Model name	Abbreviation	Equation	Fitting parameters
Five parameter models
Van Genuchten	VG1	$θ (h) = θ_{r} + (θ_{s} - θ_{r}) / {[1 + {(α h)}^{n}]}^{m}$	θ_r, θ_s, α, n, m
Fredlund-Xing	FX	$θ (h) = θ_{r} + {(θ_{s} - θ_{r}) / [\ln [2.7183 + {(α h)}^{n}]]}^{m}$	θ_r, θ_s, α, n, m
Omuto	Omuto	$θ (h) = θ_{r} + θ_{s 1} * \exp (- α_{1} h) + θ_{s 2} * \exp (- α_{2} h)$	θ_r, θ_s₁, θ_s₂, α₁, α₂
Four-parameter models
Gardner	Gard	$θ (h) = θ_{r} + (θ_{s} - θ_{r}) / [1 + α h^{n}]$	θ_r, θ_s, α, n
Brooks-Corey	BC	$θ (h) = θ_{r} + (θ_{s} - θ_{r}) / {(α h)}^{n}$	θ_r, θ_s, α, n
Kosugi	Kosugi	$θ (h) = θ_{r} + (θ_{s} - θ_{r}) Q [\ln (α h) / n]$ , Q is complimentary normal distribution function define as $Q (h) = 1 - \int_{h}^{\infty} ((\exp (- 0.5 h^{2})) / \sqrt{2 π}) d h$	θ_r, θ_s, α, n
Double exponential	Dexpo	$θ (h) = θ_{s 1} * \exp (- α_{1} h) + θ_{s 2} * \exp (- α_{2} h)$	θ_s₁, θ_s₂, α₁, α₂
Van Genuchten	VG2	$θ (h) = θ_{r} + (θ_{s} - θ_{r}) / {[1 + {(α h)}^{n}]}^{1 - 1 / n}$	θ_r, θ_s, α, n
Russo	Ruso	$θ (h) = θ_{r} + (θ_{s} - θ_{r}) / {[(1 + 0.5 * {(α h)}^{n}) * \exp (- 0.5 * (α h))]}^{2 / (n + 2)}$	θ_r, θ_s, α, n
Three-parameter models
McKee-Bumb	MB	$θ (h) = θ_{r} + (θ_{s} - θ_{r}) * \exp (- α h)$	θ_r, θ_s, α
Campbell	Camp	$θ (h) = θ_{s} * {(α h)}^{n}$	θ_s, α, n
Tani	Tani	$θ (h) = θ_{r} + (θ_{s} - θ_{r}) * [1 + (α h)] * \exp (- α h)$	θ_r, θ_s, α