Indus River Basin Glacier Melt at the Subbasin Scale

Abstract

Pakistan is the most glaciated country on the planet but faces increasing water scarcity due to the vulnerability of its primary water source, the Indus River, to changes in climate and demand. Glacier melt constitutes over one-third of the Indus River’s discharge, but the impacts of glacier shrinkage from anthropogenic climate change are not equal across all eleven subbasins of the Upper Indus. We present an exploration of glacier melt contribution to Indus River flow at the subbasin scale using a distributed surface energy and mass balance model run 2001–2013 and calibrated with geodetic mass balance data. We find that the northern subbasins, the three in the Karakoram Range, contribute more glacier meltwater than the other basins combined. While glacier melt discharge tends to be large where there are more glaciers, our modeling study reveals that glacier melt does not scale directly with glaciated area. The largest volume of glacier melt comes from the Gilgit/Hunza subbasin, whose glaciers are at lower elevations than the other Karakoram subbasins. Regional application of the model allows an assessment of the dominant drivers of melt and their spatial distributions. Melt energy in the Nubra/Shyok and neighboring Zaskar subbasins is dominated by radiative fluxes, while turbulent fluxes dominate the melt signal in the west and south. This study provides a theoretical exploration of the spatial patterns to glacier melt in the Upper Indus Basin, a critical foundation for understanding when glaciers melt, information that can inform projections of water supply and scarcity in Pakistan.

Evaluation of ERA5 and Dynamical Downscaling for Surface Energy Balance Modeling at Mountain Glaciers in Western Canada

“…Near-surface wind speeds are given at a height of 10 m above the surface for WRF and ERA5, while the AWS wind data was measured at a height of 2 m above the surface. A common correction for the difference in wind speed heights is based on the assumptions of a logarithmic wind proőle (e.g., Claremar et al, 2012;Giese et al, 2022), which rarely takes place at our study sites where katabatic ŕow with low (<3 m above surface) wind speed maxima prevail during a summer season (e.g., Fitzpatrick et al, 2017;Radić et al, 2017). The correction based on the logarithmic wind proőle may therefore introduce an additional bias (underestimation) of wind speed relative to observations.…”

Evaluation of reanalysis data and dynamical downscaling for surface energy balance modeling at mountain glaciers in western Canada

“…Near-surface wind speeds in the reanalyses and WRF are given at a height of 10 m above the surface, while the AWS wind data were measured at a height of 2 m above the surface. A common correction for the difference in wind speed heights is based on the assumption of a logarithmic wind profile (e.g., Claremar et al, 2012;Giese et al, 2022), which rarely takes place at our study sites, especially those in the interior of British Columbia, where katabatic flow with low (< 3 m above surface) wind speed maxima prevails during a summer season (e.g., Fitzpatrick et al, 2017;Radić et al, 2017). The correction based on the logarithmic wind profile may therefore introduce an additional bias (underestimation) of wind speed relative to the observed wind speed at 2 m. We therefore chose not to correct for the height difference in the wind datasets.…”

Evaluation of ERA5 and Dynamical Downscaling for Surface Energy Balance Modeling at Mountain Glaciers in Western Canada

“…Near-surface wind speeds in the reanalyses and WRF are given at a height of 10 m above the surface, while the AWS wind data was measured at a height of 2 m above the surface. A common correction for the difference in wind speed heights is based on the assumption of a logarithmic wind profile (e.g., Claremar et al, 2012;Giese et al, 2022), which rarely takes place at our study sites, especially those in the interior of British Columbia, where katabatic flow with low (<3 m above surface) wind speed maxima prevails during a summer season (e.g., Fitzpatrick et al, 2017;Radić et al, 2017). The correction based on the logarithmic wind profile may therefore introduce an additional bias (underestimation) of wind speed relative to the observed wind speed at 2 m. We therefore chose not to correct for the height difference in the wind datasets.…”