By Enrique Ortiz et Al. Executive Secretary of the NGO GH2MF2 – Association for a Participated Global Hydrological Monitoring and Flood Forecasting System. Via Settimio Mobilio 16 Int.21 – 84127 (SA) Salerno – Italy. Fiscal Code: 95165880659 The… Read More

EGU2019-132 | Orals | HS4.1.3/NH1.32 The importance of coupling meteorological and hydrological forecasts to anticipate Flash Floods. The case of Sant Llorenç des Cardassar (Illes Balears – Spain) Enrique Ortiz, Guilllermo Santana, Raúl Herrero, and Daniel Santos-Muñoz Thu,… Read More

GH2MF2 – Association for a Participated Global Hydrological Monitoring and Flood Forecasting System has joined the CSDMS Project as a member of the Hydrology Focus Research Group. The Hydrology FRG is a research group (currently 652 members), that… Read More

In this Post we want to share with the hydrological and meteorological community, the brilliant Keynote of Professor Eric F. Wood (Department of Civil and Environmental Engineering, Princeton University. Princeton NJ 08544 USA) that performed in the International… Read More

In the Non-Profit GH2MF2 association we have processed the MODIS product (Moderate Resolution Imaging Spectroradiometer) MOD16A2: MODIS / Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid V006 in the basin of the Ganges-Brahmaputra-Meghna rivers. The objective… Read More

From deterministic to probabilistic thresholds: possibly a better way to take advantage of  predictive uncertainty in flood emergency management Prof. Ezio Todini is Honorary President of Italian Hydrological Society and Co-Founder of “GH2MF2 – Association for a Participated… Read More

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… Read More

In the Non-Profit GH2MF2 project the MSWEP V2.1 product dataset (Tile 243) is being used to force and calibrate the physically distributed hydrological model based on the Koshi River Basin together with the dataset derived from the CFSR… Read More

SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture https://www.earth-syst-sci-data.net/10/267/2018/essd-10-267-2018.pdf Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present… Read More