Presentations

UPDATING FLOOD ANNUAL MAXIMA IN SOUTHERN ITALY from Salvatore Manfreda

Le sfide del Sud nella Gestione delle Risorse Idriche from Salvatore Manfreda

Use of UAS for hydraulic monitoring from Salvatore Manfreda

From Global satellite water cycle products to field scale satellite water states from Salvatore Manfreda

Soil Moisture Retrievals from Unmanned Aerial Systems (UAS) from Salvatore Manfreda

SPRINt: Strategie integrate per la Prevenzione e il monitoraggio del rischio Incendi e la sensibilizzazione delle comunità from Salvatore Manfreda

https://www.slideshare.net/Summer_School_of_Hydrology/an-integrative-information-aqueduct-to-close-the-gaps-between-global-satellite-observation-of-water-cycle-and-local-sustainable-management-of-water-resources

Use of Unmanned Aerial Systems for Hydrological Monitoring from Salvatore Manfreda

DEM-based Methods for Flood Risk Mapping at Large Scale from Salvatore Manfreda

Derivation of flow rating-curves in data-scarce environments from Salvatore Manfreda

Sistema di gestione e monitoraggio delle risorse idriche in Basilicata from Salvatore Manfreda

HYDROLAB RESEARCH ACTIVITIES from Salvatore Manfreda

La gestione delle acque meteoriche in ambito urbano from Salvatore Manfreda

Calibration of Physically based Hydrological Models from Salvatore Manfreda

Modelling Vegetation Patterns in Semiarid Environments from Salvatore Manfreda

EFFECTS CLIMATE CHANGE ON WATER RESOURCES AVAILABILITY AND VEGETATION PATTERNS from Salvatore Manfreda

Detection of Flood Prone Areas using Digital Elevation Models from Salvatore Manfreda

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Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environment

Apply for Ph.D. Position

Updating flood annual maxima in Southern Italy

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at global scale

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You missed

Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environment

Apply for Ph.D. Position

Updating flood annual maxima in Southern Italy

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at global scale