Understanding temporal stability: a long-term analysis of USDA ARS watersheds. Issue 10 (3rd October 2021)

• Record Type: Journal Article
• Authors: Coopersmith, Evan J.; Cosh, Michael H.; Starks, Patrick J.; Bosch, David D.; Holifield Collins, Chandra; Seyfried, Mark; Livingston, Stan; Prueger, John
• Is Part Of: International journal of digital earth. Volume 14:Issue 10(2021)
• Page Start: 1243
• Page End: 1254
• Publication Date: 2021-10-03
• Subjects: Soil moisture -- temporal stability -- in situ networks; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2021.1943550
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: The U.S. Department of Agriculture'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, parameterization of hydrologic models, and local agricultural decision support. However, the estimates from these networks are dependent upon their ability to provide reliable soil moisture information at a large scale. It is also not known how many network stations are sufficient to monitor watershed scale dynamics. Therefore, the objectives of this research were to: (1) determine how temporally stable these networks are, including the relationships between various sensors on a year-to-year and seasonal basis, and (2) attempt to determine how many sensors are required, within a network, to approximate the full network average. Using data from seven in situ, it is concluded that approximately 12 soil moisture sensors are sufficient in most environments, presuming their locations are distributed to capture the hydrologic heterogeneity of the watershed. 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.