Permafrost estimation model in Upper Indus Basin

Sat, 23 Sep 2023 00:00:00 +0000

Abstract

Remotely sensed topo-climatic factors, potential incoming solar radiation (PISR), land surface temperature (LST), topographic wetness index (TWI), Surface emissivity, and elevation, and machine learning techniques are used for mapping the spatial distribution of permafrost in the Tso Kar, a sub-basin of Upper Indus Basin (UIB) in Leh, Ladakh (UT). This schematic model is employed to identify remotely sensed parameters which are crucial in assessing permafrost extent over the study region. It is followed by the application and tuning of several machine learning models to deliver an expected accuracy in terms of permafrost classes demarcated over the study region based on literature. Results show that the PISR, LST and TWI are the most significant remotely sensed parameters affecting the permafrost and associated processes. Above 5000 m a.s.l., the proportion of permafrost in the study catchment is higher. Synergistic use of remote sensing image processing and machine learning techniques together provide mapping of permafrost over the region, which is elusive so far.

Permafrost in the Upper Indus Basin: An active layer dynamics

Wed, 29 Mar 2023 00:00:00 +0000

Abstract

Permafrost in the Upper Indus Basin (UIB) in Ladakh, India, is a critical water source and is less studied. Identifying permafrost and its characteristics is a crucial knowledge gap in the UIB. Thus, understanding the permafrost active layer dynamics is critical and essential due to its implications on regional hydrology, infrastructure stability, and disaster occurrence. For this purpose, an experimental site is prepared with 11 plots having two near-surface ground temperature loggers each, i.e., 22 in total, in the upper Ganglass catchment, a sub-region of the UIB, Ladakh. The permafrost active layer thickness characteristics and its thaw progression are simulated using the 1-D GEOtop model with forcing from these 22 loggers from 2016 to 2020. The snow days are calculated using the near-surface ground temperature. The simulation results show no permafrost at 4727 m a.s.l. consistently, whereas all the plots above 4900 m a.s.l. show permafrost active layer thickness, in particular, up to 4 m at 4942 m a.s.l. Permafrost characteristics significantly differ between a warmer (colder) year with low (high) snow. The mean surface offset of the catchment ranges between −0.01° and 5.5°C. These findings on permafrost and associated periglacial processes will provide a critical knowledge base for the stability of high-elevation infrastructure, glacial lakes, regional hydrology and climate, particularly for water.

Upper Indus Basin | John Mohd Wani | Cryosphere Research

Permafrost estimation model in Upper Indus Basin

Abstract

Permafrost in the Upper Indus Basin: An active layer dynamics

Abstract