Policy Insights from Comparing Evapotranspiration Estimates in the Sacramento-San Joaquin Delta

Jesse JankowskiCWEMF Annual Meeting

University Engagement Session- April 4, 2018

Principal Investigators:Josué Medellín-Azuara, Kyaw Tha Paw U, Yufang

Jin, & Jay Lund

Report Lead Team:Jesse Jankowski, Andrew Bell, Eric Kent, Jenae’

Clay, Nicholas Santos, & Jessica Badillo

Modeling Groups:Morteza Orang, Lan Liang, Martha Anderson,

Daniel Howes, Forrest Melton, Nadya Alexander, Andy Wong, & many other collaborators

Project Overview 7 participating models from 5 different groups

Monthly average ET at 30x30-meter resolution in Delta for 2015-2016

Academic, State, & Federal modelers

University field campaign group 2 years, 2015 over bare soil & 2016 over alfalfa/corn/pasture

3 measurement methods, rigorous defense of results

Geospatial, plotting, & statistical analyses of results Common raster formats, processing in Google Earth Engine

Interim “blind” comparison first year updated results

Other data support from DWR Annual land use data to evaluate results by crop

Additional CIMIS stations to improve spatial reference ET datasets

Eventual public data release to encourage further analysis2

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ET rates for individual crops

Sample of Project Results

Spatial ET distribution

ET variation between models

Model-model & model-field comparisons

Total ET volume for individual crops

Policy Insights

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~1.4 MAFY of crop ET in Delta ±11%, 40% of crop land use & 50% of crop ET is alfalfa/corn/pasture

Uncertainties mean real water for users & agencies, ensemble of ET estimates is intensive but thorough

Enhanced land use programs: irrigation methods & multi-cropping

Modeling of ET may effectively reduce reporting burdens & help quantify water transfers

Field data needed for bare soil & native vegetation, fallowing or wetland restoration may impact consumptive use

Consumptive use estimates are key for SGMA water balances

University as efficient data analyzer & model convener

Collaborative statewide ET consortium: reduce costs & improve transparency & accuracy of ET estimates

Thank You!Jesse Jankowski: jjankowski@ucdavis.edu

Project Website: tinyurl.com/DeltaET

Final Report & public datasets expected late April 2018

Financial and Research Support from:State Water Resources Control Board, California Department of Water Resources, Delta Protection Commission, Delta Stewardship Council, North Delta Water Agency, Central Delta Water Agency, South Delta Water Agency, UC Water

Extra Slides

Goal: Estimate Crop Consumptive Use in the SSJ Delta Improve information for

Water rights administration

Water balance in hydrologic and other models

Reporting of water use in irrigation

Water management and operations

Habitat conservation efforts

Employed in the Study

Field measurements

7 CU estimation methods

Land Use Survey

Unmanned Aerial Vehicles

Byproducts

Land Use Surveys 2015 and 2016

Field equipment

Bare soil consumptive use

Increased CIMIS stations in the Delta7

Source: UC Riverside

Presentation Outline

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Delta Water Management & Evapotranspiration

Project Overview

Land Use, Models, Field Campaign

Overall Results

Detailed Model & Field Comparisons

Conclusions & Policy RecommendationsSource: Water Education Foundation

Sacramento-San Joaquin Delta & Water Management

California’s water hub

Project exports

Local municipal water

Island agriculture

Wildlife habitat

San Francisco Bay-Delta Estuary

Source: UC Riverside

Complex management framework

Surface & groundwater balances

Water rights administration

Water quality maintenance

Vegetation restoration

A modeler’s paradise (or purgatory?)

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Evapotranspiration (ET) Important but uncertain element in water balances

Evaporation from soil + transpiration from plants

Affected by land use/crops, soil, irrigation, environment

1. Surface energy balance residual:

ET = (Net Radiation – Sensible Heat – Ground Heat) / Latent Heat of Vaporization

Parameters measured in field or by satellites

2. Crop coefficients:

Potential ET = Reference ET * Crop Coefficient * Adjustment

Simple approach for irrigation scheduling, published values for ag regions

Source: United Nations Food & Agricultural Org.10

Project Overview 7 participating models & field campaign

2-year study: 2015-2016 water years

Monthly average daily ET (mm/d) on 30x30 m pixels over Delta

DWR land use data for results analysis

Defined source datasets & methods

Common: Spatial CIMIS reference ET, land use data

Different: Satellite overpass dates, interpolation, cloud masking

2015 Interim Report: “off-the-shelf” estimates, blind comparison

Final results reflect refinement, group learning, access to field data

Open platform usage of GitHub & Google Earth Engine

Final Report & data release expected March 2018

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Delta Land Use Delta Service Area (DSA) = 679,725 acres

“Wall-to-wall” at 30x30-meter resolution

36 land use classes, 26 agricultural selected for study

DSA was ~70% agricultural in 2016 Fallow- 81,00 ac. (12%)

Corn- 71,000 ac. (11%)

Alfalfa- 65,000 ac. (10%)

Pasture- 42,000 ac. (6%)

Vineyards- 37,000 ac. (5%)

Increased fallowing 2015-2016- Drought, urbanization, prep for permanent crops

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Evapotranspiration Models1. DWR- California Simulation of Evapotranspiration of Applied Water

(CalSIMETAW) Crop coefficients, DAU-County resolution, tabular, potential ET

2. DWR- Delta Evapotranspiration of Applied Water (DETAW) Crop Coefficients, Delta island resolution, tabular

3. USDA-ARS- Disaggregated Atmosphere-Land Exchange Inverse (DisALEXI) Global-scale w/ satellite data

4. Cal Poly Irrigation Training & Research Center (ITRC)- Mapping Evapotranspiration at High Resolution with Internalized Calibration (ITRC-METRIC) Modified with alfalfa reference & customizations

5. NASA-Ames- Satellite Irrigation Management Support (SIMS) Satellite data w/ crop coefficients, basal crop ET

6. UC Davis- METRIC (UCD-METRIC) Standard approach with Google Earth Engine

7. UC Davis- Optimized Priestley-Taylor approach (UCD-PT) Eco-physical constraints, calibrated to field data

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Field Campaign Surface renewal & eddy covariance measurements of energy balance

2015: 4 stations on 3 fallow fields

2016: 14 stations in corn, alfalfa, & pasture

Half-hourly data & error analysis

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Results: Agricultural Delta ET (2016)

*Mean of other models for Upland Herbaceous & Sub-Irrigated Pasture

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16 Field results generally lower than model estimates

Results: ET for Major Crops

Results: Spatial ET & Variation (2016)

Yolo

North

West

Central

South

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Detailed Model & Field Comparison Daily model data on overpass dates, some with field data

Results averaged across 3x3-pixel grid (~ 2 ac.) around 14 field stations

Similar methods paired for comparison

CalSIMETAW & DETAW, ITRC & UCD-METRICs, DisALEXI & SIMS & UCD-PT

Common overpass dates between pairs

Comparison plots & statistics:

1:1 scatter for 3 major crops

Linear regression & R2

Timeseries by station

Mean Bias, RMSE, t-tests

Individual meetings with models

Reasons for differences

Prospects for convergence

Field data questions

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ET (mm/d)

Detailed Model & Field Comparison

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(Alfalfa)

All 3 Crops:Mean Bias = +0.07 mm/d ITRCRMSE = 0.89 mm/d

Conclusions (2016) Model mean ET = ~1.4 million AF/year, ±11% for all models

Alfalfa + corn + pasture = ~40% of ag land & ~50% of ET, Fallow = ~17% of ag land & ~15% of ET

More model variation in non-growing season when ET is lowest, some crops had larger differences (almonds, corn, potatoes, rice)

Common input data, similar methods, & cooperation improve model agreement, but some may never agree completely

Field ET measurements/estimates generally lower than models, caused much discussion, “Delta Breeze” & microclimates may affect ET uniquely, more bare soil data needed (2018 study underway)

ET from non-agricultural lands higher than crops, but more field data & model tuning is needed

Quantified uncertainties inherent to remote sensing & field measurements, considerable data for analysis & future studies

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Policy Recommendations

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Uncertainties are real water (~$1,000/AF), growers may sacrifice certainty & some water for security & lower overall costs

Ensemble of ET estimate models intensive but thorough

Land use surveys will benefit ET estimates, include multi-cropping & irrigation method to improve year-round data

Models may sub for diversion reporting & quantify transfers

More field data needed for bare soil & native vegetation, fallowing or wetland restoration may impact consumptive use

Consumptive use estimates will be crucial in SGMA water budgets

Collaborative California state ET consortium needed