Location: Water Management and Systems Research
Title: Understanding soil moisture regimes through analysis of long-term, spatially distributed data within a farm field: Implications for scaling and inference of hydrological fluxesAuthor
Green, Timothy | |
Barnard, David | |
Erskine, Robert - Rob | |
Sherrod, Lucretia | |
NIEMANN, JEFFREY - Colorado State University | |
Mankin, Kyle |
Submitted to: American Geophysical Union
Publication Type: Abstract Only Publication Acceptance Date: 10/25/2021 Publication Date: N/A Citation: N/A Interpretive Summary: N/A Technical Abstract: Soil moisture is a key state variable in space and time. Within an agricultural field (106 ha) in northeastern Colorado, soil moisture dynamics may be similar at many positions but vary with landscape topography, soil characteristics and land use/management. Soil water contents were measured hourly with capacitance sensors centered at depths of 30, 60 90 and 120 or 150 cm at 18 landscape positions since 2002 and using two cosmic ray sensors installed in 2015 and 2016 at summit and toe-slope positions, respectively. While much of the soil-water dynamic behavior is driven by weather patterns from event to inter-annual time scales, land management is an important driving factor. The wheat-fallow field was converted to perennial vegetation under the Conservation Reserve Program (CRP) in 2013 and 2014 based on spatially alternating crop strips. Decadal soil climatology and impacts of land management changes on space-time regimes of soil moisture in the root zone are emphasized here. Anticipated corollaries include using these data for space-time process simulation to infer unmeasured water fluxes, quantifying changes in surface runoff patterns and thresholds, and analysis of micrometeorology using an enhanced network of above-ground meteorological variables at the site. Scaling of data with different support scales and up-scaling from this field scale to larger gridded data products remains a challenge. We highlight the value of such long-term, spatially distributed data, including depths spanning the root zone that are not measured directly with remote sensing. |