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Search Results (5,766)

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27 pages, 37457 KiB  
Article
Multi-Sensor Flood Mapping in Urban and Agricultural Landscapes of the Netherlands Using SAR and Optical Data with Random Forest Classifier
by Omer Gokberk Narin, Aliihsan Sekertekin, Caglar Bayik, Filiz Bektas Balcik, Mahmut Arıkan, Fusun Balik Sanli and Saygin Abdikan
Remote Sens. 2025, 17(15), 2712; https://doi.org/10.3390/rs17152712 - 5 Aug 2025
Abstract
Floods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning [...] Read more.
Floods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning method to evaluate the July 2021 flood in the Netherlands. The research developed 25 different feature scenarios through the combination of Sentinel-1, Landsat-8, and Radarsat-2 imagery data by using backscattering coefficients together with optical Normalized Difference Water Index (NDWI) and Hue, Saturation, and Value (HSV) images and Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture features. The Random Forest (RF) classifier was optimized before its application based on two different flood-prone regions, which included Zutphen’s urban area and Heijen’s agricultural land. Results demonstrated that the multi-sensor fusion scenarios (S18, S20, and S25) achieved the highest classification performance, with overall accuracy reaching 96.4% (Kappa = 0.906–0.949) in Zutphen and 87.5% (Kappa = 0.754–0.833) in Heijen. For the flood class F1 scores of all scenarios, they varied from 0.742 to 0.969 in Zutphen and from 0.626 to 0.969 in Heijen. Eventually, the addition of SAR texture metrics enhanced flood boundary identification throughout both urban and agricultural settings. Radarsat-2 provided limited benefits to the overall results, since Sentinel-1 and Landsat-8 data proved more effective despite being freely available. This study demonstrates that using SAR and optical features together with texture information creates a powerful and expandable flood mapping system, and RF classification performs well in diverse landscape settings. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Flood Forecasting and Monitoring)
13 pages, 2022 KiB  
Article
A Practical Method for Ecological Flow Calculation to Support Integrated Ecological Functions of the Lower Yellow River, China
by Xinyuan Chen, Lixin Zhang and Lei Tang
Water 2025, 17(15), 2326; https://doi.org/10.3390/w17152326 - 5 Aug 2025
Abstract
The lower Yellow River is characterized by low water discharge and a high sediment load, resulting in a fragile aquatic ecosystem. It is important to develop a reasonable method of ecological flow calculation that can be applied to the water-scarce rivers like the [...] Read more.
The lower Yellow River is characterized by low water discharge and a high sediment load, resulting in a fragile aquatic ecosystem. It is important to develop a reasonable method of ecological flow calculation that can be applied to the water-scarce rivers like the Yellow River. In this paper, we selected the Huayuankou hydrological station in the lower Yellow River as our study site and assessed the ecological flow using several methodologies including the monthly frequency calculation method, the sediment transportation method, the habitat simulation method, and the improved annual distribution method. Based on the seasonal applicability of the four methods across months of the year, we established an ecological flow calculation method that considers the integrated ecological functions of the lower Yellow River. In this method, ecological flow in the lower Yellow River during the dry season (November to March) can be determined by using the improved annual distribution method, ecological flow in the fish spawning period (April to June) can be calculated using the habitat simulation method, and the ecological flow during the flood season (July to October) can be calculated using the sediment transportation method. The optimal ecological flow regime for the Huayuankou section was determined using the established method. The ecological flow regimes derived in our study ranged from 310 m3/s to 1532 m3/s. However, we also observed that the ecological flow has a relatively low assurance rate during the flood season in the lower Yellow River, with the assurance rate not exceeding 63%. This highlights the fact that more attention should be given in reservoir regulations to facilitating sediment transport downstream. Full article
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22 pages, 2029 KiB  
Article
A Deep Reinforcement Learning Framework for Cascade Reservoir Operations Under Runoff Uncertainty
by Jing Xu, Jiabin Qiao, Qianli Sun and Keyan Shen
Water 2025, 17(15), 2324; https://doi.org/10.3390/w17152324 - 5 Aug 2025
Abstract
Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address [...] Read more.
Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address inflow variability. This study introduces a novel deep reinforcement learning (DRL) framework that tightly couples probabilistic runoff forecasting with adaptive reservoir scheduling. We integrate a Long Short-Term Memory (LSTM) neural network to model runoff uncertainty and generate probabilistic inflow forecasts, which are then embedded into a Proximal Policy Optimization (PPO) algorithm via Monte Carlo sampling. This unified forecast–optimize architecture allows for dynamic policy adjustment in response to stochastic hydrological conditions. A case study on China’s Xiluodu–Xiangjiaba cascade system demonstrates that the proposed LSTM-PPO framework achieves superior performance compared to traditional baselines, notably improving power output, storage utilization, and spillage reduction. The results highlight the method’s robustness and scalability, suggesting strong potential for supporting resilient water–energy nexus management under complex environmental uncertainty. Full article
(This article belongs to the Section Hydrology)
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14 pages, 5448 KiB  
Article
A Study of Climate-Sensitive Diseases in Climate-Stressed Areas of Bangladesh
by Ahammadul Kabir, Shahidul Alam, Nusrat Jahan Tarin, Shila Sarkar, Anthony Eshofonie, Mohammad Ferdous Rahman Sarker, Abul Kashem Shafiqur Rahman and Tahmina Shirin
Climate 2025, 13(8), 166; https://doi.org/10.3390/cli13080166 - 5 Aug 2025
Abstract
The National Adaptation Plan of Bangladesh identifies eleven climate-stressed zones, placing nearly 100 million people at high risk of climate-related hazards. Vulnerable groups such as the poor, floating populations, daily laborers, and slum dwellers are particularly affected. However, there is a lack of [...] Read more.
The National Adaptation Plan of Bangladesh identifies eleven climate-stressed zones, placing nearly 100 million people at high risk of climate-related hazards. Vulnerable groups such as the poor, floating populations, daily laborers, and slum dwellers are particularly affected. However, there is a lack of data on climate-sensitive diseases and related hospital visits in these areas. This study explored the prevalence of such diseases using the Delphi method through focus group discussions with 493 healthcare professionals from 153 hospitals in 156 upazilas across 21 districts and ten zones. Participants were selected by district Civil Surgeons. Key climate-sensitive diseases identified included malnutrition, diarrhea, pneumonia, respiratory infections, typhoid, skin diseases, hypertension, cholera, mental health disorders, hepatitis, heat stroke, and dengue. Seasonal surges in hospital visits were noted, influenced by factors like extreme heat, air pollution, floods, water contamination, poor sanitation, salinity, and disease vectors. Some diseases were zone-specific, while others were widespread. Regions with fewer hospital visits often had higher disease burdens, indicating under-reporting or lack of access. The findings highlight the need for area-specific adaptation strategies and updates to the Health National Adaptation Plan. Strengthening resilience through targeted investment and preventive measures is crucial to reducing health risks from climate change. Full article
(This article belongs to the Section Climate and Environment)
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17 pages, 826 KiB  
Review
Mechanisms and Impact of Acacia mearnsii Invasion
by Hisashi Kato-Noguchi and Midori Kato
Diversity 2025, 17(8), 553; https://doi.org/10.3390/d17080553 - 4 Aug 2025
Abstract
Acacia mearnsii De Wild. has been introduced to over 150 countries for its economic value. However, it easily escapes from plantations and establishes monospecific stands across plains, hills, valleys, and riparian habitats, including protected areas such as national parks and forest reserves. Due [...] Read more.
Acacia mearnsii De Wild. has been introduced to over 150 countries for its economic value. However, it easily escapes from plantations and establishes monospecific stands across plains, hills, valleys, and riparian habitats, including protected areas such as national parks and forest reserves. Due to its negative ecological impact, A. mearnsii has been listed among the world’s 100 worst invasive alien species. This species exhibits rapid stem growth in its sapling stage and reaches reproductive maturity early. It produces a large quantity of long-lived seeds, establishing a substantial seed bank. A. mearnsii can grow in different environmental conditions and tolerates various adverse conditions, such as low temperatures and drought. Its invasive populations are unlikely to be seriously damaged by herbivores and pathogens. Additionally, A. mearnsii exhibits allelopathic activity, though its ecological significance remains unclear. These characteristics of A. mearnsii may contribute to its expansion in introduced ranges. The presence of A. mearnsii affects abiotic processes in ecosystems by reducing water availability, increasing the risk of soil erosion and flooding, altering soil chemical composition, and obstructing solar light irradiation. The invasion negatively affects biotic processes as well, reducing the diversity and abundance of native plants and arthropods, including protective species. Eradicating invasive populations of A. mearnsii requires an integrated, long-term management approach based on an understanding of its invasive mechanisms. Early detection of invasive populations and the promotion of public awareness about their impact are also important. More attention must be given to its invasive traits because it easily escapes from cultivation. Full article
(This article belongs to the Special Issue Plant Adaptation and Survival Under Global Environmental Change)
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19 pages, 5404 KiB  
Article
Combined Effects of Flood Disturbances and Nutrient Enrichment Prompt Aquatic Vegetation Expansion: Sediment Evidence from a Floodplain Lake
by Zhuoxuan Gu, Yan Li, Jingxiang Li, Zixin Liu, Yingying Chen, Yajing Wang, Erik Jeppesen and Xuhui Dong
Plants 2025, 14(15), 2381; https://doi.org/10.3390/plants14152381 - 2 Aug 2025
Viewed by 245
Abstract
Aquatic macrophytes are a vital component of lake ecosystems, profoundly influencing ecosystem structure and function. Under future scenarios of more frequent extreme floods and intensified lake eutrophication, aquatic macrophytes will face increasing challenges. Therefore, understanding aquatic macrophyte responses to flood disturbances and nutrient [...] Read more.
Aquatic macrophytes are a vital component of lake ecosystems, profoundly influencing ecosystem structure and function. Under future scenarios of more frequent extreme floods and intensified lake eutrophication, aquatic macrophytes will face increasing challenges. Therefore, understanding aquatic macrophyte responses to flood disturbances and nutrient enrichment is crucial for predicting future vegetation dynamics in lake ecosystems. This study focuses on Huangmaotan Lake, a Yangtze River floodplain lake, where we reconstructed 200-year successional trajectories of macrophyte communities and their driving mechanisms. With a multiproxy approach we analyzed a well-dated sediment core incorporating plant macrofossils, grain size, nutrient elements, heavy metals, and historical flood records from the watershed. The results demonstrate a significant shift in the macrophyte community, from species that existed before 1914 to species that existed by 2020. Unlike the widespread macrophyte degradation seen in most regional lakes, this lake has maintained clear-water plant dominance and experienced continuous vegetation expansion over the past 50 years. We attribute this to the interrelated effects of floods and the enrichment of ecosystems with nutrients. Specifically, our findings suggest that nutrient enrichment can mitigate the stress effects of floods on aquatic macrophytes, while flood disturbances help reduce excess nutrient concentrations in the water column. These findings offer applicable insights for aquatic vegetation restoration in the Yangtze River floodplain and other comparable lake systems worldwide. Full article
(This article belongs to the Special Issue Aquatic Plants and Wetland)
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10 pages, 3658 KiB  
Proceeding Paper
A Comparison Between Adam and Levenberg–Marquardt Optimizers for the Prediction of Extremes: Case Study for Flood Prediction with Artificial Neural Networks
by Julien Yise Peniel Adounkpe, Valentin Wendling, Alain Dezetter, Bruno Arfib, Guillaume Artigue, Séverin Pistre and Anne Johannet
Eng. Proc. 2025, 101(1), 12; https://doi.org/10.3390/engproc2025101012 - 31 Jul 2025
Abstract
Artificial neural networks (ANNs) adjust to the underlying behavior in the dataset using a training rule or optimizer. The most popular first-and second-order optimizers, Adam (AD) and Levenberg–Marquardt (LM), were compared with the aim of predicting extreme flash floods of a runoff-dominated hydrological [...] Read more.
Artificial neural networks (ANNs) adjust to the underlying behavior in the dataset using a training rule or optimizer. The most popular first-and second-order optimizers, Adam (AD) and Levenberg–Marquardt (LM), were compared with the aim of predicting extreme flash floods of a runoff-dominated hydrological system. A fully connected multilayer perceptron with a shallow structure was used to reduce complexity and limit overfitting. The inputs of the ANN were determined by rainfall–water level cross-correlation analysis. For each optimizer, the hyperparameters of the ANN were selected using a grid search and the cross-validation score on a novel criterion (PERS PEAK) mixing the persistency (PERS) and the quality of flood-peak restitution (PEAK). For an extreme and unseen event used as a test set, LM outperformed AD by 25% on all performance criteria. The peak water level of this event, 66% greater than that of the training set, was predicted by 92% after more training iterations were done by the LM optimizer. This shows that the ANN can predict beyond the ranges of the training set, given the right optimizer. Nevertheless, the LM training time was up to five times longer than that of AD during grid search. Full article
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33 pages, 2962 KiB  
Review
Evolution of Data-Driven Flood Forecasting: Trends, Technologies, and Gaps—A Systematic Mapping Study
by Banujan Kuhaneswaran, Golam Sorwar, Ali Reza Alaei and Feifei Tong
Water 2025, 17(15), 2281; https://doi.org/10.3390/w17152281 - 31 Jul 2025
Viewed by 366
Abstract
This paper presents a Systematic Mapping Study (SMS) on data-driven approaches in flood forecasting from 2019 to 2024, a period marked by transformative developments in Deep Learning (DL) technologies. Analysing 363 selected studies, this paper provides an overview of the technological evolution in [...] Read more.
This paper presents a Systematic Mapping Study (SMS) on data-driven approaches in flood forecasting from 2019 to 2024, a period marked by transformative developments in Deep Learning (DL) technologies. Analysing 363 selected studies, this paper provides an overview of the technological evolution in this field, methodological approaches, evaluation practices and geographical distribution of studies. The study revealed that meteorological and hydrological factors constitute approximately 76% of input variables, with rainfall/precipitation and water level measurements forming the core predictive basis. Long Short-Term Memory (LSTM) networks emerged as the dominant algorithm (21% of implementations), whilst hybrid and ensemble approaches showed the most dramatic growth (from 2% in 2019 to 10% in 2024). The study also revealed a threefold increase in publications during this period, with significant geographical concentration in East and Southeast Asia (56% of studies), particularly China (36%). Several research gaps were identified, including limited exploration of graph-based approaches for modelling spatial relationships, underutilisation of transfer learning for data-scarce regions, and insufficient uncertainty quantification. This SMS provides researchers and practitioners with actionable insights into current trends, methodological practices, and future directions in data-driven flood forecasting, thereby advancing this critical field for disaster management. Full article
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19 pages, 2237 KiB  
Article
Flood Season Division Model Based on Goose Optimization Algorithm–Minimum Deviation Combination Weighting
by Yukai Wang, Jun Li and Jing Fu
Sustainability 2025, 17(15), 6968; https://doi.org/10.3390/su17156968 - 31 Jul 2025
Viewed by 163
Abstract
The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. [...] Read more.
The division of the flood season is of great significance for the precise operation of water conservancy projects, flood control and disaster reduction, and the rational allocation of water resources, alleviating the contradiction of the uneven spatial and temporal distribution of water resources. The single weighting method can only determine the weight of the flood season division indicators from a certain perspective and cannot comprehensively reflect the time-series attributes of the indicators. This study proposes a Flood Season Division Model based on the Goose Optimization Algorithm and Minimum Deviation Combined Weighting (FSDGOAMDCW). The model uses the Goose Optimization Algorithm (GOA) to solve the Minimum Deviation Combination model, integrating weights from two subjective methods (Expert Scoring and G1) and three objective methods (Entropy Weight, CV, and CRITIC). Combined with the Set Pair Analysis Method (SPAM), it realizes comprehensive flood season division. Based on daily precipitation data of the Nandujiang River (1961–2022), the study determines its flood season from 1 May to 30 October. Comparisons show that: ① GOA converges faster than the Genetic Algorithm, stabilizing at T = 5 and achieving full convergence at T = 24; and ② The model’s division results have the smallest Intra-Class Differences, avoiding indistinguishability between flood and non-flood seasons under special conditions. This research aims to support flood season division studies in tropical islands. Full article
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30 pages, 4804 KiB  
Article
Deep Storage Irrigation Enhances Grain Yield of Winter Wheat by Improving Plant Growth and Grain-Filling Process in Northwest China
by Xiaodong Fan, Dianyu Chen, Haitao Che, Yakun Wang, Yadan Du and Xiaotao Hu
Agronomy 2025, 15(8), 1852; https://doi.org/10.3390/agronomy15081852 - 31 Jul 2025
Viewed by 214
Abstract
In the irrigation districts of Northern China, the flood resources utilization for deep storage irrigation, which is essentially characterized by active excessive irrigation, aims to have the potential to mitigate freshwater shortages, and long-term groundwater overexploitation. It is crucial to detect the effects [...] Read more.
In the irrigation districts of Northern China, the flood resources utilization for deep storage irrigation, which is essentially characterized by active excessive irrigation, aims to have the potential to mitigate freshwater shortages, and long-term groundwater overexploitation. It is crucial to detect the effects of irrigation amounts on agricultural yield and the mechanisms under deep storage irrigation. A three-year field experiment (2020–2023) was conducted in the Guanzhong Plain, according to five soil wetting layer depths (RF: 0 cm; W1: control, 120 cm; W2: 140 cm; W3: 160 cm; W4: 180 cm) with soil saturation water content as the irrigation upper limit. Results exhibited that, compared to W1, the W2, W3, and W4 treatments led to the increased plant height, leaf area index, and dry matter accumulation. Meanwhile, the W2, W3, and W4 treatments improved kernel weight increment achieving maximum grain-filling rate (Wmax), maximum grain-filling rate (Gmax), and average grain-filling rate (Gave), thereby enhancing the effective spikes (ES) and grain number per spike (GS), and thus increased wheat grain yield (GY). In relative to W1, the W2, W3, and W4 treatments increased the ES, GS, and GY by 11.89–19.81%, 8.61–14.36%, and 8.17–13.62% across the three years. Notably, no significant difference was observed in GS and GY between W3 and W4 treatments, but W4 treatment displayed significant decreases in ES by 3.04%, 3.06%, and 2.98% in the respective years. The application of a structural equation modeling (SEM) revealed that deep storage irrigation improved ES and GS by positively regulating Wmax, Gmax, and Gave, thus significantly increasing GY. Overall, this study identified the optimal threshold (W3 treatment) to maximize wheat yields by optimizing both the vegetative growth and grain-filling dynamics. This study provides essential support for the feasibility assessment of deep storage irrigation before flood seasons, which is vital for the balance and coordination of food security and water security. Full article
(This article belongs to the Section Water Use and Irrigation)
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15 pages, 2006 KiB  
Article
Hydrological Responses to Territorial Spatial Change in the Xitiaoxi River Basin: A Simulation Study Using the SWAT Model Driven by China Meteorological Assimilation Driving Datasets
by Dongyan Kong, Huiguang Chen and Kongsen Wu
Water 2025, 17(15), 2267; https://doi.org/10.3390/w17152267 - 30 Jul 2025
Viewed by 244
Abstract
The use of the Soil and Water Assessment Tool (SWAT) model driven by China Meteorological Assimilation Driving Datasets (CMADS) for runoff simulation research is of great significance for regional flood prevention and control. Therefore, from the perspective of production-living-ecological space, this article combined [...] Read more.
The use of the Soil and Water Assessment Tool (SWAT) model driven by China Meteorological Assimilation Driving Datasets (CMADS) for runoff simulation research is of great significance for regional flood prevention and control. Therefore, from the perspective of production-living-ecological space, this article combined multi-source data such as DEM, soil texture and land use type, in order to construct scenarios of territorial spatial change (TSC) across distinct periods. Based on the CMADS-L40 data and the SWAT model, it simulated the runoff dynamics in the Xitiaoxi River Basin, and analyzed the hydrological response characteristics under different TSCs. The results showed that The SWAT model, driven by CMADS-L40 data, demonstrated robust performance in monthly runoff simulation. The coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (NSE), and the absolute value of percentage bias (|PBIAS|) during the calibration and validation period all met the accuracy requirements of the model, which validated the applicability of CMADS-L40 data and the SWAT model for runoff simulation at the watershed scale. Changes in territorial spatial patterns are closely correlated with runoff variation. Changes in agricultural production space and forest ecological space show statistically significant negative correlation with runoff change, while industrial production space change exhibits a significant positive correlation with runoff change. The expansion of production space, particularly industrial production space, leads to increased runoff, whereas the enlargement of agricultural production space and forest ecological space can reduce runoff. This article contributes to highlighting the role of land use policy in hydrological regulation, providing a scientific basis for optimizing territorial spatial planning to mitigate flood risks and protect water resources. Full article
(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction, 2nd Edition)
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19 pages, 4896 KiB  
Article
Calculation of Connectivity Between Surface and Underground Three-Dimensional Water Systems in the Luan River Basin
by Jingyao Wang, Zhixiong Tang, Belay Z. Abate, Zhuoxun Wu and Li He
Sustainability 2025, 17(15), 6913; https://doi.org/10.3390/su17156913 - 30 Jul 2025
Viewed by 213
Abstract
While water conservancy projects continuously enhance flood control and resource allocation capabilities, the adverse impacts on basin systems, particularly the structural disruption of surface water–groundwater continuity, have become increasingly pronounced. Therefore, establishing quantitative assessment of water system connectivity as a critical foundation for [...] Read more.
While water conservancy projects continuously enhance flood control and resource allocation capabilities, the adverse impacts on basin systems, particularly the structural disruption of surface water–groundwater continuity, have become increasingly pronounced. Therefore, establishing quantitative assessment of water system connectivity as a critical foundation for optimizing spatial water distribution, maintaining ecohydrological equilibrium, and enhancing flood–drought regulation efficacy is important. Focusing on the regulated reaches of the Panjiakou, Daheiting, and Taolinkou reservoirs in the Luan River Basin, this study established and integrated a three-dimensional assessment framework that synthesizes hydrological processes, hydraulic structural effects, and human activities as three fundamental drivers, and employed the Analytic Hierarchy Process (AHP) to develop a quantitative connectivity evaluation system. Results indicate that water conservancy projects significantly altered basin connectivity: surface water connectivity decreased by 0.40, while groundwater connectivity experienced a minor reduction (0.25) primarily through reservoir seepage. Consequently, the integrated surface–groundwater system declined by 0.39. Critically, project scale governs surface connectivity attenuation intensity, which substantially exceeds impacts on groundwater systems. The comprehensive assessment system developed in this study provides theoretical and methodological support for diagnosing river connectivity, formulating ecological restoration strategies, and protecting basin ecosystems. Full article
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20 pages, 3137 KiB  
Article
The Heat Transfer Coefficient During Pool Boiling of Refrigerants in a Compact Heat Exchanger
by Marcin Kruzel, Tadeusz Bohdal, Krzysztof Dutkowski, Krzysztof J. Wołosz and Grzegorz Robakowski
Energies 2025, 18(15), 4030; https://doi.org/10.3390/en18154030 - 29 Jul 2025
Viewed by 211
Abstract
The results of experimental data on the heat transfer coefficient during the boiling of pro-ecological refrigerants in a compact tube-shell heat exchanger are presented. The boiling process occurred in the micro-space of the exchanger shell on the surface of horizontal tubes, which were [...] Read more.
The results of experimental data on the heat transfer coefficient during the boiling of pro-ecological refrigerants in a compact tube-shell heat exchanger are presented. The boiling process occurred in the micro-space of the exchanger shell on the surface of horizontal tubes, which were heated from the inside with warm water. The flow of the refrigerant was gravity-based. The heat exchanger was practically flooded with liquid refrigerant at a saturation temperature (ts), which flowed out after evaporation in a gaseous form. The tests were conducted for four refrigerants: R1234ze, R1234yf, R134a (a high-pressure refrigerant), and HFE7100 (a low-pressure refrigerant). Thermal characteristics describing the heat transfer process throughout the entire compact heat exchanger, specifically for the boiling process itself, were developed. It was found that in the case of micro-space boiling, there is an exponential dependence of the heat transfer coefficient on the heat flux density on the heated surface. Experimental data were compared to experimental and empirical data presented in other studies. Our own empirical models were proposed to determine the heat transfer coefficient for boiling in a mini-space for individual refrigerants. The proposed calculation models were also generalized for various refrigerants by introducing the value of reduced pressure into the calculation relationship. The developed relationship enables the determination of heat transfer coefficient values during boiling in a micro-space on the surface of horizontal tubes for various refrigerants with an accuracy of ±25%. Full article
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22 pages, 3267 KiB  
Article
Identifying Deformation Drivers in Dam Segments Using Combined X- and C-Band PS Time Series
by Jonas Ziemer, Jannik Jänichen, Gideon Stein, Natascha Liedel, Carolin Wicker, Katja Last, Joachim Denzler, Christiane Schmullius, Maha Shadaydeh and Clémence Dubois
Remote Sens. 2025, 17(15), 2629; https://doi.org/10.3390/rs17152629 - 29 Jul 2025
Viewed by 243
Abstract
Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that [...] Read more.
Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that offer either high spatial or temporal resolution. Persistent Scatterer Interferometry (PSI) addresses these limitations, enabling high-resolution monitoring in both domains. Sensors such as TerraSAR-X (TSX) and Sentinel-1 (S-1) have proven effective for deformation analysis with millimeter accuracy. Combining TSX and S-1 datasets enhances monitoring capabilities by leveraging the high spatial resolution of TSX with the broad coverage of S-1. This improves monitoring by increasing PS point density, reducing revisit intervals, and facilitating the detection of environmental deformation drivers. This study aims to investigate two objectives: first, we evaluate the benefits of a spatially and temporally densified PS time series derived from TSX and S-1 data for detecting radial deformations in individual dam segments. To support this, we developed the TSX2StaMPS toolbox, integrated into the updated snap2stamps workflow for generating single-master interferogram stacks using TSX data. Second, we identify deformation drivers using water level and temperature as exogenous variables. The five-year study period (2017–2022) was conducted on a gravity dam in North Rhine-Westphalia, Germany, which was divided into logically connected segments. The results were compared to in situ data obtained from pendulum measurements. Linear models demonstrated a fair agreement between the combined time series and the pendulum data (R2 = 0.5; MAE = 2.3 mm). Temperature was identified as the primary long-term driver of periodic deformations of the gravity dam. Following the filling of the reservoir, the variance in the PS data increased from 0.9 mm to 3.9 mm in RMSE, suggesting that water level changes are more responsible for short-term variations in the SAR signal. Upon full impoundment, the mean deformation amplitude decreased by approximately 1.7 mm toward the downstream side of the dam, which was attributed to the higher water pressure. The last five meters of water level rise resulted in higher feature importance due to interaction effects with temperature. The study concludes that integrating multiple PS datasets for dam monitoring is beneficial particularly for dams where few PS points can be identified using one sensor or where pendulum systems are not installed. Identifying the drivers of deformation is feasible and can be incorporated into existing monitoring frameworks. Full article
(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy II)
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27 pages, 8755 KiB  
Article
Mapping Wetlands with High-Resolution Planet SuperDove Satellite Imagery: An Assessment of Machine Learning Models Across the Diverse Waterscapes of New Zealand
by Md. Saiful Islam Khan, Maria C. Vega-Corredor and Matthew D. Wilson
Remote Sens. 2025, 17(15), 2626; https://doi.org/10.3390/rs17152626 - 29 Jul 2025
Viewed by 404
Abstract
(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate [...] Read more.
(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article
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