TY - JOUR
T1 - Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes
T2 - A multi-model validation study
AU - Veldkamp, T. I.E.
AU - Zhao, F.
AU - Ward, P. J.
AU - De Moel, H.
AU - Aerts, J. C.J.H.
AU - Schmied, H. Müller
AU - Portmann, F. T.
AU - Masaki, Y.
AU - Pokhrel, Y.
AU - Liu, X.
AU - Satoh, Y.
AU - Gerten, D.
AU - Gosling, S. N.
AU - Zaherpour, J.
AU - Wada, Y.
N1 - Funding Information:
The Global Runoff Data Centre (GRDC, 56068 Koblenz, Germany) is thanked for providing the observed discharge data. This work has been conducted under the framework of phase two of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a: www.isimip.org) and the authors want to thank the coordination team responsible for bringing together the different global hydrological modeling groups and for coordinating the research agenda, which resulted in this manuscript. The research leading to this article is partly funded by the EU 7th Framework Programme through the project Earth2Observe (grant agreement no. 603608). JZ was funded by the Islamic Development Bank. PJW received additional funding from the Netherlands Organisation for Scientific Research (NWO) in the form of a Vidi grant (016.161.324). JCJHA received funding from the Netherlands Organisation for Scientific Research (NWO) Vici (grant no. 453-14-006). YM was supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment.
Publisher Copyright:
© 2018 IOP Publishing Ltd.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/5
Y1 - 2018/5
N2 - Human activity has a profound influence on river discharges, hydrological extremes and water-related hazards. 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. The analysis is performed for 471 gauging stations across the globe for the period 1971-2010. We find that the inclusion of HIP improves the performance of the GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across the GHMs, although the level of improvement and the reasons for it vary greatly. 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. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. 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. This is due to the relative importance of the timing of return flows and reservoir operations as well as their associated uncertainties. Even with the inclusion of HIP, we find that the model performance is still not optimal. This highlights the need for further research linking human management and hydrological domains, especially in those areas in which human impacts are dominant. 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.
AB - Human activity has a profound influence on river discharges, hydrological extremes and water-related hazards. 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. The analysis is performed for 471 gauging stations across the globe for the period 1971-2010. We find that the inclusion of HIP improves the performance of the GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across the GHMs, although the level of improvement and the reasons for it vary greatly. 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. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. 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. This is due to the relative importance of the timing of return flows and reservoir operations as well as their associated uncertainties. Even with the inclusion of HIP, we find that the model performance is still not optimal. This highlights the need for further research linking human management and hydrological domains, especially in those areas in which human impacts are dominant. 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.
KW - fresh water resources
KW - global hydrological modeling
KW - human impact
KW - hydrological extremes
KW - multi-model
KW - validation
UR - http://www.scopus.com/inward/record.url?scp=85048079381&partnerID=8YFLogxK
U2 - 10.1088/1748-9326/aab96f
DO - 10.1088/1748-9326/aab96f
M3 - Letter
AN - SCOPUS:85048079381
VL - 13
JO - Environmental Research Letters
JF - Environmental Research Letters
SN - 1748-9318
IS - 5
M1 - 055008
ER -