Developing an Integrated Ocean Observing System for New Zealand

New Zealand (NZ) is an island nation with stewardship of an ocean twenty times larger than its land area. While the challenges facing NZ’s ocean are similar to other maritime countries, no coherent national plan exists that meets the needs of scientists, stakeholders or kaitiakitanga (guardianship) of NZ’s ocean in a changing climate. The NZ marine science community used the OceanObs’19 white paper to establish a framework and implementation plan for a collaborative NZ ocean observing system (NZ-OOS). Co-production of ocean knowledge with Māori will be embedded in this national strategy for growing a sustainable, blue economy for NZ. The strengths of an observing system for a relatively small nation come from direct connections between the science impetus through to users and stakeholders of an NZ-OOS. The community will leverage off existing ocean observations to optimize effort and resources in a system that has historically made limited investment in ocean observing. The goal of the community paper will be achieved by bringing together oceanographers, data scientists and marine stakeholders to develop an NZ-OOS that provides best knowledge and tools to the sectors of society that use or are influenced by the ocean

Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters

Monitoring of the phytoplankton pigment chlorophyll-a is often used as an indicator of eutrophication in coastal waters. Improved water quality monitoring using data sourced from MODIS (Moderate Resolution Imaging Spectroradiometer)-sourced data allows for infrequently sampled sites to be interrogated for long-term trends. Despite the wide availability and good spatial and temporal coverage of MODIS data, these data have had little use in operational coastal monitoring of chlorophyll-a in New Zealand. This is in part due to the poor performance of global oceanic algorithms applied in the coastal waters. Accessible algorithm tuning methods that can be validated by in situ measurements may assist the uptake of satellite data for coastal monitoring. This study presents results from regional tuning and validation of two empirical algorithm approaches, including a new simple exponential model, to estimate chlorophyll-a for two coastal locations in New Zealand. A novel method of training chlorophyll-a models using smoothed in situ data to match spatial scales of satellite observations was applied, and shows promise for improving tuned model performance. This approach shows potential for lowering barriers for researchers and coastal managers wishing to make use of the growing satellite data resource in their coastal environments