# Presentation: 079954

## Relation of Physical Measures of Streamflow Conditions to Ecological Effects of Urbanization in Streams

**USGS**
***Jeffrey Steuer; Middleton, WI***
***Krista Stensvold; Middleton, WI***
***Elise Giddings; Raleigh, NC***
***Jerad Bales; Raleigh, NC***

## Milwaukee-Green Bay

**National Water-Quality Assessment (NAWQA) Program**** *****Effects of urbanization on stream ecology***

**Milwaukee-Green Bay**
** ****30 Watersheds **

## 30 Watersheds 

**Milwaukee-Green Bay – range of urbanization**
**30 Watersheds**** **

_**Watershed Size **_
**Range = 5 – 39 mi****2**
_**Urban land cover **_
**Range = 3- 99 percent**
_**Proportion population change 1990 – 2000**_** **
***Range = -0.16 – 1.38***

**Notes:**
The Study Unit Urban Index is a GIS-based number calculated on 25 variables. Watershed boundaries indicated in figure.

## Problem review

**Compare two time series data foundations for response to urbanization and association to stream biology.  **
**Hydraulic variables (HEC); simulated hydraulic variables estimate direct ***** stream conditions***** such as velocity, depth, shear stress, turbulence, bed exposure. **
**Hydrologic condition metrics (HCM); measures *****patterns of flow conditions***** during different time periods (magnitude, duration and freq of high, low flow and flow change). **

## Eleven transects per ~150 m reach.....

- Rio (UII=10)
***Mapped imp = 1%***

- Hoods (UII=31)
***Mapped imp = 6%***

- Jambo (UII = 0)
***Mapped imp = 1%***

- Lincoln (UII=100)
***Mapped imp = 45%***

- Garners (UII=60)
***Mapped imp = 26%***

- Fox (UII=40)
***Mapped imp = 6%***

## Transect measurements of instream and 	channel conditions......

**1.Bankfull width**
**2.Wetted channel width**
**3.Depth &   velocity**

**1**

**2**

**3**

**Thalweg**

*Fitzpatrick; modified*

## Transect measurements of streambank 	characteristics.....

**4.Bank angle**
**5.Bank height (bankfull depth)**

## First data foundation – Hydraulic (HEC) variable time series.... 

**Build upon habitat geometry, reach map, photographs, reach gradient ****(*****water surface slope at low Q)***
**Hydrograph (daily and hourly)**

## Energy balance – transect to transect

- Unsteady –storage, mass balance

**Notes:**
Example figures from HEC_RAS one-dimensional flow model output

## Hydraulic model (Hec-Rasv3.1.2)

**Limitations/assumptions:**
**Crude cross section data, estimated overbank slope**
**Rough elevation data**
**One dimensional (no lateral velocity gradient)**
**HEC model output - hydraulic variable time series for 11 transects – annual period of record ****(POR)**** and  three seasons.**
**Flow**
**Wetted perimeter**
**Depth**
**Velocity**
**Stream power**
**Froude number**
**Water column Reynolds number**
**Bottom shear stress**

*~ 10,000 time series  **(30 sites, 8 variables, 11 transects, 4 POR)*

## Bottom shear stress for 11 transects at OAK Creek Each reach aggregated into a max, min, average value

- Summer POR

**Notes:**
Example time series for summer POR.  Have these for all HEC variables
To illustrate reach aggregate variable; Tau max - take peak value from each time series and take mean to aggregate for one reach value

## Maximum shear stress at 11 Oak Creek transects; two adjacent transects with lowest peak shear - variable “refug.2”

**Notes:**
Do for maximum to aggregate entire reach into one var – Tau max.  Do also for reach min, reach ave.
Do for all HEC time series var
Introduce reach refuge – spatial subset concept 

## Maximum shear stress at transects for 25 streams two transect refuge denoted in red 

- Summer POR

- Transects

**Notes:**
25 streams in figure (5 sites had transducer problems during summer period of record)

## Hydraulic model time series variables (continued...)

***Refuge concept (shear) – ******Minimum shear stress in a “refuge” (2,3,4,5,6 adjacent transects) for a range of sizes***
***Exceed a threshold (shear)- *** ***Duration and integration of shear stress above a threshold (1, 2, 5, 20, 100 dyne/cm2)***
***Fraction exposed bed – ******estimated from wetted perimeter time series and fixed bed width (photos/survey)***
***	***
***	***

***Additional variables derived from the HEC generated time series***

## Example hydraulic (HEC) variable relations 

** ****Invertebrate - Filter collector richness increased with minimum shear stress**

** ****Decreased motile algae with increase amount of exposed bed**

**Biological metrics we’ve selected - **
**Not assemblages in a multivariate fashion but do represent measures of communities which are meaningful *****(metrics that could be measured in a biomonitoring program).***

**Notes:**
Each data point represents one site (all 11 transects aggregated together).

## Example hydraulic (HEC) variable relations continued..

** ****Fish IBI had negative association with increased shear stress in the two transect refuge**

** ****Refuge.2 bottom shear stress increased with urban intensity index**

- Fall (hourly)

***Fish IBI – Lyons et al., 1992***

## Second data foundation.....

**	****Hydrologic Condition Metrics (HCM)- measure *****patterns of flow conditions***** during different time periods (magnitude, duration and freq of high, low flow and flow change). **
***	******[modified from Nature Conservancy indicators of hydrologic alteration (IHA)]***

## Examples of hydrologic condition metrics (HCMs).

- Area normalized hydrographs for a low and high UII site.  Arrows indicate rises that area (flow) was seven times the median rise (PERIODR7).  ***Pigeon had seven rises; Pokeberry had three rises.***

***Fm Giddings;  in review***

**Notes:**
Number flow events above a threshold

## Hydrologic condition metrics (HCMs) continued...Example of duration metric

- Storm hydrograph for a low and high UII site. Shaded area is portion of hydrograph above the 90 percent flow value (MDH90).  ***Pigeon was 11 hrs; Pokeberry was 43 hrs.***

***Fm Giddings; in review***

**Notes:**
Duration of storm events

## Hydrologic condition metric (HCM) – biologic relations 

** ****	****Invertebrate - EPT abundance had negative correlation with flow variation**

** ****	****Diatom richness decreased with the duration of low flow during fall POR**

## Hydrologic condition metric (HCM) relations continued.. 

** ****Fish IBI decreased with stream flashiness in the fall POR.**

**With increased urbanization the duration of high flow (exceeded 10% of the time) was shorter.**

**Notes:**
MDH_90 is median duration of 10% exceedance flow

## Hourly based metrics (HCM and HEC) computed over 3 intervals (hourly data) and annual POR (daily data)

- Flow/area

- Flow/area

**Notes:**
Figure illustrates the 3 periods of record (fall, spring, summer; hourly flow data) from which HCM and HEC variables were computed.  Fall and summer were lower flow periods. Spring POR was a period of snowmelt and high precipitation.

## Mean spearman correlation coefficients (absolute value) for 37 biologic endpoints

*[Hydrologic condition metric (HCM); Hydraulic model variable (HEC)]*

**Overall...  HCM ****relations**** ~15% stronger than HEC**

*Blue value is maximum correlation within a group*

- CHANGE HYD to HEC...

**Notes:**
Mean spearman correlation is based upon the number of relations shown in parenthesis. For example 8 fish-HCM relations
HCM can address low flow duration (important for some bio) but HEC has no such metric to address low flow duration.
Future efforts to utilize both data foundations.

## Fish IBI regression tree model build on hydraulic variable data foundation (daily data, annual POR) 

**Fish IBI regression tree model***** ******build on ***_***hydraulic variable***_*** data foundation (daily data, annual POR)**** *

## Fish IBI regression tree model build on hydrologic condition metric foundation (daily data, annual POR) 

**Fish IBI regression tree model***** ******build on ***_***hydrologic condition***_*** metric foundation (daily data, annual POR)**** *

## Hydrologic condition metric (HCM) tree regression models ~ 8% stronger (lower deviation) than hydraulic (HEC) variable models..... consistent with correlation results.

**However hydraulic variables offer potential link between reach scale change and biologic endpoint....**

**BLOT**

**Notes:**
24 models constructed.  6 biologic endpoint (fish(2), inverts (2), algae (2) across 4 POR)

## And a possible understanding to biologic mechanism.....

**And a possible understanding to biologic mechanism.....**

- Field experiment in 27 patches in 150 m reach – northeastern Spain.  Examined invertebrate loss from bed with shear stress. *Gibbins et al.; 2007.*

## Invertebrate drift became exponential at shear stress of 9 dyne/cm2 .....

*Gibbins et al.; 2007*

## Our hydraulic modeling of refuge bottom shear stress at 25 sites is consistent with that finding..... 

** ****Scraper abundance decreased with increasing shear stress in refug.2**

**Notes:**
Circled sites, based on shear stress, should have better scraper invertebrate abundance.  
Numerous factors, other than hydraulics, are important for biologic distribution.  For example, circled sites may have high chloride or organic compound concentrations.

## findings to date

**A 1- dimensional hydraulic model, based on the **_**NAWQA habitat data**_** and**_** flow record**_**, allowed us to examine hydraulic and habitat conditions throughout the water year.**
**Time series based hydrologic condition metrics (HCM) and hydraulic variables (HEC) had numerous significant biologic relations (algae, invertebrates, fish) across all POR.**
**HCM data foundation had stronger association with biology than the hydraulic data foundation.  **
**Both foundations may provide link between watershed scale change and stream biology.**
**Hydraulic variables may provide mechanistic insight and provide a link between reach scale change (restoration) and biology.**

**Notes:**
Bio endpoint encompassed a range of biologic sampling protocols (riffle, depositional, richness, abundance).
One reason HCM stronger – HCM represented DURATION of low flow; HEC did not address low Q conditions.

## Acknowledgements.....many, many people 

*Acknowledgements.....**many, many people** *

**Study design and management**
**Cathy Tate, Jerry McMahon, Tom Cuffney**
**Site installations and hydrology data collectors**
**Habitat and biology data collectors**
**Data processing – biology, hydrology, habitat**
**Hec-Ras model (30) construction and output processing**

***Many skill sets/backgrounds required.....***
***USGS/NAWQA able to provide framework***