A geographic weighted regression approach for improved total alkalinity estimates in the Northern Gulf of Mexico. (February 2022) - Dash, Padmanava; Devkota, Madhur; Mercer, Andrew E.; Ambinakudige, Shrinidhi
Abstract: Total alkalinity (TA) is one of the important parameters to show the intensity of seawater buffer against ocean acidification. TA dynamics in the northern Gulf of Mexico (N-GoM) is significantly affected by the Mississippi River. An empirical TA algorithm is offered here which accounts for the local effects of coastal processes. In situ data collected during numerous research cruises in the N-GoM were compiled and used to develop TA algorithms using sea surface temperature (SST) and sea surface salinity (SSS) as explanatory variables. After improving the coefficients and functional form of this algorithm, chlorophyll a (Chl- a ) was included as an additional explanatory variable, which worked as a proxy for addressing the pronounced effects of biological forcing on coastal waters. Finally, a geographically weighted regression algorithm was developed in the form TA = exp[Xo + X1 (SSS-35) 2 +X2 (SSSxSST) 1/2 +X3 chl- a ] to address spatial non-stationarity, which produced improved estimates of TA in the N-GoM. Highlights: Algorithms were developed to estimate total alkalinity in northern Gulf of Mexico. Use of chlorophyll a addressed the biological and chemical complexities. Geographically weighted regression produced the best estimates.