Sensitivity Analysis of Soil Hydraulic Parameters for Improved Flow Predictions in an Atlantic Forest Watershed Using the MOHID-Land Platform

Dhiego da Silva Sales,Jader Lugon Junior,David de Andrade Costa, Renata Silva Barreto Sales, Ramiro Joaquim Neves,Antonio José da Silva Neto

Eng（2025）

Cited 0|Views0

Abstract

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 (n) as the most sensitive parameter, followed by saturated water content (θs) considering the 10% variation. Additionally, a combined change in θs, n, residual water content (θr), curve adjustment parameter (α), and saturated conductivity (Ksat) values by 10% significantly improves the model’s performance, by reducing channel flow peaks and increasing baseflow.

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Key words

soil physical modelling,watershed modelling,deterministic calibration

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【要点】：本研究使用MOHID-Land模型对大西洋森林流域的土壤水力参数进行敏感性分析，以提高径流预测的准确性，并发现曲线调整参数(n)是最敏感的参数。

【方法】：研究采用基于残差导数的数学方法评估VGM参数对径流的影响，并对参数进行校准以提高模型性能。

【实验】：实验在大西洋森林流域进行，使用MOHID-Land模型，通过对五个关键参数的校准（n、θs、θr、α、Ksat），将NSE从0.16提升至0.53，表明参数校准显著提高了模型性能。