Modification of SCS-CN model for estimating event rainfall runoff for small watersheds in the Loess Plateau, China.
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摘要: 径流曲线数(SCS-CN)是预测场降雨地表径流常用的水文模型之一,由于其基本假设合理、参数易于获得而被广泛应用。然而,由于流域径流的形成受广泛存在空间或时间异质性的地形、地貌、土壤、气象、植被以及土地利用等多种因素的影响,按照标准径流曲线数模型估算的场降雨径流与实测径流相差可能很大。因此,针对特定区域、特定流域对该模型进行相应的修正是提高其径流预测精度的有效途径。本文于晋西黄土区吉县蔡家川分别以农田草地、人工林和次生林为主的3个典型小流域为对象,将2004—2011年实测的场降雨径流数据分为模型参数率定期(2004—2009年)和验证期(2010—2011年),对比标准SCS-CN模型和修正的SCS-CN模型(包括降雨量修正,降雨量与降雨强度修正,降雨量、降雨强度和初损率优化修正)预测场降雨径流的可靠性。结果表明:1)标准SCS-CN预测小流域场降水径流时,精度极差,模型拟合效率系数(E)均小于0;2)采用降雨量修正CN值预测流域地表径流精度优于标准模型,但对于小径流事件而言,预测结果会偏大,对于大径流事件,预测结果会偏小;3)基于优化降雨强度修正因子β和初损率λ模型可以提高以农田草地和人工林为主2个小流域的径流预测精度。对于以次生林为主的流域而言,仅通过降雨量修正CN值即可提高模型的预测精度,E可达0.79。反映流域储水特征的初损率λ,人工林为主的流域最小,为0.069,农田草地为主的流域次之,为0.189,次生林为主的流域,为0.200,表明次生林流域具有较好的储水效果。Abstract: The SCS-CN method is one of the most widely used hydrological models to predict surface runoff from watershed for a given rainfall event. However, runoff generation is governed by spatially and temporally heterogeneous factors including topography, landform, soil, climate, vegetation and land use, and using standard SCS-CN method to predict surface runoff, could thus result in large errors. Therefore, it is an effective way to modify the original model for particular region, specific watershed for improving the accuracy. The measured event rainfall-runoff datasets from three watersheds located in Caijiachuan watershed on the Loess Plateau of China during 2004 and 2011 were used for calibrating (2004 to 2009) and validating (2010—2011) the original and five modified SCS-CN models. The selected three watersheds are dominated by farmland and grassland, plantation forests, and secondary forests, respectively. We found that the standard SCS-CN method poorly estimated the event runoff for all three watersheds (model efficiency coefficients E less than 0). The performance of revised SCS-CN based on rainfall amount was better than the standard one even though overestimation for small runoff events and underestimation for large ones were observed across the watersheds. The optimized SCS-CN model by rainfall intensity revised and initial abstraction coefficient improved the prediction accuracy most among the five modified models for watersheds dominated by farmland and grassland and plantation forest. Interestingly, revised SCS-CN by rainfall amount only improved significantly the predicting accuracy for secondary forest dominated watershed (E=0.79). In addition, the initial abstraction coefficient (λ) was 0.069, 0.189, and 0.200 for watersheds dominated by plantation forest, farmland and grassland, and secondary forest, respectively, indicating that the water storage capacity was affected by the vegetation type.
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Key words:
- SCS-CN /
- rainfall /
- runoff /
- the Loess Plateau
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