Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR023325 |
Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches | |
Zhang, Yongqiang1; Chiew, Francis H. S.1; Li, Ming2; Post, David1 | |
2018-10-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:10页码:7859-7878 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia |
英文摘要 | Accurate prediction of runoff signatures is important for numerous hydrological and water resources applications. However, there are lack of comprehensive evaluations of various approaches for predicting hydrological signatures. This study, for the first time, introduces regression tree ensemble approach and compares it with other three widely used approaches (multiple linear regression, multiple log-transformed linear regression, and hydrological modeling) for assessing prediction accuracy of 13 runoff characteristics or signatures, using a large data set from 605 catchments across Australia. The climate, in particular, mean annual precipitation and aridity index, has the most significant influence on the runoff signatures. Physical catchment attributes including forest ratio, slope, and soil water holding capacity also have significant influence (p < 0.05) on the runoff signatures. All four approaches can predict the long-term average and high flow signatures accurately. The regression approaches can also well predict majority of the other runoff signatures, with the Nash-Sutcliffe Efficiency larger than 0.60. The regression tree ensemble outperforms the two linear regressions in predicting signatures of flow dynamics. The hydrological models, calibrated to one specific objective criterion, cannot predict many of the runoff signatures, particularly those reflecting low flows and flow dynamics. This is because in most hydrological model applications, the simulations allow satisfactory predictions of long-term average and high flow signatures. In applications where a specific runoff signature is needed, regression relationships that directly relate that runoff signature to catchment attributes give the best predictions. Here the regression tree ensemble is overall best and offers significant potential, being able to predict most of the runoff signatures very well. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000450726000040 |
WOS关键词 | FLOW DURATION CURVE ; LEAF-AREA INDEX ; UNGAUGED CATCHMENTS ; SPATIAL INTERPOLATION ; NON-STATIONARITY ; STREAMFLOW ; EVAPOTRANSPIRATION ; REGIONALIZATION ; UNCERTAINTY ; CLIMATE |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21158 |
专题 | 资源环境科学 |
作者单位 | 1.CSIRO Land & Water, Canberra, ACT, Australia; 2.CSIRO Data 61, Wembley, WA, Australia |
推荐引用方式 GB/T 7714 | Zhang, Yongqiang,Chiew, Francis H. S.,Li, Ming,et al. Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches[J]. WATER RESOURCES RESEARCH,2018,54(10):7859-7878. |
APA | Zhang, Yongqiang,Chiew, Francis H. S.,Li, Ming,&Post, David.(2018).Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches.WATER RESOURCES RESEARCH,54(10),7859-7878. |
MLA | Zhang, Yongqiang,et al."Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches".WATER RESOURCES RESEARCH 54.10(2018):7859-7878. |
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