Flood Volume Research Articles

The outburst of dammed lake Maashey (North-Chuya ridge, Central Altai)

The dammed lakes are widespread in mountainous areas and usually occur when river flow is blocked by landslides, rock glaciers, etc. Among such lakes, the most dangerous are those located in the periglacial zone and blocked by rock glaciers. Continued deglaciation of mountainous areas under changing climate conditions contributes to accumulation of large volumes of melt water in lakes, which may increase pressure on the dam, cause its failure and subsequent outburst flood. In this article we describe the development of such a lake before its outburst and the process of its outburst. The object of study was Maashei Lake (North Chuya Ridge, Central Altai) located in the zone of mountain glaciation and dammed by a rock glacier, where the lake outburst occurred in July 2012. The lake area before the outburst was 259 × 103 m2 and water volume 1.21 × 106 m3. As a result of the outburst, the lake was completely drained. We analyzed the published works on Lake Maashei, materials of our own field studies in the lake basin combined with remote sensing data. We hypothesized that the mechanism of the outburst occurred in 2012 was caused by the water erosion of the filtration channel in the dam body. The mechanism of this outburst was numerically simulated using the method presented in this article. The modeling allowed to reproduce the outburst flood hydrograph, to estimate such characteristics as maximum water discharge, volume of the outburst flood, water flow velocities and the size of the formed breach. Estimated maximum discharge was 694 m3s−1, flow velocities varied from 0.2 to 5-7 m s−1, and the outburst flood period was about 5.5 hours. The breach was formed to the full height of the dam (10 m). Its calculated morphometric characteristics were as follows: average width 47.5 m (measured 41.5 m), скоss-section area 476 m2 (measured 415 m2). The discrepancy between the modeled and measured values was about 15%.

Integrated Early Flood Prediction using Sentinel-2 Imagery with VANET-MARL-based Deep Neural RNN

<p style="line-height:200%;"><span style="font-family:"Times New Roman","serif";font-size:12.0pt;line-height:200%;" lang="EN-US">Floods are a major contributor to the destruction of infrastructure and the overall economy of afflicted countries, leading to loss of life and significant damage. Remote sensing, satellite imagery photography, global positioning system, and geographic information system (GIS) are commonly used to identify floods and analyze the associated damages. The research presented here integrates Sentinel-2 satellite images, VANET with Multi-Agent Reinforcement Learning (MARL), and a deep neural RNN for early flood prediction. Sentinel-2 imaging delivers extensive geographical and temporal data regarding land cover and water bodies, while VANET-MARL provides real-time ground-truth information and distributed decision-making capabilities. The Deep Neural RNN efficiently acquires intricate patterns from the combined data to forecast the likelihood, intensity, and scope of floods. The experimental findings clearly show that the suggested system outperforms standard methods in terms of accuracy, precision, recall, and lead time. VANET-MARL integration improves the system's ability to adapt and remain strong in changing circumstances. The results showed that the method was 94.8% accurate in early predicting floods.</span></p>

The Surface Water Potentiality in Arid and Semi-Arid Basins Using GIS and HEC-HMS Modeling, Case Study: Gebel El Sibai Watershed, Red Sea

The Red Sea region is considered one of the regions that suffer most from water scarcity among the Egyptian areas. This situation reinforces the importance of maximizing the utilization of available water sources. Rainwater and flood harvesting may form a good water source if good harvesting practices are applied. Natural pastures, Bedouin communities, and wild plants may be affected by severe droughts expected due to climate change. Additional water resources are very important to enhance the resilience of the Bedouin communities to probable droughts. Five main hydrographic basins are issued from Gebel El Sibai (+1435 m), including Wadi Esel, Wadi Sharm El Bahari, Wadi Sharm El Qibli, Wadi Wizr, and Wadi Umm Gheig. Detailed investigation of morphometric parameters, runoff/rainfall relationship, and flood volume using GIS and HEC-HMS model of each basin were estimated as well as natural vegetation. This study reveals that rainfall ranges from 84 mm to 0 mm, and a storm of 84 mm (highest event) is expected to occur every 42 years with a probability of 2.4%. Quantitative morphometric analysis implies that the area has good potential for flooding, especially Wadi Sharm El Qibli and Wadi Umm Gheig, where Wadi Sharm El Bahri represents the lowest priority for flooding. The flood volume of Umm Gheig basin is the greatest: 12 million m3 at the basin outlet with a rainfall event of 15 mm. Wadi Esel is expected to collect 8.7 million m3 due to the ratio of the impervious soil and rainfall quantity, Wadi Sharm El Bahari 2.1 million m3, Wadi Sharm El Qibli 1.6 million m3, and Wadi Wizer 1.04 million m3. Seven storage dams (SD1-SD7) were proposed to enhance the utilization of the surface water potentialities of this study area.

Optimizing urban flood management: enhancing urban drainage system efficiency under extreme rainfall events

ABSTRACT Extreme rainfall events, particularly those induced by tropical cyclones, pose a heightened risk to the urban drainage system (UDS). Existing UDSs, having been established long ago, often fail to account for the extreme rainfall caused by cyclones. To address this issue, this study designs a multi-objective intelligent scheduling model within a simulation -optimization framework, aiming to optimize the operation of urban drainage infrastructure and hydraulic structures. This is achieved by integrating the Storm Water Management Model (SWMM) with the multi-objective particle swarm optimization algorithm (MOPSO) and distinctly evaluating typhoons and torrential rains for their impact on extreme rainfall. The study results show that the multi-objective intelligent scheduling model can effectively devise operation strategies for pumping stations and weirs in the study area, thereby optimizing their use for urban drainage. The model was successful in reducing the total flood volume (TFV) and the water level fluctuation (WLF) by 3.11%–57.77% and 26.32%–65.48%, respectively. This not only mitigates urban flooding but also enhances the infrastructure stability of the UDS. The model outperformed the local adaptation strategy in most scenarios for the two selected objectives, suggesting that the efficiency can be significantly improved by optimizing UDSs without expansion of existing infrastructure or additional costs.

Comparative effect of traditional and collaborative watershed management approaches on flood components

AbstractIdentifying the critical areas of flood generation and determining the optimal measures for flood control and management (FCM) is one of the most important basics of watershed management. Therefore, the current study was carried out to prioritize sub‐watersheds (SWs) based on flood generation using physical (Ph‐MA), technical (Te‐MA), comanagerial (Com‐MA), and conjunct management approaches (Con‐MA), as well as determining the quantitative effects of the proposed FCM measures in the Cheshmeh‐Kileh Watershed, Iran. To prioritize SWs based on flood generation, geo‐environmental criteria were used in Ph‐MA, HEC‐HMS software was used in Te‐MA, and semi‐structured interviews with local stakeholders were used in Com‐MA. Finally, using the Condorcet algorithm based on game theory, SWs were prioritized in each approach. In the semi‐structured interviews, stakeholders were asked to provide suggested measures for FCM. Finally, the effect of each measure on the flood components was quantified. Based on the results, the measure of 10% improvement of forest cover in the entire watershed from the category of Con‐MA was selected as the optimal measure. In the return periods of 2, 5, 10, 20, 50, and 100 years, the effectiveness of this measure was 80.08%, 70.09%, 58.10%, 55.67%, 50.58%, and 43.62%, respectively. The influence of Con‐MA based on non‐structural and structural measures on reducing the components of peak flow and flood volume was more than other approaches. In general, it can be concluded that non‐structural measures have more effect than structural measures in FCM in the study watershed. The methods and approaches used in this study can be directly used by local executive managers, decision‐makers, and policymakers of watershed management and flood management activities.

НЕКОТОРЫЕ МОМЕНТЫ СОВЕРШЕНСТВОВАНИЯ ПРОГНОЗИРОВАНИЯ ОБЪЕМА ПОЛОВОДЬЯ В УСЛОВИЯХ ОГРАНИЧЕННЫХ ГИДРОМЕТЕОРОЛОГИЧЕСКИХ ДАННЫХ

The article deals with methods of improving the forecasting of the volume of spring flood runoff. The main objects of the study are the Oba, Ulbi and Dresvyanka rivers, which are lateral tributaries of the Shulba reservoir. These rivers belong to the type of rivers with spring-summer flooding and extended flow for more than 2...3 months. The aim of the study is to develop a methodology for flow forecasting based on the relationship between snow accumulation and river flow in spring and summer periods. The results of the study of hydrological and meteorological factors affecting runoff formation are presented, and equations for forecasting runoff during spring and summer floods and its duration are derived. The obtained multiple regression equations demonstrated high accuracy in modelling flood volumes, with correlation coefficients between observed and predicted data ranging from 0,79 to 0,91. According to S/σ calculations, the results obtained indicate good reproducibility of the observed flood volumes, with values ranging from 0,46 to 0,53.

Evaluating the effect of the land use change on discharge and flood intensity (case study: sub-basins of Kal-e Shur Sabzevar river basin, Iran)

Evaluating the effect of the land use change on discharge and flood intensity (case study: sub-basins of Kal-e Shur Sabzevar river basin, Iran)

Machine learning-based rainfall forecasting in real-time optimal operation of urban drainage systems

This paper presents a rainfall forecast-based real-time optimal operation model of urban drainage systems (UDSs) investigating the role of rainfall forecasts in predictive real-time operation of UDSs. Two rainfall forecast models, namely long short-term memory (LSTM) and optimized-by-PSO (particle swarm optimization) support vector regression, are linked to an integrated simulation–optimization model. For any given operation policies, the rainfall-runoff stormwater management model (SWMM) simulates the urban system’s operation under flooding conditions in which the operation policies are optimized by the metaheuristic harmony search (HS) optimizer. The objective function of the HS algorithm is minimizing flood volumes at control points. The presented approach is therefore a dynamic predictive real-time operation (PRTOP) model that continuously updates rainfall forecasts and operational decisions during flood events. The performance of the developed forecast-based integrated SWMM-HS simulation–optimization modeling framework is tested through its application in a real urban system case study located in Tehran, Iran. Results demonstrate that a notable reduction in flood volume (11.8%) is achieved by using the LSTM-driven rainfall forecasts in the proposed PRTOP model compared to a reactive real-time operation (RTOP) model not utilizing rainfall forecasts.

Constructing Long‐Term Hydrographs for River Climate‐Resilience: A Novel Approach for Studying Centennial to Millennial River Behavior

AbstractStudying the centennial or millennial timescale response of large rivers to changing patterns in precipitation, discharge, flood intensity and recurrence, and associated sediment erosion is critical for understanding long‐term fluvial geomorphic adjustment to climate. Long hydrographs, maintaining reliable Flow Duration Curves (FDCs), are a fundamental input for such simulations; however, recorded discharge series rarely span more than a few decades. The absence of robust methodologies for generating representative long‐term hydrographs, especially those incorporating coarse temporal resolution or lacking continuous simulations, is therefore a fundamental challenge for climate resilience. We present a novel approach for constructing multi‐century hydrographs that successfully conserve the statistical, especially frequency analysis, and stochastic characteristics of observed hydrographs. This approach integrates a powerful combination of a weather generator with a fine disaggregation technique and a continuous rainfall‐runoff transformation model. We tested our approach to generate a statistically representative 300‐year hydrograph on the Ninnescah River Basin in Kansas, using a satellite precipitation data set to address the considerable gaps in the available hourly observed data sets. This approach emphasizes the similarities of FDCs between the observed and generated hydrographs, exhibiting a reasonably acceptable range of average absolute deviation between 6% and 18%. We extended this methodology to create projected high‐resolution hydrographs based on a range of climate change scenarios. The projected outcomes present pronounced increases in the FDCs compared to the current condition, especially for more distant futures, which necessitates more efficient adaptation strategies. This approach represents a paradigm shift in long‐term hydrologic modeling.

Impact of permanent dam opening on the fate of polycyclic aromatic compounds in industrial sludges accumulated on river banks: In situ approach

The impact of permanent dam opening on the fate of organic contaminants was studied in the specific case of the Orne River industrial deposits. In the downstream part of the Orne River, the river banks were mainly constituted of steelmaking wastes accumulated for decades. Coring was performed before and after the permanent dam opening (performed in November 2019). The core layers were analysed for grain size, element content, mineralogy and PAC concentrations and distributions. The fine grain size, the high iron content (20–35 %), the presence of high temperature iron phases, the high zinc and lead contents were the main characteristics of these steelmaking sludges and came along with high polycyclic aromatic hydrocarbon (PAH) concentrations. The relative enrichment in low molecular weight PAHs associated to the abundance of furans signed the contribution of coal tar in specific layers. Element and grain size results revealed the erosion of about 12 cm of material during the first year of opening. Oxidative conditions were clearly demonstrated by the presence of gypsum along the entire length of the cores collected in 2020 and the years after. Comparing PAC features in the cores collected before and after dam opening, PAH concentrations did not show significant variations, but the molecular distribution of PACs presented significant changes, mainly in the first 30 cm. Indeed, the depletion of oxygenated PACs suggested the preferential leaching of these polar molecules. Leaching might have been enhanced by opening circumstances and/or the intense flood occurring few months after dam opening. Several PAC ratios were used to confirm the leaching and oxidative processes.

A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk

Traditional approaches to flood risk management assume flood events follow an independent, identically distributed (i.i.d.) random process from which static risk measures are computed. Modern risk accounting strategies also consider nonstationarity or long-term trends in the mean and moments of the associated flood probability distributions. However, few approaches consider how extreme hydroclimatic events cluster in both space and time, compounding damage risks. Here we introduce a compound flood risk simulator that models and conditionally forecasts future variability in regional flooding events that cluster in time, given trends and oscillations in a variable climate signal. A modular, novel integration of wavelet signal processing, nonstationary time series forecasting, k-nearest neighbor (KNN) bootstrapping, multivariate copulas, and modified Neyman-Scott (NS) event clustering process provides users the ability to model interannual and sub-annual clustering of flood risk. Our semi-parametric flood generator specifically targets the clustered temporal dynamics of jointly modeled flood intensity, duration, and frequency over a finite future period of a decade or more, thereby providing a foundation for adaptation approaches that integrate temporally clustered flood risk into planning, response and recovery.

Enhancing water management and urban flood resilience using Hazard Capacity Factor Design (HCFD) model: Case study of Eco-Delta city, Busan

This study evaluated future climate scenarios and the changes in urban flood resistance capacity in Busan Eco-Delta City using the hazard capacity factor design (HCFD) model. It analyzed the flood reduction effects of both gray and green infrastructure. Despite existing flood safety systems, there is a growing need to enhance urban flood resilience due to increasing heavy rainfall, unpredictable precipitation, and frequent typhoons driven by climate change. The HCFD model predicted urban flood volumes of Busan Eco-Delta City and analyzed the effectiveness of gray and green infrastructure in flood control. A stormwater management model (SWMM) simulated urban flood resistance capacity under the SSP1–2.6 climate change scenario. Results indicate that for an anticipated 500 mm rainfall over 3 h, green infrastructure can mitigate floods by 9 % to 17.6 %, while gray infrastructure can reduce flooding by 24 % to 32.1 %. The integration of gray and green infrastructure leads to an overall flood mitigation ranging from 47.1 % to 63.3 %. A notable contribution of this research is its predictive analysis of future flood scenarios using model-based scenario analysis and decision support algorithms, offering valuable insights into changes in urban flood resistance capacity and strategies for effective flood control decision-making.

Prototype data analysis of the dynamics of the Venice gate-barriers during an extreme storm event

The MoSE barriers system was designed and constructed at the inlets of the Venice Lagoon (Italy) in order to limit and tame the flooding events in the Lagoon areas and in the City. The success of the design and operation of the system has been demonstrated by the significant reduction in the number and intensity of floods in the lagoon since its beginning of operations in 2020. In this study, we investigate the dynamical behavior of the MoSE system at full-scale by analyzing the barriers behavior during the severe storm event of November 22nd, 2022. In particular, the dynamical response of the Chioggia barrier to waves and storm surge is studied in detail. Spectral analysis of field records, barrier and inlet modal analyses and Empirical Orthogonal Functions (EOF) techniques are applied to provide a key for interpreting the actual behavior of such a complex system during a storm event, highlighting dominant frequencies and checking for the occurrence of resonance phenomena. First, a brief review of the experimental and theoretical studies carried out over the past forty years is given. Modal patterns of gates oscillations detected via EOF analysis confirm the presence of the eigenmodes of both the barrier and the inlet; however, the gates oscillations during the considered event are mild and the hydraulic performances of the system are satisfactory for the severe event studied. Further field measurements and future severe events should be studied to reach extended conclusions.

The Impact of Catastrophic Floods on Macroinvertebrate Communities in Low-Order Streams: A Study from the Apennines (Northwest Italy)

Floods are normal components of many river regimes and, as such, they exert a significant influence at the ecosystem level. In recent decades, however, climate change has increased the frequency and intensity of floods, with serious consequences for lotic biota, particularly benthic macroinvertebrates, due to their limited mobility and sensitivity to disturbance. The impact of floods varies according to different biological parameters including the characteristics of the macrobenthic communities (taxonomic composition, morphology, behaviour, and life history traits) on one hand and various nonbiological parameters such as flood intensity, artificialisation of the river bed, the presence of dams, and many other factors on the other. Understanding these dynamics is pivotal to improve the effective management and conservation of aquatic ecosystems in the context of current climate change. The aim of this short communication is to evaluate the impact of a catastrophic flood on the macroinvertebrate community of a low-order Appennine stream (NW Italy). This will provide data regarding the varying impacts on different taxa and the recovery pattern of this significant component of the ecosystem.

Analisis Spasial Bencana Banjir Di Kota Padang Periode Tahun 2020-2024

During the period of 2020-2024, the frequency and intensity of flooding in the City of Padang showed a significant increase, impacting the community. This study aims to: 1) identify the spatial distribution of flood disasters in the City of Padang, 2) classify the types of floods that occurred, and 3) identify the impact of floods on damage and losses. This type of research is quantitative, with the population being all sub-districts in the City of Padang. The sample was taken using total sampling, covering all sub-districts affected by flooding, totaling 11 sub-districts, including Padang Barat, Padang Utara, Padang Timur, Padang Selatan, Nanggalo, Kuranji, Lubuk Begalung, Lubuk Kilangan, Pauh, Koto Tangah, and Bungus Teluk Kabung. The data analysis technique used is spatial analysis. The results of the study show that there were 121 flood events distributed across various sub-districts, with the highest occurrences in Koto Tangah (56 events) and the fewest in Padang Barat (2 events). Floods in the City of Padang are classified into three categories: fluvial floods, tidal floods, and flash floods. Fluvial floods are the most common, while tidal floods occur in Padang Barat, and flash floods occur in Lubuk Begalung. The impact of the floods includes damage and losses; Lubuk Begalung experienced the most significant damage, with 70 houses affected, and the largest losses occurred in Pauh in 2023, amounting to Rp1,786,150,000, while the lowest losses occurred in Koto Tangah in 2022, amounting to Rp18,000,000.

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of “rainfall and drainage pipeline siltation” were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

Runoff and erosion reduction benefits of vegetation during natural succession on fallow grassland slopes

Vegetation restoration is an effective and important measure for controlling soil erosion in arid and -arid regions. Both its aboveground and underground parts play a crucial role in controlling surface runoff and soil detachment on slopes. But how much the parts of vegetation contribute to the runoff and sediment reducing benefits of rill erosion on slopes is unclear. We used grassland slopes at four successional stages for simulated scouring experiments to observe how successional vegetation community structures, root characteristics, and soil structures contribute to erosion and sand production. Initial flow production time increased, and total runoff decreased. Under the scour intensities, the 11-year slope had the lowest flood peak and volume and the greatest runoff reduction benefit. The 25-year slope had the lowest sand peak and volume and the greatest sediment reduction benefit. As scour intensity increased, runoff reduction effect of vegetation at the successional stages decreased; the sediment reduction benefit remained high. PLS-PM analysis showed that the indirect effects of the aboveground and underground parts of vegetation on sand production were −0.364 and −0.439, respectively. Aboveground parts mainly embodied the regulation of runoff, in which stem count, humus mass, and biomass were the main factors affecting runoff and sand production. Underground parts mainly reflected their soil structure improvement, in which root volume density, root surface area density, and root mass density are the main explanatory variables. The direct effects of runoff and soil structure on slope rill erosion were 0.330 and −0.616, respectively, suggesting the stability of soil structure is the primary factor affecting the sand production, not erosion energy. The results provide a reference for scientific assessment of the key role of natural vegetation restoration in regional soil erosion control and the development of biological measures for soil and water conservation on the slopes of the Loess Plateau.

A predictive fuzzy logic and rule-based control approach for practical real-time operation of urban stormwater storage system

Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that use accessible, non-idealized predictive information while improving their efficiency for successive rainfall events instead of specific phases. This study developed a predictive fuzzy logic and rule-based control (PFL-RBC) approach to address the continuous control of individual storage systems. This approach incorporates total rainfall depth forecast information with an intra-storm fuzzy logic system to optimize peak flow control and several rule-based strategies for pre-storm water detention, reuse, and release control. Computational experiments were conducted using a storage tank case study to test the proposed approach under various rainfall conditions and storage sizes. The results showed that PFL-RBC outperformed static rule-based control in infrequent design storms and realistic continuous rainfall events, reducing flood peaks and volumes by 55 %∼87 % and 7 %∼20 %, respectively, and significantly increasing water detention time and reuse volume. Meanwhile, PFL-RBC required less predictive information to achieve a 6 %∼15 % advantage in peak flow control compared to optimized model predictive control. More importantly, PFL-RBC was reliable in the face of input uncertainty, with <25 % performance loss for water quantity control when the realistic forecast error ranged from -50 % to +50 %. These findings suggest that the proposed approach has great potential to enhance the efficiency and practicality of stormwater storage operations.

Adaptive Operating Rules for Flood Control of a Multi-Purpose Reservoir

Almost all multipurpose reservoirs in Romania were put into operation 30–50 years ago or even earlier. Meanwhile, a large volume of hydrologic data has been collected, and the initial design flood should be reconsidered. Consequently, the operating rules of flow control structures (bottom gates and weir gates) should be re-examined, mainly for medium and low-frequency floods. The design flood is not unique, being characterized by different shapes and time to peak, which has consequences for flood mitigation rules. Identifying the critical design flood is an important preliminary step, although it is usually neglected in flood management. Simulating the operation of the Stânca–Costești reservoir on the Prut River, it was found that the design flood corresponding to the maximum value of the compactness coefficient is the most difficult to mitigate considering the specific conditions of the dam and the reservoir: the prescribed conservation level in the reservoir, and the design flood volume of medium and rare floods that far exceeds the flood control volume. These conditions can jeopardize both dam safety and downstream flood protection. The main steps of the proposed approach are as follows: (1) developing the hydraulic model; (2) statistical processing of the registered floods and defining critical design floods for different AEPs (Annual Exceedance Probabilities); (3) deriving optimal operation rules based on a simulation-optimization model; (4) implementing real-time adaptive operation of the mechanical outlets; and (5) critically assessing the operating rules after the event. Based on the hydrological forecast, if necessary, new outlets are put into operation while keeping the ones already activated. Based on the hydrological forecast and properly operated, the safety of the Stânca–Costești dam is guaranteed even in the event of a 0.1% CC (Climate Change) flood. However, for floods greater than 1% magnitude, the carrying capacity of the downstream riverbed is exceeded. The main gaps addressed in this paper are the following: (1) the establishment of critical design floods, and (2) the adaptive operating rules of outlet devices aimed at optimizing flood control results, using short-term flood forecasts.

Comprehensive Zoning Strategies for Flood Disasters in China

The frequency of global floods has increased, posing significant threats to economic development and human safety. Existing flood risk zoning studies in disaster prevention lack integration of the natural–economic–social chain and urban resilience factors. This study addresses this gap by constructing flood disaster risk and intensity indices using data from 31 provinces and 295 prefectural-level cities in China from 2011 to 2022. These indices incorporate natural (rainfall), economic (GDP), and social (population, built-up area) indicators to assess the flood likelihood and loss degree, providing comprehensive risk and intensity ratings. The study also examines the impact of resilience factors—environmental (green space), infrastructural (rainwater pipeline density), and natural resource (watershed areas)—on flood intensity. Findings reveal that high-risk regions are mainly in the Yangtze River Basin and southern regions, while high-intensity regions are primarily in the middle and lower Yangtze River and certain northwestern cities. Increasing rainwater pipeline density mitigates flood impacts in high-risk, high-intensity areas, while expanding green spaces and pipelines are effective in high-risk, low-intensity regions. This paper proposes a comprehensive flood hazard zoning mechanism integrating natural, economic, and social factors with urban resilience, offering insights and a scientific basis for urban flood management.