Applying disaster system theory and with reference to the mechanisms that underlie agricultural drought risk, in this study, crop yield loss levels were determined on the basis of hazards and environmental and hazard-affected entities (crops). Thus, by applying agricultural drought risk assessment methodologies, the spatiotemporal distribution of maize drought risk was assessed at the national scale. The results of this analysis revealed that the overall maize drought risk decreases gradually along a northwest-to-southeast transect within maize planting areas, a function of the climatic change from arid to humid, and that the highest yield loss levels are located at values between 0.35 and 0.45. This translates to drought risks of once in every 10 and 20 years within 47.17% and 43.31% of the total maize-producing areas of China, respectively. Irrespective of the risk level, however, the highest maize yield loss rates are seen in northwestern China. The outcomes of this study provide the scientific basis for the future prevention and mitigation of agricultural droughts as well as the rationalization of related insurance.
In disasters, risk is defined as the probability of loss and depends on three factors: hazards, vulnerability, and exposure. This means that if the magnitude of any one of these factors changes, the risk will correspondingly increase or decrease [
Previous drought research mainly focused on the drought index [
China has a typical monsoon climate and is also an agricultural country with the largest population in the world. The instability of the monsoon climate in China has led to frequent drought-related disasters. Drought is the major constraining factor on maize growth and development, one of the three main national grain crops. Thus, taking maize as the target for this research, an agricultural drought risk assessment was performed by assessing physical crop vulnerabilities. Overall, many scholars have carried out a lot of research on the climatic factors on the growth and development of maize, variety maturity, suitable area, yield, and quality [
Meteorological data in this study were collected from 752 meteorological stations, with data provided by the China Meteorological Administration, including daily precipitation, daily relative humidity, daily sunshine hours, and average daily wind speed during 1961–2015.
Crop observational data were extracted from the annual reports of national agricultural meteorological observation stations stored in the archives of the China Meteorological Administration. The information in these reports includes basic crop information; crop growth periods; yield components, factors, and information; and field management processes and meteorological conditions during the growth period.
Exposure to drought-inducing hazards is a prerequisite if crops are affected by these disasters. Therefore, if a body is not exposed to an environment containing a particular hazard, an agricultural drought will not occur and the risk remains zero. The main data sources used in this study are presented in Table
Database of drought hazard-affected bodies.
Data | Information content | Sources | Year(s) |
---|---|---|---|
Land use data | National 1 : 1 million land use vector map | Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences | 2010 |
Crop yield data | Corn production in each county | Statistical Yearbook of Chinese Cities and Counties | Between 1996 and 2015 |
Crop yield data | Corn acreage in each county | Statistical Yearbook of Chinese Cities and Counties | Between 1996 and 2015 |
Crop regionalization | Regionalization of maize planting | Chinese National Atlas of Agriculture | 2003 |
Crop phenological periods | Crop phenological periods | Chinese National Atlas of Agriculture | 2003 |
Flow chart for data calculation.
SPI values for maize crop growth periods are used as the drought hazard index. This index of SPI has been commonly utilized for characterizing droughts [
Hazard-inducing factors were assessed in this study in two ways. (1) Probability risk based on the fixed drought hazard index. (2) Drought hazard index based on fixed exceeding probability. The drought hazard index probability was initially calculated, including the probability density and the probability that the hazard-inducing factor index was exceeded. The risk was then calculated using a fixed probability that the factor index was exceeded as well as the fixed drought hazard index. The fixed drought hazard index is used to calculate the probability of drought under different hazard index levels, including four levels of drought hazard index according to the data histogram: SPI less than −0.15, SPI less than −0.30, SPI less than −0.40, and SPI less than −0.45. The fixed exceeding probability is to calculate drought hazard indexes at once in 2, 5, 10, and 20 years.
Artificial neural networks (ANNs), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems. Due to their powerful capability and functionality, ANNs provide an alternative approach for many assessment problems that are difficult to solve by conventional approaches [
The difference between the actual and theoretical yields was then used as the loss in drought yield reduction, given the actual drought hazard index at each meteorological station, and the BP-ANN model was applied to simulate a drought vulnerability curve using the software MATLAB. A nonlinear statistical model was then used to fit a regression between the drought hazard index and yield loss rate data, and a vulnerability curve and corresponding equation for the common maize variety Danyu 13 were then generated (Figure
Application of BP-ANN model in the vulnerability curve simulation.
Without considering the drought mitigation capacity, while setting exposure to 1 (maize-growing regions), the risk of each assessment cell was a function of hazard index and vulnerability. Formula
Based on the SPI database of maize growth periods and fixed degrees of drought hazard index, drought probabilities were calculated via excess probability for each 1 km grid unit in the form of a series of risk maps.
The results of this study showed that, in general, given different levels of drought hazard index, the northwestern, northeastern, and northern Chinese maize regions exhibit the highest values of hazard risk across the national planting areas (Figure
Maps showing probability risks, given different maize drought hazard indexes across China. (a) SPI less than −0.15, (b) SPI less than −0.30, (c) SPI less than −0.40, and (d) SPI less than
Based on the SPI database of maize growth periods and fixed degrees of exceeding probabilities, four maps of maize drought hazard risk were calculated at different risk levels (Figure
Maps showing drought hazard indexes for China at different timescales. (a) Once in two years, (b) once in five years, (c) once in ten years, and (d) once in 20 years.
As discussed above, a drought vulnerability curve was simulated in this study by applying the BP-ANN model in the software MATLAB. A nonlinear regression model was then used to simulate a drought vulnerability curve and the corresponding regression equation, as follows:
In this expression,
The physical vulnerability curve generated in this study conforms to a logistic distribution (Figure
Calculated physical vulnerability curve for the common maize variety Danyu 13.
Using the drought-induced hazard index and the physical vulnerability curve for maize, a series of risk of loss maps for different hazard levels (for a maize hazard risk of once in every 2, 5, 10, and 20 years) across China were generated (Figures
Map showing areas of China where the drought yield loss rate is such that a risk of hazard to maize is likely to occur once in every two years.
Map showing areas of China where the drought yield loss rate is such that a risk of hazard to maize is likely to occur once in every five years.
Map showing areas of China where the drought yield loss rate is such that a risk of hazard to maize is likely to occur once in every ten years.
Map showing areas of China where the drought yield loss rate is such that a risk of hazard to maize is likely to occur once in every 20 years.
The vulnerability of agricultural hazard-affected bodies is determined by the unique physical characteristics of crops. However, by determining the relationship between drought hazard index and disaster loss percentage, a vulnerability curve for a particular hazard-affected body can be generated. A hazard, vulnerability curve, risk evaluation system for the assessment of drought risk based on physical vulnerability is therefore proposed as a result of this study.
Applying the drought risk assessment method, in this study, the spatiotemporal distribution of maize drought risk was evaluated quantitatively across China for the first time. The results of this analysis revealed that the risk of maize yield losses in China decreases along a northwest-to-southeast transect, which is caused by the climatic transition from arid to humid. Most yield loss rates at the 10-year-risk and 20-year-risk levels fall between 0.35 and 0.45 and account for 47.17% and 43.31% of the total Chinese maize-planting areas, respectively. The highest rate of yield loss at all four risk levels occurs in the northwestern Chinese maize region. It is not only related to the climate zone in which the maize areas are located but also to the regional differences in land surface conditions. While in arid and semiarid regions, the dependence on irrigation of maize planting and growth in these areas was most obvious.
Because of data limitations, a number of assumptions were necessary in this study with regard to the spatial distribution and varieties of maize crops and the homogeneity of units used for evaluation. In future analyses, it will be necessary to refine the crop types and varieties as well as planting ratios and to incorporate both disaster prevention and mitigation measures as evaluation units. The current study is based on meteorological observation data of 1960–2015, and a risk assessment under future climate change still need further study. These analytical improvements are likely to lead to more accurate risk assessments and will provide an enhanced scientific reference for the rational planning and utilization of Chinese agricultural land, the prevention and mitigation of drought, and the rationalization of an insurance system for the planting industry that incorporates a predetermined regional premium rate.
The land use data used to support the findings of this study were supplied by the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, under license and so cannot be made freely available. Requests for access to these data should be made to the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences,
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors thank all those who contributed to this study. This research was supported by the National Key R&D Program of China (Grant no.2017YFE0100800), the International Partnership Program of the Chinese Academy of Sciences (Grant no.131211KYSB20170046), the National Natural Science Foundation of China (Nos. 41671505 and 41471428), the State Key Laboratory of Earth Surface Processes and Resource Ecology (No. 2017-KF-240), and the Arid Meteorology Science Foundation, CMA (No. IAM201609).