Stephanie Eisner

Research Scientist

(+47) 904 13 678
stephanie.eisner@nibio.no

Place
Ås H8

Visiting address
Høgskoleveien 8, 1433 Ås

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Abstract

Water consumption along value chains of goods and services has increased globally and led to increased attention on water footprinting. Most global water consumption is accounted for by evaporation (E), which is connected via bridges of atmospheric moisture transport to other regions on Earth. However, the resultant source–receptor relationships between different drainage basins have not yet been considered in water footprinting. Based on a previously developed data set on the fate of land evaporation, we aim to close this gap by using comprehensive information on evaporation recycling in water footprinting for the first time. By considering both basin internal evaporation recycling (BIER; >5% in 2% of the world’s basins) and basin external evaporation recycling (BEER; >50% in 37% of the world’s basins), we were able to use three types of water inventories (basin internal, basin external, and transboundary inventories), which imply different evaluation perspectives in water footprinting. Drawing on recently developed impact assessment methods, we produced characterization models for assessing the impacts of blue and green water evaporation on blue water availability for all evaluation perspectives. The results show that the negative effects of evaporation in the originating basins are counteracted (and partly overcompensated) by the positive effects of reprecipitation in receiving basins. By aggregating them, combined net impacts can be determined. While we argue that these offset results should not be used as a standalone evaluation, the water footprint community should consider atmospheric moisture recycling in future standards and guidelines.

Abstract

As a carbon dioxide removal measure, the Norwegian government is currently considering a policy of large-scale planting of spruce (Picea abies (L) H. Karst) on lands in various states of natural transition to a forest dominated by deciduous broadleaved tree species. Given the aspiration to bring emissions on balance with removals in the latter half of the 21st century in effort to limit the global mean temperature rise to “well below” 2°C, the effectiveness of such a policy is unclear given relatively low spruce growth rates in the region. Further convoluting the picture is the magnitude and relevance of surface albedo changes linked to such projects, which typically counteract the benefits of an enhanced forest CO2 sink in high-latitude regions. Here, we carry out a rigorous empirically based assessment of the terrestrial carbon dioxide removal (tCDR) potential of large-scale spruce planting in Norway, taking into account transient developments in both terrestrial carbon sinks and surface albedo over the 21st century and beyond. We find that surface albedo changes would likely play a negligible role in counteracting tCDR, yet given low forest growth rates in the region, notable tCDR benefits from such projects would not be realized until the second half of the 21st century, with maximum benefits occurring even later around 2150. We estimate Norway's total accumulated tCDR potential at 2100 and 2150 (including surface albedo changes) to be 447 (±240) and 852 (±295) Mt CO2-eq. at mean net present values of US$ 12 (±3) and US$ 13 (±2) per ton CDR, respectively. For perspective, the accumulated tCDR potential at 2100 represents around 8 years of Norway's total current annual production-based (i.e., territorial) CO2-eq. emissions.

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Abstract

Various studies investigated the fate of evaporation and the origin of precipitation. The more recent studies among them were often carried out with the help of numerical moisture tracking. Many research questions could be answered within this context, such as dependencies of atmospheric moisture transfers between different regions, impacts of land cover changes on the hydrological cycle, sustainability-related questions, and questions regarding the seasonal and interannual variability of precipitation. In order to facilitate future applications, global datasets on the fate of evaporation and the sources of precipitation are needed. Since most studies are on a regional level and focus more on the sources of precipitation, the goal of this study is to provide a readily available global dataset on the fate of evaporation for a fine-meshed grid of source and receptor cells. The dataset was created through a global run of the numerical moisture tracking model Water Accounting Model-2layers (WAM-2layers) and focused on the fate of land evaporation. The tracking was conducted on a 1.5∘×1.5∘ grid and was based on reanalysis data from the ERA-Interim database. Climatic input data were incorporated in 3- to 6-hourly time steps and represent the time period from 2001 to 2018. Atmospheric moisture was tracked forward in time and the geographical borders of the model were located at ±79.5∘ latitude. As a result of the model run, the annual, the monthly and the interannual average fate of evaporation were determined for 8684 land grid cells (all land cells except those located within Greenland and Antarctica) and provided via source–receptor matrices. The gained dataset was complemented via an aggregation to country and basin scales in order to highlight possible usages for areas of interest larger than grid cells. This resulted in data for 265 countries and 8223 basins. Finally, five types of source–receptor matrices for average moisture transfers were chosen to build the core of the dataset: land grid cell to grid cell, country to grid cell, basin to grid cell, country to country, basin to basin. The dataset is, to our knowledge, the first ready-to-download dataset providing the overall fate of evaporation for land cells of a global fine-meshed grid in monthly resolution. At the same time, information on the sources of precipitation can be extracted from it. It could be used for investigations into average annual, seasonal, and interannual sink and source regions of atmospheric moisture from land masses for most of the regions in the world and shows various application possibilities for studying interactions between people and water, such as land cover changes or human water consumption patterns. The dataset is accessible under https://doi.org/10.1594/PANGAEA.908705 (Link et al., 2019a) and comes along with example scripts for reading and plotting the data.

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Abstract

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the ensemble mean (EM). The performance gain offered by MMC suggests that future multi-model applications consider reporting MMCs, alongside the EM and intermodal range, to provide end-users of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.

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Abstract

A robust hydrological modeling at a fine spatial resolution is a vital tool for Norway to simulate river discharges and hydrological components for climate adaptation strategies. However, it requires improvements of modelling methods, detailed observational data as input and expensive computational resources. This work aims to set up a distributed version of the HBV model with a physically based evapotranspiration scheme at 1 km resolution for mainland Norway and to calibrate/validate the model for 124 catchments using regionalized parameterizations. The Penman-Monteith equation was implemented in the HBV model and vegetation characteristics were derived from the Norwegian forest inventory combined with multi-source remote sensing data at 16 m spatial resolution. The estimated potential evapotranspiration (Ep) was compared with pan measurements and estimates from the MODerate Resolution Imaging Spectrometer (MOD16) products, the Global Land Evaporation Amsterdam Model (GLEAM) and Variable Infiltration Capacity (VIC) hydrological model. There are 5 climatic zones in Norway classified based on 4 temperature and precipitation indices. For each zone, the model was calibrated separately by optimizing a multi-objective function including the Nash-Sutcliff efficiency (NSE) and biases of selected catchments. In total, there are 85 catchments for calibration and 39 for validation. The Ep estimates showed good agreement with the measurements, GLEAM and VIC outputs. However, the MOD16 product significantly overestimates Ep compared to the other products. The discharge was well reproduced with the median daily NSE of 0.68/0.67, bias of −3%/−1%, Kling-Gupta efficiency (KGE) of 0.70/0.69 and monthly NSE of 0.80/0.78 in the calibration/validation periods. Our results showed a significant improvement compared to the previous HBV application for all catchments, with an increase of 0.08–0.16 in the median values of the daily NSE, KGE and monthly NSE. Both the temporal and spatial transferability of model parameterizations were also enhanced compared to the previous application.

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Abstract

Climate models show that global warming will disproportionately influence high‐latitude regions and indicate drastic changes in, among others, seasonal snow cover. However, current continental and global simulations covering these regions are often run at coarse grid resolutions, potentially introducing large errors in computed fluxes and states. To quantify some of these errors, we have assessed the sensitivity of an energy‐balance snow model to changes in grid resolution using a multiparametrization framework for the spatial domain of mainland Norway. The framework has allowed us to systematically test how different parametrizations, describing a set of processes, influence the discrepancy, here termed the scale error, between the coarser (5 to 50‐km) and finest (1‐km) resolution. The simulations were set up such that liquid and solid precipitation was identical between the different resolutions, and differences between the simulations arise mainly during the ablation period. The analysis presented in this study focuses on evaluating the scale error for several variables relevant for hydrological and land surface modelling, such as snow water equivalent and turbulent heat exchanges. The analysis reveals that the choice of method for routing liquid water through the snowpack influences the scale error most for snow water equivalent, followed by the type of parametrizations used for computing turbulent heat fluxes and albedo. For turbulent heat exchanges, the scale error is mainly influenced by model assumptions related to atmospheric stability. Finally, regions with strong meteorological and topographic variability show larger scale errors than more homogenous regions.

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Abstract

Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface–atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAImax), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MSNFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAImax) of managed forests in Fennoscandia, we compared our LAImax map with reference LAImax maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAImax values used in three leading land models. Comparison of the LAImax maps showed that our product provides a spatially more realistic description of LAImax in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.

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Abstract

Due to the increasing relevance of analyzing water consumption along product life cycles, the water accounting and vulnerability evaluation model (WAVE) has been updated and methodologically enhanced. Recent data from the atmospheric moisture tracking model WAM2-layers is used to update the basin internal evaporation recycling (BIER) ratio, which denotes atmospheric moisture recycling within drainage basins. Potential local impacts resulting from water consumption are quantified by means of the water deprivation index (WDI). Based on the hydrological model WaterGAP3, WDI is updated and methodologically refined to express a basin’s vulnerability to freshwater deprivation resulting from the relative scarcity and absolute shortage of water. Compared to the predecessor version, BIER and WDI are provided on an increased spatial and temporal (monthly) resolution. Differences compared to annual averages are relevant in semiarid and arid basins characterized by a high seasonal variation of water consumption and availability. In order to support applicability in water footprinting and life cycle assessment, BIER and WDI are combined to an integrated WAVE+ factor, which is provided on different temporal and spatial resolutions. The applicability of the WAVE+ method is proven in a case study on sugar cane, and results are compared to those obtained by other impact assessment methods.

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Abstract

Predicting the surface albedo of a forest of a given species composition or plant functional type is complicated by the wide range of structural attributes it may display. Accurate characterizations of forest structure are therefore essential to reducing the uncertainty of albedo predictions in forests, particularly in the presence of snow. At present, forest albedo parameterizations remain a nonnegligible source of uncertainty in climate models, and the magnitude attributable to insufficient characterization of forest structure remains unclear. Here we employ a forest classification scheme based on the assimilation of Fennoscandic (i.e., Norway, Sweden, and Finland) national forest inventory data to quantify the magnitude of the albedo prediction error attributable to poor characterizations of forest structure. For a spatial domain spanning ~611,000 km2 of boreal forest, we find a mean absolute wintertime (December–March) albedo prediction error of 0.02, corresponding to a mean absolute radiative forcing ~0.4 W/m2. Further, we evaluate the implication of excluding albedo trajectories linked to structural transitions in forests during transient simulations of anthropogenic land use/land cover change. We find that, for an intensively managed forestry region in southeastern Norway, neglecting structural transitions over the next quarter century results in a foregone (undetected) radiatively equivalent impact of ~178 Mt‐CO2‐eq. year−1 on average during this period—a magnitude that is roughly comparable to the annual greenhouse gas emissions of a country such as The Netherlands. Our results affirm the importance of improving the characterization of forest structure when simulating surface albedo and associated climate effects.

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Abstract

An intercomparison of climate change impacts projected by nine regional-scale hydrological models for 12 large river basins on all continents was performed, and sources of uncertainty were quantified in the framework of the ISIMIP project. The models ECOMAG, HBV, HYMOD, HYPE, mHM, SWAT, SWIM, VIC and WaterGAP3 were applied in the following basins: Rhine and Tagus in Europe, Niger and Blue Nile in Africa, Ganges, Lena, Upper Yellow and Upper Yangtze in Asia, Upper Mississippi, MacKenzie and Upper Amazon in America, and Darling in Australia. The model calibration and validation was done using WATCH climate data for the period 1971–2000. The results, evaluated with 14 criteria, are mostly satisfactory, except for the low flow. Climate change impacts were analyzed using projections from five global climate models under four representative concentration pathways. Trends in the period 2070–2099 in relation to the reference period 1975–2004 were evaluated for three variables: the long-term mean annual flow and high and low flow percentiles Q10 and Q90, as well as for flows in three months high- and low-flow periods denoted as HF and LF. For three river basins: the Lena, MacKenzie and Tagus strong trends in all five variables were found (except for Q10 in the MacKenzie); trends with moderate certainty for three to five variables were confirmed for the Rhine, Ganges and Upper Mississippi; and increases in HF and LF were found for the Upper Amazon, Upper Yangtze and Upper Yellow. The analysis of projected streamflow seasonality demonstrated increasing streamflow volumes during the high-flow period in four basins influenced by monsoonal precipitation (Ganges, Upper Amazon, Upper Yangtze and Upper Yellow), an amplification of the snowmelt flood peaks in the Lena and MacKenzie, and a substantial decrease of discharge in the Tagus (all months). The overall average fractions of uncertainty for the annual mean flow projections in the multi-model ensemble applied for all basins were 57% for GCMs, 27% for RCPs, and 16% for hydrological models.

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Abstract

This paper presents one of the first quantitative scenario assessments for future water sup- ply and demand in Asia to 2050. The assessment, developed by the Water Futures and Solutions (WFaS) initiative, uses the latest set of global climate change and socioeconomic scenarios and state-of-the-art global hydrological models. In Asia, water demand for irrigation, industry, and households is projected to increase substantially in the coming decades (30–40% by 2050 compared to 2010). These changes are expected to exacerbate water stress, especially in the current hotspots such as north India and Pakistan, and north China. By 2050, 20% of the land area in the Asia-Pacific region, with a population of 1.6–2 bil- lion, is projected to experience severe water stress. We find that socioeconomic changes are the main drivers of worsening water scarcity in Asia, with climate change impacts further increasing the challenge into the 21st century. Moreover, a detailed basin-level analysis of the hydro-economic conditions of 40 Asian basins shows that although the coping capacity of all basins is expected to improve due to gross domestic product (GDP) growth, some basins continuously face severe water challenges. These basins will potentially be home to up to 1.6 billion people by mid-21st century.