diff --git a/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv b/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv index e1a4c13..352f28f 100644 --- a/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv +++ b/manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv @@ -28,7 +28,6 @@ LIB,2014-10-26,-208.74866666666668 LIB,2014-10-27,-380.0002083333333 LIB,2014-10-28,-488.27825 LIB,2014-10-29,-554.2435833333333 -LIB,2014-10-30,-784.5495384615385 LIB,2014-11-03,-457.8645 LIB,2014-11-04,-670.8648333333333 LIB,2014-11-05,-900.0175 @@ -111,8 +110,6 @@ LIB,2015-09-16,-1312.0833333333333 LIB,2015-09-17,-1272.9166666666667 LIB,2015-09-18,-1245.8333333333333 LIB,2015-09-19,-1235.8333333333333 -LIB,2015-09-20,-1309.2857142857142 -LIB,2015-09-26,-1635 LIB,2015-09-27,-1773.3333333333333 LIB,2015-09-28,-1918.75 LIB,2015-09-29,-1799.1666666666667 @@ -171,8 +168,6 @@ LIB,2016-06-01,-1800 LIB,2016-06-02,-1555.4166666666667 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+RVB,2019,33,10059.081632653062 +RVB,2019,34,10128.92857142857 +RVB,2019,35,10210.119047619048 +RVB,2019,36,10977.797619047618 +RVB,2019,37,10245.833333333334 +RVB,2019,38,10742.797619047618 +RVB,2019,39,8912.857142857143 +RVB,2019,40,9099.642857142857 +RVB,2019,41,6703.928571428572 +RVB,2019,42,5704.444444444444 +RVB,2019,43,4467.7464788732395 +RVB,2019,44,5053.690476190476 +RVB,2019,45,4431.041666666667 diff --git a/manuscript_synthesis/data/processed/flow_week_avg_2013-2019.rds b/manuscript_synthesis/data/processed/flow_week_avg_2013-2019.rds new file mode 100644 index 0000000..c6b818e Binary files /dev/null and b/manuscript_synthesis/data/processed/flow_week_avg_2013-2019.rds differ diff --git a/manuscript_synthesis/src/process_data_rtm_flow.R b/manuscript_synthesis/src/process_data_rtm_flow.R index b0fc57b..da1976d 100644 --- a/manuscript_synthesis/src/process_data_rtm_flow.R +++ b/manuscript_synthesis/src/process_data_rtm_flow.R @@ -1,13 +1,15 @@ # NDFS Synthesis Manuscript # Purpose: Process and integrate the continuous flow data collected from the - # Yolo Bypass and downstream during years 2013-2019. Calculate daily averages - # which are used in figures and analysis for the NDFS synthesis manuscript. + # Yolo Bypass and downstream during years 2013-2019. Calculate daily and weekly + # averages which are used in figures and analysis for the NDFS synthesis + # manuscript. # Author: Dave Bosworth # Contacts: David.Bosworth@water.ca.gov # Load packages library(tidyverse) library(fs) +library(readxl) library(here) library(conflicted) @@ -24,11 +26,11 @@ i_am("manuscript_synthesis/src/process_data_rtm_flow.R") # Import Continuous Flow Data ----------------------------------------------- # Define directory for the continuous flow data on the NDFA SharePoint -fp_rtm_flow <- ndfa_abs_sp_path("2011-2019 Synthesis Study-FASTR/WQ_Subteam/Processed_Data/Continuous") +fp_rtm_flow_sp <- ndfa_abs_sp_path("2011-2019 Synthesis Study-FASTR/WQ_Subteam/Processed_Data/Continuous") # Import QA'ed and cleaned continuous flow data for the NDFS period of interest df_rtm_flow <- read_csv( - file = path(fp_rtm_flow, "RTM_INPUT_all_2021-04-20.csv"), + file = path(fp_rtm_flow_sp, "RTM_INPUT_all_2021-04-20.csv"), col_types = cols_only( StationCode = "c", DateTime = "c", @@ -37,61 +39,116 @@ df_rtm_flow <- read_csv( ) ) -# Import daily average tidally-filtered flow data for LIS -df_flow_tf_lis <- readRDS(here("Water_Quality/Data_Processed/LIS_SR_DailyAvgFlow_2013-2022.rds")) +# Import instantaneous and tidally-filtered flow data from LIS collected from + # 2013-2022 +df_rtm_flow_lis <- read_excel( + here("Water_Quality/Continuous_WQ/data-raw/LIS_FlowData_2013_2022.xlsx"), + sheet = "15 Min Net Flow", + skip = 10 +) # Clean and Integrate Continuous Data ------------------------------------- -# Prepare daily average tidally-filtered flow data collected at LIS to be - # combined to all other data -df_flow_tf_lis_c <- df_flow_tf_lis %>% +# Prepare tidally-filtered flow data collected at LIS to be combined to all + # other data +df_rtm_flow_lis_c <- df_rtm_flow_lis %>% transmute( StationCode = "LIS", - Date, - Flow = LIS_DailyAvgNetFlow + DateTime = round_date(force_tz(`Date & Time (PST)`, tzone = "Etc/GMT+8"), unit = "15 minute"), + Date = date(DateTime), + Flow = `Net Flow (cfs)` ) %>% drop_na(Flow) %>% # only include data within the NDFS period of interest + filter(year(Date) %in% 2013:2019) %>% ndfa_action_periods() %>% drop_na(FlowActionPeriod) %>% select(-c(Year, FlowActionPeriod)) +# Look for duplicated time stamps in the LIS flow data +df_rtm_flow_lis_c %>% + count(DateTime) %>% + filter(n > 1) +# No duplicated time stamps present in data set + # Prepare all other continuous flow data for averaging df_rtm_flow_c <- df_rtm_flow %>% + # only keep RCS, RD22, LIB, and RVB stations + filter(StationCode %in% c("RCS", "RD22", "LIB", "RVB")) %>% mutate( - # parse date-time variable and define tz as PST; add date variable + # parse date-time variable and define tz as PST; add date variables DateTime = ymd_hms(DateTime, tz = "Etc/GMT+8"), Date = date(DateTime), # use standard flow data for RCS and RD22 and tidally-filtered flow data for # LIB and RVB - FlowCombined = case_when( + Flow = case_when( StationCode %in% c("RCS", "RD22") ~ Flow, - StationCode %in% c("LIB", "RVB") ~ FlowTF, - TRUE ~ NA_real_ + StationCode %in% c("LIB", "RVB") ~ FlowTF ) ) %>% - # remove rows with NA values in FlowCombined, which only keeps RCS, RD22, LIB, - # and RVB - drop_na(FlowCombined) + # remove rows with NA values in Flow + drop_na(Flow) %>% + # remove FlowTF variable because its no longer needed + select(-FlowTF) %>% + # filter to years 2013-2019 + filter(year(Date) %in% 2013:2019) + +# Combine LIS flow data to all other flow data +df_rtm_flow_all <- + bind_rows(df_rtm_flow_lis_c, df_rtm_flow_c) %>% + # Add Year and Week variables + mutate( + Year = year(Date), + Week = week(Date) + ) # Calculate Daily Averages ------------------------------------------------ -df_flow_dv <- df_rtm_flow_c %>% +# Inspect number of measurements per day for each station +df_rtm_flow_all %>% + count(StationCode, Date, name = "NumMeas") %>% + count(StationCode, NumMeas, name = "NumDays") %>% + arrange(StationCode, desc(NumMeas)) %>% + print(n = 90) + +# Every station has at least a few days with large numbers of missing values. + # We'll only calculate daily averages for days that have 10% or fewer missing + # timestamps, which is 86-96 timestamps for LIS, RCS, and RD22 (collected at + # 15-min intervals) and 24-22 timestamps for LIB and RVB (collected at 1-hour + # intervals). + +df_flow_daily_avg <- df_rtm_flow_all %>% # calculate daily average flow values - summarize(Flow = mean(FlowCombined), .by = c(StationCode, Date)) %>% - # add daily average tidally-filtered flow data collected at LIS to all other - # daily average flow data - bind_rows(df_flow_tf_lis_c) %>% - # filter to years 2013-2019 - filter(year(Date) %in% 2013:2019) %>% + summarize( + Flow = mean(Flow), + NumMeas = n(), + .by = c(StationCode, Date) + ) %>% + # Remove days with >10% missing timestamps + filter( + StationCode %in% c("LIS", "RCS", "RD22") & NumMeas >= 86 | + StationCode %in% c("LIB", "RVB") & NumMeas >= 22 + ) %>% + select(-NumMeas) %>% arrange(StationCode, Date) +# Calculate Weekly Averages ----------------------------------------------- + +df_flow_week_avg <- df_rtm_flow_all %>% + # calculate weekly average flow values + summarize(Flow = mean(Flow), .by = c(StationCode, Year, Week)) %>% + arrange(StationCode, Year, Week) + + # Save and Export Data ---------------------------------------------------- -# Save daily average flow data as csv and rds files -df_flow_dv %>% write_csv(here("manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv")) -df_flow_dv %>% saveRDS(here("manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.rds")) +# Save daily and weekly average flow data as csv and rds files +df_flow_daily_avg %>% write_csv(here("manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.csv")) +df_flow_daily_avg %>% saveRDS(here("manuscript_synthesis/data/processed/flow_daily_avg_2013-2019.rds")) + +df_flow_week_avg %>% write_csv(here("manuscript_synthesis/data/processed/flow_week_avg_2013-2019.csv")) +df_flow_week_avg %>% saveRDS(here("manuscript_synthesis/data/processed/flow_week_avg_2013-2019.rds"))