Hydro-climatic influences of El-Nino/Southern Oscillation on nutrient loads in the southeast United States.

摘要

As global climate change becomes more of a problem, it is crucial to understand regional variability. In the southeast U.S.A., natural resource managers wish to reduce climate associated risks. This research explores the relationship between the El-Nino/Southern Oscillation (ENSO), a major driver of global climate variability, with hydrology and water quality in two watersheds.;In basin S-191 of Lake Okeechobee, we quantified relationships between 36 years of WAM simulated phosphorus (P) loading and categorical measures of ENSO. Results showed ENSO strongly affects seasonal and monthly P runoff, with significant loading in spring of El Nino and summer of La Nina years. Greater P load in certain months was consistent with greater precipitation, with large flow and nutrient flushes following drought. Observed data from the Little River Watershed (LRW) was used with wavelet analysis to quantify the significance of a teleconnection between the NINO 3.4 index and precipitation, stream flow, nitrate concentration and load. Areas of high power and inter-annual variability manifested in the 3--7 year ENSO periodic signal.;To explain the powerful relationship between ENSO and stream flow, a calibrated SWAT model of the LRW mechanistically identified how the power of a climate signal can be increased through natural processes. Using SWAT and wavelet analysis, we found that the presence of a confining layer in the LRW increased the groundwater/baseflow signal such that the ENSO signal in flow was more significant than the chaotic precipitation signal. Finally, significant 3--7 year reconstructed components from the wavelet analyses were extracted and used to create a monthly vector time series model that more accurately forecasts 1--3 month NO3 loads than time-domain signals only.;IPCC reports have concluded that climate variability and extreme events will be more common in the future. Research that focuses on understanding and predicting the effects will be increasingly helpful for making robust management decisions in an uncertain world. Models of water quality and ENSO can help stakeholders effectively manage their risk in the near future, and are a step towards faster integration of climate information into daily decision making.