The Performance of Physically Based and Conceptual Hydrologic Models: A Case Study for Makkah Watershed, Saudi Arabia

Abstract

Population growth and land use modification in urban areas require the use of accurate tools for rainfall-runoff modeling, especially where the topography is complex. The recent improvement in the quality and resolution of remotely sensed precipitation satisfies a major need for such tools. A physically-based, fully distributed hydrologic model and a conceptual semi-distributed model, forced by satellite rainfall estimates, were used to simulate flooding events in a very arid, rapidly urbanizing watershed in Saudi Arabia. Observed peak discharge for two flood events was used to compare hydrographs simulated by the two models, one for calibration and one for validation. To further explore the effect of watershed heterogeneity, the hydrographs produced by three implementations of the conceptual were compared against each other and against the output of the physically-based model. The results showed the ability of the distributed models to capture the effect of the complex topography and variability of land use and soils of the watershed. In general, the GSSHA model required less calibration and performed better than HEC-HMS. This study confirms that the semi-distributed HEC-HMS model cannot be used without calibration, while the GSSHA model can be the best option in the case of a lack of data. Although the two models showed good agreement at the calibration point, there were significant differences in the runoff, discharge, and infiltration values at interior points of the watershed.

Impacts of temporal/spatial rainfall heterogeneities on peak runoff distribution and intensities for an urban river basin of south China

“…This modeling package has many functions including two‐dimensional land surface flow, one‐dimensional stream flow, one‐dimensional infiltration, two‐dimensional groundwater, and simulation of sediment transport in shallow soils, land surfaces, rivers, and open channels. The previous research demonstrated that GSSHA could adequately capture the urban flood response and obtain high simulation accuracy without the need of “significant calibration.” It is a suitable tool for solving problem of inadequate hydrological information in urban regions (Al‐Areeq et al, 2021; Cristiano et al, 2019).…”

“…The previous research demonstrated that GSSHA could adequately capture the urban flood response and obtain high simulation accuracy without the need of "significant calibration." It is a suitable tool for solving problem of inadequate hydrological information in urban regions (Al-Areeq et al, 2021;Cristiano et al, 2019).…”

Remote Sensed and/or Global Datasets for Distributed Hydrological Modelling: A Review

“…For instance, SoilGrids by ISRIC plus The Global Lithological Map (GLiM) v1.0 data has been used by Dembele et al (2020a) [14] and Dembele et al (2020b) [68] to develop hydrological models for the Volta River basin, Africa. SoilGrids by ISRIC, in addition to Global Hydrologic Soil Groups (HYSOGs250m) data for hydrologic soil groups identification, have been used by Al-Areeq et al (2021) [69] to develop two hydrological models for the Makkah region in Saudi Arabia using Gridded Surface Subsurface Hydrologic Analysis (GSSHA) fully distributed modelling tool and Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), a semi-distributed hydrological modelling tool. Busari et al (2021) [61] used ESDB in combination with HWSD, while Dahri et al (2021) [70] HWSD in combination with High-Resolution Soil Maps of Global Hydraulic Properties (HiHydroSoil) by Future Water.…”

Mapping Groundwater Potential Zones in the Habawnah Basin of Southern Saudi Arabia: An AHP- and GIS-based Approach

“…Annual rainfall varied between 39.8 mm and 107.1 mm. The inverse distance weighted (IDW) technique was applied for mapping as it has been found to yield satisfactory outcomes [49,50]. Figure 10 shows the categorization of the rainfall map, which was rescaled to a spatial resolution of 10 square meters and classified using the natural break classification approach into five subclasses (Jenks).…”