A novel approach: community-driven snow depth measurement in Central Asia

Abstract. Central Asia is a landlocked region with its freshwater resources originating in the mountains of Pamir, Tianshan, and Hindukush. Water resources in this area are formed mainly due to seasonal snowmelt, with glacier melt being the second largest hydrological component contributing to river flow, primarily in late summer. Water resources are shared among all Central Asian countries and used mainly for agricultural production purposes as well as hydropower generation. Proper management of water resources requires an accurate assessment of water availability originating in the mountains, mainly due to snowmelt. This requires data on snow depth, which is limited in the region. Snow surveys that were initiated during the 1980s have not continued in many parts of the region. The limitation of data on snow depth observations creates a challenge in forecasting water availability with the required accuracy.

In order to cope with the challenge of data availability on snow depth measurements to improve the accuracy of hydrological forecasts, we introduced a novel approach that involved communities living in the source area of water formation to collect snow depth measurements. The project was conducted in the territory of Kyrgyzstan, Tajikistan, and Uzbekistan, and more than 1000 observations were collected in the period from February 2024 to March 2025. Figures and maps prepared for this manuscript rely on data collected in 2024. The social media channel Telegram was used to establish communication with communities living in remote areas. The observations were done voluntarily. Volunteers used a ruler as a measuring device and Telegram to send their observations every five days in the period of January to March 2024. The data on snow measurement were validated for any outliers by comparing them to the closest observations that were provided by other volunteers.

The data collected in this project were used as ground-truth data to validate MODIS snow cover data that was processed by the MODSNOW-Tool. The validation results showed over 80 % agreement of community-driven snow depth measurement and snow cover observation from remote sensing products.

In summary, community-driven snow depth data collection enhances the accuracy of mountain snow storage assessments, supports water resource forecasting, and fosters long-term resilience by empowering local participation in environmental monitoring, particularly valuable in resource-limited, remote regions like Central Asia.

The dataset is freely accessible from https://doi.org/10.5281/zenodo.17158864 (last access: 19 September 2025; Gafurov et al., 2025).