Impact Assessment of Gridded Precipitation Products on Streamflow Simulations over a Poorly Gauged Basin in El Salvador

Abstract

In this study, five open access gridded precipitation (GP) products (CFSR, MSWEPv1.1, PERSIANN-CDR, CMORPH, and CHIRPSv2.0) and local climate data were evaluated over the Grande de San Miguel (GSM) River Basin in El Salvador. The main purpose was to identify optional data sources of precipitation for hydrological modelling given that ground-based precipitation gauges in El Salvador are scarce and their data includes important temporal and spatial gaps. Firstly, a direct comparison was made between the precipitation data from the five GP products and from the rain gauges. Secondly, the SWAT model was used to simulate the streamflow regimen based on the precipitation datasets. The analysis of results showed that the models produced correct predictions, and the accuracy increased as models were calibrated to each specific precipitation product. Overall, PERSIANN-CDR produced the best simulation results, including streamflow predictions in the GSM basin, and outperformed other GP products and also the results obtained from data precipitation gauges. The findings of this research support the hydrological modelling based on open-access GP products, particularly when the data from precipitation gauges are scarce and poor.

Evaluation of satellite-based and reanalysis precipitation datasets by hydrologic simulation in the Chenab river basin

“…Precipitation data is regarded as an essential driving variable in hydrologic models (Sharannya et al 2020;Jimeno-Sáez et al 2021). At high altitudes, the scarcity, non-existence or lack of gauged observations coupled with orographic effects and complex weather systems hinders the efforts to quantify current and future water availability (Nazeer et al 2021).…”

“…Furthermore, even if precipitation data is available, its access to public use is restricted due to strict data policy (Duan et al 2019). The accuracy of the rain gauge network is also considerably influenced by flaws in instrument installation methods, losses through instrument by wetting inside walls and evaporation, and the wind effects above the gauge orifice (Jimeno-Sáez et al 2021;Senent-aparicio et al 2021). In addition to rain gauge data, the availability of alternative sources for precipitation data will enhance the hydrological modeling efforts, particularly in data-scarce regions.…”

“…The rainfall estimates from PERSIANN-CDR have been extensively applied in many studies (Ashouri et al 2015;Zhu et al 2016;Liu et al 2017Liu et al , 2018Jimeno-Sáez et al 2021). The performance of the PERSIANN-CDR precipitation product was poor in the detection of precipitation events and the amount of daily precipitation over Columbia.…”

Novel Approaches for Regionalising SWAT Parameters Based on Machine Learning Clustering for Estimating Streamflow in Ungauged Basins

“…Before calibrating the SWAT model, we performed 500 model runs and selected sensitive parameters by performing a sensitivity analysis on 10 widely used parameters (Jimenez-Navarro et al 2021;Jimeno-Sáez et al 2021;Castellanos-Osorio et al 2023) that can influence river streamflow (see Table B4 in Appendix B). In particular, we selected the SWAT parameters that obtained a p-value of less than 0.05 for each donor basin − the lower the p-value, the more sensitive the parameter.…”