Ice cliffs can act as “hot spots” for melt on debris-covered glaciers and promote local glacier mass loss. Repeat high-resolution remote-sensing data are therefore required to monitor the role of ice cliff dynamics in glacier mass loss. Here we analyze high-resolution aerial photogrammetry data acquired during the 2007, 2018, and 2019 post-monsoon seasons to delineate and monitor the morphology, distribution, and temporal changes of the ice cliffs across the debris-covered Trakarding Glacier in the eastern Nepal Himalaya. We generate an ice cliff inventory from the 2018 and 2019 precise terrain data, with ice cliffs accounting for 4.7 and 6.1% of the debris-covered area, respectively. We observe large surface lowering (>2.0 m a−1) where there is a denser distribution of ice cliffs. We also track the survival, formation, and disappearance of ice cliffs from 2018 to 2019, and find that ∼15% of the total ice cliff area is replaced by new ice cliffs. Furthermore, we observe the overall predominance of northwest-facing ice cliffs, although we do observe spatial heterogeneities in the aspect variance of the ice cliffs (ice cliffs face in similar/various directions). Many new ice cliffs formed across the stagnant middle sections of the glacier, coincident with surface water drainage and englacial conduit intake observations. This spatial relationship between ice cliffs and the glacier hydrological system suggests that these englacial and supraglacial hydrological systems play a significant role in ice cliff formation.
Climate-induced cryospheric changes can have a significant impact on the downstream water availability. In this study, the Open Global Glacier Model (OGGM) and the Glacio-hydrological Degree-day Model (GDM) are integrated to project the response of cryospheric and hydrological systems to climate change until 2100. The study area comprises six sub-basins of glacierized Koshi River basin covering Nepalese and Chinese territories. The output from OGGM is provided as input to GDM along with the spatial and hydro-meteorological data. The average glacier area change in all the sub-basins from 2021 to 2100 is estimated as 65 and 85% decrease and the average glacier volume change is estimated as 76 and 86% decrease for RCP 4.5 and 8.5 scenarios, respectively. The future simulated discharge shows an increasing trend in pre-monsoon and monsoon seasons and a decreasing trend in post-monsoon and winter seasons after 2060 in all the sub-basins, which can lead to wetter wet seasons and drier dry seasons in the far future. A shift in peak flow is observed from August to July in most of the sub-basins. The coupled modelling technique used in this study can largely improve our understanding of glacio-hydrological dynamics in the Himalayan region.
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.
The Bagmati River, which lies in central Nepal, originates in Bagdwar at an altitude of 2690 m and flows south through the Kathmandu valley. The river basin covers an area of 3750 sq. km and includes eight districts of Nepal. Flooding along this river is a common occurrence during the monsoon season. In this study, we compared rainfall events with river discharge, predicted the rate of future flooding events, and assessed the probable causes and socioeconomic impacts of flooding in the Bagmati River Basin. Data used to compare rainfall and discharge were based on rain gauge stations with the highest 24‐h records and also contributed directly to the run‐off at the Pandhera Dobhan discharge station, the lowest elevation water discharge gauge, in order to facilitate a direct comparison between basin rainfall and river discharge. Future trends in extreme 24‐h rainfall events and peak flood values were calculated for 2‐, 5‐, 10‐, 25‐, 50‐ and 100‐year recurrence intervals. Extreme 24‐h rainfall events varied between 195 mm to 552 mm among the different rain gauges, corresponding to a peak discharge at Pandhera Dobhan of 16 523 m3/s for the 100‐year return interval. We also elaborate on the probable causes of flooding in the central basin and summarise major past flood events and their impacts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.