Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2023
Sammendrag
Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. However, wetlands have numerous hydrological, ecological, and social values. They play an important role in interactions among soil, water, plants, and animals. The rich biodiversity in the vicinity of wetlands makes them invaluable. Therefore, the conservation of wetlands is highly important in today’s world. Many anthropogenic activities damage wetlands. Climate change has adversely impacted wetlands and their biodiversity. The shrinking of wetland areas and reducing wetland water levels can therefore be frequently seen. However, the opposite can be seen during stormy seasons. Since wetlands have permissible water levels, the prediction of wetland water levels is important. Flooding and many other severe environmental damage can happen when these water levels are exceeded. Therefore, the prediction of wetland water level is an important task to identify potential environmental damage. However, the monitoring of water levels in wetlands all over the world has been limited due to many difficulties. A Scopus-based search and a bibliometric analysis showcased the limited research work that has been carried out in the prediction of wetland water level using machine-learning techniques. Therefore, there is a clear need to assess what is available in the literature and then present it in a comprehensive review. Therefore, this review paper focuses on the state of the art of water-level prediction techniques of wetlands using machine-learning techniques. Nonlinear climatic parameters such as precipitation, evaporation, and inflows are some of the main factors deciding water levels; therefore, identifying the relationships between these parameters is complex. Therefore, machine-learning techniques are widely used to present nonlinear relationships and to predict water levels. The state-of-the-art literature summarizes that artificial neural networks (ANNs) are some of the most effective tools in wetland water-level prediction. This review can be effectively used in any future research work on wetland water-level prediction.
Sammendrag
På oppdrag fra Bane NOR har NIBIO overvåket vannkvalitet i resipienter som kan motta avrenning fra anleggsarbeider i forbindelse med utbygging av Follobanen. NIBIO har driftet opptil 10 målestasjoner utstyrt med multiparametersensorer for automatisk overvåking av vannkvalitet. I tillegg har det blitt tatt ut vannprøver ved opptil 15 stasjoner og utført biologiske undersøkelser ved opptil seks stasjoner. Overvåkingen har pågått i vannforekomster nedstrøms riggområdet på Åsland og i Alna i Oslo, i bekker sør for stasjonsområdet på Ski, langs anleggsområdet mellom Ski og Langhus, samt ved Sagdalsbekken i Langhus. Årsrapporten omfatter alle resultater samlet inn på disse stasjonene i 2022 og har blitt sammenlignet med tidligere resultater.
Sammendrag
Light penetration plays a vital role in lakes and drinking water reservoirs, influencing fundamental processes such as primary production and thermal budgets. The Secchi depth (ZSD) and the compensation depth (ZCD) are commonly used measurements in this context. ZSD is determined through visual inspection using a Secchi disc, while ZCD represents the depth at which photosynthetic activity balances respiration and can be measured using a quantum irradiance sensor. Through in situ water-core samples from 23 lakes within a lake district in Southeastern Norway, we observed that DNOM exerts a diverse influence on these light irradiance measurements. If DNOM concentrations are reduced to half or a quarter of the current concentration, similar to what was measured during the 1980s, the median ZCD:ZSD ratios are estimated to have decreased by approximately 30 and 60% since then, respectively. Conversely, a plausible future climate-driven doubling or quadrupling of the present DNOM concentrations are estimated to further decrease the median ZCD:ZSD ratios in the lake district with approximately 10 and 25%, respectively. From this, the ZCD:ZSD ratios seem to have experienced a considerable long-term decline attributed to both climate change and the recovery from acid rain, and a further climate-driven decrease is expected.
Sammendrag
Levels of dissolved natural organic matter (DNOM) are increasing in our boreal watercourses. This is manifested by an apparent increase in its yellow to brown colour of the water, i.e., browning. Sound predictions of future changes in colour of our freshwaters is a prerequisite for predicting effects on aquatic fauna and a sustainable operation of drinking water facilities using surface waters as raw water sources. A model for the effect of climate on colour (mg Pt L-1) has been developed for two surface raw water sources in Scotland, i.e., at Bracadale and Port Charlotte. Both sites are situated far out on the Scottish west coast, without major impact of acid rain, with limited amounts of frost, and with limited recent land-use changes. The model was fitted to 15 years long data-series on colour measurements, provided by Scottish Water, at the two sites. Meteorological data were provided by UK Met. The models perform well for both sites in simulating the variation in monthly measured colour, explaining 89 and 90% of the variation at Bracadale and Port Charlotte, respectively. These well fitted models were used to predict future changes in colour due to changes in temperature and precipitation based on median climate data from a high emission climate RCP8.5 scenario from the HadCM3 climate model (UKCP18). The model predicted an increase in monthly average colour during growing season at both sites from about 150 mg Pt L-1 to about 200 mg Pt L-1 in 2050–2079. Temperature is found to be the most important positively driver for colour development at both sites.
Forfattere
Rolf David Vogt Petr Porcal Josef Hejzlar Ma. Cristina Paule‐Mercado Ståle Haaland Cathrine Brecke Gundersen Geir Orderud Bjørnar EikebrokkSammendrag
Increasing levels of dissolved organic matter (DOM) in watercourses in the northern hemisphere are mainly due to reduced acid rain, climate change, and changes in agricultural practices. However, their impacts vary in time and space. To predict how DOM responds to changes in environmental pressures, we need to differentiate between allochthonous and autochthonous sources as well as identify anthropogenic DOM. In this study we distinguish between allochthonous, autochthonous, and anthropogenic sources of DOM in a diverse watercourse network by assessing effects of land cover on water quality and using DOM characterization tools. The main sources of DOM at the studied site are forests discharging allochthonous humic DOM, autochthonous fulvic DOM, and runoff from urban sites and fish farms with high levels of anthropogenic DOM rich in protein‐like material. Specific UV absorbency (sUVa) distinguishes allochthonous DOM from autochthonous and anthropogenic DOM. Anthropogenic DOM differs from autochthonous fulvic DOM by containing elevated levels of protein‐like material. DOM from fishponds is distinguished from autochthonous and sewage DOM by having high sUVa. DOM characteristics are thus valuable tools for deconvoluting the various sources of DOM, enabling water resource managers to identify anthropogenic sources of DOM and predict future trends in DOM
Sammendrag
ANC er nøkkelparameteren for å vurdere endringer i kjemisk vannkvalitet med endringer i sur nedbør, klima og arealbruk. Imidlertid har parameteren lav presisjon, siden den er basert på ladningsbalansen mellom mange målte verdier. Det er derfor ønskelig å utlede alternative måter å beregne ANC. ANC er et estimat for overskuddet av svake syrers baser i vannet. I naturlig vann er dette tilnærmet lik differansen mellom konsentrasjonen av H+ og summen av bikarbonat og organiske anioner i løsning. Titrert alkalitet er et mål for det samme, men som en erstatning for ANC, må verdien korrigeres for operasjonelle kilder til avvik. Her utledes og testes to teoretiske modeller og en empirisk tilpasset modell for ANC basert på målinger av alkalitet. I de fleste vann anbefales modellen basert på bikarbonat betegnet som ALK02. I svært forsuringsfølsomt vann (nær kvantifiseringsgrense for titrert alkalitet), anbefales imidlertid en empirisk tilpasset modell som erstatning for beregnet ANC.
Forfattere
Geir Orderud Petr Porcal Bjørnar Eikebrokk Jiří Sláma Rolf David Vogt Josef Hejzlar Ståle HaalandSammendrag
Several actors have an impact on the quality of drinking water, but ultimately drinking water treatment plants (DWTPs) play a decisive role in ensuring that water quality complies with public regulations. Several developing technologies are combined in water treatment processes. In this paper, we are analysing the technological development of DWTPs in the South Bohemian region of the Czech Republic. The empirical basis is five DWTPs of varying size, and data are gathered through semi-structured interviews with relevant staff inside and outside of the five DWTPs. This study identifies the interplay of factors driving technological development: public regulations, the economic capacity of local DWTP owners together with subsidies from the European Union and national authorities, political priorities by local authorities, and the knowledge network. The paper addressess learning–knowledge–change processes of DWTPs, thereby contributing to our understanding of developing competence in producing drinking water. Generally, large DWTPs are front-runners in introducing new technologies while the smaller ones are lagging. Still, private companies operating small plants on behalf of municipal owners ensure that those DWTPs are part of a wider knowledge network, aiding to introduce a necessary and cost-effective upgrade to treatment steps.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Aaron N. Koop Daniel R. Hirmas Sharon A. Billings Li Li Alejandro Cueva Xi Zhang Hang Wen Attila Nemes Lígia F.T. Souza Hoori Ajami Alejandro N. Flores Aoesta K. Rudick Annalise Guthrie Lola M. Klamm Micah Unruh Pamela L. SullivanSammendrag
Multi-scale evidence of rapid, climate-induced soil structural changes occurring at yearly to decadal timescales is mounting. As a result, it has become increasingly important to identify the properties and mechanisms controlling the development and maintenance of soil structure and associated macroporosity. This is especially relevant since macroporosity has disproportionate effects on saturated hydraulic conductivity ( ) which strongly influences water storage and flux, thus, affecting the water cycle. In this study, we use decision trees and piecewise linear regression to assess the influence of soil and climate properties on effective porosity (EP; a proxy of macroporosity) in both surface and subsurface horizons under varying land-use and management practices. Data from 1,491 pedons (3,679 horizons) spanning five ecoregions representing bioclimate (e.g., potential vegetation) across the conterminous US demonstrate that, at a continental scale, EP in surface (A) and subsurface (B) horizons is strongly dependent on the complexed fraction of the total mass of soil organic carbon (SOC) and clay; a combined fraction that we refer to as complexed organic carbon and clay (COCC). EP showed a slight positive response to COCC in A horizons but increased steeply with increasing COCC in B horizons. This is because the smaller values of COCC in B horizons reflect a larger pool of clay that has a greater potential to accommodate and complex additions of SOC promoting stronger organo-mineral bonds and the concomitant development and maintenance of soil structure in these horizons. In contrast, larger values of COCC in A horizons reflect conditions where all or most of the clay fraction is effectively complexed with SOC resulting in a larger pool of non-complexed soil organic matter with varying contrasting effects on macroporosity that ultimately mute the response of EP to increases in COCC. In surface horizons, indirect factors such as mean annual precipitation and land use were important predictors of EP, whereas COCC was more influential in controlling EP within the subsoil. The EP-COCC relationship also holds within ecoregions but its effect is mitigated by soil and climate interactions suggesting that the effect of climate on this relationship is indirect and complex. Plowed surface horizons and horizons underlying plowed layers showed greater homogenization (due to disturbance effects reducing heterogeneity in the soil) as well as a reduction in the magnitude and rate of change of EP as a function of COCC compared to undisturbed horizons. Our findings suggest that the complexed fraction of clay and SOC is important for controlling macroporosity and at ecoregion scales and that the EP-COCC relationship may be an important framework for understanding and predicting future land use- and climate-induced changes in soil hydraulic properties.