Search Results (519)

(1) Background: Aiming at low-accuracy and unclear parameter differentiation of snowmelt ice melting, rainstorm and mixed flood simulation in Northwest Chinese arid inland river basins, this study aimed to improve complex flood simulation ability and support arid area flood prediction via HEC-HMS model optimization and classification standard innovation. (2) Method: A distributed HEC-HMS model was built using topography, soil and land use data. A “meteorology, hydrology, underlying surface” flood classification method was developed, and runoff generation-concentration parameters were calibrated via trial-and-error and Latin hypercube sampling for 36 historical floods (12 each type) to verify model applicability. (3) Result: The classification accuracy reached 92%. All three flood types met simulation standards: flood peak and runoff depth error ≤ ±20%, peak time error < 3 h, average NSE = 0.76 (snowmelt: 0.82, rainstorm: 0.76, mixed: 0.70). Parameters showed gradient differences: snowmelt (CN = 65, I_a = 20 mm, k = 0.3), rainstorm (CN = 80, I_a = 10 mm, k = 0.5), mixed (parameters in between). (4) Conclusions: After parameter optimization, the HEC-HMS model is suitable for multi-source flood simulation in arid areas, and the revealed parameter laws provide a quantitative basis for flood forecasting in similar basins. Full article

Songhua River Basin, northeast China, has seen significant changes due to climate change and human activities from 1990 to 2000, when forests were largely reclaimed and agricultural land was taken up to change the terrestrial water cycle drastically. This paper investigates hydrological changes in three basins: the main stream basin of the Songhua River, the Second Songhua River Basin, and the Nenjiang River Basin. Machine learning and signal processing techniques have been applied to reconstruct historical river records with high accuracy, achieving determination coefficients exceeding 0.97. The physically based WEP model effectively simulates both natural hydrological patterns and human-induced hydrological processes in the northern Nenjiang region. Climate projections indicate clear temperature increases across all scenarios. The most significant warming is observed under the SSP5-8.5 scenario, where runoff increases by 8.52% to 12.02%t, with precipitation driving 62% to 78% of the changes. Summer runoff shows the most significant increase, while autumn runoff decreases, particularly in the Nenjiang Basin, where permafrost loss alters spring melt patterns. This change elevates flood risk in summer, with the rate of increase strongly dependent on the scenario. Water resources show strong scenario dependence, with the average growth rate of SSP5-8.5 being 4 times that of SSP1-2.6. A critical threshold is reached at a 2.5 °C increase in temperature, triggering system instability. These results emphasize the need for adaptation to spatial differences to address emerging water security challenges in rapidly changing northern regions, including nonlinear hydroclimatic responses, infrastructure resilience to flow changes, and cross-basin coordination. Full article

Making effective decisions about scaling up on-farm irrigation practices to the district level requires a comprehensive assessment of irrigation management at the farm level. In this context, a bucket-type water mass balance model was developed, calibrated, and validated over five irrigation seasons on a 121-hectare rice farm located in the lower Ter River valley (north-east Spain), to assess the water use efficiency and the impact of different irrigation practices on water savings. The model was implemented considering the spatial variability of the soils within the farm. It showed a satisfactory performance in both the calibration (2020, 2021, 2022) and validation (2023, 2024) cropping seasons, with NSE values greater than 0.50, PBIAS lower than ±20%, and RSR lower than 0.70. After model validation, the simulation of alternative water management practices revealed that the 10-day fixed-turn irrigation reduced irrigation water use by 30% compared to the traditional water management, although it may negatively impact rice yield. Simulations of an early irrigation cut-off at the end of the season and dry seeding with delayed flooding accounted for 17% and 15% irrigation water savings, respectively. The implementation of the no-runoff practice only accounted for a 6% reduction in water use. The water-saving potential of the simulated strategies was mainly driven by shortening the flooded period of rice paddies, thus demonstrating that managing the ponding water level is critical to diminishing water use in rice irrigation. Full article

The hydrology of the transboundary region encompassing the western Red River Basin headwaters, such as Devils Lake Basin (DLB) in North America, is complex and highly sensitive to climate variability, impacting water resources, agriculture, and flood risk. Understanding hydrological shifts in this region is critical, particularly given recent hydroclimatic changes. This study aimed to simulate and analyze key hydrological processes and their evolution from 1981 to 2020 using an integrated modeling approach. We employed the NASA Land Information System (LIS) framework configured with the Noah-MP land surface model and the HyMAP routing model, driven by a combination of reanalysis and observational datasets. Simulations revealed a significant increase in precipitation inputs and consequential positive net water storage trends post-1990, indicating increased water retention within the system. Snow dynamics showed high interannual variability and decadal shifts in average Snow Water Equivalent (SWE). Simulated streamflow exhibited corresponding multi-decadal trends, including increasing flows within a major DLB headwater basin (Mauvais Coulee Basin) during the period of Devils Lake expansion (mid-1990s to ~2011). Furthermore, analysis of decadal average seasonal hydrographs indicated significant shifts post-2000, characterized by earlier and often higher spring peaks and increased baseflows compared to previous decades. While the model captured these trends, validation against observed streamflow highlighted significant challenges in accurately simulating peak flow magnitudes (Nash–Sutcliffe Efficiency = 0.33 at Mauvais Coulee River near Cando). Overall, the results depict a non-stationary hydrological system responding dynamically to hydroclimatic forcing over the past four decades. While the integrated modeling approach provided valuable insights into these changes and their potential drivers, the findings also underscore the need for targeted model improvements, particularly concerning the representation of peak runoff generation processes, to enhance predictive capabilities for water resource management in this vital region. Full article

Climate change and human activities are intensifying the hydrologic cycle and increasing extreme events, challenging accurate prediction. This study builds on the Transformer architecture by introducing a sliding time window and runoff classification mechanism, enabling high-precision long-term runoff forecasting and significantly improving the simulation of extreme floods. However, the generalization ability of data-driven models remains limited in non-stationary environments. To address this issue, we further propose a hybrid framework that couples the process-based GBHM with the enhanced Transformer via bias correction. This fusion leverages the strengths of both models: the process-based model explicitly captures topographic heterogeneity, the spatial distribution of meteorological forcings, and their temporal variability, while the data-driven model excels at uncovering latent relationships among hydrological variables. The results demonstrate that the coupled model significantly outperforms traditional approaches in peak-flow prediction and exhibits superior robustness and generalizability under changing environmental conditions. Full article

Floods are among the most destructive natural disasters and can devastate human life, infrastructure, and mobility in urban areas. It is necessary to develop a simulation model suitable for disaster management to prepare for flooding and facilitate rapid response interventions. The advantage of a three-dimensional (3D) geographic information system (GIS) is that it allows researchers to perform more successful spatial analyses than traditional two-dimensional (2D) systems. In this study, real-time 3D flood simulations were created for the Bozkurt district of Kastamonu, Türkiye, integrating GIS and game engine technologies. Land use land cover (LU/LC) map, digital elevation model (DEM), soil properties and climate data of the study region constitute the input data for the hydrological model. DEM and building footprints are also used to create 3D models of the buildings in the region. Through the Soil and Water Assessment Tool (SWAT) analysis, a hydrological model that included environmental factors such as precipitation, runoff, and soil erosion was created. The average flow rate for the same period, obtained from flow monitoring stations in the Bozkurt district, was 4.64 m³/s, while the flow rate obtained with the SWAT+ model was 4.12 m³/s. Using the flow parameters obtained with SWAT, 3D flood models were developed on Unreal Engine (UE). The flood simulation created with UE and the flood disaster experienced in 2021 in the region were compared on an area basis. The obtained simulation accuracy was 88%. Full article

With climate change, the frequency and severity of localized heavy rainfalls are increasing. Thus, for urban drainage networks (UDNs), particularly those in aging cities such as Seoul, Republic of Korea, flood risk management challenges are mounting. Conventional design standards typically apply uniform roughness coefficients based on new pipe conditions, neglecting the ongoing performance degradation from physical influences. This study introduces a methodology that systematically incorporates pipe age and size into roughness coefficient scenarios for higher-accuracy 1D–2D rainfall–runoff hydrologic–hydraulic simulations. Eleven roughness scenarios (a baseline and ten aging-based scenarios) are applied across seven UDNs using historical rainfall data. The most representative scenario (S3) is identified using a Euclidean distance metric combining the peak water-level error and root mean square error. For two rainfall events, S3 yields substantial increases in the simulated mean flood volumes (75.02% and 76.45%) compared with the baseline, while spatial analysis reveals significantly expanded inundation areas and increased flood depths. These findings underscore the critical impact of pipe deterioration on hydraulic capacity and demonstrate the importance of incorporating aging infrastructure into flood modeling and UDN design. This approach offers empirical support for updating UDN design standards for more resilient flood management. Full article

This study investigates the impact of climate change on the Mudurnu Stream Basin in northwest Türkiye by analyzing climate parameters in the Mediterranean region. Historical data from EC-Earth2, HadGEM2-ES, and MPI-ESM-MR GCMs from the CMIP5 Euro-CORDEX archive were assessed, and future precipitation and temperature data were derived using five statistical bias correction methods for the selected EC-Earth2 model under RCP4.5 and RCP8.5 scenarios. The SWAT model was employed to simulate future runoff amounts for the Mudurnu Stream Basin. The findings reveal notable changes in precipitation and temperature. The annual and seasonal variations of total precipitation and average, maximum, and minimum temperatures for the RCP4.5 and RCP8.5 scenarios in the Sakarya and Mudurnu regions were analyzed and determined. The projections for future river flow indicate a significant increase in precipitation during the rainy seasons. The Mudurnu Stream mainstem will experience an increase in flow of between 70 and 140% under RCP4.5 and between 80 and 160% under RCP8.5. In the Dinsiz Stream tributary, a 32–55% increase is observed for the spring and summer months. In this context, the rainfall and runoff projections required for the estimation of potential drought and flood risks in the near and distant future were calculated. Full article

This study applies the Integrated Modification Methodology (IMM) to assess how morphology-driven, nature-based solutions reduce urban heat island (UHI) effects and flooding in Rio de Janeiro’s Cidade Nova. Multi-scale GIS diagnostics identify green continuity and vertical permeability as critical weaknesses. Simulations (Ladybug/Dragonfly) and hydrological modelling (rational method) quantify the intervention’s impact, including greening, material retrofits, and drainage upgrades. Results show a 38% increase in albedo, a 13% reduction in volumetric heat capacity, and a 30% drop in thermal conductivity. These changes reduce the peak UHI by 0.2 °C hourly, narrowing the urban–rural temperature gap to 3.5 °C (summer) and 4.3 °C (winter). Hydrologically, impervious cover decreases from 22% to 15%, permeable surfaces rise from 9% to 29%, and peak runoff volume drops by 27% (16,062 to 11,753 m³/h), mitigating flood risks. Green space expands from 7.8% to 21%, improving connectivity by 50% and improving park access. These findings demonstrate that IMM-guided interventions effectively enhance thermal and hydrological resilience in dense tropical cities, aligning with climate adaptation and the Sustainable Development Goals. Full article

Urban flooding represents a growing global concern, especially in areas with rapid urbanization, unregulated urban sprawl and climate change conditions. Conventional flood modeling approaches do not effectively capture the complex dynamics between natural watershed behavior and urban infrastructure; they typically simulate these domains in isolation. This study introduces the Watershed-BIM methodology, a three-dimensional simulation framework that integrates Building and City Information Modeling (BIM/CIM), Geographic Information Systems (GIS), Flood Risk Assessment (FRA), and Flood Risk Management (FRM) into a single framework. Autodesk InfraWorks 2024, Civil 3D 2024, and RiverFlow2D v8.14 software are incorporated in the development. The methodology enhances interoperability and prediction accuracy by bridging hydrological processes with detailed urban-scale data. The framework was tested on a real-world flash flood event in Mandra, Greece, an area frequently exposed to extreme rainfall and runoff events. A specific comparison with observed flood characteristics indicates improved accuracy in comparison to other hydrological analyses (e.g., by HEC-RAS simulation). Beyond flood depth, the model offers additional insights into flow direction, duration, and localized water accumulation around buildings and infrastructure. In this context, integrated tools such as Watershed-BIM stand out as essential instruments for translating complex flood dynamics into actionable, city-scale resilience planning. Full article

Recent flooding events in Spain have highlighted the need to develop real-time flood forecasts to estimate streamflows over the next few hours and days. Therefore, a meteorological forecast that provides possible precipitation for the upcoming hours combined with a hydrological model to simulate the rainfall-runoff processes in the basin and its flood response are needed. In this paper, a probabilistic flood forecasting tool is proposed for the Francolí river basin, located in Catalonia (Spain). For this purpose, the Real-time Interactive Basin Simulator (RIBS) distributed hydrological model was calibrated in this basin for a set of flood events. Then, a series of rainfall field forecasts based on the analog method have been used as input data in the hydrological model, obtaining a set of hydrographs for given flood events as output. Finally, a probabilistic forecast that supplies the probability distribution of the possible response flows of the Francolí river is provided for a set of episodes. Full article

Existing drainage systems have limited capacity to mitigate future climate change-induced flooding problems effectively. However, some studies have evaluated the effectiveness of integrating Water-Sensitive Urban Design (WSUD) with existing drainage systems in mitigating flooding in tropical regions. This study examined the performance of drainage systems and integrated WSUD options under current and future climate scenarios in a sub-catchment of Saltwater Creek, a tropical catchment located in Cairns, Australia. A combination of one-dimensional (1D) and two-dimensional (1D2D) runoff generation and routing models (RORB, storm injector, and MIKE+) is used for simulating runoff and inundation. Several types of WSUDs are tested alongside different climate change scenarios to assess the impact of WSUD in flood mitigation. The results indicate that the existing grey infrastructure is insufficient to address the anticipated increase in precipitation intensity and the resulting flooding caused by climate change in the Engineers Park sub-catchment. Under future climate change scenarios, moderate rainfall events contribute to a 25% increase in peak flow (95% confidence interval = [1.5%, 0.8%]) and total runoff volume (95% confidence interval = [1.05%, 6.5%]), as per the Representative Concentration Pathway 8.5 in the 2090 scenario. Integrating WSUD with existing grey infrastructure positively contributed to reducing the flooded area by 18–54% under RCP 8.5 in 2090. However, the efficiency of these combined systems is governed by several factors such as rainfall characteristics, the climate change scenario, rain barrel and porous pavement systems, and the size and physical characteristics of the study area. In the tropics, the flooding problem is estimated to increase under future climatic conditions, and the integration of WSUD with grey infrastructure can play a positive role in reducing floods and their impacts. However, careful interpretation of results is required with an additional assessment clarifying how these systems perform in large catchments and their economic viability for extensive applications. Full article

The rapid expansion of urbanization and inadequate planning have triggered a water balance crisis in many cities, manifesting as both the need for artificial lake supplementation and frequent urban flooding. Using the Xuanwu Lake watershed in Nanjing as a case study, this research aims to optimize the Blue–Green Infrastructure (BGI) network to maximize rainfall utilization within the watershed. The ultimate goal is to restore natural water balance processes and reduce reliance on artificial supplementation while mitigating urban flood risks. First, the Soil Conservation Service Curve Number (SCS–CN) model is employed to estimate the maximum potential of natural convergent flow within the watershed. Second, drawing on landscape connectivity theory, a multi-level BGI network optimization model is developed by integrating the Minimum Cumulative Resistance (MCR) model and the gravity model, incorporating both hydrological connectivity and flood safety considerations. Third, a water balance model based on the Storm Water Management Model (SWMM) framework and empirical formulas is constructed and coupled with the network optimization model to simulate and evaluate water budget performance under optimized scenarios. The results indicate that the optimized scheme can reduce artificial supplementation to Xuanwu Lake by 62.2% in June, while also ensuring effective supplementation throughout the year. Annual runoff entering the lake reaches 13.25 million cubic meters, meeting approximately 13% of the current annual supplementation demand. Moreover, under a 100-year return period flood scenario, the optimized network reduces total watershed flood volume by 35% compared to pre-optimization conditions, with flood-prone units experiencing reductions exceeding 50%. These findings underscore the optimized BGI network scheme’s capacity to reallocate rainwater resources efficiently, promoting a transition in urban water governance from an “engineering-dominated” approach to an “ecology-oriented and self-regulating” paradigm. Full article

The Cau River Basin in northeastern Vietnam is an ecologically and economically important watershed, yet it has lacked comprehensive hydrological modelling to date. Characterised by highly complex topography, diverse land use/land cover, and limited hydrometeorological data, the basin presents challenges for water resource assessment and management. This study applies the SWAT hydrological model to simulate streamflow dynamics in the Cau River Basin over a 31-year period (1990–2020) using multiple-source geospatial data, including a 30 m digital elevation model, official soil and land use maps, and daily climate records from six meteorological stations. Model calibration (1997–2008) and validation (2009–2020) were conducted using the SWAT-CUP tool, achieving strong performance with a Nash–Sutcliffe Efficiency (NSE) of 0.95 and 0.90, and R² of 0.95 and 0.91, respectively. Sensitivity analysis identified four key parameters most influential on streamflow (curve number, saturated hydraulic conductivity, soil evaporation compensation factor, and available water capacity), supporting a more focused and effective calibration process. Model results revealed substantial spatio-temporal variability in runoff, with annual surface runoff ranging from 19.8 mm (2011) to 56.4 mm (2013), generally lower in upstream sub-watersheds (<30 mm) and higher in downstream areas (>60 mm). The simulations also showed a clear seasonal contrast between the wet and dry periods. These findings support evidence-based strategies for flood and drought mitigation, inform agricultural and land use planning, and offer a transferable modelling framework for similarly complex watersheds. Full article

The efficient and rational development of hydropower in the Lancang–Mekong River Basin can promote green energy transition, reduce carbon emissions, prevent and mitigate flood and drought disasters, and ensure the sustainable development of the entire basin. In this study, based on publicly available hydrometeorological observation data and satellite remote sensing monitoring data from 2001 to 2020, a machine learning model of the Lancang–Mekong Basin was developed to reconstruct the basin’s hydrological processes, and identify the occurrence patterns and influencing mechanisms of water-related hazards. The results show that, against the background of climate change, the Lancang–Mekong Basin is affected by the increasing frequency and intensity of extreme precipitation events. In particular, Rx1day, Rx5day, R10mm, and R95p (extreme precipitation indicators determined by the World Meteorological Organization’s Expert Group on Climate Change Monitoring and Extreme Climate Events) in the northwestern part of the Mekong River Basin show upward trends, with the average maximum daily rainfall increasing by 1.8 mm/year and the total extreme precipitation increasing by 18 mm/year on average. The risks of flood and drought disasters will continue to rise. The flood peak period is mainly concentrated in August and September, with the annual maximum flood peak ranging from 5600 to 8500 m³/s. The Stung Treng Station exhibits longer drought duration, greater severity, and higher peak intensity than the Chiang Saen and Pakse Stations. At the Pakse Station, climate change and hydropower development have altered the non-drought proportion by −12.50% and +15.90%, respectively. For the Chiang Saen Station, the fragmentation degree of the drought index time series under the baseline, naturalized, and hydropower development scenarios is 0.901, 1.16, and 0.775, respectively. These results indicate that hydropower development has effectively reduced the frequency of rapid drought–flood transitions within the basin, thereby alleviating pressure on drought management efforts. The regulatory role of the cascade reservoirs in the Lancang River can mitigate risks posed by climate change, weaken adverse effects, reduce flood peak flows, alleviate hydrological droughts in the dry season, and decrease flash drought–flood transitions in the basin. The research findings can enable basin managers to proactively address climate change, develop science-based technical pathways for hydropower dispatch, and formulate adaptive disaster prevention and mitigation strategies. Full article