A Bayesian Surveillance Model to Track Variable Rainfall-Runoff Responses for Small Watersheds

TL;DR

This paper introduces a Bayesian estimation framework for tracking dynamic hydrological parameters in small watersheds, enabling improved monitoring of rainfall-runoff responses over time and space.

Contribution

The study develops and tests a Bayesian method integrated with a conceptual hydrology model, demonstrating its effectiveness in tracking parameter changes and analyzing watershed response variability.

Findings

Bayesian method accurately tracks inter-annual parameter changes.

Identified a shift in rainfall-runoff response around 1943 in Fall Creek.

Found a significant relationship between watershed size and response variability.

Abstract

Understanding dynamics of hydrological responses is essential in producing skillful runoff forecast. This can be quantitatively done by tracking changes in hydrology model parameters that represent physical characteristics. In this study, we implement a Bayesian estimation method in continuously estimating hydrology model parameters given observations of rainfall and runoff for small watersheds. The method is coupled with a conceptual hydrology model using a Gamma distribution-based Instantaneous Unit Hydrograph. The whole analytical framework is tested using synthetic data as well as observational data from the Fall Creek watershed. The results show that the Bayesian method can well track the hidden parameters that change inter-annually. Then the model is applied to examine temporal and spatial variability of the rainfall-runoff responses and we find 1) a systematic shift in the rainfall-runoff response for the Fall Creek watershed around 1943 and 2) a statistically significant relationship between rainfall-runoff responses and watershed sizes for selected NY watersheds. Our results demonstrate potential of the Bayesian estimation method as a rapid surveillance tool in monitoring and tracking changes of hydrological responses for small watersheds.