Search Results (1,176)

Rainfall–runoff models are widely used in water management and flood forecasting. In this study, we present a rainfall–runoff model to forecast hourly flows based on an artificial neural network (ANN). This model was developed and applied to the Iton watershed (northwestern France) to solve the problems of nonlinearity in the rainfall–runoff relationship resulting from karst and complex hydrogeological behaviors. The model design required several steps during which we were able to identify the model parameters and create the database needed to perform the flow rate forecast. This work has resulted in an ANN model able to perform an efficient prediction up to a 48 h time horizon. These results confirm that ANN models can play an important role in forecasting the nonlinear rainfall–runoff relationship encountered in many watersheds. Full article

As global temperatures rise, the Greenland ice sheet (GrIS) is undergoing accelerating mass loss, with significant implications for sea level rise and climate systems. Using GRACE and GRACE Follow-On (GRACE-FO) RL06 data from April 2002 to May 2023, alongside MARv3.14 regional climate model outputs (ice melting, runoff, rainfall, snowfall, and land surface temperature (LST)), we investigated the drivers of GrIS mass changes. Continuous wavelet transform analysis revealed significant annual signals in all variables except snowfall, with wavelet decomposition showing the largest annual amplitudes for ice melting (58.8 Gt/month) and runoff (44.5 Gt/month), surpassing those of GRACE/GRACE-FO (31.1 Gt/month). Cross-correlation analysis identified ice melting, runoff, rainfall, snowfall, and LST as significantly correlated with GrIS mass changes, with ice melting, runoff, and LST emerging as primary drivers, while snowfall and runoff exerted secondary influences. Temporal lags of 3, 4, 4, 7, and 4 months were observed for ice melting, runoff, rainfall, snowfall, and LST, respectively. These findings highlight the complex interplay of climatic and hydrological processes driving GrIS mass loss. Full article

In recent years, the urgent need to mitigate stormwater runoff and address urban waterlogging has garnered significant attention. Low Impact Development (LID) has emerged as a promising strategy for managing urban runoff sustainably. However, the vast array of potential LID layout combinations presents challenges in quantifying their effectiveness and often results in high construction costs. To address these issues, this study proposes a simulation-optimization framework that integrates the Storm Water Management Model (SWMM) with advanced optimization techniques to minimize both runoff volume and costs. The framework incorporates random variations in rainfall intensity within the basin, ensuring robustness under diverse climatic conditions. By leveraging a multi-objective scatter search algorithm, this research optimizes LID layouts to achieve effective stormwater management. The algorithm is further enhanced by two local search techniques—namely, the ‘cost–benefit’ local search and path-relinking local search—which significantly improve computational efficiency. Comparative analysis reveals that the proposed algorithm outperforms the widely used NSGA-II (Non-dominated Sorting Genetic Algorithm II), reducing computation time by an average of 8.89%, 16.98%, 1.72%, 3.85%, and 1.23% across various scenarios. The results demonstrate the method’s effectiveness in achieving optimal LID configurations under variable rainfall intensities, highlighting its practical applicability for urban flood management. This research contributes to advancing urban sponge city initiatives by providing a scalable, efficient, and scientifically grounded solution for sustainable urban water management. The proposed framework is expected to support decision-makers in designing cost-effective and resilient stormwater management systems, paving the way for more sustainable urban development. Full article

Urbanization leads to increased stormwater runoff, placing enormous pressure on the drainage system, including that of port cities in Hunan Province. This increases the risk of urban flooding and threatens the sustainability of the urban ecosystem. In this study, we employed the Storm Water Management Model (SWMM) to assess surface runoff and pollutant accumulation (TSS, COD, TN, and TP) under varying storm conditions and evaluate the efficacy of low-impact development (LID) measures in mitigating these impacts. The results included a peak ratio of 0.45, indicating complex concentration dynamics and good agreement with the observed rainfall patterns. The installation of permeable paving, rainwater infiltration ditches, and rainwater storage tanks reduced the peak flows by 33.3%, 30%, and 50%, respectively, with the rainwater storage tanks also reducing the total phosphorus (TP) load by 29.17%. In addition, it was found that rainwater collected in cisterns could be used not only for resource recycling but also to replenish groundwater resources. This demonstrates that low-impact development (LID) measures significantly reduce peak flows and pollutant loads and effectively promote the sustainable use of urban stormwater resources. The cost–benefit analyses show that the long-term benefits of LID systems are superior to those of traditional stormwater management systems. Therefore, LID measures can not only effectively reduce the pressure on urban drainage systems and improve flood prevention and mitigation capabilities but also promote sustainable development and the green transformation of cities. Full article

This study examines the spatio-temporal dynamics of rainfall erosivity, R, in the Umbria region (central Italy), based on a 20-year dataset of 30 min precipitation records from 54 stations. Using the RUSLE2 framework, models of varying complexity were evaluated to estimate the R-factor: the original model (Model A), and models based solely on event rainfall depth h_e or daily rainfall depth h_d. All the models show consistency in the spatial and temporal patterns of the R-factor: higher erosivity is observed in the southern and northwestern areas, while summer contributes the most to annual erosivity due to the high average intensity of rainfall events. Trend analyses indicate stationarity across most stations. Compared to Model A (mean R-factor: 1840 MJ mm ha⁻¹ h⁻¹ y⁻¹), the models based on h_e underestimate the R-factor by about 15%, whereas the R-factor derived from the h_d-dependent model is almost equivalent. The estimate from Model A is also approximately 20% higher than that of a previous study conducted on a more limited dataset. The most likely reason for this difference appears to be the formula used for estimating the R-factor. The study highlights the practicality of simplified models, which offer a viable alternative in contexts where high-resolution precipitation data are unavailable. It also demonstrates the benefits of denser station networks and longer observation periods, particularly in regions characterised by complex terrains. Full article

Flash floods are highly destructive natural disasters, particularly in arid and semi-arid regions like Egypt, where data scarcity poses significant challenges for analysis. This study focuses on the Wadi Al-Barud basin in Egypt’s Central Eastern Desert (CED), where a severe flash flood occurred on 26–27 October 2016. This flash flood event, characterized by moderate rainfall (16.4 mm/day) and a total volume of 8.85 × 10⁶ m³, caused minor infrastructure damage, with 78.4% of the rainfall occurring within 6 h. A significant portion of floodwaters was stored in dam reservoirs, reducing downstream impacts. Multi-source data, including Landsat 8 OLI imagery, ALOS-PALSAR radar data, Global Precipitation Measurements—Integrated Multi-satellite Retrievals for Final Run (GPM-FR) precipitation data, geologic maps, field measurements, and Triangulated Irregular Networks (TINs), were integrated to analyze the flash flood event. The Soil Conservation Service Curve Number (SCS-CN) method integrated with several hydrologic models, including the Hydrologic Modelling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), and European Hydrological System Model (MIKE-SHE), was applied to evaluate flood forecasting, watershed management, and runoff estimation, with results cross-validated using TIN-derived DEMs, field measurements, and Landsat 8 imagery. The SCS-CN method proved effective, with percentage differences of 5.4% and 11.7% for reservoirs 1 and 3, respectively. High-resolution GPM-FR rainfall data and ALOS-derived soil texture mapping were particularly valuable for flash flood analysis in data-scarce regions. The study concluded that the existing protection plan is sufficient for 25- and 50-year return periods but inadequate for 100-year events, especially under climate change. Recommendations include constructing additional reservoirs (0.25 × 10⁶ m³ and 1 × 10⁶ m³) along Wadi Kahlah and Al-Barud Delta, reinforcing the Safaga–Qena highway, and building protective barriers to divert floodwaters. The methodology is applicable to similar flash flood events globally, and advancements in geomatics and datasets will enhance future flood prediction and management. Full article

Urban flooding, driven by extreme rainfall events and urbanization, poses substantial risks to urban safety and infrastructure. This study employed a neighborhood-scale InfoWorks ICM model to analyze the full-process impacts of urban flooding under six rainfall return periods in Haining, China. The results reveal distinct non-linear responses from the 3-year to 50-year rainfall return period: (1) the surface runoff volume increases by 64.3%, with peak timing advancing by about one minute; (2) the overflow nodes rise from 37.35% to 63.24%, with durations over 30 min increasing by 78.6%; (3) the inundation areas expand by 164.9%, with maximum depths increasing by 0.31 m, showing significant regional disparities; and (4) high-risk zones, such as Haining People’s Square and Railway Station, require targeted interventions due to severe surface overflow and inundation. This comprehensive analysis emphasizes the need for tailored and phased flood prevention measures that address each stage of urban flooding. It provides a strong framework to guide urban planning and enhance resilience against rainfall-induced urban flooding. Full article

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin. Full article

To mitigate the loss of surface runoff and deep percolation in the water-scarce area and enhance the utilization of rainfall resources, this study adaptively determines the soil water content threshold triggering such losses by incorporating rainfall intensity (RI) and soil properties (SP) based on the model predictive control (MPC) framework. These thresholds then serve as the target soil water content before rainfall, and a model predictive control incorporating RI and SP (RISPMPC) irrigation decision-making is proposed. We conducted irrigation simulation experiments in Ya’an City, Sichuan Province, across four RI levels and six soil texture types. The results were compared with those obtained from conventional model predictive control (CMPC), rule-based closed-loop irrigation decision (RBC), and a newly developed zone-based model predictive control (ZMPC). Results demonstrate that RISPMPC enhances the utilization of rainfall resources across different scenarios. In soils with strong infiltration capabilities, such as loamy sand, loam, and clay loam, RISPMPC reduces irrigation water use by 26%, 5%, and 3% compared to RBC, CMPC, and ZMPC, respectively. In contrast, for soils with poor infiltration capabilities, including silty soil, clay A, and clay B, RISPMPC’s water-saving efficiency strongly correlates with rainfall intensity levels, achieving maximum savings of 61%, 36%, and 34% compared to the same three methods. Furthermore, in all cases, RISPMPC demonstrates the highest maximum effective rainfall utilization rate (MERU). As soil infiltration capability decreases and rainfall intensity increases, the MERU gap between RISPMPC and the other three methods widens significantly, underscoring RISPMPC’s robustness in environments where rainwater utilization is challenging. Therefore, RISPMPC can improve the utilization efficiency of rainwater resources and effectively alleviate agricultural water scarcity issues. Full article

Laterite is the predominant zonal soil in China’s southernmost tropical rainforest and monsoon forest regions, where typhoons are the primary source of precipitation. These storms pose significant risks of land and soil degradation due to heavy rainfall. In recent years, a substantial area of sloping land has been converted to agricultural use in these regions, predominantly for the cultivation of crops grown in laterite soil. These activities contribute to soil erosion, exacerbate environmental challenges, and hinder the pursuit of sustainable development. There is a paucity of research reports on the processes and mechanisms of runoff and sediment on sugarcane-cropped slopes in regions with laterite soil under heavy rainfall conditions. In this study, four different heavy rainfall scenarios of 75, 100, 125, and 150 mm/h were designed to assess the impact on sugarcane growth at four key stages and to measure the resulting effects on initial runoff time, surface runoff, and sediment yield from laterite soil slopes under controlled laboratory conditions. The results showed that the Horton model explained much of the variation in infiltration rate on the sugarcane-cropped laterite slopes. The cumulative sediment yield on the sugarcane-cropped laterite slopes followed a second-degree polynomial function. The initial runoff time, infiltration intensity, runoff intensity, and sediment yield were all linearly related to the leaf area index (LAI) and rainfall intensity on the sugarcane-cropped slope surface. The leaf area index exerted a greater influence on the initial runoff time and infiltration intensity than rainfall intensity. However, rainfall intensity exerted a greater influence on the runoff intensity and sediment yield than the leaf area index. Compared with the bare sloping land, the average sediment yield was reduced by 12.2, 33.1, 58.2, and 64.9% with the sugarcane growth stages of seedling, tillering, elongation, and maturity, respectively. Full article

The Haidian District was, historically, rich in water resources. However, with urban development, the groundwater levels have declined, and most rivers have lost their ecological baseflows. To restore the aquatic ecosystems, the district has implemented a cyclic water network and advanced water replenishment projects. Nonetheless, the existing replenishment strategies face challenges, such as an insufficient scientific basis, lack of data, and high energy consumption. There is an urgent need to develop a scientifically robust ecological water replenishment system and optimize pump station scheduling to enhance water resource management efficiency. This study addresses the ecological water replenishment needs of seasonal rivers by integrating the Literature method, Rainfall-Runoff method, and R2cross method to develop a comprehensive approach for calculating the ecological flow and water depth. The proposed method simultaneously meets the ecological functionality and landscape requirements of seasonal rivers. Additionally, the SWMM model is employed to design intelligent pump station scheduling rules, optimizing the replenishment efficiency and energy consumption. Through field measurements and data collection, the ecological water demands of the river channels in different areas are assessed. Using a hydrodynamic model, the dynamic variations in the ecological flow and water depth are simulated. For the Cuihu, Daoxianghu, and Yongfeng areas, this study reveals that the current replenishment volume is insufficient to meet the landscape and ecological needs of the rivers. Most rivers require a 20–30% increase in water levels, with the Dazhai qu needing a substantial rise from 0.17 m to 0.3 m, representing an increase of 76%. Additionally, the results demonstrate that intelligent pump station scheduling can significantly reduce operating costs and energy consumption by dynamically adjusting the replenishment timing and flow rates. This approach optimizes the intervals between equipment activation and deactivation, thereby balancing ecological and energy-saving goals. This research not only provides technical support for the precise calculation of ecological replenishment volumes and the intelligent management of pump stations, but also offers scientific references for water resource management in similar regions. The findings will enhance the ecological functions and landscape quality of the rivers in the Haidian District while promoting refined and intelligent regional water resource management. Moreover, this study presents innovative solutions and theoretical foundations for water resource regulation under the backdrop of climate change. Full article

Pluvial flooding, driven by increasingly impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure are exacerbating flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of a soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach for the Tiffany Run watershed, Baltimore City. This study utilized different extreme storm events, a high-resolution (1 m) LiDAR Digital Terrain Model (DTM), building footprints, and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, the City Catchment Analysis Tool (CityCAT), to simulate urban flood dynamics. The pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 m in most areas, with hydrologic soil groups C and D, especially downstream of the study area. The post-soil rehabilitation simulation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 m. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following the rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. The validation using a contingency matrix demonstrated true-positive rates of 0.75, 0.50, 0.64, and 0 for the selected events, confirming the model’s capability at capturing real-world flood occurrences. Full article

Expansive soils are widely distributed in tropical and subtropical regions and are highly sensitive to moisture variations, posing significant challenges to slope stability. Rainfall infiltration alters the hydro-mechanical behavior of expansive soils, increasing the risk of landslides and slope failures. Understanding the infiltration dynamics under different slope conditions is therefore essential for improving slope stability management and disaster mitigation. To investigate the mechanisms governing the long-term stability of steep expansive soil slopes, this study designed and constructed a multi-slope combination model test box. Model experiments were conducted on rainfall-induced expansive soil slopes with varying gradients to analyze the interaction between surface runoff and seepage under different rainfall conditions. The results demonstrate that slope gradient plays a crucial role in the rainfall infiltration process. As the slope gradient decreases, the time required for runoff initiation increases, and rainfall infiltration becomes the dominant process, while runoff plays a secondary role. This effect is more pronounced at lower slope gradients. Furthermore, as the slope gradient increases, the variation in soil moisture content decreases, and the influence of rainfall on deeper soil layers is reduced. Beyond a certain threshold, further increases in slope angle result in a diminished effect on enhancing surface runoff and limiting infiltration. Additionally, steeper slopes exhibit a slower rise in soil moisture content during rainfall events. The results also indicate that as the slope gradient increases, the depth of soil affected by rainfall becomes shallower, and the migration speed of the wetting front decreases. The findings of this study provide valuable insights into slope hydrodynamics and serve as a scientific basis for sustainable slope management and soil conservation in expansive soil regions. Full article

The Australian Bureau of Meteorology provides flood forecasting and warning services across Australia for most major rivers in Australia, in cooperation with other government, local agencies and emergency services. As part of this service, the Bureau issues a flood watch product to provide early advice on a developing situation that may lead to flooding up to 4 days prior to an event. This service is based on (a) an ensemble of available Numerical Weather Prediction (NWP) rainfall forecasts, (b) antecedent soil moisture, stream and dam conditions, (c) hydrological forecasts using event-based models and (d) expert meteorological and hydrological input by Bureau of Meteorology staff, to estimate the risk of reaching pre-specified river height thresholds at locations across the continent. A flood watch provides information about a developing weather situation including forecasting rainfall totals, catchments at risk of flooding, and indicative severity where required. Although there is uncertainty attached to a flood watch, its early dissemination can help individuals and communities to be better prepared should flooding eventuate. This paper investigates the utility of forecasts of daily gridded national runoff to inform the risk of riverine flooding up to 7 days in advance. The gridded national water balance model (AWRA-L) runoff outputs generated using post-processed 9-day Numerical Weather Prediction hindcasts were evaluated as to whether they could accurately predict exceedance probabilities of runoff at gauged locations. The approach was trialed over 75 forecast locations across North East Australia (Queensland). Forecast 3-, 5- and 7-day accumulations of runoff over the catchment corresponding to each location were produced, identifying whether accumulated runoff reached either 95% or 99% historical levels (analogous to minor, moderate and major threshold levels). The performance of AWRA-L runoff-based flood likelihood was benchmarked against that based on precipitation only (i.e., not rainfall–runoff transformation). Both products were evaluated against the observed runoff data measured at the site. Our analysis confirmed that this runoff-based flood likelihood guidance could be used to support the generation of flood watch products. Full article

This paper presents a novel methodology for the event-based calibration of urban drainage models based on conceptual simulation of external sub-catchments and physical representation of underground channels. Following the setup of the numerical model of an urban drainage system and the definition of the list of parameters, the methodology proposed is based on two steps, namely the application of sensitivity analysis for the identification of influent parameters and the calibration of the model on each event considering the reduced set of influent parameters by means of an optimizer. The methodology is applied to the Cascina Scala urban catchment in Pavia, northern Italy, for which a rainfall/runoff dataset is available for 14 events. In the applications, the urban drainage system is constructed in the EPA-SWMM environment, and a genetic algorithm is used for calibration. The results prove that the model parameterized with the innovative methodology features a very good fit to experimental data concerning hydrographs at the exit of the catchment while offering significant computational advantages compared with the usual calibration approach. Full article