Inverse modeling of unsaturated flow using clusters of soil texture and pedotransfer functions

Characterization of heterogeneous soil hydraulic parameters of deep vadose zones is often difficult and expensive, making it necessary to rely on other sources of information. Pedotransfer functions (PTFs) based on soil texture data constitute a simple alternative to inverse hydraulic parameter estimation, but their accuracy is often modest. Inverse modeling entails a compromise between detailed description of subsurface heterogeneity and the need to restrict the number of parameters. We propose two methods of parameterizing vadose zone hydraulic properties using a combination of k-means clustering of kriged soil texture data, PTFs, and model inversion. One approach entails homogeneous and the other heterogeneous clusters. Clusters may include subdomains of the computational grid that need not be contiguous in space. The first approach homogenizes within-cluster variability into initial hydraulic parameter estimates that are subsequently optimized by inversion. The second approach maintains heterogeneity through multiplication of each spatially varying initial hydraulic parameter by a scale factor, estimated a posteriori through inversion. This allows preserving heterogeneity without introducing a large number of adjustable parameters. We use each approach to simulate a 95 day infiltration experiment in unsaturated layered sediments at a semiarid site near Phoenix, Arizona, over an area of 50 × 50 m2 down to a depth of 14.5 m. Results show that both clustering approaches improve simulated moisture contents considerably in comparison to those based solely on PTF estimates. Our calibrated models are validated against data from a subsequent 295 day infiltration experiment at the site

Signature of Obliquity and Eccentricity in Soil Chronosequences

Periodic shifts in Earth\u27s orbit alter incoming solar radiation and drive Quaternary climate cycles. However, unambiguous detection of these orbitally driven climatic changes in records of terrestrial sedimentation and pedogenesis remains poorly defined, limiting our understanding of climate change‐landscape feedbacks, impairing our interpretation of terrestrial paleoclimate proxies, and limiting linkages among pedogenesis, sedimentation, and paleoclimatic change. Using a meta‐analysis, we show that Quaternary soil ages preserved in the modern record have periodicities of 41 and 98 kyr, consistent with orbital cycles. Further, soil ages predominantly date to periods of low rates of climatic change following rapid climate shifts associated with glacial‐to‐interglacial transitions. Soil age appears linked to orbital cycles via climate‐modulated sediment deposition, which may largely constrain soil formation to distinct climate periods. These data demonstrate a record of widespread orbital cyclicity in sediment deposition and subsequent pedogenesis, providing a key insight into soil‐landscape evolution and terrestrial paleo‐environment changes

Controlled Experiments of Hillslope Coevolution at the Biosphere 2 Landscape Evolution Observatory: Toward Prediction of Coupled Hydrological, Biogeochemical, and Ecological Change

Understanding the process interactions and feedbacks among water, porous geological media, microbes, and vascular plants is crucial for improving predictions of the response of Earth’s critical zone to future climatic conditions. However, the integrated coevolution of landscapes under change is notoriously difficult to investigate. Laboratory studies are limited in spatial and temporal scale, while field studies lack observational density and control. To bridge the gap between controlled laboratory and uncontrollable field studies, the University of Arizona built a macrocosm experiment of unprecedented scale: the Landscape Evolution Observatory (LEO). LEO comprises three replicated, heavily instrumented, hillslope-scale model landscapes within the environmentally controlled Biosphere 2 facility. The model landscapes were designed to initially be simple and purely abiotic, enabling scientists to observe each step in the landscapes’ evolution as they undergo physical, chemical, and biological changes over many years. This chapter describes the model systems and associated research facilities and illustrates how LEO allows for tracking of multiscale matter and energy fluxes at a level of detail impossible in field experiments. Initial sensor, sampler, and soil coring data are already providing insights into the tight linkages between water flow, weathering, and microbial community development. These interacting processes are anticipated to drive the model systems to increasingly complex states and will be impacted by the introduction of vascular plants and changes in climatic regimes over the years to come. By intensively monitoring the evolutionary trajectory, integrating data with mathematical models, and fostering community-wide collaborations, we envision that emergent landscape structures and functions can be linked, and significant progress can be made toward predicting the coupled hydro-biogeochemical and ecological responses to global change

Using Pedotransfer Functions in Vadose Zone Models for Estimating Groundwater Recharge in Semiarid Regions

Process-based vadose zone models are becoming common tools for evaluating spatial distributions of groundwater recharge (GR), but their applications are restricted by complicated parameterizations, especially because of the need for highly nonlinear and spatially variable soil hydraulic characteristics (SHCs). In an attempt to address the scarcity of field SHC data, pedotransfer functions (PTF) were introduced in earlier attempts to estimate SHCs. However, the accuracy of this method is rarely questioned in spite of significant uncertainties of PTF-estimated SHCs. In this study, we investigated the applicability of coupling vadose zone models and PTFs for evaluating GR in sand and loamy sand soils in a semiarid region and also their sensitivity to lower boundary conditions. First, a data set containing measured SHCs was used in the simulations. A second data set contained correlated SHCs drawn from the covariance matrix of the first data set. The third SHC data set used was derived from a widely used PTF. Although standard deviations for individual parameters were known for this PTF, no covariance matrix was available. Hence, we assumed that the parameters of this PTF were uncorrelated, thereby potentially overestimating the volume of the parameter space. Results were summarized using histograms of GR for various sets of input parameters. Under the unit gradient flow lower boundary condition, the distributions of GR for sand and loamy sand significantly overlap. Values of GR based on mean SHCs (or GR*) generally lie off the mode of the GR distribution. This indicates that the routinely used method of taking GR* as a regional representation may not be viable. More importantly, the computed GR largely depends in a nonlinear fashion on the shape factor n in the van Genuchten model. Under the same meteorological conditions, a coarser soil with a larger n generally produces a higher GR. Therefore, the uncertainty in computed GR is largely determined by the uncertainty in estimated n by PTFs (e.g., mean and standard deviation). Under the constant head lower boundary condition, upward soil moisture flux may exist from the lower boundary. Especially for regions with shallow water tables where upward flux exists, choosing an appropriate lower boundary condition is more important than selecting SHC values for calculating GR. The results show that the distribution of GR is less scattered and GR is more intense if the constant head lower boundary is located at deeper depths

A High-Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically-Based Water Retention Model

This dataset provides global maps of mean values and standard deviations of five soil hydraulic parameters based on Kosugi water retention model in 1 km resolution. Calculations are estimated from Kosugi K3 pedotransfer function model (using sand, silt, clay percentage, and bulk density as input) based on the surface soil of SoilGrids 1 km data set (Hengl et al., 2014). The dataset is in GeoTIFF format, which can be read by R, python, Matlab, etc, and most GIS software. If you use the dataset, please cite our publication ^_^ . Yonggen Zhang, Marcel G. Schaap, and Yuanyuan Zha. (2018). A High-Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically-Based Water Retention Model, Water Resources Research, 54. https://doi.org/10.1029/2018WR02353

A High-Resolution Global Map of Soil Hydraulic Properties Produced by a Hierarchical Parameterization of a Physically Based Water Retention Model

A correct quantification of mass and energy exchange processes among Earth's land surface, groundwater, and atmosphere requires an accurate parameterization of soil hydraulic properties. Pedotransfer functions (PTFs) are useful in this regard because they estimate these otherwise difficult to obtain characteristics using texture and other ubiquitous soil data. Most PTFs estimate parameters of empirical hydraulic functions with modest accuracy. In a continued pursuit of improving global-scale PTF estimates, we evaluated whether improvements can be obtained when estimating parameters of hydraulic functions that make physically based assumptions. To this end, we developed a PTF that estimates the parameters of the Kosugi retention and hydraulic conductivity functions (Kosugi, 1994, , 1996, ), which explicitly assume a lognormal pore size distribution and apply the Young-Laplace equation to derive a corresponding pressure head distribution. Using a previously developed combination of machine learning and bootstrapping, the developed five hierarchical PTFs allow for estimates under practical data-poor to data-rich conditions. Using an independent global data set containing nearly 50,000 samples (118,000 retention points), we demonstrated that the new Kosugi-based PTFs outperformed two van Genuchten-based PTFs calibrated on the same data. The new PTFs were applied to a 1x1km(2) global map of texture and bulk density, thus producing maps of the parameters, field capacity, wilting point, plant available water, and associated uncertainties. Soil hydraulic parameters exhibit a much larger variability in the Northern Hemisphere than in the Southern Hemisphere, which is likely due to the geographical distribution of climate zones that affect weathering and sedimentation processes.National Natural Science Foundation of China [41807181]6 month embargo; published online: 6 November 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

2009. Quantification of uncertainty in pedotransfer function-based parameter estimation for unsaturated flow modeling

[1] While pedotransfer functions (PTFs) have long been applied to estimate soil hydraulic parameters for unsaturated flow and solute transport modeling, the uncertainty associated with the estimates is often ignored. The objective of this study is to evaluate uncertainty of the PTF-estimated soil hydraulic parameters and its effect on numerical simulation of moisture flow. Contributing to the parameter estimation uncertainty are (1) the PTF intrinsic uncertainty caused by limited data used for PTF training and (2) the PTF input uncertainty in pedotransfer variables (i.e., PTF inputs). The PTF intrinsic uncertainty is assessed using the bootstrap method by generating multiple bootstrap realizations of the soil hydraulic parameters; the realizations follow normal or lognormal distributions. The PTF input variables (i.e., bulk density and soil texture) are obtained using the cokriging technique. The PTF input uncertainty is quantified by assuming that the cokriging estimates follow a normal distribution. Our results show that the PTF input uncertainty dominates over the PTF intrinsic uncertainty and determines the spatial distribution of the PTF parameter estimation uncertainty. When the parameter estimation uncertainty is included, the spatial variability of the measured soil hydraulic parameters is better captured. This is also the case for the observed moisture contents, whose spatial variability is well bracketed by the prediction intervals. However, this is only possible after the PTF input uncertainty is considered. These results suggest that additional sample acquisition for the PTF input variables would have a more favorable impact on reduction of the parameter estimation uncertainty than collecting additional soil hydraulic parameter measurements for PTF development

Prospecting for In Situ Resources on the Moon and Mars Using Wheel-Based Sensors

The Apollo and Russian missions during 1970's were reviewed to rediscover the type and distribution of minerals on the Moon. This study revealed that the Moon is a relatively barren place in mineral content when compared with the Earth. Results from the Lunar minerals brought back to Earth, indicate that the Moon lacks water, hydroxyl ions, and carbon based minerals. Our approach to prospecting utilizes a vehicle with sensors embedded in a wheel that allow measurements while the vehicle is in motion. Once a change in soil composition is detected, decision making software stops the vehicle and analytical instruments perform a more definitive analysis of the soil. The focus of this paper is to describe the instrumentation and data from the wheel-based sensors