Preliminary Study of Computational Time Steps in a Physically Based Distributed Rainfall–Runoff Model

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Rainfall–runoff models are not perfect, and the suitability of a model structure depends on catchment characteristics and data. It is important to investigate the pros and cons of a rainfall–runoff model to improve both its high- and low-flow simulation. The production and routing components of the GR4J and IHACRES models were combined to create two new models. Specifically, the GR_IH model is the combination of the production store of the GR4J model and the routing store of the IHACRES model (vice versa in the IH_GR model). The performances of the new models were compared to those of the GR4J and IHACRES models to determine components improving the performance of the two original models. The suitability of the parameters was investigated with sensitivity analysis using 40 years’ worth of spatiotemporally different data for five catchments in Australia. These five catchments consist of two wet catchments, one intermediate catchment, and two dry catchments. As a result, the effective rainfall production and routing components of the IHACRES model were most suitable for high-flow simulation of wet catchments, and the routing component improved the low-flow simulation of intermediate and one dry catchments. Both effective rainfall production and routing components of the GR4J model were suitable for low-flow simulation of one dry catchment. The routing component of the GR4J model improved the low- and high-flow simulation of wet and dry catchments, respectively, and the effective rainfall production component improved both the high- and low-flow simulations of the intermediate catchment relative to the IHACRES model. This study provides useful information for the improvement of the two models.

Section: Calibration Methodsmentioning

confidence: 99%

Component Combination Test to Investigate Improvement of the IHACRES and GR4J Rainfall–Runoff Models

Shin¹,

Kim

2021

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“…In this study, it was important to evaluate the sensitivity of all the parameters used in the GRM, unlike previous studies (e.g., references [16,17]) that used only a few parameters of the GRM to calibrate the parameters. The results of this study can be used as an important guideline for researchers who use the GRM as to which parameters should be most carefully calibrated.…”

Section: Discussionmentioning

confidence: 99%

“…Runoff is calculated using the kinematic wave equation, and infiltration is calculated using the Green-Ampt model. The GRM can properly simulate short-term flood events using a short simulation time interval, such as a 10 min time interval (e.g., in References [17,18]). The governing equations of GRM are as follows.…”

Section: Grid-based Rainfall-runoff Modelmentioning

confidence: 99%

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Sensitivity Analysis to Investigate the Reliability of the Grid-Based Rainfall-Runoff Model

Shin¹,

Choi

2018

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This study aimed to assess the suitability of the parameters of a physically based, distributed, grid-based rainfall-runoff model. We analyzed parameter sensitivity with a dataset of eight rainfall events that occurred in two catchments of South Korea, using the Sobol’ method. Parameters identified as sensitive responded adequately to the scale of the rainfall events and the objective functions employed. Parameter sensitivity varied depending on rainfall scale, even in the same catchment. Interestingly, for a rainfall event causing considerable runoff, parameters related to initial soil saturation and soil water movement played a significant role in low flow calculation and high flow calculation, respectively. The larger and steeper catchment exhibited a greater difference in parameter sensitivity between rainfall events. Finally, we found that setting an incorrect parameter range that is physically impossible can have a large impact on runoff simulation, leading to substantial uncertainty in the simulation results. The proposed analysis method and the results from our study can help researchers using a distributed rainfall-runoff model produce more reliable analysis results.

“…For rainfall-runoff simulations, we utilized a grid-based rainfall-runoff model (GRM) based on a kinematic wave model [19]. The GRM can simulate surface runoff, infiltration, and subsurface flow, and consider control by hydraulic structures [20][21][22][23].…”

Section: Rainfall-runoff Modelingmentioning

confidence: 99%

Effects of Rainfall Spatial Distribution on the Relationship between Rainfall Spatiotemporal Resolution and Runoff Prediction Accuracy

Kim

2020

We studied how rainfall spatial distribution affects the relationship between rainfall spatiotemporal resolution and runoff prediction accuracy under real field conditions. We gathered radar rainfall and discharge data for three rainfall events. These rainfall-runoff events were then reproduced using a kinematic wave model. Modeling accuracy was estimated quantitatively using the Nash–Sutcliffe model efficiency coefficient and peak discharge ratio. Normalized root-mean-square error ( nRMSE ), skewness ( S k ), and second scaled spatial moment of catchment rainfall ( δ 2 ) were employed to quantify rainfall spatial distribution characteristics. By relating the accuracy of modeling results to the rainfall spatial characteristics using various rainfall spatiotemporal resolutions, we found that the modeling results converged to a value as the nRMSE , | S k | and | 1 − δ 2 | decreased. That is, rainfall spatial distributions affect the relationship between lower limit of rainfall spatiotemporal resolution for runoff models and runoff prediction accuracy.