2012 20132011

±

0 10 205KM

Allocated Irrigation(mm/yr)

01-100101-500501-1000>1000

±

0 10 205KM

Irrigation Water Scarcity

Adequate Water RightsLight ScarcityMedium ScarcityHeavy Scarcity

A B

C

Integrated Modeling of Social and Biophysical ProcessesInfluencing Water Availability in Southwest Idaho: Update on

Irrigation and Climate Change Integration

Connections, Integration and Synergies

Treasure Valley Project, Alternative Futures

Treasure Valley Progress Update 1: Irrigation

Modeling Framework

• Southwest Idaho: semi-arid, hot-dry summer, relatively wet winter

• Most populous area in Idaho

• Rapidly growing population

• Shifting urban-agriculture interface

• Complex water management

• Irrigation dominated agricultural activities & human influenced hydrological processes

• Many diverse stakeholders

• What will the Treasure Valley land use look like?

• How will the water availability change?

• How will human decision making influence the trajectory of change?

Research Questions

Fig. 5. Water right loop showing how irrigated water is allocated based on available water from the stream, water demand at each integrated decision unit (IDU) and water rights information (e.g. Water

rights priority dates, water use code). Currently, groundwater from wells are considered to be unlimited.

stream reach discharge (SRD)

Add WD to out stream use (OSU)

Flow Model

HRUs

Water Right Loop

SRD=SRD-OSUIDU Loop

next WR

aggregate SRD to HRU

available reach discharge (ARD)

most senior WR

yes

no

groundwater

unlimited supply

next junior WR

noyes

IDU water demand (WD)

yes

no

next IDU

no

yes

all WR been considered?

shutoff criterion

met?

yes

this WR is shutoff for the rest of the loops

note for future shutoff decision

no

surface water?

>0?

ARD>WD?

all IDU been considered?

Fig .4. . Local water rights data (PODs and POUs) from Idaho Department of Water Resources (Irrigation water use only).

• Local water rights dataset are integrated into a semi-conceptual hydrologic model

• 4,838 Points of Diversions (PODs) and 3,859 Places of Use (POUs) are appropriated for irrigation use

• 78% of the PODs use groundwater as water source, and only 22% use surface water as water source

• Surface water is the main water source on the diverted water volume.

• Prior Appropriation Doctrine (first in time is first in right)

• Flow time step: available water and IDU water demand

• Calculation unit: Hydrologic response unit (HRU);

• Surface water: stream reach

• Groundwater: assumed to be unlimited

• Irrigation: allocated in water right loop

• Climate: Using historical weather observations and future downscaled GCM projections to drive Weather Generator

• Hydrology: Semi-conceptual HBV model

• Irrigation: Following local water rights constraints

• Population and Land Use: Dynamic regression models.

ScenarioGenerator

HydrologySnow-Melting

Soil-Response

Groundwater-Runoff

Water Rights

Policies

Climate

Landscape Change

and Water AvailabilityScenarios

PopulationGrowth Model

Urban Expansion and

Land Use

Zoning

Economics

Historical Trend

Future Pathways

Khila Dahal Amy Steimke Andrea LeonardDevelopers at

Oregon State U

Nancy Glenn&Josh

Johnston

Julie Heath & Kathryn Demps

Eric Lindquist & Jen Schneider

Stakeholders

Closely Engaged

Broadly Engaged

Progress Update 2: Climate ChangeData

extraction

Summarization

Latin Hypercube Sampling

Statistical analysis

Statistical weather generator

Downscaled General Circulation Models

Local climate data(2071-2100)

Monthly climate variables

Representative ranges of monthly variables

Ensembles of monthly variables

Ensembles of monthly variables

Envision Scenario

Generator

RCP 4.5 RCP 8.5 RCP 4.5 RCP 8.5

0 100 200 300 3650

50

100

Dai

ly D

ishc

harg

e (m

3 /s)

Days

0 100 200 300 3650

5

10x 10

8

Cum

ulat

ive

Dis

chag

e (m

3 )

Daily ObservationsDaily SimulationsCumulative ObservationsCumulative Simulations

0 100 200 300 3650

50

100D

aily

Dis

hcha

rge

(m3 /s

)

Days

0 100 200 300 3650

5

10x 10

8

Cum

ulat

ive

Dis

chag

e (m

3 )

Daily ObservationsDaily SimulationsCumulative ObservationsCumulative Simulations

0 100 200 300 3650

50

100

Dai

ly D

ishc

harg

e (m

3 /s)

Days

0 100 200 300 3650

5

10x 10

8

Cum

ulat

ive

Dis

chag

e (m

3 )

Daily ObservationsDaily SimulationsCumulative ObservationsCumulative Simulations

• Irrigation simulated within the constraints ofwater rights, water demand, and availablewater

• Model captures annual and monthly irrigationwater use patterns (Fig. 6. A and B)

• Model reveals spatial irrigation water usepatterns (Fig. 6. C)

• Model calibrated and partially validatedbased on historical discharge (Fig. 6. D)

D

Fig. 1. Location of the Treasure Valley area and the included major cities, major highways, streams and the New York Canal.

Fig. 2. Conceptual modeling framework for projections of future land use and water availability scenarios. Light green boxes represents the work that is mostly tackled. Green boxes represents the on-going work.

Fig. 3. The work is focusing on future scenarios of the Treasure Valley. A team of scientists, engineers and stakeholders are contributing to the integration of knowledge.

Fig. 6. Annual allocated and unsatisfied irrigation water (Panel A); Monthly allocated and unsatisfied irrigation water (Panel B); Spatial explicit map of water allocation, taking year 2013 as an example

(Panel C); Observed New York Canal Discharge and the simulation diversion rate (Panel D).

• Three scenarios: Historical trend, and 2 future Representative Concentration Pathways (RCP 4.5 and RCP 8.5)

• 11 downscaled General Circulation Models (GCMs) in each future scenario

• 12 climate variables are analyzed and summarized to generate representative monthly ranges of each variable

• Latin Hypercube method to randomly sample 10 sets of monthly climate statistics within representative ranges of the variables

• Statistical weather generator (WXGN) to generate 10 ensembles of daily climate for each climate set under each RCP

Fig. 7. Workflow of climate change scenarios

Fig. 8. Boxplot of monthly climate variables over 11 GCMs (taking maximum temperature (Tmax), standard deviation of maximum temperature (Sdmx), precipitation, standard deviation of

precipitation (Sdrf) as examples). Both higher temperature and higher precipitation rates in RCP 8.5. Precipitation has larger variance between GCMs than temperature. The large variance indicates that

an ensemble of climate realizations are necessary to capture the variations of climate change.

Han, B. et al. Spatially distributed simulation of intensively managed hydrologic systems: coupling biophysical and social systems to evaluate potential water scarcity (manuscript). Han, B. et al. Integrated Modeling of Social and Biophysical Processes Influencing Water Availability in Southwest Idaho: Preliminary Results. Agricultural Water Management (in review) .

• Run the model for the ensemble ofscenarios of climate change, andproject the water use and waterscarcity patterns by 2100

• Integrate the crop choice modelresults into Envision

• Integrate urban water use in themodel

Broader Impacts• Advances the science by building a

framework for water use projectionsthat is applicable to semi-aridregions with limited water source

• The modeling outcome can inform stakeholders with better decision-making.Outcomes

Fig. 9. An example showing fully integrated population growth, land use change, climate

change, evapotranspiration and irrigation rates in the Treasure Valley.

Future Work

Bangshuai Han1, Alejandro Flores1, Shawn Benner 1

, Amy Steimke1, Andrea Leonard1, Khila Dahal2, Josh Johnston1,and many more…1Geosciences, Boise State University 2Public Policy Research Center, Boise State UniversityContact: bangshuaihan@gmail.com