The GRoW project WELLE offers a webinar on December 3rd and 10th to introduce its recently developed web-based tool for calculating a company’s water footprint – the Organizational Water Footprint tool (OWF).
The multi-scale hydrological substance emission model MoRE (Modelling of Regionalized Emissions) was further developed and optimized by GRoW researchers and will now be used by the associated MuDaK-WRM project partner Universidade Federal do Paraná (UFPR) in the Paranapanema River in Southern Brazil.
As part of the GRoW-project TRUST, researchers are training an Artificial Intelligence (AI), with the help of which aerial photos can be used to make statements about soil properties.
We are happy to announce the GRoW final conference, which will take place in Berlin in June 2020:
Save the Date: 23.- 24. June 2020
Location: ESMT Berlin, Schlossplatz 1, 10178 Berlin, Germany
In spring 2019, the GRoW-project TRUST signed an agreement on future cooperation with the newly elected municipality of San Andrés de Tupicocha.
Christian D. León, coordinator of the GRoW-project TRUST, participated in a seminar in Lima as a panelist, talking about the nexus between water, health and climate change.
A video by DIE GEWÄSSER-EXPERTEN! shows aerial photographs and 3D models generated during various field trips in South Africa.
At this year's General Assembly of the International Union of Geodesy and Geophysics (IUGG) in Montréal, Prof. Dr. Harald Kunstmann, coordinator of the GRoW project SaWaM, moderated a session on decision-based forecasting for sub-seasonal and seasonal forecasts.
Dr. Konstantin Scheihing, project engineer of OOWV Oldenburg-East-Frisian Water Board, will hold a presentation on behalf of the GRoW project go-Cam during the 3rd edition of IFAT Africa - Trade Fair for Water, Sewage, Refuse and Recycling.
The Journal of Hydrology features a publication by members of the project team InoCottonGROW. The article "Spatially distributed model calibration of a highly managed hydrological system using remote-sensing-derived ET data" deals with the challenge of calibrating a spatially distributed hydrological model that meets the local specifics of a heavily used agricultural region.
