Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations

Recent studies argue that hydrological models must be configured for the specific objectives they need to address. Model configuration involves multiple model decision steps, such as choice of model structure, spatial discretization, spatial representation of forcing and performance metrics to calibrate them. We investigated the influence of each of the decisions in turn in a standardized framework to understand how differences in these decisions impact flood simulations. For this purpose, we used the capability of Structure for Unifying Multiple Modeling Alternatives (SUMMA) model as it allows a straightforward comparison of different model structure choices and can easily be reconfigured for different spatial organization. We also employed a sequential sensitivity analysis using the Efficient Elementary Effect (EEE) method to select the sensitive parameters for each model structure considered in this study. This allowed us to check if the dominant hydrological process of the catchment can be captured irrespective of the model structure choice. Identification of the most sensitive parameters also helped us to scale down the dimensionality of the problem both in terms of computational demand and complexity. Based on the various choices of model decisions, 36 unique model configurations are constructed and each of them are separately calibrated by the performance metrics. The impact and relative importance of modeling decisions are quantified by Analysis of Variance(ANOVA) and effect size respectively. As a test case, we use accurate simulation of flood peaks (expressed as deviation in peak flow, deviation in peak time and relative volume error) that occurred in the Netravathi basin of Karnataka, India to study the impact of the model decisions. The extensive sensitivity analysis and model calibration required for the study is carried out by parallel processing using a supercomputer. For floods, the choice of spatial discretization of the modeling domain is the most impacting decision, followed by the choice of objective function during calibration, model structure and spatial representation of forcing, respectively, for the catchment. The results provide key insights regarding the ideal option a modeler must choose for each modeling decision for simulating floods in Netravathi. More generally, this study shows that model configuration decisions that are anecdotally often made based on convenience or habit can strongly impact simulation accuracy for specific modeling purposes. Alongside the need to quantify more traditional uncertainty sources, such as data and parameter uncertainty, there is a need to quantify the impact of these model configuration decisions.