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Compute 1d water level data from the FLYS3 water level MQ and a gauging station according to the INFORM 3-method Flood1 (Flut1)

Source: R/waterLevelFlood1.R
waterLevelFlood1.Rd

This function computes a 1d water level according to the INFORM flood duration method Flood1 (Flut1) and stores it as column w of an S4 object of type WaterLevelDataFrame. First the function obtains the reference water level MQ from df.flys. This reference water level is then shifted by the difference between measured water and the FLYS3 water level for MQ at the specified gauging station. Here it is provided mainly for historical reasons and more advanced functions like waterLevel or waterLevelPegelonline should be used.

Usage 

waterLevelFlood1(wldf, gauging_station, w, uuid, shiny = FALSE)

Arguments

wldf

an object of class WaterLevelDataFrame.

gauging_station

must be type character with a length of one. Permitted values are: 'SCHOENA', 'PIRNA', 'DRESDEN', 'MEISSEN', 'RIESA', 'MUEHLBERG', 'TORGAU', 'PRETZSCH-MAUKEN', 'ELSTER', 'WITTENBERG', 'COSWIG', 'VOCKERODE', 'ROSSLAU', 'DESSAU', 'AKEN', 'BARBY', 'SCHOENEBECK', 'MAGDEBURG-BUCKAU', 'MAGDEBURG-STROMBRUECKE', 'MAGDEBURG-ROTHENSEE', 'NIEGRIPP AP', 'ROGAETZ', 'TANGERMUENDE', 'STORKAU', 'SANDAU', 'SCHARLEUK', 'WITTENBERGE', 'MUEGGENDORF', 'SCHNACKENBURG', 'LENZEN', 'GORLEBEN', 'DOEMITZ', 'DAMNATZ', 'HITZACKER', 'NEU DARCHAU', 'BLECKEDE', 'BOIZENBURG', 'HOHNSTORF', 'ARTLENBURG', 'GEESTHACHT', 'IFFEZHEIM', 'PLITTERSDORF', 'MAXAU', 'PHILIPPSBURG', 'SPEYER', 'MANNHEIM', 'WORMS', 'NIERSTEIN-OPPENHEIM', 'MAINZ', 'OESTRICH', 'BINGEN', 'KAUB', 'SANKT GOAR', 'BOPPARD', 'BRAUBACH', 'KOBLENZ', 'ANDERNACH', 'OBERWINTER', 'BONN', 'KOELN', 'DUESSELDORF', 'RUHRORT', 'WESEL', 'REES', 'EMMERICH'.

w

If the wldf does not supply a valid non-NA time slot, it is possible to execute the function with the help of this optional parameter. Otherwise getGaugingDataW or getPegelonlineW provide gauging data internally.

uuid

must be type character with a length of one. Permitted values are: '7cb7461b-3530-4c01-8978-7f676b8f71ed', '85d686f1-55b2-4d36-8dba-3207b50901a7', '70272185-b2b3-4178-96b8-43bea330dcae', '24440872-5bd2-4fb3-8554-907b49816c49', 'b04b739d-7ffa-41ee-9eb9-95cb1b4ef508', '16b9b4e7-be14-41fd-941e-6755c97276cc', '83bbaedb-5d81-4bc6-9f66-3bd700c99c1f', 'f3dc8f07-c2bb-4b92-b0b0-4e01a395a2c6', 'c093b557-4954-4f05-8f5c-6c6d7916c62d', '070b1eb4-3872-4e07-b2e5-e25fd9251b93', '1ce53a59-33b9-40dc-9b17-3cd2a2414607', 'ae93f2a5-612e-4514-b5fd-9c8aecdd73c7', 'e97116a4-7d30-4671-8ba1-cdce0a153d1d', '1edc5fa4-88af-47f5-95a4-0e77a06fe8b1', '094b96e5-caeb-46d3-a8ee-d44182add069', '939f82ec-15a9-49c8-8828-dc2f8a2d49e2', '90bcb315-f080-41a8-a0ac-6122331bb4cf', 'b8567c1e-8610-4c2b-a240-65e8a74919fa', 'ccccb57f-a2f9-4183-ae88-5710d3afaefd', 'e30f2e83-b80b-4b96-8f39-fa60317afcc7', '3adf88fd-fd7a-41d0-84f5-1143c98a6564', '133f0f6c-2ca1-4798-9360-5b5f417dd839', '13e91b77-90f3-41a5-a320-641748e9c311', 'de4cc1db-51cb-4b62-bee2-9750cbe4f5c4', 'f4c55f77-ab80-4e00-bed3-aa6631aba074', 'e32b0a28-8cd5-4053-bc86-fff9c6469106', 'cbf3cd49-91bd-49cc-8926-ccc6c0e7eca4', '48f2661f-f9cb-4093-9d57-da2418ed656e', '550e3885-a9d1-4e55-bd25-34228bd6d988', 'c80a4f21-528c-4771-98d7-10cd591699a4', 'ac507f42-1593-49ea-865f-10b2523617c7', '6e3ea719-48b1-408a-bc55-0986c1e94cd5', 'c233674f-259a-4304-b81f-dce1f415d85b', 'a26e57c9-1cb8-4fca-ba80-9e02abc81df8', '67d6e882-b60c-40d3-975c-a6d7a2b4e40a', '6aa1cd8e-e528-4bcb-ba8e-705b6dcb7da2', '33e0bce0-13df-4ffc-be9d-f1a79e795e1c', 'd9289367-c8aa-4b6a-b1ad-857fec94c6bb', 'b3492c68-8373-4769-9b29-22f66635a478', '44f7e955-c97d-45c8-9ed7-19406806fb4c', 'b02be240-1364-4c97-8bb6-675d7d842332', '6b774802-fcb5-49ae-8ecb-ecaf1a278b1c', 'b6c6d5c8-e2d5-4469-8dd8-fa972ef7eaea', '88e972e1-88a0-4eb9-847c-0925e5999a46', '2cb8ae5b-c5c9-4fa8-bac0-bb724f2754f4', '57090802-c51a-4d09-8340-b4453cd0e1f5', '844a620f-f3b8-4b6b-8e3c-783ae2aa232a', 'd28e7ed1-3317-41c5-bec6-725369ed1171', 'a37a9aa3-45e9-4d90-9df6-109f3a28a5af', '665be0fe-5e38-43f6-8b04-02a93bdbeeb4', '0309cd61-90c9-470e-99d4-2ee4fb2c5f84', '1d26e504-7f9e-480a-b52c-5932be6549ab', '550eb7e9-172e-48e4-ae1e-d1b761b42223', '2ff6379d-d168-4022-8da0-16846d45ef9b', 'd6dc44d1-63ac-4871-b175-60ac4040069a', '4c7d796a-39f2-4f26-97a9-3aad01713e29', '5735892a-ec65-4b29-97c5-50939aa9584e', 'b45359df-c020-4314-adb1-d1921db642da', '593647aa-9fea-43ec-a7d6-6476a76ae868', 'a6ee8177-107b-47dd-bcfd-30960ccc6e9c', '8f7e5f92-1153-4f93-acba-ca48670c8ca9', 'c0f51e35-d0e8-4318-afaf-c5fcbc29f4c1', 'f33c3cc9-dc4b-4b77-baa9-5a5f10704398', '2f025389-fac8-4557-94d3-7d0428878c86', '9598e4cb-0849-401e-bba0-689234b27644'.

shiny

logical determing whether columns (section, weight_x, weight_y) relevant for the plotShiny()-function are appended to the resulting WaterLevelDataFrame.

Value

An object of class WaterLevelDataFrame.

Details

This function computes a water level based on the reference water level MQ from df.flys. Since the function only shifts this single reference water level to make it fit to the measured water level, no interpolation is needed. Therefore the shiny columns have constant values of section <- 1, weight_x <- 1 and weight_y <- shift.

References

Rosenzweig S, Giebel H, Schleuter M (2011). “Ökologische Modellierungen für die Wasser- und Schifffahrtsverwaltung – Das integrierte Flussauenmodell INFORM in seiner neuesten Fassung (Version 3). Bundesanstalt für Gewässerkunde, Koblenz, Germany.” doi:10.5675/bfg-1667 .

Bundesanstalt für Gewässerkunde (2016). “FLYS – Flusshydrologischer Webdienst.” http://www.bafg.de/DE/08_Ref/M2/03_Fliessgewmod/01_FLYS/flys_node.html.

Examples 

wldf <- WaterLevelDataFrame(river   = "Elbe",
                            time    = as.POSIXct("2016-12-21"),
                            station = seq(257, 262, 0.1))
wldf1 <- waterLevelFlood1(wldf, "ROSSLAU")
wldf2 <- waterLevelFlood1(wldf, "DESSAU")

wldf1$w - wldf2$w
#>  [1] -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28
#> [13] -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28
#> [25] -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28
#> [37] -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28 -0.28
#> [49] -0.28 -0.28 -0.28