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Title:   Author  item
                                            HAIN, C. - University Of Marylanditem
                                            Crow, Wadeitem
                                            Anderson, Marthaitem
                                            YILMAZ, M.T. - Collaborator
  Submitted to: Journal of Hydrometeorology
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 1/7/2015
                                    Publication  Date: 6/1/2015
                                    Citation: Hain, C., Crow, W.T., Anderson, M.C., Yilmaz, M. 2015. Diagnosing neglected soil moisture source/sink processes via a thermal infrared-based two-source energy balance model. Journal of Hydrometeorology. 16:1070-1086. doi: 010.1175/JHM-D-14-0017.1.Interpretive Summary: Hydrologic models are important tools for examining the impact of: agricultural management, seasonal drought, and long-term climate change on water resource availability. Unfortunately, such models typically lack a number of processes which are important for determining the soil water balance in agricultural landscapes. These processes include: irrigation, direct root extraction from groundwater, tile drainage and sub-surface lateral water flow into riparian areas. The neglect of these processes can seriously degrade our ability to monitor water resource availability in agricultural catchments. To date, efforts to include these processes in hydrologic models have been hampered by a lack of direct observational data quantifying their impact on the surface water and energy balance. In order to address this known problem, this paper describes the application of a new remote-sensing-based technique for detecting areas where irrigation, tile drainage, and groundwater processes play a significant role in shaping the surface water and energy balance. The new information provided by this technique can be used by federal agencies to improve their ability to monitor agricultural drought and predict the impact of climate change on soil water availability within agricultural landscapes.
                                Technical Abstract: In recent years, increased attention has been paid to the role of previously neglected water source (e.g., irrigation, direct groundwater extraction and inland water bodies) and sink (e.g., tile drainage) processes on the surface energy balance. However, effects to parameterize these processes within Land Surface Models (LSMs) have generally been hampered by a lack of appropriate observational tools for directly observing the impact(s) of such processes on surface energy fluxes. One potential strategy for quantifying these impacts are direct comparisons between bottom-up surface energy fluxes predictions from a one-dimensional, free-drainage LSM with top-down energy flux estimates derived via thermal-infrared remote sensing. The neglect of water source (and/or sink) processes in the bottom-up LSM can be potentially diagnosed through the presence of systematic energy flux biases relative to the bottom-down remote sensing retrieval. Based on this concept, we introduce the ALEXI Source/Sink for EvapoTranspiration (ASSET) index derived from comparisons between Atmosphere Land Exchange Inverse (ALEXI) remote sensing latent heat flux retrievals and comparable estimates obtained from the Noah LSM v3.2. Comparisons between ASSET index values and known spatial variations of groundwater depth, irrigation extent, inland water bodies and tile drainage density within the contiguous United States, verify the ability of ASSET to accurately reflect the impact of neglected soil water source/sink processes on patterns of surface energy fluxes. Consequently, ASSET appears to provide valuable information for on-going efforts to improve the parameterization of new water source/sink processes within modern LSMs.


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