Sensitivity Analysis of the Rainfall–Runoff Modeling Parameters in Data-Scarce Urban Catchment

Abstract

Rainfall–runoff phenomena are among the main processes within the hydrological cycle. In urban zones, the increases in imperviousness cause increased runoff, originating floods. It is fundamental to know the sensitivity of parameters in the modeling of an urban basin, which makes the calibration process more efficient by allowing one to focus only on the parameters for which the modeling results are sensitive. This research presents a formal sensitivity analysis of hydrological and hydraulic parameters—absolute–relative, relative–absolute, relative–relative sensitivity and R2—applied to an urban basin. The urban basin of Tuxtla Gutiérrez, Chiapas, in Mexico is an area prone to flooding caused by extreme precipitation events. The basin has little information in which the records (with the same time resolution) of precipitation and hydrometry match. The basin model representing an area of 355.07 km2 was characterized in the Stormwater Management Model (SWMM). The sensitivity analysis was performed for eight hydrological parameters and one hydraulic for two precipitation events and their impact on the depths of the Sabinal River. Based on the analysis, the parameters derived from the analysis that stand out as sensitive are the Manning coefficient of impervious surface and the minimum infiltration speed with R2 > 0.60. The results obtained demonstrate the importance of knowing the sensitivity of the parameters and their selection to perform an adequate calibration.

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

“…The sensitivity analysis [10] was conducted in this study to understand the effects of the two model parameters (i.e., X 1 and X 2 ). We randomly selected three runoff stations (X.44, X.64, and X.240) as the representative for all 37 runoff stations due to the analysis sensitivity.…”

“…Kwak et al [9] also used the rainfall-runoff model to reconstruct the missing runoff time-series information. Similarly, Ballinas-González et al [10] studied the sensitivity analysis of the rainfall-runoff modeling parameters in the data-scarce urban catchment. Lerat et al [11] proposed the alternative method for calibrating daily rainfall-runoff models to monthly streamflow data when no daily streamflow data recorded.…”

Application of Medical Material Inventory Model Under Deep Learning in Supply Planning of Public Emergency

“…In view of this characteristic, the particularity of the scarce material inventory strategy should be carefully considered. The establishment method of the inventory management model adopts the regular order method, as shown in Figure 5 [29]. Inventory management should appropriately increase the amount of safety inventory, and establish a scientific inventory management model, striving to reduce inventory, reducing inventory costs, and giving full play to the role of funds and materials.…”

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in The Southern Basin of Thailand

“…The sensitivity analysis [ 10] was experimented on in our research work to understand the effects of the model's two parameters: X1 and X2. We randomly selected three runoff stations (X.44, X.64, and X.240) as the representative for all 37 runoff stations due to the experiment's sensitivity.…”

“…Kwak,et al [ 9] also used the rainfall-runoff model to reconstruct the missing runoff time-series information. Similarly, Ballinas-González, et al [ 10] studied the sensitivity analysis of the rainfall-runoff modeling parameters in the data-scarce urban catchment. Lerat, et al [ 11] proposed the alternative method for calibrating daily rainfall-runoff models to monthly streamflow data when no daily streamflow data recorded.…”