How evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change

Krysanova, Valentina; Zaherpour, Jamal; Didovets, Iulii; Gosling, Simon N.; Gerten, Dieter; Hanasaki, Naota; Schmied, Hannes Müller; Pokhrel, Yadu; Satoh, Yusuke; Tang, Qiuhong; Wada, Yoshihide

doi:10.1007/s10584-020-02840-0

Cited by 35 publications

(32 citation statements)

References 53 publications

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“…These 58 basins are distributed among eight hydrobelts, as defined by Meybeck et al (2013) (see Fig. 1 in Krysanova et al 2020). Compared to the Köppen classification of climate zones, the classification of land areas in hydrobelts considers watershed boundaries and other geo-hydrological factors, and therefore, it lends itself well to applications in hydrological modeling.…”

Section: Case Study Areasmentioning

confidence: 99%

“…The drainage areas of 14 of the 58 basins are less than 100,000 km 2 , and 10 basins have drainage areas larger than 1 million square kilometers. The characteristics of these basins can be found in Table S1 in Krysanova et al (2020) and Table 1 in Gädeke et al (2020). The case study areas where the regional-scale models were applied ( Fig.…”

Section: Case Study Areasmentioning

confidence: 99%

“…The following climate data were used in the research reported in the papers of this Special Issue. For evaluation of the regional-scale models in the historical period, three reanalysis datasets were used: EWEMBI Lange 2018 In turn, global hydrological models (gHMs) were evaluated using model runs driven by WFDEI and GSWP3 (Kim et al 2014) data for 57 river basins worldwide (Krysanova et al 2020) and by WATCH, WFDEI, GSWP3, and Princeton (Sheffield et al 2006) data for six Arctic basins (Gädeke et al 2020). The continental-scale model E-HYPE applied EFAS-Meteo data (Ntegeka et al 2013) with resolution of 5 km (Hundecha et al 2020).…”

Section: Climate Data and Scenariosmentioning

confidence: 99%

“…& Evaluation of performance of six global water models in the historical period was done for 57 large river basins on six continents, considering monthly and long-term mean monthly dynamics of discharge at outlets, based on four common metrics summarized to an aggregated index (AI, ranging from 0 to 1, the higher the better) (Krysanova et al 2020). Next, a comparison of projected impacts in terms of magnitude of change and spreads was performed for 12 selected river basins using (i) all models, applying the ensemble mean approach (EM), and (ii) only satisfactorily performing models, applying weighting coefficients (WCO) estimated based on the evaluation results.…”

Section: C) Multi-basin Studies With the Global-scale Modelsmentioning

confidence: 99%

“…The importance of model performance is closely connected with the necessity of model evaluation, which reveals how well the models perform in the historical reference period against observed river discharge and other variables. The model evaluation could be done for the calibrated and validated models applied at the regional or catchment scales (see Gelfan et al 2020), and also for uncalibrated models, which are usually applied at the global scale (see overview of papers in Krysanova et al 2020). For a comparison of the performances of the regional-and globalscale hydrological models, see Hattermann et al (2017).…”

Section: Introductionmentioning

confidence: 99%

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How evaluation of hydrological models influences results of climate impact assessment—an editorial

2020

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This paper introduces the Special Issue (SI) “How evaluation of hydrological models influences results of climate impact assessment.” The main objectives were as follows: (a) to test a comprehensive model calibration/validation procedure, consisting of five steps, for regional-scale hydrological models; (b) to evaluate performance of global-scale hydrological models; and (c) to reveal whether the calibration/validation methods and the model evaluation results influence climate impacts in terms of the magnitude of the change signal and the uncertainty range. Here, we shortly describe the river basins and large regions used as case studies; the hydrological models, data, and climate scenarios used in the studies; and the applied approaches for model evaluation and for analysis of projections for the future. After that, we summarize the main findings. The following general conclusions could be drawn. After successful comprehensive calibration and validation, the regional-scale models are more robust and their projections for the future differ from those of the model versions after the conventional calibration and validation. Therefore, climate impacts based on the former models are more trustworthy than those simulated by the latter models. Regarding the global-scale models, using only models with satisfactory or good performance on historical data and weighting them based on model evaluation results is a more reliable approach for impact assessment compared to the ensemble mean approach that is commonly used. The former method provides impact results with higher credibility and reduced spreads in comparison to the latter approach. The studies for this SI were performed in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).

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Section: Case Study Areasmentioning

confidence: 99%

Section: Case Study Areasmentioning

confidence: 99%

Section: Climate Data and Scenariosmentioning

confidence: 99%

Section: C) Multi-basin Studies With the Global-scale Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How evaluation of hydrological models influences results of climate impact assessment—an editorial

2020

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Climate change projections of continental-scale streamflow across the Mississippi River Basin

Lewis

Lytle

Tavakoly

2022

Theor Appl Climatol

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A large body of scientific research has demonstrated a changing climate, which affects river flow regimes and extreme flood frequencies and magnitudes. The magnitude and frequency of extreme events are of critical importance in the evaluation of river systems to inform flood risk reduction under current and future conditions. The global climate projections from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) datasets were used by the Variable Infiltration Capacity (VIC) land surface model to produce a runoff dataset, implementing a Bias-Correction Spatial Disaggregation (BCSD) approach. The resulting runoff was then used as input to the Routing Application for Parallel computatIon of Discharge (RAPID) river routing model to simulate daily flows within all 1.2 million Mississippi River Basin river reaches for years 1950 through 2099. This research effort analyzed the performance of the models for the historical time period, comparing with the observations at 64 gage locations for 16 different climate models. A recurrence interval analysis was performed to determine the 2-, 5-, 10-, 50-, 100-, 500-, and 1000-year events within both the historical and projected time periods, highlighting the relative changes predicted into the future. Anticipated seasonal changes are demonstrated by comparing monthly average streamflows for three different time periods (1951–2005, 2006–2049, and 2050–2099). Results indicate that the hydrologic conditions of the Lower Mississippi River are not stationary. Based on all 16 models considered in this study, the median of the model projections shows an 8% increase in the 100-year return period discharge at Vicksburg, Mississippi, into the future time period, although the full range of 16 models varies widely from − 11 to + 85% change in the 100-year discharge in the future.

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Performance evaluation of global hydrological models in six large Pan-Arctic watersheds

et al. 2020

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Global Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds.

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How evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change

Cited by 35 publications

References 53 publications

How evaluation of hydrological models influences results of climate impact assessment—an editorial

How evaluation of hydrological models influences results of climate impact assessment—an editorial

Climate change projections of continental-scale streamflow across the Mississippi River Basin

Performance evaluation of global hydrological models in six large Pan-Arctic watersheds

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