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Abstract Uncertainty attribution in water supply forecasting is crucial to improve forecast skill and increase confidence in seasonal water management planning. We develop a framework to quantify fractional forecast uncertainty and partition it between (1) snowpack quantification methods, (2) variability in post‐forecast precipitation, and (3) runoff model errors. We demonstrate the uncertainty framework with statistical runoff models in the upper Tuolumne and Merced River basins (California, USA) using snow observations at two endmember spatial resolutions: a simple snow pillow index and full‐catchment snow water equivalent (SWE) maps at 50 m resolution from the Airborne Snow Observatories. Bayesian forecast simulations demonstrate a nonlinear decrease in the skill of statistical water supply forecasts during warm snow droughts, when a low fraction of winter precipitation remains as SWE. Forecast skill similarly decreases during dry snow droughts, when winter precipitation is low. During a shift away from snow‐dominance, the uncertainty of forecasts using snow pillow data increases about 1.9 times faster than analogous forecasts using full‐catchment SWE maps in the study area. Replacing the snow pillow index with full‐catchment SWE data reduces statistical forecast uncertainty by 39% on average across all tested climate conditions. Attributing water supply forecast uncertainty to reducible error sources reveals opportunities to improve forecast reliability in a warmer future climate.
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This content will become publicly available on August 1, 2026
Forecasting Missouri riverflow with SARIMA models: A data-driven framework for adaptive water resourcemanagement
A statistical water balance and time series modeling framework is developed to analyze and forecast the Missouri River’s monthly flow at Bismarck from 1954 to 2024. Integrating traditional hydrological components precipitation, evaporation, upstream inflow, tributaries with ARIMA and SARIMA models enable detection of long-term and seasonal trends. Model fit is rigorously assessed by AIC, AICc, BIC, Nash-Sutcliffe Efficiency, and visual diagnostics with credible intervals. Stationarity is evaluated through ADF and KPSS tests to guide model selection. The final SARIMA framework, incorporating Box-Cox transformation and outlier adjustment, produces reliable forecasts with quantified uncertainty for both typical and extreme hydrologic conditions. These forecasts are vital for river management and policy, demonstrating how statistical rigor and visual assessment underpin adaptive water management strategies.
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- Award ID(s):
- 1839895
- PAR ID:
- 10649594
- Publisher / Repository:
- International Journal of Statistics and Applied Mathematics
- Date Published:
- Journal Name:
- International Journal of Statistics and Applied Mathematics
- Volume:
- 10
- Issue:
- 8
- ISSN:
- 2456-1452
- Page Range / eLocation ID:
- 147 to 159
- Subject(s) / Keyword(s):
- Missouri river, flow forecasting, SARIMA, ARIMA, time series modeling, water balance, hydrological modeling, river discharge, seasonal trends, climate impact, ecosystem management, statistical forecasting, stationarity tests, reservoir operations, flood risk, water resource planning, uncertainty quantification, validation, upscaling, upscaling, environmental monitoring
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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