Don Pierson

Uppsala University;GLEON

 Recent Activity

ABSTRACT:

The European Union Water JPI (http://www.waterjpi.eu/) has funded the project BLOOWATER (Supporting tools for the integrated management of drinking water reservoirs contaminated by Cyanobacteria and cyanotoxins (https://www.bloowater.eu/) The main objective of the BLOOWATER project is to produce information resources for Public water supply systems to prepare and respond to the risk of the cyanotoxins in drinking water. Practically the project proposes innovative technological solutions aim to develop a methodological approach based on the integration of monitoring techniques and treatment of water affected by toxic blooms. BLOOWATER aims to create forecasting models and systems of surveillance and early warning of toxic blooms to perform immediate actions such as opportune potabilization treatment. The project intends to develop and implement methods to treat cyanobacteria laden water with more efficient processes, to define diagnostic protocols through the use of innovative techniques for water monitoring, and create forecasting models and systems of surveillance and early warning of toxic blooms. Combined these actions will allow water treatment fallibilities to optimally adjust treatment plant operations in response to the onset of cyanobacteria blooms.

To develop cyanobacteria forecasts two different but complimentary methods are being tested

1) The use of Process based models, in this case the combination of the GOTM Hydrodynamic model and the SELMA biogeochemical model coupled using the Framework for Biogechemical Models (FABM) SELMA simulates the biomass of a generic cyanobacteria group and we will test if this can be of useful predictor of cyanobacteria blooms

2) Use of machine learning based models that will be forced and trained on the same data sets used to simulate and verify the process based models, but which may also take as imput data generated by the process based models.

Here we provide an archive of forcing data and measured lake chemistry and phytoplankton data that will be used by BLOOWATER to develop and test model forecasts using both process based modeling and machine learning approaches.

Data are provided for Lake Erken Sweden a primary case study site in the BLOOWATER project

All data files are formatted for use with the GOTM version 5.3 (https://gotm.net/) and SELMA models that are coupled by the frame work for biogeochemical models (https://github.com/fabm-model). The lake model was calibrated using the Parallel Sensitivity Analysis and Calibration tool ParSAC (https://bolding-bruggeman.com/portfolio/parsac/) The measured data used for calibration in the format used by ParSAC are also included in this archive

Additional data and machine learning workflows developed by the BLOOWATER project are available at https://github.com/Shuqi-Lin/Algal-bloom-prediction-machine-learning

Show More

ABSTRACT:

The European Union Water JPI (http://www.waterjpi.eu/) has funded the project PROGNOS (Predicting In-Lake Responses to Change Using Near Real Time Models http://prognoswater.org/). PROGNOS developed an integrated approach that couples high frequency (HF) lake monitoring data to dynamic lake water quality models to forecast short-term changes in lake water quality. Here we provide an archive that demonstrates model simulations that were run on Lake Erken as part of the PROGNOS project. Simulations were run using the GOTM (https://gotm.net/)and SELMA models that are coupled by the frame work for biogeochemical models (https://github.com/fabm-model). The lake model was calibrated using the program acpy (https://bolding-bruggeman.com/portfolio/acpy/) The measured data used for calibration in the format used by acpy are also included in this archive

All data were collected from Lake Erken the site of the Uppsala University Limnology field station (http://www.ieg.uu.se/erken-laboratory/). The simulation period is between 2004-2018, and we used a 5 year (1999-2003) model spin-up period prior to simulation and calibration. Model forcing data are obtained from meteorological stations at the Erken laboratory, and from Erken's routine monitoring program. To calibrate the model data sets of HF water temperature, chlorophyll fluorescence and dissolved oxygen are combined with laboratory measurement from the routine monitoring program. Calibration data for nutrients are only available from the Erken laboratories routine monitoring program All data files are formated as required by the lake models. Files that define the model parameterization (xml and yaml) are also included

Show More

ABSTRACT:

The European Union Water JPI (http://www.waterjpi.eu/) has funded the project PROGNOS (Predicting In-Lake Responses to Change Using Near Real Time Models http://prognoswater.org/). PROGNOS developed an integrated approach that couples high frequency (HF) lake monitoring data to dynamic lake water quality models to forecast short-term changes in lake water quality. Here we provide an archive the the HF monitoring data sets that were used by PROGNOS project Partner Uppsala Universtiy to calibrate and verify the performance of the GOTM (https://gotm.net/)and SELMA models that are coupled by the frame work for aquatic biogeochemical models (https://github.com/fabm-model). All data were collected from Lake Erken the site of the Uppsala University Limnology field station (http://www.ieg.uu.se/erken-laboratory/). HF data is from 2015, 2016, 2017, and 2018, years when there was good coverage of the three main categories of data that are needed for water quality modeling: 1) meteorological data; 2) water temperature data; and 3)lake biogeochemical data. These data are in the format routinely collected,and can contain additional measurements that are not actually used in the model simulations.

Show More

 Contact

Resources
All 0
Collection 0
Resource 0
App Connector 0
Resource Resource

ABSTRACT:

The European Union Water JPI (http://www.waterjpi.eu/) has funded the project PROGNOS (Predicting In-Lake Responses to Change Using Near Real Time Models http://prognoswater.org/). PROGNOS developed an integrated approach that couples high frequency (HF) lake monitoring data to dynamic lake water quality models to forecast short-term changes in lake water quality. Here we provide an archive the the HF monitoring data sets that were used by PROGNOS project Partner Uppsala Universtiy to calibrate and verify the performance of the GOTM (https://gotm.net/)and SELMA models that are coupled by the frame work for aquatic biogeochemical models (https://github.com/fabm-model). All data were collected from Lake Erken the site of the Uppsala University Limnology field station (http://www.ieg.uu.se/erken-laboratory/). HF data is from 2015, 2016, 2017, and 2018, years when there was good coverage of the three main categories of data that are needed for water quality modeling: 1) meteorological data; 2) water temperature data; and 3)lake biogeochemical data. These data are in the format routinely collected,and can contain additional measurements that are not actually used in the model simulations.

Show More
Resource Resource

ABSTRACT:

The European Union Water JPI (http://www.waterjpi.eu/) has funded the project PROGNOS (Predicting In-Lake Responses to Change Using Near Real Time Models http://prognoswater.org/). PROGNOS developed an integrated approach that couples high frequency (HF) lake monitoring data to dynamic lake water quality models to forecast short-term changes in lake water quality. Here we provide an archive that demonstrates model simulations that were run on Lake Erken as part of the PROGNOS project. Simulations were run using the GOTM (https://gotm.net/)and SELMA models that are coupled by the frame work for biogeochemical models (https://github.com/fabm-model). The lake model was calibrated using the program acpy (https://bolding-bruggeman.com/portfolio/acpy/) The measured data used for calibration in the format used by acpy are also included in this archive

All data were collected from Lake Erken the site of the Uppsala University Limnology field station (http://www.ieg.uu.se/erken-laboratory/). The simulation period is between 2004-2018, and we used a 5 year (1999-2003) model spin-up period prior to simulation and calibration. Model forcing data are obtained from meteorological stations at the Erken laboratory, and from Erken's routine monitoring program. To calibrate the model data sets of HF water temperature, chlorophyll fluorescence and dissolved oxygen are combined with laboratory measurement from the routine monitoring program. Calibration data for nutrients are only available from the Erken laboratories routine monitoring program All data files are formated as required by the lake models. Files that define the model parameterization (xml and yaml) are also included

Show More
Resource Resource

ABSTRACT:

The European Union Water JPI (http://www.waterjpi.eu/) has funded the project BLOOWATER (Supporting tools for the integrated management of drinking water reservoirs contaminated by Cyanobacteria and cyanotoxins (https://www.bloowater.eu/) The main objective of the BLOOWATER project is to produce information resources for Public water supply systems to prepare and respond to the risk of the cyanotoxins in drinking water. Practically the project proposes innovative technological solutions aim to develop a methodological approach based on the integration of monitoring techniques and treatment of water affected by toxic blooms. BLOOWATER aims to create forecasting models and systems of surveillance and early warning of toxic blooms to perform immediate actions such as opportune potabilization treatment. The project intends to develop and implement methods to treat cyanobacteria laden water with more efficient processes, to define diagnostic protocols through the use of innovative techniques for water monitoring, and create forecasting models and systems of surveillance and early warning of toxic blooms. Combined these actions will allow water treatment fallibilities to optimally adjust treatment plant operations in response to the onset of cyanobacteria blooms.

To develop cyanobacteria forecasts two different but complimentary methods are being tested

1) The use of Process based models, in this case the combination of the GOTM Hydrodynamic model and the SELMA biogeochemical model coupled using the Framework for Biogechemical Models (FABM) SELMA simulates the biomass of a generic cyanobacteria group and we will test if this can be of useful predictor of cyanobacteria blooms

2) Use of machine learning based models that will be forced and trained on the same data sets used to simulate and verify the process based models, but which may also take as imput data generated by the process based models.

Here we provide an archive of forcing data and measured lake chemistry and phytoplankton data that will be used by BLOOWATER to develop and test model forecasts using both process based modeling and machine learning approaches.

Data are provided for Lake Erken Sweden a primary case study site in the BLOOWATER project

All data files are formatted for use with the GOTM version 5.3 (https://gotm.net/) and SELMA models that are coupled by the frame work for biogeochemical models (https://github.com/fabm-model). The lake model was calibrated using the Parallel Sensitivity Analysis and Calibration tool ParSAC (https://bolding-bruggeman.com/portfolio/parsac/) The measured data used for calibration in the format used by ParSAC are also included in this archive

Additional data and machine learning workflows developed by the BLOOWATER project are available at https://github.com/Shuqi-Lin/Algal-bloom-prediction-machine-learning

Show More