EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using recent public glacier data sets to calibrate glacier melt models and drive hydrological models in Central Asia: Facilitating hydrological modelling workflows

Beatrice Marti1, Dirk Karger2, and Tobias Siegfried3
Beatrice Marti et al.
  • 1hydrosolutions Ltd, Zurich, Switzerland (
  • 2WSL, Birmensdorf, Switzerland (
  • 3hydrosolutions Ltd, Zurich, Switzerland (

Central Asia's river systems are largely fed by reliable snow and glacier melt which allows agricultural production in the dry lowlands and hydropower production. However, climate forcing is changing faster than ever and predictions of river discharge relying on past observations (as are currently applied by Central Asia’s Hydrometeorological Agencies) may no longer pass stringent quality criteria for good forecasts. There is a growing need for hydrological models for nexus studies and the feasibility study for small hydropower plants in the region. Central Asia is a large region with a sparse hydrometeorological monitoring network which makes it difficult to calibrate hydrological models with traditional methods. It is therefore good news that the amount of remotely sensed data or data from reanalysis products has been increasing in both quantity, and quality in the past few years. Such data offers a huge potential to improve hydrological modelling efforts but the required pre-processing of such data often exceeds the capacities of local stakeholders in Central Asia which does not allow them to valorize these data. As local workflows being digitized, tools need to be developed to facilitate the integration of improved model forcing and modelling techniques in applied hydrology. 

The present study uses the daily CHELSA-W5E5 v1.1 data set at daily 1km by 1km resolution, which is an ERA5 derivative with corrections for high mountain regions, to force degree-day melt models for glaciers and semi-distributed hydrological models using HBV. We combine the data from the Randolph Glacier Inventory in the region with recently available information on individual glacier elevation change (2000 - 2019), thickness and glacier discharge (2000 - 2016) to calibrate degree-day melt models for glaciers in Central Asia and to estimate daily glacier discharge until the end of the century for the 4 GCM models of the CIMIP6 climate projections with the highest priority for the region and for 4 socio-economic scenarios (i.e. 16 modeling scenarios). We also validate existing glacier volume, length and area scaling relationships for Central Asian glaciers from the literature. 

The glacier discharge time series is used as a source to a semi-distributed hydrological model to estimate the future water availability of the river Koksu,  a tributary to the Shakhimardan catchment in the south of the Fergana valley, and is a key input for the design of a small-hydropower plant. We further demonstrate a workflow to calibrate the snow components of the HBV modules in the hydrological model using the high mountain snow reanalysis product. 

We strive to streamline the use of such novel data products in the hydrological modelling process for Central Asian river basins by developing a suite of publicly available R packages & vignettes that facilitate data processing and modelling. The presented modelling effort is part of the ongoing EU Horizon 2020 project Hydro4U which aims at promoting sustainable small-hydropower solutions in Central Asia. The project's demonstration site of Shakhimardan is especially interesting because of its sensitive transboundary nature and the potential for socio-economic development in this remote enclave which is frequently cut off from power supply.

How to cite: Marti, B., Karger, D., and Siegfried, T.: Using recent public glacier data sets to calibrate glacier melt models and drive hydrological models in Central Asia: Facilitating hydrological modelling workflows, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7983,, 2022.


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