Application of a Conceptual Hydrological Model for Streamflow Prediction Using Multi-Source Precipitation Products in a Semi-Arid River Basin

Abstract

Management of the freshwater resources in a sustained manner requires the information and understanding of the surface water hydrology and streamflow is of key importance in this nexus. This study evaluates the performance of eight different precipitation products (APHRODITE, CHRS CCS, CHRS CDR, CHIRPS, CPC Global, GPCC, GPCP, and PERSIANN) for streamflow prediction in two sub-catchments (Chirah and Dhoke Pathan) of the data-scarce Soan River Basin (SRB) in Pakistan. A modified version of the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) known as HBV-light was used to generate streamflow. The model was separately calibrated and validated with observed and estimated precipitation data for streamflow simulation with optimized parameterization. The values of R2, NSE, KGE and PBIAS obtained during the calibration (validation) period for the Chirah sub-catchment were 0.64, 0.64, 0.68 and −5.6% (0.82, 0.81, 0.88 and 7.4%). On the other hand, values of R2, NSE, KGE, and PBIAS obtained during the calibration (validation) period for the Dhoke Pathan sub-catchment were 0.85, 0.85, 0.87, and −3.4% (0.82, 0.7, 0.73 and 6.9%). Different ranges of values were assigned to multiple efficiency evaluation metrics and the performance of precipitation products was assessed. Generally, we found that the performance of the precipitation products was improved (higher metrics values) with increasing temporal and spatial scale. However, our results showed that APHRODITE was the only precipitation product that outperformed other products in simulating observed streamflow at both temporal scales for both Chirah and Dhoke Pathan sub-catchments. These results suggest that with the long-term availability of continuous precipitation records with fine temporal and spatial resolutions, APHRODITE has the high potential to be used for streamflow prediction in this semi-arid river basin. Other products that performed better were GPCC, GPCP, and CHRS CCS; however, their scope was limited either to one catchment or a specific time scale. These results will also help better understand surface water hydrology and in turn, would be useful for better management of the water resources.

Study on a Hybrid Hydrological Forecasting Model SCE-GUH by Coupling SCE-UA Optimization Algorithm and General Unit Hydrograph

“…These models effectively capture the variations and attenuation of flood events, maintaining the simulation errors of peak flow and peak time within acceptable limits. Nevertheless, all models typically underestimate the magnitude of flood peaks, a common observation in other lumped models [6]. The performance of the four models is ranked as follows: SCE-GUH > GUH > SCE-NIUH > NIUH.…”

“…The relative error (RE) is calculated by multiplying the ratio of the absolute error of a measurement to the actual value by 100%. Equation (6) shows the calculation. In the evaluation of flash flood models, RE is commonly employed to assess the reliability of the simulated flood peak discharge:…”

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Flash Flood Risk Assessment in the Asir Region, Southwestern Saudi Arabia, Using a Physically-Based Distributed Hydrological Model and GPM IMERG Satellite Rainfall Data

“…The rainy season is from November to April, where the average annual rainfall ranges between 400-700 mm/y [22]. The return period of the highest precipitation ranges from 2 to 200 years [14], which is highly variable. Figure 3 shows the average rainfall from 2002 to 2020, where most of the basin is located in the humid region.…”

Precipitation Extremes over India in a Coupled Land–Atmosphere Regional Climate Model: Influence of the Land Surface Model and Domain Extent

“…All these events adversely affect the hydrological cycle, agriculture, and the lives of people [1][2][3]. These extreme events have profound impacts on the understanding of water hydrological dynamics for the efficient distribution, accessibility, and sustainable stewardship of freshwater resources for the proper functioning of a multitude of ecosystems [4,5]. The crop yield accuracy (deficiency) simulated by crop models is dependent on the precision of input fields such as temperature or rainfall.…”