Within the ‘Convective and Orographically-induced Precipitation Study’ (COPS) 2007 in Southwest Germany and Northeast France a soil moisture monitoring network was installed. The aim of the network is to identify the interaction between the temporal and spatial variability of the soil moisture field and its influence on the energy balance and the moisture availability in the planetary boundary layer. The network is comprised of a large number of newly developed low-cost soil moisture sensors based on the frequency-domain reflectometry method (FDR). In total 47 soil moisture stations within the COPS domain were each equipped with two to four sensors simultaneously measuring vertical profiles of soil moisture and soil temperature down to 50 cm depth. This contribution describes the soil moisture network, its installation procedure and the calibration of the sensor output signal. Furthermore we discuss the soil texture distribution within the study area and present first analyses of the spatio-temporal soil moisture variability during a 13 month period from June 2007 till June 2008 based on regional differences and site specific properties (altitude and soil texture). Results show that the altitude plays a key role for the overall soil moisture pattern relative to the area mean due to the direct linkage to precipitation patterns. Soil texture controls the vertical soil moisture gradient relative to the near surface soil moisture, as their properties control water storage and drainage characteristics. Both factors significantly influence regional soil moisture patterns in Southwest Germany. Zusammenfassung –

Spatio-temporal soil moisture variability in Southwest Germany observed with a new monitoring network within the COPS domain

[1] Recent work by Isham et al. and Rodriguez-Iturbe et al. has characterized the space-time variability of soil moisture through its analytically derived covariance function which depends on soil properties, vegetation structure, and rainfall patterns typical of a region. This paper uses such characterization to address the strategies and methodologies for the sampling of soil moisture fields. The focus is on the estimation of the long-term mean soil moisture and the daily soil moisture averaged over a given area as a function of the network geometry, number of stations, number of sampling days and landscape heterogeneity. It is found that the spatial geometry of the network has a significant impact on the sampling of the average soil moisture over an area in any particular day, while it is much less relevant for the sampling of the long-term mean daily soil moisture over the region. In the latter case, the length of the record is a commanding factor in what concerns the variance of estimation, specially for soils with shallow rooted vegetation. Spatial vegetation heterogeneity plays an important role on the variance of estimation of the soil moisture, being particularly critical for the sampling of the average soil moisture over an area for a given day.

On the Spatial and Temporal Sampling of Soil Moisture Fields.

[1] Data from the Washita '92 experiment illustrate the spatial and temporal variability of surface soil moisture. Statistical moments revealed that there is no significant skewness or kurtosis in the distribution. Over the drying period the variance decreased with time; however, the coefficient of variation did not significantly change, indicating that scaled variability remains roughly constant. Regression analysis showed that soil texture, vegetation density, and time since precipitation accounted for approximately 55% of the variability of surface soil moisture. There are also strong indications of persistence in the spatial structure representing perhaps topography and solar exposure, which was maintained and explained 31% of the total variability. The methodology proposed herein should be generally applicable in the analysis of any spatially variable land surface data set as it evolves with time.

Variability of Surface Soil Moisture at the Watershed Scale

Granger causality (GC) is used in the econometrics literature to identify the presence of one- and two-way coupling between terms in noisy multivariate dynamical systems. Here we test for the presence of GC to identify a soil moisture (S) feedback on precipitation (P) using data from Illinois. In this framework S is said to Granger cause P if F(P t |Ω t− Δ t )≠F(P t |Ω t− Δ t −S t− Δ t ) where F denotes the conditional distribution of P, Ω t− Δ t represents the set of all knowledge available at time t−Δt, and Ω t− Δ t −S t− Δ t represents all knowledge except S. Critical for land–atmosphere interaction research is that Ω t− Δ t includes all past information on P as well as S. Therefore that part of the relation between past soil moisture and current precipitation which results from precipitation autocorrelation and soil water balance will be accounted for and not attributed to causality. Tests for GC usually specify all relevant variables in a coupled vector autoregressive (VAR) model and then calculate the significance level of decreased predictability as various coupling coefficients are omitted. But because the data (daily precipitation and soil moisture) are distinctly non-Gaussian, we avoid using a VAR and instead express the daily precipitation events as a Markov model. We then test whether the probability of storm occurrence, conditioned on past information on precipitation, changes with information on soil moisture. Past information on precipitation is expressed both as the occurrence of previous day precipitation (to account for storm-scale persistence) and as a simple soil moisture-like precipitation-wetness index derived solely from precipitation (to account for seasonal-scale persistence). In this way only those fluctuations in moisture not attributable to past fluctuations in precipitation (e.g., those due to temperature) can influence the outcome of the test. The null hypothesis (no moisture influence) is evaluated by comparing observed changes in storm probability to Monte-Carlo simulated differences generated with unconditional occurrence probabilities. The null hypothesis is not rejected (p>0.5) suggesting that contrary to recently published results, insufficient evidence exists to support an influence of soil moisture on precipitation in Illinois.

Investigating Soil Moisture Feedbacks on Precipitation With Tests of Granger Causality

Soil moisture data, obtained from four AmeriFlux sites in the US, were examined using an ecohydrological framework. Sites were selected for the analysis to provide a range of plant functional type, climate, soil particle size distribution, and time series of data spanning a minimum of two growing seasons. Soil moisture trends revealed the importance of measuring water content at several depths throughout the rooting zone; soil moisture at the surface (0-10 cm) was approximately 20-30%) less than that at 50-60 cm. A modified soil moisture dynamics model was used to generate soil moisture probability density functions at. each site. Model calibration results demonstrated that the commonly used soil matric potential values for finding the vegetation stress point and field content may not be appropriate, particularly for vegetation adapted to a water-controlled environment. Projections of future soil moisture patterns suggest that two of the four sites will become severely stressed by climate change induced alterations to the precipitation regime. (C) 2006 Elsevier Ltd. All rights reserved.

/pdf/an-analysis-of-soil-moisture-dynamics-using-multi-year-data-374cp1l5ao.pdf

An analysis of soil moisture dynamics using multi-year data from a network of micrometeorological observation sites - eScholarship

This thesis deals with aspects of the spatial and temporal variability of soil moisture in relation to atmospheric conditions. It is first illustrated that the combination of seasonality in atmospheric forcing and non-linearity in the relation between soil moisture and the losses from the soil moisture reservoir result in a bimodal temporal distribution. Next, the impact of root water uptake on the coupled dynamics of soil moisture and evapotranspiration is investigated by a dimensionless analytical model. Subsequently, observations of evapotranspiration are used to study characteristic soil moisture response times under water-limited conditions for different biomes. Typically, these values are in the order of 15–25 days. In order to investigate the accuracy with which areal soil moisture can be estimated from a limited number of point-scale observations, rank stability analysis is applied to different fieldor catchment-scale soil moisture datasets. It is shown that temporal changes in the spatial soil moisture distribution prevent perfect rank stability. Covariance analysis of Monte-Carlo simulations reveals that these changes in spatial soil moisture variability are caused by spatially variable root water uptake during drydrown, and drainage during (re)wetting. These simulations also reveal that the nonlinear relation between soil moisture and evapotranpiration in combination with climate variability induces hysteresis (i.e., non-uniqueness) in the relation between mean soil moisture and its variability. A linear analytical model is used to characterize space-time variability of surface soil moisture fields in response to minor rainfall events. The results are in good agreement with observations made during a recent field experiment conducted under very dry conditions.

/pdf/soil-moisture-dynamics-and-land-surface-atmosphere-2qje6bhlkm.pdf

Soil moisture dynamics and land surface-atmosphere interaction

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Soil moisture dynamics and land surface-atmosphere interaction