us army corps of engineers building strong ® step 2: inventory and forecast planning principles...

US Army Corps of Engineers

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STEP 2: INVENTORY AND FORECASTPlanning Principles & Procedures – FY 11

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Objectives

Define inventory and forecast

Describe data collection process and requirements

Discuss forecasting techniques

Identify most important output of Step 2

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Why Inventory?

Describe historic and existing conditions

Refine and expand problems and opportunities

Define future without project condition

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How to Inventory

Information Gathering Strategy

Develop and describe in Project Management Plan (PMP)

Re-evaluate

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Information Gathering Strategy

Geographic Area

Relevancy

Quantity

Accuracy

Timing

Period of time

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Examples of Data Used in Planning

Foundation characteristics InfrastructureGeologic formations MineralsLand classifications Drainage patternsAquifer characteristics YieldsRain gauge data Precipitation recordsEvaporation rates Gauging stations locationsStream flow records Sediment loadsGroundwater quality Surface water qualityFlora and fauna Historic structuresEndangered species Significant resourcesHabitat types PopulationLand uses Structure valuesStructure elevations Income distributionEmployment Commodity demand Stakeholders Commodity movementsVessel fleet characteristics

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Example Data Flood Management Study

Data Needed Purpose Suggested Sources

Water surface elevations by flood frequency

Define floodplain.

Estimate damages/benefits.

Formulate plans.

Hydrologic/hydraulic analyses

Property at risk of flooding Estimate damages/benefits.

Formulate plans.

Hydrologic/hydraulic analyses.

Flooded area maps.

Structure/Content values Estimate damages/benefits.

Formulate plans

Field surveys.

Commercial data (Marshall & Swift, others)

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Example Data Flood Risk Management Study (Cont.)

Data Needed Purpose Suggested Sources

Depth-damage curves Estimate damages/benefits.

Generic curves.

Site specific surveys.

Ecological resources impacted by plans

Develop mitigation measures.

Published data.

Resource agencies.

Field surveys.

Unit costs Cost estimates MCACES.

Commercial sources.

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Data Collection

Potential sources:►Primary

• From original research

►Secondary

• Collected by others

• Internet

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Planners Website

http://usace.army.mil/CECW/PlanningCOP/Pages/psa.aspx

Planner's Study Aids:Coastal Storm Damage Reduction Ecosystem Restoration Flood Risk Management Hydropower Navigation Recreation Water Supply/Water Quality Watershed Formulation and Evaluation

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For Additional Information on Available Data

Flood Risk Management► Stuart A. Davis, IWR► Eric Thaut, SPD

Deep-Draft/Inland Navigation► Ian Mathis, IWR► Navigation Data Center, IWR► Wesley Walker, LRD► Bernard Moseby, SAM

Ecosystem Restoration► Jodi Staebell, MVR

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Criteria to Identify Data Needed

Required► Laws► Policies► Regulations

Nice to Know

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Helpful or Harmful?

Recent reports Checklists “Typical” study Available information Templates

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COLUMN 1

COLUMN 2

COLUMN 3

COLUMN 4

COLUMN 5

AGE

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

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COLUMN 1

DREAM

COLUMN 2

COLUMN 3

COLUMN 4

COLUMN 5

AGE

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

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COLUMN 1

DREAM

COLUMN 2

COLUMN 3

2011 COLUMN 4

COLUMN 5

AGE

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

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COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4 COLUMN 5

AGE

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

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COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4

2021 COLUMN 5

AGE

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

BUILDING STRONG®

COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4

2021 COLUMN 5

$2021$

AGE

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

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Inventory and Forecast

Four Conditions:► Existing condition.► Historic condition.► Future without condition.► Future with condition.

Target

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COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4

2021 COLUMN 5

$2021$

AGE EXISTING CONDITION

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

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COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4

2021 COLUMN 5

$2021$

AGE

HISTORIC CONDITION

EXISTING CONDITION

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

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COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4

2021 COLUMN 5

$2021$

AGE

HISTORIC CONDITION

EXISTING CONDITION

FUTURE WITHOUT

CONDITION

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

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COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4

2021 COLUMN 5

$2021$

AGE

HISTORIC CONDITION

EXISTING CONDITION

FUTURE WITHOUT

CONDITION

FUTURE WITH

CONDITION

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

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COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4

2021 COLUMN 5

$2021$

AGE

TARGET

HISTORIC CONDITION

EXISTING CONDITION

FUTURE WITHOUT

CONDITION

FUTURE WITH

CONDITION

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

HOME ADDRESS

CAR MODEL

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Inventory and Forecast

Four Conditions:► Existing condition.► Historic condition.► Future without condition.► Future with condition.

Target. Three types of comparisons:

► With-and-without analysis.► Before-and-after analysis.► Gap analysis.

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COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4

2021 COLUMN 5

$2021$

AGE

TARGET

HISTORIC CONDITION

EXISTING CONDITION

FUTURE WITHOUT

CONDITION

FUTURE WITH

CONDITION

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

WITH AND WITHOUT ANALYSIS

HOME ADDRESS

CAR MODEL

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COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4

2021 COLUMN 5

$2021$

AGE TARGET

HISTORIC CONDITION

EXISTING CONDITION

FUTURE WITHOUT CONDITION

FUTURE WITH CONDITION

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

WITH AND WITHOUT ANALYSIS

HOME ADDRESS

BEFORE AND…

…AFTER ANALYSIS

CAR MODEL

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COLUMN 1

DREAM

COLUMN 2

2001 COLUMN 3

2011 COLUMN 4

2021 COLUMN 5

$2021$

AGE

TARGET

HISTORIC CONDITION

EXISTING CONDITION

FUTURE WITHOUT

CONDITION

FUTURE WITH

CONDITION

FIRST NAME

LAST NAME

HAIR COLOR

JOB TITLE

WITH AND WITHOUT ANALYSIS

HOME ADDRESS

BEFORE AND…

…AFTER ANALYSIS

CAR MODEL

GAP… …ANALYSIS

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Inventory and Forecast

Four Conditions:► Existing condition.► Historic condition.► Future without condition.► Future with condition.

Target Three types of comparisons:

► With-and-without analysis (Col. 5 - Col.4).► Before-and-after analysis (Col. 5- Col. 3).► Gap analysis (Col. 5- Col. 1).

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FORECASTING

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References ER 1105-2-100, Appendix E, Paragraph E-3a(2)

ER 1105-2-100, Appendix E, Sections II and X ►Specific guidance for each Civil Works mission

Without Project Condition Handbook

Handbook on Deep Draft Forecasting

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What is a Forecast and Why Do it?

What: Informed guess about the future, a future condition or scenario

Why: Anticipate future conditions that are uncertain.

To define the base condition, the future without project condition and the future with projects condition

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Forecasts are based on:

Data

Expertise and Experience

Assumptions

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What do we forecast?

Variables

Scenarios (Conditions)

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Variables to forecast: those that affect decisions

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Conditions to Forecast

Base Condition - condition at the time of implementation (base year)

Without Condition - condition if no action takes place (period of analysis)

With Condition - condition if a particular course of action is taken (period of analysis)

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Forecast Without Project Condition

Primary outcome of Step 2. Forecast of the future, without any action to solve

the problem at hand. Does not mean “no change”. Most likely condition or use RBA Used for plan evaluation (with vs. without)

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Assumptions

Data, expertise and experience help make an informed forecast and thus reduce uncertainty.

Assumptions address the remaining uncertainty.

When uncertainty is significant, assumptions are also significant.

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Annual Hotel Revenues

010

2030

405060

7080

90100

1985 1990 1995 2000 2005 2010 2015 2020 2025 2030

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Forecasting Methods Quantitative

►Percentage growth►Regression►Simulation►Mathematical programming

Qualitative►Subjective judgment►Expert opinion

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Criteria to Select Forecasting Method

Purpose

Reliability of Data

Level of Disaggregation

Uncertainties and Scenarios

Short vs. Long Term

BASE YR TIME +20YEARS

0-

10-

20-

BASE YR TIME +20YEARS

0-

10-

20-

Existing atExisting atbase yrbase yr

BASE YR TIME +20YEARS

0-

10-

20-

Existing atbase yr

TargetTarget

BASE YR TIME +20YEARS

0-

10-

20-

Existing atbase yr

Target

FutureFuture Without (ER)

Without (ER)

Future Without (FDR/NAV)

Future Without (FDR/NAV)

BASE YR TIME +20YEARS

0-

10-

20-

Existing atbase yr

Target

Future Without (ER)

Future with Plan AFuture with Plan A

BASE YR TIME +20YEARS

0-

10-

20-

Existing

Target

Future Without (ER)

Future with Plan AFuture with Plan A

BASE YR TIME +20YEARS

0-

10-

20-

Existing

Target

Future Without (ER)

Future with Plan AFuture with Plan A

Before& AfterBefore& After

BASE YR TIME +20YEARS

0-

10-

20-

Existing

Target

Future Without (ER)

Future with Plan AFuture with Plan A

With & WithoutWith & Without

BASE YR TIME +20YEARS

0-

10-

20-

Existing

Target

Future Without (ER)

Future with Plan AFuture with Plan A

With & WithoutWith & Without

GapGap

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Summary In Step 2, data collected is used to verify or re-define

problems and opportunities identified in Step 1

Future without project condition is primary outcome of Step 2

Collect only information you need and use it

“Without” or “No Action” does not mean no change

Assumptions matter.

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Forecaster’s Motto

OFTEN WRONG

BUT NEVER IN DOUBT