Currently there is no established standard for tools to forecast ecosystem response to restoration projects or a generic approach that will entirely encompass all needs and expectations of ecosystem resilience.

Ecosystem forecasting is limited by a number of factors including inadequate initialization information, unknown boundary conditions, inaccurate model physics and atmospheric forcing functions, and inadequate algorithm development of geomorphic and ecological responses to geophysical processes. Moreover, establishing skill levels in coast wide forecasting is also limited by inadequate validation data and the ambiguity of defining skill.

To effectively model the Mississippi River delta, a much more comprehensive and dynamic approach is needed. This includes the ability to couple models, invoke dynamic algorithms based on streams of sensor and satellite data, locate appropriate data and computational resources, and create necessary workflows on demand, all in real time. Such an approach could enhance planning restoration strategies, ecological forecasting, placement of future sensors, control of water diversion for salinity control, or predict/control harmful algal blooms.

This project will help improve management decisions to sustain ecosystem productivity and lessen the impacts from extreme natural events and human activities. Restoration science and engineering needs tools to forecast ecosystem state change associated with specific restoration measures. System performance is critical to assessment of effective restoration techniques. The modeling tools developed here will assist in understanding system performance to large-scale manipulations.