developing data consistency with models and local

Developing Data Consistency with Models and Local Knowledge in the Tulare Lake Hydrologic Region for Estimation of Agricultural Water Demand

CWEMF 2017 Annual MeetingMarch 21, 2017

PRESENTERFrank Qian, RMC a Woodard & Curran CompanyCOLLABORATORSSteve Ewert, Cynthia Moffett, Morteza Orang (DWR)Mesut Cayar, Saquib Najmus (RMC)

Tulare Lake HR

10.9 million acres

Accounts for 38% of total GW use in the state (2005-2010 average)

6 critically overdrafted basins

Water budget related data available through CWP and C2VSim

Agricultural Water Demand

Quantity of water needed to grow crops

Part of this need is met by precipitation

Remainder of this needs is met by Applied water (AW) which accounts for irrigation efficiencies

Three models are compared

CalAg Model

CalSIMETAW Model

C2VSim Model

California Agricultural Water Use Model (CalAg)

Developed by the DWR’s Northern Region to estimate monthly ETc and ETAW

Uses monthly pan evaporation and pan coefficient data

Simulates and aggregates volumes of on-farm crop consumptive use and applied water

Simulates historical monthly on-farm consumptive and applied water use through input of crop, soil and water supply characteristics; climatic conditions; and crop management practices.

Input data are developed for average on-farm conditions by crop type for a given region, typically a DAU/county subarea.

California Simulation of Evapotranspiration of Applied Water (CalSIMETAW)

Developed by DWR Water Use Efficiency Branch

Estimates daily soil water balance to determine ETc and ETaw for use in the California Water Plan Update.

Daily weather data, derived from monthly PRISM climate data and daily U.S. National Climate Data Center climate station data to cover California with 4×4 km grid spacing.

Uses SSURGO soil characteristic data and crop information with precipitation and ETc data to generate hypothetical water balance irrigation schedules to determine ETaw

California Central Valley Simulation Model (C2VSim)

Developed by DWR Bay Delta Office

Central Valley IWFM integrated numerical

October 1921 through September 2009.

Dynamically calculates crop water demands, allocates contributions from precipitation, soil moisture and surface water diversions, and calculates the groundwater pumpage required to meet the remaining demand.

Simulates the historical response of the Central Valley’s groundwater and surface water flow system to historical stresses

Supply and Demand Estimate Comparison

-

2,000.0

4,000.0

6,000.0

8,000.0

10,000.0

12,000.0

14,000.0

16,000.0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

TAF

Water Year

Urban Demand - Subregion TL

CWP-CalAgCALSIMETAWC2VSim

-

2,000.0

4,000.0

6,000.0

8,000.0

10,000.0

12,000.0

14,000.0

16,000.0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

TAF

Water Year

Ag Demand - Subregion TL


-

2,000.0

4,000.0

6,000.0

8,000.0

10,000.0

12,000.0

14,000.0

16,000.0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

TAF

Water Year

Surface Water Deliveries - Subregion TL


-

2,000.0

4,000.0

6,000.0

8,000.0

10,000.0

12,000.0

14,000.0

16,000.0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

TAF

Water Year

Groundwater Pumping - Subregion TL


Data Comparison: C2VSim vs CalSIMETAW

Crop Acreage: There is substantial difference in the input data for crop acreage and crop types between the models.

ETc (Potential Crop Evapotranspiration): There is substantial difference in the ETc values used in the two models.

Irrigation period: Irrigation periods for similar crops are not the same in the models.

Irrigation efficiency: There is substantial difference in the irrigation efficiency numbers used in the models.

Precipitation and effective precipitation: There are differences in how effective precipitation is computed in the models.

Curve number: C2VSim uses the curve number method to compute the runoff and effective precipitation that reflect an equivalent representation in CalSIMETAW, and this can be adjusted for consistency between the models.

Reuse fractions: Reuse fractions are not simulated in CalSIMETAW.

Crop Acreage Comparison (C2VSim vs CalSIMETAW)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

PASTURE ALFALFA SUGAR BEET FIELD CROPS TRUCK CROPS TOMATO (HANDPICKED)

TOMATO(MACHINE

PICKED)

ORCHARD GRAINS VINEYARD COTTON CITRUS & OLIVES

Acr

es

C2VSim CALSIMETAW

Ag Supply Requirement after Crop Acreage Adjustment

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

TAF

C2VSim Historical CalSIMETAW C2VSim Crop Acreage

C2VSim with Adjusted Crop Acreage

C2VSim Historical Calibration

CalSIMETAW

Crop Evapotranspiration Coefficient (ETc)

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

TAF

C2VSim Historical CalSIMETAW C2VSim ETc

C2VSim with Adjusted Crop Acreage and ETc


CalSIMETAW

Ag Supply Requirement after Irrigation Period

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

TAF

C2VSim Historical CalSIMETAW C2VSim Irrigation Period

C2VSim with Adjusted Crop Acreage, ETc, Irrigation Efficiency and Period


CalSIMETAW

Irrigation Efficiency (C2VSim vs CalSIMETAW)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Pasture Alfalfa Sugar Beets Field Crops Truck Crops Tomato Tomato - HandPicked

Tomato -Machine Picked

Orchard Grains Vineyards Cotton Citrus andOlives

C2VSim CalSimetaw

Ag Supply Requirement after Irrigation Efficiency

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

TAF

C2VSim Historical CalSIMETAW C2VSim Irrigation Efficiency

C2VSim with Adjusted Crop Acreage, ETc, and Irrigation Efficiency


CalSIMETAW

Ag Supply Requirement after Curve Number

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

TAF

C2VSim Historical CalSIMETAW C2VSim No Reuse, Curve Number

C2VSim with Adjusted Crop Acreage, ETc, Irrigation Efficiency and Period, Curve Number, and Reuse


CalSIMETAW

Discrepancy with CalAg still Exists

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

TAF

C2VSim Historical CalSIMETAW C2VSim No Reuse, Curve Number CalAg

3 MAF vs CalAg

C2VSim with Adjusted Crop Acreage, ETc, Irrigation Efficiency and Period, Curve Number, and Reuse

CalAg


CalSIMETAW

Crop Acreage Data Update

Data from DWR annual crop acreage estimates

Crops categories 13 vs 20 Corn, Dry Beans, Safflower, Other Field grouped into Field Crops

Almond, Pistachio, other Deciduous grouped into Orchard

Cucurbits, Onions & Garlic, Potatoes, and Truck Crops grouped into Truck Crops

Mapping Methodology

Evapotranspiration, Crop Coefficients Data Update

CalAg crop coefficient is outdated

Updated Kc for alfalfa, subtropical tree crops and garlic & onions

Revised reference ET based on PRISM and CIMIS data

ETc – Original and Revised

0

1

2

3

4

5

6

ETc

(Fee

t)

Original Revised

Irrigation Efficiency and Period Data Update

Started with Consumed Fraction from CalAg model DWR County Land Use Surveys

“Irrigation Methods” data

Seasonal Application Efficiency System efficiency tables

Discussion lead to increase in CF

CalSIMETAW Irrigation Efficiency Comparison: Original and Revised

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Original Revised

Stepwise Data Consistency and Model Results

Water Year

Run:

C2VSIM_V0

Original

Historical

Calibration

13 crop types

Run 374 FG

IWMF v. 3.02

Run:

CALSIMETAW_V

0

i. 13 crop

ii. C2VSIM

Subregions

Run:

C2VSIM_V1

i. 13 cop

ii. Adj. Crop

acreage to match

CALSIMETAW

acreages

Run:

C2VSIM_V2

I. 13 crop

Ii. Adj. Crop

Acreage

iIi. Adj. ETc

Run:

C2VSIM_V3

i. 13 crop

ii. Adj. Crop

Acreage

iii. Adj. ETc

iv. Irrig. Period

Run:

C2VSIM_V4

i. 13 crop

ii. Adj. Crop

Acreage

iii. Adj. ETc

iv. Irrig. Period

v. Irrig. Eff.

Run:

C2VSIM_V5

i. 13 crop

ii. Adj. Crop

Acreage

iii. Adj. ETc

iv. Irrig. Period

v. Irrig. Eff.

vi. No Reuse

vi. Curve Number

Run: (20 crops)

CALSIMETAW_V1

i. C2VSIM

Subregions

Run: (20 crops)

C2VSIM_V6

i. Same Crop

Acreage as

CALSIMETAW

ii. Same Etc (Crop

Acres)

iii. Same Irrig.

Period

iv. Same Irrig. Eff

v. No Reuse

vi. Adj. Curve

Number (+4 from

Originial C2VSIM)

1999 9,091 13,092 8,853 10,592 11,401 11,116 13,155 11,279 10,833

2000 8,825 13,507 8,074 11,014 12,103 11,769 13,847 11,647 11,620

2001 8,333 13,153 8,296 10,625 11,648 11,339 13,464 11,204 11,211

2002 8,897 13,806 9,041 11,687 12,455 12,158 13,987 11,767 11,607

2003 8,355 13,470 8,421 10,995 11,833 11,548 13,638 11,281 11,200

2004 8,722 13,914 9,268 11,852 12,647 12,369 14,152 11,818 11,737

2005 7,285 12,509 7,389 9,706 10,320 10,123 12,899 10,543 10,352

2006 7,355 13,242 7,763 11,065 11,781 11,606 13,989 10,990 11,293

2007 8,679 14,075 9,464 12,191 12,877 12,675 14,182 11,593 11,533

2008 8,501 14,714 9,028 12,569 13,114 12,915 14,685 12,117 11,977

2009 8,509 14,392 9,100 12,363 12,852 12,665 14,424 11,819 11,659

Average 8,414 13,625 8,609 11,333 12,094 11,844 13,857 11,460 11,366

5.2 MAF difference 94 TAF difference

Evolution of Agricultural Demand Estimates in the Tulare Lake Hydrologic Region using CALSIMETAW and C2VSIM

20

cro

p r

un

s fo

r b

oth

mo

del

s

Final Ag Supply Requirement Estimate Comparison

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

TAF

C2VSim Historical Calibration CALSIMETAW Revised C2VSim Revised CALSIMETAW

Revised C2VSim and CalSIMETAW


CalSIMETAW

Final Ag Supply Requirement Estimate Comparison

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

TAF

C2VSim Historical Calibration CALSIMETAW Revised C2VSim Revised CALSIMETAW CalAg


CalSIMETAW

Revised C2VSim and CalSIMETAW

CalAg

Conclusion

All 3 models have sound analytical foundation

Revision of crop parameters resulted in much more realistic estimates of ag demand

Similar updates are planned for other HRs for CalSIMETAW for CWP Update 2018

developing data consistency with models and local

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