Location: Hydrology and Remote Sensing LaboratoryTitle: Understanding temporal stability: A long-term analysis of USDA ARS watersheds
|COOPERSMITH, E. - Collaborator|
|Starks, Patrick - Pat|
|Bosch, David - Dave|
|Holifield Collins, Chandra|
Submitted to: International Journal of Digital Earth
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/10/2021
Publication Date: 6/22/2021
Citation: Coopersmith, E., Cosh, M.H., Starks, P.J., Bosch, D.D., Holifield Collins, C.D., Seyfried, M.S., Livingston, S.J., Prueger, J.H. 2021. Understanding temporal stability: A long-term analysis of USDA ARS watersheds. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2021.1943550.
Interpretive Summary: Validation of satellite and model soil moisture products often use soil moisture in situ network time series as a ground truth. Networks are often assumed stable over time, but there has been little research on demonstrating the consistency of a network’s estimates and variability over time. Thus, a study was conducted on a variety of in situ soil moisture networks to determine how the variability compares from one season to the next and one year to the next. This study finds that watershed networks are able to adequately capture a full range of soil moisture conditions within one calendar year, which is encouraging for network scaling activities and validation campaigns.
Technical Abstract: The USDA’s Agricultural Research Service (USDA-ARS) maintains seven in situ soil moisture networks throughout the continental United States, some since 2002. These networks are crucial for understanding the spatial and temporal extent of droughts in their historical context, for the parameterization of hydrologic models, for inputs to General Circulation Models (GCMs), for comparison with soil moisture estimates gathered via remote sensing and for local agricultural decision support. But the estimates from these networks are dependent upon their ability to provide reliable soil moisture information at a large scale. Therefore, it is necessary to determine how temporally stable these networks are, including the relationships between various sensors on a year-to-year basis (i.e. are the wettest and driest locations consistently so each year?) and on a seasonal basis (i.e. is the wettest sensor in April also the wettest sensor in August?). Additionally, this analysis will attempt to determine how many sensors are required, within a network, to approximate the full network average. In this regard, it is possible to install a temporary network containing a suitable number of sensors for an appropriate length of time, glean stable relationships between locations, and retain these insights moving forward with fewer sensor resources.