Forecasting cyanobacterial blooms using high frequency monitoring data

Anouk Blauw, Henk Van den Boogaard, Annelies Hommersom, Floris Boogaard, Rui de Lima, Janneke Schenkel, Elena Uibel, Marieke Eleveld, Lilith Kramer, Valesca Harezlak, Miguel Dioniso

Research output: Contribution to conferenceAbstractProfessional

8 Downloads (Pure)

Abstract

Cyanobacterial blooms can be toxic to humans swimming in affected waters. According to the European Bathing Water Directive bathing waters should be closed during cyanobacterial blooms. In the Netherlands, cyanobacteria monitoring in all official bathing water locations is usually performed every two weeks during the bathing season. In face of the large temporal and spatial
variability of cyanobacterial bloom dynamics this monitoring frequency however is too low for adequate early warnings to the public.
High frequency monitoring and forecasting models can provide information on cyanobacterial blooms in between the regular monitoring dates and for a few days into the future. In the H2020 project EOMORES, we have combined observational data from a spectral camera (Ecowatch) near a Dutch bathing site with fluorescence data from an underwater drone to analyse the variability of
cyanobacterial blooms at short temporal and spatial scales. The results are used in a short term forecasting model of cyanobacterial blooms (AlgaeRadar) and a 3D scum forecasting model (EWACS). The AlgaeRadar is cross-validated with biweekly data from other bathing water sites and shows improved model performance compared to an earlier version that was built with only biweekly data.
For the site with high-frequency chlorophyll observations the near-real time data are assimilated in the model to further enhance the model performance. Model performance of EWACS is verified using high frequency pictures from the Ecowatch station, showing scum layers on the water. This allowed us to validate and calibrate the EWACS model. Model validation abilities were in the past
also limited by to the patchy nature and high temporal variability of the scum layers, which was not covered by sparse scum observations. With the resulting models, early warnings for cyanobacterial blooms are more reliable than those from the current practice that are merely based on biweekly monitoring data. For the protection of public health this provides better opportunities as well.
Original languageEnglish
Publication statusPublished - Oct 2018
Event18th international conference on harmful algae from ecosystems to socio Ecosystems - Nantes, France
Duration: 21 Oct 201826 Oct 2018
https://www.icha2018.com/

Conference

Conference18th international conference on harmful algae from ecosystems to socio Ecosystems
CountryFrance
CityNantes
Period21/10/1826/10/18
Internet address

Keywords

  • water
  • climate
  • climateadaption

Cite this

Blauw, A., Van den Boogaard, H., Hommersom, A., Boogaard, F., de Lima, R., Schenkel, J., ... Dioniso, M. (2018). Forecasting cyanobacterial blooms using high frequency monitoring data. Abstract from 18th international conference on harmful algae from ecosystems to socio Ecosystems, Nantes, France.
Blauw, Anouk ; Van den Boogaard, Henk ; Hommersom, Annelies ; Boogaard, Floris ; de Lima, Rui ; Schenkel, Janneke ; Uibel, Elena ; Eleveld, Marieke ; Kramer, Lilith ; Harezlak, Valesca ; Dioniso, Miguel. / Forecasting cyanobacterial blooms using high frequency monitoring data. Abstract from 18th international conference on harmful algae from ecosystems to socio Ecosystems, Nantes, France.
@conference{d78f27e4e5274c69a53578593c3bc2b9,
title = "Forecasting cyanobacterial blooms using high frequency monitoring data",
abstract = "Cyanobacterial blooms can be toxic to humans swimming in affected waters. According to the European Bathing Water Directive bathing waters should be closed during cyanobacterial blooms. In the Netherlands, cyanobacteria monitoring in all official bathing water locations is usually performed every two weeks during the bathing season. In face of the large temporal and spatialvariability of cyanobacterial bloom dynamics this monitoring frequency however is too low for adequate early warnings to the public.High frequency monitoring and forecasting models can provide information on cyanobacterial blooms in between the regular monitoring dates and for a few days into the future. In the H2020 project EOMORES, we have combined observational data from a spectral camera (Ecowatch) near a Dutch bathing site with fluorescence data from an underwater drone to analyse the variability ofcyanobacterial blooms at short temporal and spatial scales. The results are used in a short term forecasting model of cyanobacterial blooms (AlgaeRadar) and a 3D scum forecasting model (EWACS). The AlgaeRadar is cross-validated with biweekly data from other bathing water sites and shows improved model performance compared to an earlier version that was built with only biweekly data.For the site with high-frequency chlorophyll observations the near-real time data are assimilated in the model to further enhance the model performance. Model performance of EWACS is verified using high frequency pictures from the Ecowatch station, showing scum layers on the water. This allowed us to validate and calibrate the EWACS model. Model validation abilities were in the pastalso limited by to the patchy nature and high temporal variability of the scum layers, which was not covered by sparse scum observations. With the resulting models, early warnings for cyanobacterial blooms are more reliable than those from the current practice that are merely based on biweekly monitoring data. For the protection of public health this provides better opportunities as well.",
keywords = "water, climate, climateadaption, water, klimaatverandering",
author = "Anouk Blauw and {Van den Boogaard}, Henk and Annelies Hommersom and Floris Boogaard and {de Lima}, Rui and Janneke Schenkel and Elena Uibel and Marieke Eleveld and Lilith Kramer and Valesca Harezlak and Miguel Dioniso",
year = "2018",
month = "10",
language = "English",
note = "18th international conference on harmful algae from ecosystems to socio Ecosystems ; Conference date: 21-10-2018 Through 26-10-2018",
url = "https://www.icha2018.com/",

}

Blauw, A, Van den Boogaard, H, Hommersom, A, Boogaard, F, de Lima, R, Schenkel, J, Uibel, E, Eleveld, M, Kramer, L, Harezlak, V & Dioniso, M 2018, 'Forecasting cyanobacterial blooms using high frequency monitoring data' 18th international conference on harmful algae from ecosystems to socio Ecosystems, Nantes, France, 21/10/18 - 26/10/18, .

Forecasting cyanobacterial blooms using high frequency monitoring data. / Blauw, Anouk; Van den Boogaard, Henk; Hommersom, Annelies; Boogaard, Floris; de Lima, Rui; Schenkel, Janneke; Uibel, Elena; Eleveld, Marieke ; Kramer, Lilith ; Harezlak, Valesca; Dioniso, Miguel.

2018. Abstract from 18th international conference on harmful algae from ecosystems to socio Ecosystems, Nantes, France.

Research output: Contribution to conferenceAbstractProfessional

TY - CONF

T1 - Forecasting cyanobacterial blooms using high frequency monitoring data

AU - Blauw, Anouk

AU - Van den Boogaard, Henk

AU - Hommersom, Annelies

AU - Boogaard, Floris

AU - de Lima, Rui

AU - Schenkel, Janneke

AU - Uibel, Elena

AU - Eleveld, Marieke

AU - Kramer, Lilith

AU - Harezlak, Valesca

AU - Dioniso, Miguel

PY - 2018/10

Y1 - 2018/10

N2 - Cyanobacterial blooms can be toxic to humans swimming in affected waters. According to the European Bathing Water Directive bathing waters should be closed during cyanobacterial blooms. In the Netherlands, cyanobacteria monitoring in all official bathing water locations is usually performed every two weeks during the bathing season. In face of the large temporal and spatialvariability of cyanobacterial bloom dynamics this monitoring frequency however is too low for adequate early warnings to the public.High frequency monitoring and forecasting models can provide information on cyanobacterial blooms in between the regular monitoring dates and for a few days into the future. In the H2020 project EOMORES, we have combined observational data from a spectral camera (Ecowatch) near a Dutch bathing site with fluorescence data from an underwater drone to analyse the variability ofcyanobacterial blooms at short temporal and spatial scales. The results are used in a short term forecasting model of cyanobacterial blooms (AlgaeRadar) and a 3D scum forecasting model (EWACS). The AlgaeRadar is cross-validated with biweekly data from other bathing water sites and shows improved model performance compared to an earlier version that was built with only biweekly data.For the site with high-frequency chlorophyll observations the near-real time data are assimilated in the model to further enhance the model performance. Model performance of EWACS is verified using high frequency pictures from the Ecowatch station, showing scum layers on the water. This allowed us to validate and calibrate the EWACS model. Model validation abilities were in the pastalso limited by to the patchy nature and high temporal variability of the scum layers, which was not covered by sparse scum observations. With the resulting models, early warnings for cyanobacterial blooms are more reliable than those from the current practice that are merely based on biweekly monitoring data. For the protection of public health this provides better opportunities as well.

AB - Cyanobacterial blooms can be toxic to humans swimming in affected waters. According to the European Bathing Water Directive bathing waters should be closed during cyanobacterial blooms. In the Netherlands, cyanobacteria monitoring in all official bathing water locations is usually performed every two weeks during the bathing season. In face of the large temporal and spatialvariability of cyanobacterial bloom dynamics this monitoring frequency however is too low for adequate early warnings to the public.High frequency monitoring and forecasting models can provide information on cyanobacterial blooms in between the regular monitoring dates and for a few days into the future. In the H2020 project EOMORES, we have combined observational data from a spectral camera (Ecowatch) near a Dutch bathing site with fluorescence data from an underwater drone to analyse the variability ofcyanobacterial blooms at short temporal and spatial scales. The results are used in a short term forecasting model of cyanobacterial blooms (AlgaeRadar) and a 3D scum forecasting model (EWACS). The AlgaeRadar is cross-validated with biweekly data from other bathing water sites and shows improved model performance compared to an earlier version that was built with only biweekly data.For the site with high-frequency chlorophyll observations the near-real time data are assimilated in the model to further enhance the model performance. Model performance of EWACS is verified using high frequency pictures from the Ecowatch station, showing scum layers on the water. This allowed us to validate and calibrate the EWACS model. Model validation abilities were in the pastalso limited by to the patchy nature and high temporal variability of the scum layers, which was not covered by sparse scum observations. With the resulting models, early warnings for cyanobacterial blooms are more reliable than those from the current practice that are merely based on biweekly monitoring data. For the protection of public health this provides better opportunities as well.

KW - water

KW - climate

KW - climateadaption

KW - water

KW - klimaatverandering

M3 - Abstract

ER -

Blauw A, Van den Boogaard H, Hommersom A, Boogaard F, de Lima R, Schenkel J et al. Forecasting cyanobacterial blooms using high frequency monitoring data. 2018. Abstract from 18th international conference on harmful algae from ecosystems to socio Ecosystems, Nantes, France.