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Advances in Subseasonal to Seasonal Prediction Relevant to Water Management in the Western United States

Agniv Sengupta, Bohar Singh, Michael J. DeFlorio, Colin Raymond, Andrew W. Robertson, Xubin Zeng, Duane E. Waliser, Jeanine Jones | October 1st, 2022


Water management in the semiarid western United States is a challenging endeavor that evolves from year to year based on large-scale atmospheric and oceanic conditions. This region has frequently experienced prolonged periods of drought with substantial socioeconomic impacts; the most recent instance involves California entering its third consecutive drought year. As context for the economic implications of drought, California’s enacted state budget for the fiscal year 2021/22 authorizes more than $600 million for the California Department of Water Resources for providing immediate drought support to local water agencies (State of California 2021a); this amount is part of a package of more than $5 billion over 4 years for water resilience and drought preparedness (State of California 2021b). For drought preparedness, state and local water managers would like to rely on skillful forecasts on time scales that span the spectrum between weather forecasts and climate predictions. Despite recent improvements in developing subseasonal (2–6-weeks lead) and seasonal (2–6-months lead) prediction systems that compete with and/or outperform existing dynamical and statistical models (e.g., Gibson et al. 2021; Switanek et al. 2020; DeFlorio et al. 2019), forecasting limitations have hindered crucial management decisions for western U.S. water managers, especially related to longer-lead planning, budgeting, resource allocation, and better preparedness for wet and dry extremes (DeFlorio et al. 2021). Improved understanding of physical phenomena and processes governing weather and climate extremes and utilization of emerging forecast methodologies are critical to developing skillful subseasonal to seasonal (S2S) forecast products relevant to water management.

Keywords

water supply forecasting