Vulnerability and Adaptation Assessments Hands-On Training Workshop Impact, Vulnerability and...

Vulnerability and Adaptation Assessments

Hands-On Training Workshop

Impact, Vulnerability and Adaptation Assessmentfor the Agriculture Sector

Outline

Climate change, agriculture and food security

Methods, tools, and datasets

Practical applications

Integrated assessments

Climate Change, Agriculture, and Food Security

Climate change is one stress among many affecting agriculture and the population that depends on it

Multiple Interactions, Vulnerability, and Adaptation

Social vulnerability

Climate change

Economic, social,

demographic, land usechanges

Systems and social groups that needs to

adapt

Systems and social groups that needs to

adapt

Mozambique, floods

Social Vulnerability

“Starvation is the characteristic of some people not having enough food to eat. It is not the characteristic of there being not enough food to eat. While the later can cause the former, it is but one of many possible causes.”

Multiple Interactions, Stakeholders Define Adaptation

ScientistsScientists

Policy makersPolicy

makers

Civil stake-holders

Civil stake-holders

Multiple Interactions

Climate change is one stress among many now affecting agriculture and the population that depends on it

Integration of results is essential to formulate assessments relevant to policy

Potential future consequences depend on: The region and the agricultural system

[Where?] The magnitude [How much? Scenarios are

important.] The socioeconomic response [What happens

in response to change? Adaptive capacity (internal adaptation) and planned adaptation.]

Where? Systems and Social Groups

Cassava production, Mozambique

Vegetable production, Egypt

Coffee production, Kenya

How Much? Climate and SRES Scenarios

Precipitation change

Temperature changeHad CM2 model, 2050s

What Happens in Response to Change?

Adaptive capacity (internal adaptation)

Planned adaptation

Climate Change Affects Crop Production

Changes in biophysical conditions Changes in socioeconomic conditions in response to

changes in crop productivity (farmers’ income; markets and prices; poverty; malnutrition and risk of hunger; migration)

POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2

CARBON DIOXIDEFERTILIZATION

LONGERGROWINGSEASONS

INCREASEDPRECIPITATION

MOREFREQUENTDROUGHTS

PESTS

HEATSTRESS

FASTERGROWINGPERIODS

INCREASEDFLOODING ANDSALINIZATION

POSSIBLE BENEFITS

POSSIBLE DRAWBACKS

CO2

CARBON DIOXIDEFERTILIZATION

LONGERGROWINGSEASONS

INCREASEDPRECIPITATION

MOREFREQUENTDROUGHTS

PESTS

HEATSTRESS

FASTERGROWINGPERIODS

INCREASEDFLOODING ANDSALINIZATION

Percentage change in average crop yields for the Hadley Center global climate change scenario (HadCM2). Direct physiological effects of CO2 and crop adaptation are taken into account. Crops modeled are wheat, maize, and rice.Source: NASA/GISS; Rosenzweig and Iglesias, 1994.

2020s

2050s

2080s

Yield Change (%)

-30 -20 -10 -5 -2.5 0 2.5 5 10 20 30 40

How Might Global Climate Change Affect Food Production?

Limits to Adaptation

Technological limits (e.g., crop tolerance to water-logging or high temperature; water reutilization)

Social limits (e.g., acceptance of biotechnology)

Political limits (e.g., rural population stabilization may not be optimal land use planning)

Cultural limits (e.g., acceptance of water price and tariffs)

Developed-Developing Country Differences

Scenario A1FI A2a A2b A2c A2c B1a B2b

C02 (ppm) 810 709 709 709 527 561 561

World (%) -5 0 0 -1 -3 -2 -2

Developed (%) 3 8 6 7 3 6 5

Developing (%) -7 -2 -2 -3 -4 -3 -5

Developed-Developing) (%)

10 10 8 10 7 9 9

Potential change (%) in national cereal yields for the 2080s (compared with 1990) using the HadCM3 GCM and SRES scenarios (Parry et al., 2004)

Additional People at Risk of Hunger

30

9

69

50

7

60

34

5

43

0

10

20

30

40

50

60

70

80

2020 2050 2080

Add

ition

al M

illio

ns o

f Peo

ple

Unstabilised

Stabilised at 750ppmv


30

9

69

50

7

60

34

5

43

0

10

20

30

40

50

60

70

80

2020 2050 2080

Add

ition

al M

illio

ns o

f Peo

ple

30

9

69

50

7

60

34

5

43

0

10

20

30

40

50

60

70

80

2020 2050 2080

Add

ition

al M

illio

ns o

f Peo

ple

Unstabilised



Parry et al., 2004

Additional People at Risk of Hunger (continued)

Overall, the potential for additional people with risk of hunger is greater with the “unstabilized” scenario, although there are decadal variations

In all decades, the “unstabilized” scenario is the warmest In the 2020s, the warming is beneficial for aggregated

crop production In the 2080s, the warming exceeds the threshold of

optimal crop tolerance in many low latitude regions with more people at risk

Interaction and Integration: Water

0

40

80

120

2020 2050 2080

Po

pula

tion

(m

illio

ns)

Additional population under extreme stress of water shortage

Conclusions

Although global production appears stable . . . . . . regional differences in crop production are

likely to grow stronger through time, leading to a significant polarization of effects . . .

. . . with substantial increases in prices and risk of hunger amongst the poorer nations

Most serious effects are at the margins (vulnerable regions and groups)

Methods, Tools, and Datasets

1. The framework

2. The choice of the research methods and tools

Demand-driven methods: responding to stakeholders

Key characteristics, strengths, weaknesses Examples

3. Datasets: sources, scales, reliability

Frameworks

Adaptation Policy Framework (APF), US Country Studies, IPCC, seven steps

All have the essential common elements Problem definition Selection and testing of methods Application of scenarios (climate and

socioeconomic) Evaluation of vulnerability and adaptation

The studies may want to use a framework as guidance or draw from the best elements of all of them

Demand-Driven Methods

Need quantitative estimates Models are assisting tools Surveys are assisting tools for

designing adaptation options Key variables for agronomic and

socioeconomic studies: crop production, land suitability, water availability, farm income, …

Quantitative Methods and Tools

Experimental Analogues (spatial and temporal) Production functions (statistically derived) Agroclimatic indices Crop simulation models (generic and crop-specific) Economic models (farm, national, and regional) –

Provide results that are relevant to policy Social analysis tools (surveys and interviews) –

Allow the direct input of stakeholders (demand-driven science), provide expert judgment

Integrators: GIS

Experimental: Effect of Increased C02

Near Phoenix, Arizona, scientists measure the growth of wheat surrounded by elevated levels of atmospheric CO2. The study, called Free Air Carbon Dioxide Enrichment (FACE), is to measure CO2 effects on plants. It is the largest experiment of this type ever undertaken. http://www.ars.usda.gov

http://www.whitehouse.gov/media/gif/Figure4.gif

Experimental

Value

Spatial scale of results Season to decades

Time to conduct analysis Site

Data needs 4 to 5

Skill or training required 1

Technological resources 4 to 5

Financial resources 4 to 5

Range for ranking is 1 (least amount) to 5 (most demanding).

Example: growth chambers, experimental fields.

Analogues: Drought, Floods

Africa vegetation health (VT - index) Vegetation health: Red – stressed, Green – fair, Blue – favorableSource: NOAA/NESDIS

Analogues: Drought

Wheat yield in Spain

0%

20%

40%

60%

80%

100%

All Dry Normal Wet

Pro

babi

lity

of y

ield

(%

) high

medium

low

Analogues (space and time)

Value

Spatial scale of results Decades

Time to conduct analysis Site to region

Data needs 1 to 2

Skill or training required 1 to 3




Example: existing climate in another area or in previous time

Dryland Yield

Predicted Values

Yr PP Change (%)

150100500-50-100-150

Dry

land

Yie

ld (

kg h

a-1)

8000

6000

4000

2000

0

Irrigation

Predicted Values

Yr PP Change (%)

150100500-50-100-150

Irrig

atio

n (m

m)

400

300

200

100

0

Statistically derived functions (Almeria – Wheat)Yield Irrigation demand

Production Functions

Iglesias et al., 1999

Production Functions

Value


Time to conduct analysis Site to globe

Data needs 2 to 4





Example: Derived with empirical data.

Agroclimatic Indices

Length of the growing periods (reference climate, 1961-1990). IIASA-FAO, AEZ

Agroclimatic Indices

Value


Time to conduct analysis Site to globe

Data needs 1 to 3





Example: FAO, etc.

Water

Carbon

Nitrogen

Crop Models

Based on

Understanding of plants, soil, weather, management

Calculate

Require

Growth, yield, fertilizer & water requirements, etc

Information (inputs): weather, management, etc

Models – Advantages

Models are assisting tools, stakeholder interaction is essential

Models allow to ask “what if” questions, the relative benefit of alternative management can be highlighted: Improve planning and decision making Assist in applying lessons learned to policy

issues Models permit integration across scales,

sectors, and users

Models – Limitations

Models need to be calibrated and validated to represent reality

Models need data and technical expertise Models alone do not provide an answer,

stakeholder interaction is essential

Crop Models

Value

Spatial scale of results Daily to centuries


Data needs 4 to 5





Example: CROPWAT, CERES, SOYGRO, APSIM, WOFOST, etc.

Economic Models

Consider both producers and consumers of agricultural goods (supply and demand)

Economic measures of interest include: How do prices respond to production

amounts? How is income maximized with different

production and consumption opportunities?

Economic Models (continued)

Microeconomic: Farm Macroeconomic: Regional economies All: Crop yield is a primary input (demand is

the other primary input) Economic models should be built bottom-up

Farm Models – Differences

Small holder farmer Commercial farmer

Strategy of production Stabilize food production Maximize income

Risk Malnutrition and migration Debt and cessation of activity

Source of risk Weather Weather, markets and policies

Non-structural risk avoidance mechanisms

Virtually nonexistent Insurance, credit, legislation

Inputs and farm assets Very low Very significant

Price of food crops Local for primary crops and partially global for industrial crops, with some interference of governments

Global with some interference of policies

Agricultural Trade Models

Parry et al., 1999.

Social Sciences Tools

Surveys and interviews Allow the direct input of stakeholders

(demand-driven science), provide expert judgment in a rigorous way

Surveys and Interviews

Development of adaptation options with stakeholders

Surveys to StakeholdersDesigning Adaptation Options

Stakeholder group

Adaptation

Level 1

Adaptation

Level 2

Adaptation

Level 3

Small-holder farmers or farmers' groups

Tactical advice on changes in crop calendar and water needs

Management of risk in water availability (quantity and frequency)

Education on water-saving practices and changes in crop choices

Commercial farmers

Tactical on improving cash return for water and land units

Investment in irrigation technology; Risk-sharing (e.g., insurance)

Private sector participation in development of agro-businesses

Resource Managers

Education on alternatives for land and water management

Integrated resource management for water and land

Alternatives for the use of natural resources and infrastructure

Economic and Social Tools

Value

Spatial scale of results Yearly to centuries


Data needs 4 to 5





Examples: Farm, econometric, I/O, national economies, BLS, …

Integrators: GIS

Integrators: GIS

Value

Spatial scale of results monthly to centuries

Time to conduct analysis region

Data needs 5


Technological resources 5

Financial resources 5


Example: …. All possible applications ….

Conclusions

The merits of each approach vary according to the level of impact being studied, and they may frequently be mutually supportive

For example, simple agroclimatic indices often provide the necessary information on how crops respond to varying rainfall and temperature in wide geographical areas; crop-specific models are use to test alternative management that can in turn be used as a component for an economic model that analyses regional vulnerability or national adaptation strategies

Therefore, a mix of approaches is often the most rewarding

Datasets

Data are required data to define climatic, nonclimatic environmental, and socioeconomic baselines and scenarios

Data are limited Discussion on supporting databases and

data sources

Valencia - Dec-Feb T(C) 1900-2000

8

9

10

11

12

13

1880 1900 1920 1940 1960 1980 2000 2020

Valencia - Jun-Aug T(C) 1900-2000

21

22

23

24

25

26

1880 1900 1920 1940 1960 1980 2000 2020

Valencia - Annual T(C) 1900-2000

15

16

17

18

19

20

1880 1900 1920 1940 1960 1980 2000 2020

IPCC Working Group 1: “A Collective Picture of a Warming World”

Source of data: GISS/NASA

Climate

FAOCLIM

Valladolid - Annual Precipitation (mm)

200

300

400

500

600

700

1951

1955

1959

1963

1967

1971

1975

1979

1983

1987

1991

1995

1999

Precipitation Annual 1901-1995

Source of data: NOAA, NCDC

1: Evergreen needle leaf forests2: Evergreen broad leaf forests3: Deciduous needle leaf forests4: Deciduous broad leaf forests5: Mixed forests6: Woodlands

Global Land Cover Classification

7: Wooded grasslands/shrubs8: Closed bushlands or shrublands9: Open shrublands10: Grasses11: Croplands12: Bare13: Mosses and lichens

De Fries et al., 1998

Population

Map of the night-time city lights of the worldDMSP: NASA and NOAA

Lights are Related to Income and Population

Soils: FAO

Agricultural GDP as share of total GDP

FAO and the World Bank

Food aid received from external sources 2000

Countries facing exceptional food emergencies due to the drought August 2001

FAO

USGS, FEWS, USAID

FEWS NET in cooperation with USGS and US AID

Botswana – village flood watch

Carbon sequestration Environmental monitoring

and information system Land cover performance Madagascar conservation Rift Valley fever Sahel land use Sustainable tree crops

The projected change in annual temperature and precipitation for the 2050s compared to the present day, for two GCMs, when the climate models are driven with an increase in greenhouse gas concentrations defined by the IPCC “business-as-usual” scenario.

Irrigation Area Tunisia (1970 - 1998)

50

150

250

350

450

1970 1975 1980 1985 1990 1995Year

Iirrg

Are

a (

ha

x 1

00

0)

FAO Data USDA ERS Data

Data: Scales, Sources, Reliability

Practical Applications Policy Questions

What components of the farming system are particularly vulnerable and may thus require special attention?

Can the water/irrigation systems meet the stress of changes in water supply/demand?

Will climate significantly affect domestic agriculture?

http://www.icasanet.org/ http://www.clac.edu.eg

International Consortium for Agricultural Systems Applications

Question: What components of the farming system are particularly vulnerable, and may thus require special attention? – crop models (e.g., DSSAT)

Question: What components of the farming system are particularly vulnerable, and may thus require special attention? – crop models (e.g., DSSAT)

Practical Applications: DSSAT

Practical Applications: DSSAT

1. Overview and previous examples of use

2. Guided use of model (three practical applications to be done by the participants)

DSSAT Decision Support System for

Agrotechnology Transfer

Components Description

Databases Weather, soil, genetics, pests, experiments, economics

Models Crop models (maize, wheat, rice, barley, sorghum, millet, soybean, peanut, dry bean, potato, cassava, etc.)

Supporting software

Graphics, weather, pests, soil, genetics, experiments, economics

Applications Validation, sensitivity analysis, seasonal strategy, crop rotations

Input Requirements

Weather: Daily precipitation, maximum and minimum temperatures, solar radiation

Soil: Soil texture and soil water measurements

Management: planting date, variety, row spacing, irrigation and N fertilizer amounts and dates, if any

Crop data: dates of anthesis and maturity, biomass and yield, measurements on growth and Leaf Area Index (LAI)

Source: Iglesias et al., 1999

Crop Model Validation

Examples

Can optimal management be an adaptation option for maize production in Zimbabwe?

Can adaptation be achieved by optimizing crop varieties?

Does the start of the rainy season affect maize yield in Kasungu, Central Malawi?

Can Optimal Management be an Adaptation Option for Maize Production in Zimbabwe?

Agroclimatic zones

Muchena, 1994

Impacts: Zimbabwe

Gueru Banket Chisumbanje

Impacts of climate change: CERES-Maize model

Muchena, 1994

Adaptation: Zimbabwe

Increased inputs and improve management:

Fertilizer Fertilizer and irrigation

Adaptation strategies in Gueru: CERES-Maize model

Muchena, 1994

Crop Coefficients Corn

. Juvenile phase (growing degree days base 8°C from emergence to end of the juvenile phase)

. Photoperiod sensitivity

. Grain filling duration (growing degree days base 8 from silking to physiological maturity)

. Potential kernel number

. Potential kernel weight (growth rate)

P1

P2P5

G2G5

P1

P2P5

G2G5

Can Adaptation be Achieved by Optimizing Crop Varieties?

315 325 335 345 355

160

150

140

130

120

110

100

90

80

NU

MB

ER

OF

GR

OW

ING

DA

YS

DAY OF YEAR, START OF RAINS

EARLY RAINS NORMAL LATE RAINSRAINS

SIMULATED MEAN MAIZE YIELDS (t/ha)

1.83 1.38 0.95

Does the Start of the Rainy Season Affect Maize Yield in Kasungu, Central Malawi?

Practical Applications

1. Effect of management (nitrogen and irrigation) in wet and dry sites (Florida, USA, and Syria)

2. Effect of climate change on wet and dry sites

Sensitivity analysis to changes in temperature and precipitation (thresholds) and CO2 levels

3. Adaptation: Changes in management to improve yield under climate change

Application 1. Management

Objective: Getting started

Weather

Syria Florida, USA

SR (MJ m2 day1) 19.3 16.5

T Max (°C) 23.0 27.4

T Min (°C) 8.5 14.5

Precipitation (mm) 276.4 1364.3

Rain Days (num) 55.7 114.8

Input Files Needed

Weather Soils Cultivars Management files (*.MZX files) description of

the experiment

Open DSSAT . . .

Weather file

Soilfile

Genotype file (Definition of cultivars)

Examine the Data Files . . .

Location of the Cultivar File . . .

Select the Cultivar File . . .

Examine the Cultivar File . . .

Location of the Weather File . . .

Selection of the Weather File . . .

Examine the Weather File . . .

Calculate Monthly Means . . .

Calculate Monthly Means . . . (continued)

Program to Generate Weather Data . . .

Location of the Input Experiment File . . .

Select the Experiment File . . .

Examine the Experiment File (Syria)

Examine the Experiment File (Florida)

. . . The Experiment File Can Be Edited Also With a Text Editor (Notepad)

Start Simulation …

Running . . .

Select Experiment . . .

Select Treatment . . .

View the Results . . .

Select Option . . .

Retrieve Output Files for Analysis

C:/DSSAT35/MAIZE/SUMMARY.OUT C:/DSSAT35/MAIZE/WATER.OUT C:/DSSAT35/MAIZE/OVERVIEW.OUT C:/DSSAT35/MAIZE/GROWTH.OUT C:/DSSAT35/MAIZE/NITROGEN.OUT

There are DOS text files Can be imported into Excel

Management: Maize Yield Florida and Syria

0

2000

4000

6000

8000

10000

12000

Rainfed Low N Rainfed High N Irrig Low N Irrig High N

Gra

in Y

ield

(k

g/h

a)

Florida

Syria

Analyse and Present Results

Exp 2. Sensitivity to Climate

Objective: Effect of weather modification

Start Simulation . . .

Sensitivity Analysis . . .

Select Option …

Climate Change: Maize Yield Florida

0

500

1000

1500

2000

2500

Florida Base Florida -50% pp

Gra

in Y

ield

(k

g/h

a)

Analyze Results . . .

Exp 3. Adaptation

Objective: For advanced participants …

http://www.fao.org/ag/agl/aglw/cropwat.htm

CROPWAT is a decision support system for irrigation planning and management.

Can the water/irrigation systems meet the stress of changes in water supply/demand? – irrigation models (e.g., CROPWAT)

Can the water/irrigation systems meet the stress of changes in water supply/demand? – irrigation models (e.g., CROPWAT)

http://www.clac.edu.eg

Experiments

1. Calculate ET0

2. Calculate crop water requirements

3. Calculate irrigation requirements for several crops in a farm

Start CROPWAT …

Retrieve Climate File . . .

Examine Temperature . . .

Examine ET0 . . .

Calculate ET0 . . .

Examine Rainfall . . .

Retrieve Crop Parameters . . .

View Progress of Inputs . . .

Define and View Crop Areas Selected . . .

Define Irrigation Method . . .

Input Data Completed . . .

Calculate Irrigation Demand . . .

Calculate Irrigation Schedule . . .

View Results . . .

Will climate significantly affect domestic agriculture? – model integration; GIS integration

Will climate significantly affect domestic agriculture? – model integration; GIS integration

Integration of Agriculture and Other Sectors

Discussion on how to integrate the V&A methods and tools into comprehensive assessments relevant to policy

Examples Agriculture – land use, water use (Egypt) Agriculture – socioeconomic issues (Mediterranean) Agriculture – water (Global)

Integrated Assessment in Egypt

Source: Strzepek et al., 1999

Aim Analysis of no regret options for the future

Current vulnerability• Dependence on the Nile as the primary water source• Large traditional agricultural base• Long coastline already undergoing both intensifying

development and erosion• Problems derived from population increase• Agriculture entirely based on irrigation (water from

the Nile, and to lesser degree from groundwater)• Soil conditions and water quality deteriorating

Integrated Assessment in Egypt

Methods Scenario development Vulnerability evaluation using

agronomic, economic, and water allocation models

Results: Future vulnerability Significant decreases in crop yield and

agronomic water use efficiency with climate change

Overall crop production further deteriorated as result of a reduction in agricultural land due to sea-level intrusion, and population increase

Adaptation: Limits of Current Technology

2002 Egypt Morocco Spain Tunisia

Area (1000ha) 100,145 44,655 50,599 16,361Population (1000) 70,507 30,072 40,977 9,728Population 2030 (1000) 109,111 42,505 39,951 12,351Population in agriculture (% of total) 35 35 7 24Population in rural areas (% of total) 57 43 22 33Population in rural areas 2030 (% of total) 46 29 15 22

Agricultural Area (% of total) 3 69 58 55Irrigation area (% of agricultural) 100 4 12 4Wheat Yield (kg/ha) (World = 2,678) 6,150 1,716 2,836 3,853

Agricultural Imports (million $) 3,688 1,740 12,953 1,022Agricultural Exports (million$) 774 811 16,452 391Fertiliser Consumption (kg/ha) 392 12 74 12

Crop Drought Insurance No No Yes NoAgricultural Subsidies Low Low High LowAgriculture, value added (% of GDP) 17 14 4 12GDP Per capita (US$) UN derived from purchasing power parity (PPP) 4,000 3,900 21,200 6,800

Data: FAOSTAT

Pioneer, April 00 - 128

Adaptation

On-farm adaptation: Use of alternative existing varieties and optimization of the timing of planting may improve yield levels or water use (no cost). In Egypt this is a very limited option.

Essential changes in resource management (crops, water and land) would lead not only to adaptation to climate change but also to the overall improvement of the agricultural systems (no regret options).

Explicit guidance to farmers regarding optimal crop selection, irrigation, and fertilization. Should institute strong incentives to avoid excessive water use.

Socioeconomic Issues

Policy, stakeholders Technology

Understanding the Stakeholder Linkages and Decision Process

Small Farmers (80%)

Regional Policy Makers

V&A Assessment

Central Policy Maker

(Ministry of agriculture)

Technical Policy Makers

National commissions

Extension service

Dec

isio

ns

Project

Extension Service

Policy Decisions

Adaptation is, in part, a political process, and information on options reflects different views about the long-term future of resources, economies, and societies. (Downing, 2001)

Current and projected water demand (%)

1996 2030 Drinking 11.5 17.7Irrigation 83.7 73.5Tourism 0.7 1.5Industrial 4.1 7.3

Tunisia: National Strategy on Water Management

Resources management Mobilization, storage (over 1,000 hill

reservoirs in 10 years), and transfer of the resources Use of the nonconventional resources: saline and wastewater for irrigation

(95,400 and 7,600 ha) Desalinization

Demand management Water saving in irrigation (up to 60% government subsidies), industry, and

other uses

Crop Liberalization

Example: the recent Egyptian policy of crop liberalization is giving farmers the possibility of adapting to more suitable crops in each area; as result of this policy, the area sown with cotton has sharply decreased in recent years while the cereal area has increased.

Drought Management in the Mediterranean

Disaster management could be an effective adaptation option

Decreasing drought vulnerability is a “win-win” adaptation option

Water for Agriculture

WATER is a fundamental requirement for agriculture. That requirement is certain to increase along with the growth of population and living standards, especially in view of the prospect of a warmer climate imposed by the enhanced greenhouse effect.

0

200

400

600

800

1000

1200

1400

1600

Brazil China US

1995

Low

High

Population (millions)2020

WATBALStreamflow

PET

WATBALStreamflow

PET

CLIMATEPrecip., Temp.

Solar Rad.

CLIMATEPrecip., Temp.

Solar Rad.

WEAP

EvaluationPlanning

WEAP

EvaluationPlanning

CERESCrop water

demand

CERESCrop water

demand

CROPWATRegionalirrigation

CROPWATRegionalirrigation

SCENARIOSGCMs

variability

SCENARIOSGCMs

variability

SCENARIOSPopulation, Development,

Technology

SCENARIOSPopulation, Development,

Technology

GISS Precipitation Change 2050

GISS Temperature Change 2050

Methods

Methods

Crop yields, water demands, and nitrogen leaching are estimated with process based crop models (calibrated and validated). The ratios (Kc) between simulated and actual crop ET are used to estimate regional water demand with CROPWAT, and are then adjusted by a regional irrigation efficiency.

Working with Different Models: Consistency, Scales, Calibration

Projections Using the Suite of Models

Changes in runoff, water demands, and water system reliability

Actual changes in crop yield based on consistent projections of changes in water supply and demand

Changes in environmental stress due to human use of water resources

Changes in water quality

Vulnerability and Adaptation Assessments Hands-On Training Workshop Impact, Vulnerability and...

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