Estimating the parameters of a monthly hydrological model using hydrological signatures

Abstract

In the most common Bayesian framework for estimating the parameters of a hydrological model (time domain), the specification of the likelihood function can be challenging. In addition, scarcely gauged regions might be hard to model, due to the lack of sufficient timeseries to calibrate the model. To circumvent these problems, the present study seeks to evaluate the applicability of hydrological signatures and Approximate Bayesian Computation methods to estimating the parameters and analyzing the uncertainty of a hydrological model (signature domain). We used the GR2M monthly model, aiming to approximate the signatures estimated from the simulated timeseries to those calculated from the monitoring data. As a result, we found KGEs of over 0.91 and 0.83 for most signatures in the calibration and validation periods, respectively (0.95 and 0.90 in the time domain). The uncertainty intervals varied from signature to signature, with the tendency of being smaller for the signature-domain than for the time-domain.