Robust global sensitivity analysis of a river management model to assess nonlinear and interaction effects

Abstract

Abstract. The simulation of routing and distribution of water through a regulated river system with a river management model will quickly result in complex and nonlinear model behaviour. A robust sensitivity analysis increases the transparency of the model and provides both the modeller and the system manager with a better understanding and insight on how the model simulates reality and management operations.In this study, a robust, density-based sensitivity analysis, developed by Plischke et al. (2013), is applied to an eWater Source river management model. This sensitivity analysis methodology is extended to not only account for main effects but also for interaction effects. The combination of sensitivity indices and scatter plots enables the identification of major linear effects as well as subtle minor and nonlinear effects.The case study is an idealized river management model representing typical conditions of the southern MurrayDarling Basin in Australia for which the sensitivity of a variety of model outcomes to variations in the driving forces, inflow to the system, rainfall and potential evapotranspiration, is examined. The model outcomes are most sensitive to the inflow to the system, but the sensitivity analysis identified minor effects of potential evapotranspiration and nonlinear interaction effects between inflow and potential evapotranspiration.

Spring hydrograph simulation of karstic aquifers: Impacts of variable recharge area, intermediate storage and memory effects

“…Density based sensitivity indices (DSI) have become progressively widespread in Global Sensitivity Analysis (GSA) applications across different hydrological modeling fields (e.g. Liu et al 2006, Pappenberger et al 2008, Peeters et al 2014, Rajabi et al 2015. In the DSI approach proposed by the which named PAWN, the model output distribution is characterized by data sample Cumulative Distribution Function (CDF) rather than its PDF.…”

A simple and efficient method for global sensitivity analysis based on cumulative distribution functions

“…To the authors' knowledge, the few examples are Pappenberger et al (2008) for the entropy-based indices; and Castaings et al (2012), Anderson et al (2014 and Peeters et al (2014) for the d-sensitivity measure.…”

Spatial GR4J conceptualization of the Tamor glaciated alpine catchment in Eastern Nepal: evaluation of GR4JSG against streamflow and MODIS snow extent

“…Common schemes for conducting sensitivity analysis include variance‐based schemes such as Monte Carlo analysis; however, Peeters et al . () and Plischke et al (2013) have highlighted the complexity of conducting sensitivity analysis when the solution space is complex and non‐linear (which it often is). Nonetheless, the robustness of the model to uncertainties was tested by varying precipitation data and ground‐water parameter bounds (one‐at‐a‐time) and assessing the Monte Carlo parameter distributions returned by DREAM.…”