Dynamic Effects of Climate and Land Use Policies on Water Yield in Drylands—A Case Study in the Northwest of China
Abstract
:1. Introduction
Progress in Assessing Water Yield and the Literature Gap
2. Materials and Methods
2.1. Study Area
2.2. Methods
2.2.1. InVEST Model
2.2.2. Trend Analysis
2.2.3. Comparative Experiment
2.2.4. Patch-Generating Land Use Simulation (PLUS) Model
2.3. Data Sources and Preprocessing
2.3.1. Data Preprocessing for Water Yield Calculation
2.3.2. Data Preprocessing for Land Use Simulation
3. Results
3.1. Changes in Climate and Land Use Patterns
3.2. Spatial-Temporal Variation of Water Yield
3.3. Effects of Climate and Land Use Change on Water Yield
3.3.1. Effect of Climate Change
3.3.2. Effect of Land Use Change
3.4. Water Yield Simulation under Different Land Use Policy Scenarios
4. Discussion
4.1. Driving Factors of Water Yield Variation
4.2. Policy Implications for Land Use Planning
4.3. Uncertainties and Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Factor name | Category | Factor Name |
---|---|---|---|
Social-economic factor | GDP density | Natural factor | Accessibility to water land |
Population density | Soil type | ||
Accessibility to municipality | Elevation | ||
Accessibility to primary road | Slope | ||
Accessibility to highway | Annual average precipitation | ||
Accessibility to railway | Annual average temperature |
Land Use Policy Scenarios | Descriptions |
---|---|
Natural Development (ND) scenario | The area of various land types is predicted by Markov-chain method, assuming that the transition probability between various land use types from 2020 to 2030 is the same as that from 2010 to 2020. |
Regional-Greening-Dominated (RGD) scenario | The area of various land types is predicted by adjusted Markov matrix in which the transition from forest and grassland to other lands is forbidden. |
Grain-Security-Dominated (GSD) scenario | The area of various land types is predicted by adjusted Markov matrix in which the transition from cropland to forest or grassland is forbidden while the transition probability from forest and grassland to farmland remains the same as that in the original matrix. |
Data type | Data description | Data source | Data usage |
---|---|---|---|
Meteorological data | Precipitation data from 2000 to 2020 Temperature data from 2000 to 2020 | China Meteorological Science Data Center (http://data.cma.cn/) The accessed date: 17 December 2021 | Calculation of reference evapotranspiration and water yield |
Extraterrestrial radiation | The United Nations Food and Agriculture Organization (FAO) Irrigation Drainage Paper [54] | ||
Future precipitation and temperature | Coupled Model Intercomparison Project (Phase 6) (https://esgf-node.llnl.gov/projects/cmip6/) The accessed date: 26 March 2022 | ||
Soil data | Soil reference depth, clay, sand, silt and organic matter contents, in China soil map based on harmonized world soil database (HWSD) (v1.1) | National cryosphere desert data center (https://www.ncdc.ac.cn) The accessed date: 16 August 2021 | Calculation of plant available water capacity and water yield |
Hydrologic data | Measured surface runoff and water resource quantity, in the water resources bulletin of Hohhot, Baotou, Ordous and Yulin | Municipal water conservancy bureau of Hohhot, Baotou, Ordous and Yulin The accessed date: 21 March 2022 | Calibration of Z parameter in InVEST model |
Land use data | The 30 m annual land cover datasets and its dynamics in China from 1990 to 2020 | Earth System Science Data (https://essd.copernicus.org) The accessed date: 5 April 2022 | Calculation of water yield; rule-mining of land expansion |
Road data | Highway, railway, primary road, etc | OpenstreetMap (https://www.openstreetmap.org) The accessed date: 13 July 2021 | Rule-mining of land expansion |
Topographic data | Digital elevation model (DEM) | Resource and Environment Sciences and Data Center, Chinese Academy of Science (https://www.resdc.cn) The accessed date: 18 January 2022 | Rule-mining of land expansion |
Socio-economic data | GDP density raster Population density raster Location of municipalities | Resource and Environment Sciences and Data Center, Chinese Academy of Science (https://www.resdc.cn) The accessed date: 18 January 2022 | Rule-mining of land expansion |
Land Use Type | 2000 | |||||||
---|---|---|---|---|---|---|---|---|
Cropland | Forest | Grassland | Water | Barren | Built-Up Land | Other Land | ||
2020 | Cropland | 14,807.92 | 8.74 | 10,865.22 | 124.84 | 522.86 | 179.33 | 2.15 |
Forest | 38.28 | 376.32 | 623.11 | 0.72 | 1.17 | 1.44 | 7.98 | |
Grassland | 15,036.61 | 441.80 | 104,141.73 | 129.43 | 11,522.93 | 300.39 | 7.12 | |
Water | 185.10 | 1.26 | 197.17 | 270.84 | 65.30 | 15.76 | 0.31 | |
Barren | 81.69 | 0.90 | 2923.99 | 22.43 | 9061.72 | 10.36 | 0.00 | |
Built-up land | 979.35 | 1.17 | 975.74 | 57.83 | 129.52 | 471.16 | 0.28 | |
Other land | 0.00 | 3.51 | 4.05 | 0.00 | 0.09 | 0.00 | 0.36 |
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An, L.; Zhong, S.; Shen, L. Dynamic Effects of Climate and Land Use Policies on Water Yield in Drylands—A Case Study in the Northwest of China. Water 2022, 14, 3940. https://doi.org/10.3390/w14233940
An L, Zhong S, Shen L. Dynamic Effects of Climate and Land Use Policies on Water Yield in Drylands—A Case Study in the Northwest of China. Water. 2022; 14(23):3940. https://doi.org/10.3390/w14233940
Chicago/Turabian StyleAn, Li, Shuai Zhong, and Lei Shen. 2022. "Dynamic Effects of Climate and Land Use Policies on Water Yield in Drylands—A Case Study in the Northwest of China" Water 14, no. 23: 3940. https://doi.org/10.3390/w14233940
APA StyleAn, L., Zhong, S., & Shen, L. (2022). Dynamic Effects of Climate and Land Use Policies on Water Yield in Drylands—A Case Study in the Northwest of China. Water, 14(23), 3940. https://doi.org/10.3390/w14233940