A geographic weighted regression approach for improved total alkalinity estimates in the Northern Gulf of Mexico

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-alpha) 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 + X-1(SSS-35)(2)+X-2(SSSxSST)(1/2)+X(3)chl-alpha] to address spatial non-stationarity, which produced improved estimates of TA in the N-GoM.

Automated summary

An empirical algorithm for total alkalinity (TA) in the northern Gulf of Mexico (N-GoM) was developed, taking into account the local effects of coastal processes. Sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll a (Chl-alpha) were used as explanatory variables, producing improved estimates of TA in the region.