Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas

Abstract

In this study, a distributed biosphere hydrological model with three-layer energy-balance snow physics [an improved version of the Water and Energy Budget-based Distributed Hydrological Model (WEB-DHM-S)] is applied to the Dudhkoshi region of the eastern Nepal Himalayas to estimate the spatial distribution of snow cover. Simulations are performed at hourly time steps and 1-km spatial resolution for the 2002/03 snow season during the Coordinated Enhanced Observing Period (CEOP) third Enhanced Observing Period (EOP-3). Point evaluations (snow depth and upward short-and longwave radiation) at Pyramid (a station of the CEOP Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region. The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating the model's capability to accurately capture the spatiotemporal variations in snow cover across the study area. The qualitative pixelto-pixel comparisons for the snow-free and snow-covered grids reveal that the simulations agree well with the MODIS data to an accuracy of 90%. Simulated nighttime land surface temperatures (LST) are comparable to the MODIS LST (MOD11A2) with mean absolute error of 2.428C and mean relative error of 0.778C during the study period. The effects of uncertainty in air temperature lapse rate, initial snow depth, and snow albedo on the snow-cover area (SCA) and LST simulations are determined through sensitivity runs. In addition, it is found that ignoring the spatial variability of remotely sensed cloud coverage greatly increases bias in the LST and SCA simulations. To the authors' knowledge, this work is the first to adopt a distributed hydrological model with a physically based multilayer snow module to estimate the spatial distribution of snow cover in the Himalayan region.

Review of studies on land use and land cover change in Nepal

“…These mapping exercises indicated that remote-sensing (RS) data and geographic information system (GIS) tools are more reliable for snow cover mapping. Further snow cover and glacier change research has been carried out using various methods, datasets and techniques in Nepal in a major part of the Himalayan regions (Armstrong 2010;Bolch et al 2012), as well as at the national level (Shrestha and Joshi 2009;Bajracharya et al 2014) or at a smaller scale (Maskey et al 2011;Shrestha et al 2012;Kuhle 2014;Shea et al 2014). The overall small-scale mapping of various classes of LULC in Nepal is summarized in the Table 2, with LULC categories, spatial and temporal coverage, sources of data and references.…”

Optimizing Snowfall Correction Factor for Radar-Amedas Precipitation Using Distributed Snow Model (Web-DHM-S) and Modis Snow Cover Data

“…The snowmelt model applied in this study is the WEB-DHM-S (Water and Energy Budget -based Distributed Hydrological Model with improved snow physics) 11,12) . WEB-DHM-S is the improved version of WEB-DHM 13) in its snow physics in the distributed biosphere hydrological modeling framework.…”

Simulating Long-Term Hydrological Processes in Cold Region River Basin

“…The bulk density of ice for new snowfall is calculated following the formulation used in the CROCUS snow model 15) . The detailed description of the model equations and snow physics is comprehensively described by Shrestha et al 9,10) . A hillslope-driven runoff scheme employing a kinematic wave flow routing method is adopted in calculating runoff.…”

“…Model output is taken as the maximum snow extent over 8-day, corresponding to the dates of the MODIS dataset. A model-output pixel was determined snow covered when the snow depth is greater or equal to 4cm 10) . As evident from the Fig.…”

“…Bhatti et al 8) constructed physical based Water and Energy Budget -based Distributed Hydrological Model with improved snow physics (WEB-DHM-S) 9,10) to investigate the impact of climate change on snowmelt runoff and snow water equivalent (SWE) by forcing the model with past (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000) and future (2046-2065) AOGCMs data in the Shubuto River basin, Hokkaido, Japan. However; barriers to widespread use of climate change projections in water resources studies include long-term data availability and the knowledge to appropriately use this information for specific watershed studies.…”