1. High Resolution Forecasting of Summer Drought in the Western United States.
- Author
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Abolafia‐Rosenzweig, Ronnie, He, Cenlin, Chen, Fei, Ikeda, Kyoko, Schneider, Timothy, and Rasmussen, Roy
- Subjects
DROUGHT management ,DROUGHT forecasting ,ATMOSPHERIC models ,NATURAL disasters ,SPATIAL systems ,LEAD time (Supply chain management) ,SPATIAL resolution - Abstract
Drought monitoring and forecasting systems are used in the United States (U.S.) to inform drought management decisions. Drought forecasting efforts have often been conducted and evaluated at coarse spatial resolutions (i.e., >10‐km), which miss key local drought information at higher resolutions. Addressing the importance of forecasting drought at high resolutions, this study develops statistical models to evaluate 1‐ to 3‐month lead time predictability of meteorological and agricultural summer drought across the western U.S. at a 4‐km resolution. Our high‐resolution drought predictions have statistically significant skill (p ≤ 0.05) across 70%–100% of the western U.S., varying by evaluation metric and lead time. 1‐ to 3‐month lead time drought forecasts accurately represent monitored summer drought spatial patterns during major drought events, the interannual variability of drought area from 1982 to 2020 (r = 0.84–0.93), and drought trends (r = 0.94–0.97). 71% of western U.S summer drought area interannual variability can be explained by cold‐season (November–February) climate conditions alone allowing skillful 3‐month lead time predictions. Pre‐summer drought conditions (represented by drought indices) are the most important predictors for summer drought. Thus, the statistical models developed in this study heavily rely on the autocorrelation of chosen agricultural and meteorological drought indices which estimate land surface moisture memory. Indeed, prediction skill strongly correlates with persistence of drought conditions (r ≥ 0.73). This study is intended to support future development of operational drought early warning systems that inform drought management. Plain Language Summary: Droughts are complex and devastating natural disasters with severe implications in the western U.S. for energy production, agricultural yields, and wildfires, particularly during summer months. Drought monitoring and forecasting systems are used in the U.S. to inform drought management decisions. However, existing products and analyses are typically performed at coarse spatial resolutions (>10‐km). Because summer drought is spatially variable at finer resolutions than current drought monitoring and forecasting systems, it is valuable to develop seasonal drought forecasting systems at higher spatial resolutions. In this study we present a novel statistical modeling framework to forecast summer drought conditions at a high resolution (4‐km) at 1‐ to 3‐month lead times across the western U.S. Our models provide skillful predictions of drought across most of the western U.S., but there is degraded skill in areas where drought tends to be less persistent. Future research can expand on our work by predicting drought at finer temporal resolutions while maintaining the high spatial resolution used in this study's methodology. This research is intended to support development of future operational drought early warning systems that provide high spatial resolution forecasts. Key Points: Statistical models accurately predict summer meteorological and agricultural drought at 1‐, 2‐, and 3‐month lead times at a 4‐km resolutionMost of the interannual variability in summer drought area can be explained by preceding cold‐season climate conditionsStatistical models rely heavily on the autocorrelation of drought indices in drought forecasts [ABSTRACT FROM AUTHOR]
- Published
- 2023
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