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QUADRAT DTP: Development of a land surface temperature index glacio-hydrological model

   School of Geosciences

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  Dr Shaktiman Singh, Dr D Mullan, Dr Lydia Sam, Dr Anshuman Bhardwaj, Prof B Rea, Prof Matteo Spagnolo  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

The Hindu Kush-Himalaya (HKH) mountains contain considerable freshwater reservoirs in the form of snow, glaciers, lakes, permafrost, and wetlands supporting a population of ⁓1.3 billion (Singh et al., 2016). The HKH harbours ⁓50% (by area) of all the world’s glaciers outside the polar realm. These are particularly sensitive to minor changes in temperature and precipitation due to their latitudinal position, high ablation rates and specific accumulation processes. Several studies show that Himalayan glaciers are retreating in general with few exceptions [1]. These changes and their future implications are not fully understood owing primarily to the uncertainties caused by the scarcity of spatiotemporally continuous observed data available in the region. The geophysical models that integrate the hydrological cycle with glaciological process in a catchment, also referred to as glacio-hydrological models, can function as an important tool to estimate and predict the basin-scale mass balance and its effect on meltwater and river discharge (Kumar et al., 2016). There are several temperature-index based glacio-hydrological models which have been developed for the estimation of discharge and mass balance in a glacierised catchment, but all these models are primarily dependent on the spatially extrapolated near-surface air temperature observation from meteorological stations.

The novelty of the present project relies on exploiting the strong relationship established between remotely sensed land surface and air temperatures for different climatic and elevation zones in the Himalaya (Singh et al., 2019), so that the spatially continuous land surface temperature could be used instead of the spatially extrapolated air temperature observations in the glacio-hydrological model. The project will benefit from available remote sensing data [Sentinel, Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), Gravity Recovery and Climate Experiment (GRACE) and Tropical Rainfall Measuring Mission (TRMM), reanalysis climate data from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA 5, Earth System Grid Federation (ESGF) and Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] and observed air temperature, precipitation, discharge, mass balance data and established melt factors for debris-covered ice, debris-free ice and snow. Through this PhD project, we aim to develop and apply a glacio-hydrological model that will use land surface temperature for estimation of discharge and mass balance in glaciated catchments in the Himalaya. The model will be first set up, validated, and applied in the Baspa basin, Western Himalaya and subsequently applied to, and tested in, other glaciated catchments in the Himalaya.   


  1. Multitemporal mapping of glaciers (including debris cover and debris free) and land cover in the study area. 
  2. Identifying a reliable regional reanalysis climate dataset available and downscaling it using observed land surface temperature and precipitation data. 
  3. Using the downscaled data in a glacio-hydrological model and validating the output against observed data. 
  4. Applying the validated glacio-hydrological model regionally for estimating future variation in discharge in response to various modelled future climate scenarios. 
  5. Estimating the proportion of the contribution of ice and snow melt and precipitation to the overall discharge and their spatiotemporal changes. 

Candidate Background: Experience in glaciology, mapping and GIS. Experience in numerical modelling and the use of Python are desirable, but not essential.

More project details are available here:

How to apply: 

Funding Notes

QUADRAT studentships are open to UK and overseas candidates. Funding covers:
• A monthly stipend for accommodation and living costs, based on UKRI rates (currently £17,668 pa for 2022/23, updated annually)
• Fees (home rate tuition fees and/or fee waiver for overseas fees, where applicable)
• Research and training costs
For further information before applying please check full funding and eligibility information:


1. Singh et al., 2016
2. Kumar et al., 2016
3. Singh et al., 2019
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