BAITSSS

{{Infobox software

| name = BAITSSS

| developer = Ramesh Dhungel and group

| programming language = Python (programming language), shell script, GDAL, numpy

| genre = Evapotranspiration modeling, irrigation simulation, surface temperature simulation, soil moisture simulation, Geographic information system

}}

BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution) is biophysical Evapotranspiration (ET) computer model that determines water use, primarily in agriculture landscape, using remote sensing-based information. It was developed and refined by Ramesh Dhungel and the water resources group at University of Idaho's Kimberly Research and Extension Center since 2010. It has been used in different areas in the United States including Southern Idaho, Northern California, northwest Kansas, Texas, and Arizona.

History of development

BAITSSS originated from the research of Ramesh Dhungel, a graduate student at the University of Idaho, who joined a project called "Producing and integrating time series of gridded evapotranspiration for irrigation management, hydrology and remote sensing applications" under professor Richard G. Allen.{{Cite web|url=https://reeis.usda.gov/web/crisprojectpages/0226143-producing-and-integrating-time-series-of-gridded-evapotranspiration-for-irrigation-management-hydrology-and-remote-sensing-applications.html|title=Univ of Idaho submitted to: Producing and Integrating Time Series of Gridded Evapotranspiration for Irrigation Management, Hydrology and Remote Sensing Applications| work=Research, Education & Economics Information System (REEIS) | publisher=United States Department of Agriculture | access-date=2019-11-02}}

In 2012, the initial version of landscape model was developed using the Python IDLE environment using NARR weather data (~ 32 kilometers). Dhungel submitted his PhD dissertation in 2014 where the model was called BATANS (backward averaged two source accelerated numerical solution).{{Cite web|url=https://digital.lib.uidaho.edu/digital/collection/etd/id/829/|title=Time Integration of Evapotranspiration Using a Two Source Surface Energy Balance Model Using Narr Reanalysis Weather Data and Satellite Based Metric Data|last=Dhungel|first=Ramesh|date=May 2014|via=University of Idaho digital library | url-status=live|archive-url=https://web.archive.org/web/20191019152037/https://digital.lib.uidaho.edu/digital/collection/etd/id/829/ |archive-date=2019-10-19 |access-date=2019-10-19}} The model was first published in Meteorological Applications journal in 2016 under the name BAITSSS

as a framework to interpolate ET between the satellite overpass when thermal based surface temperature is unavailable. The overall concept of backward averaging was introduced to expedite the convergence process of iteratively solved surface energy balance components which can be time-consuming and can frequently suffer non-convergence, especially in low wind speed.

In 2017, the landscape BAITSSS model was scripted in Python shell, together with GDAL and NumPy libraries using NLDAS weather data (~ 12.5 kilometers). The detailed independent model was evaluated against weighing lysimeter measured ET, infrared temperature (IRT) and net radiometer of drought-tolerant corn and sorghum at Conservation and Production Research Laboratory in Bushland, Texas by group of scientists from USDA-ARS and Kansas State University between 2017 and 2020. Some later development of BAITSSS includes physically based crop productivity components, i.e. biomass and crop yield computation.{{Cite web|url=https://www.ageconomics.k-state.edu/research/research-publications/2018%20Publications%20.pdf|title=Agricultural Economics, Staff, Programs, and Publications – Kansas State University|last=|first=|date=|website=|url-status=live|archive-url=https://web.archive.org/web/20211013190648/https://www.ageconomics.k-state.edu/research/research-publications/2018%20Publications%20.pdf |archive-date=2021-10-13 |access-date=}}

Rationale

File:BAITSSS components.png

The majority of remote sensing based instantaneous ET models use evaporative fraction (EF) or reference ET fraction (ET{{sub|r}}F), similar to crop coefficients, for computing seasonal values, these models generally lack the soil water balance and Irrigation components in surface energy balance. Other limiting factors is the dependence on thermal-based radiometric surface temperature, which is not always available at required temporal resolution and frequently obscured by factors such as cloud cover. BAITSSS was developed to fill these gaps in remote sensing based models liberating the use of thermal-based radiometric surface temperature and to serve as a digital crop water tracker simulating high temporal (hourly or sub-hourly) and spatial resolution (30 meter) ET maps.{{Cite web|url=https://www.k-state.edu/today/students/announcement/?id=46653|title='It's all about water' Global Food Systems meeting|website=www.k-state.edu|language=en-US|access-date=2019-10-21}} BAITSSS utilizes remote sensing based canopy formation information, i.e. estimation of seasonal variation of vegetation indices and senescence.

Approach and model structure

Surface energy balance is one of the commonly utilized approaches to quantify ET (latent heat flux in terms of flux), where weather variables and vegetation Indices are the drivers of this process. BAITSSS adopts numerous equations to compute surface energy balance and resistances where primarily are from Javis, 1976,{{Cite book|title=The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field.|last=Jarvis, P.G.|date=1976|oclc=709369248}} Choudhury and Monteith, 1988,{{Cite journal|last1=CHOUDHURY|first1=BJ|last2=MONTEITH|first2=JL|date=1988-01-15|title=A four-layer model for the heat budget of homogeneous land surfaces|journal=Quarterly Journal of the Royal Meteorological Society|volume=114|issue=480|pages=373–398|doi=10.1256/smsqj.48005|issn=1477-870X}} and aerodynamic methods or flux-gradient relationship equations{{Citation|last1=Hatfield|first1=J.L.|title=Aerodynamic Methods for Estimating Turbulent Fluxes|date=2005|work=Micrometeorology in Agricultural Systems|publisher=American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America|isbn=978-0-89118-268-9|last2=Baker|first2=J.M.|last3=Prueger|first3=John H.|last4=Kustas|first4=William P.|doi=10.2134/agronmonogr47.c18|url=http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=2399&context=usdaarsfacpub}}{{Cite journal|last1=Pagán|first1=Brianna|last2=Maes|first2=Wouter|last3=Gentine|first3=Pierre|last4=Martens|first4=Brecht|last5=Miralles|first5=Diego|date=2019-02-18|title=Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates|journal=Remote Sensing|language=en|volume=11|issue=4|pages=413|bibcode=2019RemS...11..413P|doi=10.3390/rs11040413|issn=2072-4292|doi-access=free|hdl=1854/LU-8629757|hdl-access=free}} with stability functions associated with Monin–Obukhov similarity theory.

=Underlying fundamental equations of surface energy balance=

Latent heat flux (LE)

The aerodynamic or flux-gradient equations of latent heat flux in BAITSSS are shown below. $e^{o}_c$ is saturation vapor pressure at the canopy and $e^{o}_s$ is for soil, $e_a$ is ambient vapor pressure, r{{sub|ac}} is bulk boundary layer resistance of vegetative elements in the canopy, r{{sub|ah}} is aerodynamic resistance between zero plane displacement (d) + roughness length of momentum (z{{sub|om}}) and measurement height (z) of wind speed, r{{sub|as}} is the aerodynamic resistance between the substrate and canopy height (d +z{{sub|om}}), and r{{sub|ss}} is soil surface resistance.

$LE_{c} = \frac{\rho_a c_{p} }{{\gamma}} \bigl(\frac{e^{o}_c - e_a }{{r_{ac} + r_{ah} + r_{sc}}}\bigr)\ \And \ LE_{s} = \frac{\rho_a c_{p} }{{\gamma}} \bigl(\frac{e^{o}_s - e_a }{{r_{as} + r_{ah} + r_{ss}}}\bigr)$ File:LE H.png (LE) and sensible heat flux (H) as electrical analogy showing various resistances (soil surface resistance : r{{sub|ss}} and canopy resistance: r{{sub|sc}}) and surface temperatures (canopy temperature: T{{sub|c}} and soil surface temperature: T{{sub|s}}).]]

Sensible heat flux (H) and surface temperature calculation

The flux-gradient equations of sensible heat flux and surface temperature in BAITSSS are shown below.

$H_{c} = {\rho_a c_{p} } \bigl(\frac{T_c - T_a }{{r_{ah} + r_{ac}}}\bigr)\Longleftrightarrow T_{c} = \frac{H_{c}(r_{ah} + r_{ac}) }{{\rho_a c_{p}}} + {T_a }$

$H_{s} = {\rho_a c_{p} } \bigl(\frac{T_s - T_a }{{r_{ah} + r_{as}}}\bigr)\Longleftrightarrow T_{s} = \frac{H_{s}(r_{ah} + r_{as}) }{{\rho_a c_{p}}} + {T_a }$

Canopy resistance (r{{sub|sc}})

Typical Jarvis type-equation of r{{sub|sc}} adopted in BAITSSS is shown below, R{{sub|c-min}} is the minimum value of r{{sub|sc}}, LAI is leaf area index, f{{sub|c}} is fraction of canopy cover, weighting functions representing plant response to solar radiation (F{{sub|1}}), air temperature (F{{sub|2}}), vapor pressure deficit (F{{sub|3}}), and soil moisture (F{{sub|4}}) each varying between 0 and 1.

File:Convergence pattern of aerodynamic resistance.png

$r_{sc} = \frac{R_{c-min}}{\frac{LAI} {f_c} F_1 F_2 F_3 F_4 }$

=Equations of soil water balance and irrigation decision=

Standard soil water balance equations for soil surface and the root zone are implemented in BAITSSS for each time step, where irrigation decisions are based on the soil moisture at the root zone.

Data

=Input=

File:Temperature simulation updated.tif from BAITSSS (composite surface) compared to measured Infrared Temperature (IRT) and air temperature of corn between 22 May and 28 June 2016 near Bushland, Texas.]] ET models, in general, need information about vegetation (physical properties and vegetation indices) and environment condition (weather data) to compute water use. Primary weather data requirements in BAITSSS are solar irradiance (R{{sub|s↓}}), wind speed (u{{sub|z}}), air temperature (T{{sub|a}}), relative humidity (RH) or specific humidity (q{{sub|a}}), and precipitation (P). Vegetation indices requirements in BAITSSS are leaf area index (LAI) and fractional canopy cover (f{{sub|c}}), generally estimated from normalized difference vegetation index (NDVI). Automated BAITSSS can compute ET throughout United States using National Oceanic and Atmospheric Administration (NOAA) weather data (i.e. hourly NLDAS: North American Land Data Assimilation system at 1/8 degree; ~ 12.5 kilometers), Vegetation indices those acquired by Landsat, and soil information from SSURGO.

=Output=

BAITSSS generates large numbers of variables (fluxes, resistances, and moisture) in gridded form in each time-step. The most commonly used outputs are evapotranspiration, evaporation, transpiration, soil moisture, irrigation amount, and surface temperature maps and time series analysis.

Model features

File:Time series BAITSSS.png a) transpiration (T), b) evaporation (E{{sub|ss}}), c) mean soil moisture at root zone (θ{{sub|root}}), d) mean soil moisture at surface (θ{{sub|sur}}), e) evapotranspiration (ET), f) gridded precipitation (P), and simulated irrigation (I{{sub|rr}}; bar plot) of sampled pixel at Sheridan 6 (SD-6) LEMA (100° 38′ 22″ W, 39° 21′ 38″ N) between May 10 and September 15, Kansas, United States. Shade represents 5-year maximum and minimum and the black line represents mean value.|471x471px]]

class="wikitable"

! Feature

! Description

Two-source energy balance

|BAITSSS is a two-source energy balance model (separate soil and canopy section) which is integrated by fraction of vegetation cover (f{{sub|c}}) based on vegetation indices.

Two-layers soil water balance

| BAITSSS simulates soil surface moisture (θ{{sub|sur}}) and root zone moisture (θ{{sub|root}}) layers are related to the dynamics of evaporative (E{{sub|ss}}) and transpirative (T) flux. Capillary rise (CR) from the layer below root zone into the root zone layer is neglected. The soil moisture at both layers is restricted to field capacity (θ{{sub|fc}}).

Surface temperature

|BAITSSS iteratively solves surface temperature inverting flux-gradient equations of H at the soil surface (subscript s) (T{{sub|s}}) and canopy level (subscript c) (T{{sub|c}}) for each time step using continuous weather variables and surface roughness defined by vegetation Indices.

Ground heat flux of soil

| BAITSSS estimates ground heat flux (G) of soil surface based on sensible heat flux (H{{sub|s}}) or net radiation (R{{sub|n_s}}) of soil surface and neglects G on vegetated surface.

Transpiration

|Variable canopy conductance in terms of canopy resistance (r{{sub|sc}}), based on the Jarvis-type algorithm is used to compute transpiration.

Evaporation

|Evaporation (E{{sub|ss}}) in BAITSSS is computed based on soil resistance (r{{sub|ss}}) and soil water content in soil surface layer (upper 100 millimeters of soil water balance).

Irrrigation

|BAITSSS simulates irrigation) in agricultural landscapes{{Cite journal |last1=He |first1=Ruyan |last2=Jin |first2=Yufang |author-link2=Yufang Jin |last3=Kandelous |first3=Maziar |last4=Zaccaria |first4=Daniele |last5=Sanden |first5=Blake |last6=Snyder |first6=Richard |last7=Jiang |first7=Jinbao |last8=Hopmans |first8=Jan |date=2017-05-05 |title=Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations |journal=Remote Sensing |language=en |volume=9 |issue=5 |pages=436 |bibcode=2017RemS....9..436H |doi=10.3390/rs9050436 |issn=2072-4292 |doi-access=free}}{{Cite journal |last1=Dhungel |first1=Ramesh |last2=Anderson |first2=Ray G. |last3=French |first3=Andrew N. |last4=Saber |first4=Mazin |last5=Sanchez |first5=Charles A. |last6=Scudiero |first6=Elia |date=2022-08-26 |title=Assessing evapotranspiration in a lettuce crop with a two-source energy balance model |url=https://doi.org/10.1007/s00271-022-00814-x |journal=Irrigation Science |volume=41 |issue=2 |pages=183–196 |language=en |doi=10.1007/s00271-022-00814-x |s2cid=251871952 |issn=1432-1319}} by mimicking a tipping-bucket approach (applied to surface as sprinkler or sub-surface layer as drip), using Management Allowed Depletion (MAD), and soil water content regimes at rooting depth (lower 100-2000 millimeters of soil layer).

Biomass and Yield

|BAITSSS computes above biomass from transpiration efficiency normalized by vapor pressure deficit and grain fraction by empirical function of biomass.

File:Sheridan 6 ET.jpg-Simulated cumulative seasonal evapotranspiration (mm at 30 m spatial resolution) from hourly weather data from NLDAS and Vegetation Indices from Landsat using automated BAITSSS assuming 0.5 MAD between 10 May and 15 September 2013 for regulated groundwater management district; SD-6 LEMA, Kansas, United States (black circles, water rights shapes).]]

Agriculture system applications and recognition

BAITSSS was implemented to compute ET in southern Idaho for 2008, and in northern California for 2010. It was used to calculate corn and sorghum ET in Bushland, Texas for 2016, and multiple crops in northwest Kansas for 2013–2017.{{Cite web |url=https://howwerespond.aaas.org/community-spotlight/kansas-farmers-minimize-water-use-as-the-southern-great-plains-become-more-arid/ |title=Kansas Farmers Minimize Water Use as the Southern Great Plains Become More Arid |website=How We Respond |language=en-US |access-date=2019-09-20}}{{Cite web |url=https://kwo.ks.gov/docs/default-source/regional-advisory-committees/upper-republican-rac/upper-republican-rac-meeting-notes/upper-republican---june-2019.pdf?sfvrsn=6a028214_0 |title=Upper Republican Regional Advisory Committee |website=kwo.ks.gov |url-status=live |access-date=2019-09-21|archive-url=https://web.archive.org/web/20191105163427/https://kwo.ks.gov/docs/default-source/regional-advisory-committees/upper-republican-rac/upper-republican-rac-meeting-notes/upper-republican---june-2019.pdf?sfvrsn=6a028214_0 |archive-date=2019-11-05 }}{{Cite web |url=https://conferences.k-state.edu/govwater/sessions/2016-faculty-staff-professional-poster-abstracts/ |title=Effective agricultural water management strategy with biophysical evapotranspiration algorithm (BAITSSS) |url-status=live|archive-url=https://web.archive.org/web/20181115101739/http://conferences.k-state.edu:80/govwater/sessions/2016-faculty-staff-professional-poster-abstracts/ |archive-date=2018-11-15 }} BAITSSS has been widely discussed among the peers around the world, including Bhattarai et al. in 2017 and Jones et al. in 2019. United States Senate Committee on Agriculture, Nutrition and Forestry listed BAITSSS in its climate change report.{{Cite web|url=https://www.agriculture.senate.gov/imo/media/doc/USDA%20Climate%20Change%20Research%20Jan17%20Aug19_FINAL.pdf|title=Peer-Reviewed Research on Climate Change by USDA Authors January 2017-August 2019|url-status=live|archive-url=https://web.archive.org/web/20191106201454/https://www.agriculture.senate.gov/imo/media/doc/USDA%20Climate%20Change%20Research%20Jan17%20Aug19_FINAL.pdf |archive-date=2019-11-06 }} BAITSSS was also covered by articles in Open Access Government,{{Cite web|url=https://www.openaccessgovernment.org/water-for-plant-growth-the-foundation-of-the-global-food-supply-and-ecosystem-services/55166/|title=Water for plant growth: The foundation of the global food supply and ecosystem services|last=Eccleston|first=Sally|date=2018-11-30|website=Open Access Government|language=en-GB|access-date=2019-11-13}}{{Cite web|url=https://ogallala.tamu.edu/news/|title=News {{!}} Ogallala Aquifer Program|website=ogallala.tamu.edu|access-date=2019-11-13}} Landsat science team,{{Cite web|url=https://www.usgs.gov/land-resources/nli/landsat/landsat-science-team-publications#t|title=Landsat Science Team Publications|last=|first=|date=|website=|url-status=live|archive-url=https://web.archive.org/web/20200423065324/https://www.usgs.gov/land-resources/nli/landsat/landsat-science-team-publications |archive-date=2020-04-23 |access-date=}} Grass & Grain magazine,{{cite web|url=https://www.grassandgrain.com/archived-newspaper-editions/file/2019/10October/gg_10-01-19_sect_1.pdf.html|title=Sheridan 6 LEMA a success story in water conservation and management }} National Information Management & Support System (NIMSS), {{cite web |title=Soil, Water, and Environmental Physics to Sustain Agriculture and Natural Resources |url=https://www.nimss.org/projects/view/publications/18606}} terrestrial ecological models, {{Cite journal|last1=Li|first1=Sinan|last2=Zhang|first2=Li|last3=Ma|first3=Rui|last4=Yan|first4=Min|last5=Tian|first5=Xiangjun|date=2020-11-01|title=Improved ET assimilation through incorporating SMAP soil moisture observations using a coupled process model: A study of U.S. arid and semiarid regions|journal=Journal of Hydrology|language=en|volume=590|pages=125402|doi=10.1016/j.jhydrol.2020.125402|bibcode=2020JHyd..59025402L|s2cid=224904819|issn=0022-1694|doi-access=free}} key research contribution related to sensible heat flux estimation and irrigation decision in remote sensing based ET models.{{Cite journal|last1=Mohan|first1=M. M. Prakash|last2=Kanchirapuzha|first2=Rajitha|last3=Varma|first3=Murari R. R.|date=2020-10-15|title=Review of approaches for the estimation of sensible heat flux in remote sensing-based evapotranspiration models|journal=Journal of Applied Remote Sensing|volume=14|issue=4|page=041501|doi=10.1117/1.JRS.14.041501|bibcode=2020JARS...14d1501M|issn=1931-3195|doi-access=free}}{{Cite journal|last1=Zhang|first1=Jingwen|last2=Guan|first2=Kaiyu|last3=Peng|first3=Bin|last4=Jiang|first4=Chongya|last5=Zhou|first5=Wang|last6=Yang|first6=Yi|last7=Pan|first7=Ming|last8=Franz|first8=Trenton E.|last9=Heeren|first9=Derek M.|last10=Rudnick|first10=Daran R.|last11=Abimbola|first11=Olufemi|date=2021-02-08|title=Challenges and opportunities in precision irrigation decision-support systems for center pivots|journal=Environmental Research Letters|volume=16|issue=5|page=053003|doi=10.1088/1748-9326/abe436|bibcode=2021ERL....16e3003Z|issn=1748-9326|doi-access=free}}

In September 2019, the Northwest Kansas Groundwater Management District 4 (GMD 4) along with BAITSSS received national recognition from American Association for the Advancement of Science (AAAS).{{Citation|title=How We Respond – Communities and Scientists Taking Action on Climate Change| date=15 September 2019 |url=https://www.youtube.com/watch?v=QIxcTbn2tUM|language=en|access-date=2019-11-14}}{{Cite web|url=https://howwerespond.aaas.org/|title=How We Respond: Stories of Community Response to Climate Change|website=How We Respond|language=en-US|access-date=2019-09-18}}{{Cite web|url=https://gmd4.org/|title=GMD 4|last=|first=|date=|website=www.gmd4.org|url-status=live|archive-url=https://web.archive.org/web/20020610013031/http://gmd4.org:80/ |archive-date=2002-06-10 |access-date=2019-11-11}}{{Cite web|url=https://krwa.net/ONLINE-RESOURCES/News-Article/urlid/2206|title=Kansas Rural Water Association > ONLINE RESOURCES > News Article|website=krwa.net|access-date=2019-11-12}}{{Cite web|url=https://www.farmprogress.com/weather/report-spotlights-sheridan-county-climate-effort|title=Report spotlights Sheridan County climate effort|date=2019-09-20|website=Farm Progress|language=en|access-date=2019-11-12}} AAAS highlighted 18 communities across the U.S. that are responding to climate change{{Cite web|url=https://www.eurekalert.org/pub_releases/2019-09/aaft-wr082819.php|title='How We Respond' spotlights how US communities are addressing climate change impacts|website=EurekAlert!|language=en|access-date=2019-11-18}}{{Cite journal|title=Science Magazine|journal=Science|date=25 October 2019|volume=366|issue=6464|pages=436–437|doi=10.1126/science.366.6464.436|last1=Hoy|first1=Anne Q.|s2cid=211388071|doi-access=free}}{{Cite web|url=https://www.earth.com/news/communities-preparing-climate-change/|title=How American communities are preparing for the impacts of climate change • Earth.com|website=Earth.com|language=en|access-date=2019-11-18}} including [https://www.youtube.com/watch?time_continue=2&v=-7YfP1zSDzI&feature=emb_logo Sheridan County, Kansas] to prolong the life of Ogallala Aquifer by minimizing water use where this aquifer is depleting rapidly due to extensive agricultural practices . AAAS discussed the development and use of intricate ET model BAITSSS and Dhungel's and other scientists efforts supporting effective use of water in Sheridan County, Kansas.

Furthermore, Upper Republican Regional Advisory Committee of Kansas (June 2019) and GMD 4 discussed possible benefit and utilization of BAITSSS for managing water use, educational purpose, and cost-share. A short story about Ogallala Aquifer Conservation effort from Kansas State University and GMD4 using ET model was published in Mother Earth News (April/May 2020),{{cite web |url=https://www.motherearthnews.com/green-transportation/cities-move-toward-renweable-power-transit-zm0z2002znad/ |title = Cities Move Toward Renewable Power for Transit

|date = 5 March 2020

}} and Progressive Crop Consultant.{{Cite web|last=Scudiero|first=Elia|date=2021-07-08|title=Can Artificial Intelligence Enhance the Profit and Environmental Sustainability of Agriculture?|url=https://progressivecrop.com/2021/07/can-artificial-intelligence-enhance-the-profit-and-environmental-sustainability-of-agriculture/|access-date=2021-10-16|website=Progressive Crop Consultant|language=en}}

Example application

=Groundwater and Irrigation=

File:Hysteresis Study.png

Dhungel et al., 2020, combined with field crop scientists, systems analysts, and district water managers, applied BAITSSS at the district water management level focusing on seasonal ET and annual groundwater withdrawal rates at Sheridan 6 (SD-6) Local Enhanced Management Plan (LEMA) for five years period (2013-2017) in northwest, Kansas, United States. BAITSSS simulated irrigation was compared to reported irrigation as well as to infer deficit irrigation within water right management units (WRMU). In Kansas, groundwater pumping records are legal documents and maintained by the Kansas Division of Water Resources. The in-season water supply was compared to BAITSSS simulated ET as well-watered crop water condition.

=Evapotranspiration Hysterisis and Advection=

File:Evapotranspiration uncertainty.png

A study related to ET uncertainty associated with ET hysteresis (Vapor pressure and net radiation) were conducted using lysimeter, Eddy covariance (EC), and BAITSSS model (point-scale) in an advective environment of Bushland, Texas. Results indicated that the pattern of hysteresis from BAITSSS closely followed the lysimeter and showed weak hysteresis related to net radiation when compared to EC. However, both lysimeter and BAITSSS showed strong hysteresis related to VPD when compared to EC.{{Citation needed|date=June 2023}}

=Lettuce Evapotranspiration=

A study related to lettuce evapotranspiration was conducted at Yuma, Arizona using BAITSSS between 2016 and 2020, where model simulated ET closely followed twelve eddy covariance sites

= Challenges and limitations =

Simulation of hourly ET at 30 m spatial resolution for seasonal time scale is computationally challenging and data-intensive.{{Cite web|title=Point to Landscape-Scale: Traits in Agroecology|url=http://eesa.lbl.gov/event/point-to-landscape-scale-traits-in-agroecology/|access-date=2020-06-12|website=Earth and Environmental Sciences Area|language=en-US}} The low wind speed complicates the convergence of surface energy balance components as well. The peer group Pan et al. in 2017 and Dhungel et al., 2019 pointed out the possible difficulty of parameterization and validations of these kinds of resistance based models. The simulated irrigation may vary than that actually applied in field.

References

{{reflist|refs=

Peer reviewed papers from Dhungel and others:

{{Cite journal|last1=Dhungel|first1=Ramesh|last2=Allen|first2=Richard G.|last3=Trezza|first3=Ricardo|last4=Robison|first4=Clarence W.|date=2016|title=Evapotranspiration between satellite overpasses: methodology and case study in agricultural dominant semi-arid areas|journal=Meteorological Applications|language=en|volume=23|issue=4|pages=714–730|bibcode=2016MeApp..23..714D|doi=10.1002/met.1596|issn=1469-8080 | publication-date=October 2016|doi-access=free}}
{{Cite journal |last1=Dhungel |first1=Ramesh |last2=Aiken |first2=Robert |last3=Colaizzi |first3=Paul |last4=Lin |first4=Xiaomao |last5=Baumhardt |first5=Roland |last6=Brauer |first6=David |last7=Marek |first7=Gary |last8=Evett |first8=Steven |last9=O'Brien |first9=Dan |date=April 11, 2019 |title=Increased bias in evapotranspiration modeling due to weather and vegetation indices data sources |url=https://www.ars.usda.gov/research/publications/publication/?seqNo115=354882 |journal=Agronomy Journal |volume=111 |issue=3 |pages=1407–1424 |doi=10.2134/agronj2018.10.0636 |access-date=2019-04-09 |via=Agricultural Research Service|doi-access=free |bibcode=2019AgrJ..111.1407D }}
{{Cite journal |last1=Dhungel |first1=Ramesh |last2=Aiken |first2=Robert |last3=Colazzi |first3=Paul D. |last4=Lin |first4=Xiaomao |last5=O'Brien |first5=Dan |last6=Baumhardt |first6=Roland Louis |last7=Brauer |first7=David |last8=Marek |first8=Gary W. |last9=Evett |first9=Steve |date=June 3, 2019 |title=Evaluation of the uncalibrated energy balance model (BAITSSS)for estimating evapotranspiration in a semiarid, advective climate |url=https://www.ars.usda.gov/research/publications/publication/?seqNo115=348805 |journal=Hydrological Processes |volume=33 |issue=15 |page= |doi=10.1002/hyp.13458 |bibcode=2019HyPr...33.2110D |s2cid=146551438 |access-date=2019-04-09 |via=Agricultural Research Service}}
{{Cite journal|last1=Dhungel|first1=Ramesh|last2=Aiken|first2=Robert|last3=Lin|first3=Xiaomao|last4=Kenyon|first4=Shannon|last5=Colaizzi|first5=Paul D.|last6=Luhman|first6=Ray|last7=Baumhardt|first7=R. Louis|last8=O'Brien|first8=Dan|last9=Kutikoff|first9=Seth|last10=Brauer|first10=David K.|date=October 21, 2019 | title=Restricted water allocations: Landscape-scale energy balance simulations and adjustments in agricultural water applications|url=https://www.ars.usda.gov/research/publications/publication/?seqNo115=365507|journal=Agricultural Water Management|volume=227|pages=105854|doi=10.1016/j.agwat.2019.105854|access-date=2019-10-23 |via=Agricultural Research Service|doi-access=free}}
{{Cite journal|last1=Dhungel|first1=Ramesh|last2=Allen|first2=Richard G.|last3=Trezza|first3=Ricardo|date=2016-06-09|title=Improving iterative surface energy balance convergence for remote sensing based flux calculation|journal=Journal of Applied Remote Sensing|language=en|volume=10|issue=2|pages=026033|doi=10.1117/1.JRS.10.026033|bibcode=2016JARS...10b6033D|s2cid=124338728|issn=1931-3195}}
{{Cite journal|last1=Dhungel|first1=Ramesh|last2=Aiken|first2=Rob|last3=Lin|first3=Xiaomao|last4=Kutikoff|first4=Seth|last5=Zuo|first5=Guanqiang|year=2019|title=B31L-2467 - An integrated analytic framework for water and crop production in agroecosystems|url=https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/499126|journal=AGU Fall Meeting | publisher=American Geophysical Union}}
{{Cite journal|last1=Dhungel|first1=Ramesh|last2=Aiken|first2=Robert|last3=Evett|first3=Steven R.|last4=Colaizzi|first4=Paul D.|last5=Marek|first5=Gary|last6=Moorhead|first6=Jerry E.|last7=Baumhardt|first7=R. Louis|last8=Brauer|first8=David|last9=Kutikoff|first9=Seth|last10=Lin|first10=Xiaomao|date=2020-12-14|title=Energy Imbalance and Evapotranspiration Hysteresis under an Advective Environment: Evidence from Lysimeter, Eddy Covariance, and Energy Balance Modelling|url=https://onlinelibrary.wiley.com/doi/10.1029/2020GL091203|journal=Geophysical Research Letters|volume=48|language=en|doi=10.1029/2020GL091203|s2cid=230594532|issn=0094-8276}}

Kansas GMD 4 meeting minutes:

{{Cite web|url=https://www.gmd4.org/BoardMeetings/19-08-Minutes.pdf|title=Minutes: Northwest Kansas Groundwater Management District 4 August 08, 2019 Board Meeting|last=|first=|date=|website=|url-status=live|archive-url=https://web.archive.org/web/20191108165155/https://www.gmd4.org/BoardMeetings/18-11-Minutes.pdf |archive-date=2019-11-08 |access-date=}}
{{Cite web|url=https://www.gmd4.org/BoardMeetings/19-06-Minutes.pdf|title=Minutes: Northwest Kansas Groundwater Management District 4 June 13, 2019 Board Meeting|last=|first=|date=|website=|url-status=live|archive-url=https://web.archive.org/web/20191108165155/https://www.gmd4.org/BoardMeetings/18-11-Minutes.pdf |archive-date=2019-11-08 |access-date=}}
{{Cite web|url=https://www.gmd4.org/BoardMeetings/19-01-Minutes.pdf|title=Minutes: Northwest Kansas Groundwater Management District 4 January 10, 2019 Board Meeting|last=|first=|date=|website=|access-date=}}
{{Cite web|url=https://www.gmd4.org/BoardMeetings/18-11-Minutes.pdf|title=Minutes Northwest Kansas Groundwater Management District 4 November 1, 2018 Board Meeting|last=|first=|date=|website=|url-status=live|archive-url=https://web.archive.org/web/20191108165155/https://www.gmd4.org/BoardMeetings/18-11-Minutes.pdf |archive-date=2019-11-08 |access-date=}}

.{{Cite journal |url=https://scisoc.confex.com/scisoc/2018am/meetingapp.cgi/Paper/113852 |title=Next Generation Landscape Evapotranspiration Tools: Is It Feasible?. |website=ASA, CSSA, and SSSA - ASA, CSSA, and CSA International Annual Meeting |date=6 November 2018 |access-date=2019-05-11}}

Discussion among peers:

{{Cite journal |last1=Bhattarai |first1=Nishan |last2=Wagle |first2=Pradeep |last3=Gowda |first3=Prasanna H. |last4=Kakani |first4=Vijaya G. |date=2017 |title=Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions |journal=ISPRS Journal of Photogrammetry and Remote Sensing |language=en |volume=133 |pages=128–141 |bibcode=2017JPRS..133..128B |doi=10.1016/j.isprsjprs.2017.10.010|doi-access=free }}
{{Cite journal|last1=Jones|first1=Andrew S.|last2=Andales|first2=Allan A.|last3=Chávez|first3=José L.|last4=McGovern|first4=Cullen|last5=Smith|first5=Garvey E. B.|last6=David|first6=Olaf|last7=Fletcher|first7=Steven J.|title=Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part I: A Review of Metrics and Adjoint Methods|journal=JAWRA Journal of the American Water Resources Association|language=en|doi=10.1111/1752-1688.12810|issn=1752-1688|year=2019|volume=56|issue=2|pages=187–200|doi-access=free}}
{{Cite journal |last1=Jin |first1=Yufang |last2=He |first2=Ruyan |last3=Marino |first3=Giulia |last4=Whiting |first4=Michael |last5=Kent |first5=Eric |last6=Sanden |first6=Blake L. |last7=Culumber |first7=Mae |last8=Ferguson |first8=Louise |last9=Little |first9=Cayle |last10=Grattan |first10=Stephen |last11=Paw U |first11=Kyaw Tha |date=2018-11-15 |title=Spatially variable evapotranspiration over salt affected pistachio orchards analyzed with satellite remote sensing estimates |journal=Agricultural and Forest Meteorology |volume=262 |pages=178–191 |doi=10.1016/j.agrformet.2018.07.004 |bibcode=2018AgFM..262..178J |issn=0168-1923|doi-access=free }}
{{Cite journal |last1=Majozi |first1=Nobuhle |last2=Mannaerts |first2=Chris |last3=Ramoelo |first3=Abel |last4=Mathieu |first4=Renaud |last5=Mudau |first5=Azwitamisi |last6=Verhoef |first6=Wouter |date=2017-03-24 |title=An Intercomparison of Satellite-Based Daily Evapotranspiration Estimates under Different Eco-Climatic Regions in South Africa |journal=Remote Sensing |language=en |volume=9 |issue=4 |pages=307 |doi=10.3390/rs9040307 |bibcode=2017RemS....9..307M |issn=2072-4292|doi-access=free |hdl=2263/61449 |hdl-access=free }}
{{Cite journal |last1=Pagán |first1=Brianna |last2=Maes |first2=Wouter |last3=Gentine |first3=Pierre |last4=Martens |first4=Brecht |last5=Miralles |first5=Diego |date=2019-02-18 |title=Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates |journal=Remote Sensing |language=en |volume=11 |issue=4 |pages=413 |doi=10.3390/rs11040413 |bibcode=2019RemS...11..413P |issn=2072-4292|doi-access=free |hdl=1854/LU-8629757 |hdl-access=free }}
{{Cite journal|last1=Khand|first1=Kul|last2=Taghvaeian|first2=Saleh|last3=Gowda|first3=Prasanna|last4=Paul|first4=George|date=2019-03-02|title=A Modeling Framework for Deriving Daily Time Series of Evapotranspiration Maps Using a Surface Energy Balance Model|journal=Remote Sensing|volume=11|issue=5|pages=508|doi=10.3390/rs11050508|bibcode=2019RemS...11..508K|issn=2072-4292|doi-access=free}}
{{Cite journal|last1=Numata|first1=Izaya|last2=Khand|first2=Kul|last3=Kjaersgaard|first3=Jeppe|last4=Cochrane|first4=Mark|last5=Silva|first5=Sonaira|date=2017-01-06|title=Evaluation of Landsat-Based METRIC Modeling to Provide High-Spatial Resolution Evapotranspiration Estimates for Amazonian Forests|journal=Remote Sensing|volume=9|issue=1|pages=46|doi=10.3390/rs9010046|bibcode=2017RemS....9...46N|issn=2072-4292|doi-access=free}}
{{Cite thesis|degree=PhD|last=Khand|first=Kul Bikram|date=2019-05-01|title=Assessment and Development of Remotely Sensed Evapotranspiration Modeling Approaches|url=https://shareok.org/handle/11244/321595|language=en-US}}
{{Cite journal|last1=Akhtar|first1=Fazlullah|last2=Awan|first2=Usman|last3=Tischbein|first3=Bernhard|last4=Liaqat|first4=Umar|date=2018-06-18|title=Assessment of Irrigation Performance in Large River Basins under Data Scarce Environment—A Case of Kabul River Basin, Afghanistan|journal=Remote Sensing|language=en|volume=10|issue=6|pages=972|doi=10.3390/rs10060972|bibcode=2018RemS...10..972A|issn=2072-4292|doi-access=free}}
{{Cite journal|last1=He|first1=Ruyan|last2=Jin|first2=Yufang|last3=Kandelous|first3=Maziar|last4=Zaccaria|first4=Daniele|last5=Sanden|first5=Blake|last6=Snyder|first6=Richard|last7=Jiang|first7=Jinbao|last8=Hopmans|first8=Jan|date=2017-05-05|title=Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations|journal=Remote Sensing|volume=9|issue=5|pages=436|doi=10.3390/rs9050436|bibcode=2017RemS....9..436H|issn=2072-4292|doi-access=free}}