Muhammad Zahir

Freshwater reservoirs face increasing threats from eutrophication and anthropogenic nutrient enrichment, yet practical multicriteria tools for ranking site-specific vulnerability remain underutilized. This study applies the PROMETHEE/GAIA multicriteria decision analysis framework to rank water quality vulnerability at five sampling sites (L1–L5) in Shanzai Reservoir, Fujian Province, China, using ten water quality parameters (TN, TP, COD, DO, Chl-a, pH, temperature, N:P ratio, transparency, and carbon ratio) measured monthly from April 2023 to April 2024. The PROMETHEE II complete ranking and the GAIA biplot together provide both a spatial vulnerability ranking and parameter-level diagnostic visualization. The Reservoir Centre (L5) ranked first (Φ = +0.32), exhibiting the most favorable water quality, while the River Channel (L3) ranked last (Φ = −0.44), with mean TN (1.15 mg/L) and TP (0.088 mg/L) exceeding Chinese Class III standards and Chl-a (35.89 µg/L) surpassing eutrophication thresholds. Intermediate rankings: L4 (Φ = +0.20), L1 (Φ = 0.00), L2 (Φ = −0.04). Spatial vulnerability followed a clear zone-level gradient: the riverine zone (L1, L3) was most vulnerable, the transitional zone (L4) showed intermediate performance, and the lacustrine zone (L2, L5) was most favorable, consistent with reservoir hydrodynamic theory. The GAIA biplot revealed that nutrient criteria (TN, TP, Chl-a) were the primary drivers separating site vulnerability classes. A sensitivity analysis across eight weighting scenarios confirmed that L3 ranked last in all scenarios (Φ = −0.450 to −0.694), demonstrating the robustness of the recommendation to prioritize intervention at the river channel inflow zone. These findings offer a practical, reproducible decision-support framework for water quality management prioritization in subtropical freshwater reservoirs, subject to confirmation through multi-year monitoring programs.

Harmful Algal Blooms (HABs) cause significant ecological damage and public health issues in freshwater reservoirs globally. While in situ sampling is essential for monitoring HABs, it is often costly and time-consuming. Remote sensing offers a rapid and cost-effective alternative for detecting HABs. This study integrates Sentinel-2 multispectral imagery with in-situ observations to monitor HABs in Dongzhang Reservoir, Fujian, China. We analyzed the Normalized Difference Vegetation Index and Normalized Difference Chlorophyll Index (NDCI) to monitor chlorophyll-a concentrations in this dynamic water body. In situ water samples were collected at three locations to determine the phytoplankton density, and these counts were correlated with spectral indices. Our findings indicate that the Dongzhang Reservoir experiences high algal biomass during spring and summer, as evidenced by seasonal variations in spectral index values. The NDCI, which leverages the red edge wavelength, provided the most accurate identification of chlorophyll-a. Linear regression analysis revealed strong correlations between phytoplankton density and remote sensing values in spring, summer, and autumn of 2022, with R² values of 0.81, 0.74, and 0.62, respectively. Two key factors driving HAB proliferation in 2022 were total phosphorus levels and water surface temperatures, which were positively correlated with phytoplankton density (R² = 0.83 for total phosphorus and R² = 0.81 for water temperature). In 2023, total phosphorus remained the main factor, with an R² of 0.75. HAB events were more severe in 2022, affecting 34.41% of the reservoir area in May, compared with 22.15% in 2023. This study demonstrated that Sentinel-2 imagery captures greater spatial and temporal heterogeneity of algal blooms than traditional in situ samples do, highlighting the potential of remote sensing as a crucial tool for monitoring algal bloom dynamics in reservoirs and other water bodies. Complementing in situ samples with Sentinel-2 images, owing to their high temporal resolution, can increase monitoring efforts. The implementation of appropriate management strategies can mitigate HABs in Dongzhang Reservoir.

This study employed an innovative participatory geographic information system (PGIS) approach to evaluate the health of reservoirs and their socioecological importance to communities within the Shanzai sub‐catchment. The participation rate was 100% in all five communities, with 53% of participants were women. Statiscial analysis shows that algal bloom negatively correlate with less fish productivity and positively with unhealthy reservoir indicators. In contrast, clean water positively correlates with healthy reservoir indicators, while algal blooms consistently show negative correlations with all indicators of healthy reservoir. These findings show current reservoir health, with 41% of participants recognizing reservoirs as healthy, 23% as unhealthy and 36% responded as moderate. Laboratory analysis identified 30 phytoplankton genera, with Cyanophyta as the dominant group. Highest phytoplankton density was observed in May, followed by June and April, providing crucial insights into the seasonal dynamics of reservoir ecosystems. Sentinel‐2 imagery further highlighted algal bloom fluctuations, the bloom extent particularly increased during May 2023, supporting water quality measurements and validating algae as a community‐identified indicator. This study underlines the value and accuracy of community‐driven insights in environmental monitoring. The alignment of participatory mapping, laboratory analyses, and remote sensing demonstrates the efficiency of PGIS in managing freshwater resources. By fostering knowledge exchange, this approach promotes sustainable resource monitoring and conservation. These findings represent a significant contribution to the advancement of reservoir monitoring and underscore the importance of prioritizing community‐driven initiatives in environmental research.

Groundwater management (quantity and quality) is a pressing concern in Islamabad amid the challenges posed by climate change and urbanization. This study leverages the Water Quality Index (WQI), coupled with advanced remote sensing (RS) and geographic information system (GIS) applications, to provide a comprehensive assessment of groundwater dynamics in the city. Groundwater samples from 40 tube wells were analyzed using standard methods, and spatial distribution patterns of water quality variables were mapped applying an integrated GIS framework. Geological and hydrological data collected from the Capital Development Authority (CDA) supported the mapping of water table depths, bore depths, and water quality features. Key findings revealed significant hydrogeological variations, with sectors G-8, G-7, G-9, and G-11 exhibiting elevated electrical conductivity (EC) levels, peaking at 1054.5 µS/cm, surpassing permissible limits. The WQI indicated excellent to good quality of all the collected samples except one found unfit for drinking. Land use and land cover (LULC) analysis revealed extensive urbanization, exacerbating groundwater contamination risks. This study underscores the interconnectedness of urban growth, geological features, groundwater quality deterioration, and sustainability. The findings provide actionable insights for policymakers and urban planners to mitigate groundwater contamination and ensure sustainable resource management in Islamabad.

This study assessed the impacts of anthropogenic and climate change on harmful algal blooms (HABs) in the Dongzhang and Shanzai Reservoirs from 1990 to 2023, focusing on land use and land cover change (LULCC) analysis. Through supervised classification of Landsat imagery, the study examined changes in cropland, forest, shrub, grassland water, barren and built‐up areas in the watersheds of both reservoirs and monitor HABs events. The Dongzhang catchment saw forest cover increase from 72.0% in 1990 to 75.3% in 2010 before decreasing to 71.7% in 2022, while cropland and built‐up areas fluctuated. Similarly, Shanzai's forest cover declined from 94.4% in 1996 to 91.4% in 2022, with cropland increasing. Significant HAB events in Dongzhang Reservoir peaked in 1990, 2010 and 2022, affecting up to 40% of the reservoir's area in 2022. In Shanzai Reservoir, HAB events showed an increasing trend from 1996 to 2018, with a peak in 2018 when 40% of the reservoir's area was affected. Land use changes exacerbate HABs, as seen in declining forest cover and increasing cropland and built‐up areas. Analysis using Landsat imagery showed HAB occurrences increasing from 1990 to 2023, with rising water temperatures indicating potential future increases. Addressing anthropogenic activities and climate change are vital to prevent and manage HABs, requiring improved monitoring and early warning systems. With remote sensing, water quality issues can be identified and managed. The findings emphasize the need for continuous monitoring, improved LULCC analysis, sediment pollution research and effective management strategies to protect water quality and ecosystem health under climate change. Recommendations include long‐term monitoring, nutrient input investigations, modelling studies, early warning systems and adaptation strategies.