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基于耦合融雪模块WAS模型的流域径流模拟与组分解析

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[目的]在气候变化背景下,深入解析径流组分对识别降水模式改变、气温升高等气候变化下的径流响应具有重要意义。为提高WAS模型在高寒山区的径流模拟精度,从而支撑径流组分的准确深入解析。[方法]以毛家村水库上游流域为例,采用度日因子法与双温度阈值法综合构建了融雪模块,将其与WAS模型进行耦合,并将改进后WAS模型应用于 2001-2020 年毛家村水库流域径流模拟与组分解析。根据水源与演化路径不同,将径流划分为基流、降雨地表径流和融雪地表径流3 部分,并根据年内不同时期水文过程的主导因素不同,利用不同时期的实测径流分别对地下水参数、土壤参数和融雪参数进行逐步分级率定。[结果]结果显示:耦合融雪模块WAS模型相较于原WAS模型,在春季融雪期的模拟精度有显著提升,改进后WAS模型在率定期与验证期NSE 分别为 0。85、0。78,R2 分别为0。93、0。91,优于原WAS模型的模拟效果。径流组分解析结果显示,季节尺度上,基流、降雨地表径流、融雪地表径流对春季径流的贡献分别为 58。1%、19。1%、22。8%,对夏季径流的贡献分别为30。7%、67%、2。3%,秋、冬季径流中,无融雪地表径流,基流、降雨地表径流对秋季径流的贡献分别为 46。4%、53。6%,对冬季径流的贡献分别为 78。8%、21。2%;年尺度上,基流、降雨地表径流和融雪地表径流对总径流的贡献分别为 41。7%、53。6%和 4。8%。[结论]WAS模型无论改进前后均可以从总体上模拟出径流的长期动态变化,耦合融雪模块的WAS模型可以更加精准的模拟出春季融雪期径流过程,提高了模型在高寒山区的适用性。降雨地表径流是该流域径流的主要组成部分,其次为基流,融雪地表径流最少。但融雪对于抑制春季径流衰减具有重要作用,基流是构成冬、春两季径流的主要部分,对于维持冬季与春季径流水平具有重要意义。
Simulation and component analysis of watershed runoff based on coupled snowmelt module WAS modeling
[Objective]In the context of climate change,in-depth analysis of runoff components is important for identifying runoff responses under climate change such as changes in precipitation patterns and increases in temperature.In order to improve the runoff simulation accuracy of the WAS model in highland mountainous areas,and thus to support the accurate and in-depth analy-sis of runoff components,the WAS model was developed.[Methods]Taking the upstream watershed of Maojiacun Reservoir as an example,the snowmelt module was constructed by using the degree-day factor method and the dual temperature threshold method in an integrated way,which was coupled with the WAS model,and the improved WAS model was applied to simulate and analyze the runoff and components in the watershed of Maojiacun Reservoir for the period of 2001-2020.According to the differ-ent water sources and evolution paths,the runoff is divided into three parts:baseflow,rainfall surface runoff and snowmelt sur-face runoff,and according to the different dominant factors of hydrological processes in different periods of the year,the measured runoff in different periods is used to determine the graded rates of the groundwater parameters,soil parameters and snowmelt pa-rameters,respectively.[Results]The result show that the coupled snowmelt module WAS model significantly improves the simu-lation accuracy in the spring snowmelt period compared with the original WAS model,and the improved WAS model is better than the original WAS model in the rate period and validation period with NSEs of 0.85 and 0.78,and R2s of 0.93 and 0.91,respec-tively.The runoff component analysis result showed that on the seasonal scale,baseflow,rainfall surface runoff,and snowmelt surface runoff contributed 58.1%,19.1%,and 22.8%to the spring runoff,and 30.7%,67%,and 2.3%to the summer run-off,respectively,and that there was no snowmelt surface runoff in the fall and winter runoff,and that baseflow and rainfall sur-face runoff contributed 46.4%to the fall runoff,respectively,53.6%,and 78.8%and 21.2%for winter runoff,respectively;on an annual scale,baseflow,rainfall surface runoff,and snowmelt surface runoff contributed 41.7%,53.6%,and 4.8%to total runoff,respectively.[Conclusion]The WAS model can simulate the long-term dynamic changes of runoff in general before and after the improvement,and the WAS model coupled with the snowmelt module can more accurately simulate the runoff process during the spring snowmelt period,which improves the applicability of the model in the mountainous areas of the plateau.Rainfall surface runoff is the main component of runoff in this watershed,followed by baseflow,and snowmelt surface runoff is the least.However,snowmelt plays an important role in spring runoff replenishment,and baseflow constitutes the majority of winter and spring runoff,which is important for maintaining winter and spring runoff levels.

water cycleWAS modelsnowmelt runoffrunoff simulationrunoff component analysisclimate changerainfalldynamic changes

段天池、桑学锋、殷峻暹、倪红珍

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中国水利水电科学研究院,北京 100038

河海大学 水安全与水科学协同创新中心,江苏 南京 211100

水循环 WAS模型 融雪径流 径流模拟 径流组分 气候变化 降雨 动态变化

2024

水利水电技术(中英文)
水利部发展研究中心

水利水电技术(中英文)

CSTPCD北大核心
影响因子:0.456
ISSN:1000-0860
年,卷(期):2024.55(10)