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<p>This study evaluates the utility of Satellite Precipitation Products (SPPs) as an alternative data source, focusing on the Global Precipitation Climatology Project (GPCP) One-Degree Daily Precipitation Dataset (1DD) in the West Seti River Basin, Nepal. The purpose of this study was to assess whether SPPs, specifically the GPCP 1DD dataset, can serve as a reliable alternative to limited ground-based rainfall data for hydrological modelling in the West Seti River Basin (WSRB). The performance of both raw and bias-corrected GPCP 1DD data (1997–2015) was assessed against 18 ground stations using statistical indices. The raw data showed significant underestimation (PBIAS = -54.70%) and low detection skill (POD = 0.392, CSI = 0.259). Bias correction using the Linear Scaling method effectively removed the mean bias (PBIAS = 0%), though RMSE increased from 12.85 to 16.55&#xa0;mm/day. A semi-distributed hydrological model was developed in HEC-HMS and demonstrated satisfactory performance during calibration (NSE = 0.773, R<sup>2</sup> = 0.786) and validation (NSE = 0.691, R<sup>2</sup> = 0.697). The study concludes that while raw GPCP 1DD data is unreliable for direct hydrological application, its bias-corrected version is a viable alternative for rainfall-runoff modelling in the data-scarce far-western mountainous region of Nepal.</p>

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Evaluation of SPP and hydrological modelling of West Seti River Basin using HEC-HMS

  • 期刊论文

  • 发表日期：

    2026年6月24日

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  • I. Dahal,
  • A. Khanal,
  • M. Kafle,
  • A. Khanal

摘要

This study evaluates the utility of Satellite Precipitation Products (SPPs) as an alternative data source, focusing on the Global Precipitation Climatology Project (GPCP) One-Degree Daily Precipitation Dataset (1DD) in the West Seti River Basin, Nepal. The purpose of this study was to assess whether SPPs, specifically the GPCP 1DD dataset, can serve as a reliable alternative to limited ground-based rainfall data for hydrological modelling in the West Seti River Basin (WSRB). The performance of both raw and bias-corrected GPCP 1DD data (1997–2015) was assessed against 18 ground stations using statistical indices. The raw data showed significant underestimation (PBIAS = -54.70%) and low detection skill (POD = 0.392, CSI = 0.259). Bias correction using the Linear Scaling method effectively removed the mean bias (PBIAS = 0%), though RMSE increased from 12.85 to 16.55 mm/day. A semi-distributed hydrological model was developed in HEC-HMS and demonstrated satisfactory performance during calibration (NSE = 0.773, R2 = 0.786) and validation (NSE = 0.691, R2 = 0.697). The study concludes that while raw GPCP 1DD data is unreliable for direct hydrological application, its bias-corrected version is a viable alternative for rainfall-runoff modelling in the data-scarce far-western mountainous region of Nepal.