# Presentation: 693004

## Topics covered in Hill and Tiedeman, 2007(covered in class)

- Transient
- Initial conditions
- Transient observations
- Transient parameters
- Transport
- Select processes to include
- Define source geometry and concentrations
- Scale issues
- Numerical issues: model accuracy and execution time
- Transport observations
- Transport parameters
- Recalibrating models – see book, p. 227-228

## Transient: Initial conditions

- Use results of a steady-state simulation
- Ensures that transient response is the result of imposed transient characteristics
- Often possible in ground-water, but not always possible in some fields. For example, atmospheric modeling
- Use contoured measurements from a selected time.

## Transient Observations 

- Processes behind measures transient response being simulated?
- Yes: time-consistent observations
- Often need to average high-frequency data for the long-time steps common to gw models
- If unrepresented processes produce random errors with a mean of zero, they can be represented through the weighting.

## Transient Observations: Differencing

- As observations, use an initial value and the subsequent changes over time.
- Differencing eliminates errors that produce an offset and emphasizes the transient response.

## Transient Observations: Differencing

- Initial head

- Subsequent changes in head

## Transient Observations: Weighting

- Are the errors correlated or independent over time?
- If errors are temporally correlated, it may be important to represent this. No one has investigated.
- Differencing reduces the error, so results in larger weights on the subsequent changes (for example, the drawdowns)

## Transport

- Concentration observations
- Selected articles
- Highlights from three of the articles

## Regression Using Concentration Observations 

- Often most efficient to first derive likely advective travel from the concentration measurements, and calibrate using a flow advective travel model (for example, MODFLOW and the ADV Package or MODPATH). It is easier to calibrate an advective-dispersive model once simulated flow directions approximate actual flow directions.
- Use UCODE_2005, PEST, OSTRICH, etc. The flow and transport can be solved by the same computer program or by different programs. For example, MODFLOW and MT3DMS.

## Selected Transport Articles

- Anderman, Hill, Poeter, 1996, Ground Water.
- Example of deriving and using an advective transport observation. Reproduced in the ADV documentation.
- Barlebo, Hill, Rosbjerg, Jensen, 1998, Nordic Hydrology.
- Example of transport model accuracy given calibration with head data only and with concentrations.
- Mehl and Hill, 2001, Ground Water.
- Compares the 4 solution methods in MT3D and one programmed by Steffen Mehl. Considers the how differences in the solution methods affect sensitivity analysis and parameter estimation.
- Barth and Hill, 2005a,b, Journal of Contaminant Hydrology.
- Uses the capabilities of MODFLOW and MT3DMS to represent processes important to viral transport. Investigates sensitivity and correlation of parameters, utility of observations, and effect of nonlinearity.
- Tiedeman and Hill, 2006, chapter in Thangarayan (ed.)
- Hill and Tiedeman, 2007, chapter 9.

**Notes:**
These are studies with which I am familiar that I thought were instructive. Others are likely to choose other examples.  mch

## From Barlebo +, 1998

- Presented as part of guideline 10.
- Book,
- fig 15.14,
- p. 372.

- Book,
- fig 15.12,
- p. 371.

## Importance of including confidence intervals in the comparison to reasonable values

- Here, the confidence intervals help show that the problem with an unreasonable estimate obtained using heads only is that the observation data provide inadequate information toward estimating this parameter value.

- Barlebo +, 1998, Grindsted landfill (Denmark) transport model

- Presented as part of guideline 10.
- Book, fig 15.14, p. 372.

**Notes:**
Because of large confidence interval for case with heads only, is unclear whether unreasonable estimate indicates model error, or whether the head data are inadequate to estimate the K parameter.

Addition of concentration data caused estimate and confidence interval  to be reasonable, showing that original problem was inadequate observation data.

- From Mehl and Hill, 2001

## From

- Same parameters values.
- Different
- numerical methods.

- Mehl
- and
- Hill,
- 2001

## From

- Same parameters values.
- Different
- numerical methods.

- With K parameters estimated

- Mehl
- and
- Hill,
- 2001

## Reactive-Transport Test Case[Barth and Hill, Journal of Contaminant Hydrology, 2005a,b; based on experimental results from Schijven et al., 1999 ]

- Homogeneous.
- Processes (to simulate virus transport):
-  advection
-  dispersion
-  sorption (to represent physical-chemical filtration)
-  reaction mechanisms (to represent virus inactivation)

## Sensitivity analysis: How much information from all Observations for each Parameter? CSS

- Sensitivity analysis**: **How much information from all Observations for each Parameter? CSS

**Observations**
-  heads, flow, conservative transport moments, and virus transport

** ****Hydraulic**

** ****Conservative transport**

** ****Reactive transport**

**Process parameters**

**Numeric parameter**
**Transport time-step size**

## Sensitivity analysis: Are Parameter estimates unique? PCC

- COV(_b_) = s2(_X_T__ _X_)

- Sensitivity analysis**: **Are Parameter estimates unique? PCC

| Parameter 1 | Parameter 2 | PCC |
| --- | --- | --- |
| Porosity (   ) | Hyd. Cond. ( K ) | 0.91 |
| Sorption ( ) | In-sol. Inact. (1) | -0.97 |

## Different ways of using concentration data

- Interpreted advective transport “observations”
- Moments of the concentration data.
- First moment = advective transport
- Second moment=spread of plume
- Interpreted time-concentration or space-concentration curves or contours.
- Be careful about interpretation!
- Concentration measurements scaled by maximum value or source value

## Catchment zones

- figure contributed by Ruth Davison and Sascha Oswald

**Notes:**
It is important to put lots of particles around a well so that you get the full catchment and the point of stagnation

## Tracing contamination pathways

**Cell size affects path line results **

- Zheng, C., 1994, *Analysis of particle tracking errors associated with spatial discretization, *Ground Water.

**	****	****(especially in areas where gradient changes)**

## Source characterization

- There has been recent work on source characterization by, for example, Neupauer and Wilson (WRR)
- Trying to estimate source concentrations makes it much harder to develop a transport model