Incorporating El Niño Southern Oscillation (ENSO)-Induced Climate Variability for Long-Range Hydrologic Forecasting and Stream Water Quality Protection

Abstract

El Niño Southern Oscillation (ENSO) has been found to have a strong predictable effect on streamflow in different parts of the world. Since ENSO has potential relationship to seasonal and inter-annual variability in streamflow, identifying the potential linkage between streamflow and ENSO and applying that linkage to data-driven model can improve the streamflow simulation and forecasting. That is, streamflow forecasting using sea surface temperature (SST) can be useful in ENSO-affected regions. In addition, ENSO might have teleconnection with stream water quality. One of the sources of stream water quality degradation is point source pollution which is regulated under the “National Point Discharge Elimination System (NPDES)” permitting process. Since conventional NPDES permits do not consider seasonal to inter-annual climate variability, they are either under-protective or over-protective of stream water quality. Application of ENSO information in NPDES permitting can be useful for stream water quality protection. Further, ENSO signals can be utilized to predict total organic carbon (TOC) loads, which form disinfection byproducts during chlorination of drinking water. Therefore, the specific objectives of this research were: (i) to demonstrate that the data-driven model, Adaptive Neuro Fuzzy Inference System (ANFIS) that incorporates SST and sea level pressure (SLP) can simulate streamflow as good as the Loading Simulation Program C++ (LSPC++), (ii) to quantify the long-range streamflow forecasting skill of the ANFIS model with the fusion of SST and predicted climate data, (iii) to demonstrate how ENSO information can be incorporated to improve NPDES permitting system in a complex river system using a watershed linked hydrodynamic and water quality model, and (iv) to predict TOC loads quantitatively in different ENSO phases using climate data and ENSO indices, and forecast TOC load qualitatively using a fuzzy logic approach. It was found that: (i) the performance of the ANFIS model was comparable to the LSPC model, (ii) streamflow forecast using SST at one month lead time was satisfactory, (iii) ENSO information was useful for regulating point sources for stream water quality protection, and (iv) the TOC load was correlated with the ENSO phase; therefore, TOC load was predicted both quantitatively and qualitatively in different ENSO phases.