A New Tool for Mapping Water Yield in Cold Alpine Regions
Abstract
:1. Introduction
2. Methodology
2.1. Seasonal Water Yield Model
2.2. Model Revision
2.2.1. Considering the Effect of Frozen Ground on Water Yield
2.2.2. Considering the Effect of Snow Cover on Water Yield
2.2.3. Model Calibration, Validation, and Evaluation
- Determine if the model performance improves after the model revision.
- Conduct sensitivity analysis (α, β, and γ) to identify the optimal parameter combination.
- Since α, β, and γ only affect baseflow and not quickflow, we compared the monthly quickflow values of SWY1, SWY2, SWY3, and SWY4 with those obtained from the Eckhardt filter method to select the best SWY for modeling quickflow at the preliminary stage (we calculated daily quickflow based on the Eckhardt filter method, then summed them into monthly).
- We employed the method of Hamel et al. to analyze the sensitivity of parameters in YAR, YER, and LAR [27]. We started by using the default value for α (1/12) and γ (1) and changed β from 0 to 1 with the increments of 0.2 to analyze parameter sensitivity. Similarly, we set 1 for β and γ and adjusted α equal to 1/12, 1/6, and 1/3. We also repeated the previous analyses for γ by varying γ from 0 to 1 in increments of 0.2.
- Using the various parameter combinations obtained from the best SWY for modeling quickflow, as determined in the first step, we compared the annual water yield values. First, we summed the 12 quickflow outputs of SWY to obtain the annual quickflow, and then added the annual quickflow and baseflow to calculate the annual water yield. We subsequently compared this to the observed streamflow data to determine the optimal parameter combination.
- Using the optimal parameter combination obtained from the third step, we applied it to SWY1, SWY2, SWY3, and SWY4. We then compared the annual baseflow values obtained from these models with those from the Eckhardt filter method. To accomplish this, we initially computed the daily baseflow using the observed daily runoff data with the Eckhardt filter method. Subsequently, we summed the daily baseflow of the Eckhardt filter method to obtain the annual baseflow. This allowed us to investigate whether the best SWY for modeling baseflow was also the optimal choice for simulating quickflow.
3. Case Study
3.1. Study Area
3.2. Datasets
3.2.1. Meteorological Data
3.2.2. Hydrological Data
3.2.3. Land Use/Land Cover (LULC) Data
3.2.4. Soil Data
- (1)
- Hydrologic Soil Group raster (used as the soil group before revision) and Saturate Hydraulic Conductivity rasters (used to revise the soil group) from FutureWater (https://www.futurewater.eu/2015/07/soil-hydraulic-properties/) (accessed on 4 May 2021).
- (2)
- China meteorological assimilation datasets for the SWAT model-soil temperature version 1.0 (http://data.tpdc.ac.cn/) (accessed on 5 November 2020) [69].
- (3)
3.3. Parameters
4. Results
4.1. Sensitivity Analyses
4.2. Model Calibration, Validation, and Evaluation
4.3. Effects of Frozen Ground and Snow Cover
5. Discussion
5.1. Incorporating Frozen Ground and Snow Cover Improved Model Performance
5.2. Limitations of the Modeling Approach
6. Conclusions
- (1)
- The performance of the SWY was best in the scenario that accounted for the effects of both frozen ground and snow cover, while the scenario that did not consider these effects had the worst performance. Furthermore, the model performance improved when considering the effects of frozen ground or snow cover on water yield.
- (2)
- Snow cover affects water yield through processes of melting and sublimation, while frozen ground acts as an aquitard, reducing infiltration and thus affecting the distribution of both spatial and temporal quickflow and baseflow.
- (3)
- Without considering the effect of snow cover on water yield, the annual average baseflow and water yield of the TRHR would be overestimated by 13 mm (47.58 × 108 m3/yr) and 14 mm (51.24 × 108 m3/yr), respectively. Similarly, if the effect of frozen ground on water yield were not considered, there would be an annual overestimation of about 6 mm of quickflow as baseflow.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Inputs | Format | Source (before Processing into Model Inputs) |
---|---|---|
Monthly Precipitation (revised by the method shown in Figure 1b.) | Raster (1 km) | China Meteorological Data Service Center (http://data.cma.cn) (accessed on 1 January 2021). |
Monthly Reference Evapotranspiration (ET0) | Raster (1 km) | China Meteorological Data Service Center (http://data.cma.cn) (accessed on 1 January 2021). |
Annual LULC Maps | Raster (1 km) | Chinese Academy of Environmental Science Data Center (https://www.resdc.cn/) (accessed on 5 June 2021). |
Annual Soil Group (revised by the method shown in Figure 1a.) | Raster (1 km) | The soil temperature data was downloaded from the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/) (accessed on 5 November 2020) [69]. Hydrologic Soil Group raster (used as the soil group before revision) and Saturate Hydraulic Conductivity rasters (used to revise the soil group) from FutureWater (https://www.futurewater.eu/2015/07/soil-hydraulic-properties/) (accessed on 4 May 2021). |
Biophysical Table (12 months in a CSV) | CSV | CN was downloaded from the United States Department of Agriculture [70]. Kc values were from FAO [68]. |
Rain Events (12 months in a CSV) | CSV | China Meteorological Data Service Center (http://data.cma.cn) (accessed on 1 January 2021). |
DEM | Raster (1 km) | Geospatial Data Cloud http://www.gscloud.cn/ (accessed on 5 November 2020). |
AOI (Area of Interest) | Vector | National Tibetan Plateau Data Center (http://data.tpdc.ac.cn/) (accessed on 5 November 2020) [71]. |
The Critical Temperature Threshold of Soil Freezing (Tf) 1 | - | −8 °C [72]. |
The Threshold Temperature For Differentiating Rain and Sleet (T1) 1 | 5 °C [54]. | |
The Threshold Temperature For Differentiating Snow and Sleet (T2) 1 | 2 °C [55]. | |
TFA (Threshold Flow Accumulation) 1 | - | 3000 |
α; β; γ 1 | - | Default (1/12; 1; 1) |
Parameters | YAR | YER | LAR | ||||||
---|---|---|---|---|---|---|---|---|---|
R2 | NSE | PBIAS | R2 | NSE | PBIAS | R2 | NSE | PBIAS | |
α = 1/6, β = 1, γ = 1 | 0.40 | −0.30 | 21.67 | 0.75 | 0.61 | 7.43 | 0.55 | −0.49 | 9.80 |
α = 1/4, β = 1, γ = 1 | 0.40 | 0.07 | 14.69 | 0.74 | 0.68 | 3.77 | 0.55 | −0.49 | 9.73 |
α = 1/3, β = 1, γ = 1 | 0.37 | 0.36 | −1.56 | 0.73 | 0.69 | −2.81 | 0.55 | −0.46 | 9.54 |
α = 1/12, β = 0, γ = 1 | 0.43 | −1.42 | −32.66 | 0.69 | −0.88 | −33.03 | 0.56 | −3.46 | −28.96 |
α = 1/12, β = 0.2, γ = 1 | 0.47 | 0.41 | −0.14 | 0.73 | 0.59 | −7.98 | 0.57 | 0.12 | −6.99 |
α = 1/12, β = 0.4, γ = 1 | 0.42 | 0.18 | 11.32 | 0.76 | 0.72 | 0.50 | 0.62 | 0.32 | 1.02 |
α = 1/12, β = 0.6, γ = 1 | 0.41 | −0.02 | 16.57 | 0.75 | 0.68 | 3.98 | 0.59 | 0.03 | 5.53 |
α = 1/12, β = 0.8, γ = 1 | 0.41 | −0.16 | 19.39 | 0.75 | 0.65 | 5.73 | 0.56 | −0.20 | 7.66 |
α = 1/12, β = 1, γ = 0 | 0.45 | −121.84 | −286.37 | 0.71 | −95.48 | −260.84 | 0.60 | −212.67 | −224.99 |
α = 1/12, β = 1, γ = 0.2 | 0.51 | −16.93 | −107.33 | 0.77 | −18.76 | −113.03 | 0.61 | −44.49 | −101.77 |
α = 1/12, β = 1, γ = 0.4 | 0.34 | −3.53 | −47.22 | 0.77 | −5.27 | −62.10 | 0.60 | −12.06 | −50.84 |
α = 1/12, β = 1, γ = 0.6 | 0.32 | −1.06 | −23.75 | 0.73 | −12.09 | −48.07 | 0.60 | −2.92 | −22.50 |
α = 1/12, β = 1, γ = 0.8 | 0.42 | 0.21 | 0.67 | 0.70 | 0.32 | −12.51 | 0.57 | −0.49 | −4.75 |
α = 1/12, β = 1, γ = 1 | 0.40 | −0.27 | 21.16 | 0.74 | −0.44 | 7.44 | 0.56 | −0.33 | 8.62 |
YAR | YER | LAR | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | NSE | PBIAS | R2 | NSE | PBIAS | R2 | NSE | PBIAS | ||
QF | SWY1 | 0.56 | 0.54 | 17.62 | 0.53 | 0.47 | 23.45 | - | - | - |
SWY2 | 0.71 | 0.69 | −5.91 | 0.65 | 0.55 | −2.55 | - | - | - | |
SWY3 | 0.65 | 0.62 | 21.57 | 0.62 | 0.53 | 23.34 | - | - | - | |
SWY4 | 0.77 | 0.76 | 1.25 | 0.72 | 0.65 | 1.34 | - | - | ||
BF | SWY1 | 0.52 | −0.02 | −14.08 | 0.62 | 0.18 | −17.39 | - | - | |
SWY2 | 0.56 | 0.26 | −8.62 | 0.74 | 0.45 | −12.41 | - | - | - | |
SWY3 | 0.58 | 0.50 | −1.30 | 0.77 | 0.70 | −5.66 | - | - | - | |
SWY4 | 0.61 | 0.55 | 3.73 | 0.82 | 0.80 | −1.00 | - | - | - | |
WY | SWY1 | 0.41 | −0.02 | −10.94 | 0.70 | 0.53 | −8.80 | 0.57 | −0.15 | −8.30 |
SWY2 | 0.41 | −0.02 | −10.94 | 0.70 | 0.53 | −8.80 | 0.57 | −0.15 | −8.30 | |
SWY3 | 0.47 | 0.41 | 0.54 | 0.76 | 0.72 | 1.04 | 0.62 | 0.32 | 1.02 | |
SWY4 | 0.47 | 0.41 | 0.54 | 0.76 | 0.72 | 1.04 | 0.62 | 0.32 | 1.02 |
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Zhao, L.; Chen, R.; Yang, Y.; Liu, G.; Wang, X. A New Tool for Mapping Water Yield in Cold Alpine Regions. Water 2023, 15, 2920. https://doi.org/10.3390/w15162920
Zhao L, Chen R, Yang Y, Liu G, Wang X. A New Tool for Mapping Water Yield in Cold Alpine Regions. Water. 2023; 15(16):2920. https://doi.org/10.3390/w15162920
Chicago/Turabian StyleZhao, Linlin, Rensheng Chen, Yong Yang, Guohua Liu, and Xiqiang Wang. 2023. "A New Tool for Mapping Water Yield in Cold Alpine Regions" Water 15, no. 16: 2920. https://doi.org/10.3390/w15162920