Multistep optimization of HyPix model for flexible vertical scaling of soil hydraulic parameters

Efficient simulation of water-flow processes in the vadose zone is crucial to increase agricultural productivity within environmental limits. This requires deriving detailed soil hydraulic parameters of the soil profile that is highly challenging, particularly for heterogeneous soils. We therefore developed an alternative indirect methodology to calibrate the hydraulic parameters from soil water content time series measured at multiple depths by using the new physically based hydrological model HyPix. We propose a novel, efficient, multistep optimization algorithm for layered soils that derives an optimal set of hydraulic parameters for a desired number of soil layers. For each selected soil layer, HyPix derives five physical, bimodal, Kosugi hydraulic parameters that describe the soil water retention and hydraulic conductivity by using a novel algorithm that reduces the degree of sensitivity and freedom of the parameters. The optimization algorithm upscales the soil hydraulic parameters by gradually incorporating the soil heterogeneity. This method overcomes the problems associated with optimization of the hydraulic parameters of each layer individually, which leads to poor results because it does not represent the cohesive soil water dynamics across the unsaturated zone. We tested the method using soil water content measurements at different depths at five heterogeneous experimental sites in New Zealand. We show how the accuracy of the simulated water balance components increases with the number of soil layers. The multistep optimization upscales a detailed, layered profile of soil hydraulic parameters into a model with fewer layers. The methodology developed provides an estimate of the uncertainty of using a reduced number of soil layers. We also show that a pedological description can provide an indication of the minimum soil layers of vertical discretization required to accurately compute the soil water balance components.New Zealand Ministry of Business, Innovation and Employment throught the ‘Winning Against Wildings, C09X1611’‘Next Generation S-map, C09X1612’ research programmesy University of Granada/CBUA, Spai

How important is the description of soil unsaturated hydraulic conductivity values for simulating soil saturation level, drainage and pasture yield?

Accurate simulation of soil water dynamics is a key factor when using agricultural models for guiding management decisions. However, the determination of soil hydraulic properties, especially unsaturated hydraulic conductivity, is challenging and measured data are scarce. We investigated the use of APSIM (Agricultural Production Simulation Model) with SWIM3 as the water module, based on Richards equation and a bimodal pore system, to determine likely ranges of the hydraulic conductivity at field capacity (K-10; assumed at a matric potential of −10 kPa) for soils representing different drainage characteristics. Hydraulic conductivity measurements of soils with contrasting soil drainage characteristics and values for K-10 were extracted from New Zealand’s national soil database. The K-10 values were then varied in a sensitivity analysis from 0.02 to 5 mm d−1 for well-drained soils, from 0.02 to 1 mm d−1 for moderately well-drained soils, and from 0.008 to 0.25 mm d−1 for poorly drained soils. The value of K-10 had a large effect on the time it took for the soil to drain from saturation to field capacity. In contrast, the saturated hydraulic conductivity value had little effect. Simulations were then run over 20 years using two climatic conditions, either a general climate station for all seven different soils, or site-specific climate stations. Two values for K-10 were used, either the APSIM default value, or the soil-specific measured K-10. The monthly average soil saturation level simulated with the latter has a better correspondence with the morphology of the seven soils. Finally, the effect of K-10 on drainage and pasture yield was investigated. Total annual drainage was only slightly affected by the choice of K-10, but pasture yield varied substantially.Ministry of Business, Innovation and Employment’s Endeavour Fund, through the Manaaki Whenua-led ‘Next Generation S-map’ research programme, C09X161

Derivation of physically based soil hydraulic parameters in New Zealand by combining soil physics and hydropedology

Field-characterised soil morphological data (to 1 m depth) and modelled soil water release characteristics are recorded in the S-map database for soils cover- ing approximately 40% of New Zealand's soil area. This paper shows the devel- opment of the Smap-Hydro database that estimates hydraulic parameters by synergising soil morphologic data recorded in S-map and soil physics. The Smap-Hydro parameters were derived using the bi-modal Kosugi hydraulic function. The validity of the Smap-Hydro parameters was tested by applying them within an uncalibrated physically based hydrological model (HyPix) and comparing results with soil water content, θ, measured with Aquaflex soil moisture probes (0–40 cm deep) at 24 sites across New Zealand. The HyPix model provided an excellent fit with observed soil water content for 25% of the sites, a good fit for 33% of the sites and a poor fit for 42% of the sites. Applying the model to all soils in the S-map database required adjustments for the occurrence of rock fragments, hydraulic discontinuities caused by soil pans and required the addition of boundary conditions for water tables and the occurrence of impermeable rock. A discussion on how we can further syner- gise the development of pedotransfer functions with knowledge of soil physics is provided

Improved prediction of water retention curves for fine texture soils using an intergranular mixing particle size distribution model

Laboratory measurements to derive the soil water retention curve, , are time consuming and expensive. We present a cost-effective alternative using particle size distribution (PSD) and saturated water content. We propose a novel physical conceptual intergranular mixing PSD model (IMP model) which derives from PSD, exploiting the relation between particle size and pore size distributions and the intergranular arrangement of the soil particles. The IMP model successfully predicts for fine texture soil, which is the most challenging soil texture to be modelled. With our novel model, reliable can be obtained using only three general fitting parameters without needing to assume any particular type of soil particle packing, with mean Nash–Sutcliffe efficiency coefficient of 0.92 for 259 soils. The IMP model can accurately predict for fine texture soils because: a) it implements an intergranular mixing function that accounts for soil pores not all being perfectly spherical and takes into consideration the intergranular rearrangement (mixing) of the particles, which allows neighbouring particles to have different sizes resulting in variations in pore radius and pore shape of the corresponding pore fraction; b) it overcomes the absence of PSD data for sizes smaller than the clay fraction by developing a normalised form of the Young–Laplace capillary equation; and c) the residual pore volume accounting for water strongly bound to solid particles or in very small pores is incorporated as a function of the clay fraction

A Linking Test that investigates the feasibility of inverse modelling: application to a simple rainfall interception model for Mt Gambier, southeast South Australia

International audienceInterception loss has an important influence on the water yield of forested areas. Nevertheless, in most studies stemflow is not measured, therefore the question of how to determine the feasibility of optimizing interception and stemflow parameters simultaneously by matching daily simulated throughfall to fortnightly measurements of cumulative throughfall is an important one. By applying a daily empirical interception model, a goodness fit of 2·2 mm/day is obtained between observed and simulated cumulative throughfall. However, by applying the simple but robust Linking Test, it was shown that the parameters are non-unique and falsely linked, i.e. inter-relationships between different vegetation parameter sets give similar throughfall but non-unique net precipitation. The Linking Test investigates the causes of obtaining falsely linked parameters and shows that objective equifinality is not the source of the problem. Objective equifinality occurs when an inappropriate objective function is used. The Linking Test also shows that falsely linked parameters are not caused by measuring throughfall on a non-daily basis (termed frequency sampling equifinality). By expanding the interception model to the second degree, it was found that the non-uniqueness is due to the inherent nature of interception and stemflow functions that behave similarly and therefore can easily compensate each other (termed similarity equifinality). It is also shown that a simple daily empirical exponential interception model developed for conifers in the uplands of the United Kingdom is suitable to model interception in Pinus radiata plantations in the Mediterranean climate of southern Australia by using only daily gross precipitation data and two parameters

Bimodal unsaturated hydraulic conductivity derived from water retention parameters by accounting for clay‒water interactions: deriving a plausible set of hydraulic parameters

We developed a novel, lognormal, pore-scale, unsaturated hydraulic conductivity model, K(ψ)model, which does not require saturated hydraulic conductivity, Ks, as an input parameter. K(ψ)model is derived solely from hydraulic parameters describing a bimodal, lognormal, pore-scale, soil water retention curve θ(ψ). The K(ψ)model is based on the Hagen‒Poiseuille equation, which represents the soil as a bundle of parallel, non-intersecting capillary tubes. To improve the modelling of fine-textured soils we introduced a novel model to consider the clay‒water interaction. This model assumes that clay‒water interaction occurs for soils having more than 30% of clay and an effective matrix porosity greater than 35%. Compared to previously developed models, the K(ψ)model does not require the use of integrals and can be computed from a spreadsheet and distinguishes between macropore (non-equilibrium) and matrix (equilibrium) flows. The K(ψ)model gives improved results when the hydraulic parameters are dynamically constrained and when θ(ψ) describes a bimodal, lognormal distribution. Suggested Reviewers

Deriving physical and unique bimodal soil Kosugi hydraulic parameters from inverse modelling

International audienceHydraulic parameters define the water retention, θ(ψ), and the unsaturated hydraulic conductivity, K(θ), functions. These functions are usually obtained by fitting experimental data using inverse modelling. The drawback of inverting the hydraulic parameters is that they suffer from non-uniqueness and the optimal hydraulic parameters may not be physical. To reduce the non-uniqueness, it is necessary to invert the hydraulic parameters simultaneously from observations of θ(ψ) and K(θ), and ensure the measurements cover the full range of θ from saturated to oven dry. The challenge of using bimodal θ(ψ) and K(θ) compared to unimodal functions is that it requires double the number of parameters, one set for the matrix and another set for the macropore domain. The objective of this paper is to address this shortcoming by deriving a procedure to reduce the number of parameters to be optimized to obtain a unique physical set of bimodal soil Kosugi hydraulic parameters from inverse modelling. To achieve this, we (1) derive residual volumetric soil water content from the Kosugi standard deviation parameter of the soil matrix, (2) derive macropore hydraulic parameters from the water pressure head threshold between macropore and matrix flow, and (3) dynamically constrain the Kosugi hydraulic parameters of the soil matrix. The procedure successfully reduces the number of optimized hydraulic parameters and dynamically constrains the hydraulic parameters without compromising the fit of the θ(ψ) and K(θ) functions, and the derived hydraulic parameters are more physical. The robustness of the methodology is demonstrated by deriving the hydraulic parameters exclusively from θ(ψ) and Ks data, enabling satisfactory prediction of K(θ) even when no additional K(θ) data are available

Sensitivity analysis of land and water productivities predicted with an empirical and a process-based root water uptake function

Rootzone hydraulic conditions govern root water uptake and transpiration under drought stress. Process-based approaches to predict the soil water status are advocated for an improved simulation of soil hydrology and crop yield. We investigated the sensitivity to system parameters in root water uptake simulation using a process- based function (MFlux) and an empirical function (Feddes) embedded in the SWAP hydrological model, applied to irrigated pasture scenarios in New Zealand. Data from two locations and three soils were used to simulate 42 growing seasons. The sensitivity analysis of both Feddes and MFlux parameters was performed for a rainfed and two irrigated scenarios, one triggering irrigation based on relative evapotranspiration (I–ETr), the other based on common practice using total available water (I–TAW). Results confirm that some parameters of the MFlux function are more sensitive than those from the Feddes function and both functions support the I–ETr criterion to optimize the water use in grazed pastures in New Zealand

HyPix: 1D physically based hydrological model with novel adaptive time-stepping management and smoothing dynamic criterion for controlling Newton–Raphson step

International audienceThe newly developed open-source Hydrological Pixel model, HyPix, written in the fast and flexible Julia lan- guage, efficiently solves the mixed form of the Richardson–Richards’ equation (RRE). HyPix uses a cell-centred, finite-volume scheme for the spatial discretization, with an implicit Euler scheme for the temporal discretization, by using the weighted average inter-cell hydraulic conductivity. HyPix includes the following modules: (a) rainfall interception, (b) root water uptake with compensation algorithm and root growth, (c) soil evaporation, (d) ponding using a novel method for computing sorptivity, and (e) runoff. HyPix includes a wide range of top and boundary conditions (flux, pressure, free drainage). To control the Newton–Raphson iterations, HyPix incorporates a novel dynamic physical smoothing criterion, which improves not only the model performance but also its accuracy compared with using the traditional absolute convergence criterion. To control the time-step, the traditional physical time-step management based on changes in the soil water content was specifically designed to solve RRE based on soil water content. This work adapts the time-step management such that it is specifically designed to solve RRE based on soil water pressure without introducing further parameters. The novel time-step management also requires only one parameter and was found to be more efficient than the traditional time-step management. HyPix implements an option to solve the derivatives numerically, enabling the RRE to be modified and tested (e. g., the inter-cell hydraulic conductivity) by changing only a few lines of code. Numerically calculating de- rivatives was found to be as accurate as deriving the derivatives analytically, and only 10–25% slower.The well-established hydrological model HYDRUS was used to validate HyPix without the sink term. The HyPix results show good agreement to HYDRUS, validating the algorithms implemented in HyPix. Even for challenging conditions, HyPix can provide accurate and reliable results using the recommended standard op- tions. Moreover, the algorithm developed in HyPix is more efficient than the one used in HYDRUS, particularly for coarse texture soils. The recommended options were also tested by running HyPix with sink term using field data