Seasonal snow cover in mountainous regions represents a critical natural freshwater reserve for arid and semi-arid areas of Central Asia. This study evaluates the long-term (2000–2024) spatiotemporal dynamics of snow cover in the Arys River basin, located within the Western Tien Shan. The research utilizes daily satellite data from MODIS Terra and Aqua, along with data from the MODSNOW automated processing system. Terra-Aqua composite imagery was employed to minimize cloud cover effects. Satellite-derived estimates were validated against observational data from five meteorological stations of the Republican State Enterprise (RSE) “Kazhydromet”. The results indicate significant interannual variability in snow cover extent: the snow-covered area during the cold season ranged from 16.2% to 54.1%, with a mean value of 34.4%. Trend analysis revealed a weak negative trend, while Sen’s slope estimator showed an average annual reduction in snow cover area of 0.37% per year. The most pronounced decline in snow accumulation was observed in mid-elevation mountain zones. These findings suggest potential increased risks to seasonal water availability in the Arys River basin and, more broadly, across the Syr Darya basin under ongoing climate change conditions. The results provide a scientific basis for quantifying climate impacts and developing adaptation strategies for integrated water resources management in Central Asia.

Political tensions over water management in the Central Asia region have intensified since the Soviet era, as ecological issues like the drying Aral Sea and seasonal hydropower disruptions impact downstream countries. The paper discusses the region’s water governance challenges, including climate-induced uncertainties, water resource demands, and the limited capacity of local research institutions. While Central Asia’s governments have initiated reforms, including new infrastructure and agreements, resilience in water management remains underdeveloped. The paper also explores the role of higher education institutions in fostering capacity-building for sustainable governance, emphasizing the need for local expertise and regional collaboration. The proposed establishment of a Central Asian water research platform aims to enhance science-policy integration, promote sustainable water governance, and support informed regional cooperation on transboundary water issues.

Due to the long memory of snow processes, statistically based seasonal streamflow prediction models in snow-dominated catchments can successfully leverage, but also typically rely on, snowpack estimates. Using mountainous catchments in central Asia as a case study, we demonstrate how seasonal hydrological forecasts benefit from incorporating large-scale climate oscillations (COs). Firstly, we examine the teleconnections between the major COs and peak precipitation season in eight catchments across the Pamir Mountains and the Tian Shan from February to June. We then employ a machine learning (ML) framework that incorporates snow water equivalent (SWE) and dominant CO indices as predictors for mean discharge from April to September. Our workflow leverages an ensemble technique with multiple SWE estimates from near-time global data sources and diverse types of explainable machine learning models. We find that the winter states of the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) enhance SWE-based forecasts of seasonal discharge in the study catchments. We identify three instances in which the inclusion of COs as additional predictors could be instrumental for snowpack-based seasonal streamflow forecasting: (1) when forecasts are issued at extended lead times and accumulated SWE is not yet representative of seasonal terrestrial water storage, (2) when climate variability during the forecasted season plays a larger role in shaping seasonal discharge, and (3) when SWE estimates for a catchment are subject to larger uncertainty. Our approach provides a useful way to reduce uncertainties in seasonal discharge predictions in data-scarce, snowmelt-dominated catchments.

Snow is an important hydrological component in Central Asia. The snowmelt contributes to about 50 % of total water formation in the region, depending on geographic conditions. Many hydro-meteorological phenomena such as floods or drought conditions can be triggered by snowmelt amounts in Central Asia. The amount of snow accumulation in the mountains of Tian-Shan and Pamir also defines the availability of water for summer months to be used for agricultural production or re-filling of reservoirs for energy production in the winter period. Thus, it is of high importance to better understand the seasonal variation of snow and if the over the global average climate warming in the region is affecting the processes related to snow accumulation and melt.In this study, we analyze 22 years of daily Moderate Resolution Imaging Radiometer (MODIS) snow cover data that was processed using the MODSNOW-Tool, including cloud elimination. Additionally, observed snow depth data from meteorological stations were used to estimate trends related to snow cover change. We used several parameters such as snow cover duration, snow depth, snow cover extent, and snowline elevation to analyze changes. We conducted this analysis in 18 river basins across the Central Asian domain with each river basin having different geographic conditions and the results show varying tendencies. In many river basins, a clear decrease of snow cover was found to be significant, whereas in some river basins also increase in the snow cover extent in particular months could be identified. We attributed the changes related to snow cover to available historical temperature and precipitation records from meteorological stations to better understand the driving forces. The results of this study indicate seasonal snow cover variations but also potential water shortages in particular months as well as water abundance in months where water demand is not high in Central Asia.

Droughts pose significant challenges to water resources, agriculture, and socioeconomic stability, particularly in regions susceptible to climatic extremes such as Central Asia (CA) with its complex topography and diverse ecosystem. In the past several years there has been a substantial decrease in water storage in the region which further could lead to socioeconomic instability. Water is mainly used for irrigation and hydropower production in the region.In CA, the availability of ground observations is restricted, with most of the measurement stations being outdated since the Soviet era with little or no data sharing between the countries. Consequently, the utilization of widely available remotely sensed data proves advantageous in overcoming these limitations and improving the accuracy of water availability assessment in the region.CA relies predominantly on water resources derived from the melting of snow and glaciers in the Pamir, Tian Shan, and Hindukush mountains. In the study, we consider the two largest upstream river basins, Amu Darya and Naryn, the eastern headstream of Syr Darya. These two largest rivers in CA are crucial sources of water in the region, supporting agriculture and the ecosystem in the whole of CA.The study specifically focuses on evaluating snow cover and Snow Water Equivalent (SWE) during the winter months, especially preceding the onset of drought periods, and the Total Water Storage (TWS) in the drought months. The objective is to comprehend and quantify the correlation between these climatic elements and historical droughts, utilizing the Drought Severity Index (DSI) and the widely used Standardized Precipitation Index (SPI). DSI is based on the TWS value that is derived from the GRACE and GRACE-FO satellite missions. It shows a significant decrease in water storage in both basins since the start of the GRACE mission in 2002, with more intense arid conditions in the last 6 years. SPI-6 and SPI-9 based on precipitation and SWE data, show a slight increase in the trend in the Amu Darya basin, while in Naryn all indices show an increase in drought periods. This indicates that the arid conditions in the summer months in the Amu Darya basins are driven by human-induced water depletion. Finally, all indices can depict severe droughts in 2008, 2011 and 2018 in both basins. The study shows the potential of using globally available TWS data for drought assessment on a regional scale such as in CA.

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).

Winter snow accumulation is important for summer water supply in Central Asia, and contributes more than 50 % to the annual runoff. The region’s water availability is highly dominated by snow reserves in the mountain, which will be affected by climate change. Volumetric snow data play a vital role for hydrologic forecast in mountainous river basins, where snow is considered as a dominating hydrological component. This study quantifies decadal snow depth changes in the Western Tian-Shan in the Chirchik River Basin in Uzbekistan. The snow depth measurements from Uzhydromet have been used in this research. The historical changes in snow depth has been statistically analyzed for the 1963-2020 hydrological years. Correspondingly, the impact of climatic factors (temperature and precipitation) on snow dynamics were assessed as well. The results of hydrometeorological parameters such as snow depth, air temperature at 2 meters and precipitation were plotted as the trend line on monthly, seasonal, and annual scales. To verify statistical significance of the trend dynamics, the slope method and the Mann-Kendall trend test were applied. Our results show that snow cover (duration) days were significantly decreased by 4 days per decade or 21 days for 57 years from 1963 to 2020. Particularly, the initial occurrence of a permanent snow onset day was significantly delayed by 3 days per decade or 16 days for 57 years. Likewise, annual peak snow depth day was significantly shifted earlier by 4 days per decade or 20 days for 57 years. Interestingly, the maximum snow depth did not change statistically significant, but we observe a decline of 3.33 cm per decade or 19 cm for 57 years. Overall, we conclude that the duration of snow cover (snow reserve) has significantly decreased in the Chirchik basin due to climate warming in the last 57 years.

Central Asia is facing a water shortage due to the negative impacts of climate change. Water resources in this region originate mainly in the mountains of Pamir and Tian-Shan due to snow-and glacier melt. Thus, it is important to understand variations in the cryosphere (snow and glaciers) in this region to foster climate change adaptation measures.This study focuses on the analysis of spatio-temporal changes of snow and glaciers in the Amu Darya, Syr Darya and Zerafshan river basins in Central Asia. Due to limited availability of observational network in the region, we used, besides available station data, also remote sensing-based snow cover area data for the period of 2000-2023. As for the glacier change analysis, we used a degree-day modelling approach to assess changes of glacier thickness in the period of 2000-2023. Eight glaciers were chosen for modelling purposes that are all located in the selected eight river basins for this study. Spatio-temporal analysis of snow cover area change show significantly decreasing number of snow cover days above a certain elevation in Upper Amu Darya and Upper Syr Darya river basins. In both river basins, there are regions with up to 40 days less snow coverage between 2000 and 2023. In the Upper Syr Darya river basins this change is observed in the Akshiirak Massif area, whereas in the Amu Darya River Basin, this change is observed in the Murghab area in the far western part of the river basin. Below a certain elevation zone, there are also areas with increased number of snow cover days of up to 10 days. The attribution of this change into meteorological parameters leads to various hypothesis. The modelling results of glacier thickness change was validated against glacier area evolution that was derived using the Landsat images. In most of the river basins, a maximum of 60-70 meters of ice thickness loss was estimated with an increase of ice thickness of some glaciers in the accumulation area of about 10-15 meters. However, in two of the valley glaciers (Vanch and Zerafshan River Basins), higher amount of glacier thickness loss was estimated in the last 23 years.The study suggests quantified cryosphere changes in the last 23 years for Central Asian region and emphasizes the need for climate change adaptation as the water resources originating in the mountains of the region (water towers) are important for socio-economic stability.

Study Region: Central Asia region, where snow and glacier covered Tian Shan and Pamir mountains serve as “Water Tower” to supply populated areas downstream with essential freshwater resources, predominantly for agricultural production in summer and for energy production in winter. Study Focus: To identify the impact of irrigation practices on large-scale water storage by analyzing the spatial dynamics of water storage in Central Asia from sink to source using satellite-based Terrestrial Water Storage (TWS) variations from six GRACE(-FO) solutions and snow cover from MODIS. New Hydrological Insights for the Region: Snow Cover Index (SCI), a new proxy index for snow accumulation shows that GRACE-based TWS dynamics agree very well with snow accumulation and melt for seasonal and inter-annual variations. The spatial patterns of seasonal TWS variations show water re-distribution from the mountain region to the downstream parts, where agricultural fields are irrigated. In these areas, the maximum storage in summer observed with the majority of GRACE solutions can be attributed to the diversion of water inflow from the mountain ranges into vast areas of agricultural land. The ability of GRACE to observe both natural and human-induced water re-distribution in Central Asia indicates its potential for regional-scale monitoring applications in water resources management.

Abstract. Due to the long memory of snow processes, statistical seasonal streamflow predictions in snow-dominated catchments typically rely on snowpack estimates. Using mountainous catchments in Central Asia as a case study, we demonstrate how seasonal hydrological forecasts benefit from incorporating large-scale climate oscillations (COs). First, we examine the teleconnections between the major COs and peak precipitation season in eight catchments across the Pamir and Tian-Shan mountains from February to June. We then employ a machine learning framework that incorporates snow water equivalent (SWE) and dominant COs indices as predictors for mean discharge from April to September. Our workflow leverages an ensemble technique that uses multiple SWE estimates from near-time global data sources and diverse types of explainable machine-learning models. We find that the winter states of the El Niño-Southern Oscillation and the North Atlantic Oscillation enhance SWE-based forecasts of seasonal discharge in the study catchments. We identify three instances in which the inclusion of COs as additional predictors could be instrumental for snowpack-based seasonal streamflow forecasting: 1) when forecasts are issued at extended lead times and accumulated SWE is not yet representative of seasonal terrestrial water storage; 2) when climate variability during the forecasted season plays a larger role in shaping seasonal discharge; and 3) SWE estimates for a catchment are subject to larger uncertainty. Our approach provides a novel way to reduce uncertainties in seasonal discharge predictions in data-scarce snowmelt-dominated catchments.

In the face of climate change and socio-economic developments, water scarcity is a tremendous challenge. In particular, a significant portion of the world’s population rely on water from cryospheric sources such as snow and/or glacier fed mountain rivers. However, the data coverage in mountain regions is often sparse, which substantially hampers the assessment of climate impacts on hydrological systems. Furthermore, the large impact of climate change on snow and glacier hydrology require physically sound hydrological models. The gap between the growing need for sustainable water resources management, low data availability and uncertain hydrological projections calls for new approaches. To close this gap, a modular modelling framework was developed to foster the use of complementary data sets in hydrological models. The framework enables a flexible combination of remote sensing and in situ data for model calibration and validation providing a multi-model and multi-input ensemble. The additional consideration of data regarding snow covered area, snow water equivalent and soil moisture allows for physically meaningful representations of key hydrological processes, even in the absence of a dense network of meteorological stations and river discharge gauges. Case studies in the European Alps (Inn and Adige/Etsch) and in Central Asia (Ala Archa and Karadarya) illustrate the high value of this approach for physically meaningful representations of the hydrological processes. Furthermore, a high impact of glacier retreat on future water availability was found for the highly glacierised basins of the Fagge river in the upper part of the Inn basin and the Ala Archa river.

Droughts are one of the most severe natural hazards, causing extensive economical losses and often regional conflicts due to water scarcity. Central Asia is especially sensitive to droughts because the region heavily depends on water for hydropower and irrigation. The drought warning system is needed for water-related political discussions and decision-makers in the region to mitigate potential water governance and make action plans for agriculture depending on the water availability. Central Asia has limited ground observations and additionally a lot of outdated measurement stations from the Soviet era, therefore the usage of remotely sensed data is beneficial in this region. Here we use several drought indices to analyse historical droughts and possibly predict future droughts. Already developed indices such as Drought Severity Index (DSI) based on GRACE and GRACE-FO total water storage anomaly (TWSA) data and a widely used Standard Precipitation Index (SPI), based only on precipitation, were calculated. Additionally, other climate factors were investigated and their statistical relationship with DSI and SPI, such as groundwater and particularly, snow cover and snow water equivalent (SWE). We applied near real-time monthly TWSA data from GRACE/GRACE-FO and MSWEP (Multi-Source Weighted-Ensemble Precipitation). Daily snow cover data from MODIS was used and Copernicus products for groundwater and SWE. In Central Asia, most of the water resources come from snow and glacier melt, coming from the Pamir, Tian Shan and Hindukush mountains, and the study was focused on assessing the snow cover and SWE in the winter months, particularly before the drought period. The goal was to understand and quantify the relationship between these climate factors and historical droughts based on DSI and SPI. Ultimately, the established relationship between climate factors and drought indices could provide an early warning for the upcoming arid and drought period. The obtained results show that drought conditions can be well identified using the remote sensing information in Central Asia. The results also show that the region is experiencing more frequent drought conditions in the last 5 years.

This paper investigates the hydro-climatic reasons behind the desiccation of the Hamun Lakes in the Iran-Afghanistan border. We analyzed changes in the flow of the Hirmand River (90 percent of the total inflow to the lakes) at the international border, and precipitation over this river’s sub-basin during 1960-2016 by calculating standardized indices for precipitation (SPI) and discharge (SDI). We applied Normalized Difference Spectral Indices using satellite images from 1987-2021 to observe monthly areal change of the lakes. The results show that the major cause of desiccation is upstream water regulation which severely reduced the Hirmand River inflow delivery to the lakes. Also, recently constructed reservoirs, near the lakes, compounded the effect of upstream water regulation to aggravate the situation. There is a discernible shift in the relation between the Hirmand River flow at the border and upstream precipitation before and after 2004. In 1960-2003, high Hirmand River inflows were expected due to high precipitation, while the flow declined after 2004 despite large amounts of upstream precipitation. Although a long period of drought from 1998-2004 decreased the lakes’ area, the lake system is primarily falling victim to anthropogenic flow reduction in the transboundary basin. Increased regulation of flows and use of water for irrigation in Afghanistan and Iran underscores the necessity of bilateral dialogues between the two countries to consider environmental flow of the lakes. The lakes’ shrinkage places socio-economic stress on an already-vulnerable region with public health implications as the exposed lake beds turn into major sources of dust storms.

The availability of water resources in Central Asia depends greatly on snow accumulation in the mountains of Tien-Shan and Pamir. It is important to precisely forecast water availability as it is shared by several countries and has a transboundary context. The impact of climate change in this region requires improving the quality of hydrological forecasts in the Naryn river basin. This is especially true for the growing season due to the unpredictable climate behavior. A real-time monitoring and forecasting system based on hydrological watershed models is widely used for forecast monitoring. The study’s main objective is to simulate hydrological forecasts for three different hydrological stations (Uch-Terek, Naryn, and Big-Naryn) located in the Naryn river basin, the main water formation area of the Syrdarya River. We used the MODSNOW model to generate statistical forecast models. The model simulates the hydrological cycle using standard meteorological data, discharge data, and remote sensing data based on the MODIS snow cover area. As for the forecast at the monthly scale, the model considers the snow cover conditions at separate elevation zones. The operation of a watershed model includes the effects of climate change on river dynamics, especially snowfall and its melting processes in different altitude zones of the Naryn river basin. The linear regression models were produced for monthly and yearly hydrological forecasts. The linear regression shows R2 values of 0.81, 0.75, and 0.77 (Uch-Terek, Naryn, and Big-Naryn, respectively). The correlation between discharge and snow cover at various elevation zones was used to examine the relationship between snow cover and the elevation of the study. The best correlation was in May, June, and July for the elevation ranging from 1000–1500 m in station Uch-Terek, and 1500–3500 m in stations Naryn and Big-Naryn. The best correlation was in June: 0.87; 0.76; 0.84, and May for the elevation ranging from 1000–3500 m in station Uch-Terek, and 2000–3000 m in stations Naryn and Big-Naryn. Hydrological forecast modeling in this study aims to provide helpful information to improve our under-standing that the snow cover is the central aspect of water accumulation.

Snow is an important hydrological component in Central Asia. The snowmelt contributes to about 50 % of total water formation in the region, depending on geographic conditions. Many hydro-meteorological phenomena such as floods or drought conditions can be triggered by snowmelt amounts in Central Asia. The amount of snow accumulation in the mountains of Tian-Shan and Pamir also defines the availability of water for summer months to be used for agricultural production or re-filling of reservoirs for energy production in the winter period. Thus, it is of high importance to better understand the seasonal variation of snow and if the over the global average climate warming in the region is affecting the processes related to snow accumulation and melt. In this study, we analyze 22 years of daily Moderate Resolution Imaging Radiometer (MODIS) snow cover data that was processed using the MODSNOW-Tool, including cloud elimination. Additionally, observed snow depth data from meteorological stations were used to estimate trends related to snow cover change. We used several parameters such as snow cover duration, snow depth, snow cover extent, and snowline elevation to analyze changes. We conducted this analysis in 18 river basins across the Central Asian domain with each river basin having different geographic conditions and the results show varying tendencies. In many river basins, a clear decrease of snow cover was found to be significant, whereas in some river basins also increase in the snow cover extent in particular months could be identified. We attributed the changes related to snow cover to available historical temperature and precipitation records from meteorological stations to better understand the driving forces. The results of this study indicate seasonal snow cover variations but also potential water shortages in particular months as well as water abundance in months where water demand is not high in Central Asia.

Central Asia is facing a water shortage due to the negative impacts of climate change and demographic development. Water resources in this region originate mainly in the mountains of Pamir and Tian-Shan due to snow-and glacier melt. However, a limited observation network is available in these mountain systems and many are malfunctioning. Thus, the region needs new innovative methods to forecast seasonal and sub-seasonal water availability to ensure better water resources management and mitigate hydro-meteorological risks.In this study, we present the results of our efforts for many years to develop a forecasting tool and implementation in the region. Since the region has limited observed meteorological data, we use primarily remote sensing data on snow cover for this purpose. We apply the MODIS snow cover data that is processed, including cloud removal, using the MODSNOW-Tool. We have applied this tool, which can be used to monitor snow cover in an operational mode and forecast water availability for the vegetation period but also for the monthly scale using the multiple linear regression method.Our results show that snow is important in most of the river basins and can also be used as a single predictor to forecast seasonal water availability. Especially, in remote areas with limited observations, this approach gives a possibility of forecasting water availability for different time period. Besides seasonal hydrological forecast, the MODSNOW-Tool was also used to forecast water availability for upcoming months. The validity of forecasts were tested against observed discharge for the last 20 years and mostly above 70 % verification was achieved. Additionally to remote sensing based snow cover data, observed meteorological information was also used as predictors and improved the validity of forecast models in some river basins.The implementation of the MODSNOW-Tool to improve the hydrological forecast was done for 28 river basins in Central Asia that are located in the territories of five post-Soviet countries Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. The MODSNOW-Tool was also implemented at the National Hydrometeorological Services (NHMS) of each post-Soviet country.

This paper investigates the hydrologic and water management reasons behind the desiccation of the Hamun Lakes in the Iran-Afghanistan border region. We analysed changes in Hirmand (or Helmand) River flow, the main tributary providing 70% of the lakes’ total inflow, and precipitation during 1960-2016 by calculating standardized indices for precipitation (SPI) and discharge (SDI). Also, we applied Normalized Difference Spectral Indices (NDSIs) using satellite images from 1987 to present to observe monthly areal change of the lakes. The transboundary water body is responding to changes in regional water management, which has severely reduced the lakes’ inflow. Upstream water regulation in Afghanistan coupled with reservoir construction on the Iranian side has caused nearly full desiccation of major parts of the lake system. There is a discernible shift in the relation between the Hirmand River flow at the international border and upstream precipitation over the lakes’ basin before and after 2004. From 1960 to 2003, high river flows were expected to feed the lakes due to high precipitation over the basin. However, the Hirmand River flow at the border declined after 2004 despite large amounts of upstream precipitation, including the largest recorded amounts, especially in the Hindu Kush mountains. Further, environmental water stress caused by anthropocentric water management in Iran by reservoir construction has impacted the area of the lakes. Although a long period of drought from 1998-2004, i.e. climatic driver, decreased the lakes’ area, the lake system is primarily falling victim to anthropogenic flow alterations in the transboundary river basin. The lakes’ shrinkage places socio-economic stress on an already-vulnerable region with important public health implications as the exposed lake beds turn into major sources of sand and dust storms.

The regional research network Water in Central Asia (CAWa) funded by the German Federal Foreign Office consists of 18 remotely operated multi-parameter stations (ROMPSs) in Central Asia. These stations were installed by the German Research Centre for Geosciences (GFZ) in Potsdam, Germany, in close cooperation with the Central-Asian Institute for Applied Geosciences (CAIAG) in Bishkek, Kyrgyzstan; the national hydrometeorological services in Tajikistan and Uzbekistan; the Ulugh Beg Astronomical Institute in Tashkent, Uzbekistan; and the Kabul Polytechnic University, Afghanistan. The primary objective of these stations is to support the establishment of a reliable data basis of meteorological and hydrological data especially in remote areas with extreme climate conditions for applications in climate and water monitoring in Central Asia. Up to now, 10 years of data have been provided for an area of scarce station distribution and with limited open-access data which can be used for a wide range of scientific or engineering applications. The data described in this paper are made publicly available with the digital object identifier (DOI) https://doi.org/10.5880/GFZ.1.2.2020.002 (Zech et al., 2020) or via the Sensor Data Storage System (SDSS) at http://sdss.caiag.kg (last access: 22 February 2021).

The alpine cryosphere, including snow, glaciers and permafrost, is critical to water management in the Aral Sea Basin (ASB) and larger Central Asia (CA) under the changing climate, as it stores large amounts of water in its solid forms. Most cryospheric components in the Aral Sea Basin are close to melting point, and hence very vulnerable to a slight increase in air temperature with significant consequences to long-term water availability and to water resources variability and extremes. Current knowledge about different components of the cryosphere and their connection to climate in the Basin and in the entire Central Asia region varies. While it is advanced in the topics of snow and glaciers, knowledge on permafrost is rather limited. Observed trends in runoff point in the direction of increasing water availability in July and August at least until mid-century and increasing possibility for water storage in reservoirs and aquifers. However, eventually this will change as glaciers waste away. Future runoff may change considerably after mid-century and start to decline if not compensated by increasing precipitation. Cryosphere monitoring systems are the basis for sound estimates of water availability and water-related hazards associated with snow, glaciers and permafrost. They require a well-distributed observational network for all cryospheric variables. Such systems need to be re-established in the Basin after the breakup of the Soviet Union in the early 1990s. This process is slowly emerging in the region. Collaboration between local operational hydro-meteorological services and the academic sector, and with international research networks, may improve the observational capabilities in high-mountain regions of CA in general and in the ASB in particular.