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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (6): 852-875    DOI: 10.1007/s40333-024-0101-4     CSTR: 32276.14.s40333-024-0101-4
Research article     
Temporal and spatial variation and prediction of water yield and water conservation in the Bosten Lake Basin based on the PLUS-InVEST model
CHEN Jiazhen1,2, KASIMU Alimujiang1,2,*(), REHEMAN Rukeya3, WEI Bohao1, HAN Fuqiang1, ZHANG Yan1
1School of Geography and Tourism, Xinjiang Normal University, Urumqi 830054, China
2Xinjiang Key Laboratory of Lake Environment and Resources in Arid Zone, Urumqi 830054, China
3School of Geography and Tourism, Shaanxi Normal University, Xi'an 710062, China
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Abstract  

To comprehensively evaluate the alterations in water ecosystem service functions within arid watersheds, this study focused on the Bosten Lake Basin, which is situated in the arid region of Northwest China. The research was based on land use/land cover (LULC), natural, socioeconomic, and accessibility data, utilizing the Patch-level Land Use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models to dynamically assess LULC change and associated variations in water yield and water conservation. The analyses included the evaluation of contribution indices of various land use types and the investigation of driving factors that influence water yield and water conservation. The results showed that the change of LULC in the Bosten Lake Basin from 2000 to 2020 showed a trend of increasing in cultivated land and construction land, and decreasing in grassland, forest, and unused land. The unused land of all the three predicted scenarios of 2030 (S1, a natural development scenario; S2, an ecological protection scenario; and S3, a cultivated land protection scenario) showed a decreasing trend. The scenarios S1 and S3 showed a trend of decreasing in grassland and increasing in cultivated land; while the scenario S2 showed a trend of decreasing in cultivated land and increasing in grassland. The water yield of the Bosten Lake Basin exhibited an initial decline followed by a slight increase from 2000 to 2020. The areas with higher water yield values were primarily located in the northern section of the basin, which is characterized by higher altitude. Water conservation demonstrated a pattern of initial decrease followed by stabilization, with the northeastern region demonstrating higher water conservation values. In the projected LULC scenarios of 2030, the estimated water yield under scenarios S1 and S3 was marginally greater than that under scenario S2; while the level of water conservation across all three scenarios remained rather consistent. The results showed that Hejing County is an important water conservation function zone, and the eastern part of the Xiaoyouledusi Basin is particularly important and should be protected. The findings of this study offer a scientific foundation for advancing sustainable development in arid watersheds and facilitating efficient water resource management.

Key words： PLUS model      InVEST model      Bosten Lake Basin      water yield      water conservation      land-use simulation      Geodetector     
Received: 26 February 2024      Published: 30 June 2024
Corresponding Authors: *KASIMU Alimujiang (E-mail: alimkasim@xjnu.edu.cn)
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CHEN Jiazhen
KASIMU Alimujiang
REHEMAN Rukeya
WEI Bohao
HAN Fuqiang
ZHANG Yan
Cite this article:

CHEN Jiazhen, KASIMU Alimujiang, REHEMAN Rukeya, WEI Bohao, HAN Fuqiang, ZHANG Yan. Temporal and spatial variation and prediction of water yield and water conservation in the Bosten Lake Basin based on the PLUS-InVEST model. Journal of Arid Land, 2024, 16(6): 852-875.

URL:

http://jal.xjegi.com/10.1007/s40333-024-0101-4     OR     http://jal.xjegi.com/Y2024/V16/I6/852

Fig. 1 Location (a) and distribution of land use/land cover (LULC) (b) of the Bosten Lake Basin in Xinjiang Uygur Autonomous Region, China. Note that Figure 1a is based on the standard map (新S(2023)064) of the Map Service System (http://xinjiang.tianditu.gov.cn/bzdt_code/bzdt.html) marked by the Xinjiang Uygur Autonomous Region Platform for Common Geospatial Information Services, and the standard map has not been modified. DEM, digital elevation model.
Dimension Factor Data source Spatial
resolution		 Year
Land use type Land use/land cover (LULC) https://www.resdc.cn 90 m 2000, 2010, and 2020
Natural factor Average annual precipitation https://www.geodata.cn 1 km 2000-2020
Average annual temperature https://www.resdc.cn 1 km 2000-2020
Average annual potential evapotranspiration (PET) http://data.tpdc.ac.cn 1 km 2000-2020
Digital elevation model (DEM) https://www.gscloud.cn 90 m /
Slope https://www.gscloud.cn 90 m /
Normalized difference vegetation index (NDVI) http://www.nesdc.org.cn 1 km 2020
Soil texture https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12 1 km /
Soil type https://www.resdc.cn 1 km /
Soil organic matter https://data.tpdc.ac.cn 1 km /
Soil depth https://www.isric.org 1 km 2016
Socioeconomic factor Population density (POP) https://www.gscloud.cn 1 km 2020
Gross domestic product (GDP) https://www.resdc.cn 1 km 2020
Nighttime light (NTL) https://www.ngdc.noaa.gov 500 m 2020
Accessibility factor Euclidean distance from each pixel to the nearest railway https://openmaptiles.org 90 m /
Euclidean distance from each pixel to the nearest national highway https://openmaptiles.org 90 m 2020
Euclidean distance from each pixel to the nearest provincial highway https://openmaptiles.org 90 m 2020
Euclidean distance from each pixel to the nearest county highway https://openmaptiles.org 90 m 2020
Euclidean distance from each pixel to the nearest city-level road https://openmaptiles.org 90 m 2020
Euclidean distance from each pixel to the nearest administrative quarter https://openmaptiles.org 90 m 2020
Euclidean distance from each pixel to the nearest railway station https://openmaptiles.org 90 m 2020
Table 1 Data sources of factors involved in this study
Parameter Land use type
Cultivated land Forest Grassland Water body Glacier Construction land Unused land
Crop evapotranspiration coefficient 0.65 1.00 0.65 1.00 0.50 0.30 0.30
Root depth (mm) 300.00 2000.00 500.00 1000.00 10.00 10.00 10.00
Table 2 Value of parameters used in the InVEST model for the calculation of water yield and water conservation
Fig. 2 Flow chart of this study. PLUS, Patch-level Land Use Simulation; InVEST, Integrated Valuation of Ecosystem Services and Tradeoffs; LULC, land use/land cover; LEAS, land expansion analysis strategy; CI, contribution index.
Fig. 3 Spatial distribution of the selected influence factors of LULC variation in the Bosten Lake Basin
Result Area of
catchment
(km2)		 Average annual runoff
(×108 m3)		 Average annual groundwater
(×108 m3)		 Average annual snowmelt
(×108 m3)		 Total volume of water yield
(×108 m3)
The reference volume of water yield (Li et al., 2003; Chen et al., 2022; Chen et al., 2023) 18,541 36.59 13.00 9.96 39.63
The simulated volume of water yield by InVEST model 39.64
Table 3 Validation result of water yield in the Kaidu River Basin simulated by the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model
Criterion Interaction type
q(X1∩X2)<Min[q(X1), q(X2)] Nonlinear weakening
Min[q(X1), q(X2)]<q(X1∩X2)<Max[q(X1), q(X2)] Single-factor nonlinear weakening
q(X1∩X2)>Max[q(X1), q(X2)] Dual-factor enhancement
q(X1∩X2)=q(X1)+q(X2) Independence
q(X1∩X2)>q(X1)+q(X2) Nonlinear enhancement
Table 4 Criterion and type of interaction between pair of driving factors
Fig. 4 Spatial distribution of LULC in the Bosten Lake Basin in 2000 (a), 2010 (b), and 2020 (c)
Fig. 5 LULC transfer in the Bosten Lake Basin from 2000 to 2020. (a), 2000-2010; (b) 2010-2020; (c), 2000-2020.
Land use type Aera (km2)
2000 2010 2020 2030
S1 S2 S3
Cultivated land 3272.37 4968.81 5797.50 6589.69 5595.73 6589.69
Forest 1519.75 1134.75 1049.79 982.94 1089.44 982.89
Grassland 40,250.41 39,913.98 39,040.11 38,305.29 39,240.16 38,317.19
Water body 1243.58 1322.97 1363.04 1402.40 1374.12 1402.42
Glacier 829.35 599.90 597.82 600.74 598.86 601.37
Construction land 219.34 389.20 548.90 602.58 585.32 590.09
Unused land 32,977.25 31,982.45 31,914.88 31,828.43 31,828.43 31,828.42
Table 5 Area of each land use type in the Bosten Lake Basin in 2000, 2010, 2020, and the three predicted scenarios of 2030
Fig. 6 Spatial distribution of LULC in the Bosten Lake Basin in the three predicted scenarios of 2030. (a), scenario S1 (natural development scenario); (b), scenario S2 (ecological protection scenario); (c), scenario S3 (cultivated land protection scenario).
Fig. 7 Spatial distribution of water yield in the Bosten Lake Basin in 2000 (a), 2010 (b), 2020 (c), and the three predicted scenarios of 2030 (d, e, and f). The glacier and lake areas, which are shown as white areas, were excluded from the calculation of water yield (CAO et al., 2023).
Fig. 8 Spatial distribution of water conservation in the Bosten Lake Basin in 2000 (a), 2010 (b), 2020 (c), and the three predicted scenarios of 2030 (d, e, and f). The glacier and lake areas, which are shown as white areas, were excluded from the calculation of water conservation (CAO et al., 2023).
Fig. 9 Contribution index (CI) of each land use type to water yield in the Bosten Lake Basin in 2000, 2010, and 2020
Land use type Average annual water yield (mm)
2000 2010 2020 2030
S1 S2 S3
Cultivated land 1.76 1.83 2.19 2.22 2.17 2.24
Forest 55.52 74.34 68.30 69.91 69.29 69.82
Grassland 118.41 128.78 128.58 130.99 127.84 130.94
Water body 131.21 205.75 198.26 200.96 196.90 200.69
Glacier 409.89 421.49 420.93 419.50 419.51 419.28
Construction land 5.81 0.55 0.34 1.41 0.66 1.27
Unused land 117.03 104.45 109.36 109.57 109.57 109.57
Table 6 Average annual water yield of each land use type in the Bosten Lake Basin in 2000, 2010, 2020, and the three predicted scenarios of 2030
Fig. 10 CI of each LULC land use type to water conservation in the Bosten Lake Basin in 2000, 2010, and 2020
Land use type Average annual water conservation (mm)
2000 2010 2020 2030
S1 S2 S3
Cultivated land 0.32 0.32 0.35 0.37 0.35 0.36
Forest 2.48 4.93 4.84 5.00 4.84 4.99
Grassland 6.05 6.62 6.67 6.79 6.64 6.78
Water body 5.37 7.13 6.83 7.56 6.78 7.09
Glacier 6.25 5.16 5.08 5.25 5.09 5.26
Construction land 0.96 0.32 0.30 0.49 0.30 0.50
Unused land 4.10 3.21 3.32 3.32 3.32 3.32
Table 7 Average annual water conservation of each land use type in the Bosten Lake Basin in 2000, 2010, 2020, and the three predicted scenarios of 2030
Fig. 11 Interactive detection of driving factors of water yield variation in the Bosten Lake Basin. PET, potential evapotranspiration; NDVI, normalized difference vegetation index; NTL, nighttime light. ***, significant at P<0.001 level; **, significant at P<0.01 level.
Fig. 12 Interactive detection of driving factors of water conservation variation in the Bosten Lake Basin. ***, significant at P<0.001 level; **, significant at P<0.01 level.
The level of importance of water conservation function Level Water conservation (mm)
Generally important Ⅰ [0, 5.00)
Mildly important Ⅱ [5.00, 10.00)
Moderately important Ⅲ [10.00, 20.00)
Highly important Ⅳ [20.00, 35.00)
Extremely important Ⅴ [35.00, ∞)
Table 8 Classification of water conservation function important level
Fig. 13 Spatial distribution of the level of importance of water conservation function. The glacier and lake areas, which are shown as white areas, were excluded from the calculation of water conservation (CAO et al., 2023).
Fig. 14 Average annual precipitation, temperature, and PET in the Bosten Lake Basin from 2000 to 2020
Fig. 15 LULC transfer in the Bosten Lake Basin between 2020 and the three predicted scenarios of 2030. (a), from 2020 to the scenario S1 of 2030; (b), from 2020 to the scenario S2 of 2030; (c), form 2020 to the scenario S3 of 2030.
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