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Tropical forest loss and degradation in watersheds disrupt essential ecosystem services that regulate water flow, often causing devastating floods that impact agricultural productivity and impoverish downstream communities. Despite its importance, evaluations of the interconnectedness between the depletion of hydrological services and flooding lack an evidence-base in the Global South and, therefore, have little influence on policy dialogue. In this study, we focus on the forest-rich province of Aceh, Indonesia, using local and national newspaper articles to compile information on flood events between 2011 and 2018. We explored spatio-temporal flood patterns with a combination of climatic, topographic, and environmental factors. We compiled 2,029 reported flood events in mainland Aceh located in 20 of the 21 districts/cities, with a disproportionately high occurrence (71%) in four districts. The trend of flood events exhibited an increasing pattern between 2011 and 2018. Over this period, floods displaced ~158,000 people and damaged ~24,500 houses and ~11,500 ha of agricultural land. Our generalized linear mixed-effect model found that reported flood events were more likely to occur in areas with lower tree cover, more oil palm plantations, and higher precipitation. Areas with a lower human population density and higher poverty rates were found to be most susceptible to flooding events. Our findings highlight the critical link between forest preservation and flood prevention, and the irreplaceable role that forests play in ensuring the well-being of local communities, especially those affected by poverty. Our study underscores the importance of considering these interconnected factors in future land use and economic development plans and policies. [ABSTRACT FROM AUTHOR]

The present study using a 90-m resolution Shuttle Radar Topography Mission (SRTM), digital elevation model (DEM), and geospatial techniques and an in-depth study of geomorphic and structural characteristics has been carried out for the Cauvery River basin (CRB) of India. The study of terrain characteristics and their evolution has, in fact, been transformed in recent years by the combination of remote sensing data such as the SRTM, DEM with GIS technology. The analysis involves morphometric assessments of the drainage system, focusing on linear, aerial, and relief aspects. Additionally, the longitudinal profile analysis synthesizes the influence of lithology on the basin's characteristics. Notably, Cauvery is identified as the 8th order drainage basin, covering a substantial area of 85,071.6 km2. River basin structural control is found to be low to moderate, according to the bifurcation ratio study. Because of the hard rock lithology, there is less stream frequency (0.22) and drainage density (0.6 km/km2), which indicates that there may be more permeability in the strata below the surface, river's mean stream length, which varies from 1.8 to 241.4 for all orders, and the stream length ratio, which varies from 0.38 to 0.65, which has a surface area of 85,071.6 sq. km and a circumference of roughly 2573.2 km. The vastness of the basin, as seen by its area and perimeter, emphasizes how important it is to the dynamics of peak flow and runoff in hydrological processes. The basin is extremely elongated, as indicated by the shape parameters, which makes managing flood control within the basin comparatively easier and river watershed management. [ABSTRACT FROM AUTHOR]

Many tidal marshes have been lost by past land use changes, but are nowadays increasingly restored and created to provide valuable ecosystem services such as nature‐based flood and erosion protection along estuarine shorelines. To be functional for flood and shoreline erosion protection, restored and created tidal marshes should develop erosion resistant sediment beds. Here, we investigated which factors drive the spatial variations in sediment strength and erosion resistance in a developing tidal marsh restoration site. Our results show that decreasing tidal inundation frequency, decreasing sedimentation rate, and better drainage led to stronger consolidation in deeper sediment layers. This consolidation resulted in greater sediment strength, quantified here by shear strength and penetration resistance. Generally, sediment strength was greater when sediment had higher bulk density, while a higher water and fine fraction (= clay and silt) content decreased sediment strength. Overall, all measurement locations were relatively erosion resistant, likely caused by the dense root network and cohesive sediment. To restore or create resilient tidal marshes for nature‐based flood and shoreline erosion protection, we should thus aim for sites with relatively low tidal inundation frequency, moderate sedimentation rates, and cohesive sediment mixtures of clay, silt, and sand, which are well drained and have potential for vegetation establishment. These conditions have a high likelihood of resulting in restored or created tidal marshes that contribute to nature‐based flood and shoreline erosion protection. [ABSTRACT FROM AUTHOR]

Purpose: Concrete gravity dams are important structures for flood control and hydraulic power generation, but they can be vulnerable to seismic activity due to ground movements that trigger crack propagation. Design/methodology/approach: To better understand the factors that affect the stability of concrete gravity dams against concrete fracture during earthquakes, a concrete plastic damage model has been utilized with two new expressions to simulate compressive and tensile damage variables. Findings: The findings showed that the crack patterns were strongly influenced by the concrete's strength. The simulation results led to the proposal of appropriate concrete properties aimed at minimizing damage. These findings, together with the proposed model, offer significant insights that can enhance the safety and stability of concrete gravity dam structures. Originality/value: This study offers a comprehensive analysis of concrete behavior under varying grades and introduces simple and robust expressions for evaluating concrete parameters in plastic damage models. The versatility of these expressions enables accurate simulation of stress-strain curves for different grades, resulting in excellent agreement between model results and experimental findings. The simulation of the Koyna Dam case study demonstrates a similarity in crack patterns with previous simulations and field observations. [ABSTRACT FROM AUTHOR]

Sealing of surfaces alters the natural hydrological conditions, thereby increasing the urban flood risk. Rainwater management in urban environments has traditionally been performed using conventional urban drainage systems (UDS). However, low impact development (LID) solutions have been recognised in recent decades as effective in controlling runoff. The present work aims to evaluate the efficiency of LID techniques in flood control in a closed basin characterised by high urbanisation for three extreme precipitation events in the city of Natal, Brazil, using 1D/2D modelling. Two techniques were tested, namely bioretention cells (BCs) and rain barrels (RBs) alone or combined. The existing UDS in the area proved unable to control flooding of the basin. Water depths exceeded 0.5 m in all events. The efficiency of the LID solutions evaluated was extremely low, with reductions of less than 1% for highest hazard class. Combining the techniques produced better performance than implementing isolated techniques. [ABSTRACT FROM AUTHOR]

Reservoirs balance multiple conflicting objectives, including flood control and water supply. In California, shifts in seasonal hydrologic patterns under climate change will amplify the difficulties in balancing flood control with water supply. Current flood control policies are based on fixed seasonal rule curves determined by the observed timing and magnitude of floods in the record. These rule curves generally require the release of wet season inflows, reducing the available stored water for use during the dry season. Here we investigate the potential for forecast-informed reservoir operations (FIRO) to increase water supply availability while minimizing additional flood risk at 14 reservoirs in the Sacramento, San Joaquin, and Tulare river basins. We use a differential evolution algorithm to train risk-based reservoir operation policies with an ensemble of historical forecasts over the period 2013–2023. Results show an average 8.1% increase in storage normalized by capacity, though this varies across reservoirs. The forecast-informed policies also reduce the occurrence of high-magnitude releases throughout the system. The accumulation of benefits is sensitive to the timing and magnitude of flood events, and most of the cumulative benefit is obtained during a few years. Under cross-validation, we find that large floods are needed in the training data to avoid overfitting the policy. We further examine the relationship between reservoir properties and FIRO benefits, finding that the ratio of peak inflow magnitude to maximum safe release correlates with increased storage under the FIRO policy, while the ratio of mean inflow to capacity correlates to the reduction of high-magnitude releases. This study highlights how adaptive reservoir management policies can yield water supply benefits without an increase in flood risk, given adequate historical data for policy training. These policies may be a valuable adaptation to climate change but require careful validation and out-of-sample testing. [ABSTRACT FROM AUTHOR]

Egø Engsø is an artificial Danish suburban lake created in 2006, primarily for nutrient retention and flood control. Expanding submerged macrophyte cover (dominated by Stoneworts Chara spp. and Pondweeds Potamogeton spp.) attracted moulting concentrations of 600 mute swans Cygnus olor, 1100 greylag geese Anser anser and 280 Canada geese Branta canadensis. These unexpected additions to the avifauna benefit from reliable food supplies and effective protection from recreational disturbance on and near the water surface. Egø Engsø is a model of enabling intense human activity at a waterbird moulting site, and confirming appropriate planning can accommodate multiple functional objectives following wetland creation. [ABSTRACT FROM AUTHOR]

Flood forecasting is crucial for flood prevention and mitigation in both watersheds and urban areas. In watersheds, the development of flood forecasting methods is relatively mature, while their application in urban areas is still in the research and exploration stage. In practice, high-precision hydrological and hydrodynamic modeling during large floods is constrained to varying degrees by power, timeliness, and transmission conditions, whereas the unit hydrograph method is simple, efficient, has fewer constraints, and plays an important role. To address the current situation, this study introduces an improved synthetic unit hydrograph method to evaluate its practical effectiveness in watershed and urban flood forecasting. Firstly, based on the Snyder's synthetic unit hydrograph method, the flooding process in Muyangxi watershed in Fujian Province was calculated and compared with the results of the Xin'anjiang model and the measured data; secondly, based on the Snyder's synthetic unit hydrograph method, the urban synthetic unit hydrograph was established by taking into account the impermeability of the sub-surface and the slope, which was then applied to the calculation of the flooding process line in the urban area of Zhengzhou, and the differences of the simulation results between this method and the mainstream SWMM model were compared. The differences between this method and the mainstream SWMM model simulation results are compared. The results show that the synthetic unit hydrograph method is close to the measured and hydrological model results in the selected watersheds, and the forecast pass rate is 91.7%, with no peak-present time difference; compared with the SWMM model results, the relative error of the peak flow of the most floods is within 10%, and the coefficient of certainty of the floods in 72.7% is above 0.9. The example application shows that compared with the hydrological and hydrodynamic modeling methods, the partial rise and fall process of the flood calculated by the synthetic unit hydrograph method is better than the model results, which are more in line with the actual process, and its calculation is concise. The synthetic unit hydrograph method requires fewer parameters, and has strong implementation and popularity. The research further verifies the superiority of the synthetic unit hydrograph method in flood forecasting and provides an effective method for flood process calculation. [ABSTRACT FROM AUTHOR]

Affected by human activities, the naturally occurring river network in the southeastern plain of Yinzhou has gradually evolved into a natural–artificial composite water system, and changes in river connectivity due to changes in river network systems have caused water security problems, including urban flooding. To clarify the river connectivity change and its relationship with the urbanization process, this paper discusses an evaluation method for river connectivity based on complex networks and cellular automata (CA) from the perspective of complex systems, quantitatively analyzes the spatial–temporal characteristics of the structural and functional river connectivity in the study area during the 1990s–2020s, and reveals the impact of river nodes and chains on the connectivity level under the disturbance of natural or human factors. The results contained the following revelations: ① River connectivity showed a decreasing trend in the initial and rapid development stages of urbanization from the 1990s to the 2010s and a limited increasing trend in the optimization and upgrading stages from the 2010s to the 2020s. ② River network degradation and ongoing connectivity decline are found in the northeastern part of the study area. The highest river connectivity exists in Dongqianhukaifaqu. ③ The number of river nodes and chains should be maintained at approximately 80% for normal river connectivity. The nodes of high degree in the inflow area are listed in the key protection areas. ④ Changes in river connectivity are significantly correlated with the urbanization process. Changes in the functional connectivity level affect the magnitude of a flood. This study provides a theoretical basis for river network connectivity improvement and flood prevention in plain areas. [ABSTRACT FROM AUTHOR]

Accurate assessment of water surface velocity (WSV) is essential for flood prevention, disaster mitigation, and erosion control within hydrological monitoring. Existing image-based velocimetry techniques largely depend on correlation principles, requiring users to input and adjust parameters to achieve reliable results, which poses challenges for users lacking relevant expertise. This study presents RivVideoFlow, a user-friendly, rapid, and precise method for WSV. RivVideoFlow combines two-dimensional and three-dimensional orthorectification based on Ground Control Points (GCPs) with a deep learning-based multi-frame optical flow estimation algorithm named VideoFlow, which integrates temporal cues. The orthorectification process employs a homography matrix to convert images from various angles into a top-down view, aligning the image coordinates with actual geographical coordinates. VideoFlow achieves superior accuracy and strong dataset generalization compared to two-frame RAFT models due to its more effective capture of flow velocity continuity over time, leading to enhanced stability in velocity measurements. The algorithm has been validated on a flood simulation experimental platform, in outdoor settings, and with synthetic river videos. Results demonstrate that RivVideoFlow can robustly estimate surface velocity under various camera perspectives, enabling continuous real-time dynamic measurement of the entire flow field. Moreover, RivVideoFlow has demonstrated superior performance in low, medium, and high flow velocity scenarios, especially in high-velocity conditions where it achieves high measurement precision. This method provides a more effective solution for hydrological monitoring. [ABSTRACT FROM AUTHOR]

Dynamic topology and power-controlled lightweight mobile devices usually affect the performance of wireless ad hoc networks and have always been a major focus of research. Data transmission between a source-destination pair is governed by different types of routing protocols like proactive, reactive, hybrid, geographical, etc. Reactive routing techniques are highly preferred due to on-demand source-based routing with limited space requirements. To get a stable route, the routing process should consider the link availability between the communicating nodes. If any two nodes along the route become unreachable, the communication could be interrupted. This could cause the receiver to lose data and experience longer delays. In this paper, we suggest a way to improve the existing DSR protocol. The focus is on choosing a stable route with less control overhead. During the route request phase, every participating node calculates its link availability time (LAT) for each adjacent link based on the current position of the neighbor and receiving signal strength. Links with lesser LATs than a threshold are not considered for route setup hence the flooding of control packets is reduced. Simulation has been performed over the basic DSR and proposed improvements over DSR in terms of control overhead, successful data transmission rate, and the number of route errors generated in the network. The findings show that previous knowledge of link availability during the routing phase aids in the discovery of more stable routes with lower control overhead, thereby promoting reliable data delivery and improving the overall network performance. [ABSTRACT FROM AUTHOR]

Previous studies show that the fuzzy-based approach predicts incoming floods better than machine learning (ML). However, with numerous observation points, difficulties in manually determining fuzzy rules and membership values increase. This research proposes a novel fuzzy logic-based learning (FLBL) that embeds missing data imputations and a fuzzy rule optimization strategy to enhance ML performance while still benefiting from fuzzy theory. The simple moving average handles sensors' missing data. The logical mapping is used for fuzzification automation and fuzzy rule generation. The join function between the Szymkiewicz-Simpson coefficient similarity and max function is applied to optimize a fuzzy rules model. The case study uses observation data from three rivers traversing three districts in Semarang City. As a result, FLBL achieves 97.87% accuracy in predicting flood, outperforming the decision tree (96%) and the neural network (73.07%). This work is significant as a part of preventive flood-related disaster plans. [ABSTRACT FROM AUTHOR]

Highlights: A LightGBM-based water level prediction model was developed for floodplain lakes. The RMSE values of the model's one-day-ahead prediction range from 0.09 to 0.10 m. The rank of the driving factors of Poyang Lake water level change was identified. Timely and accurate prediction of water levels is crucial for managing floodplain lakes with important ecosystem services, especially for flood prevention. Floodplain lakes are hydrologically complex and incredibly variable systems. Their water levels respond nonlinearly to external disturbances, complicating their prediction. Machine learning methods, especially the ensemble learning strategy, provide an excellent choice for solving such nonlinear problems. Based on the Light Gradient Boosting Machine (LightGBM) method, a water level prediction model was developed for Poyang Lake, the largest freshwater lake in China, featuring seasonal flooding and drying under strong river-lake interaction. Daily lake water levels at typical stations and discharge of the Yangtze River and the five main inflowing rivers from 2003 to 2019 were collected for training and testing the proposed model. Combinations of input variables are important for the model performance. The past information of water levels in Poyang Lake can improve the prediction accuracy significantly. Compared with the predictions without considering past water level information, the root mean square error values of the model considering past water levels have decreased from 0.49 to 0.62 m to 0.09–0.10 m for one-day ahead prediction. Therefore, the proposed model with optimized input variables can be an effective tool for the water level prediction of floodplain lakes. Note that the prediction accuracy decreases with the extension of the prediction period. Importance rank analysis shows that the water levels at different typical stations of Poyang Lake have diverse responses to the Yangtze River and the main five inflowing rivers, providing new insights into river-lake interaction. It also implied that the lake regulation should be deliberately conducted for such a large lake with a complex flow regime. [ABSTRACT FROM AUTHOR]

Despite its crucial role in flood defense for downstream regions, the catastrophic breach of the Kakhovka Dam on June 6, 2023, along the Dnipro River in Ukraine caused extensive flooding and damage both upstream and downstream. In addition, the subsequent significant drying up of the dam reservoir poses serious challenges, including hindered electricity generation, compromised flood control measures, and disrupted aquatic ecosystems. This study aims to address knowledge gaps related to the event by employing multi-temporal change detection of pre- and post-event Sentinel-1 synthetic aperture radar (SAR) imagery, analyzed using the Google Earth Engine (GEE) platform, to map flood extent and impacts. Furthermore, we assessed the impacts of dam breaches on soil organic carbon (SOC) sequestration potential in both the drying reservoir region upstream and the flooded areas downstream. The results estimated the total area of the flood extent to be approximately 379.41 km2, with an overall accuracy (OA) of 94% and a Kappa index (K) of 0.89. Quantitative analysis revealed that 81.15 km2 of urban areas, 82.59 km2 of agricultural lands, and 215.56 km2 of herbaceous wetlands were submerged by floodwaters. Both flooding and reservoir drawdown from dam collapses can significantly affect soil organic carbon (SOC) sequestration rates in affected soils. The quantification of post-disaster impacts underscores the pressing need for restoration practices and sustainable management efforts to lessen the environmental impacts and enhance the recovery of the affected regions. [ABSTRACT FROM AUTHOR]

Morphometric analysis is important for conservation and management of watersheds including flood control measures, which is in turn related to topographical components. Prioritization of sub-watersheds based on morphometric factors is carried out to understand the vulnerability of different sub-watersheds to hazards like flooding, soil erosion and groundwater storage. It is carried out in the initial phase so as to enhance the efficacy in selection of water and soil management and remedial practices. In the present study, prioritization is attempted using multi criteria approach based on VIKOR on the sub-watersheds of Mahe river basin in southwest India vis-à-vis their susceptibility to flooding. Thirteen morphometric factors pertaining to twelve sub-watersheds of the river basin are used in the present analysis and they are classified (High, Moderate and Low) based on their risk of flooding. Sub-watersheds 3–8, upstream of the Mahe were identified as being the most prone to flooding. Sub-watersheds 1 and 2, offer a moderate risk of flooding, since the advantage of prominent runoff in their upper reaches is inhibited by the laterite dominated terrain downstream. The sub-basins 9–13 in the coastal plains, forming ~ 60% of total area of Mahe basin, fall in 'Least' category. The prioritization of sub-watersheds based on Multi Criteria Decision Analysis of their morphological factors is found to be useful in the discrimination of flooding prone areas. This would help in the selection of suitable management and remedial measures for the sub-watersheds based on their hazard of flooding. [ABSTRACT FROM AUTHOR]

Naumure Multipurpose Project (NMP) featuring a 169 m high Concrete Face Rock Filled Dam (CFRD) is the proposed reservoir project in the West Rapti River with an installed capacity of 218.34 MW. Most of the rivers in Nepal carry significant sediment loads that will consequently catalyze reservoir sedimentation. This phenomenon prevails as the primary factor in reducing reservoirs useful life, making ineffective for both flood control and hydroelectricity generation. Ultimately, such process of sedimentation has adverse impacts on projects economic feasibility and long-term sustainability. Therefore, the objective of this research was to examine the expected sediment deposition pattern in the NMP reservoir throughout its operational period by employing 1D HEC-RAS model to simulate the sedimentation process. The model was simulated for 10, 20, 30, 40 and 50 years. Yang's equation as a sediment transport function, Active layer as a bed sorting method and Toffaleti as a fall velocity method were best suited for the river reach. The delta deposition was formed between 11 km and 22 km upstream of the dam region in the Jhimruk river, with the sediment deposition depth reaching peaks of about 23 m, 38 m, 39 m, 41 m and 49 m in 10, 20, 30, 40 and 50 years, respectively. Similarly, the delta deposition was formed between 13 km and 33 km upstream of dam region in the Madi river, with the sediment deposition depth reaching peaks of about 47 m, 62 m, 60 m, 68 m and 75 m in 10, 20, 30, 40 and 50 years respectively. Headcutting of delta deposition occurred between 20 and 30 year due to high flood during low stage of reservoir. Furthermore, the study revealed that about 6.22%, 11.61%, 15.94%, 22.96% and 25.65% of the storage capacity of NMP reservoir will be depleted in 10, 20, 30, 40 and 50 years respectively. [ABSTRACT FROM AUTHOR]

The "Returning Farmland to Lakes" (RFTL) project began in China following the catastrophic 1998 floods. It aims to recover flood storage capacity and mitigate flood risk to agriculture and people. This flood adaptation strategy divides the floodplain into three types of restoration polders with different flood control levels (double restoration polders, single restoration polders, and storage polders) and polders for intensive production and living (nonrestoration polders). During the substantial flooding in the Poyang Lake Basin in 2020, the double and single restoration polders were operated for flood diversion for the first time since 1999. This event provided an opportunity to assess the effectiveness of the RFTL project. Using satellite observations of rice planting and flooding areas, we found that 86% of paddy rice areas (3,400 km²) in the basin were successfully protected due to the timely flood diversion into different levels of polders. Compared to 1998, the flooded rice areas decreased overall by 58% (18 to 92% in different types of polders). Thus, the RFTL project has enhanced regional agricultural resistance to floods. A more comprehensive assessment of the RFTL project, including other ecosystem services and functions, is necessary in the future for regional sustainable development. [ABSTRACT FROM AUTHOR]

This article investigates long-term adaptation and resilience to floods at World Heritage sites through a case study of Hoi An Ancient Town, Quang Nam, Vietnam. Drawing on fieldwork observation, document review, and unstructured interviews conducted in Hoi An, we examine the enduring processes and experiences of co-living with floods. The article considers residents’ ability to comprehend, acknowledge, and address the challenges of flooding using the conceptual lens of adaptation and adversity acceptance as resilience. Our findings reveal an inseparable bond between residents and their built environment, encompassing invaluable lessons and experiences, local beliefs, resource availability, and adaptive strategies aimed at harmonious cohabitation with flooding described as ‘leaning into nature’. Strikingly, residents have developed their own practices and identities shaped by the town’s inundation by flood waters over a few centuries. Various modern flood prevention measures trace their origins to historical efforts, underscoring the vital role of traditional knowledge and practices in coping with the impacts of climate change. Those efforts are also framed within the local policy context such as Heritage Management Plan, Disaster Risk Management Plan, and Regulation for the Protection of the UNESCO-listed Hoi An Ancient Town. [ABSTRACT FROM AUTHOR]

The prevailing challenge centers on the limited application of single‐purpose flood control reservoirs transitioning to multi‐purpose reservoirs, which, despite their potential, often fall short in addressing the escalating demands for diverse water resource management. These challenges are compounded by outdated operational rules and changing environmental conditions. This research develops a framework to enhance the dual functionality of reservoirs, initially designed for flood control, to also support water supply through the determination of maximum safe water levels (MSWLs). Utilizing historical inflow data and reservoir simulation models, the study identifies opportunities for optimizing United States Army Corps of Engineers Louisville District reservoirs. It highlights certain reservoirs as ideal for augmenting water supply capabilities without compromising flood control performance. Others remain critical for flood management due to limited water supply potential, underscoring the importance of maintaining a focus on flood control. The findings illuminate the intricate balance required between managing flood risks and enhancing water supply, indicating that precise operational adjustments can significantly improve reservoir sustainability and efficiency. This method offers a viable pathway to convert single purpose reservoirs into multi‐purpose reservoirs, meeting growing water demands while ensuring robust flood mitigation, and making a step toward better water utilization. [ABSTRACT FROM AUTHOR]

The present study embarks on a strategic initiative to prioritize flood-prone watersheds, aiming for the optimal placement of watershed structures within the Siah Khor watershed, situated in the northeast of Islamabad West in Kermanshah province. Addressing the complexities of watershed management and flood control, the research employs an integrated methodological approach. This approach harmoniously combines multivariable decision systems, particularly the VIKOR(Vlsekriterijumska optimizacija I Kompromisno Resenkje) and TOPSIS(Technique for order preference by similarity to ideal solution) methods, with the advanced capabilities of the HEC-HMS hydrological model. The synergy of these methodologies facilitates a comprehensive analysis, leading to the precise identification and ranking of critical sub-basins. Such a targeted approach significantly enhances the allocation of resources, ensuring that flood mitigation efforts are both effective and efficient. Delving deeper into the nuances of watershed management, the study meticulously examines the pivotal criteria essential for informed decision-making in constructing watershed dams. It introduces an innovative, structured hierarchical model that effectively weights these criteria, thereby augmenting the clarity and precision of decisions. This comprehensive framework not only streamlines the decision-making process but also fosters a more systematic and principled approach to managing watershed regions. Through its meticulous analysis and strategic integration of sophisticated models, the study makes a substantial contribution to the field of watershed management. It presents a robust, multi-faceted strategy for flood prevention and control, demonstrating the potential for significant advancements in environmental management and sustainable development. Research Highlights: Multi-Variable Decision Systems in Flood Prioritization. Identifying Critical Sub-Basin for Flood Control. Model Validation: ROC Curve and AUC Analysis. Summary of Findings: According to the VIC method, Sub-basin 7 takes precedence for implementing watershed management structures in the Siahkhor watershed, followed by Sub-basins 1, 27, 13, and 9, respectively. The suitability assessment of locations for constructing stone and cement dams using TOPSIS method identified Sub-basin 7 in the Siahkhor watershed as having the highest priority for such construction. Flood susceptibility of sub-basins was prioritized using HEC-HMS model. Among the 32 proposed structures, approximately 15% were classified within the top six priority levels by HEC-HMS model, while around 40% were classified within the top fifteen priority levels. The flood susceptibility prioritization map of sub-basins based on the (f) index indicated that sub-basins located upstream have the highest importance in flood control. Therefore, implementing flood control operations in areas farther from the watershed outlet is more suitable. [ABSTRACT FROM AUTHOR]

The partially catastrophic effects of large floods, such as the events of 2002 and 2013 in Germany and Austria for example, clearly show the vulnerability of settlement structures along large rivers. In the case of impounded rivers with run-of-river hydropower plants, the main question is whether a reduction of floods can be achieved with flood-adapted, intelligent control of barrages - to date, the operational objective has been the safe discharge of floodwater; targeted reduction is currently an unoccupied field due to its complexity. Specifically, the interaction with the operation of a polder for flood retention is of special interest, as polders are a much-discussed and effective element of flood protection, especially at higher discharges, that directly influences the hydrograph shape. Extensive analyses and sensitivity investigations of a multitude of parameters and measured values, using a complex model system, are needed in order to investigate the reduction potential of the named technical flood protection measures and the development of control strategies relevant to practice. It is essential to base the developed controlling strategies on operationally available data, as this is the only way to make the determined reductions relevant for practical operation. [ABSTRACT FROM AUTHOR]

Clarifying the impact of driving forces on multi-temporal-scale (annual, quarterly and monthly) runoff changes is of great significance for watershed water resource planning. Based on monthly runoff data and meteorological data of the Jialing River (JLR) during 1982–2020, the Mann–Kendall tendency testing approach was first applied to analyze variation tendencies of multi-timescale runoff. Then, abrupt variation years of runoff were determined using Pettitt and cumulative anomaly mutation testing approaches. The ABCD model was employed for simulating hydrological change processes in the base period and variation period. Finally, influences of climatic and anthropic factors on multi-scalar runoff were computed using the multi-scalar Budyko formula. The following conclusions were drawn in this study: (1) The mutation year of discharge was 1993; (2) the monthly runoff in the JLR presented a "single peak" distribution, and the concentration degree and concentration period in the JLR both showed an insignificant reduction trend; (3) anthropic factors were the dominant factor for spring runoff variations; climatic factors were the dominant factor on annual, summer, fall and winter runoff variations; (4) except for November, climatic factors were the dominant factor causing runoff changes in the other 11 months. This study has important reference value for water resource allocation and flood control decisions in the JLR. [ABSTRACT FROM AUTHOR]

Compound channels play an important role in hydraulic and hydrological engineering. Many rivers and streams have naturally compound channels, which are important for both flood control and river ecosystems. When predicting discharge, interval predictions are usually more realistic and reliable than traditional point predictions made using ML models. In this study, the authors used three well-known machine learning techniques (Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Artificial Neural Networks (ANN)) to predict compound channel discharge. To accurately assess uncertainty, two resampling techniques were employed: Bootstrap (B) and Jackknife after Bootstrap (JB). The proposed approach reflects the impacts of statistical uncertainties using prediction confidence intervals and the reliability of point prediction. The JB resampling technique has proven to offer superior accuracy in terms of point prediction and prediction intervals when compared to Bootstrap. Bootstrapping did not yield superior outcomes compared with the standalone ML models. The JB-ANN and JB-ANFIS models outperformed the JB-SVM and Bootstrap ML models in the testing phase, as shown by various statistical measures. This study suggests that the JB-ANN approach is a reliable and robust tool for accurately predicting compound discharges. The findings reveal that when the models have low accuracy in point prediction, the level of uncertainty in predicting the interval also increases. The innovation of this research is enhancing the examination of uncertainties in compound channel discharge by incorporating two resampling techniques to analyze three different ML models. [ABSTRACT FROM AUTHOR]

This study conducted a comprehensive analysis of flood control and water quality simulation for the urban water system in Runan County, with the aim of evaluating the conditions of urban waterlogging and changes in water quality before and after the management of the urban water system. This provides significant scientific basis and technical support for the water system management and water environment protection in Runan County and similar urban areas. The study employed a rapid modeling technique assisted by ArcGIS and the SWMM (Storm Water Management Model) for the first time to conduct an in-depth simulation study on the regulation of floods and the improvement of water quality in the central urban area of Runan County. By simulating design rainfall scenarios of 1-h and 6-h durations for return periods of 3, 5, 10, 20, and 50 years, a detailed analysis was performed on the extent of flood inundation in the urban area of Runan County under different rainfall conditions and the flood detention capacity of the river channels. Furthermore, for rainfall scenarios of 3, 10, and 50-year return periods, a meticulous simulation analysis was conducted on the water quality response and the attenuation process of pollutant concentrations in the main river channels. The results of the study revealed the significant effects of river channel regulation on enhancing the connectivity of the water system and the capacity for flood control: the regulation of river channels significantly increased the cross-sectional area and the volume of water in the channels, leading to a reduction in pollutant concentrations along the direction of the river flow. Comparing the data before and after the management, the extent of the flood-prone area and the volume of water entering the river in the non-managed water system were 1.4 times and 59% of those after management, respectively, indicating that the comprehensive management of the water system significantly reduced the risk of waterlogging. In addition, the self-purification capacity of the river channels and the efficiency of pollutant degradation improved significantly after the management, effectively enhancing the water quality environment. [ABSTRACT FROM AUTHOR]

Streamflow prediction offers crucial information for managing water resources, flood control, and hydropower generation. Yet, reliable streamflow prediction is challenging due to the complexity and nonlinearity of the rainfall-runoff relationship. This study investigated the comparative performance of the newly integrated self-attention-based deep learning (DL) model, SA-Conv1D-BiGRU with Conv1D-LSTM, and bidirectional long short-term memory (Bi-LSTM) models for streamflow prediction under different time-series conditions, and a range of variable input combinations based on flood events. All datasets passed quality control procedures, and the time lag for generating input series was established through Pearson correlation analysis. 80% of the data was used for training, whereas 20% was used to evaluate the model's performance. The performance of the models was evaluated using three metrics: mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R²). The findings reveal the excellent potential of DL models for streamflow prediction, with the SA-Conv1D-BiGRU model outperforming other models under different time-series characteristics. Despite the complexity, the Conv1D-LSTM models did not outperform the Bi-LSTM model. In conclusion, the results are condensed into themes of model variability and time-series characteristics. Consequently, different architectures in DL models had a greater influence on streamflow prediction accuracy than input time lags and time-series features. [ABSTRACT FROM AUTHOR]

Continuous hydrological records at the desired site with spatial and temporal coverage are essential to water resources management and flood prevention. Hydrological gauging stations and observations are limited at transboundary Himalayan River basins in developing countries. This study is carried out to estimate discharge at ungauged sites from donor catchments based on calibrated discharge. In the Tamakoshi River basin, Spatial Process in HYdrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV)-light, and Hydrologic Engineering Centre Hydrologic Modelling System (HEC HMS) models were used for hydrological simulation. The ungauged discharge estimated at Benighat is based on daily, monthly, and annual bases from upstream gauged stations. Statistical indices were obtained as NSE >0.62, R² >0.77, and PBias <26% in the simulation of these models. The low flow prediction is more reliable than the high flow prediction. The deviation in predicted flow mostly appeared in high flow periods. All the hydrological models simulated ungauged streamflow were similar in the flow pattern, and the estimated discharge at the ungauged receiver site was greater than at the donor site. Comparative simulations are better alternatives to estimate ungauged discharge than individual simulations. This research will support future reference to generate streamflow data at ungauged sites and can help fill the data recording gap at similar mountain river basins. [ABSTRACT FROM AUTHOR]

(1) Background: Willow forests are well established as nature-based solutions contributing to flood protection in the riverine environment. With climate change, storm surges in winter may increasingly expose downstream floodplain forests to seawater pulses. The effects of seawater pulses on willows are unknown, as previous studies focused on long-term exposure effects. (2) Methods: We studied the resilience of the floodplain forest foundation species Salix alba L. to seawater pulses during winter. This corresponds to the effects of storm surges in the North Sea region on floodplain willow trees in downstream river stretches during their dormant stage. Seawater pulses were applied from November to May on vegetative propagules. The plants were placed on flooding stairways at three levels in a mesocosm experiment under ambient conditions in Zealand, NL. (3) Results: Twice-applied 48 h seawater pulses during winter led to increasing salinity in the soil where vegetative propagules were placed. Ninety-five percent of the plants developed leaves, shoots, and roots, and juvenile trees were established in the following spring. Although the aboveground and belowground dry masses decreased with increasing short-term seawater flooding, they increased from April to May. (4) Conclusions: The seawater pulse caused a growth-delaying effect in the young experimental propagation plants of Salix alba. Contrary to earlier findings on the growth-inhibiting effects on S. alba under long-term salinity treatments, we show that S. alba is resilient to short-term seawater pulses experienced during the dormant (winter) stage. This is good news for the inclusion of S. alba in nature-based flood defense schemes in downstream riverine stretches. [ABSTRACT FROM AUTHOR]

Coastal ecosystems provide critical storm and flood protection but are rapidly degrading worldwide, making their restoration urgent. We evaluated whether successional facilitation, where pioneers facilitate climax species, could be leveraged to accelerate coastal dune revegetation. A survey spanning 270 km of Southeast U.S. coastline revealed that Panicum amarum (bitter panicum) supported higher plant richness than Uniola paniculata (sea oats) and that sea oat cover was 230% greater on mature dunes than disturbed dunes, suggesting bitter panicum functions as a pioneer and sea oats as a climax species. A reciprocal transplant experiment confirmed this interpretation: bitter panicum stem production and height fell by 37% and >20 cm, respectively, when planted proximate to sea oats versus in isolation, whereas sea oats produced 38% more and >12 cm taller stems when planted proximate to bitter panicum versus in isolation. A second experiment evaluating the density‐dependence of this facilitative interaction revealed that sea oats transplanted into low densities of bitter panicum grew >15% taller than isolated and high‐density treatments. However, within 7 months, wave inundation eliminated over 60% of propagules in both experiments. To explore foredune inundation frequency and its implications for dune revegetation, we applied empirical wave runup models at 101 locations throughout Volusia County, Florida. While disturbance frequency varied seasonally and annually, sites with low dune crests and steep beach slopes experienced frequent inundation (>50 events/year). Given the interactions between geomorphology and vegetation success, we present a decision matrix to guide managers in determining optimal revegetation methods tailored to project goals and site conditions. [ABSTRACT FROM AUTHOR]

The paper demonstrates the effectiveness of the SNOWED dataset, specifically designed for identifying water bodies in Sentinel-2 images, in developing a remote sensing system based on deep neural networks. For this purpose, a system is implemented for monitoring the Po River, Italy's most important watercourse. By leveraging the SNOWED dataset, a simple U-Net neural model is trained to segment satellite images and distinguish, in general, water and land regions. After verifying its performance in segmenting the SNOWED validation set, the trained neural network is employed to measure the area of water regions along the Po River, a task that involves segmenting a large number of images that are quite different from those in SNOWED. It is clearly shown that SNOWED-based water area measurements describe the river status, in terms of flood or drought periods, with a surprisingly good accordance with water level measurements provided by 23 in situ gauge stations (official measurements managed by the Interregional Agency for the Po). Consequently, the sensing system is used to take measurements at 100 "virtual" gauge stations along the Po River, over the 10-year period (2015–2024) covered by the Sentinel-2 satellites of the Copernicus Programme. In this way, an overall space-time monitoring of the Po River is obtained, with a spatial resolution unattainable, in a cost-effective way, by local physical sensors. Altogether, the obtained results demonstrate not only the usefulness of the SNOWED dataset for deep learning-based satellite sensing, but also the ability of such sensing systems to effectively complement traditional in situ sensing stations, providing precious tools for environmental monitoring, especially of locations difficult to reach, and permitting the reconstruction of historical data related to floods and draughts. Although physical monitoring stations are designed for rapid monitoring and prevention of flood or other disasters, the developed tool for remote sensing of water bodies could help decision makers to define long-term policies to reduce specific risks in areas not covered by physical monitoring or to define medium- to long-term strategies such as dam construction or infrastructure design. [ABSTRACT FROM AUTHOR]

Durant, D. and Kernéïs, E., 2024. Investigating the farm resilience of polder environments after seawater flooding: Results from Storm Xynthia (2010). Journal of Coastal Research, 40(5), 887–900. Charlotte (North Carolina), ISSN 0749-0208. Climate forecasts predict that global warming will cause sea-levels to rise and will increase the frequency of storms in the future, thus also increasing the risk of submersion of coastal agricultural areas. Yet very few studies examine the consequences of seawater flooding, from either an agricultural or an environmental point of view. This study therefore investigates the resilience of coastal farming systems through the case study of the Saint Laurent de la Prée research farm hit by the Storm Xynthia in 2010. Two aspects of resilience are analysed here: (i) the soil characteristics of the system (indicators: salinity, sodicity and structural stability) after the submersion, and the system's ability to recover its productive capacity on croplands (crop yields) and on permanent grasslands (fodder production); and (ii) the ecological resilience of permanent grasslands (indicators: species richness and diversity). Results showed that the system recovered its productive capacity in 2012, so after two years, when the research farm engineers recorded a return of the crops and grasslands to a production level that they considered satisfactory. For crop soils, there was a quick recovery of salinity levels, but the structural stability after the storm was largely degraded and had not totally recovered by 2012. On grasslands, the farm suffered two successive years of low grassland productivity, partially due to a temporary shift in plant species' composition. Finally, two dimensions of resilience are discussed here: (i) the speed of recovery; and (ii) the ability to learn from the storm, in view of the increasing risks of coastal submersion in the future. As it appears that flood protection policy must consider local and long-term impacts of seawater flooding on agricultural lands, this study can provide useful information for further research on the foresight scenarios and adaptive strategies required. [ABSTRACT FROM AUTHOR]

This paper addresses the limitations of existing interest forwarding strategies in Named Data Networks (NDN) over Low-power and Lossy Networks (LLNs). While current strategies utilize listening periods to minimize duplicate retransmissions, they suffer from indiscriminately propagating interests across the network, resulting in excessive retransmissions. Conversely, strategies with propagation control often neglect challenges posed by producer mobility. In response, we propose an efficient forwarding strategy that not only mitigates duplicate retransmissions but also governs interest propagation through the integration of probabilities and estimated distances to data producers. Additionally, the strategy adeptly manages producer mobility through judicious flooding control. In contrast to prior studies primarily benchmarking against Blind Flooding (BF) and Deferred Blind Flooding (DBF), our work presents the first comprehensive evaluation of the new forwarding scheme against six established strategies. Through extensive simulations, we demonstrate the superiority of our proposed forwarding solution across key performance metrics, including sent packets, retrieval latency, success rate, and energy consumption in various scenarios. [ABSTRACT FROM AUTHOR]

To address the concerns regarding a disused tailings pond, a closure engineering design and dam flood control safety evaluation were conducted. The proposed reservoir closure treatment methods, including adding flood drainage system (spillway), plugging the existing overflow tower and drainage pipe, were developed in combination with the construction conditions of the reservoir area. Then, hydrological calculations and flood routing for the tailings pond were performed, and the flood control safety of the tailings pond was evaluated. The results of the study indicate that after the designed closed pond treatment method for the tailings pond is adopted, a 1000-year design flood would result in a flood elevation of 0.7 m, a minimum safety super-elevation of 2.0 m, and a minimum dry beach of approximately 130 m. These findings are in compliance with the relevant regulations and requirements of the specifications, and thus ensure the flood control safety of the tailings pond. [ABSTRACT FROM AUTHOR]

Streamflow forecasting holds a pivotal role in the effective management of water resources, flood control, hydropower generation, agricultural planning, and environmental conservation. This study assessed the effectiveness of a stacked Multilayer Perceptron-Random Forest (MLP-RF) ensemble model for short- to medium-term (7 to 15 days ahead) daily streamflow forecasts in the UK. The stacked model combines MLP and RF, enhancing generalization by capturing complex nonlinear relationships and robustness to noisy data. Stacking reduces bias and variance by aggregating predictions and addressing differing sources of bias and variance in MLP and RF. Furthermore, this ensemble model is computationally inexpensive. The study also examined the impact of different meta-learner algorithms, Elastic Net (EN), Isotonic Regression (IR), Pace Regression (PR), and Radial Basis Function (RBF) Neural Networks, on model performance. For 1-day ahead forecasts, all models performed well (Kling Gupta efficiency, KGE, from 0.921 to 0.985, mean absolute percentage error, MAPE, from 3.59 to 13.02%), with minimal impact from the choice of meta-learner. At 7-day ahead forecasts, satisfactory results were obtained (KGE from 0.876 to 0.963, MAPE from 11.53 to 24.55%), while at the 15-day horizon, accuracy remained reasonable (KGE from 0.82 to 0.961, MAPE from 18.31 to 34.38%). The RBF meta-learner generally led to more accurate predictions, particularly affecting low and peak flow rates. RBF consistently outperformed in predicting low flow rates, while EN excelled in predicting flood flow rates in many cases. For estimating total discharged water volume, all models exhibited low relative error (< 0.08). [ABSTRACT FROM AUTHOR]

Climate change disproportionately impacts coastal residents in the United States. Existing studies document institutional efforts to adapt to sea level rise through projects like seawalls, beach nourishment, and property acquisitions to protect communities from rising seas. Such studies capture institutional adaptations, but do not include ad hoc adaptations by homeowners impacted by climate change. How are homeowners adapting to climate hazards? This paper analyzes ethnographic and interview data from 100 households in two coastal counties in North Carolina, a state with one of the most climate vulnerable shorelines in the country. This analysis of homeowner response considers ad hoc adaptations along the North Carolina coast. Results show that homeowners recognize climate hazards and regularly adapt on their own within the context of institutionally maintained flood protection infrastructure and transportation access to the places where they live. Residents are aware of and attempt to access support for home adaptations when programs or funds are available to them after disasters and do so with varying levels of success, though the more pervasive adaptations to chronic stress are not supported by government programs or insurance mechanisms. Ad hoc adaptations may provide short-term protection from climate hazards but have questionable long-term efficacy as sea levels rise and storm strength and frequency increases. Leaving communities and households to adapt on their own as chronic climate hazards outpace institutional response exacerbates existing inequalities by relying on residents with different levels of resources and agency to adapt. [ABSTRACT FROM AUTHOR]

With the acceleration of global climate change and urbanization, the frequency and impact of flood disasters are increasing year by year, making flood emergency management increasingly crucial for safeguarding people's lives, property, and societal stability. To enhance the accuracy of river flow prediction, this study employs an Improved Gray Wolf Optimization Algorithm (IGWO) to optimize parameters of the Long Short-Term Memory Network (LSTM) model. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy of river flow prediction, achieving higher precision and better generalization compared to traditional machine learning algorithms. This method provides more reliable data support for flood warning systems, aiding in the accurate prediction of flood occurrence timing and intensity, thereby providing scientific basis for flood prevention and mitigation efforts. Moreover, this approach supports hydro-logical research, enhancing understanding of river water cycle processes and ecosystem changes. [ABSTRACT FROM AUTHOR]

Accurate flood forecasting is a crucial process for predicting the timing, occurrence, duration, and magnitude of floods in specific zones. This prediction often involves analyzing various hydrological, meteorological, and environmental parameters. In recent years, several soft computing techniques have been widely used for flood forecasting. In this study, flood forecasting for the Narmada River at the Hoshangabad gauging site in Madhya Pradesh, India, was conducted using an Artificial Neural Network (ANN) model, a Fuzzy Logic (FL) model, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. To assess their capacity to handle different levels of information, three separate input data sets were used. Our objective was to compare the performance and evaluate the suitability of soft computing data-driven models for flood forecasting. For the development of these models, monthly discharge data spanning 33 years from six gauging sites were selected. Various performance measures, such as regression, root mean square error (RMSE), and percentage deviation, were used to compare and evaluate the performances of the different models. The results indicated that the ANN and ANFIS models performed similarly in some cases. However, the ANFIS model generally predicted much better than the ANN model in most cases. The ANFIS model, developed using the hybrid method, delivered the best performance with an RMSE of 211.97 and a coefficient of regression of 0.96, demonstrating the potential of using these models for flood forecasting. This research highlighted the effectiveness of soft computing techniques in flood forecasting and established useful suitability criteria that can be employed by flood control departments in various countries, regions, and states for accurate flood prognosis. [ABSTRACT FROM AUTHOR]

In view of the problems including incomplete basic data of flood control hazards of mountainous railway and difficulty of flood control site hidden hazards investigation, a technical scheme of railway flood control hidden hazards investigation based on high-precision oblique photogrammetry real 3D modeling technology was proposed, and a high-precision oblique photogrammetry real 3D modeling process and model optimization method were established. The practical application scenarios of 3D technology in railway flood control were summarized systematically. The application practice of real 3D flood control hidden danger investigation carried out in a railway in southern China shows that the real 3D technology can provide full elements and three-dimensional geographic information for flood control hidden danger investigation, meet the global overview of macro information and local detailed characteristics of flood control works to obtain information, and solve the difficult problems, such as manual inspection of blind areas in mountainous areas. The research results show that, the adoption of the technology can greatly improve the efficiency of hidden danger detection, and the accuracy of hidden danger identification can reach more than 95%, which significantly improves the comprehensiveness and accuracy of flood control hidden danger detection. [ABSTRACT FROM AUTHOR]

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High-impact river floods are often caused by very extreme precipitation events with return periods of several decades or centuries, and the design of flood protection measures thus relies on reliable estimates of the corresponding return values. However, calculating such return values from observations is associated with large statistical uncertainties due to the limited length of observational time series, uneven spatial distributions of rain gauges and trends associated with anthropogenic climate change. Here, 100-year return values of daily precipitation are estimated on a global grid based on a large data set of model-generated precipitation events from ensemble weather prediction. In this way, the statistical uncertainties in the return values can be substantially reduced compared to observational estimates due to the substantially longer time series. In spite of a general agreement in spatial patterns, the model-generated data set leads to systematically higher return values than the observations in many regions, with statistically significant differences, for instance, over the Amazon, western Africa, the Arabian Peninsula and India. This might be linked to an overestimation of tropical extreme precipitation in the model or an underestimation of extreme precipitation events in observations, which, if true, would have important consequences for practical water management. [ABSTRACT FROM AUTHOR]

Dams play a pivotal role in providing essential services such as energy generation, water supply, and flood control. However, their stability is crucial, and continuous monitoring is vital to mitigate potential risks. The Mosul Dam is one of the most interesting infrastructures in Iraq because it was constructed on alternating beds of karstified and gypsum which required continuous grouting due to water seepage. Therefore, the ongoing maintenance issues raised international concerns about its stability. For several years the dam indicated a potential for disastrous failure that could cause massive flooding downstream and pose a serious threat to millions of people. This research focuses on comprehensive statistical assessments of the dam geodetic network points across multiple epochs of long duration. Through the systematic application of three statistical tests and the predictive capabilities of the Kalman filter, safety and long-term stability are aimed to be enhanced. The analysis of the dam's geodetic network points shows a consistent trend of upstream-to-downstream movement. The Kalman filter demonstrates promising outcomes for displacement prediction compared to least squares adjustment. This research provides valuable insights into dam stability assessment, aligns with established procedures, and contributes to the resilience and safety of critical infrastructure. The outcome of this paper can encourage future studies to build upon the foundation presented. [ABSTRACT FROM AUTHOR]

Due to the influence of water and sediment conditions, engineering projects, channel erosion and siltation, river-related factors, and human activities (such as adjustments in floodplain production structures and village construction), there have been significant variations in the transverse gradient of the floodplain in the lower Yellow River. An irrational transverse gradient can lead to the rapid conversion of gravitational potential energy into kinetic energy during the flood evolution process, resulting in increased flow velocity and inundated areas. Exploring reasonable transverse gradients can provide technical support for floodplain management. Using "flood risk assessment" as a keyword, research papers from the Web of Science core database and CNKI published in the past five years were collected. Through a VOS viewer analysis of indicators, a flood risk assessment model based on the "Source–Path–Receptor–Consequence–Resilience" framework was established. A two-dimensional water and sediment model was used to simulate flood inundation scenarios with different transverse gradients in the same flood event, evaluate flood risks in the floodplain, and determine the optimal transverse gradient based on flood risk levels. The results indicate that, compared to low transverse gradients, moderate and high transverse gradients have a more significant driving effect on flood inundation, increasing flood risk opportunities for floodplains. Lower transverse gradients (i.e., TG = 10LG = 1.25‰) are the most favorable for flood protection in the floodplain after flood inundation. [ABSTRACT FROM AUTHOR]

There exist conflicts of interest between upstream and downstream regions in flood control and drainage; how to balance these conflicts and achieve collaborative flood management remains an important scientific problem. To explore a balanced governance strategy, this study took the Demonstration Zone of Green and Integrated Ecological Development of the Yangtze River Delta, which consists of three separate administrative regions, as the research domain. Using evolutionary game theory, the study conducts a comparative analysis of the interests between upstream and downstream areas. It introduces external drivers, such as the intervention of higher-level administrative bodies and incentive-constraining policies, along with internal balancing mechanisms like bidirectional compensation. The goal is to explore collaborative strategies and cooperation mechanisms that can balance the conflicts of interest between upstream and downstream areas. Results indicate that: (1) The final collaborative strategy was closely related to factors such as the cost of conflict, the amount of two-way compensation, additional benefits of flood control and drainage, and the intensity of incentive constraints. (2) Incorporating a reasonable two-way compensation and reward and punishment mechanism into the evolutionary game theory model can promote the model to a stable strategy. (3) The external driving mechanisms aim to coordinate the conflicts between upstream and downstream regions through incentive or constraint policies, which help motivate and encourage proactive collaboration in flood control and drainage management. The internal balancing mechanism is responsible for compensating for economic losses caused by imbalances, thereby creating pressure that fosters regional cooperation in flood control and drainage governance. In a word, the collaborated management mechanism helps provide a more balanced strategy across different administrative regions. [ABSTRACT FROM AUTHOR]

Water security, as a crucial component of national security, plays a significant role in maintaining regional stability and ensuring the healthy and rapid development of the economy and society. The Yellow River water-receiving area of Henan Province (YRWAR-HN) is selected as the research area in this study. Firstly, a comprehensive evaluation index system is constructed based on the actual water security problems of the research area, and the single index quantification–multiple indices syntheses–poly-criteria integration method (SMI-P) is introduced to quantify the water security degree of 14 cities in the YRWAR-HN from 2010 to 2021. Then, the obstacle degree model is used to identify the key obstacle indexes that restrict the improvement of water security. Finally, the harmonious behavior set optimization method is adopted to carry out the regulation of water security, and the improvement path of water security in the YRWAR-HN is formulated. The results indicate the following: (1) the water security degree of the YRWAR-HN shows a fluctuating upward trend, increasing from 0.4348 (2010) to 0.6766 (2021), a significant rise of 55.61%. The water security level improves from the relatively unsafe level to the relatively safe level. Hebi City exhibits the fastest rate of water security improvement, while Xinxiang City shows the slowest rate. (2) The density of the river network (X1) and the proportion of investment in water conservancy and environmental protection in the total investment (X15) are the two indexes with the highest obstacle degree, with the average obstacle degrees being 15.09% and 10.79%, respectively. (3) The combination of the composite regulation scenario and improvement Path 2 is the optimal regulation strategy for water security in the YRWAR-HN. From the implementation process, Luoyang, Sanmenxia, Jiyuan, Xuchang, and Shangqiu may prioritize improving their flood and drought disaster defense capabilities and emergency response capabilities, continuously enhancing the flood prevention and disaster reduction system. Zhengzhou, Kaifeng, Xinxiang, Jiaozuo, Anyang, Hebi, Pingdingshan, and Zhoukou may prioritize resolving the regional water supply and demand conflicts, balancing development and conservation, actively seeking transboundary and external water transfers, and strengthening the capacity for water conservation and intensive utilization. Puyang City may prioritize enhancing its comprehensive water environment management capabilities, increasing investment in water conservancy and the environment, improving production processes, reducing pollutant emissions, and mitigating agricultural non-point source pollution. [ABSTRACT FROM AUTHOR]

The multi-reservoir flood control operation (MRFCO) problem is characterized by high dimensions and multiple constraints. These features pose significant challenges to algorithms aiming to solve the MRFCO problem, requiring them not only to handle high-dimensional variables effectively but also to manage constraints efficiently. The Horned Lizard Optimization Algorithm (HLOA) performs excellently in handling high-dimensional problems and effectively integrates with penalty functions to manage constraints. However, it still exhibits poor convergence when dealing with certain benchmark functions. Therefore, this paper proposes the Enhanced Horned Lizard Optimization Algorithm (EHLOA), which incorporates Circle initialization and two strategies for avoiding local optima, thereby enhancing HLOA's convergence performance. Firstly, EHLOA was tested on benchmark functions, where it demonstrated strong robustness and scalability. Then, EHLOA was applied to the MRFCO problem at the upper section of Lanzhou of the Yellow River in China, showing excellent convergence capabilities and the ability to escape local optima. The reduction rates of flood peaks achieved by EHLOA for the two millennial floods and two decamillennial floods were 55.6%, 52.8%, 58.1%, and 56.4%, respectively. Additionally, the generated operation schemes showed that the reservoir volumes changes were reasonable, and the discharge processes were stable under EHLOA's operation. Overall, EHLOA can be considered a reliable algorithm for addressing the MRFCO problem. [ABSTRACT FROM AUTHOR]

With urban expansion, traditional drainage systems in densely populated cities face significant challenges, leading to frequent flooding and pollution issues. Deep tunnel drainage systems emerge as an innovative approach, offering underground storage for excess precipitation and alleviating urban inundation. This research investigates the deployment of a deep tunnel system in Guangzhou's densely populated urban core. By integrating with existing networks, this system aims to curtail over-flow contamination and boost sewage-handling capacity. Successful implementation hinges on the thorough evaluation and synchronization with broader urban development objectives. In Guangzhou, where traditional methods fall short, deep tunnels present a viable option. This study explores techniques for identifying drainage deficiencies, devising enhancements, and refining citywide strategies. Economic analysis indicates that deep tunnels are more cost-effective than conventional drainage upgrades, offering long-term benefits for land conservation and drainage efficiency. Following implementation, these systems markedly enhance sewage management, diminish overflow incidents, and improve pollution mitigation. Although initial investments are substantial, the enduring advantages in land preservation and drainage efficiency are significant. Thus, deep tunnel systems emerge as a practical flood control solution for high-density urban areas like Guangzhou, fostering sustainable metropolitan growth. [ABSTRACT FROM AUTHOR]

Due to the complexity of terrain and climate in the mountain–plain transition zone, it is difficult to simulate and forecast the flow discharge of river basins accurately. The poor regularity of the river thus leads to uncertain flood control scheduling. Meanwhile, reservoirs and flood detention areas are constructed to store and divert water when severe floods threaten the safety of the basin. In order to improve the accuracy of flood forecasts and the effectiveness of flood control, a hydrological and 1D/2D hydrodynamic coupling model was developed to enable the joint computation of multiple objects, including mountainous streams, plains river networks, hydraulic control structures, and flood detention areas. For the hydrological component, the Xin'anjiang model with the Muskingum module is employed to simulate mountainous flow discharge. For the hydrodynamic component, the Saint–Venant equations and shallow water equations are applied to estimate flood processes in rivers and on land surfaces, respectively. The Dongtiaoxi River Basin in Zhejiang Province, China, serves as the case study, where river flow is influenced by both upstream mountainous floods and downstream backwater effects. Using the integrated model, flood routing and scheduling are simulated and visualized. Both the Xin'anjiang model and the 1D hydrodynamic model demonstrate over 80% acceptability in calibration and validation, confirming their robustness and reliability. Meanwhile, inundation in flood detention areas can be effectively estimated by coupling the 1D and 2D hydrodynamic models with a flood diversion scheduling model. The coupled model proves capable of simulating flood routing in complex river basins that include mountains, plains, and hydraulic control structures, accounting for the interactions between hydrological elements. These findings provide a new perspective on flood simulation in other similarly complex river basins. [ABSTRACT FROM AUTHOR]

The development of sponge cities advocates for sustainable urban rainwater management, effectively alleviating urban flood disasters, reducing non-point-source pollution, and promoting the recycling of rainwater resources. Low-Impact Development (LID) serves as a key strategy in this context, providing essential support for urban rainwater control and pollution reduction. To investigate the runoff control effects of LID measures and to reveal the relationship between facility runoff control performance and installation scale, this study focuses on a sponge community in Beijing. A SWMM model was constructed to analyze the rainwater flood control and pollutant load reduction effects of different LID facilities, including bio-retention cells, green roofs, and permeable pavements. Using evaluation indicators such as surface runoff, node overflow, and pollutant control rates, this study examined how facility performance varies with installation scale under different rainfall conditions. The combination scheme of LID equipment optimal configuration is designed by using multiple criteria decision analysis (MCDA) and cost–benefit theory. The results indicate significant differences in performance among the various LID facilities across different rainfall scenarios. Specifically, the optimal installation proportion for runoff and overflow control of permeable pavements were found to be between 30% and 70%. Green roofs demonstrate superior performance in handling extreme rainfall events, while bio-retention cells exhibit significant effectiveness in controlling Total Suspended Solids (TSSs). Through comprehensive performance evaluation, this study identified the optimal combination scale under a 3-year rainfall recurrence interval as 30% permeable pavements, 20% green roof, and 60% bio-retention cells. This combination effectively leverages the strengths of each facility, ensuring system stability and efficiency while also demonstrating optimal management efficiency in cost–benefit analyses. The findings of this research provide valuable insights for future urban water management and infrastructure development. [ABSTRACT FROM AUTHOR]

The operation of hydraulic projects within plain river networks to mitigate floods can alter river network connectivity patterns, subsequently affecting flood processes. This study employed the MIKE 11 model to simulate flood processes under three different river network connectivity scenarios. Based on the simulations, we propose a method to evaluate flood intensity severity by integrating three flood characteristic indices: Slope of the Flow Duration Curve (SFDC), Rising Climb Index (RCI), and Flashiness Index (FI). These indices assess the overall magnitude of change, the rate of rise, and process fluctuations, respectively. Results indicate that changes in river network connectivity significantly impact RCI and SFDC, more than FI. Compared to the natural river network connectivity mode, changes in urban or watershed river network connectivity resulted in a significant decrease in RCI values by 3–37% or 18–38% across various return periods, with the rate of change in RCI values increasing as the return period lengthened. The impact of urban river network connectivity changes on SFDC within the Changzhou urban area was more pronounced under high-magnitude storm conditions, causing a 61% reduction. Furthermore, changes in watershed river network connectivity had a larger effect on SFDC under low-magnitude storm conditions than under high-intensity storms. Over 80% of the rivers under natural connectivity conditions exhibited flood intensity severity of Level III or higher, particularly in the Chenshu–Qingyang area. The alterations in connectivity significantly decreased flood intensity severity, with 85% to 91% of rivers showing the lowest flood intensity severity of Level I. Under a 100-year rainstorm scenario, flood risk shifted from within the flood protection envelope to outside it in the Changzhou urban area. The results will provide an important scientific basis for regional flood management in plains with dense rivers. [ABSTRACT FROM AUTHOR]

The lower course of the Yellow River is a "hanging river" across the hinterland of China, and the safety of its flood control measures/systems is closely tied to the stability of the nation. Ensuring high-quality, sustainable development of the lower Yellow River floodplain while maintaining flood safety is crucial for the entire Yellow River Basin. Previous studies have primarily focused on the overall development of the Yellow River Basin or the economic–ecological coupling development of cities along the river, often neglecting the flood safety development of the floodplain. This study optimizes the socio-economic–flood-safety–ecological (SFE) system of the typical downstream Landong floodplain within the Yellow River Basin. The system dynamics model (SDM) can simulate the dynamic behavior of SFE systems by constructing mathematical models that incorporate feedback loops and time delays. The primary components include causal loop modules and stock-flow modules. Then, a coupling coordination degree model for the Landong floodplain is established using a comprehensive subjective and objective weighting method, assessing the SFE system's coordination under five scenarios: inertial development, economic development, environmental protection, flood safety, and sustainable development. The results of historical and validity tests indicate that the SDM can effectively simulate the coupling coordination degree of the SFE system. The study results suggest that the coupling coordination degree increases the most under the sustainable development scenario, indicating that the development of the Landong floodplain should not only focus on socio-economic growth, but should also consider flood safety and ecological concerns. In addition, comprehensive regulation from socio-economic, flood safety, and ecological environment indicators are necessary to achieve high-quality, coordinated development. This study has significant implications for policy formulation and management to achieve high-quality and sustainable development in the downstream floodplain of the Yellow River. [ABSTRACT FROM AUTHOR]

Canal dikes in low‐lying polders, as well as in other regions worldwide, are critical infrastructure for flood protection and water management. The subsurface water conditions can cause dike failures during excessive rainfall and prolonged periods of drought. There is a lack of multi‐year monitoring of subsurface water conditions in canal dikes and an insufficient understanding of their geohydrological behavior. This study provides and analyses a novel multiyear data set of soil moisture and hydraulic heads (from February 2020 until March 2023) from a monitoring network covering various canal dikes with different characteristics in the western Netherlands. The data, including two extremely dry summers, highlight the impact of meteorological variations on the subsurface water conditions. Non‐hydrostatic hydraulic head levels were observed during droughts that can be detrimental to dike stability and that are often not accounted for in safety assessments for drought situations. The effectiveness of various meteorological drought indicators applied to subsurface water conditions was evaluated: the precipitation deficit is the most reliable measure and outperforms the standardized drought indicators (SPEI and SPI). The drought recovery of dikes was analyzed to understand seasonal transitions and the sequence of different failure mechanisms, during dry and wet situations. This analysis also reveals differences between meteorological, soil moisture, and groundwater droughts, highlighting soil's storage capacity after drought and the limitations of meteorological drought indicators as proxies for soil moisture and groundwater. The insights from this study enhance assessments, inspection procedures and the identification of weak spots of dikes and other earthworks of infrastructure. Key Points: Novel multiyear observations of soil moisture and hydraulic heads from various canal dikes reveal geohydrological behaviorThe precipitation deficit emerges as the most reliable meteorological drought indicator and can be used as an indicator of dike safetyThe drought recovery lasted 4.5 months in 2022, which is important for the transition between seasons and different failure mechanisms [ABSTRACT FROM AUTHOR]