BAITSSS-The Daily Ledger

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.

BAITSSS

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). 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.

BAITSSS

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.

BAITSSS

Rationale

The majority of remote sensing based instantaneous ET models use evaporative fraction (EF) or reference ET fraction (ETF), 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. 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, Choudhury and Monteith, 1988, and aerodynamic methods or flux-gradient relationship equations 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. 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 (d) + roughness length of momentum (z) and measurement height (z) of wind speed, r is the aerodynamic resistance between the substrate and canopy height (d +z), and r is soil surface resistance.