# Presentation: 090514

## An Introduction toOSU StreamWood

**An Introduction to****OSU StreamWood**

- Mark A. Meleason2**,**** ****Daniel J. Sobota****1****, **Stanley V. Gregory3
- 1Washington State University, Vancouver Campus
- 2USDA Forest Service Pacific Northwest Research Station
- 3Department of Fisheries and Wildlife, Oregon State University

**Notes:**
Thanks Cheryl for having me.  Today, I will give an introduction to a riparian forest model that was developed by my colleagues while I was OSU.  Mark would be here, but his wife has a C-section scheduled today for their second child.  But Mark wanted to let you all know that it was a tough choice for him.  This material has primarily been developed by Mark and Stan.  I’d like this to be interactive, so please stop if you have questions along the way.

## Presentation Outline 

**Model Description**
**Types of Applications**
**Simulation Example**

**Notes:**
To start, I’d like to go over the basic organization of this talk.  I will start off by giving a descriptive over of the OSU streamwood.  Next I will go over the types of applications it can currently perform.  For the conclusion of the talk, I will work through a simulation example in the model.

## I.  Model Description

**Model Overview**
**Model Components**
**Model Performance**

**Notes:**
Okay, let’s begin with the model description.  I’ll start with the basic model overview, discuss in more detail the forest and stream model components, and give some information on model performance, including sensitivity and accuracy

## OSU StreamWood predicts...

**STANDING STOCK of wood**
**   ****(Breakage, movement, and decay)**
**MEANS and VARIANCE**
**   ****(Individual–based Stochastic)**
**GENERAL trends**
**Scales: Time – ANNUAL**
**                 ****Space – MULTIPLE REACH**

**Notes:**
Most wood models predict recruitment for a given time interval.  SW differs in that in-channel dynamics are applied through time.

SW is run with a Monte Carlo procedure (random effects) so get a distribution of possible standing stocks for a given year.

A trade off: we did not want a model so specific that it could not easily be used at another site
GENERAL: representative of a  regional trend (hypothetical) – outcomes for a given forest type and stream conditions

SW predicts standing stocks of wood at an annual time step and can be run for decades, centuries, or milenia

The model is broken up into multiple reaches so that, if desired, one can implement different management practices in different stream sections and examine how they influence wood dynamics downstream.

## STREAMWOOD

**Forest**

**Stream**

**Tree Recruitment**

**Tree Growth**

**Tree Mortality**

**Log Recruitment**

**Log Breakage**

**Log Movement**

** ****Decomposition**

**Forest Harvest**

**Notes:**
Two models in 1:  The output of the forest model is the input of the stream model
Emphases?: individual based – Monte Carlo

## STREAMWOOD

**Tree Recruitment**

**Tree Growth**

**Tree Mortality**

**Log Recruitment**

**Log Breakage**

**Log Movement**

** ****Decomposition**

**Forest Harvest**

**Forest**

**Stream**

**Notes:**
First the forest model.  Option to use a forest gap model within SW or import other results.  

## Forest Inputs

**Forest Gap–Phase Model (w/I SW)**
**JABOWA (Botkin et al., 1972)**
**Individual-based, Monte Carlo**** **
**ORGANON and FVS (G&Y models)**
**User defined**

**Notes:**
The Forest gap model based on the JABOWA family of models

Alternative, import he results of other FM or make up your own.

## Riparian Zone

**no cut**

**partial cut**

**Riparian Zone**

**Harvest**
**Regime**

**stream **

**forest**

**upland**

**Notes:**
Can define regions of no-cut, partial cut, and upslope cutting/thinning.

Adding the ability to import FVS and ORGANON to obtain more sophisticated thinning regimes

## STREAMWOOD

**Tree Recruitment**

**Tree Growth**

**Tree Mortality**

**Log Recruitment**

**Log Breakage**

**Log Movement**

** ****Decomposition**

**Forest Harvest**

**Forest**

**Stream**

**Notes:**
Stream model takes the trees that have died and determines which fall into the channel

For now, no snag dynamics – trees fall the year they die

Like the FM, the wood model is individual-based (each log is run through the 3 in-channel processes each year)

## STREAMWOOD

**Tree Recruitment**

**Tree Growth**

**Tree Mortality**

**Log Recruitment**

**Log Breakage**

**Log Movement**

** ****Decomposition**

**Forest Harvest**

**Forest**

**Stream**

**Notes:**
Conversion of a tree to a log and includes entry breakage

## Tree Fall Regime

**directional**
**fall**** **

**random**
**fall **

**Tree Fall Regime**

**stream **

**forest**

**random**
**fall **
**or**

**directional**
**fall**** **

**Notes:**
Can define regions of no-cut, partial cut, and upslope cutting/thinning.

Adding the ability to import FVS and ORGANON to obtain more sophisticated thinning regimes

## STREAMWOOD

**Tree Recruitment**

**Tree Growth**

**Tree Mortality**

**Log Recruitment**

**Log Breakage**

**Log Movement**

** ****Decomposition**

**Forest Harvest**

**Forest**

**Stream**

**Notes:**
All logs break but data sparse – used first principles (broken stick model) and known distributions of length frequency distributions – e.g.,  reverse-j in OG forests

## Tree Entry Breakage

**Notes:**
Definition of a log – can be partially outside the channel.  (e.g., C3 is outside the channel and would not be included)

Standing stock measures include: 
1.  and 2.  total wood count and volume of all logs intercepting at least 1 bfw
3.  Count of all completely within BFW
4.  In-channel volume (m^3 / ha of stream bed)

## In-channel Breakage

**Does the log break?**
**residence time**
**top diameter**
**If so where?**
**Variations on broken stick model**
**Break location related to diameter**

**Notes:**
Assumed that chance of breakage increases with residence time in the stream, 

and that smaller log will have a greater chance of breakage than a larger log (all else being equal).

## Predicted vs. Observed

**Notes:**
Example of model validation – comparison of frequency length distributions from observed (Mack Creek, OG) and simulated.

## STREAMWOOD

**Tree Recruitment**

**Tree Growth**

**Tree Mortality**

**Log Recruitment**

**Log Breakage**

**Log Movement**

** ****Decomposition**

**Forest Harvest**

**Forest**

**Stream**

**Notes:**
Two questions: does the log move and is so, how far?

## Chance of Log Movement

**Does the log move?**
**Function of:**
**FLOW (peak annual flow)**
**Number of Key Pieces**
**Length outside of channel**
**Length to bankfull width**

**Notes:**
Multiple logistic regression of a 12-yr tagged log data set – smoothed to make more “Ideal” 

## Chance of Movement: No Key Pieces, 100% Within Channel

**Notes:**
Response curve for chance of log movement for no key pieces and 100% in-channel.  This is one example from a series - adjustable for “retentive” (e.g., large boulders) or non-retentive streams.  

## Distance of Log Movement

**If it does move, then how far?**
**Single negative exponential model**
***k***** = average travel distance**
**	****(units of bank full width)**
**Assumed independent of piece size and channel characteristics**

## Distance Moved, Mack Creek

**Notes:**
Mack Data

## STREAMWOOD

**Tree Growth**

**Tree Mortality**

**Log Recruitment**

**Log Breakage**

**Log Movement**

** ****Decomposition**

**Forest Harvest**

**Forest**

**Stream**

**Tree Recruitment**

**Notes:**
Final in-channel process is decay

## Decomposition

**Single negative exponential**
**Represents microbial decay and physical abrasion**
**Species-specific aquatic and terrestrial rates**

## The Value of Models

- “Models of course, are never true, but fortunately it is only necessary that they be useful.”
- “For it is usually needful only that they not be grossly wrong.”
- Box, G. E. P. 1979.  Some problems of statistics and everyday life.  J. Am. Stat. Assoc. 74: 1-4

**Notes:**
Before launching into model applications,  let’s state as clearly as possible the value of models:

## Model Performance Evaluation“Truth is the intersection of independent lies” (Levins1970)

- Absolute Tests difficult for most models
- Using realistic input parameters:
- Reasonable agreement with available data
- And derived characteristics  (e.g., log length frequency distribution)
- Sensitivity Analysis: ID critical variables

**Notes:**
Difficult to prove a model – in fact, a model is either undergoing improvement (refinements based on discrepancies with real data or additional information about the system) or dead (not very useful).  The refinement of a model is one area of usefulness.

## Slide 25

**Notes:**
Onward to some applications of this....

## II. Sample Applications

- Vary, riparian width, no-cut width, and upland rotation length
- Characterizing variability of wood volume for a given forest type

**Notes:**
The first may be the most important to managers (e.g., mean response) but the variability of wood volume is also important in characterizing wood in streams through time.

## Forest Basal Area: Standard Run

**Notes:**
Gap-phase model within StreamWood, western Cascades.  Basal area of 3 main spp and total 

## Forest Plantation Basal Areas

**Notes:**
Simple clear cut to stream bank using 3 rotation ages. 

Of course this is illegal, but this is a model!  A test of the simplest case 

Standard run is a 50-m wide native forest buffer without cutting (start from 0)

## Volume From Plantation Forests

**Notes:**
See that the clearcuts achieve their levels prior to first cut

## Plantation Forests: 6-m Buffer 

**Notes:**
Now add a 6-m buffer and see that that the not wide enough to achieve site potential (120 yr old tree not tall enough to contribute much beyound 6 m.)

## Plantation Forests: 10-m Buffer

## Plantation Forests: 15-m Buffer

## Total Volume by Buffer Width

**Notes:**
Simple function of buffer width – wider the no-cut buffer, more wood (but with diminishing returns)

## Study Conclusions

- 6-m buffer: 32% of site potential
- 30-m buffer: 90% of site potential
- Plantation forests: maximum 1st cut

## Slide 35

**Notes:**
Second case study.  Characterizing wood variability for a forests of similar age and type for a give stream size.

## Simulated Wood Volume Waihaha Basin, New Zealand

**Simulated Wood Volume ****Waihaha Basin, New Zealand**

**Notes:**
Simulation of wood in a New Zealand Stream (used the forest-gap model LINKNZ).

Total wood volume / 200 m reach.  

The bars are SD 

Let’s look at the variation of the last year  (1800) by each iteration
Since we ran it for 2000 iterations, n = 2000

## Volume Frequency DistributionYear 1800, Waihaha, NZ

**Notes:**
Relative frequency distribution of wood volume for year 1800.  Note that the range is from 0-5 to >50 m3 / 100m)

## Cumulative Frequency Volume Distribution Waihaha, NZ

**Cumulative Frequency Volume Distribution Waihaha, NZ**

**Notes:**
Now make is cumulative frequency by selected years.
Median wood volume (e.g., where 50% of the iterations [ n = 2000] aka reach estimates) for yr 400 were twice that of yr 200 – note also the longer tail

Also, the forest height is greatest in a mid-successional sere, thus yr 1800 is less than yr600.

Actually, the variability is also influence by the length of reach sampled – a longer sample reach will generally have a lower median but there is a finite limit to the length of a reach with “uniform” conditions (e.g., tributary junction etc.) so can base on standard protocol.

So, difficult to characterize as a “target” when a range of possible wood volumes are possible for a given forest type and stream width.

## Slide 39

## III.  Simulation Example

**4-reach system using the internal forest model (no harvest activity)**
**Bank full width = 10 m, length =200 m**
**Run for 200 years, 100 iterations**

**Notes:**
A simple run

## Slide 41

**Notes:**
The opening GUI with the reach tree expanded

Click on Basin Environment

## Slide 42

**Notes:**
These are variables set to the Basin level of the model – e.g., minimum log dimensions are 10 cm top d and 1 m length.

Note that the Riparian Forest Model or Import Riparian Data is greyed out.
  This is because the type of forest input was defined by the use in basin file that defines the nature of the stream system (number of reaches, tree and log files to import, and species file  to import)

Now click on the Reach environment

## Slide 43

**Notes:**
This defines the characteristics of the reach – again, these values were stated in the basin file 

Ok let’s do the run. Close this file and open Run 

## Slide 44

**Notes:**
Check  use Monte Carlo and press run

## Slide 45

**Notes:**
Selected the top and bottom reach for total volume (lower 2 lines) and total count (top 2 lines).  Volumes are very similar because larger pcs tend to be less mobile;  count tend to increase as go downstream.

## Final Thoughts

- Designed to be flexible
- Currently v2 is under construction
- Includes “StreamLine” – a 1-reach system
- Imports ORGANON and/or FVS dead tree files
- Latest release version on HJA LTER website
-    http://www.fsl.orst.edu/lter/data/tools/models/
- Developer: Mark Meleason (streamwoodv1@hotmail.com)

## Questions?