High resolution global standardized drought indices

The dataset consists of the standardized precipitation (SPI) and the standardized precipitation evapotranspiration index (SPEI) index with a 30 arcsec (~1km) horizontal resolution. Data for precipitation rates (pr) and potential evapotranspiration (pet) are taken from CHELSA V2.1 (https://doi.org/10.16904/envidat.228.v2.1). Both data sets are monthly time series from 1980 to 2018 at 30 arcsec. and have the unit of kg m-2 month-1. SPEI is a more reliable measure of drought than SPI as it additionally considers the effect of potential evapotranspiration. Potential evapotranspiration (pet) is the amount of water per area per time that can evaporate at the soil surface or be transpired by plants in absence of water supply limitations. SPEI effectively estimates for a given location how the climatic water balance, i.e., the difference between precipitation rate (pr) and potential evapotranspiration (pet), relates to the long-term mean. Here, pet was calculated with the Penman-Montheith approach, assuming surface conductance of a reference crop of 12 cm height following the definition of the Food and Agriculture Organization of the United Nations (FAO), originating from CHELSA-BIOCLIM+ database (https://www.doi.org/10.16904/envidat.332). To obtain global time series of SPEI, in each pixel the frequency distribution of water balance estimates (pr-pet) is approximated with a log-logistic probability distribution function. We calculate monthly SPEI using a 12-month memory and approximate the parameters of the log-logistic probability distribution based on the time series from 1980-2018. Such a 12-month time window is suitable to capture long-term drought events that are long enough to impose substantial impacts on agriculture, hydrology, and ecosystems while ones at shorter time scales would be more appropriate to detect short-term drought. For each month, SPEI is calculated considering the respective water-balance value and the values of the eleven preceding months. Here we use SPEI-12 in December to represent the overall water-balance condition of the respective year. The resulting SPEI data has a horizontal resolution of 130 arcsec (~1km). The calculation of SPI and SPEI is done in R with the package SPEI (https://www.doi.org/10.32614/CRAN.package.SPEI).本数据集包含空间分辨率为30角秒（约1千米）的标准化降水指数（Standardized Precipitation Index, SPI）与标准化降水蒸散指数（Standardized Precipitation Evapotranspiration Index, SPEI）。降水速率（precipitation rates, pr）与潜在蒸散量（potential evapotranspiration, pet）的原始数据来源于CHELSA V2.1数据集（https://doi.org/10.16904/envidat.228.v2.1）。该数据集包含1980年至2018年的逐月时间序列，空间分辨率为30角秒，单位为kg·m⁻²·月⁻¹。相较于SPI，SPEI是更为可靠的干旱衡量指标，因其额外纳入了潜在蒸散的影响。潜在蒸散量（pet）指在无供水限制的条件下，土壤表面可蒸发或植物可蒸腾的单位面积单位时间的水量。SPEI可针对特定位置，有效估算气候水平衡——即降水速率pr与潜在蒸散量pet的差值——与长期均值的对应关系。本次研究中，潜在蒸散量pet采用Penman-Montheith方法计算，参考联合国粮食及农业组织（Food and Agriculture Organization of the United Nations, FAO）的定义，以高度为12厘米的参考作物表面导度为参数，数据来源于CHELSA-BIOCLIM+数据库（https://doi.org/10.16904/envidat.332）。为获取全球尺度的SPEI时间序列，我们针对每个像元，将水平衡估算值pr-pet的频率分布通过对数logistic概率分布函数进行拟合。本次研究采用12个月的记忆窗口计算逐月SPEI，并基于1980-2018年的时间序列拟合对数logistic概率分布的参数。12个月的时间窗口适用于捕捉对农业、水文与生态系统造成显著影响的长期干旱事件，而更短的时间尺度则更适合检测短期干旱。针对每个月份，SPEI的计算会纳入当月的水平衡值以及此前11个月的对应数值。本次研究以12月的SPEI-12代表对应年份的整体水平衡状况。最终生成的SPEI数据集空间分辨率为130角秒（约1千米）。SPI与SPEI的计算通过R语言中的SPEI包完成（https://doi.org/10.32614/CRAN.package.SPEI）。

搜集汇总

背景与挑战

背景概述

该数据集提供全球高分辨率标准化干旱指数，包括标准化降水指数（SPI）和标准化降水蒸散指数（SPEI），水平分辨率为30弧秒（约1公里），覆盖1980年至2018年的月度时间序列。SPEI通过结合潜在蒸散数据，能更可靠地评估干旱情况，数据基于CHELSA V2.1的降水和潜在蒸散信息计算得出。