Estimating Soil Hydraulic Parameters by Inverse Modeling with PEST

Abstract

Core Ideas Coupling SWAP with PEST, soil moisture and ET are included in inverse modeling of soil water flow. Multi‐objective optimization could improve model predictions and reduce parameter uncertainty. Observation errors and frequency and spatial arrangement impact predictions and uncertainty. The accurate estimation of soil hydraulic properties is an important part of the application of hydrological models for quantifying water transport in the vadose zone. Various inverse models have been developed to solve hydraulic properties optimization problems, and the accuracy of the results obtained by different algorithms is still debated because of the inherent ill‐posedness of such problems. In this study, we coupled an agrohydrological Soil–Water–Atmosphere–Plant (SWAP) model with an independent parameter estimation program (PEST) to calibrate the soil hydraulic parameters and investigate the uncertainty in the parameter estimations. The objectives of this study were to assess to what extent the SWAP model can be calibrated from a single observation (only soil moisture, θ) and further to investigate whether involving additional evapotranspiration fluxes including actual evapotranspiration (ETa), actual evaporation (Ea), or actual transpiration (Ta) can lead to an improvement in model predictions and a reduction in parameter uncertainty. Extensive synthetic experiments were conducted to achieve the objectives. Both double (winter wheat [Triticum aestivum L.]–summer maize [Zea mays L.]) and single (only winter wheat or only summer maize) cropping systems were considered in the multi‐objective optimizations. The results indicate that although the addition of evapotranspiration fluxes does not necessarily improve the accuracy of the soil moisture prediction, it can reduce the parameter uncertainty for both single‐ and double‐cropping systems. Moreover, the parameter estimation of the topsoil layer could greatly benefit from the addition of Ea, whereas the addition of Ta could help reduce the parameter uncertainty of the lower layers.

Bayesian Inversion of Soil Hydraulic Properties from Simplified Evaporation Experiments: Use of DREAM(ZS) Algorithm

“…At first, we introduced a stochastic oscillation on the potential evaporation in the synthetic experiment of the loam example, which was synthetically determined from a stochastic Gauss noise [55]:…”

Thermal, Moisture, and Solute Transport Responses Effects on Unsaturated Soil Hydraulic Parameters Estimation

“…The soil moisture is the main regulator of the soil thermal and hydrochemical properties. Although advancements have been made in understanding heat, water, and solute transport in unsaturated soil, the estimation of characteristic parameters by considering transient water content and heat transfer processes remains a great challenge in soil science community (Abbaspour et al, 2001;Li et al, 2017;Ritter et al, 2003). Accurate model in conjunction to effective, reliable strategies are needed to estimate parameters of unsaturated zone (Baroni et al, 2010).…”

“…In these approaches, the model measurements are constructed such as to identify optimum characteristic parameters of the problem. This allows fast computation of problem parameters without any constraints on the form of the complexity of the model (Abbasi et al, 2003;Lemoubou et al, 2019;Li et al, 2017). Initial and boundary conditions are easily implemented based on equivalent inverse procedure.…”

“…This is generally linked to many difficulties such as a weak formulation and/or solution of the forward model, multiple parameter estimations, and high intercorrelation among parameters (Abbaspour et al, 2001;Baroni et al, 2010;Seki et al, 2015). This posed apparent constraints to formulate appropriate objective function which even in more complex models may not be sensitive enough to one or more parameters estimate (Li et al, 2017;Wohling et al, 2008). Despite the more pronounce advantage offers with inverse strategies, a major difficulty of partially saturated domains remains in well evaluating and estimating parameters of coupled transport model which also relies to an appropriate approximation of the forward solution.…”