考虑融雪量的多变量综合干旱指数的构建与评估

Development and evaluation of a multivariate composite drought index accounting for snow

【目的】为解决大多数干旱评估中雪因素常被忽略的问题,需要开发适用于高寒融雪流域的干旱指数。【方法】根据潜在蒸散量、融雪量、降雨量计算水分增益/损失,然后利用Copula函数计算水分增加/损失与土壤水分的联合分布概率,从而构建了一个基于降雨、融雪量、潜在蒸散量和土壤水分新的标准化融雪综合干旱指数(SSCDI),并比较和评估了3月和6月时间尺度的SSCDI在黄河源区的干旱监测性能。【结果】结果表明,Fischer-Hinzmann Copula是构建SSCDI的最佳连接函数;新构建的SSCDI与SPEI和MSDI相比,同SSI的拟合程度更好(R^(2)高于0.7);SSCDI比SPEI和MSDI更符合观测到的干旱变化。【结论】特别是在高寒流域,对雪因素的考虑提高了SSCDI的干旱监测能力,并且SSCDI能够同时监测出SPEI和MSDI所监测到的干旱情况,总体而言,SSCDI可用于识别和监测高寒区不同时间尺度的气象和农业综合干旱。

[Objective]In order to address the issue of neglecting snow factors in most drought assessments,it is necessary to develop a drought index that is suitable for alpine snowmelt watersheds.[Methods]Water gain/loss is calculated based on potential evapotranspiration,snowmelt and rainfall.The Copula function is used to calculate the joint distribution probability of water gain/loss and soil water.A new standardized snow melt drought index(SSCDI)based on rainfall,snowmelt,potential evapotranspiration,and soil moisture is constructed.The drought monitoring performance of SSCDI at 3-month and 6-month time scales in the source region of the Yellow River is compared and evaluated.[Results]The results show that the Fischer-Hinzmann Copula is the best connection function for constructing SSCDI.Compared with SPEI and MSDI,the newly constructed SSCDI fit SSI better(R^(2) was higher than 0.7).SSCDI is more consistent with observed changes in meteorological and agricultural drought than SPEI and MSDI.[Conclusion]Especially in alpine basins,consideration of snow improves the drought monitoring capability of SSCDI.SSCDI can monitor the drought situation monitored by SPEI and MSDI at the same time,and can be used to identify and monitor meteorological and agricultural drought at different time scales in alpine regions.