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Booming urbanization due to a fast-growing population results in more impervious areas, less infiltration, and hence greater flood peak and runoff. Clear understanding of flood responses in regions with different levels and expansions of urbanization is of great importance for regional urban planning. In this study, comparison of flooding responses to urbanization processes in terms of flood peak and runoff volume in the upper, middle, and lower Xiang River Basin (XRB), China, was carried out using the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model. From 2005 to 2015, urbanization level and intensity were higher in the lower XRB compared to the upper and middle XRB, and the overall expansion rate of urban areas was 112.8%. Modeling results by the HEC-HMS model indicate elevated flood peak discharges and volumes due to fast urbanization in the XRB from the 1980s to 2015. This rapid increase is particularly the case in the lower XRB. The study also revealed different hydrological responses of flood regimes—urbanization tends to have larger impacts on peak flood flow rather than on flood volume in the lower XRB, which further corroborated urbanization-induced intensifying flood processes in terms of peak flood flow. Urbanization has increasing impacts on flood volume from the upper to the lower XRB, which can be attributed to accumulated runoff down the river system. This study provides a reference for basin-wide land use and urban planning as well as flood hazard mitigation.

For several years now, the problem of extreme rainfall events has been acutely felt in Algeria, particularly the risk of flooding which threatens several towns, which have already faced this hazard with tragic consequences. To fight against floods, a long series of flood flow measurements is necessary, which unfortunately is not the case most of the time (Daifalah et al Technol Sci Rev Sum 34:74–84, 2017). This is the case of the Wadi Biskra catchment area. The stopping of the hydrometric stations of El kantara, and Djemoura; controlling this catchment area, over an area of 2787 km 2 , poses a serious data problem. To overcome this handicap, we have used the values of maximum flows, the recent series available covers a period of 28 years (1968–1995). Whereas the Djemoura station: the recent series available covers a period of 22 years (1972/1993), which fits well with the Gumbel law. From the adjustment line, the values of probable floods for different return periods of 2–1000 years were determined. To protect the population against floods caused by floods, operational and reliable forecasting tools are required. The rain-flow model uses knowledge of rainfall. It is applicable at any point in the hydrographic network. Among these hydrological models, a probabilistic model (Gradex) has been chosen to assess the risks of extreme floods, and which allows extrapolation to different return periods. To achieve our second objective, which is the possibility of reconstructing flood flows using empirical formulae based on precipitation. The two formulas used for the determination of the flood flows (Turazza formula and Sokolvosky formula) gave identical results to those measured at the gauging station.

The hydro-meteorological factors influencing flood timing and magnitude are shifting due to natural and anthropogenic climate change. Regionally, the association between floods and their driving factors/descriptors is complex. This necessitates a deeper understanding of flood generation to enhance forecasting, modeling, and risk analyses—critical aspects of effective flood management. Thus, to better understand flood generation in India, we investigate the dominant flood-generating descriptors and their relationship with watershed characteristics across central and southern peninsular India using circular statistics. We find that flood generation is primarily influenced by soil moisture and precipitation excess, dominating 89% of the analyzed (231) watersheds. In particular, larger watersheds (>70000 km2) are predominantly influenced by soil moisture, while smaller ones (<16000 km2) are influenced by precipitation. Interestingly, watersheds covering similar areas produce higher flood flows if predominantly influenced by soil moisture. The explicit evaluation suggests a positive influence of antecedent soil moisture (ASM) on flood flows across all watersheds. An attempt to relate the morphological characteristics with flood descriptors reveals a positive (negative) influence of the topographic wetness index (TWI) on annual maximum flows for soil moisture-dominated (precipitation-dominated) watersheds. This indicates that ponding/accumulation is a driving (limiting) factor for soil moisture (precipitation) dominated watersheds. The relative importance of the ASM compared to precipitation decreases when the precipitation intensity (PI) increases, implying exchanges of influence at certain levels of PI. Further exploration could reveal insights into the interplay between ASM and precipitation, crucial for flood magnitude and hazard assessments. Given that flood behavior is significantly influenced by dominant descriptors, it is advisable to adopt a segregated approach in analyzing flood escalation under climate change. In addition, incorporation of dominant flood descriptors into cascade flood modeling is essential for enhancing flood hazard and risk modeling.

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).

Numerous anthropogenic activities like the construction of large dams, storages, and barrages changed the watershed characteristics impacting ecosystem health. In this study, the hydrological alterations (HAs) that have occurred in the Bhima River due to the construction of the Ujjani dam were analyzed. The hydraulic analysis is also performed to determine the hydraulic parameter and recommend the lowest flow release from the dam for improving ecosystem health. Fifty-eight years of data starting from the year 1960 to 2018 were gathered at Yadgir station, which is located downstream of the Ujjani dam. The data were divided into pre- and post-construction river flow discharge. To check for the change in the river flow regime for the post-dam construction period, HA was calculated using Flow Health Software (FHS). The results demonstrate that the dam impoundment reduces high flows primarily by storing flood flow for water supply, irrigation, etc. The velocity and depth provided by the environmental design flow for a flow health (FH) score of 0.62 give a very good habitat to fishes. A minimum release of 24.8 m3/s from the dam is recommended. This study will help policymakers mitigate the impacts of degrading ecosystem health of the Bhima River.

This study is concerned with flood risk that can be assessed by integrating GIS, hydraulic modelling and required field information. A critical point in flood risk assessment is that while flood hazard is the same for a given area in terms of intensity, the risk could be different depending on a set of conditions (flood vulnerability). Clearly, risk is a function of hazard and vulnerability. This study aims to introducing a new approach of assessing flood risk, which successfully addresses this above-mentioned critical issue. The flood risk was assessed from flood hazard and vulnerability indices. Two-dimensional flood flow simulation was performed with Delft3D model to compute floodplain inundation depths for hazard assessment. For the purpose of flood vulnerability assessment, elements at risk and flood damage functions were identified and assessed, respectively. Then, finally flood risk was assessed first by combining replacement values assessed for the elements and then using the depth–damage function. Applying this approach, the study finds that areas with different levels of flood risk do not always increase with the increase in return period of flood. However, inundated areas with different levels of flood depth always increase with the increase in return period of flood. The approach for flood risk assessment adopted in this study successfully addresses the critical point in flood risk study, where flood risk can be varied even after there is no change in flood hazard intensity.

Floods cause extensive damage, especially if they affect large regions. Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable hydrologic model ideally reproduces both local flood characteristics and spatial aspects of flooding under current and future climate conditions. However, uncertainties in simulated floods can be considerable and yield unreliable hazard and climate change impact assessments. This study evaluates the extent to which models calibrated according to standard model calibration metrics such as the widely used Kling–Gupta efficiency are able to capture flood spatial coherence and triggering mechanisms. To highlight challenges related to flood simulations, we investigate how flood timing, magnitude, and spatial variability are represented by an ensemble of hydrological models when calibrated on streamflow using the Kling–Gupta efficiency metric, an increasingly common metric of hydrologic model performance also in flood-related studies. Specifically, we compare how four well-known models (the Sacramento Soil Moisture Accounting model, SAC; the Hydrologiska Byråns Vattenbalansavdelning model, HBV; the variable infiltration capacity model, VIC; and the mesoscale hydrologic model, mHM) represent (1) flood characteristics and their spatial patterns and (2) how they translate changes in meteorologic variables that trigger floods into changes in flood magnitudes. Our results show that both the modeling of local and spatial flood characteristics are challenging as models underestimate flood magnitude, and flood timing is not necessarily well captured. They further show that changes in precipitation and temperature are not always well translated to changes in flood flow, which makes local and regional flood hazard assessments even more difficult for future conditions. From a large sample of catchments and with multiple models, we conclude that calibration on the integrated Kling–Gupta metric alone is likely to yield models that have limited reliability in flood hazard assessments, undermining their utility for regional and future change assessments. We underscore that such assessments can be improved by developing flood-focused, multi-objective, and spatial calibration metrics, by improving flood generating process representation through model structure comparisons and by considering uncertainty in precipitation input.

Flash flooding has become more prominent under climate change, threatening people's life and property. Post‐event investigations of recent events emphasize the role of floating debris, such as vehicles, in exacerbating damage. Few modeling methods and tools have been developed to simulate the full‐process dynamics of floating debris driven by large‐scale flood waves in real world. In this work, a fully coupled model is developed for simulating the full‐process interactive movements of vehicles driven by flash flood hydrodynamics, from entrainment, transport to deposition. The proposed coupled modeling system consists of a finite volume shock‐capturing hydrodynamic model solving the 2D shallow water equations and a 3D discrete element method (DEM) model. The proposed two‐way coupling approach estimates the hydrostatic and hydrodynamic forces acting on solid objects using the water depth and velocity predicted by the hydrodynamic model; the resulting counter forces on the fluid flow are then considered by adding extra source terms in the hydrodynamic model. A multi‐sphere method is further embedded in the DEM model to better represent vehicle shapes. New calculation modules are further implemented to represent the vehicle entrainment, contact and stopping motions. The coupled model is applied to reproduce a flash flood event hit Boscastle in the UK in 2004. Over 100 vehicles were moved and carried downstream by the highly transient flood flow. The model well predicts the hydrodynamics, interactive transport process and the final locations of vehicles. The proposed coupled model provides a new tool for simulating large‐scale flash flooding processes, including debris dynamics. Key Points A new coupled model for simulation of entrainment, transport and deposition of vehicles driven by and interacting with flood hydrodynamics The model is used to reproduce a flash flood event that moved over 100 vehicles, with results consistent with post‐event report and survey Increasing number of floating vehicles alters flood hydrodynamics and intensifies debris‐debris and debris‐fluid interactions

On August 11, 2021, one of the most destructive flood disasters occurred in the Western Black Sea Basin of Turkey. The flood resulted in the death of 76 individuals, 30,000 people being affected by the disaster. A maximum precipitation depth of 400 mm/day was recorded at one station, indicating a return period exceeding 500 years for the rainfall event. During a two-day site visit immediately following the flooding event, damages to infrastructures, water structures, bridges, retaining walls, roads, and private houses were observed in the Bozkurt and Ayancık regions. Based on the observations, the flood wave propagated through the initial meandering river bed and floodplain, exceeding the channelized river bed capacity. Due to the massive sediment transport and drifting of trees, several bridges have been blocked and overflown where the basements of the structures in these regions were flooded. The enormous flood flow triggered extensive scouring on bridge piers, building foundations, and retaining walls, eventually causing the walls and bridges to collapse. The collapse of structures blocked the waterway and amplified the backwater effect when combined with the sediment transport. The total collapse of the retaining walls in some sections of the stream caused accelerated scouring in the foundations of the nearby buildings. Damages were also observed on the side roads along the river beds. This paper evaluated the driving mechanism of damages caused by flood flow from hydrological, structural, and geotechnical perspectives. Based on these observations and assessments, recommendations on engineering design guidelines for structures close to the floodplain, such as bridges, retaining walls, and side roads, were elaborated. Emphasis was placed on the flood-resistant design of these structures to develop a comprehensive approach for flood risk management.

The starting point for this study is the simulation study of Metcalfe et al. (2018) which suggested that retention times of the order of 10 h are required for natural flood management storage features to have a maximum effect on large flow peaks. A analysis of the celerity characteristics for some log jams at Tebay Gill, Cumbria (upland UK), suggests that the impacts of storage in slowing the celerities is only of the order of minutes. An analysis of storage-discharge dynamics based on observations at 4 jams reveals that the dynamics of storages can be represented with time constants of between 3 and 213 min, still well short of those required to maximise the effects for larger flood flows. That is not to say that there will not be reduction in flood peaks for smaller events, only that for large events the effects will be limited. A spreadsheet tool for retention times has been developed to help in the design of new schemes that is freely available.