Sensitivity Analysis of Soil Hydraulic Parameters for Improved Flow Predictions in an Atlantic Forest Watershed Using the MOHID-Land Platform
Soil controls water distribution, which is crucial for accurate hydrological modeling. MOHID-Land is a physically based, spatially distributed model that uses van Genuchten–Mualem (VGM) functions to calculate water content in porous media. The hydraulic soil parameters of VGM are dependent on soil t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Eng |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4117/6/4/65 |
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| Summary: | Soil controls water distribution, which is crucial for accurate hydrological modeling. MOHID-Land is a physically based, spatially distributed model that uses van Genuchten–Mualem (VGM) functions to calculate water content in porous media. The hydraulic soil parameters of VGM are dependent on soil type and are typically estimated from experimental data; however, they are often obtained using pedotransfer functions, which carry significant uncertainty. As a result, calibration is frequently required to account for both the natural spatial variability of soil and uncertainties estimation. This study focuses on a representative Atlantic Forest watershed. It assesses the sensitivity of channel flow to VGM parameters using a mathematical approach based on residuals derivative, aimed at enhancing soil calibration efficiency for MOHID-Land. The model’s performance significantly improved following calibration, considering only five parameters. The NSE improved from 0.16 on the base simulation to 0.53 after calibration. A sensitivity analysis indicated the curve adjustment parameter (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi></mrow></semantics></math></inline-formula>) as the most sensitive parameter, followed by saturated water content (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi></mrow><mrow><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula>) considering the 10% variation. Additionally, a combined change in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi></mrow><mrow><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi></mrow></semantics></math></inline-formula>, residual water content (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>θ</mi></mrow><mrow><mi>r</mi></mrow></msub></mrow></semantics></math></inline-formula>), curve adjustment parameter (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>α</mi></mrow></semantics></math></inline-formula>), and saturated conductivity (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>K</mi></mrow><mrow><mi>s</mi><mi>a</mi><mi>t</mi></mrow></msub></mrow></semantics></math></inline-formula>) values by 10% significantly improves the model’s performance, by reducing channel flow peaks and increasing baseflow. |
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| ISSN: | 2673-4117 |