Surface  hydrology

Gauging

Flood risk assessment requires reliable hydrological  and hydraulic models. Reliable models require reliable data (precipitation, evapotranspiration, streamflow, etc.). For this reason, the following services are provided:

• Advanced hydrometrics employing hand-held surface flow velocity radar (SVR) and image velocimetry techniques along with sophisticated mathematical approaches (entropy maximization).   https://doi.org/10.1080/02626667.2022.2030059 

• Development of rating curves.

• Development and calibration of hydrological and hydraulic models.

Monitoring network

Currently operates 16 hydro-telemetric stations, six of which are of NOA‘s design, in the Peloponnese and in Attica, Greece. Measured data are transmitted to NOAs’ Server, where they are automatically processed, quality controlled and stored in a Data Base; the data are freely available to users through the OpenHi.net platform (openhi.net), or upon request (hydronet@noa.gr). A prime service prospect of the HYDRO-NET system, with its real-time observations, is Flood Warning.

• Installation of online stations with automated data transmission.

• Near-real time hydrologic data (stream water level) freely available at the platform https://system.openhi.net/?q=observatory 

• Development of in-house tele-hydrometric stations – at ~50% cost of commercial stations with additional features (e.g., connection with camera).

Early warning systems

Low computational burden flood modelling are currently studied (research project LOCOBUFLO, May 2021 - Apr 2023). This study will assess the most promising low computational burden flood models, and the most suitable for the small to medium-sized water basins of Greece will be adapted and applied to representative case studies. The final product will be a pilot service that will provide short-term forecast in a typical water basin in Greece. Visit the project page.

Hydrological model boosting

The results of hydrological models can be post processed to obtain additional valuable information. The first step is to identify whether the hydrological model captures successfully the deterministic relationship between inputs and outputs. This can be accomplished with an RNN model (https://doi.org/10.3390/hydrology9010005). Then, the model results can be further processed with a KNN model to obtain both the confidence interval and a less biassed simulation (https://doi.org/10.3390/hydrology9060101).