Assessment of Uncertainty in Grid-Based Rainfall-Runoff Model Based on Formal and Informal Likelihood Measures

Abstract

Damage prevention from the local storms and typhoons in Korea, the development of a rainfall-runoff model reflecting local geological, meteorological and physical characteristics is necessary. The accuracy of the rainfall-runoff model is influenced by the various uncertainty factors that can occur in the modeling processes, including input data, model parameters, modeling simplification, and so on. Thus, the objectives of this study are (1) to estimate runoff for two rainfall events using Grid Rainfall-Runoff Model (GRM); (2) to quantify the uncertainty of the GRM model using the Generalized Likelihood Uncertainty Estimation (GLUE) method, and (3) to assess the uncertainty ranges of the GRM based on different likelihood functions. For this, two rainfall events were implemented to the GRM in the Cheongmicheon watershed, and informal likelihood functions (LNSE, LPBIAS, LRSR, and LLOG) based on the fitness indices (NSE, PBIAS, RSR, and Log-normal) were used for uncertainty analysis and quantification using GLUE method. As a result, the GRM parameters varied according to the different rainfall patterns even in the same watershed. In addition, among the GRM parameters, the CRC (Channel Roughness Coefficient) and CSHC (Correction factor for Soil Hydraulic Conductivity) characteristics are the most sensitive. Moreover, this study showed that the uncertainty range of the GRM model can be changed with the subjective selection of likelihood functions and thresholds. The GRM model is open source and has good accessibility. Especially, this open-source model allows various approaches to disaster prevention plans such as flood forecasting and flood insurance policies. In addition, if the parameter range of GRM is quantified and standardized at domestic watersheds, it is expected that the reliability of the rainfall-runoff simulation can be increased by the reduction of the uncertainty factors.

Parameter Optimization of SWMM Model Using Integrated Morris and GLUE Methods

“…Nevertheless, for a complex hydrological model, it is common to involve more parameters in model optimization. Often more than one parameter in different models and study areas can be used to obtain more accurate parameter intervals by uncertainty analysis [57][58][59]. Considering a suitable range of uncertainty parameter values after sensitivity analysis for model parameter optimization will further improve the model simulation accuracy.…”

Risk Assessment of Landslide Collapse Disasters along National Highways Based on Information Quantity and Random Forest Coupling Methods: A Case Study of the G331 National Highway

“…The quantitative research method establishes a mathematical relationship between geological disasters and indicators using specific data, resulting in findings with a relatively high accuracy [9]. However, this research method is easily affected by the challenges of data collection, and the efficiency of data collection will impact the research progress [10]. Common quantitative research methods include the information quantity method [11], the logistic regression method [12], the frequency ratio method [13], the weight of evidence method [14], and so on.…”