Modelling Data for Predicting Cyanobacteria Blooms - JPIWater Project BLOOWATER

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欧盟水联合倡议（European Union Water JPI，http://www.waterjpi.eu/）资助了BLOOWATER项目——针对受蓝藻及蓝藻毒素污染的饮用水水库综合管理支持工具（Supporting tools for the integrated management of drinking water reservoirs contaminated by Cyanobacteria and cyanotoxins，https://www.bloowater.eu/）。BLOOWATER项目的核心目标是为公共供水系统提供信息资源，以应对饮用水中的蓝藻毒素风险，做好风险准备与响应工作。本项目提出了创新性技术解决方案，旨在开发一套集成监测技术与有毒水华影响水体治理的方法学框架。BLOOWATER旨在构建有毒水华的预测模型、监测与预警系统，以便及时开展适宜的饮用水处理等应对行动。本项目将开发并实施高效处理载蓝藻水体的工艺方法，通过创新监测技术制定水质诊断规程，同时搭建有毒水华的预测模型与监测预警系统。上述举措将助力水处理设施根据蓝藻水华的爆发情况，最优调整水厂的运行参数。 为开发蓝藻预测模型，项目团队正在测试两种互补的差异化方法： 1) 基于过程的模型：本次将结合GOTM水动力模型与SELMA生物地球化学模型，并通过生物地球化学模型框架（Framework for Biogeochemical Models, FABM）实现耦合。SELMA可模拟通用蓝藻类群的生物量，我们将验证其能否作为蓝藻水华的有效预测因子。 2) 基于机器学习的模型：该模型将使用与过程模型相同的数据集进行训练与验证，同时也可接入过程模型生成的数据作为输入。 本数据集提供了强迫数据、实测湖泊化学与浮游植物数据档案，供BLOOWATER项目用于开发并测试基于过程建模与机器学习方法的模型预测方案。 本数据集以瑞典埃尔肯湖（Lake Erken）为例，该湖是BLOOWATER项目的核心案例研究站点。 所有数据文件均适配版本5.3的GOTM模型（https://gotm.net/）以及通过生物地球化学模型框架耦合的SELMA模型（https://github.com/fabm-model）。本次湖泊模型校准所使用的并行敏感性分析与校准工具为ParSAC（https://bolding-bruggeman.com/portfolio/parsac/），采用ParSAC要求格式存储的校准用实测数据也一并收录于本数据档案中。 此外，BLOOWATER项目开发的额外数据集与机器学习工作流可在https://github.com/Shuqi-Lin/Algal-bloom-prediction-machine-learning获取。