A Comprehensive Approach to Develop a Hydrological Model for the Simulation of All the Important Hydrological Components: The Case of the Three-River Headwater Region, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Description
2.3. Setup of HEC-HMS
2.4. Calibration and Validation
2.4.1. Estimation of Physical Parameters
2.4.2. Estimation of Process Parameter
- First, search and separate single isolated storm event from streamflow time series.
- Plot a streamflow hydrograph for a single isolated storm event on a semi-logarithmic graph, as shown in Figure 4.
- Estimate the recession constant (k) for both the recession curves of baseflow and interflow. We used regression analysis on a semi-logarithmic graph. Baseflow is contributed by the deep groundwater layer (GW2) and interflow from the shallow groundwater layer (GW1) [24].
- Estimate the storage coefficient (Sc), storage capacity (St), and storage depth (St/area) with the following equations for both GW1 and GW2:
Soil Texture | Area (km2) | % of the Total Area | Soil Depth (mm) | Porosity (cm3/cm3) | Field Capacity (cm3/cm3) | KS (mm/h) | Infiltration (mm/h) |
---|---|---|---|---|---|---|---|
Clay | 2481 | 2 | 1000 | 0.49 | 0.41 | 0.6 | 2 |
Clay loam | 1926 | 2 | 1000 | 0.48 | 0.36 | 2.3 | 5 |
Loam | 40,205 | 34 | 654 | 0.46 | 0.28 | 13.2 | 10 |
Sand | 630 | 1 | 1000 | 0.4 | 0.1 | 210 | 25 |
Sandy loam | 72,088 | 61 | 300 | 0.44 | 0.18 | 25.9 | 20 |
Silt loam | 670 | 1 | 1000 | 0.49 | 0.31 | 6.8 | 7 |
2.4.3. Sensitivity Analysis
2.5. Baseflow Separation
2.6. Terrestrial Water Storage
2.7. Actual Evapotranspiration Estimation
3. Results
3.1. Calibration and Validation with Streamflow
3.2. Validation of Other Hydrological Components
3.2.1. Soil Moisture Content
3.2.2. Baseflow
3.2.3. Terrestrial Water Storage
3.2.4. Snow Water Equivalent
3.2.5. Actual Evapotranspiration
3.3. Uncertainties and Limitations
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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SR | Data Type | Spatial/Temporal Resolution | Source | Availability |
---|---|---|---|---|
1 | Streamflow data | Daily | Hydrology and Water Resources Survey Bureau of Qinghai province | 1980–2015 |
2 | Climate data | Daily | Qinghai Meteorological Bureau (QMB) | 1980–2015 |
3 | DEM | 90 m | NASA’s Shuttle Radar Topography Mission (SRTM), Version 004 [31] (http://srtm.csi.cgiar.org) (accessed on 20 August 2019). | Updated 2008 |
4 | Land Use Land Cover | 1 km | Global Land Cover Characteristics [32] (https://earthexplorer.usgs.gov/) (accessed on 20 August 2019). | 1993 |
5 | Soil characteristics | 1 km | Harmonized World Soil Database Version 1.2 (http://www.fao.org/soils-portal/) (accessed on 20 August 2019) [33] | Update 2013 |
6 | Snow Water Equivalent (SWE)/snow depth | 25 km/daily, monthly | Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), Version 2 [34] | 2002/6–2011/10 |
1°/daily | Canadian Sea Ice and Snow Evolution (CanSISE) [35] | 1981–2010 | ||
0.25°/daily | Environmental and Ecological Science Data Center for West China (WESTDC) [36] | 1979–2019 | ||
7 | Soil Moisture Content | 0.25°/daily | European Space Agency (ESA) Climate Change Initiative Soil Moisture product (ESA-CCI-SM_v4.7), | 1978–2019 |
0.25°/daily | China Soil Moisture Dataset from Microwave Data Assimilation (ITP-LDAS) [37] | 2002–2011 | ||
0.5°′0.625°/diurnal | Modern-Era Retrospective analysis for Research and Applications (MERRA-2) [38] | 1980–2020 | ||
8 | GRACE data | 300 km/monthly | Geo-forschungs-Zentrum Potsdam (GFZP), University of Texas-Center for Space Research (UT-CSR), and UT-CSR Mascons [39], | 2002–2020 |
9 | Leaf Area Index | 0.25°/monthly | Global Monthly Mean Leaf Area Index Climatology, 1981–2015 [40] | 1981–2015 |
10 | Evapotranspiration | 4 km/monthly | TERRACLIMATE [41] | 1958–2019 |
0.1°/monthly | Terrestrial evapotranspiration dataset across China, version 1.5 [42] | 1982–2017 | ||
500 m/8-daily | MOD16A2 Version 6 [43] | 2000–2020 |
Code | Description | % of the Total Area | Canopy Storage (mm) |
---|---|---|---|
1 | Urban and Built-Up Land | 0.00 | 0.5 |
2 | Dryland Cropland and Pasture | 0.47 | 1.5 |
5 | Cropland/Grassland Mosaic | 0.01 | 2.0 |
6 | Cropland/Woodland Mosaic | 0.02 | 2.0 |
7 | Grassland | 88.61 | 2.0 |
8 | Shrubland | 1.99 | 2.5 |
9 | Mixed Shrubland/Grassland | 1.66 | 2.2 |
10 | Savanna | 1.05 | 2.0 |
11 | Deciduous Broadleaf Forest | 0.15 | 3.0 |
12 | Deciduous Needleleaf Forest | 0.13 | 2.0 |
14 | Evergreen Needleleaf Forest | 0.01 | 2.0 |
15 | Mixed Forest | 0.81 | 3.0 |
16 | Water Bodies | 1.51 | 0.0 |
17 | Herbaceous Wetland | 0.02 | 1.0 |
18 | Wooded Wetland | 0.00 | 1.0 |
19 | Glacier | 0.12 | 0.0 |
21 | Wooded Tundra | 3.43 | 2.0 |
Surface | Slope (%) | Max Surface Storage (mm) |
---|---|---|
Paved impervious area | NA | 3.2–6.6 |
Steep, smooth slopes | >30 | 1.0 |
Moderate to gentle slopes | 5–30 | 12.7–6.4 |
Flat, furrowed land | 0–5 | 50.8 |
Jimai | Maqu | Tangnaihai | Zhimenda | Xiangda | |
---|---|---|---|---|---|
Calibration | |||||
E | 0.66 | 0.85 | 0.83 | 0.74 | 0.69 |
R2 | 0.73 | 0.88 | 0.89 | 0.83 | 0.73 |
PVD (%) | 4.45 | 3.60 | 6.24 | 8.96 | −4.29 |
NRMSE | 0.46 | 0.30 | 0.30 | 0.51 | 0.43 |
Validation-1 | |||||
E | 0.69 | 0.83 | 0.90 | 0.77 | 0.74 |
R2 | 0.77 | 0.83 | 0.90 | 0.82 | 0.78 |
PVD | 12.8 | 3.29 | −1.32 | 8.68 | 6.66 |
NRMSE | 0.39 | 0.35 | 0.27 | 0.49 | 0.49 |
Validation-2 | |||||
E | 0.62 | 0.82 | 0.82 | 0.79 | 0.61 |
R2 | 0.71 | 0.82 | 0.82 | 0.81 | 0.65 |
PVD | 21.07 | −4.38 | −0.78 | 1.36 | −1.13 |
NRMSE | 0.52 | 0.42 | 0.40 | 0.48 | 0.55 |
Soil Moisture Content | Snow Water Equivalent | Baseflow | Terrestrial Water Storage Changes | Actual Evapotranspiration | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ESA-CCI-SM | MERRA-2 | ITP-LDAS | CanSISE | AMSR-E | WESTDC | DRF | GFZ | UT-CSR | UT-CSR_MASCON | TERRACLIMATE | MOD16A2 | TEDAC | |
Analysis Period 2001–2010 | 2001–2010 | 1981–2015 | 2002–2015 | 2000–2010 | |||||||||
The Source of the Lancang River (SLR) | |||||||||||||
R | 0.72 | 0.60 | 0.44 | 0.51 | 0.75 | 0.72 | 0.95 | 0.72 | 0.70 | 0.76 | 0.89 | 0.79 | 0.87 |
RMSE | 0.031 | 0.027 | 0.033 | 6.5 | 12.5 | 6.0 | 29.0 | 20.0 | 22.1 | 17.5 | 15.3 | 23.9 | 24.3 |
PVD (%) | 4.5 | −6.6 | 9.0 | 64.1 | −51.7 | 111.8 | 18.1 | 11.0 | −5.3 | 34.3 | 0.1 | −38.6 | −36.3 |
The Source of the Yellow River (SYER) | |||||||||||||
R | 0.72 | 0.74 | 0.56 | 0.82 | 0.73 | 0.81 | 0.96 | 0.70 | 0.75 | 0.79 | 0.85 | 0.80 | 0.83 |
RMSE | 0.029 | 0.020 | 0.022 | 3.6 | 6.3 | 2.5 | 100.8 | 21.7 | 20.4 | 17.7 | 21.9 | 24.5 | 22.2 |
PVD (%) | 6.7 | 5.7 | −2.4 | −38.4 | −59.8 | 22.4 | 7.4 | 17.4 | 9.1 | 18.6 | −13.5 | −33.2 | −25.4 |
The Source of the Yangtze River (SYAR) | |||||||||||||
R | 0.80 | 0.84 | 0.49 | 0.24 | 0.33 | 0.32 | 0.96 | 0.64 | 0.66 | 0.55 | 0.86 | 0.73 | 0.81 |
RMSE | 0.034 | 0.015 | 0.035 | 9.7 | 8.2 | 9.9 | 113.2 | 19.2 | 19.2 | 28.7 | 17.3 | 23.6 | 24.6 |
PVD (%) | 11.5 | −0.9 | −10.2 | 318.4 | −11.3 | 410.4 | 20.1 | 16.3 | 5.1 | −23.8 | −3.5 | −42.2 | −37.6 |
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Mahmood, R.; Jia, S. A Comprehensive Approach to Develop a Hydrological Model for the Simulation of All the Important Hydrological Components: The Case of the Three-River Headwater Region, China. Water 2022, 14, 2778. https://doi.org/10.3390/w14182778
Mahmood R, Jia S. A Comprehensive Approach to Develop a Hydrological Model for the Simulation of All the Important Hydrological Components: The Case of the Three-River Headwater Region, China. Water. 2022; 14(18):2778. https://doi.org/10.3390/w14182778
Chicago/Turabian StyleMahmood, Rashid, and Shaofeng Jia. 2022. "A Comprehensive Approach to Develop a Hydrological Model for the Simulation of All the Important Hydrological Components: The Case of the Three-River Headwater Region, China" Water 14, no. 18: 2778. https://doi.org/10.3390/w14182778