BAITSSS originated from the research of Ramesh Dhungel, a graduate student at the
University of Idaho,[2] 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.[3]
In 2012, the initial version of
landscape model was developed using the
PythonIDLE environment using NARR weather data (~ 32 kilometers).[1] Dhungel submitted his
PhD dissertation in 2014 where the model was called BATANS (backward averaged two source accelerated numerical solution).[1][2] 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.[1] 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.[1]
In 2017, the landscape BAITSSS model was scripted in Python
shell, together with
GDAL and
NumPy libraries using NLDAS weather data (~ 12.5 kilometers).[1] The detailed independent model was evaluated against weighing
lysimeter measured
ET, infrared temperature (IRT) and
net radiometer of
drought‐tolerantcorn 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.[1] Some later development of BAITSSS includes physically based crop productivity components, i.e.
biomass and
crop yield computation.[1][4][5]
Surface energy balance is one of the commonly utilized approaches to quantify
ET (
latent heatflux 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,[9] Choudhury and Monteith, 1988,[10] and
aerodynamic methods or flux-gradient relationship equations[11][12] with stability functions associated with
Monin–Obukhov similarity theory.
Underlying fundamental equations of surface energy balance
The
aerodynamic or flux-gradient equations of
latent heat flux in BAITSSS are shown below. is saturation
vapor pressure at the
canopy and is for soil, is ambient
vapor pressure, rac is bulk
boundary layer resistance of vegetative elements in the canopy, rah is
aerodynamic resistance between zero plane displacement (d) +
roughness length of momentum (zom) and measurement height (z) of
wind speed, ras is the aerodynamic resistance between the substrate and canopy height (d +zom), and rss is soil surface resistance.[1]
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.[1]
BAITSSS is a two-source energy balance model (separate soil and canopy section) which is integrated by fraction of vegetation cover (fc) based on vegetation indices.
Two-layers soil water balance
BAITSSS simulates soil surface moisture (θsur) and root zone moisture (θroot) layers are related to the dynamics of evaporative (Ess) 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 (θfc).
BAITSSS estimates ground heat flux (G) of soil surface based on sensible heat flux (Hs) or net radiation (Rn_s) of soil surface and neglects G on vegetated surface.
BAITSSS simulates
irrigation (Irr) in
agriculturallandscapes[13][14] 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).
BAITSSS was implemented to compute ET in southern
Idaho for 2008, and in northern California for 2010.[1] It was used to calculate
corn and
sorghum ET in
Bushland, Texas for 2016, and multiple crops in northwest
Kansas for 2013–2017.[1][15][16][4] BAITSSS has been widely discussed among the
peers around the world, including Bhattarai et al. in 2017 and Jones et al. in 2019.[17]United States Senate Committee on Agriculture, Nutrition and Forestry listed BAITSSS in its
climate change report.[18] BAITSSS was also covered by articles in
Open Access Government,[6][19]Landsat science team,[20] Grass & Grain magazine,[21] National Information Management & Support System (NIMSS), [22] terrestrial ecological models, [23] key research contribution related to sensible heat flux estimation and irrigation decision in remote sensing based ET models.[24][25]
Furthermore, Upper Republican Regional Advisory Committee of
Kansas (June 2019)[16] and GMD 4[34] 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),[35] and Progressive Crop Consultant.[36]
Example application
Groundwater and Irrigation
Dhungel et al., 2020,[1] 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
cropwater condition.
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.[1] 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]
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 [14]
Challenges and limitations
Simulation of hourly
ET at 30 m spatial resolution for
seasonal time scale is computationally challenging and
data-intensive.[1][37] The low
wind speed complicates the convergence of surface energy balance components as well.[1] The peer group Pan et al. in 2017 [14] and Dhungel et al., 2019 [1] 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.[1]
See also
METRIC, another model developed by University of Idaho that uses Landsat satellite data to compute and map evapotranspiration
SEBAL, uses the surface energy balance to estimate aspects of the
hydrological cycle. SEBAL maps evapotranspiration, biomass growth, water deficit and soil moisture
^Jarvis, P.G. (1976). The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field.
OCLC709369248.
^CHOUDHURY, BJ; MONTEITH, JL (1988-01-15). "A four-layer model for the heat budget of homogeneous land surfaces". Quarterly Journal of the Royal Meteorological Society. 114 (480): 373–398.
doi:
10.1256/smsqj.48005.
ISSN1477-870X.