Sound water resources planning and management requires adequate data with sufficient spatial and temporal resolution. This is especially true in the context of irrigated agriculture, which is one of the main consumptive users of the world’s freshwater resources. Existing remote sensing methods for the management of irrigated agricultural systems are often based on empirical cropland data that are difficult to obtain, and that put into question the transferability of mapping algorithms in space and time. Here we implement an automatic irrigation mapping procedure in Google Earth Engine that uses surface reflectance satellite imagery from different sensors. The method is based on unsupervised training of a pixel-by-pixel classification algorithm within image regions identified through unsupervised object-based segmentation, followed by multi-temporal image analysis to distinguish productive irrigated fields from non-productive and non-irrigated areas. Ground-based data are not required. The final output of the mapping algorithm are monthly and annual irrigation maps (30 m resolution). The novel method is applied to the Central Asian Chu and Talas River Basins that are shared between upstream Kyrgyzstan and downstream Kazakhstan. We calculate the development of irrigated areas from 2000 to 2017 and assess the classification results in terms of robustness and accuracy. Based on seven available validation scenes (in total more than 2.5 million pixels) the classification accuracy is 77–96%. We show that on the Kyrgyz side of the Talas basin, the identified increasing trends over the years are highly significant (23% area increase between 2000 and 2017). In the Kazakh parts of the basins the irrigated acreages are relatively stable over time, but the average irrigation frequency within Soviet-era irrigation perimeters is very low, which points to a poor physical condition of the irrigation infrastructure and inadequate water supply.

This study uses a new dataset on gauge locations and catchments to assess the impact of 21st-century climate change on the hydrology of 221 high-mountain catchments in Central Asia. A steady-state stochastic soil moisture water balance model was employed to project changes in runoff and evaporation for 2011–2040, 2041–2070, and 2071–2100, compared to the baseline period of 1979–2011. Baseline climate data were sourced from CHELSA V21 climatology, providing daily temperature and precipitation for each subcatchment. Future projections used bias-corrected outputs from four General Circulation Models under four pathways/scenarios (SSP1 RCP 2.6, SSP2 RCP 4.5, SSP3 RCP 7.0, SSP5 RCP 8.5). Global datasets informed soil parameter distribution, and glacier ablation data were integrated to refine discharge modeling and validated against long-term catchment discharge data. The atmospheric models predict an increase in median precipitation between 5.5% to 10.1% and a rise in median temperatures by 1.9 °C to 5.6 °C by the end of the 21st century, depending on the scenario and relative to the baseline. Hydrological model projections for this period indicate increases in actual evaporation between 7.3% to 17.4% and changes in discharge between + 1.1% to –2.7% for the SSP1 RCP 2.6 and SSP5 RCP 8.5 scenarios, respectively. Under the most extreme climate scenario (SSP5-8.5), discharge increases of 3.8% and 5.0% are anticipated during the first and second future periods, followed by a decrease of -2.7% in the third period. Significant glacier wastage is expected in lower-lying runoff zones, with overall discharge reductions in parts of the Tien Shan, including the Naryn catchment. Conversely, high-elevation areas in the Gissar-Alay and Pamir mountains are projected to experience discharge increases, driven by enhanced glacier ablation and delayed peak water, among other things. Shifts in precipitation patterns suggest more extreme but less frequent events, potentially altering the hydroclimate risk landscape in the region. Our findings highlight varied hydrological responses to climate change throughout high-mountain Central Asia. These insights inform strategies for effective and sustainable water management at the national and transboundary levels and help guide local stakeholders.

In today’s intrinsically connected world, the Water–Food–Energy–Climate Nexus (WFEC Nexus) concept provides a starting point for informed and transparent decision-making based on the trade-offs and synergies between different sectors, including aquatic ecosystems, food security, energy production, and climate neutrality. The WFEC Nexus approach is particularly applicable in regions requiring transboundary water management, such as Central Asia. Unfortunately, this region with unevenly distributed water resources—consisting of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan—is characterized by data scarcity, which limits informed decision-making. However, open-source geodata is becoming increasingly available. This paper aims to fill Central Asia’s WFEC Nexus data gap by providing an overview of key data. We collected geodata through an integrated survey of stakeholders and researchers, stakeholder consultation, and literature screening. Sixty unique datasets were identified, belonging to one of six thematic categories: (1) climate, (2) hydrology, (3) geography and topography, (4) geomorphology, (5) ecology, and (6) anthropogenic uses. For each dataset, a succinct description, including a link to the online source, is provided. We also provide possible applications of using the presented datasets, demonstrating how they can assist in conducting various studies linked to the WFEC Nexus in Central Asia and worldwide.

The presence of ephemeral ponds and perennial lakes in the Sudano-Sahelian region of West Africa is strongly variable in space and time. Yet, they have important ecological functions and societies are reliant on their surface waters for their lives and livelihoods. It is essential to monitor and understand the dynamics of these lakes to assess past, present, and future water resource changes. In this paper, we present an innovative approach to unravel the sediment and water balance of Lac Wégnia, a small ungauged lake in Mali near the capital of Bamako. The approach uses optical remote sensing data to identify the shoreline positions over a period of 22 years (2000–2021) and then attributes water surface heights (WSHs) to each observation using the lake bathymetry. We then present a novel methodology to identify and quantitatively analyze deposition and erosion patterns at lakeshores and in lake beds. The method therefore represents a significant advancement over previous attempts to remotely monitor lakes in the West African drylands, since it considers not only changes in water depth to explain recent declining trends in lake areas, but also changes in the storage capacity. At Lac Wégnia, we recognize silting at the tributaries to the lake, but overall, erosion processes are dominant and threaten the persistence of the lake because of progressive erosion through the natural levee at the lake outlet. This factor contributes 66 %±18 % to the decreasing WSH trend, while 34 %±18 % of the dry-season lake level changes are explained by increasing evaporation from the lake and by possibly falling groundwater tables. Due to the decreasing reservoir capacity of the lake, WSHs are declining even in the wet season in spite of positive rainfall patterns.