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USGS2025

Characterizing projected future droughts for south Florida (2056–2095)

Balance anomalies, defined as the deviation of monthly precipitation minus reference evapotranspiration from their long-term monthly historical means (1950–2005), were computed for regions in south Florida and temporally averaged over 6- and 12-month timescales to identify meteorological drought events during a historical (1950–2005) and future (2056–2095) period of interest for 40 CMIP5 general circulation models (GCM) and scenario combinations downscaled by the Multivariate Adaptive Constructed Analogs method. Under the assumption of stomatal resistance ( r s ) remaining constant at the historical standard value (70 s/m), 81% of models project declines in monthly balance anomalies in the future compared to historical, with multimodel ensemble mean declines of 3.7 in/year under RCP4.5 and 8.7 in/year under RCP8.5. Drought events were identified from the downscaled model projections, their characteristics (duration and intensity) extracted, and their historical joint distributions validated against those derived from historical observational datasets. The future joint distributions of drought characteristics were compared across models using hierarchical clustering. A climate model summary plot and table were developed based on these methods to guide climate model selection for hydrologic modeling in support of water-supply planning at the South Florida Water Management District (SFWMD). The model summary plot for the entire SFWMD shows that 35% of GCM/training-dataset combinations have historical joint distributions of drought characteristics that are significantly different at the 10% level from those derived from observational gridded data, whereas 39% of GCM/scenario/training-dataset combinations have future joint distributions that are significantly different from historical. A sensitivity analysis was performed assuming r s increasing with increasing CO 2 .

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USGS2022

Development of projected depth-duration frequency curves (2050–89) for south Florida

Planning stormwater projects requires estimates of current and future extreme precipitation depths for events with specified return periods and durations. In this study, precipitation data from four downscaled climate datasets are used to determine changes in precipitation depth-duration-frequency curves from the period 1966–2005 to the period 2050–89 primarily on the basis of Representative Concentration Pathways 4.5 and 8.5 emission scenarios from the Coupled Model Intercomparison Project Phase 5. The four downscaled climate datasets are (1) the Coordinated Regional Downscaling Experiment (CORDEX) dataset, (2) the Localized Constructed Analogs (LOCA) dataset, (3) the Multivariate Adaptive Constructed Analogs (MACA) dataset, and (4) the Jupiter Intelligence Weather Research and Forecasting Model (JupiterWRF) dataset. Change factors—multiplicative changes in expected extreme precipitation magnitude from current to future period—were computed for grid cells from the downscaled climate datasets containing National Oceanic and Atmospheric Administration Atlas 14 stations in central and south Florida. Change factors for specific durations and return periods may be used to scale the National Oceanic and Atmospheric Administration Atlas 14 historical depth-duration-frequency values to the period 2050–89 on the basis of changes in extreme precipitation derived from downscaled climate datasets. Model culling was implemented to select downscaled climate models that best captured observed historical patterns of precipitation extremes in central and south Florida. Overall, a large variation in change factors across downscaled climate datasets was found, with change factors generally greater than one and increasing with return period. In general, median change factors were higher for the south-central Florida climate region (1.05–1.55 depending on downscaled climate dataset, duration, and return period) than for the south Florida climate region (1–1.4 depending on downscaled climate dataset, duration, and return period) when considering best performing models for both areas, indicating a projected overall increase in future extreme precipitation events.

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