Long-term data collection at USDA experimental sites for studies of ecohydrology.
Authors:
Moran, M. Susan Peters, Debra P.C. McClaran, Mitchel P. Nichols, Mary H. Adams, Mary B. USDA, ARS USDA, FS
Source:
Ecohydrology. 2008 Dec., v. 1, issue 4, p. 377-393.
NALT Subjects:
watersheds agricultural watersheds forested watersheds forest hydrology hydrologic models watershed hydrology USDA hydrologic data literature reviews United States
Other Subjects:
experimental watersheds ecohydrology
Issue Date:
Dec-2008
Abstract:
The science of ecohydrology is characterized by feedbacks, gradual trends and extreme events that are best revealed with long-term experimental studies of hydrological processes and biological communities. In this review, we identified 81 US Department of Agriculture (USDA) experimental watersheds, forests and ranges with data records of more than 20 years measuring important ecosystem dynamics such as variations in vegetation, precipitation, climate, runoff, water quality and soil moisture. Through a series of examples, we showed how USDA long-term data have been used to understand key ecohydrological issues, including (1) time lag between cause and effects, (2) critical thresholds and cyclic trends, (3) context of rare and extreme events and (4) mechanistic feedbacks for simulation modelling. New analyses of network-wide, long-term data from USDA experimental sites were used to illustrate the potential for multi-year, multi-site ecohydrological research. Three areas of investigation were identified to best exploit the unique spatial distribution and long-term data of USDA experimental sites: convergence, cumulative synthesis and autocorrelation. This review underscored the need for continuous, interdisciplinary data records spanning more than 20 years across a wide range of ecosystems within and outside the conterminous USA to address major crosscutting problems facing ecohydrology. Conversely, the heightened interest in ecohydrology has impacted USDA experimental sites by encouraging new long-term data collection efforts and adapting existing long-term data collection networks to address new science issues.
