Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
This presentation discusses three challenges to simulate water and energy cycles in arid and semi-arid regions such as the U.S. Great Plains. The North American Land Data Assimilation System (NLDAS) is used as an example to illustrate these issues. The first challenge is due to the relative lack in-situ observations in this region compared to other parts of the U.S. The observation usually used for land-surface model calibration is USGS streamflow; however, there are few streamflow gauges measuring unregulated flows in this region. In addition, there are much fewer long-term (e.g., 10-20 years) in-situ evapotranspiration (ET), sensible/latent heat flux, soil moisture/ temperature observations available. The second challenge is due to the lack of scientific understanding for land surface physical processes, including soil and hydrology physical process (e.g., groundwater dynamics), in arid/semi-arid regions where sand is a dominant soil type. For example, when precipitation is partitioned into ET and total runoff in arid and semi-arid regions, most of the precipitation becomes ET and only a small part of the precipitation goes into total runoff. A 10% to 20% error in observed precipitation can lead to 100% errors in runoff simulation. Therefore, even when soil and hydrologic parameters are calibrated using USGS observed streamflow, these parameters do not necessarily improve ET simulation as the latter has a larger portion. Potential reasons may be due to lack of some physical processes such as groundwater and irrigation, as well as inappropriate partitioning of evapotranspiration into bare soil evaporation, canopy evaporation, and transpiration. The third challenge is how to select calibration reference datasets. In the Great Plains, although there are few in-situ observations, there are quite a lot of reference gridded datasets such as remote sensing ET data (e.g., MODIS, ALEXI, SEBS, and GLEAM), FLUXNET-based gridded ET, and other reanalysis ET products. In addition, FLUXNET-based gridded sensible heat, remotely sensed land surface temperature, soil moisture, terrestrial water storage (e.g., GRACE), and radiation fluxes also exist. They are good alternatives to calibrate the model soil and hydrologic parameters. However, as these reference products have their own errors and uncertainties, it is a challenge on how to best use them to calibrate the models. For this presentation, the NLDAS land-surface models are examined by (1) investigating error estimation of these references and the variables selected for model calibration and (2) using high efficient calibration method, high performance computer, and parallel computing model code to speed up calibration process. Final, the prospects within NLDAS system are discussed.
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