Location: Hydrology and Remote Sensing LaboratoryTitle: Analyzing the variability of remote sensing and hydrologic model evapotranspiration products in a watershed in Michigan
|HERMAN, M.R. - Michigan State University|
|NEJADHASHEMI, A.P. - Michigan State University|
|HERNANDEZ-SUAREZ, J.S. - Michigan State University|
Submitted to: Journal of the American Water Resources Association
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/5/2020
Publication Date: 5/13/2020
Citation: Herman, M., Nejadhashemi, A., Hernandez-Suarez, J., Sadeghi, A.M. 2020. Analyzing the variability of remote sensing and hydrologic model evapotranspiration products in a watershed in Michigan. Journal of the American Water Resources Association. https://doi.org/10.1111/1752-1688.12849.
Interpretive Summary: Advances in satellite technology have led to the availability of global remote sensing datasets that can be used to supplement gaps in observed hydrological data. In this study, the temporal and spatial variabilities of eight remotely sensed actual evapotranspiration (Eta) datasets along with ETa output of a hydrological model were evaluated. The temporal analyses of the datasets showed that there was no noticeable trend in similarities between specific datasets at both monthly and seasonal scales. This is reflective of weather and vegetation cover trends within the region. Nevertheless, the lack of patterns among the datasets showed that temporal variation is less influential when compared to spatial variation. Overall, the ETa product that was able to differentiate amongst all the major landuses for all seasons was the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. All the other datasets tested, except for the Atmosphere-Land Exchange Inverse (ALEXI), were able to differentiate between landuses for at least one season. Since this comparison analysis was performed on only one watershed, future studies are needed to expand this analysis to different climatological zones. Findings from this study help our understanding of how each ETa product performs across the global landscape and to make sure that the correct ETa dataset is selected for further model calibration and testing.
Technical Abstract: Monitoring the movement of water across the Earth is becoming more and more important as the demand for freshwater rises. However, current monitoring techniques are often unable to provide enough resolutions to inform decision making. This is particularly true for actual evapotranspiration (ETa). Nevertheless, advances in satellite technology has led to the availability of global remote sensing datasets that can be used to supplement gaps in observed hydrological data. However, it is often challenging to identify the right dataset for different spatial and temporal scales. Therefore, the goal of this paper is to statistically explore the spatial and temporal performance of remotely sensed ETa datasets, a remotely sensed ensemble in a region that lacks observed data. The remotely sensed datasets were further compared with Eta results from a physically-based hydrologic model to examine the differences and describe discrepancy among them. All of these datasets were compared through the use of Generalized Least-Square estimations that compared ETa datasets on temporal (i.e., monthly and seasonal basis) and spatial (i.e., landuse) scales at both watershed and subbasin levels. Results showed a lack of patterns among the datasets when evaluating the monthly ETa variations; however, the seasonal aggregated data presented a better pattern and less variances and statistical difference at the 0.05 level during spring and summer compare to fall and winter months. Meanwhile, spatial analysis of the datasets showed that the MOD16A2 500 m ETa product was the most versatile of the tested datasets, being able to differentiate between landuses during all seasons. This was due to the fact that the MOD16A2 500 m dataset had the highest spatial resolution and was thus able to capture more spatial variability in ETa. Furthermore, the use of an averaging ensemble was able to improve overall ETa performance in the study area. Finally, the ETa output of the hydrological model was found to be similar to several of the ETa products (MOD16A2 1 km, NLDAS-2: Noah, and NLDAS-2: VIC) use 44 d in this study. These similarities were driven using similar governing equations.