Global Maps of Streamflow Characteristics Based on Observations from Several Thousand Catchments
Global Maps of Streamflow Characteristics Based on Observations from Several Thousand Catchments
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DOI：
10.1175/jhm-d-14-0155.1
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发表时间：
2015-07
影响因子：
3.8
通讯作者：
H. Beck;A. Roo;A. Dijk
中科院分区：
文献类型：
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作者：
H. Beck;A. Roo;A. Dijk
无资料流域径流流量Q估算是水文学家面临的最大挑战之一。利用全球3000至4000个中小流域(10-10000平方公里)的Q观测数据，训练神经网络集成，以根据流域的气候和地形特征来估计Q特征。总共选择了17个Q特征，包括年平均Q、基流指数和若干流量百分位数。估算Q特性的决定系数从基流退缩常数的0.55到Q计时的0.93不等。总体而言，气候指数在预测指标中占据主导地位。与土壤和地质学相关的预测指标相对不重要，可能是因为它们的数据质量。随后，将训练好的神经网络集成在整个无冰陆地表面上进行空间应用，得到Q特性的全球地图(分辨率为0.125°)。这些地图有几个独特的壮举..。
AbstractStreamflow Q estimation in ungauged catchments is one of the greatest challenges facing hydrologists. Observed Q from 3000 to 4000 small-to-medium-sized catchments (10–10 000 km2) around the globe were used to train neural network ensembles to estimate Q characteristics based on climate and physiographic characteristics of the catchments. In total, 17 Q characteristics were selected, including mean annual Q, baseflow index, and a number of flow percentiles. Testing coefficients of determination for the estimation of the Q characteristics ranged from 0.55 for the baseflow recession constant to 0.93 for the Q timing. Overall, climate indices dominated among the predictors. Predictors related to soils and geology were relatively unimportant, perhaps because of their data quality. The trained neural network ensembles were subsequently applied spatially over the entire ice-free land surface, resulting in global maps of the Q characteristics (at 0.125° resolution). These maps possess several unique feat...