Evaluating macropore flow with temporal electrical resistivity imaging in riparian areas

Riparian soils are uniquely susceptible to the formation of macropores, voids with preferential flow in comparison to surrounding strata, which are hypothesized to promote fast transport of water through soil layers. Electrical Resistivity Imaging (ERI) can locate spatial heterogeneities in soil wetting patterns caused by preferential flow through macropores, thus optimizing the design of riparian buffers. Temporal ERI (TERI) imaging was conducted in a fine and coarse field setting with artificial macropores to evaluate flow under unsaturated simulated rainfall conditions and saturated infiltrometer conditions.Results from field data show that while macropores are detectable using TERI datasets, this results in an average field setting would detect the wetted zone in the vicinity of a macropore, not the macropore itself. The results were similar for both the primary fine grain soil site in Oklahoma as well as the coarse grain site in North Carolina. TERI data indicate that without artificial rainfall or macropores in low noise conditions, a single macropore would not be detected, a wetted zone would be the best detection. In a field evaluation of naturally occurring macropores, the TERI technique would detect the wetted zone around a macropore similar to an area of increased hydraulic conductivity in a heterogeneous soil matrix. The findings from the first set of experimentation indicate an appropriate resolution and electrode spacing for the second experiment in this thesis. The second experiment entails the tracer velocity mapping of alluvial soil. Preliminary results show TERI as a viable method for calculating the fluid velocity along a series of vertical profiles in the coarse-grained North Carolina field site

Development of soil moisture profiles through coupled microwave–thermal infrared observations in the southeastern United States

The principle of maximum entropy (POME) can be used to develop vertical soil moisture (SM) profiles. The minimal inputs required by the POME model make it an excellent choice for remote sensing applications. Two of the major input requirements of the POME model are the surface boundary condition and profile-mean moisture content. Microwave-based SM estimates from the Advanced Microwave Scanning Radiometer (AMSR-E) can supply the surface boundary condition whereas thermal infrared-based moisture estimated from the Atmospheric Land EXchange Inverse (ALEXI) surface energy balance model can provide the mean moisture condition. A disaggregation approach was followed to downscale coarse-resolution ( ∼ 25&thinsp;km) microwave SM estimates to match the finer resolution ( ∼ 5&thinsp;km) thermal data. The study was conducted over multiple years (2006–2010) in the southeastern US. Disaggregated soil moisture estimates along with the developed profiles were compared with the Noah land surface model (LSM), as well as in situ measurements from 10 Natural Resource Conservation Services (NRCS) Soil Climate Analysis Network (SCAN) sites spatially distributed within the study region. The overall disaggregation results at the SCAN sites indicated that in most cases disaggregation improved the temporal correlations with unbiased root mean square differences (ubRMSD) in the range of 0.01–0.09&thinsp;m3&thinsp;m−3. The profile results at SCAN sites showed a mean bias of 0.03 and 0.05 (m3&thinsp;m−3); ubRMSD of 0.05 and 0.06 (m3&thinsp;m−3); and correlation coefficient of 0.44 and 0.48 against SCAN observations and Noah LSM, respectively. Correlations were generally highest in agricultural areas where values in the 0.6–0.7 range were achieved.</p

Modeling Soil Moisture Profiles in Irrigated Fields by the Principle of Maximum Entropy

Vertical soil moisture profiles based on the principle of maximum entropy (POME) were validated using field and model data and applied to guide an irrigation cycle over a maize field in north central Alabama (USA). The results demonstrate that a simple two-constraint entropy model under the assumption of a uniform initial soil moisture distribution can simulate most soil moisture profiles that occur in the particular soil and climate regime that prevails in the study area. The results of the irrigation simulation demonstrated that the POME model produced a very efficient irrigation strategy with minimal losses (about 1.9% of total applied water). However, the results for finely-textured (silty clay) soils were problematic in that some plant stress did develop due to insufficient applied water. Soil moisture states in these soils fell to around 31% of available moisture content, but only on the last day of the drying side of the irrigation cycle. Overall, the POME approach showed promise as a general strategy to guide irrigation in humid environments, such as the Southeastern United States