Search Results (1,721)

Urban inland flooding has become a serious problem in many cities because heavy rain often exceeds the capacity of drainage systems. In Japan, GIS-based evacuation maps are commonly used to support disaster preparedness, but they still have several limitations. In particular, they do not avoid flooded road segments and cannot generate multiple evacuation options at the same time. This study proposes an improved evacuation route method using the free and open-source software QGIS. The method combines flood-depth data with road network processing to remove roads where the predicted water depth is higher than 0.5 m. It also provides several evacuation paths to different shelters at the same time. A case study in Kurashiki City, Okayama Prefecture, demonstrates that about 1.37% of the road network becomes unusable during an inland-flood scenario. Several existing evacuation routes also pass through hazardous areas, but the QGIS-based method avoids these areas in most cases. Since the workflow uses only built-in QGIS functions and does not require programming or plug-ins, it is easy to reproduce and apply in other regions. This study offers a practical and low-cost method to support inland-flood evacuation planning for local governments. Full article

Our research aims to provide a comparative analysis of water governance components by presenting the complexity of water-covered land use by the extractive industry in terms of legal, economic, and environmental protection aspects in Poland and Malaysia, along with the corresponding regulations and their implications. This paper discusses the legal forms of land ownership and use, as well as the currently applied principles for calculating fees for using state-owned water covered land that contains mineral deposits. We also present a comparison of selected technologies for the extraction of sand and gravel aggregates under water with their environmental impact. This research highlights the need for specialized valuation frameworks tailored to the geological and regulatory landscape of Poland and Malaysia. We suggest that the market value of land located above a mineral deposit, calculated individually for each deposit-property, should serve as the basis for calculating the lease fee. This discussion should encompass not only the principles and methodology involved in estimating the magnitudes of lease rents on mining industry and its profitability, but also the identification and criteria for assessing the risks associated with ongoing or planned mining ventures and concerns about the protection of river ecosystems. Our research contributes in providing data to stakeholders on extractive industry that operates within flowing and standing inland waters. The key finding of our research is that, in our opinion, the water governance frameworks in Poland and Malaysia are inadequate for protecting public finances and for internalizing the environmental externalities inherent in the economics of mining. Full article

The use of aircraft for cloud seeding to enhance rainfall serves as an effective meteorological intervention and plays a vital role in ensuring ecological security within the context of the low-altitude economy. This study utilized ground-based precipitation observations from the Shiyang River Basin, in conjunction with Landsat satellite remote sensing imagery (2000–2024), regional historical regression, vegetation index retrieval, and spectral mixture analysis, to evaluate the effectiveness of aircraft-based cloud seeding for enhancing rainfall. The normalized difference vegetation index and the fraction of vegetation cover were calculated to examine the spatiotemporal dynamics and growth patterns of surface vegetation before and after the implementation of this rainfall enhancement measure, thus offering a quantitative assessment of the ecological restoration effect in the Shiyang River Basin. A novel application of cloud-seeding technology for ecological recovery has been developed. It provides one of the first quantitative assessments of aircraft-based cloud seeding in inland river basins of China, linking meteorological intervention directly to measurable ecological restoration outcomes. The findings indicate that: (1) Aircraft-based cloud seeding for rainfall enhancement has yielded significant results, with an average relative precipitation increase of 20.8% (p < 0.1%) in the operational area; (2) Following the commencement of this rainfall enhancement practice in 2010, normalized difference vegetation index and fraction of vegetation cover values within the study area have shown a marked increase, with the percentage of regions with low vegetation coverage declining from 30.36% to 25.21%; and (3) Since the implementation of this measure in 2010, vegetation conditions in the Shiyang River Basin have generally stabilized, demonstrating substantial improvement and a reduction in degradation. The percentage of regions classified as improved or slightly improved increased significantly, from 14.20% before the implementation of this measure to 36.24%, indicating a transition in the vegetation ecosystem from localized enhancement to overall improvement. These results demonstrate that ecological restoration efforts in the Shiyang River Basin have shown considerable improvement after the introduction of aircraft-based cloud-seeding operations, resulting in significant increases in vegetation coverage throughout extensive regions of the basin. The research connects scientific results to policy and management, suggesting that low-altitude economy-based cloud seeding can play a key role in water resource management, ecological stability, and climate resilience. Full article

Satellite remote sensing is an important approach for monitoring lake water environments. However, in regions with frequent cloud and rainfall, optical remote sensing imagery often suffers from extensive data gaps caused by cloud cover, rainfall, and sun glint, which severely limit its continuity and reliability for long-term monitoring. To address this issue, this study uses Lake Taihu—a typical eutrophic lake located in a cloudy and rainy region—as a case study and systematically compares four representative gap-filling methods: Kriging Interpolation, Savitzky–Golay (SG) Filtering, Data Interpolating Empirical Orthogonal Functions (DINEOF), and the Data Interpolating Convolutional Auto Encoder (DINCAE). The results show that traditional methods retain some accuracy under low missing-data conditions (for Kriging: R = 0.84, RMSE = 7.85 μg/L; for SG Filtering: R = 0.88, RMSE = 6.67 μg/L), but tend to produce over-smoothing or distorted estimations in cases of extensive gaps or highly dynamic environments. In contrast, both DINEOF and DINCAE capture the spatiotemporal variability of chlorophyll-a more effectively, maintaining relatively high accuracy and robustness even when the missing rate exceeds 60% (for DINEOF: R = 0.84, RMSE = 6.91 μg/L; for DINCAE: R = 0.79, RMSE = 8 μg/L). Based on the optimal algorithm, a seamless long-term dataset of chlorophyll-a concentration covering Lake Taihu can be constructed, providing a solid data foundation for eutrophication trend analysis and algal bloom early warning. This study demonstrates the effectiveness of integrating statistical and deep learning approaches for lake water color remote sensing data reconstruction, offering important implications for enhancing continuous monitoring of lake water environments and supporting ecological management decisions. Full article

Climate change is intensifying droughts and threatening water security worldwide, particularly in arid and semi-arid regions. Israel’s innovative response has been to integrate large-scale desalination into its water supply and climate resilience strategy, recently constructing the Reverse Water Carrier, a pioneering project that conveys desalinated seawater from the Mediterranean inland to Lake Kinneret (Sea of Galilee). This study examines the objectives, rationale, and feasibility of this system as a model for climate-resilient water management. Using a qualitative case study approach, it evaluates the project across four dimensions: water security, environmental sustainability, economic feasibility and regional cooperation. Data were drawn from policy documents, expert interviews, and government reports. The analysis finds that replenishing the Kinneret with surplus desalinated water enhances national water reliability, reduces salinity, stabilizes agricultural production, and provides a critical emergency reserve, while introducing manageable energy and ecological trade-offs. Although long-term sustainability will depend on continued efficiency improvements and adaptive management, Israel’s experience demonstrates how inter-basin desalination transfers can strengthen water security and offer a replicable framework for other regions confronting climate-induced scarcity. Full article

Salt marshes are dynamic coastal environments that play a critical role in sediment transport, nutrient cycling, and carbon sequestration. However, the geomorphic factors that influence water flow and material exchange in and between marshes of different size, shape and type remain poorly understood. In this study, we compare the morphology of three distinct marsh landscapes within the ACE Basin, South Carolina: a natural salt marsh (Ethan’s Island), a reclaimed agricultural marsh (Alligator Marsh), and a natural marsh at the upland forest interface (Hannah’s Marsh). Using high-resolution digital elevation models and estimates for drainage density and drainage efficiency, we quantify the similarities and differences in morphology between the different marsh types, and discuss potential implications of our findings in regard to water flow and material exchange. We found that drainage density and drainage efficiency are not always positively correlated and, importantly, the agricultural marsh displays the highest drainage efficiency, despite a drainage density that is comparable to the natural marshes. This finding reveals that the unique linear, interconnected structure of creek networks in the agricultural marsh yields higher efficiency. Further, we found that the natural marsh with the most complex, meandering and fragmented creek networks displayed the lowest drainage efficiency, despite having the highest drainage density. Together, these findings suggest that both drainage density and drainage efficiency should be considered separately, and that drainage efficiency is largely influenced by the structure and spatial arrangement of creek networks with a marsh. Given the relatively higher drainage efficiency of the ag-marsh landscapes, we speculate that such landscapes may enhance the flow of water and sediment between inland areas and the coastal ocean, a process that can help marshes migrate landward as sea levels rise. Full article

Chlorophyll-a (Chl-a) concentration is a key parameter for assessing the degree of eutrophication and the algal bloom risk in water bodies. Accurate and robust monitoring of Chl-a is crucial for effective water quality management of inland and coastal optically complex Case-II waters. This study proposes a stratified integrated framework that combines optical water type (OWT) classification and multi-index blending models and evaluates the capability of ZY-1 02E hyperspectral imagery in the retrieval of Chl-a concentration in Case-II waters. This research is based on ZY-1 02E hyperspectral remote sensing images and ground synchronous measurement data from four typical water bodies in China (Dongpu Reservoir, Nanyi Lake, Tangdao Bay, and Moon-lake Reservoir). Using Fuzzy C-Means (FCM) clustering combined with spectral feature analysis, three different OWTs were identified, and the bands sensitive to Chl-a for each water type were recognized. Subsequently, the most suitable semi-empirical indices (BR, TBI) were selected, and a new suspended matter correction index (SMCI) was constructed by integrating spectral bands and TSM data specifically for high-turbidity waters to facilitate the retrieval of Chl-a concentration. The RMSE and MAPE of the model constructed based on the unclassified dataset were 3.1586 μg·L⁻¹ and 30.82%, respectively. When the stratified ensemble method based on optical water type classification was employed, the overall RMSE and MAPE were reduced to 1.5832 μg·L⁻¹ and 16.36%. The results demonstrate that this hierarchical ensemble framework significantly improved the retrieval accuracy of Chl-a concentration. An uncertainty assessment of the Chl-a retrieval model for highly turbid waters incorporating SMCI was conducted using the Monte Carlo method, revealing a mean coefficient of variation of 0.0567 and a coverage rate of 95.65% for the 95% confidence interval, indicating high predictive stability and reliability of the model. This study emphasizes the importance of the integrated framework strategy that combines OWTs classification and multi-index blending models for accurate and robust remote sensing estimation of Chl-a concentration under optically complex environmental conditions. It confirms the application potential of ZY-1 02E hyperspectral data in monitoring Chl-a in inland and near-coastal waters at medium and small scales. Full article

Microplastics (MPs) are increasingly recognized as emerging contaminants in aquatic environments; however, their occurrence and fate in tropical wastewater treatment systems remain poorly understood. This study provides the first inland–island comparison of MP removal in wastewater treatment plants (WWTPs) across Thailand’s Eastern Economic Corridor. Influent and effluent samples were collected from six WWTPs, encompassing five treatment types: oxidation ditch, aerated lagoon, stabilization pond, aerated tank, and sand filtration combined with reverse osmosis. Polymeric composition and size distribution were examined in parallel with conventional water quality indicators. Across all sites, polyethylene (PE) and polypropylene dominated influent MPs, together accounting for 57–92% of total abundance. Inland plants received heterogeneous municipal wastewater, including domestic inputs and agricultural runoff. In contrast, island facilities consistently showed PE-enriched influents (45–60%) in site F, reflecting tourism-driven reliance on single-use plastics and personal care products. In addition, several minor polymers were identified, including poly (vinyl stearate) (up to 26%), polyamide, polytetrafluoroethylene and ethylene–butyl acrylate, highlighting overlooked pathways of MP entry into WWTPs. Fine MPs (100–300 μm) comprised over two-thirds of influent particles, with stabilization ponds reaching 16,000 MP m⁻³. Removal efficiency ranged from 86.0% to 98.5%. Spearman’s correlation and multiple linear regression analyses revealed strong positive relationships between MPs and both total suspended solids (TSS) and turbidity. Suspended solids parameters emerged as the most reliable predictor of MP abundance (adjusted R² = 0.91, p = 0.001). This finding highlights TSS coupled with turbidity as a practical, cost-effective indicator for monitoring MPs in tropical WWTPs. To achieve greater accuracy, a larger dataset should be built and further analyzed. Full article

Hulun Lake, the largest freshwater lake in the Eurasian steppe, is a critically climate-sensitive water body facing severe ecological threats. This systematic review synthesizes multidisciplinary evidence from 1961 to 2025 to examine the characteristics and drivers of its water environment and quality evolution. The findings reveal that the primary driver of the lake’s hydrological degradation shifted from natural climate variability to anthropogenic land-use change around 1998. While ecological water diversion has partially alleviated water scarcity, it also introduces a significant external nutrient load, creating a paradox where increased water volume coincides with aggravated eutrophication. Furthermore, overgrazing in the catchment not only enhances conventional runoff pollution but also facilitates a unique “tumbleweed-mediated cross-media pollution” pathway. This review concludes that the restoration of Hulun Lake necessitates a shift from singular water quantity regulation to an integrated management strategy that concurrently addresses water quantity, quality, and aquatic ecosystem health. The insights gained are crucial for informing the sustainable management of Hulun Lake and other inland lakes in cold, arid regions under global change. Full article

Coastal managers worldwide must prepare for changes in annual wave overtopping events due to climate change and sea-level rise. Research often assesses overtopping discharges by extreme events at a sea wall crest, typically using data from physical models or empirical rules based on scaled experiments. Here, we analyse a unique 1-year field dataset of coastal wave overtopping, from SW England, to determine the number of individual waves, regardless of their size, overtopping two locations across a coastal structure. The coastal conditions causing the most frequent overtopping differ from those driving it landward, complicating hazard communications for multiuse infrastructure. These data are the first field observations covering a year of tide, wave and wind conditions that cause overtopping of a vertical sea wall. Storms have a minimal (<2%) contribution to the number of tides associated with overtopping and the prevailing wave direction was not that associated with most overtopping events. Overtopping histograms identify the variability in the most likely time of overtopping relative to high tide for different wave categories across the structure. Sea-level rise, beach lowering and climate change will influence the annual number of waves overtopping in future. Change will be a complex balance between overtopping by different wave categories due to their likelihood of coincidence with water levels that do not cause depth-limitation over the foreshore or (partial-)reflection off the structure. It is possible the number of waves overtopping will reduce at the crest of a sea wall, while more of those overtopping waves will travel further inland. Full article

This study addresses the observation of floating plastic debris in freshwater environments using an Unmanned Aerial Vehicle (UAV) multi-sensor strategy. An experimental campaign is described where an heterogeneous plastic assemblage, namely a plastic target, and a naturally occurring leaf-litter mat are observed by a UAV platform in the Lake Calore (Avellino, Southern Italy) within the framework of the “multi-layEr approaCh to detect and analyze cOastal aggregation of MAcRo-plastic littEr” (ECOMARE) Italian Ministry of Research (MUR)-funded project. Three UAV platforms, equipped with optical, multispectral, and thermal sensors, are adopted, which overpass the two targets with the objective of analyzing the sensitivity of optical radiation to plastic and the possibility of discriminating the plastic target from the natural one. Georeferenced orthomosaics are generated across the visible, multispectral (Green, Red, Red Edge, Near-Infrared—NIR), and thermal bands. Two novel indices, the Plastic Detection Index (PDI) and the Heterogeneity Plastic Index (HPI), are proposed to discriminate between the detection of plastic litter and natural targets. The experimental results highlight that plastics exhibit heterogeneous spectral and thermal responses, whereas natural debris showed more homogeneous signatures. Green and Red bands outperform NIR for plastic detection under freshwater conditions, while thermal imagery reveals distinct emissivity variations among plastic items. This outcome is mainly explained by the strong NIR absorption of water, the wetting of plastic surfaces, and the lower sensitivity of the Mavic 3′s NIR sensor under high-irradiance conditions. The integration of optical, multispectral, and thermal data demonstrate the robustness of UAV-based approaches for distinguishing anthropogenic litter from natural materials. Overall, the findings underscore the potential of UAV-mounted remote sensing as a cost-effective and scalable tool for the high-resolution monitoring of plastic pollution over inland waters. Full article

As global climate change intensifies, hydrological processes in arid inland river basins are undergoing profound transformations, posing severe challenges to regional water security and ecological stability. This study aims to develop a coupled SWAT-LSTM model integrating glacier melt processes to simulate runoff dynamics in the Keria River basin under climate change, providing a basis for local water resource management. Based on natural monthly runoff observations from the Langgan hydrological station (1961–2015), glacier data extracted from Landsat 8 remote sensing imagery (2013–2019), and downscaled data from the CMIP6 Multi-Model Ensemble (MME), this study constructed a SWAT-LSTM coupled model to simulate future scenarios (2026–2100). Research indicates that this hybrid model significantly enhances the accuracy of hydrological simulations in high-altitude glacier-fed catchments. The Nash efficiency coefficient (NSE) during the validation period reached 0.847, representing a 15% improvement over the SWAT model. SSP5-8.5 is identified as a high-risk scenario, underscoring the urgency of emissions reduction; SSP1-2.6 represents the most desirable pathway, with its relatively stable pattern offering sustained advantages for long-term water resource management in the basin. The study further reveals a negative feedback mechanism between glacier ablation and runoff increase, validating the regulatory role of Jiyin Reservoir’s “store during floods to compensate for droughts” operation strategy in balancing basin water resources. This study explores the coupling path between the physical model and the deep learning model, and provides an effective integration scheme for the hydrological simulation of the global watershed with ice–snow meltwater as the main recharge runoff, especially for the adaptive management of water resources in inland river basins in arid areas. Full article

Conventional pixel-wise satellite-derived bathymetry (SDB) models face dual challenges: physical ambiguity from variable water quality and spatial incoherence from ignoring geographic context. This study addresses these limitations by proposing and validating OptiFusionStack, a novel two-stage physio-spatial synergistic framework that operates without in situ optical data for model calibration. The framework first generates diverse, physics-informed predictions by integrating Quasi-Analytical Algorithm (QAA)-derived inherent optical properties (IOPs) with multiple base learners. Critically, it then constructs a multi-scale spatial context by computing neighborhood statistics over an experimentally optimized 9 × 9-pixel window. These physical priors and spatial features are then effectively fused by a StackingMLP meta-learner. Validation in optically diverse environments demonstrates that OptiFusionStack significantly surpasses the performance plateau of pixel-wise methods, elevating inversion accuracy (e.g., R² elevated from 0.66 to >0.92 in optically complex inland waters). More importantly, the framework substantially reduces spatial artifacts, producing bathymetric maps with superior spatial coherence. A rigorous benchmark against several state-of-the-art, end-to-end deep learning models further confirms the superior performance of our proposed hierarchical fusion architecture in terms of accuracy. This research offers a robust and generalizable new approach for high-fidelity geospatial modeling, particularly under the common real-world constraint of having no in situ data for optical model calibration. Full article

Accurate monitoring of total suspended matter (TSM) concentration is essential for aquatic ecosystem protection and water quality assessment. Multispectral remote sensing provides an effective approach for large-scale TSM monitoring. However, robust retrieval models are difficult to develop due to limited in situ data. This study presents a Deep Feature Extraction–Machine Learning fusion framework that integrates a pre-trained back-propagation neural network (BPNN) with support vector regression (SVR) to enhance TSM retrieval. High-level spectral features extracted by BPNN are used as inputs to SVR (termed DFE-SVR) for regional TSM retrieval, using in situ measurements from five inland lakes in Jiangsu and Anhui Provinces, China. The generated TSM maps showed spatial patterns consistent with TSM concentration distributions visually observed in true-color imagery. Validation results demonstrated that DFE-SVR outperformed BPNN and SVR models, achieving R² of 0.85 and 0.90 and RMSE of 7.95 and 4.76 mg/L for GF-1 and Sentinel-2 imagery, respectively. Compared with SVR models using principal component analysis or band combinations, DFE-SVR reduced RMSE by over 20%. Under reduced training samples, the DFE-SVR model also maintained higher stability and accuracy. These findings showed its potential for multispectral water quality monitoring with limited in situ data. Full article

The Tarim River Basin, a core region for economic development and ecological security in China’s inland arid areas, faces the pressing challenge of synergistically improving land use efficiency to resolve human-land conflicts under water resource constraints and achieve sustainable development. Based on the “economic-social-ecological” benefit coordination theory, this study constructs a land use efficiency evaluation system with 16 indicators and integrates the coupling coordination degree model and gravity model to quantitatively analyze the spatiotemporal differentiation patterns and coupling mechanisms of land use efficiency in the basin from 1990 to 2020. Results show that economic and social benefits of land use increased during this period, exhibiting a “high-north, low-south” spatial pattern, while ecological benefits remained relatively high but declined gradually. The coupling coordination degree of subsystem benefits displayed significant spatial heterogeneity, with an overall upward trend, where composite factors emerged as the primary constraint. Spatially, land use efficiency coupling coordination evolved from “core polarization” to “axial expansion” and finally “networked synergy,” with stronger linkages concentrated in oasis irrigation districts. These findings provide theoretical support for ecological conservation, water management, and policy-making in southern Xinjiang, offering pathways to synergize the “economic-social-ecological” system and promote sustainable development in arid regions. Full article