基于陆气耦合模型的浙江省区域干旱过程预报分析

    Forecasting analysis on regional drought process in ZheJiang Province based on land - air coupled model

    • 摘要: 干旱及其准确预报一 直 是 水 文 气 象 领 域 研 究 的 重 难 点问 题 。为 探 索 准 确 可 行 的 干 旱 预 报 方 法 , 基 于 VIC模型与欧洲中期数值天气预报中心 (ECMWF)预报气象数据建立的陆气耦 合 干 旱 预 报 模 型 ,计 算 监 测 与 预报土壤含水量 ,分析土壤含水量距平指数(SMAPI)的时空分布特征 ,在此基础上评估耦合模型对 浙 江 省 干 旱的预报能力 。结果表明:① 从预见期来看 ,0 ~ 11 d 预见期的 SMAPI预报值与监测值在绝对误差 、RMSE和 R值上表现较好 , 即模型在旬尺度上预报效果最佳 ;② 该模型的旬尺度预报可以在时间变化过程上准确把握 未来干旱的发展趋势 ,在空间预报上与监测的 SMAPI值 网 格 分 布 一 致 性 可 达 90% 以 上;③ 模 型 对 浙 江 省西 北少雨平原区预报性能更佳 ,且对土壤含水量变化较剧烈地区的旱情预报趋势描述更好 。研究成果可为浙江 省干旱灾害防范预警提供科技支撑 。

       

      Abstract: Drought and its accurate prediction have always been a major problem in the field of hydrometeorology.To explore accurate and feasible drought forecasting methods,a land-air coupled drought forecasting model based on the VIC model and the European Center for Medium-Range Numerical Weather Forecasting (ECMWF) forecasting meteorological data was established to calculate monitored soil water content and predicted soil water content and to analyze the spatial and temporal distribution characteristics of the Soil Moisture Anomaly Percentage Index (SMAPI),based on which we assessed the drought forecasting ability of the coupled model over Zhejiang Province.The results show that:① for the foresight period,the forecasted and monitored SMAPI values in the foresight period of 0~1l d perform better in terms of absolute error,RMSE,and R-value,which shows the model has the best forecasting effect in the 10-day scale;② The 10-days scale forecast of the model can accurately grasp the development trend of the future droughts in the process of temporal change,and the consistency of the spatial forecast with the grid distribution of the monitored SMAPI values can reach 90% or more;③ The model has better prediction performance in the plain area with little rainfall in the northwest of Zhejiang Province,and better describes the trend of drought prediction in the area with more drastic changes in soil water content.The research results can provide scientific and technological support for drought disaster prevention and early warning in Zhejiang Province.

       

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