Last updated: 2021-01-06
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Knit directory: globalIRmap/analysis/
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Two streamflow gauging station datasets were used as the source of training and cross-validation data for study models — the World Meteorological Organization Global Runoff Data Centre (GRDC) database (n ≈ 10,000) and a complementary subset of the Global Streamflow Indices and Metadata archive (GSIM, n ≈ 25,000), a compilation of twelve free-to-access national and international streamflow gauging station databases.
Whereas the GRDC offers daily water discharge values for most stations, GSIM only contains time series summary indices computed at the yearly, seasonal and monthly resolution (calculated from daily records whose open-access release is restricted for some of the compiled data sources). Therefore, we used the GRDC database as the core of our training/testing set and complemented it with a subset of streamflow gauging stations from GSIM.
A GSIM station was included only if:
i) it was not already part of the GRDC database,
ii) it included auxiliary information on the drainage area of the monitored reach (for reliably associating it to RiverATLAS), and if it was located either
iii) on an IRES or
iv) in a river basin which did not already contain a GRDC station (assessed based on level 5 sub-basins of the global BasinATLAS database52, average sub-basin area = 2.9 x 104 km2).
We applied the described GSIM selection criteria to balance the relative amount of non-perennial vs. perennial records and the spatial distribution of stations in the model training dataset.
Each station in the combined dataset was geographically associated with a reach in the RiverATLAS stream network and every discharge time series was quality-checked through statistical and manual outlier detection (see Supplementary Information B1 for details on these procedures). Non-perennial gauging stations were only included in the dataset if they were free of anomalous zero-flow values (e.g. from instrument malfunction, gauge freezing, tidal flow reversal). We also excluded stations whose streamflow was potentially dominated by reservoir outflow regulation (i.e. with a degree of regulation > 50% or whose discharge time series exhibited an obvious alteration from natural flow permanence, see Supplementary Information B1), as flow regulating structures may change the flow class of a river either from perennial to non-perennial or vice-versa depending on their mode and rules of operation. We further narrowed our selection by adding only gauging stations with streamflow time series spanning at least 10 years — excluding years with more than 20 days of missing records for the calculation of this criterion and in subsequent analysis. Finally, we classified stations as non-perennial if their recorded discharge dropped to zero at least one day per year on average over the years of record, and as perennial otherwise. Stations with at least one zero-flow day per year on average (i.e. non-perennial) but without a zero-flow day during 20 consecutive valid years of data (those with ≤ 20 missing days), anywhere in their record, were deemed either to have experienced a shift in flow intermittence class (regardless of the direction of the shift) or to have ceased to flow due to exceptional conditions of drought and were also excluded.
Based on these selection criteria, the training dataset contained data for 3,967 perennial river reaches and for 1,388 non-perennial reaches, with 45 and 34 years of daily streamflow data on average, respectively, across all continents (except Antarctica).
Create data.table with gauge id, characteristics, X, Y, reason for removal, and embedded image
analyzemerge_gaugeir <- function(in_GRDCgaugestats, in_GSIMgaugestats, yearthresh,
in_gaugep, inp_resdir, plotseries = FALSE) {
### Analyze GSIM data ####################################
GSIMstatsdt <- rbindlist(in_GSIMgaugestats)
#Outliers from examining plots of ir time series (those that were commented out were initially considered)
GSIMtoremove_o1800_irartifacts <- c(
'AT_0000014', #remove - changed flow permanence
'BR_0000286', #remove - Tapajos river. Impossible that it dries out.
'BR_0000581', #remove - 0 flow happened only once at the end of record
'BR_0000786', #remove - probably perennial
'BR_0000862', #remove - probably perennial
'BR_0001011', #remove - probably perennial
'BR_0001116', #remove - probably naturally perennial
'BR_0001133', #remove - probably naturally perennial
'CA_0001057', #remove - only one 0 flow event in 28 years
'CA_0003488', #remove - only one 0 flow event
'CA_0003526', #remove - only one 0 flow event
'ES_0000078', #remove - first 20 years without 0 flow
'ES_0000084', #remove - probably perennial, first 20 years without 0 flow
'ES_0000388', #remove - record is too discontinued to assess permanence, probably perennial; besides, it looks regulated
'ES_0000444', #remove - only one 0 valid flow occurrence
'ES_0000525', #remove - very discontinued record. Seems to have changed flow permanence
'ES_0000729', #remove - seems to have shifted
'ES_0000785', #remove - erroneous
'ES_0000786', #remove - looks regulated
'ES_0000816', #remove - doesn't look reliable
'ES_0000856', #remove - 0 flow occured only once in record
'ES_0000892', #remove - probably perennial
'ES_0000806', #remove - probably perennial, 0 flow occurred only once
'ES_0000910', #remove - changed flow permanence
'ES_0000986', #remove - 0 flow occured only once
'ES_0000996', #remove - changed flow permanence
'ES_0001020', #remove - hard to tell flow permanence
'ES_0001052', #remove - regulated
'ES_0001082', #remove - changed flow permanence, either regulated or...
'ES_0001085', #remove - changed flow permanence
'ES_0001116', #remove - occurred only the last year not the first 22 years
'ES_0001162', #remove - most occurrences seem buggy
'FI_0000156', #remove - no occurrences in first 60% of record
'FR_0000052', #remove - no occurrences in first 20 years
'HU_0000017', #remove - only one zero flow occurence in 25 years
'IE_0000014', #remove - no 0 flow occurrence in first 25 years
'IN_0000023', #remove - no 0 flow occurrence in first 20 years
'IN_0000045', #remove - no 0 flow occurrence in first 20 years
'IN_0000046', #remove - changed flow permanence
'IN_0000050', #remove - changed flow permanence
'IN_0000063', #remove - changed flow permanence
'IN_0000064', #remove - changed flow permanence
'IN_0000113', #remove - only one 0 flow occurrence
'IN_0000124', #remove - changed flow permanence
'IN_0000125', #remove - changed flow permanence
'IN_0000134', #remove - changed flow permanence
'IN_0000170', #remove - only one 0 flow occurrence
#'IN_0000190', #checked- looked regulated
'IN_0000255', #remove - changed flow permanence
'IN_0000283', #remove - changed flow permanence
'IN_0000312', #remove - changed flow permanence
'MZ_0000010', #remove - changed flow permanence
'NO_0000028', #remove - changed flow permanence
'NO_0000030', #remove - occurred only once
'RU_0000189', #remove - occurred only once
'RU_0000265', #remove - changed flow permanence
'RU_0000358', #remove - changed flow permanence
'TZ_0000018', #remove - hard to tell but probably changed flow permanence
'US_0005106', #remove - hard to tell, probably changed flow permanence
'US_0005874', #remove - changed flow permanence
'US_0005876', #remove - changed flow permanence
'US_0006103', #remove - regulated
#'US_0006156', #checked - regulated, barrier made it perennial?
'US_0006396', #remove - buggy looking pre-1960
'US_0006440', #remove - not enough data to tell, seemed like changed flow permanence
'US_0006537', #remove - changed flow permanence
'ZA_0000074', #remove - missing data
'ZA_0000268', #remove - seemingly changed flow permanence
'ZA_0000270' #remove - changed flow permanence
)
GSIMtoremove_o1961_irartifacts <- c(
#'AR_0000014', #maybe regulated --- checked
'AT_0000021', #remove
'AT_0000026', #remove
'AT_0000038', #remove
'AT_0000059', #remove
'BR_0000557', #inspected - confirmed dry channel visually
'BR_0000662', #is regulated -- remove
#'BR_0000664', #change of regime, maybe regulated --- but was intermittent before
'BR_0000706', #remove
'BR_0000717', #remove --regulated intermittency
'BR_0000778', #remove, too much missing data
'BR_0001104', #remove
#'CA_0003473', downstream of natural lake, all good
#'CA_0003526', #downstream of lakes, maybe low lake levels
'CA_0003544', #already in GRDC and a bit buggy looking
'CN_0000004', #remove
'CN_0000009', #remove
'CN_0000010', #remove
'CN_0000012', #remove
'CN_0000013', #remove
'CN_0000021', #remove
'CN_0000022', #remove
'CN_0000026', #remove
'CN_0000029', #remove
'CN_0000038', #remove - got dewatered by dam
'CN_0000043', #remove
'CN_0000062', #remove
#'ES_0000525', #maybe regulated --- check
'ES_0000581', #remove
'ES_0000660', #remove -- regulated
#'ES_0000676', #looks regulated --- but probably intermittent before
'ES_0000794', #remove - same as 0000832
'ES_0000784', #remove -- same as 'ES_0000785'
'ES_0000818', #remove - same as ES_0000785 and ES_0000784 which are not intermittent
'ES_0000841', #remove -- regulated
'ES_0000958', #remove -- 0s seem erroneous
'FI_0000107', #remove -- lake inlet, maybe just become standing water when high water level
#'IE_0000014', #downstream of lake -- must be drop in lake levels
'IN_0000105', #remove -- downstream of major dams -- no records pre-1968 time of building
'IN_0000159', #remove -- regulated, no records before dam
'IN_0000280', #remove -- regulated, no records before dam. intermittency started after building of Parambikulam dam
#'IN_0000309', #intermittent before building of reservoir in 1988
'NA_0000050', #remove, unreliable, 0s and interpolations
#'NO_0000018', #checked -- downstream of natural lake. probably water level decrease
'NO_0000044', #remove
'NO_0000090', #remove
'SE_0000058', #remove -- downstream of dam, didn't use to be intermittent. and experiences ice
#'US_0001855', #maybe rounded values --- check
#'US_0001861', #maybe rounded values --- check
#'US_0001868', #maybe rounded avlues --- check
'US_0002247', #remove -- regulated -- GRDC 4149415 upstream not intermittent
'US_0002248', #remove -- regulated just downstream of 2247
'US_0002791', #on usgs website: "close proximity to the Ohio River. During periods of high water on the Ohio River, the computed discharge at this site may be incorrectly displayed due to the backwater effect created."
#'US_0003591', #maybe rounded values --- check
# 'US_0003774', #maybe rounded values --- check
# 'US_0003836', #maybe rounded values --- check
# 'US_0004023', #maybe rounded values --- check
# 'US_0004216', #maybe rounded values --- check
# 'US_0004232', #maybe rounded values --- check
# 'US_0004658', #maybe rounded values --- check
'US_0004773', #remove - regulated. no records prior to reservoir buiding
#US_005099, #confirmed dried bed on imagery
# 'US_0005161', #maybe rounded values --- check
# 'US_0005177', #maybe rounded values --- check
#'US_0005303', #looks fine on usgs website and imagery. just small
#'US_0005596', #looks fine, small
#'US_0005597', #looks fine, small
# 'US_0005622', #maybe rounded values --- check
# 'US_0005623', #maybe rounded values --- check
# 'US_0005684', #maybe rounded values --- check
# 'US_0005687', #maybe rounded values --- check
'US_0005732', #remove -- regime shift because of Lake Arcadia (reservoir)
#'US_0005859', #maybe rounded values --- check
#'US_0005879', #maybe rounded values --- check
# 'US_0006073', #maybe rounded values --- check
# 'US_0006109', #maybe rounded values --- check
# 'US_0006154', #maybe rounded values --- check
'US_0006155', #currently regulated but used to be intermittent but same as 6156 so remove
#'US_0006156', #currently regulated but used to be intermittent so keep
#'US_0006301', #maybe rounded values --- check
#'US_0006327', #maybe rounded values --- check
#'US_0006387', #maybe rounded values --- check
#'US_0006206', #all good, confirmed dry with imagery
#'US_0006574', #maybe regulated? ---checked all good
#'US_0006975', #maybe rounded values --- check
#'US_0006984', #maybe rounded values --- check
#'US_0006985', #maybe rounded values --- check
#'US_0006986', #maybe rounded values --- check
'US_0008607', #regulated. 1978 intermittency started after building reservoir
'US_0008687', #regulated. intermittency started after bui;lding reserovir 1935
# 'US_0008726', #maybe rounded values --- check
# 'US_0008779', #maybe rounded values --- check
'ZA_0000008', #remove
'ZA_0000084' #remove
)
# readformatGSIMmon(
# GSIMstatsdt[gsim_no == 'US_0000546', path]) %>%
# .[MIN != 0, min(MIN)]
#----- Check flags in winter IR for GSIM
wintergaugesall_GSIM <- plot_winterir(
dt = GSIMstatsdt, dbname = 'gsim', inp_resdir = inp_resdir,
yearthresh = 1800, plotseries = plotseries)
#Check suspicious canadian ones
GSIMwintermeta <- in_gaugep[in_gaugep$gsim_no %in% wintergaugesall_GSIM$gsim_no,]
canadians_toinspect <- in_gaugep[in_gaugep$gsim_no %in%
paste0('CA_000', c(3469, 3473, 3526, 3544, 6082, 6122)),]$reference_no
# if (!dir.exists(hy_dir())) download_hydat()
# cancheck <- lapply(canadians_toinspect, function(refno) {
# merge(hy_daily(station_number = refno),
# hy_stn_regulation(station_number = refno),
# by='STATION_NUMBER') %>%
# setDT
# }) %>%
# rbindlist
# cancheck[REGULATED==T, unique(STATION_NUMBER)] #No regulated station
# cancheck[Value==0, .N, by=.(STATION_NUMBER, Symbol)]
#E - Estimate: no measured data available for the day or missing period,
# and the water level or streamflow value was estimated by an indirect method
#A - Partial Day: daily mean value was estimated despite gaps of more than
# 120 minutes in the data string or missing data not significant enough to
# warrant the use of the E symbol.
#B - Ice conditions: value was estimated with consideration for the presence
# of ice in the stream. Ice conditions alter the open water relationship
# between water levels and streamflow.
#D - Dry: stream or lake is "dry" or that there is no water at the gauge.
# This symbol is used for water level data only.
#R - Revised: The symbol R indicates that a revision, correction or addition
# ` has been made to the historical discharge database after January 1, 1989.
# ggplot(cancheck[Value > 0, ], aes(x=Date, y=Value, color=Symbol)) +
# geom_vline(data=cancheck[is.na(Value),], aes(xintercept = Date), color='grey', alpha=1/4) +
# geom_point(alpha=1/6) +
# geom_point(data=cancheck[Value==0,]) +
# facet_wrap(~STATION_NUMBER, scales='free') +
# theme_classic()
#Remove 06NB002, 06AF001 — CA_0003544, CA_0003473
#Check others 'CN_0000047', 'NO_0000018', 'RU_0000089',
#'RU_0000391', 'RU_0000393', 'RU_00000395', 'RU_0000436',
#'RU_0000470', 'US_0008687')
check <- readformatGSIMmon(GSIMstatsdt[gsim_no == 'US_0008687',path])
#Remove CN_
GSIMtoremove_winterIR <- c('CA_0003544', #erroneous patterns (abnormally high values)
'CA_0003473', #sudden peak — unsure about estimated discharge under ice conditions
'CN_0000047', #Anomalous change from near 0 discharge to 150 m3/s, no explanation)
'NO_0000018', #Record for 18 years without a 0, 0s every month for last two years before discontinuation
'RU_0000391', #Stopped recording during the winter the last ~10 years. Maybe questionable winter data
'RU_0000393', #Didn't record during the winter for the first 20 years. Maybe questionable winter data
'RU_0000436', #Same
'RU_0000470', #Same
'US_0008687' #Just downstream of reservoir which appears to have caused intermittence
)
#----- Check flags in coastal IR for GSIM
GSImcoastalirall <- plot_coastalir(in_gaugep = in_gaugep, dt = GSIMstatsdt,
dbname = 'gsim', inp_resdir = inp_resdir,
yearthresh = 1800, plotseries = plotseries)
#Already checked CA_0006122
GSIMtoremove_coastalIR <- c('NO_0000044',
'NO_0000090')
#------ Remove stations with unstable intermittent flow regime
#Remove those which have at least one day per year of zero-flow day but instances
#of no zero-flow day within a 20-year window — except for three gauges that have a slight shift in values but are really IRES
GSIMtoremove_unstableIR <- GSIMstatsdt[
(mDur_o1800 >= 1) & (!movinginter_o1800), gsim_no]
### Analyze GRDC data ####################################
GRDCstatsdt <- rbindlist(in_GRDCgaugestats)
#Remove all gauges with 0 values that have at least 99% of integer values as not reliable (see GRDC_NO 6140700 as example)
GRDCtoremove_allinteger <- GRDCstatsdt[integerperc_o1800 >= 0.99 &
intermittent_o1800 == 1, GRDC_NO]
#Outliers from examining plots of ir time series (those that were commented out were initially considered)
GRDCtoremove_o1800_irartifacts <- c(
1134300, #remove - Appears to have been a change in flow permanence, but too much missing data
1159830, #check missing values - probably perennial
1160785, #remove - changed flow permanence
1160800, #remove - changed flow permanence
1160850, #remove - changed flow permanence
1160881, #remove - changed flow permanence
1196141, #remove - doesn't look reliable, hard to assess long term flow permanence
1199410, #remove - changed flow permanence
1259500, #remove - hard to assess long term flow permanence
1428500, #remove - changed flow permanence
1434200, #remove - almost all integers, impossible to tell flow permanence
1434300, #remove - almost all integers
1491815, #remove - changed flow permanence
1591110, #remove - doesn't look reliable, changed flow permanence
3652050, #remove - looks naturally perennial
3652200, #remove - looks natural perennial (first 15 years)
4208372, #remove
4208655, #remove - insufficient data to tell flow permanence
4208855, #remove - insufficient data to tell flow permanence
4208857, #remove - probably not intermittent only occured once - missing years suggest perennial too
4213566, #remove - probably perennial
4214297, #remove - insufficient data to tell flow permanence
4214298, #remove - probably perennial
4769200, ##remove - insufficient data to tell flow permanence
5204170, #remove - probably changed flow permanence
5405095, ##remove - hard to tell whether actually intermittent (change at beginning, maybe regulated?)
5708200 #remove - probably perennial
)
GRDCtoremove_o1961_irartifacts <- c(#1104800, #Keep
1134500, #Goes from 1 cm3/s to 0. Occurs only one time in entire record.
1159302, #Anomalous zero values
1159303, #weird patterns, isolated 0s, sudden jumps and capped at 77
#1159320, #0 values for the first 14 years but doesn't affect post-1961
#1159325, #0 values for most record. probably episodic and due to series of agricultural ponds
1159510, #0 values for most record, on same segment as 1159511 but weird record, remove.
#1159512, #0 values for first 4 years. Otherwise no real reason to doubt.
#1159520, #values seem capped after 1968, otherwise seem fine. Could just be rating curve
1160101, #0 values are erroneous (goes from 1-10 m3/s to 0 then right back up )
1160310, #Only at the beginning of record. Now regulated. Probably dried because of dam building or natural regime
1160340, #pretty definitely erroneous. Missing data must have been labeled as 0.
#1160378, #just downstream of dam. reservoir fully dry on satellite imagery, so keep as intermittent even when not regulated
#1160420, #decreases to 0 appear a bit sudden but ok
1160427, #0s at beginning of record, must be missing. Wouldn't be considered intermittent without that.
1160435, #discard. 0s are mostly missing data. unreliable
1160440, #only 0 values
1160470, #Maybe discard. change of rating curve in 1947, mostly missing data until 1980 but truly intermittent based on imagery
#1160510, #sudden shift in regime 1974. maybe change in bed morphology or dam building
1160511, #unreliable, remove
#1160540, #only 0 - nodata for first 15 years. seemingly good data post 1979. Keep.
#1160580, #change of regime post-1974 from intermittent to perennial. not sure why. on same river as 1160510
#1160635, #0 values post-1985 seem abrupt but enough values otherwise to make it intermittent
1160650, #0 values are outliers
1160670, #0 values are outliers due to dam just upstream
#1160675, #Some outliers but otherwise most 0 values seem believable
1160701, #0 values look like outliers
1160756, #most 0 values look like outliers
1160770, #only happened once. maybe outlier zeros
1160780, #unreliable record. remove
#1160785, #some 0 values look like outliers but most seem believable
#1160790, #weird record pre 1963 but does not affect much. rating curve must have changed
1160795, #most 0 values look like outliers
1160825, #first 2 years of data are 0s, some other outlying 0s but enough believable ones to be intermittent
1160840, #only 2 zero values are believable, others are outliers
#1160850, #unsure
1160880, #outlying 0 values. Tugela river. Perennial
1160900, #most 0 values look like outliers
1160911, #Remove. only one zero-flow vlaues appears right
1160971, #check record. most zero-flow values look outliers.
1160975, #Remove. unreliable
1196100, #Half of the zero-flow values are outliers
1196102, #Remove, unreliable
#1196160, #Some outlying 0 values but most are good
1197500, #Remove, unreliable
1197540, #Check, abrupt 0 values. probably remove
1197591, #Check, abrupt 0 values. probably remove
1197700, #Check, abrupt 0 values. probably remove
#1197740, #several outlying 0 flow values, but most are believable
1199100, #Most 0s are outliers
1199200, #Remove
#1199410, #a few outliers pre-1990 but otherwise good
1259800, #Remove, 0 values come from integer-based record
1286690, #Remove, it seems that values were rounded to second decimal
1289230, #Change in regime past 1963, only one outlier zero flow value after, remove
1428400, #0 values come from integer-based record
1434810, #0 values come from integer-based record
#1491870, #some outliers but most 0s are believable
1494100, #outlier 0s
#1496351, #regulated but naturally intermittent
1591720, #buggy, some values also look interpolated
1591730, #anomalous drops
1733600, #0 values come from integer-based record and outlier
1837410, #Check, abrupt 0 values. probably remove
#1837430, #Essentially the same as 1837410. Naturally intermittent before dam
#1896501, #Faulty values but intermittent
1897550, #outlier 0s
1898501, #outlier 0s
1992400, #nearly half of 0s are seemingly outliers
1992600, #Outlier 0 values
2549230, #Remove
2588500, #Remove
2588551, #Remove
2588630, #Remove
2588640, #Remove
2588708, #Remove
2588820, #Remove
2589230, #Remove
2589370, #Remove
2591801, #Remove
2694450, #Remove
2917100, #Remove
2969081, #Remove
2999920, #0s come from integer-vased values
3627900, #Remove
#3650460, #some issues but enough believable intermittent ones
3650470, #0s come from integer-based values
#3650475, #earlier values pre-1973 are erroneous but post-2002 are valid 0s
#3650610, #integers pre-1960s but still intermittent after
3650640, #lots of outliers. remove
3650649, #change of flow regime due to dam building but intermittent before
3650690, #weird values but intermittent
3650928, #weird. remove
3652135, #some outliers, but enough 0s otherwise
3844460, #Remove
#4101451, #station downstream also has 0s
4103700, #zero values are outliers
#4113304, #Not super representative of current regime
4122150, #just downstream of dam.
4148955, #just downstream of dam which made it intermittent. May even be tidally influenced
4149411, #Remove
#4150605, #just downstream of lwesville dam in Dallas. previously intermittent as well but will be removed anyways as >50% dor
#4151513, #Looks regulated but will be removed as > 50% regulated #################### good example for showing effect of regulation
4152120, #regulated. intermittency occured right at the building of Alamo Lake on the Bill William
4208195, #0s stem from interpolation
#4213540, #one outlier, rest is good although occurred only once in 1965..
#4213675, #looks funny but just downstream of natural lake
4213905, #Regulated. hence the intermittency
4213911, #Is regulated, remove
4214075, ##Remove
4234300, #remove. regulated
4243610, #remove, regulated ############################ Good example of regulation
4351710, #Remove. 0s due to outlier and integer-based values
4355500, #Remove. outlier values. regulated
4357510, #Remove
4773050, #look erroneous (going from 140L/s to 0 in one day. must be a rating curve issue)
5101020, #Remove
5101101, #Remove
5101130, #Remove
5101201, #Remove
#5101290, #valkues before 2000 aren't great. But 0 values post 2000 are believable
5101305, #most 0 values are wrong
5101380, #Remove
5101381, #Remove
5109110, #Remove
5109200, #Remove, 0 values at beginning, interpolations used, not reliable
5109230, #Remove
5109251, #Remove
5202140, #Remove
5202145, #Most 0s look erroneous
5202228, #Regulated? Remove
5302229, #Remove
5302251, #Remove
5302261, #Remove
#5405046, #canal through adelaide - sturt river
#5405095, #looks wierd but intermittent it is
5606130, #Remove
5608100, #Remove
5803160, #Remove
5864500, #Remove. rounded to 10L/s
5870100, #remove, rounded to 100L/S
6119100, #Remove, rounded to 10L/s
#6128220, #Weird. occurred only once but seems believable
6140700, #Remove, perfect example of what an integer-based record involves
6401800, #Remove, became regulated
6442300, ##Remove, perfect example of what an integer-based record involves
6444250, #Remove
6444350, #Remove
6444400, #Check, abrupt 0 values
6935570 #Remove, rounded to 10L/s
)
#plotGRDCtimeseries(GRDCstatsdt[GRDC_NO == 4101450,])
#### Check intermittent record
# checkno <- 6444400 #GRDC_NO
# check <- checkGRDCzeroes( #Check area around 0 values
# GRDCstatsdt, in_GRDC_NO=checkno, period=15, yearthresh=1800,
# maxgap=20, in_scales='free', labelvals = F)
# checkno %in% GRDCtoremove_allinteger #Check whether all integers
# in_gaugep[in_gaugep$GRDC_NO==checkno & !is.na(in_gaugep$GRDC_NO), "dor_pc_pva"] #check DOR
# GRDCstatsdt[GRDC_NO == checkno, integerperc_o1800] #Check % integers
#Outliers from examining plots of perennial time series (those that were commented out were initially considered)
#Try to find those:
# whose low flow plateaus could be 0s
# whose perennial character is dam-driven or maybe irrigation driven (changed from IR to perennial but hard to find)
# whose missing data are actually 0s
# whose quality is too low to be reliable
GRDCtoremove_o1800_pereartifacts <- c(
1159800, #look regulated
#1160302, #looked regulated -- but nothing obvious
4101200, #low flows may be zeros as they plateau unless all integers
4118850, #low flows plateau
4125903, #too regulated
4126351, #looks too regulated (as far as the record goes)
4148850 #unsure, missing data have lots of zeros
)
GRDCtoremove_o1961_pereartifacts <- c(
1160331, #remove- Lower plateaus are likely overestimated 0 values
#1593100 #checked - clearly not intermittent but really bad quality
1593751, #remove - missing values seem to contain intermittency
1599111, #remove - maybe going down to 0 in missing data
1160788, #remove - looks like plateauing at 0 but rating curve is off
1899100, #remove-maybe intermittent now. intermittent last year of record (missing gap)
3628200, #remove- lower values may be 0s but rating curve is off
3652030, #remove- 0s in missing years and low flows in other years may also be 0s
4115225, #remove -looks like it became regulated and may have otherwised become intermittent
4146610, #remove- low flows may be 0s but rating curve is off
4151801, #remove - regulated and may otherwise go dry Rio Grande
4152651, #remove - regulated by blue mesa reservoir, may have been intermittent otherwise ######### good example
4208610, #remove - too much missing data but if not would be intermittent ###################good example of that
4213055, #remove- too much missing data but if not would be intermittent
4213802, #remove - identical to 4213801
4214320, #remove - lower values may be 0s and in missing years
#4231620, #check -- maybe regulated but would probably otherwise be perennial
4362100, #remove = lower values may be 0s and in missing years
5606090, #remove - lower values may be 0s
56064140, #remove - lower values may be 0s
6123630, #remove- lower values may be 0s
6233410, #remove - looks erroneous
6335020, #remove - looks identical to 633060
6335050, #remove - looks identical to 6335060
6337503, #remove -looks heavily regulated. cannot tell whether may have been intermittent before
6442100, #remove - identical to 6442600
6935146, #remove - looks identical to 6935145
6972350, #remove
6935600 #remove - identical to 6935145
)
#---------- Check flags in winter IR
plot_winterir(dt = GRDCstatsdt, dbname = 'grdc', inp_resdir = inp_resdir,
yearthresh = 1800, plotseries = plotseries)
#Checked for seemingly anomalous 0s. Sudden decreases.
#Check for flags, check satellite imagery, station name, check for construction of reservoir
#If no way to explain, remove or if caused by reservoir/dam that is not in GranD
#4220310 and 4243610, just downstream of dams that are in GranD — should be taken in account
GRDCtoremove_winterIR <- c('2588640', #Sudden shift
'2589230',#Sudden shift
'4213540',#Sudden shift
'4214075',#Sudden shift
'6401800' #Just downstream of a reservoir that is not in GranD
)
#------ Check time series of stations within 3 km of seawater
GRDCcoastalirall <- plot_coastalir(in_gaugep = in_gaugep, dt = GRDCstatsdt,
dbname = 'grdc', inp_resdir = inp_resdir,
yearthresh = 1800, plotseries = plotseries)
GRDCcoastalirall[, unique(readformatGRDC(path)$Flag), by=GRDC_NO]
#Nothing obviously suspect beyond those that ad already been flagged
#Inspect statistics for 4208857, 4213531 as no flow days occurred only one year
# ID = '6976300'
# GRDCstatsdt[GRDC_NO == ID,]
# check <- readformatGRDC(GRDCstatsdt[GRDC_NO == ID,path])
# unique(check$Flag)
#
# plotGRDCtimeseries(GRDCstatsdt[GRDC_NO == ID,], outpath=NULL)
#------ Remove stations with unstable intermittent flow regime
#Remove those which have at least one day per year of zero-flow day but instances
#of no zero-flow day within a 20-year window — except for three gauges that have a slight shift in values but are really IRES
GRDCtoremove_unstableIR <- GRDCstatsdt[
(mDur_o1800 >= 1) & (!movinginter_o1800) &
!(GRDC_NO %in% c(1160115, 1160245, 4146400)), GRDC_NO]
#Before cleaning
GRDCtoremove_all <- unique(c(GRDCtoremove_allinteger,
GRDCtoremove_o1800_irartifacts,
GRDCtoremove_o1800_pereartifacts,
GRDCtoremove_o1961_irartifacts,
GRDCtoremove_o1961_pereartifacts,
GRDCtoremove_winterIR,
GRDCtoremove_unstableIR))
GRDCstatsdt[intermittent_o1800 == 1 & totalYears_kept_o1800 >= 10, .N]
GRDCstatsdt[intermittent_o1800 == 1 & totalYears_kept_o1800 >= 10 &
!(GRDC_NO %in% GRDCtoremove_all), .N]
### Check changes in GSIM discharge data availability and flow regime over time ####
GSIMstatsdt_clean <- GSIMstatsdt[!(gsim_no %in% c(GSIMtoremove_o1961_irartifacts,
GSIMtoremove_o1800_irartifacts,
GSIMtoremove_coastalIR,
GSIMtoremove_winterIR,
GSIMtoremove_unstableIR)),]
mvars <- c('intermittent_o1800',
'intermittent_o1961',
'intermittent_o1971')
alluv_formatGSIM <- melt(GSIMstatsdt_clean,
id.vars = c('gsim_no',
paste0('totalYears_kept_o',
c(1800, 1961, 1971))),
measure.vars = mvars) %>%
.[totalYears_kept_o1800 < 10 & variable %in% mvars, value := NA] %>%
.[totalYears_kept_o1961 < 10 & variable %in% mvars[2:3], value := NA] %>%
.[totalYears_kept_o1971 < 10 & variable %in% mvars[3], value := NA] %>%
.[, count := .N, by=.(variable, value)]
### Check changes in GRDC discharge data availability and flow regime over time ####
GRDCstatsdt_clean <- GRDCstatsdt[!(GRDC_NO %in% GRDCtoremove_all),]
alluv_formatGRDC <- melt(GRDCstatsdt_clean,
id.vars = c('GRDC_NO',
paste0('totalYears_kept_o',
c(1800,1961, 1971))),
measure.vars = mvars) %>%
.[totalYears_kept_o1800 < 10 & variable %in% mvars, value := NA] %>%
.[totalYears_kept_o1961 < 10 & variable %in% mvars[2:3], value := NA] %>%
.[totalYears_kept_o1971 < 10 & variable %in% mvars[3], value := NA] %>%
.[, count := .N, by=.(variable, value)]
###Analyze change in number of gauges with different intermittency criterion
irsensi_format <- melt(rbind(GRDCstatsdt_clean, GSIMstatsdt_clean,
use.names=TRUE, fill=T)[totalYears_kept_o1961 >= 10,],
id.vars = c('GRDC_NO', 'gsim_no'),
measure.vars = paste0('mDur_o', c(1800, 1961, 1971))) %>%
.[!is.na(value) & value >0,] %>%
setorder(variable, -value) %>%
.[, cumcount := seq(.N), by=.(variable, is.na(GRDC_NO))]
ggirsensi <- ggplot(irsensi_format, aes(x=value, y=cumcount,
color=variable, linetype=is.na(GRDC_NO))) +
geom_line(size=1.1) +
coord_cartesian(expand=0, clip='off') +
scale_x_sqrt(breaks=c(1, 5, 10, 30, 90, 180, 365),
labels=c(1, 5, 10, 30, 90, 180, 365)) +
geom_vline(xintercept=c(1, 5)) +
annotate(geom='text', x=c(1.7,6.5), y=150, angle=90,
label=c(sum(irsensi_format[value==1 & variable=='mDur_o1800',
max(cumcount), by=is.na(GRDC_NO)]$V1),
sum(irsensi_format[value==5 & variable=='mDur_o1800',
max(cumcount), by=is.na(GRDC_NO)]$V1))) +
theme_classic()
plots <- grid.arrange(
ggalluvium_gaugecount(dtformat = alluv_formatGRDC, alluvvar = 'GRDC_NO'),
ggalluvium_gaugecount(dtformat = alluv_formatGSIM, alluvvar = 'gsim_no'),
ggirsensi
)
### Bind GRDC and GSIM records ####################################
databound <- rbind(GRDCstatsdt_clean,
GSIMstatsdt_clean,
use.names=TRUE, fill=T)
return(list(plots=plots, data=databound))
}Create a map with all gauges and their characteristics
#Render image based on directory of graphs in github server <- function(input, output, session) { # Send a pre-rendered image, and don’t delete the image after sending it output\(preImage <- renderImage({ # When input\)n is 3, filename is ./images/image3.jpeg filename <- normalizePath(file.path(‘./images’, paste(‘image’, input$n, ‘.jpeg’, sep=’’)))
# Return a list containing the filename and alt text
list(src = filename,
alt = paste("Image number", input$n))}, deleteFile = FALSE) }
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] grid stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] visNetwork_2.0.9 tidyhydat_0.5.0
[3] raster_3.3-13 quantreg_5.67
[5] SparseM_1.78 profvis_0.3.6
[7] tictoc_1.0 stringr_1.4.0
[9] sf_0.9-6 scales_1.1.0
[11] rnaturalearthdata_0.1.0 rnaturalearth_0.1.0
[13] rgeos_0.5-5 rgdal_1.5-16
[15] sp_1.4-2 reprex_0.3.0
[17] rbin_0.2.0 ranger_0.12.1
[19] qs_0.23.2 poweRlaw_0.70.6
[21] plyr_1.8.6 patchwork_1.1.0
[23] paradox_0.4.0 olsrr_0.5.3
[25] mvtnorm_1.1-1 mlr3viz_0.3.0
[27] mlr3tuning_0.3.0 mlr3pipelines_0.3.0
[29] mlr3learners_0.3.0 mlr3_0.6.0
[31] mgcv_1.8-33 nlme_3.1-147
[33] maps_3.3.0 lubridate_1.7.9
[35] kableExtra_1.2.1 gstat_2.0-6
[37] gridExtra_2.3 ggpubr_0.4.0
[39] ggnewscale_0.4.3 ggalluvial_0.12.2
[41] ggplot2_3.3.0 gdalUtils_2.0.3.2
[43] furrr_0.1.0 future.callr_0.5.0
[45] future.apply_1.6.0 future_1.18.0
[47] drake_7.12.5 dplyr_1.0.2
[49] data.table_1.13.2 cowplot_1.0.0
[51] mandrake_1.0.0 facetscales_0.1.0.9000
[53] bigstatsr_1.2.3 edarf_1.1.1
[55] mlr3learners.partykit_0.2.1.9000 mlr3spatiotempcv_0.0.0.9004
[57] ggplotify_0.0.5 rprojroot_1.3-2
loaded via a namespace (and not attached):
[1] R.utils_2.9.2 tidyselect_1.1.0 htmlwidgets_1.5.1
[4] mlr3misc_0.5.0 munsell_0.5.0 mmpf_0.0.5
[7] base64url_1.4 units_0.6-7 codetools_0.2-16
[10] bbotk_0.2.1 withr_2.2.0 colorspace_1.4-1
[13] filelock_1.0.2 knitr_1.29 uuid_0.1-4
[16] rstudioapi_0.11 ggsignif_0.6.0 listenv_0.8.0
[19] git2r_0.27.1 lgr_0.3.4 txtq_0.2.3
[22] vctrs_0.3.4 generics_0.0.2 xfun_0.17
[25] R6_2.4.1 bigassertr_0.1.3 doParallel_1.0.15
[28] gridGraphics_0.5-1 assertthat_0.2.1 promises_1.1.0
[31] gtable_0.3.0 globals_0.12.5 conquer_1.0.2
[34] processx_3.4.2 goftest_1.2-2 workflowr_1.6.2
[37] MatrixModels_0.4-1 rlang_0.4.7 flock_0.7
[40] splines_4.0.2 rstatix_0.6.0 broom_0.5.6
[43] checkmate_2.0.0 BiocManager_1.30.10 yaml_2.2.1
[46] bigparallelr_0.2.3 abind_1.4-5 backports_1.1.10
[49] httpuv_1.5.4 tools_4.0.2 ellipsis_0.3.0
[52] Rcpp_1.0.4.6 progress_1.2.2 classInt_0.4-3
[55] purrr_0.3.4 ps_1.3.3 prettyunits_1.1.1
[58] zoo_1.8-8 haven_2.3.1 fs_1.5.0
[61] magrittr_1.5 openxlsx_4.1.5 spacetime_1.2-3
[64] storr_1.2.1 matrixStats_0.56.0 stringfish_0.14.2
[67] hms_0.5.3 evaluate_0.14 rio_0.5.16
[70] readxl_1.3.1 compiler_4.0.2 tibble_3.0.1
[73] KernSmooth_2.23-16 crayon_1.3.4 R.oo_1.23.0
[76] htmltools_0.4.0 later_1.0.0 tidyr_1.0.3
[79] RcppParallel_5.0.2 DBI_1.1.0 RApiSerialize_0.1.0
[82] Matrix_1.2-18 car_3.0-9 cli_2.0.2
[85] R.methodsS3_1.8.0 parallel_4.0.2 igraph_1.2.5
[88] forcats_0.5.0 pkgconfig_2.0.3 rvcheck_0.1.8
[91] foreign_0.8-78 xml2_1.3.2 roxygen2_7.1.1
[94] foreach_1.5.0 webshot_0.5.2 rvest_0.3.6
[97] callr_3.4.3 digest_0.6.25 pracma_2.2.9
[100] rmarkdown_2.3 cellranger_1.1.0 intervals_0.15.2
[103] nortest_1.0-4 curl_4.3 jsonlite_1.6.1
[106] lifecycle_0.2.0 carData_3.0-4 viridisLite_0.3.0
[109] fansi_0.4.1 pillar_1.4.4 lattice_0.20-41
[112] httr_1.4.2 glue_1.4.0 xts_0.12-0
[115] zip_2.1.1 FNN_1.1.3 iterators_1.0.12
[118] class_7.3-16 stringi_1.4.6 renv_0.9.3
[121] e1071_1.7-3