Water Security Indicator Model - Global Land Data Assimilation System (WSIM-GLDAS) Monthly Grids, Version 1
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The Water Security Indicator Model - Global Land Data Assimilation System (WSIM-GLDAS) Monthly Grids, Version 1 data set identifies and characterizes surpluses and deficits of freshwater, and the parameters determining these anomalies, at monthly intervals over the period January 1948 to December 2014. The data set uses the land surface model outputs from NASA's Global Land Data Assimilation System, covering the global extent, to generate anomaly values for the following parameters at a gridded resolution of 0.25 degrees: temperature, precipitation, soil moisture, potential minus actual evapotranspiration, runoff, total blue water (flow-accumulated runoff), composite index of water surplus, and composite index of water deficits. These data are provided in terms of return periods, scientific Units, and standardized (normalized) anomalies, and are computed over 1-month, 3-month, 6-month, and 12-month temporal periods of accumulation, referred to as integration periods. Anomaly values are present in terms of return periods with respect to a fitted Generalized Extreme Value (GEV) probability distribution function over a historical baseline period of January 1950 to December 2009, at a global spatial resolution of 0.25 degrees over the monthly, 3-month, 6-month, and 12-month periods of integration. Parameter values (location, scale, shape) of the fitted GEV probability distribution, which are fit separately for each calendar month, are distributed per parameter for each integration period.
《全球陆地数据同化系统水安全指标模型月度网格(WSIM-GLDAS)版本1数据集》旨在识别和描述1948年1月至2014年12月期间,淡水盈余与不足及其决定性参数的异常情况,并按月度进行表征。该数据集采用美国宇航局全球陆地数据同化系统(GLDAS)的土地表面模型输出,覆盖全球范围,以0.25度网格分辨率生成以下参数的异常值:温度、降水量、土壤湿度、潜在实际蒸散量差值、径流、总蓝水(流量累积径流)、水盈余综合指标和水赤字综合指标。这些数据以回报期、科学单位和标准化(归一化)异常值的形式提供,并计算了1个月、3个月、6个月和12个月的累积时间周期,即积分周期。异常值以回报期形式表示,基于1950年1月至2009年12月的历史基线时期的拟合广义极值(GEV)概率分布函数,在全球0.25度空间分辨率下,针对月度、3个月、6个月和12个月的积分周期。对于每个日历月份,拟合GEV概率分布的参数值(位置、尺度、形状)分别进行拟合,并针对每个积分周期按参数进行分发。
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