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has been cited by the following article:

• TITLE: Estimating Soil Hydraulic Parameters Characterizing Rainwater Infiltration and Runoff Properties of Dryland Floodplains

  AUTHORS: Sabelo S. W. Mavimbela, Leon D. van Rensburg

  KEYWORDS: Floodplain Soil Types, Hydrus-1D Model, Inverse Parameter Optimisation, Objective Function, Parameter Sensitivity, Sedimentary Crust, Rainfall Simulation

  JOURNAL NAME: Computational Water, Energy, and Environmental Engineering, Vol.8 No.1, January 29, 2019

  ABSTRACT: The two-layered (0 - 50 and 50 - 250 mm) surface horizon hydraulic parameters of three dryland floodplain soil-types under aquafer water management in Postmasburg, Northern Cape Province of South Africa were estimated with HYDRUS-1D model. Time dependent water infiltration measurements at 30 and 230 mm depths from simulated rainfalls on undisturbed 1 m2 small plots with intensities of 1.61 (high), 0.52 (medium) and 0.27 (low) mm·min-1, were minimised using a two-step inversion. Firstly, separate optimisation of the van Genuchten-Mualem model parameters for the two surface-horizon layers and secondly, simultaneous optimisation for the joint two-layered horizon with first step optimal parameters entered as initial values. The model reproduced transient water-infiltration data very well with the Nash-Sutcliffe model efficiency coefficient (NSE) of 0.99 and overestimated runoff (NSE; 0.27 to 0.98). The upper surface horizon had highly optimised and variable parameters especially θs and Ks. Optimal Ks values from higher soil surface bulk-density (≥1.69 g·cm-3) were lower by at least one order of magnitude to double ring infiltrometers and water infiltration properties were different (P α and n parameter values corresponded well with texture of the Addo (Greysols), Augrabies (Ferralsols) and Brandvlei (Cambisols) soil types. However, θs and Ksshowed greater sensitivity to model output and exerted greater influence on dryland floodplain water-infiltration and runoff characteristics. Increasing rainfall simulation period to attain near-surface saturated conditions and inclusion of surface ponding data in the inverse problem could considerable improve model prediction of hydro-physical parameters controlling surface-subsurface water distribution in fluvial environments.