| 刘爽,范兴科.基于简单气象因子的干旱指数计算及其适用性分析[J].干旱地区农业研究,2024,(1):242~251 |
| 基于简单气象因子的干旱指数计算及其适用性分析 |
| Calculation and applicability analysis of drought index based on simple meteorological factors |
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| DOI：10.7606/j.issn.1000-7601.2024.01.25 |
| 中文关键词: 气象干旱 气温 相对湿度 缺水度指数（WSI） 陕西省 |
| 英文关键词:meteorological drought air temperature relative humidity water scarcity index (WSI) Shaanxi Province |
| 基金项目:“十三五”国家重点研发计划项目（2016YFC0501703）；唐仲英基金会资助项目；陕西省现代节水农业工程技术研究中心后补助项目（2021GCZX-16） |
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| 中文摘要: |
| 为建立可对短期干旱进行合理监测表征的气象干旱指数，从气象干旱发生的机制——水分供需矛盾（主要为降水量与蒸散量两大因素）出发，以旬尺度的蒸散缺水量占需水量的比值定义一个气象干旱指数——缺水度指数(WSI)，潜在蒸散量(PET)采用气温(T，℃)及相对湿度(RH，%)两大极易获取的气象因子计算，同时利用陕西省30个气象站点2000—2020年的逐日气象观测资料分析WSI的适用性特征。结果表明：在不考虑风和大气压的影响条件下，相对于Penman-Monteith方程，基于T和RH估算PET误差较小，均方根误差(RMSE)和平均绝对误差(MAE)均值分别为1.17 mm和0.82 mm，但62.1%的数据计算结果偏小，部分站点80%以上数据偏小。对陕西省不同区域近年来旱情发展变化的研究表明，WSI能够识别出陕西省旱情易发区域及时段，同时对于短期干旱事件具有较强的识别能力，较MCI指数能够更快地捕捉旱情发生，同时更加灵活简便，可以应用于气象干旱的监测预报和评估。 |
| 英文摘要: |
| In order to establish a meteorological drought index that can effectively monitor short\|term droughts, this study, based on the mechanism of meteorological drought occurrence\|namely the imbalance between water supply and demand (primarily influenced by precipitation and evapotranspiration), introduced a drought index termed the water scarcity index (WSI). This index was defined by the ten\|day scale evapotranspiration deficit as a proportion of the water demand. The potential evapotranspiration (PET) was calculated using easily accessible meteorological factors: temperature (T, ℃) and relative humidity (RH, %). Using daily meteorological observation data from 30 weather stations in Shaanxi Province from 2000 to 2020, we analyzed the applicability characteristics of WSI. The results indicated that, without considering the effects of wind and atmospheric pressure, the error in estimating PET based on T and RH was relatively small compared to the Penman\|Monteith equation. The root mean square error (RMSE) and mean absolute error (MAE) were 1.17 mm and 0.82 mm. However, 62.1% of the data results were underestimated, with some stations having over 80% of data underestimated. Research on the recent drought development changes in different regions of Shaanxi Province showed that WSI can identify drought\|prone areas and periods in Shaanxi. It also possesses a strong ability to recognize short\|term drought events. WSI outperforms MCI in swiftly detecting droughts, offering greater flexibility and simplicity for monitoring, forecasting, and evaluating meteorological drought conditions. |
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