%0 Journal Article %1 1748-9326-13-6-065015 %A Zaherpour, Jamal %A Gosling, Simon N %A Mount, Nick %A Schmied, Hannes Müller %A Veldkamp, Ted I E %A Dankers, Rutger %A Eisner, Stephanie %A Gerten, Dieter %A Gudmundsson, Lukas %A Haddeland, Ingjerd %A Hanasaki, Naota %A Kim, Hyungjun %A Leng, Guoyong %A Liu, Junguo %A Masaki, Yoshimitsu %A Oki, Taikan %A Pokhrel, Yadu %A Satoh, Yusuke %A Schewe, Jacob %A Wada, Yoshihide %D 2018 %J Environmental Research Letters %K climate\_impacts, extremes, floods, hydrological\_modelling, %N 6 %P 065015 %T Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts %U http://stacks.iop.org/1748-9326/13/i=6/a=065015 %V 13 %X Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models’ ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model—a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.

@article{1748-9326-13-6-065015, abstract = {Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models’ ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model—a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.}, added-at = {2018-10-03T10:38:13.000+0200}, author = {Zaherpour, Jamal and Gosling, Simon N and Mount, Nick and Schmied, Hannes Müller and Veldkamp, Ted I E and Dankers, Rutger and Eisner, Stephanie and Gerten, Dieter and Gudmundsson, Lukas and Haddeland, Ingjerd and Hanasaki, Naota and Kim, Hyungjun and Leng, Guoyong and Liu, Junguo and Masaki, Yoshimitsu and Oki, Taikan and Pokhrel, Yadu and Satoh, Yusuke and Schewe, Jacob and Wada, Yoshihide}, biburl = {https://www.bibsonomy.org/bibtex/2e9666c213c7902402f486e1a276b23c2/rutgerdankers}, interhash = {48a57e7403bcc052e1f79abbf7a3a7ae}, intrahash = {e9666c213c7902402f486e1a276b23c2}, journal = {Environmental Research Letters}, keywords = {climate\_impacts, extremes, floods, hydrological\_modelling,}, number = 6, pages = 065015, timestamp = {2018-10-03T10:38:13.000+0200}, title = {Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts}, url = {http://stacks.iop.org/1748-9326/13/i=6/a=065015}, volume = 13, year = 2018 }