全球智库信息集成服务系统(G2TT): Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study.

G2TT

来源类型	Article
规范类型	其他
DOI	10.1088/1748-9326/aab96f
	Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study.
	Veldkamp T; Zhao F; Ward PJ; Moel H de; Aerts JCJH; Müller Schmied H; Portmann FT; Masaki Y
发表日期	2018
出处	Environmental Research Letters : 1-28
出版年	2018
语种	英语
摘要	Human activities have a profound influence on river discharge, 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 the mean, high, and low flows. The analysis is performed for 471 gauging stations across the globe and for the period 1971-2010. We find that the inclusion of HIP improves the performance of 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 GHMs, although the level of improvement and reasons for improvement vary greatly by GHM. The inclusion of HIP leads to a significant decrease in the bias of long-term mean monthly discharge in 36-73% of the studied catchments, and an improvement in modelled 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 simulated high-flows, it can lead to either increases or decreases in low-flows. This is due to the relative importance of the timing of return flows and reservoir operations and their associated uncertainties. Even with the inclusion of HIP, we find that model performance still not optimal. This highlights the need for further research linking the human management and hydrological domains, especially in those areas with a dominant human impact. 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.
主题	Water (WAT)
URL	http://pure.iiasa.ac.at/id/eprint/15220/
来源智库	International Institute for Applied Systems Analysis (Austria)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/131404
推荐引用方式 GB/T 7714	Veldkamp T,Zhao F,Ward PJ,et al. Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study.. 2018.

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