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

Drought is probably the most complex and severe natural disaster, which affects more people than any other type of natural hazard (Wilhite 2000). Globally, it has the widest range of influence, the longest duration of prevalence, and causes huge economic loss (Sheffield et al. 2014).

The risk of drought disasters is their possible impact on social, economic, and natural environmental systems (Zhang 2004). Owing broadly to a lack of data and varying research perspectives, most of the drought disaster risk research does not involve the essential characteristics of the risk of loss caused by drought disaster and only focuses on drought risk. Researchers use the theory of risk formation based on natural disasters for drought risk assessment. A common tool for drought risk assessment is to estimate the drought indices using observable meteorological and hydrological data, which can be used as an individual index or as a combination of other indices. A variety of drought indices have been proposed, such as the Palmer drought severity index (PDSI) (Vasiliades et al. 2011), the standardized precipitation index (SPI) (Raziei et al. 2013), the standardized precipitation evapotranspiration index (SPEI) (Hernandez & Uddameri 2014), the soil moisture deficit index (SMDI) (Narasimhan & Srinivasan 2005), and the normalized difference vegetation index (NDVI) (Choudhury et al. 1987). Integrated analysis of the frequency, recurrence period, exposure, and vulnerability are the variables that are used to characterize the risk posed by droughts (Birkmann 2007). Commonly used exposure and vulnerability indicators include land use (Martin et al. 2016), socioeconomic indicators (Dabanli 2018), geographic information (Belal et al. 2012), and crop growth (Zhang 2004). However, the severity of the risks identified through these drought indices is based on a stationary Markov stochastic process. In other words, the future development of drought disaster risk is only related to the historical drought risk situation, where the corresponding probability does not change because of the translation of time, and hence, only a static risk is assessed. Thus, the results obtained are limited to the probability distribution of the risk of drought disasters for a specific period.

The principle of the dynamic assessment of risk due to droughts considers spatiotemporal change as the key constraint. The results of the assessment process and risk characterization change with time and space, reflecting the impact of the drought disaster risk for different periods with changes in the indicators. Certain studies have attempted to establish a dynamic vulnerability assessment model for drought disasters by fitting the vulnerability curves between the drought indices and the drought disaster loss rates in different periods, achieving the transition from the static risk assessment (Zhang et al. 2019), to a certain extent. Risk assessment usually considers loss factors, drought indices, and the nature of drought disasters; however, these cannot accurately reflect all the characteristics of the risks caused by drought disasters, as they are complex. Studies dynamically assessing the risk of drought disasters by considering all four factors (i.e., hazard, exposure, vulnerability, and capacity for drought mitigation) of drought disasters are rare. Therefore, with increased data availability and improved computation techniques, a dynamic risk assessment of drought disasters based on the spatiotemporal change in hazards, exposure, vulnerability, and capacity for drought mitigation is essential.

Research on the dynamic assessment of natural disaster risks has not progressed as desired owing to the negligence of time-dependent changes in risks, resulting in non-technical conceptual studies (Huang 2015). In 1988, the Office of Science and Technology Information of the U.S. Department of Energy developed the Plant Risk Status Information Management System (PRISIM) to ensure the safety of nuclear power plants in Arkansas. Inspectors can rapidly obtain monitoring information and use it to update the results of the risk analysis to achieve a dynamic assessment of risks. Jain & Davidson (2007) systematically studied the dynamic changes in hurricane risks and proposed that these risks change over time owing to changes in the number, type, location, vulnerability, and value of buildings. Lopez-Nicolas et al. (2017) proposed a risk assessment framework for economic loss based on agricultural droughts, which integrated a random time series model for predicting runoff and water storage, a statistical regression model based on water balance, and a crop value assessment based on irrigation water and crop prices. This model dynamically assesses the risk of drought disasters through the price fluctuations caused by water shortages. Zhang et al. (2015) analyzed the relationship between water shortage and drought risk in three stages (before, during, and after the drought disaster) and showed that the contributions of the hazard factors to the risk varied at the different stages.

The current trend in drought disaster research is the transformation from drought crisis management to drought risk management (Wilhite 2016). Dynamic monitoring and assessment of drought disasters at different scales is the most vital component of drought risk management. A dynamic risk assessment of drought disaster can reveal their spatiotemporal evolution mechanisms and provide essential inputs for the scientific formulation of disaster reduction plans. The main purpose of this study is to propose a conceptual model for the dynamic risk assessment of drought disasters through the construction of a mathematical model based on the interactions between the factors of drought at different times. To achieve this, the analytic hierarchy process (AHP) (Saaty 1987) method that uses weight coefficients of the hazard, exposure, vulnerability, and capacity for drought mitigation indicators was developed. This model was applied to the study area to dynamically analyze the integrated risk of drought disaster.

The study area, Jiangxi Province (Figure 1), is located in southeastern China (from 113°34′36″ E to 118°28′58″ E and from 24°29′14″ N to 30°04′41″ N). It spans about 620 km from north to south and is 490 km wide from east to west. The total land area of the province is 166,948 km², accounting for 1.74% of the total land area of China. It is located near the Tropic of Cancer and belongs to a subtropical warm and humid monsoon climate. It is warm and rainy in spring and hot and humid in summer. The annual average temperature ranges from 16.3 to 19.5 °C, increasing from north to south, and the province experiences 240–307 days of a frost-free period per annum. The average annual temperature of northeast Jiangxi, the northwestern mountainous region of Jiangxi, and the Poyang Lake Plain is 16.3–17.5 °C. The average annual temperature of Gannan Basin is 19.0–19.5 °C, and the extreme maximum temperature is above 40 °C at one of the hottest areas in the middle reaches of the Yangtze River. The daily average temperature is stable over 10 °C for a duration of 240–270 days, and the active accumulated temperature is 5,000–6,000 °C within this period. The average annual precipitation in Jiangxi Province is about 1,600 mm. During the year, precipitation is distributed as follows: the wet period accounts for about 46% of the whole year, mainly concentrated in April, May, and June; the dry period accounts for 21%, mainly concentrated in January, September, October, November, and December; and the intermediate period accounts for about 33%, mainly concentrated in February, March, July, and August.

According to the change characteristics of each of the four factors for drought disaster, the change in drought disaster risks over time and space is mainly affected by the status of each factor and their interrelationships. The four influencing factors for the dynamic risk assessment of drought disasters include hazard, exposure, vulnerability, and capacity for drought mitigation. The hazard is expressed by the degree of deviation of the meteorological and hydrological factors for various types of drought-induced events from normal values. The data pertaining to rainfall, evapotranspiration, and temperature from 12 meteorological stations influencing the formation of drought events in the study area during 2000–2013 were used. Land-use information pertaining to the study area was extracted from Landsat thematic mapper (TM) images with a spatial resolution of 30 m. The ratios of the areas of each land-use type were derived for each city within Jiangxi Province and used as the exposure factor. Vulnerability to a disaster (by society, environment, etc.) is a measure of the inability to resist or respond to a hazard (Cutter & Finch 2008). The vegetation health coefficient was calculated using the NDVI derived from SPOT VEGETATION 10-day synthesis (VGT S10) instruments to obtain the sensitivity of the disaster-bearing component. The agricultural population and its region-wise ratios represent the scale of the annual disaster-tolerant component. The capacity for drought mitigation was calculated using capacities of the beneficial reservoirs and the regions serviced by them. Table 1 provides detailed information on the data used in this study for the dynamic risk assessment of drought disasters in Jiangxi Province.

There are two types of non-drought moments during a drought event: (1) the time when the SPEI value is greater than −1 occurs in the middle of the drought event and (2) the time when the SPEI is greater than −1 occurs at the beginning and end of the drought event. When the SPEI value is greater than −1, is greater than zero. Months with an SPEI greater than −1 occur at different times during the drought event, and the associated risks differ. In such situations, cannot be calculated using formula (3). For the first case, the risk index for a particular month is calculated by adding the average hazard risk value of the month (when the SPEI value of the preceding and following months was less than −1) and the value of the month. In the second case, is the risk index.

The risk assessment based on the four factors of disaster theory is in a primary stage, and there are still significant differences and uncertainties in the judgment of the importance of each factor by different experts. AHP and fuzzy analytical hierarchy process (FAHP) (Chang 1996) are commonly used methods to determine the weight coefficients of each index in the model. The AHP method makes use of the characteristics of behavioral science to quantify the empirical judgment of decision-makers. When the factor structure is complex and there is a lack of necessary data, this method is more practical. FAHP is a highly complex process compared with AHP. Therefore, in this study, AHP was adopted to calculate the weight coefficient of each factor. In the selection of the scale, compared with the original one-to-nine scale, the four-factor scale makes it easier to judge the importance of each index, and the consistency check process is omitted. This method is simple, fast, and the accuracy conforms to the calculation requirements. Through this method, the four-factor scale of drought disasters was constructed by approximately replacing the results of direct expert investigations.

Table 2 indicates that vulnerability is the most important factor, followed by the hazard exposure and mitigation capacity factors. The main feature of a drought disaster is the loss of the hazard-bearing component. The hierarchy achieved here forms the core of distinguishing between a drought disaster and drought. The interplay of the four factors listed in Table 2 reflects the characteristics of drought disasters.

The achieved weights for the four factors through the AHP method were redistributed according to the relationship between the feature layer and each of the indicator layers, with the final calculation of the weight coefficients of the indicators in the model (Table 3).

Jiangxi is a major agricultural province and an important grain production area in China. However, due to the impact of climate change in recent years, drought disasters have occurred frequently. Depending on the information on the duration, intensity, and area of influence of drought disasters recorded in the ‘China Meteorological disaster Yearbook’, ‘China disaster Chronicle,’ and ‘Jiangxi Yearbook’. In the summer of 2003, Jiangxi Province suffered sustained high temperature and drought, and the drought continued into winter. The total rainfall for the three seasons of summer, autumn, and winter in Jiangxi Province in 2003 was the lowest since the records began in 1959. In June, drought disaster occurred in central Jiangxi Province; in July, drought disaster occurred in Shangrao, Jingdezhen; severe drought disaster occurred in Nanchang from August to October, and the disaster aggravated in the south-central area of Jiangxi Province from October to November. In 2013, the average temperature in Jiangxi Province was the second-highest in history, and the amount of summer precipitation was 37% less than during the same period in normal years. In July, drought disasters occurred in the central and southern parts of Jiangxi Province; in August, drought disasters spread to the north; and from August to October, drought disasters occurred throughout the province, while severe drought disasters occurred in Nanchang. The high temperatures and severe droughts in 2003 and 2013 had a widespread, long duration, and resulted in severe disasters, which were rare in recorded history. Thus, we selected 2003 and 2013 as typical years for the dynamic risk assessment of Jiangxi Province.

The SPEI was utilized as the drought identification index. As the study area is located in the subtropical monsoon climate region, the water cycle is rapid, such that the monthly scale is more sensitive, which can reflect the development trend of drought events (Duan 2013). Simultaneously, the monthly dynamic risk assessment can conform to the requirements of disaster risk management for the early warning of drought disasters and achieve the purpose of effectively mitigating the impact of drought disasters (Grasso & Singh 2011). Therefore, the monthly scale was selected for drought event identification and drought disaster risk analysis. Finally, the annual scale was selected to analyze the total dynamic change process in drought disaster risk from 2003 to 2013.

The risk presented by drought disaster events, calculated based on the SPEI probability of each meteorological station, was used to obtain the spatial distribution of the risk using the inverse distance weighted (IDW) spatial interpolation method (Figure 3). The study area was dominated by a summer drought in 2003, which occurred in the central and northeast regions of the study area in June and July. This drought spread throughout the entire province between August and September, and gradually eased from north to south from October to November.

The disaster reduction capacity of each city/region before the occurrence of the drought was calculated based on the cumulative rainfall of the cities/regions in the study area 3 months before the drought. It was assumed that each reservoir only has a 4-month drought resistance capacity, and the disaster reduction capacity was considered the same for the months in between. Finally, the integrated risk for each city/region was calculated. As shown in Figures 3 and 4, the drought phenomenon first appeared in the central area of the study area in June. Since agriculture is mainly distributed in the central region of Jiangxi Province, the scope and intensity of the integrated drought disaster risk are higher than the risk of drought events. At the same time, Nanchang had the largest population, and its inhabitants were more vulnerable. Therefore, the overall risk for Nanchang was higher than that of the surrounding Jiujiang and Shangrao areas. The drought occurred throughout the province in July, and Nanchang was affected by the drought, and the integrated risk continued to increase. At this time, the reservoirs in Ji'an, Ganzhou, and Fuzhou began to supply water, and the integrated risk of the three places was reduced. In August and September, droughts occurred in all cities in the study area. Owing to the lack of disaster reduction capabilities of the reservoirs in Nanchang, its integrated risk was highest among all cities. With the easing of the drought conditions in the northern part of the study area in October and November, the integrated risk of drought disasters in Nanchang was reduced. However, the exposure of Xinyu City (mainly drylands and paddy fields) was higher than that of the surrounding cities such that when the drought continued in the central region, its integrated risk was significantly higher than that of the other areas.

Similarly, analyses were performed for 2013. Figure 5 shows that the study area suffered a summer drought in 2013. In July, the drought mainly occurred in the southern Ganzhou region, and it gradually spread from the south to the northeast. In October, in the majority of the areas, its intensity was at a maximum, as evidenced by the risk values. By December, the drought showed a decreasing trend from the north, and the south got relieved. The northern regions of Yichun, Jiujiang, Nanchang, Shangrao, and Jingdezhen had minimum risk levels. Ji'an did not experience drought, with a risk value of zero.

As shown in Figures 5 and 6, the drought in July mainly occurred in the southern region, but the central region, with concentrated agriculture, had maximum exposure and vulnerability, resulting in a higher integrated risk in the central and southern regions than in the other regions. The capacity of the reservoirs for each city in August was investigated to compare the impact of disaster reduction capacity computed based on the dynamic risk assessment model for 2013. Nanchang, as the provincial capital, has accelerated its urbanization in recent years. The impervious surface of the city has increased significantly, and the exposure mainly affected by vegetation coverage showed a decreasing trend. However, the vulnerability affected by population and vegetation health has increased rapidly, coupled with the weak disaster reduction capacity of the reservoirs in Nanchang. Therefore, the risk for Nanchang was the highest among all cities/regions. Ji'an meteorological station did not show drought event, and hence, Ji'an reservoir was not considered in the analysis. The magnitude of the integrated drought risk for each city/region in the study area was closely related to the risk of hazards and the vulnerability of the affected component: the higher the risk of hazards, the greater the vulnerability, and the greater the magnitude of the integrated risk. It is important to develop both engineering and non-engineering measures of drought mitigation scientifically and economically, for the development of the country. Finally, in terms of the spatial patterns, the integrated drought disaster risk intensity in the northern region was higher than that in the southern region, which, hence, requires more attention. The results of dynamic drought disaster risk assessment accord with the actual situation.

For a typical year, the monthly scale was used to characterize the changes in drought and the drought disaster risk during the year. In this section, the annual scale data were adopted by the model's various impact factors to thoroughly analyze the dynamic changes of drought disaster risk in Jiangxi Province from 2003 to 2013 (Figure 7). During these years, the integrated risk of drought disasters in 2003 and 2013 was the highest. The province was affected by drought disasters, and the areas of severe drought were mainly distributed in the north-central part of Jiangxi. The integrated risk of drought disasters in 2007, 2009, and 2011 was relatively serious, mainly distributed in the northern region, and the degree of risk decreased from north to south. In other years, the drought disaster risk intensity was relatively small. Due to the high population density and advanced crop production in the central and northern regions, the impact of drought disasters was particularly evident. Coupled with the weak disaster reduction capacity, Nanchang City, which had the largest population and the most developed economy, has always had the highest risk of drought disasters in the years with more serious drought disasters.

Static assessments of drought disaster risk do not address issues pertaining to disaster risk zoning, recurrence period, and the changes in the disaster risk over time. The risk of drought disasters is not static; it changes with time (Wilhite et al. 2014). The development of drought risk assessments should be a dynamic process that changes continuously with time. Based on the research on the dynamic assessment of natural disaster risks, this study proposed a dynamic assessment model of drought disasters based on the four factors of drought disasters over time.

The experimental results show that, compared with the drought risk at a monthly scale calculated based on the SPEI probability of each meteorological station, the results of the dynamic risk assessment model were more consistent with the actual situation recorded in the yearbook. The integrated drought disaster risk showed an increasing, followed by a decreasing trend during the drought disaster process, which reflects the cumulative change processes with respect to the risk. At an annual scale, the drought disaster risk was greatly affected by drought, but dynamic changes in factors such as vegetation coverage, artificial impervious surface, population, and disaster reduction capability would also directly affect the distribution and intensity of the drought disaster risk. The experimental results were consistent with the yearbook records. This study, therefore, indicates that the risk of drought disasters changes dynamically with spatiotemporal changes because the influencing factors of hazards, vulnerability, exposure, and the capacity for drought mitigation are also changing dynamically. The dynamic risk assessment model for drought disasters and the risk indicators proposed were validated in this study. Therefore, the results of this research provide an innovative method for the risk management of drought disasters.

In summary, compared with previous drought disaster assessment models, the improvements yielded by this model are as follows: (1) Dynamic integration of the impact factors of drought disasters into the model. This includes the SPEI of the study area, land use, and NDVI obtained from remote sensing analysis, surface rainfall, population, and reservoir volume. Due to the limitation of the data acquisition capacity, the time scales of the factors are different. However, they were able to reflect the dynamic changes in various factors affecting drought disasters at monthly and annual scales. (2) The results of the drought disaster risk calculated by the model are dynamic. The model calculation results are at a monthly or annual scale, which can reflect the dynamic change process of the drought disaster risk within the specified time.

Although the monthly and annual scales of the model were selected to identify drought events and assess the drought disaster risk in the study area, it is limited by the existing observation and data collection conditions. In future studies, by integrating the influencing factors collected at a smaller time scale or live data, such as real-time monitoring of rainfall, evapotranspiration, soil water content, reservoir scheduling information, or night light remote sensing data, at a unified scale, we will be able to receive more timely dynamic information on drought disaster risks to provide decision support for drought disaster risk managers. Meanwhile, the selection of drought disaster indicators remains in an exploratory stage. For example, in the evaluation of the vulnerability indicators, only two influencing factors, namely the ecological vulnerability coefficient and the ratio of non-agricultural to agricultural population, were selected. However, the vulnerability response caused by drought is in all aspects of human social activities, and it is difficult to quantify the vulnerability to drought disasters through only two factors. The lack of other indicators is due to insufficient research on socioeconomic attributes, and it is difficult to collect data on the corresponding time scale. In addition, the model should be verified in more study areas with different climatic conditions to improve the common applicability of the model.

Based on a conceptual model of the dynamic risk assessment of drought disaster, the sub-indices of hazard, exposure, vulnerability, and capacity for drought mitigation for a certain period and their interrelationships between time and space were analyzed. A mathematical model for the dynamic risk assessment of drought disasters was developed. Monthly and annual time scales were selected for the validation of the developed model.

The results show that when compared with traditional static assessment methods, the dynamic disaster risk assessment model proposed in this study can accurately reflect the distribution and magnitude of the drought disaster risk at a specified time. Furthermore, the model reflected the dynamic change characteristics in drought disaster risk with time.

Conceptualization, P. A.; methodology, B. C.; software, B. C.; validation, D. Y.; formal analysis, M. H.; resources, D. Y.; data curation, D. Y.; writing – original draft preparation, B. C.; writing – review and editing, P. A.; visualization, B. C.; supervision, P. A.; project administration, D. Y.; and funding acquisition, P. A., M. H., and H. L.

This work was supported by the National Natural Science Foundation of China (grant number 91846203), the National Key Research and Development Program of China (grant number 2017YFC0405701), the Fundamental Research Funds for the Central Universities (grant number 2018B610X14), and the Graduate Research and Innovation Projects of Jiangsu Province (grant number KYCX18_0583).

No conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all authors for publication.

All relevant data are available from an online repository or repositories.