Search Results (466)

Investigating the coevolution between ecosystem services (ES) and human well-being (HWB) holds significant implications for achieving the sustainable operation of human–environment systems. However, limited research has focused on ES-HWB interactions in ecotourism-dominated counties. To address this gap, this study takes Chun’an County in Zhejiang Province, China, as a case study, with the research objective of exploring the processes, patterns, and mechanisms of the coevolution between ecosystem services (ES) and human well-being (HWB) in ecotourism-dominated counties. By integrating multi-source heterogeneous data, including land use data, the normalized difference vegetation index (NDVI), and statistical records, and employing methods such as the dynamic equivalent factor method, the PLUS model, the coupling coordination degree model, and comprehensive evaluation, we analyzed the synergistic evolution of ES-HWB in Chun’an County from 2000 to 2020. The results indicate that (1) the ecosystem service value (ESV) fluctuated between 30.15 and 36.85 billion CNY, exhibiting a spatial aggregation pattern centered on the Qiandao Lake waterbody, with distance–decay characteristics. The PLUS model confirms ecological conservation policies optimize ES patterns. (2) The HWB index surged from 0.16 to 0.8, driven by tourism-led economic growth, infrastructure investment, and institutional innovation, facilitating a paradigm shift from low to high well-being at the county level. (3) The ES-HWB interaction evolved through three phases—disordered, antagonism, and coordination—revealing tourism as a key mediator driving coupled human–environment system sustainability via a pressure–adaptation–synergy transmission mechanism. This study not only advances the understanding of ES-HWB coevolution in ecotourism-dominated counties, but also provides a transferable methodological framework for sustainable development in similar regions. Full article

Inland waters, serving as crucial carbon sinks and pivotal conduits within the global carbon cycle, are essential targets for carbon assessment under global warming and carbon neutrality initiatives. However, the extensive spatial distribution and inherent sampling challenges pose fundamental difficulties for monitoring dissolved organic carbon (DOC) in these systems. Since 2010, remote sensing has catalyzed a technological revolution in inland water DOC monitoring, leveraging its advantages for rapid, cost-effective long-term observation. In this critical review, we systematically evaluate research progress over the past two decades to assess the performance of remote sensing products and existing methodologies in DOC retrieval. We provide a detailed examination of diverse remote sensing data sources, outlining their application characteristics and limitations. By tracing uncertainties in retrieval outcomes, we identify atmospheric correction, spatial heterogeneity, and model and data deficiencies as primary sources of uncertainty. Current retrieval approaches—direct, indirect, and machine learning (ML) methods—are thoroughly scrutinized for their features, effectiveness, and application contexts. While ML offers novel solutions, its application remains nascent, constrained by limited waterbody-specific samples and model constraints. Furthermore, we discuss current challenges and future directions, focusing on data optimization, feature engineering, and model refinement. We propose that future research should (1) employ integrated satellite–air–ground observations and develop tailored atmospheric correction for inland waters to reduce data noise; (2) develop deep learning architectures with branch networks to extract DOC’s intrinsic shortwave absorption and longwave anti-interference features; and (3) incorporate dynamic biogeochemical processes within study regions to refine retrieval frameworks using biogeochemical indicators. We also advocate for multi-algorithm collaborative prediction to overcome the spectral paradox and unphysical solutions arising from the single data-driven paradigm of traditional ML, thereby enhancing retrieval reliability and interpretability. Full article

In this article, we examine water chemistry and carbonate precipitation in the alkaline waterbody Lake Van in Turkey, analyzing the possible role of microbial communities in stromatolite formation. Lake Van represents a unique environment characterized by high salinity and pH and extensive microbial communities, as revealed by SEM observation. Microbial activity, including that of cyanobacteria, can influence carbonate precipitation processes, leading to the formation of authigenic carbonates through physicochemical or metabolic mechanisms such as photosynthesis or sulfate reduction. In these environments, which are often dominated by cyanobacteria, carbonate precipitation can be influenced by biologically induced processes. This study presents new data on the hydrochemistry of lake water, focusing on the behavior of rare-earth elements (REEs) in this water and the carbon and oxygen isotopic compositions of carbonate microbial mats. The oxygen isotope data suggest that inorganic carbonate precipitation is the dominant process, but a biological influence on inorganic precipitation cannot be ruled out. For a deeper understanding of the role of biological processes in Lake Van, further studies on microbialites are needed. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

The traditional method for the establishment of the green laser underwater transmission model is purely based on the laser transmission mechanism in waterbodies, while neglecting a few exterior conditions. This paper proposes a novel method to establish the underwater transmission model from a maximum measurement depth perspective by refining the dynamic relationship between the effective received power P_A and the background noise power P_B. Different from the traditional empirical model of fixed P_A/P_B, this method combines the sensor, flight, and environmental parameters of airborne LiDAR (ALB) to achieve the dynamic calibration of P_A and P_B. In particular, the empirical relationship between the maximum underwater measurement depth and the laser attenuation coefficient, coupled parameters, etc., is considered. The established model is verified by different types of experiments. The experimental results discovered that the errors are approximately 0.86 m and 1.28, under the same water conditions, when compared to the existing models. The validation experiments demonstrated that the errors for the maximum depth prediction were 0.38 m (indoor tank), 1.58 m (indoor swimming pool), 0.44 m (Li River, Guangxi), and 1.20 m (Beibu Gulf, Pacific Ocean). The experimental results demonstrated that the established model enables us to widely predict the maximum water depth measurable using an airborne LiDAR under different environmental conditions. Full article

Rapid suburbanization can alter catchment flow regime and increase stormwater runoff, posing threats to sensitive ecosystems. Applications of Nature-based Solutions (NbS) have increasingly been adopted as part of integrated water management efforts to tackle the hydrological impact of urbanization with co-benefits for improved urban resilience, sustainability, and community well-being. However, the implementation of NbS can be hindered by gaps in performance assessment. This paper introduces a physically based dynamic modeling approach to assess the performance of a nature-based storage facility designed to capture excess runoff from an urbanizing catchment (Armstrong Creek catchment) in Geelong, Australia. The study adopts a numerical modelling approach, supported by extensive field monitoring of water levels over a 2.5-year period. The model provides a decision support tool for Geelong local government in managing stormwater runoff to protect Lake Connewarre, a Ramsar-listed wetland under the Port Phillip Bay (Western Shoreline) and Bellarine Peninsula. Runoff is currently managed via a set of operating rules governing gate operations that prevents flows into the ecological sensitive downstream waterbody from December to April (drier periods in summer and most of autumn). Comparison with observed water level data at three monitoring stations for a continuous simulation period of May 2022 to October 2024 demonstrates satisfactory to excellent model performance (NSE: 0.55–0.79, R²: 0.80–0.89, ISE rating: excellent). Between 1670 × 10³ m³ and 2770 × 10³ m³ of runoff was intercepted by the nature-based storage facility, representing a 56–70% reduction in stormwater discharge into Lake Connewarre. Our model development underscores the importance of understanding and incorporating user interventions (gate operations and emergency pumping) from the standard operation plan to better manage catchment runoff. As revealed by the seasonal flow analysis for consecutive years, adaptive runoff management practices, capable of responding to rainfall variability, should be incorporated. Full article

Freshwater cyanobacterial harmful algal blooms (FCHABs) alter zooplankton communities, often adversely, through the production of cyanotoxins. While Daphnia magna is frequently used to evaluate the impact of toxicants, it is not commonly found in tropical waters; cladocerans from tropical and subtropical waterbodies should be used in bioassays. Here, we evaluated the impact of crude cyanobacteria extracts on three common, native species (Daphnia laevis, Ceriodaphnia dubia, and Simocephalus vetulus) based on acute and chronic bioassays. We analyzed the toxicity of cyanobacterial consortium collected from Lake Zumpango, Mexico. The FCHAB was dominated by Planktothrix agardhii (1.16 × 10⁶ ind mL⁻¹). A series of freeze/thaw/sonification cycles at 20 kHz was used to extract the toxic metabolites and the concentration of dissolved microcystin-LR equivalents was measured using an ELISA immunological kit. S. vetulus was the most sensitive species, with a median lethal concentration of 0.43 compared to 1.19 µg L⁻¹ of D. magna at 48 h. S. vetulus was also the most sensitive in chronic evaluations, showing a negative rate of population increase (−0.10 d⁻¹) in experiments with 20% crude extract. Full article

Flood hazards and their disastrous consequences disrupt economic activity and threaten human lives globally. From a remote sensing perspective, since floods are often triggered by extreme climatic events, such as heavy rainstorms or tropical cyclones, the efficacy of using optical remote sensing data for disaster and damage mapping is significantly compromised. In many flood events, obtaining cloud-free images covering the affected area remains challenging. Nonetheless, considering that floods are the most frequent type of natural disaster on Earth, optical remote sensing data should be fully exploited. In this article, firstly, we will present a critical review of remote sensing data and machine learning methods for global flood-induced damage detection and mapping. We will primarily consider two types of remote sensing data: moderate-resolution multi-spectral data and high-resolution true-color or panchromatic data. Big and semantic databases available for advanced machine learning to date will be introduced. We will develop a set of best-use case scenarios for using these two data types to conduct water-body and built-up area mapping with no to moderate cloud coverage. We will cross-verify traditional machine learning and current deep learning methods and provide both benchmark databases and algorithms for the research community. Last, with this suite of data and algorithms, we will demonstrate the development of a cloud-computing-supported computing gateway, which houses the services of both our remote-sensing-based machine learning engine and a web-based user interface. Under this gateway, optical satellite data will be retrieved based on a global flood alerting system. Near-real-time pre- and post-event flood analytics are then showcased for end-user decision-making, providing insights such as the extent of severely flooded areas, an estimated number of affected buildings, and spatial trends of damage. In summary, this paper’s novel contributions include (1) a critical synthesis of operational readiness in flood mapping, (2) a multi-sensor-aware review of optical limitations, (3) the deployment of a lightweight ML pipeline for near-real-time mapping, and (4) a proposal of the GloFIM platform for field-level disaster support. Full article

Surface water is the primary freshwater supply for Earth. Small lakes and ponds provide important ecological and economic services to society but are often left undocumented, or their documentation is outdated, due to their small sizes and temporal dynamics. This study tested the feasibility of the new Surface Water and Ocean Topography (SWOT) mission regarding the 3D documentation of small waterbodies in a coastal area of South Carolina, USA. Via deep learning using a recent 15 cm aerial image, small waterbodies (>0.02 ha) were extracted at an average precision score of 0.81. The water surface elevation (WSE) of each waterbody was extracted using the SWOT Level-2 Water Mask Pixel Cloud (PIXC) product, with the data collected on 1 June 2023. Using a statistical noise-removal approach, the average WSE values of small waterbodies revealed a significant correlation (Pearson’s r = 0.64) with their bottom elevations. Via spatial interpolation, the water levels of small waterbodies across the study area were generally aligned with the state-reported Cone of Depression of ground water surfaces in underlying aquifers. While the WSE measurements of SWOT pixel points are noisy due to the land–water interactions in small waterbodies, this study indicates that the SWOT PIXC product could provide a valuable resource for assessing freshwater availability to assist in water-use decision-making. Full article

Standardizing data collection, management, and analysis processes can improve the reliability and efficiency of fisheries monitoring programs, yet few studies have examined the operationalization of these tasks within agency settings. We reviewed the Mississippi Department of Wildlife, Fisheries, and Parks, Fisheries Bureau’s inland recreational fisheries monitoring program—a 30+-year effort to standardize field protocols, data handling procedures, and automated analyses through a custom-built computer application, the Fisheries Resources Analysis System (FRAS). Drawing on quantitative summaries of sampling trends and qualitative interviews with fisheries managers, we identified key benefits, challenges, and opportunities associated with the Bureau’s standardization efforts. Standardized procedures improved sampling consistency, data reliability, and operational efficiency, enabling the long-term tracking of fish population and angler metrics across more than 270 managed waterbodies. However, challenges related to analytical transparency and spatiotemporal comparisons persist. Simulations indicated that under current conditions, 5.8, 22.9, and 37.1 years would be required to sample (boat electrofishing) 50%, 75%, and 95% of the Bureau’s waterbodies at least once, respectively; these figures should translate to other agencies, assuming similar resource availability per waterbody. The monitoring program has reduced manual processing effort and enhanced staff capacity for waterbody-specific management, yet several opportunities remain to improve efficiency and utility. These include expanding FRAS functionalities for trend visualization, integrating mobile field data entry to reduce transcription errors, linking monitoring results with management objectives, and enhancing automated report generation for management support. Strengthening these elements could not only streamline workflows but better position agencies to apply standardized data in adaptive management embedded into the monitoring program. Full article

Diclofenac, a nonsteroidal anti-inflammatory drug widely used worldwide, has been detected in waterbodies at concentrations ranging from ng L⁻¹ to µg L⁻¹. Although diclofenac is not a persistent compound, aquatic organisms may be exposed to this drug for extended periods due to its incorporation into the environment by continuous release from hospitals and municipal discharges. This study aimed to evaluate the toxic effects of diclofenac on the microalga Pseudokirchneriella subcapitata, the cladoceran Daphnia curvirostris, and zebrafish embryos (Danio rerio). Toxicity bioassays for the microalga were performed according to the OECD 201 protocol with diclofenac concentrations of 0, 6.25, 12.5, 25, 50, 75, and 100 mg L⁻¹. For the determination of acute toxicity in the cladoceran (48 h), concentrations of 0, 10, 20, 30, 40, 50, and 60 mg L⁻¹ were tested; in subchronic bioassays, the effect of the drug on the reproductive parameters of D. curvirostris was determined for 21 days with sublethal concentrations of 10.3, 14.4, 17.2, and 21.3 mg L⁻¹. Toxicity bioassays on zebrafish embryos were performed according to the OECD 236 protocol, using concentrations of 0, 1, 2, 4, 6, 8, and 10 mg L⁻¹ of diclofenac. The results confirmed the toxic effects of the drug. The IC₅₀ for the microalga was 16.57 mg L⁻¹, while the LC₅₀ for D. curvirostris and D. rerio was 32.29 and 6.27 mg L⁻¹, respectively. In the microalga, chlorophyll-a and carotenoids increased at a concentration of 3.62 mg L⁻¹ of diclofenac; however, chlorophyll-b decreased at the highest drug concentration (13.51 mg L⁻¹). Protein and lipid concentrations in P. subcapitata exposed to all concentrations were higher than in the control. Chronic diclofenac exposure did not affect the survival of D. curvirostris; however, the cumulative progeny and number of clutches significantly decreased, and the age of first reproduction was delayed at all drug concentrations. Protein concentration in D. curvirostris hatchlings was higher at all diclofenac concentrations; in contrast, the amount of lipids and carbohydrates decreased significantly. In D. rerio, the hatching rate decreased by 40, 51.6, and 80% at concentrations of 6, 8, and 10 mg L⁻¹ diclofenac, respectively, and exposure to the drug caused lethal effects such as coagulation at 24 and 48 hpf; sublethal effects such as edema and curved tail were also observed at concentrations of 2 to 10 mg L⁻¹, and the effects increased with increasing concentration up to 144 hpf. The results demonstrate the vulnerability of aquatic organisms to the toxic effects of diclofenac, suggesting that discharging it into water bodies should be regulated to prevent potential ecological impacts on the various trophic levels of freshwater biota. Full article

The increasing severity of the urban heat island (UHI) effect is a consequence of rapid urban expansion and global climate change. The urban center of Da Nang, Vietnam, is currently experiencing severe UHI effects combined with increasingly frequent heatwaves. This study employs advanced machine learning techniques—including natural gradient boosting machine and deep neural network—to model the spatial variation in UHI intensity. The explanatory variables include topographical features, distances to coastlines and rivers, land cover types, built-up density, greenspace density, bareland density, waterbody density, and distance to wetlands. Experimental results show that the machine learning models successfully explain 90% of the variation in UHI intensity. To identify the primary factors influencing UHI intensity, Shapley additive explanations are utilized. Additionally, a neural network-based cellular automata model is implemented to project future land cover changes. The proposed framework is then employed to forecast UHI intensity in Da Nang’s urban center in 2040. Based on the prediction results, the area with extremely high UHI intensity is expected to increase by 3.7%. The area with high UHI intensity is projected to rise by 4.6%, while the area with medium UHI intensity is anticipated to expand by 12.6%. Notably, it is forecasted that the areas with extremely low and low UHI intensity are forecasted to decrease by 3.9% and 40.8%, respectively. The findings from this study can be useful to assist urban planners in establishing effective mitigation strategies for reducing the impact of UHI effects. Full article

Chironomidae are characterised by cosmopolitan distribution, high abundance and diversity in different aquatic environments, which makes them ideal for studying changes in freshwater ecosystems. To understand the environmental factors influencing chironomid communities, we analysed how altitude and waterbody type (hydromorphological features) affect their composition at 75 study sites from 49 watercourses. A total of 110 chironomid taxa from five subfamilies were identified, with Prodiamesa olivacea, Rheocricotopus fuscipes and Cricotopus bicinctus being the most frequent species. The lowest values of all alpha diversity components were recorded in communities collected from watercourses at altitudes up to 500 m a.s.l., while the highest values were observed in small mountainous rivers and streams. Beta diversity showed that taxa turnover was the dominant component in all situations analysed. Communities in large rivers with fine substrate were characterised by the lowest taxa turnover and the highest levels of nestedness, indicating the existence of an ecological gradient that reduces the number of taxa from one site to another. We identified indicator taxa for different altitudes, as well as groups of taxa that are typical for different waterbody types. Furthermore, the combination of four water parameters (oxygen saturation, conductivity, concentration of ammonium and nitrates) had the strongest influence on the chironomid community composition in the studied watercourses. Full article

Tire wear particles (TWPs), a form of microplastics (MPs) pollution, are transported into waterbodies through stormwater runoff, leading to environmental pollution and impacts on associated biota. Here, we investigated the effectiveness of stormwater filter socks filled with rice husk biochar or pine tree woodchips in reducing TWP pollution in urban runoff in Oxford, Mississippi. Triplicate runoff samples were collected upstream and downstream of the biofilters at two sites during two storm events at peak flow within minutes of the start of the storm and after 30 min. Samples were analyzed for TWPs using a combination of stereomicroscopy, micro-attenuated total reflectance Fourier transform infrared spectroscopy (µ-ATR-FTIR), and scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDX). Concentrations (TWPs/L) upstream of the biofilter were variable but highest at the start of the runoff, dropping from an average of 2811 ± 1700 to 476 ± 63 after 30 min at site 1 and from 2702 ± 353 to 2356 ± 884 at site 2. Biochar was more effective than woodchips (p < 0.05) at removing TWPs, reducing concentrations by an average of 97.6% (first use) and 85.3% (second use) compared to 66.2% and 54.2% for woodchips, respectively. Biochar was particularly effective at removing smaller TWPs (<100 µm). Both materials became less effective with use, suggesting fewer available trapping sites and the need for removal and replacement of the material with time. Overall, this study suggests that biochar and woodchips, alone or in combination, deserve further scrutiny as a potential cost-effective and sustainable method to mitigate the transfer of TWPs to aquatic ecosystems and associated biota. Full article

Short-term observations suggest that environmental changes affect the diversity and composition of soil fungi, significantly influencing forest resilience, plant diversity, and soil processes. However, time-series experiments should be supplemented with geobiological archives to capture the long-term effects of environmental changes on fungi–soil–plant interactions, particularly in undersampled, floristically diverse tropical forests. We recently conducted trnL-P6 amplicon sequencing to generate a sedimentary ancient DNA (sedaDNA) record of the regional catchment vegetation of the tropical waterbody Lake Towuti (Sulawesi, Indonesia), spanning over one million years (Myr) of the lake’s developmental history. In this study, we performed 18SV9 amplicon sequencing to create a parallel paleofungal record to (a) infer the composition, origins, and functional guilds of paleofungal community members and (b) determine the extent to which downcore changes in fungal community composition reflect the late Pleistocene evolution of the Lake Towuti catchment. We identified at least 52 members of Ascomycota (predominantly Dothiodeomycetes, Eurotiomycetes, and Leotiomycetes) and 12 members of Basidiomycota (primarily Agaricales and Polyporales). Spearman correlation analysis of the relative changes in fungal community composition, geochemical parameters, and paleovegetation assemblages revealed that the overwhelming majority consisted of soil organic matter and wood-decaying saprobes, except for a necrotrophic phytopathogenic association between Mycosphaerellaceae (Cadophora) and wetland herbs (Alocasia) in more-than-1-Myr-old silts and peats deposited in a pre-lake landscape, dominated by small rivers, wetlands, and peat swamps. During the lacustrine stage, vegetation that used to grow on ultramafic catchment soils during extended periods of inferred drying showed associations with dark septate endophytes (Ploettnerulaceae and Didymellaceae) that can produce large quantities of siderophores to solubilize mineral-bound ferrous iron, releasing bioavailable ferrous iron needed for several processes in plants, including photosynthesis. Our study showed that sedaDNA metabarcoding paired with the analysis of geochemical parameters yielded plausible insights into fungal-plant-soil interactions, and inferred changes in the paleohydrology and catchment evolution of tropical Lake Towuti, spanning more than one Myr of deposition. Full article