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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #319551

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Improving root-zone soil moisture estimations using dynamic root growth and crop phenology

item Crow, Wade
item Kustas, William - Bill

Submitted to: Advances in Water Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/15/2015
Publication Date: 12/31/2015
Citation: Hashemian, M., Ryu, D., Crow, W.T., Kustas, W.P. 2015. Improving root-zone soil moisture estimations using dynamic root growth and crop phenology. Advances in Water Resources. 86(A):170-183.

Interpretive Summary: The integration of remote sensing observations with soil water balance modeling provides a valuable tool for monitoring the availability of soil water for uptake by agricultural crops. However, the successful application of such models requires that they accurately simulate the manner in which the crop roots extract water from the soil. This paper examines the performance of an existing soil water balance model and demonstrates that it does not realistically simulate the root uptake of water by a developing agricultural crop. In response to this motivation, the paper goes on to develop and apply a new root-water uptake parameterization which is demonstrated to improve the overall performance of the soil water balance model. This improved model is of value for agricultural applications like irrigation scheduling and drought monitoring which require tracking the temporal dynamics of soil water available over time. Eventually, it could lead to more efficient irrigation practices and improved mitigation to lessen the detrimental impact of agricultural drought.

Technical Abstract: Water Energy Balance (WEB) Soil Vegetation Atmosphere Transfer (SVAT) modelling can be used to estimate soil moisture by forcing the model with observed data such as precipitation and solar radiation. Recently, an innovative approach that assimilates remotely sensed thermal infrared (TIR) observations into WEB-SVAT to improve the results has been proposed. However, the efficacy of the model-observation integration relies on the model’s realistic representation of soil water processes. We explore methods to improve the soil water processes of a simple WEB-SVAT model by adopting and incorporating an exponential root water uptake model with water stress compensation and establishing a more appropriate soilbiophysical linkage between root-zone moisture content, above-ground states and biophysical indices. The existing WEB-SVAT model is extended to a new Multi-layer WEB-SVAT with Dynamic Root distribution (MWSDR) that has 5 soil layers and dynamic root distribution. Impacts of plant root depth variations, growth stages and phenological cycle of plant on transpiration are considered in developing stages. Hydrometeorological and biogeophysical measurements collected from two experimental sites, one in Dookie, Victoria, Australia and the other in Ponca, Oklahoma, USA, are used to validate the new model. Results demonstrate that MWSDR provides improved soil moisture, transpiration and evaporation predictions which, in turn, can provide an improved physical basis for assimilating remotely sensed data into the model. Results also show the importance of an adequate representation of vegetationrelated transpiration process for an appropriate simulation of water transfer in a complicated system of soil, plants and atmosphere.