Journal of Geographical Sciences >
Evaluation of farmland losses from sea level rise and storm surges in the Pearl River Delta region under global climate change
Author: Kang Lei (1989-), PhD, specialized in economic geography and regional development. E-mail: kangleiwawa1989@163.com
*Corresponding author: Ma Li (1975-), Associate Professor, specialized in economic geography and regional development. E-mail:mali@igsnrr.ac.cn
Received date: 2015-10-05
Accepted date: 2015-11-10
Online published: 2016-04-25
Supported by
National Basic Research Program of China, No.2012CB955702
Copyright
The Pearl River Delta on China’s coast is a region that is seriously threatened by sea level rise and storm surges induced by global climate change, which causes flooding of large areas of farmland and huge agricultural losses. Based on relevant research and experience, a loss evaluation model of farmland yield caused by sea level rise and storm surges was established. In this model, the area of submerged farmland, area of crops, and per unit yield of every type of crop were considered, but the impact of wind, flooding time, changes in land use and plant structure were not considered for long-term prediction. Taking the Pearl River Delta region in Guangdong as the study area, we estimated and analyzed the spatial distribution and loss of farmlands for different scenarios in the years 2030, 2050, and 2100, using a digital elevation model, land-use data, local crop structure, rotation patterns, and yield loss ratios for different submerged heights obtained from field survey and questionnaires. The results show that the proportion of submerged farmlands and losses of agricultural production in the Pearl River Delta region will increase gradually from 2030 to 2100. Yangjiang, Foshan, and Dongguan show obvious increases in submerged farmlands, while Guangzhou and Zhuhai show slow increases. In agricultural losses, vegetables would sustain the largest loss of production, followed by rice and peanuts. The greatest loss of rice crops would occur in Jiangmen, and the loss of vegetable crops would be high in Shanwei and Jiangmen. Although losses of peanut crops are generally lower, Jiangmen, Guangzhou, and Shanwei would experience relatively high losses. Finally, some measures to defend against storm surges are suggested, such as building sea walls and gates in Jiangmen, Huizhou, and Shanwei, enforcing ecological protection to reduce destruction from storm surges, and strengthening disaster warning systems.
Key words: sea level rise; storm surge; submerged farmland; loss of farmland; Pearl River Delta
KANG Lei , Ma Li , LIU Yi . Evaluation of farmland losses from sea level rise and storm surges in the Pearl River Delta region under global climate change[J]. Journal of Geographical Sciences, 2016 , 26(4) : 439 -456 . DOI: 10.1007/s11442-016-1278-z
Figure 1 Location of the Pearl River Delta region |
Table 1 Scenarios of future sea level rise and storm surges in the Pearl River Delta region |
Scenario | 2030 | 2050 | 2100 | |
---|---|---|---|---|
The predicted submerged elevation | High-estimation | 4.5006 m | 4.5822 m | 4.7862 m |
Low-estimation | 3.2376 m | 3.2992 m | 3.4532 m |
Table 2 Crop loss ratios under different submerged heights in the Pearl River Delta region (%) |
Submerged height (cm) | Vegetables | Peanuts | Rice |
---|---|---|---|
<30 | 20 | 26.52 | 24.79 |
30-50 | 40 | 30.63 | 34.89 |
50-80 | 80 | 35.83 | 36.11 |
80-120 | 85 | 45.83 | 40.57 |
120-150 | 100 | 50.5 | 54.21 |
>150 | 100 | 100 | 100 |
Figure 2 Future spatial distribution of submerged farmlands under different submerged heights caused by sea level rise and storm surges in the Pearl River Delta region |
Table 3 Future estimates and statistics of submerged farmland areas for each city in the Pearl River Delta region under different scenarios of sea level rise and storm surge (ha) the east. Storm surge disasters due to typhoons would cause large areas of farmland to be submerged with a low-estimation of nearly ten thousand hectares. |
2030 | 2050 | 2100 | ||||
---|---|---|---|---|---|---|
City | Low-estimation | High-estimation | Low-estimation | High-estimation | Low-estimation | High-estimation |
Guangzhou | 19462.75 | 25585.39 | 19500.64 | 25649.55 | 19609.74 | 25786.43 |
Shenzhen | 113.05 | 174.42 | 113.13 | 174.59 | 114.87 | 175.31 |
Zhuhai | 10400.83 | 12778.43 | 10400.92 | 12778.43 | 10401.19 | 12778.83 |
Foshan | 7027.02 | 11785.66 | 7061.93 | 11817.74 | 7145.29 | 11900.38 |
Huizhou | 1847.21 | 5406.00 | 1847.21 | 5406.00 | 1847.21 | 5407.12 |
Shanwei | 9687.18 | 17683.60 | 9719.22 | 17701.91 | 9802.11 | 17753.99 |
Dongguan | 5852.58 | 8046.46 | 5879.61 | 8066.82 | 5936.81 | 8109.69 |
Zhongshan | 8286.49 | 9776.66 | 8288.93 | 9779.15 | 8295.36 | 9781.61 |
Jiangmen | 29054.78 | 39236.80 | 29116.03 | 39317.22 | 29280.68 | 39538.11 |
Yangjiang | 3456.50 | 7253.27 | 3496.31 | 7398.01 | 3588.59 | 7823.26 |
Total | 95188.39 | 137726.69 | 95423.93 | 138089.42 | 96021.85 | 139054.73 |
Figure 3 Future distribution of farmlands with different submerged grades under different scenarios of sea level rise and storm surges in the Pearl River Delta region |
Table 4 Future estimates and statistics of farmlands with different submerged heights under different scenarios of sea level rise and storm surge in the Pearl River Delta region (ha) |
Submerged height (cm) | 2030 | 2050 | 2100 | |||
---|---|---|---|---|---|---|
Low-estimation | High-estimation | Low-estimation | High-estimation | Low-estimation | High-estimation | |
<30 | 34969.53 | 1248.19 | 34888.2 | 1284.78 | 1155.5 | 1393.04 |
30-50 | 996.68 | 737.03 | 1021.05 | 779.14 | 34564.41 | 838.25 |
50-80 | 1405.93 | 38771.32 | 1407.89 | 38713.34 | 1482.76 | 38610.98 |
80-120 | 1783.21 | 1540.95 | 1798.32 | 1557.07 | 1809.44 | 1685.78 |
120-150 | 30947.32 | 1203.16 | 30949.37 | 1199.17 | 30797.96 | 1156.06 |
>150 | 25085.72 | 94226.04 | 25359.1 | 94555.92 | 26211.78 | 95370.62 |
Table 5 Prediction of future yield losses in the Pearl River Delta region (ton) |
Year | City | Rice | Peanut | Vegetable | |||
---|---|---|---|---|---|---|---|
Low-estimation | High-estimation | Low-estimation | High-estimation | Low-estimation | High-estimation | ||
2030 | Guangzhou | 44250.58 | 80894.28 | 2435.99 | 4486.31 | 543554.90 | 911988.78 |
Shenzhen | 0.00 | 0.00 | 0.10 | 0.33 | 1765.37 | 5575.28 | |
Zhuhai | 11138.30 | 20098.71 | 272.44 | 498.54 | 67771.14 | 105780.57 | |
Foshan | 5116.29 | 14673.56 | 396.95 | 1144.22 | 147810.86 | 393660.84 | |
Huizhou | 2796.75 | 17315.66 | 371.33 | 2191.06 | 13337.00 | 125669.49 | |
Shanwei | 33841.96 | 102444.30 | 2021.39 | 6079.56 | 111099.73 | 343857.01 | |
Dongguan | 1361.14 | 3402.31 | 17.63 | 44.61 | 87187.01 | 174153.96 | |
Zhongshan | 13576.67 | 24521.15 | 148.90 | 273.56 | 251815.27 | 370180.06 | |
Jiangmen | 84215.45 | 158611.04 | 2971.60 | 5666.90 | 148206.39 | 251388.43 | |
Yangjiang | 14565.46 | 45469.40 | 1373.26 | 4262.24 | 32283.07 | 100692.40 | |
2050 | Guangzhou | 44639.75 | 81125.77 | 2457.99 | 4499.40 | 545807.69 | 914217.72 |
Shenzhen | 0.00 | 0.00 | 0.10 | 0.34 | 1780.08 | 5586.24 | |
Zhuhai | 11138.63 | 20099.55 | 272.45 | 498.57 | 67774.00 | 105783.56 | |
Foshan | 5152.17 | 14746.68 | 399.82 | 1149.85 | 149189.54 | 394747.15 | |
Huizhou | 2796.75 | 17316.38 | 371.33 | 2191.24 | 13337.00 | 125675.57 | |
Shanwei | 34161.79 | 102741.42 | 2040.94 | 6097.18 | 112019.25 | 344359.76 | |
Dongguan | 1377.48 | 3417.51 | 17.81 | 44.82 | 88286.41 | 174870.86 | |
Zhongshan | 13616.11 | 24542.48 | 149.32 | 273.82 | 252269.09 | 370339.58 | |
Jiangmen | 84370.01 | 159005.43 | 2977.47 | 5680.63 | 148519.36 | 251916.70 | |
Yangjiang | 14827.65 | 46201.10 | 1396.41 | 4329.56 | 32887.11 | 102185.00 | |
2100 | Guangzhou | 47787.00 | 81659.83 | 2569.66 | 4529.02 | 594658.37 | 919894.34 |
Shenzhen | 0.00 | 0.00 | 0.11 | 0.34 | 2325.41 | 5611.74 | |
Zhuhai | 11678.93 | 20101.01 | 278.22 | 498.61 | 73192.26 | 105787.18 | |
Foshan | 5788.34 | 14912.33 | 425.10 | 1162.67 | 177264.07 | 397644.78 | |
Huizhou | 3691.22 | 17319.43 | 419.97 | 2191.84 | 22366.52 | 125692.26 | |
Shanwei | 39089.44 | 103461.80 | 2194.19 | 6139.36 | 136623.15 | 345721.44 | |
Dongguan | 1517.80 | 3454.87 | 18.83 | 45.34 | 100786.15 | 176421.63 | |
Zhongshan | 14361.25 | 24569.57 | 153.93 | 274.14 | 270467.39 | 370721.07 | |
Jiangmen | 88808.87 | 160097.57 | 3054.90 | 5720.41 | 161054.99 | 253251.39 | |
Yangjiang | 17042.22 | 48276.50 | 1526.59 | 4525.91 | 39368.10 | 106694.87 |
Table 6 Future yield losses and proportions of main crops under different scenarios of sea level rise and storm surge in the Pearl River Delta region |
Rice | Peanut | Vegetable | ||
---|---|---|---|---|
2030 | Low-estimated losses (×104 tons) | 21.09 | 1.00 | 140.48 |
Proportion accounting for the study area (%) | 8.03 | 5.60 | 13.28 | |
Proportion accounting for Guangdong (%) | 1.99 | 1.15 | 5.17 | |
High-estimated losses (×104 tons) | 46.74 | 2.46 | 278.29 | |
Proportion accounting for the study area (%) | 17.79 | 13.79 | 26.30 | |
Proportion accounting for Guangdong (%) | 4.41 | 2.83 | 10.24 | |
2050 | Low-estimated losses (×104 tons) | 21.21 | 1.01 | 141.19 |
Proportion accounting for the study area (%) | 8.07 | 5.64 | 13.34 | |
Proportion accounting for Guangdong (%) | 2.00 | 1.16 | 5.19 | |
High-estimated losses (×104 tons) | 46.92 | 2.48 | 278.97 | |
Proportion accounting for the study area (%) | 17.86 | 13.86 | 26.36 | |
Proportion accounting for Guangdong (%) | 4.42 | 2.84 | 10.26 | |
2100 | Low-estimated losses (×104 tons) | 22.98 | 1.06 | 157.81 |
Proportion accounting for the study area (%) | 8.75 | 5.96 | 14.91 | |
Proportion accounting for Guangdong (%) | 2.17 | 1.22 | 5.80 | |
High-estimated losses (×104 tons) | 47.39 | 2.51 | 280.74 | |
Proportion accounting for the study area (%) | 18.04 | 14.04 | 26.53 | |
Proportion accounting for Guangdong (%) | 4.47 | 2.88 | 10.33 |
The authors have declared that no competing interests exist.
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