# Presentation: 024842

## Adaptive Implementation Modeling and Monitoring for  TMDL Refinement

- K.H. Reckhow
- Duke University

## Project Objectives

** ****Develop an adaptive implementation modeling and monitoring strategy (AIMMS) for TMDL improvement.**

** ****Apply and evaluate AIMMS on the Neuse Estuary TMDL in North Carolina.**

## Slide 3

## Target Reduction

- with 95% Confidence

## We need predictions to guide TMDL decision making, so what should we do?

***Adaptive Implementation***
**We can “learn while doing;” that is, we can observe how the real system (the actual waterbody) responds, and then use that information to augment and improve the prediction for the modeled system.**

## How might we conduct adaptive implementation?

*** ******Step 2:*****  After the TMDL is implemented (i.e., nonpoint & point source pollution controls in place), a *****properly-designed***** monitoring & research program is established; this program can be focused on assessment of particular pollutant controls and/or on overall waterbody compliance with standards.**

-  ***Step 3:  *****The pre-implementation model forecast (from step 1) is combined with the post-implementation monitoring (from step 2); this provides the best overall estimate of TMDL success and provides the basis for any necessary revisions to the TMDL.**

**How might we conduct adaptive implementation?**

## Adaptive Implementation: Bayesian Analysis

**Adaptive Implementation: Bayesian Analysis**

- Water Quality Criterion Concentration

## Post (TMDL) Implementation Questions

** ****Has compliance with the water quality standard been achieved?**

** ****If compliance has not been achieved, what pollutant reduction actions did not respond as predicted?**

## Determining the specific aspects (Who does what? Where?) of the TMDL implementation plan, and designing the post-implementation monitoring, are important decisions:

** ****Should TMDL implementation be focused on the most cost-effective pollutant reduction strategies?**

** ****Should implementation be directed toward activities that provide learning opportunities?**

## NeuBERN

**NeuBERN**
**Bayes Net**
**Estuary Model**

## Neuse SPARROW Model

**Neuse SPARROW Model**

- Stream
- Load at reach i

- Sources

- Land-to-water
- transport

- Aquatic
- transport

- Error

- n = number of sources
- j = reaches in the set J(I)

**Notes:**
The SPARROW model is an example of an empirical modeling approach, where the model specification in informed by process concepts.

The SPARROW approach specifies a non-linear model that imposes a mass balance on contaminant flux

The SPARROW model specifies that annual mean load (mass per time) at each monitoring station is a function of the contaminant source in upstream reaches, a land-to-water delivery factor, an in-stream delivery factor, and multiplicative error term.

The operation of the SPARROW model depends on information contained in the stream network data set. Data on nutrient sources, land-to-water delivery factors and in-stream loss factors are developed at the individual reach level. 
The stream network is used to accumulate information about basin characteristics for all upstream reaches and to estimate the cumulative effect of in-stream nutrient processing

## Tasks Completed or Underway

** ****NeuBERN and SPARROW models have been linked within a Bayesian framework (WinBUGS)**

** ****NeuBERN is being re-specified to add more mechanism.**

** ****SPARROW has been re-calibrated to address spatial correlation and improve parameter estimators.**

## Issues to Address in Year 2

** ****Model time step**

** ****Representing land use (pollutant load) change in SPARROW**

** ****Role of stakeholders**

** ****Addressing lags in system response – use of observational data**

** ****Monitoring program design (sensitivity analysis)**

## Expected Outcomes

** ****Re-assessment of the Neuse nitrogen TMDL**

** ****Development of guidelines and procedures for adaptive implementation of TMDLs**

** ****Determination of effective roles for stakeholders**