Journal
ENVIRONMENTAL RESEARCH LETTERS
Volume 13, Issue 6, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1748-9326/aac547
Keywords
global hydrological models; land surface models; human impacts; extreme events; model evaluation; model validation
Funding
- German Ministry of Education and Research [01LS1201A]
- Islamic Development Bank
- University of Nottingham Faculty of Social Sciences
- Leibniz Competition [SAW-2013 PIK-5]
- EU FP7 HELIX project [603864]
- National Natural Science Foundation of China [41625001, 41571022]
- Beijing Natural Science Foundation [8151002]
- Southern University of Science and Technology [G01296001]
- Office of Science of the US Department of Energy as part of the Integrated Assessment Research Program
- US DOE [DE-AC05-76RLO1830]
- Ministry of the Environment, Japan [S-10]
- Japan Society for the Promotion of Science KAKENHI [16H06291]
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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.
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