Search Results (226)

Extreme rainstorms are difficult to predict and often result in catchment-scale rainfall flooding, leading to substantial economic losses globally. Enhancing the numerical computational efficiency of flood models is essential for improving flood forecasting capabilities. This study presents an integrated hydrological–hydrodynamic model accelerated using GPU (Graphics Processing Unit) technology to perform high-efficiency and high-precision rainfall flood simulations at the catchment scale. The model couples hydrological and hydrodynamic processes by solving the fully two-dimensional shallow water equations (2D SWEs), incorporating GPU-accelerated parallel computing. The model achieves accelerated rainstorm flooding simulations through its implementation on GPUs with parallel computing technology, significantly enhancing its computational efficiency and maintaining its numerical stability. Validations are conducted using an idealized V-shaped catchment and an experimental benchmark, followed by application to a small catchment on the Chinese Loess Plateau. The computational experiments reveal a strong positive correlation between grid cell numbers and GPU acceleration efficiency. The results also demonstrate that the proposed model offers better computational accuracy and acceleration performance than the single-GPU model. This GPU-accelerated hydrological–hydrodynamic modeling framework enables rapid, high-fidelity rainfall flood simulations and provides critical support for timely and effective flood emergency decision making. Full article

Rainfall-based methods have been employed for computing hydrographs in urban drainage systems. However, their implementation often introduces uncertainty in various aspects, such as the selection of a unit hydrograph, the choice of abstraction methods, and the formulas used to calculate the time of concentration, among others. Conventional consultancy studies tend to oversimplify catchment representation by treating it as a homogeneous unit or discretizing it into a few segments with simplified flood routing. This research proposes a streamlined methodology for computing hydrographs, considering the sub-basins’ heterogeneity. The methodology is based on the principles of proportionality and superposition. A sensitivity analysis of the proposed methodology is conducted, considering both homogeneous and heterogeneous catchments and the temporal distribution of rainfall. The proposed methodology is applied to the catchment of the Ricaurte channel, located in Cartagena de Indias (Colombia), with a watershed area of 728.8 ha. It has proven effective in representing a recorded simultaneous rainfall-runoff event, achieving a Root Mean Square Error of 3.93% in estimating the total volume of the measured hydrographs. A key advantage of the methodology, compared to traditional rainfall–runoff approaches, is that it does not require an extensive number of parameters to be calibrated. It may be utilized to estimate extreme flood events in urban areas with limited data availability, relying on minimal data inputs. Full article

Climate-induced extreme rainfall events are increasing the intensity and frequency of flash floods, highlighting the urgent need for advanced flood management systems in climate-resilient cities. This study introduces an Intelligent Flood Control Decision Support System (IFCDSS), a novel AI-driven solution for real-time flood forecasting and automated pump operations. The IFCDSS integrates multiple advanced tools: machine learning for rapid short-term water level forecasting, NSGA-III for multi-objective optimization, the TOPSIS for robust multi-criteria decision-making, and the ANFIS for real-time pump control. Implemented in the flood-prone Zhongshan Pumping Station catchment in Taipei, the IFCDSS leveraged real-time sensor data to deliver accurate water level forecasts within five seconds for the next 10–30 min, enabling proactive and informed operational responses. Performance evaluations confirm the system’s scientific soundness and practical utility. Specifically, the ANFIS achieved strong accuracy (R² = 0.81), with most of the prediction errors being limited to a single pump unit. While the conventional manual operations slightly outperformed the IFCDSS in minimizing flood peaks—due to their singular focus—the IFCDSS excelled in balancing multiple objectives: flood mitigation, energy efficiency, and operational reliability. By simultaneously addressing these dimensions, the IFCDSS provides a robust and adaptable framework for urban environments. This study highlights the transformative potential of intelligent flood control to enhance urban resilience and promote sustainable, climate-adaptive development. Full article

Accurately assessing the accessibility and equity of urban public sports facilities is essential for improving public service provision and enhancing residents’ well-being. However, most existing studies rely on administrative units such as subdistricts and communities, often overlooking the multi-level structure of such facilities and failing to reflect their distribution within the spatial scope of residents’ daily activities. To address this gap, this study adopted the residential neighborhood as the basic unit of analysis and developed an integrated methodological framework combining the average nearest neighbor index, kernel density estimation, a Gaussian-based two-step floating catchment area method, the Gini coefficient, and location quotient analysis. When applied to Shanghai, the framework revealed distinct spatial patterns across facility levels, exhibiting scale-dependent characteristics. Community-level and residential-level sports facilities were found to be relatively accessible, whereas city-level and subdistrict-level sports facilities showed limited accessibility, particularly in peripheral suburbs. All facility levels exhibited varying degrees of spatial inequality, highlighting persistent issues of spatial justice. These findings provide empirical evidence to inform the spatial optimization of public sports facilities and to promote more equitable access to urban public services. Full article

Recent progress in Artificial Intelligence and Machine Learning (AIML) has accelerated improvements in the prediction performance of many hydrological processes. Yet, flood prediction remains a challenging task due to its complex nature. Two common challenges afflicting the task are flood volatility and the sensitivity and complexity of flood generation attributes. This study explores the application of Recurrent Neural Networks (RNNs)—specifically Vanilla Recurrent Neural Networks (VRNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in flood prediction and assessment. By integrating catchment-specific hydrological and meteorological variables, the RNN models leverage sequential data processing to capture the temporal dynamics and seasonal patterns characteristic of flooding. These models were employed across diverse terrains, including mountainous watersheds in the state of South Carolina, USA, to examine their robustness and adaptability. To identify significant hydrological events for flash flood analysis, a discharge frequency analysis was conducted using the Pearson Type III distribution. The 1-year and 2-year return period flows were estimated based on this analysis, and the 1-year return flow was selected as a conservative threshold for flash flood event identification to ensure a sufficient number of training instances. Comparative benchmarking with the National Water Model (NWM v3.0) revealed that the RNN-based approaches offer notable enhancements in capturing the intensity and timing of flood events, particularly for short-duration and high-magnitude floods (flash floods). Comparison of predicted disharges with the discharge recorded at the gauges revealed that GRU had the best performance as it achieved the highest mean NSE values and exhibited low variability across diverse watersheds. LSTM results were slightly less consistent compared to the GRU albeit achieving satisfactory performance, proving its value in hydrological forecasting. In contrast, VRNN had the highest variability and the lowest NSE values among the three. The NWM model trailed the machine learning-based models. The study highlights the efficacy of the RNN models in advancing hydrological predictions. Full article

Freshwater habitats and biota are among the most threatened worldwide. In Europe, significant efforts are being taken to counteract detrimental human impacts on nature. In line with these efforts, the MERLIN project funded by the H2020 program focuses on mainstreaming ecosystem restoration for freshwater-related environments at the landscape scale. Additionally, the Dammed Fish project focuses on one of the main threats affecting European Networks—artificial fragmentation of the river. Meeting the objectives of both projects to work on a large, pan-European scale, we developed a novel spatial database for river units. These spatial units, named River Restoration Units (R2Us), abide by river network functioning while creating the possibility of aggregating multiple data sources with varying resolutions to size-wise comparable units. To create the R2U, we set a methodological framework that departs from the Catchment Characterization and Modelling—River and Catchment Database v2.1 (CCM2)—together with the capabilities of the River Network Toolkit (v2) software (RivTool) to implement a seven-step methodological procedure. This enabled the creation of 11,557 R2U units in European sea outlet river basins along with their attributes. Procedure outputs were associated with spatial layers and then reorganized to create a relational database with normalized data. Under the MERLIN project, R2Us have been used as the spatial analysis unit for a large-scale analysis using multiple input datasets (e.g., ecosystem services, climate, and European Directive reporting data). This database will be valuable for river management and conservation planning, being particularly well suited for large-scale restoration planning in accordance with European Nature legislation. Full article

Rising nitrate contamination in water systems poses significant risks to public health and ecosystem stability, necessitating advanced modeling to understand nitrate dynamics more accurately. This study applies the long short-term memory (LSTM) modeling to investigate the hydrologic and environmental factors influencing nitrate concentration dynamics in rivers and aquifers across the state of Alabama in the southeast of the United States. By integrating dynamic data such as streamflow and groundwater levels with static catchment attributes, the machine learning model identifies primary drivers of nitrate fluctuations, offering detailed insights into the complex interactions affecting multi-year nitrate concentrations in natural aquatic systems. In addition, a novel LSTM-based approach utilizes synthetic surface water nitrate data to predict groundwater nitrate levels, helping to address monitoring gaps in aquifers connected to these rivers. This method reveals potential correlations between surface water and groundwater nitrate dynamics, which is particularly meaningful given the lack of water quality observations in many aquifers. Field applications further show that, while the LSTM model effectively captures seasonal trends, limitations in representing extreme nitrate events suggest areas for further refinement. These findings contribute to data-driven water quality management, enhancing understanding of nitrate behavior in interconnected water systems. Full article

In the 20-year period from 2003 to 2022, water runoff and suspended sediment load in a forested gully system with a total length of 7.5 km was recorded. The branching gully system cuts through an agricultural catchment of 1.24 km² located within the loess plateau of the Nałęczów Plateau (E Poland). A rain gauge was installed close to the watershed and in the gully mouth water runoff was monitored using a limnigraph, installed with a water gauge on a Thomson triangular weir. To determine suspended sediment concentrations during erosion episodes, water was sampled at a frequency dependent on the rate and duration of the runoff. The aim of the monitoring studies was to specify the relationship between flow rate and suspended concentration for different water runoff conditions, which were used to calculate sediment load. The contribution of propluvial and pronivial runoff to gully development was assessed, particularly in the transport of sediment out of the catchment, and unit denudation rates were calculated as less than 1000 Mg km⁻² year⁻¹, average 173 Mg km⁻² year⁻¹, and during a single event as a maximal 900 Mg km⁻². Full article

Performance plays a critical role in the practical use of global streamflow reanalysis. This paper presents the combined use of random forest and the Shapley additive explanation to examine the mechanism by which catchment attributes influence the accuracy of streamflow estimates in reanalysis products. In particular, the reanalysis generated by the Global Flood Awareness System streamflow is validated by streamflow observations provided by the Catchment Attributes and MEteorology for Large-sample Studies dataset. Results highlight that with regard to the Kling–Gupta efficiency, the reanalysis surpasses mean flow benchmarks in 93% of catchments across the continental United States. In addition, twelve catchment attributes are identified as major controlling factors with spatial patterns categorized into five clusters. Topographic characteristics and climatic indices are also observed to exhibit pronounced influences. Streamflow reanalysis performs better in catchments with low precipitation seasonality and steep slopes or in wet catchments with a low frequency of precipitation events. The partial dependence plot slopes of most key attributes are consistent across the four seasons but the slopes’ magnitudes vary. Seasonal snow exhibits positive effects during snow melting from March to August and negative effects associated with snowpack accumulation from September to February. Catchments with very low precipitation seasonality (values less than −1) show strong seasonal variation in streamflow estimations, with negative effects from June to November and positive effects from December to May. Overall, this paper provides useful information for applications of global streamflow reanalysis and lays the groundwork for further research into understanding the seasonal effects of catchment attributes. Full article

In the vicinity of the Adamów power plant, which operates in the catchment area of the Kiełbaska river, there is a significant shortage of water resources caused by the intensive use of water by the energy industry and agriculture. The development of the plant by replacing the outdated coal-fired (lignite-fired) units with modern gas and steam units may contribute significantly to reducing the negative impact on the environment and reduce the demand for water resources relative to coal technology. Gas and steam units are a much more energy-efficient technology. This implies a lower demand for water, a reduction in pollutant emissions, and greater operational flexibility, which enables the units to adapt to changing hydrological and environmental conditions. The high efficiency of these units limits the need for frequent water-refilling, while allowing for a more sustainable and stable production of energy. Based on an analysis of hydrological data for the years 2019–2023, it was estimated that water stress is observed in this catchment area on 198 days per year, which accounts for c.a. 54% of the hydrological year. Therefore, it is assumed that inter-catchment pumping stations with a flow of 0.347 m³∙s⁻¹ will be required. This sets the demand for water at 5.95 million m³ per year. The planned water transfer will be carried out from Jeziorsko reservoir on the Warta river through the catchment area of Teleszyna river. Moreover, there are plans for the reconstruction of the layout of Kiełbaska Duża and Teleszyna rivers, which would involve the restoration of natural run-offs, following the discontinuation of open-pit lignite mining. This will additionally be supported by the reduced demand for water in the water use system when using the modernised power plant. The analysed data made it possible to develop hydrological scenarios that take the future reduction in water stress into account by implementing plans to restore the former hydrographic system in the region. These investments would also foresee the creation of new retention reservoirs (in former mining pits) with a capacity of nearly 900 million m³, which will significantly increase the region’s water resources and retention potential, supporting hydrological and energy security for the years to come. Full article

Accessibility to metro services is often evaluated based on the locations of stations. However, compared to the location of station itself, focusing on its entrances/exits offers a more accurate approach to assessing station supply and demand levels. Despite this, research focusing on the supply of and demand for metro services concerning metro entrances and exits remains limited. This study employed multi-source geospatial data from Xiamen, China, to examine the supply and demand dynamics of metro stations with a particular emphasis on entrances/exits. In the first phase, we treated entrances/exits as supply facilities and used land plot boundaries as the fundamental spatial units for accessibility calculations. Taking into account the layout characteristics of entrances/exits, along with the traffic generation of various land-use types, we employed the Gaussian two-step floating catchment area (G2SFCA) method to gauge the supply and demand levels of plots. Subsequently, we computed the spatial supply-and-demand relationships of station entrances/exits for both station-level and shared usage level of entrances/exits. We found that the accessibility from plots to entrances/exits diverged from previously observed spatial distribution trends, being higher in city centers, regional boundaries, and terminal stations and lower in transitional areas. Moreover, “metro accessibility” and the “imbalance index of entrances/exits” are associated with the primary functions of stations and the surrounding urban development; yet they exhibit spatial heterogeneity. The stations with a high value for “imbalanced index of entrances/exits” were always near some business parks, and “metro accessibility” seemed to be more easily affected by location factors. Based on two metrics, stations were categorized into four types, each displaying unique characteristics regarding location distributions, entrance/exit configurations, and commuting passenger sources. This research aims to identify the phenomenon of unfair transport in metro service from the perspective of their entrances, inform the optimization of metro station designs, and tailor planning recommendations, ultimately enhancing transport equity and contributing to sustainable urban built environments. Full article

Climate change has increased the intensity and frequency of weather systems, increasing the risk of inundation in urban areas. To mitigate these risks, not only rivers but also entire catchments need to be managed, and the use of infiltration and retention units needs to be expanded. The ability to evaluate the effects of promoting infiltration and retention in catchments using distributed hydrological models, clarify the three-dimensional behavior of exfiltration from catchments into natural base soils, and parameterize this flow as a one-dimensional hypothetical water flux is essential. Using VGFlow2D (Forum8) and field observations, numerical analyses were conducted to parametrize the flux and assess the features of q/Ks values, representing the volume of three-dimensional water exfiltration from stormwater inlet bases into natural soils relative to the saturated hydraulic conductivity (Ks) of the soils. The findings were integrated into the hydrological model Infoworks ICM (Innovyze) by adding a single parameter, the “exfiltration loss rate”, to each inlet without increasing computational demands. The obtained q/Ks values were compared to previously reported values, and variations were evaluated using infiltration theory. Full article

The summer drought in the United Kingdom (UK) in 2022 produced significant speculation concerning how its termination may impact and interact with the soil resource. Whilst knowledge regarding soils and droughts exists in the scientific literature, a coherent understanding of the wider range of impacts on soil properties and functions has not been compiled for temperate soils. Here, we draw together knowledge from studies in the UK and other temperate countries to understand how soils respond to drought, and importantly what and where our knowledge gaps are. First, we define the different types of droughts and their frequency in the UK and provide a brief overview on the likely societal impacts that droughts place on the soil and related ecosystems. Our focus is on ‘agricultural and ecosystem drought’, as this is when soils experience dry periods affecting crops and ecosystem function, followed by rewetting. The behaviour of moisture in soils and the key processes that contribute to its storage and transport are examined. The principal changes in the physical, chemical, and biological properties of soils resulting from drought, and rewetting (i.e., drought termination) are discussed and their extensive interactions are demonstrated. Processes that are involved in the rewetting of soils are explored for soil and catchment-scale soil responses. Lastly, soils’ recovery after drought is considered, knowledge gaps are identified, and areas to improve understanding are highlighted. Full article

Digital technology has brought drastic changes to the design methods, values, and design tools of neighbourhood public spaces, thereby changing the behavioural patterns of people in neighbourhood public spaces. Therefore, people’s requirements for urban public space have changed and are characterised by high efficiency, high precision, humanization, and high aesthetics. Scholars at home and abroad have conducted many studies and practises on the application of digital technology to neighbourhood design, but there is a lack of systematic research practises on the use and analysis of multi-faceted data. This paper selects the Xiaomi Beijing Changping II design project, simulates and deduces the physical environment of its location, simulates the results of the natural environment by using the data of sunshine, wind, and water catchment in the physical environment, and then simulates the crowd’s action paths by combining kinetic algorithms and ant algorithms to optimise the design methods, processes, and results of the neighbourhood public space on the basis of this research. The research team designed five groups of programmes for the project based on different design methods and processes, and conducted a comparative study of the five groups of programmes through the hierarchical analysis method in conjunction with the fuzzy comprehensive evaluation method, as well as discussing them in conjunction with the actual bidding results of the project and the scoring conclusions of the industry experts. The results of the study show that the design scheme for neighbourhood public space based on physical environment simulation and crowd simulation is better able to take advantage of the analytical and predictive advantages of the technology and unite with the designer’s aesthetic interests, balancing the data objectivity and aesthetic subjectivity in the design process. The method is more likely to achieve a design solution that combines systematicity, foresight, rationality, and aesthetics, and provides an empirical case for the application of data simulation in public space, aiming to improve the rationality of public space design and solve the data-objective problems faced by the design of public space at this stage. Full article

Global urbanization, population growth, and climate change have considerably impacted water resources, making sustainable water resource management (WRM) essential. Understanding the changes in hydrological components is important for effective WRM, particularly in cities such as Higashi-Hiroshima, which is known for its saké brewing industry. This study used the Soil and Water Assessment Tool (SWAT) with Hydrological Response Units (HRUs) to achieve high spatial precision in assessing the impacts of land use change and climate variability on hydrological components in a suburban catchment in western Japan. Over the 30-year study period (1980s–2000s), land use change was the main driver of hydrological variability, whereas climate change played a minor role. Increased surface runoff, along with decrease in groundwater recharge, evapotranspiration, and baseflow, resulted in an overall reduction in water yield, with a 34.9% decrease in groundwater recharge attributed to the transformation of paddy fields into residential areas. Sustainable WRM practices, including water conservation, recharge zone protection, and green infrastructure, are recommended to balance urban development with water sustainability. These findings offer valuable insights into the strategies for managing water resources in rapidly urbanizing regions worldwide, emphasizing the need for an integrated WRM system that considers both land use and climate change impacts. Full article