J.A. Huisman

On the value of soil moisture measurements in vadose zone hydrology: A review

[1]xa0We explore and review the value of soil moisture measurements in vadose zone hydrology with a focus on the field and catchment scales. This review is motivated by the increasing ability to measure soil moisture with unprecedented spatial and temporal resolution across scales. We highlight and review the state of the art in using soil moisture measurements for (1) estimation of soil hydraulic properties, (2) quantification of water and energy fluxes, and (3) retrieval of spatial and temporal dynamics of soil moisture profiles. We argue for the urgent need to have access to field monitoring sites and databases that include detailed information about variability of hydrological fluxes and parameters, including their upscaled values. In addition, improved data assimilation methods are needed that fully exploit the information contained in soil moisture data. The development of novel upscaling methods for predicting effective moisture fluxes and disaggregation schemes toward integrating large-scale soil moisture measurements in hydrological models will increase the value of soil moisture measurements. Finally, we recognize a need to develop strategies that combine hydrogeophysical measurement techniques with remote sensing methods.

Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion

Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion A.C. Hinnell 1 , T.P.A. Ferre 1 , J.A. Vrugt 2 , J.A. Huisman 3 , S. Moysey 4 , J Rings 3 , and M.B. Kowalsky 5 Hydrology and Water Resources, University of Arizona, Tucson, AZ, 85721-0011 Center for Nonlinear Studies (CNLS), Mail Stop B258, Los Alamos, NM 87545 ICG 4 Agrosphere, Forschungszentrum Julich, 52425 Julich, Germany Environmental Engineering and Earth Sciences, Clemson University, Clemson, S.C. 29634 Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720 Abstract There is increasing interest in the use of multiple measurement types, including indirect (geophysical) methods, to constrain hydrologic interpretations. To date, most examples integrating geophysical measurements in hydrology have followed a three-step, uncoupled inverse approach. This approach begins with independent geophysical inversion to infer the spatial and/or temporal distribution of a geophysical property (e.g. electrical conductivity). The geophysical property is then converted to a hydrologic property (e.g. water content) through a petrophysical relation. The inferred hydrologic property is then used either independently or together with direct hydrologic observations to constrain a hydrologic inversion. We present an alternative approach, coupled inversion, which relies on direct coupling of hydrologic models and geophysical models during inversion. We compare the abilities of coupled and uncoupled

Seasonal and event dynamics of spatial soil moisture patterns at the small catchment scale

[1] Our understanding of short- and long-term dynamics of spatial soil moisture patterns is limited due to measurement constraints. Using new highly detailed data, this research aims to examine seasonal and event-scale spatial soil moisture dynamics in the topsoil and subsoil of the small spruce-covered Wustebach catchment, Germany. To accomplish this, univariate and geo-statistical analyses were performed for a 1 year long 4-D data set obtained with the wireless sensor network SoilNet. We found large variations in spatial soil moisture patterns in the topsoil, mostly related to meteorological forcing. In the subsoil, temporal dynamics were diminished due to soil water redistribution processes and root water uptake. Topsoil range generally increased with decreasing soil moisture. The relationship between the spatial standard deviation of the topsoil soil moisture (SD� ) and mean water content (� ) showed a convex shape, as has often been found in humid temperate climate conditions. Observed scatter in topsoil SD� (� ) was explained by seasonal and event-scale SD� (� ) dynamics, possibly involving hysteresis at both time scales. Clockwise hysteretic SD� (� ) dynamics at the event scale were generated under moderate soil moisture conditions only for intense precipitation that rapidly wetted the topsoil and increased soil moisture variability controlled by spruce throughfall patterns. This hysteretic effect increased with increasing precipitation, reduced root water uptake, and high groundwater

Electromagnetic induction calibration using apparent electrical conductivity modelling based on electrical resistivity tomography

Electromagnetic parameters of the subsurface such as electrical conductivity are of great interest for non-destructive determination of soil properties (e.g., clay content) or hydrologic state variables (e.g., soil water content). In the past decade, several non-invasive geophysical methods have been developed to measure subsurface parameters in situ . Among these methods, electromagnetic (EM) induction appears to be the most efficient one that is able to cover large areas in a short time. However, this method currently does not provide absolute values of electrical conductivity due to calibration problems, which hinders a quantitative analysis of the measurement. In this study, we propose to calibrate EM induction measurements with electrical conductivity values measured with electrical resistivity tomography (ERT). EM induction measures an apparent electrical conductivity at the surface, which represents a weighted average of the electrical conductivity distribution over a certain depth range, whereas ERT inversion can provide absolute values for local conductivities as a function of depth. EM induction and ERT measurements were collected along a 120-metre-long transect. To reconstruct the apparent electrical conductivity measured with EM induction, the inverted ERT data were used as input in an electromagnetic forward modelling tool for magnetic dipoles over a horizontally layered medium considering the frequencies and offsets used by the EM induction instruments. Comparison of the calculated and measured apparent electrical conductivities nshows very similar trends but a shift in absolute values, which is attributed to system calibration problems. The observed shift can be corrected for by linear regression. This new calibration strategy nfor EM induction measurements now enables the quantitative mapping of electrical conductivity nvalues over large areas.

Soil hydrology: Recent methodological advances, challenges, and perspectives

Technological and methodological progress is essential to improve our understanding of fundamental processes in natural and engineering sciences. In this paper, we will address the potential of new technological and methodological advancements in soil hydrology to move forward our understanding of soil water related processes across a broad range of scales. We will focus on advancements made in quantifying root water uptake processes, subsurface lateral flow, and deep drainage at the field and catchment scale, respectively. We will elaborate on the value of establishing a science-driven network of hydrological observatories to test fundamental hypotheses, to study organizational principles of soil hydrologic processes at catchment scale, and to provide data for the development and validation of models. Finally, we discuss recent developments in data assimilation methods, which provide new opportunities to better integrate observations and models and to improve predictions of the short-term evolution of hydrological processes.

An empirical vegetation correction for soil water content quantification using cosmic ray probes

Cosmic ray probes are an emerging technology to continuously monitor soil water content at a scale significant to land surface processes. However, the application of this method is hampered by its susceptibility to the presence of aboveground biomass. Here we present a simple empirical framework to account for moderation of fast neutrons by aboveground biomass in the calibration. The method extends the N0-calibration function and was developed using an extensive data set from a network of 10 cosmic ray probes located in the Rur catchment, Germany. The results suggest a 0.9% reduction in fast neutron intensity per 1 kg of dry aboveground biomass per m2 or per 2 kg of biomass water equivalent per m2. We successfully tested the novel vegetation correction using temporary cosmic ray probe measurements along a strong gradient in biomass due to deforestation, and using the COSMIC, and the hmf method as independent soil water content retrieval algorithms. The extended N0-calibration function was able to explain 95% of the overall variability in fast neutron intensity.

Inverse determination of heterotrophic soil respiration response to temperature and water content under field conditions

Heterotrophic soil respiration is an important flux within the global carbon cycle. Exact knowledge of the response functions for soil temperature and soil water content is crucial for a reliable prediction of soil carbon turnover. The classical statistical approach for the in situ determination of the temperature response (Q10 or activation energy) of field soil respiration has been criticised for neglecting confounding factors, such as spatial and temporal changes in soil water content and soil organic matter. The aim of this paper is to evaluate an alternative method to estimate the temperature and soil water content response of heterotrophic soil respiration. The new method relies on inverse parameter estimation using a 1-dimensional CO2 transport and carbon turnover model. Inversion results showed that different formulations of the temperature response function resulted in estimated response factors that hardly deviated over the entire range of soil water content and for temperature below 25°C. For higher temperatures, the temperature response was highly uncertain due to the infrequent occurrence of soil temperatures above 25°C. The temperature sensitivity obtained using inverse modelling was within the range of temperature sensitivities estimated from statistical processing of the data. It was concluded that inverse parameter estimation is a promising tool for the determination of the temperature and soil water content response of soil respiration. Future synthetic model studies should investigate to what extent the inverse modelling approach can disentangle confounding factors that typically affect statistical estimates of the sensitivity of soil respiration to temperature and soil water content.

Predicting subgrid variability of soil water content from basic soil information

Knowledge of unresolved soil water content variability within model grid cells (i.e., subgrid variability) is important for accurate predictions of land-surface energy and hydrologic fluxes. Here we derived a closed-form expression to describe how soil water content variability depends on mean soil water content (σθ( )) using stochastic analysis of 1-D unsaturated gravitational flow based on the van Genuchten-Mualem (VGM) model. A sensitivity analysis showed that the n parameter strongly influenced both the shape and magnitude of the maximum of σθ( ). The closed-form expression was used to predict σθ( ) for eight data sets with varying soil texture using VGM parameters obtained from pedotransfer functions that rely on available soil information. Generally, there was good agreement between observed and predicted σθ( ) despite the obvious simplifications that were used to derive the closed-form expression. Furthermore, the novel closed-form expression was successfully used to inversely estimate the variability of hydraulic properties from observed σθ( ) data.

Integrated analysis of waveguide dispersed GPR pulses using deterministic and Bayesian inversion methods

Ground-penetrating radar (GPR) data affected by waveguide dispersion are not straightforward to analyse. Therefore, waveguide dispersed common midpoint measurements are typically interpreted using so-called dispersion curves, which describe the phase velocity as a function of frequency. These dispersion curves are typically evaluated with deterministic optimization algorithms that derive the dielectric properties of the subsurface as well as the location and depth of the respective layers. However, these methods do not provide estimates of the uncertainty of the inferred subsurface properties. Here, we applied a formal Bayesian inversion methodology using the recently developed DiffeRential Evolution Adaptive Metropolis DREAM (ZS) algorithm. This Markov Chain Monte Carlo simulation method rapidly estimates the (non-linear) parameter uncertainty and helps treat the measurement error explicitly. We found that the frequency range used in the inversion has an important influence on the posterior parameter estimates, essentially because parameter sensitivity varies with measurement frequency. Moreover, we established that the measurement error associated with the dispersion curve is frequency dependent and that the estimated model parameters become severely biased if this frequency-dependent nature of the measurement error is not properly accounted for. We estimated these frequency-dependent measurement errors together with the model parameters using the DREAM (ZS) algorithm. The posterior distribution of the model parameters derived in this way compared well with inversion results for a reduced frequency bandwidth. This more subjective method is an alternative to reduce the bias introduced by this frequencydependent measurement error. Altogether, our inversion procedure provides an integrated and objective methodology for the analysis of dispersive GPR data and appropriately treats the measurement error and parameter uncertainty.

Estimating effective model parameters for heterogeneous unsaturated flow using error models for bias correction

[1]xa0Estimates of effective parameters for unsaturated flow models are typically based on observations taken on length scales smaller than the modeling scale. This complicates parameter estimation for heterogeneous soil structures. In this paper we attempt to account for soil structure not present in the flow model by using so-called external error models, which correct for bias in the likelihood function of a parameter estimation algorithm. The performance of external error models are investigated using data from three virtual reality experiments and one real world experiment. All experiments are multistep outflow and inflow experiments in columns packed with two sand types with different structures. First, effective parameters for equivalent homogeneous models for the different columns were estimated using soil moisture measurements taken at a few locations. This resulted in parameters that had a low predictive power for the averaged states of the soil moisture if the measurements did not adequately capture a representative elementary volume of the heterogeneous soil column. Second, parameter estimation was performed using error models that attempted to correct for bias introduced by soil structure not taken into account in the first estimation. Three different error models that required different amounts of prior knowledge about the heterogeneous structure were considered. The results showed that the introduction of an error model can help to obtain effective parameters with more predictive power with respect to the average soil water content in the system. This was especially true when the dynamic behavior of the flow process was analyzed.