Industrial ecology of earth resources Industrial ecology of earth resources EAEE E4001EAEE E4001

MANAGING THE WATER RESOURCESMANAGING THE WATER RESOURCES

INVITED LECTURE BY PROF. UPMANU LALL INVITED LECTURE BY PROF. UPMANU LALL   

Decision Analysis Tools for Total Maximum Daily Loads

- EPA's Water Quality Management Program

Presentation by Prof. Upmanu Lall,Earth and Environmental Engineering

Outline• The Playing Field

– The Clean Water Act, Water Quality and the EPA

• The Problem– Management or Regulation ? Data, Science,

Problem Scale and Decisions

• An Approach– Emphasis on Science to support Decision Process– Bayes Networks + Packaging

Fires plagued the Cuyahoga beginning in 1936 when a spark from a blow torch ignited floating debris and oils. Fires erupted on the river several more times before June 22, 1969, when a river fire captured national attention when Time magazine described the Cuyahoga as the river that "oozes rather than flows" and in which a person "does not drown but decays." This event helped spur an avalanche of pollution control activities resulting in the Clean Water Act, Great Lakes Water Quality Agreement, and the creation of the federal and state Environmental Protection Agencies.

http://www.cnn.com/NATURE/9906/22/saving.cuyahoga/

National Environmental Policy Act, 1969: Environmental Assessments (EA's) and Environmental Impact Statements (EIS's) for all federal activities

Federal Water Pollution Control Act 1972 : Regulates discharges of pollutants to waters

Endangered Species Act, 1973: Conservation of threatened/endangered plants and animals and the habitats in which they are found

The Safe Drinking Water Act, 1974, 1996: Protect the quality of all waters actually or potentially designed for drinking use, whether from above ground or underground sources. EPA to establish safe standards of purity and required all public water systems to comply with primary (health) standards. State governments, also encourage attainment of secondary standards (nuisance).

The Clean Water Act 1977: Focus on toxics. EPA gets authority to set effluent standards on an industry basis (technology-based) and water quality standards for all contaminants in surface waters. The CWA makes it unlawful for any person to discharge any pollutant from a point source into navigable waters unless a permit (NPDES) is obtained.

Comprehensive Environmental Response, Compensation, and Liability Act, 1980: Federal “Superfund” to clean up uncontrolled or abandoned hazardous-waste sites as well as accidents, spills, and other emergency releases of pollutants and contaminants into the environment

The Clean Water Act 1987: authorized citizen suit provisions, and funded sewage treatment plants (POTW's) under the Construction Grants Program. EPA can delegate many permitting, administrative, and enforcement aspects of the law to state governments.

Resource Conservation & Recovery Act, 1976, 1986: Underground Storage Tanks, Non-Haz Waste

Key Legislation mandating EPA’s role in Water

EPA

Assessed Rivers, Lakes, and Estuaries Meeting All Designated Uses 1994/1996 Using Latest State Information Reported - EPA

Percent of Impaired Waters - 1998

-EPA

Major Water Quality Concerns Today• Non-point source Pollution (e.g., Agricultural Sources, Urban Runoff)

– Nutrients (P, N), Sediment, Stream temperature, Dissolved Oxygen, Pathogens, Pesticides

– Organics in Urban Runoff

– Eutrophication

– Endangered Species/Habitat/Riparian Zone, Recreation Impacts

– Climate Variability and Dynamic Range of Biophysical Processes

– Rapid Urbanization/Shifts in Land Use

– Competition between Environmental and Agricultural/M&I demands

– Paucity of Data in Space and Time

– Limited Understanding of Long Term Impacts in Ephemeral Streams

Sediments

Nutrients

Pathogens

Dissolved Oxygen

Metals

Habitat

pH

Suspended Solids

Temperature

Flow Alterations

Pesticides

Noxious Plants

Turbidity

Fish Contamination

Ammonia

% of Water Segments180

-EPA

Sediment Runoff Potential - 1990-1995

Pesticide Runoff Potential - 1990 -1995

Nitrogen Runoff Potential - 1990 -1995

Fish Consumption Advisories - 1997

Key Points

• TMDLs - legal domain dominates process - policy or science issue ?

• Major Public Sector Expenditures and Urgency - Legal Impetus

• 40,000 TMDLs @ $100k each = $4 billion EPA expense in 10 yrs

• There are significant data gaps to even evaluate existing conditions

• Natural climatic variability leads to short and long range effects on the landscape that complicates the assessment of sediment, nutrient, and other inter-related “loads” that result from modified land use practices

• Various “best” management practices have been proposed. Little is known about their efficacy

• How does one develop management/regulation plans for specific watersheds in this environment ?– Scale of watershed, location of source, Equity

The “Local” Decision Problem

Collaborative Decision Making as a Watershed Management Approach

- The Physical Scientists Role

TMDL: Total Maximum Daily Loads

Key aspects:

For each stream reach listed for a specific pollutant

• Identify Beneficial Use

•Identify Sources (point and non-point) & Background Loading

•Allocate Loads to all Point and Non-Point Sources + Margin Of Safety such that water quality standard(s) relative to beneficial use designation are met

Decision

Regulatory Constraints

Management GoalsStakeholders

Management OptionsTime Point &

Time Frame

Information Scale, Relevance, Availability, Source and Cost

Natural ProcessesE.g. Climate, Geology

Water Bodies

Values of Activity Outcomes

Human Activities

Eco-environmental State

Natural StateDesired State

Typical “Stakeholders”

• Regional EPA Offices

• State Departments of Environmental Quality, Water Rights, Fish and Wildlife, Natural Resources, Agriculture, Planning and Budget

• The Western Governors Association (WGA)

• Federal Land and Resource Managers – e.g., USFS, BLM, USBR, NPS, USFWS, NRCS, USDA, DOE, DOD

• Association of County Governments and County Planners, Irrigation and Water Conservation Districts, Major Industries and Utilities

• Environmental Organizations and Action Groups, Farm Groups

• Information Suppliers: The scientific community - Universities and other Researchers

THE TMDL EQUATION as per EPA

TMDL = Sum of WLA + Sum of LA + MOS

• TMDL = WQ Standard for Beneficial Use Designation

• WLA = Waste Load Allocation for each point sourceNPDES Permits

• LA = Load Allocation for each non-point sourceBMPs

• MOS = Margin of Safety (e.g. 20% of TMDL) Socio-Economic Factors ? Uncertainty ? Variability ?

Develop for each “reach”. Watershed Sequencing ? Trading ?

Current Modeling Approaches Lumped, and spatially distributed simulation models for overland and channel

flow generation given landscape information and climatic time series Associated models for modeling the transport, reactions, decay and mixing of

contaminants in a river channel and/or non-point source load generation Lumped and spatially distributed discrete time simulation models for groundwater

flow and transport and surface-water exchanges "GIS Models" that use spatial landscape data and simple physics or statistical

approaches to estimate mean fluxes at a point or perform discrete time simulations of the system given assumptions on exogenous climate and pollutant application

Statistical approaches based on linear or nonlinear (including neural network) regressions to provide estimates of the mean value (and variance) of loads and fluxes given specific indicators.

Statistical indicators of ecological condition of the watershed Ecological models of habitat dynamics conditional on exogenous forcings

• Statistical and dynamical models of economic markets and user preferences

Focus on data and decision questions or on unit process modeling ?

Do decision makers react to results of process simulations or to the chance of key outcomes ?

Source: Jarrell (1999)

Approach• TMDL Management Plan = translate regulatory needs,

competing stakeholder objectives into monitoring system design and watershed “operation” strategy given evolving data and goals

• Focus on Variables/Processes needed for decision analysis and their interconnections - represent as a directed network

• Recognize that unit process models provide means to connect variables on network

• Explicitly consider role of natural variability and ignorance in defining uncertainty - Bayes network

• Communication - Visualization / Presentation Tools– Simcity/Simearth motif with BayesNet providing SimRules

– Limiting Probabilities vs Time Simulation ?

Example1 point & 1 Non-point source

Concern: Dissolved Oxygen

Natural Area 1

Natural Area 2

Natural Area 3

Ranch

Beet Factory

Reach 1

Reach 2

Reach 3

Margin of Safety - Some thoughts

MOS = TMDL - Total Loads

FS = DOmean/TMDL

Riparian Buffer

Secondary Treatment

Probability of critical DO in reach 3 due to Beet Factory

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

012345678910

DO (mg/l)

DO pdf w/ Beet factory only

DOmean=6.6

FS=1.34

DO<TMDL 3.5% of time

Probability of Critical DO in reach 3 due to Ranch

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0246810

DO (mg/l)

DO pdf w/ Ranch only

DOmean=7

FS=1.4

DO<TMDL 11% of time

Probability of Critical DO in reach 3 with Ranch and Beet Factory

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

012345678910

DO (mg/l)

DO pdf w/ Beet factory + ranch

DOmean=6.2

FS=1.24

DO<TMDL 23% of time

The NPS (Ranch) has a higher FS than Beet Factory, but worse risk of violation of the standard

For the case with both the beet factory and the ranch, we have a FS>1, but the TMDL is violated 23% of the time. Is this acceptable for ecological impacts ?

Probability of critical DO in reach 3 with Beet Factory Treatment

0

0.02

0.04

0.06

0.08

012345678910

DO (mg/l)

DO pdf w/ Beet treatment

DOmean=7

FS=1. 4

DO<TMDL 12% of time

Probability of critical DO in reach 3 with Ranch Riparian Zoning

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

012345678910

DO (mg/l)

DO pdf w/ Riparian Buffer

DOmean=7

FS=1.4

DO<TMDL 5% of time

Probability of critical DO in reach 3 with Ranch Riparian Zoning and Beet Factory Treatment

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

012345678910

DO (mg/l)

DO pdf w/ Beet treatment + riparian buffer

DOmean=7.2

FS=1.44

DO<TMDL 1% of time

The 2 treatment options individually lead to the same FS, but the Riparian buffer leads to a lower risk of TMDL violation. Trading ?

Is a 5% risk acceptable or do we need both options to reduce the risk to 1% ?

Choosing a Margin of Safety

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0.4 0.6 0.8 1

Margin of Safety (Reliability)

Expected CostExpected benefit

Expected Net benefit

Setting up a Bayesian Network to Determine the Probabilities of Outcomes

Example1 point & 1 Non-point source

Concern: Dissolved Oxygen

Natural Area 1

Natural Area 2

Natural Area 3

Ranch

Beet Factory

Reach 1

Reach 2

Reach 3

Riparian Buffer

Secondary Treatment

Network Diagram of Key Elements of the Physical System

Bayes Network if “Local” Conditional Probability Distributions are defined for each Parent-Child Link set

NA1 BOD

Ranch BOD

NA2 BOD

R1 DO R2

DO

R3 DOcrit

Beet BOD

Economic Conditions

Climate Conditions

Treatment

Riparian Buffer Costs and Benefits

Beet BOD

R3 DOcrit

Ranch BOD

The Bayes Network can be reduced to focus on key items of interest

Ranch BOD Beet BOD Reach 3 critical DO (mg/l)(mg/l) (mg/l) 0 - 5 5 - 8 >80-30 0-10 0 0.3 0.730-60 0-10 0.1 0.5 0.460-90 0-10 0.2 0.6 0.20-30 10-20 0.1 0.4 0.530-60 10-20 0.25 0.65 0.160-90 10-20 0.35 0.65 0

Conditional Probabilities are evaluated from data or simulation models or can be specified by an expert

Stoddard Diversion

Lost Cr.

Echo Cr.

Chalk Cr.

Silver Cr.

East Canyon Cr.

Rockport Res.

East Canyon Res.

Lost Cr. Res.

Echo Res.

East Canyon Reservoir, UtahA case study

Concerns• Current problems

– loss of cold water fisheries (stream and flat water)

– reduction in use of State Park at East Canyon Reservoir

• Annual Visitors (300K in 1986, now <100K)

– periodic low dissolved oxygen in stream

– eutrophication of reservoir

• Future concerns

– development - population to grow 400% in 20-30 years

– conversion of agricultural to urban lands

– interbasin water transfers that may reduce flushing

Goals

• Control of Contaminant Discharges into East Canyon Cr. and Reservoir– Determination of total loading of a particular pollutant into

a water body

• point and non-point sources

• overland flow

• ground water

– Need for discharge management to achieve water quality goals

Approach

• Analysis of data– identification of data sources

• U.S. EPA STORET repository, USGS repository, State of Utah, Results of university and other studies, data held by stakeholders

– probability distributions for hydrologic, water quality, and contaminant source inputs

• exploratory data analysis, parametric distribution fitting, non-parametric distribution ‘fitting’

– methods for finding probability distributions of outputs• Bayes nets, deterministic models

East Canyon Conceptualization

Kimball Junction

East Canyon Reservoir

Snyderville Basin WWTP

TP

Temp

Flow TPTemp

Flow

Snyderville WWTP - Point Source

Kimball Junction - Headwater

East Canyon Reservoir

East Canyon Creek

Models to route probabilities

• Input Variables from Historical Data• QUAL2E/UNCAS - EPA developed and

maintained– stream water quality and temperature

• BOD/DO

• nutrients

• algae/Coliform bacteria

• user-defined constituents

– detailed representation of stream hydraulics

– error propagation integral to model

Results - Reservoir InflowFrequency Distribution - Total Phosphorus

0

100

200

300

400

500

600

700

800

0.001 0.01 0.1 1 10

Reservoir Influent P concentration, mg/L

Fre

quency

Target Value

ResultsReservoir Status

KFLOW KP WFLOW WP L M HHigh Probability of H L L H H 0.01 0.01 0.98

L M H H 0.01 0.01 0.97L H H H 0.03 0.03 0.94L H M H 0.01 0.19 0.80H H M H 0.12 0.12 0.76

High Probability of M L H M M 0.10 0.75 0.15H L M H 0.17 0.73 0.10H M M M 0.15 0.70 0.15L M M M 0.30 0.68 0.01L L M M 0.31 0.68 0.01L L H M 0.06 0.67 0.27

High Probability of L H L M M 0.42 0.42 0.15

RP

Prior Water

Quality

Stream flow

Forecast

Treatment

Cost

Net BenefitTotal Cost

Benefit

Monitoring Strategy

Treatment Strategy Posterio

rWater

Quality

Monitoring Cost

A future TMDL Strategy as a Bayes Net

An Approach to the Analysis of Sediment Loads

Questions Addressed

• Where are the most erodible soils located within the watershed?

• Where do the eroded materials move to; i.e., what stream segment?

• What are the high priority areas to address erosion/ sedimentation for the watershed?

• What stream segments are at greatest risk from sedimentation?

• How would implementation of BMPs in selected areas of the watershed affect the erosion rate, mass, and loading into the receiving streams?

• How is the amount of eroded material attenuated as it moves over and through different areas with varying land cover/use, soils, and slope?

GIS input data• 30 m DEM based on USGS data

• River Reach Files

• SSURGO soils data (USDA-NRCS)

Hierarchical Estimation of Sediment Load

H3

H1

H2 C1

C2

C3

LH3

DH3TH3

LC3

TC3 DC3

LH2

DH2TH2

LH1

TH1 DH1

LC1

TC1 DC1

LC2

TC2 DC2

D: Detachment, T: Transport, L: Load

HillslopesLH1 = Min(DH1, TH1), LH2 = Min(DH2, TH2), LH3 = Min(DH3, TH3)

ChannelsLC1 = Min(DC1+LH1, TC1), LC2 = Min(DC2+LH2, TC2),LC3 = Min(DC3+LC1+LC2+LH3, TC3)

A Bayesian Framework

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6

Distribution of Hillslope Transport Capacity (TH)

With Riparian Buffer

No Riparian Buffer

Precip

RB%H1

Full Bayesian Dependence Network

Consolidated Bayesian DependenceNetwork

LC3RB%H2

RB%H3

LH3

DH3TH3

LC3

TC3 DC3

LH2

DH2TH2

LH1

TH1 DH1

LC1

TC1 DC1

LC2

TC2 DC2

To

all D

and

T n

odes

To TH3Precip

RB%H1

RB%H2

RB%H3

Watershed responseevaluated in GIS

Decision variables

Functional Elements of Computer Package

DatabaseSpatial, Environmental, Economic and Regulatory

Electronic LibraryCase studies, beneficial uses, stakeholder information

Decision Process GuideWalk through decision process, flag data, prescribe analyses

System Representation Tools Physical representation of the landscape and control points, pollution sources and sinks Process representation using Bayes networks

Analysis ToolsStatistical and Mechanistic Models

Load allocation•Visualize system interconnections•Display causal network•Apply management options•Assess optimal economic load allocation strategy

Information•Deterministic Models•Regional statistical analysis

Sub-basin assessmentFor each reach in sub-basin:•Determine beneficial use designation•Determine standards for selected pollutant•Identify sources•Characterize loads from each source•Generate probabilities from data •Identify management options•Evaluate management options

Project Setup•Identify sub-basin•Organize reaches by stream order•Determine listing status•Select pollutant for TMDL•Organize data sets

Using the System to Develop TMDLs

Project Environment A standalone Windows-PC application (no extra software required). Live links to local or Internet-based data sources, GIS capabilities and a guidance system that walks the user through the data analysis, modeling and decision-making processes.

Internal Data Processing -Graphical Scripting

A schematic-based data processing interface provides the user with an intuitive “drag and drop” ability to process and prepare both temporal and spatial data for use in the system.

Commercial GIS products such as ArcView are used to conduct a regional data analysis of all water quality stations in similar watersheds.

External Data Processing-Regional Analysis

Total Nitrogen

Contributing Area in

Buffer ZoneContributing Basin Area

Land Use in Buffer

ZoneSoil Type

Monthly Precipitation

Average Slope in

Buffer ZoneMedium Medium Medium 2 2 High High

High Medium High 3 2 Medium MediumLow Medium Low 1 3 High High

Medium Medium Medium 2 1 Medium MediumHigh Medium High 3 2 High HighLow Medium Low 1 2 High HighLow Medium Low 1 2 High HighHigh Medium High 3 2 High HighHigh Medium High 3 3 High High

Medium Medium Medium 2 2 High HighHigh High High 3 2 High HighHigh Medium High 3 2 Medium Medium

Medium Medium Medium 2 3 High HighHigh Medium High 3 2 High HighLow Medium Low 1 2 Medium Medium

Medium Medium Medium 2 1 Medium MediumLow Medium Low 1 2 High MediumHigh Medium High 3 2 High HighHigh Medium High 3 2 High HighHigh High High 3 2 Medium Medium

Medium Medium Medium 2 2 Medium MediumLow Medium Low 1 2 Medium LowLow Medium Low 1 2 High High

Medium Medium Medium 2 3 High HighLow Medium Low 1 1 Medium Low

Regional Statistical AnalysisA conditional probability table is generated from monitoring stations throughout the adjoining ecoregions.

This table is used to provide estimates of nonpoint source loadings.

GIS-Based Interaction

User selects a sub watershed by clicking on the map. A recursive algorithm selects connected streams and builds a topological network.

Data Extraction and Manipulation

Extracted data include:

stream network, point sources, monitoring stat-ions, land use, soil type, contributing area, average slope, mean annual precipitation and elevations.

Nonpoint Source Modeling

These data will be used to generate nonpoint source loadings for the reaches based on the conditional probability table developed from the ecoregion-wide causal relationship analysis.

Bayesian Network Generation

A Bayesian belief network or causal network is generated from the GIS layers. The network includes control points, point and nonpoint sources, management options, monitoring stations and cost/benefits.