An integrated modeling system for estimating glacier and snow melt driven streamflow from remote sensing and earth system data products in the Himalayas

Abstract

Keywords:Himalayas Glacier melt Energy balance Stream flow s u m m a r y Quantification of the contribution of the hydrologic components (snow, ice and rain) to river discharge in the Hindu Kush Himalayan (HKH) region is important for decision-making in water sensitive sectors, and for water resources management and flood risk reduction. In this area, access to and monitoring of the glaciers and their melt outflow is challenging due to difficult access, thus modeling based on remote sensing offers the potential for providing information to improve water resources management and decision making. This paper describes an integrated modeling system developed using downscaled NASA satellite based and earth system data products coupled with in-situ hydrologic data to assess the contribution of snow and glaciers to the flows of the rivers in the HKH region. Snow and glacier melt was estimated using the Utah Energy Balance (UEB) model, further enhanced to accommodate glacier ice melt over clean and debris-covered tongues, then meltwater was input into the USGS Geospatial Stream Flow Model (Geo-SFM). The two model components were integrated into Better Assessment Science Integrating point and Nonpoint Sources modeling framework (BASINS) as a user-friendly open source system and was made available to countries in high Asia. Here we present a case study from the Langtang Khola watershed in the monsoon-influenced Nepal Himalaya, used to validate our energy balance approach and to test the applicability of our modeling system. The snow and glacier melt model predicts that for the eight years used for model evaluation (October 2003-September 2010, the total surface water input over the basin was 9.43 m, originating as 62% from glacier melt, 30% from snowmelt and 8% from rainfall. Measured streamflow for those years were 5.02 m, reflecting a runoff coefficient of 0.53. GeoSFM simulated streamflow was 5.31 m indicating reasonable correspondence between measured and model confirming the capability of the integrated system to provide a quantification of water availability.Published by Elsevier B.V.

Black carbon and the Himalayan cryosphere: A review

“…However, the processes affecting Himalayan glaciers remain poorly understood (Bolch et al, 2012). The changes to snow and ice in the HKH region have implications that go deeper than climate: more than 1.3 billion people are supported by major rivers originating in the HKH region, and a large fraction of source water in areas can be attributed to snow and glacier melt water (Brown et al, 2014). Changes to the quantity and timing of seasonal melt has broad implications for the livelihood of a large portion of the Earth's population.…”

How much cryosphere model complexity is just right? Exploration using the conceptual cryosphere hydrology framework

“…For a complete representation of a multi-layered cryosphere system (either multiple snow layers or snow and ice layers), the internal energy of each layer and heat transfer between the layers must be considered. In many computational models, e.g., that described in Hock and Holmgren (2005), SnowModel (Liston and Elder, 2006a), and the Better Assessment Science Integrating point and Nonpoint Sources modeling framework (BASINS; Brown et al, 2014), sensible and latent energy of snow are considered but sensible energy of glaciers is ignored. Some models, including SnowModel (Liston and Elder, 2006a), have the option to calculate the state properties of multiple snow layers.…”

Developing Daily Cloud‐Free Snow Composite Products From MODIS and IMS for the Tienshan Mountains

“…As a major component of the cryosphere, snow plays an important role in the Earth's climate system by affecting the surface energy budget, water cycle, primary productivity, and surface gas exchange (Dong, 2018;Dong & Menzel, 2016a, 2016b. Understanding its variability is critical for assessing spatial snow cover variability, climate/weather forecasting models, snow phenology changes, and trend diagnose in snow cover growth and decline (Brown et al, 2014;Estilow et al, 2015;Gao et al, 2012;Peng et al, 2013). Snow accumulation and ablation are sensitive to temperature change, and snow cover, due to its high albedo, can also directly affect land surface temperature by reflecting incoming short-wave solar radiation (Kongoli & Bland, 2000).…”