Spatiotemporal Water Yield Variations and Influencing Factors in the Lhasa River Basin, Tibetan Plateau
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Seasonal Water Yield Model
2.2.1. Quick Flow
2.2.2. Local Recharge
2.2.3. Baseflow
2.3. Baseflow Separation
2.4. Sensitivity Analysis: Morris Screening Method
2.5. Quantifying Relative Contributions of Influencing Factors
2.6. Data Sources
3. Results
3.1. Sensitivity Analysis and Model Validation
3.2. Changing Trends of Influencing Factors
3.2.1. Precipitation
3.2.2. Land Cover
3.2.3. Normalized Difference Vegetation Index (NDVI)
3.3. Water Yield Change
3.4. Relative Contributions of Influencing Factors
4. Discussion
4.1. Model Performance and Uncertainties
4.2. Driving Force Analysis: Climate Change and Human Activities in Combination
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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No. | Parameters | Definition | Range | Unit |
---|---|---|---|---|
1 | Precipitation (P) | Average annual precipitation | (50, 500) | mm |
2 | Reference evapotranspiration (RET) | Potential evaporation of a hypothetical vegetation with an abundant water supply | (50, 500) | mm |
3 | Curve number (CN) | An empirical parameter used in hydrology for predicting direct runoff or infiltration from rainfall excess | (10, 100) | - |
4 | Crop/vegetation coefficient (Kc) | Properties of plants used in predicting evapotranspiration | (0.1, 1) | - |
5 | Threshold flow accumulation (TFA) | The number of upstream cells that must flow into a cell before it is considered part of a stream | (1000, 10000) | - |
6 | Rain events (RE) | Average number of monthly rain events | (2, 20] | - |
Name | Data Type | Resolution | Time Availability | Source |
---|---|---|---|---|
Precipitation | Excel | - | 1955–2015, monthly | China Meteorological Data Service Center |
Land cover | Raster | 30 m | 1990, 2000, 2010, 2015 | Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences |
NDVI | Raster | 250 m | 1990, 2000, 2010, 2015 | Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences |
Stream flow | Excel | - | 2006–2014, daily | China Institute of Water Resources and Hydropower Research |
DEM | Raster | 30 m | 2015 | Geospatial Data Cloud (http://www.gscloud.cn/) |
Soil texture | Raster | 1 km | - | Harmonized World Soil Database (http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/zh/) |
CN | Excel | - | - | USDA (United States Department of Agriculture) handbook [42] |
Kc | Excel | - | - | FAO (Food and Agriculture Organization of the United Nations) guidelines [45] |
Reference evapotranspiration | Raster | 1 km | Monthly | CGIAR CSI dataset (https://cgiarcsi.community/) |
Land Cover Type | 1990 | 2000 | 2010 | 2015* | Area Change in Land Cover between 1990 and 20151 | Proportion Change in Land Cover between 1990 and 2015 |
---|---|---|---|---|---|---|
Forest | 80.01 | 80.21 | 80.73 | 80.75 | 0.74 | 0.92% |
Shrub | 5077.63 | 5077.64 | 5077.72 | 5065.31 | −12.33 | −0.24% |
Alpine meadow | 8561.66 | 8562.13 | 8561.94 | 8682.04 | 120.38 | 1.41% |
Alpine steppe | 7629.94 | 7631.09 | 7613.59 | 7702.02 | 72.09 | 0.94% |
Sparse grassland | 7388.28 | 7400.13 | 7401.72 | 7152.10 | −236.19 | −3.20% |
Farmland | 613.00 | 598.68 | 584.79 | 583.83 | −29.17 | −4.76% |
Barren land | 2098.49 | 2104.65 | 2105.12 | 2108.88 | 10.39 | 0.50% |
Artificial surface | 90.29 | 102.97 | 117.59 | 164.87 | 74.57 | 82.59% |
Water | 174.65 | 174.42 | 188.70 | 231.07 | 56.42 | 32.30% |
Snow/Glaciers | 964.57 | 946.62 | 946.62 | 860.31 | −104.26 | −10.81% |
Time Period | 1990–2000 | 2000–2010 | 2010–2015 | ||||||
---|---|---|---|---|---|---|---|---|---|
Influencing Factor | ΔP | ΔL | ΔN | ΔP | ΔL | ΔN | ΔP | ΔL | ΔN |
Δ Baseflow | 22.80 | −0.09 1 | −25.78 | −76.68 | 0.06 | −3.46 | 1.77 | −0.88 | 1.97 |
Δ Local Recharge | 22.81 | −0.09 | −28.91 | −90.74 | 0.08 | −3.96 | 4.21 | 2.71 | −0.67 |
Δ Quick flow | 1.58 | 0.03 | 0.00 | −8.93 | 0.01 | 0.00 | −0.61 | −0.03 | 0.00 |
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Lu, H.; Yan, Y.; Zhu, J.; Jin, T.; Liu, G.; Wu, G.; Stringer, L.C.; Dallimer, M. Spatiotemporal Water Yield Variations and Influencing Factors in the Lhasa River Basin, Tibetan Plateau. Water 2020, 12, 1498. https://doi.org/10.3390/w12051498
Lu H, Yan Y, Zhu J, Jin T, Liu G, Wu G, Stringer LC, Dallimer M. Spatiotemporal Water Yield Variations and Influencing Factors in the Lhasa River Basin, Tibetan Plateau. Water. 2020; 12(5):1498. https://doi.org/10.3390/w12051498
Chicago/Turabian StyleLu, Huiting, Yan Yan, Jieyuan Zhu, Tiantian Jin, Guohua Liu, Gang Wu, Lindsay C. Stringer, and Martin Dallimer. 2020. "Spatiotemporal Water Yield Variations and Influencing Factors in the Lhasa River Basin, Tibetan Plateau" Water 12, no. 5: 1498. https://doi.org/10.3390/w12051498