Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide—a synthesis

Krysanova, Valentina; Vetter, Tobias; Eisner, Stephanie; Huang, Shaochun; Pechlivanidis, Ilias; Strauch, Michael; Gelfan, Alexander; Kumar, Rohini; Aich, Valentin; Arheimer, Berit; Chamorro, Alejandro; Griensven, Ann van; Kundu, Dipangkar; Lobanova, Anastasia; Mishra, Vimal; Plötner, Stefan; Reinhardt, Julia; Seidou, Ousmane; Wang, Xiaoyan; Wortmann, Michel; Zeng, Xiangyong; Hattermann, Fred F.

doi:10.1088/1748-9326/aa8359

Cited by 122 publications

(85 citation statements)

References 43 publications

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“…As outlined by many studies (e.g., Haddeland et al, 2011;Gudmundsson et al, 2012;Pechlivanidis and Arheimer, 2015;Zhang et al, 2016;Krysanova et al, 2017), performance of both regional and global models generally reduces when there is a transition from wet to dry conditions. In semiarid regions, satellites have several limitations in capturing rainfall intensities due to the local, convective nature of the precipitation, and they often overestimate the occurrence of rainfall because raindrops are likely to evaporate (i.e sub-cloud evaporation) before reaching the surface (Dinku et al, 2010;Sunilkumar et 5 al., 2015;Beck et al, 2017c).…”

Section: Cross-scale Comparison Of River Discharges From Continental mentioning

confidence: 99%

Toward continental hydrologic–hydrodynamic modeling in South America

Paiva¹,

Siqueira²,

Fleischmann³

et al. 2018

Preprint

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Abstract. Providing reliable estimates of streamflow and hydrological fluxes is a major challenge for water resources management over national and transnational basins in South America. Global hydrological models and land surface models are a possible solution to simulate the terrestrial water cycle at the continental scale, but issues on parameterization and limitations in representing lowland river systems put into question their utility for basin-scale analysis and to deliver daily 15 discharges to meet local needs. In an attempt to overcome such limitations, we extended a regional, fully coupled hydrologic-hydrodynamic model (MGB) to the continental domain of South America and assessed its performance using daily river discharges, water levels from independent sources (in situ, satellite altimetry), estimates of terrestrial water storage (TWS) and evapotranspiration (ET) from remote sensing and other available global datasets. In addition, river discharges were compared with outputs from global models acquired through the eartH2Observe project (HTESSEL/CaMa-20 Flood, LISFLOOD and WaterGAP3), providing the first cross-scale assessment (regional/continental  global models) that makes use of spatially consistent daily discharge data. A satisfactory representation of discharges and water levels was obtained (NSE > 0.6 in 55 % of the cases) and MGB was able to capture patterns of seasonality and magnitude of TWS and ET especially over the largest basins of South America. Continental-scale modeling significantly improved discharge estimates when compared with global models, which resulted in a large number of gauges with negative (or close to 0) NSE 25 values. Models were largely affected by positive bias mainly over East/Northeast Brazil and Argentina as well as over regions of Sao Francisco and Parnaiba basins, while major issues on flow timing were observed in regions affected by floodplain processes such as the Amazon, La Plata, Tocantins-Araguaia, Orinoco and Magdalena basins. We state that efforts in calibrating rainfall-runoff parameters within large basins are necessary to simulate daily river discharges appropriately in this continent, but implementing a hydrodynamic routing component is also important. We hope that our 30 study provides further insights about hydrological simulation in South America, helping to reduce the gap between global and regional hydrological modeling communities.Hydrol. Earth Syst. Sci. Discuss., https://doi

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Section: Cross-scale Comparison Of River Discharges From Continental mentioning

confidence: 99%

Toward continental hydrologic–hydrodynamic modeling in South America

Paiva¹,

Siqueira²,

Fleischmann³

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…Predictions of the consequences of anthropogenic and natural changes in the environment are necessary to understand and support decisions regarding water-resources management, water pollution, and flood control [1][2][3]. Such predictions are made with hydrological models ranging from lumped to fully-distributed setups [4][5][6][7]. However, using a model to mimic the real world has proven to be challenging, due to In this study, we investigate the effect of different spatial input data (DEM, LULC and soil maps) on model uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Effects of Input Data Content on the Uncertainty of Simulating Water Resources

Camargos

Jülich

Houska

et al. 2018

Water

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Abstract:The widely used, partly-deterministic Soil and Water Assessment Tool (SWAT) requires a large amount of spatial input data, such as a digital elevation model (DEM), land use, and soil maps. Modelers make an effort to apply the most specific data possible for the study area to reflect the heterogeneous characteristics of landscapes. Regional data, especially with fine resolution, is often preferred. However, such data is not always available and can be computationally demanding. Despite being coarser, global data are usually free and available to the public. Previous studies revealed the importance for single investigations of different input maps. However, it remains unknown whether higher-resolution data can lead to reliable results. This study investigates how global and regional input datasets affect parameter uncertainty when estimating river discharges. We analyze eight different setups for the SWAT model for a catchment in Luxembourg, combining different land-use, elevation, and soil input data. The Metropolis-Hasting Markov Chain Monte Carlo (MCMC) algorithm is used to infer posterior model parameter uncertainty. We conclude that our higher resolved DEM improves the general model performance in reproducing low flows by 10%. The less detailed soil-map improved the fit of low flows by 25%. In addition, more detailed land-use maps reduce the bias of the model discharge simulations by 50%. Also, despite presenting similar parameter uncertainty (P-factor ranging from 0.34 to 0.41 and R-factor from 0.41 to 0.45) for all setups, the results show a disparate parameter posterior distribution. This indicates that no assessment of all sources of uncertainty simultaneously is compensated by the fitted parameter values. We conclude that our result can give some guidance for future SWAT applications in the selection of the degree of detail for input data.

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“…While multiple climate models and greenhouse gas emission scenarios have been used to capture the ensemble of climate scenarios in the past two decades, such studies are often limited to the choice of a single hydrologic model 17,20,22,[25][26][27] . Despite studies indicating that choice of hydrologic model can produce substantial differences in hydrologic projections, at times exceeding the mean signal from climate scenarios 11 , the use of multiple hydrological models has only begun to gain traction 21,28,29 .The selection of appropriate hydrologic model(s) in the modeling framework remains a challenge, as decisions so subjective in nature require careful consideration of several factors, including model applicability, suitable spatiotemporal scale of implementation, availability of computational resources, quality of meteorological forcings and land surface parameters, and the overall technical feasibility 30 . While certain applications-such as hydrodynamic modeling applied at watershed scales-warrant fine-scale outputs from hydrologic modeling (<100 m) 31 , the scalability of these implementations at regional scales is an obvious challenge.…”

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confidence: 99%

Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States

Gangrade

Kao

McManamay

2020

Sci Rep

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This study uses a high-resolution, process-based modeling framework to assess the impacts of changing climate on water resources for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. A 33-member ensemble of hydrologic projections was generated using 3 distributed hydrologic models (Precipitation-Runoff Modeling System, Variable Infiltration Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity. These hydrologic models were driven by dynamically downscaled and bias-corrected future climate simulations from 11 Coupled Model Intercomparison Project Phase 5 global climate models under Representative Concentration Pathway 8.5 emission scenario, with 40 years each in baseline and future (2011-2050) periods. The hydroclimate response, in general, projects an increase in mean seasonal precipitation, runoff, and streamflow. The high and low flows are projected to increase and decrease, respectively, in general, suggesting increased likelihood of extreme rainfall events and intensification of the hydrologic cycle. The uncertainty associated with the ensemble hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice of climate model is more critical than the choice of hydrologic model for the studied region. this study provides in-depth insights of hydroclimate response and associated uncertainties to support informed decisions by water resource managers.A changing climate is projected to intensify regional and global hydrologic cycles 1,2 . These alterations in hydrologic cycles will potentially increase the frequency and magnitudes of hydroclimate extremes such as floods and droughts, and potentially impact water resource availability due to changes in the seasonality of streamflow and runoff 3-5 . The future hydrologic projections are, therefore, important to inform mitigation and adaptation strategies aimed at addressing impacts of climate change in addition to increasing water demands. Moreover, reliable estimates of hydroclimate extreme trends can ensure better preparedness of society and infrastructure from threats arising from extreme events and their socioeconomic impacts 6 .Studies assessing climate change impacts on future hydrology at regional or catchment scales often adapt a standard procedure involving the use of a hierarchical hydro-meteorological framework (hereinafter "modeling framework"), including selection of the following key elements: (a) greenhouse gas emission scenario, (b) global climate model (GCM), (c) downscaling method (statistical or dynamical), (d) bias correction of downscaled data (if required), and (e) hydrologic model 7-10 . These hydrologic projections are inevitably associated with uncertainties introduced at each stage of the modeling framework. In addition to external factors such as natural variability and the choice of emission scenarios, many uncertainties are model-related, such as model assumptions, structures, accuracy, initial conditions, calibration procedures, training datasets,...

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Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide—a synthesis

Cited by 122 publications

References 43 publications

Toward continental hydrologic–hydrodynamic modeling in South America

Toward continental hydrologic–hydrodynamic modeling in South America

Effects of Input Data Content on the Uncertainty of Simulating Water Resources

Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States

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