Modeling spatial variability and transport processes in a glacial till soil

The objective of this study was to investigate the spatial variability of different physical and chemical properties of soils, and their role on transport processes of chemicals to groundwater sources in a glacial till soil of central Iowa;Measurements were made to determine the in-situ saturated hydraulic conductivity (K[subscript] sat) of a glacial till soil at sixty six sites in a tillage established plot. One hundred thirty-two data points on K[subscript] sat collected at two soil depths along two bisecting perpendicular transects, were used to develop semivariogram models, in conjunction with split-window median polish approach. Nested structure with an overall range of 60 m was found for the K[subscript] sat at a depth of 30 cm below the soil surface. K[subscript] sat values at 15 cm depth were found as structureless random noise;Another data set was collected on nitrate-nitrogen (NO[subscript]3-N) concentration in soil water, soil moisture content, and soil profile NO[subscript]3-N content in the same field under two different tillage practices using a different sampling pattern. Data on NO[subscript]3-N concentration in the soil water collected at 175 grid points arranged on a three dimensional (3-D) grid, were compared for spatial distribution patterns as function of the tillage system. Results of this study indicated transitional spatial structure of NO[subscript]3-N distribution, both in vertical and horizontal directions, under conventional tillage. In contrast, nugget and linear type semivariograms were observed for the no tillage system, in the vertical and horizontal directions, respectively;Data on soil moisture content, NO[subscript]3-N concentration in soil water, and soil total NO[subscript]3-N contents in the soil profile collected at five soil depths (30, 60, 90, 120, and 150 cm below the ground surface) in a tile drained plot were studied for coregionalization. This study indicated that well-structured cross semivariograms existed between depths of 60 and 90 cm, and 90 and 120 cm, for NO[subscript]3-N concentration and soil moisture content. Strong negative correlation between soil moisture content and NO[subscript]3-N concentration resulted in negative cross-semivariograms at 90 and 120 cm depths;A deterministic simulation model was developed with an effective hydraulic conductivity parameter based on the spatial correlation length in place of an average hydraulic conductivity parameter, to simulate the major water and nitrate transport processes for predicting the NO[subscript]3-N losses to subsurface drainage systems. (Abstract shortened by UMI.

Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithm: 1. Conceptual modeling

We used a genetic algorithm (GA) to identify soil water retention (h) and hydraulic conductivity K(h) functions by inverting a soil-water-atmosphere-plant (SWAP) model using observed near-surface soil moisture (0-5 cm) as search criterion. Uncertainties of parameter estimates were estimated using multipopulations in GA and considering data and modeling errors. Three hydrologic cases were considered: (1) homogenous free-draining soil column, (2) homogenous soil column with shallow water table, and (3) heterogeneous soil column under free-drainage condition, considering three different rainfall patterns in northern Texas. Results (cases 1 and 2) showed the identifiability of soil hydraulic parameters improving at coarse and fine scales of the soil textural class. Medium-textured soils posed identifiability problems when the soil is dry. Nonlinearity in (h) and K(h) is greater at drier conditions, and some parameters are less sensitive for estimation. Flow regimes controlled by upward fluxes were found less successful, as the information content of observed near-surface data may no longer influence the hydrologic processes in the subsurface. The identifiability of soil hydraulic parameters was found better when the soil profile is predominantly draining. In case 3, top soil layer hydraulic properties were defined using near-surface data alone as criterion. Adding evapotranspiration (ET) improved identification of the second soil layer, although not all parameters were identifiable. Under uncertainties, (h) was found to be well defined while K(h) is more uncertain. Finally, we applied the method to a validation site in Little Washita watershed, Oklahoma, where derived effective soil hydraulic properties closely matched the measured ones at the field site

Spatiotemporal analyses of soil moisture from point to footprint scale in two different hydroclimatic regions

This paper presents time stability analyses of soil moisture at different spatial measurement support scales (point scale and airborne remote sensing (RS) footprint scale 800 m × 800 m) in two different hydroclimatic regions. The data used in the analyses consist of in situ and passive microwave remotely sensed soil moisture data from the Southern Great Plains Hydrology Experiments 1997 and 1999 (SGP97 and SGP99) conducted in the Little Washita (LW) watershed, Oklahoma, and the Soil Moisture Experiments 2002 and 2005 (SMEX02 and SMEX05) in the Walnut Creek (WC) watershed, Iowa. Results show that in both the regions soil properties (i.e., percent silt, percent sand, and soil texture) and topography (elevation and slope) are significant physical controls jointly affecting the spatiotemporal evolution and time stability of soil moisture at both point and footprint scales. In Iowa, using point‐scale soil moisture measurements, the WC11 field was found to be more time stable (TS) than the WC12 field. The common TS points using data across the 3 year period (2002–2005) were mostly located at moderate to high elevations in both the fields. Furthermore, the soil texture at these locations consists of either loam or clay loam soil. Drainage features and cropping practices also affected the field‐scale soil moisture variability in the WC fields. In Oklahoma, the field having a flat topography (LW21) showed the worst TS features compared to the fields having gently rolling topography (LW03 and LW13). The LW13 field (silt loam) exhibited better time stability than the LW03 field (sandy loam) and the LW21 field (silt loam). At the RS footprint scale, in Iowa, the analysis of variance (ANOVA) tests show that the percent clay and percent sand are better able to discern the TS features of the footprints compared to the soil texture. The best soil indicator of soil moisture time stability is the loam soil texture. Furthermore, the hilltops (slope ∼0%–0.45%) exhibited the best TS characteristics in Iowa. On the other hand, in Oklahoma, ANOVA results show that the footprints with sandy loam and loam soil texture are better indicators of the time stability phenomena. In terms of the hillslope position, footprints with mild slope (0.93%–1.85%) are the best indicators of TS footprints. Also, at both point and footprint scales in both the regions, land use–land cover type does not influence soil moisture time stability

Parameter conditioning with a noisy Monte Carlo genetic algorithm for estimating effective soil hydraulic properties from space

The estimation of effective soil hydraulic parameters and their uncertainties is a critical step in all large-scale hydrologic and climatic model applications. Here a scale-dependent (top-down) parameter estimation (inverse modeling) scheme called the noisy Monte Carlo genetic algorithm (NMCGA) was developed and tested for estimating these effective soil hydraulic parameters and their uncertainties. We tested our method using three case studies involving a synthetic pixel (pure and mixed) where all modeling conditions are known, and with actual airborne remote sensing (RS) footprints and a satellite RS footprint. In the synthetic case studies under pure (one soil texture) and mixed-pixel (multiple soil textures) conditions, NMCGA performed well in estimating the effective soil hydraulic parameters even with pixel complexities contributed by various soil types and land management practices (rain-fed/irrigated). With the airborne and satellite remote sensing cases, NMCGA also performed well for estimating effective soil hydraulic properties so that when applied in forward stochastic simulation modeling it can mimic large-scale soil moisture dynamics. The results also suggest a possible scaling down of the effective soil water retention curve (h) at the larger satellite remote sensing pixel compared with the airborne remote sensing pixel. However, it did not generally imply that all effective soil hydraulic parameters should scale down like the soil water retention curve. The reduction of the soil hydraulic parameters was most profound in the saturated soil moisture content ( sat) as we relaxed progressively the soil hydraulic parameter search spaces in our satellite remote sensing studies. Overall, the NMCGA framework was found to be very promising in the inverse modeling of remotely sensed near-surface soil moisture for estimating the effective soil hydraulic parameters and their uncertainties at the remote sensing footprint/climate model grid

Performance evaluation of two popular antennas designed using a Bacteria Foraging Algorithm

AbstractTwo popular antennas such as the Yagi-Uda Array (YUA) and the Log Periodic Dipole Array (LPDA) with the same number of dipole elements are optimally designed using Bacteria Foraging Algorithm (BFA). BFA being one of the successful optimization algorithms, used to optimize many design parameters of these two antennas to get a number of desired performance parameters. A YUA is designed here, mainly to realize high directivity, input-impedance (Zin) close to 50Ω, high Front To Back Ratio (FTBR), high Front-to-maximum-Side-Lobe-Level (FSLL), low Half Power Beam Width (HPBW), and appreciable bandwidth, whereas a LPDA is designed here, mainly to achieve high bandwidth, average Zin close to 50Ω, high average FTBR, high average FSLL, low average HPBW, and appreciable average directivity. The successful design approaches, application and comparative study of these two antennas presented here can also be extended to other antennas

Hot Spots and Persistence of Nitrate in Aquifers Across Scales

Nitrate-N (NO3 -- N) is one of the most pervasive contaminants in groundwater. Nitrate in groundwater exhibits long-term behavior due to complex interactions at multiple scales among various geophysical factors, such as sources of nitrate-N, characteristics of the vadose zone and aquifer attributes. To minimize contamination of nitrate-N in groundwater, it is important to estimate hot spots (>10 mg/L of NO3 -- N), trends and persistence of nitrate-N in groundwater. To analyze the trends and persistence of nitrate-N in groundwater at multiple spatio-temporal scales, we developed and used an entropy-based method along with the Hurst exponent in two different hydrogeologic settings: the Trinity and Ogallala Aquifers in Texas at fine (2 km × 2 km), intermediate (10 km × 10 km) and coarse (100 km × 100 km) scales. Results show that nitrate-N exhibits long-term persistence at the intermediate and coarse scales. In the Trinity Aquifer, overall mean nitrate-N has declined with a slight increase in normalized marginal entropy (NME) over each decade from 1940 to 2008; however, the number of hot spots has increased over time. In the Ogallala Aquifer, overall mean nitrate-N has increased with slight moderation in NME since 1940; however, the number of hot spots has significantly decreased for the same period at all scales

Soil hydraulic properties in one-dimensional layered soil profile using layer-specific soil moisture assimilation scheme

We developed a layer-specific soil-moisture assimilation scheme using a simulation-optimization framework, Soil-Water-Atmosphere-Plant model with genetic algorithm (SWAP-GA). Here, we explored the quantification of the soil hydraulic properties in a layered soil column under various combinations of soil types, vegetation covers, bottom boundary conditions and soil layering using idealized (synthetic) numerical studies and actual field experiments. We demonstrated that soil layers and vertical heterogeneity (layering arrangements) could impact to the uncertainty of quantifying soil hydraulic parameters. We also found that, under layered soil system, when the subsurface flows are dominated by upward fluxes, e.g., from a shallow water table, the solution to the inverse problem appears to be more elusive. However, when the soil profile is predominantly draining, the soil hydraulic parameters could be fairly estimated well across soil layers, corroborating the results of past studies on homogenous soil columns. In the field experiments, the layer-specific assimilation scheme successfully matched soil moisture estimates with observations at the individual soil layers suggesting that this approach could be applied in real world conditions

Effective hydraulic parameters for steady state vertical flow in heterogeneous soils,

[1] In hydroclimate and land-atmospheric interaction models, effective hydraulic properties are needed at large grid scales. In this study, the effective soil hydraulic parameters of the areally heterogeneous soil formation are derived by conceptualizing the heterogeneous soil formation as an equivalent homogeneous medium and assuming that the equivalent homogeneous soil will approximately discharge the same total amount of flux and produce same average pressure head profile in the formation. As compared to previous effective hydraulic property studies, a specific feature of this study is that the derived effective hydraulic parameters are mean-gradient-dependent (i.e., vary across depth). Although areal soil heterogeneity was formulated as parallel homogeneous stream tubes in this study, our results appear to be consistent with the previous findings of meangradient unsaturated hydraulic conductivit