Description: This study addresses two critical barriers to the use of passive Distributed Temperature Sensing (DTS) for large-scale, high-resolution monitoring of soil moisture. In recent research, a particle batch smoother (PBS) was developed to assimilate sequences of temperature data at two depths into Hydrus-1D to estimate soil moisture as well as soil thermal and hydraulic properties. However, this approach was limited to bare soil and assumed that the cable depths were perfectly known. In order for passive DTS to be more broadly applicable as a soil hydrology research and remote sensing soil moisture product validation tool, it must be applicable in vegetated areas. To address this first limitation, the forward model (Hydrus-1D) was improved through the inclusion of a canopy energy balance scheme. Synthetic tests were used to demonstrate that without the canopy energy balance scheme, the PBS estimated soil moisture could be even worse than the open loop case (no assimilation). When the improved Hydrus-1D model was used as the forward model in the PBS, vegetation impacts on the soil heat and water transfer were well accounted for. This led to accurate and robust estimates of soil moisture and soil properties. The second limitation is that, cable depths can be highly uncertain in DTS installations. As passive DTS uses the downward propagation of heat to extract moisture-related variations in thermal properties, accurate estimates of cable depths are essential. Here, synthetic tests were used to demonstrate that observation depths can be jointly estimated with other model states and parameters. The state and parameter results were only slightly poorer than those obtained when the cable depths were perfectly known. Finally, in-situ temperature data from four soil profiles with different, but known, soil textures were used to test the proposed approach. Results show good agreement between the observed and estimated soil moisture, hydraulic properties, thermal properties and observation depths at all locations. The proposed method resulted in soil moisture estimates in the top 10 cm with RMSE values typically 〈 0.04 3 /m 3 . This demonstrates the potential of detecting the spatial variability of soil moisture and properties in vegetated areas from Passive DTS data. This article is protected by copyright. All rights reserved.

Description: Soil moisture patterns are commonly thought to be dominated by land surface characteristics, such as soil texture, at small scales and by atmospheric processes, such as precipitation, at larger scales. However, a growing body of evidence challenges this conceptual model. We investigated the structural similarity and spatial correlations between mesoscale (∼1-100 km) soil moisture patterns and land surface and atmospheric factors along a 150-km transect using 4-km multisensor precipitation data and a cosmic-ray neutron rover, with a 400-m diameter footprint. The rover was used to measure soil moisture along the transect 18 times over 13 months. Spatial structures of soil moisture, soil texture (sand content), and antecedent precipitation index (API) were characterized using autocorrelation functions and fitted with exponential models. Relative importance of land surface characteristics and atmospheric processes were compared using correlation coefficients ( r ) between soil moisture and sand content or API. The correlation lengths of soil moisture, sand content, and API ranged from 12-32 km, 13-20 km, and 14-45 km, respectively. Soil moisture was more strongly correlated with sand content ( r = -0.536 to -0.704) than with API for all but one date. Thus, land surface characteristics exhibit coherent spatial patterns at scales up to 20 km, and those patterns often exert a stronger influence than do precipitation patterns on mesoscale spatial patterns of soil moisture.

Description: Agriculture must increase production for a growing population while simultaneously reducing its environmental impacts. These goals need not be in tension with one another. Here we outline a vision for improving both the productivity and environmental performance of agriculture in the U.S. Midwest, also known as the Corn Belt. Mean annual precipitation has increased throughout the region over the past 50 years, consistent with climate models that attribute the increase to a warming troposphere. Stream gauge data indicate that higher precipitation has been matched or exceeded by higher stream flows, contributing to flooding, soil loss, and excessive nutrient flux to the Gulf of Mexico. We propose increasing landscape hydrologic storage through construction of ponds and restoration of wetlands to retain water for supplemental irrigation while also reducing flood risks. Primary productivity is proportional to transpiration, and analysis shows that in the U.S. Midwest both can be sustainably increased with supplemental irrigation. The proposed strategy should reduce interannual yield variability by limiting losses due to transient drought, while facilitating adoption of cropping systems that “perennialize” the landscape to take advantage of the full potential growing season. When implemented in concert, these practices should reduce the riverine nitrogen export that is a primary cause of hypoxia in the Gulf of Mexico. Erosive sediment losses should also be reduced through the combination of enhanced hydrologic storage and increased vegetative cover. Successful implementation would require watershed-scale coordination among producers and landowners. An obvious mechanism to encourage this is governmental farm policy.

Description: In north-central Oklahoma eastern redcedar ( Juniperus virginiana ) encroachment into grassland is widespread and is suspected of reducing streamflow, but the effects of this encroachment on soil hydraulic properties are unknown. This knowledge gap creates uncertainty in understanding the hydrologic effects of eastern redcedar encroachment and obstructs fact-based management of encroached systems. The objective of this study was to quantify the effects of eastern redcedar encroachment into tallgrass prairie on soil hydraulic properties. Leaf litter depth, soil organic matter, soil water repellency, soil water content, sorptivity, and unsaturated hydraulic conductivity were measured near Stillwater, OK along 12 radial transects from eastern redcedar trunks to the center of the grassy intercanopy space. Eastern redcedar encroachment in the second half of the twentieth century caused the accumulation of 3 cm of hydrophobic leaf litter near the trunks of eastern redcedar trees. This leaf litter was associated with increased soil organic matter in the upper six cm of soil under eastern redcedar trees (5.96 % by mass) relative to the grass-dominated intercanopy area (3.99 % by mass). Water repellency was more prevalent under eastern redcedar than under grass, and sorptivity under eastern redcedar was 0.10 mm s -1/2 , one seventh the sorptivity under adjacent prairie grasses (.68 mm s -1/2 ). Median unsaturated hydraulic conductivity under grass was 2.52 cm h -1 , four times greater than under eastern redcedar canopies (.57 cm h -1 ). Lower sorptivity and unsaturated hydraulic conductivity would tend to decrease infiltration and increase runoff, but other factors such as rainfall interception by the eastern redcedar canopy and litter layer, and preferential flow induced by hydrophobicity must be examined before the effects of encroachment on streamflow can be predicted. Copyright © 2011 John Wiley & Sons, Ltd.

Description: Since groundwater is Earth's largest pool of freshwater, understanding the sensitivity of deep drainage to climate, soils, and land-cover is critical to managing water resources. To better understand controls on this critical flux in the context of woody encroachment, we determined the sensitivity of deep drainage to climate, soil texture, soil compaction, rooting depth, growing season duration, and plant water stress response using Hydrus-1D to simulate deep drainage. To evaluate the simulation results, we compared these results with ground measurements at two anchor sites. At both anchor sites Hydrus-1D predictions of deep drainage matched measured values within the errors inherent in ground measurements. Sensitivity analysis suggested greatest sensitivity of deep drainage to climate (24 mm yr -1 ) and rooting depth (12 mm yr -1 ), moderate sensitivity to growing season duration (5 mm yr -1 ) and soil texture (4 mm yr -1 ), and lowest sensitivity to topsoil compaction and plant water stress response (3 mm yr -1 ). The sensitivity analysis indicated the relative importance of the plant-related factors considered was, in decreasing order, rooting depth, growing season duration, and plant water stress response—factors that change concomitantly as a result of forestation or woody encroachment. Further ground-truth measurements of woody encroachment effects on deep drainage are needed to confirm or refine the results of this simulation modeling study. This article is protected by copyright. All rights reserved.

Description: This study demonstrated a new method for mapping high resolution (spatial: 1 m, and temporal: 1 hour) soil moisture by assimilating distributed temperature sensing (DTS) observed soil temperatures at intermediate scales. In order to provide robust soil moisture and property estimates, we first proposed an adaptive particle batch smoother algorithm (APBS). In the APBS, a tuning factor, which can avoid severe particle weight degeneration, is automatically determined by maximizing the reliability of the soil temperature estimates of each batch window. A multiple truth synthetic test was used to demonstrate the APBS can robustly estimate soil moisture and properties using observed soil temperatures at two shallow depths. The APBS algorithm was then applied to DTS data along a 71 m transect, yielding an hourly soil moisture map with meter resolution. Results show the APBS can draw the prior guessed soil hydraulic and thermal properties significantly closer to the field measured reference values. The improved soil properties in turn remove the soil moisture biases between the prior guessed and reference soil moisture, which was particularly noticeable at depth above 20 cm. This high resolution soil moisture map demonstrates the potential of characterizing soil moisture temporal and spatial variability and reflects patterns consistent with previous studies conducted using intensive point scale soil moisture samples. The intermediate scale high spatial resolution soil moisture information derived from the DTS may facilitate remote sensing soil moisture product calibration and validation. In addition, the APBS algorithm proposed in this study would also be applicable to general hydrological data assimilation problems for robust model state and parameter estimation. This article is protected by copyright. All rights reserved.