Effects of Input Data Content on the Uncertainty of Simulating Water Resources
Abstract
:1. Introduction
- (1)
- Global model input datasets, which consider general information and may not represent the heterogeneity of the study area.
- (2)
- Regional model input datasets, with fine resolution of the catchment characteristics, commonly not available for free and computationally demanding.
2. Materials and Methods
2.1. Study Area
2.2. Soil and Water Assessment Tool (SWAT) Model
2.3. Data Input and Model Setup
2.3.1. Digital Elevation Model (DEM)
- The pan-European elevation data EU-DEM (D1) is a 3D raster dataset with 30 m resolution and a vertical accuracy of 2.9 m [46,47]. This hybrid product is a weighted averaging of the Shuttle Radar Topography Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER GDEM).
- The regional DEM (D2) was provided by the Luxembourg Institute of Science and Technology (LIST). The D2 data has a spatial resolution of 5 m.
2.3.2. Land Use/Land Cover (LULC)
- The pan-European CORINE Land Cover 2006 (L1) is a compilation of national LULC datasets. Its production was based on agreed methodology and carried out by the European Environment Agency (EEA) under the framework of the Copernicus program [52]. L1 has a 100 m spatial resolution and represents the LULC status close to the year 2006 with an accuracy above 85%.
- The regional LULC map (L2) titled Occupation Biophysique du Sol (2007) was also provided by the LIST.
2.3.3. Soils
- The Harmonized World Soil Database (HWSD, version 1.21) (S1) is a 1 km spatial resolution global soil map produced jointly by the International Institute for Applied Systems Analysis IIASA and the Food and Agriculture Organization of the United Nations (FAO) [53]. The database includes commonly used soil parameters and texture classes. S1 presents one soil class for the Wiltz watershed having two layers, one 0.3 m deep and the other 1 m deep.
- SoilGrids (S2) is a 250 m spatial resolution global soil database developed jointly by International Soil Reference and Information Centre (ISRIC—World Soil Information) and other collaborators [54]. S2 has six soil layers with depths of 0.05 m, 0.15 m, 0.30 m, 0.60 m, 1.00 m, and 2.00 m. It provides standard soil properties (soil texture, bulk density, soil organic carbon content, etc.) per each grid cell and layer. To adjust the database to the necessity of SWAT input format, we categorized the cell values according to the soil texture, separating them into three classes: (i) loamy sands and silty-loamy sands with a high percentage of sand and a low percentage of clay (≤17%); (ii) silty loams with a high percentage of silt ≥50%; (iii) and sandy loams and slightly clayey loams with clay >7% and silt <50%.
2.4. Calibration, Parameters, and Uncertainty Analysis
3. Results and Discussion
3.1. Posterior Model Performance
3.2. Uncertainty Analysis
3.3. Posterior Parameter Distribution
4. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Model Setup | Number of Subbasins | Number of HRUs | Watershed Area (km2) | Stream Network Length (km) |
---|---|---|---|---|
D1S1L1 | 8 | 90 | 103.0 | 33.5 |
D2S1L1 | 8 | 89 | 104.2 | 33.0 |
D1S1L2 | 8 | 99 | 103.0 | 33.5 |
D2S1L2 | 8 | 100 | 104.2 | 33.0 |
D1S2L1 | 8 | 173 | 103.0 | 33.5 |
D2S2L1 | 8 | 174 | 104.2 | 33.0 |
D1S2L2 | 8 | 199 | 103.0 | 33.5 |
D2S2L2 | 8 | 200 | 104.2 | 33.0 |
Parameter Name | Parameter Definition | Parameter Factor | SWAT Default | Prior lower Bound | Prior Upper Bound | Units |
---|---|---|---|---|---|---|
SFTMP | Snowfall temperature | Replace | 1 | −5 | 5 | °C |
SMTMP | Snowmelt base temperature | Replace | 0.5 | −5 | 5 | °C |
CHN | Manning’s roughness coefficient, n | Replace | 0.014 | 0.01 | 0.25 | mm h−1 |
CHK | Hydraulic conductivity of channel | Replace | 0 | 0.001 | 150 | mm h−1 |
ALPHA_BF | Base flow alpha factor | Replace | 0.048 | 0.001 | 0.99 | - |
GW_DELAY | Groundwater delay time | Replace | 31 | 0 | 31 | days |
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Camargos, C.; Julich, S.; Houska, T.; Bach, M.; Breuer, L. Effects of Input Data Content on the Uncertainty of Simulating Water Resources. Water 2018, 10, 621. https://doi.org/10.3390/w10050621
Camargos C, Julich S, Houska T, Bach M, Breuer L. Effects of Input Data Content on the Uncertainty of Simulating Water Resources. Water. 2018; 10(5):621. https://doi.org/10.3390/w10050621
Chicago/Turabian StyleCamargos, Carla, Stefan Julich, Tobias Houska, Martin Bach, and Lutz Breuer. 2018. "Effects of Input Data Content on the Uncertainty of Simulating Water Resources" Water 10, no. 5: 621. https://doi.org/10.3390/w10050621
APA StyleCamargos, C., Julich, S., Houska, T., Bach, M., & Breuer, L. (2018). Effects of Input Data Content on the Uncertainty of Simulating Water Resources. Water, 10(5), 621. https://doi.org/10.3390/w10050621