Soil moisture variability of root zone profiles within SMEX02 remote sensing footprints

Remote sensing of soil moisture effectively provides soil moisture at a large scale, but does not explain highly heterogeneous soil moisture characteristics within remote sensing footprints. In this study, field scale spatio-temporal variability of root zone soil moisture was analyzed. During the Soil Moisture Experiment 2002 (SMEX02), daily soil moisture profiles (i.e., 0–6, 5–11, 15–21, and 25–31 cm) were measured in two fields in Walnut Creek watershed, Ames, Iowa, USA. Theta probe measurements of the volumetric soil moisture profile data were used to analyze statistical moments and time stability and to validate soil moisture predicted by a simple physical model simulation. For all depths, the coefficient of variation of soil moisture is well explained by the mean soil moisture using an exponential relationship. The simple model simulated very similar variability patterns as those observed.As soil depth increases, soil moisture distributions shift from skewed to normal patterns. At the surface depth, the soil moisture during dry down is log-normally distributed, while the soil moisture is normally distributed after rainfall. At all depths below the surface, the normal distribution captures the soil moisture variability for all conditions. Time stability analyses show that spatial patterns of sampling points are preserved for all depths and that time stability of surface measurements is a good indicator of subsurface time stability. The most time stable sampling sites estimate the field average root zone soil moisture value within ±2.1% volumetric soil moisture.     

A new tool for hillslope hydrologists: spatially distributed groundwater level and soilwater content measured using electromagnetic induction

New methods for obtaining and quantifying spatially distributed subsurface moisture are a high research priority in process hydrology. We use simple linear regression analyses to compare terrain electrical conductivity measurements (EC) derived from multiple electromagnetic induction (EMI) frequencies to a distributed grid of water‐table depth and soil‐moisture measurements in a highly instrumented 50 by 50 m hillslope in Putnam County, New York. Two null hypotheses were tested: H0₍₁₎, there is no relationship between water table depth and EC; H0₍₂₎, there is no relationship between soil moisture levels and EC. We reject both these hypotheses. Regression analysis indicates that EC measurements from the low frequency EM31 meter with a vertical dipole orientation could explain over 80% of the variation in water‐table depth across the test hillslope. Despite zeroing and sensitivity problems encountered with the high frequency EM38, EC measurements could explain over 70% of the gravimetrically determined soil‐moisture variance. The use of simple moisture retrieval algorithms, which combined EC measurements from the EM31 and EM38 meters in both their vertical and horizontal orientations, helped increase the r² coefficients slightly. This first hillslope hydrological analysis of EMI technology in this way suggests that it may be a promising method for the collection of a large number of distributed soilwater and groundwater depth measurements with a reasonable degree of accuracy. Copyright © 2003 John Wiley & Sons, Ltd.     

Sensitivity of soil moisture field evolution to rainfall forcing

In this paper the temporal behaviour of soil moisture is modelled and statistically characterized by use of the zero‐dimensional model for soil moisture dynamics and the rectangular pulses Poisson process model for rainfall forcing. The mean, covariance and spectral density function of soil moisture (both instantaneous and locally averaged cases) are analytically derived to evaluate its sensitivity to the model parameters. Finally, the probability density function of soil moisture is derived to evaluate the effect of rainfall forcing. All the model parameters used have been tuned to the Monsoon '90 data. Results can be summarized as follows. (1) Only the soil moisture model parameters (η and nZ_r) are found to affect the autocorrelation function in a distinguishable manner. On the other hand, both the rainfall model parameter (θ) and the effective soil depth (nZ_r) are found to be of impact to the soil moisture spectrum. However, as the smoothing (or damping) effect of soil is so dominant, about ±20% variation of one parameter seems not to affect significantly the second‐order statistics of soil moisture. (2) More difference can be found by applying a longer averaging time, which is found to obviously decrease the variance but increase the correlation even though no overlapping between neighbouring soil moisture data was allowed. (3) Among rainfall model parameters, the arrival rate (λ) was found to be most important for the soil moisture evolution. When increasing the arrival rate of rainfall, the histogram of soil moisture shifts its peak to a certain value as well as becomes more concentrated around the peak. However, by decreasing the arrival rate of rainfall, a much smaller (almost to zero) mean value of soil moisture was estimated, even though the total volume of rainfall remained constant. This indicates that desertification may take place without decreasing the total volume of rainfall. Copyright © 2005 John Wiley & Sons, Ltd.     

Soil moisture dynamics in coastal savanna soils in the tropics under different soil management practices

Abstract

Soil moisture is important for crop cultivation and its adequacy to meet crop-water requirements is determined by the degree of soil management practised and the quantity of water applied to the soil. This study investigates soil moisture dynamics on three plots: Bare (clean, weeds removed), Weedy (kept weedy), and Mulched (cleared of weeds and fully covered with grass mulch) during rainy and dry periods at the Teaching and Research Farm at the University of Cape Coast, in the coastal savanna zone of Ghana. Soil moisture dynamics under different levels of soil compaction were also studied. A Massey Ferguson tractor (MF265) was used to compact the soil at various levels by making 0, 1, 5, 9 and 13 passes. During both the rainy and the dry periods, moisture retention in the soil under bare, weedy and mulched plots increased with depth. During the rainy period, the mean soil moisture retention was in the order: Mulched > Weedy > Bare at both 0–20 cm and 20–40 cm depths. Within a 7–day period, soil moisture measurements from a day after heavy rainfall (intensity > 7 mm h^?1) gave mean moisture losses of 2.7, 4.1 and 3.9% for the Bare, Weedy and Mulched plots, respectively. During the dry period, however, the mean soil moisture retention was of the order: Mulched > Bare > Weedy at both 0–20 cm and 20–40 cm depths. Mean moisture loss during a 7–day dry period was 4.5, 2.9 and 3.4% for the Bare, Weedy and Mulched plots, respectively. Under different levels of soil compaction, the mean moisture retention in the soil increased from 8.3% at 0 pass to 17.8% at 13 passes within the 0–20 cm depth, whilst it decreased from 13.3 to 5.9% from 0 to 13 passes, respectively, within the 20–40 cm depth. It was realized that at less than two passes, the mean soil moisture retention within the 0–20 cm depth was less than the mean moisture retention within the 20–40 cm depth, but the converse happened for more than two passes. The study showed that mulching the soil surface helped to retain enough soil moisture during both the rainy and the dry seasons. Also, soil with high sand content required some sort of soil compaction in order to retain enough moisture at the crop rooting zone.     

Spatial soil moisture scaling structure during Soil Moisture Experiment 2005

Soil moisture state and variability control many hydrological and ecological processes as well as exchanges of energy and water between the land surface and the atmosphere. However, its state and variability are poorly understood at spatial scales larger than the fields (i.e. 1 km²) as well as the ability to extrapolate field scale to larger spatial scales. This study investigates soil moisture profiles, their spatial organization, and physical drivers of variability within the Walnut Creek watershed, Iowa, during Soil Moisture Experiment 2005 and relates the watershed scale findings to previous field‐scale results. For all depths, the watershed soil moisture variability was negatively correlated with the watershed mean soil moisture and followed an exponential relationship that was nearly identical to that for field scales. This relationship differed during drying and wetting. While the overall time stability characteristics were improved with observation depth, the relatively wet and dry locations were consistent for all depths. The most time stable locations, capturing the mean soil moisture of the watershed within ± 0·9% volumetric soil moisture, were typically found on hill slopes regardless of vegetation type. These mild slope locations consistently preserve the time stability patterns from field to watershed scales. Soil properties also appear to impact stability but the findings are sensitive to local variations that may not be well defined by existing soil maps. Copyright © 2010 John Wiley & Sons, Ltd.     

Regional scale spatial and temporal variability of soil moisture in a prairie region

Understanding the dynamics of spatial and temporal variability of soil moisture at the regional scale and daily interval, respectively, has important implications for remote sensing calibration and validation missions as well as environmental modelling applications. The spatial and temporal variability of soil moisture was investigated in an agriculturally dominated region using an in‐situ soil moisture network located in central Saskatchewan, Canada. The study site evaluated three depths (5, 20, 50 cm) through 139 days producing a high spatial and temporal resolution data set, which were analysed using statistical and geostatistical means. Processes affecting standard deviation at the 5‐cm depth were different from the 20‐cm and 50‐cm depths. Deeper soil measurements were well correlated through the field season. Further analysis demonstrated that lag time to maximum correlation between soil depths increased through the field season. Temporal autocorrelation was approximately twice as long at depth compared to surface soil moisture as measured by the e‐folding frequency. Spatial correlation was highest under wet conditions caused by uniform rainfall events with low coefficient of variation. Overall soil moisture spatial and temporal variability was explained well by rainfall events and antecedent soil moisture conditions throughout the Kenaston soil moisture network. It is expected that the results of this study will support future remote sensing calibration and validation missions, data assimilation, as well as hydrologic model parameterization for use in agricultural regions. Copyright © 2016 John Wiley & Sons, Ltd.     

Soil moisture–temperature relationships: results from two field experiments

This paper analyses data from two field experiments in Chickasha, Oklahoma, and Tifton, Georgia, carried out in July 1999 and June 2000 respectively. The observations on soil moisture at two depths, viz. 0–2·5 and 0–5·0 cm, surface temperature, and temperatures at 1, 5 and 10 cm depths are analysed. The relationship between the soil moisture and the temperature variability in time is examined as a function of vegetation type and location. Results from these experiments show that, during drydown, surface temperature shows an increase that corresponds to a decrease in the soil moisture. Linear models for prediction of soil moisture (at both depths) using surface temperature observations are examined. Copyright © 2003 John Wiley & Sons, Ltd.     

Role of time variant and invariant terms on soil moisture spatio-temporal variability in a subhumid coastal wetland

The knowledge of soil moisture spatio-temporal variability is highly relevant for water resources management. This paper reports an analysis of the spatial–temporal variability of soil moisture data for a small to medium-scale soil-sensors network in a coastal wetland of southwestern Spain. Measurements were taken from five sites located in the Doñana National Park over the time-period of one hydrological year from September 2017 to September 2018. The total area of the soil-sensors network shows an extension about 25 × 3 km. Soil moisture data was separated into time invariant (the temporal mean of the whole period at each site) and time-variant terms (the deviations of soil moisture from the mean, or anomalies). The time-invariant component was generally the main contributor to the total spatial variance of soil moisture and it was mostly controlled by the groundwater levels in the area. Nevertheless, the time variant terms have a huge effect on soil moisture variability in very dry states. Characteristic convex time-dependent patterns for this field site were found between spatially averaged soil moisture and its variability. This information could be used for the up and downscaling of soil moisture from satellite data. Those patterns of relation between spatial mean and variability of soil moisture were only affected by heavy rainfalls giving rise to hysteretic behaviour. This study shows that even though groundwater level is a time-variant variable, it significantly affects soil moisture's time-variant but also time-invariant terms due to the different average groundwater level depths at the different sites.     

Scaling characteristics of spatial patterns of soil moisture from distributed modelling

Characterizing the spatial dynamics of soil moisture fields is a key issue in hydrology, offering an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, the statistical structure of soil moisture patterns is examined using modelled soil moisture obtained from the North American Land Data Assimilation System (NLDAS) at 0.125° resolution. The study focuses on the vertically averaged soil moisture in the top 10 cm and 100 cm layers. The two variables display a weak dependence for lower values of surface soil moisture, with the strength of the relationship increasing with the water content of the top layer. In both cases, the variance of the soil moisture follows a power law decay as a function of the averaging area. The superficial layer shows a lower degree of spatial organization and higher temporal variability, which is reflected in rapid changes in time of the slope of the scaling functions of the soil moisture variance. Conversely, the soil moisture in the top 100 cm has lower variability in time and larger spatial correlation. The scaling of these patterns was found to be controlled by the changes in the soil water content. Results have implications for the downscaling of soil moisture to prevent model bias.     

Sensitivity of soil parameters in unsaturated zone modelling and the relation between effective,laboratory and in situ estimates

Simulation of soil moisture content requires effective soil hydraulic parameters that are valid at the modelling scale. This study investigates how these parameters can be estimated by inverse modelling using soil moisture measurements at 25 locations at three different depths (at the surface, at 30 and 60 cm depth) on an 80 by 20 m hillslope. The study presents two global sensitivity analyses to investigate the sensitivity in simulated soil moisture content of the different hydraulic parameters used in a one‐dimensional unsaturated zone model based on Richards' equation. For estimation of the effective parameters the shuffled complex evolution algorithm is applied. These estimated parameters are compared to their measured laboratory and in situ equivalents. Soil hydraulic functions were estimated in the laboratory on 100 cm³ undisturbed soil cores collected at 115 locations situated in two horizons in three profile pits along the hillslope. Furthermore, in situ field saturated hydraulic conductivity was estimated at 120 locations using single‐ring pressure infiltrometer measurements. The sensitivity analysis of 13 soil physical parameters (saturated hydraulic conductivity (K_s), saturated moisture content (θ_s), residual moisture content (θ_r), inverse of the air‐entry value (α), van Genuchten shape parameter (n), Averjanov shape parameter (N) for both horizons, and depth (d) from surface to B horizon) in a two‐layer single column model showed that the parameter N is the least sensitive parameter. K_s of both horizons, θ_s of the A horizon and d were found to be the most sensitive parameters. Distributions over all locations of the effective parameters and the distributions of the estimated soil physical parameters from the undisturbed soil samples and the single‐ring pressure infiltrometer estimates were found significantly different at a 5% level for all parameters except for α of the A horizon and K_s and θ_s of the B horizon. Different reasons are discussed to explain these large differences. Copyright © 2004 John Wiley & Sons, Ltd.     

Information and complexity measures applied to observed and simulated soil moisture time series

Abstract

Time series of soil moisture-related parameters provide important insights into the functioning of soil water systems. Analysis of patterns within such time series has been used in several studies. The objective of this work was to compare patterns in observed and simulated soil moisture contents to understand whether modelling leads to a substantial loss of information or complexity. The time series were observed at four plots in sandy soils within the USDA-ARS OPE3 experimental watershed, for a year; precipitation and evapotranspiration (ET) were measured and estimated, respectively, and used for soil water flow simulation with the HYDRUS-1D software. The information content measures are the metric entropy and the mean information gain, and complexity measures are the fluctuation complexity and the effective measure complexity. These measures were computed based on the binary encoding of soil moisture time series, and used probabilities of patterns, i.e. probabilities of joint or sequential appearance of symbol sequences. The information content of daily soil moisture time series was much smaller than that of rainfall data, and had higher complexity, indicating that soil worked essentially as an information filter. Information content and complexity decreased and increased with depth, respectively, demonstrating the increase in the information filtering action of soil. The information measures of simulated soil moisture content were close to those of the measurements, indicating the successful simulation of patterns in the data. The spatial variability of the information measures for simulated soil moisture content at all depths was less pronounced than the one of measured time series. Compared with precipitation and estimated ET, soil moisture time series had more structure and less randomness in this work. The information measures can provide useful complementary knowledge about model performance and patterns in observation and modelling results.

Citation Pan, F., Pachepsky, Y. A., Guber, A. K., & Hill, R. L. (2011) Information and complexity measures applied to observed and simulated soil moisture time series. Hydrol. Sci. J. 56(6), 1027–1039.     

Seasonal variation of soil respiration under different land use/land cover in arid region

Through combining the soil respiration with the main environmental factors under the planting shelterbelt (Populus woodland) and the natural desert vegetation (Tamarix ramosissima+Phragmites communis community and Haloxylon ammodendron community) in the western Junngar Basin, the difference in soil respiration under different land use/land cover types and the responses of soil respiration to temperature and soil moisture were analyzed. Results showed that the rate of soil respiration increased with temperature. During the daytime, the maximum soil respiration rate occurred at 18:00 for the Populus woodland, 12:00 for T. ramosissima+Ph. communis community, and 14:00 for H. ammodendron community, while the minimum rate all occurred at 8:00. The soil respiration, with the maximum rate in June and July and then declining from August, exhibited a similar trend to the near-surface temperature from May to October. During the growing season, the mean soil respiration rates and seasonal variation differed among the land use/land cover types, and followed the order of Populus woodland > T. ramosissima+Ph. communis community > H. ammodendron community. The difference in the soil respiration rate among different land use/land cover types was significant. The soil respiration of Pouplus woodland was significantly correlated with the near-surface temperature and soil temperature at 10 cm depth (P<0.01) in an exponential manner. The soil respiration of T. ramosissima+Ph. communis and H. ammodendron communities were all linearly correlated with the near-surface temperature and soil surface temperature (P<0.01). Based on the near-surface temperature, the calculated Q₁₀ of Populus woodland, T. ramosissima+Ph. communis community and H. ammodendron community were 1.48, 1.59 and 1.63, respectively. The integrated soil respiration of the three land use/land cover types showed a significant correlation with the soil moisture at 0–5 cm, 5–15 cm and 0–15 cm depths (P<0.01). The quadratic model could best describe the relationship between soil respiration and soil moisture at 0–5 cm depth (P<0.01).     

Evidence of multiplicity in the P-travel time curve beyond 30 degrees

Slowness measurements on first and later arrivals from earthquakes in the Philippine and Taiwan regions recorded at the Warramunga array in Australia indicate abrupt decreases in slowness of the first arrival as well as triplications in the travel time curve at epicentral distances of about 38 and 43°. These results imply the presence of regions of rapid or discontinuous velocity increase at depths of about 900 and 1050 km, respectively. Between these regions of sharp velocity increases the dT/dΔ measurements indicate that the velocity gradients are lower than those determined by previous investigators. The observed extensions of the 650- and 770-km branches out to 50° can be explained in terms of the triplications if small negative velocity gradients of the order of 0.1 km/s per 100 km exist between 650–770 and 770–900 km depths. An alternative explanation of these observed extensions may be provided in terms of underside reflections from the bottom of the velocity discontinuities. Either of the two explanations require sharp velocity gradients at the depth of the velocity discontinuities. These observations are at variance with earth models where the P-wave velocity increases continuously with depth below a depth of 650 km.     

Soil moisture variation in relation to topography and land use in a hillslope catchment of the Loess Plateau, China

The profile characteristics and the temporal dynamics of soil moisture variation were studied at 26 locations in Da Nangou catchment (3.5 km²) in the loess area of China. Soil moisture measurements were performed biweekly at five depths in the soil profile (0–5, 10–15, 20–25, 40–45 and 70–75 cm) from May to October 1998 using Delta-T theta probe. Soil moisture profile type and temporal variation type and their relationship to topography and land use were identified by detrended canonical correspondence analysis (DCCA) and correlation analysis. The profile distribution of time-averaged soil moisture content can be classified into three types i.e. decreasing-type, waving-type and increasing-type. The profile features of soil moisture (e.g. profile gradient and profile variability) are influenced by different environmental factors. The profile type of soil moisture is only attributed to land use while profile gradient and profile variability of soil moisture is mainly related to land use and topography (e.g. landform type and slope). The temporal dynamics of layer-averaged soil moisture content is grouped into three types including three-peak type, synchro-four-peak type and lagged-four-peak type. These types are controlled by topography rather than by land use. The temporal dynamic type of soil moisture shows significant correlation with relative elevation, slope, aspect, while temporal variance displays significant relation with slope shape. The mean soil moisture is related to both the profile and dynamics features of soil moisture and is controlled by both land use and topography (e.g. aspect, position, slope and relative elevation). The spatial variability of soil moisture across landscape varies with both soil depths and temporal evolution.     

Evaluating soil moisture estimation from ground-penetrating radar hyperbola fitting with respect to a systematic time-domain reflectometry data collection in a boreal podzolic agricultural field

The upper 30 cm of the soil profile, which hosts the majority of the root biomass, can be considered as the shallow agricultural root zone of most temperate crops. The electromagnetic wave velocity in the soil obtained from reflection hyperbolas in ground-penetrating radar (GPR) data can be used to estimate soil moisture (SM). Finding shallow hyperbolas in a radargram and minimizing the subjective error associated with the hyperbola fitting are the main challenges in this approach. Nevertheless, we were motivated by the recent improvements of hyperbola fitting algorithms, which can reduce the subjective error and processing time. To overcome the difficulty of finding very shallow hyperbolas, we applied the hyperbola fitting method to reflections ranging from 27- to 50-cm depth using a 500-MHz centre-frequency GPR and compared the estimated moisture with vertically installed, 30-cm-long time-domain reflectometry (TDR) sensors. We also compared TDR and GPR sample areas in a 2-D plane using different GPR survey types and different hyperbola depths. SM measured with TDR and GPR were not significantly different according to Mann–Whitney's test. Our analyses showed that a root mean square error of 0.03 m³ m⁻³ was found between the two methods. In conclusion, the proposed method might be suitable to estimate SM with an acceptable accuracy within the root zone if the soil profile is fairly uniform within the application depth range.     

Spatial explicit soil moisture analysis: pattern and its stability at small catchment scale in the loess hilly region of China

Soil moisture is essential for plant growth and terrestrial ecosystems, especially in arid and semi‐arid regions. This study aims to quantify the variation of soil moisture content and its spatial pattern as well as the influencing factors. The experiment is conducted in a small catchment named Yangjuangou in the loess hilly region of China. Soil moisture to a depth of 1 m has been obtained by in situ sampling at 149 sites with different vegetation types before and after the rainy season. Elevation, slope position, slope aspect, slope gradient and vegetation properties are investigated synchronously. With the rainy season coming, soil moisture content increases and then reaches the highest value after the rainy season. Fluctuation range and standard deviation of soil moisture decrease after a 4‐month rainy season. Standard deviation of soil moisture increases with depth before the rainy season; after the rainy season, it decreases within the 0‐ to 40‐cm soil depth but then increases with depths below 40 cm. The stability of the soil moisture pattern at the small catchment scale increases with depth. The geographical position determines the framework of soil moisture pattern. Soil moisture content with different land‐use types is significantly increased after the rainy season, but the variances of land‐use types are significantly different. Landform and land‐use types can explain most of the soil moisture spatial variations. Soil moisture at all sample sites increases after the rainy season, but the spatial patterns of soil moisture are not significantly changed and display temporal stability despite the influence of the rainy season. Copyright © 2013 John Wiley & Sons, Ltd.     

Factors affecting soil moisture spatial variability for a humid forest hillslope

Soil moisture is an important variable in explaining hydrological processes at hillslope scale. The distribution of soil moisture along a hillslope is related to the spatial distribution of the soil properties, the topography, the soil depth, and the vegetation. In order to investigate the factors affecting soil moisture, various environmental data were collected from a humid forest hillslope in this study. Several factors (the wetness index; the contributing area; the local slope; the soil depth; the composition of sand, silt, and clay; the scaling parameter; the hydraulic conductivity; the tree diameter at breast height; and the total weighted basal area) were evaluated for their effect on soil moisture and its distribution over the hillslope at depths of 10, 30, and 60 cm. Both linear correlation analysis and empirical orthogonal function analysis indicated that the soil texture was a dominant factor in soil moisture distribution. The impact of soil hydraulic conductivity was important for all soil moisture ranges at a depth of 30 cm, but those at 10 and 60 cm were limited to very wet and dry conditions, respectively. The relationships of the various factors with the spatial variability of soil moisture indicated the existence of a threshold soil moisture that is related to the composition of the soil and the factors related to the distribution of water in the study area.     

Root-zone soil moisture estimation from assimilation of downscaled Soil Moisture and Ocean Salinity data

The crucial role of root-zone soil moisture is widely recognized in land–atmosphere interaction, with direct practical use in hydrology, agriculture and meteorology. But it is difficult to estimate the root-zone soil moisture accurately because of its space-time variability and its nonlinear relationship with surface soil moisture. Typically, direct satellite observations at the surface are extended to estimate the root-zone soil moisture through data assimilation. But the results suffer from low spatial resolution of the satellite observation. While advances have been made recently to downscale the satellite soil moisture from Soil Moisture and Ocean Salinity (SMOS) mission using methods such as the Disaggregation based on Physical And Theoretical scale Change (DisPATCh), the assimilation of such data into high spatial resolution land surface models has not been examined to estimate the root-zone soil moisture. Consequently, this study assimilates the 1-km DisPATCh surface soil moisture into the Joint UK Land Environment Simulator (JULES) to better estimate the root-zone soil moisture. The assimilation is demonstrated using the advanced Evolutionary Data Assimilation (EDA) procedure for the Yanco area in south eastern Australia. When evaluated using in-situ OzNet soil moisture, the open loop was found to be 95% as accurate as the updated output, with the updated estimate improving the DisPATCh data by 14%, all based on the root mean square error (RMSE). Evaluation of the root-zone soil moisture with in-situ OzNet data found the updated output to improve the open loop estimate by 34% for the 0–30 cm soil depth, 59% for the 30–60 cm soil depth, and 63% for the 60–90 cm soil depth, based on RMSE. The increased performance of the updated output over the open loop estimate is associated with (i) consistent estimation accuracy across the three soil depths for the updated output, and (ii) the deterioration of the open loop output for deeper soil depths. Thus, the findings point to a combined positive impact from the DisPATCh data and the EDA procedure, which together provide an improved soil moisture with consistent accuracy both at the surface and at the root-zone.     

Relationship between soil moisture of near surface and multiple depths of the root zone under heterogeneous land uses and varying hydroclimatic conditions

This paper presents an assessment of the relationship between near-surface soil moisture (SM) and SM at other depths in the root zone under three different land uses: irrigated corn, rainfed corn and grass. This research addresses the question whether or not near-surface SM can be used reliably to predict plant available root zone SM and SM at other depths. For this study, a realistic soil-water energy balance process model is applied to three locations in Nebraska representing an east-to-west hydroclimatic gradient in the Great Plains. The applications were completed from 1982 through to 1999 at a daily time scale. The simulated SM climatologies are developed for the root zone as a whole and for the five layers of the soil profile to a depth of 1·2 m. Over all, the relationship between near-surface SM (0–2·5 cm) and plant available root zone SM is not strong. This applies to all land uses and for all locations. For example, r estimates range from 0·02 to 0·33 for this relationship. Results for near-surface SM and SM of several depths suggest improvement in r estimates. For example, these estimates range from − 0·19 to 0·69 for all land uses and locations. It was clear that r estimates are the highest (0·49–0·69) between near-surface and the second layer (2·5–30·5 cm) of the root zone. The strength of this type of relationship rapidly declines for deeper depths. Cross-correlation estimates also suggest that at various time-lags the strength of the relationship between near-surface SM and plant available SM is not strong. The strength of the relationship between SM modulation of the near surface and second layer over various time-lags slightly improves over no lags. The results suggest that use of near-surface SM for estimating SM at 2·5–30 cm is most promising. Copyright © 2007 John Wiley & Sons, Ltd.     

Spatio‐temporal distribution of near‐surface and root zone soil moisture at the catchment scale

Soil moisture is highly variable both spatially and temporally. It is widely recognized that improving the knowledge and understanding of soil moisture and the processes underpinning its spatial and temporal distribution is critical. This paper addresses the relationship between near‐surface and root zone soil moisture, the way in which they vary spatially and temporally, and the effect of sampling design for determining catchment scale soil moisture dynamics. In this study, catchment scale near‐surface (0–50 mm) and root zone (0–300 mm) soil moisture were monitored over a four‐week period. Measurements of near‐surface soil moisture were recorded at various resolutions, and near‐surface and root zone soil moisture data were also monitored continuously within a network of recording sensors. Catchment average near‐surface soil moisture derived from detailed spatial measurements and continuous observations at fixed points were found to be significantly correlated (r² = 0·96; P = 0·0063; n = 4). Root zone soil moisture was also found to be highly correlated with catchment average near‐surface, continuously monitored (r² = 0·81; P < 0·0001; n = 26) and with detailed spatial measurements of near‐surface soil moisture (r² = 0·84). The weaker relationship observed between near‐surface and root zone soil moisture is considered to be caused by the different responses to rainfall and the different factors controlling soil moisture for the soil depths of 0–50 mm and 0–300 mm. Aspect is considered to be the main factor influencing the spatial and temporal distribution of near‐surface soil moisture, while topography and soil type are considered important for root zone soil moisture. The ability of a limited number of monitoring stations to provide accurate estimates of catchment scale average soil moisture for both near‐surface and root zone is thus demonstrated, as opposed to high resolution spatial measurements. Similarly, the use of near‐surface soil moisture measurements to obtain a reliable estimate of deeper soil moisture levels at the small catchment scale was demonstrated. Copyright © 2007 John Wiley & Sons, Ltd.