Data collection network to support ecosystem forecasting for the Barataria Basin - Mississippi River domain

Abstract

Ecosystem forecasting is limited by a number of uncertainties including inadequate initialization information, unknown boundary conditions, inaccurate model physics and atmospheric forcing functions, and inadequate algorithm development of geomorphic and ecological responses to hydrodynamic and geophysical processes. Monitoring can help reduce these uncertainties by providing numerical information on those variables that can improve simulation capabilities. A data collection network was designed for the Mississippi River, south of Tarbert Landing, Ms. to Venice, La., and for the inland and nearshore waters of Barataria Basin, La. The network considered existing monitoring efforts that effectively link oceanographic and water quality observations to system drivers to support ecosystem hindcasting, nowcasting, and forecasting capabilities. The design was driven by the diverse needs of the modeling community and utilized questionnaire surveys, workshops, and web- based applications to inventory past and present modeling applications, domains, and attributes. The use of existing monitoring stations, including variables measured and frequency of data collection, were also considered, as well as recommendations on where new monitoring stations should be located, what types of data should be collected, and at what temporal frequency. Compiled databases are presented as a series of GIS layers to represent overlap between existing and requested stations, and priorities are established for either maintaining or augmenting existing stations or deploying new stations. An optimization plan will be prepared from the results of this study that will provide a rationale for the ecosystem forecasting data collection network that includes a description of existing monitoring programs to support ecosystem forecasting and a set of justifiable locations for "megastations" that meet the needs of the modeling and assessment communities.