Search Results
Evaluation of water balance components in the Elbe river catchment simulated by the regional climate model CCLM
2014, Volkholz, J., Grossman-Clarke, S., Hattermann, F.F., Böhm, U.
For investigations of feedbacks between the hydrological cycle and the climate system, we assess the performance of the regional climate model CCLM in reconstructing the water balance of the Elbe river catchment. To this end long-term mean precipitation, evapotranspiration and runoff are evaluated. Extremes (90th percentile) are also considered in the case of precipitation. The data are provided by a CCLM presentday simulation for Europe that was driven by large-scale global reanalyses. The quality of the model results is analyzed with respect to suitable reference data for the period 1970 to 1999. The principal components of the hydrological cycle and their seasonal variations were captured well. Basin accumulated, averaged daily precipitation, evapotranspiration and runoff differ by no more than 10% from observations. Larger deviations occur mainly in summer, and at specific areas.
Three perceptions of the evapotranspiration landscape: Comparing spatial patterns from a distributed hydrological model, remotely sensed surface temperatures, and sub-basin water balances
2013, Conradt, T., Wechsung, F., Bronstert, A.
A problem encountered by many distributed hydrological modelling studies is high simulation errors at interior gauges when the model is only globally calibrated at the outlet. We simulated river runoff in the Elbe River basin in central Europe (148 268 km2) with the semi-distributed eco-hydrological model SWIM (Soil and Water Integrated Model). While global parameter optimisation led to Nash-Sutcliffe efficiencies of 0.9 at the main outlet gauge, comparisons with measured runoff series at interior points revealed large deviations. Therefore, we compared three different strategies for deriving sub-basin evapotranspiration: (1) modelled by SWIM without any spatial calibration, (2) derived from remotely sensed surface temperatures, and (3) calculated from long-term precipitation and discharge data. The results show certain consistencies between the modelled and the remote sensing based evapotranspiration rates, but there seems to be no correlation between remote sensing and water balance based estimations. Subsequent analyses for single sub-basins identify amongst others input weather data and systematic error amplification in inter-gauge discharge calculations as sources of uncertainty. The results encourage careful utilisation of different data sources for enhancements in distributed hydrological modelling.
Effect of climate change on hydrology, sediment and nutrient losses in two lowland catchments in Poland
2017, Marcinkowski, P., Piniewski, M., Kardel, I., Szcześniak, M., Benestad, R., Srinivasan, R., Ignar, S., Okruszko, T.
Future climate change is projected to have significant impact on water resources availability and quality in many parts of the world. The objective of this paper is to assess the effect of projected climate change on water quantity and quality in two lowland catchments (the Upper Narew and the Barycz) in Poland in two future periods (near future: 2021-2050, and far future: 2071-2100). The hydrological model SWAT was driven by climate forcing data from an ensemble of nine bias-corrected General Circulation Models-Regional Climate Models (GCM-RCM) runs based on the Coordinated Downscaling Experiment-European Domain (EURO-CORDEX). Hydrological response to climate warming and wetter conditions (particularly in winter and spring) in both catchments includes: lower snowmelt, increased percolation and baseflow and higher runoff. Seasonal differences in the response between catchments can be explained by their properties (e.g., different thermal conditions and soil permeability). Projections suggest only moderate increases in sediment loss, occurring mainly in summer and winter. A sharper increase is projected in both catchments for TN losses, especially in the Barycz catchment characterized by a more intensive agriculture. The signal of change in annual TP losses is blurred by climate model uncertainty in the Barycz catchment, whereas a weak and uncertain increase is projected in the Upper Narew catchment.
Aerial and surface rivers: Downwind impacts on water availability from land use changes in Amazonia
2018, Weng, W., Luedeke, M., Zemp, D., Lakes, T., Kropp, J.
The abundant evapotranspiration provided by the Amazon forests is an important component of the hydrological cycle, both regionally and globally. Since the last century, deforestation and expanding agricultural activities have been changing the ecosystem and its provision of moisture to the atmosphere. However, it remains uncertain how the ongoing land use change will influence rainfall, runoff, and water availability as findings from previous studies differ. Using moisture tracking experiments based on observational data, we provide a spatially detailed analysis recognizing potential teleconnection between source and sink regions of atmospheric moisture. We apply land use scenarios in upwind moisture sources and quantify the corresponding rainfall and runoff changes in downwind moisture sinks. We find spatially varying responses of water regimes to land use changes, which may explain the diverse results from previous studies. Parts of the Peruvian Amazon and western Bolivia are identified as the sink areas most sensitive to land use change in the Amazon and we highlight the current water stress by Amazonian land use change on these areas in terms of the water availability. Furthermore, we also identify the influential source areas where land use change may considerably reduce a given target sink's water reception (from our example of the Ucayali River basin outlet, rainfall by 5–12ĝ€% and runoff by 19–50ĝ€% according to scenarios). Sensitive sinks and influential sources are therefore suggested as hotspots for achieving sustainable land–water management.
Impacts of future deforestation and climate change on the hydrology of the Amazon Basin: A multi-model analysis with a new set of land-cover change scenarios
2017, Guimberteau, M., Ciais, P., Pablo, Boisier, J., Paula Dutra Aguiar, A., Biemans, H., De Deurwaerder, H., Galbraith, D., Kruijt, B., Langerwisch, F., Poveda, G., Rammig, A., Andres Rodriguez, D., Tejada, G., Thonicke, K., Von, Randow, C., Randow, R., Zhang, K., Verbeeck, H.
Deforestation in Amazon is expected to decrease evapotranspiration (ET) and to increase soil moisture and river discharge under prevailing energy-limited conditions. The magnitude and sign of the response of ET to deforestation depend both on the magnitude and regional patterns of land-cover change (LCC), as well as on climate change and CO2 levels. On the one hand, elevated CO2 decreases leaf-scale transpiration, but this effect could be offset by increased foliar area density. Using three regional LCC scenarios specifically established for the Brazilian and Bolivian Amazon, we investigate the impacts of climate change and deforestation on the surface hydrology of the Amazon Basin for this century, taking 2009 as a reference. For each LCC scenario, three land surface models (LSMs), LPJmL-DGVM, INLAND-DGVM and ORCHIDEE, are forced by bias-corrected climate simulated by three general circulation models (GCMs) of the IPCC 4th Assessment Report (AR4). On average, over the Amazon Basin with no deforestation, the GCM results indicate a temperature increase of 3.3ĝ€°C by 2100 which drives up the evaporative demand, whereby precipitation increases by 8.5 %, with a large uncertainty across GCMs. In the case of no deforestation, we found that ET and runoff increase by 5.0 and 14ĝ€%, respectively. However, in south-east Amazonia, precipitation decreases by 10ĝ€% at the end of the dry season and the three LSMs produce a 6ĝ€% decrease of ET, which is less than precipitation, so that runoff decreases by 22 %. For instance, the minimum river discharge of the Rio Tapajós is reduced by 31ĝ€% in 2100. To study the additional effect of deforestation, we prescribed to the LSMs three contrasted LCC scenarios, with a forest decline going from 7 to 34ĝ€% over this century. All three scenarios partly offset the climate-induced increase of ET, and runoff increases over the entire Amazon. In the south-east, however, deforestation amplifies the decrease of ET at the end of dry season, leading to a large increase of runoff (up to +27ĝ€% in the extreme deforestation case), offsetting the negative effect of climate change, thus balancing the decrease of low flows in the Rio Tapajós. These projections are associated with large uncertainties, which we attribute separately to the differences in LSMs, GCMs and to the uncertain range of deforestation. At the subcatchment scale, the uncertainty range on ET changes is shown to first depend on GCMs, while the uncertainty of runoff projections is predominantly induced by LSM structural differences. By contrast, we found that the uncertainty in both ET and runoff changes attributable to uncertain future deforestation is low.
Assessment of climate change impacts on water resources in three representative ukrainian catchments using eco-hydrological modelling
2017, Didovets, I., Lobanova, A., Bronstert, A., Snizhko, S., Maule, C.F., Krysanova, V.
The information about climate change impact on river discharge is vitally important for planning adaptation measures. The future changes can affect different water-related sectors. The main goal of this study was to investigate the potential water resource changes in Ukraine, focusing on three mesoscale river catchments (Teteriv, UpperWestern Bug, and Samara) characteristic for different geographical zones. The catchment scale watershed model-Soil and Water Integrated Model (SWIM)-was setup, calibrated, and validated for the three catchments under consideration. A set of seven GCM-RCM (General Circulation Model-Regional Climate Model) coupled climate scenarios corresponding to RCPs (Representative Concentration Pathways) 4.5 and 8.5 were used to drive the hydrological catchment model. The climate projections, used in the study, were considered as three combinations of low, intermediate, and high end scenarios. Our results indicate the shifts in the seasonal distribution of runoff in all three catchments. The spring high flow occurs earlier as a result of temperature increases and earlier snowmelt. The fairly robust trend is an increase in river discharge in the winter season, and most of the scenarios show a potential decrease in river discharge in the spring.
The role of spatial variability of soil moisture for modelling surface runoff generation at the small catchment scale
1999, Bronstert, A., Bárdossy, A.
The effects of spatial variability of soil moisture on surface runoff generation at the hillslope and small catchment scale were studied. The model used is physically based accounting for the relevant hydrological processes during storm runoff periods. A case study investigating the effects on runoff generation in a loessy small catchment is presented. In this study the storm rainfall response was modelled using different distribution patterns of the initial soil moisture content, and where different initial soil moisture fields were generated by using both interpolation methods and stochastic simulation methods. It is shown that spatial variability of pre-event soil moisture results in an increase in runoff production compared to averaged values. It is of particular importance to note the combined organised/stochastic variability features, that is, the superposition of systematic and random features of soil moisture dominate local generation of surface runoff. In general one can say that the stronger the organised heterogeneity is, the more important is an adequate and refined interpolation technique which is capable of accounting for complex spatial trends. The effects of soil moisture variations are of particular importance for storms, where the produced runoff volume is just a small fraction of precipitation.
Statistical characteristics of surrogate data based on geophysical measurements
2006, Venema, V., Bachner, S., Rust, H.W., Simmer, C.
In this study, the statistical properties of a range of measurements are compared with those of their surrogate time series. Seven different records are studied, amongst others, historical time series of mean daily temperature, daily rain sums and runoff from two rivers, and cloud measurements. Seven different algorithms are used to generate the surrogate time series. The best-known method is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm, which is able to reproduce the measured distribution as well as the power spectrum. Using this setup, the measurements and their surrogates are compared with respect to their power spectrum, increment distribution, structure functions, annual percentiles and return values. It is found that the surrogates that reproduce the power spectrum and the distribution of the measurements are able to closely match the increment distributions and the structure functions of the measurements, but this often does not hold for surrogates that only mimic the power spectrum of the measurement. However, even the best performing surrogates do not have asymmetric increment distributions, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found deviations of the structure functions on small scales.
The future sea-level contribution of the Greenland ice sheet: A multi-model ensemble study of ISMIP6
2020, Goelzer, Heiko, Nowicki, Sophie, Payne, Anthony, Larour, Eric, Seroussi, Helene, Lipscomb, William H., Gregory, Jonathan, Abe-Ouchi, Ayako, Shepherd, Andrew, Simon, Erika, Agosta, Cécile, Alexander, Patrick, Aschwanden, Andy, Barthel, Alice, Calov, Reinhard, Chambers, Christopher, Choi, Youngmin, Cuzzone, Joshua, Dumas, Christophe, Edwards, Tamsin, Felikson, Denis, Fettweis, Xavier, Golledge, Nicholas R., Greve, Ralf, Humbert, Angelika, Huybrechts, Philippe, Le clec'h, Sebastien, Lee, Victoria, Leguy, Gunter, Little, Chris, Lowry, Daniel P., Morlighem, Mathieu, Nias, Isabel, Quiquet, Aurelien, Rückamp, Martin, Schlegel, Nicole-Jeanne, Slater, Donald A., Smith, Robin S., Straneo, Fiammetta, Tarasov, Lev, van de Wal, Roderik, van den Broeke, Michiel
The Greenland ice sheet is one of the largest contributors to global mean sea-level rise today and is expected to continue to lose mass as the Arctic continues to warm. The two predominant mass loss mechanisms are increased surface meltwater run-off and mass loss associated with the retreat of marine-terminating outlet glaciers. In this paper we use a large ensemble of Greenland ice sheet models forced by output from a representative subset of the Coupled Model Intercomparison Project (CMIP5) global climate models to project ice sheet changes and sea-level rise contributions over the 21st century. The simulations are part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6).We estimate the sea-level contribution together with uncertainties due to future climate forcing, ice sheet model formulations and ocean forcing for the two greenhouse gas concentration scenarios RCP8.5 and RCP2.6. The results indicate that the Greenland ice sheet will continue to lose mass in both scenarios until 2100, with contributions of 90-50 and 32-17mm to sea-level rise for RCP8.5 and RCP2.6, respectively. The largest mass loss is expected from the south-west of Greenland, which is governed by surface mass balance changes, continuing what is already observed today. Because the contributions are calculated against an unforced control experiment, these numbers do not include any committed mass loss, i.e. mass loss that would occur over the coming century if the climate forcing remained constant. Under RCP8.5 forcing, ice sheet model uncertainty explains an ensemble spread of 40 mm, while climate model uncertainty and ocean forcing uncertainty account for a spread of 36 and 19 mm, respectively. Apart from those formally derived uncertainty ranges, the largest gap in our knowledge is about the physical understanding and implementation of the calving process, i.e. the interaction of the ice sheet with the ocean. © Author(s) 2020.