TY - JOUR A1 - Frieler, Katja A1 - Schauberger, Bernhard A1 - Arneth, Almut A1 - Balkovic, Juraj A1 - Chryssanthacopoulos, James A1 - Deryng, Delphine A1 - Elliott, Joshua A1 - Folberth, Christian A1 - Khabarov, Nikolay A1 - Müller, Christoph A1 - Olin, Stefan A1 - Pugh, Thomas A. M. A1 - Schaphoff, Sibyll A1 - Schewe, Jacob A1 - Schmid, Erwin A1 - Warszawski, Lila A1 - Levermann, Anders T1 - Understanding the weather signal in national crop-yield variability JF - Earths future N2 - Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations. Y1 - 2017 U6 - https://doi.org/10.1002/2016EF000525 SN - 2328-4277 VL - 5 SP - 605 EP - 616 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Schaller, Jörg A1 - Puppe, Daniel A1 - Kaczorek, Danuta A1 - Ellerbrock, Ruth A1 - Sommer, Michael T1 - Silicon cycling in soils revisited JF - Plants : open access journal N2 - Silicon (Si) speciation and availability in soils is highly important for ecosystem functioning, because Si is a beneficial element for plant growth. Si chemistry is highly complex compared to other elements in soils, because Si reaction rates are relatively slow and dependent on Si species. Consequently, we review the occurrence of different Si species in soil solution and their changes by polymerization, depolymerization, and condensation in relation to important soil processes. We show that an argumentation based on thermodynamic endmembers of Si dependent processes, as currently done, is often difficult, because some reactions such as mineral crystallization require months to years (sometimes even centuries or millennia). Furthermore, we give an overview of Si reactions in soil solution and the predominance of certain solid compounds, which is a neglected but important parameter controlling the availability, reactivity, and function of Si in soils. We further discuss the drivers of soil Si cycling and how humans interfere with these processes. The soil Si cycle is of major importance for ecosystem functioning; therefore, a deeper understanding of drivers of Si cycling (e.g., predominant speciation), human disturbances and the implication for important soil properties (water storage, nutrient availability, and micro aggregate stability) is of fundamental relevance. KW - andosols KW - clay neoformation KW - crop yield KW - land use change KW - micro KW - aggregate stability KW - phytoliths KW - sediments KW - silicon cycling KW - silicon KW - extraction methods KW - silicon pore water speciation Y1 - 2021 U6 - https://doi.org/10.3390/plants10020295 SN - 2223-7747 VL - 10 IS - 2 PB - MDPI CY - Basel ER - TY - JOUR A1 - Hoan, Tran Viet A1 - Richter, Karl-Gerd A1 - Borsig, Nicolas A1 - Bauer, Jonas A1 - Ha, Nguyen Thi A1 - Norra, Stefan T1 - An improved groundwater model framework for aquifer structures of the quaternary-formed sediment body in the southernmost parts of the Mekong Delta, Vietnam JF - Hydrology : open access journal N2 - The Ca Mau peninsula (CMP) is a key economic region in southern Vietnam. In recent decades, the high demand for water has increased the exploitation of groundwater, thus lowering the groundwater level and leading to risks of degradation, depletion, and land subsidence, as well as salinity intrusion in the groundwater of the whole Mekong Delta region. By using a finite element groundwater model with boundary expansion to the sea, we updated the latest data on hydrogeological profiles, groundwater levels, and exploitation. The basic model setup covers seven aquifers and seven aquitards. It is determined that the inflow along the coastline to the mainland is 39% of the total inflow. The exploitation of the study area in 2019 was 567,364 m(3)/day. The most exploited aquifers are the upper-middle Pleistocene (qp(2-3)) and the middle Pliocene (n(2)(2)), accounting for 63.7% and 24.6%, respectively; the least exploited aquifers are the upper Pleistocene and the upper Miocene, accounting for 0.35% and 0.02%, respectively. In the deeper aquifers, qp(2-3) and n(2)(2), the change in storage is negative due to the high exploitation rate, leading to a decline in the reserves of these aquifers. These groundwater model results are the calculations of groundwater reserves from the coast to the mainland in the entire system of aquifers in the CMP. This makes groundwater decision managers, stakeholders, and others more efficient in sustainable water resources planning in the CMP and Mekong Delta (MKD). KW - groundwater modeling KW - hydrogeology KW - aquifers system KW - water balance; KW - validation of model KW - Ca Mau peninsula KW - Kien Giang KW - Soc Trang KW - Hau Giang KW - Bac Lieu Y1 - 2022 U6 - https://doi.org/10.3390/hydrology9040061 SN - 2306-5338 VL - 9 IS - 4 PB - MDPI CY - Basel ER - TY - JOUR A1 - Reinecke, Robert A1 - Trautmann, Tim A1 - Wagener, Thorsten A1 - Schüler, Katja T1 - The critical need to foster computational reproducibility JF - Environmental research letters KW - reproducibility KW - models KW - software KW - Open Science Y1 - 2022 U6 - https://doi.org/10.1088/1748-9326/ac5cf8 SN - 1748-9326 VL - 17 IS - 4 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Didovets, Iulii A1 - Krysanova, Valentina A1 - Bürger, Gerd A1 - Snizhko, Sergiy A1 - Balabukh, Vira A1 - Bronstert, Axel T1 - Climate change impact on regional floods in the Carpathian region JF - Journal of hydrology : Regional studies N2 - Study region: Tisza and Prut catchments, originating on the slopes of the Carpathian mountains. Study focus: The study reported here investigates (i) climate change impacts on flood risk in the region, and (ii) uncertainty related to hydrological modelling, downscaling techniques and climate projections. The climate projections used in the study were derived from five GCMs, downscaled either dynamically with RCMs or with the statistical downscaling model XDS. The resulting climate change scenarios were applied to drive the eco-hydrological model SWIM, which was calibrated and validated for the catchments in advance using observed climate and hydrological data. The changes in the 30-year flood hazards and 98 and 95 percentiles of discharge were evaluated for the far future period (2071-2100) in comparison with the reference period (1981-2010). New hydrological insights for the region: The majority of model outputs under RCP 4.5 show a small to strong increase of the 30-year flood level in the Tisza ranging from 4.5% to 62%, and moderate increase in the Prut ranging from 11% to 22%. The impact results under RCP 8.5 are more uncertain with changes in both directions due to high uncertainties in GCM-RCM climate projections, downscaling methods and the low density of available climate stations. KW - Climate change impact KW - Floods KW - Hydrological modelling KW - SWIM KW - Tisza KW - Prut KW - Carpathians KW - Ukraine Y1 - 2019 U6 - https://doi.org/10.1016/j.ejrh.2019.01.002 SN - 2214-5818 VL - 22 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Devitt, Laura A1 - Neal, Jeffrey A1 - Wagener, Thorsten A1 - Coxon, Gemma T1 - Uncertainty in the extreme flood magnitude estimates of large-scale flood hazard models JF - Environmental research letters : ERL / Institute of Physics N2 - The growing worldwide impact of flood events has motivated the development and application of global flood hazard models (GFHMs). These models have become useful tools for flood risk assessment and management, especially in regions where little local hazard information is available. One of the key uncertainties associated with GFHMs is the estimation of extreme flood magnitudes to generate flood hazard maps. In this study, the 1-in-100 year flood (Q100) magnitude was estimated using flow outputs from four global hydrological models (GHMs) and two global flood frequency analysis datasets for 1350 gauges across the conterminous US. The annual maximum flows of the observed and modelled timeseries of streamflow were bootstrapped to evaluate the sensitivity of the underlying data to extrapolation. Results show that there are clear spatial patterns of bias associated with each method. GHMs show a general tendency to overpredict Western US gauges and underpredict Eastern US gauges. The GloFAS and HYPE models underpredict Q100 by more than 25% in 68% and 52% of gauges, respectively. The PCR-GLOBWB and CaMa-Flood models overestimate Q100 by more than 25% at 60% and 65% of gauges in West and Central US, respectively. The global frequency analysis datasets have spatial variabilities that differ from the GHMs. We found that river basin area and topographic elevation explain some of the spatial variability in predictive performance found in this study. However, there is no single model or method that performs best everywhere, and therefore we recommend a weighted ensemble of predictions of extreme flood magnitudes should be used for large-scale flood hazard assessment. KW - large-scale flood hazard models KW - global hydrological model KW - flood risk Y1 - 2021 U6 - https://doi.org/10.1088/1748-9326/abfac4 SN - 1748-9326 VL - 16 IS - 6 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Mtilatila, Lucy Mphatso Ng'ombe A1 - Bronstert, Axel A1 - Shrestha, Pallav A1 - Kadewere, Peter A1 - Vormoor, Klaus Josef T1 - Susceptibility of water resources and hydropower production to climate change in the tropics BT - the case of Lake Malawi and Shire River Basins, SE Africa JF - Hydrology : open access journal N2 - The sensitivity of key hydrologic variables and hydropower generation to climate change in the Lake Malawi and Shire River basins is assessed. The study adapts the mesoscale Hydrological Model (mHM) which is applied separately in the Upper Lake Malawi and Shire River basins. A particular Lake Malawi model, which focuses on reservoir routing and lake water balance, has been developed and is interlinked between the two basins. Climate change projections from 20 Coordinated Regional Climate Downscaling Experiment (CORDEX) models for Africa based on two scenarios (RCP4.5 and RCP8.5) for the periods 2021-2050 and 2071-2100 are used. An annual temperature increase of 1 degrees C decreases mean lake level and outflow by 0.3 m and 17%, respectively, signifying the importance of intensified evaporation for Lake Malawi's water budget. Meanwhile, a +5% (-5%) deviation in annual rainfall changes mean lake level by +0.7 m (-0.6 m). The combined effects of temperature increase and rainfall decrease result in significantly lower flows in the Shire River. The hydrological river regime may change from perennial to seasonal with the combination of annual temperature increase and precipitation decrease beyond 1.5 degrees C (3.5 degrees C) and -20% (-15%). The study further projects a reduction in annual hydropower production between 1% (RCP8.5) and 2.5% (RCP4.5) during 2021-2050 and between 5% (RCP4.5) and 24% (RCP8.5) during 2071-2100. The results show that it is of great importance that a further development of hydro energy on the Shire River should take into account the effects of climate change, e.g., longer low flow periods and/or higher discharge fluctuations, and thus uncertainty in the amount of electricity produced. KW - Lake Malawi Basin KW - Shire River Basin KW - lake water balance KW - climate change impacts in the tropics KW - hydropower generation KW - response surface analysis KW - sensitivity analysis Y1 - 2020 U6 - https://doi.org/10.3390/hydrology7030054 SN - 2306-5338 VL - 7 IS - 3 PB - MDPI CY - Basel ER - TY - JOUR A1 - Seleem, Omar A1 - Ayzel, Georgy A1 - Costa Tomaz de Souza, Arthur A1 - Bronstert, Axel A1 - Heistermann, Maik T1 - Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany JF - Geomatics, natural hazards and risk N2 - Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available. KW - Urban pluvial flood susceptibility KW - convolutional neural network KW - deep KW - learning KW - random forest KW - support vector machine KW - spatial resolution; KW - flood predictors Y1 - 2022 U6 - https://doi.org/10.1080/19475705.2022.2097131 SN - 1947-5705 SN - 1947-5713 VL - 13 IS - 1 SP - 1640 EP - 1662 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Seleem, Omar A1 - Ayzel, Georgy A1 - Bronstert, Axel A1 - Heistermann, Maik T1 - Transferability of data-driven models to predict urban pluvial flood water depth in Berlin, Germany JF - Natural Hazards and Earth System Sciences N2 - Data-driven models have been recently suggested to surrogate computationally expensive hydrodynamic models to map flood hazards. However, most studies focused on developing models for the same area or the same precipitation event. It is thus not obvious how transferable the models are in space. This study evaluates the performance of a convolutional neural network (CNN) based on the U-Net architecture and the random forest (RF) algorithm to predict flood water depth, the models' transferability in space and performance improvement using transfer learning techniques. We used three study areas in Berlin to train, validate and test the models. The results showed that (1) the RF models outperformed the CNN models for predictions within the training domain, presumable at the cost of overfitting; (2) the CNN models had significantly higher potential than the RF models to generalize beyond the training domain; and (3) the CNN models could better benefit from transfer learning technique to boost their performance outside training domains than RF models. Y1 - 2023 U6 - https://doi.org/10.5194/nhess-23-809-2023 SN - 1684-9981 SN - 1561-8633 VL - 23 IS - 2 SP - 809 EP - 822 PB - Copernicus CY - Göttingen ER - TY - JOUR A1 - Rottler, Erwin A1 - Francke, Till A1 - Bürger, Gerd A1 - Bronstert, Axel T1 - Long-term changes in central European river discharge for 1869–2016 BT - impact of changing snow covers, reservoir constructions and an intensified hydrological cycle JF - Hydrology and Earth System Sciences N2 - Recent climatic changes have the potential to severely alter river runoff, particularly in snow-dominated river basins. Effects of changing snow covers superimpose with changes in precipitation and anthropogenic modifications of the watershed and river network. In the attempt to identify and disentangle long-term effects of different mechanisms, we employ a set of analytical tools to extract long-term changes in river runoff at high resolution. We combine quantile sampling with moving average trend statistics and empirical mode decomposition and apply these tools to discharge data recorded along rivers with nival, pluvial and mixed flow regimes as well as temperature and precipitation data covering the time frame 1869-2016. With a focus on central Europe, we analyse the long-term impact of snow cover and precipitation changes along with their interaction with reservoir constructions. Our results show that runoff seasonality of snow-dominated rivers decreases. Runoff increases in winter and spring, while discharge decreases in summer and at the beginning of autumn. We attribute this redistribution of annual flow mainly to reservoir constructions in the Alpine ridge. During the course of the last century, large fractions of the Alpine rivers were dammed to produce hydropower. In recent decades, runoff changes induced by reservoir constructions seem to overlap with changes in snow cover. We suggest that Alpine signals propagate downstream and affect runoff far outside the Alpine area in river segments with mixed flow regimes. Furthermore, our results hint at more (intense) rain-fall in recent decades. Detected increases in high discharge can be traced back to corresponding changes in precipitation. KW - empirical mode decomposition KW - atmospheric blocking KW - heavy precipitation KW - streamflow trends KW - climate-change KW - rhine basin KW - time-series KW - events KW - Switzerland KW - variability Y1 - 2020 U6 - https://doi.org/10.5194/hess-24-1721-2020 SN - 1027-5606 SN - 1607-7938 VL - 24 IS - 4 SP - 1721 EP - 1740 PB - Copernicus CY - Göttingen ER -