TY - JOUR
T1 - El-Niño/Southern Oscillation (ENSO) influences on monthly NO3 load and concentration, stream flow and precipitation in the Little River Watershed, Tifton, Georgia (GA)
AU - Keener, V. W.
AU - Feyereisen, G. W.
AU - Lall, U.
AU - Jones, J. W.
AU - Bosch, D. D.
AU - Lowrance, R.
N1 - Funding Information: The research was supported in part through grants from the National Oceanic and Atmospheric Administration – NOAA – Climate Program Office (NOAA-CPO) Grant Number NJ17RJ1226 , the US Department of Agriculture – Cooperative State Research, Education, and Extension Services (USDA-CSREES) Grant Number 2009-38890-19911 , and developed under the auspices of the Southeast Climate Consortium (SECC).
PY - 2010/2/15
Y1 - 2010/2/15
N2 - 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 the best known pattern. Its global effects on weather, hydrology, ecology and human health have been well documented. 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 establish a teleconnection between ENSO and the Little River Watershed (LRW), GA., as seen in a shared 3-7 year mode of variability for precipitation, stream flow, and nutrient load time series. Univariate wavelet analysis of the NINO 3.4 index of sea surface temperature (SST) and of precipitation, stream flow, NO3 concentration and load time series from the watershed was used to identify common signals. 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. 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 the NINO 3.4 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, facilitating management in high risk seasons.
AB - 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 the best known pattern. Its global effects on weather, hydrology, ecology and human health have been well documented. 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 establish a teleconnection between ENSO and the Little River Watershed (LRW), GA., as seen in a shared 3-7 year mode of variability for precipitation, stream flow, and nutrient load time series. Univariate wavelet analysis of the NINO 3.4 index of sea surface temperature (SST) and of precipitation, stream flow, NO3 concentration and load time series from the watershed was used to identify common signals. 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. 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 the NINO 3.4 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, facilitating management in high risk seasons.
KW - ENSO
KW - El Niño
KW - Hydrology
KW - Nutrients
KW - Southeast US
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=74549188082&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=74549188082&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2009.12.008
DO - 10.1016/j.jhydrol.2009.12.008
M3 - Article
SN - 0022-1694
VL - 381
SP - 352
EP - 363
JO - Journal of Hydrology
JF - Journal of Hydrology
IS - 3-4
ER -