Drought monitoring based on TIGGE and distributed hydrological model in Huaihe River Basin, China.
Drought monitoring based on TIGGE and distributed hydrological model in Huaihe River Basin, China.
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DOI：
10.1016/j.scitotenv.2016.02.115
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
Junfang Zhao;J. Xu;Xingmei Xie;Hou-quan Lu
中科院分区：
文献类型：
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作者：
Junfang Zhao;J. Xu;Xingmei Xie;Hou-quan Lu
干旱评估对于制定措施以减少农业脆弱性，从而确保依赖农业的人的生计是重要的。本研究使用了四个全球集成天气预报系统：中国气象局、欧洲中期天气预报中心、英国气象局和美国国家环境预报中心，这些系统保存在2006年至2010年的THORPEX(观测系统研究和可预报性实验)互动大全球集合(TIGGE)档案中。基于XXT(第一个X代表新安江，第二个X代表混合型，T代表TOPMODEL)分布式水文模型的结果，以及土壤水分观测和数字高程模型(DEM)数据，建立了淮河流域中国流域的综合干旱等级。为了剔除短期波动对观测到的土壤水分的影响，计算了30天移动平均值。移动平均的使用显著改善了观测土壤水分与模拟土壤水分亏缺深度之间的相关性。最后，建立了观测土壤水分与模拟土壤水分亏缺深度之间的线性回归模型。回归系数−为154.23，确定性回归系数为0.5872，相关系数为0.77。根据土壤水分和土壤水分亏缺深度计算的干旱等级的趋势是相同的，并且等级在一个水平内一致。我们的研究结果强调了在使用不同的土壤水分指标评估干旱时综合干旱等级的重要性，以便获得对干旱状况的更全面的预测。
Drought assessment is important for developing measures to reduce agricultural vulnerability and thereby secure the livelihoods of those who depend on agriculture. This study uses four global ensemble weather prediction systems: the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the UK Met Office (UKMO), and the US National Centres for Environmental Prediction (NCEP) in the THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE) archive from 2006 to 2010. Based on results from the XXT (the first X denotes Xinanjiang, the second X denotes hybrid, and the T denotes TOPMODEL) distributed hydrological model, as well as soil moisture observations and digital elevation model (DEM) data, synthesized drought grades were established in the Huaihe River Basin of China. To filter out the impact of short-term fluctuations on observed soil moisture, a 30-day moving average was calculated. Use of the moving average significantly improves the correlation between observed soil moisture and simulated soil water deficit depth. Finally, a linear regression model describing the relationship between observed soil moisture and simulated soil water deficit depth was constructed. The deterministic regression coefficient was 0.5872, the correlation coefficient was 0.77, and the regression coefficient was − 154.23. The trends in drought grades calculated using soil moisture and soil water deficit depth were found to be the same, and the grades agreed to within one level. Our findings highlight the importance of synthesizing drought grading when assessing drought using different soil moisture indicators in order to obtain a more comprehensive forecast of drought conditions.