|BUNTING, D.P. - University Of Arizona
|KURC, S.A. - University Of Arizona
|GLENN, E.P. - University Of Arizona
|NAGLER, P.L. - University Of Arizona
|Scott, Russell - Russ
Submitted to: Journal of Arid Environments
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/30/2014
Publication Date: 12/1/2014
Citation: Bunting, D., Kurc, S., Glenn, E., Nagler, P., Scott, R.L. 2014. Insights for empirically modeling evapotranspiration in¿uenced by riparian and upland vegetation in semiarid regions. Journal of Arid Environments. 111:42-52. doi:75910.1016/j.jaridenv.2014.06.007.
Interpretive Summary: A common goal for water resource managers in the southwestern United States, and arid and semiarid regions worldwide, is to ensure long-term water supplies for increasing human populations. In these regions, evapotranspiration (ET) is an important component of the water budget and has large implications for water resources management. We used satellite data and measured ET from three riparian-influenced and two upland, water-limited sites to develop a computer model that could estimate ET with satellite and rainfall data alone. We found that two separate models were needed: one for the riparian sites and another for the dry upland sites due to the complete dependency of the upland sites on rainfall. These models can be used in combination to estimate total annual ET across a watershed provided that each region is classified appropriately. This approach improves accuracy of ET estimates at large scales while accounting for daily to seasonal fluctuations in ET.
Technical Abstract: Water resource managers aim to ensure long-term water supplies for increasing human populations. Evapotranspiration (ET) is a key component of the water balance and accurate estimates are important to quantify safe allocations to humans while supporting environmental needs. Scaling up ET measurements from small spatial scales has been problematic due to spatiotemporal variability. Satellite data provide spatially distributed remote sensing products that account for seasonal climate and vegetation variability. We used MODIS products [i.e., Enhanced Vegetation Index (EVI) and nighttime land surface temperatures (LSTn)] to create an empirical ET model calibrated using measured ET from three riparian-influenced and two upland, water-limited flux tower sites. Results showed that combining all sites introduced systematic bias, so we developed separate models to estimate riparian and upland ET. While EVI and LSTn were the main drivers for ET in riparian sites, precipitation replaced LSTn as the secondary driver of ET in upland sites. Riparian ET was successfully modeled using an inverse exponential approach (r2 = 0.92) while upland ET was adequately modeled using a multiple linear regression approach (r2 = 0.77). These models can be used in combination to estimate ET at basin scales provided each region is classified and precipitation data is available. This approach improves accuracy of ET estimates at large scales while accounting for daily to seasonal fluctuations.