The increasing variability of hydroclimatic conditions, water scarcity, and growing competition for water across sectors require advanced, scalable tools for monitoring water resources and supporting resilient, evidence-based decision-making. This PhD project aims to develop an integrated framework for monitoring and assessing rivers and inland waters. This framework will combine satellite remote sensing, camera-based systems, citizen science, in situ sensing, historical records and process-based hydrological modelling. It will support the assessment of water quantity and quality, as well as operational monitoring and water availability forecasting.
The project is based on the idea that combining multi-source Earth observation data with physically based models can help to overcome the current fragmentation of water monitoring data, reduce uncertainty in the assessment of river discharge and water quality, and improve our understanding of current and future water availability in the context of climate change. Copernicus Sentinel-2 and Sentinel-1 imagery, satellite altimetry (e.g. Sentinel-3 and SWOT), drone- and camera-based observations, and citizen science data will be combined to create consistent, higher-frequency information. This information will feed into process-based hydrological and water quality models, providing the evidence base and decision support indicators needed for cross-sectoral water management and nexus governance.
The research is organised around four components: (i) the harmonisation and integration of historical, in situ and catchment data into a coherent, multi-source database; (ii) the in situ and low-cost monitoring of river stage, discharge and water quality proxies via camera systems and citizen science observations; (iii) the satellite-based retrieval of hydrological and water quality variables, including water flux, suspended and floating material, turbidity and chlorophyll a, building on Copernicus Sentinel and related Earth observation products; and (iv) the integration of these variables into process-based hydrological and water quality modelling to reconstruct, forecast and assess scenarios for water quantity and quality. These components will converge to create a citizen-science-driven, multi-channel tool for large-scale water state assessment, designed to exploit the full potential of innovative monitoring systems.
Strong background in hydrology or water resources; experience with hydrological/hydraulic modelling;
Knowledge of climate change impacts on the hydrological cycle;
Familiarity with calibration and uncertainty analysis;
Understanding of stochastic methods for extremes;
Proficiency in Python, R or MATLAB;
Experience with GIS and remote sensing data;
Ability to work with alternative data sources (e.g., reanalysis, citizen science);
Good communication skills in English;
Capacity for independent research and interdisciplinary collaboration.
HydroLAB coordinated by Prof. Salvatore Manfreda is operating in the department DICEA of the University of Naples Federico II which is a leading institute in hydraulic construction and hydrological studies particularly devoted in the optimization of water management systems. HydroLAB is developing new innovative technologies for environmental monitoring using remote sensing and camera systems. The environment is a stimulating and challenging one with a strong and significant international dimension.