# Risk Assessment of Agricultural Drought Disaster on the Huaibei Plain of China Based on the Improved Connection Number and Entropy Information Diffusion Method

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{2}. The study area comprises the cities of Suzhou, Huaibei, Bozhou, Fuyang, Bengbu, and Huainan.

^{3}in the study area, accounting for only half of the average level in the province and a quarter of that in China. Corresponding to the relatively scarce water resources, the total value of agricultural production in all six cities in the study area reached 1158.7 billion Chinese yuan (170 billion USD), while the total population working in agriculture in the study area reached about 25 million in 2017 [23,24,25]. These two agricultural indicators account for about half of the total in Anhui Province, suggesting that the study area is an important region for agricultural production in the province. In addition, according to the available statistics, the central disaster region of agricultural drought in the province is in the Huai River Basin within the study area [23,24,25].

## 3. Methodology

#### 3.1. Development of an Evaluation Indicator System

- The disaster breeding environment, including precipitation, evaporation, and amount of water resources. It is the average value from the year in question to 10 years prior.
- The disaster factors, including annual average temperature and shallow groundwater depth. The 12-month Standardized Precipitation Index (SPI) was calculated using monthly rainfall data from 1983 to 2017 provided by the six national weather stations located in the six cities.
- The disaster affected body, including the area of grain planting, cultivated land, dry land, and drought-affected land, along with the value of agricultural production, agricultural water consumption, and total grain output.
- Disaster prevention and mitigation measures, including the area of effective irrigated land, per capita disposable income of rural households, and water supply capacity for drought-resistant water sources.

#### 3.2. Evaluation Method

#### 3.2.1. Determination of the Weight of the Agricultural Drought Evaluation Indicators

_{k}} (where n

_{k}is the number of indicators in subsystem k), adding the importance ranking value to a questionnaire. Smaller ranking values indicate greater importance of the indicator.

_{e}(k, j) denotes the importance ranking value of one expert for indicator j in subsystem k (e = 1, 2,…, n

_{e}) and n

_{e}is the total number of experts. The fuzzy complementary judgment matrix A

^{k}was obtained using Equation (2) [26]:

_{k}; and l = 1, 2,…, n

_{k}; a(k, j, l) indicate the relative importance of indicator j and indicator l in subsystem k. The fuzzy complementary judgment matrix A

^{k}met the conditions 0 ≤ a(k, j, l) ≤ 1, a(k, j, l) + a(k, l, j) = 1, and a(k, j, l) = 0.5, meaning that indicator j was as important as indicator l; and a(k, j, l) > 0.5, meaning that indicator j was more important than indicator l.

^{k}and to calculate the subjective weight of each indicator {w

_{s}(k, j)|k = 1, 2, 3, 4; j = 1, 2,…, n

_{k}}. If A

^{k}met the following condition in Equation (3), it was referred to as the fuzzy consistency judgment matrix [23]:

_{k}. If A

^{k}was completely consistent:

^{k}[27,28]. If this value was not greater than a certain threshold, then A

^{k}was said to have satisfactory consistency, and, when A

^{k}did not have satisfactory consistency, a further matrix correction was required.

^{k}refers to the correction matrix of A

^{k}, and, for simplicity, the weights of B

^{k}were still recorded as {w

_{s}(k, j)|k = 1, 2,…, 5; j = 1, 2,…, n

_{k}}, resulting in the minimum fuzzy B

^{k}being called the optimal fuzzy consistency judgment matrix of A

^{k}[29]:

_{k}) is the consistency index coefficient, with smaller CIC(n

_{k}) values indicating a higher degree of consistency. Numerous calculation experiments have been reported [23,26,29], with data indicating that, when the CIC(n

_{k}) value is less than 0.2, A

^{k}can be considered to have satisfactory consistency with an acceptable sort weight. Otherwise, it is necessary to adjust parameter d until A

^{k}exhibits satisfactory consistency.

#### 3.2.2. Assessment of Agricultural Drought Disaster

_{A-B}= S/m + (F/m) × I + (P/m) × J

_{r}(i, g)=a

_{r}(i, g) + b

_{r}(i, g)I + c

_{r}(i, g)J

_{r}(i, g) represents the connection numbers between the assessment samples and grades; i is the year (i = 1, 2,…, N); g is the grade (g = 1, 2,…, G); and G = 5 in this study. In addition, smaller g values indicate a smaller degree of ADD; a

_{r}(i, g), b

_{r}(i, g), and c

_{r}(i, g) are the connection components; a

_{r}(i, g) represents the identity of assessment samples and grade g in year i; b

_{r}(i, g) represents the discrepancy; and c

_{r}(i, g) represents the opposition. Then each connection component can be calculated by Equation (9):

_{r}(i, g) represents the sum of indicator weights within grade g in year i. ξ

_{ba}(i, g) is the grey correlation coefficient of b

_{r}(i, g) and a

_{r}(i, g), with the calculation performed according to Equation (10) [31]:

_{ba}(i, g) = |b

_{r}(i, g) – a

_{r}(i, g)|, Δ

_{ba}(i, g) is the absolute value of difference; ${\Delta}_{\mathrm{min}}=\underset{i}{\mathrm{min}}\underset{g}{\mathrm{min}}\left|{b}_{r}(i,g)-{a}_{r}(i,g)\right|$; and ${\Delta}_{\mathrm{max}}=\underset{i}{\mathrm{max}}\underset{g}{\mathrm{max}}\left|{b}_{r}(i,g)-{a}_{r}(i,g)\right|$. This resulted in the correlation between b

_{r}(i, g) and a

_{r}(i, g) being defined as shown in Equation (11):

_{ba}is the average value of all grey correlation coefficients. Similarly, r

_{bc}can be calculated by Equations (10) and (11). Here, the difference coefficient I in Equation (8) was calculated using Equation (12) as follows:

_{ba}and r

_{bc}(if r

_{ba}> r

_{bc}, then I = r

_{ba}; if r

_{ba}< r

_{bc}, then I = −r

_{bc}), it has the limitation of losing important information when r

_{ba}and r

_{bc}are close. We allocated the difference coefficient according to their association, which ensured better utilization of the information.

_{r}(i, g), the connection numbers for agricultural drought disaster assessment CN (r, i) can be obtained as shown by Equation (13):

_{r}(i, g) = 0.5 + 0.5 × u

_{r}(i, g). CN (r, i) was divided into four grades, as shown in Table 1.

#### 3.2.3. Risk Assessment of Agricultural Drought Disaster

_{i}|i = 1, 2,…, N}, the set of risk assessment domains is U = {u

_{m}|m = 1, 2,…, n}. u

_{m}is the discrete real value obtained by the dispersion in the interval [0, 1]. Therefore, S can spread the carried information to all points in U and the corresponding expression is shown in Equation (14) [18,21]:

_{i}is transformed into interval [0,1] by s

_{i}= 0.2 × CN (r, i) and h is the information diffusion coefficient, which is generally determined empirically. Hence, in this study it was determined by the principle of maximum entropy, as shown in Equation (15):

_{r}(s

_{i}, u

_{m}) is established. The membership function of the corresponding fuzzy subset is established as shown in Equation (19) [18,35]:

_{m}is established, as shown in Equation (20) [18,35]:

## 4. Results

#### 4.1. Weight Analysis of Evaluation Indicators of ADD of the Huaibei Plain in Anhui Province

#### 4.2. Assessment of Agricultural Drought Disaster of the Huaibei Plain

_{ba}(i, g) and ξ

_{bc}(i, g) were obtained by applying the evaluation criteria and the weights of the indicators shown in Figure 3 to Equations (9) and (10). The evaluation indicator criteria were divided by the collected indicator values. Then, using Equations (8), (11), and (12), the connection numbers between the assessment samples and grades u

_{r}(i, g) for the Huaibei Plain from 2008 to 2017 were obtained. The normalized connection numbers u*

_{r}(i, g) were calculated and their annual averages are shown in Table 2.

_{r}(i, g). The smaller is the value of g, the smaller is the degree of agricultural drought disaster; therefore, as shown in Table 2, the highest value in Huaibei was 0.317 and the connection with g = 1 (no drought). For the other cities, the biggest numbers were those with g = 2 (mild drought) and g = 3 (moderate drought) in their corresponding u*

_{r}(i, g). These findings illustrate that through the comprehensive analysis of a 10-year dataset, except for Huaibei, the remaining five cities of the Huaibei Plain generally experienced mild to moderate degrees of agricultural drought disaster from 2008 to 2017.

#### 4.3. Risk Assessment of Agricultural Drought Disaster for the Huaibei Plain

_{m}|u

_{m}= 0.1, 0.15, 0.2, 0.25,..., 1} and substituting Equation (18) into Equation (14), the agricultural drought index f

_{r}(s

_{i}, u

_{m}) values for each city in the plain were obtained.

_{r}(u

_{m}) of the agricultural drought disaster index s

_{i}fell at u

_{m}, as calculated by Equations (19) and (20). The transcending probability P

_{r}(u

_{m}) at u

_{m}was obtained using Equation (21). It was found that the ADDI of each city was reduced, generally in the interval of u

_{m}∈[0.5, 0.7], and therefore the probability value p

_{r}(u

_{m}) was refined in the modified interval and the corresponding transcending probability was established. The inverse of transcending probability is the year return, and the year returns of s

_{i}at different u

_{m}values are shown in Table 3.

_{m}≥ 0.5 in the Huaibei Plain was once every 1.6 years, while the frequency at u

_{m}≥ 0.7 was once every 22.1 years. This result clearly shows that the impact of ADD, in terms of both frequency and extent, was large. Therefore, ADD was one of the most important natural disasters affecting the Huaibei Plain, highlighting that ADD prevention and control measures are important considerations in natural disaster risk management in the future. The transcending probability P

_{r}(u

_{m}) values across the whole interval are shown in Figure 5.

_{m}∈[0,0.5], u

_{m}∈(0.5,0.7], and u

_{m}∈(0.7,1]. The transcending probability P

_{r}(u

_{m}) of s

_{i}falls in these three intervals, indicating the different probabilities of ADD occurring to different degrees.

_{i}falls in the range of u

_{m}∈[0,0.5], indicating that the risk of ADD events occurring is not high in each city. Figure 5a shows that most areas except Huaibei are light green, which indicates that the area faces a high risk of ADD. Combined with Table 3, these findings show that the frequency of ADD at u

_{m}> 0.5 is once every 2.8 years in Huaibei, with the possibility of ADD events every 2–3 years, representing the lowest ADD risk of all of the cities assessed. The average frequency for the southern four cities is once every 1.2 years, which is more frequent than the 2.3 years in Huaibei and Bozhou in the north. In the present study, the southern region was found to be nearly twice as likely to be struck by ADD as the northern region.

_{i}falls in the range of u

_{m}∈(0.5, 0.7], indicating mild and moderate degrees of ADD risk in each city. The probability that Suzhou and Bengbu in the eastern region will experience ADD is higher than that of Fuyang and Bozhou in the western region. Combined with Table 3, the average frequency of ADD at u

_{m}> 0.6 for Fuyang and Bozhou is once every 2.6 years and at u

_{m}> 0.7 is once every 25.3 years. In contrast, the frequency of ADD at u

_{m}> 0.6 for Suzhou and Bengbu is once every 2.1 years and at u

_{m}> 0.7 is once every 21 years. Based on these findings, the risk of ADD events in Suzhou and Bengbu is higher than in Fuyang and Bozhou.

_{i}falls in the range of u

_{m}∈(0.7, 1], indicating the chances of severe ADD in each city. Most parts of the map are light red, indicating lower risk. Combined with Table 3, it can be seen that the probability of severe ADD occurring across the whole Huaibei Plain is relatively small, with an average frequency of 22.3 years. Except for Huainan, which is once every 11 years in the south, the average frequency of ADD at u

_{m}> 0.7 is once every 25 years. Therefore, it is considered that Huainan represents the city at highest risk for ADD on the Huaibei Plain in Anhui Province.

## 5. Discussion

^{3}/kg, while in Huaibei and Bozhou it is about 0.15 m

^{3}/kg (Figure 8c). This shows that agricultural production in Huaibei and Bozhou has more effective utilization of irrigation water and is less likely to be affected by water shortages. Expect for Huainan and Huaibei, the rate of effective irrigation area of other cities is relatively small (Figure 8d), indicating that the rural water conservancy infrastructure in this area needs to be improved urgently, and the comprehensive agricultural production capacity needs to be improved.

## 6. Conclusions

- Using weight analysis, the disaster factors and the disaster affected body were found to be key elements of the agricultural drought disaster system in small units such as the Huaibei Plain. It was also found that the average annual precipitation from the year in question to 10 years prior (X1), 12-month SPI (X4), the annual average temperature (X5), water consumption per kilogram of grain production (X10), percentage of drought-affected area (X11), and rate of effective irrigation area (X12) are more important than other indicators in their corresponding subsystems.
- Based on a comprehensive analysis of the connection numbers CN (r, i) for the Huaibei Plain from 2008 to 2017, five cities (except Huaibei) had a mild to moderate degree of ADD in the period assessed. The conditions during 2011–2013 were relatively serious, especially in 2013. The chance of ADD in the southern area, represented by Fuyang, Huainan, and Bengbu, was greater than in the northern area, represented by Bozhou and Huaibei. Furthermore, ADD in the southern region was more serious than in the north.
- The frequency of ADD for each city is mainly once every 1–3 years, and, with an increased agricultural drought disaster index value, the frequency is significantly reduced. The frequency of severe and above-grade ADD is once every 10–30 years. The risk of ADD for the Huaibei Plain is characterized by frequent, mainly mild to moderate risk, making prevention and control measures key for effective risk management of natural disasters in the future.
- The southern region of the study area was found to be nearly twice as likely to be struck by ADD as the northern region. Meanwhile, the eastern region has a higher frequency of severe and above-grade events than the western region. The frequency of ADD is once every 2.8 years for Huaibei, which is lower than the average value of the southern four cities. Huainan, in contrast, has a frequency of severe and above-grade ADD of once every 11 years. Based on the risk assessment results above, the cities of the Huaibei Plain were ranked from high to low risk as: Huainan > Bengbu > Suzhou > Fuyang > Bozhou > Huaibei.
- In this study, some suggestions were proposed for ADD prevention and mitigation based on the analysis of risk assessment results and the background of disaster formation. It is necessary for high-risk cities in the study area, such as Huainan and Bengbu, to improve the resilience of their agricultural systems in the future by optimizing planting structures and enhancing irrigation water efficiency. These results confirm overall that the proposed method is feasible and effective and that the results are reasonable.

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Maybank, J.; Bonsal, B.; Jones, K.; Lawford, R.G.; O′Brien, E.G.; Ripley, E.A.; Wheaton, E. Drought as a natural disaster. Atmos. Ocean
**1995**, 33, 195–222. [Google Scholar] [CrossRef] - Song, Y.L.; Elisabeth, S.; Chen, D.L.; Dong, W.J. Influence of Climate Change on Winter Wheat Growth in North China during 1950–2000. J. Meteorol. Res.
**2005**, 19, 501–510. [Google Scholar] [CrossRef] [Green Version] - Ying, G.U.; Jing, N.L.; Jin, L. Analysis on characteristics and situation of drought disasters during past 60 years in China. Water Resour. Hydropower Eng.
**2010**, 41, 71–74. [Google Scholar] [CrossRef] - Ye, T.; Shi, P.J.; Wang, J.A.; Liu, L.Y.; Fan, Y.D.; Hu, J.F. China′s drought disaster risk management: Perspective of severe droughts in 2009–2010. Int. J. Disaster Risk Sci.
**2012**, 3, 84–97. [Google Scholar] [CrossRef] [Green Version] - Zhang, Q.; Han, L.Y.; Zhang, L.Y.; Wang, J.S. Analysis on the Character and Management Strategy of Drought Disaster and Risk under the Climatic Warming. Adv. Earth Sci.
**2014**, 29, 80–91. [Google Scholar] - Motha, R.P. Disaster reduction planning and response: The example of national drought policy in USA. Nat. Disasters Extrem. Events Agric.
**2005**, 179–193. [Google Scholar] [CrossRef] - Mishra, A.K.; Singh, V.P. A review of drought concepts. J. Hydrol. (Amst.)
**2010**, 391, 202–216. [Google Scholar] [CrossRef] - Liu, L.; Liu, S.; Liu, P. Synthetic analysis and quantitative estimation of the agricultural vulnerability to drought disaster in Hunan province. J. Nat. Disasters
**2002**, 11, 78–83. [Google Scholar] [CrossRef] - Qu, Y.P.; Gao, H.; Lü, J.; Su, Z.; Cheng, X.; Sun, H. Agricultural drought disaster risk assessment in China based on the regional disaster system theory. J. Hydraul. Eng.
**2015**, 46, 908–917. [Google Scholar] [CrossRef] - Dalezios, N.R.; Blanta, A.; Spyropoulos, N.V.; Tarquis, A.M. Risk identification of agricultural drought for sustainable Agroecosystems. Nat. Hazards Earth Syst. Sci.
**2014**, 14, 2435–2448. [Google Scholar] [CrossRef] [Green Version] - He, B.; Wu, J.; Lü, A.; Cui, X.; Zhou, L.; Liu, M.; Zhao, L. Quantitative assessment and spatial characteristic analysis of agricultural drought risk in China. Nat. Hazards
**2012**, 66, 155–166. [Google Scholar] [CrossRef] - Krishna, V. Risk, vulnerability, and asset-based approach to disaster risk management. Int. J. Sociol. Soc. Policy
**2004**, 24, 1–48. [Google Scholar] [CrossRef] - Xu, K.; Xu, X.Y.; Li, A.H.; Yang, D.W. Assessing agricultural drought disaster risk in Chengde city using stochastic method. Trans. Chin. Soc. Agric. Eng.
**2013**, 29, 139–146. [Google Scholar] [CrossRef] - Potopová, V.; Štěpánek, P.; Možný, M.; Türkott, L.; Soukup, J. Performance of the standardised precipitation evapotranspiration index at various lags for agricultural drought risk assessment in the Czech Republic. Agric. For. Meteorol.
**2015**, 202, 26–38. [Google Scholar] [CrossRef] - Qu, Y.P.; Sun, H.Q.; Su, Z.C. Dynamic assessment of agricultural drought disasters risk based on crop growth model. Agric. Res. Arid Areas
**2013**, 31, 231–236. [Google Scholar] - Sun, Z.Y.; Zhang, J.Q.; Yan, D.H.; Wu, L.; Guo, E.L. The impact of irrigation water supply rate on agricultural drought disaster risk: A case about maize based on EPIC in Baicheng City, China. Nat. Hazards
**2015**, 78, 23–40. [Google Scholar] [CrossRef] - Zhao, H.Y.; Gao, G.; Yan, X.D.; Zhang, Q. Risk assessment of agricultural drought using the CERES-Wheat model: A case study of Henan plain, China. Clim. Res.
**2011**, 50, 247–256. [Google Scholar] [CrossRef] [Green Version] - Huang, C.F.; Liu, X.L.; Zhou, G.X.; Li, X.J. Agricultural natural disaster risk assessment method according to the historic disaster data. J. Nat. Disasters
**1998**, 7, 1–9. [Google Scholar] - Feng, L.H.; Luo, G.Y. Flood risk analysis based on information diffusion theory. Hum. Ecol. Risk Assess.
**2008**, 14, 1330–1337. [Google Scholar] [CrossRef] - Liu, J.F.; Li, S.; Wu, J.; Liu, X.J.; Zhang, J.Q. Research of influence of sample size on normal information diffusion based on the Monte Carlo method: Risk assessment for natural disasters. Environ. Earth Sci.
**2018**, 77, 480. [Google Scholar] [CrossRef] - Li, M.G.; Zhou, C.S.; Lian, L. Agricultural flood and drought risk Assessment in China based on entropy information diffusion theory. J. Nat. Resour.
**2017**, 32, 620–631. [Google Scholar] [CrossRef] - Yu, X.B.; Yu, X.R.; Ji, Z.H.; Lu, Y.Q. Risk assessment of typhoon disaster in China′s South-East coastal areas-based on information diffusion theory. J. Catastr.
**2019**, 34, 73–77. [Google Scholar] [CrossRef] - Chen, M.L.; Ning, S.W.; Cui, Y.; Jin, J.L.; Zhou, Y.L.; Wu, C.G. Quantitative Assessment and Diagnosis for Regional Agricultural Drought Resilience Based on Set Pair Analysis and Connection Entropy. Entropy
**2019**, 21, 373. [Google Scholar] [CrossRef] [Green Version] - Anhui Municipal Bureau of Statistics. Anhui Statistical Yearbook, 2009–2018; China Statistics Press: Beijing, China, 2018.
- Department of Water Resources of Anhui Province. Anhui Water Resources Bulletin, 2008–2017; China Water & Power Press: Beijing, China, 2017.
- Cui, Y.; Feng, P.; Jin, J.L.; Liu, L. Water resources carrying capacity evaluation and diagnosis based on set pair analysis and improved the entropy weight method. Entropy
**2018**, 20, 359. [Google Scholar] [CrossRef] [Green Version] - Lu, Y.J. Weight calculation method of fuzzy analytical hierarchy process. Fuzzy Syst. Math.
**2002**, 16, 79–85. [Google Scholar] - Song, G.X.; Yang, D.L. Methods for identifying and improving the consistency of fuzzy judgment matrix. Syst. Eng.
**2003**, 21, 110–116. [Google Scholar] - Jin, J.L.; Wu, K.Y.; Li, R.Z. Region water security evaluation method based on information entropy and improved fuzzy analytic hierarchy process. J. Hydroelectr. Eng.
**2007**, 26, 61–66. [Google Scholar] - Zhao, K.Q. Set Pair Analysis and its Preliminary Application; Zhejiang Science and Technology Press: Hangzhou, China, 2000; pp. 1–180. [Google Scholar]
- Liu, P.J.; Zhao, Z.L.; Yan, X. Grey relational analysis on the effects of rainfall factors on runoff and sediment in the sloping farmland with different plants in the central south of Shandong province. Meteorol. Environ. Res.
**2011**, 2, 52–55. [Google Scholar] - Kose, E.; Burmaoglu, S.; Kabak, M. Grey relational analysis between energy consumption and economic growth. Grey Syst. Theory Appl.
**2013**, 3, 291–304. [Google Scholar] [CrossRef] - Yu, H.; Zhang, K. The application of grey relational analysis model in the performance evaluation of agricultural product logistics companies. Appl. Mech. Mater.
**2014**, 687, 5165–5168. [Google Scholar] [CrossRef] - Jin, J.L.; Fu, J.; Wei, Y.M.; Jiang, S.M.; Zhou, Y.L.; Liu, L.; Wang, Y.Z.; Wu, C.G. Integrated risk assessment method of waterlog disaster in Huaihe River Basin of China. Nat. Hazards
**2015**, 75, 155–178. [Google Scholar] [CrossRef] - Hao, L.; Zhang, X.; Liu, S. Risk assessment to China’s agricultural drought disaster in county unit. Nat. Hazards
**2011**, 61, 785–801. [Google Scholar] [CrossRef] - Liu, Y.Q.; You, M.; Zhu, J.L.; Wang, F.; Ran, R.P. Integrated risk assessment for agricultural drought and flood disasters based on entropy information diffusion theory in the middle and lower reaches of the Yangtze River, China. Int. J. Disaster Risk Reduct.
**2019**, 38, 1–14. [Google Scholar] [CrossRef] - Kaplan, S.; Garrick, B.J. On the quantitative definition of risk. Risk Anal.
**1981**, 1, 11–27. [Google Scholar] [CrossRef] - Jin, J.L.; Li, J.Q.; Zhou, Y.L.; Fei, Z.Y.; Jiang, S.M.; Yuan, X.C.; He, J. Theoretical framework of drought risk assessment. J. Catastr.
**2014**, 29, 1–10. [Google Scholar] [CrossRef] - He, B.; Wu, J.J.; Lu, A.F. New advances in agricultural drought risk study. Prog. Geogr.
**2010**, 29, 557–564. [Google Scholar]

**Figure 3.**The four subsystems and each indicator in them for the indicator evaluation system of Huaibei Plain in Anhui Province: (

**a**) disaster breeding environment subsystem; (

**b**) disaster factors subsystem; (

**c**) disaster-affected body subsystem; and (

**d**) disaster prevention and mitigation measures subsystem.

**Figure 5.**Agricultural drought disaster (ADD) risk assessment maps of the cities of the Huaibei Plain in Anhui Province: (

**a**) when u

_{m}≤ 0.5; (

**b**) when 0.5 < u

_{m}≤ 0.7; and (c) when u

_{m}> 0.7.

**Figure 6.**The average value in cities of 12-month Standardized Precipitation Index (SPI) across the Huaibei Plain from 2008 to 2017.

**Figure 7.**Percentage of drought-affected areas in the cities across the Huaibei Plain from 2008 to 2017.

**Figure 8.**Percentage of drought-affected areas in the cities across the Huaibei Plain from 2008 to 2017.

Evaluation Indicator | Connection Numbers | Agricultural Drought Disaster Index | Agricultural Drought Disaster |
---|---|---|---|

g | CN (r, i) | s_{i} | |

1 | [0, 2.5] | [0, 0.5] | No drought |

2 | (2.5, 3] | (0.5, 0.6] | Grade I (mild drought) |

3 | (3, 3.5] | (0.6, 0.7] | Grade II (moderate drought) |

4 | (3.5, 4] | (0.7, 0.8] | Grade III (severe drought) |

5 | [4, 5] | [0.8, 1] | Grade IV (extremely severe drought) |

**Table 2.**Average annual values of the normalized connection numbers u*

_{r}(i, g) for Huaibei Plain from 2008 to 2017.

Huaibei | Bozhou | Suzhou | Bengbu | Fuyang | Huainan | |
---|---|---|---|---|---|---|

u*_{r}(i, 1) | 0.317 | 0.186 | 0.146 | 0.156 | 0.189 | 0.069 |

u*_{r}(i, 2) | 0.306 | 0.253 | 0.223 | 0.234 | 0.234 | 0.200 |

u*_{r}(i, 3) | 0.183 | 0.236 | 0.269 | 0.263 | 0.226 | 0.323 |

u*_{r}(i, 4) | 0.125 | 0.202 | 0.248 | 0.234 | 0.227 | 0.224 |

u*_{r}(i, 5) | 0.069 | 0.123 | 0.113 | 0.114 | 0.125 | 0.184 |

**Table 3.**Year returns of agricultural drought disaster index (ADDI) (s

_{i}) at different values of u

_{m}.

s_{i} | City | u_{m} | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

0.5 | 0.52 | 0.54 | 0.56 | 0.58 | 0.6 | 0.62 | 0.64 | 0.66 | 0.68 | 0.7 | ||

Year return | Huaibei | 2.8a | 2.9a | 2.9a | 3.2a | 4.0a | 5.2a | 6.4a | 8.1a | 14.4a | 18.5a | 30.4a |

Bozhou | 1.8a | 1.8a | 1.9a | 2.1a | 2.3a | 2.5a | 3.4a | 6.2a | 12.4a | 14.7a | 25.8a | |

Suzhou | 1.2a | 1.3a | 1.5a | 1.7a | 1.9a | 2.3a | 2.9a | 5.1a | 11.0a | 13.0a | 21.3a | |

Bengbu | 1.2a | 1.2a | 1.4a | 1.5a | 1.6a | 1.9a | 2.7a | 4.2a | 8.2a | 11.0a | 20.7a | |

Fuyang | 1.4a | 1.5a | 1.7a | 1.9a | 2.1a | 2.7a | 3.6a | 6.2a | 12.8a | 14.5a | 24.8a | |

Huainan | 1.1a | 1.1a | 1.1a | 1.2a | 1.3a | 1.5a | 1.8a | 2.2a | 3.8a | 5.0a | 9.8a | |

Average | 1.6a | 1.6a | 1.8a | 1.9a | 2.2a | 2.7a | 3.5a | 5.3a | 10.3a | 12.8a | 22.1a |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Chen, M.; Ning, S.; Jin, J.; Cui, Y.; Wu, C.; Zhou, Y.
Risk Assessment of Agricultural Drought Disaster on the Huaibei Plain of China Based on the Improved Connection Number and Entropy Information Diffusion Method. *Water* **2020**, *12*, 1089.
https://doi.org/10.3390/w12041089

**AMA Style**

Chen M, Ning S, Jin J, Cui Y, Wu C, Zhou Y.
Risk Assessment of Agricultural Drought Disaster on the Huaibei Plain of China Based on the Improved Connection Number and Entropy Information Diffusion Method. *Water*. 2020; 12(4):1089.
https://doi.org/10.3390/w12041089

**Chicago/Turabian Style**

Chen, Menglu, Shaowei Ning, Juliang Jin, Yi Cui, Chengguo Wu, and Yuliang Zhou.
2020. "Risk Assessment of Agricultural Drought Disaster on the Huaibei Plain of China Based on the Improved Connection Number and Entropy Information Diffusion Method" *Water* 12, no. 4: 1089.
https://doi.org/10.3390/w12041089