Near‐surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithm: 1. Conceptual modeling

Abstract

We used a genetic algorithm (GA) to identify soil water retention θ(h) and hydraulic conductivity K(h) functions by inverting a soil‐water‐atmosphere‐plant (SWAP) model using observed near‐surface soil moisture (0‐5 cm) as search criterion. Uncertainties of parameter estimates were estimated using multipopulations in GA and considering data and modeling errors. Three hydrologic cases were considered: (1) homogenous free‐draining soil column, (2) homogenous soil column with shallow water table, and (3) heterogeneous soil column under free‐drainage condition, considering three different rainfall patterns in northern Texas. Results (cases 1 and 2) showed the identifiability of soil hydraulic parameters improving at coarse and fine scales of the soil textural class. Medium‐textured soils posed identifiability problems when the soil is dry. Nonlinearity in θ(h) and K(h) is greater at drier conditions, and some parameters are less sensitive for estimation. Flow regimes controlled by upward fluxes were found less successful, as the information content of observed near‐surface data may no longer influence the hydrologic processes in the subsurface. The identifiability of soil hydraulic parameters was found better when the soil profile is predominantly draining. In case 3, top soil layer hydraulic properties were defined using near‐surface data alone as criterion. Adding evapotranspiration (ET) improved identification of the second soil layer, although not all parameters were identifiable. Under uncertainties, θ(h) was found to be well defined while K(h) is more uncertain. Finally, we applied the method to a validation site in Little Washita watershed, Oklahoma, where derived effective soil hydraulic properties closely matched the measured ones at the field site.

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

“…Feasible ranges of the parameters (i.e., search spaces in a modified-microGA) for each model were defined based on literature related to the model parameter sensitivity and to accommodate a diversity of soils ranging from clay to sandy loam Liu et al, 2004;Ines and Mohanty, 2008;Rosero et al, 2010;Shin et al, 2012].…”

“…They showed that GA found better optimized model parameters than others, although a large number of computational resources were required. Further, the near-surface [Ines and Mohanty, 2008] and layer-specific data assimilation [Shin et al, 2012] approaches using GA coupled with SWAP based on inversion model were developed for quantifying effective soil hydraulic properties in the homogeneous and heterogeneous soil profiles. Their findings indicated that the estimated effective soil parameters at the near-surface and subsurface layers can be adequately conditioned by GA.…”

Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications

“…With the restarting technique, the MOGA provides new genetic materials through the creep and jump mutation operators [Ines and Honda, 2005]. The MOGA always remembers the previous (gÀ1) elite chromosomes and reproduce in the next generation [Ines and Mohanty, 2008a]. We integrated a random resampling (ensemble e) algorithm (IBM Programmers' Guide) [Efron, 1982] into the MOGA for searching more unknown spaces, called EMOGA.…”

Development of a Web-Based L-THIA 2012 Direct Runoff and Pollutant Auto-Calibration Module Using a Genetic Algorithm

“…GA is one of the existing powerful search algorithms developed by Holland [18] and Goldberg [24]. To date, GAs have been extended/improved through various updated versions [24][25][26][27][28][29]. Parameters (P 1 = {CN i = 1,…,M }; P 2 = {EMC j = 1,…,N }) to be searched are represented by chromosomes (genes) in an array.…”