Search Results (549)

Small lakes (<10 km²) provide a range of ecosystem services but are often overlooked in both monitoring efforts and limnological studies. Remote sensing has been increasingly used to complement in situ monitoring or to provide water colour data for unmonitored inland water bodies. However, due to spatial, radiometric, and spectral constraints, it has been heavily focused on large lakes. Sentinel-2 MSI is the first sensor with the capability to consistently retrieve a wide range of essential water quality variables, such as chlorophyll-a concentration (chl-a) and water transparency, in small water bodies, and to provide long time series. Here, we provide and validate a framework for retrieving two variables, chl-a and turbidity, over lakes with diverse optical characteristics using Sentinel-2 imagery. It is based on GRS for atmospheric and sun glint correction, WaterDetect for water detection, and inversion models that were automatically selected based on two different sets of optical water types (OWTs)—one for each variable; for chl-a, we produced a blended product for improved spatial representation. To validate the approach, we compared the products with more than 600 in situ data from 108 lakes located in the Adour–Garonne river basins, ranging from 3 to ∼5000 ha, as well as remote sensing reflectance (Rrs) data collected during 10 field campaigns during the summer and spring seasons. Rrs retrieval (n = 65) was robust for bands 2 to 5, with MAPE varying from 15 to 32% and achieving correlation from 0.74 up to 0.92. For bands 6 to 8A, the Rrs retrieval was much less accurate, being influenced by adjacency effects. Glint removal significantly enhanced Rrs accuracy, with RMSE improving from 0.0067 to 0.0021 sr⁻¹ for band 4, for example. Water quality retrieval showed consistent results, with an MAPE of 56%, an RMSE of 11.4 mg m⁻³, and an r of 0.76 for chl-a, and an MAPE of 47%, an RMSE of 9.7 NTU, and an r of 0.87 for turbidity, and no significant effect of lake area or lake depth on retrieval errors. The temporal and spatial representations of the selected parameters were also shown to be consistent, demonstrating that the framework is robust and can be applied over lakes as small as 3 ha. The validated methods can be applied to retrieve time series of chl-a and turbidity starting from 2016 and with a frequency of up to 5 days, largely expanding the database collected by water agencies. This dataset will be extremely useful for studying the dynamics of these small freshwater ecosystems. Full article

After the 2011 earthquake, lake water depletion has become a widespread issue in Sikkim, especially in regions classified as high to very high seismic zones, where many lakes have turned into seasonal water bodies. This study investigates Chochen Lake in the Barapathing area of Sikkim’s Pakyong district, which is facing severe water seepage and instability. The problem, intensified by the 2011 seismic event and ongoing local construction, is examined through subsurface fracture mapping using Vertical Electrical Sounding (VES) and profiling techniques. A statistical factor method, applied to interpret VES data, helped identify fracture patterns beneath the lake. Results from two sites (VES-1 and VES-2) reveal significant variations in weathered and semi-weathered soil layers, indicating fractures at depths of 17–50 m (VES-1) and 20–55 m (VES-2). Higher fracture density near VES-1 suggests increased settlement risk and ground displacement compared to VES-2. Contrasting resistivity values emphasize the greater instability in this zone and the need for cautious construction practices. The findings highlight the role of seismic-induced fractures in ongoing water depletion and underscore the importance of continuous dewatering to stabilize the swampy terrain. Full article

The uMlazi River receives effluents from wastewater work before feeding the Shongweni Dam. However, local communities are consuming fish from this dam for protein supplements. This study was undertaken to investigate the metal concentrations in the water and sediment, the general health of Coptodon rendalli and Oreochromis mossambicus, and metal bioaccumulation. Sampling was conducted during the dry (July–August) and wet seasons (November and December) in 2021. Water was sampled using acid-pre-treated sampling bottles, whereas sediment was collected using the Van Veen grab at the inflow, middle, and dam wall. Fish were collected, and their tissues were digested using aqua regia. Metal concentrations were measured using inductively coupled plasma optical emission spectroscopy (ICP-OES). This data manuscript reports the physical parameters of the water and concentrations of antimony, arsenic, cadmium, copper, iron, manganese, lead, selenium, and strontium in the water and sediment from the Shongweni Dam. Moreover, the fish morphometric data and metal concentrations observed in the muscle are also presented. This data could be used as baseline information on metal concentrations in the Shongweni Dam. Moreover, it provides insight into the potential impact of wastewater effluents on metal increases in freshwater bodies. Full article

This study investigates ground deformations in the southeastern Po Plain (northern Italy), focusing on the Bologna area—a densely populated region affected by natural and anthropogenic subsidence. Ground deformations in the area result from geological processes (e.g., sediment compaction and tectonic activity) and human activities (e.g., ground water production and underground gas storage—UGS). We apply a multidisciplinary approach integrating subsurface geology, ground water production, advanced differential interferometry synthetic aperture radar—DInSAR, gas storage data, and land use information to characterize and analyze the spatial and temporal variations in vertical ground deformations. Seasonal and trend decomposition using loess (STL) and cluster analysis techniques are applied to historical DInSAR vertical time series, targeting three representatives areas close to the city of Bologna. The main contribution of the study is the attempt to correlate the lateral extension of ground water bodies with seasonal ground deformations and water production data; the results are validated via knowledge of the geological characteristics of the uppermost part of the Po Plain area. Distinct seasonal patterns are identified and correlated with ground water production withdrawal and UGS operations. The results highlight the influence of superficial aquifer characteristics—particularly the geometry, lateral extent, and hydraulic properties of sedimentary bodies—on the ground movements behavior. This case study outlines an effective multidisciplinary approach for subsidence characterization providing critical insights for risk assessment and mitigation strategies, relevant for the future development of CO₂ and hydrogen storage in depleted reservoirs and saline aquifers. Full article

Turbidity is a crucial parameter for assessing the ecological health of aquatic ecosystems, particularly in shallow tropical lakes that are subject to climatic variability and anthropogenic pressures. Lake Chapala, the largest freshwater body in Mexico, has experienced persistent turbidity and sediment influx since the 1970s, primarily due to upstream erosion and reduced water inflow. In this study, we utilized Landsat satellite imagery in conjunction with near-synchronous in situ reflectance measurements to monitor spatial and seasonal turbidity patterns between 2023 and 2025. The surface reflectance was radiometrically corrected and validated using spectroradiometer data collected across eight sampling sites in the eastern sector of the lake, the area where the highest rates of horizontal change in turbidity occur. Based on the relationship between near-infrared reflectance and field turbidity, second-order polynomial models were developed for spring, fall, and the composite annual model. The annual model demonstrated acceptable performance (R² = 0.72), effectively capturing the spatial variability and temporal dynamics of the average annual turbidity for the whole lake. Historical turbidity data (2000–2018) and a particular case study in 2016 were used as a reference for statistical validation, confirming the model’s applicability under varying hydrological conditions. Our findings underscore the utility of empirical remote-sensing models, supported by field validation, for cost-effective and scalable turbidity monitoring in dynamic tropical lakes with limited monitoring infrastructure. Full article

Accurate land cover classification is vital for informed water resource management, especially in irrigation-dependent regions facing increased climate variability. Using fused multi-sensor remote sensing imagery from Landsat 8 and Sentinel-2, this study assesses the effectiveness of three machine learning classifiers: Random Forest (RF), Gradient Tree Boosting (GTB), and Naive Bayes (NB) in creating land cover maps for the Tisza-Körös Valley Irrigation System (TIKEVIR) in Hungary. Water bodies, built-up areas, forests, grasslands, and major crops were among the important land cover categories that were classified for the two agricultural seasons (2018 and 2022). RF performed consistently in 2022 and reached its best accuracy in 2018 (OA = 0.87, KC = 0.83, PI = 0.94). While NB’s performance in 2022 remained less consistent, GTB’s performance increased. The findings show that RF works effectively for generating accurate land cover data, providing useful information for regional monitoring, and assisting in water and environmental management decision-making. Full article

This research addresses urban heat island intensification driven by urbanization using Dianchi Lake in Kunming, China, as a case study, aiming to quantitatively evaluate the spatial extent, intensity, and land cover sensitivity differences in the cooling effects of large urban water bodies across dry/wet seasons and complex urban landscapes (forest, cropland, and impervious surfaces) to provide a scientific basis for optimizing thermal environments in low-latitude plateau cities. Based on Landsat 8/9 satellite data from dry (January) and wet (May) seasons in 2020 and 2023 used for land surface temperature (LST) retrieval combined with land use data, buffer zone gradient analysis was adopted to quantify the spatial heterogeneity of key cooling indicators within 0–1500 m lakeshore buffers. The results demonstrated significant seasonal differences. The wet season showed a greater cooling extent (600 m) and higher intensity (6.0–6.6 °C) compared with the dry season (400 m; 2.4–3.9 °C). The land cover responses varied substantially, with cropland having the largest influence (600 m), followed by impervious surfaces (400 m), while forest exhibited a minimal effective cooling range (100 m) but localized warming anomalies at 200–400 m. Sensitivity analysis confirmed that impervious surfaces were the most sensitive to water-cooling, followed by cropland, whereas forest showed the lowest sensitivity. Full article

Macrophytes are important components of aquatic ecosystems performing essential ecological functions. Their species composition and density reflect the ecological status of water bodies. The optimal ratio of morphological types of macrophytes is an important condition for preventing eutrophication. The aim of the study is to analyse the species composition, distribution, and density of macrophytes in the Vyzhyvka River (Ukraine) in a seasonal aspect (2023–2024) under constant physical and chemical characteristics of water. To assess the seasonal dynamics of water quality, changes in indicators in three representative areas were analysed. The MIR method of environmental indexation of watercourses was used to assess the ecological state of the river. The water quality in the Vyzhyvka River at all test sites corresponds to the second class of the “good” category with the trophic status of “mesotrophic”. This is confirmed by the identified species diversity, which includes 64 species of higher aquatic and riparian plants. Among the various morphological types of macrophytes, submerged rooted forms account for only 10.56% of the total species composition. To ensure a functional balance between submerged and other forms of macrophytes, a scientifically based approach is proposed, which involves the use of mineral raw materials of local origin, in particular, mining and quarrying wastes rich in silicon, calcium and other mineral components. The results obtained are of practical value for water management, environmental protection, and ecological reclamation and can be used to develop effective measures to restore river ecosystems. Full article

The use of pharmaceuticals and personal care products (PPCPs) is steadily growing as the world’s population both increases and ages. Many of these products are released into the environment via municipal wastewater treatment plants and onsite wastewater treatment systems (septic tanks). Consequently, it is essential to ascertain whether these contaminants pose any risk to aquatic organisms who live in the water bodies receiving this waste. Risk quotients (RQ) are a commonly used method to do so. For our pilot study, we undertook such analysis for three trophic levels: algae, crustaceans, and fish from two small lakes, one fed by septic tanks and the other not. This research was conducted in 2021 from the end of the dry season and through most of the wet season in west central Florida, USA. Of the 14 PPCPs measured, six had RQs that posed a risk to all three trophic levels. This risk increased during the wet season. Both lakes, regardless of whether they directly received PPCPs from septic tanks or not, had some level of risk. However, the lake without septic tanks had a smaller risk, both in elevated RQs and the occurrence to the various species. Of the PPCPs measured, DEET, caffeine, and theophylline posed the greatest risk. Full article

Heavy metal pollution in aquatic ecosystems is a critical environmental issue worldwide. In these ecosystems, the seston adsorbs heavy metals from the water and introduces them into the food web, causing potential environmental and health risks. This study analyses how heavy metals (Cd, Hg, Cr, Cu, Pb, Fe, and Mn) are distributed in the water and seston of the Tampamachoco Lagoon, an ecosystem affected by pollution from a thermoelectric plant and by hydrometeorological variability, both of which influence their concentrations. The relationships among metal distribution, physicochemical variables, and the influence of plant emissions in three seasons (rainy, northerly windstorms, and dry) were analyzed. The metal concentrations in seston (Fe > Mn > Pb > Cu > Cr > Hg) were up to four times higher than in the water column (Fe > Mn > Cr > Cd > Pb > Cu > Hg), emphasizing the key role of particulate matter in metal transport and bioavailability. Particularly, the Cd concentrations exceeded WHO thresholds by 527.6% in the water column during the rainy season, while Hg and Pb exceeded the thresholds of the Mexican criteria for the protection of marine aquatic life by 4.05% and 41.6%, respectively. Principal Component Analyses revealed distinct spatiotemporal distribution patterns for metals in water and seston, reflecting the combined effects of natural variability and anthropogenic inputs. The strong association between metals and seston indicates continued contamination and potential risks to aquatic ecosystems. These findings highlight the environmental impact of metals on seston and the need for monitoring to assess aquatic ecosystems’ health. Our results highlight the importance of understanding how metals are distributed between seston and water, and how climate variability affects pollutant redistribution patterns. We propose that water quality regulations need to be rethought and redirected towards the achievement of new strategic objectives that truly integrate the different pollutant sources whose final destination is water bodies, so as to protect and conserve biodiversity and aquatic ecosystems. Full article

Water management is a significant challenge, stimulating synergies between scientists and practitioners to create new tools and approaches to streamline decision making in this field. The assessment and monitoring of freshwater quality in surface water bodies are crucial for sustainable and safe water management. The main objectives of this study were to analyze the characteristics and properties of Chirita lake, assess seasonal variations in water quality, determine compliance with national environmental legislation, and perform a comparison with monitoring systems in other European lakes. The study used data that determined water quality indicators for a five-year period, from 2020 to 2024, considering temperature, turbidity, pH, conductivity, alkalinity, hardness, organic matter, nitrates, nitrites, ammonium, and chlorides. The statistical analysis technique based on the Pearson correlation coefficient was used to evaluate the seasonal correlations of water quality parameters in Chirita lake and to extract the essential parameters for assessing seasonal variations in river water quality. The results obtained indicated that the indicators considered important for water quality variation in one season may not be important in another season, except for organic matter and conductivity, which showed a significant contribution to water quality variation throughout the four seasons. This study demonstrated that lake water is classified as first class, according to national regulations. These results provide valuable support for local authorities to develop effective strategies for water quality management and the prevention of eutrophication processes in reservoirs. Full article

The pollution of water bodies has deteriorated the quality of freshwater and the health of the natural ecosystem. In the present study, the water quality index (WQI) was used to evaluate the spatial and temporal contamination levels in Lake Yuriria, Guanajuato, Mexico. Water quality was monitored at 27 different locations (monitoring points) in the dry season (April) and after the rainy season (November), measuring 21 physicochemical water parameters, 2 biological parameters, and 19 metal concentrations. The data analysis revealed that Yuriria Lake is a eutrophic water body. Six monitoring points exhibited a poor WQI (25–50) in April, and seven monitoring sites were classified as having poor water quality in November. The remaining monitoring points showed a WQI categorized as fair (51–70) in both periods. The present study analyzes an extensive distribution of monitoring points over the lake’s surface in two periods, showing a significant spatial and temporal representation of water quality. In addition, the major pollution sources identified include agricultural runoff and effluents from a nearby waterway and freshwater river. Finally, the key physicochemical parameters that determined the water quality were identified. BOD₅, NH₄⁺, P, orthophosphates, DO, conductivity, TSS, and color were linked to anthropogenic pollution sources, and Li, Ni, Zn, Cd, Ba, and Pb concentrations were linked to natural contamination sources. This study demonstrates the utility and versatility of these methodologies in water quality research, and it is the first spatial and temporal WQI analysis of Yuriria Lake. Full article

Dinder Biosphere Reserve (DBR), a UNESCO-recognized biodiversity hotspot in Sudan, faces escalating land-use pressure. We analyzed land cover changes from 2019 to 2024 using Sentinel-2 imagery processed in Google Earth Engine. A Random Forest classifier identified five land cover classes: water, built-up areas, vegetation, bare land, and crops. The transition matrix revealed significant changes over this period. About 1501 km² of vegetation and 1648 km² of cropland were converted to bare land. Built-up areas lost 95 km² to bare land. Bare land remained largely unchanged (4749 km²), while water bodies were the most stable (13,473 km² unchanged). Only minor transitions involved water (27.6 km² to vegetation, 15.2 km² to bare land). Notably, 411 km² of cropland and 1773 km² of bare land transitioned to vegetation, indicating some regrowth. These land cover changes reflect a dynamic interplay between degradation and recovery processes; however, the results should be interpreted with caution due to potential classification inaccuracies, seasonal variation in imagery, and absence of field validation. Continued satellite monitoring is essential to guide adaptive land management and safeguard ecosystem function in DBR. Full article

Flood risk to rice production has previously been examined in terms of river basins or administrative units, incorporating data about the flood year, inundated area, precipitation, elevation, and impacts. However, there is limited knowledge about this topic, as most flood impact studies have focused on loss and damage to people and the economy. It remains important to identify how flood risk to rice production can be better identified within a long-term, community-based, analytical framework. In addition, flood risk studies in Sri Lanka tend to focus on single-year flood events within an administrative boundary, making it difficult to fully comprehend risks to rice production. This paper aims to fill these gaps by investigating long-term flood risk levels on rice production. With this aim, we collected and analyzed information about rice production, geospatial data, and 15-year precipitation records. Temporal-spatial maps were generated using Google Earth Engine JavaScript coding, Google Earth Pro, and OpenStreetMap. In addition, focus group discussions with farmers and key informant interviews were conducted to verify the accuracy of online information. The collected data were analyzed using descriptive statistics, GIS, and linear regression analysis methods. Regarding rice production impacts, we found that floods in the years 2006–2007, 2010–2011, and 2014–2015 had significant impacts on rice production with 20.5%, 75.8%, and 16.6% reductions, respectively. Flood risk maps identified low-, medium-, and high-risk areas based on 15-year flood events, elevation, proximity to water bodies, and 15-year flood-induced damage to rice fields. High risk areas were further studied through field discussions and interviews, showing the connection between past floods and poor water governance practices in terms of dam management. Our linear regression analysis found a marginal negative correlation between total seasonal rainfall and rice production. Full article

With the rapid development of urbanization in rural areas of China, various environmental issues have become increasingly prominent, particularly the water pollution problems in small rural watersheds, which have garnered considerable attention. Comprehensive management of small watersheds requires an initial analysis of the sources and characteristics of water pollution. This study focuses on small rural watersheds in the Pearl River Delta. Based on the characteristics of the watersheds, 35 water quality monitoring stations were set up to collect water quality data. Cluster analysis was used to study the spatial distribution characteristics of water quality indicators at each monitoring point. Further, factor analysis methods (PCA/FA) and Absolute Principal Component Scores-Multiple Linear Regression (APCS-MLR) models were employed to identify water quality influencing factors and quantify pollution source contributions. Finally, the comprehensive index method for eutrophication assessment was used to evaluate and analyze the potential eutrophication pollution risk in the watersheds. The results indicate significant pollution in the water quality of rural small watersheds in the study area, with varying degrees of pollution over time and space. During the wet season, water quality is mainly influenced by agricultural nutrients, followed by biochemical factors. In the normal and dry seasons, water quality is primarily affected by oxygen-consuming organic pollutants, followed by eutrophication factors. The comprehensive eutrophication evaluation shows that the overall water quality in the watershed is better during the wet season, with a lower risk of eutrophication; during the normal season, the overall water quality is poorer, with the highest eutrophication risk in the midstream; during the dry season, the upstream and midstream water quality is better, while the downstream water quality is poorer. In contrast, the pond water exhibits a higher risk of eutrophication during the wet season compared to the normal and dry seasons. This is mainly due to the peak of fish farming during the wet season, which results in a heavier load on the water body. This study provides effective data support for the water environment management of rapidly developing rural small watersheds. Full article