Attribution of Flux Partitioning Variations between Land Surface Models over the Continental US
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Attribution of Flux Partitioning Variations between Land Surface Models over the Continental US
  • Published Date:

    2018

  • Source:
    Remote Sens. 2018, 10(5), 751
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Attribution of Flux Partitioning Variations between Land Surface Models over the Continental US
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  • Description:
    Accurate quantification of the terrestrial evapotranspiration ( ET ) components of plant transpiration (T), soil evaporation (E) and evaporation of the intercepted water (I) is necessary for improving our understanding of the links between the carbon and water cycles. Recent studies have noted that, among the modeled estimates, large disagreements exist in the relative contributions of T, E and I to the total ET . As these models are often used in data assimilation environments for incorporating and extending ET relevant remote sensing measurements, understanding the sources of inter-model differences in ET components is also necessary for improving the utilization of such remote sensing measurements. This study quantifies the contributions of two key factors explaining inter-model disagreements to the uncertainty in total ET : (1) contribution of the local partitioning and (2) regional distribution of ET . The analysis is conducted by using outputs from a suite of land surface models in the North American Land Data Assimilation System (NLDAS) configuration. For most of these models, transpiration is the dominant component of the ET partition. The results indicate that the uncertainty in local partitioning dominates the inter-model spread in modeled soil evaporation E. The inter-model differences in T are dominated by the uncertainty in the distribution of ET over the Eastern U.S. and the local partitioning uncertainty in the Western U.S. The results also indicate that uncertainty in the T estimates is the primary driver of total ET errors. Over the majority of the U.S., the contribution of the two factors of uncertainty to the overall uncertainty is non-trivial.
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