Journal of Geographical Sciences ›› 2019, Vol. 29 ›› Issue (6): 1021-1038.doi: 10.1007/s11442-019-1642-x
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Received:
2018-10-15
Accepted:
2018-12-21
Online:
2019-06-25
Published:
2019-06-25
Contact:
Jing LI
E-mail:lijing@snnu.edu.cn
About author:
Author: Zeng Li (1993-), Master, specialized in resources and environment remote sensing and GIS.E-mail:
Supported by:
Li ZENG, Jing LI. A Bayesian belief network approach for mapping water conservation ecosystem service optimization region[J].Journal of Geographical Sciences, 2019, 29(6): 1021-1038.
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Table 1
Conditional probability table for the evapotranspiration node"
Vegetation Type | Precipitation | Evapotranspiration | |||
---|---|---|---|---|---|
Highest | High | Medium | Low | ||
Highest | Highest | 0.0647 | 52.15 | 38.991 | 8.794 |
Highest | High | 0.104 | 55.123 | 37.712 | 7.061 |
Highest | Medium | 0.16 | 58.139 | 35.251 | 6.451 |
Highest | Low | 0.314 | 80.038 | 19.586 | 0.0628 |
High | Highest | 1.036 | 48.705 | 26.425 | 23.834 |
High | High | 1.104 | 25.153 | 49.202 | 24.54 |
High | Medium | 1.139 | 22.322 | 47.41 | 29.129 |
High | Low | 0.704 | 48.415 | 50.792 | 0.088 |
Medium | Highest | 0.813 | 0.675 | 12.462 | 86.049 |
Medium | High | 3.265 | 2.664 | 41.71 | 52.36 |
Medium | Medium | 2.248 | 5.154 | 34.65 | 57.948 |
Medium | Low | 1.249 | 8.005 | 90.68 | 0.0662 |
Low | Highest | 4.66 | 1.553 | 9.709 | 84.078 |
Low | High | 32.642 | 2.554 | 26.257 | 38.547 |
Low | Medium | 15.281 | 2.488 | 23.099 | 59.133 |
Low | Low | 17.687 | 4.082 | 77.751 | 0.68 |
Table 2
State classification of water conservation factors"
No. | Variables | State | Rank of state | Range |
---|---|---|---|---|
1 | Precipitation (mm/a) | Highest | 1 | 817-996 |
High | 2 | 681-817 | ||
Medium | 3 | 529-681 | ||
Low | 4 | 0-529 | ||
2 | Vegetation type | Highest | 1 | Evergreen broad-leaved forest, evergreen coniferous forest, deciduous broad-leaved forest, deciduous coniferous forest, arbor garden, sparse forest, mixed coniferous and broad-leaved forest |
High | 2 | Shrubs, deciduous broad-leaved shrubs, arbors, sparse shrubs | ||
Medium | 3 | Herbaceous green space, herbaceous marsh, grass, dry land, paddy field, sparse grassland, grassland | ||
Low | 4 | Mining, industrial land, rivers, lakes, transportation, residential, open ground, bare soil, bare rock, reservoirs, canals, desert / sandy land | ||
3 | Land use | Cropland | 1 | Cropland |
Forest | 2 | Forest | ||
Grassland | 3 | Grassland | ||
Water area | 4 | Water area | ||
Urban land | 5 | Urban land | ||
Unused land | 6 | Unused land | ||
4 | Soil type | Highest | 1 | Swampy soil, paddy soil, silted black soil, tidal soil, gray-brown soil type silt, cinnamon soil type silt, slightly salinized silt |
High | 2 | Subalpine meadow soil, mountain brown soil, mountain leached cinnamon soil, dark brown soil, brown soil, mountain grassland soil, coarse bone soil, cinnamon soil, yellow-brown soil, mountain meadow grassland soil | ||
Medium | 3 | Cultivation of mountain leached cinnamon soil, cultivation of mountain cinnamon soil, cultivation of mountain carbonate cinnamon soil, cultivation of mountain meadow grassland soil, cultivation of mountain cinnamon soil, cultivation of mountain carbonate | ||
Low | 4 | Alluvial soil, new soil, purple soil, red soil, aeolian sand, yellow cinnamon soil, lithic soil, loessial soil, limestone soil | ||
5 | Evapotranspiration (mm/a) | Highest | 1 | 10913-65535 |
High | 2 | 6003-10913 | ||
Medium | 3 | 4668-6003 | ||
Low | 4 | 1765-4668 | ||
6 | Surface runoff (mm/a) | Highest | 1 | 59-89 |
High | 2 | 34-59 | ||
Medium | 3 | 15-34 | ||
Low | 4 | 0-15 | ||
7 | Water conservation (t/ha·a) | Highest | 1 | 1103-1468 |
High | 2 | 917-1103 | ||
Medium | 3 | 163-917 | ||
Low | 4 | 0-163 |
Table 3
Social and economic statistics and scenario design in the study area"
Region | GDP growth rate (%) | Growth rate of permanent population (%) | ||||||
---|---|---|---|---|---|---|---|---|
2015 | Planning | Protection | Development | 2015 | Planning | Protection | Development | |
Xi’an | 11.53 | 12 | 10 | 15 | 4.64 | 6 | 4.5 | 8 |
Weinan | 7.71 | 7.5 | 6 | 9 | 3.46 | 4 | 3.5 | 6 |
Baoji | 6.27 | 6.5 | 5.5 | 8 | 3.55 | 4 | 3.5 | 6 |
Tongchuan | 0.65 | 0.6 | 0.5 | 1 | 3.79 | 4 | 3.8 | 4.5 |
Xianyang | 12.08 | 13 | 11 | 15 | 3.98 | 5 | 4.0 | 6 |
Yangling | 13.57 | 14 | 10 | 15 | 4.93 | 6 | 5.0 | 6.5 |
Tianshui | 8.9 | 9 | 8 | 10 | 0.35 | 0.4 | 0.3 | 0.6 |
Table 4
Error matrix of water conservation suitability prediction"
Predicted results of water conservation | |||||
---|---|---|---|---|---|
Actual results of water conservation | Highest | High | Medium | Low | Sum of rows |
Highest | 92 | 9 | 0 | 0 | 101 |
High | 38 | 59 | 0 | 0 | 97 |
Medium | 1 | 1 | 0 | 0 | 2 |
Low | 0 | 0 | 0 | 0 | 0 |
Sum of column | 131 | 69 | 0 | 0 | 200 |
Overall accuracy | 75.5% |
Table 6
Sensitivity of water conservation services to each node"
Node name | Variance reduction | Relative percentage |
---|---|---|
Water conservation | 1.49285 | 100 |
Precipitation | 0.53235 | 35.7 |
Evapotranspiration | 0.11014 | 7.38 |
0.10628 | 7.12 | |
Surface runoff | 0.05903 | 3.95 |
Vegetation type | 0.02189 | 1.47 |
Soil type | 0.00015 | 0.00975 |
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