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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.

Recent extreme rainfall events have led to frequent flash flood-debris flow hazards in the mountainous regions of western and northern Beijing, China, resulting in significant loss of life and property. To decipher the formation mechanism and hazard characteristics of these local disasters, the July 2023 flash flood-debris flow in Hantai Village (HTV) and Hantai Gully (HTG) of Changping District, Beijing, was selected as the focus of this study. This study acquired topographic and geological data of the study area through regional disaster investigation, field surveys in severely affected regions, and indoor assessments and analyses, utilizing 1: 1000 topographic maps and 1: 20,000 geological maps sourced from unmanned aerial vehicle (UAV) remote sensing images. Then, the characteristics of rainfall and the associated hazard processes (flash flood and debris flow) were systematically investigated and analyzed. By further exploring the formation mechanism of these events, relevant hazard prevention and mitigation suggestions were proposed. The results indicate that the total flood volume in the upstream channel of HTG is approximately 6870 m3 within 1 h, demonstrating significant destructive power. The leading edge of the flash flood/debris flow exits the gully (the southern entrance of HTV) at an instantaneous speed of 74.47 km/h and maintained a speed of 16.42 km/h by the time it reached the northern exit of HTV (outlet of the HTG), with a movement time of 1.83 min. This brief event duration presents significant challenges for timely evacuation. This catastrophic flash flood-debris flow event is identified as a compound hazard triggered by extreme rainfall from the remnant circulation of Typhoon Doksuri, with the sudden increase in surface runoff amount due to short-term heavy rainfall being the primary cause. It is hypothesized that the debris flow occurred later than the flash flood or during the weakening period of the flash flood and was subsequently eroded by weak flood flows, demonstrating clear characteristics of a mutual transformation between flash flood and debris flow. This study provides fundamental support and reference for hazard prevention and response improvement in other small watersheds.

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 increase in atmospheric CO2 concentration elevates atmospheric temperature and enhances water storage capacity. This could lead to more extreme precipitation events, while simultaneously, higher temperatures may cause land and soil to dry out. Such alterations create ambiguous expectations regarding the direction of hydrological changes in the following decades. This work quantifies streamflow changes on flood discharges in South America based on the MGB‐SA hydrodynamic‐hydrological model forced with the Climate Model Intercomparison Project (CMIP6) ensemble of climate projections. Future projections indicate that floods are expected to increase in magnitude and become up to five times more frequent in Southern Brazil, a region recently impacted by unprecedented flooding. In contrast, ecosystems such as the Amazon and the Pantanal will likely experience less frequent floods in the coming decades. These projections have relevant implications not only for flood risk in populated areas but also for ecological dynamics. Plain Language Summary Global warming is a phenomenon that is increasing the planet's temperature. This increase leads to more water retained in the atmosphere, potentially increasing precipitation. However, the temperature rise might also increase the evapotranspiration of water in the soil. All these alterations in the water cycle affect river flows and floods. In this work, we quantified the alterations for South America's large rivers using climate projections from CMIP6 and the hydrological model MGB‐SA. Rainfall projections show an increase of 20% in 5 years return period (RP) rainfall and up to 60 % in 100 years RP rainfall. In the following decades, floods might be up to five times more frequent in Southern Brazil, a region recently affected by unprecedented floods. In contrast, the Amazon and Pantanal basins show projections of less frequent floods, which might negatively affect its ecosystem. Key Points CMIP6 projects a rise in maximum daily rainfall across most of South America, though flood flows will not uniformly increase Projections show flood intensification over Southern Brazil, a region recently affected by disastrous floods Amazon, Pantanal, and Bananal wetlands may have decreased flood events, negatively impacting ecosystems

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

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).

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.

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.

Flood risks across the Pearl River basin, China, were evaluated using a peak flood flow dataset covering a period of 1951–2014 from 78 stations and historical flood records of the past 1000 years. The generalized extreme value (GEV) model and the kernel estimation method were used to evaluate frequencies and risks of hazardous flood events. Results indicated that (1) no abrupt changes or significant trends could be detected in peak flood flow series at most of the stations, and only 16 out of 78 stations exhibited significant peak flood flow changes with change points around 1990. Peak flood flow in the West River basin increased and significant increasing trends were identified during 1981–2010; decreasing peak flood flow was found in coastal regions and significant trends were observed during 1951–2014 and 1966–2014. (2) The largest three flood events were found to cluster in both space and time. Generally, basin-scale flood hazards can be expected in the West and North River basins. (3) The occurrence rate of floods increased in the middle Pearl River basin but decreased in the lower Pearl River basin. However, hazardous flood events were observed in the middle and lower Pearl River basin, and this is particularly true for the past 100 years. However, precipitation extremes were subject to moderate variations and human activities, such as building of levees, channelization of river systems, and rapid urbanization; these were the factors behind the amplification of floods in the middle and lower Pearl River basin, posing serious challenges for developing measures of mitigation of flood hazards in the lower Pearl River basin, particularly the Pearl River Delta (PRD) region.

Accurately modeling and predicting flood flows across multiple sites within a watershed presents significant challenges due to potential issues of insufficient accuracy and excessive computational demands in existing methodologies. In response to these challenges, this study introduces a novel approach centered around the use of vine copula models, termed RDV-Copula (reduced-dimension vine copula construction approach). The core of this methodology lies in its ability to integrate and extract complex data before constructing the copula function, thus preserving the intricate spatial–temporal connections among multiple sites while substantially reducing the vine copula's complexity. This study performs a synchronization frequency analysis using the devised copula models, offering valuable insights into flood encounter probabilities. Additionally, the innovative approach undergoes validation by comparison with three benchmark models which vary in dimensions and nature of variable interactions. Furthermore, the study conducts stochastic simulations, exploring both unconditional and conditional scenarios across different vine copula models. Applied in the Shifeng Creek watershed, China, the findings reveal that vine copula models are superior in capturing complex variable relationships, demonstrating significant spatial interconnectivity crucial for flood risk prediction in heavy-rainfall events. Interestingly, the study observes that expanding the model's dimensions does not inherently enhance simulation precision. The RDV-Copula method not only captures comprehensive information effectively but also simplifies the vine copula model by reducing its dimensionality and complexity. This study contributes to the field of hydrology by offering a refined method for analyzing and simulating multi-site flood flows.