Land is a limited yet vital resource, which is fundamental for food security and national stability. Ensuring its effective use requires modern tools to support informed decision making. This study evaluates land suitability for irrigation in the Kyzylorda region of Kazakhstan, with the objective of identifying areas appropriate for crop expansion. A multidimensional approach was employed, integrating soil properties, topography, proximity to water sources, and existing land use patterns, with suitability classes defined according to the Food and Agriculture Organization’s framework. The Analytic Hierarchy Process, based on pair-wise comparisons and Saaty’s scale, was used to assign weights to each factor. The findings reveal that 30% of highly suitable land and 80% of suitable land remain uncultivated. Conversely, 10% of the current cropland is located in marginally suitable area—likely a result of historical land use decisions or the limited availability of optimal land. This research responds to the specific challenges of arid climates and water scarcity by providing a decision-support tool that promotes sustainable land use, strengthens food security, and encourages responsible land management.

Understanding the interactions among mountain snow resources, river discharge, and irrigated agriculture is essential for sustainable water management in transboundary river basins of Central Asia. This study evaluated the relationships among high-elevation snow conditions, hydroclimatic factors, summer river discharge, and agricultural vegetation dynamics in the transboundary Chu River Basin during 2001–2025 using MODIS snow-cover, snow-persistence, and NDVI products together with meteorological and hydrological observations. The results showed that snow cover was widespread during winter but declined substantially during spring, while the Mann–Kendall analysis indicated that no statistically significant trend predominated across most of the high-elevation snow zone, although significant decreases in snow persistence occurred across 12.83% of the analyzed area. For summer cropland NDVI, 61.15% of the analyzed area showed no significant trend, while significant decreases and increases occurred across 26.24% and 12.62%, respectively. Spring temperature exhibited a significant negative correlation with snow persistence (r = −0.64, p < 0.001), indicating the sensitivity of mountain snow resources to warming conditions. Summer river discharge was strongly correlated with summer precipitation (r = 0.77, p < 0.0001), vegetated cropland area (r = 0.76, p < 0.0001), and mean summer NDVI (r = 0.69, p < 0.001). In addition, snow persistence showed a significant positive relationship with summer river discharge (r = 0.63, p < 0.001), vegetated cropland area (r = 0.52, p = 0.008), and mean summer NDVI (r = 0.42, p = 0.036), suggesting a linkage between high-elevation snow conditions, seasonal water availability, and downstream agricultural vegetation. Canonical correlation analysis further revealed a strong multivariate association between hydroclimatic and hydroagricultural variables (Rc = 0.900, Rc² = 0.810). Overall, the findings indicate significant statistical linkages among climate conditions, snow resources, river discharge, and irrigated agriculture, providing a scientific basis for water-resource management and climate-change adaptation in transboundary mountain-fed basins.

Sustainable expansion of irrigated agriculture requires the identification of areas with favorable biophysical conditions while also considering water-resource and infrastructure constraints. This study assessed the biophysical land suitability for potential irrigation development within the Ertis Water Management Basin (WMB), Kazakhstan, using an integrated remote sensing, geographic information system (GIS), and Analytic Hierarchy Process (AHP) framework. Four criteria—slope, normalized difference vegetation index (NDVI), land use/land cover (LULC), and soil texture—were standardized into five suitability classes and integrated using an AHP-based weighted overlay. The derived weights were 0.51 for slope, 0.26 for NDVI, 0.14 for LULC, and 0.09 for soil texture, with a consistency ratio of approximately 1%. The resulting assessment classified approximately 10% of the basin as highly suitable (S1), 43% as suitable (S2), 18% as moderately suitable (S3), 2% as marginally suitable (S4), and 27% as not suitable (NS), indicating that approximately 53% of the basin falls within the two highest suitability classes. Regional analysis showed that substantial shares of suitable land are concentrated in Pavlodar and Abay Regions, while suitable areas were identified across a wide range of distances from the river network. These findings indicate considerable biophysical potential for irrigation development across the Ertis WMB, with suitable areas occurring both near and farther from the river network. The resulting suitability map provides a spatial basis for identifying areas where more detailed assessments of water availability, irrigation infrastructure, and development feasibility can subsequently be undertaken.