Open water evaporation of Lake IJssel, scripts and data used for regression analyses in the study 'Evaporation from a large lowland reservoir – observed dynamics during a warm summer'.
doi: 10.4121/16913308
Scripts in the programming language R and accompanying datasets that are used for regression analysis in the study 'Evaporation from a large lowland reservoir – observed dynamics during a warm summer'. The analysis provides insight in which variables can explain open water evaporation of Lake IJssel, the Netherlands, measured at two locations for the years 2019 and 2020. There are 4 scripts for each location (i.e. Stavoren and Trintelhaven): for (1) the hourly and (2) daily temporal scale, and for analysis based on (3) our own observations (doi: 10.4121/16601675), and (4) routinely measured data by the Royal Netherlands Meteorological Institute (KNMI -https://www.knmi.nl/nederland-nu/klimatologie/uurgegevens (last access: 01-11-2021)) and the Directorate-General for Public Works and Water Management (Rijkswaterstaat - https://waterinfo.rws.nl/#!/kaart/watertemperatuur/ (last access: 01-11-2021)). The datasets consist of evaporation data, and five possible explanatory variables: global radiation, wind speed, vertical vapour pressure gradient,
vapour pressure deficit, and water temperature. The datasets of the routinely measured data consist of combined data that is sourced from KNMI (global radiation,
wind speed, vapour pressure gradient, vapour pressure deficit) and Rijkswaterstaat (water temperature). For more information about the datasets that
are based on our own observations, have a look at doi:10.4121/16601675 (related dataset).
- 2021-11-02 first online
- 2023-11-22 published, posted
- SWM-EVAP: Smart Water Management in a complex environment: improving the monitoring and forecasting of surface evaporation (grant code ALWTW.2016.049) [more info...] Dutch Research Council
DATA
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README.txt - 10,425 bytesMD5:
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Regression_Stavoren_daily_revised.R - 8,241 bytesMD5:
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Regression_Stavoren_daily_routine_revised.R - 10,284 bytesMD5:
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Regression_Stavoren_hourly_revised.R - 8,204 bytesMD5:
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Regression_Stavoren_hourly_routine_revised.R - 10,056 bytesMD5:
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Regression_Trintelhaven_daily_revised.R - 7,755 bytesMD5:
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Regression_Trintelhaven_daily_routine_revised.R - 10,351 bytesMD5:
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Regression_Trintelhaven_hourly_revised.R - 8,567 bytesMD5:
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Regression_Trintelhaven_hourly_routine_revised.R - 40,640 bytesMD5:
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Stavoren_daily_2019_revised.txt - 45,167 bytesMD5:
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Stavoren_daily_2019_routine_revised.txt - 29,084 bytesMD5:
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Stavoren_daily_2020_revised.txt - 34,882 bytesMD5:
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Stavoren_daily_2020_routine_revised.txt - 430,095 bytesMD5:
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Stavoren_hourly_2019_revised.txt - 566,599 bytesMD5:
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Stavoren_hourly_2019_routine_revised.txt - 370,429 bytesMD5:
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Stavoren_hourly_2020_revised.txt - 459,099 bytesMD5:
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Stavoren_hourly_2020_routine_revised.txt - 35,409 bytesMD5:
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Trintelhaven_daily_2019_revised.txt - 53,852 bytesMD5:
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Trintelhaven_daily_2019_routine_revised.txt - 38,236 bytesMD5:
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Trintelhaven_daily_2020_revised.txt - 57,726 bytesMD5:
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Trintelhaven_daily_2020_routine_revised.txt - 411,827 bytesMD5:
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Trintelhaven_hourly_2019_revised.txt - 748,281 bytesMD5:
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Trintelhaven_hourly_2019_routine_revised.txt - 530,623 bytesMD5:
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Trintelhaven_hourly_2020_revised.txt - 998,015 bytesMD5:
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Trintelhaven_hourly_2020_routine_revised.txt -
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