Towards a generic model evaluation metric for non-normally distributed measurements in water quality and ecosystem models

Abstract

Model evaluation is a crucial process in model development and quantified model evaluation metrics play a pivotal role in model calibration and validation. However, in current water quality and ecosystem models, conventional model evaluation metrics are derived from hydrological models and often overlook the non-normal distribution of water quality and ecological state variables. In this study, we proposed a series of metrics that consider non-normal distributions by modifying components of the Kling-Gupta efficiency. Subsequently, we tested the existing metrics and newly proposed metrics using four different synthetic datasets and the actual simulation case of total phosphorus, chlorophyll a, and dissolved oxygen concentrations. The results demonstrate that the newly proposed metric, Fu-Zhang efficiency, which replaces the ratio of standard deviations with the ratio of interquartile ranges to measure dispersion similarity, is more suitable for evaluating simulation results with lots of outliers in the measurements. Our study reveals the hidden perils in conventional integrated model evaluation metrics of water quality and ecosystem models and calls for the development of comprehensive and feasible quantitative evaluation frameworks to drive the next paradigm shift.