Next Generation Landscape Evapotranspiration Tools

Next Generation Landscape Evapotranspiration Tools: Is It Feasible? Ramesh Dhungel1, Robert Aiken2, Xiaomao Lin1, Paul Colaizzi3, David K. Brauer3, Steven R. Evett3, Gary W. Marek3, Louis Baumhardt3, Dan O’Brien2 1Kansas

State University, Department of Agronomy, Kansas, USA State University, Northwest Research—Extension Center, Colby, Kansas, USA 3Conservation and Production Research Laboratory, USDA—ARS, Bushland, Texas, USA 2Kansas

2018 ASA Meeting | Nov. 6 | Baltimore, MD

What we envision as “Next Generation Landscape Evapotranspiration Tools” • Address “uncertainty in evapotranspiration modeling” at satellite overpass, between the overpass (where no thermal images are available) and large scale distributed models (Google Scholar Metrics, about 114,100 search results on 10/11/2018). • Solve complete energy balance (Penman-Monteith reverts to complete energy balance with appropriate parameterization (Dhungel et al., 2014)) including convective exchange (flux gradients for LE and H, with stability correction). • Incorporate canopy and soil resistances, integrating responses to environmental conditions. • Accommodate irrigation and precipitation through soil water balance.

• Quantify evaporation and transpiration components of ET. • Utilize open source high-level programming language and library for model development, advanced computation, and automation. • Utilize readily available input data sources, providing access to stakeholders including farmers and their advisors. 2018 ASA Meeting | Nov. 6 | Baltimore, MD

What is BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution)? • Developed by Dr. Ramesh Dhungel and water resources group at University of Idaho (Dhungel et al., 2016) to calculate evapotranspiration at point to regional scales (new model). • Establishes radiative, convective, and conductive components of the surface energy balance for soil and canopy surfaces. • Utilizes continuous weather (solar irradiance, Ta, RH, uz, P) and vegetation indices (LAI and fc) in hourly and 30m grid. • Adopts canopy resistances formulation from Jarvis function. • Iteratively solves surface energy balance components with Monin-Obukhov stability (flux-gradient equations of latent and sensible heat) to calculate surface temperature at soil surface and canopy level each time step. 2018 ASA Meeting | Nov. 6 | Baltimore, MD

What is BAITSSS? Contd. • Implements basic (capacity-type) two-layered soil water balance model, i.e., soil surface (100mm) and root zone (400mm to 2000mm) to track soil evaporation and transpiration separately, respectively. • Mimics irrigation (tipping-bucket approach, applied to surface or sub-surface layer) using Management Allowed Depletion (MAD) and threshold moisture at root zone. • Uses Python Shell based programming (open-source library), and automated gridded weather data from NLDAS or weather stations and vegetation indices such as measured LAI or satellite or aircraft input acquisition (NDVI and LAI) (point scale hourly data ~ about an hour for annual run). • https://sites.google.com/site/dhun9265/BAITSSS 2018 ASA Meeting | Nov. 6 | Baltimore, MD

Can BAITSSS serve as “Next Generation Landscape ET tools”? BAITSSS neither interpolate ETrF or EF, nor calculate potential or reference ET. 1

Rather it is a continuous physical process based on flux gradient on each pixel.

2

c

3

c Integrated

Integrated

Figure 1 Model for a) latent heat (LE) and sensible heat flux (H) of BAITSSS surface energy balance components, b) landscape ET from BAITSSS utilizing hourly NLDAS and linearly interpolated NDVI and LAI from satellite acquisition, c) Soil water balance and irrigation sub-model (Dhungel et al., 2018 ASA Meeting | Nov. 6 | Baltimore, MD 2016).

BAITSSS evaluation (Point Scale - Texas) • Maize ET, quantified in field lysimeter, with associated soil and weather data observations, Conservation and Production Laboratory, USDA-ARS, Bushland, TX.

• BAITSSS was executed and evaluated with 15-minute ground-based weather data from lysimeter site between May 23 (DOY 143) and September 26 (DOY 270), 2016 for continuous 127 days, including periods with regional advection. • Blind test was conducted without prior evaluating lysimeter data. • LAI was measured on multiple dates by destructive sampling method and linearly interpolated based on the dates after planting. • Actual irrigation timing and amounts were input to the model (subsurface). • Evaluation period includes from emergence to maturity with a wide range of environmental, surface (bare, partial, and full cover), and plant physiological conditions (emergence to leaf senescence period). 2018 ASA Meeting | Nov. 6 | Baltimore, MD

BAITSSS vs. Lysimeter measured ET-Texas

Cum. ET within ~ +1% accuracy

Figure 2 Scatterplot of a) daily and b) hourly ET between revised BAITSSS and lysimeter of corn between DOY 143 and DOY 270, 2016 for Bushland TX. Linear regression (red lines) and one-to-one correspondence (black line) are also indicated.

Figure 3 Comparison (uncalibrated model) and revised of daily ET of corn based on BAITSSS, lysimeter, and revised between DOY 143 and DOY 270, 2016 for Bushland TX.


BAITSSS vs. Surface temperature Temperature in Kelvin

Temperature in oC

Figure 4 a) BAITSSS simulated hourly averaged temperature capturing drying and wetting events after precipitation while compared to IRT (bottom), surface energy balance components (top) at lysimeter site with maize between May 22 (DOY 143, emergence) and June 8 (DOY 160), 2016, Bushland, TX.

Figure 5 An illustration of corn surface temperature simulated from BAITSSS vs. Landsat based thermal surface temperature at ~ 11 am on 08/27/2013, Kansas.

Fluxes

FWT Input NLDAS Input

Tcom

Rn

Fluxes, resistances, and environmental variables (Texas)

rsc

Measured Values

DOY FIGURE 6 a) Hourly-averaged surface energy flux components, net radiation, temperatures, and canopy resistances of corn between 28 June (DOY 180) and 27 August (DOY 240) 2016 for Bushland TX.

Figure 7 Scatterplot of hourly a) surface temperature, b) net radiation from BAITSSS from FWT input, c) surface temperature, and d) net radiation from gridded input compared to IRT and net radiometer of corn DOY 143 and DOY 270, 2016 for Bushland TX. Diel intervals (0:00-9:00, 10:00-16:00, 17:00-24:00) are indicated by symbol color; linear regression (red lines) and one-to-one correspondence (black line) as also indicated.

BAITSSS applications • Understand confounding factors of ET modeling such as local environment, advection, and heterogeneity. • Improve estimation accuracy for multi-year crop systems ranging from dryland to full irrigation. • Develop physically based canopy productivity model (aboveground biomass and yield estimation). • Irrigation management decisions. • Others (insurance guidelines, educational programs). • Overall, effective agricultural water management, for instance, Kansas’ GMD4 water management rules for Sheridan 6 LEMA 2018 ASA Meeting | Nov. 6 | Baltimore, MD

Point to Landscape scale - Do corn consume water equally? (BAITSSS on GMD4- Kansas) NLDASweather data

Simulated Irrigation

Difference in vegetation indices

Landsatvegetation indices

Evapotranspiration (inches)

Biomass (megagrams per hectare)

Grain fraction (bushels per acre)

from transpiration efficiency, normalized by vpd B = kT/vpd

from empirical function of biomass G = .75 *B - 2500

Figure 8 Cumulative ET (irrigated corn), biomass and grain fraction simulated from BAITSSS between 05/10/2013 and 09/15/2013 of corn for Township 9S, Range 41W, Kansas.

Partitioning of ET, soil moisture, irrigation from BAITSSS (GMD4- Kansas)

Figure 9 Simulated daily evapotranspiration (evaporation and transpiration), precipitation, and simulated irrigation between 05/10/2013 and 09/15/2013 of corn for the pixel represented in red (Figure 8), in Township 9S, Range 41W, Kansas, USA.

Vegetation indices

Figure 10 Simulated hourly soil moisture at surface and root zone with surface-applied irrigation between 05/10/2013 and 09/15/2013 of corn for the pixel represented in red (Figure 8), in Township 9S, Range 41W, Kansas, USA.

Quantifying biases in ET modeling using BAITSSS: Weather and vegetation indices variation (Texas) NLDAS Gridded Input

One scale to another- representation problem

Low LAI ~ - 5%

Modeled ET

All Measured ~ +4%

Field Weather Tower Figure 11 Hourly scatterplot from field weather tower (FWT) and gridded NLDAS of corn between 22 May (DOY 143) and 26 September (DOY 270) 2016 for Bushland TX. Diel intervals (0:00-9:00, 10:00-16:00, 17:00-24:00) are indicated by symbol color; one-to-one correspondence (black line) as indicated.

Lysimeter ET

All Gridded ~ +25%

Figure 12 Daily scatterplot of ET from twelve combinations of gridded data compared to lysimeter measured ET of corn between DOY 143 and DOY 270, 2016 for Bushland TX. DOYs (143-180, 181-240, 241270) are indicated by symbol color; linear regression (red lines) and one-to-one correspondence (black line) are indicated.

Quantifying biases Contd.: Daily ET using BAITSSS (Texas) SWS grass reference at 250m from Lysimeter site

Standard Weather Station (SWS)

One microclimate to another- representation problem

~ +20% bias

Field Weather Tower (FWT)

Figure 14 Daily evapotranspiration between BAITSSS simulated from standard weather station (SWS) data, and lysimeter measured between DOY 143 and DOY 270, 2016 for Bushland TX; a) time sequence of daily values and b) scatterplot of daily values. Linear regression (red lines) and one-to-one correspondence (black line) are indicated.

Figure 13 Hourly scatterplot weather data from field weather tower (FWT) and standard weather station (SWS) of corn between DOY 143 and DOY 270, 2016 for Bushland TX. DOY (143-180, 181-250, 251-270) is indicated by symbol color; one-to-one correspondence 2018 ASA Meeting | Nov. 6 | Baltimore, MD (black line) as indicated.

Checklist of landscape ET modeling and accuracy measures Variables Initial soil moisture and soil types (FC, WP)

Types Surface and root zone etc.

Availability √

Irrigation type and MAD Vegetation indices Continuous weather variables

Drip/Sprinkler etc. LAI, NDVI, fc etc. Incoming solar radiation, wind speed, air temperature, relative humidity, and precipitation etc.

√ √ √

Data sources ET comparison and instrumental biases

Field/ Standard/ Gridded etc. Lysimeter/ Eddy covariance etc.

√ √

Models (advantages and weakness)

Single source, two sources, satellite based, distributed etc.

√


Continuous weather variables with accurately measured vegetation indices

Concluding remarks • Accuracy of initial soil moisture, irrigation type, and input variables were vital to simulating diel ET after wetting and drying events. • Bias in input are likely propagated in ET modeling and needs to be systematically quantified. • Iterative convergence and internal calibration of energy balance components can be challenging with low wind speed conditions. • Uncertainty in actual irrigation vs. simulated irrigation may influence simulated surface temperature. • Further investigation of minimum canopy resistance, soil surface resistance, evaporation model, influence of vapor pressure deficit, local and regional advection. 2018 ASA Meeting | Nov. 6 | Baltimore, MD

BAITSSS inputs and products

Acknowledgments This work was supported by the Ogallala Aquifer Program, a consortium of the USDA Agricultural Research Service, Kansas State University, Texas AgriLife Research, Texas AgriLife Extension Service, Texas Tech University, and West Texas A&M University. Lysimeter and evapotranspiration research at USDA, Bushland, Texas were supported by USDA-ARS National Program 211, Water Availability and Watershed Management.

Questions? Open Source - BAITSSS algorithm on Python NumPy, pandas, and GDAL

BAITSSS algorithm called on shell

Envisioning public-private partnership for water and food security