BAITSSS

BAITSSS Evaluation against Lysimeter Measured ET

BAITSSS Model

Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution (BAITSSS) (Dhungel et al., 2016, 2019, 2019a, 2020, 2020a) is a tool for tracking crop water use and yield. BAITSSS is an advanced two-source energy balance and two-layer soil water balance biophysical evapotranspiration algorithm (Figure 1). Surface temperatures (T_s; soil surface temperature and T_c; canopy temperature) in BAITSSS are iteratively solved at each time step (Figures 1 and 2) using complete aerodynamic equations of latent heat flux (LE) and sensible heat flux (H). The pixel scale variations of surface roughness (z_om, z_oh, Z₁), zero plane displacement (d), and the height of canopy (h_c) in BAITSSS are estimated based on vegetative indices. BAITSSS uses a Jarvis-type formulation to compute canopy resistance to quantify the transpiration (T) and a simplified soil surface resistance (r_ss) formulation to quantify evaporation (E_ss). The automated BAITSSS tool utilizes Python-based libraries (bindings of OGR, NumPy, scipy, and GDAL Geospatial Data Abstraction Library, see http://www.gdal.org)) with a package manager conda (“Conda — Conda documentation,” n.d.) and shell scripting for the various operations on the file system.

Figure 1: Modeling scheme for LE and H, soil water balance of soil surface (dashed line), and root zone control volume that includes the soil surface of BAITSSS (left) and typical irrigation sub-model adopted in BAITSSS (right) (Dhungel et al., 2016, 2019, 2019a, 2020) (For symbols, please visit BAITSSS Features).

Figure 2: Flowchart of automated BAITSSS surface energy balance (symbols in BAITSSS Features) .

Figure 3: BAITSSS Inputs

Major Inputs

Gridded BAITSSS is driven by weather variables (primarily from NLDAS) and remote sensing (from Landsat) based canopy formation (NDVI, LAI; Figure 3). Inference of seasonal canopy formation provided by linear interpolation between successive Landsat images. Input parameters include wind speed (u_z) at 10 m, air temperature (T_a) at 2 m, specific humidity (q_a) at 2 m, incoming solar irradiance (R_s↓), precipitation (P), and surface runoff (S_run). Weather data (hourly, ~ 12.5 km spatial resolution) can be downloaded from NLDAS (https://hydro1.gesdisc.eosdis.nasa.gov/data/NLDAS/NLDAS_FORA0125_H.002/1979/006/). The soil available water capacity (θ_awc) and soil volumetric water content at field capacity (θ_fc) metadata, associated with spatially referenced soil mapping units, are used from SSURGO.

Supplementary inputs:

National Land Cover Database (NLCD)

Cropland data layer (CDL)

BAITSSS Evaluation against Lysimeter Measured ET

BAITSSS was evaluated for a fully irrigated grain sorghum (2014) and corn (2016) crops at a lysimeter site near Bushland, Texas (Figures 4 and 5). BAITSSS was executed and evaluated with ground-based weather data near lysimeter site and later compared lysimeter measured ET, infrared thermometer (IRT) surface temperature, and net radiation from net radiometers in an advective weather of Bushland, TX. The evaluation period of corn includes from planting to maturity with a wide range of environmental (wetting and drying), surface (bare, partial, and full cover), and plant physiological conditions (growing to leaf senescence period).

Figure 4: Scatterplot of daily modeled vs lysimeter measured ET (a) sorghum between 23 August (DOY 235) and 01 October (DOY 274) 2014, (b) corn between 22 May (DOY 143) and 26 September (DOY 270) 2016 for Bushland, Texas.

Figure 5: Daily plots of ET from BAITSSS compared to lysimeter (a) 2014 sorghum and (b) 2016 corn.

Example Output

BAITSSS generates a large number of variables. BAITSSS is capable of computing landscape consumptive water use along with irrigation within a single-pixel (i.e. 30 m spatial resolution hourly scale) throughout the USA (Figure 6).

Figure 6: Automated gridded BAITSSS output: Visual inspection of (a) simulated seasonal evapotranspiration, (b) simulated seasonal irrigation from BAITSSS (c) seasonal mean NDVI, and (d) seasonal mean LAI from Landsat at 30 m spatial resolution between 10 May and 15 September 2008 at Sheridan 6 (SD-6) Local Enhanced Management Areas (LEMA), Kansas, USA .

Figure 7: Automated gridded BAITSSS output: Simulated soil water balance components of 0.5 MAD sprinkler irrigation of sampled pixel (100° 38′ 22″ W, 39° 21′ 38″ N) between 10 May and 15 September 2013 for SD-6 LEMA, Kansas, USA. The black vertical bar plots are simulated irrigation and red are gridded precipitation input.

References

Dhungel R, Aiken R, Colaizzi P, et al. Evaluation of uncalibrated energy balance model (BAITSSS) for estimating evapotranspiration in a semiarid, advective climate. Hydrological Processes. 2019;1–21. https://doi.org/10.1002/hyp.13458

Dhungel, R., Allen, R. G., Trezza, R., & Robison, C. W. (2016). Evapotranspiration between satellite overpasses: methodology and case study in agricultural dominant semi‐arid areas. Meteorological Applications, 23(4), 714-730.

Dhungel, R., R. Aiken, P. D. Colaizzi, X. Lin, R. L. Baumhardt, S. R. Evett, D. K. Brauer, G. W. Marek, and D. O’Brien. 2019a. Increased Bias in Evapotranspiration Modeling Due to Weather and Vegetation Indices Data Sources. Agronomy Journal. 0. doi:10.2134/agronj2018.10.0636

Dhungel, R., Allen, R. G., & Trezza, R. (2016). Improving iterative surface energy balance convergence for remote sensing based flux calculation. Journal of Applied Remote Sensing, 10(2), 026033.

Dhungel R, Aiken R, X.Lin, et al., 2019. Restricted water allocations: Landscape-scale energy balance simulations and adjustments in agricultural water applications . Agricultural Water Management doi.org/10.1016/j.agwat.2019.105854 .

Dhungel, R., Aiken, R., Evett, S. R, Colaizzi, P., Marek, G., Moorhead, J., Baumhardt, L., Brauer, D., Kutikoff, S., Lin, X. Energy Imbalance and Evapotranspiration Hysteresis under an Advective Environment: Lessons Learned from Lysimeter, Eddy Covariance Tower, and Energy Balance Model. Geophysical Research Letters doi.org/10.1029/2020GL091203.

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