Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts
Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts
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通过考虑人类影响的六个全球规模水文模型对平均和极端径流进行全球评估
DOI：
10.1088/1748-9326/aac547
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发表时间：
2018-06
影响因子：
6.7
通讯作者：
Yadu Pokhr
中科院分区：
文献类型：
--
作者：
Jamal Zaherpour;Simon N Gosling;Nick Mount;Hannes Muller Schmied;Ted I E Veldkamp;Rutger Dankers;Stephanie Eisner;Dieter Gerten;Lukas Gudmundsson;Ingjerd Haddel;Naota Hanasaki;Hyungjun Kim;Guoyong Leng;Junguo Liu;Yoshimitsu Masaki;Taikan Oki;Yadu Pokhr
全球尺度的水文模型通常用于评估全世界的水资源短缺、洪水灾害和干旱。最近将人类活动纳入这些模型的努力，使得与观测的比较更加现实。在这里，我们评估了参与部门间影响模型相互比较项目（ISIMIP2a）第二阶段的六个模型的集成模拟。我们模拟了40个集水区的月径流，这些集水区在空间上分布在8个全球水文带。每个模型的性能和合奏的意思是检查他们的能力，以复制观测到的平均和极端径流在人为影响的条件下。应用一种新的综合评价指标来量化模型模拟月径流时间序列的能力表明，该模型一般表现更好，在潮湿的赤道和北方水文带比干燥的南部水文带。当模型输出在时间上汇总以评估平均年径流和极端径流时，模型表现得更好。然而，我们发现一个总的趋势，在大多数模型对高估的平均年径流量和所有指标的上限和下限极端径流。这些模型很难捕捉季节周期的时间，特别是在北方水文带，而在南方水文带，模型很难再现季节周期的大小。值得注意的是，在所有的水文指标，集合平均未能表现得比任何个人模型，这一发现挑战了普遍持有的看法，模型集合估计提供上级性能超过个人模型。该研究强调需要继续开发和改进模型。它还建议，应谨慎总结模拟模式合奏的基础上，其平均输出。
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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影响因子：
3.8
作者：
H. Beck;A. Roo;A. Dijk
通讯作者：
H. Beck;A. Roo;A. Dijk
DOI：
10.1002/2015wr018247/full
发表时间：
2016-04
期刊：
--
影响因子：
--
作者：
B. Hylke;Van Dijk Albert I.j.m.;D. Arie;G. Diego;R. McvicarTim.;Schellekens Jaap;Bruijnzeel L. Adrian
通讯作者：
B. Hylke;Van Dijk Albert I.j.m.;D. Arie;G. Diego;R. McvicarTim.;Schellekens Jaap;Bruijnzeel L. Adrian
影响因子：
6.7
作者：
Xingcai Liu;Q. Tang;H. Cui;M. Mu;D. Gerten;S. Gosling;Y. Masaki;Y. Satoh;Y. Wada
通讯作者：
Xingcai Liu;Q. Tang;H. Cui;M. Mu;D. Gerten;S. Gosling;Y. Masaki;Y. Satoh;Y. Wada
影响因子：
--
作者：
B. Decharme
通讯作者：
B. Decharme
影响因子：
5.2
作者：
B. Sanderson;R. Knutti
通讯作者：
B. Sanderson;R. Knutti