BAITSSS
BAITSSS is biophysical Evapotranspiration 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, and Texas.
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.In 2012, the initial version of landscape model was developed using the Python IDLE environment using NARR weather data. Dhungel submitted his PhD dissertation in 2014 where the model was called BATANS. 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. The detailed independent model was evaluated against weighing lysimeter measured ET, infrared temperature and net radiometer of drought‐tolerant corn at Conservation and Production Research Laboratory in Bushland, Texas by group of scientists from USDA-ARS and Kansas State University between 2017 and 2019. Some later development of BAITSSS includes physically based crop productivity components, i.e. biomass and crop yield computation.
Rationale
The majority of remote sensing based instantaneous ET models use evaporative fraction or reference ET fraction, 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 and spatial resolution ET maps. BAITSSS utilizes remote sensing based canopy formation information, i.e. estimation of seasonal variation of vegetation indices and senescence.Approach and model structure
is one of the commonly utilized approaches to quantify ET, 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, Choudhury and Monteith, 1988, and aerodynamic methods or flux-gradient relationship equations with stability functions associated with Monin–Obukhov similarity theory.Underlying fundamental equations
Latent heat fluxThe 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, r is bulk boundary layer resistance of vegetative elements in the canopy, r is aerodynamic resistance between zero plane displacement + roughness length of momentum and measurement height of wind speed, r is the aerodynamic resistance between the substrate and canopy height, and r is soil surface resistance.
and sensible heat flux as electrical analogy showing various resistances and surface temperatures.
Sensible heat flux and surface temperature calculation
The flux-gradient equations of sensible heat flux and surface temperature in BAITSSS are shown below.
'''Canopy resistance
Typical Jarvis type-equation of r adopted in BAITSSS is shown below, R is the minimum value of r, LAI is leaf area index, f is fraction of canopy cover, weighting functions representing plant response to solar radiation, air temperature, vapor pressure deficit, and soil moisture each varying between 0 and 1.
Data
Input
models, in general, need information about vegetation and environment condition to compute water use. Primary weather data requirements in BAITSSS are solar irradiance, wind speed, air temperature, relative humidity or specific humidity, and precipitation. Vegetation indices requirements in BAITSSS are leaf area index and fractional canopy cover, generally estimated from normalized difference vegetation index. Automated BAITSSS can compute ET throughout United States using National Oceanic and Atmospheric Administration weather data, Vegetation indices those acquired by Landsat, and soil information from SSURGO.Output
BAITSSS generates large numbers of variables 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
| Feature | Description |
| Two-source energy balance | BAITSSS is a two-source energy balance model which is integrated by fraction of vegetation cover based on vegetation indices. |
| Two-layers soil water balance | BAITSSS simulates soil surface moisture and root zone moisture layers are related to the dynamics of evaporative and transpirative flux. Capillary rise from the layer below root zone into the root zone layer is neglected. The soil moisture at both layers is restricted to field capacity. |
| Surface temperature | BAITSSS iteratively solves surface temperature inverting flux-gradient equations of H at the soil surface and canopy level 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 of soil surface based on sensible heat flux or net radiation of soil surface and neglects G on vegetated surface. |
| Transpiration | Variable canopy conductance in terms of canopy resistance, based on the Jarvis-type algorithm is used to compute transpiration. |
| Evaporation | Evaporation in BAITSSS is computed based on soil resistance and soil water content in soil surface layer. |
| Irrrigation | BAITSSS simulates irrigation in agricultural landscapes by mimicking a tipping-bucket approach, using Management Allowed Depletion, and soil water content regimes at rooting depth. |
| Biomass and Yield | BAITSSS computes above biomass from transpiration efficiency normalized by vapor pressure deficit and grain fraction by empirical function of biomass. |
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. 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. BAITSSS was also covered by articles in Open Access Government,, Landsat science team, Grass & Grain magazine, and National Information Management & Support System.In September 2019, the Northwest Kansas Groundwater Management District 4 along with BAITSSS received national recognition from American Association for the Advancement of Science. AAAS highlighted 18 communities across the U.S. that are responding to climate change including 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. 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.Furthermore, Upper Republican Regional Advisory Committee of Kansas 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.