The GH2MF2 System is integrated into a Decision Support System (DSS) platform, based on geographical data. The DSS is an innovative web application (for Pcs, Tablets, Mobile phones) not requiring installation (a web browser and an internet connection only) or updating (all upgrading being deployed on the remote server). The proposed platform is meant to be participative and interactive. To meet this objective, all information used in the system must be freely accessible to users, including the hydrological model, and allowed to set-up the models for the benefit of the community.
The novel aspect of the proposed project and its platform is that any user will be allowed to set-up, calibrate and validate hydrological models, by downloading from the platform the base maps (e.g. digital elevation model) and the hydro-meteorological forcing data. Validated models (according to provided standards) will be then uploaded on the platform to be part of the daily run of the system and to be available to other users, who can view and interrogate the platform for forecasts in the basins. The hydrological simulations will also be processed by the platform for their visualization and for diffusion of warnings when extreme conditions (e.g. floods and droughts) are reached. The platform comprises the following components:
- Web portal with a dashboard to share, download and upload hydrological datasets;
- Blog and social media pages where users can exchange impressions, suggestions and comments on the use of the platform and their simulations, and the possibility of upload geo-referenced observed water levels and other relevant information useful for other users;
- Decision Support System based on the latest generation Web-GIS, which enables the user to view and query on any 1×1 km2 cell the evolution of modeled variables in the form of Multi-Temporal output raster maps: rainfall, temperature, soil moisture (surface layer and deep layer), evapotranspiration, snow water equivalent (SWE), percolation, discharge flow in the channel network and surface flow.
To meet the requirements of availability, stability, interoperability and portability, a systematic architecture of three tiers, including the presentation, application and database and model, is considered. The Web GIS DSS will be built upon a platform of proven open source component including Geoserver, Open Layers, ExtJs, PostgreSql with PostGis extension, GDAL libraries. It implements Open Geospatial Consortium (OGC) standards, including Web Map Server (WMS), and Web Feature Service (WFS). The main available functions of the platform are:
- WebGis-portal with user-oriented interface, designed for facilitating remote access to the key operational tools;
- Pre-processing of historical and real-time hydrological data freely available, such as those provided by the WMO Hydrological Observing System (WHOS);
- Pre-processing of observed/historical and near real time Precipitation/Temperature derived by Satellite and Reanalysis datasets, such as NASA TRMM 3B42RT, PERSIANN-CCS, MSWEP, Reanalysis ERA5-ECMWF and CFSR-NCEP-NOAA;
- Pre-processing of ensemble numerical weather predictions (NWP) such us GFS 0.25º;
- Parameterization of a continuous and distributed hydrological model with reference base maps at global resolution of 1×1 km2;
- Customization of model parameters based on user expert knowledge (e.g. based on other data sources or after calibration for specific catchments);
- Batch simulations with historical hydro-meteorological data;
- Forecasts with real-time data and NWP;
- Visualization and download of model outputs, including both catchment distributed state variables (e.g. soil water content, snow cover, SWE, evapotranspiration, flow discharge in every cell of fluvial network grid) and integrated catchment response (e.g. flow discharge), at the user-required time-resolution (from one hour to one year);
- Simulation performance is evaluated by comparing simulated and observed discharge at selected gauged catchment outlets, e.g. those provided by WMO Hydrological Observing System (WHOS). The performance will be evaluated with the common statistics such as: correlation, root mean square error, Nash-Sutcliffe efficiency index;
- Predictive uncertainty of the catchment discharge is evaluated with the Model Conditional Processor approach (MCP model);
- Alerts and warnings for low flows and floods, based on user-assigned probabilities that the simulated discharge is below or above user defined thresholds.