Supplement to "Feedback between channel conveyance and flood frequency under stage–discharge rating curve uncertainty: Insights from the Pulangi River (Philippines)"

README

This supplement contains the data and code associated with the manuscript “Feedback between channel conveyance and flood frequency under stage–discharge rating curve uncertainty: Insights from the Pulangi River (Philippines),” which has been submitted to a journal for possible publication. Any questions or clarifications are welcome. 

Code, Data, and Attribute Descriptions

Uploaded are two directories (indicated in bold font, with their contents detailed below) containing input data and various outputs of the analyses performed for our case study on the Pulangi River at Lumayong (Philippines). Please consider this description equivalent to an omnibus README file for the deposited files. Note that we rescued the hydrometric data considered herein from the archives of the Water Projects Division (WPD) of the Philippine Department of Public Works and Highways (DPWH).  

• Two R-related files are included in this deposit: Pln_RC.R, the R script file, and Pln_RC_glbenv.RData, which contains stored objects generated from the script based on the last execution. The script consists of admittedly too many lines of code (e.g., for data wrangling, formal analyses, and producing figures used in the manuscript). Please be guided by the outline and the comments and kindly reach out lest issues with the code arise.

• Pln_RC_in_Zenodo contains the following CSV files and a subdirectory; these files were used as inputs to the R script for formal analyses.

• Pln-H_arch.csv contains the mean daily stage values [“H_bar_arch”] (m) for every day in the 2004—2020 sub-period [“Date”] (YYYY-MM-DD). The stages in this file are already corrected for gross errors.

• Pln-DM-filtered.csv contains the following information on the gaugings performed on the Pulangi at Lumayong (1983—2020): (i) date [“Date”] (YYYY-MM-DD); (ii) stage [“H”] (m); (iii) discharge in L/s [“Q_lps”] and m3/s [“Q_cms”]; (iv) wetted area [“A_sqm”] (m2); (v) mean flow velocity [“Vel_mps”] (m/s); (vi) channel width [“W_m”] (m); (vii) mean flow depth [“D_ave_m”] (m); (viii) location of the measurement cross-section with respect to the staff gauge, with negative values meaning downstream of the gauge and positive values meaning upstream of the gauge [“XS_loc_wrt_gage”]; (ix) maximum flow depth [“Max_Depth_m”] (m); and (x) minimum streambed elevation [“MINSBE”] (m). Note that this file includes only gaugings that passed our filtering step for measurement location consistency.

• Pln-XS-csv.csv contains data on the gauging transects: gauging ID [“GaugingID”]; date [“Date”] (YYYY-MM-DD); lateral distance from a fixed initial point [“Lat_distance”] (m); width of the gauging vertical [“Width_vert..m.”] (m); depth relative to the water surface [“Depth..m.”] (m); stage at the gauging vertical [“H_m_vert..m.”] (m); and elevation with respect to a fixed arbitrary datum at the station [“Elev..m.”] (m).

• Pln-DM_ofcl_rating.csv details the validity periods of the official rating curves used by the DPWH. It contains the following information: (i) subperiod [“Sub-period”]; (ii) rating curve [“Rating”]; (iii) start date of the validity period [“Start_date”] (YYYY-MM-DD); and (iv) end date of the validity period (“End_date”] (YYYY-MM-DD). 

• Three CSV files corresponding to the rating tables are included: Pln-DM - RatingTable_A.csv, Pln-DM - RatingTable_B.csv, and Pln-DM - RatingTable_C.csv. It has the following columns: (i) stage [“H”] (m); (ii) discharge in L/s [“Q_lps”]; and (iii) discharge in m3/s [“Q_cms”].

• Pln_Q_rated is a subdirectory containing just one CSV file, Pulangi_dpwh_rtd.csv. It has the following columns: year [“YEAR”] (YYYY); day [“DAY”] (DD); and columns for the months of January to December, under which are the officially rated or “processed” discharge estimates in L/s. 

• Pln_RC_in_Zenodo contains only one RDS file, Pln_DM_BDRC_tour.rds, which is the saved R object for the Bayesian rating curve modeling results. Generating said object may take time if re-run from scratch, so reading in this RDS can save on computational cost.