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(A) Elevation map of South Asia. (B) Land cover types in the study area as generated with the MODIS MCD12 dataset.
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The substantial reliance of South Asia (SA) to rain-based agriculture makes the region
susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered th...
Contexts in source publication
Context 1
... study focuses on SA, which is situated between 5 • to 40 • N and 60 • to 100 • E ( Figure 1A). This area has a diverse range of climatic zones, including tropical, sub-tropical, mountainous, humid, alpine, dry land, and desert areas with bimodal rainfall (summer and winter monsoon) regimes. ...
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... Asia has a total land area of 5.2 million km 2 [10]. Figure 1A demonstrates the typical elevation above sea level and the overall geographical location, while Figure 1B indicates MODIS-based land cover for SA. ...
Context 3
... Asia has a total land area of 5.2 million km 2 [10]. Figure 1A demonstrates the typical elevation above sea level and the overall geographical location, while Figure 1B indicates MODIS-based land cover for SA. ...
Context 4
... study focuses on SA, which is situated between 5 • to 40 • N and 60 • to 100 • E ( Figure 1A). This area has a diverse range of climatic zones, including tropical, sub-tropical, mountainous, humid, alpine, dry land, and desert areas with bimodal rainfall (summer and winter monsoon) regimes. ...
Context 5
... Asia has a total land area of 5.2 million km 2 [10]. Figure 1A demonstrates the typical elevation above sea level and the overall geographical location, while Figure 1B indicates MODIS-based land cover for SA. ...
Context 6
... Asia has a total land area of 5.2 million km 2 [10]. Figure 1A demonstrates the typical elevation above sea level and the overall geographical location, while Figure 1B indicates MODIS-based land cover for SA. ...
Context 7
... study focuses on SA, which is situated between 5 • to 40 • N and 60 • to 100 • E ( Figure 1A). This area has a diverse range of climatic zones, including tropical, sub-tropical, mountainous, humid, alpine, dry land, and desert areas with bimodal rainfall (summer and winter monsoon) regimes. ...
Context 8
... Asia has a total land area of 5.2 million km 2 [10]. Figure 1A demonstrates the typical elevation above sea level and the overall geographical location, while Figure 1B indicates MODIS-based land cover for SA. ...
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... However, few stud- ies have employed remote sensing techniques to investigate the spatio-temporal dynamics of rangeland degradation specifically in the understudied context of Bhakkar District. This study embarks on an exploration of rangeland degradation issues, with a specific focus on Bhakkar District in the Punjab province of Pakistan, employing advanced remote sensing techniques and key vegetation indices ( Aslam et al. 2024b ;Sajjad et al. 2023 ;Zhao et al. 2024 ;Tian et al. 2020 ;Shahzaman et al. 2021 ). This study integrates participatory mapping, geographic information systems (GIS), and remote sensing to assess land use and land cover (LULC) changes in Chunati Wildlife Sanctuary, Bangladesh, highlighting the crucial role of local community engagement and advanced spatial technologies in monitoring environmental transformations Zafar et al. 2021 ;Yu et al. 2021 ;Ren et al. 2022 ). ...
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Rangelands globally face escalating threats from overgrazing, land conversion, and climate change. This study investigates spatio-temporal rangeland degradation patterns in Pakistan's Bhakkar District, a semi-arid region dependent on fragile pastoral ecosystems, over the past four decades at 10-yr intervals (1990, 20 0 0, 2010, 2020). Remote sensing offers a valuable tool for monitoring these vast yet understudied dry-land environments. We employed Landsat satellite data and machine learning algorithms to map land cover change and analyze vegetation health indicators. The random forest classifier achieved high accuracy (94%) in delineating six land cover categories-water, built-up, forest, cropland, rangeland, and barren land. Classified rangeland area declined by over 25%, largely due to agricultural expansion. Vegetation indices showed mixed trends, with decreases in enhanced vegetation index but marginal improvement in normalized difference vegetation index. Meanwhile, rising land surface temperatures pointed to increased aridity. These concerning changes underscore the urgent need for conservation policies tailored to community needs through participatory engagement. Rangeland degradation threatens the livelihoods and welfare of pastoral communities reliant on these ecosystems. Integrated solutions centered on adaptation and resilience can promote sustainability in Bhakkar's marginal dryland environments. This study demonstrates the power of satellite monitoring coupled with social research for implementing holistic strategies to address the globally prevalent threat of rangeland disruption.
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Groundwater utilization for several purposes such as irrigation in agriculture, industry, and domestic use substantially impacts water storage. Groundwater Storage Anomaly (GWSA) estimates have improved owing to the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) advancements. However, the characterization of GWSA fluctuation hotspots has been hindered by the coarse resolution of GRACE data. To better measure groundwater storage and depletion variations throughout an area and identify GWSA variation hotspots, a fine spatial resolution of GWSA estimations is required. Therefore, due to the coarse resolution of GRACE measurements, the eXtreme Gradient Boosting (XGBoost) model was developed to simulate fine resolution 0.1° GWSA combining climatic variables (soil moisture storage, evapotranspiration, temperature, surface runoff, and rainfall) from improved spatial high resolution FLDAS (Famine Early Warning Systems Network Land Data Assimilation System) model derived data and geospatial variables (elevation, slope, and aspect) extracted from Digital Elevation Model (DEM). A correlation of 0.98 demonstrated that the XGBoost model successfully simulated groundwater storage at a finer scale over the Upper Indus Plain Aquifer (UIPA). The findings suggested that the UIPA's groundwater storage has been depleted at an annual rate of 0.44 km3/yr which was 7.94 km3 in total between 2003 and 2020. According to the results, there seems to be consistency between the downscaled and original GWSA regarding temporal and spatial variability. The results were verified to show an improved correlation of 0.77 between the downscaled and the in-situ GWSA, compared to 0.75 between the GRACE-derived and the in-situ GWSA.
... The water class was increased but in the year 2000 there was a major decline in both lakes and the area was shrined but after 2010 and 2020 the area is increased. Table 3 shows the transfer change of Landuse landcover with the percentage of total area from the year 1990 to 2020 (Nielsen et al., 2020;Shahzaman et al., 2021). A stratified random sampling strategy was adopted for collecting reference data. ...
Although it is a globally significant wetland, the ecological environment of Soon valley (Ucchali and Khabeki wetland lakes, Pakistan) is being negatively impacted by the rapid transformation of its land-use change brought about by economic and human activities. Through supervised classification and interpretation, we extract land use categories from Landsat remote sensing images acquired between 1990 and 2020. Using Google Earth Engine with machine learning algorithms (e.g., Smile CART Classifier), we generate four maps depicting different types of land use with a 10-year time horizon. In statistics, Built-up area increased by 18 % between 1990 and 2020, while barren lands declined by 31 %. Wetland and mudflat extent exhibited fluctuations but demonstrated an overall 21 % reduction since 1990. Adopting a land use transfer matrix and a dynamic stance toward land use, this paper examines the factors that led to the change in wetland landuse in Soon Valley (Ucchali and Khabeki wetland lakes, Pakistan). The results show that between 1990 and 2020, there will be a significant shift in the wetland land use types in Soon valley, with a relatively wide change range for builtup land, mudflat and barren land, and an upward trend. Overall, we saw a reduction in wetland and grassland areas, and a sizable amount of land was diverted from other uses. This land was used primarily for building sites, rice paddies, and other dry purposes. From a dynamic standpoint, the range of change was relatively small as compared to the woodland area expanded. Overall, results indicating a gentle transitioning of one type of land use to another, with the mudflat wetland area decreasing first and then increasing. Although the population was not much larger, but played a significant role with precipitation which may be another driver of land use land cover (LULC) change in the Uchhali-Khabeki wetland lakes, as implied by correlation analysis. The foregoing study of LULC remote sensing is valuable for the preservation of Uchhali and kha-beki Lake's wetlands and the administration of the lake's land resources.
... Agriculture is another critical sector that depends on TWS. Changes in TWS can have significant impacts on agricultural production, and by monitoring these changes, could lead to informed decisions about irrigation practices, crop selection, and water management strategies (Shahzaman et al., 2021). ...
This study utilized data from the NASA Gravity Recovery and Climate Experiment (GRACE) to examine the variability of the terrestrial water storage anomaly (TWSA) in Iraq between 2002 and 2019. The analysis focused on six grid cells representing the Iraqi territory. The season trend decomposition (SLT) method was used to
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... The elevated chloride concentration results from industrial and sewage effluent contamination, leachate, and the dissolution of sedimentary rocks, leading to an alteration in water taste. While not posing adverse effects generally, it can be impactful for vulnerable individuals [49]. In accordance with WHO standards, the permissible chloride level in potable water should not surpass 250 mg/L. ...
This study undertook an assessment of 24 physiochemical parameters at over 1094 sites to compute the water quality index (WQI) across the upper and central Punjab regions of Pakistan. Prior to the WQI calculation, an analytical hierarchy process (AHP) was employed to assign specific weights to each water quality parameter. The categorization of WQI into distinct classes was achieved by constructing a pairwise matrix based on their relative importance utilizing Saaty's scale. Additionally, the groundwater quality status for irrigation and drinking purposes across various zones in the study area was delineated through the integration of WQI and geostatistical methodol-ogies. The findings revealed discernible heavy metal issues in the Lahore division, with emerging microbiological contamination across the entire study region, potentially attributed to untreated industrial effluent discharge and inadequately managed sewerage systems. The computed indices for the Lahore, Sargodha, and Rawalpindi divisions fell within the marginal to unfit categories, indicating water quality concerns. In contrast, the indices for other divisions were in the medium class, suggesting suitability for drinking purposes. Scenario analysis for developing mitigation strategies indicated that primary treatment before wastewater disposal could rehabilitate 9% of the study area, followed by secondary (35%) and tertiary (41%) treatments. Microbiological contamination (27%) emerged as the predominant challenge for water supply agencies. Given the current trajectory of water quality deterioration, access to potable water is poised to become a significant public concern. Consequently, government agencies are urged to implement appropriate measures to enhance overall groundwater quality for sustainable development.
... Furthermore, rainfall data was incorporated to analyze its potential influence on the wetland dynamics. The flow chart below ( Fig. 2) illustrates the systematic process adopted in the research to gather, analyze, and interpret the data for modeling and mapping the wetlands' dynamics [29,33,34]. In this research, a combination of satellite images from various sources was utilized to study wetland dynamics. ...
Wetlands are important in many ways, including hydrological cycles, ecosystem diversity, climate change, and economic activity. Despite the Ramsar Convention's awareness programmes, the importance of wetlands is frequently disregarded in underdeveloped countries. The Ramsar Convention recognises 2491 wetlands worldwide, 19 of which are in Pakistan. The goal of this study is to use satellite sensor technology to identify neglected wetlands in Pakistan. The key goals of this research are to analyse water quality, monitor ecological changes, and comprehend the impact of climate change on the aforementioned wetlands. We used approaches like supervised classification and TCW to identify wetlands. To detect climate-induced changes, a change detection index was used to Quick Bird imagery. TCG and the NDTI were also employed to examine the water quality and ecological changes in these wetlands. Sentinel-2 data between 2016 and 2019 were used in the analysis. Furthermore, watershed analysis was carried out using ASTER DEM data. Modis data was used to calculate the LST (°C) of the selected wetlands, while rainfall (mm) data was collected from ANN databases. According to the study's findings, in 2016, Borith, Phander, Upper Kachura, Satpara, and Rama Lake held 22.73%, 20.79%, 23.01%, 24.63%, and 23.03% water, respectively. In 2019, the water ratios for these lakes were 23.40%, 22.10%, 22.43%, 25.01%, and 24.56%. These findings emphasise the need of taking preventative actions to protect these wetlands in order to improve ecosystem dynamics in the future. As a result, it is critical that the relevant authorities implement the necessary conservation measures.
