Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019WR026752 |
On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation | |
Guo, Danlu1,2; Zheng, Feifei2; Gupta, Hoshin3; Maier, Holger R.2,4 | |
2020-03-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2020 |
卷号 | 56期号:3 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia; Peoples R China; USA |
英文摘要 | Conceptual rainfall-runoff (CRR) models are widely used for runoff simulation and for prediction under a changing climate. The models are often calibrated with only a portion of all available data at a location and then evaluated independently with another part of the data for reliability assessment. Previous studies report a persistent decrease in CRR model performance when applying the calibrated model to the evaluation data. However, there remains a lack of comprehensive understanding about the nature of this "low transferability" problem and why it occurs. In this study we employ a large sample approach to investigate the robustness of CRR models across calibration/validation data splits. Specially, we investigate (1) how robust is CRR model performance across calibration/evaluation data splits, at catchments with a wide range of hydroclimatic conditions; and (2) is the robustness of model performance somehow related to the hydroclimatic characteristics of a catchment? We apply three widely used CRR models, GR4J, AWBM and IHACRE_CMD, to 163 Australian catchments having long-term historical data. Each model was calibrated and evaluated at each catchment, using a large number of data splits, resulting in a total of 929,160 calibrated models. Results show that (1) model performance generally exhibits poor robustness across calibration/evaluation data splits and (2) lower model robustness is correlated with specific catchment characteristics, such as higher runoff skewness and aridity, highly variable baseflow contribution, and lower rainfall-runoff ratio. These results provide a valuable benchmark for future model robustness assessments and useful guidance for model calibration and evaluation. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000538000800026 |
WOS关键词 | HYDROLOGICAL MODELS ; CLIMATE ; SENSITIVITY ; SIMULATION ; ENSEMBLE ; IMPACT ; TRANSFERABILITY ; BENCHMARKING ; UNCERTAINTY ; PROJECTIONS |
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/280566 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Melbourne, Dept Infrastruct Engn, Parkville, Vic, Australia; 2.Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou, Peoples R China; 3.Univ Arizona, Dept Hydrol & Atmospher Sci, Tucson, AZ USA; 4.Univ Adelaide, Sch Civil Environm & Min Engn, Adelaide, SA, Australia |
推荐引用方式 GB/T 7714 | Guo, Danlu,Zheng, Feifei,Gupta, Hoshin,et al. On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation[J]. WATER RESOURCES RESEARCH,2020,56(3). |
APA | Guo, Danlu,Zheng, Feifei,Gupta, Hoshin,&Maier, Holger R..(2020).On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation.WATER RESOURCES RESEARCH,56(3). |
MLA | Guo, Danlu,et al."On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Data Set Splits Selection: A Large Sample Investigation".WATER RESOURCES RESEARCH 56.3(2020). |
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