Can a Spatially Distributed Hydrological Model Effectively Analyze Hydrological Processes in the Nepal Himalaya River Basin?

• Published in: Environmental modeling & assessment 2024-12, Vol.29 (6), p.1037-1058
• Main Authors: Pradhan, Ananta Man Singh, Silwal, Gunjan, Shrestha, Suchita, Huynh, Thanh-Canh, Dawadi, Sarita
• Format: Article
• Language: English
• Subjects: Applications of Mathematics; Calibration; Cell size; Climate change; Climate models; Climatic conditions; Discharge; Earth and Environmental Science; Environment; Flow duration; Flow duration curves; Glaciers; Glaciohydrology; Global climate; Goodness of fit; Hydroclimate; Hydrologic models; Hydrology; Impact analysis; Math. Appl. in Environmental Science; Mathematical Modeling and Industrial Mathematics; Meteorological data; Monsoons; Operations Research/Decision Theory; Parameter sensitivity; Parameter uncertainty; Precipitation; River basins; Rivers; Sensitivity analysis; Snow; Temperature dependence
• ISSN: 1420-2026; 1573-2967
• DOI: 10.1007/s10666-024-09975-9

In regions characterized by snow and glaciers, the selection of appropriate methodologies and the use of suitable hydrological models are imperative due to heightened vulnerability to the impacts of global climate change. This study focuses on the Tamor River basin (TRB) in the Eastern Himalaya of Nepal, aiming to employ an advanced hydrological model to enhance understanding and prediction. We used the SPHY-cryo-hydro-climate-impact model to simulate the daily discharge and to estimate the contribution of different runoff components both annually and seasonally. The model was chosen based on physical processes representation, the ability to simulate snow and glacier melt, and the computational efficiency to project the daily discharge simulations in changing climate. Utilizing meteorological data with high temporal (daily) and spatial (250 m × 250 m grid cell size) resolution from 1995 to 2012, the model undergoes calibration (1996–2004) and validation (2005–2012) using a manual approach due to the limited parameters requiring calibration. The model’s performance is evaluated using goodness of fit (GoF) functions, including NSE, KGE, and VD, with satisfactory results (NSE ≥ 70%, VD  0.7) for both calibration and validation periods at two monitoring stations (690 and 684). The study reveals that snow and ice melt significantly contribute to pre-monsoon flow (March, April, and May), constituting approximately 35% of the total flow, while their influence diminishes during post-monsoon seasons (October and November). Sensitivity analyses indicate the model’s responsiveness to both temperature and precipitation forcings, with temperature exerting a more pronounced impact, particularly on temperature-dependent melt modules and the state of precipitation at higher elevations. However, uncertainties persist in the model outputs, primarily attributed to input forcings, model structure, and parameters derived from literature rather than field measurements. The observed discrepancy between flow duration curves suggests that calibrated parameters remain partially optimized and subject to uncertainty. This study underscores the complexity of hydrological modeling in snow and glacier-dominated regions and emphasizes the need for continued refinement to enhance predictive accuracy amid evolving climatic conditions.