Hydrological Modeling – Collaborate with us, for a better Earth in a moment of change, all ideas or proposals are welcome
There are many current hydrological models, almost as many as research groups of different research entities or university departments.
Hydrological models can be aggregated, semi-distributed, distributed conceptually or physically based, stochastic and black-box or data-driven (ANN’s), which is true, is that there is no universal model, an interesting reflection is the one made in 2002 and published in Advances in Water Resources 25 (8): 1313-1334: Advances in the use of observed spatial patterns of catchment hydrological response – doi: 10.1016 / S0309-1708 (02) 00060-X
In the Non-Profit GH2MF2 project we have initially started to use the physically distributed model based TOPKAPI-eXtended because it is free to use and open source software and part of the members of the association have contributed to its development.
Your hydrological model and your knowledge is important for the objectives of the Non-Profit Project GH2MF2, do you want to collaborate? get in touch with us at email@example.com
You can model the basins in which we have started working or propose other basins in which you have a background and are useful to add in the open platform WEBGIS GH2MF2
The WEBGIS platform is able to incorporate other hydrological models, because it intends to be multi-model, and that is why we invite the International Hydrological Community to collaborate in this Non-Profit project by making available their models and collaborating in their incorporation to make a post- processed to estimate the Predictive Uncertainty based on the MCP (A Model Conditional Processor to Assess Predictive Uncertainty in Flood Forecasting) published in International Journal of River Basin Management (Todini, E. https://doi.org/10.1080/15715124.2008.9635342)
If you want to know more about the proposed methodology on which the Model Conditional Processor is based, you can download the paper published in HESS 2011 “Recent developments in predictive uncertainty assessment based on the model conditional processor approach” (Coccia. G and Todini, E. doi:10.5194/hess-15-3253-2011)
The following video shows the benefits of the methodology proposed in the project NON-Profit GH2MF2 in the excellent presentation by Coccia, G.: “MULTI-TEMPORAL UNCERTAINTY PROCESSORS FOR REAL TIME FLOOD FORECASTING“, with examples in the River Basins of the Po River( Italy) and Baron Folk River (USA) making use of three Hydrological Models widely used in the world and sanctioned by their high quality: TOPKAPI-eXtended, TETIS (GIHMA, UPV) and a Model of Neural Networks ANN:
Presented at International Symposium on Distributed Hydrological Modelling, University of Bologna To mark the 70th birthday of Prof. Ezio Todini, 5-7 June 2013.
Prof. Patrick Enda O’Connell – University of Newcastle upon Tyne
Prof. Alberto Montanari – DICAM – University of Bologna
Prof. Enzo Farabegoli – BiGeA – University of Bologna