Phd in INFORMATION AND COMMUNICATION TECHNOLOGY (ICT)

Thesis title: Investigating the snow cover in the Italian Central Apennines using numerical modelling, remote sensing techniques and in situ measurements

The goal of this study is to investigate the potentials of different approaches in estimating the physical variables that characterize the snow cover of the Central Apennines, a part of the Apennines mountains, a mountain range that crosses the entire Italian peninsula from north-west to south-east. With the first approach we wanted to investigate the ability of snow cover models to reproduce the observed snow height and extent during winter season 2018-2019, using forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: i) Noah LSM, a single-layer Eulerian model; ii) Alpine3D, a multi-layer Lagrangian model. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at regional scale with a spatial resolution of 3 km. While Noah LSM is already online coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D. First we validate the WRF simulations of air temperature, relative humidity, wind speed, incoming shortwave radiation and daily precipitation using a dense network of automatic weather stations. Then we evaluate the performances of both WRF-Noah and WRF-Alpine3D by comparing model simulations with snow heights measurements provided by a quality-controlled network of snow stations located in Central Apennines. We find that WRF-Alpine3D model produces a bias almost 4 times smaller compared to the WRF-Noah model for the snow height estimation, and more than 2 times smaller for the daily snow height variation estimation. However, the WRF-Noah model is slightly better than WRF-Alpine3D to reproduce the snow cover area observed with respect to the Moderate Resolution Imaging Spectroradiometer (MODIS), even though both models tend to overestimate it. We finally show that snow settlement rate in WRF-Alpine3D is mainly driven by densification, whereas in WRF-Noah there is also an important contribution of snow melting especially at high elevation. With the second approach instead we wanted to estimate snow cover area, height, and density in complex orography regions. The retrieval method, subdivided into classification and estimation subsequent stages, is based on 2 artificial neural networks (ANNs) trained by a forward DInSAR response model coupled with Alpine3D snow cover numerical model outputs. Auxiliary satellite training data from satellite visible-infrared MODIS imager as well as digital elevation model and land cover database are used to discriminate wet and dry snow areas. For snow cover classification the ANN-based retrieval methodology is combined with a fuzzy-logic scheme and compared with well-established decision threshold approaches using C-band backscattering coefficient data. For snow height and density estimation, the proposed methodology is compared with the well-known analytical inverse method and 2 model-based statistical techniques (linear regression and maximum likelihood). The validation is carried out in Central Apennines, using in situ snow data collected between December 2018 and February 2019. Results show that the ANN-based technique has a snow cover area classification accuracy more than 80% using MODIS reference maps. The estimation bias and a root mean square error are equal to about 0.5 cm and 20 cm for snow height and to 5 kg/m3 and 80 kg/m3 for snow density. As expected, worse results are associated to low DInSAR coherence between 2 repeat passes and to snow melting periods. With the third approach we wanted to directly measure the snow cover physical properties on the field, using both automatic and manual techniques, in order to lay the basis for a systematic monitoring of the Central Apennines snowpack. We set up a measurement site on Campo Imperatore plateau, at 1494 m a.s.l., in the core of the Central Apennines. The measurement site is made of automatic weather-snow station (AWSS) and an area reserved for manual measurements. From November 2020 to end of April 2021 we measured: i) air temperature, relative humidity, wind speed, wind direction, liquid precipitation, incoming shortwave radiation, reflected shortwave radiation, snow height, snow surface temperature, soil surface temperature at 30 minutes frequency with the AWSS; ii) vertical profiles of snow temperature, grain size, grain shape, snow hardness and snow density digging snow pits on 10 different days. The detailed information provided by the manual vertical profiles together with the automatic measurements collected nearby, constitute a unique database in Central Apennines, which may help to better understand the snow cover evolution in that region and develop more accurate snowpack numerical models and remote sensing techniques.