Surface Water Changes Research Articles

Assessing of monthly surface water changes impact on thermal human discomfort in Baghdad

In urban areas, surface water bodies play an important role in mitigating thermal discomfort, which is mainly caused by increasing air temperatures. Based on daily temperature and relative humidity data recorded by the Baghdad weather station for the two years 2018 and 2021, the monthly human discomfort index was calculated and then combined with monthly surface water areas extracted by a modified normalized difference water index using Sentinel-2A satellite imagery for the same period. The results show that the winter and most spring months of these years have no discomfort, and the summer months (July and August) in 2021 have the highest discomfort with severe thermal stress due to the large deficit in rainfall events. The monthly relationship between urban water surfaces and the level of the discomfort index was also studied, which was non-linear and followed the exponential decay function. This means that as the amount of surface water increased, the levels of the discomfort index decreased exponentially until no discomfort conditions existed.

Multi-Decadal Impact of Mine Waters in Przemsza River Basin, Upper Silesian Coal Basin, Southern Poland

Anthropogenic increases in the salinity of surface waters are referred to as secondary salinization. In surface waters, salinity levels can vary significantly due to various natural and anthropogenic influences. This article presents multi-decadal observations of changes in surface water salinity in the highly industrialized region in southern Poland. The case study of the Przemsza River is an example of the significant impacts of industrial, mainly coal mining, activities that have changed the chemical and biological characteristics of water bodies. The presented research revealed that impacts on salinity and water body status due to mining discharges will be difficult or even impossible to restore, considering the process of transition of the coal sector. In the Przemsza river basin, almost 42% less mine water was discharged in 2023 than in 1991. Parallelly, the salinity of mine waters discharged from deeper levels of active coal mines has increased due to the geochemical gradient (the total load of chlorides and sulfates was 534.8 MgCl−+SO42− per day in 1991, while in 2023 the total salinity load was 480.1 MgCl−+SO42− per day). Moreover, of the 19 active mine water discharges in 1991, only 11 remain in 2023, while the observed salinity of surface water in the Przemsza watershed increased rapidly from an average of 2000 µS·cm−1 to 6700 µS·cm−1 due to the significant drought and adverse hydrological conditions, which represent low flows never observed before (three times lower flows in the mouth of the Przemsza River in the period 2021–2023 compared to the previous decades 1991–2020). Impacts on water bodies will continue to occur regardless of mining activities in the area—it should be noted that at the end of exploitation, mine water rebound and flooding do not automatically reduce long-lasting impacts on surface waters. Therefore, salinization is a growing threat that might be amplified by climate change. While industrial and urban impacts on surface water change its characteristics, the future challenge of proper water management with a holistic approach is necessary with proper monitoring data collection and river flow-dependent and surface water salinity-dependent discharge of wastewater in the river basin.

Satellite observations of surface water dynamics and channel migration in the Yellow River since the 1980s

Study regionthe Yellow River (YR) in China. Study focusDue to climate change and human activities, YR channel morphology has undergone significant spatiotemporal variations. Yet, a comprehensive understanding of channel migration in YR and its driving factors remains unclear. Here, we developed a multi-index water extraction method to track the changes in surface water and river channel migration of YR based on Landsat imagery since the 1980s. New hydrological insights for the regionWe find that the average surface water area of YR over the past four decades is 4013 km2, with 73.5 % of permanent surface water. Notably, the surface water extent has experienced a 9 % increase since the 1980s, while the river channel has undergone a 12.2 % decrease. The YR channel’s centerline exhibits diverse change patterns across the entire basin, which can be broadly categorized into six types ranging from unchanged to reverse migration. We identify that climate, particularly temperature and precipitation, contributed 71 % of channel changes in the upper reaches, while 65 % of changes in the lower reaches are from human activities, including reservoir operations and water management policies. Our results unveil the variations in water extent and channel migration of the YR, offering new insights into the interactions between channel migration and climate change and human activities in the YR over the past four decades.

Landsat data reveal lake deoxygenation worldwide

Dissolved oxygen (DO) is a fundamental requirement for the survival of aquatic organisms, which plays a crucial role in shaping the structure and functioning of aquatic ecosystems. However, the long-term DO change in global lakes remains unknown due to limited available data. To address this gap, we integrate Landsat data and geographic features to develop DO estimation models for global lakes using machine learning approaches. The results demonstrated that the trained random forest (RF) model has better performance (R2 = 0.72, and RMSE = 1.24 mg/L) than artificial neural network (ANN) (R2 = 0.66, and RMSE = 1.39 mg/L), support vector machine regression (SVR) (R2 = 0.62, and RMSE = 1.45 mg/L) and extreme gradient boosting (XGBoost) (R2 = 0.72, and RMSE = 1.29 mg/L). Then, we used the trained RF model to reveal the DO long-term (1984–2021) change in surface water (epilimnetic) of 351,236 global lakes with water area ≥ 0.1 km2. The results show that the average epilimnetic DO concentration of global lake was 9.72 ± 1.07 mg/L, with higher DO in the polar regions (latitude > 66.56 °) (10.87 ± 0.54 mg/L) and lower in the equatorial regions (−5 ° < latitude < 5 °) (6.29 ± 0.63 mg/L). We also find widespread deoxygenation in surface water of global lakes, with a rate of − 0.036 mg/L per decade. Meanwhile, the number of lakes and surface area that experiencing DO stress are continuously increasing, with rate of 39 and 212.85 km2, respectively. Our results offer a comprehensive dataset of DO variation spanning nearly 40 years, furnishing valuable insights for formulating effective management strategies, and enhancing the maintenance of the health of aquatic ecosystems.

Economic valuation of changes in ecosystem services of 77 Ramsar wetlands in West Asia over 37 years (1984–2021)

Summary In the West Asia region, the vulnerability of Ramsar Convention wetlands due to unsustainable utilization driven by water scarcity continues to grow. Here, a global surface water product generated by the European Joint Research Centre was used to assess changes in surface water in 77 wetlands listed under the Ramsar Convention over a 37-year period (1984–2021). By combining this product with a quantitative valuation model, estimates were made of the economic value of the ecosystem services provided by these wetlands, enabling the determination of the economic losses resulting from any reduction in surface water. We show that 20% (7550 km2) of permanent surface waters in Ramsar sites have disappeared or are no longer classified as permanent. Based on this, USD 106 billion of the economic value of wetlands ecosystem services have been lost. Additionally, 33% (12 100 km2) of seasonal surface waters in these wetlands have experienced a decrease in area. Iran and Iraq account for 90% of water losses, primarily in 34 wetlands (30 in Iran and 4 in Iraq). These findings underscore the urgent need for water management policies and conservation strategies in the West Asia region.

Cloud computing and spatial hydrology for monitoring the Buyo and Kossou reservoirs in Côte d'Ivoire

The Buyo and Kossou reservoirs are crucial for water supply, agricultural irrigation, and hydroelectric power generation in Côte d'Ivoire. However, climate change threatens the stability and availability of these water resources by increasing rainfall variability, extending drought periods, and intensifying extreme weather events. These challenges underscore the need for precise and continuous monitoring of water levels and surface areas to ensure sustainable management. Due to the scarcity of gauging stations, the objective of this study is to leverage cloud computing technologies along with altimetric and satellite data, for effective reservoir monitoring. Tools like the EO-Africa program's Innovation Lab and Google Earth Engine (GEE), along with advanced image processing software such as PyGEE-SWToolbox and AlTis, were used to process large datasets from the Sentinel-1, Sentinel-2, and Sentinel-3 satellites. These satellites delivered extensive, high-resolution imagery and altimetric data, crucial for monitoring changes in the reservoirs. The processed data were validated with in-situ measurements, yielding a Root Mean Square Error (RMSE) of less than 0.4 m and a correlation coefficient exceeding 0.90. The results highlighted water surface and level changes from 2016 to 2022, with downward trends and seasonal variations closely aligning with in-situ measurements. The study also revealed that the relationship between water levels and surface areas is influenced by both precipitation and the hydrological regimes of the Bandama and Sassandra rivers, demonstrating the complexity of water dynamics in these reservoirs. This research emphasizes the effectiveness of integrating spatial hydrology with cloud computing tools for fast and accurate monitoring of large reservoir. The use of these advanced technologies provides near real-time, reliable, and easily accessible data, offering a significant advantage for water resource management in Côte d'Ivoire.

Seasonally inundated area extraction based on long time-series surface water dynamics for improved flood mapping

Accurate extraction of Seasonally Inundated Area (SIA) is pivotal for precise delineation of Flood Inundation Area (FIA). Current methods predominantly rely on Water Inundation Frequency (WIF) to extract SIA, which, due to the lack of analysis of dynamic surface water changes, often yields less accurate and robust results. This significantly hampers the rapid and precise mapping of FIA. In the study, based on the Harmonic Models constructed from Long Time-series Surface Water (LTSW) dynamics, an SIA extraction approach (SHM) was introduced to enhance their accuracy and robustness, thereby improving flood mapping. The experiments were conducted in Poyang Lake, a region characterized by active hydrological phenomena. Sentinel-1/2 remote sensing data were utilized to extract LTSW. Harmonic analysis was applied to the LTSW dataset, using the amplitude terms in the harmonic model to characterise the frequency of variation between land and water for the surface units, thus extracting the SIAs. The results reveal that the harmonic model parameters are capable of portraying SIA. In comparison to the commonly used WIF thresholding method for SIA extraction, the SHM approach demonstrates superior accuracy and robustness. Leveraging the SIA extracted through SHM, a higher level of accuracy in FIA extraction is achieved. Overall, the SHM offers notable advantages, including high accuracy, automation, and robustness. It offers reliable reference water extents for flood mapping, especially in areas with active and complex hydrological dynamics. SHM can play a crucial role in emergency response to flood disasters, providing essential technical support for natural disaster management and related departments.

Drivers of Daily Water Level Fluctuation of Shallow Groundwater in the Inner Delta of the River Danube

Groundwater flow systems are influenced by the changes in surface waters as well as climatic factors. These teleconnections significantly increase in cases of extreme weather conditions. To prepare and mitigate the effect of such phenomena, the background factors that create and influence natural processes must be recognized. In the present study, 94 shallow groundwater (SGW) wells’ water level time series were analyzed in the inner delta of the River Danube (Europe) the Szigetköz region to explore which factors contribute to the development of diurnal periodicity of SGW and what its drivers are. The relationship between surface meteorological processes and SGW dynamics in the Szigetköz region was investigated using hourly data from monitoring wells. Hourly water temperature data exhibited weak correlations with meteorological parameters. However, daily averaged data revealed stronger correlations, particularly between SGW levels and air temperature and potential evapotranspiration. Diurnal periodicity in SGW fluctuations correlated strongly with potential evapotranspiration. The study also demonstrated the role of capillary fringe dynamics in linking surface evapotranspiration with SGW fluctuations. Changes in groundwater levels, even small, can significantly affect soil moisture, vegetation, and ecosystem functioning, highlighting the sensitivity of the unsaturated zone to SGW fluctuations driven by surface processes.

A Surface Water Extraction Method Integrating Spectral and Temporal Characteristics

Remote sensing has been applied to observe large areas of surface water to obtain higher-resolution and long-term continuous observation records of surface water. However, limitations remain in the detection of large-scale and multi-temporal surface water mainly due to the high variability in water surface signatures in space and time. In this study, we developed a surface water remote sensing information extraction model that integrates spectral and temporal characteristics to extract surface water from multi-dimensional data of long-term Landsat scenes to explore the spatiotemporal changes in surface water over decades. The goal is to extract open water in vegetation, clouds, terrain shadows, and other land cover backgrounds from medium-resolution remote sensing images. The average overall accuracy and average kappa coefficient of the classification were verified to be 0.91 and 0.81, respectively. Experiments applied to China’s inland arid area have shown that the method is effective under complex surface environmental conditions.

Использование данных дистанционного зондирования Земли для анализа межгодовой динамики площади Тениз-Коргалжынских озер

The Teniz-Korgalzhyn lake system has the status of an international Ramsar site and is the most important ornithological center in Central Asia. It includes more than 60 shallow fresh- and salt water bodies among which the largest salt ones are Big and Small Teniz and a group of fresh Korgalzhyn lakes. Fluctuations in water levels there and changes in their area determine the degree of favorable habitat conditions for key ornithological species. A quantitative assessment of the water levels’ interannual dynamics in 2006–2019 was carried out using remote sensing and geoinformation mapping methods. The choice of the methodology to use multispectral data for water surface area study is substantiated. Calculations are made, and a corresponding map of the bodies’ water surface area dynamics is compiled. Four periods with multidirectional dynamics of changes in it were identified. They are caused by both natural and anthropogenic factors and differ in intensity for fresh and salt lakes. Fluctuations of fresh Korgalzhyn ones are mostly associated with natural and climatic impact. Salty Big and Small Teniz are characterized by more significant changes in water surface the area, caused primarily by human activities, including periodic breakthroughs of retaining dams. Maximum deviations towards the increase were noted for fresh ones in 2016–2019, and for saline in 2014–2019. The observed decrease in the total area of the water surface in the period from 2006 to 2013 was mainly associated with the Small and Big Teniz

Concepts for compensation of wave effects when measuring through water surfaces in photogrammetric applications

Abstract. A common problem when imaging and measuring through moving water surfaces is the quasi-random refraction caused by waves. The article presents two strategies to overcome this problem by lowering the complexity down to a planer air/water interface problem. In general, the methods assume that the shape of the water surface changes randomly over time and that the water surface moves around an idle-state (calm planar water surface). Thus, moments at which the surface normal is orientated vertically should occur more frequently than others should. By analysing a sequence of images taken from a stable camera position these moments could be identified – this can be done in the image or object space. It will be shown, that a simple median filtering of grey values in each pixel position can provide a corrected image freed from wave and glint effects. This should have the geometry of an image taken through calm water surface. However, in case of multi camera setups, the problem can be analysed in object space. By tracking homological underwater features, sets of image rays hitting accidently horizontal orientated water surface areas can be identified. Both methods are described in depth and evaluated on real and simulated data.

ON DEVELOPMENT OF OIL FIELDS

Using a literature review, the article concludes that the displacement of oil by surfactants affects interrelated factors: by affecting the surface tension at the oil-water-rock interface, SAM adsorption occurs on the surface of pore channels, rock surface water and oil wetting changes, the oil layer is broken up and washed from the rock surface, the dispersion of oil in water stabilizes, the phase permeability of the porous medium changes, and finally, the forced displacement of oil by the water phase occurs. Keywords: gravity, elastic, capillary forces, adhesion, adsorption.

Multi-factor weighted image fusion method for high spatiotemporal tracking of reservoir drawdown area and its vegetation dynamics

Reservoir drawdown areas (RDAs) with distinct dry-wet cycles and vegetation dynamics have emerged as significant hotspots for carbon-related activities. However, high-resolution spatiotemporal tracking of the variations and vegetation dynamics of RDAs remains challenging because they often change dramatically and are controlled by both human activities and natural factors. Herein, a modified image fusion method was proposed to capture rapid variations in RDAs by integrating impact factor information into the analysis. The capability of the proposed method was tested in the Danjiangkou (DJK) Reservoir as it is the largest artificial freshwater lake in Asia with a highly variable RDA, since it is surrounded by gently sloping plains or hills. The results showed that the modified workflow produced reliable predictions (r=0.83,RMSE=0.097) compared with the original Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) workflow (r=0.60,RMSE=0.195), demonstrating improved capability for mapping water surface changes and vegetation dynamics. Using the proposed method, the 15-d variations in the RDA were derived from 2013 to 2022 with a 30-m resolution. The interannual maximum RDA was estimated to be 278 km2 after the dam was elevated in 2013. The Normalized Difference Vegetation Index (NDVI) decreased as inundation frequency (IF) increased. Mean NDVI in the growing season (May–October) decreased by 0.109 (17.6 %) and 0.156 (26.0 %) under 30 %–40 % IF and 60 %–70 % IF, respectively, compared with the vegetation under 0 %–10 % IF, which was referred as “natural” vegetation considering its rare inundation. Moreover, the mean growing season length decreased to only 63 and 19 d for 30 %–40 % IF and 60 %–70 % IF, respectively. Furthermore, 77.3 % of the RDA exhibited a decrease in NDVI, whereas 22.7 % showed an unusual increase, possibly due to the selection of dominant species well-adapted to inundation during vegetation succession. Overall, this study not only proposed a new method for the high spatiotemporal monitoring of RDAs, but also highlighted the importance of variations and vegetation dynamics within RDAs for accurate estimation of their carbon budgets.

Surface water and geomorphological changes of the Blue Nile dynamics associated with the Grand Ethiopian Renaissance Dam (GERD): a multi-temporal analysis

ABSTRACT The Blue Nile River is an indispensable source of livelihood for over 90 million people in Ethiopia, Sudan, and Egypt. Recently, Ethiopia has constructed the Grand Ethiopian Renaissance Dam (GERD), the largest in Africa, which could potentially alter the geomorphology of the river regime, and impact downstream countries. This study offers the first comprehensive analysis of the geomorphological alterations and water surface coverage changes of the Blue Nile River due to the construction and operation of the GERD. For this, we use multi-temporal Landsat 5 and Sentinel-2 satellite imagery spanning the period from 2009 to 2022, supplemented with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation products to discern the potential influence of climate variability on the observed changes. The findings are examined using the Before-After-Control-Impact (BACI) model, revealing significant fluctuations in the water surface coverage area across different sections of the river, particularly following the filling stages of the GERD. An analysis at every 10 km is performed to assess the extent of GERD’s influence on the Blue Nile, which indicates no notable alterations in surface area or geomorphology within the first 30 km of downstream river flow. The study also determines that changes from 2011 to 2018 are primarily associated with the upgrade activities of the Roseires Dam, while alterations along the Blue Nile from 2018 to 2019 are linked to climate variability. The observed changes between 2020 and 2022 can largely be attributed to the filling stages of the GERD. Notably, these changes are most pronounced in the water retaining areas upstream of the GERD dam. The results also show that Roseires Dam could play a crucial role as a buffer, mitigating potential impacts imposed by the GERD, and serving as a conduit between the GERD and downstream communities.

Changes in Surface and Terrestrial Waters in the China–Pakistan Economic Corridor Due to Climate Change and Human Activities

Changes in Surface and Terrestrial Waters in the China–Pakistan Economic Corridor Due to Climate Change and Human Activities

Impact of Ecological Water Transfer Project on Vegetation Recovery in Dried-Up Kongque River, Northwest China

The ecological water transfer project (EWTP) plays a pivotal role in reinstating the flow of dried-up rivers in arid regions, promoting river connectivity and vegetation resurgence. An essential facet in ensuring the efficacious execution of the EWTP lies in determining the optimal duration of irrigation to facilitate vegetation recovery. Nevertheless, comprehensive reports concerning the EWTP process in arid river ecosystems are scarce. Here, we leverage remote sensing imagery to assess changes in surface water and vegetation dynamics before and after the implementation of the EWTP in a dried-up river. The results show that before the EWTP (1987–2016), riparian vegetation’s mean normalized difference vegetation index (NDVI) decreased from 0.181 to 0.066. After EWTP (2017–2022), the river’s flow was restored for a distance of 347 km. This restoration resulted in the formation of 81.47 km2 of intermittent water bodies along the river. The mean NDVI increased from 0.065 to 0.093. As irrigation duration increased, the NDVI growth rate exhibited an initial rise followed by a subsequent decline, reaching its peak growth rate by irrigating for 18 days per year. The regions showing increased NDVI values exhibited a pronounced spatial correlation with the areas subjected to water transfer. These improvements in NDVI were predominantly concentrated on both sides of the river within a 550 m range. Interestingly, as moves farther away from the river, the growth rate of NDVI exhibited an initial increase followed by a subsequent decline. The pinnacle of NDVI growth rate materialized at a distance of 40–50 m from the river. These findings reveal the response characteristics of desert riparian vegetation to EWTP, providing valuable insights for selecting appropriate water transfer timing in future EWTP.

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

A comprehensive analysis of surface water changes in Dau Tieng Lake, Vietnam, across dry and wet seasons

This study investigates the seasonal dynamics of Dau Tieng Lake near Tay Ninh, Vietnam, employing remote sensing technology, specifically the Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI). Focused on the period from 2016 to 2020, the research aims to detect changes in the water surface area during dry and wet seasons. As the largest reservoir for water supply and irrigation in Vietnam, Dau Tieng Lake faces operational challenges due to seasonal rainfall variations and increasing water demands. The results reveal significant differences in water body areas between wet and dry seasons, with MNDWI proving more effective in capturing these variations compared to NDVI. The study concludes that the seasonal change in Dau Tieng Lake is 21.76 km2 (NDVI) and 25.55 km2 (MNDWI), emphasizing the latter's superiority in accurately detecting water body changes. These findings contribute valuable insights for improved reservoir management and sustainable utilization of water resources in the region.

Mapping Water Bodies and Wetlands from Multispectral and SAR Data for the Cross-Border River Basins of the Polish–Ukrainian Border

Using remote sensing data to accurately record water surface changes over large areas is crucial in monitoring water resources. However, mapping water surfaces from remote sensing data has its advantages and disadvantages. This study presents a method for mapping water surfaces and wetlands based on Sentinel-1/-2 data over a study area of more than 26,000 km2 in three river basins, the Bug, Dniester, and San, located along the Polish–Ukrainian border. To achieve this goal, an image processing algorithm with additional options was developed (special filters, type classification, and post-classification), which minimized the shortcomings and increased the accuracy of the method. As a result, by using optical and radar data, it was possible to create maps of water bodies in the study area in the driest month of the year from 2018 to 2021. The results were evaluated numerically and graphically. The accuracy of the method was assessed using the Kappa coefficient. For optical data, the lowest value was 76.28% and the highest was 88.65%; for radar data, these values were 87.61% and 97.18%, respectively. When assessing accuracy, the highest values were achieved for overall accuracy (OA), with a maximum of 0.95 (for SAR) and 0.91 (for optical data). The highest values were in user accuracy (UA), with a maximum value of 1 for both SAR and optical data.

Farmers on the front line: Perceptions, practices and discrepancies from the Aral Sea's Karakalpakstan and Khorezm regions

AbstractUndesirable changes in surface water and groundwater resources and land quality for biophysical and institutional reasons will further endanger the livelihoods of people in Central Asia. The farmers' understanding of these problems and the adaptation and solution strategies they opt for are the critical variables in devising relevant policies. Our findings captured significant disparities between farmer‐perceived water shortages and officially documented water availability, as well as soil salinity discrepancies. Farmers' coping strategies, including crop alterations and water‐saving measures, often lead to trade‐offs, such as reduced crop yields. The study highlights the need to consider farmer perceptions and practices along with official data when designing policies. Effective policymaking must consider this dynamic interplay and the multifaceted challenges faced by farmers in these vulnerable Aral Sea regions.