Sources of uncertainty in hydrological climate impact assessment: a cross-scale study

Hattermann, Fred F.; Vetter, Tobias; Breuer, Lutz; Su, Buda; Daggupati, Prasad; Donnelly, Chantal; Fekete, B M; Flörke, Martina; Gosling, Simon N.; Hoffmann, Peter; Liersch, Stefan; Masaki, Yoshimitsu; Motovilov, Yu. G.; Müller, Christoph; Samaniego, Luis; Stacke, Tobias; Wada, Yoshihide; Tao, Yang; Krysanova, Valentina

doi:10.1088/1748-9326/aa9938

Cited by 129 publications

(77 citation statements)

References 41 publications

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“…Since the meteorological forcings for this study were only limited by one run per GCM, the internal variability is therefore integral in determining GCM uncertainty. Nevertheless, the findings of this study align with other studies that focused on quantifying the major sources of uncertainty over various regions [8][9][10]23,44 .…”

Section: Role Of Climate Versus Hydrological Models and Uncertainty Esupporting

confidence: 88%

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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Section: Role Of Climate Versus Hydrological Models and Uncertainty Esupporting

confidence: 88%

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 might be because of the sensitivity of peak discharge to extreme precipitation, snowmelt (or soil freezing) and soil moisture excess [50], which was mistaken in the simulations cannot entirely be corrected by the LSTMs. Moreover, the biases in the climate forcing data, the representation of real hydrological process and the limitation of routing could further limit the capacity to accurately predict correct timing [10,11,46,51]. Thus, the improvement of timing might depend more on the traditional GHMs-CaMa-Flood model chain than tools like LSTM used in this study, e.g.…”

Section: Improvement Of Ghms-based Flood Simulations Using Machine Lementioning

confidence: 99%

“…The GHMs performances varied considerably across river basins and flood-relevant indices, due to their different process representation and spatially generalized parameters [45]. The large inter-model differences in seasonal variability might be due to different process descriptions related to human impacts and regulations in the GHMs [5,46].…”

Section: Evaluation Of Ghms-based Flood Simulation Under Different CLmentioning

confidence: 99%

“…In general, the uncertainties of GHMs-based flood simulations might come from (1) the biases in climate forcing data as inputs of the GHMs, (2) the errors in describing hydrological processes (representation of hydrological process and discrepancy of models with real hydrological process) at the scale of grid cell (3) the limitation of river routing approach such as model parameters related to physical routing process [10,11,[46][47][48]. The large variations in GHMs' performance in different basins, climate zones and various flood-relevant indices, call for a careful selection of GHMs and improvement in techniques adopted for future flood simulations.…”

Section: Evaluation Of Ghms-based Flood Simulation Under Different CLmentioning

confidence: 99%

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Evaluation and machine learning improvement of global hydrological model-based flood simulations

Tao

Sun

Gentine

et al. 2019

Environ. Res. Lett.

108

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A warmer climate is expected to accelerate global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the accuracy and improvement of global hydrological models (GHMs)-based flood simulation is insufficient for most applications. Here we compared flood simulations from five GHMs under the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) protocol, against those calculated from 1032 gauging stations in the Global Streamflow Indices and Metadata Archive for the historical period 1971-2010. A machine learning approach, namely the long short-term memory units (LSTM) was adopted to improve the GHMs-based flood simulations within a hybrid physics-machine learning approach (using basin-averaged daily mean air temperature, precipitation, wind speed and the simulated daily discharge from GHMs-CaMa-Flood model chain as the inputs of LSTM, and observed daily discharge as the output value). We found that the GHMs perform reasonably well in terms of amplitude of peak discharge but are relatively poor in terms of their timing. The performance indicated great discrepancy under different climate zones. The large difference in performance between GHMs and observations reflected that those simulations require improvements. The LSTM used in combination with those GHMs was then shown to drastically improve the performance of global flood simulations (especially in terms of amplitude of peak discharge), suggesting that the combination of classical flood simulation and machine learning techniques might be a way forward for more robust and confident flood risk assessment.

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“…Although global-scale models can provide valuable spatiotemporal estimates of water fluxes and projections of those estimates (Sood and Smakthin, 2015), their ability to reproduce discharge observations at basin scale and to address practical water management issues is still limited (Archfield et al, 2015;Hattermann et al, 2018). Inaccuracies in runoff estimation from GHMs and LSMs may be first attributed to the uncertainty in global satellite precipitation products (Tian and Peters-Lidard, 2010;Sperna Weiland et al, 2015), but several studies have shown considerable differences between model outputs even when using the same meteorological forcing, given the lack of knowledge about runoff generation processes and deficiencies in parameter estimation (e.g., Haddeland et al, 2011;Gudmundsson et al, 2012;Zhou et al, 2012;Beck et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

Toward continental hydrologic–hydrodynamic modeling in South America

Siqueira

Paiva

Fleischmann

et al. 2018

Hydrol. Earth Syst. Sci.

124

133

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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 about parameterization and limitations in representing lowland river systems can place constraints on these models to meet local needs. In an attempt to overcome such limitations, we extended a regional, fully coupled hydrologic–hydrodynamic model (MGB; Modelo hidrológico de Grandes Bacias) to the continental domain of South America and assessed its performance using daily river discharge, 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 discharge was compared with outputs from global models acquired through the eartH2Observe project (HTESSEL/CaMa-Flood, LISFLOOD and WaterGAP3), providing the first cross-scale assessment (regional/continental × global models) that makes use of spatially distributed, daily discharge data. A satisfactory representation of discharge and water levels was obtained (Nash–Sutcliffe efficiency, NSE > 0.6 in 55 % of the cases) and the continental model was able to capture patterns of seasonality and magnitude of TWS and ET, especially over the largest basins of South America. After the comparison with global models, we found that it is possible to obtain considerable improvement on daily river discharge, even by using current global forcing data, just by combining parameterization and better routing physics based on regional experience. Issues about the potential sources of errors related to both global- and continental-scale modeling are discussed, as well as future directions for improving large-scale model applications in this continent. We hope that our study provides important insights to reduce the gap between global and regional hydrological modeling communities.

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Sources of uncertainty in hydrological climate impact assessment: a cross-scale study

Cited by 129 publications

References 41 publications

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

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

Evaluation and machine learning improvement of global hydrological model-based flood simulations

Toward continental hydrologic–hydrodynamic modeling in South America

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