The large variation in performance between GHMs, regions and performance indicators, calls for a careful selection of GHMs, model components and evaluation metrics in future model applications. This highlights the need for further research linking human management and hydrological domains, especially in those areas in which human impacts are dominant. Even with the inclusion of HIP, we find that the model performance is still not optimal. This is due to the relative importance of the timing of return flows and reservoir operations as well as their associated uncertainties.
Whilst the inclusion of HIP leads to decreases in the simulated high-flows, it can lead to either increases or decreases in the low-flows. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. The inclusion of HIP leads to a significant decrease in the bias of the long-term mean monthly discharge in 36%–73% of the studied catchments, and an improvement in the modeled hydrological variability in 31%–74% of the studied catchments. This finding is robust across the GHMs, although the level of improvement and the reasons for it vary greatly. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments.
We find that the inclusion of HIP improves the performance of the GHMs, both in managed and near-natural catchments. The analysis is performed for 471 gauging stations across the globe for the period 1971–2010. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of mean, high- and low-flows. Human activity has a profound influence on river discharges, hydrological extremes and water-related hazards. 1 Institute for Environmental Studies (IVM), VU Amsterdam, the NetherlandsĢ Potsdam Institute for Climate Impact Research, Potsdam, Germanyģ Department of Geography, University of California, Santa Barbara, Santa Barbara, United States of AmericaĤ Institute of Physical Geography, Goethe-University Frankfurt, Frankfurt, Germanyĥ Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt, GermanyĦ National Institute for Environmental Studies, Tsukuba, Japanħ Department of Civil and Environmental Engineering, Michigan State University, Michigan, United States of AmericaĨ Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People's Republic of Chinaĩ International Institute for Applied Systems Analysis, Laxenburg, Austriaġ0 Department of Geography, Humboldt-Universität zu Berlin, Berlin, Germanyġ1 School of Geography, University of Nottingham, Nottingham, United Kingdomġ2 Department of Physical Geography, Utrecht University, the Netherlandsġ3 Author to whom any correspondence should be addressed.
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