Structured variation in the frequency spectrum of critical hydrologic variables can have important implications for the design and management of water resources infrastructure, yet traditional hydrologic frequency analysis often ignores the influence of exogenous factors that can both precede and exert control over hydrologic responses. Moreover, emerging literature that has addressed predictable low-frequency oscillations in the probabilistic nature of hydrologic variables has focused almost exclusively on flood flows. This study explores a new approach for conditioning the frequency spectrum of hydrologic extremes on seasonal predictors and applies the method to annual minimum 7 day low flows, a critical low-flow statistic often utilized in water quality management and planning. A semiparametric local likelihood method is used to condition quantile estimates of the 7 day low flow on year-to-year hydroclimatic forecasts for two major rivers in the northeast United States. The local likelihood approach is employed in a Bayesian framework in which regional information is used to inform prior distributions of model parameters. The method is compared against a baseline approach that applies a static Bayesian inference with noninformative priors to derive unconditional parameter and quantile estimates. The implications of the approach for the efficacy of water quality regulations and as an adaptation to climate change are discussed.
Steinschneider, S; Brown, C
Forecast-informed low-flow frequency analysis in a Bayesian framework for the northeastern United States
Water Resources Research