Parameterizing Haverkamp Model From the Steady‐State of Numerically Generated Infiltration: Influence of Algorithms for Steady‐State Selection

ABSTRACT BEST (Beerkan Estimation of Soil Transfer parameters) methods of soil hydraulic characterisation are widely applied for estimating sorptivity, S , and saturated hydraulic conductivity, K s . Calculating these properties requires choosing the β and γ parameters of the Haverkamp infiltration model. These parameters can be obtained from numerically simulated three‐dimensional (3D) infiltration runs reaching steady‐state. This investigation tested dependence of the estimated β and γ parameters on the algorithm for steady‐state selection using simulated 3D cumulative infiltrations for different soils and initial conditions. Two algorithms used the original simulation outputs and included using (i) a threshold defining steadiness (T‐algorithm) and (ii) the last four data points, yielding a reference value of steady‐state infiltration rate (R‐algorithm). A third algorithm, similar to the R‐algorithm, was applied to previously re‐sampled infiltration data at fixed time intervals (RR‐algorithm). The intercept, b s , of the straight line fitted to the data describing steady‐state on the cumulative infiltration plot depended on the applied algorithm more than the slope of this line. Consequently, β varied with the applied algorithm more than γ . The RR‐algorithm, yielding 0.62 ≤ β ≤ 1.99 and 0.74 ≤ γ ≤ 0.98, was preferred since it mediated between advantages and disadvantages of T‐ and R‐algorithms. The influence of the choice of proper values for β and γ on the estimates of S and K s was evaluated using BEST. Using the default values of β (0.6) and γ (0.75) yielded accurate estimates of S but not of K s . Soil dependent β and γ values should be used in this case. A check of the reliability of the estimates of b s can be made by a sequential analysis of the cumulative infiltration data. Future developments include considering sources differing in size and establishing if the suggested β and γ values apply in general to the available BEST algorithms.

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Publication Details

Journal
Hydrological Processes
Published
2024-11-01
DOI
https://doi.org/10.1002/hyp.15330
Citations
2
Primary Topic
Soil and Unsaturated Flow
Type
article
Field-Weighted Citation Impact
0.52
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article

Parameterizing Haverkamp Model From the Steady‐State of Numerically Generated Infiltration: Influence of Algorithms for Steady‐State Selection

Massimo Iovino, Vincenzo Bagarello, Laurent Lassabatère, Dario Autovino et al.
2 citations
Hydrological Processes
Soil and Unsaturated Flow
0.52
article

Parameterizing Haverkamp Model From the Steady‐State of Numerically Generated Infiltration: Influence of Algorithms for Steady‐State Selection

Massimo Iovino, Vincenzo Bagarello, Laurent Lassabatère, Dario Autovino, Denız Yılmaz
article en
2 citations

Abstract

ABSTRACT BEST (Beerkan Estimation of Soil Transfer parameters) methods of soil hydraulic characterisation are widely applied for estimating sorptivity, S , and saturated hydraulic conductivity, K s . Calculating these properties requires choosing the β and γ parameters of the Haverkamp infiltration model. These parameters can be obtained from numerically simulated three‐dimensional (3D) infiltration runs reaching steady‐state. This investigation tested dependence of the estimated β and γ parameters on the algorithm for steady‐state selection using simulated 3D cumulative infiltrations for different soils and initial conditions. Two algorithms used the original simulation outputs and included using (i) a threshold defining steadiness (T‐algorithm) and (ii) the last four data points, yielding a reference value of steady‐state infiltration rate (R‐algorithm). A third algorithm, similar to the R‐algorithm, was applied to previously re‐sampled infiltration data at fixed time intervals (RR‐algorithm). The intercept, b s , of the straight line fitted to the data describing steady‐state on the cumulative infiltration plot depended on the applied algorithm more than the slope of this line. Consequently, β varied with the applied algorithm more than γ . The RR‐algorithm, yielding 0.62 ≤ β ≤ 1.99 and 0.74 ≤ γ ≤ 0.98, was preferred since it mediated between advantages and disadvantages of T‐ and R‐algorithms. The influence of the choice of proper values for β and γ on the estimates of S and K s was evaluated using BEST. Using the default values of β (0.6) and γ (0.75) yielded accurate estimates of S but not of K s . Soil dependent β and γ values should be used in this case. A check of the reliability of the estimates of b s can be made by a sequential analysis of the cumulative infiltration data. Future developments include considering sources differing in size and establishing if the suggested β and γ values apply in general to the available BEST algorithms.

Hydrological ProcessesVol. 38(11)
Université Claude Bernard Lyon 1 (FR), Institut polytechnique de Grenoble (FR), Centre National de la Recherche Scientifique (FR), Institut des Géosciences de l'Environnement (FR), Institut de Recherche pour le Développement (FR), École Nationale des Travaux Publics de l'État (FR), Munzur University (TR), Université Grenoble Alpes (FR), University of Palermo (IT)
Life in Land
Openalex Percentile: Top 35%
Soil and Unsaturated Flow
0.52
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