A probabilistic framework for robust master recession curve parameterization

Abstract

Master recession curve (MRC) representing the long-term catchment streamflow recession is widely used to estimate catchment storage-discharge relationship and predict low flows. Recession analysis of the streamflow functional form (-dQ/dt∼Q) is an effective way to construct MRC. However, the great variability of recession processes among events makes it difficult to parameterize the recession processes by deterministic methods and indicates the recession rate could be thought of as a random variable. In this study, a probabilistic approach (Parameterized Binning-percentile Method, or PBM) is proposed to construct MRCs based on -dQ/dt∼Q analysis. The probabilistic PBM is introduced by describing the distribution of recession rate at partitioned -dQ/dt intervals by a Gamma distribution. MRCs at any percentile can be obtained by fitting regenerated data points of -dQ/dt∼Q in each interval. The PBM is validated by both numerically generated and observed hydrographs. It shows that the PBM is robust in capturing the distribution of recession rate and can adapt to various observed hydrographs with different recession characteristics. It is more accurate to generate probabilistic MRCs compared to the individual recession method in Q∼t form and quantile regression in -dQ/dt∼Q form. Further, MRCs from traditional deterministic methods can viewed as special cases of the probabilistic framework. Our newly proposed probabilistic framework can be used to quantify the statistical distributions of low flows, which can be helpful in regional water resources management during dry seasons.