INTEGRATED MANAGEMENT OF LAND AND WATER RESOURCES FOR ENVIRONMENTAL AND ECONOMIC SUSTAINABILITY IN THE NORTHEAST U.S.
Location: Pasture Systems & Watershed Management Research
Title: El-Niño Southern Oscillation (ENSO) influences on monthly NO3 load and concentration, stream flow and precipitation in the Little River Watershed, Tifton, GA
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 30, 2009
Publication Date: February 15, 2010
Citation: Keener, V.W., Feyereisen, G.W., Lall, U., Jones, J.W., Bosch, D.D., Lowrance, R.R. 2010. El-Niño Southern Oscillation (ENSO) influences on monthly NO3 load and concentration, stream flow and precipitation in the Little River Watershed, Tifton, GA. Journal of Hydrology. 381(3-4):352-363.
Interpretive Summary: Stream flow volume and water quality in the southeastern United States effects river and estuary ecological health. The climate pattern known as the El-Niño/Southern Oscillation (ENSO), which is characterized by a 3-7 year periodicity in equatorial Pacific Sea Surface Temperatures (SST’s), has strong effects on precipitation and stream flow in the southeastern United States and may also affect water nutrient concentrations and loads. This study evaluates the relationship between ENSO and precipitation, stream flow, nitrate-nitrogen concentration, and nitrate-nitrogen loads in southern Georgia’s Little River Watershed. Results confirm that SST’s in the equatorial Pacific cause a significant change in precipitation, stream flow and nitrate-nitrogen load, and to a lesser degree nitrate-nitrogen concentration in southern Georgia. The study identified a lag time of three months between changes in the ENSO signal and nitrate-nitrogen load in the Little River Watershed. The results can be utilized in a time series model to predict monthly nutrient loads based on predictions of short-term climate variability, potentially identifying high risk seasons and providing response time for management options.
As climate variability increases, it is becoming increasingly critical to find predictable patterns that can still be identified despite overall uncertainty. The El-Niño/Southern Oscillation is one such pattern; a climate phenomenon with global effects on weather, hydrology, ecology and human health, characterized by a 3-7 year periodicity in equatorial Pacific Sea Surface Temperatures. Climate variability manifested through ENSO has strong effects in the southeast United States, seen in precipitation and stream flow data. However, climate variability may also affect water quality in nutrient concentrations and loads, and have impacts on ecosystems, health, and food availability in the southeast. In this research, we confirmed a teleconnection between ENSO and the Little River Watershed, GA., as seen in a shared 3-7 year mode of variability for precipitation, stream flow, and nutrient load time series. Univariate wavelet analysis was used on the NINO 3.4 index of SST to find modes of variability significant from red noise with 90% confidence, compared with wavelet analysis of precipitation, stream flow, and NO3 concentration and load time series to identify areas of similar periodicity. Shared 3-7 year modes of variability were seen in all variables, most strongly in precipitation, stream flow and nutrient load in strong El Niño years, and least in nutrient concentration. The significance of shared 3-7 year periodicity over red noise with 95% confidence in SST and precipitation, stream flow, and NO3 load time series was confirmed through cross-wavelet and wavelet-coherence transforms, in which common high power and co-variance were computed for each set of data. The strongest 3-7 year shared power was seen in SST and stream flow data, while the strongest co-variance was seen in SST and NO3 load data. The strongest cross-correlation was seen as a positive value between SST and NO3 load with a three-month lag. The teleconnection seen in the LRW between the NINO 3.4 index and precipitation, stream flow, and NO3 load can be utilized in a model to predict monthly nutrient loads based on short-term climate variability, adding management options for high risk seasons.