A Signature-Based Hydrologic Efficiency Metric for Model Calibration and Evaluation in Gauged and Ungauged Catchments

Abstract

Rainfall-runoff models are commonly evaluated against statistical evaluation metrics. However, these metrics do not provide much insight into what is hydrologically wrong if a model fails to simulate observed streamflow well and they are also not applicable for ungauged catchments. Here, we propose a signature-based hydrologic efficiency (SHE) metric by replacing the statistical components of current efficiency metrics with hydrologic signatures that can be regionalized for model evaluation in ungauged catchments. We test our new efficiency metric across 633 catchments from Great Britain. Strong correlations with Spearman rank and Pearson correlation values around 0.8 are found between our proposed metric and commonly used statistical evaluation metrics (NSE, KGE, and NP) demonstrating that the proposed SHE metric is related to existing metrics as much as these metrics are related to each other. For ungauged catchments, we regionalize the three signatures included in SHE and find that 78% of catchments have an absolute difference of SHE values between gauged and ungauged cases of less than 0.2. This difference varies depending on the quality of the regionalized bias and variance signature values. It means that the SHE metric is applicable for model evaluation in ungauged catchments if its signatures can be regionalized well. When applying the SHE metric in other domains with different hydrologic properties, modellers should carefully consider the components of the proposed metric with signatures best suited to their research domains and their regionalization potential.