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Flooding disasters occur frequently in China, especially on roads, which seriously affect people's normal travel and even threaten their lives. The current technologies for monitoring road waterlogging are inefficient, and there is an urgent need for an efficient method to monitor road waterlogging. Accurate monitoring of road waterlogging is helpful for the government to issue policies and personnel to take preventive measures. Therefore, this article proposed a real-time monitoring method for road waterlogging based on improved YOLOv8. Through the YOLOv8 algorithm, a convolutional block attention module (CBAM) attention mechanism was added to the neck structure network to enhance the important features of waterlogging areas, suppress general features, and improve the accuracy of identifying road waterlogging. In addition, perspective transformation and pixels were used to calculate the waterlogging area. The article studied the road waterlogging in the new campus of Hebei University of Engineering. The results show that the accuracy of this method reaches 93.83%, which can accurately identify the road waterlogging surface and output the waterlogging area in real time, meeting the monitoring needs.

In the context of global climate change, the frequency of waterlogging is increasing. Therefore, to elucidate the effects of waterlogging under real precipitation conditions on the physiological characteristics of peanuts and the underlying mechanics and to provide a theoretical basis for timely protective measures, this study involved a waterlogging disaster simulation experiment in the field environment and a waterlogging stress control experiment in the potting environment. It was found that sufficient water had a positive effect on the growth and development of peanuts (Arachis hypogaea L.) during the 3–5 days period at the beginning of waterlogging. However, as the duration of waterlogging increased, excess water inhibited the growth of peanuts, with a stronger inhibitory effect on the development of pods. A comparison of the two different experimental models found that in the potting environment, water circulation was not smooth, and the intensity of waterlogging was higher than in the field environment experiment, resulting in the effect of waterlogging being advanced by one observation stage (2 days) in the potting environment. Furthermore, using a novel fluorescence imaging system, an analysis of variations in the physiological characteristics of leaf sections demonstrated that the chlorophyll fluorescence in the leaves of the peanut plant exhibited a specific pattern in response to waterlogging stress. [ABSTRACT FROM AUTHOR]

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Under the comprehensive influence of climate, the current urban waterlogging problem is becoming serious. The pipe network system is difficult to cope with the drainage pressure independently due to aging and laying difficulties. The frequent waterlogging problem has put the protection of historic and cultural districts to a major test, and it is urgent to comprehensively improve the waterlogging resilience of historic and cultural districts. The article takes the Ruijin Liaowuping historic and cultural district as an example, analyzes the interaction between the characteristics of historic and cultural districts(traditional water management system and tourism activities)and the waterlogging disasters. On the basis, the article analyzes the mechanism of waterlogging risk in the historic and cultural district with the conceptual model of urban resilience, and builds an index system for evaluating the waterlogging resilience of the historic and cultural district. The system is based on three aspects: external disturbance, ontological fragility and adaptive resilience. The study shows that the low-resilience area in the district is in the densely populated area of historic buildings inside the historic and cultural district, which is difficult to penetrate. This area is a major resilience weak point due to the prominent phenomenon of private construction and the lack of disaster response capacity while the vulnerability of the built environment is outstanding. Based on the measurement results, the targeted spatial optimization strategies and planning adjustment suggestions are proposed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, the global climate has changed sharply, which led to frequent floods. The specific high-intensity and extreme rainfall, and the consequent flood events occurred in the urban areas have damaged the safety and property of residents seriously. There was a torrential rainfall event happened in Zhengzhou on July 20th, 2021, which caused the most serious urban pluvial flood disaster since 1949 in China. Many studies have been done to explore the cause and mechanism of formation as well as the characteristics of the serious rainstorm, in order to improve the urban flood prevention and control. This study systematically reviewed the urban flooding disaster of "7⋅20" in Zhengzhou, analyzed the research and main results of urban waterlogging in recent years and focused on the Zhengzhou "7⋅20" torrential rain waterlogging disaster, analyzed the return period, spatial-temporal distribution and formation mechanism of the storm event. This study analyzed the mechanism of flood formation and prevention, as well as the main problems in urban drainage and flood prevention infrastructure, flood emergency command, risk management and urban planning. The results showed that the "7⋅20" torrential rain in Zhengzhou had the characteristics of extreme and difficult to predict, and the single-day and cumulative precipitation both exceeded the historical extreme values. The coupling and comprehensive influence of typhoon, topography and "rain island effect", the heavy precipitation weather process was caused. "7⋅20" heavy rainfall urban flooding disaster exposed the obvious shortcomings of drainage and waterlogging infrastructure and construction in Zhengzhou city. There were bottlenecks in the river defense system. and inadequate emergency facilities and management capacity. Based on the above problems, it was necessary to adjust the waterlogging prevention and control standards appropriately, to strengthen the flood risk management and planning and construction control measures, build the engineering system of external flood waterlogging and prevention and control, and to strengthen the intelligent dispatching and emergency command and decision-making ability of urban flood. [ABSTRACT FROM AUTHOR]

【Background and objective】 Waterlogging is a natural disaster facing agricultural production in many countries across the world. Global warming is expected to increase its frequency. The objective of this paper is to analyze the spatiotemporal variation of climate-induced waterlogging for winter wheat in Jianghuai Region in central-southern China. 【Method】 The analysis was based on archived meteorological data measured from 143 weather stations across Anhui and Jiangsu provinces, from which we calculated the annual and monthly precipitation, potential evapotranspiration of the winter wheat. We divided the two provinces into a number of sub-regions based on climate and analyzed the data in each sub-region using the principal component analysis and K-means cluster methods. The spatiotemporal variation in waterlogging for each sub-region was calculated from daily standardized antecedent precipitation evapotranspiration index. 【Result】 ①From November to next February, long-lasting waterlogging was mostly likely to occur in the Jianghuai watershed and eastern Yangtze River, while from March to May, it was more likely to happen in the south and less likely in the north of the region. ②In west Yangtze River and in the high mountain areas, the occurrence of waterlogging in each 10 days increased as the crop grew from seedling to harvest, while in the Jianghuai watershed and east Yangtze River, the occurrence of waterlogging was independent of the growing season. In the north of the Huaihe River, the occurrence of waterlogging in the seedling stage was more frequent than in other growing stages. ③Intra-annual variation of waterlogging in each 10-day showed a significant increase from later November to middle of next February, and the variation coefficients was high. 【Conclusion】 Climate-induced waterlogging for winter wheat in Jiangsu and Anhui provinces is high but its occurrence varies spatiotemporally. These results are helpful for developing mitigating methods to alleviate the detrimental impact of waterlogging in the two provinces.

【Objective】 The aim of this paper is to propose a new method to improve the accuracy of the prediction of water level at drainage sluice stations after rainfalls. 【Method】 The analysis was based on field-measured water level data spanning over one year. We collected hydrological data measured from 2010—2020 from two sluice stations at Xijiakou andTianguan, respectively, in the Sihu Basin, and used two machine learning methods -support vector machine regression (SVR) and regression tree - to predict the most unfavorable conditions of the sluice stations after rainfalls. 【Result】 Both SVR and the regression tree model are able to predict the maximum sluice water level after rainfalls in the two stations with R2>0.80. On average, the two models worked better for the Xijiakou station than for the Tianguan station. The selection of the kernel function has a consequence for the SVR model, with the linear kernel function working better. The regression tree model was slightly better than the SVR model. 【Conclusion】 The maximum sluice water level following rainfalls can be predicted reasonably well using the characteristic variables of the sluice water level, rainfall intensity, rainfall duration, and drainage flow of the pumping station. It is necessary to train the machine learning methods for different sluice stations to find the most accurate one.

The viticulture area around the Bohai Bay is the largest grape producing area in China. Waterlogging disaster is a major agricultural meteorological disaster in China, which seriously threatens grape production. Waterlogging indexes are utilized on field crops widely, but most of them can only be evaluated after the end of the growing season, which lacks the timeliness of monitoring and evaluating the process of waterlogging disasters. Taking the main grape producing areas in the Bohai Rim of China as the research object, the waterlogging grade index is constructed based on the daily meteorological data, grape growth stage data and grape waterlogging historical disaster data from 303 meteorological stations in the study area from 1980 to 2019. In the process of index construction, the influence of previous water surplus and deficit status on the current waterlogging process is fully considered, and the climate adaptability of crops in a certain place is considered. The daily waterlogging index of grapes is constructed by referring to the relative humidity index method of crops. Taking historical disaster inversion and disaster process analysis as the main line, Lilliefors test of normal distribution and t-distribution interval estimation method are used to construct the grape waterlogging disaster grade index system suitable for the main grape producing areas around the Bohai Bay, starting from the duration and intensity of waterlogging disaster. Based on the classification index of grape waterlogging disasters constructed above, the frequency of waterlogging disasters at each site in the Bohai Rim from 1980 to 2019 is counted, and the probability of disasters at each site is obtained by using information diffusion theory. Considering the probability and intensity of waterlogging disasters of each grade, the risk index of grape waterlogging in each station in the region is calculated. The results show that the occurrence range of waterlogging disaster in the same growth period of grape decreases with the increase of the disaster level, while the occurrence range of severe waterlogging disaster in different growth periods gradually increases with the advancement of development process. The risk of grape waterlogging is relatively low during the period of bud-shoot growth and flowering and fruit-setting, while the high-risk period of grape waterlogging is the period of fruit expansion and coloring and maturity. The high-risk areas of grape waterlogging disaster are mainly located in the southeast of Shandong Province, the southeast of Liaoning Province and the northeast of Hebei Province.

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Waterlogging has always been a natural disaster that troubles the development of human society. The crop reduction caused by waterlogging has threatened food security. The research on risk index, risk level and risk evaluation of waterlogging has been one of the hot topics, which has important theoretical and practical significance. In this paper, the Luoshan Drainage Area in Four-lake Watershed is chosen as the study area. The waterlogging disaster index system is established, and the index weights are calculated by using the AHP and CRITIC method. Then the waterlogging risk assessment model is established, and the risk degree under different mitigation measures is analyzed. The results show that by improving the drainage capacity to the planning level, increasing the water level, and improving the water-drought ratio at the same time, the comprehensive waterlogging risk decreases the most in the study area. Compared with the current situation, the proportion of high-risk area decreases by 100%, 78.05% and 54.44% respectively when the rainfall intensity in the flood season occurs once in 5 years, 10 years and 20 years, and the proportion of the low-risk area decreases by 72.96% when the rainfall intensity in thr flood season occurs every 3 years. [ABSTRACT FROM AUTHOR]

According to high-precision geographic information, drainage facilities and drainage operation model of urban Fuzhou, the urban rainstorm waterlogging simulation model in Fuzhou was established. The Urban Rainstorm Waterlogging Simulation Model in Fuzhou was used to simulate the waterlogging disaster caused by three typical rainstorm types in Fuzhou history and rainfall cases with different return periods. The simulated and actual values of ponding water depth in three rainfall types were evaluated. The results show that the percentages of water accumulation points with the absolute error between simulated and actual water accumulation depth less than 10 cm were 50%, 78% and 76%, respectively, for the three rainfall types. This model is more capable of simulating accumulated water for the rainfall type of long duration and uniform rainfall intensity than for that of short-term heavy rainfall. The simulated accumulated water was less than the real accumulated water in the rainfall type of short-term heavy rainfall. The model was used to evaluate the risk of waterlogging in Fuzhou city under different rainfall recurrence periods. The results show that the model can objectively reflect the risk distribution of waterlogging in the urban area in Fuzhou under the rainfall in different reappearing periods.

Waterlogging disasters cause significant threats to road transportation system. Scholars are currently focusing on the manner in which disaster resilience of road transportation system can be measured to improve the disaster control capability of an entire road transportation system. However, existing studies lack of quantitative analysis on the waterlogging resilience of road transportation system. This study analyzed the validity of network system structure by combining percolation theory on the basis of the validity view of system functions proposed by Pimm to construct a waterlogging resilience measurement model of road transportation systems and recognize the weak region of waterlogging resilience during a quantitative study. A resilience measurement model of an urban road transportation system in the context of waterlogging disasters was constructed. On this basis, the lost key connections, which were used as the weak region of waterlogging resilience in road transportation system, were recognized. Results demonstrated that the constructed waterlogging resilience measurement model could quantify the waterlogging resilience of the road transportation system throughout the process and highlight the dynamic features of waterlogging disasters. In the weak resilience region recognized in the quantitative study based on percolation theory, the percolation phenomenon of system network may disappear once the key connections are broken. Conclusions offset the shortcomings of previous resilience quantitative studies in the lack of waterlogging disaster analysis. The study results of waterlogging resilience of road transport system are applied directly to optimal decision making on resilience by combining the topology of system network and the post-disaster behavioral characteristics of users. This study provides a new method and decision-making idea to evaluate the disaster resilience of road transportation system. [ABSTRACT FROM AUTHOR]

In recent years, global warming has led to frequent waterlogging disaster (WLD), hindering the normal growth and development of crops, affecting the sustainable development of the peanut industry and food security in China. Henan Province, the main peanut-producing area with frequent WLD, was selected as the study area. Based on the soil-crop-atmosphere continuum system, this study constructed a comprehensive waterlogging index (CWI) to monitor waterlogging events. To determine the applicability of CWI, the standardized precipitation evapotranspiration index (SPEI) was calculated. A risk assessment model was constructed to evaluate the hazards of WLD during the different stages of development of spring peanut. The water stress at different growth stages of peanut on crucial physiological indices were studied to determine the natural vulnerability curve of WLD. As a consequence, based on the "Four-Factor" theory of disaster formation, this study established a dynamic WLD assessment model for peanut and evaluated the comprehensive risk of WLD in Henan from 1990 to 2020. The results indicated that the CWI is capable of accurately identifying waterlogging events and peanut responded significantly differently to water stress; the longer the duration of stress in the late growth period (LGP), the higher was the rate of yield reduction. The comprehensive risk of WLD to peanut rose from north to south during the three growth periods. During the LGP, high-risk regions constituted approximately 34.3% of the total population. This study provides a scientific basis and technical support for better exploration of peanut growth under waterlogging stress to manage and reduce the risk of WLD. • The comprehensive waterlogging index (CWI) can be used to identify waterlogging events. • Different water stress days in three periods had different effects on the growth of spring peanut. • Water stress in the late growth period had the greatest influence on the yield of spring peanut. • The risk was evaluated through "Four-Factor" theory, while the south of Henan had high-risk levels. [ABSTRACT FROM AUTHOR]

Waterlogging disasters are one of the most destructive meteorological disasters, which lead to crop yield reduction and cause a great threat to humanity and economic structure. This study presents the methodology and procedure for dynamic risk assessment of waterlogging disasters for maize in Midwest of Jilin Province, China. We took the representative waterlogging disaster years of 1994, 2005, and 2010 as examples, the growth-stage waterlogging index was established to assess the waterlogging disaster hazard by using standard antecedent precipitation index and the relative humidity index. Maize growing data and maize planting area data were combined to assess the waterlogging disaster vulnerability of maize, in which the CERES-Maize model was used to simulate the growth of maize at a daily time step for each grid. Based on the theory of natural disaster risk, the dynamic risk assessment model of waterlogging disaster for maize was built. In this study, the risk indexes were divided into five classes by using an optimal partition method. The grid GIS technology was used to map the spatial distribution of data and the grade of waterlogging disaster risk at a resolution of 5000 × 5000 m. The results show that areas with very low waterlogging disaster risk are mainly located in western and northeastern regions; in contrast, very high and high waterlogging disaster risk levels are mainly located in southern and central regions. Meanwhile, high risk areas at different growth stages gradually spread from the southwestern to the Midwestern and southeastern regions. This study could help the government when they make strategic decisions regarding food security in China, and the method of dynamic waterlogging risk disaster assessment could also be applied for other crops to control and prevent the occurrence and development of waterlogging disasters and reduce their adverse influence. [ABSTRACT FROM AUTHOR]

Waterlogging disasters in the lakeside areas of shallow lakes that located in plain regions are sensitive to lake-level fluctuations. However, there are very few studies on the influences of lake-level fluctuations on waterlogged lakeside areas from a large lake basin perspective. This paper proposes an integrated hydrodynamic model employing the MIKE software to contribute to the existing literature by filling the gap constituted by the lack of an estimation of the impacts of lake-level fluctuations on waterlogging disasters by relevant models. First, a coupled one-dimensional and two-dimensional hydrodynamic model is established to simulate the waterlogging routing in the lakeside area around Nansi Lake (NL) in addition to the flood routing in NL and its tributaries. Second, the model is calibrated and verified by two measured flood events in July 2007 and July 2008; the results indicate that the model can correctly simulate the drainage process of pumping stations in the lakeside area, as well as the interactions between the waterlogging drainage and lake-level fluctuations. Third, the process of waterlogging in the lakeside area of NL is simulated under different rainfall events and initial lake-level conditions. Fourth, based on the results of the model, this paper illustrates the influences of lake-level fluctuations on the waterlogged area around the lake, as well as the different responses of waterlogging in different areas to lake-level fluctuations in NL and the main cause for these differences. Finally, based on the results of the model, this paper presents some implications for waterlogging simulations and drainage system design. [ABSTRACT FROM AUTHOR]