Basic example
You can use the get_county_cd function to get a vector of all counties within a state:
get_county_cd(state = c("Georgia", "Alabama"))## [1] "01001" "01003" "01005" "01007" "01009" "01011" "01013" "01015"
## [9] "01017" "01019" "01021" "01023" "01025" "01027" "01029" "01031"
## [17] "01033" "01035" "01037" "01039" "01041" "01043" "01045" "01047"
## [25] "01049" "01051" "01053" "01055" "01057" "01059" "01061" "01063"
## [33] "01065" "01067" "01069" "01071" "01073" "01075" "01077" "01079"
## [41] "01081" "01083" "01085" "01087" "01089" "01091" "01093" "01095"
## [49] "01097" "01099" "01101" "01103" "01105" "01107" "01109" "01111"
## [57] "01113" "01115" "01117" "01119" "01121" "01123" "01125" "01127"
## [65] "01129" "01131" "01133" "13001" "13003" "13005" "13007" "13009"
## [73] "13011" "13013" "13015" "13017" "13019" "13021" "13023" "13025"
## [81] "13027" "13029" "13031" "13033" "13035" "13037" "13039" "13043"
## [89] "13045" "13047" "13049" "13051" "13053" "13055" "13057" "13059"
## [97] "13061" "13063" "13065" "13067" "13069" "13071" "13073" "13075"
## [105] "13077" "13079" "13081" "13083" "13085" "13087" "13089" "13091"
## [113] "13093" "13095" "13097" "13099" "13101" "13103" "13105" "13107"
## [121] "13109" "13111" "13113" "13115" "13117" "13119" "13121" "13123"
## [129] "13125" "13127" "13129" "13131" "13133" "13135" "13137" "13139"
## [137] "13141" "13143" "13145" "13147" "13149" "13151" "13153" "13155"
## [145] "13157" "13159" "13161" "13163" "13165" "13167" "13169" "13171"
## [153] "13173" "13175" "13177" "13179" "13181" "13183" "13185" "13187"
## [161] "13189" "13191" "13193" "13195" "13197" "13199" "13201" "13205"
## [169] "13207" "13209" "13211" "13213" "13215" "13217" "13219" "13221"
## [177] "13223" "13225" "13227" "13229" "13231" "13233" "13235" "13237"
## [185] "13239" "13241" "13243" "13245" "13247" "13249" "13251" "13253"
## [193] "13255" "13257" "13259" "13261" "13263" "13265" "13267" "13269"
## [201] "13271" "13273" "13275" "13277" "13279" "13281" "13283" "13285"
## [209] "13287" "13289" "13291" "13293" "13295" "13297" "13299" "13301"
## [217] "13303" "13305" "13307" "13309" "13311" "13313" "13315" "13317"
## [225] "13319" "13321"You can use the get_gages function to pull all gages within a county or counties. For example, to get information on all gages for Miami-Dade county, you can run:
library(dplyr)
get_gages("12086", start_date = "1988-01-01", end_date = "2015-01-01") %>%
slice(1:5)## # A tibble: 5 x 8
## agency_cd site_no
## <chr> <chr>
## 1 USGS 022908295
## 2 USGS 251203080480600
## 3 USGS 251154080471900
## 4 USGS 251033080440800
## 5 USGS 02289040
## # ... with 6 more variables: station_nm <chr>, site_tp_cd <chr>,
## # dec_lat_va <dbl>, dec_long_va <dbl>, county_cd <chr>, DA <dbl>You can use these two functions together within a pipe chain. For example, to get information on all the gages in Virginia in 2015, you can run:
va_gages <- get_county_cd("Virginia") %>%
get_gages(start_date = "2015-01-01", end_date = "2015-12-31")
head(va_gages)## agency_cd site_no station_nm
## 1 USGS 03207800 LEVISA FORK AT BIG ROCK, VA
## 2 USGS 02013000 DUNLAP CREEK NEAR COVINGTON, VA
## 3 USGS 02014000 POTTS CREEK NEAR COVINGTON, VA
## 4 USGS 02018500 CATAWBA CREEK NEAR CATAWBA, VA
## 5 USGS 02011800 JACKSON RIVER BL GATHRIGHT DAM NR HOT SPGS, VA
## 6 USGS 02055100 TINKER CREEK NEAR DALEVILLE, VA
## site_tp_cd dec_lat_va dec_long_va county_cd DA
## 1 ST 37.35372 -82.19569 51027 297.0
## 2 ST 37.80290 -80.04700 51005 162.0
## 3 ST 37.72901 -80.04228 51005 153.0
## 4 ST 37.46819 -80.00532 51023 34.3
## 5 ST 37.94846 -79.94922 51005 345.0
## 6 ST 37.41763 -79.93532 51023 11.7The function sends county-by-county requests to the USGS water services server (https://waterservices.usgs.gov) and, if there are data for gages within that county, returns that data and adds it to the dataframe returned by the function. This function may result in a message about a 404 HTTP status for some counties. This means that no data was found for one of the counties being queried but will not affect proper running of the function for other counties. Internally, this function uses the whatNWISsites function from the package, dataRetrieval, but adds the county FIPS code for each gage, allowing users to join gage data with other data identified by county FIPS. This function also uses the readNWISsites function from the dataRetrieval package to obtain the drainage area of the watershed at the selected gage. This can be used as a proxy for relative river size.
Once you have a list of gage numbers for which you would like to pull flow data, you can use the get_flow_data function to pull that data. For example, to pull data for all the stream gage flow data for 1999 for gages in Southhampton County, VA (FIPS code 51175), you can run:
southampton_gages <- get_gages("51175", start_date = "1999-01-01",
end_date = "1999-12-31")
southampton_data <- get_flow_data(southampton_gages, start_date = "1999-01-01",
end_date = "1999-12-31")The output from get_flow_data is a data frame with daily streamflow for the queried gages over the requested dates. The data frame has three columns: “site_no” = gage number, “date” = date of observation, and “discharge” = mean daily flow in cubic feet per second (USGS parameter code 00060):
head(southampton_data)## site_no date discharge
## 1 02047000 1999-01-01 685
## 2 02047000 1999-01-02 631
## 3 02047000 1999-01-03 714
## 4 02047000 1999-01-04 1300
## 5 02047000 1999-01-05 2190
## 6 02047000 1999-01-06 2830library(ggplot2)
ggplot(southampton_data, aes(x = date, y = discharge, color = site_no)) +
geom_line() + theme_classic() +
labs(y = "mean daily flow (cubic ft / s)", color = "gage site #")
To determine when a flood occurred, we need a value to determine a gage-specific value of streamflow that consitutes a “flood”. Expected streamflow values vary substantially from gage to gage, so it is criticial to determine a sensible threshold for each gage included in a study before trying to identify floods at each gage.
One way to get gage-specific flood thresholds is with the find_nws function, which gets the National Weather Service flood discharge threshold, when available, for a gage site. Note there are four NWS thresholds for each gage, representing cutoffs for different levels of floods: “Action”, “Flood”, “Moderate”, and “Major”. These thresholds are available through the Advanced Hydrologic Prediction Service’s “River Stage” information. They are originally in feet for stages, which we convert to streamflow (in cubic feet per second) using USGS rating tables for each gage (pulled using the readNWISrating function from the dataRetrieval package).
Gages may have some, all, or none of these NWS flood thresholds available. If a gage does not have any of the four thresholds, it is excluded from the output of the find_nws function. For example, to get “Moderate” flood thresholds for the Virginia gages, you can run:
va_nws <- find_nws(site_no = va_gages$site_no, type = "moderate")
head(va_nws)## site_no flood_val
## 1 01652500 8400.246
## 2 03168000 55378.500
## 3 02060500 28306.221
## 4 01654000 3370.084
## 5 03173000 17958.120
## 6 02011400 8008.627summary(va_nws$flood_val)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1719 8062 14039 22290 28306 90491Many USGS gages do not have NWS flood thresholds for any of the four categories of floods. Therefore, use of the NWS flood thresholds to identify floods may severely limit the sample size of the data output.
Another way to get gage-specific flood thresholds is with the find_q2 function, which calculates the median annual flood for each gage using a minimum of 20 years of USGS annual peak flow data:
va_q2 <- find_q2(site_no = va_gages$site_no)
head(va_q2)## # A tibble: 6 x 3
## site_no flood_val years
## <chr> <dbl> <int>
## 1 01613900 923.0 56
## 2 01615000 2355.0 68
## 3 01620500 691.5 70
## 4 01621050 543.0 23
## 5 01622000 7440.0 87
## 6 01625000 5740.0 89summary(va_q2$flood_val)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 113.5 2606.2 4970.0 9439.8 9476.7 70766.7You can compare the results from these two methods for sites where you can get both values (note that both axes are shown on a log-10 scale):
va_flood_stage <- va_nws %>%
rename(flood_nws = flood_val) %>%
inner_join(va_q2, by = "site_no") %>%
rename(flood_q2 = flood_val)
ggplot(va_flood_stage, aes(x = flood_q2, y = flood_nws)) +
geom_point(alpha = 0.5) +
geom_abline(aes(intercept = 0, slope = 1), linetype = 3) +
geom_smooth(method = "lm", se = FALSE) +
scale_x_log10(labels = scales::comma) + scale_y_log10(labels = scales::comma) +
labs(x = "Streamflow threshold for\nflood based on USGS Q2",
y = "Streamflow threshold for flood based\non NWS flood height thresholds") +
theme_classic() +
expand_limits(x = c(min(va_flood_stage$flood_q2, va_flood_stage$flood_nws),
max(va_flood_stage$flood_q2, va_flood_stage$flood_nws)),
y = c(min(va_flood_stage$flood_q2, va_flood_stage$flood_nws),
max(va_flood_stage$flood_q2, va_flood_stage$flood_nws)))
For the Virginia monitors, you can see that the flood values from the “moderate” NWS flood heights and the Q2 method are well-correlated, although NWS values tend to be consistently higher than Q2 values.
Whichever flood threshold you pick, it can be joined to the time series streamflow data obtained with the get_flow_data function, and flood status can be determined on each day based on whether streamflow that day exceeded the threshold. For example, the following code can be used to add a binary flood variable to the streamflow data pulled earlier for Southampton County, VA:
southampton_q2 <- find_q2(site_no = southampton_gages$site_no)
southampton_data <- southampton_data %>%
left_join(southampton_q2, by = "site_no") %>%
select(-years) %>%
mutate(flood = discharge >= flood_val)
head(southampton_data)## site_no date discharge flood_val flood
## 1 02047000 1999-01-01 685 7980 FALSE
## 2 02047000 1999-01-02 631 7980 FALSE
## 3 02047000 1999-01-03 714 7980 FALSE
## 4 02047000 1999-01-04 1300 7980 FALSE
## 5 02047000 1999-01-05 2190 7980 FALSE
## 6 02047000 1999-01-06 2830 7980 FALSEHere are the time series for the two streamgages in the county, with horizontal lines added for the flood threshold used for each gage:
library(ggplot2)
ggplot(southampton_data, aes(x = date, y = discharge)) +
geom_line() + theme_classic() +
geom_point(aes(color = flood), alpha = 0.5, size = 1.2) +
facet_wrap(~ site_no, ncol = 1) +
geom_hline(data = southampton_q2, aes(yintercept = flood_val),
linetype = 3) +
labs(y = "mean daily flow (cubic ft / s)", color = "Flood status")
Once you have data on gages, flood values, and flow data, you can also get flood summaries by site over the selected date range using the flood_analysis function. For each site, this function calculates the average “peak” (ratio of observed discharge to flood threshold discharge), maximum peak, and number of days where a flood occured. It also classifies flood magnitude. If the NWS flood threshold is used, this is a simple binary of “Flood” or “No Flood”. If the Q2 flood threshold is used, flood magnitude is classified based on “max_peak” value: “None” (<1), “Minor” (1-1.5), “Moderate” (1.5-2), “Major” (2-5), and “Extreme” (>5). For example, to get flood summary statistics by gage for the Virginia gages, you can run:
va_counties <- get_county_cd("Virginia")
va_flow_data <- get_flow_data(va_gages, start_date = "2015-01-01",
end_date = "2015-12-31")
va_floods <- flood_analysis(flow_data = va_flow_data, peaks = va_q2,
gages = va_gages, county_cd = va_counties,
threshold = "Q2")## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vectorhead(va_floods, 3)## # A tibble: 3 x 14
## site_no county_cd lat long avg_peak flood_dur max_peak
## <chr> <chr> <dbl> <dbl> <dbl> <int> <dbl>
## 1 02072000 51067 36.78069 -80.02476 0.26735068 8 3.953871
## 2 02040000 51007 37.42154 -77.85889 0.12193951 5 1.880193
## 3 03524500 51195 36.92927 -82.45626 0.06564179 2 1.876866
## # ... with 7 more variables: num_missing <int>, Q2 <lgl>, DA <dbl>,
## # size <dbl>, state <fctr>, county <fctr>, flood <fctr>ggplot(va_floods, aes(x = max_peak, fill = flood)) +
geom_histogram()## Warning: Removed 31 rows containing non-finite values (stat_bin).
There is also a function that will allow you to get county-level aggregate statistics from this gage-level summary. Any counties with no gages or for which the gages didn’t have flow data or flood threshold values are also included. If the Q2 flood threshold is used, this function gives the percentage of gages in the county above each flood magnitude classification (e.g. “Minor”, “Moderate”, etc.):
va_county_stats <- county_aggregates(flood_stats = va_floods)
head(va_county_stats)## # A tibble: 6 x 12
## county_cd county state num_gage avg_peak max_peak minor
## <chr> <fctr> <fctr> <int> <dbl> <dbl> <dbl>
## 1 51001 accomack:main virginia 2 NaN -Inf 0
## 2 51003 albemarle virginia 4 0.3655210 0.5557300 0
## 3 51005 alleghany virginia 4 0.7473739 0.8574018 0
## 4 51007 amelia virginia 2 1.5193316 1.8801932 100
## 5 51009 amherst virginia 1 NaN -Inf 0
## 6 51011 appomattox virginia 1 0.3571429 0.3571429 0
## # ... with 5 more variables: moderate <dbl>, major <dbl>, extreme <dbl>,
## # max_dur <dbl>, avg_dur <dbl>You can use the run_flood function to put all of this together, and pull all flood summaries by either gage (output = "gage"), county (output = "county"), or both (output = "both") for either a set of counties or all counties in a state. Because it is time consuming to pull flow data, it is more efficient to set (output = "both") and then extract gage or county level data from the list rather than running each separately.
For example, to get all the flood statistics by gage for all gages with available data in Virginia, you can run:
va_floods <- run_flood(state = "Virginia", start_date = "2015-01-01",
end_date = "2015-12-31", threshold = "Q2",
output = "gage")## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vectorhead(va_floods)## # A tibble: 6 x 14
## site_no county_cd lat long avg_peak flood_dur max_peak
## <chr> <chr> <dbl> <dbl> <dbl> <int> <dbl>
## 1 02072000 51067 36.78069 -80.02476 0.26735068 8 3.953871
## 2 02040000 51007 37.42154 -77.85889 0.12193951 5 1.880193
## 3 03524500 51195 36.92927 -82.45626 0.06564179 2 1.876866
## 4 03527000 51169 36.64871 -82.75044 0.09350164 2 1.810000
## 5 02071530 51141 36.77847 -80.24922 0.06066979 1 1.788618
## 6 02069700 51141 36.57097 -80.12949 0.06708210 1 1.749420
## # ... with 7 more variables: num_missing <int>, Q2 <dbl>, DA <dbl>,
## # size <dbl>, state <fctr>, county <fctr>, flood <fctr>Similarly, to get county-level data for counties in Florida in 2004, you can run:
fl_floods <- run_flood(state = "Florida", start_date = "2004-01-01",
end_date = "2004-12-31", threshold = "Q2",
output = "county")## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vectorhead(fl_floods)## # A tibble: 6 x 12
## county_cd county state num_gage avg_peak max_peak minor moderate
## <chr> <fctr> <fctr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 12001 alachua florida 1 2.0661376 2.0661376 100.0 100
## 2 12003 baker florida 2 3.2362483 3.4654378 100.0 100
## 3 12005 bay florida 1 0.9708029 0.9708029 0.0 0
## 4 12007 bradford florida 1 NaN -Inf 0.0 0
## 5 12009 brevard florida 8 2.4755538 4.7323944 87.5 75
## 6 12011 broward florida 1 NaN -Inf 0.0 0
## # ... with 4 more variables: major <dbl>, extreme <dbl>, max_dur <dbl>,
## # avg_dur <dbl>These output can be mapped using the map_flood function. If the data was collected by gage, this will show a point map with the flood level at each gage. The size of the point corresponds to the size of the stream, based on either the median flood value (Q2) or the drainage area (DA). This defaults to Q2 but can be changed using the weight input to the run_flood function:
map_flood(va_floods) ## Warning: Removed 31 rows containing missing values (geom_point).
For county-level data, this will create a choropleth indicating the percent of gages in each county with flood magnitude above a user-specified flood category: “Low” (0-20%), “Moderate” (20-40%), “Moderate-High”" (40-60%), “High” (60-80%), and “Very High” (80-100%).
map_flood(fl_floods)## Warning in if (county_stats$state %in% c("puerto rico", "alaska",
## "hawaii")) {: the condition has length > 1 and only the first element will
## be used
map_flood(fl_floods, category = "major")## Warning in if (county_stats$state %in% c("puerto rico", "alaska",
## "hawaii")) {: the condition has length > 1 and only the first element will
## be used
The long_term_flood function is very similar to the run_flood function except it accepts a data frame as input with three columns: county_cd, start_date, and end_date. This allows you to analyze floods across multiple date ranges and multiple counties. For example, if we wanted to examine April flooding in three counties in northern Virginia we would create the following input data frame:
county_cd <- c(rep("51013", 5), rep("51107", 5), rep("51059", 5))
start_date <- rep(c("2010-04-01", "2011-04-01", "2012-04-01", "2013-04-01", "2014-04-01"), 3)
end_date <- rep(c("2010-04-30", "2011-04-30", "2012-04-30", "2013-04-30", "2014-04-30"), 3)
input_df <- data.frame(county_cd = county_cd, start_date = start_date, end_date = end_date, stringsAsFactors = FALSE)
head(input_df)## county_cd start_date end_date
## 1 51013 2010-04-01 2010-04-30
## 2 51013 2011-04-01 2011-04-30
## 3 51013 2012-04-01 2012-04-30
## 4 51013 2013-04-01 2013-04-30
## 5 51013 2014-04-01 2014-04-30
## 6 51107 2010-04-01 2010-04-30It is important that these variables are character strings and not factors. The flood analysis can then be performed:
#With default values
va_floods <- long_term_flood(input_df)
va_gage_output <- va_floods[[1]]
head(va_gage_output)## site_no start_date end_date county_cd lat long avg_peak
## 1 01652500 2010-04-01 2010-04-30 51013 38.84333 -77.08586 0.005887097
## 2 01652500 2011-04-01 2011-04-30 51013 38.84333 -77.08586 0.006847634
## 3 01652500 2012-04-01 2012-04-30 51013 38.84333 -77.08586 0.004709785
## 4 01652500 2013-04-01 2013-04-30 51013 38.84333 -77.08586 0.004625376
## 5 01652500 2014-04-01 2014-04-30 51013 38.84333 -77.08586 0.020453548
## 6 01646000 2010-04-01 2010-04-30 51059 38.97594 -77.24581 0.031427007
## flood_dur max_peak num_missing Q2 DA size state
## 1 0 0.02203226 0 3100.000 12.6 3.491362 virginia
## 2 0 0.04096774 0 3100.000 12.6 3.491362 virginia
## 3 0 0.06225806 0 3100.000 12.6 3.491362 virginia
## 4 0 0.02906452 0 3100.000 12.6 3.491362 virginia
## 5 0 0.23419355 0 3100.000 12.6 3.491362 virginia
## 6 0 0.07116788 0 1826.667 57.8 3.261659 virginia
## county flood
## 1 arlington None
## 2 arlington None
## 3 arlington None
## 4 arlington None
## 5 arlington None
## 6 fairfax Noneva_county_output <- va_floods[[2]]
head(va_county_output)## # A tibble: 6 x 14
## county_cd start_date end_date county state num_gage max_peak
## <chr> <chr> <chr> <fctr> <fctr> <int> <dbl>
## 1 51013 2010-04-01 2010-04-30 arlington virginia 1 0.02203226
## 2 51013 2011-04-01 2011-04-30 arlington virginia 1 0.04096774
## 3 51013 2012-04-01 2012-04-30 arlington virginia 1 0.06225806
## 4 51013 2013-04-01 2013-04-30 arlington virginia 1 0.02906452
## 5 51013 2014-04-01 2014-04-30 arlington virginia 1 0.23419355
## 6 51059 2010-04-01 2010-04-30 fairfax virginia 2 0.07116788
## # ... with 7 more variables: avg_peak <dbl>, minor <dbl>, moderate <dbl>,
## # major <dbl>, extreme <dbl>, max_dur <dbl>, avg_dur <dbl>If you are interested in seeing when flooding occurred over a set time period, you can perform a time series analysis using the time_series_flood function. This has similar inputs to the run_flood function. This will also output a flood_metric value which is the fraction of gages in the county experiencing a flood on that date, weighted by river size. River size is calculated as either the logarithm of the median annual flood (Q2, the default) or as the logarithm of the drainage area. This can be changed using the weight input. For example, if you wanted to examine when flooding occurred in Virginia from 2010 to 2015:
va_time_series <- time_series_flood(state = "Virginia", start_date = "2010-01-01",
end_date = "2015-12-31", threshold = "NWS",
flood_type = "flood", weight = "Q2")
va_gage_output <- va_time_series[[1]]
head(va_gage_output)## site_no date lat long county_cd Q2 DA size
## 1 02013000 2013-05-07 37.80290 -80.04700 51005 5516.667 162.0 3.741677
## 2 02018000 2013-07-04 37.66596 -79.91144 51023 7520.000 329.0 3.876218
## 3 02011500 2010-01-25 38.06957 -79.89700 51017 5510.000 134.0 3.741152
## 4 02011400 2010-01-25 38.04235 -79.88144 51017 3870.000 157.0 3.587711
## 5 02011470 2010-01-25 38.19040 -79.81172 51017 3112.500 75.6 3.493109
## 6 02011470 2011-04-28 38.19040 -79.81172 51017 3112.500 75.6 3.493109
## discharge flood_val flood_ratio state county flood
## 1 8090 5906.626 1.369648 virginia alleghany Flood
## 2 8520 8059.431 1.057147 virginia botetourt Flood
## 3 5050 4613.418 1.094633 virginia bath Flood
## 4 3470 3134.765 1.106941 virginia bath Flood
## 5 3360 2556.761 1.314163 virginia bath Flood
## 6 2630 2556.761 1.028645 virginia bath Floodva_county_output <- va_time_series[[2]]
head(va_county_output)## # A tibble: 6 x 10
## county_cd date county state num_gage max_peak avg_peak
## <chr> <date> <fctr> <fctr> <int> <dbl> <dbl>
## 1 51089 2010-01-25 henry virginia 2 1.073097 0.8541252
## 2 51105 2015-03-05 lee virginia 1 1.539506 1.5395064
## 3 51113 2010-01-25 madison virginia 2 1.914846 1.1225070
## 4 51113 2010-03-13 madison virginia 2 1.934091 1.0769796
## 5 51113 2011-04-17 madison virginia 2 1.295809 0.7585690
## 6 51113 2013-01-31 madison virginia 2 1.199585 0.7002309
## # ... with 3 more variables: no_flood <dbl>, yes_flood <dbl>,
## # flood_metric <dbl>The county-level output can be plotted using the time_series_plot function which shows bar charts of the timing and magnitude of floods during the selected time period. You can select values for start_date and end_date to change the x-limits on the plots or the default is to show the full time period including any flood. For example, here is the plot for Halifax County, VA:
time_series_plot(va_county_output[va_county_output$county == "halifax", ])
The results of the time_series_flood function can be mapped using the time_series_map function. This creates one map per day with data. These figures can be saved to a file and used to create a movie or gif using an external program (e.x. ImageJ or ImageMagick). In order to get the full time series requested, use filter_data = FALSE in the time_series_flood function. The following code looks at flooding in Texas after Hurricane Harvey hit in September 2017:
tx <- time_series_flood(state = "Texas", start_date = "2017-08-24", end_date = "2017-09-10", filter_data = FALSE)
time_series_map(tx[[1]])The selection of Q2 or DA as a weight can affect results. These metrics are both meant to represent river size (larger rivers have a larger drainage area and median annual flood). Q2 is a more direct measurement of river size since it is based on the actual magnitude of river flows. Drainage area is a good proxy, however, when Q2 values are not available. The relationship between these two metrics can be compared for the va_gage_output data:
ggplot(va_gage_output, aes(x = DA, y = Q2)) +
geom_point(alpha = 0.5) +
geom_abline(aes(intercept = 0, slope = 1), linetype = 3) +
geom_smooth(method = "lm", se = FALSE) +
scale_x_log10(labels = scales::comma) + scale_y_log10(labels = scales::comma) +
labs(x = "Gage Drainage Area",
y = "Gage Q2") +
theme_classic() +
expand_limits(x = c(min(va_gage_output$Q2, va_gage_output$DA),
max(va_gage_output$Q2, va_gage_output$DA)),
y = c(min(va_gage_output$Q2, va_gage_output$DA),
max(va_gage_output$Q2, va_gage_output$DA)))## Warning: Removed 5 rows containing non-finite values (stat_smooth).## Warning: Removed 5 rows containing missing values (geom_point).
There is clearly a strong relationship between DA and Q2, although Q2 values (in this case) are far larger than DA. However, only one of these variables would be used at one time to compare relative river size so this will not affect the analysis.
The selection of the weight impacts the value of weighted flood_metric. We can compare the flood_metric values for the va_county_output using DA or Q2 as the weighting variable:
va_county_output2 <- time_series_flood(state = "Virginia", start_date = "2010-01-01",
end_date = "2015-12-31", threshold = "NWS",
flood_type = "flood", weight = "DA")[[2]]
ggplot() +
geom_histogram(data = va_county_output, aes(x = flood_metric, fill = "Q2"), binwidth = 0.03) +
geom_histogram(data = va_county_output2, aes(x = flood_metric, y = - ..count.., fill = "DA"), binwidth = 0.03)
In this case, the results are not substantially different, althought the DA weights tend to give slightly higher flood_metric values. To get full, unfiltered results, set the filter_data input of time_series_flood to FALSE. Unweighted percentages of gages experiencing a flood on a given date are also returned (yes_flood column for NWS flood thresholds and by flood magnitude, e.g. minor, for Q2 flood thresholds).
