Wind-induced sea level blowouts, measured as negative storm surge or extreme low water (ELW), produce public safety hazards and impose economic costs (e.g., to shipping). In this paper, we use a regional hydrodynamic numerical model to test the effect of historical environmental change and the time scale, direction, and magnitude of wind forcing on negative and positive surge events in the New York Harbor (NYH). Environmental sensitivity experiments show that dredging of shipping channels is an important factor affecting blowouts while changing ice cover and removal of other roughness elements are unimportant in NYH. Continuously measured water level records since 1860 show a trend towards smaller negative surge magnitudes (measured minus predicted water level) but do not show a significant change to ELW magnitudes after removing the sea-level trend. Model results suggest that the smaller negative surges occur in the deeper, dredged modern system due to a reduced tide-surge interaction, primarily through a reduced phase shift in the predicted tide. The sensitivity of surge to wind direction changes spatially with remote wind effects dominating local wind effects near NYH. Convergent coastlines that amplify positive surges also amplify negative surges, a process we term inverse coastal funneling.

E. coli (EC) concentrations of the upstream boundary, tributaries, and stormwater in the lower Passaic River at Paterson, New Jersey, were modeled using multivariate polynomial regression (MPR). Baseflow indexes (BFIs) and river flows from upstream and downstream boundaries of the study area were used as predictors. The MPR models were developed by stepwise addition of the candidate terms. The candidate terms were selected based on their $t$ -statistics and the final term was selected based on the Nash-Sutcliffe efficiency (NSE) of the overall model. The NSE values of the models ranged from 0.61 to 0.88. The boundary concentrations were earlier modeled using symbolic regression without BFI as a predictor, resulting in a set of highly complex models for the same data. This study demonstrates the suitability of BFI as a water quality predictor and the importance of identifying suitable predictors to develop defensible empirical water quality models. Further, the relation between EC concentrations and BFI could be used to infer whether the predominant pollutant source at a location is independent of rainfall or is rainfall driven.