BAITSSS

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• Updated and added report from American Association for the Advancement of Science (AAAS) report; "How we respond" for Climate Change

https://howwerespond.aaas.org/community-spotlight/kansas-farmers-minimize-water-use-as-the-southern-great-plains-become-more-arid/?fbclid=IwAR1_cUEhilQaiJjxDKCzo8FK7gwMubSreyl2zegm0Xbnnf8VcP4eI7ctGtI

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• Added Urban Climate News

http://www.urban-climate.org/wp-content/uploads/IAUC073.pdf

• Open Access Government

https://www.openaccessgovernment.org/water-for-plant-growth-the-foundation-of-the-global-food-supply-and-ecosystem-services/55166/

• Added Upper Republican Regional Advisory Committee Meeting and multiple GMD4 meeting minutes

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 Archived 2019-11-05 at the Wayback Machine

• Added Ogallala Aquifer Program news

https://ogallala.tamu.edu/news/

• Added Kansas State University news

https://www.k-state.edu/today/students/announcement/?id=46653

• Added multiple independent citations around the world from Journals and reports

• Updated the lead paragraph to better understand this, please let me know.

• Added small section of history as per found in web.

• Thanks DGG for nicely editing.

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BAITSSS

Developer(s)	Ramesh Dhungel and group
Written in	Python (programming language), shell script, GDAL, numpy
Engine
Operating system	Microsoft Windows
Type	Evapotranspiration modeling, irrigation simulation, surface temperature simulation, Geographic information system

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BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution) is a Evapotranspiration (ET) computer model which 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, and Texas.

History of development

University of Idaho's Kimberly Research and Extension Center Water Resources Group is prominent on ET work in irrigated agriculture and natural systems (see METRIC, Standardization of Reference Evapotranspiration). BAITSSS was developed during the PhD , which was a part of project "Producing and integrating time series of gridded evapotranspiration for irrigation management, hydrology and remote sensing applications". Majority of remote sensing based instantaneous ET models use evaporative fraction (EF) or reference ET fraction (ETrF), similar to crop coefficients, for computing seasonal values, these ET models generally lack the soil water balance and Irrigation components in surface energy balance. BAITSSS was developed to fill these gaps in remote sensing based models and to serve as a digital crop water tracker simulating high temporal (hourly or sub-hourly) and spatial resolution (30 meter).

In 2012, the initial version of model was developed using the Python IDLE environment using NARR weather data (~ 32 kilometers). The model was first published in Meteorological Applications journal in 2016 as a framework to interpolate ET between the satellite overpass when thermal based surface temperature is unavailable.

In 2017, the BAITSSS model is 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 lysimeters at USDA-ARS, Conservation and Production Research Laboratory in Bushland, Texas.

Approach

Schematic of BAITSSS latent heat flux (LE) and sensible heat flux (H)

Surface energy balance is one of the commonly utilized approaches to quantify evapotranspiration (latent heat flux in terms of flux), where weather variables and vegetation Indices are the drivers of this process. BAITSSS is an integrated two-source surface energy balance and two-layered soil water balance biophysical land surface model. BAITSSS computes latent heat flux (LE) and sensible heat flux (H) in the energy balance using aerodynamic methods or flux-gradient relationship equations with stability functions associated with Monin–Obukhov similarity theory. Variable canopy conductance in terms of canopy resistance (r_sc), based on the Jarvis-type algorithm is used to compute transpiration. Evaporation in BAITSSS is computed based on soil resistance (r_ss) and soil water content in soil surface layer (upper 100 millimeters of soil water balance). BAITSSS iteratively solves surface temperature at the soil surface (subscript s) (T_s) and canopy level (subscript c) (T_c) for each time step using continuous weather variables and surface roughness defined by vegetation Indices. BAITSSS simulates irrigation in agricultural landscapes by mimicking a tipping-bucket approach (applied to surface or sub-surface layer), using Management Allowed Depletion (MAD), and soil water content regimes at rooting depth (lower 100-2000 millimeters of soil layer).

Surface energy balance and temperature formulation

The aerodynamic or flux-gradient equations of latent heat flux and sensible heat flux in BAITSSS are shown below. $e_c^o$ is saturation vapor pressure at the canopy and $e_s^o$ is for soil, $e_a$ is ambient vapor pressure, r_ac is bulk boundary layer resistance of vegetative elements in the canopy, r_ah is aerodynamic resistance between zero plane displacement (d) + roughness length of momentum (z_om) and measurement height (z) of wind speed, r_as is the aerodynamic resistance between the substrate and canopy height (d +z_om), and r_ss is soil surface resistance.

$L{E}_c=\frac{{\rho }_a{c}_p}{\gamma }\left(\frac{e_c^o-e_a}{r_{ac}+r_{ah}+r_{sc}}\right)\mathrm{\&}L{E}_s=\frac{{\rho }_a{c}_p}{\gamma }\left(\frac{e_s^o-e_a}{r_{as}+r_{ah}+r_{ss}}\right)$

$H_c={\rho }_a{c}_p\left(\frac{T_c-T_a}{r_{ah}+r_{ac}}\right)⟺T_c=\frac{H_c(r_{ah}+r_{ac})}{{\rho }_a{c}_p}+T_a$

$H_s={\rho }_a{c}_p\left(\frac{T_s-T_a}{r_{ah}+r_{as}}\right)⟺T_s=\frac{H_s(r_{ah}+r_{as})}{{\rho }_a{c}_p}+T_a$

Typical Jarvis type-equation of r_sc adopted in BAITSSS is shown below, R_c-min is the minimum value of r_sc, LAI is leaf area index, f_c is fraction of canopy cover, weighting functions representing plant response to solar radiation (F₁), air temperature (F₂), vapor pressure deficit (F₃), and soil moisture (F₄) each varying between 0 to 1.

$r_{sc}=\frac{R_{c-min}}{\frac{LAI}{f_c}{F}_1{F}_2{F}_3{F}_4}$

Data Requirements

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_s↓), wind speed (u_z), air temperature (T_a), relative humidity (RH) or specific humidity (q_a), and precipitation (P). Vegetation indices requirements in BAITSSS are leaf area index (LAI) and fractional canopy cover (f_c), generally estimated from normalized difference vegetation index (NDVI). Automated BAITSSS can compute ET throughout USA 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.

Agriculture system applications

BAITSSS has been used throughout USA where some of published reports were to compute landscape ET in southern Idaho (2008), and northern California (2010), calculating corn and sorghum ET in Bushland, Texas (2016), landscape ET and water right for multiple crops in northwest Kansas (2013-2017). Bhattarai et al., 2017 and Jones et al., 2019 discussed the implications of advanced ET calculation procedure and improved soil water balance model of BAITSSS to enhance the performance of the remote sensing based surface energy balance models under dry conditions. However, Pan et al., 2017 pointed out the possible difficulty of parameterization and validations of these kinds of resistance based models. BAITSSS has been widely discussed among the peers around the world for various water management issues in agriculture sector (some are listed here).

In September 2019, Northwest Kansas Groundwater Management District 4 (GMD 4) along with BAITSSS received national recognition from American Association for the Advancement of Science (AAAS) "How we respond to climate change initiative . AAAS highlighted the effort of stakeholders of 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 . Furthermore, AAAS discussed the development and use of BAITSSS for effective use of water in Sheridan County, Kansas. Some other studies like The United States Senate Committee on Agriculture, Nutrition and Forestry list of Climate Change (01/2017 - 08/2019), Open Access Government (December 2018) and Urban Climate News (September, 2019) had incorporated ET study from BAITSSS in their report. Upper Republican Regional Advisory Committee Meeting of Kansas (June 2019) and GMD 4 discussed possible benefit and utilization of BAITSSS for managing water use, educational purpose, and cost-share. Landsat science team publication and Kansas State University, agricultural economics had incorporated the ET study from BAITSSS.

See also

• METRIC
• SEBAL

External links

• BAITSSS at Sites.google.com

Category:Remote sensing Category:Hydrology models Category:Water resources management Category:Irrigation Category:Computer-aided engineering software Category:Numerical climate and weather models

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