Disaster Prevention In China Research Articles

Improving Seasonal Prediction of Summer Precipitation in the Middle–Lower Reaches of the Yangtze River Using a TU-Net Deep Learning Approach

Abstract The two-step U-Net model (TU-Net) contains a western North Pacific subtropical high (WNPSH) prediction model and a precipitation prediction model fed by the WNPSH predictions, oceanic heat content, and surface temperature. The data-driven forecast model provides improved 4-month lead predictions of the WNPSH and precipitation in the middle and lower reaches of the Yangtze River (MLYR), which has important implications for water resources management and precipitation-related disaster prevention in China. When compared with five state-of-the-art dynamical climate models including the Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0) and four models participating in the North American Multi-Model Ensemble (NMME) project, the TU-Net produces comparable skills in forecasting 4-month lead geopotential height and winds at the 500- and 850-hPa levels. For the 4-month lead prediction of precipitation over the MLYR region, the TU-Net has the best correlation scores and mean latitude-weighted RMSE in each summer month and in boreal summer [June–August (JJA)], and pattern correlation coefficient scores are slightly lower than the dynamical models only in June and JJA. In addition, the results show that the constructed TU-Net is also superior to most of the dynamical models in predicting 2-m air temperature in the MLYR region at a 4-month lead. Thus, the deep learning-based TU-Net model can provide a rapid and inexpensive way to improve the seasonal prediction of summer precipitation and 2-m air temperature over the MLYR region. Significance Statement The purpose of this study is to examine the seasonal predictive skill of the western North Pacific subtropical high anomalies and summer rainfall anomalies over the middle and lower reaches of the Yangtze River region by means of deep learning methods. Our deep learning model provides a rapid and inexpensive way to improve the seasonal prediction of summer precipitation as well as 2-m air temperature. The work has important implications for water resources management and precipitation-related disaster prevention in China and can be extended in the future to predict other climate variables as well.

Study on the Application of LSTM in Meteorological Element Prediction based on Computer Software

The 21st century is the era of big data. With the rapid development of computer technology, we can store more meteorological data, which increases the meteorological data. As people put forward higher requirements for meteorological information, we need to collect and forecast abundant meteorological laws. With the rapid development of computer technology, deep learning has opened a new era of artificial intelligence. Deep learning technology has brought opportunities to the development of meteorological forecasting technology, which has played an important role in basic weather forecast, economic activities and disaster prevention in China. First of all, this paper analyzes the important role of meteorological elements prediction. Then, this paper analyzes the deep learning LSTM algorithm. Finally, this paper makes an empirical analysis.

Key Factors and Coupling Relationships of Collaborative Governance for Disaster Prevention in China's Coastal Cities.

In order to discover the key factors for the successful implementation of the collaborative management of disaster prevention and mitigation in coastal cities and their causal coupling, based on the hypothesis, the main factor analysis method was used to analyze the key factors of the various subsystems of disaster prevention and mitigation, and to verify the correlation between the main factors and the successful implementation of coordinated governance for disaster prevention and mitigation in coastal cities. Interpretative structural model (ISM) is used to explore the coupling relationship between subsystems and key factors within each subsystem. The results show that there have 6 factors, including emergency organization coordination, plan construction coordination, monitoring and early warning coordination, information sharing coordination, emergency command coordination, and emergency resource coordination are positively related to the successful implementation of coastal city disaster prevention and mitigation collaborative governance; emergency rescue coordination and emergency decision-making coordination are not directly related to the successful implementation of disaster prevention and mitigation collaborative governance in coastal cities. When the main factors work together, the impact on the successful implementation of disaster prevention and mitigation collaborative governance in coastal cities is not obvious. There are obvious hierarchical relationships and clear influence paths between key factors within each main factor.

Trends, change points and spatial variability in extreme precipitation events from 1961 to 2017 in China

Abstract Extreme precipitation events vary with different sub-regions, sites and years and show complex characteristics. In this study, the temporal variations, trends with significance and change points in the annual time series of 10 extreme precipitation indices (EPIs) at 552 sites and in seven sub-regions were analyzed using the modified Mann–Kendall test and sequential Mann–Kendall analysis. Three representative (extremely wet, normal and extremely dry) years from 1961 to 2017 were selected by the largest, 50%, and smallest empirical frequency values in China. The spatiotemporal changes in the EPIs during the three representative years were analyzed in detail. The results showed that during 1961–2017, both the consecutive wet or dry days decreased significantly, while the number of heavy precipitation days had no significant trend, and the other seven wet EPIs increased insignificantly. The abrupt change years of the 10 EPIs occurred 32 and 40 times from 1963 to 1978 and from 1990 to 2016, respectively, regardless of sub-region. The extremely dry (or wet) events mainly occurred in western (or southwestern) China, implying a higher extreme event risk. The extremely wet, normal and extremely dry events from 1961 to 2017 occurred in 2016, 1997 and 2011 with empirical frequencies of 1.7%, 50% and 98.3%, respectively. In addition, 1998 was the second-most extremely wet year (empirical frequency was 3.7%). The monthly precipitation values were larger from February to August in 1998, forming a much earlier flood peak than that of 2016. The 10 EPIs had close connections with Normalized Difference Vegetation Indexes during the 12 months of 1998 and 2016. This study provides useful references for disaster prevention in China.

Rainfall threshold determination for flash flood warning in mountainous catchments with consideration of antecedent soil moisture and rainfall pattern

Flash flood disaster is a prominent issue threatening public safety and social development throughout the world, especially in mountainous regions. Rainfall threshold is a widely accepted alternative to hydrological forecasting for flash flood warning due to the short response time and limited observations of flash flood events. However, determination of rainfall threshold is still very complicated due to multiple impact factors, particular for antecedent soil moisture and rainfall patterns. In this study, hydrological simulation approach (i.e., China Flash Flood-Hydrological Modeling System: CNFF-HMS) was adopted to capture the flash flood processes. Multiple scenarios were further designed with consideration of antecedent soil moisture and rainfall temporal patterns to determine the possible assemble of rainfall thresholds by driving the CNFF-HMS. Moreover, their effects on rainfall thresholds were investigated. Three mountainous catchments (Zhong, Balisi and Yu villages) in southern China were selected for case study. Results showed that the model performance of CNFF-HMS was very satisfactory for flash flood simulations in all these catchments, especially for multimodal flood events. Specifically, the relative errors of runoff and peak flow were within ± 20%, the error of time to peak flow was within ± 2 h and the Nash–Sutcliffe efficiency was greater than 0.90 for over 90% of the flash flood events. The rainfall thresholds varied between 93 and 334 mm at Zhong village, between 77 and 246 mm at Balisi village and between 111 and 420 mm at Yu village. Both antecedent soil moistures and rainfall temporal pattern significantly affected the variations of rainfall threshold. Rainfall threshold decreased by 8–38 and 0–42% as soil saturation increased from 0.20 to 0.50 and from 0.20 to 0.80, respectively. The effect of rainfall threshold was the minimum for the decreasing hyetograph (advanced pattern) and the maximum for the increasing hyetograph (delayed pattern), while it was similar for the design hyetograph and triangular hyetograph (intermediate patterns). Moreover, rainfall thresholds with short time spans were more suitable for early flood warning, especially in small rural catchments with humid climatic characteristics. This study was expected to provide insights into flash flood disaster forecasting and early warning in mountainous regions, and scientific references for the implementation of flash flood disaster prevention in China.

Maize drought disaster risk assessment of China based on EPIC model

Digital Agriculture is one of the important applications of Digital Earth. As the global climate changes and food security becomes an increasingly important issue, agriculture drought comes to the focus of attention. China is a typical monsoon climate country as well as an agricultural country with the world's largest population. The East Asian monsoon has had a tremendous impact upon agricultural production. Therefore, a maize drought disaster risk assessment, in line with the requirements of sustainable development of agriculture, is important for ensuring drought disaster reduction and food security. Meteorology, soil, land use, and agro-meteorological observation information of the research area were collected, and based on the concept framework of ‘hazard-inducing factors assessment (hazard)-vulnerability assessment of hazard-affected body (vulnerability curve)-risk assessment (risk),’ importing crop model EPIC (Erosion-Productivity Impact Calculator), using crop model simulation and digital mapping techniques, quantitative assessment of spatio-temporal distribution of maize drought in China was done. The results showed that: in terms of 2, 5, 10, and 20 year return periods, the overall maize drought risk decreased gradually from northwest to southeast in the maize planting areas. With the 20 year return period, high risk value regions (drought loss rate ≥0.5) concentrate in the irrigated maize region of Northwest china, ecotone between agriculture and animal husbandry in Northern China, Hetao Irrigation Area, and north-central area of North China Plain, accounting for 6.41% of the total maize area. These results can provide a scientific basis for the government's decision-making in risk management and drought disaster prevention in China.

Framework of National Non-Structural Measures for Flash Flood Disaster Prevention in China

In recent years, disasters caused by flash floods with many casualties have occurred frequently in China. In order to effectively prevent flash flood disasters, the State Council approved the National Flash Flood Control Planning (NFFCP) in 2006. In this planning, non-structural measures are recommended as the first step to be adopted in the prevention of flash floods, debris flow and landslide disasters caused by heavy rainfall. In order to effectively build up a comprehensive non-structural measure system for flash flood disaster prevention, the Ministry of Water Resources has asked the local authorities to conduct studies in 103 pilot counties aiming to explore practical schemes. Based on the experience from these pilot counties, the construction of non-structural measures on flash flood disaster prevention for 1836 counties was officially initiated in 2010. The contents of non-structural measures for flash flood disaster prevention at county level include eight aspects. This paper will systematically illustrate the main contents of this national project for the construction of non-structural measures for flash flood disaster prevention in China. This work could provide a reference for other countries and regions in terms of flash flood prevention.