2012 20132011
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0 10 205KM
Allocated Irrigation(mm/yr)
01-100101-500501-1000>1000
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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: [email protected]