Drought and Moisture Surplus for the Conterminous United States, Annual Data 5-Year Windows (Image Service)

The Moisture Deficit and Surplus map uses moisture difference z-score (MDZ) datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station to represent drought and moisture surplus across the contiguous United States. A z-score is a statistical method for assessing how different a value is from the mean. Mean moisture values over 1-year windows were derived from monthly historical precipitation and temperature data from PRISM, between 1900 and 2023, and compared against a 1900-2017 baseline. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits (droughts) or surpluses. Thus, the dark orange areas on the map indicate a 1-year window with extreme drought, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361 Detailed technical methods for this analysis are available here: https://www.fs.usda.gov/treesearch/pubs/43361. This is derived from monthly PRISM temperature and precipitation data, located here: ftp://prism.nacse.org/monthly/. Monthly temperature data are used to calculate potential evapotranspiration (PET) using the Thornthwaite PET equation. Monthly precipitation and PET data are then used to calculate a moisture index (MI) for each month within a 1-year time window. The mean moisture index (MMI) across the months of the target window is compared to an appropriate long-term normal, in this case the average of the MMI for all windows between 1900 and 2017. Then, a moisture difference z-score (MDZ) is calculated from the MMI for the window of interest. This is done by subtracting the 1900-2017 normal MMI from the MMI for a given year, and then dividing by the standard deviation over the baseline period. Equations for calculating modified moisture index are adopted from Willmott, C.J. and Feddema, J.J. 1992. A more rational climatic moisture index. Professional Geographer 44(1): 84-87. The z-score values were then reclassified using the classification scheme below: z-score less than -2 -- extremely dry compared to normal conditions z-score -2 to -1.5 -- severely dry compared to normal conditions z-score -1.5 to -1 -- moderately dry compared to normal conditions z-score -1 to -0.5 - mildly dry compared to normal conditions z-score -0.5 to 0.5 -- near normal conditions z-score 0.5 to 1 -- mildly wet compared to normal conditions z-score 1 to 1.5 -- moderately wet compared to normal conditions z-score 1.5 to 2 -- severely wet compared to normal conditions z-score more than 2 -- extremely wet compared to normal conditions.本水分盈亏分布图采用美国林务局南方研究站（Forest Service Southern Research Station）的Frank Koch、John Coulston与William Smith科研团队开发的水分差异z分数（moisture difference z-score, MDZ）数据集，用以表征美国本土（contiguous United States）的干旱与水分盈余状况。z分数（z-score）是一种统计学方法，用于衡量某一数值与均值的偏离程度。研究以1900年至2023年间PRISM发布的逐月历史降水与气温数据为基础，推导得到1年时间窗口内的平均水分值，并将其与1900-2017年的基准期均值进行对比。z值越大，与平均气候条件的偏离程度越高，对应水分亏缺（干旱）或盈余的程度也越严重。因此，地图上的深橙色区域代表相较于过去百年平均水平，该1年时间窗口内遭遇极端干旱。 如需了解此类地图的制作方法详情，可参阅以下出版物：https://www.fs.usda.gov/treesearch/pubs/43361。本分析的详细技术方法亦可通过该链接获取。 本数据集源自PRISM逐月气温与降水数据，数据获取地址为：ftp://prism.nacse.org/monthly/。研究利用Thornthwaite潜在蒸散（potential evapotranspiration, PET）公式，基于逐月气温数据计算潜在蒸散量；随后结合逐月降水与潜在蒸散量数据，计算1年时间窗口内各月的水分指数（moisture index, MI）。将目标窗口内各月的平均水分指数（mean moisture index, MMI）与对应长期基准均值（本研究中为1900-2017年所有时间窗口的MMI平均值）进行对比，进而针对目标时间窗口计算水分差异z分数（MDZ）：即通过将目标年份的MMI减去1900-2017年基准期MMI均值，再除以基准期内的标准差得到。修正后水分指数的计算公式参考自Willmott, C.J.与Feddema, J.J.于1992年发表的《更具合理性的气候水分指数》（载于《Professional Geographer》44(1): 84-87）。 随后研究采用以下分级方案对z分数值进行重分类： - z分数 < -2：相较于平均水平极端干旱 - -2 ≤ z分数 < -1.5：相较于平均水平严重干旱 - -1.5 ≤ z分数 < -1：相较于平均水平中度干旱 - -1 ≤ z分数 < -0.5：相较于平均水平轻度干旱 - -0.5 ≤ z分数 ≤ 0.5：接近平均水平 - 0.5 ≤ z分数 < 1：相较于平均水平轻度湿润 - 1 ≤ z分数 < 1.5：相较于平均水平中度湿润 - 1.5 ≤ z分数 < 2：相较于平均水平严重湿润 - z分数 > 2：相较于平均水平极端湿润