Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

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 © 2018 The Author(s). Published by IOP Publishing Ltd Environ. Res. Lett. 13 (2018) 065015 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.

Nonlinearity of Runoff Response to Global Mean Temperature Change Over Major Global River Basins

“…For each climate model and each RCP scenario, relative change in the mean and interannual variability of basin-averaged runoff was calculated between the preindustrial era (1850-1900) and each 30-year future period corresponding to a prescribed GMT rise varying from 0.1 to 3°C at an interval of 0.1°C (see Text S1 in the supporting information). Although a recent study reported that multimodel ensemble mean is not always superior to individual models (Zaherpour et al, 2018), we did not try to select specific climate models from the pool of CMIP5 according to their performance. Here the multimodel ensemble medians (MMs) estimated from the subsets formed by the total 34 climate models under 4 RCP scenarios were calculated and used for analysis following previous studies (Mote et al, 2011;Pierce et al, 2009;Reichler & Kim, 2008).…”

Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

“…Müller Schmied et al () showed that by taking into account uncertainties of climate and land cover data, global discharge was changed by 5% whereas actual evapotranspiration was unaffected. In a number of studies, the performance of the WGHM in the simulation of discharge, reservoir, radiation, and evapotranspiration was evaluated, and it was found that the model estimate in the gridded and basin scales generally outperforms other global models (Masaki et al, ; Müller Schmied et al, 2016b; Veldkamp et al, ; Wartenburger et al, ; Zaherpour et al, , ; Zhao et al, ). The comparison of seasonal amplitudes of the model to GRACE data showed that WGHM underestimates TWSA in the Southern Hemisphere and the tropics but compares favourably with GRACE in the Northern Hemisphere (Scanlon et al, ).…”

A hydrological model for root zone water storage simulation on a global scale

“…ISIMIP is a community-driven global platform that supports for model inter-comparison studies at both global and regional scales, while ISIMIP2a focuses on the historical period and all the models are driven by four state-of-the-art climate forcing . Since the ISIMIP2a simulations are widely studied and discussed in many studies Müller Schmied et al, 2016;Gernaat et al, 2017;Zaherpour et al, 2018), the comparison between WAYS and ISIMIP2a models can provide added-value for evaluation in addition to examine only with reference data. To make the 15 climate forcing consistent with WAYS model, only the GSWP3 driven simulations are used for comparison.…”

“…ISIMIP2a models show a clear trend of overestimation in some of the basins (Mississippi, Ganges, Yangtze, Parana and Murray Darling), where the spread of the runoff ensembles are also large. This is partly due to the reason that some of the ISIMIP2a models are not calibrated at all (Zaherpour et al, 2018), whilst the WAYS is calibrated to a Composite Monthly Runoff data set which assimilates the monitored river discharge (Fekete et al, 2011). 30 In Mekong rive basin, all the models show a high consistency in monthly runoff generation with a narrow spread of the ensemble.…”