Search

Recently, frequent flood disasters in China have seriously threatened economic development and public safety. This paper addresses the need for a dynamic urban flood emergency knowledge management system in emergency management departments and the lack of systematic knowledge among emergency managers regarding urban flood control. A multimodal knowledge graph-based urban flood emergency knowledge management model was constructed to enhance the decision-making capabilities of emergency management departments, improve the efficiency of public emergency evacuation, and reduce losses from urban flood disasters by analyzing the shortcomings of the existing emergency management system. An intelligent and dynamic flood emergency knowledge management model was built. This paper integrates multimodal knowledge graph technology to establish a multimodal emergency knowledge management framework for urban flood control. It develops and simulates the proposed model's application scenarios for urban flood emergency evacuation using the Flocking algorithm on the NetLogo platform. Through simulation experiments, the practicality and effectiveness of the model in real flood disaster situations were examined, particularly in simulating crowd evacuation behavior. The study found that the model significantly improves the accuracy of information and decision-making speed during emergency responses and supports emergency management departments in conducting targeted and personalized emergency decisions. This research provides a scientific basis for emergency management departments to optimize their emergency response strategies to flood disasters and serves as a reference and example for the application of multimodal knowledge graph technology in emergency management. [ABSTRACT FROM AUTHOR]

In order to study the technology of emergency disposal and provide insights into the landslide dam breach and rescue processes, a hyperbolic erosion dam breach model was used to predict the peak discharges of 10.11 and 11.03 Baige barrier lakes in the upper reach of the Jinsha River in northwest of China on October and November 2018. The results showed peak discharge values of 10,000 and 44,370 m3/s in the 10.11 and 11.03 Baige barrier lakes, respectively. Therefore, it is proposed to conduct a series of emergency disposals of the 11.03 barrier lake, including manual excavation of a drainage channel, demolition of the cofferdam at the Suwalong Hydropower Station under construction, and discharge of the Liyuan Hydropower Station Reservoir in operation. For the 11.03 barrier lake, the obtained results showed that: (1) the excavation of a 13.5 m deep drainage channel reduced the peak discharge by 24%, which is consistent with the predicted peak discharge of 34,400 m3/s. (2) The demolition of the cofferdam of the Suwalong Station under construction avoids superimposing and magnification of the flood peak. Indeed, in the absence of this measure, the peak discharge might be 1.6 times its original size. The demolition of the cofferdam reduced the impact of peak discharge on downstream areas. (3) The Liyuan Reservoir effectively decreased the peak discharge by 37%, thereby reducing the flood control pressure of the downstream cascade stations and minimizing the flood disaster-related losses through the joint dispatch of the cascade reservoirs. (4) The joint emergency response mechanisms, such as joint interministerial consultation, central-local government coordination, emergency response social mobilization, and joint dispatch of cascade reservoirs, were important and effective in the Baige landslide dams disposal. The present paper provides a systematic back analysis and further insights into the rescue process, key technologies, and emergency measures for the successive barrier lakes. It also provides a reference for emergency rescue of similar landslide dams and risk control of cascade hydropower stations in river basins. [ABSTRACT FROM AUTHOR]

The design flood for a coastal basin based on model simulations can be impacted by various conditions other than design storm, such as tide level. However, the design storm and tide level are usually investigated separately, and the relation between them has not been sufficiently studied, which may have unexpected impacts on model simulations. This paper presents a bivariate assessment framework that aims to evaluate the coordination between design storm and tide level using a copula-based joint distribution to derive the conditional probability of coincidences between design storm and tide level intervals. We apply this framework to the Tai Lake Basin (TLB) in the eastern coast of China and investigate what tide level along its coastline is appropriate for guiding flood control planning. Our findings reveal that the currently-used tide level along the southern coast of TLB is too low and should be raised by approximately 0.35 m and 0.12 m under maximum 1-day and 3-day storms, respectively. Meanwhile, the tide levels for the north and east coasts are appropriate. This paper provides a comprehensive understanding of the interplay between design storm and tide level in a large coastal basin, offering insights into the design of flood protection systems. [ABSTRACT FROM AUTHOR]

With the large-scale development and grid connection of renewable energy, hydropower faces more intense and frequent peak shaving and frequency regulation, giving rise to water level fluctuations and frequently forced sluice adjustments at hydropower stations. This paper proposes a model that combines "offline calculation" and "online search". First, feasible sluice opening combinations for different water levels at each hydropower station are calculated offline, and a sluice operation strategy table is constructed. Subsequently, an optimal sluice operation strategy is searched online according to the real-time water level and various regulatory requirements. As an example, we select three hydropower stations in the middle reach of the Dadu River in China, namely, Pubugou, Shenxigou, and Zhentouba. The results show that the total number of adjustments of the sluices of the cascade hydropower stations was reduced from 1195 to 675, a reduction of 43.5%, and the leading hydropower station, Pubugou, met water level control requirements, whereas the fluctuations in the water level of the two downstream daily regulating hydropower stations, Shenxigou and Zhentouba, were reduced by 1.38 m and 0.55 m, respectively. The results indicate that the sluices of hydropower stations were optimally used under high-intensity peak shaving and frequency regulation. [ABSTRACT FROM AUTHOR]

Global warming has changed the thermal and dynamic environment of climate effects, further exacerbating the uneven distribution of water in China within the year, and the threat of drought will continue to intensify. Flood control and drought resistance are equally important at present. How to reasonably utilize the flood control capacity of reservoirs and raise the flood control level is the key to alleviating the supply-demand contradiction of water resources in China. In the face of new situations, combining the existing operation regulations of the Three Gorges Reservoir and long series of historical runoff data, this paper takes the Three Gorges Reservoir as the object and formulates different flood control level schemes to analyze the benefits and risks of raising the flood control level of the Three Gorges Reservoir. The results indicate that relying on the current forecast level and the joint operation of upstream reservoirs, the flood control risk of the Three Gorges Reservoir flood control level rising to 155-157 m is controllable. It will not lower the flood control standards of Jingjiang and increase the risk of backwater flooding in the reservoir area. When the flood control level of the Three Gorges is raised to 153 m, the average annual power genertion reached its maximum value of 102.9 billion kWh, an increase of 16.7% compared to the initial design value. Consistent with years of statistical law, the Three Gorges Reservoir reaches its maximum power generation capacity in both wet and normal years when the flood control level increases to 153 m. Higher flood control level in dry years, resulting in better power generation efficiency. The flood control benefits are negatively correlated with the flood control level, but the average annual flood detention volume of different schemes varies by 1.5 billion m3. It has changed little. The benefits of water resources are positively correlated with the flood control level. The starting water level of the Three Gorges Reservoir increases synchronously with the rise in the flood control level. When the flood control level rises to 147 m, the full storage rate of the Three Gorges Reservoir can reach 100% by the end of October. For every 1m increase in flood control level, the average annual outflow of water during the storage period can increase by about 500 million m3. Relying on the current forecast level and the joint operation of upstream reservoirs, the flood control risk of the Three Gorges Reservoir s flood control water level rising to 155- 157 m is controllable. Considering both benefits and risks, the flood control level of the Three Gorges Reservoir can be raised to 153 m at present. [ABSTRACT FROM AUTHOR]

The Huangpu River, which flows into the Yangtze Estuary, runs through Shanghai Municipality in China. During the flood season, typhoon-induced surges result in extremely high water levels in the Huangpu River, which is the main factor threatening flood control safety in Shanghai. Based on observations of water levels at three stations along the Huangpu River from 2008 to 2021, together with wind and rainfall, this paper investigates: the trends of the annual highest storm surges; the differences in periodic variation and magnitude of surges during the spring-neap cycle; the longitudinal variation of surges; and the impact of rainfall. The results show that the annual highest surges at three stations all show a significant increasing trend, which is caused by the increasing wind speed, and the intensity and duration of rainfall during typhoon events, induced by the increasing frequency of landfall and close-to-YE (Yangtze Estuary) typhoon events, and strong and very strong typhoon events. The periodic variation and magnitude of surges show clear differences during the spring-neap cycle, resulting from the largest nonlinear surge-tide interaction during spring tide and the smallest during neap tide. Heavy rainfall can reduce the probability of 6–7 h and 2.7–3.3 h periods, and change the phase of the periodic variation, inducing partial main peaks and maximum surges during ebb or low tide. The longitudinal variation of storm surge along the Huangpu River can be divided into three types: decreasing upstream, first increasing then decreasing, and increasing upstream. Heavy and long-duration rainfall increases the water discharge from the basin to the river, amplifying the surge, with the largest impact in the upper reach and the smallest in the lower reach, which is the main factor inducing the latter two types, particularly the third type. • Typhoon-induced surge heights show a significant increasing trend in recent years. • Periodic variation and magnitude of surges show clear differences during a spring-neap cycle. • The longitudinal variations of storm surge show different characteristics. • Heavy rainfall amplifies the storm surge, changes its phase, and influences its longitudinal variation. [ABSTRACT FROM AUTHOR]

Climate change and human activities exert significant impact on the mechanism of runoff generation and confluence. Comprehending the reasons of runoff change is crucial for the sustainable development of water resources. Taking the Upper Shule River as the research area, the M-K test and the moving t test were used to diagnose the runoff mutation time. Furthermore, the slope changing ratio of cumulative quantity method (SCRCQ), climate elasticity method, and Budyko equation were utilized to quantitatively evaluate the impacts and contribution rates of climate change and human activities. The following results were obtained: (1) The Upper Shule River experienced a significant increase in runoff from 1972 to 2021, with 1998 marking the year of abrupt change. (2) The runoff sensitivity showed a downward trend from 1972 to 2021. The main factor affecting the decrease in runoff sensitivity was the characteristic parameters of underlying surface (n), followed by precipitation (P), while the influence of potential evapotranspiration (ET0) was the weakest. (3) The response of runoff changes to runoff sensitivity and influencing factors were 90.32% and 9.68%, respectively. (4) The results of three attribution methods indicated that climate change was the primary factor causing the alteration of runoff in the Upper Shule River. The research results supplement the hydrological change mechanisms of the Upper Shule River and provide a scientific basis for future water resources management and flood control measures. [ABSTRACT FROM AUTHOR]

The source area of the Yellow River (SAYR) in China is an important water yield and water-conservation area in the Yellow River. Understanding the variability in rainfall and flood over the SAYR region and the related mechanism of flood-causing rainfall is of great importance for the utilization of flood water resources through the optimal operation of cascade reservoirs over the upper Yellow River such as Longyangxia and Liujiaxia, and even for the prevention of flood and drought disasters for the entire Yellow River. Based on the flow data of Tangnaihai hydrological station, the rainfall data of the SAYR region and NCEP-NCAR reanalysis data from 1961 to 2020, three meteorological conceptual models of flood-causing rainfall—namely westerly trough type, low vortex shear type, and subtropical high southwest flow type—are established by using the weather-type method. The mechanism of flood-causing rainfall and the corresponding flood characteristics of each weather type were investigated. The results show that during the process of flood-causing rainfall, in the westerly trough type, the mid- and high-latitude circulation is flat and fluctuating. In the low vortex shear type, the high pressures over the Ural Mountains and the Okhotsk Sea are stronger compared to other types in the same period, and a low vortex shear line is formed in the west of the SAYR region at the low level. The rain is formed during the eastward movement of the shear line. In the subtropical high southwest flow type, the low trough of Lake Balkhash and the subtropical high are stronger compared to other types in the same period. Flood-causing rainfall generally occurs in areas with low-level convergence, high-level negative vorticity, low-level positive vorticity, convergence of water vapor flux, a certain amount of atmospheric precipitable water, and low-level cold advection. In terms of flood peak increment and the maximum accumulated flood volume, the westerly trough type has a long duration and small flood volume, and the low vortex shear type and the subtropical high southwest flow type have a short duration and large flood volume. [ABSTRACT FROM AUTHOR]

• A framework is proposed to analyze compound floods with multiple driving factors. • Local rainfall has a larger impact on the flooding levels of the GBA, China. • Flooding area is about 50 km2 and 1.3 m flooded depth for the worst scenario. • Findings have important implications to GBA's flooding control and management. Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods for complex urban river networks remain relatively scarce. This paper proposes a framework to simulate the coastal compound flooding process, with a series of scenarios designed to explore the main flooding drivers and their worst possible combination. The approach is used to quantify the induced flooding consequences, in which a two-dimensional model is employed to conduct a case study for a city with complex river networks. The Qianshan River basin in Zhuhai City, located in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China is used as the case study. Application results show that: (i) the occurrence and properties of flooding in this coastal area are jointly affected by upstream river flows, downstream tidal levels and local rainfall intensity with different contribution levels, (ii) compared to tidal levels, local rainfall intensity has a larger impact on the flooding levels of the study region, and (iii) the worst scenario where a 50-year return period upstream river flow co-occurring with high tide and a 100-year return period rainfall can produce a flooding area up to 50 km2 for the study region, with an average of 1.3 m flooded depth. The proposed framework with its findings offers an in-depth understanding and quantification of coastal urban flood characteristics within the GBA of China. [ABSTRACT FROM AUTHOR]

Accurately assessing the risk of flash floods is a fundamental prerequisite for defending against flash flood disasters. The existing methods for assessing flash flood risk are constrained by unclear key factors and challenges in elucidating disaster mechanisms, resulting in less-than-ideal early warning effectiveness. This article is based on official statistics of flash flood disaster data from 2017 to 2021. It selects eight categories of driving factors influencing flash floods, such as rainfall, underlying surface conditions, and human activities. Subsequently, a geographical detector is utilized to analyze the explanatory power of each driving factor in flash flood disasters, quantifying the contribution of each factor to the initiation of flash flood; the flash flood potential index (FFPI) was introduced to assess the risk of flash flood disasters in China, leading to the construction of a comprehensive assessment framework for flash flood risk. The results indicate that (1) Flash floods are generally triggered by multiple factors, with rainfall being the most influential factor, directly causing flash floods. Soil type is the second most influential factor, and the combined effects of multiple factors intensify the risk of flash floods. (2) The southeastern, southern, and southwestern regions of China are considered high-risk areas for flash floods, with a high danger level, whereas the northwestern, northern, and northeastern plain regions exhibit a lower danger level. The above research results provide reference and guidance for the prevention and control of flash flood disasters. [ABSTRACT FROM AUTHOR]

Accurate prediction of lake water level is of great significance for flood prevention, reservoir scheduling, and ecological protection. However, the change in lake water level is influenced by multiple factors, and water level data as a time series also have the characteristics of complexity, which leads to difficulty in water level prediction. In view of this, a hybrid CEEMDAN–BiGRU–SVR–MWOA (CBSM) framework is proposed here for lake water level prediction based on multiple sources of environmental, hydrological and meteorological factors. Firstly, the lake water level is decomposed into modal data with different frequencies using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, the modal data are divided into internally and externally controlled items using the maximum information coefficient (MIC). Then, multivariate prediction is performed by combining the external data using bi-directional gated recurrent unit (BiGRU). The prediction results are combined using support vector regression (SVR) for prediction with higher accuracy. A modified whale optimization algorithm (MWOA) is used to optimize the parameters of CEEMDAN and the hyperparameters of BiGRU based on permutation entropy and mean square error, respectively. The proposed CBSM was applied to Dongting Lake, China, and the Nash–Sutcliffe efficiency of the prediction results reached 0.997, which was better than those of other benchmark models or frameworks, and the Diebold Mariano (DM) test further demonstrated the superiority of the proposed CBSM. Thus, this research provides a new and effective method for accurately simulating and predicting lake water levels. [ABSTRACT FROM AUTHOR]

The seasonal design flood (SDF) plays an important role in guiding water resources management and floodwater utilization. To estimate the SDF, studies have established the joint distribution of flood peak (i.e., annual maximum daily discharge) and timing (i.e., occurrence date) and assumed a monotonic dependence structure (MDS) between these two flood elements within the bivariate framework. However, the MDS assumption cannot fully represent the flood seasonality dynamics, which might affect the reliable assessments of the bivariate flood risks. To solve this issue, this study proposes a mixed-copula function to model a nonmonotonic dependence structure (NMDS) between flood peak and timing, and then derives the SDFs under nonstationary conditions by using the equivalent reliability method. As the NMDS can characterize the turnaround relationship between peak discharge and timing, the NMDS performs better at modeling observed (1951–2019) flood peaks than the traditional MDS, as reported by a case study in the Wujiang River Basin, China. The results suggest that, within the flood season from May to October, the NMDS-based estimation framework produces higher SDFs in June and July but smaller SDFs in the remaining months than the annual design flood. This allows for reducing the flood control storage capacity to improve floodwater utilization downstream without increasing flood risk. After further investigating the potential effects of a changing environment on peak discharges in the future (2020–2099), it is expected that reservoir regulation might play a more important role in flattening the seasonal fluctuations in floods. These results provide rich information as references for flood risk management and floodwater resources utilization under a changing environment. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting has important implications for urban operation and flood prevention. Radar echo extrapolation is a common method in precipitation nowcasting. Using deep learning models to extrapolate radar echo data has great potential. The increase of lead time leads to a weaker correlation between the real rainfall evolution and the generated images. The evolution information is easily lost during extrapolation, which is reflected as echo attenuation. Existing models, including generative adversarial network (GAN)-based models, have difficulty curbing attenuation, resulting in insufficient accuracy in rainfall prediction. To solve this issue, a spatiotemporal process enhancement network (GAN-argcPredNet v2.0) based on GAN-argcPredNet v1.0 has been designed. GAN-argcPredNet v2.0 curbs attenuation by avoiding blurring or maintaining the intensity. A spatiotemporal image correlation (STIC) prediction network is designed as the generator. By suppressing the blurring effect of rain distribution and reducing the negative bias by STIC attention, the generator generates more accurate images. Furthermore, the discriminator is a channel–spatial (CS) convolution network. The discriminator enhances the discrimination of echo information and provides better guidance to the generator in image generation by CS attention. The experiments are based on the radar dataset of southern China. The results show that GAN-argcPredNet v2.0 performs better than other models. In heavy rainfall prediction, compared with the baseline, the probability of detection (POD), the critical success index (CSI), the Heidke skill score (HSS) and bias score increase by 18.8 %, 17.0 %, 17.2 % and 26.3 %, respectively. The false alarm ratio (FAR) decreases by 3.0 %. [ABSTRACT FROM AUTHOR]