Integrating the PLUS-InVEST Model to Project Water Conservation Dynamics and Decipher Climatic Drivers in the Chengdu–Chongqing Economic Zone Under Multiple Future Scenarios
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Methodology
2.2.1. PLUS Model
- Phase 1: Calibration and Validation
- Phase 2: Predictive Simulation
2.2.2. InVEST Model
- (1)
- Water Yield Calculation Module
- (2)
- Water Conservation Capacity Calculation Module
2.2.3. Spatial Autocorrelation
2.2.4. Hot Spot Analysis
2.3. Data Sources
3. Results and Analysis
3.1. Analysis of Land Use Evolution Trends in the Chengdu–Chongqing Economic Zone
3.1.1. Evolution of Land Use Status
3.1.2. Land Use Evolution Trends Under Future Scenarios
3.2. Analysis of Water Conservation Function Evolution Trends
3.2.1. Evolution of Water Conservation Function
3.2.2. Water Conservation Function Levels Evolution Trends Under Future Scenarios
3.3. Analysis of Spatial Aggregation Characteristics of Water Conservation Function
3.3.1. Spatial Autocorrelation Analysis of Water Conservation Function in the Chengdu–Chongqing Economic Zone
3.3.2. Current Spatial Aggregation Characteristics of Water Conservation Function in the Chengdu–Chongqing Economic Zone
3.3.3. Spatial Aggregation Characteristics of Water Conservation Function Under Future Scenarios in the Chengdu–Chongqing Economic Zone
4. Discussion
5. Conclusions
- (1)
- The PLUS model demonstrated high accuracy (Kappa coefficient: 0.81–0.82) in simulating 30 m resolution land use data for the Chengdu–Chongqing Economic Zone, providing reliable support for large-scale water conservation function predictions.
- (2)
- From 2000 to 2020, the water conservation function exhibited a gradient differentiation characterized by “contraction of low-value zones and expansion of high-value zones.” Under both ND and EP scenarios, water conservation function during 2030–2050 remained lower than 2020 levels.
- (3)
- Under the ND scenario, the proportions of highly important and extremely important grades in 2020, 2030, 2040, and 2050 were 5.02%, 0.51%, 0.11%, and 3.97%, respectively. Spatiotemporal differentiation manifested as “high-value clustering in Chengdu’s urban core and northeastern regions, with low-value diffusion in peripheral mountainous and central areas.”
- (4)
- The ND scenario showed a cold and hot spot pattern of “low-value diffusion and high-value fragmentation,” while the EP scenario exhibited “low-value integration and high-value synergy.”
- (5)
- Divergences in water conservation function between SSP1-1.9 and SSP2-4.5 scenarios underscored the interactive effects of climate policy intensity and human activities. These findings reveal the evolutionary characteristics of water conservation function under varying development pressures, offering critical insights for regional ecological security and sustainable development.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Dataset Type | Name | Source | Resolution (m) | Model Role | Preprocessing Steps |
---|---|---|---|---|---|
Basic Geographic Data | DEM | Geospatial Data Cloud | 30 | Slope extraction; one of the driving factors for PLUS model | Extracted study area using clipping function in ArcGIS |
Study Area Boundary Data | National Geographic Information Public Service Platform | / | Define boundary scope | Selected and exported study area boundary using ArcGIS | |
Land Use | LULC Imagery (2000, 2010, 2020) | Resource and Environmental Science Data Center, Chinese Academy of Sciences | 30 | Training and validation of PLUS model; raster input for InVEST water model | Reclassified into five categories (cropland, forest, grassland, water body, built-up land, unused land) and clipped to study area using ArcGIS |
Environmental-driven Data | Road Data | OpenStreetMap | / | Random forest driving factor for PLUS model | Imported and clipped in ArcGIS |
NDVI (Normalized Difference Vegetation Index) | Geospatial Data Cloud | 500 | Driving factor for PLUS model | Atmospheric correction, projection, and other processing using ENVI | |
Climate Data | Annual Precipitation (2000, 2010, 2020) | National Qinghai-Tibet Plateau Scientific Data Center | 1000 | Input for InVEST model to calculate water yield | Raster projection and raster calculation using ArcGIS |
Potential Evapotranspiration (2000, 2010, 2020) | |||||
Scenario-driven Data | Annual Precipitation (SSP1-1.9, SSP2-4.5) | National Qinghai-Tibet Plateau Scientific Data Center | 1000 | Input for InVEST model to calculate water yield | Raster projection and raster calculation using ArcGIS |
Potential Evapotranspiration (SSP1-1.9) | |||||
Soil Data | Soil Type | World Soil Database | 1000 | Input for InVEST model and water conservation calculation | Raster projection and raster calculation using ArcGIS |
Root Restriction Layer Depth | Literature-shared data [30] | 1000 | Input for InVEST model | Extracted by mask and raster calculation in ArcGIS |
Scenario Models | Year | Moran’s I | Z-Score | p Value | Variance |
---|---|---|---|---|---|
Current Status | 2000 | 0.424 | 7.543 | 0 | 0.004 |
2010 | 0.463 | 8.263 | 0 | 0.003 | |
2020 | 0.282 | 5.192 | 0 | 0.003 | |
ND Scenario under SSP1-1.9 | 2030 | 0.448 | 8.061 | 0 | 0.003 |
2040 | 0.509 | 9.127 | 0 | 0.003 | |
2050 | 0.441 | 7.856 | 0 | 0.003 | |
EP Scenario under SSP1-1.9 | 2030 | 0.446 | 8.027 | 0 | 0.003 |
2040 | 0.508 | 9.114 | 0 | 0.003 | |
2050 | 0.436 | 7.780 | 0 | 0.003 |
Year | Region | Natural Development (ND) Scenario | Ecological Protection (EP) Scenario |
---|---|---|---|
2030 | Hot spot | 28.32% | 28.32% |
Cold Spot | 12.83% | 12.83% | |
2040 | Hot spot | 22.57% | 22.57% |
Cold Spot | 12.82% | 12.82% | |
2050 | Hot spot | 18.79% | 18.79% |
Cold Spot | 20.72% | 19.29% |
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Zhu, K.; Li, S.; Huang, W.; Hou, P.; Liu, Y.; Liu, J.; Li, Z. Integrating the PLUS-InVEST Model to Project Water Conservation Dynamics and Decipher Climatic Drivers in the Chengdu–Chongqing Economic Zone Under Multiple Future Scenarios. Hydrology 2025, 12, 184. https://doi.org/10.3390/hydrology12070184
Zhu K, Li S, Huang W, Hou P, Liu Y, Liu J, Li Z. Integrating the PLUS-InVEST Model to Project Water Conservation Dynamics and Decipher Climatic Drivers in the Chengdu–Chongqing Economic Zone Under Multiple Future Scenarios. Hydrology. 2025; 12(7):184. https://doi.org/10.3390/hydrology12070184
Chicago/Turabian StyleZhu, Kangwen, Suqiong Li, Wei Huang, Peng Hou, Yaqun Liu, Jian Liu, and Zihui Li. 2025. "Integrating the PLUS-InVEST Model to Project Water Conservation Dynamics and Decipher Climatic Drivers in the Chengdu–Chongqing Economic Zone Under Multiple Future Scenarios" Hydrology 12, no. 7: 184. https://doi.org/10.3390/hydrology12070184
APA StyleZhu, K., Li, S., Huang, W., Hou, P., Liu, Y., Liu, J., & Li, Z. (2025). Integrating the PLUS-InVEST Model to Project Water Conservation Dynamics and Decipher Climatic Drivers in the Chengdu–Chongqing Economic Zone Under Multiple Future Scenarios. Hydrology, 12(7), 184. https://doi.org/10.3390/hydrology12070184