mehta nifa talk-final

Predictability of Impacts of Decadal Climate Variability on Water and Crop Yields in the Missouri River Basin, and Its Use in Agricultural Adaptation

Vikram M. MehtaThe Center for Research on the Changing Earth System, Catonsville,

Maryland

Vikram Mehta NIFA PDs’ Meeting, San Francisco, CA

17 December 2016

Supported by the U.S. Department of Agriculture – National Institute of Food and Agriculture Grant 2011-67003-30213 under the NSF-USDA-DOE Earth System Modeling Program.

⦿ Objectives of this project

⦿ Importance of the Missouri River Basin (MRB)

⦿ Decadal climate variability (DCV) impacts on MRB water and crops

⦿ Data and models

⦿ Major accomplishments and other outcomes

⦿ The future?

The Team

Vikram Mehta, Norman Rosenberg, Katherin Mendoza, and Hui WangThe Center for Research on the Changing Earth System, Catonsville, Maryland

Cody Knutson, Nicole Wall, Tonya Haigh, and Tonya BernadtNational Drought Mitigation Center, Univ. of Nebraska – Lincoln, Nebraska

Bruce McCarl, Mario Fernandez, Pei Huang, Jinxiu Ding, Theepakorn Jithitikulchai

Raghavan Srinivasan, Prasad Daggupati, and Deb DebjaniTexas A & M University, College Station, Texas

Amita Mehta and Saikumar PopuriNASA-UMBC Joint Center for Earth System Technology, Catonsville,

Maryland


17 December 2016

Using the Missouri River Basin as a case study:

☞ To develop an ‘end-to-end’, decadal climate and impacts prediction system

☞ To develop an adaptive water and agriculture management system


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Overarching Objectives

Importance of the Missouri River BasinMissouri River Basin

Largest river basin in the US

Covers 500,000sq. miles,10 States, many Native American reservations, parts of Alberta and Saskatchewan

Value of crops and livestock over$100 billion per year

117 million acres cropland, only 12 million acres irrigated

Produces 46% of wheat, 22% of grain corn, 34% of cattle in the United States

Dependence on the Missouri River for drinking water, irrigation and industrial needs, hydro-electricity, recreation, navigation, and fish and wildlife habitat

Over 2000 urban centers of various sizes


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Urban Areas in the Missouri River Basin


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Over 2000 urban areas

Increasing competition for water among various sectors

Droughts and Water in the Main Stem Reservoirs

Water in the Main Stem Reservoirs


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Severe to Extreme Drought Area

(Lower figure, courtesy Kevin Grody, USACE)

Pacific Decadal Oscillation and wheat production in the MRB


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Average production6.6 million tons

Average production13.8 million tons

Correlation coefficients between the PDO and wheat production time series0.5 without smoothing and 0.65 after smoothing all time series.

Decadal Climate and Impacts Information for Decision Support in the Missouri River Basin

DCVphenomena

Influences on Basin hydro-

meteorology

Influenceson

AgricultureUrban water

IndustriesNavigationRecreation

Others

Rural andurban

economies; Local,

regional, national,

international economies

Adaptation strategies

viaunderstanding, prediction, and

scenario development

Data, information,

and decision- support systems

Active involvement of stakeholders

and policymakers

Applications In various

sectors

A system adaptableto other river basins also


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Data and Models

◉ Observed streamflow data from U.S. Geological Survey: Many gauge locations, 1961 - 2010

◉ Crop yield estimates from the USDA – National Agricultural Statistical Service (NASS): County totals, 1961 - 2010

◉Observations-based precipitation, daily max. and min. temperatures, surface winds, surface air humidity: 12 km x 12 km, 1961 - 2010

◉Decadal sea-surface temperature and hydro-meteorological predictions by 4 Earth System Models (NCAR-CCSM4, GFDL CM2.1, UKMO-HadCM3, and MIROC5): Monthly, various spatial resolutions, 1961 to 2010

◉Calibrated and validated 12 km x 12 km version of the Soil and Water Assessment Tool (SWAT) for the entire MRB

◉ Water and crop choices model RIVERSIM, optimized for the MRBVikram Mehta NIFA PDs’ Meeting, San Francisco, CA

17 December 2016

Hybrid Dynamical-Statistical Prediction System for Decadal Climate and

Hydro-meteorology

1961 – 2010ocean-

atmosphere- land hindcasts

Earth System Model (ESM)

1961-2010=me history

of greenhouse

gases, volcanic and

other aerosol op=cal

depths, solar radia=on; Projected values

aFer 2010 Ensemble ini=aliza=on system; 10-year experiments

ini=alized in 1960, 1970, …, 2000

2011 – 2036ocean-

atmosphere- land forecasts

Dynamical System for SST Prediction(CMIP5)

Models used in this study


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GFDL – CM2.1 UKMO – HadCM3 MIROC5NCAR – CCSM4

SWAT Setup and Calibration - Validation

�Characterization of ~ 14,000 watersheds�Sub-watersheds and streams�Land use – land cover at30 m resolution, crop rotation and irrigation�Irrigated land and soil data�Precipitation, temperature, winds, solar radiation data at 12 km x 12 km�Crop yield calibration; Winter and spring wheat, corn (dryland and irrigated), soybean (dryland and irrigated)�Water yield (total surface and base flow) calibration�Water abstractions and other man- made changes not captured

Calibration and validation in each of these 11 land use classes


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Types of SWAT Experiments

SWATObserved Hydro- meteorological data; 1961 - 2010

Idealized Hydro- meteorological data from DCV scenarios

Water yield, stream flow, crop yields

Comparison with observed data; 1961 - 2010

SWAT Water yield, stream flow, crop yields

Inter-comparison of impacts of scenarios

SWATHindcast Hydro- meteorologicaldata; 1961 - 2010

Water yield, stream flow, crop yields

Comparison with observed data; 1961 - 2010


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Observed and SWAT-simulated streamflow anomalies (cu. m/s) in wet (1982-86) and dry (1987-90) Epochs

Dry: 1987 - 90


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USGS

SWAT

Wet: 1982 - 86

USGS

SWAT

Decreased flows in western Montana and northern Kansas, and increased flows elsewhere

Increased flows in western Montana and northern Kansas, and decreased flows elsewhere

Observed and SWAT-simulated winter wheat yield anomalies (t/ha) in wet (1982-86) and dry (1987-90) Epochs

Wet: 1982 - 86 Dry: 1987 - 90

Decreased yields in western Montana and southeast MRB, and increased yields elsewhere

Increased yields in western Montana and southeast MRB, and decreased yields elsewhere

NASS


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SWAT

NASS

SWAT

Selected sub-basins for development of predictability and adaptation methodologies

Platte

Lower Grand

James

Marias


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Decadal prediction of the Pacific Decadal Oscillation index by the MIROC5 Earth System Model: 1981-2020

Vertical dashed lines – forecast start times

Shading:±1 std. dev. of ensemble

Impacts prediction periods indicatedby

PDO+ 1982-842003-06

PDO- 1988-902007-13


17 December 2016

ehta NIFA PDs’ Meeting, San Francisco, CA

Winter wheat prediction in the Platte River sub-basin

Winter wheat yield anomaly prediction with 50-year NASS data statistics

PDO+

Winter wheat yield anomaly prediction with SWAT

Actual 1982-84

Actual yield anomalies

PDO-

Actual 1988-90

Predicted PDO+ 1982-84 Predicted PDO- 1988-90

Vikram M 17 December 2016

ehta NIFA PDs’ Meeting, San Francisco, CA

Barley prediction in the Marias sub-basin

Barley yield anomaly prediction with 50-year NASS data statistics

Barley yield anomaly prediction with SWAT

Actual 1982-84



Actual 1988-90

PDO+ PDO-

Vikram M 17 December 2016

Corn prediction in the James River sub-basin

Corn yield anomaly prediction with 50-year NASS data statistics



Actual 2003-06


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Actual 2007-13

Why is there value?�Decision makers would likely make adjustments for revised expectations of crop yields and water supply.v If farmers knew water demands would be higher and supplies smaller, they might plant a smaller area.

v If they knew some crop yields would be lower and others higher, they might shift crop mix.

v If water planners knew water would be short, they might encourage conservation, reduce use, and negotiate options to buy out some agricultural water rights.

�These actions compared to actions based on the historical climatic record would generate value.


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Value of decadal climate information to MRB agriculture

Difference

82 29 53

• Modeling Scope– 16 crops, 411 counties in MRB; irrigated and

dryland cropping; municipal and industrial water use– DCV impacts on 5 crops: wheat (spring and winter),

corn, sorghum, soybeans; hydrological flow balance; reservoir storage

• Uncertainty about which combination of phases of major DCV phenomena will occur in the next one year

• Includes crop insurance

Value of DCV Information (in million $)


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Perfect information on next year’s DCV phase relative to

historical frequency

Reduced uncertainty on next year’s DCV phase (based on this

year’s phase) relative to historical frequency

Water and Agriculture Choices RIVERSIM model

NIFA PDs’ Meeting, San Francisco, CA

Decadal climate variability based crop adaptation options for farmersExample: Corn – soybean – wheat – hay planting choices in

the Platte River sub-basin, given DCV phase combination prediction

PDO+, TAG- PDO-, TAG+

Vikram Mehta 17 December 2016

Corn Corn

Hay Hay

Soybeans Soybeans

Winter wheat

Winter wheat

Summary of accomplishments

• Simulations of DCV phenomena and their decadal predictability assessed in four Earth System Models (ESMs)

• Statistical hydro-meteorological prediction system, using predicted DCV indices as predictors, developed

• Statistical downscaling scheme developed for ESM data

• Very high resolution land use-hydrology-crop model Soil and Water Assessment Tool (SWAT) calibrated and validated for the MRB; water and crop yield simulation and prediction experiments conducted


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Summary of accomplishments

•Skillful, multiyear to decadal prediction of indices of the PDO and other DCV phenomena possible in some cases, especially since the 1980s.

•These predicted DCV indices are shown to be useful for multiyear to decadal prediction of water yields, streamflow, and crop yields in the Missouri River Basin.

•The predicted crop yields are shown to be useful for, among other applications, adaptation of choice of crops to plant.

•Value of DCV information to MRB agricultural economy estimated at $30 – 80 million per year.


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Research Capacity-building

Project people: 6 senior scientists, 2 research scientists, 1 post-doctoral scientist,1 research associate, 5 Ph.D. students, 16 undergrad. students, 3 outreach specialists, 1 web programmer, 1 administrative officer, 1 information technologist

Minorities and Under-represented groups: 6 women, 1

Hispanic Institution network: 1 non-profit

organization and 3 Universities Project website:

Missouri.crces.org

Stakeholder Advisory Team (SAT): Representatives from water, agriculture, and natural resources management sectors in the MRB; academics; state and federal officials


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Other outcomes

Completed Ph.D.s: 4 (Texas A & M University)

Ph.D. in progress: 1 (University of Maryland – Baltimore County (UMBC))

Undergraduate student interns: 16 in UMBC under the NSF Research Experience for Undergraduates (REU) Program

Papers published: 7; Papers in preparation: 4

Conference/Workshop and other talks and posters: 10


17 December 2016

The Future?

●Decadal prediction of water and crop yields at the county level and development of fine-scale adaptation options in the MRB.

●County-level assessment of value of DCV information to agricultural economy in the MRB.

● Simulation and prediction of coupled food-energy-water securities in the MRB and the Mississippi River Basin.

● Adaptation of models and methodologies to the Mississippi and Ohio River Basins to develop prediction and adaptation systems for water, crops, and water-borne transportation of agricultural and other materials/products.


17 December 2016

Thank you!


17 December 2016

mehta nifa talk-final

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