models quantify the relationship between water flows ... · dsm2-sjr model hydraulic sub-model...

Models Quantify the Relationship Between Water Flows/Levels and

Ecological Endpoints

National Conference on Ecosystem Restoration

Los Angeles, CA

July 20-24, 2009

Joseph V. DePinto, Todd M. Redder,

Scott Bell, Laura Weintraub

LimnoTech (www.Limno.com)

Ann Arbor, MI

Presentation Outline

� Background: two initiatives that recognize importance of hydrology/hydraulics to ecosystem structure and function

� ESWM framework of The Nature Conservancy

� Great Lakes Water Compact� Great Lakes Water Compact

� Previous Flow/Level – Ecological Response Modeling

� Lake Ontario – St. Lawrence River Regulation Evaluation (IERM)

� Muskegon River Watershed linked flow – ecological response modeling (GLECO)

� Conceptual Approach for Sacramento – San Joaquin Bay Delta System

Ecological Sustainable Water Management - The Nature Conservancy (from Richter, et al., 2003)

� Goal: Meet human needs for water by storing and diverting water in a manner that sustains ecological sustains ecological integrity of aquatic ecosystems

� Signed by all Great Lakes States

� Separate Agreement with Canada (Ontario, Quebec)

� Goal: Protect, conserve, restore, improve and effectively manage the Waters and Water

Great Lakes Water Compact

effectively manage the Waters and Water Dependent Natural Resources of the Basin

� Specifically: prevent significant adverse impacts of water Withdrawals and Losses on the Basin's ecosystems and watersheds

Moses Saunders Dam

(Plan 1958DD)

Lower St.

Lawrence River

Quebec

Trois-Rivieres

Lake Ontario – St. Lawrence River Water Level/Flow Regulation Study

Goal: Evaluate existing regulation plan and recommend alternative plan that best satisfies the needs of multiple interests: environment, riparian landowners, hydropower, commercial navigation, recreational boating, water supplies)

Lake OntarioUpper St.

Lawrence River

Toronto

Montreal

Rochester

Canada

U.S.

Trois-Rivieres

Integrated Ecological Response Model (IERM)

� Designed to compare response of Ecological Performance Indicators (PI’s) to alternative Hydrologic/Hydraulic (H&H) conditions� Compare alternative Regulation Plans under a given Basin Supply Scenario

�� Other stressors assumed constant for comparisonOther stressors assumed constant for comparison

� Composed of sub-models for each PI group� PI response algorithms only as complex as data will allow� PI response algorithms only as complex as data will allow� Range of complexity from simple empirical relationships (PI vs water level function) to more complex process-oriented population sub-models

� Work with researchers to integrate the science� Build Conceptual model: understand data availability & connections between various studies (2002-04)

� Identify specific performance indicators and associated metrics

� Evaluate and interpret the model

Reduce Analysis to 32 “Key” PIs

Lake Ontario /

Upper St. Lawrence

Key PIs (19)

SAR (4)

Mammal (1)

Herptiles (0)

Fish (11)

Vegetation

(1)

Lower St. Lawrence

Key PIs (13)

Birds (2)Fish (11)

Vegetation

(0)Fish (3)

Birds (4)

Herptiles (1)

Mammal (1)

SAR (4)

Key PIs based on:• Representativeness/significance

• Certainty

• Sensitivity to regulation

• Geographic coverage

IERM Conceptual Model (Lake Ontario)

Wetland Habitat

Lake Ontario

Water Level

•Weekly WL time series

Water Temperature

•Nearshore temperature

•Wetland temperatureWetland Birds

•Frequency of low-

water years

•Frequency of high-

water years

• Nest access

•Nest flooding

• Wetland access

•Stranding potential

•Weighted

usable area

Wetland Habitat

Lake Ontario

Water Level

•Weekly WL time series

Water Temperature

•Nearshore temperature

•Wetland temperatureWetland Birds

•Frequency of low-

water years

•Frequency of high-

water years

• Nest access

•Nest flooding

• Wetland access

•Stranding potential

•Weighted

usable area

•Meadow marsh area

•Cattail area

•Floating leaf area

•Suitable habitat area

(acres)

• Nesting success

Fish (multiple species)

•Year-class strength (no./yr)

•Biomass (kg/ha)

•Production (no./ha/yr)

Muskrats

• Muskrat houses per acre

•Cattail usage

•Timing of

spawning events

•Habitat loss due to

flooding/stranding

•Weighted usable

area for various life

stages

Endangered Species


Amphibians/Reptiles


•WL fluctuations

•Flood

magnitude/duration

•Weighted

usable area

•Weighted

usable area

•Meadow marsh area

•Cattail area

•Floating leaf area


(acres)

• Nesting success

Fish (multiple species)

•Year-class strength (no./yr)

•Biomass (kg/ha)

•Production (no./ha/yr)

Muskrats

• Muskrat houses per acre

•Cattail usage

•Timing of

spawning events

•Habitat loss due to

flooding/stranding

•Weighted usable

area for various life

stages

Endangered Species


Amphibians/Reptiles


•WL fluctuations

•Flood

magnitude/duration

•Weighted

usable area

•Weighted

usable area

Wetland Plant Sub-Model (LO/USL)

LO/USL Water Level Time

Series

•Flooding – elevations

inundated for 4 consecutive

QM during growing season

•Dewatering – elevations dry

during entire growing season

% Species Composition @

Specific Elevations

•Barrier Beach

•Drowned River Mouth

•Protected Embayment

•Unprotected Embayment

Mo

del

In

pu

ts Su

b-M

od

el Ou

tpu

ts

Feed to Faunal

Sub-Models

Wetland Plant

PI Measures

“Typical” Wetland

Topography

•Barrier Beach




Total Estimated Area of

Plant Species (ha)

•Barrier Beach



•Unprotected EmbaymentLO/USL Wetland Area

•Barrier Beach





Su

b-M

od

el I

np

uts

Mo

del O

utp

uts

Feed to Faunal

Sub-Models

Wetland Plant

Effects

IERM “PI Time Series” Diagram

IERM “Target” Diagram

IERM Plan Evaluation Results (Lake Ontario / Upper River – 19 PIs)

10

15

20

Net # o

f P

Is w

/ S

ignific

ant G

ain

s

Historical (1900-2000)

Stochastic #1 - Wettest Century

Stochastic #2 - Driest Century

Stochastic #3 - Like Historical

Stochastic #4 - Longest Drought

-10

-5

0

5

Plan A

Net # o

f P

Is w

/ S

ignific

ant G

ain

s

Plan A Plan D Plan B PreProject

IERM Plan Evaluation Results (Entire LOSL System – 32 PIs)

10

15

20

Net # o

f P

Is w

/ S

ignific

ant G

ain

s

Historical (1900-2000)

Stochastic #1 - Wettest Century

Stochastic #2 - Driest Century

Stochastic #3 - Like Historical

Stochastic #4 - Longest Drought

-10

-5

0

5

Plan A

Net # o

f P

Is w

/ S

ignific

ant G

ain

s

Plan A Plan D Plan B PreProject

Channel

Modifications

Natural Hydrological &

Climatological Forcings

Other Management Actions

& System Stressors

River Channel Flow

Regime

Drainage Basin

Properties

Water

Use

Watershed

Runoff Quantity

Watershed

Runoff Quality

•Nutrient loads

•Solids loads

Groundwater

Flow Regime

Str

esso

rsF

low

R

esponse

Addressing Great Lakes Withdrawal IssuesGreat Lakes Watershed Ecosystem Model (GLECO)

Ass

essm

ent

Ind

icat

ors Riparian Wetland

Vegetation

•Plant diversity

Water Quality•Nutrients

•Solids

River Hydraulics

•Flow/Volume•Depth

•Velocity

Temperature

Fish Habitat

•Weighted usable

habitat area

Fish Population

GLECO development and application to Muskegon Watershed (MI)

� Configure Hydrologic Simulation Program – FORTRAN (HSPF) to watershed based on geomorphic, hydrologic, land use/cover data

� Link HSPF to required ecological sub-models

� Calibrate flow and water quality parameters at various sampling locations� Establish baseline condition for relevant ecological endpoints.� Establish baseline condition for relevant ecological endpoints.

� Run model forecasts of withdrawal scenarios & compare to baseline

Model Scenario Application: Little Muskegon River

� Scenario “A”

� Single withdrawal

� Withdrawal of 5 MGD from the Little Muskegon River

catchment #1

� Scenario “B”:

Cumulative withdrawal� Cumulative withdrawal

� Withdrawal of 5 MGD from the Little Muskegon River

catchment #1 & catchment #2 (10 mgd total)

� Withdrawals modify channel hydraulics and water

temperature

� Withdrawals reduce suitable spawning habitat area

for brown trout by 20-50%

0.0

0.2

0.4

0.6

0.8

1.0

Hab

itat

Su

itab

ilit

y In

dex (

HS

I)

Brown Trout: Spawning Habitat Suitability Functions

� Brown trout spawning

habitat is impacted by

water temperature, depth,

and stream velocity.

� Based on U.S. Fish &

Wildlife Service brown trout 0.0

0 10 20 30 40 50 60 70 80

Water Temperature (deg. F)

Hab

itat

Su

itab

ilit

y In

dex (

HS

I)

Wildlife Service brown trout

habitat report (1986).

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.5 1.0 1.5 2.0

Stream Depth (feet)

Hab

itat

Su

itab

ilit

y In

dex (

HS

I)

0.0

0.2

0.4

0.6

0.8

1.0

0.0 1.0 2.0 3.0 4.0

Stream Velocity (ft/sec)

Hab

itat

Su

itab

ilit

y In

dex (

HS

I)

Withdrawal Scenarios for Little Muskegon River sub-basin

� Model generates geographic comparisons of withdrawal

scenarios (red, green) relative to baseline (blue) for

spawning habitat (average annual weighted suitable area):

GW: 5 mgdMuskegon

GW: 5 mgd

GW: 5 mgd

GW: 5 mgd

Little Muskegon River

Muskegon

River

Comparison of Habitat Results

� Model generates temporal comparisons of

scenario simulation results relative to

baseline:

� The Bay Delta system is managed both in terms of water quantity and water quality.

� There are multiple stakeholders with varying priorities

� Reclamation and other management and project decisions must consider ecological impacts

Sacramento – San Joaquin Bay Delta Integrated Ecological Response Model: Concepts

Reclamation Project

20

Reclamation Project Dam

Canal

New flow mgmt. plan

Modification of System Conditions

Flow volume

Velocity

Temperature

Salinity

Ecosystem Receptor

Fish

Waterfowl

Wetland habitat

Bay Delta Integrated Ecological Response Model: Challenges

� Must provide for protection and recovery of endangered and sensitive species, as well as protection and restoration of water supplies

� Multiple projects to consider, each may affect multiple environmental driversmultiple environmental drivers

� Multiple ecological receptors: delta smelt, Chinook salmon, green sturgeon, riparian wetlands, migratory waterfowl, etc.

� Cumulative impacts may exist

� Must overlay management on natural hydrometeorological conditions (climate change?)

21

DSM2-SJR Model

Hydraulic Sub-Model (HYDRO)

•Predicted flow @

Vernalis

Water Quality Sub-Model (QUAL)

•Predicted salinity in

delta area

Math

em

ati

cal M

od

els

SJR System OperationsTributary boundary inflows,

diversions, return flows

Hydrology

Sys

tem

S

tre

ss

ors

Vernalis delta area

Delta Smelt

•Population indices:

1) SJR-Sacramento

confluence

2) Suisun Bay

Chinook Salmon

•Smolt out-migration

(mean flow in spring)

•Adult escapement

(mean flow in fall)

Ecological Response Sub-Models

Predicted flow @

Vernalis

Predicted salinity

in delta area

Math

em

ati

cal M

od

els

22

Model Scenario Application: Little Muskegon River

Little Muskegon

River Watershed

models quantify the relationship between water flows ... · dsm2-sjr model hydraulic sub-model...

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