ERDC Grant for Parameterizing Next-Generation Ecological Models to Predict Aquatic Species Growth

Program Description

A.   Short Description of Funding Opportunity

ERDC seeks applications for integrating and parameterizing high-quality field and experimental mesocosm data into dynamic general vegetation model(s) (Gen Veg).

B.    Background

Accurately predicting ecosystem responses to aquatic nuisance species and changing climate conditions is difficult. The USACE requires information about future conditions to make decisions on how to maintain and operate its water resource projects and infrastructure. The USACE requires ecological models to inform its decisions. There is a need to elevate the accuracy of ecological models by incorporating more dynamic parameters and coupling them to physical process models. This research effort is focused on compiling field and experimental mesocosim data collected on aquatic vegetation in response to changing water quality and hydrology variables and integrating this data into general vegetation growth model (Gen Veg). This research effort focuses on 1) compiling existing data sets of vegetation growth in response to various water quality and hydrology changes; and 2) integrating data into Gen Veg, via parameterization, model evaluation, testing and communication.

C.    Program Description/Objective:

This project will establish an interdisciplinary collaboration between USACE and a University partner. This collaboration will compile, integrate, and parameterize wetland vegetation growth data from data collected from the field and controlled experiments. This collaboration seeks to improve dynamic general vegetation growth model(s) with a module focused on the response of riparian vegetation to future conditions (i.e., changes in hydrology, water quality). Successful proposals will also (a) clearly identify question(s) the proposed project will seek to answer (i.e., project technical objectives); (b) clearly describe the data analytic skills required to answer those question(s) (i.e., data quality objectives); and (c) describe envisioned project deliverables by task and by year. Proposals that demonstrate intent to maximize use of existing data sets generated from past collaborations, and activities are required. Successful proposals will identify quantitative and qualitative success criteria for each project task and objective; identification of go/no-go decision points at the end of each year is also encouraged.

This project will:

1)     Compile existing data sets collected from the field and experiments on aquatic vegetation growth in response to changes in water quality and hydrology. 

2)     Integrate data sets into dynamic vegetation growth model(s).

3)     Parameterize, test, evaluate and communicate model updates.

Findings will be reported to the public through technical reports, technical notes, journal articles, presentations, attendance of In Progress Reviews, as appropriate and needed.