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Sensitivity and uncertainty analysis of model hypoxia estimates for the Texas‐Louisiana shelf
Author(s) -
Mattern Jann Paul,
Fennel, Katja,
Dowd Michael
Publication year - 2013
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
eISSN - 2169-9291
pISSN - 2169-9275
DOI - 10.1002/jgrc.20130
Subject(s) - environmental science , surface runoff , hypoxia (environmental) , uncertainty analysis , sensitivity (control systems) , generalized additive model , climatology , hydrology (agriculture) , statistics , ecology , mathematics , geology , biology , organic chemistry , electronic engineering , oxygen , engineering , chemistry , geotechnical engineering
Numerical ocean models are becoming increasingly important tools for marine research and for management of marine resources. It is therefore crucial that uncertainty in model predictions and model sensitivity to errors in the model inputs be quantified. We performed a combined sensitivity and uncertainty analysis for a realistic physical‐biological model of the Texas‐Louisiana shelf in the northern Gulf of Mexico. The model simulates the major physical and biological processes involved in the formation of the hypoxic zone that develops on the shelf every summer. With the help of a statistical emulator technique, we introduced uncertainty in selected model inputs and assessed the effects of these uncertainties on the predicted development and spatial distribution of bottom hypoxia. The uncertain inputs we examined belong to two categories: (i) biological inputs including river nutrient concentration, phytoplankton growth rate and initial and boundary conditions of biological variables, and (ii) physical inputs including freshwater river runoff, wind forcing, and mixing coefficients. We show that uncertainty in different inputs has distinct effects on model output which vary in magnitude, time, and space. Uncertainty in physical inputs was found to have a strong impact on estimates of hypoxia, e.g., hypoxic area estimates vary by more than 40%, due to a 20% variation in the freshwater river runoff.