Evaluation of precipitation elasticity using precipitation data from ground and satellite-based estimates and watershed modeling in Western Nepal

•We evaluated the performance of satellite-based precipitation estimates (SPEs).•Soil and Water Assessment Tool (SWAT) was used to simulate river discharge.•After the implementation of bias-correction, all SPEs revealed improved results.•We find the precipitation elasticity of the study area is ∼1.5...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of hydrology. Regional studies 2021-02, Vol.33, p.100768, Article 100768
Hauptverfasser: Talchabhadel, Rocky, Aryal, Anil, Kawaike, Kenji, Yamanoi, Kazuki, Nakagawa, Hajime, Bhatta, Binod, Karki, Saroj, Thapa, Bhesh Raj
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•We evaluated the performance of satellite-based precipitation estimates (SPEs).•Soil and Water Assessment Tool (SWAT) was used to simulate river discharge.•After the implementation of bias-correction, all SPEs revealed improved results.•We find the precipitation elasticity of the study area is ∼1.5.•This study supports the application of SPEs for hydrological modelling. West Rapti River (WRR) basin, Western Nepal. Hydrologic modeling requires an accurate precipitation data at a high spatial resolution, which is often limited in many regions of the globe. As a complement to the ground (gauge) precipitation data, satellite-based precipitation estimates (SPEs) appear useful. At first, this study evaluated performance of three different SPEs, namely i) CHIRPS, ii) PERSIANN-CCS, and iii) IMERG, with respect to gauge data using different event detection and quantification indices. Soil Water Assessment Tool (SWAT), a semi-distributed hydrologic model, was used to simulate the river discharge. We then analysed precipitation elasticity, as a first kind of such study in Nepalese river basin, by scaling the precipitation input in both positive and negative directions (ranging from -20 % to +20 %) in order to explore basin response on likely alteration of precipitation. A non-parametric precipitation elasticity was finally computed for three different cases: 1) observed river discharge, 2) gauge-based simulated river discharge, and 3) SPEs-based simulated river discharge. IMERG proved to be superior among three SPEs. All SPEs showed improved results after implementation of different levels of bias-correction where daily precipitation data were corrected using linear correction factors computed at a mean monthly scale. Computed correction factors are replicable to nearby basins. Precipitation elasticity of the study area ranged from +1.3 to +2.0 (approximately +1.5) which indicates that a 1.0 % change in precipitation will result in 1.5 % change in river discharge.
ISSN:2214-5818
2214-5818
DOI:10.1016/j.ejrh.2020.100768