Risk Assessment Of Drought Disaster Research Articles

Study on agricultural drought disaster risk assessment in Heilongjiang reclamation area based on SSAPSO optimization projection pursuit model.

Heilongjiang reclamation area serves as a crucial hub for commodity grain production and strategic reserves in China, playing a vital role in maintaining national food security. Investigating the assessment of agricultural drought risk in this region can yield valuable insights into spatial and temporal variations in drought risk. Such insights can aid in formulating effective strategies for disaster prevention and mitigation, thereby minimizing food losses caused by drought disasters. This study employs a comprehensive indicator system comprising 17 indicators categorized into hazard, exposure, vulnerability, and resistance capacity. The projection pursuit model is applied to evaluate regional drought risk, while the PSO algorithm, optimized by the SSA algorithm, addresses the limitations of low local search ability and search accuracy during the large-scale search process of the PSO optimization algorithm. This study examines and compares the optimization and convergence capabilities of three algorithms: real number encoding-based genetic algorithm (RAGA), particle swarm optimization algorithm (PSO), and sparrow algorithm-based improved particle swarm optimization algorithm (SSAPSO). The analysis demonstrates that SSAPSO exhibits superior optimization performance and convergence properties, establishing it as a highly effective algorithm for optimization tasks. The findings reveal the following trends: over time, agricultural drought risk in Heilongjiang reclamation area has generally declined, with fluctuations observed in hazard and vulnerability, an increase in exposure, and a continuous enhancement of resistance capacity. Spatially, the western region exhibits significantly higher agricultural drought risk compared to the eastern region, primarily due to elevated hazard and vulnerability, coupled with lower resistance capacity. As the agricultural economy grows and agricultural expertise accumulates, the risk of agricultural drought decreases. However, variations in economic growth among different regions lead to diverse spatial distributions of risk.

Connection number structure-based information diffusion model for agricultural drought disaster risk assessment: A case study in Jianghuai watershed area

The information diffusion model is widely used in natural disaster risk assessment because of its operationalization, low data requirements and clear significance of the evaluation results. However, the commonly used normal information diffusion model is based on an average distance assumption, and the diffusion widow with h is directly affected by sample size and extreme values, especially when the sample size is more than 11, which may lead to unstable results of normal diffusion, with typical uncertainty. Considering the influence of the whole samples on the diffusion points, incorporating the connection number method, a connection number structure-based information diffusion model was proposed, and also a relative connection number method is proposed to calculate the connection number, which was applied to the risk assessment of drought disasters in Jianghuai watershed area. The results show that the connection number structure-based information diffusion model reduces the direct influence of sample extremes and sample size, eliminates the constraint of the average distance assumption in the traditional model, and performs more consistently with increasing sample size for risk assessment. And the relative connection number method can contain the traditional connection number expression and offers a wider application. A study of drought risk in the Jianghuai watershed shows that the indices of agriculture drought-suffered, drought-affected and drought-damaged fluctuated greatly, but the trends were generally consistent with each other from 1990 to 2020. It is more likely to suffer from drought in the area, but the disaster rate after drought tends to decrease over time. With the increase in drought intensity, the disaster risk will decrease significantly compared to the drought risk. The connection number structure-based information diffusion model has no two-point proximity principle and the average distance assumption, with better stability in the disaster risk assessment for different samples size compared with the traditional model, which will have more extensive application in risk assessment.

Agricultural drought disaster risk assessment in Shandong Province, China

Agricultural drought disaster risk assessment in Shandong Province, China

A New Agricultural Drought Disaster Risk Assessment Framework: Coupled a Copula Function to Select Return Periods and the Jensen Model to Calculate Yield Loss

China is one of the regions with the most frequent drought disasters and serious social and economic losses. Agricultural drought is the most serious natural disaster. Due to climate change, the regional agricultural drought risk assessment has always been the focus of the academic circle. This study takes Zunyi City as an example, which is the most typical city of karst landform development. The monthly precipitation data set of ground meteorological observation stations in Zunyi City from 1956 to 2020 was selected, and the drought characteristic variables were extracted by the coupled use of the precipitation anomaly percentage (Pa) index and the theory of runs. A copula function was applied to establish the joint distribution model of characteristic variables, obtaining the drought frequency and drought return periods. Combined with the Jensen model, the agricultural drought loss rate under different drought return periods in the target year (2020) was calculated and evaluated. The results showed that the Gumbel-Hougaard copula function was suitable for the joint distribution of drought joint variables in Zunyi City. From 1956 to 2020, fewer droughts occurred in Zhengan and Wuchuan, and the most droughts took place in Fenggang, Meitan, and Yuqing. The average drought duration in each county was about 1.5 months, and the average drought severity was about 0.35 in spatial distribution. Crop loss rate caused by drought increased and the affected area expanded with the increase of drought return periods (5, 10, 20, 50, and 100 years) in temporal distribution. Meanwhile, the drought disaster was most drastic in the eastern region, followed by the south, north, west, and central area. The results were highly consistent with the historical drought in Zunyi City, which verified the validity of the model. This study could provide scientific knowledge for drought resistance and reasonable mitigation programing for the security of the regional agricultural production and the sustainability of social and economic development.

System Structure–Based Drought Disaster Risk Assessment Using Remote Sensing and Field Experiment Data

With the impacts of climate change and human activities, agricultural drought disaster losses have increased remarkably. Drought disaster risk assessment is a prerequisite for formulating disaster reduction strategies and ensuring food security. However, drought disaster risk is a complex system, and quantitative assessment methods reflecting the risk formation mechanism are still rarely reported. This study presented a chain transmission system structure of drought disaster risk, which meant that drought disaster loss risk R was derived from drought hazard H by the transformation of drought disaster vulnerability V. Based on this point, firstly, a drought hazard curve between drought intensity and drought frequency was established using remote sensing data and the copula function. Then, a crop loss calculation approach under various drought events and drought resistance capacity scenarios was achieved by two-season field experiments and the AquaCrop model. Finally, a loss risk curve cluster of “drought frequency–drought resistance capacity–yield loss rate” was proposed by the composition of the above two quantitative relationships. The results of the case study for summer maize in Bengbu City indicated that the average yield loss rate under 19 droughts occurring during the growth period of maize from 1982 to 2017 was 24.51%. High risk happened in 1988, 1992, 1994, 2001, and 2004, with the largest loss rate in 2001, up to 65.58%. Overall, droughts with a return period less than two years occurred frequently during the growth period of summer maize in Bengbu, though the loss risk was still controllable. In conclusion, the results suggest that the loss risk curve provides a new effective method of drought disaster risk quantitative assessment from a physical mechanism perspective, which lays a scientific foundation for decision-making in risk management.

A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks.

As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed.

Risk assessment of drought disaster in summer maize cultivated areas of the Huang-Huai-Hai plain, eastern China.

Agricultural drought risk analysis is useful for reducing probable drought risk in the background of global warming. This study aims to identify spatiotemporal characteristics of drought and drought disaster risk in the summer maize growth period under climate change condition. In this research, we use daily datasets from 79 meteorological stations and the maize yield data in the Huang-Huai-Hai (HHH) plain, eastern China during the period 1960-2015. The drought disaster risk index (DDRI) model was applied to assess the drought disaster risk. The maize drought disaster risk maps were drawn under current and future climate change conditions. The results showed that the high DDRI was distributed in northern region and low DDRI was distributed in most of southern region in the HHH plain. During the summer maize growth period, the DDRI decreased gradually from the northern to southern region. The results also exhibited that under the RCP4.5 (Representative Concentration Pathway 4.5) scenario, about one half of the HHH plain belonged to the slight and sub-slight DDRI region in the future 80years. Overall, our results demonstrated that the DDRI model provided an accurate assessment in both spatial and temporal scales and had a theoretical guidance for improving the adaptation of crop production. Elevating maize drought risk management helps to lessen the anticipated risk to crop production in the HHH plain under the context of climate change.

A 3D Copula Method for the Impact and Risk Assessment of Drought Disaster and an Example Application

Droughts have more impact on crops than any other natural disaster. Therefore, drought risk assessments, especially quantitative drought risk assessments, are significant in order to understand and reduce the negative impacts associated with droughts, and a quantitative risk assessment includes estimating the probability and consequences of hazards. In order to achieve this goal, we built a model based on the three-dimensional (3D) Copula function for the assessment of the proportion of affected farmland areas (PAFA) based on the idea of internally combining the drought duration, drought intensity, and drought impact. This model achieves the “internal combination” of drought characteristics and drought impacts rather than an “external combination.” The results of this model are not only able to provide the impacts at different levels that a drought event (drought duration and drought intensity) may cause, but are also able to show the occurrence probability of impact at each particular level. We took Huize County and Mengzi County in Yunnan Province as application examples based on the meteorological drought index (SPI), and the results showed that the PAFAs obtained by the method proposed in this paper were basically consistent with the actual PAFAs in the two counties. Moreover, due to the meteorological drought always occurring before an agricultural drought, we can get SPI predictions for the next month or months and can further obtain more abundant information on a drought warning and its impact. Therefore, the method proposed in this paper has values both on theory and practice.

Drought disaster risk management based on optimal allocation of water resources

Drought risk management has gradually emerged as an important discipline and the traditional negative drought management changes to active drought management. Drought risk assessment and control are the core of drought risk management. In this study, based on precipitation anomaly (Pa) and soil moisture content anomaly index, the stochastic drought index model was established to calculate the drought distribution under different probability. Considering risk of disaster (H), vulnerability of the environment (S), exposure of the disaster bearing body (V), and disaster prevention and mitigation capability (C), a water resource optimization allocation model based on drought disaster risk assessment model was established to minimize the regional drought disaster risk. The developed models were used in Heilongjiang Province, China, and the results showed that: (1) the drought indexes based on the stochastic method can reflect the regional drought under different probabilities, providing managers with comprehensive drought information to manage the disaster; (2) the optimal allocation of water resources can reduce the risk of drought disaster in drought-prone months and drought-prone areas; and (3) studying drought risk assessment and regulation considering grain yield can be used to effectively understand and alleviate drought effects in the study area, reduce farmers' economic losses and ensure local food security.

Application of the Hidden Markov Model to Agricultural Drought Disaster Risk Assessment

Application of the Hidden Markov Model to Agricultural Drought Disaster Risk Assessment

Dynamic risk assessment of drought disaster: a case study of Jiangxi Province, China

Abstract The dynamic risk assessment of drought is crucial in the transition from the crisis management model to the risk management model, which can reveal the evolution mechanism of drought disasters. Due to a lack of data and research perspectives, most current studies are still based on static risk assessment. This study proposes a conceptual model for the dynamic risk assessment of droughts based on the probability of their occurrence and potential impacts. The developed dynamic risk index considers the hazard, exposure, vulnerability, and capacity for drought mitigation. The analytic hierarchy process (AHP) method was used to determine the weight coefficient of each indicator in the model. The novelty of the proposed model lies in the integration of four elements of drought disasters with spatiotemporal characteristics. Jiangxi Province, which is frequently affected by drought, was selected as the study area to validate the proposed model. Experimental results demonstrate that the proposed model rapidly reflects the degree of drought disaster risk caused by drought events and the influencing factors at monthly and annual scales. Moreover, the datasets based on the influencing factors of drought disasters in different regions have a good commonality in the proposed model.

Risk evaluation of agricultural drought disaster using a grey cloud clustering model in Henan province, China

Frequent drought is a critical limiting factor for regional agriculture development. Accurately evaluating regional agricultural drought risk contributes to raising disaster risk management and reducing drought disaster losses. This paper proposes a grey cloud clustering model based on panel data to assess the agricultural drought disaster risk of Henan province. The new model includes two parts. One component is indicator and time weights determination which uses the grey incidence analysis methods, the maximum deviation and maximum entropy principle, respectively. The other part is the construction of grey cloud possibility function which calculates the drought disaster risk grade. Then, the proposed model is applied for assessing the agricultural drought disaster risk of Henan Province at the regional scale, and the time scale selected is from 2012 to 2016. The agricultural drought disaster presents different spatial distribution in drought hazard, exposure, damage sensitivity and drought resistance capacity. The comprehensive risk evaluation results indicate that the drought risk in the most northern and southern regions of Henan Province is lower, while the western, mid-western and eastern regions have a higher risk. Compared with the traditional assessment models for disaster risk, the proposed model can recognize the random, fuzzy and grey uncertainties of the agricultural drought disaster system. Thus, it is a beneficial tool for drought disaster risk assessment and can help to make some suggestions for drought disaster mitigation.

Trends and risk evolution of drought disasters in Tibet Region, China

The risk posed by natural disasters can be largely reflected by hazard and vulnerability. The analysis of long-term hazard series can reveal the mechanisms by which risk changes. Drought disasters are one of the main types of disaster in the Tibet Region (TR) of China. In this study, using statistical drought disasters data in the TR from 1912 to 2012 and socio-economic statistics for five periods between 1965 and 2015, and adopting standard statistical analyses, a wavelet analysis, and a risk assessment model, we first construct the index system for drought disaster risk assessment, and then assess the risk of drought disasters and analyze the mechanisms of changes in risk. The results showed that the occurrence of drought in the TR had three distinct cycles during this study periods, with durations of 5, 15, and 27 years respectively. The frequency of drought in the TR showed increasing trends, and the cycle of drought had been prolonged. From 1965 to 2015, the risk of drought disaster in the TR is significantly increased with the growth rate of 6.8% in high-risk area. In addition, the severity of drought had enhanced, especially in Qamdo. The increased vulnerability locally and significantly enhanced hazard of drought disaster, with a shrinkage of 16.3% in the low-value area and an expansion of 7.4% in the high-value area, being the determinants of drought disaster risk. Therefore, agricultural areas of the TR are the focal locations where risk of drought disaster needs to be managed.

Characteristics of meteorological drought pattern and risk analysis for maize production in Xinjiang, Northwest China

Xinjiang is an important maize cultivation area in Northwest China. Investigating the relationship between meteorological drought and climate-driven maize yield can help to mitigate the negative impacts of drought on agricultural production in Xinjiang. In this study, multi-source data, including meteorological, agricultural, and socio-economic data, are collected to analyze the spatiotemporal patterns of drought and assess drought disaster risk. The standardized precipitation evapotranspiration index is calculated to classify different drought grades in the study area. The main results are as follows: (1) the relationship between drought and climate-driven maize yield is determined using a stepwise regression analysis, which indicates that drought conditions occurring from May to July are crucial for the yield in Xinjiang; (2) the modified Mann-Kendall test detects that the frequency-intensity-coverage of drought from May to July shows a decreasing tendency as a whole in the study area; (3) the correlation analysis shows that the multivariate ENSO index may be the dominant force in regional drought evolution; and (4) a drought disaster risk assessment system is constructed based on principles of natural disaster management. The drought disaster risk is higher in southwestern and northwestern Xinjiang, areas that should deserve more attention.

Risk assessment of drought disaster in typical area of corn cultivation in China

This study assesses the risk of corn drought disaster in China under current climate conditions and builds a predictable relationship between drought disaster risk and yield losses. Two regions, Jilin and Henan, have been selected to represent two typical areas of corn cultivation. Risk is assessed from the aspects of hazard and vulnerability. Hazard presented as drought frequency and intensity. The sensitivity of corn to droughts that happened in different corn-growing stages and regional irrigation ability are used to reflect vulnerability. The results show that drought hazard and sensitivity in Jilin are much more serious than Henan, and irrigation ability is better in Henan. As a result, the drought disaster risk in Jilin is high; corn faces severe drought stress. The average corn yield loss rates during 1978–2010 in Jilin and Henan were 9.94 and 6.91 %, respectively. The correlation between corn yield losses and drought disaster risk is significant (r = 0.711). The results can help to guide future agriculture planning and drought adaptation polices.

The impact of irrigation water supply rate on agricultural drought disaster risk: a case about maize based on EPIC in Baicheng City, China

This study presents a methodology for risk analysis, assessment, and combination of drought disasters under the different irrigational levels in Baicheng City, which is supported by run theory, copula functions, crop growth model, and technique of natural disaster risk assessment from the viewpoints of climatology, geography, hydrology, agricultural science, disaster science, environmental science, and so on. Along with the global warming, the occurrences of water-related disasters become more frequent and more serious. It is necessary to determine the laws of the relationship between irrigational ability and the loss caused by drought. Drought events were identified by using run theory; the drought frequency was calculated by using copula function; the loss of every drought event was simulated by using EPIC model; and the relationship curves under the different irrigational supply conditions between the drought frequency and the yield reduction rate of the drought event were fitted to assess the impact of irrigational supply rate on the loss caused by drought. The results show that in the range of crop water demand, the loss caused by drought decreases as the result of the increase in irrigational supply rate; however, their variations are not proportional. The loss caused by the certain frequency drought event under the certain irrigational supply condition could be calculated by the curve of drought disaster risk assessment constructed by this study. The results obtained from this study are specifically intended to support local and national governmental agencies on agricultural disaster management.

Risk Assessment of Agricultural Drought Disaster in Southern China

Risk assessment of drought disaster is necessary for the sustained agriculture development under the background of global climate change, and, meanwhile, it is an urgent scientific issue needed to be solved in agricultural risk assessment discipline. In this paper, collecting the areas covered by drought disasters, areas affected by drought disasters, areas with no harvest by drought disasters, areas planted, and yield per unit area data, agricultural drought disasters losses of the southern five provinces in China were calculated. The best probability density function was obtained according to the goodness of fit test results. Then, the value at risk (VaR) from financial market risk research method was used to the effective measure of agricultural drought risk. The results show that, in southern five provinces of our research region, risk of agricultural drought disaster in Yunnan and Guizhou is greater than other three provinces. In the scenario of 50 years and 100 years for a return, the crop loss caused by drought will reach 13.6% and 17.4% in Yunnan province, and the crop loss will be more serious in Guizhou province and be up to 15.3% and 18.1%. The above results can provide multifaceted information about drought risk that can help to guide management of drought disasters.

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.

Dynamic risk assessment of drought disaster for maize based on integrating multi-sources data in the region of the northwest of Liaoning Province, China

The traditional studies on drought disaster risk were based on the ground point data, which were unable to realize the continuity of space and the timeliness. It is shown that the monitoring and evaluation precision on drought were reduced significantly. However, remote sensing data in adequate spatial and temporal resolution can overcome these limitations. It can better monitor the crop in large area dynamically. This study presents a methodology for dynamic risk analysis and assessment of drought disaster to maize production in the northwest of Liaoning Province based on remote sensing data and GIS from the viewpoints of climatology, geography and disaster science. The model of dynamic risk assessment of drought disaster was established based on risk formation theory of natural disaster, and the expression of risk by integrating data came from sky, ground and space. The risk indexes were divided into four classes by data mining method, and the grade maps of drought disaster risk were drawn by GIS. It is shown that the spatial and temporal risk distributions of maize at each growth stage changed over time. The model has been verified against reduction in maize yield caused by drought. It demonstrated the reasonability, feasibility and reliability of the model and the methodology. The dynamic risk assessment of regional drought disaster for maize can be used as a tool, which can timely monitor the status (the possibility and extent of drought) and trends of regional drought disaster. The results obtained in this study can provide the latest information of regional drought disaster and the decision-making basis of disaster prevention and mitigation for government management and farmers.

Assessment on Typical Drought Risk for Wheat Production in China Based on Natural Vulnerability

Food security is the base of human survival and national stability.,of which food supply-demand balance variation and its induced food price fluctuation are the two major concerns.Global food production has been kept on increase since 2000,though in a slow pace,which should be able to meet the basic diet requirement.According to the 4th IPCC report,however,from the end of 2007,the worldwide food supply problem has been paid great attention to due to a sky rocket of food price.The earth is getting warmer and the frequency of drought is increased. Consequently,the intensification of water stress to crops,resulted from both the long term climate change and increase of extreme weather events,may lead to the reduction of crop yield and the increase of drought disasters. To understand the mechanism of crop drought disaster process is the key to the understanding of food security and hence a possible solution to the problem.In the past researches,the concepts of physical vulnerability and social vulnerability are not always yet explicitly distinguished or separated regarding to agricultural drought disaster risk assessment,which restricts the understanding of process-based mechanism of disaster risk dramatically.The major focuses of studies on drought disaster risk assessment range from statistical analysis and probability analysis to prediction of drought hazard or disaster losses,whilst few attached importance to the quantitative separation of physical and social vulnerabilities. The goals of this paper are to explore and quantify the physical vulnerability and drought hazard and risk.The detailed process is as follows:Firstly,agricultural drought disaster risk assessment model is developed based on the physical vulnerability of crops.This model covers agricultural drought hazard possibility distribution estimation, physical vulnerability curve estimation,land use-based exposure assessment,coping capacity integration and risk assessment.Secondly,a case study on two typical wheat species in China is carried out,and a quantitative physical vulnerability to agricultural drought hazards is analyzed using a physical-process based crop growth model and Erosion Productivity Impact Calculator(EPIC).At the beginning,the one-dimension(point scale) crop growth model EPIC with a capability of depicting water stress and temperature stress is extended to a two-dimension(area scale) model,and the Spatial Erosion Productivity Impact Calculator(S-EPIC) can be used to simulate crop yield under different intensity scenarios of drought.Two extreme scenarios for crop growth and yield simulation were investigated. Finally,the maps of drought risk occurring every 2,5,10 and 20 years were charted separately based on the calculated probable distribution of drought hazard and the vulnerability curve.The results reveal that the wheat yield loss caused by drought is decreased from the northwestern part to the southeastern part of China.