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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (12): 1669-1693    DOI: 10.1007/s40333-025-0064-0     CSTR: 32276.14.JAL.02500640
Research article     
Trade-off and synergy effects, driving factors, and spatial optimization of ecosystem services in the Wuding River Basin of China: A study based on the Bayesian Belief Network approach
FAN Liangwei1, WANG Ni1,2,*(), WANG Tingting1, LIU Zheng1, WAN Yong1, LI Zhiwei1
1College of Water Resources and Hydropower, Xi'an University of Technology, Xi'an 710048, China
2State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Xi'an University of Technology, Xi'an 710048, China
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Abstract  

The Wuding River Basin, situated in the Loess Plateau of northern China, is an ecologically fragile region facing severe soil erosion and imbalanced ecosystem service (ES) functions. However, the mechanisms driving the spatiotemporal evolution of ES functions, as well as the trade-offs and synergies among these functions, remain poorly understood, constraining effective watershed-scale management. To address this challenge, this study quantified four ES functions, i.e., water yield (WY), carbon storage (CS), habitat quality (HQ), and soil conservation (SC) in the Wuding River Basin from 1990 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model, and proposed an innovative integration of InVEST with a Bayesian Belief Network (BBN) to nonlinearly identify trade-off and synergy relationships among ES functions through probabilistic inference. A trade-off and synergy index (TSI) was developed to assess the spatial interaction intensity among ES functions, while sensitivity and scenario analyses were employed to determine key driving factors, followed by spatial optimization to delineate functional zones. Results revealed distinct spatiotemporal variations: WY increased from 98.69 to 120.52 mm; SC rose to an average of 3.05×104 t/hm2; CS remained relatively stable (about 15.50 t/km2); and HQ averaged 0.51 with localized declines. The BBN achieved a high accuracy of 81.9% and effectively identified strong synergies between WY and SC, as well as between CS and HQ, while clear trade-offs were observed between WY and SC versus CS and HQ. Sensitivity analysis indicated precipitation (variance reduction of 9.4%), land use (9.8%), and vegetation cover (9.1%) as key driving factors. Spatial optimization further showed that core supply and ecological regulation zones are concentrated in the central-southern and southeastern basin, while ecological strengthening and optimization core zones dominate the central-northern and southeastern margins, highlighting strong spatial heterogeneity. Overall, this study advances ES research by combining process-based quantification with probabilistic modeling, offering a robust framework for studying nonlinear interactions, driving mechanisms, and optimization strategies, and providing a transferable paradigm for watershed-scale ES management and ecological planning in arid and semi-arid areas.

Key words： ecosystem service functions      trade-offs and synergies      Bayesian Belief Network      spatial pattern optimization      Wuding River Basin     
Received: 28 May 2025      Published: 31 December 2025
Corresponding Authors: *WANG Ni (E-mail: wangni@xaut.edu.cn)
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FAN Liangwei
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Cite this article:

FAN Liangwei, WANG Ni, WANG Tingting, LIU Zheng, WAN Yong, LI Zhiwei. Trade-off and synergy effects, driving factors, and spatial optimization of ecosystem services in the Wuding River Basin of China: A study based on the Bayesian Belief Network approach. Journal of Arid Land, 2025, 17(12): 1669-1693.

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http://jal.xjegi.com/10.1007/s40333-025-0064-0     OR     http://jal.xjegi.com/Y2025/V17/I12/1669

Fig. 1 General overview of the Wuding River Basin based on digital elevation model (DEM) (a) and distribution of primary land use types in 2020 (b)
Data name Data time Unit Resolution Source of data
Digital elevation model (DEM) - m 30 m Geospatial Data Cloud
(https://www.gscloud.cn/)
Land use 1990, 2000, 2010, and 2020 - 30 m Center for Resources and Environmental Sciences (https://www.resdc.cn/)
Normalized Difference Vegetation Index (NDVI) 2020 - 30 m Resource Ecology Discipline Data Center (https://www.resdc.cn/)
Bedrock depth - mm 250 m ISRIC Global Dataset (https://data.isric.org/)
Temperature 2020 °C 1000 m National Earth Systems Science Data Center (http://www.geodata.cn/)
Soil - - 1000 m Harmonized World Soil Database (HWSD)
(https://www.fao.org/soils-portal/en/)
Population density 2020 persons/km2 1000 m World Population Dataset
(https://www.worldpop.org/)
Precipitation 1990, 2000, 2010, and 2020 mm 1000 m National Qinghai-Tibet Plateau Data Center (https://data.tpdc.ac.cn/home/)
Potential evapotranspiration (PET) 1990, 2000, 2010, and 2020 mm 1000 m National Qinghai-Tibet Plateau Data Center (https://data.tpdc.ac.cn/home/)
Root depth - mm 1000 m ISRIC Global Dataset (https://data.isric.org/)
Table 1 Main data sources in this study
Land use type Cabove (t/km2) Cbelow (t/km2) Csoil (t/km2) Cdead (t/km2)
Cropland 3.16 0.36 91.63 0.92
Woodland 43.54 10.84 101.76 1.75
Grassland 0.38 4.31 79.02 0.06
Water bodies 0.32 0.00 0.00 0.00
Construction land 1.87 4.73 69.16 0.19
Unused land 1.26 1.21 14.63 0.84
Table 2 Carbon pool values assigned to each land use type
Threat factor Maximum distance of influence (km) Weight Spatial decay type
Cropland 2.6 0.26 Exponential
Construction land 5.8 0.73 Linear
Unused land 2.0 0.25 Exponential
Table 3 Weights assigned to each threat source
Land use type Habitat suitability Susceptibility
Cropland Construction land Unused land
Cropland 0.45 0.3 0.5 0.4
Woodland 1.00 0.7 0.7 0.7
Grassland 0.75 0.5 0.3 0.3
Water bodies 0.85 0.6 0.7 0.7
Construction land 0.00 0.0 0.0 0.0
Unused land 0.00 0.0 0.0 0.0
Table 4 Habitat suitability and susceptibility to threat sources for different land use types
Land use type USLE-C USLE-P Land use type USLE-C USLE-P
Cropland 0.22 0.35 Water bodies 1.00 0.00
Woodland 0.03 1.00 Construction land 0.20 0.00
Grassland 0.07 1.00 Unused land 1.00 1.00
Table 5 USLE-C and USLE-P values assigned for different land use types
No. Driving factor VIF
1 Land use 1.742
2 Slope 3.902
3 Vegetation cover (indicated by NDVI) 1.916
4 Average annual temperature (using temperature for simplification) 4.965
5 Annual precipitation (using precipitation for simplification) 5.337
6 PET 2.822
7 Rainfall erosion 3.243
8 Population density 1.013
9 Soil erodibility 1.712
Table 6 Variance inflation factor (VIF) values of the selected driving factors
Network node Typology Level and range Unit
Water yield (WY) Continuous Lower: 0.00-101.52; low: 101.52-174.03;
high: 174.03-235.26; higher: 235.26-410.91		 mm
Carbon storage (CS) Continuous Lower: 0.00-3.67; low: 3.67-12.12;
high: 12.12-20.08; higher: 20.08-23.91		 t/km2
Habitat quality (HQ) Continuous Lower: 0.00-0.16; low: 0.16-0.45;
high: 0.45-0.80; higher: 0.80-1.00		 -
Soil conservation (SC) Continuous Lower: 0.00-6.14; low: 6.14-20.11;
high: 20.11-37.98; higher: 37.98-142.45		 104 t/hm2
Population density Continuous Lower: 0-1149; low: 1149-4698;
high: 4698-10,753; higher: 10,753-26,622		 persons/km2
NDVI Continuous Lower: 0.29-0.34; low: 0.34-0.50;
high: 0.50-0.66; higher: 0.66-1.00		 -
Precipitation Continuous Lower: 0-467; low: 467-520;
high: 520-580; higher: 580-648		 mm
PET Continuous Lower: 0-1089; low: 1089-1118;
high: 1118-1139; higher: 1139-1206		 mm
Rainfall erosion Continuous Lower: 0-1425; low: 1425-1719;
high: 1719-2040; higher: 2040-2398		 MJ•mm/(hm2•h•a)
Soil erodibility Continuous Lower: 0.000-0.005; low: 0.005-0.010;
high: 0.010-0.018; higher: 0.018-0.022		 t•hm2•h/(hm2•MJ•mm)
Temperature Continuous Lower: 0.00-8.80; low: 8.80-9.50;
high: 9.50-10.10; higher: 10.10-12.00		 °C
Slope Continuous Lower: 0.00-2.21; low: 2.21-5.74;
high: 5.74-9.93; higher: 9.93-28.15		 °
Land use Discrete Cropland, woodland, grassland, water bodies, construction land, and unused land -
Table 7 Grading results for each network node
Fig. 2 Spatial distribution of WY (a1-a4), CS (b1-b4), HQ (c1-c4), and SC (d1-d4) in the Wuding River Basin from 1990 to 2020. WY, water yield; CS, carbon storage; HQ, habitat quality; SC, soil conservation.
Fig. 3 Prior probability distributions of ecosystem service functions and driving factors in the Wuding River Basin based on the Bayesian Belief Network (BBN) model. The bar charts represent the prior probabilities of each state. The arrows indicate the directed edges from parent nodes to child nodes. The values below the prior probabilities for each variable indicate mean±SD. For land use, it does not have a meaningful mean or standard deviation since the variable is categorical.
Network node Precision (%) Logarithmic loss Quadratic algorithmic loss Spherical Gain
WY 68.0 0.7177 0.4089 0.7552
CS 85.8 0.3321 0.2046 0.8867
HQ 77.4 0.5176 0.3219 0.8199
SC 96.6 0.1012 0.0536 0.9711
Average 81.9 0.4172 0.2473 0.8582
Table 8 Results of the precision assessment of ecosystem service functions
Fig. 4 Posterior probability trends of the remaining three ecosystem service functions when the prior probabilities of WY (a), CS (b), HQ (c), and SC (d) are sequentially set to 100.0% for each state from lower to higher
Fig. 5 Spatial distribution of the trade-off and synergy index (TSI) among ecosystem service functions during the periods of 1990-2000, 2000-2010, and 2010-2020. (a1-a3), WY_CS; (b1-b3), WY_HQ; (c1-c3), WY_SC; (d1-d3), CS_HQ; (e1-e3), CS_SC; (f1-f3), HQ_SC. In the figure, blank areas represent regions where no significant trade-off or synergy intensity was observed between the two ES functions.
Fig. 6 Results of sensitivity analyses of ecosystem service functions
Fig. 7 Changes in posterior probabilities relative to prior probabilities for each driving factor under different scenarios. (a), Scenario I; (b), Scenario II; (c), Scenario III; (d), Scenario IV.
Ecosystem service
function		 Optimal state subset Overall conditional probability of WY at each state's maximum
Rainfall erosion Precipitation NDVI Lower Low High Higher
WY Low Low High 0.960 0.029 0.007 0.004
Higher Higher Higher 0.007 0.978 0.013 0.002
Low Low Lower 0.177 0.362 0.456 0.005
Low Low Low 0.111 0.111 0.222 0.556
Ecosystem service
function		 Optimal state subset Overall conditional probability of CS at each state's maximum
Land use NDVI PET Lower Low High Higher
CS Water bodies Lower High 0.401 0.545 0.027 0.027
Unused land Lower Low 0.001 0.998 0.001 0.000
Grassland High Low 0.000 0.017 0.982 0.000
Woodland Higher High 0.003 0.003 0.293 0.701
Ecosystem service
function		 Optimal state subset Overall conditional probability of HQ at each state's maximum
Land use NDVI PET Lower Low High Higher
HQ Unused land Lower Low 0.867 0.067 0.033 0.033
Grassland High High 0.049 0.829 0.098 0.024
Cropland High High 0.002 0.072 0.924 0.002
Woodland High Lower 0.003 0.110 0.609 0.278
Ecosystem service
function		 Optimal state subset Overall conditional probability of SC at each state's maximum
Precipitation Rainfall erosion Slope Lower Low High Higher
SC Lower Lower Lower 0.991 0.003 0.003 0.003
Higher Higher Low 0.007 0.989 0.002 0.002
Lower Lower Higher 0.333 0.167 0.333 0.167
- - - - - - -
Table 9 Optimal state subsets of ecosystem service function node states at maximum conditional probabilities
Fig. 8 Regional pattern optimization of WY (a), CS (b), HQ (c), and SC (d)
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