Long-term data collection at USDA experimental sites for studies of ecohydrology

TitleLong-term data collection at USDA experimental sites for studies of ecohydrology
Publication TypeConference Paper
Year of Publication2008
AuthorsMoran M.S., Peters DC, McClaran M., Nichols M.H., Adams M.
Conference Name28th Conference on Agriculture and Forest Meteorology
Date PublishedApril 28, 2008
ARIS Log Number223457
Keywordsabstract, ecohydrology, ecology, hydrology, long-term, watersheds
AbstractThe goal of this review is to show the value of long-term, continuous data from the network of USDA experimental watersheds, forests and ranges for studying the interaction between ecology and hydrology, termed ecohydrology. We identified 81 USDA experimental sites 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) critical thresholds and cyclic trends, 2) time lags between cause and effects, 3) context of rare and extreme events and 4) land surface simulation modeling. New analyses of network-wide, long-term data from USDA experimental sites were used to explore the scales of temporal and spatial measurement required for ecological and hydrological research. The results underscored the need for continuous, interdisciplinary data records spanning more than 20 years across a wide range of ecosystems within and outside the conterminous U.S. for key ecohydrological research. The basic conclusion is that USDA experimental sites play a unique and important role in addressing the major crosscutting problems facing ecohydrology, such as spatial complexity and scaling, thresholds, and feedbacks and interactions. 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.