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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 #299329

Title: Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River Basin

Author
item YILMAZ, M - Collaborator
item Anderson, Martha
item ZAITCHIK, B - Johns Hopkins University
item HAIN, C - University Of Maryland
item Crow, Wade
item OZODOGAN, M - University Of Wisconsin
item CHUN, J - Johns Hopkins University
item EVANS, J - Collaborator

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/11/2013
Publication Date: 1/17/2014
Publication URL: http://handle.nal.usda.gov/10113/59958
Citation: Yilmaz, M.T., Anderson, M.C., Zaitchik, B.F., Hain, C., Crow, W.T., Ozodogan, M., Chun, J.A., Evans, J. 2014. Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River Basin. Water Resources Research. 50(1):386-408.

Interpretive Summary: Spatial maps of consumptive water use, or evapotranspiration (ET), over large regions are critical for an array of water resource management applications and for monitoring global water security factors. In many major global river basins, there is a paucity of ground-based observations available to validate ET mapping products. The evaluation of these products is further hindered by a reluctance to share data between countries in transnational basins. In such data sparse regions, one method for validation is to compare output from multiple independent approaches to mapping ET. In this paper, this approach is applied to the Nile River basin, comparing ET maps generated using satellite remote sensing data in an energy balance model and with a hydrologic model that assumes water balance. The maps compared well over much of the basin, but areas of persistent discrepancy were identified. The water balance model failed in areas of intensive irrigation,such as in the Nile Delta and Gezira irrigation scheme, and in the extensive wetlands in the Sudd region due to incomplete representation of all components of the water budget. The satellite product showed biases in areas of persistent cloud cover, where surface data could not be retrieved on a regular basis. The approach described here revealed important issues that need to be addressed in each modeling system, and can be readily applied in other data sparse basins worldwide.

Technical Abstract: Regional evapotranspiration (ET) can be estimated using diagnostic remote sensing models, generally based on principles of energy balance, or with spatially distributed prognostic models that simultaneously balance both the energy and water budgets over landscapes using predictive equations for land surface temperature and moisture states. Each modeling approach has complementary advantages and disadvantages, and in combination they can be used to obtain more accurate ET estimates over a variety of land and climate conditions, particularly for areas with limited ground truth data. In this study, energy and water flux estimates from the diagnostic Atmosphere-Land Exchange (ALEXI) and prognostic Noah land surface models are compared over the Nile River basin between 2007 and 2011. A second remote sensing dataset, generated with the Penman-Monteith approach as implemented in the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16 ET product, is also included as a comparative technique. In general, spatial and temporal distributions of flux estimates from ALEXI and Noah are similar in regions where the climate is temperate and local rainfall is the primary source of water available for ET. However, the diagnostic ALEXI model is better able to retrieve ET signals not directly coupled with the local precipitation rates, for example over irrigated agricultural areas or regions influenced by shallow water tables. These hydrologic features are not well-represented by either Noah or MOD16. Evaluation of consistency between diagnostic and prognostic model estimates can provide useful information about relative product skill, particularly over regions where ground data are limited or non-existent as in the Nile basin.