Holger Lange
Seniorforsker
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Sofia Junttila Jonas Ardö Zhanzhang Cai Hongxiao Jin Natascha Kljun Leif Klemedtsson Alisa Krasnova Holger Lange Anders Lindroth Meelis Mölder Steffen M. Noe Torbern Tagesson Patrik Vestin Per Weslien Lars EklundhSammendrag
Northern forest ecosystems make up an important part of the global carbon cycle. Hence, monitoring local-scale gross primary production (GPP) of Northern forest is essential for understanding climatic change impacts on terrestrial carbon sequestration and for assessing and planning management practices. Here we evaluate and compare four methods for estimating GPP using Sentinel-2 data in order to improve current available GPP estimates: four empirical regression models based on either the 2-band Enhanced Vegetation Index (EVI2) or the plant phenology index (PPI), an asymptotic light response function (LRF) model, and a light-use efficiency (LUE) model using the MOD1732 algorithm. These approaches were based on remote sensing vegetation indices, air temperature (Tair), vapor pressure deficit (VPD), and photosynthetically active radiation (PAR). The models were parametrized and evaluated using in-situ data from eleven forest sites in North Europe, covering two common forest types, evergreen needleleaf forest and deciduous broadleaf forest. Most of the models gave good agreement with eddy covariance-derived GPP. The VI-based regression models performed well in evergreen needleleaf forest (R2 = 0.69–0.78, RMSE = 1.97–2.28 g C m−2 d−1, and NRMSE =9-11.0%, eight sites), whereas the LRF and MOD17 performed slightly worse (R2 = 0.65 and 0.57, RMSE = 2.49 and 2.72 g C m−2 d−1, NRMSE = 12 and 13.0%, respectively). In deciduous broadleaf forest all models, except the LRF, showed close agreements with the observed GPP (R2 = 0.75–0.80, RMSE = 2.23–2.46 g C m−2 d−1, NRMSE = 11–12%, three sites). For the LRF model, R2 = 0.57, RMSE = 3.21 g C m−2 d−1, NRMSE = 16%. The results highlighted the necessity of improved models in evergreen needleleaf forest where the LUE approach gave poorer results., The simplest regression model using only PPI performed well beside more complex models, suggesting PPI to be a process indicator directly linked with GPP. All models were able to capture the seasonal dynamics of GPP well, but underestimation of the growing season peaks were a common issue. The LRF was the only model tending to overestimate GPP. Estimation of interannual variability in cumulative GPP was less accurate than the single-year models and will need further development. In general, all models performed well on local scale and demonstrated their feasibility for upscaling GPP in northern forest ecosystems using Sentinel-2 data.
Forfattere
Holger LangeSammendrag
Oversikt over ICOS-Norge terrestrisk. Karbobalansen for Hurdal skogen innenfor fotavtrykk for det første hele kalenderår med målinger, 2022.
Forfattere
Marleen Pallandt Bernhard Ahrens Marion Schrumpf Holger Lange Sönke Zaehle Markus ReichsteinSammendrag
Soil organic carbon (SOC) is the largest terrestrial carbon pool, but it is still uncertain how it will respond to climate change. Especially the fate of SOC due to concurrent changes in soil temperature and moisture is uncertain. It is generally accepted that microbially driven SOC decomposition will increase with warming, provided that sufficient soil moisture, and hence enough C substrate, is available for microbial decomposition. We use a mechanistic, microbially explicit SOC decomposition model, the Jena Soil Model (JSM), and focus on the depolymerization of litter and microbial residues by microbes. These model processes are sensitive to temperature and soil moisture content and follow reverse Michaelis-Menten kinetics. Microbial decomposition rate V of the substrate [S] is limited by the microbial biomass [B]: V = Vmax * [S] * [B]/(kMB + [B]). The maximum reaction velocity, Vmax, is temperature sensitive and follows an Arrhenius function. Also, a positive correlation between temperature and kMB-values of different enzymes has been empirically shown, with Q10 values ranging from 0.71-2.80 (Allison et al., 2018). Q10 kMB-values for microbial depolymerization of microbial residues would be low compared to those of a (lignified) litter pool. An increase in kMB leads to a lower reaction velocity (V) and V becomes less temperature sensitive at low substrate concentrations. In this work we focus on the following questions: “how do temperature and soil moisture changes affect modelled heterotrophic respiration through the Michaelis-Menten term? Is there a temperature compensation effect on modelled decomposition rate because of the counteracting temperature sensitivities of Vmax and kMB?” We model these interactions under a mean warming experiment (+3.5 °K) as well as three soil moisture experiments: constant soil moisture, a drought, and a wetting scenario.
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Volkmar Timmermann Isabella Børja Nicholas Clarke Rune Eriksen Jostein Gohli Gro Hylen Jane Uhd Jepsen Paal Krokene Holger Lange Helge Meissner Nina Elisabeth Nagy Jørn-Frode Nordbakken Sverre Solberg Halvor Solheim Ole Petter Laksforsmo Vindstad Bjørn Økland Wenche AasSammendrag
Skogens helsetilstand påvirkes i stor grad av klima og værforhold, enten direkte ved tørke, frost og vind, eller indirekte ved at klimaet påvirker omfanget av soppsykdommer og insektangrep. Klimaendringene og den forventede økningen i klimarelaterte skogskader gir store utfordringer for forvaltningen av framtidas skogressurser. Det samme gjør invaderende skadegjørere, både allerede etablerte arter og nye som kan komme til Norge i nær framtid. I denne rapporten presenteres resultater fra skogskadeovervåkingen i Norge i 2021 og trender over tid for følgende temaer: (i) Landsrepresentativ skogovervåking; (ii) Skogøkologiske analyser og målinger av luftkjemi på de intensive overvåkingsflatene; (iii) Overvåking av bjørkemålere i Troms og Finnmark; (iv) Barkbilleovervåkingen 2021 og mulig overgang til to generasjoner; (v) Asiatisk askepraktbille – en dørstokkart? (vi) Overvåking av askeskuddsyke; (vii) Andre spesielle skogskader i 2021.
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Holger Lange Jaana Bäck Georg Jocher Natascha Kljun Anne Klosterhalfen Alexander Knohl Natalia Kowalska Adam Kristensson Corinna Rebmann Teresa Saura-Yera Alberto VilagrosaSammendrag
Utilizing forest ecosystems to mitigate climate change effects and to preserve biodiversity requires detailed insights into the feedbacks between forest type, climatic and soil conditions, and in particular forest management history and practice. Analysis of long-term observations at the site level, remote sensing proxies and understanding relevant biogeochemical and biophysical processes are key to achieving these insights. In the recently started EU H2020 project “CLimate Mitigation and Bioeconomy pathways for sustainable FORESTry” (CLIMB-FOREST), we address these issues based on intensely monitored sites with flux measurements (ICOS, Fluxnet), other ecosystem research and observation networks (eLTER, National Forest Inventories), remotely sensed observations and process understanding. This presentation outlines the activities of CLIMB-FOREST regarding (1) carbon stocks and fluxes according to stand age, species distribution, management and disturbance history; (2) biophysical effects of forest structure; (3) effects and importance of short-lived climate forcers (e.g. BVOCs) and (4) management and extreme event (drought, fire) impact on SOC and N dynamics. We also outline how the gained knowledge informs scenario runs of the Vegetation and Earth System Model RCA-GUESS in the project.
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Toby Marthews Holger Lange Alberto Martinez-de la Torre Richard J. Ellis Sarah E. Chadburn Martin G. de KauweSammendrag
The role of soil in current climate models is reviewed and discussed, with a focus on developments over the last two decades. Soil modeling may be divided into three major parts: simulation of soil hydrological dynamics, soil biogeochemistry and the soil thermal environment. Each of these three major parts is summarized with a brief description of current best practice and developments. Specific issues and modifications relevant to four extreme environments are highlighted: drylands, tropical moist and wet forests, cold regions, and peatlands and wetlands. Finally, current advances in the areas of hyperresolution and coupled model environments are discussed, which we see as the two leading edges of current soil model development.
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Heat Field Deformation (HFD) is a widely used method to measure sap flow of trees based on empirical relationships between heat transfer within tree stems and the sap flow rates. As an alternative, the Linear Heat Balance (LHB) method implements the same instrumental configuration as HFD but calculates the sap flow rates using analytical equations that are derived from fundamental conduction-convection heat transfer theories. In this study, we systematically compared the sap flow calculated using the two methods based on data that were recorded using the same instrument. The measurements were conducted on four Norway spruce trees. We aimed to evaluate the discrepancies between the sap flow estimates from the two methods and determine the underlying causes. Diurnal and day-to-day patterns were consistent between the sap flow estimates from the two methods. However, the magnitudes of the estimated sap flow were different between them, where LHB resulted in much lower estimates in three trees and slightly higher estimates in one compared to HFD. We also observed larger discrepancies in negative (reversed flow) than in positive sap flow, where the LHB resulted in lower reversed flow than HFD. Consequently, the seasonal budget estimated by LHB can be as low as ∼20% of that estimated by HFD. The discrepancies can be mainly attributed to the low wood thermal conductivities for the studied trees that lead to substantial underestimations using the LHB method. In addition, the sap flow estimates were very sensitive to the value changes of the empirical parameters in the calculations and, thus, using a proper case-specific value is recommended, especially for the LHB method. Overall, we suggest that, despite the strong theoretical support, the correctness of LHB outputs depends largely on the tree individuals and should be carefully evaluated.
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As a way to estimate evapotranspiration (ET), Heat Field Deformation (HFD) is a widely used method to measure sap flow of trees based on empirical relationships between heat transfer within tree stems and the sap flow rates. As an alternative, the Linear Heat Balance (LHB) method implements the same instrumental configuration as HFD but calculates the sap flow rates using analytical equations that are derived from fundamental conduction-convection heat transfer equations.
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The three-dimensional structure of forest canopies is essential for light use efficiency, photosynthesis and thus carbon sequestration. Therefore, high-quality characterization of canopy structure is critical to improving our carbon cycle estimates by Earth system models and better understanding disturbance impacts on carbon sequestration in forested ecosystems. In this context, a widely used observable is the Leaf Area Density (LAD) and its integral over the vertical dimension, the Leaf Area Index (LAI). A multitude of methods exists to determine LAD and LAI in a forest stand. In this contribution, we use a mature Norway spruce forest surrounding an ICOS flux tower at Hurdal site (NO-Hur) to investigate LAD and LAI with six different methods: field campaigns using (1) the Plant Canopy Analyzer LAI-2000; (2) the LaiPen LP 110; (3) Digital Hemispheric Photography at a set of plots within the area; (4) a Lidar drone flight covering the footprint area of the tower; (5) an airborne Lidar campaign, and (6) a satellite LAI product (MODIS). The horizontal spatial structure of LAI values is investigated using marked point process statistics. Intercomparison of the methods focusses not only on biases and root mean squared errors, but also on the spatial patterns observed, quantifying to which extent a simple bias correction between the methods is sufficient to make the different approaches match to each other.
Forfattere
Inge Dox Tore Skrøppa Mieke Decoster Peter Prislan Aina Gascó Jožica Gričar Holger Lange Matteo CampioliSammendrag
Historically, the autumn dynamics of deciduous forest trees have not been investigated in detail. However, autumn phenological events, like onset of loss of canopy greenness (OLCG), onset of foliar senescence (OFS) and cessation of wood growth (CWG), have an important impact on tree radial growth and the entire ecosystem's seasonal dynamics. Here, we monitored the leaf and wood phenological events of silver birch (Betula pendula) at four different sites in Ås, southeastern Norway: (a) a natural mature stand, (b) a plantation on former agricultural ground, (c) young natural trees, and (d) young trees in pots under different fertilization levels. The study took place over four consecutive years (from 2017 to 2020), with a particular focus on 2018, a year in which there was a severe summer drought, and the next year, 2019, which featured more normal conditions. First, we provided a description of birch phenology within its mid-north distributional. Second, we showed that drought advanced CWG by about 5 to 6 weeks and it delayed OLCG and OFS up to 30 days. Third, we observed an unexpected advance in OLCG in 2019 compared to 2018 (30 days) and 2020 (14 days). OFS presented similar dynamics as OLCG, whereas CWG was advanced only in 2018. These findings might indicate lag-effects of severe drought on the next year autumn leaf phenology but not on wood growth. On the other hand, the comparison between the natural stand and the plantation showed that, under drought conditions, wood growth is more sensitive to site fertility than autumn leaf phenology. In summary, our study elucidated the autumn dynamics of an important deciduous forest species in the northern temperate zone and showed unexpected impacts of a severely dry and warm summer on the current and next year leaf phenology.
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Holger LangeSammendrag
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Bertold Mariën Dimitri Papadimitriou Titta Kotilainen Paolo Zuccarini Inge Dox Melanie Verlinden Thilo Heinecke Joachim Mariën Patrick Willems Mieke Decoster Aina Gascó Holger Lange Josep Peñuelas Matteo CampioliSammendrag
Accurate estimations of phenophases in deciduous trees are important to understand forest ecosystems and their feedback on the climate. In particular, the timing of leaf senescence is of fundamental importance to trees’ nutrient stoichiometry and drought tolerance and therefore to trees’ vigor and fecundity. Nevertheless, there is no integrated view on the significance, and direction, of seasonal trends in leaf senescence, especially for years characterized by extreme weather events. Difficulties in the acquisition and analyses of hierarchical data can account for this. We collected four years of chlorophyll content index (CCI) measurements in thirty-eight individuals of four deciduous tree species (Betula pendula, Fagus sylvatica, Populus tremula and Quercus robur) in Belgium, Norway and Spain, and analyzed these data using generalized additive models for location, scale and shape (GAMLSS). As a result, (I) the phenological strategy and seasonal trend of leaf senescence in these tree species could be clarified for exceptionally dry and warm years, and (II) the daily average (air) temperature, global radiation, and vapor pressure deficit could be established as main drivers behind the variation in the timing of the senescence transition date. Our results show that the onset of the re-organization phase in the leaf senescence, which we approximated and defined as local minima in the second derivative of a CCI graph, was in all species mainly negatively affected by the average temperature, global radiation and vapor pressure deficit. All together the variables explained 89 to 98% of the variability in the leaf senescence timing. An additional finding is that the generalized beta type 2 and generalized gamma distributions are well suited to model the chlorophyll content index, while the senescence transition date can be modeled using the normal-exponential-student-t, generalized gamma and zero-inflated Box-Cox Cole and Green distributions for beech, oak and birch, and poplar, respectively.
Forfattere
Marleen Pallandt Bernhard Ahrens Sujan Koirala Holger Lange Markus Reichstein Marion Schrumpf Sönke ZaehleSammendrag
The role of soil moisture for organic matter decomposition rates remains poorly understood and underrepresented in Earth System Models (ESMs). We apply the Dual Arrhenius Michaelis-Menten (DAMM) model to a selection of ESM soil temperature and moisture outputs to investigate their effects on decomposition rates, at different soil depths, for a historical period and a future climate period. Our key finding is that the inclusion of soil moisture controls has diverging effects on both the speed and direction of projected decomposition rates (up to ± 20%), compared to a temperature-only approach. In the top soil, the majority of these changes is driven by substrate availability. In deeper soil layers, oxygen availability plays a relatively stronger role. Owing to these different moisture controls along the soil depth, our study highlights the need for depth-resolved inclusion of soil moisture effects on decomposition rates within ESMs. This is particularly important for C-rich soils in regions which may be subject to strong future warming and vertically opposing moisture changes, such as the peat soils at northern high latitudes.
Forfattere
Inge Dox Bertold Mariën Paolo Zuccarini Lorene J. Marchand Peter Prislan Jožica Gričar Omar Flores Friederike Gehrmann Patrick Fonti Holger Lange Josep Peñuelas Matteo CampioliSammendrag
Wood growth phenology of temperate deciduous trees is less studied than leaf phenology, hindering the understanding of their interaction. In order to describe the variability of wood growth and leaf phenology across locations, species and years, we performed phenological observations of both xylem formation and leaf development in three typical temperate forest areas in Western Europe (Northern Spain, Belgium and Southern Norway) for four common deciduous tree species (Fagus sylvatica L., Betula pendula Roth., Populus tremula L. and Quercus robur L.) in 2018, 2019 and 2020, with only beech and birch being studied in the final year. The earliest cambial reactivation in spring occurred at the Belgian stands while the end of cambial activity and wood growth cessation generally occurred first in Norway. Results did not show much consistency across species, sites or years and lacked general patterns, except for the end of cambial activity, which occurred generally first in birch. For all species, the site variation in phenophases (up to three months) was substantially larger than the inter-annual variability (up to six weeks). The timeline of bud-burst and cambium reactivation, as well as of foliar senescence and cessation of wood growth, were variable across species even with the same type of wood porosity. Our results suggest that wood growth and leaf phenology are less well connected than previously thought. Linear models showed that temperature is the dominant driver of wood growth phenology, but with climate zone also having an effect, especially at the start of the growing season. Drought conditions, on the other hand, have a larger effect on the timing of wood growth cessation. Our comprehensive analysis represents the first large regional assessment of wood growth phenology in common European deciduous tree species, providing not only new fundamental insights but also a unique dataset for future modelling applications.
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Overgang fra en til to generasjoner av stor granbarkbille med varmere klima vil kunne øke mengden av skader i norske skoger på grunn av to angrepsperioder. Sommertemperaturene i Norge har vært økende siden 1970-tallet og en utviklingsmodell for stor granbarkbille basert på temperatursummer viser økt hyppighet av to fullførte generasjoner i de varmeste lokalitetene de siste tiårene. Mens feltstudier og utviklingsmodellen fra Sør-Norge i den varme sommeren 1975 viste at granbarkbillen ikke rakk å fullføre en påbegynt andre generasjon, viste utviklingsmodellen full gjennomføring av to generasjoner for flere barkbilleangrepne lokaliteter i Vestfold i 2020. I disse lokalitetene observerte vi også fullt utviklete granbarkbiller under barken i november...
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In the Bramke valley (western Harz mountains, North Germany), three forested headwater catchments have been monitored since decades. A broad range of observables relevant to forestry, hydrology, hydrochemistry and ecosystem research allows to compare different approaches to environmental monitoring; each of them has its own set of relevant observables. The basic temporal resolution is daily for hydrometeorology and bi-weekly for streamwater chemistry; standing biomass of the Norway spruce stands is measured every couple of years. Tree growth (site index) has changed between and within rotation periods (of up to 129 years); changes in soil nutrient pools are typical variables used to explain this nonstationary forest growth when the spatial-temporal scales match. In hydrology, transport mechanisms of water and solutes through catchment soils are used to model and predict runoff and its chemistry. Given the homogeneity of the area in terms of geology, soils and topography as well as climate, differences between the catchments in the Bramke valley are mostly related to forestry variables. The catchments exhibit long-term changes and spatial gradients related to atmospheric deposition, management and changing climate. After providing a short multivariate summary of the dataset, we present several nonlinear metrics suitable to detect and quantify subtle changes and to describe different behavior, both between different variables from the same catchment, as well as for the same variable across catchments. Soil water potential and solution chemistry are further links between forestry and hydrology. However, at Lange Bramke, similar to other catchment studies, the evaluation of these data sets has not converged to a consistent, realistic model at the catchment scale. We hypothesize that this lack of model integration is due to theoretical rather than technical limits. A possible representation of these limits might be phrased in a category theory approach. How to cite: Hauhs, M., Meesenburg, H., and Lange, H.: Long-term monitoring of vegetation and hydrology in headwater catchments and the difficulties to embrace data-oriented and process-oriented approaches, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7684, https://doi.org/10.5194/egusphere-egu21-7684, 2021.
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Jan-Peter George Wei Yang Hideki Kobayashi Tobias Biermann Arnaud Carrara Edoardo Cremonese Matthias Cuntz Silvano Fares Giacomo Gerosa Thomas Grünwald Niklas Hase Michael Heliasz Andreas Ibrom Alexander Knohl Bart Kruijt Holger Lange Jean-Marc Limousin Denis Loustau Petr Lukeš Riccardo Marzuoli Meelis Mölder Leonardo Montagnani Johan Neirynck Matthias Peichl Corinna Rebmann Marius Schmidt Francisco Ramon Lopez Serrano Kamel Soudani Caroline Vincke Jan PisekSammendrag
Leaf area index (LAI) is a key ecological indicator for describing the structure of canopies and for modelling energy exchange between atmosphere and biosphere. While LAI of the forest overstory can be accurately assessed over large spatial scales via remote sensing, LAI of the forest understory (LAIu) is still largely ignored in ecological studies and ecosystem modelling due to the fact that it is often too complex to be destructively sampled or approximated by other site parameters. Additionally, so far only few attempts have been made to retrieve understory LAI via remote sensing, because dense canopies with high LAI are often hindering retrieval algorithms to produce meaningful estimates for understory LAI. Consequently, the forest understory still constitutes a poorly investigated research realm impeding ecological studies to properly account for its contribution to the energy absorption capacity of forest stands. This study aims to compare three conceptually different indirect retrieval methodologies for LAIu over a diverse panel of forest understory types distributed across Europe. For this we carried out near-to-surface measurements of understory reflectance spectra as well as digital surface photography over the extended network of Integrated Carbon Observation System (ICOS) forest ecosystem sites. LAIu was assessed by exploiting the empirical relationship between vegetation cover and light absorption (Beer-Lambert- Bouguer law) as well as by utilizing proposed relationships with two prominent vegetation indices: normalized difference vegetation index (NDVI) and simple ratio (SR). Retrievals from the three methods were significantly correlated with each other (r = 0.63–0.99, RMSE = 0.53–0.72), but exhibited also significant bias depending on the LAI scale. The NDVI based retrieval approach most likely overestimates LAI at productive sites when LAIu > 2, while the simple ratio algorithm overestimates LAIu at sites with sparse understory vegetation and presence of litter or bare soil. The purely empirical method based on the Beer-Lambert law of light absorption seems to offer a good compromise, since it provides reasonable LAIu values at both low and higher LAI ranges. Surprisingly, LAIu variation among sites seems to be largely decoupled from differences in climate and light permeability of the overstory, but significantly increased with vegetation diversity (expressed as species richness) and hence proposes new applications of LAIu in ecological modelling.
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Forests have climate change mitigation potential since they sequester carbon. However, their carbon sink strength might depend on management. As a result of the balance between CO2 uptake and emission, forest net ecosystem exchange (NEE) reaches optimal values (maximum sink strength) at young stand ages, followed by a gradual NEE decline over many years. Traditionally, this peak of NEE is believed to be concurrent with the peak of primary production (e.g., gross primary production, GPP); however, in theory, this concurrence may potentially vary depending on tree species, site conditions and the patterns of ecosystem respiration (Reco). In this study, we used eddy-covariance (EC)-based CO2 flux measurements from 8 forest sites that are dominated by Norway spruce (Picea abies L.) and built machine learning models to find the optimal age of ecosystem productivity and that of CO2 sequestration. We found that the net CO2 uptake of Norway spruce forests peaked at ages of 30-40 yrs. Surprisingly, this NEE peak did not overlap with the peak of GPP, which appeared later at ages of 60-90 yrs. The mismatch between NEE and GPP was a result of the Reco increase that lagged behind the GPP increase associated with the tree growth at early age. Moreover, we also found that newly planted Norway spruce stands had a high probability (up to 90%) of being a C source in the first year, while, at an age as young as 5 yrs, they were likely to be a sink already. Further, using common climate change scenarios, our model results suggest that net CO2 uptake of Norway spruce forests will increase under the future climate with young stands in the high latitude areas being more beneficial. Overall, the results suggest that forest management practices should consider NEE and forest productivity separately and harvests should be performed only after the optimal ages of both the CO2 sequestration and productivity to gain full ecological and economic benefits. How to cite: Zhao, J., Lange, H., and Meissner, H.: Mismatch between the optimal ages for ecosystem productivity and net CO2 sequestration in Norway spruce forests, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4257, https://doi.org/10.5194/egusphere-egu21-4257, 2021.
Vitenskapelig – On the Future Role of Symbols in Environmental Modelling
Michael Hauhs, Holger Lange
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Computer models use symbols in various ways adapted from mathematics, computer science, engineering and the natural sciences. Model applications in ecology often seek to represent future states of ecosystems, a task that has been difficult to achieve. Reflection upon the role of symbols in these models may help to disentangle the various sources and contributions to these perceptions of the environment. The modi of time (past, present, future) are here represented by corresponding forms of modelling as narration, performance, and simulation. All three occur in ecological modelling, and transitions between them may be indicative of modelling limits. Given the difficulties of representing the future of ecosystems and finding relevant analogies in the history of ecosystem use, the most challenging task for contemporary ecological models is to perform appropriately with respect to (Big) monitoring Data. We use an analogy between an environmental crisis in natural history and the current Anthropocene to demonstrate the limits of symbols in modelling which are intended to provide an abstract representation. A shift in emphasis on the engineering and computational aspect is proposed for organizing a sustainable human-environment relationship in the Anthropocene.
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Jan Pisek Angela Erb Lauri Korhonen Tobias Biermann Arnaud Carrara Edoardo Cremonese Matthias Cuntz Silvano Fares Giacomo Gerosa Thomas Grünwald Niklas Hase Michal Heliasz Andreas Ibrom Alexander Knohl Johannes Kobler Bart Kruijt Holger Lange Leena Leppänen Jean-Marc Limousin Francisco Ramon Lopez Serrano Denis Loustau Petr Lukes Lars Lundin Riccardo Marzuoli Meelis Mölder Leonardo Montagnani Johan Neirynck Matthias Peichl Corinna Rebmann Eva Rubio Margarida Santos-Reis Crystal Schaaf Marius Schmidt Guillaume Simioni Kamel Soudani Caroline VinckeSammendrag
Information about forest background reflectance is needed for accurate biophysical parameter retrieval from forest canopies (overstory) with remote sensing. Separating under- and overstory signals would enable more accurate modeling of forest carbon and energy fluxes. We retrieved values of the normalized difference vegetation index (NDVI) of the forest understory with the multi-angular Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo data (gridded 500 m daily Collection 6 product), using a method originally developed for boreal forests. The forest floor background reflectance estimates from the MODIS data were compared with in situ understory reflectance measurements carried out at an extensive set of forest ecosystem experimental sites across Europe. The reflectance estimates from MODIS data were, hence, tested across diverse forest conditions and phenological phases during the growing season to examine their applicability for ecosystems other than boreal forests. Here we report that the method can deliver good retrievals, especially over different forest types with open canopies (low foliage cover). The performance of the method was found to be limited over forests with closed canopies (high foliage cover), where the signal from understory becomes too attenuated. The spatial heterogeneity of individual field sites and the limitations and documented quality of the MODIS BRDF product are shown to be important for the correct assessment and validation of the retrievals obtained with remote sensing.
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Holger LangeSammendrag
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In a young Norway spruce stand (planted in 2012) at Hoxmark, Southeast Norway, Net Ecosystem Exchange (NEE) was measured using Eddy Covariance. The data were carefully processed with time-dependent stand parameters (i.e. canopy height), a detailed footprint analysis and calculated at 30 min temporal resolution. Photosynthetic Active Radiation (PAR) as the primary driver for carbon uptake was also available at the site. Despite its young age, the plantation already acted as a net carbon sink according to the annual NEE budget, e.g. by ca. 300 g C m-2 in 2019. However, the response of the system depended strongly on hydrometeorological conditions. We demonstrate this by investigating the relationship between NEE and PAR for this system in a temporally local fashion (30 days moving windows), using a Michaelis-Menten approach involving three parameters. Although the regression captured up to ca. 80% of the variance, the parameter estimates differed substantially throughout the season, and were contrasting between the very dry year 2018 and the close to normal year 2019. Comparison with other EC-equipped sites in a future study will clarify whether this variable sensitivity is due to the young age or is a pattern pertaining also to mature spruce stands. https://doi.org/10.5194/egusphere-egu21-5028
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Soil respiration is an important ecosystem process that releases carbon dioxide into the atmosphere. While soil respiration can be measured continuously at high temporal resolutions, gaps in the dataset are inevitable, leading to uncertainties in carbon budget estimations. Therefore, robust methods used to fill the gaps are needed. The process-based non-linear least squares (NLS) regression is the most widely used gap-filling method, which utilizes the established relationship between the soil respiration and temperature. In addition to NLS, we also implemented three other methods based on: 1) artificial neural networks (ANN), driven by temperature and moisture measurements, 2) singular spectrum analysis (SSA), relying only on the time series itself, and 3) the expectation-maximization (EM) approach, referencing to parallel flux measurements in the spatial vicinity. Six soil respiration datasets (2017–2019) from two boreal forests were used for benchmarking. Artificial gaps were randomly introduced into the datasets and then filled using the four methods. The time-series-based methods, SSA and EM, showed higher accuracies than NLS and ANN in small gaps (<1 day). In larger gaps (15 days), the performance was similar among NLS, SSA and EM; however, ANN showed large errors in gaps that coincided with precipitation events. Compared to the observations, gap-filled data by SSA showed similar degree of variances and those filled by EM were associated with similar first-order autocorrelation coefficients. In contrast, data filled by both NLS and ANN exhibited lower variance and higher autocorrelation than the observations. For estimations of the annual soil respiration budget, NLS, SSA and EM resulted in errors between −3.7% and 5.8% given the budgets ranged from 463 to 1152 g C m−2 year−1, while ANN exhibited larger errors from −11.3 to 16.0%. Our study highlights the two time-series-based methods which showed great potential in gap-filling carbon flux data, especially when environmental variables are unavailable.
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Lorène Julia Marchand Inge Dox Jožica Gričar Peter Prislan Sebastien Leys Jan Van den Bulcke Patrick Fonti Holger Lange Erik Matthysen Josep Peñuelas Paolo Zuccarini Matteo CampioliSammendrag
We explored the inter-individual variability in bud-burst and its potential drivers, in homogeneous mature stands of temperate deciduous trees. Phenological observations of leaves and wood formation were performed weekly from summer 2017 to summer 2018 for pedunculate oak, European beech and silver birch in Belgium. The variability of bud-burst was correlated to previous’ year autumn phenology (i.e. the onset of leaf senescence and the cessation of wood formation) and tree size but with important differences among species. In fact, variability of bud-burst was primarily related to onset of leaf senescence, cessation of wood formation and tree height for oak, beech and birch, respectively. The inter-individual variability of onset of leaf senescence was not related to the tree characteristics considered and was much larger than the inter-individual variability in bud-burst. Multi-species multivariate models could explain up to 66% of the bud-burst variability. These findings represent an important advance in our fundamental understanding and modelling of phenology and tree functioning of deciduous tree species.
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The rapidly expanding field of machine learning (ML) provides many methodological opportunities which match very well with the needs and challenges of hydrological research. Due to extended measurement networks, more frequent automatic measurements of hydrological variables, and not the least increasing use of remote sensing products, the era of big data surely has arrived in hydrology. Process-based models are usually developed for certain spatiotemporal scales, not fitting easily to the scope of the new datasets. Automatic methods that learn patterns and generalizations have been demonstrated to be superior in many applications. The chapter provides an overview of some of the most important machine learning algorithms which have been used in the hydrological literature. It will be shown that there is no single best method among them, but instead a spectrum of methods should be utilized, from highly flexible ones to more parsimonious learning methods, depending on the specific hydrological application, research question, and data availability. Most machine learning techniques require a calibration and a validation dataset for training. As these data are usually correlated in time and space, the problem of bias-variance tradeoff arises will be discussed as a simple example. The presentation of ML algorithms, roughly following chronological order, is discussed starting with artificial neural networks through support vector machines to gradient boosting machines. As data streams increase, these and other machine learning techniques will play an ever more important role in hydrology.
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Bei der Modellierung von Ökosystemen treffen zwei Seiten des technischen Fortschritts aufeinander, einerseits die neuen Möglichkeiten, vertreten durch die Informatik und die Verbreitung von Computern, andererseits die indirekten Wirkungen moderner Zivilisationen auf die Umwelt des Menschen und die Biosphäre. In diesem Beitrag geht es um die Möglichkeit einer Zusammenschau dieser zwei Seiten der Moderne vom Standpunkt der ökologischen Modellbildung.
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The notion of the Anthropocene does not fit well into the frame of scientific disciplines. The proposed onset of a new geological epoch has become closely linked with human history and with notions such as purposeful human actions. Purposefulness, however, is also subject to interpretation in the humanities and does not fit into analytical methods in Earth sciences. Scholars have taken up this challenge and engage with Earth scientists in public discourse on the Anthropocene. Due to the lack of a common frame of reference, discussions suffer from incompatible abstractions, notions, methods and results. Here, we propose an abstract model-framework facilitating communication between Earth scientists and scholars. In Earth sciences, models are often employed to provide a representation of an independent reality which imposes limits to growth. In the humanities, self-reference and reflexivity of modernity at all scales including the globe becomes a key issue. In the former view models can be decomposed and locally tested, in the latter models and concepts involving human action need to be considered in all their contextual and semantic relations. Typically, such concepts, for example in anthropology, do not come in a mathematical language. Nevertheless, we suggest that a common reference can be sought in an abstract model language, rather than in realistic models. Category theory and formal notions developed in computer science may provide such an abstract framework to accommodate the apparently incompatible views of models and concepts, which are considered as successful by their respective home disciplines. Diverse models such as examples from game theory (economics), from dynamic system theory (Earth science) and from a classification of ethnocosmologies (anthropology) can be formulated as different instances within a joint and abstract framework. Such a framework allows to investigate implications of the Anthropocene for logical similarities with past environmental events by seeking historical analogies (for example with the great oxygenation event) or formulating consistency requirements for the future (for example by defining sustainability). The prize for the common basis is a strict ‘epistemic hygiene’, avoiding most ontological assumptions and criticisms which often appear as dear to Earth scientists and scholars, but which may prevent a more fruitful exchange on an urgent interdisciplinary topic
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The measurement network Integrated Carbon Observation System (ICOS) is dedicated to the quantification of fluxes of CO2, H2O, N2O and CH4 at the boundary between vegetation surfaces and the lower atmosphere. The implementation of observations sites follows strict protocols and a challenging labelling process to ensure standardized intercomparable observations. We report on our experiences in attempting to establish the only Norwegian ICOS Ecosystem site thus far, NO-Hur, located in an old-growth spruce forest at Hurdal in Southeast Norway. NOHur is planned as a class 2 site, with the option to an upgrade to class 1 later. The instrumentation and sensors needed, the requirements for spatial homogeneity and a detailed analysis of a digital terrain model are presented. The current status of the tower construction, the preliminary measurements obtained with the existing ICOScertified equipment at a test site, and the plans for integrating the measurements operationally into the network are shown
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The measurement network Integrated Carbon Observation System (ICOS) is dedicated to the quantification of fluxes of CO2, H2O, N2O and CH4 at the boundary between vegetation surfaces and the lower atmosphere. The implementation of observations sites follows strict protocols and a challenging labelling process to ensure standardized intercomparable observations. We report on our experiences in attempting to establish the only Norwegian ICOS Ecosystem site thus far, NO-Hur, located in an old-growth spruce forest at Hurdal in Southeast Norway. NOHur is planned as a class 2 site, with the option to an upgrade to class 1 later. The instrumentation and sensors needed, the requirements for spatial homogeneity and a detailed analysis of a digital terrain model are presented. The current status of the tower construction, the preliminary measurements obtained with the existing ICOScertified equipment at a test site, and the plans for integrating the measurements operationally into the network are shown
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The Integrated Carbon Observation System (ICOS) research infrastructure is aimed at quantifying and understanding the greenhouse gas balance of Europe and neighboring regions. ICOS-Norway brings together the leading Norwegian institutes for greenhouse gas observations in the three Earth system domains atmosphere, ocean, and terrestrial ecosystems, providing world-leading competence, which is integrated into one jointly funded and operated infrastructure. This provides Norway with a state-of-the-art research infrastructure embedded in European and global efforts. Even though each Earth system domain was part of dedicated research infrastructures prior to the establishment of ICOS-Norway, the greenhouse gas community in Norway was divided and there was minimal collaboration across the Earth system domains. The overall goal of ICOS-Norway is to provide accurate and accessible data on, as well as integrated assessments of, the Norwegian carbon balance at regional scale, across the land, ocean, and atmosphere. ICOS-Norway has thus led to an increased impact of environmental observing systems in Norway and surrounding seas, easily seen through the number of publications and new proposals generated as collaborative efforts. This poster presents the ICOS-Norway infrastructure, including plans for expansion and long-term funding.
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Forfattere
Marleen Pallandt Bernhard Ahrens Markus Reichstein Holger Lange Marion Schrumpf Sönke ZaehleSammendrag
The role of soil moisture on organic matter decomposition remains poorly understood and underrepresented in coupled global climate models. Traditionally, organic matter decomposition is represented as simple first- or second order kinetics in such models, using mostly empirical functions for temperature and moisture controls, and without considering microbial interactions. We use the Dual Michaelis-Menten (DAMM) model (Davidson et al. 2012) to simulate simultaneous temperature and moisture controls on decomposition rates. Microbial controls on decomposition in relation to changes in soil moisture and temperature are implicitly simulated with DAMM: Soil moisture affects the available substrate (SOC) and oxygen available for decomposition and reduces the maximal, temperature driven decomposition rate (Vmax). We apply the DAMM model on vertically resolved data from the most recent coupled model intercomparison project (CMIP5) and gridded global SOC values (SoilGrids). We study the potential decomposition rates for a historic period (1976 - 2006) and a period under the RCP8.5 climate change scenario (2070-2099) for 5 soil layers up to 1m depth. Our key finding is that the inclusion of soil moisture controls has diverging effects on both the speed and direction of projected decomposition rates, compared to a temperature-only approach. The majority of these changes are driven by soil moisture through substrate limitation, rather than oxygen diffusion limitation. In deeper soil layers, oxygen diffusion limitation plays a stronger role. Our study highlights the need for inclusion of soil moisture interactions in coupled global climate models. Our findings could be particularly important for boreal soils, which store a major fraction of Earth’s SOC stocks and where temperature increases and soil moisture changes are expected to be largest.
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Holger LangeSammendrag
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Marleen Pallandt Bernhard Ahrens Sönke Zaehle Marion Schrumpf Holger Lange Markus ReichsteinSammendrag
Soil organic carbon (SOC) is the largest terrestrial carbon pool. Changes in the hydrological cycle affect C-cycle turnover, with potential effects on the global C balance’s response to global change. However, large scale model representations of the sensitivity of soil carbon to soil moisture, through decomposition and interactions with nutrient cycles, are largely empirical to semi-empirical and uncertain. To better represent these dynamics, the aims of this PhD project* are to: • Investigate the role of soil moisture on SOC decomposition over a vertical profile; • Assess which moisture controls are (most) important in a multi-layered, mechanistic soil biogeochemistry model, the Jena Soil Model (JSM, Fig 2); • Update and improve the representations of soil moisture dynamics in JSM and evaluate this model for multiple sites along a moisture gradient and global scale.
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Bernhard Ahrens Marleen Pallandt Markus Reichstein Holger Lange Marion Schrumpf Sonke ZaehleSammendrag
The Jena Soil Model (JSM) is a multi-layer mechanistic soil biogeochemistry model with explicit representations of vertical transport, mineral sorption, and microbial control on decomposition rates. Reaction rates are further modified by temperature and moisture. While temperature determines the maximum reaction velocity (Vmax), moisture reduces this rate nonlinearly if either the diffusion of substrate is restricted (at low soil moisture) or oxygen availability for microbes is limited (at wet conditions). This moisture control on soil organic matter formation and decomposition is represented with the Dual Arrhenius Michaelis-Menten (DAMM) model concept (Davidson et al. 2012) and influences the reaction rates of microbial depolymerisation of litter and microbial residue pools as well as DOC (dissolved organic matter) uptake. Sorption of DOM and microbial residues to mineral surfaces is moisture dependent through a Langmuir sorption approach. We will validate the carbon cycle representation of moisture control on soil organic matter decomposition in JSM by comparing simulations with measured carbon stocks and respiration rates from different ecosystems ranging from boreal upland forests and wetlands to Mediterranean savannas. The modular structure of JSM will allow us to investigate the effect of moisture control on each decomposition step (depolymerisation, microbial uptake and growth, and OC sorption) separately.
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Many nonlinear methods of time series analysis require a minimal number of observations in the hundreds to thousands, which is not always easy to achieve for observations of environmental systems. Eddy Covariance (EC) measurements of the carbon exchange between the atmosphere and vegetation provide a noticeable exception. They are taken at high temporal resolution, typically at 20 Hz. This generates very long time series (many millions of data points) even for short measurement periods, rendering finite size effects unimportant. In this presentation, we investigate high-resolution raw data of 3D wind speed, CO2 concentrations, water vapor and temperature measured at a young forest plantation in Southeast Norway since July 2018. Guiding for the analysis is the gain or added value of the high resolution compared to more aggregated data, i.e. the scaling behavior of nonlinear properties of the time series. We present results of complexity analysis, Tarnopolski diagrams, q-Entropy, Hurst analysis, Empirical Mode Decomposition and Singular System Analysis. This provides detailed insights into the nature of dynamics of carbon fluxes across this system boundary at different temporal scales.
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Günter Blöschl Marc F.P. Bierkens Antonio Chambel Christophe Cudennec Georgia Destouni Aldo Fiori James W. Kirchner Jeffrey J. McDonnell Hubert H.G. Savenije Murugesu Sivapalan Christine Stumpp Elena Toth Elena Volpi Gemma Carr Claire Lupton Josè Salinas Borbála Széles Alberto Viglione Hafzullah Aksoy Scott T. Allen Anam Amin Vazken Andréassian Berit Arheimer Santosh Aryal Victor Baker Earl Bardsley Marlies H. Barendrecht Alena Bartosova Okke Batelaan Wouter R. Berghuijs Keith Beven Theresa Blume Thom Bogaard Pablo de Amorim Borges Michael E. Böttcher Gilles Boulet Korbinian Breinl Mitja Brilly Luca Brocca Wouter Buytaert Attilio Castellarin Andrea Castelletti Xiaohong Chen Yangbo Chen Yuanfang Chen Peter Chifflard Annette Dathe Holger Lange Jacob Sebastian Haugaard Mernild Thomas Skaugen Ingelin SteinslandSammendrag
This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.
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Holger Lange Sebastian Sippel Britta Aufgebauer Michael Hauhs Christina Bogner Henning MeesenburgSammendrag
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Holger LangeSammendrag
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Remote sensing observations provide important information about vegetation and carbon dynamics on large scales, flux towers in situ measurements at the plot scale. Events important for ecological processes, such as hydrometeorological extremes, often happen at spatiotemporal scales between those covered by these two data sources. We discuss the event detection rates of ecological in situ networks as a function of their size and design. Using extreme reductions of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), available from satellite missions, as a proxy for substantial losses in Gross Primary Productivity (GPP), we rank historical events according to their severity, and show how many would have been detected with a given number of randomly placed sites, discuss the problem of clustering of sites, and compare the theoretical results with the existing networks FLUXNET and NEON. The further spatio-temporal expansion of the ICOS network should carefully consider the size distribution of extreme events in order to be able to monitor their impacts on the terrestrial biosphere.
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Holger LangeSammendrag
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Der Begriff des Anthropozäns fordert auf zu einer neuen Zusammenarbeit zwischen Natur-, Technik-, Sozial- und Kulturwissenschaften.
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Jan Pisek Henning Buddenbaum Fernando Camacho Joachim Hill Jennifer L.R. Jensen Holger Lange Zhili Liu Arndt Piayda Yonghua Qu Olivier Roupsard Shawn P. Serbin Svein Solberg Oliver Sonnentag Anne Thimonier Francesco VuoloSammendrag
Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘p-theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p-theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empirically-based CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate p-values for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data.
Forfattere
Sebastian Sippel Markus Reichstein Xuanlong Ma Miguel D. Mahecha Holger Lange Milan Flach Dorothea FrankSammendrag
Purpose of the Review Weather and climate extremes substantially affect global- and regional-scale carbon (C) cycling, and thus spatially or temporally extended climatic extreme events jeopardize terrestrial ecosystem carbon sequestration. We illustrate the relevance of drought and/or heat events (“DHE”) for the carbon cycle and highlight underlying concepts and complex impact mechanisms. We review recent results, discuss current research needs and emerging research topics. Recent Findings Our review covers topics critical to understanding, attributing and predicting the effects of DHE on the terrestrial carbon cycle: (1) ecophysiological impact mechanisms and mediating factors, (2) the role of timing, duration and dynamical effects through which DHE impacts on regional-scale carbon cycling are either attenuated or enhanced, and (3) large-scale atmospheric conditions under which DHE are likely to unfold and to affect the terrestrial carbon cycle. Recent research thus shows the need to view these events in a broader spatial and temporal perspective that extends assessments beyond local and concurrent C cycle impacts of DHE. Summary Novel data streams, model (ensemble) simulations, and analyses allow to better understand carbon cycle impacts not only in response to their proximate drivers (drought, heat, etc.) but also attributing them to underlying changes in drivers and large-scale atmospheric conditions. These attribution-type analyses increasingly address and disentangle various sequences or dynamical interactions of events and their impacts, including compensating or amplifying effects on terrestrial carbon cycling.
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Klimaendringer gir mer ekstremvær. Hetebølger og tørke kan gi redusert karbonlagring i skog og frigjøre klimagasser til atmosfæren.
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Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks based on time series. Values (the nodes of the network) of the time series are linked to each other if there is no value higher between them. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytic results can be obtained for the degree distribution, the local clustering coefficient distribution, the mean path length, and others. HVGs have the potential to discern between deterministic-chaotic and correlated-stochastic time series. We investigate a set of around 150 river runoff time series at daily resolution from Brazil with an average length of 65 years. Most of the rivers are exploited for power generation and thus represent heavily managed basins. We investigate both long-term trends and human influence (e.g. the effect of dam construction) in the runoff regimes (disregarding direct upstream operations). HVGs are used to determine the degree and distance distributions. Statistical and information-theoretic properties of these distributions are calculated: robust estimators of skewness and kurtosis, the maximum degree occurring in the time series, the Shannon entropy, permutation complexity and Fisher Information. For the latter, we also compare the information measures obtained from the degree distributions to those using the original time series directly, to investigate the impact of graph construction on the dynamical properties as reflected in these measures. We also show that a specific pretreatment of the time series conventional in hydrology, the elimination of seasonality by a separate z-transformation for each calendar day, changes long-term correlations and the overall dynamics substantially and towards more random behaviour. Moreover, hydrological time series are typically limited in length and may contain ties, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. Focus is on one hand on universal properties of the HVG, common to all runoff series, and on site-specific aspects on the other.
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Marleen Pallandt Bernhard Ahrend Sönke Zaehle Marion Schrumpf Holger Lange Markus ReichsteinSammendrag
Soil organic carbon (SOC) is the largest terrestrial carbon pool. Changes in the hydrological cycle affect C-cycle turnover, with potential effects on the global C balance’s response to global change. However, large scale model representations of the sensitivity of soil carbon to soil moisture, through decomposition and interactions with nutrient cycles, are largely empirical to semi-empirical and uncertain. To better represent these dynamics, the aims of this PhD project* are to: • Investigate the role of soil moisture on SOC decomposition over a vertical profile; • Assess which moisture controls are (most) important in a multi-layered, mechanistic soil biogeochemistry model, the Jena Soil Model (JSM, Fig 2); • Update and improve the representations of soil moisture dynamics in JSM and evaluate this model for multiple sites along a moisture gradient and global scale
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Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks from time series. The values of the time series are considered as the nodes of the network and are linked to each other if there is no larger value between them, such as they can “see” each other. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytical results can be obtained for network-derived quantities such as the degree distribution, the local clustering coefficient distribution, the mean path length, and others. HVGs have the potential to discern between deterministic-chaotic and correlated-stochastic time series. Here, we investigate the sensitivity of the HVG methodology to properties and pre-processing of real-world data, i.e., time series length, the presence of ties, and deseasonalization, using a set of around 150 runoff time series from managed rivers at daily resolution from Brazil with an average length of 65 years. We show that an application of HVGs on real-world time series requires a careful consideration of data pre-processing steps and analysis methodology before robust results and interpretations can be obtained. For example, one recent analysis of the degree distribution of runoff records reported pronounced sub-exponential “long-tailed” behavior of North American rivers, whereas another study of South American rivers showed hyper-exponential “short-tailed” behavior resembling correlated noise.We demonstrate, using the dataset of Brazilian rivers, that these apparently contradictory results can be reconciled by minor differences in data-preprocessing (here: small differences in subtracting the seasonal cycle). Hence, data-preprocessing that is conventional in hydrology (“deseasonalization”) changes long-term correlations and the overall runoff dynamics substantially, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. After carefully accounting for these methodological aspects, the HVG analysis reveals that the river runoff dataset shows indeed complex behavior that appears to stem from a superposition of short-term correlated noise and “long-tailed behaviour,” i.e., highly connected nodes. Moreover, the construction of a dam along a river tends to increase short-term correlations in runoff series. In summary, the present study illustrates the (often substantial) effects of methodological and data-preprocessing choices for the interpretation of river runoff dynamics in the HVG framework and its general applicability for real-world time series.
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Tonje Ingeborg Økland Jørn-Frode Nordbakken Holger Lange Ingvald Røsberg Kjersti Holt Hanssen Nicholas ClarkeSammendrag
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Holger LangeSammendrag
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Many nonlinear methods of time series analysis require a minimal number of observations in the hundreds to thousands, which is not always easy to achieve for observations of environmental systems. As a result, finite size effects often hamper proper interpretation of the results; the estimation of the correlation dimension, Lyapunov exponents or KolmogorovSinai entropies, to name a few, is plagued by huge uncertainties. Eddy Covariance (EC) measurements of the carbon exchange between the atmosphere and vegetation provide a noticeable exception. The turbulent wind fields transporting carbon dioxide to the surface layer show variability over a large range of spatiotemporal scales, and their quantification demand a high temporal resolution, typically at 20 Hz. This generates very long time series even for short measurement periods; usually, the raw data are aggregated to carbon cycle observables, like Gross Primary Productivity (GPP) or Net Ecosystem Exchange (NEE) at half-hourly time steps. In this presentation, we investigate the high-resolution raw data of 3D wind speed and CO2 concentrations measured at a young forest plantation in Southeast Norway since July 2018. After introducing the EC technique and the Integrated Carbon Observation System (ICOS), we present results of complexity analysis, Tarnopolski diagrams, q-Entropy and Hurst analysis, and Empirical Mode Decomposition. This provides insights into not only whether the young forest stand is actually a source or sink of carbon, but also when, how and how strong carbon uptake and release are taking place at the site, and the nature of dynamics of carbon fluxes across this system boundary in general.
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National parks are established to reduce human influence on nature and contribute to species conservation, biodiversity and ecological services. Other states of protection like the UNESCO world heritage sites, for example, are created for maintaining culturally important places or lifestyles. In the Matobo Hills (Zimbabwe) both states of protection are present, a national park and a world heritage site. In addition, the land outside the National Park belongs to two different systems of ownership, namely “common” (i.e. community-owned) and “not-common” (privately or governmentally owned) land. In this paper, we investigated how the state of protection and the ownership affected the land use and land cover. We derived maps using Landsat images from 1989, 1998 and 2014 by supervised classification with Random Forests. To compensate for the lack of ground data we inferred past land use and land cover from recent observations combining photographs, Google Earth images and change detection. We could identify four classes, namely shrub land, forest, patchy vegetation and agricultural area. The Matobo National Park showed a stable composition of land cover during the study period and the main changes were observable in the surroundings. Outside the national park, forest increased by about 7%. The common lands have changed substantially and their agricultural area decreased. We attribute this development to the Fast Track Land Reform, which took place in the early 2000s. Our approach shows that combining information on recent land cover with change detection allows to study the temporal development of protected areas.
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Holger Lange Jogeir N. Stokland Bernhard Ahrens Lise Dalsgaard Stephanie Eisner Hanna Lee Toby Marthews Helge Meissner Sebastian SippelSammendrag
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Background and aims Decomposition of the finest harvest residues is important for the carbon and nutrient cycle in forest ecosystems both before and after tree felling. We assumed that decomposition is dependent on harvest residue fraction and chemistry, soil temperature and moisture, and aimed at determining decomposition rates and nutrient dynamics of needles, twigs and fine roots from newly felled Picea abies trees in two sites with different climate and topography. Methods Decomposition of needles, twigs and fine roots in mesh bags was followed for up to six years and four years in harvesting sites in eastern and western Norway, respectively. The western site had a more humid climate and a steeper terrain than the eastern site. Results The mass loss after two years was significantly higher for needles (49–59%) than for twigs and fine roots (29–38%). Between sites, there was no significant difference between mass loss for neither needles nor twigs. Nitrogen accumulated in needles during the first year, but 35% of initial needle N had been released after three years. The initial needle and twig decomposition rate was dependent on soil moisture and topographic aspect. During the first three years, needle lignin concentrations retarded whereas P concentrations stimulated needle mass loss. For twigs, P concentrations stimulated mass loss, whereas higher soil temperatures reduced it. Conclusions Lignin and P concentrations of plant parts and soil temperature were the most important factors for the first three-year mass loss. The slow release of nutrients shows the importance of remaining needles, twigs and fine roots as a long-time nutrient source in the ecosystems under study.
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Nicholas Clarke Silje Skår O. Janne Kjønaas Kjersti Holt Hanssen Tonje Ingeborg Økland Jørn-Frode Nordbakken Toril Drabløs Eldhuset Holger LangeSammendrag
Short-term (three to four years) effects of forest harvesting on soil solution chemistry were investigated at two Norway spruce sites in southern Norway, differing in precipitation amount and topography. Experimental plots were either harvested conventionally (stem-only harvesting, SOH) or whole trees, including crowns, twigs and branches were removed (whole-tree harvesting, WTH), leaving residue piles on the ground for some months before removal. The WTH treatment had two sub-treatments: WTH-pile where there had been piles and WTH-removal, from where residues had been removed to make piles. Increased soil solution concentrations of NO3–N, total N, Ca, Mg and K at 30 cm depth, shown by peaks in concentrations in the years after harvesting, were found at the drier, less steep site in eastern Norway after SOH and WTH-pile, but less so after WTH-removal. At the wetter, steeper site in western Norway, peaks were often observed also at WTH-removal plots, which might reflect within-site differences in water pathways due largely to site topography.
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For å verifisere beregningsmetoder og modeller for endringer av karbonlagre i skogsjord brukt i UNFCCC rapporteringen er det behov for data fra jordprøver med gjentak over tid og der prøvetakingsmetoder er konsistente. I Norge finnes ikke denne typen data på nasjonalt / landsdekkende nivå. For å møte behovet for verifisering av beregningsmetodikken brukt i UNFCCC rapporteringen innenfor rammene av eksisterende data med metodisk konsistens over tid, er det i denne undersøkelsen gjennomført ny prøvetaking av jord og vegetasjon i to etablerte forsøksfelt i skog i sør-øst Norge der vi fra før av både har data fra tidligere jordprøveanalyser og tilvekstdata for trær i skogsbestand. De to forsøksfeltene ligger på Nordmoen i Akershus (etablert 1973 og tilplantet 1974) og i Skiptvet i Østfold (etablert 1976 i eksisterende foryngelse med supplerplanting i 1977). Med nye jordprøver, biomassemålinger og vegetasjonsanalyser i 2011 gir dette to tidsserier på hhv. 38 og 34 år med hensyn på endringer i jordkarbon og inngangsverdier i beregningsmodellene. Den eksperimentelle behandlingen i Skiptvet omfatter ulik grad av treslagsblanding av bjørk og gran på de enkelte forsøksrutene, mens på Nordmoen sammenliknes rene bestand av hhv. bjørk, gran og furu. De klimatiske forhold er tilnærmet like, mens jordsmonntypen er ulik med næringsfattig sandjord på Nordmoen og næringsrik leirjord i Skiptvet. Resultatene fra forsøkene er begrenset til å representere klimatiske og vegetasjonsmessige forhold på Østlandet (og forhold tilsvarende de to lokalitetene), og forsøksfeltene er dermed ikke representative eksempelvis for kystnære og kontinentale strøk.
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Sebastian Sippel Jakob Zscheischler Martin Heimann Holger Lange Miguel D. Mahecha Geert Jan van Oldenborgh Friederike E.L. Otto Markus ReichsteinSammendrag
Daily precipitation extremes and annual totals have increased in large parts of the global land area over the past decades. These observations are consistent with theoretical considerations of a warming climate. However, until recently these trends have not been shown to consistently affect dry regions over land. A recent study, published by Donat et al. (2016), now identified significant increases in annual-maximum daily extreme precipitation (Rx1d) and annual precipitation totals (PRCPTOT) in dry regions. Here, we revisit the applied methods and explore the sensitivity of changes in precipitation extremes and annual totals to alternative choices of defining a dry region (i.e. in terms of aridity as opposed to precipitation characteristics alone). We find that (a) statistical artifacts introduced by data pre-processing based on a time-invariant reference period lead to an overestimation of the reported trends by up to 40 %, and that (b) the reported trends of globally aggregated extremes and annual totals are highly sensitive to the definition of a "dry region of the globe". For example, using the same observational dataset, accounting for the statistical artifacts, and based on different aridity-based dryness definitions, we find a reduction in the positive trend of Rx1d from the originally reported +1.6 % decade−1 to +0.2 to +0.9 % decade−1 (period changes for 1981–2010 averages relative to 1951–1980 are reduced to −1.32 to +0.97 % as opposed to +4.85 % in the original study). If we include additional but less homogenized data to cover larger regions, the global trend increases slightly (Rx1d: +0.4 to +1.1 % decade−1), and in this case we can indeed confirm (partly) significant increases in Rx1d. However, these globally aggregated estimates remain uncertain as considerable gaps in long-term observations in the Earth's arid and semi-arid regions remain. In summary, adequate data pre-processing and accounting for uncertainties regarding the definition of dryness are crucial to the quantification of spatially aggregated trends in precipitation extremes in the world's dry regions. In view of the high relevance of the question to many potentially affected stakeholders, we call for a well-reflected choice of specific data processing methods and the inclusion of alternative dryness definitions to guarantee that communicated results related to climate change be robust.
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We investigate a set of long-term river runoff time series at daily resolution from Brazil, monitored by the Agencia Nacional de Aguas. A total of 150 time series was obtained, with an average length of 65 years. Both long-term trends and human influence (water management, e.g. for power production) on the dynamical behaviour are analyzed. We use Horizontal Visibility Graphs (HVGs) to determine the individual temporal networks for the time series, and extract their degree and their distance (shortest path length) distributions. Statistical and information-theoretic properties of these distributions are calculated: robust estimators of skewness and kurtosis, the maximum degree occurring in the time series, the Shannon entropy, permutation complexity and Fisher Information. For the latter, we also compare the information measures obtained from the degree distributions to those using the original time series directly, to investigate the impact of graph construction on the dynamical properties as reflected in these measures. Focus is on one hand on universal properties of the HVG, common to all runoff series, and on site-specific aspects on the other. Results demonstrate that the assumption of power law behaviour for the degree distribtion does not generally hold, and that management has a significant impact on this distribution. We also show that a specific pretreatment of the time series conventional in hydrology, the elimination of seasonality by a separate z-transformation for each calendar day, is highly detrimental to the nonlinear behaviour. It changes long-term correlations and the overall dynamics towards more random behaviour. Analysis based on the transformed data easily leads to spurious results, and bear a high risk of misinterpretation.
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Isabella Børja Douglas L. Godbold Jan Světlík Nina Elisabeth Nagy Roman Gebauer Josef Urban Daniel Volařík Holger Lange Paal Krokene Petr Čermák Toril Drabløs EldhusetSammendrag
Global warming will most likely lead to increased drought stress in forest trees. We wanted to describe the adaptive responses of fine roots and fungal hyphae, at different soil depths, in a Norway spruce stand to long-term drought stress induced by precipitation exclusion over two growing seasons. We used soil cores, minirhizotrons and nylon meshes to estimate growth, biomass and distribution of fine roots and fungal hyphae at different soil depths. In control plots fine roots proliferated in upper soil layers, whereas in drought plots there was no fine root growth in upper soil layers and roots mostly occupied deeper soil layers. Fungal hyphae followed the same pattern as fine roots, with the highest biomass in deeper soil layers in drought plots. We conclude that both fine roots and fungal hyphae respond to long-term drought stress by growing into deeper soil layers.
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O. Janne Kjønaas Tonje Ingeborg Økland Kjell Andreassen Arne Stuanes Jørn-Frode Nordbakken Holger Lange Gro HylenSammendrag
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Holger Lange Nicholas Clarke O. Janne Kjønaas Jørn-Frode Nordbakken Sebastian Sippel Tonje Ingeborg ØklandSammendrag
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Holger LangeSammendrag
Long time series of environmental variables are reflecting the dynamics of ecosystems. Data on climate, water, carbon, nutrients and other observables provide the key to understand terrestrial systems and to detect trends, systemic changes and responses, e.g. to changing climate, disturbances, or management. We present a number of diagnostic measures, based on symbolic dynamics or order statistics, which quantify the information content and the complexity of environmental time series. Three examples for the application of complexity measures in environmental sciences will be provided: Earth System Models and their ability to reproduce observations of Gross Primary Productivity, the dynamics of river runoff, and long-term behavior of ion concentrations in stream water from a monitoring site in Germany. Diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.
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Holger LangeSammendrag
Time Series, models and uncertainty: process-based and data-based approaches to environmental problems
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Holger LangeSammendrag
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Convergent Cross Mapping (CCM) has recently been introduced by Sugihara et al. for the identification and quantification of causal relationships among ecosystem variables. In particular, the method allows to decide on the direction of causality; in some cases, the causality might be bidirectional, indicating a network structure. We extend this approach by introducing a method of surrogate data to obtain confidence intervals for CCM results. We then apply this method to time series from stream water chemistry. Specifically, we analyze a set of eight dissolved major ions from three different catchments belonging to the hydrological monitoring system at the Bramke valley in the Harz Mountains, Germany. Our results demonstrate the potentials and limits of CCM as a monitoring instrument in forestry and hydrology or as a tool to identify processes in ecosystem research. While some networks of causally linked ions can be associated with simple physical and chemical processes, other results illustrate peculiarities of the three studied catchments, which are explained in the context of their special history.
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Sebastian Sippel Holger Lange Miguel D. Mahecha Michael Hauhs Paul Bodesheim Thomas W. Kaminski Fabian Gans Osvaldo A. RossoSammendrag
Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. We demonstrate here that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide dataanalytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.
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Holger LangeSammendrag
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Sebastian Sippel Jakob Zscheischler Martin Heimann Holger Lange Miguel D. Mahecha Geert Jan van Oldenborgh Friederike E.L. Otto Markus ReichsteinSammendrag
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Lise Dalsgaard Rasmus Astrup Clara Antón Fernández Signe Kynding Borgen Johannes Breidenbach Holger Lange Aleksi Lehtonen Jari LiskiSammendrag
Boreal forests contain 30% of the global forest carbon with the majority residing in soils. While challenging to quantify, soil carbon changes comprise a significant, and potentially increasing, part of the terrestrial carbon cycle. Thus, their estimation is important when designing forest-based climate change mitigation strategies and soil carbon change estimates are required for the reporting of greenhouse gas emissions. Organic matter decomposition varies with climate in complex nonlinear ways, rendering data aggregation nontrivial. Here, we explored the effects of temporal and spatial aggregation of climatic and litter input data on regional estimates of soil organic carbon stocks and changes for upland forests. We used the soil carbon and decomposition model Yasso07 with input from the Norwegian National Forest Inventory (11275 plots, 1960–2012). Estimates were produced at three spatial and three temporal scales. Results showed that a national level average soil carbon stock estimate varied by 10% depending on the applied spatial and temporal scale of aggregation. Higher stocks were found when applying plot-level input compared to country-level input and when long-term climate was used as compared to annual or 5-year mean values. A national level estimate for soil carbon change was similar across spatial scales, but was considerably (60–70%) lower when applying annual or 5-year mean climate compared to long-term mean climate reflecting the recent climatic changes in Norway. This was particularly evident for the forest-dominated districts in the southeastern and central parts of Norway and in the far north. We concluded that the sensitivity of model estimates to spatial aggregation will depend on the region of interest. Further, that using long-term climate averages during periods with strong climatic trends results in large differences in soil carbon estimates. The largest differences in this study were observed in central and northern regions with strongly increasing temperatures.
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We investigate a set of long-term (several decades) time series for the runoff at river gauges at daily resolution. They are monitored by the Agencia Nacional de Aguas, and time series provided by the Operador Nacional do Sistema Elétrico, Brazil. A total of 150 time series was obtained, with an average length of 73 years. Both long-term trends as well as the influence of extreme events on the dynamical behaviour are analyzed. We use Horizontal Visibility Graphs (HVGs) to determine the individual temporal networks for the time series, and extract their degree distributions. Statistical and information-theoretic properties of these distributions are calculated: robust estimators of skewness and kurtosis, the maximum degree occurring in the time series, the Shannon entropy, permutation complexity and Fisher Information. For the latter, we also compare the information measures obtained from the degree distributions to those using the original time series directly, to investigate the impact of graph construction on the dynamical properties as reflected in these measures. Focus is on one hand on universal properties of the HVG, common to all runoff series, and on site-specific aspects on the other. We show that a specific pretreatment of the time series conventional in hydrology, the elimination of seasonality by a separate z-transformation for each calendar day, is highly detrimental to the nonlinear behaviour. It changes long-term correlations and the overall dynamic towards more random behaviour. Analysis based on the transformed data easily leads to spurious results, and bear a high risk of misinterpretation.
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Measuring energy and matter fluxes between the atmosphere and vegetation using the Eddy Covariance (EC) technique is the state-of-the-art method to quantify carbon exchange between terrestrial ecosystems and their surrounding. The EC equipment is usually mounted onto a flux tower reaching higher than the local canopy. Today, more than 600 flux towers are in operation worldwide. The methodological requirements lead to high sampling frequency (20 Hz) and thus to the production of very long time series. These are related to temperature, wind components, water vapour, heat and gas exchange, and others. In this chapter, the potential of Recurrence Analysis (RA) to investigate the dynamics of this atmosphere-vegetation boundary system is elucidated. In particular, the effect of temporal resolution, the identification of periods particular suitable for reliable EC flux calculations, and the detection of transitions between dynamical regimes will be highlighted.
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Tonje Ingeborg Økland Jørn-Frode Nordbakken Holger Lange Ingvald Røsberg O. Janne Kjønaas Kjersti Holt Hanssen Nicholas ClarkeSammendrag
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Nicholas Clarke Hugh Cross Toril Drabløs Eldhuset Kjersti Holt Hanssen Ari Hietala O. Janne Kjønaas Holger Lange Jørn-Frode Nordbakken Tonje Ingeborg Økland Ingvald Røsberg Silje SkårSammendrag
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Effects of clear-cut harvesting on ground vegetation plant species diversity and their cover are investigated at two Norway spruce sites in southern Norway, differing in climate and topography. Experimental plots at these two sites were either harvested conventionally (stem-only harvesting) or whole trees including crowns, twigs and branches were removed (whole-tree harvesting), leaving residue piles on the ground for some months. We compare the number of plant species in different groups and their cover sums before and after harvesting, and between the different treatments, using non-parametric statistical tests. An overall loss of ground vegetation biodiversity is induced by harvesting and there is a shift in cover of dominant species, with negative effects for bryophytes and dwarf shrubs and an increase of graminoid cover. Differences between the two harvesting methods at both sites were mainly due to the residue piles assembled during whole-tree harvesting and the physical damage made during the harvesting of residues in these piles. The presence of the residue piles had a clear negative impact on both species numbers and cover. Pile residue harvesting on unfrozen and snow-free soil caused more damage to the forest floor in the steep terrain at the western site compared to the eastern site.
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Lise Dalsgaard Holger Lange Line Tau Strand Ingeborg Callesen Signe Kynding Borgen Jari Liski Rasmus AstrupSammendrag
Soil organic carbon (C), accumulated over millennia, comprise more than half of the C stored in boreal and temperate forest landscapes. We used the Norwegian national forest inventory and soil survey network (n = 719, no deep organic soils) to explore the validity of a deterministic model representation of this pool (Yasso07). We statistically compared simulated and measured soil C stocks and related differences (measured – simulated) to site factors (drainage, topography, climate, vegetation, C-to-N ratio, and soil classification). Median C stocks were 5.0 kg C·m−2 (model) and 14.5 kg C·m−2 (measurements). Soil C differences related to site factors (r2 of 0.16 to 0.37). For Brunisols, Gleysols, and wet Organic soils, differences related primarily to topographic wetness. For Regosols, Podzols, and Dystric Eluviated Brunisols, they related to climate, profile depth, and, in some cases, drainage class and site index. We argue that soil moisture regimes in our study area overrule tree productivity effects in the determination of soil C stocks and present conditions for soil formation that the model cannot (and does not explicitly) account for. These are processes such as humification and podsolization that involve eluviation and illuviation of dissolved organic C (DOC) with sesquioxides to form spodic B horizons and carbon enrichment due to hampered decomposition in frequently anoxic conditions.
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Tonje Ingeborg Økland Jørn-Frode Nordbakken Holger Lange Ingvald Røsberg O. Janne Kjønaas Kjersti Holt Hanssen Nicholas ClarkeSammendrag
Whole-tree harvest (WTH), i.e. harvesting of forest residues (twigs, branches and crown tops) in addition to stems, for bioenergy purposes may lead to biodiversity loss and changes in species composition in forest ground vegetation, which in turn also will affect soil properties. Effects of clear-cut harvesting on ground vegetation have been investigated at two Norway spruce sites in southern east and western Norway, respectively, differing in climate and topography. Experimental plots at these two sites were either harvested conventionally (stem-only harvest, SOH), leaving harvest residues spread on the site,or WTH was carried out, with the residues collected into piles at the site for six - nine months prior to removal. Vegetation plots in the eastern site were established and analysed before WTH and SOH in 2008 and reanalysed after harvesting in 2010, 2012 and 2014. In the western site vegetation plots were established before WTH and SOH in 2010 and reanalysed after harvesting in 2012 and 2014 (and planned for 2016). All vegetation plots are permanently marked. Pre-as well as post-harvesting species abundances of all species in each vegetation plot were each time recorded as percentage cover (vertical projection) and subplot frequency. Environmental variables (topographical, soil physical, soil chemical, and tree variables) were recorded only once; before WTH and SOH. Effec ts of WTH and SOH on ground vegetation biodiversity and cover are presented.
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Livsløpsanalyser (LCA) er analyser der man tar hele produktets livsløp med i vurderingen. Metodikken ble opprinnelig utviklet for å vurdere ulike embalasjeprodukter, men er nå videreutviklet for å kunne brukes i svært mange ulike sammenhenger. I dag brukes livsløpsanalyser for eksempel i beregninger knyttet til utslipp av klimagasser og danner grunnlag for mer omfattende vurderinger av produkters eller prosessers totale miljøbelastning.
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The eddy covariance (EC) method is one key method to quantify fluxes in biogeochemical cycles in general, and carbon and energy transport across the vegetation-atmosphere boundary layer in particular. EC data from the worldwide net of flux towers (Fluxnet) have also been used to validate biogeochemical models. The high resolution data are usually obtained at 20 Hz sampling rate but are affected by missing values and other restrictions. In this contribution, we investigate the nonlinear dynamics of EC fluxes using Recurrence Analysis (RA). High resolution data from the site DE-Bay (Waldstein-Weidenbrunnen) and fluxes calculated at half-hourly resolution from eight locations (part of the La Thuile dataset) provide a set of very long time series to analyze. After careful quality assessment and Fluxnet standard gapfilling pretreatment, we calculate properties and indicators of the recurrent structure based both on Recurrence Plots as well as Recurrence Networks. Time series of RA measures obtained from windows moving along the time axis are presented. Their interpretation is guided by three different questions: (1) Is RA able to discern periods where the (atmospheric) conditions are particularly suitable to obtain reliable EC fluxes? (2) Is RA capable to detect dynamical transitions (different behavior) beyond those obvious from visual inspection? (3) Does RA contribute to an understanding of the nonlinear synchronization between EC fluxes and atmospheric parameters, which is crucial for both improving carbon flux models as well for reliable interpolation of gaps? (4) Is RA able to recommend an optimal time resolution for measuring EC data and for analyzing EC fluxes? (5) Is it possible to detect non-trivial periodicities with a global RA? We will demonstrate that the answers to all five questions is affirmative, and that RA provides insights into EC dynamics not easily obtained otherwise.
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In this chapter, the potential of Recurrence Analysis (RA) for applications in the biogeosciences is demonstrated. We investigate the fraction of absorbed photosynthetically active radiation (FAPAR), an index based on multispectral reflectance properties of land surfaces which relates to the carbon uptake by plants. FAPAR is available with global coverage from satellites. We combine observations from two sensors, SeaWifs on board SeaStar and MERIS on board Envisat, to produce time series with 10 days resolution for a period of 14 years (1998–2011) at a spatial resolution of 0.5∘ latitude × 0.5∘ longitude. After careful quality checking and gap-filling, more than 30,000 individual time series are obtained covering all terrestrial ecosystems and climates apart from Antarctica and major deserts. To characterize the different dynamical behaviour as a function of spatial location, we employ Recurrence Quantification Analysis (RQA) and Recurrence Network Analysis (RNA). They deliver detailed information on the nonlinear dynamics in phase space through embedding. RQA and network measures are calculated for individual time series using identical recurrence parameters, and results are visualized on world maps. Taken together, the recurrence analysis leads to a partitioning of the terrestrial biosphere into regions with distinct dynamical patterns of photosynthetic activity.
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O. Janne Kjønaas Nicholas Clark Toril Drabløs Eldhuset Ari Hietala Hugh Cross Kjersti Holt Hansen Tonje Ingeborg Økland Holger Lange Jørn-Frode Nordbakken Ingvald RøsbergSammendrag
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O. Janne Kjønaas Nicholas Clarke Toril Drabløs Eldhuset Ari Hietala Hugh Cross Kjersti Holt Hanssen Tonje Ingeborg Økland Holger Lange Jørn-Frode Nordbakken Ingvald RøsbergSammendrag
Tree harvest and different harvesting methods may affect the soil carbon (C) pool in forest ecosystems. In con- ventional stem-only timber harvesting (SOH), branches and tops that are left in the forests may contribute to the build-up of the soil carbon pool. In whole-tree harvesting (WTH), inputs of organic matter from branches and tops are strongly reduced. We established field experiments at Gaupen, SE and Vindberg, SW Norway, to study the short-term effects of SOH and WTH on processes affecting the accumulation and loss of soil C. Logging residues on the WTH plots were collected in piles that were removed after 6 months, rendering two sub treatments (WTH- pile and WTH-removal areas). We weighed selected trees and logging residues, surveyed understorey biomass production, quantified pre-harvest soil C and nutrient pools down to 30 cm. Soil respiration was measured and soil water sampled monthly during the growing season, while temperature and moisture were measured continuously. Organic and mineral horizons were incubated at different temperatures to estimate potential C and N mineraliza- tion, and deep sequencing of the ITS2 barcode region of fungal DNA was performed on the samples. Litterbags were deployed in the SOH plots. The logging residues amounted to 2.2-2.4 kg C m-2 At Gaupen, the mean in situ soil respiration rates increased following harvest with all treatments, but were significantly higher in WTH-pile and SOH relative to the WTH- removal areas in the first year as well as the fourth year of treatment. The former rates included aboveground decomposing needles and twigs but excluded coarser branches. The observed increase in the WTH-removal areas may be related to decomposing roots, as well as to increased C mineralization partly due to the higher soil tem- peratures following harvest. Soil temperature was the single most important factor explaining the variability in soil respiration rates over all treatments. At Vindberg, a decrease in soil respiration was observed with all treatments in the second and third years following harvest. At both sites, decomposition of logging residues from needles was more rapid relative to twigs and fine roots. The decomposing residues released a substantial amount of nitrogen which was gradually reflected in the soil water at 30 cm soil depth. A considerable increase in the NO3-N concen- tration also in the WTH-removal areas in the second year following harvest suggests an increase in N availability from decomposing fine roots and/or soil organic matter. The increased N availability in the WTH-removal areas was supported by results from short term lab incubations of undisturbed soil from the forest floor. The changes in the WTH-removal areas were also reflected in the soil fungal diversity: saprophytic ascomycetes on decaying plant material showed a striking increase in all treatments. For the WTH-removal areas, this may, again, be related to the increased input of root litter; however, the decrease in mycorrhizal basidiomycete species and the vigorous increase of ascomycetes following harvest may also affect the C mineralization of soil organic matter.
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Holger Lange Lise Dalsgaard Signe Kynding Borgen O. Janne Kjønaas Ingeborg Callesen Jari Liski Line Tau StrandSammendrag
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Holger Lange Sebastian Sippel Miguel D. Mahecha Osvaldo A. Rosso Kirsten Thonicke Susanne Rolinski Anja Rammig Christian BeerSammendrag
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In an attempt to discern stochastic and deterministic parts of measured signals, we analyze time series from the viewpoint of ordinal pattern statistics. After choosing a suitable embedding dimension $D$, the occurrencies of all $D!$ patterns form a probability distribution $P$. The latter is input to information and complexity functionals describing, e.g., chaotic regimes or stochastic properties due to long-range correlations. Here, we use an information quantifier which is local in pattern probability space, the Fisher information $F$. This is calculable only after fixing a pattern coding scheme, i.e. numbering each and every pattern. It has been demonstrated that $F$ discerns different dynamic regimes for the logistic map to a certain extent; however, this depends on the details of the coding scheme. Here, we seek to find an optimal coding scheme for long-range correlated stochastic processes, mimicking many records e.g. from the geosciences. To increase the contrast between colored noise and deterministic processes, $F$ should be minimal for the former. Structurally similar ordinal patterns should be located adjacent to each other. Similarity is related to the number of inversions in the respective patterns. In practical terms, it is impossible to try all $D!!$ coding schemes whenever$D > 3$; however, we demonstrate a classification of coding schemes into equivalence classes based on the number of "jumps" in the patterns. These are used to improve the Keller and Lehmer coding schemes. The approach has a potential to provide an analytical understanding of the Fisher information for stochastic processes. Results for these optimizations will be shown for both the logistic map and colored ($k$-) noise. As a byproduct, an innovative method to estimate the scaling exponent $k$ emerges. Finally, we comment shortly on the importance of finite size effects, which is always an issue when dealing with observed data.
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Lise Dalsgaard Clara Antón Fernández Rasmus Astrup Signe Kynding Borgen Johannes Breidenbach Holger Lange Jogeir N. Stokland Gunnhild SøgaardSammendrag
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The analysis of temporal geospatial data has provided important insights into global vegetation dynamics, particularly the interaction among different variables such as precipitation and vegetation indices. Nevertheless, this analysis is not a straightforward task due to the complex relationships among different systems driving the dynamics of the observed variables. Aiming at automatically extracting information from temporal geospatial data, we propose a new approach to detect stochastic and deterministic patterns embedded into time series and illustrate its effectiveness through an analysis of global geospatial precipitation and vegetation data captured over a 14 year period. By knowing such patterns, we can find similarities in the behavior of different systems even if these systems are characterized by different dynamics. In addition, we developed a novel determinism measure to evaluate the relative contribution of stochastic and deterministic patterns in a time series. Analyses showed that this measure permitted the detection of regions on the global map where the radiation absorbed by the vegetation and the incidence of rain occur with similar patterns of stochasticity. The methods developed in this study are generally applicable to any spatiotemporal data set and may be of particular interest for the analysis of the vast amount of remotely sensed geospatial data currently being collected routinely as part of national and international monitoring programs.
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Foredrag – Hva forteller modellene våre?
Silje Skår, Susanne Eich-Greatorex, Trine Aulstad Sogn, ...
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Nicholas Clarke Toril Drabløs Eldhuset O. Janne Kjønaas Tonje Ingeborg Økland Kjersti Holt Hanssen Wenche Aas Trine Aulstad Sogn Kjell Andreassen Susanne Eich-Greatorex Helja-Sisko Helmisaari Ari Hietala Erik Eid Hohle Staffan Jacobson Holger Lange Jørn Frode Nordbakken Karsten Raulund-Rasmussen Ingvald Røsberg Silje Skår Volkmar Timmermann Andreas TutturenSammendrag
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Tonje Ingeborg Økland Jørn-Frode Nordbakken Holger Lange Ingvald Røsberg O. Janne Kjønaas Nicholas Clarke Kjersti Holt HanssenSammendrag
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Tonje Ingeborg Økland Jørn-Frode Nordbakken Holger Lange Ingvald Røsberg O. Janne Kjønaas Kjersti Holt Hanssen Nicholas ClarkeSammendrag
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Nicholas Clarke Toril Drabløs Eldhuset Kjersti Holt Hanssen Ari Hietala O. Janne Kjønaas Holger Lange Jørn-Frode Nordbakken Tonje Ingeborg Økland Ingvald RøsbergSammendrag
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Nicholas Clarke Toril Drabløs Eldhuset Kjersti Holt Hanssen Ari Hietala O. Janne Kjønaas Holger Lange Jørn-Frode Nordbakken Tonje Ingeborg Økland Ingvald RøsbergSammendrag
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O. Janne Kjønaas Nicholas Clarke Toril Drabløs Eldhuset Kjersti Holt Hanssen Ari Hietala Holger Lange Jørn-Frode Nordbakken Tonje Ingeborg Økland Ingvald RøsbergSammendrag
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O. Janne Kjønaas Nicholas Clarke Toril Drabløs Eldhuset Ari Hietala Hugh Cross Tonje Ingeborg Økland Jørn-Frode Nordbakken Holger Lange Ingvald Røsberg Kjersti Holt HanssenSammendrag
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Reliable methods are required to predict changes in soil carbon stocks. Process-based models often require many parameters which are largely unconstrained by observations. This induces uncertainties which are best met by using repeated measurements from the same sites. Here, we compare two carbon models, Yasso07 and Romul, in their ability to reproduce a set of field observations in Norway. The models are different in the level of process representation, structure, initialization requirements and calibration- and parameterization strategy. Field sites represent contrasting tree species, mixture and soil types. The number of repetitions of C measurements varies from 2 to 6 over a period of up to 35 years, and for some of the sites, which are part of long-term monitoring programs, plenty of auxiliary information is available. These reduce the danger of overparametrization and provide a stringent testbed for the two models. Focus is on the model intercomparison, using identical site descriptions to the extent possible, but another important aspect is the upscaling of model results to the regional or national scale, utilizing the Norwegian forest inventory system. We suggest that a proper uncertainty assessment of soil C stocks and changes has to include at least two (and preferably more) parametrized models.
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Holger Lange Lise Dalsgaard Signe Kynding Borgen Silje Skår O. Janne Kjønaas Kjell AndreassenSammendrag
Reliable methods are required to predict changes in soil carbon stocks. Process-based models often require many parameters which are largely unconstrained by observations. This induces uncertainties which are best met by using repeated measurements from the same sites. Here, we compare two carbon models, Yasso07 and Romul, in their ability to reproduce a set of field observations in Norway. The models are different in the level of process representation, structure, initialization requirements and calibration- and parameterization strategy. Field sites represent contrasting tree species, mixture and soil types. The number of repetitions of C measurements varies from 2 to 6 over a period of up to 35 years, and for some of the sites, which are part of long-term monitoring programs, plenty of auxiliary information is available. These reduce the danger of overparametrization and provide a stringent testbed for the two models. Focus is on the model intercomparison, using identical site descriptions to the extent possible, but another important aspect is the upscaling of model results to the regional or national scale, utilizing the Norwegian forest inventory system. We suggest that a proper uncertainty assessment of soil C stocks and changes has to include at least two (and preferably more) parametrized models.
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A wide range of forest products and industries have been examined in life cycle analyses (LCA). Life cycle data are essential for identifying forestry operations that contribute most to carbon emissions. Forestry can affect net CO2 emissions by changing carbon stocks in biomass, soil and products, by supplying biofuels to replace fossil fuels as well as by establishing new forests. The transport of forest products is crucial to greenhouse gas (GHG) emissions. We conceptualize the chain from seed production, silviculture, harvesting, and timber transport to the industry as a system. Inputs to the system are energy and fuel, the output represents GHG emissions. The reference functional unit used for the inventory analysis and impact assessment is one cubic meter of harvested timber under bark. GHG emissions from forestry in East Norway were calculated for the production of one such unit delivered to the industry gate in 2010 (cradle-to-gate inventory), showing that timber transport from the forest to the final consumer contributed with more than 50 % to the total GHG emissions. To assess uncertainty of model approaches, the LCA was conducted with two different models, SimaPro and GaBi, both using the Ecoinvent database with data adapted to European conditions.
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A wide range of forest products and industries have been examined in life cycle analyses (LCA). Life cycle data are essential for identifying forestry operations that contribute most to carbon emissions. Forestry can affect net CO2 emissions by changing carbon stocks in biomass, soil and products, by supplying biofuels to replace fossil fuels as well as by establishing new forests. The transport of forest products is crucial to greenhouse gas (GHG) emissions. We conceptualize the chain from seed production, silviculture, harvesting, and timber transport to the industry as a system. Inputs to the system are energy and fuel, the output represents GHG emissions. The reference functional unit used for the inventory analysis and impact assessment is one cubic meter of harvested timber under bark. GHG emissions from forestry in East Norway were calculated for the production of one such unit delivered to the industry gate in 2010 (cradle-to-gate inventory), showing that timber transport from the forest to the final consumer contributed with more than 50 % to the total GHG emissions. To assess uncertainty of model approaches, the LCA was conducted with two different models, SimaPro and GaBi, both using the Ecoinvent database with data adapted to European conditions.
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Nicholas Clarke Toril Drabløs Eldhuset Kjersti Holt Hanssen O. Janne Kjønaas Holger Lange Jørn-Frode Nordbakken Ingvald Røsberg Tonje Ingeborg ØklandSammendrag
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Nicholas Clarke Toril Drabløs Eldhuset O. Janne Kjønaas Tonje Ingeborg Økland Kjersti Holt Hanssen Wenche Aas Trine Aulstad Sogn Kjell Andreassen Susanne Eich-Greatorex Helja-Sisko Helmisaari Ari Hietala Erik Eid Hohle Staffan Jacobson Holger Lange Jørn Frode Nordbakken Karsten Raulund-Rasmussen Ingvald Røsberg Silje Skår Volkmar Timmermann Andreas TutturenSammendrag
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Over the past 40 years, a new multidisciplinary field of study has emerged which is characterised by at least two major changes in the way some scientists treat systems. First, it is increasingly accepted that we cannot fully understand the laws that govern a system simply by studying its parts, nor can we fully understand the behaviour of the parts without placing them in the context of the larger system in which they are embedded. This realization, which has arisen as we face the limits of reductionist science, has given rise to the development of new models and methods that facilitate the study of systems across multiple scales of organization. Second, the notions of equilibrium and predictability in natural systems, developed in the 19th Century and continuously pursued until far into the 20th Century, are being rejected in favour of models that embrace variability, diversity, continual change and adaptation as the status quo. Traditional analytical models that assume a stable equilibrium are being replaced by new approaches that facilitate the exploration of a system’s natural range of variation and its possible emergent responses to changing external conditions. The implications of this new field, now known as complexity science, are manifest across disciplines, fundamentally changing the way we study, analyze and perceive natural systems. We provide an overview of complexity science in the context of forest management.
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The quality of surface water and groundwater is closely related to flow paths in the vadose zone. Therefore, dye tracer studies are often carried out to visualise flow patterns in soils. These experiments provide images of stained soil profiles and their evaluation demands knowledge in hydrology as well as in image analysis and statistics. The classical analysis consists of image classification in stained and non-stained parts and calculation of the dye coverage (i.e. the proportion of staining). The variation of this quantity with depth is interpreted to identify dominant flow types. While some feature extraction from images of dye-stained profiles is necessary, restricting the analysis to the dye coverage alone might miss important information. In our study we propose to use several index functions to extract different (ideally complementary) features. We associate each image row with a feature vector (i.e. a certain number of image function values) and use these features to cluster the image rows to identify similar image areas. Because images of stained profiles might have different reasonable clusterings, we calculate multiple consensus clusterings. Experts can explore these different solutions and base their interpretation of predominant flow type on quantitative (objective) criteria.
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Tonje Ingeborg Økland Jørn Frode Nordbakken Holger Lange Ingvald Røsberg Janne O. Kjønaas Kjersti Holt Hanssen Toril Drabløs Eldhuset Nicholas ClarkeSammendrag
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Fine roots contribute to net primary production in forests, but knowledge of fine root longevity and turnover is still incomplete and limited to few tree species. In this study, we used minirhizotrons to compare fine root biomass, longevity and turnover of Pinus sylvestris L., Betula pendula Roth and Picea abies (L) Karst. in southern Sweden. Minirhizotron tubes were installed in 2006 and root images were taken in 2007–2010. Soil cores were used to estimate fine root biomass. Soil samples were taken from the humus layer and from 0 to 10 cm, 10 to 20 cm and 20 to 30 cm depth in the mineral soil. Only images from the humus layer and the upper 10 cm of mineral soil were included in root analysis. Spruce has a higher aboveground production than pine and birch in southern Sweden and this was reflected in larger fine root biomass as well as higher fine root biomass production. The annual tree fine root biomass production (humus and 0–30 cm in mineral soil) was 73, 78 and 284 g m−2 in pine, birch and spruce stands, respectively. Thicker fine roots tended to live longer. The majority of the fine roots were thinner than 0.5 mm in diameter, with a turnover rate (KM) of 0.4 year−1. When comparing all fine roots, i.e. all roots 0–2 mm, pine had the highest longevity, 1120 days, compared with 900 days for spruce and 922 days for birch (KM).
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When calculating the Bandt and Pompe ordinal pattern distribution from given time series at depth D, some of the D! patterns might not appear. This could be a pure finite size effect (missing patterns) or due to dynamical properties of the observed system (forbidden patterns). For pure noise, no forbidden patterns occur, contrary to deterministic chaotic maps. We investigate long time series of river runoff for missing patterns and calculate two global properties of their pattern distributions: the Permutation Entropy and the Permutation Statistical Complexity. This is compared to purely stochastic but long-range correlated processes, the k-noise (noise with power spectrum f−k), where k is a parameter determining the strength of the correlations. Although these processes closely resemble runoff series in their correlation behavior, the ordinal pattern statistics reveals qualitative differences, which can be phrased in terms of missing patterns behavior or the temporal asymmetry of the observed series. For the latter, an index is developed in the paper, which may be used to quantify the asymmetry of natural processes as opposed to artificially generated data.
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Alexander Radebach Jonathan F. Donges Reik V. Donner Susana Barbosa Holger Lange Jürgen KurthsSammendrag
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Reik V. Donner Susana Barbosa Jonathan F. Donges Jose Matos Alexander Radebach Holger Lange Jürgen KurthsSammendrag
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The ingrowth core method is widely used to assess fine root (diameter < 2 mm) production but has many inherent deficiencies. In this study, we modified this method by adopting mini ingrowth cores (diameter 1.2 cm), extending sample intervals to a growing season, and developing new models to quantify the concurrent production, mortality and decomposition, and applied them to a secondary Mongolian oak (Quercus mongolica Fischer ex Ledebour) forest. Annual fine root production, mortality and decomposition estimated by our method were 2.10 ± 0.23, 1.78 ± 0.20 and 0.85 ± 0.13 t ha−1, respectively, and 33.3% of the production was decomposed in the growing season. The production estimate using our method was significantly higher than those using two long-term ingrowth core (sample interval >2 months) methods. However, it was significantly lower than that using the short-term ingrowth core (sample interval <2 months) method, presumably due to the lower root competition and less decomposition occurring in the short-term cores. The fine root estimates using our method in the growing season were generally higher than those using the forward and continuous inflow methods but lower than those using the backward method. Our method reduces the disturbances in roots and soil, minimizes the sampling frequency and improves the quantification of fine root decomposition during the sample intervals. These modifications overcome the limitations associated with the previous ingrowth core methods. Our method provides an improved alternative for estimating fine root production, mortality and decomposition.
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Groundwater pollution by agrochemicals, degradation of soil quality and pollution of aquatic ecosystems by agricultural drainage waters have become an issue in the last decades. Flow processes in the vadose zone are closely related to these problems. In general, water flow in soils can be classified into two major categories: uniform and non-uniform (preferential) flow (In: U.S. National Committee for Rock Mechanics, Conceptual Models of Flow and Transport in the Fractured Vadose Zone, 2001, pp.149-187). The former describes a relatively slow movement of water through the porous soil matrix and can be modelled by Richard”s equation. The latter comprises all flow types where water bypasses a portion of the soil matrix and flows through localised (i.e. preferential) paths. Unlike uniform flow, preferential flow is hardly predictable because the assumptions of Richard”s equation of a homogeneous representative elementary volume characterised by a single value of water potential, water content and hydraulic conductivity are frequently violated (Eur J Soil Sci, 2007; 58:523-546)....
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Isabella Børja Jan Svtlík Toril Drabløs Eldhuset Holger Lange Frank Newell Kidder Douglas Lawrence GodboldSammendrag
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A crucial process of the terrestrial carbon cycle is photosynthetic uptake through plants. This may be quantified by calculating the fraction of absorbed photosynthetic active radiation (fapar), based on multispectral reflectance properties of the earth surface. The fapar index is available with global coverage from satellite sensors.Here, we combine two satellite missions, SeaWifs on board OrbView2 and MERIS on board Envisat, to produce time series with 10 days resolution for a period of 14 years (19982011) at a spatial resolution of 0.5 latitude x 0.5 longitude. These more than 50000 individual time series represent a huge range of dynamical behavior with respect to variability, periodicities and correlation structure.To characterize differences as a function of spatial location or distance, we employ Recurrence Quantification Analysis (RQA) and Recurrence Network Analysis (RNA). Two strategies are followed. On one hand, RQA and network variables are calculated for individual time series using identical recurrence parameters, and compared to see whether differences between them resemble different climate regimes, biomes, plant functional types or landuse classes. On the other hand, a multivariate extension of RNA will be exploited to see whether networks within networks occur, i.e. whether RNA provides sufficient contrasts to discern different clusters of pixels on the globe.Taken together, the recurrence analysis might lead to a new classification of the terrestrial biosphere which in turn can be compared to existing partitioning based on climate and/or vegetation properties. A number of technical issues will be addressed as well, such as the impact of the finite length of the series (504 values each), the necessity to gapfill parts of the data, the stability of network variables against changes in the recurrence parameters, or the computational challenges involved in the multinetwork analysis of many series. http://dames.pik-potsdam.de/Abstracts.pdf
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Our aim is to investigate the temporal dynamics of the Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) on a global scale and its relation to the main meteorological variables across space. We focus on complex patterns in time, which are neither regular (trend and seasonality) nor random (noise), but somewhere in between. We quantify complexity and information content or entropy using methods from order statistics and complexity sciences.Time series with high entropy are difficult to predict, whereas time series with high complexity are difficult to describe. This leads to a spatially explicit characterization of complex patterns in a very sensitive way. We use FAPAR observations (SeaWiFS and MERIS, 1998 to 2012) along with gridded global surface air temperature, precipitation and shortwave radiation.All these time series are explored on a pixelbypixel basis and clustered according to a very recent classification system of the land surface. In addition, we quantify the time reversal asymmetry of these data. We compare environmental time series with data from a stochastic candidate process temporally symmetric and long range correlated artificial knoise.Results were plotted in the ComplexityversusEntropy plane (CH plane), showing the particular footprint of each variable in a very sensitive way. Visualized in world maps, results revealed unexpected complex pattern in some dry regions, in particular on pixels surrounding deserts and in eastern Sahara. In this respect, the results provide a new classification of the climate and the biosphere. http://dames.pik-potsdam.de/Abstracts.pdf
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We exploit two recently developed informationtheoretic quantities (or measures) designed to quantify information content and complexity of ordered data (time series), respectively. Both are based on order statistics of given data sets, and probe into the shortterm structure of the data only due to finite length restrictions. Their usage requires fixing one parameter, word length or order depth D. The information measure is the orderbased Shannon entropy HS, and the complexity measures is the JensenShannon divergence CJS. The latter requires a chosen reference distribution, i.e. CJS represents a class of measures. Entropy HS and complexity CJS of data series may be represented against each other in a twodimensional diagram which we will refer to as ComplexityEntropy Causality Plane, or CECP. Very long realizations of classic stochastic processes and chaotic deterministic maps each obey one location in the CECP, specific for the process. This can be used to differentiate chaos from correlated noise (Rosso et al. 2007), which is notoriously difficult otherwise. For observed data, a mixture of deterministic (signal) and stochastic (noise) parts is to be expected. We use an ensemble of longterm river runoff time series as example, which are known to exhibit powerlaw decaying longrange correlations. We compare these data with a longrange correlated candidate process, the k noise, from the perspective of order statistics and the CECP. Although these processes resemble runoff series in their correlation behavior and may be even tuned to any runoff series by changing the value of k, the CECP locations and in particular the order pattern statistics reveals qualitative differences between them. We give a detailed account of these differences, and use them to conclude on the deterministic nature of the (shortterm) dynamics of the runoff time series. The proposed methodology also represents a stringent test bed for hydrological or other environmental models. http://dames.pik-potsdam.de/Abstracts.pdf
Forfattere
Holger LangeSammendrag
We calculate entropy and complexity of runoff time series and artificially generated series with long-range correlations. Entropy and complexity of data series may be represented against each other in a two-dimensional diagram which we will refer to as Complexity-Entropy Causality Plane, or CECP. We use a recently developed framework for these two indicators based on order statistics. It is well-known that runoff, as all other environmental time series actually measured, is a mixture of deterministic (signal) and stochastic (noise) parts, the latter due to noise inherent in the measurement process and externally induced by natural processes. The distinction between signal and noise is notoriously difficult and subject to much debate. In our approach, the observed series are compared to purely stochastic but long-range correlated processes, the k noise, where k is a parameter determining the strength of the correlations. Although these processes resemble runoff series in their correlation behavior and may be even tuned to any runoff series by changing the value of k, the CECP locations and in particular the order pattern statistics reveals qualitative differences between runoff and k noise. We use these differences to conclude on the deterministic nature of the (short-term) dynamics of the runoff time series. The proposed methodology also represents a stringent test bed for hydrological models.
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Holger LangeSammendrag
Spatial dependencies among environmental variables are often quantified by spatial autocorrelation functions. The latter basically assume linearity and isotropy, requirements usually not satisfied for measured data. Typical symptoms of violated assumptions are biased parameter estimations. Relaxing the assumptions of linear dependencies and isotropy, we present a conceptual generalization of spatial analysis where locally varying anisotropies in the geographical space are uncovered by investigating nonlinear dependencies among observations. The framework is illustrated by generalizing two examples: distance decay relations and spatial filtering. The proposed alternative is based on geodesic ecological and anisotropic spatial distances.
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Long-term monitoring of headwater semi-natural catchments is used to document persistence and changes in ecosystems. At three headwater catchments in the Bramke basin in Northern Germany, physical and chemical variables in rainfall, soil solution from various depths (20–300 cm) and streamwater have been monitored. The Lange Bramke catchment is largely covered by a Norway spruce (Picea abies, Karst.) stand planted in the 1950ies. Over 29 years, 4310 water samples from streamwater and 5475 soil water samples were analysed for major constituents. Both linear methods (principal component analysis (PCA) and cross correlation (CC)) as well as non-linear methods (isometric feature mapping (ISOMAP) and maximum variance unfolding (MVU)) were used to analyze the spatiotemporal patterns of dissolved major ion concentrations in soil solution and streamwater. This approach provides a multiscale characterisation of links between soil water and streamwater at the catchment scale. Pattern identification augments the interpretation of processes in terms of transport and storage. The long time scales were dominated by trends in ions implicated in soil acidification. This reflects the decreasing input of acid deposition. At the annual scale, where hydrological effects dominate, each of the three adjacent catchments showed different patterns. Various empirical and process-based models have been applied in the past to the Bramke catchments. Results of the data-oriented approach can be used to indicate the potential and limits of process-oriented models for this data set.
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Remote sensing of the activity of vegetation in relation to environmental conditions provides an invaluable basis for investigating the spatiotemporal dynamics and patterns of variability for ecosystem processes. We investigate the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) using SeaWiFS satellite observations from 1998 to 2005 and ancillary meteorological variables from the CRU-PIK dataset with a global coverage at a spatial resolution of 0.5o x 0.5o. A pixel-by-pixel spectral decomposition using Singular System Analysis leads to a global “classification” of the terrestrial biosphere according to prevalent time-scale dependent dynamics of fAPAR and its relation to meteorology. A complexity analysis and a combined subsignal extraction and dimensionality reduction reveals a series of dominant geographical gradients, separately for different time scales. At the annual scale, which explains around 50% of the fAPAR variability as a global average, patterns largely resemble the biomes of the world as mapped by biogeographical methods, and are driven by temperature and by pronounced rain seasons in the tropics. On shorter time scales, fAPAR fluctuations are exclusively driven by water supply, inducing, e.g., semiannual cycles in the equatorial belt of Africa or the Indo-Gangetic Plain. For some regions however, in particular South America, altitude, mean temperature, drought probability and fire occurrences are parameters that seem to shape the spatial patterns of fAPAR across time scales. Overall, we provide a first global multiscale characterization of fAPAR and highlight different mechanisms in land-surface-climate couplings.
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There is a great demand for involving rapid, non destructive and less time consuming methods for quick control and prediction of soil quality, environmental monitoring, and other precision measurements in agriculture. Near infrared reflectance spectroscopy (NIRS) is considered as an appropriate alternative method to conventional analytical methods for large scale measurements. The objective of this study was to investigate the possibilities of NIRS for prediction of some chemical properties of soil samples. A total of 97 samples from Stara Zagora, Kazanlak and Gurkovo region taken from the 0-40 cm layer were collected. Soil types were Luvisols, Vertisols, Fluvisols and Rankers. The samples were analyzed for total phosphorus by spectrometric determination of phosphorus soluble in sodium hydrogen carbonate solution, total nitrogen by Kjeldahl method, pH (H O)-potentiometrically and electrical conductivity (EC). 2 The spectral data of all air dried samples were measured using an Perkin Elmer Spectrum One NTS, FT-NIR Spectrometer, within the range from 700 to 2500 nm. Partial Least Squares (PLS) regression was used to built models to determine soil chemical parameters from the NIR spectra. Two-third of the samples were used as a calibration set and the remaining samples as independent validation test set. The best model was obtained for total nitrogen with a coefficient of determination r=0,91, standard error of calibration SEP=336 mg/kg, and the ratio of the standard variation of the reference data to the SEP, indicating the performance of the calibration, of RPD=2,3. The accuracy of prediction was poor for electrical conductivity. The results obtained clearly indicated that NIRS had the potential to predict some soil components rapidly and without sample preparation.
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A changing climate will likely influence the selection of tree species in the future, and this may in turn affect the size of the pools and fluxes of carbon. Tree species differ in growth rate, fine-root turnover and quality of litter and tend to produce different types of understory vegetation. In Sweden three tree species (Norway spruce [Picea abies] 43%, Scots pine [Pinus sylvestris] 39% and birch [Betula spp.] 11%) dominate. In the present study we used field experiments in southern Sweden to test if these tree species differed in root distribution and turnover.
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For tracer studies at the catchment scale, travel times are often assumed to be stationary. We question the validity of this assumption. We analyzed a series of tracer experiments conducted under exceptionally controlled conditions at Gårdsjön, Sweden. The Gårdsjön G1 catchment was covered by a roof underneath which natural throughfall has been replaced by artificial irrigation with a pre-defined chemical composition. This unique setup was used to perform replicated catchment scale Br tracer experiments under steady state storm flow conditions in five different years. A log-normal distribution function was fitted to all Br breakthrough curves. Fitted parameter values differed significantly for some of the experiments. These differences were not only related to the slightly different hydrologic boundary and initial conditions for the experiments, but also to seasonal changes in catchment properties that may explain the different flow paths during the experiments. We conclude that the travel time distribution is not only linked to discharge but also explicitly related to other water fluxes such as evapotranspiration, and that it is not stationary even under steady-state flow conditions. Since the attenuation of soluble pollutants is fundamentally linked to the travel times of water through the subsurface of a catchment, it is of crucial importance to understand the latter in detail. However, it is still unclear which are the dominant processes controlling their distribution.
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Anna Maria Jönsson Susanne Harding Paal Krokene Holger Lange Åke Lindelöw Bjørn Økland Hans Peter Ravn Leif Martin SchroederSammendrag
The Eurasian spruce bark beetle, Ips typographus, is one of the major forest insect pests in Europe, capable of mass-attacking and killing mature Norway spruce trees. The initiation and development of a new generation are strongly controlled by temperature and a warmer climate may affect the number of generations that is produced per year and hence the outbreak dynamics. Experimental knowledge regarding reproductive diapause adaptations is, however, too sparse for largescale assessments of future trends. We developed a model description of diapause induction, and used gridded observational temperature data to evaluate multiple combinations of day length and temperature thresholds to find the model parameterisation most coherent with I. typographus monitoring data from Scandinavia. The selected model parameterisation is supported by European literature data, though further experimental studies are required to analyse population specific adaptations and capacity for adjustments to changing climate conditions. Implementing the model description of reproductive diapause in a temperature driven model of bark beetle phenology (swarming activity and development from egg to mature bark beetle), enabled us to assess the length of the late summer swarming period that is a critical determinant of the risk of forest damage. By using regional climate model data we show that higher temperatures can result in increased frequency and length of late summer swarming events, producing a second generation in southern Scandinavia and a third generation in lowland parts of central Europe. Reproductive diapause will not prevent the occurrence of an additional generation per year, but the day length cues may restrict the length of the late summer swarming period.
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Jan Svtlík Isabella Børja Toril Drabløs Eldhuset Holger Lange Frank Newell Kidder Douglas Lawrence GodboldSammendrag
Fungi are the main degraders of organic matter and are associated symbiotically with over 80% of terrestrial plants (Smith and Read 1997). Thus, the extent of the mycelial network is an indicator of the decomposing or symbiotic activity. Although the importance of fungi in soil is undisputable, the determination of the extent of hyphal mats and the hyphal biomass is difficult to assess. Methods for estimating hyphae in soil are mostly based on the gridline intersect method originally developed to determine the root length or recently by measuring of the ergosterol content, fungal sterol found in the cell membranes....
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Markus Reichstein Miguel D. Mahecha Nuño Carvalhais Gitta Lasslop Sonia I. Seneviratne Holger Lange Alessandro Cescatti Mirco MigliavaccaSammendrag
The release of carbon dioxide (CO2) from the land surface via different respiratory processes is a major flux in the global carbon cycle, antipodal to CO2 uptake via photosynthesis. Understanding the sensitivity of respiratory processes to temperature is central for quantifying the climate–carbon cycle feedback. In a recent study we approximated the sensitivity of terrestrial ecosystem respiration to air temperature (Q10) across 60 FLUXNET sites. For this objective, we developed a novel methodology that circumvents seasonally confounding effects. Contrary to previous findings, our results suggest that Q10 is independent of mean annual temperature, does not differ among biomes, and is confined to values around 1.4 ± 0.1. However, the shape of the strong relation between photosynthesis and respiration is highly variable among sites. The results may partly explain a less pronounced climate–carbon cycle feedback than suggested by current carbon cycle climate models. In the talk we put our findings into context with other recent results and critically discuss their potential for evaluating temperature sensitivity of respiration in terrestrial biosphere models and parameterizing future land surface schemes.
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Holger LangeSammendrag
In ecosystem research, data-driven approaches to modeling are of major importance. Models are more often than not shaped by the spatiotemporal structure of the observations: an inverse modeling approach prevails. Here, I investigate the insights obtained from Recurrence Quantification Analysis of observed ecosystem time series. As a typical example of available long-term monitoring data, I choose time series from hydrology and hydrochemistry. Besides providing insights into the nonstationary and nonlinear dynamics of these variables, RQA also enables a detailed and temporally local model-data comparison.
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Miguel D. Mahecha Markus Reichstein Nuno Carvalhais Gitta Lasslop Holger Lange Sonia I. Seneviratne Rodrigo Vargas Christof Ammann M. Altaf Arain Alessandro Cescatti Ivan A. Janssens Mirco Migliavacca Leonardo Montagnani Andrew D. RichardsonSammendrag
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Miguel D. Mahecha Markus Reichstein Nuño Carvalhais Gitta Lasslop Holger Lange Sonia I. Seneviratne Rodrigo Vargas Christof Ammann M. Altaf Arain Alessandro Cescatti Ivan A. Janssens Mirco Migliavacca Leonardo Montagnani Andrew D. RichardsonSammendrag
We estimated the sensitivity of terrestrial ecosystem respiration to air temperature across 60 FLUXNET sites by minimizing the effect of seasonally confounding factors. Graf et al. now offer a theoretical perspective for an extension of our methodology. However, their critique does not change our main findings and, given the currently available observational techniques, may even impede a comparison across ecosystems.
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The airborne laser scanning (ALS) penetration rate, i.e. the ratio of ground echoes to total echoes, is a proxy for gap fraction. Hence, ALS has a potential for monitoring forest properties that are related to gap fraction, such as leaf area index, canopy cover and disturbance. Furthermore, two gap types may be distinguished: While a pulse that only produces a ground echo most likely hit a large, between-tree gap, a pulse that produces a ground echo as the last of several returns most likely hit smaller, within-canopy gaps. This may be utilized to distinguish between disturbance types such as defoliation and tree removal. However, the ALS penetration rate needs to be calibrated with gap fraction measurements in the field, because it is influenced by technical properties of the acquisition. The aim of this study was to quantify the magnitude of this influence, by comparing repeated acquisitions with different technical specifications. We had at hand 12 ALS acquisitions which could be combined into six pairs, from four spruce and pine dominated forests in Norway. We established 20x20 m grids, and for each grid cell we extracted three penetration variables: first echo penetration, last-of-many echo penetration, and total (i.e., first and last echo). We log-transformed the penetration variables (P1 and P2) from two laser acquisitions, and fitted the no-intercept, linear model log(P1) = log(P2), applying total least squares regression analysis. In a majority of the cases, the penetration variables were very similar, i.e. they deviated by <10%. For the first echo penetration the slopes varied from 0.87 to 1.07 and the R2 values ranged between 0.91 and 0.99. For the last-of-many echo penetration, there was generally weaker correspondence with slopes varying from 0.78 to 1.02, and R2 values ranging from 0.60 to 0.94. Finally, for the total penetration there was again stronger agreement with slopes in the range 0.83-1.03 and R2 values from 0.88 to 0.99. In conclusion, it seems that the penetration ability of different ALS scans in many cases are very similar, and further research may reveal ranges of standardized settings for which field inventory can be redundant.
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Miguel D. Mahecha Markus Reichstein Martin Jung Sonia I. Seneviratne Sönke Zaehle Christian Beer Maarten C. Braakhekke Nuno Carvalhais Holger Lange Guerric Le Maire Eddy MoorsSammendrag
Terrestrial biosphere models are indispensable tools for analyzing the biosphere-atmosphere exchange of carbon and water. Evaluation of these models using site level observations scrutinizes our current understanding of biospheric responses to meteorological variables. Here we propose a novel model-data comparison strategy considering that CO2 and H2O exchanges fluctuate on a wide range of timescales. Decomposing simulated and observed time series into subsignals allows to quantify model performance as a function of frequency, and to localize model-data disagreement in time. This approach is illustrated using site level predictions from two models of different complexity, Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and Lund-Potsdam-Jena (LPJ), at four eddy covariance towers in different climates. Frequency-dependent errors reveal substantial model-data disagreement in seasonal-annual and high-frequency net CO2 fluxes. By localizing these errors in time we can trace these back, for example, to overestimations of seasonal-annual periodicities of ecosystem respiration during spring greenup and autumn in both models. In the same frequencies, systematic misrepresentations of CO2 uptake severely affect the performance of LPJ, which is a consequence of the parsimonious representation of phenology. ORCHIDEE shows pronounced model-data disagreements in the high-frequency fluctuations of evapotranspiration across the four sites. We highlight the advantages that our novel methodology offers for a rigorous model evaluation compared to classical model evaluation approaches. We propose that ongoing model development will benefit from considering model-data (dis)agreements in the time-frequency domain.
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Today the spruce bark beetle Ips typographus is always univoltine in Northern Europe including Norway and completes development from egg to adult between May and August. Further south in Europe, development is bivoltine with the completion of two generations in most years. A temperature-driven developmental model suggests that by 2070-2100 the voltinism of I. typographus will change dramatically in Norway. If summers become only 2.5°C warmer than today bivoltinism can be expected every single year in the major spruce growing areas in S-Norway. This is likely to have dramatic effects on forestry since two generations per year will give two, instead of one, attack periods each summer. In addition to increasing the number of attacked trees the effect of the attacks may also be more severe, as Norway spruce is more susceptible to beetle attacks later in the summer. However, climate change will probably also change the phenology of Norway spruce and thus its susceptibility to attack by I. typographus and its phytopathogenic fungal associates. We are currently modelling how tree resistance varies with temperature and tree phenology in order to provide more well-founded advice to forest managers on the interaction between bark beetles and tree in a future climate.
Forfattere
Miguel D. Mahecha Markus Reichstein Nuno Carvalhais Gitta Lasslop Holger Lange Sonia I. Seneviratne Rodrigo Vargas Christof Ammann M.Altaf Arain Alessandro Cescatti Ivan A. Janssens Mirco Migliavacca Leonardo Montagnani Andrew D. RichardsonSammendrag
The respiratory release of carbon dioxide (CO2) from the land surface is a major flux in the global carbon cycle, antipodal to photosynthetic CO2 uptake. Understanding the sensitivity of respiratory processes to temperature is central for quantifying the climate–carbon cycle feedback. We approximated the sensitivity of terrestrial ecosystem respiration to air temperature (Q10) across 60 FLUXNET sites with the use of a methodology that circumvents confounding effects. Contrary to previous findings, our results suggest that Q10 is independent of mean annual temperature, does not differ among biomes, and is confined to values around 1.4 ± 0.1. The strong relation between photosynthesis and respiration, by contrast, is highly variable among sites. The results may partly explain a less pronounced climate–carbon cycle feedback than suggested by current carbon cycle climate models.
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Understanding the feedback between terrestrial biosphere processes and meteorological drivers is crucial to ecosystem research as well as management. For example, remote sensing of the activity of vegetation in relation to environmental conditions provides an invaluable basis for investigating the spatiotemporal dynamics and patterns of variability. We investigate the Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) using SeaWiFS satellite observations from 1998 to 2005 and ancillary meteorological variables from the CRU-PIK dataset. To what extent do precipitation and temperature dominate the terrestrial photosynthetic activity on monthly to interannual time scales? A spectral decomposition using Singular System Analysis leads to a global ‘classification’ of the terrestrial biosphere according to prevalent time-scale dependent dynamics of fAPAR and its relation to the meteorology. A complexity analysis and a combined subsignal extraction and dimensionality reduction reveals a series of dominant geographical gradients, separately for different time scales. Here, we differentiate between three time scales: on short time scales (compared to the annual cycle), variations in fAPAR coincide with corresponding precipitation dynamics. At the annual scale, which explains around 50% of the fAPAR variability as a global average, patterns largely resemble the biomes of the world as mapped by biogeographic methods.At longer time scales, spatially coherent patterns emerge which are induced by precipitation and temperature fluctuations combined. However, we can also identify regions where the variability of fAPAR on specific time scales cannot be traced back to climate and is apparently shaped by other geoecological or anthropogenic drivers. http://uregina.ca/prairies/assets/Prairie_Summit_Final_Program.pdf
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Determining the feedbacks between terrestrial biosphere processes and the meteorological drivers (here precipitation and temperature) is crucial to ecosystem research. In this context, the continuous monitoring of the earth surface provides an invaluable basis for investigating the spatiotemporal dynamics in the activity of vegetation in relation to environmental conditions. Here, we seek to identify which patterns of variability in the meteorological drivers dominate the terrestrial photosynthetic activity from monthly to interannual time scales (resp. fluctuation frequencies). We investigate the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) using SeaWiFS observations from 1998 to 2005 and ancillary meteorological variables. A spectralanalysis leads to a global `classification` of the terrestrial biosphere according to prevalent scale dependent dynamics of fAPAR and its relation to the meteorology. A combined subsignal extraction and dimensionality reduction reveals a series of dominant geographical gradients on specific time scales. E.g. we uncover spatially coherent patterns at low frequencies and show where these are induced by precipitation or temperature fluctuations. We also show where high frequency variations (relative to the annual cycle) in fAPAR coincide with corresponding precipitation dynamics. However, we can also identify regions where the variability of fAPAR on specific time scales cannot be traced back to climate and is apparently shaped by other geoecological or anthropogenic drivers. http://www.terrabites.net/fileadmin/user_upload/terrabites/PDFs/Programme_Book_TERRABITES.pdf
Forfattere
Toril Drabløs Eldhuset Isabella Børja Nicholas Clarke Kjersti Holt Hanssen O. Janne Kjønaas Holger Lange Ingvald Røsberg Tonje Ingeborg ØklandSammendrag
Hogstavfall er nøkkelen til økt satsing på bioenergi i Norge. Men vil dette påvirke bærekraften i skogøkosystemet og skogproduksjonen? Blir skogsjorda mer næringsfattig? Endres sammensetningen av arter i vegetasjonen? Vil artsmangfoldet bli redusert? Blir det mindre av de soppene som bryter ned planterester? Dette er noen av spørsmålene vi prøver å besvare gjennom prosjektet «Økologiske virkninger av økt biomasseuttak fra skog i Norge» (ECOBREM), som varer fra 2009 til 2013.
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Information retrieval from spatiotemporal data cubes is key to earth system sciences. Respective analyses need to consider two fundamental issues: First, natural phenomena fluctuate on different time scales. Second, these characteristic temporal patterns induce multiple geographical gradients. Here we propose an integrated approach of subsignal extraction and dimensionality reduction to extract geographical gradients on multiple time scales. The approach is exemplified using global remote sensing estimates of photosynthetic activity. A wide range of partly well interpretable gradients is retrieved. For instance, well known climate-induced anomalies in FAPAR over Africa and South America during the last severe ENSO event are identified. Also, the precise geographical patterns of the annual–seasonal cycle and its phasing are isolated. Other features lead to new questions on the underlying environmental dynamics. Our method can provide benchmarks for comparisons of data cubes, model runs, and thus be used as a basis for sophisticated model performance evaluations.
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The utilization history of the Lange Bramke catchment and the northern Harz mountains is dominated by ore mining. Historical documents were used to provide ample evidence that forestry and water utilization were managed according to administrative goals in a largely centralized manner. However, the perception of the landscape and its function and purpose have changed significantly over the centuries. In particular, the distinction between renewable (such as forests) and non-renewable resources (such as ore deposits) is a rather modern one, as is the principle of sustainability. This change in perception is apparent from the type of maps used, the different conflicts on property and exploitation rights, and the request for quantitative inventories of resources, appearing only quite late in the mining history. The remnants of smelters and charcoal production still demonstrate the importance of historical land use for proper interpretation of monitoring data.
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In European forests, standing stocks are currently higher than ever during the last decades, in part due to reduced logging or the abandonment of agricultural land. However, data from intensive monitoring plots reveal an increased growth even without direct human intervention.We used a set of 363 plots from 16 European countries to investigate the influence of environmental factors on forest growth: nitrogen, sulphur and acid deposition, temperature, precipitation and drought, for Norway spruce, Scots pine, common beech and European as well as sessile oak.We used existing information on site productivity, stand age and stand density to estimate expected growth. Relative tree growth, i.e., the ratio between actual growth within a five-year period and expected growth, was then related to environmental factors in a stepwise multiple regression.The results consistently indicate a fertilizing effect from nitrogen deposition, with roughly one percent increase in site productivity per kg of nitrogen deposition per ha and year, or 20 kg C fixation per kg N deposition. This was most pronounced for plots having soil C/N ratios above 25. We also found a positive albeit less clear relationship between relative growth and summer temperatures.From the study, we cannot conclude on any detrimental effects on growth from sulphur and acid deposition or from drought periods. A very recent study from the U.S., comprising 4800 plots and 24 tree species, confirms our results. However, we also show that the magnitude of N deposition effects on global forest C balance is currently a highly controversial matter, and comment on this debate. http://www.cef-cfr.ca/uploads/Colloque/Programme10_5.pdf
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The Lange Bramke catchment has been investigated as a monitored catchment for 60 years. However, its utilization history even dates back to medieval times, and is well documented in part. The intense interplay between ore mining, forestry, and water resources exploitation left remains such as scoriae piles and modified forest growth, e.g. due to local pollution at smelter locations. It is demonstrated that considering local land use history is important for a proper understanding and interpretation of modern monitoring data. A theoretical framework is proposed for the integration of the two data sources. This requires a joint approach combining two modelling paradigms, the functional one dominating in current ecosystem research, and an interactive one which best characterizes the human–environment relationship in historic times.
Forfattere
Holger LangeSammendrag
Skogøkosystemer binder karbon fra atmosfæren via fotosyntesen, men frigjør også karbon gjennom respirasjon. Nye beregninger viser hvor mye temperaturen påvirker denne balansen. Vi finner en nesten universell sammenheng mellom temperatur og utslipp fra økosystemer, og denne økningen i utslipp er mindre enn tidligere antatt. Det gjør klimamodellene mer pålitelige – og det kan være gode nyheter for klimaet vårt.
Forfattere
Svein Solberg Mathias Dobbertin Gert Jan Reinds Holger Lange Kjell Andreassen Paloma Garcia Fernandez Anders Hildingsson Wim de VriesSammendrag
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Skog har en viktig klimaregulerende effekt ved å binde CO2 og redusere konsentrasjonen av klimagasser i atmosfæren. Det nordlige barskogsbeltet er særlig viktig fordi høyere temperaturer gir økt tilvekst og dermed mulig økt CO2-binding. Men det kan være grunner til å revurdere dette bildet av skogen som en slags karbon-bank i klimaets tjeneste. Som i den virkelige bankverden finnes det nemlig røvere i barskogen også - i form av tredrepende barkbiller og deres medfølgende blåvedsopper.
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The CO2 uptake of the terrestrial biosphere (Gross Primary Productivity, GPP) plays a key role in the global carbon balance. This carbon flux cannot be determined directly on a global scale. Yet, the remotely sensed Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a valuable proxy for GPP. This study aims at characterizing global FAPAR dynamics on different temporal scales and extracting corresponding spatial structures. The time series were analyzed to uncover the presence and extent of trends, and to identify quasi-oscillatory patterns from intra- to interannual time scales....
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A catchment provides ecosystem data along with (relatively) simple, operationally defined boundaries. In addition runoff is an integrated measure of the hydrochemical ecosystem response, which can be represented by fluxes at the weir. Integration at the weir occurs first of all with respect to spatial scales. Almost all fluid output leaves a (tight) catchment at this point. Evaluation of the runoff dynamics (quantity and quality) is primarily concerned with temporal scales. The Lange Bramke catchment study with its four runoff series from forested catchments (spring and weir at Lange Bramke, weirs at Dicke Bramke and Steile Bramke) provides an exceptionally comprehensive data set. The following scales and processes can be considered, when interpreting temporal variations in runoff data: above the time scale of forest rotation (species composition, biomass accumulation, timber export, soil nutrient pools) at decadal time scales up to a full forest rotation of about 100-120 years (changes in forest growth rate, changes in deposition, climate change, insect outbreaks) at annual time scales (uptake, transpiration) at hourly to weekly time scales of hydrological events (precipitation, runoff, dilution effects of solvents).
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In high-latitude areas, landscapes with flat or moderate relief areas usually contain lakes and mires. The identification of flowpaths in such areas is a difficult issue. The increasing availability of high resolution topography from airborne Lidar measurements offers new opportunities for automatic or semi-automatic channel extraction from DEMs in small watersheds, substantially outperforming the hydrographic network in conventional digital maps....
Forfattere
Holger Lange Nicholas Clarke O. Janne Kjønaas Wenche Aas Kjell Andreassen Isabella Børja Harald Bratli Susanne Eich-Greatorex Toril Drabløs Eldhuset Kjersti Holt Hansen Tonje Ingeborg Økland Ingvald Røsberg Trine Aulstad Sogn Volkmar TimmermannSammendrag
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Forfattere
Nicholas Clarke O. Janne Kjønaas Wenche Aas Kjell Andreassen Isabella Børja Harald Bratli Susanne Eich-Greatorex Toril Drabløs Eldhuset Kjersti Holt Hanssen Holger Lange Tonje Økland Ingvald Røsberg Trine Aulstad Sogn Volkmar TimmermannSammendrag
In Norway, it is planned to double the stationary use of bioenergy from all sources by up to 14 TWh before 2020, with much of this increase coming from forest resources, including residues like branches and tops (which are not much used today) being removed after tree harvest. This removal will reduce the supply of nutrients and organic matter to the forest soil, and may in the longer term increase the risk for future nutrient imbalance, reduced forest production, and changes in biodiversity and ground vegetation species composition. However, field experiments have found contrasting results (e.g. Johnson and Curtis 2001; Olsson et al. 1996). Soil effects of increased biomass removal will be closely related to soil organic matter (SOM) dynamics, litter quality, and turnover rates. The SOM pool is derived from a balance between above- and below-ground input of plant material and decomposition of both plants and SOM. Harvest intensity may affect the decomposition of existing SOM as well as the build-up of new SOM from litter and forest residues, by changing factors like soil temperature and moisture as well as amount and type of litter input. Changes in input of litter with different nutrient concentrations and decomposition patterns along with changes in SOM decomposition will affect the total storage of carbon, nitrogen and other vital nutrients in the soil. To quantify how different harvesting regimes lead to different C addition to soil, and to determine which factors have the greatest effect on decomposition of SOM under different environmental conditions, two Norway spruce forest systems will be investigated in the context of a research project starting in 2008/2009, one in eastern and one in western Norway, representing different climatic and landscape types. At each location, two treatment regimes will be tested: Conventional harvesting, with residues left on-site (CH) Aboveground whole-tree harvest, with branches, needles, and tops removed (WTH). Input of different forest residues will be quantified post harvest. Soil water at 30 cm soil depth will be analysed for nutrients and element fluxes will be estimated to provide information about nutrient leaching. Soil respiration will be measured, along with lab decomposition studies under different temperature and moisture regimes. Long term in situ decomposition studies will be carried out in the WTH plots using three different tree compartments (needles, coarse twigs, fine roots) decomposing in litter bags, in order to determine their limit value. The structure of the fungal community will be determined by soil core sampling and use of molecular techniques allowing qualitative and quantitative estimation. Understorey vegetation will be sampled to determine the biomass, and the frequency of all vascular plants, bryophytes and lichens will be estimated. After harvesting, replanting will be carried out. Seedling survival, causes of mortality and potential damage, growth, and needle nutrients will be monitored. Results from these studies will be used to identify key processes explaining trends observed in two series of ongoing long-term whole-tree thinning trials. We shall combine knowledge obtained using field experiments with results of modelling and data from the Norwegian Monitoring Programme for Forest Damage and National Forest Inventory. This will help us to predict and map the ecologically most suitable areas for increased harvesting of branches and tops on a regional scale based on current knowledge, and to identify uncertainties and additional knowledge needed to improve current predictions.
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Minirhizotrons, transparent acrylic tubes inserted in the soil, are well suited for long term, non destructive, in situ observations of fine roots. In minirhizotrons, the fine roots are regularly photographed and the root images are visually evaluated according to their status as living, dead or disappeared. This evaluation gives the background for further statistical treatment to estimate the fine root longevity. It is inherent in the minirhizotron technique that a large group of roots will be described as “disappeared” due to grazing, overgrowing by other roots, unclear images or other reasons. Because the fraction of disappeared roots is substantial in some cases, this has consequences for the interpretation of the longevity results. We processed three years of minirhizotron images from Norway spruce stands in southeast Norway (30 yr old) and northern Finland (60 yr old). Of all processed fine roots 32 and 23% was evaluated as disappeared in Norway and Finland, respectively. When roots labelled as disappeared were pooled together with dead ones, the fine root longevity estimates, using the Kaplan Meier method, decreased almost by a factor of two (401 and 433 days), as opposed to labeling them as censored observations (770 and 750 days for Norway and Finland, respectively). Here we demonstrate how the early decision making on the fine root status bears consequences on the resulting longevity estimates. The implications will be discussed
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Long-term monitoring meteorological, hydrological and hydrochemical data from small catchments are irreplaceable witnesses of past environmental conditions. This insight shaped the formation of the Long-Term Ecological Research Network (LTER) in the US, but European as well as German siblings are under preparation. Among the European forested monitoring sites, the Bramke catchments in the Harz mountains present a particularly well-documented case, with daily runoff measurements starting after World War II-related reparation cuts in 1948, and surface water chemistry being observed since the 1970ies. Originally powered by research on erosion, then by acid rain research and the then-prominent “forest decline”, a large set of hydrochemical variables (major ions) is available now with basically weekly resolution. Previously tightly connected to academic research at the University of Göttingen, routine measurements are by now performed by local forest authorities, ensuring forthcoming continuity even when public attention should shift away again from climate change research.
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Stream flow observations from a geographical region are known to often exhibit strong common behavior. In order to assess the temporal evolution of the complexity of stream flow time series from all over Europe, we employ the recently developed Linear Variance Decay dimension density (δLV D). It is estimated as the parameter of an exponential decay function fitted to the remaining variances of the eigenvalues of a covariance matrix. Scaling between zero and one, δLV D can be interpreted as a measure of the proportion of linear independent components in a multivariate record.....
Forfattere
Svein Solberg Andreas Brunner Kjersti Holt Hanssen Holger Lange Erik Næsset Miina Rautiainen Pauline StenbergSammendrag
In this study we demonstrate how airborne laser scanning (ALS) can be applied to map effective leaf area index (LAI(e)) in a spruce forest, after being calibrated with ground based measurements. In 2003 and 2005, ALS data and field estimates of LAI(e) were acquired in a Norway spruce forest in SE Norway. We used LI-COR's LAI-2000 (R) Plant canopy analyzer ("LAI-2000") and hemispherical images ("HI") for field based estimates of LAI(e). ALS penetration rate calculated from first echoes and from first and last echoes was strongly related to field estimates of LAI(e). We fitted regression models of LAI(e) against the log-transformed inverse of the ALS penetration rate, and in accordance with the Beer-Lambert law this produced a linear, no-intercept relationship. This was particularly the case for the LAI-2000, having R-2 values > 0.9. The strongest relationship was obtained by selecting ALS data from within a circle around each plot with a radius of 0.75 times the tree height. We found a slight difference in the relationship for the two years, which can be attributed to the differences in the ALS acquisition settings. The relationship was valid across four age classes of trees representing different stages of stand development, except in one case with newly regenerated stands which most likely was an artifact. Using LAI(e) based on HI data produced weaker relationships with the ALS data. This was the case even when we simulated LAI-2000 measurements based on the HI data. (C) 2009 Elsevier Inc. All rights reserved.
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Evaluation of climate change consequences and national carbon reporting such as under the Kyoto protocol require long-term monitoring of carbon fluxes. We report on an ongoing project aimed at a national-level assessment of the terrestrial carbon sequestration potential under present conditions and under various climate and land use change scenarios, in particular in terms of their temperature effect. We develop empirical models for national soil carbon stock assessment and evaluate process-based soil carbon models for prediction of future carbon dynamics.....
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General questions that arise while investigating hydrological extremes are whether these have distinct spatial and temporal variations and how these variations are linked to mean flow conditions. We analyze a large set of European stream flow series. Based on daily observations we derive annual series of stream flow deciles ranging from the minimum to the maximum, resulting in a set of eleven series for each station representing the year to year variability of the flow regimes....
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Life on earth depends on water and where running water occurs on earth, there is life. Nevertheless, existing modelling approaches in hydrology almost completely neglect the biological aspects of water flow. We claim that ignoring biological behaviour and interaction in catchment runoff modelling is too restrictive, and that computational theories can be used to formalise behaviour and interaction and model the biological impact on runoff. To demonstrate this, starting with a general classification of catchment behaviour, as documented in runoff data, we will use symbolic dynamics to quantify randomness and complexity in the time series. This approach shows that runoff records from very different catchments show common behaviour. This behaviour can be fitted to a one-parametric curve, stratified into three regions. In this manner, it becomes possible to represent and classify types of interactive behaviour that cannot be generated algorithmically. This suggests that physically based catchment models do not properly represent all types of interactive behaviour, and that signatures of biological interaction are present in runoff data.
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Over the last decades the forestry sciences have been opened for new topics and methods. In addition to traditional forestry topics they have participated in environmental and ecosystem research. So far this type of research has been perceived as “applied”. From the modelling perspective there has been a misunderstanding among the participating disciplines of the character of knowledge being applied. Here we introduce two types of models of forest utilization and discuss their possibilities and limits for forestry sciences. The first perspective of forests is the one dominating in modelling today and in forestry sciences. It has been adopted from physics. The second perspective of forests has implicitly been adopted in the past for pragmatic reasons.
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Many time series analysis methods depend on equally spaced observations with no data point missing. If this condition is met, powerful techniques are available that identify temporal structures such as trends or periodic phenomena or non