The trophic index is often used to monitor the primary production of lakes. In Brandenburg, Germany, lakes are sampled several times every three years between April and October. The trophic index is then calculated from the values for phosphorus concentration, turbidity and chlorophyll-a content. This is usually only done for lakes that are monitored according to the Water Framework Directive (area > 50 ha). The low temporal resolution in combination with natural annual variations makes trend analysis of trophic levels very difficult and a high proportion of lakes are excluded from this monitoring.
Satellite images can be used to obtain information on chlorophyll-a and turbidity. Phosphorus, as a nutrient for algae, also has an indirect effect on water color. There are already many indices based on the Copernicus Sentinel-2 program, such as the Normalized Difference Chlorophyll Index, which can be used for real-time water monitoring. In addition, annual data are essential for lake management to identify long-term trends. The trophic index is a widely used and easily interpreted indicator in this regard.
The AD4GD project explored i) which bands of the Sentinel-2 images are best suited for estimating trophic state, ii) how the data can be temporally aggregated within a season, and iii) whether one pixel within a lake is sufficient to reliably describe the trophic state of the lake. Especially the latter was necessary to apply the method to small lakes where regular monitoring is not available.
The developed Normalized Difference Trophic Index (NDTI), aggregated over the months of April to October, best represented the trophic index based on measured values. It was developed and validated using 294 lakes in Brandenburg with trophic data between 2018 and 2022 and is defined for a satellite image as
NDTI_image=(B5-B2)/(B5+B2)
Band 5 describes the near infrared reflectance at 705 nm, band 2 the reflectance of blue light at 490 nm. In oligotrophic lakes, band 2 reflectance usually dominates and the index is below zero. The trophic index based on in-situ measurements is best calculated from monthly values. Similarly, NDTIimage is first averaged monthly and then seasonally (April to October in Germany).
The resulting NDTIseason was found to be highly correlated with the in-situ data for the available years (Pearson correlation coefficient between 0.83 and 0.92). Thus, it allows a comparison of the trophic state of lakes in the Brandenburg region. The data are available at an annual resolution, which is three times more frequent than the conventional analysis. This allows a much more reliable trend analysis, which can be used to monitor the success of water quality improvement measures or to identify water quality problems more quickly. Small lakes can be included in the monitoring without much effort.
A first sensitive analysis has shown that the classification of eutrophic water bodies is more reliable than that of oligotrophic water bodies. Further factors influencing the accuracy of the method will be investigated in a subsequent sensitivity analysis.

Release for PROMISCES D 2.3 Toolbox fate & transport modelling

The present report summarizes the benefits of the eleven digital solutions demonstrated within DWC-WP2 in the form of fact sheets. The document aims to help cities and water utilities in finding appropriate solutions for their operational, environmental or public health deficits. The report is the final version which was submitted in Nov. 2022 after incorporating the recommendations and amendments by the EC.

The Data Management Plan (DMP) is a guidance document, which introduces a series of clear rules and procedures to improve data management during the project and foster the reuse of publications and data in open access.

This report describes the main functionalities the SMART-Control web-based tool T1B Quantitative microbial risk assessment. The tool helps to quantify the pathogen occurrence in source water and their removal by various treatment steps at MAR facilities by using a probabilistic approach. The interactive web-based QMRA tool supports the evidence-based risk assessment to minimize water-related infectious diseases.

Subsurface travel time from the area of recharge to the point of abstraction during MAR is a critical parameter to ensure sufficient attenuation for hygienic parameters and other undesired substances. A new simulation tool has been developed by the SMART-Control project partners KWB and TUD for determination of groundwater hydraulic residence time (HRT) using seasonal temperature fluctuations observed in recharge water and MAR recovery wells. This tool represents a proxy for quick, costs-effective and reliable control of travel time during aquifer passage. Time series of seasonal temperature measurements observed in surface water and abstraction wells can be fitted to sinusoidal functions. Peak values represented as local maxima and local minima and turning points from the fitted sinusoidal curves are used for the approximation of travel times between surface water and abstraction well. The calculated values are adjusted by a thermal retardation factor. The developed tool is userfriendly and offers the possibility to use existing hystorical temperature measurements as well as online sensor data. Data acquisition is resolved through the internal connectivity with other web-tools developed within the SMART-Control project, providing thus an integrated simulation environment.

Zum Forschungsdatenmanagement zählen alle Aktivitäten, die mit der Aufbereitung, Speicherung, Archivierung und Veröffentlichung von Forschungsdaten verbunden sind. Die Bedeutung des Forschungsdatenmanagements ist in den vergangenen Jahren immens gestiegen. Grund dafür sind die großen Datenmengen, die im Zuge der Digitalisierung und Automatisierung von Prozessen anfallen und neue Herausforderungen an deren Verwaltung und Verarbeitung stellen, die mit den bisherigen Werkzeugen schwer bewältigt werden können. Dies gilt auch für Daten in der Wasserforschung. Der nachhaltige Zugang zu Forschungsdaten und die Erstellung von Datenmanagementplänen werden zunehmend von Forschungsförderern verlangt. Am Kompetenzzentrum Wasser Berlin gGmbH (KWB) werden im Rahmen von Forschungsprojekten eine Vielzahl von Daten verarbeitet, die entweder selbst erhoben oder von Projektpartnern zur Verfügung gestellt werden. Dazu zählen Messdaten, Metadaten, Fotos/Videos, Bestands- und Zustandsdaten und verarbeitete Daten (z.B. Zeitreihen, aggregierte Werte, Ergebnisse aus Computersimulationen). Um solche Daten nachhaltig nutzbar zu machen, zu verwalten und zu verarbeiten, sind standardisierte Prozesse, Werkzeuge und Methoden zu entwickeln, die eine projektübergreifende Reproduzierbarkeit der Ergebnisse gewährleisten. Ziel des Projektes FAKIN (Forschungsdatenmanagement an kleinen Instituten) war es, ein solches Forschungsdatenmanagement (FDM) für das KWB in Zusammenarbeit mit den Projektwissenschaftlern zu erarbeiten und unternehmensweit zu etablieren. Damit sollte das Vorhaben als übertragbares Fallbeispiel für das FDM an kleinen, aber stark vernetzten außeruniversitären Forschungsinstituten dienen.

The Data Management Plan (DMP) is a guidance document which introduces a series of clear rules and procedures to improve data management during the project and foster the reuse of publications and data in open access. ; Version submitted to EU (v0.1.0)

This report summarizes the results of Life Cycle Assessment, Water footprinting, and quantitative microbial and chemical risk assessment for selected demosites of water reuse in Europe, measuring the potential impacts of different types of water reuse on environment and human health. The case studies show that water reuse is often preferable from an environmental point of view in areas with water scarcity problems if compared to other alternatives such as water import or seawater desalination. Potential risks of water reuse for ecosystems or human health can be adequately managed if suitable processes for reclaimed water treatment are used and operated correctly. However, the study also shows the trade-offs between a higher level of reclaimed water treatment and increased environmental impacts from associated efforts in energy, chemicals and infrastructure. This inherent trade-off requires a site-specific assessment of reuse schemes to choose an adequate treatment scheme for risk management with reasonable global environmental impacts.