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/43361Detailed 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.