Estimation of the Budyko model parameter for small basins in China
Bai, Peng1; Liu, Xiaomang1; Zhang, Dan2; Liu, Changming1
Corresponding AuthorLiu, Xiaomang(liuxm@igsnrr.ac.cn)
AbstractAt the mean annual scale, water availability of a basin is substantially determined by how much precipitation will be partitioned into evapotranspiration and run-off. The Budyko framework provides a simple but efficient tool to estimate precipitation partitioning at the basin scale. As one form of the Budyko framework, Fu's equation has been widely used to model long-term basin-scale water balance. The major difficulty in applications of Fu's equation is determining how to estimate the curve shape parameter omega efficiently. Previous studies have suggested that the parameter omega is closely related to the long-term vegetation coverage on large river basins globally. However, on small basins, the parameter omega is difficult to estimate due to the diversity of controlling factors. Here, we focused on the estimation of omega for small basins in China. We identified the major factors controlling the basin-specific (calibrated) omega from nine catchment attributes based on a dataset from 206 small basins (<= 50,000 km(2)) across China. Next, we related the calibrated omega to the major factors controlling omega using two statistical models, that is, the multiple linear regression (MLR) model and artificial neural network (ANN) model. We compared and validated the two statistical models using an independent dataset of 80 small basins. The results indicated that in addition to vegetation, other landscape factors (e.g., topography and human activity) need to be considered to capture the variability of omega on small basins better. Contrary to previous findings reached on large basins worldwide, the basin-specific omega and remote sensing-based vegetation greenness index exhibit a significant negative correlation. Compared with the default omega value of 2.6 used in the Budyko curve method, the two statistical models significantly improved the mean annual ET simulations on validation basins by reducing the root mean square error from 114 mm/year to 74.5 mm/year for the MLR model and 70 mm/year for the ANN model. In comparison, the ANN model can provide a better omega estimation than the MLR model.
KeywordBudyko curve evaporation water balance water resources
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[41601034] ; National Natural Science Foundation of China[51979263] ; National Natural Science Foundation of China[41571024]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaWater Resources
WOS SubjectWater Resources
WOS IDWOS:000486519100001
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Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorLiu, Xiaomang
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Bai, Peng,Liu, Xiaomang,Zhang, Dan,et al. Estimation of the Budyko model parameter for small basins in China[J]. HYDROLOGICAL PROCESSES,2019:14.
APA Bai, Peng,Liu, Xiaomang,Zhang, Dan,&Liu, Changming.(2019).Estimation of the Budyko model parameter for small basins in China.HYDROLOGICAL PROCESSES,14.
MLA Bai, Peng,et al."Estimation of the Budyko model parameter for small basins in China".HYDROLOGICAL PROCESSES (2019):14.
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