Stochastic characterization of the onset of and recovery from hypoxia in Tokyo Bay, Japan: Derived distribution analysis based on “strong wind” events

Abstract

This paper uses derived distribution analysis to explore the process controls of the onset of and recovery from hypoxic conditions in Tokyo Bay, Japan. A conceptual, lumped model of dissolved oxygen (DO) dynamics in Tokyo Bay is proposed and, through comparison with a three‐dimensional simulation model, is verified to have sufficient accuracy for the prediction of the onset of and recovery from hypoxia. This conceptual DO model was implemented in continuous simulation mode, with 14 years of wind data and data on streamflow entering the Tokyo Bay, and was used to identify and quantify the various process controls of the onset of and recovery from hypoxia. The underlying process controls were identified to be streamflow discharge, as well as duration and strength of both northeast “positive” winds and southwest “negative” winds. The analysis helped to isolate, in particular, the potential for rapid and strong recovery from hypoxia due to “strong negative winds”, (i.e., negative winds that exceed a wind speed threshold of 10 m s−1) and the critical roles of the duration of these strong winds and the antecedent DO concentration on the strength of DO recovery. Motivated by these results, derived distribution analysis is adopted to predict the strength of DO recovery during periods of strong winds, using a simplified model of DO recovery, focused on isolated strong winds, that explicitly captures the effects of both wind duration and antecedent DO concentration.