In lanl/bci.elm.fates.hydrology: Hydrology outputs from ELM-FATES calibrated at BCI

knitr::opts_chunk$set(echo = TRUE)

Scripts in this repo are a part of the workflow associated with the following manuscript:

Chitra-Tarak, R, C Xu, S Aguilar, K Anderson-Teixeira, J Chambers, M Detto, B Faybishenko, RA Fisher, R Knox, C Koven, L Kueppers, N Kunert, SJ Kupers, NG McDowell, BD Newman, SR Paton, R Pérez, L Ruiz, L Sack, JM Warren, BT Wolfe, C Wright, SJ Wright, J Zailaa, SM McMahon (2021) Hydraulically vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytologist. https: //doi.org/10.1111/nph.17464

For the full work-flow and output datasets associated with the above manuscript see the following archived dataset:

Chitra-Tarak R, Xu C, Aguilar S, Anderson-Teixeira K, Chambers J, Detto M, Faybishenko B, Fisher R, Knox R, Koven C et al. 2020. Soil water potentials (1990–2018) from a calibrated ELM-FATES, and rooting depth analyses scripts, PA-BCI, Panama. 2.0. NGEE Tropics Data Collection. doi: 10.15486/ngt/1696806

Workflow Part II: ELM-FATES calibration for Soil Water Potentials

1. Check forcing data

code/1.0_Checking_met_data.R

2. Generate parameter files

1. FATES param files dependencies:

   • Parameter range for roob_b parameters are generated here: 2.0_root_parameters_b.R
   • Can verify that different root a can give same b: data-raw/estimation of root_b_par_same_b_for_different as.xlsx Other ranges are based on literature, or Zailaa et al. 2020

2. ELM param files dependencies: fpi_max ranges are generated from here:

   • First throughfall from Zimmermann et al and rain data from STRI at Lutz are linked: code/3.0_Throughfall.R
   • As rainfall data is not given in Zimmermann and throughfall data is collected for rainfall events (duration, amount and gap between events), rainfall that composes those events are inferred in Excel. Then range of interception is found for events > 10 mm: data-raw/throughfall_rain_hourly_for_inspection_new.xlsx

3. Surface data files dependencies: Soil texture data and soil organic content data is gathered and defined here: code/4.0_Surfdata_texture_options.R But these are not used by the model as it is forced with

   1. soil characteristic curves (Soil Water Content to Soil Water Potential) using Stephan Kupers' data, as well as, bci.hydromet/data-raw/soil_retention_curves_stephan.R

   2. soil hydraulic conductivity, using Godsey & Stallard et al defined here: code/5.0_Surfdata_Ksat_obs_and_bootstrapped_param.R

4. Surface data for ELM, ELM parameters and FATES paramater files code/6.0_Generate_parameter files.R These are transferred to the server.

3. Run simulations

1. Follow Google Doc 3.0_Running ELM-FATES with parameter ensembles @ Shared drive/Rutuja_work/web_only/ https://docs.google.com/document/d/1qqbkQGHMG8BMfrUejUlkBU3PvUqPuW0Pc6CBLBQzZZk/edit#heading=h.gjdgxs

2. Thus run ensemble members and extract simulations on the server with code/8.0_main.R (which in turn sources code/7.0_fun_extract.R) and transfer back to the desktop storing at a specific dated location such as data-raw/extract/2019-10-14_5000

4. Calibration and Sensitivity Analyses

1. Use code/09.0_ELM-FATES_output.R to generate RMSE or Rsq values between simulations and hydrological observations, plot best-fits for individual fluxes and run sensitivity analyses. These outputs are compiled in individual-flux-best-fits.html and Report.html

2. Use code/10.0_ELM-FATES_params_bestfit.R to generate objective function to choose best-fit simulations that fits all individual fluxes well (QRUNOFF, AET, Soil moisture by depth)

3. Confirm that individual fluxes are well captured by thus chosen best-fits: code/11.0_ELM-FATES_output_bestfit.R

4. Re-Run simulations for best-fits for the entire study period

Now for the chosen best-fits re-run simulations covering the entire time-period of 1985-2018 Follow Google Doc 4.0_Running ELM-FATES for best-fit parameter ensembles @ Shared drive/Rutuja_work/web_only/ https://docs.google.com/document/d/1V2UK_iSdXmkq3jR3TyowW7YllvrC7OVi/edit#heading=h.gjdgxs (and code/12.0_Prep_for_Best-fit_case_runs.R to generate par.sam numbers seperated by commas)

5. Extract, plot and save Soil Water Potentials as a data-package

1. Extract data from server and transfer to desktop at the following location data-raw/extract/2019-10-14_5000/best-fits,

2. Then extract and save full swp, btran time-series to be used for generating the data package. code/13.0_ELM-FATES_full best-fit_swp.R

3. Plot the entire time-series of SWC, SWP and ELM-FATES generated BTRAN for best-fit simulations. code/14.0_Plotting best-fits_full.R

Copyrights

© 2021. Triad National Security, LLC. All rights reserved. LANL C20130.

This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.

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groundhog.day = "2021-01-01"
groundhog.library('rmarkdown', groundhog.day)
rmarkdown::render("Readme.rmd", output_format = "pdf_document")
rmarkdown::render("Readme_data.rmd", output_format = "pdf_document")

lanl/bci.elm.fates.hydrology documentation built on Dec. 21, 2021, 8:50 a.m.