Comparative Study of Hydrology and Icemelt in Three Nepal River Basins Using the Glacio-Hydrological Degree-Day Model (GDM) and Observations From the Advanced Scatterometer (ASCAT)

Abstract

An assessment of the water supply and its seasonal and annual changes over the century in the High Mountain Asia (HMA) region is of increasing interest due to its potential impact on one-sixth of the global population. In order to understand the changing hydrology and snow and ice melt, we used remotely sensed Advanced Scatterometer (ASCAT) observations of glacier melt (GM) and a distributed and gridded Glacio-hydrological Degree-day Model (GDM) in three river basins: Tamor, Trishuli and Marsyangdi. The GDM-estimated contribution of snowmelt, icemelt, rainfall and baseflow in river flows is found to be most accurate in the Trishuli River basin, with Nash-Sutcliffe efficiency (NSE) between the estimated and observed discharges of 0.81 and volume differences of −0.5%, and reasonably accurate in the Tamor River basin, with NSE of 0.69 and volume difference of −7.51%. Similarly, NSE of 0.81 and volume difference of 4.64% in Marsyangdi River basin. We find strong similarities in the timing of glacier melting using the GDM and from observations from the ASCAT GM, determining the seasonal start of glacier melting to within 6 days on average. In all basins ASCAT GM observes melting at higher elevations relative to GDM, average of 5,328 m a.s.l. Systematic differences in glacier melting area determined by modeling and satellite observations indicate ASCAT may have suboptimal resolution, view geometry and/or polarimetry for delineating glacier melting at the process-scale in complex topography, especially in the ablation zone. This is the first step in examining the remote sensing products that could potentially be incorporated into hydrologic models to increase the accuracy of the hydrologic flow as well as the ability to estimate river discharge in other basins with limited data.

Future projection of cryospheric and hydrologic regimes in Koshi River basin, Central Himalaya, using coupled glacier dynamics and glacio-hydrological models

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Fig. 3.Flowchart showing the workflow involved in OGGM (Maussion and others, 2018) and GDM (Kayastha and Kayastha, 2020a; Kayastha and others, 2020b).

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“…Climate-induced cryospheric changes such as glacier retreat and decrease in snow cover extent can largely influence the timing, magnitude and distribution of seasonal discharge in the river system (Alford and Armstrong, 2010; Immerzeel and others, 2013; Lutz and others, 2014; Kayastha and Kayastha, 2020a; Kayastha and others, 2020b). The cryospheric regime has been enduring drastic alterations since the last few decades and those changes are mainly associated with increasing temperature (Liu and Chen, 2000; Xu and others, 2009; Radić and others, 2013; Shea and others, 2015; Bolch and others, 2019).…”

Does Data Availability Constrain Temperature-Index Snow Models? A Case Study in a Humid Boreal Forest

“…These simple TI algorithms yielded reasonable estimates for open sites with shorter vegetation (O1 and O2) but not at forested sites (M1, M2 and M3). Several previous studies reported similar limitations and proposed modifications to TI algorithms that included supplemental variables or processes such as solar radiation [3,29,31,65], variable melt factor [3,31,[34][35][36], cold content [3,32,50], and other forest factors that lower the melting rate beneath the canopy [3]. A similar path was explored here, exploiting the best performing TI algorithm (A1) in a second phase by including these additional variables/processes.…”

“…Snow models can be organized into two main categories: energy balance (EB) models [12][13][14]16,[25][26][27] and temperature-index (TI) models [3,[28][29][30][31][32][33][34][35][36]. They all aim to estimate how the snow water equivalent (SWE), which ultimately affects streamflow dynamics, evolves throughout winter.…”

Author response to Referee #1

“…Based on comparisons with SEB modeling, and the physical basis of SAR measurements we have a high degree of confidence in our methodology and in the ability of the SAR backscatter to detect melting and in data-poor regions such as HMA remote sensing records of this type provide vital information supporting the understanding of surface melt on mountain glaciers. This is crucial to understand SEB on glacier and associated links to climate and downstream hydrological and ecological processes (Kayastha, Steiner et al 2019). Figure AR1.…”