Research Project Proposals

Risk, Climate Change and Sustainable Development

Title: Ocean and atmosphere coupled processes

Proposer(s): Claudia Pasquero (University of Milano-Bicocca), Antonio Parodi (CIMA)

Curriculum: Risk, Climate Change and Sustainable Development

Description:

Significant progress has been made toward understanding the two-way coupling between the ocean and atmosphere, but many exciting and challenging research opportunities remain. This calls predominantly about prediction systems making use of a coupled atmosphere ocean models, referring to a numerical model of the atmosphere, usually with an associated land-surface model, which is 'two-way coupled' (at least daily, but preferably hourly) to a numerical model of the ocean.

The exchange of coupling fields – variables like sea surface temperature and currents from ocean to atmosphere, and heat and momentum fluxes from atmosphere to ocean – is often accomplished by the use of a separate coupling code (like OASIS-MCT, Craig et al., 2017) or framework (like ESMF, Valcke et al., 2012) providing a flexible way of linking component models and controlling the exchange and interpolation of coupling fields.

The added value of coupled models is that changes in one model (e.g., evolution of sea surface temperatures in the ocean model in response to the atmospheric state) can directly and immediately influence the other model (e.g., modified heat and moisture fluxes into the atmospheric boundary layer and beyond) and its predictive capabilities (Ricchi et al. 2021).

In ocean-only models, the absence of any feedback on the atmospheric forcing variables, e.g. winds, temperature and humidity, can cause many inaccuracies (Griffies et al., 2009). A number of recent studies (see e.g. Renault et al., 2016, Meroni et al. 2020) have focussed on the effect of including ocean currents in the calculation of the atmosphere-ocean momentum exchange.

Furthermore, the impact of different choices regarding the “sequencing” of model components, (e.g., running models concurrently or sequentially, the use of averaged or instantaneous fields if not coupling every time-step) are often ignored but will have impacts for stability, conservation and accuracy.

There is a lot still to understand regarding air-sea coupling, particularly at short spatial and temporal scales and with reference to the genesis, evolution and decay of severe sea-atmosphere processes.

Along these lines, this PhD thesis will use the WRF-ROMS modelling suite to study ocean and atmosphere coupled processes with special reference to the interplay between sea surface patterns, clouds formation and rainfall processes (Meroni et al. 2018), for a broad range of mesoscale processes in the Mediterranean area including back-building mesoscale convective systems, medicanes (Ragone et al. 2018), and marine storms at large. The current and future availability of remote sensing observations of coupled sea-atmosphere processes will be also considered (e.g. HARMONY mission, López-Dekker et al. 2021).

Link to the group or personal webpage

References:

  1. Craig, A., Valcke, S., & Coquart, L. (2017). Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3. 0. Geoscientific Model Development, 10(9), 3297-3308.

  2. Griffies, S. M., Biastoch, A., Böning, C., Bryan, F., Danabasoglu, G., Chassignet, E. P., ... & Yin, J. (2009). Coordinated ocean-ice reference experiments (COREs). Ocean modelling, 26(1-2), 1-46.

  3. López-Dekker, P., Biggs, J., Chapron, B., Hooper, A., Kääb, A., Masina, S., ... & Rommen, B. (2021, July). The Harmony Mission: End of Phase-0 Science Overview. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 7752-7755). IEEE.

  4. Meroni, A. N., Parodi, A., & Pasquero, C. (2018). Role of SST patterns on surface wind modulation of a heavy midlatitude precipitation event. Journal of Geophysical Research: Atmospheres, 123(17), 9081-9096.

  5. Meroni, A. N., Giurato, M., Ragone, F., & Pasquero, C. (2020). Observational evidence of the preferential occurrence of wind convergence over sea surface temperature fronts in the Mediterranean. Quarterly Journal of the Royal Meteorological Society, 146(728), 1443-1458.

  6. Ragone, F., Mariotti, M., Parodi, A., Von Hardenberg, J., & Pasquero, C. (2018). A climatological study of western mediterranean medicanes in numerical simulations with explicit and parameterized convection. Atmosphere, 9(10), 397.

  7. Renault, L., Molemaker, M. J., McWilliams, J. C., Shchepetkin, A. F., Lemarié, F., Chelton, D., ... & Hall, A. (2016). Modulation of wind work by oceanic current interaction with the atmosphere. Journal of Physical Oceanography, 46(6), 1685-1704.

  8. Ricchi, A., Bonaldo, D., Cioni, G., Carniel, S., & Miglietta, M. M. (2021). Simulation of a flash-flood event over the Adriatic Sea with a high-resolution atmosphere–ocean–wave coupled system. Scientific reports, 11(1), 1-11.

  9. Valcke, S., Balaji, V., Craig, A., DeLuca, C., Dunlap, R., Ford, R. W., ... & Vertenstein, M. (2012). Coupling technologies for earth system modelling. Geoscientific Model Development, 5(6), 1589-1596.

Title: Unlocking the potential of Artificial Intelligence for hydrologic science and technology

Proposer(s): Luca Ferraris (DIBRIS), Francesco Avanzi (CIMA), Simone Gabellani (CIMA), Antonio Parodi (CIMA)

Curriculum: Risk, Climate Change and Sustainable Development

Description:

Hydrology, the science of the water cycle, has always benefited from models (Savenije, 2009). These models can be employed to forecast near- to far-future conditions, or to simulate historical patterns and so gain insight into catchment functioning (Avanzi et al., 2020). Owing to both epistemic uncertainties related to the description of poorly understood processes and the large spatial extent and the variety of scales involved in the water cycle, these models are always a tradeoff across first-principle physics, parsimony, and computational requirements (Pagano et al., 2014). These tradeoffs are particularly relevant in mountain regions, where data are sparse and often unreliable, while hydrologic processes are further complicated by snow and glacier accumulation and melt (Avanzi et al., 2021b).

To remedy inherent simplifications involved in models, hydrologists have often employed several techniques based on data, including data assimilation (Piazzi et al., 2018, Silvestro et al., 2021) and model calibration (Silvestro et al., 2013). The core idea behind these techniques is either to fit model parameters to observed data (calibration) or to correct is as it runs, and independent observations become available (assimilation). Besides computational requirements, other recurring issues in hydrology in this regard are that data availability is limited, data are biased and/or affected by noise, and/or models include a comparatively large number of parameters and as such are prone to overfitting on such limited – and often noisy -- observed data (Beven and Freer, 2001).

The advent of remote sensing, cloud computing, and Artificial Intelligence techniques is promising to revolutionize environmental sciences, including hydrology, by both increasing the amount and augmenting the quality of available data and providing new approaches to add value to these data and make them more useful to models. Thus, the ultimate goal of this PhD is to unlock the potential of remote sensing, cloud computing, and Artificial Intelligence techniques in hydrologic science and technology and to move them closer to a fully data-driven field.

In this regard, the objectives of the PhD will be:

  • Perform an in-depth state of the art of existing data-driven approaches and their use in hydrology;

  • Develop a data-driven approach to quality assurance and quality control (QA/QC) that will be employed to pre-process environmental data;

  • Develop a data-driven approach to deep data assimilation to use these quality-checked data into an operational cryospheric and hydrologic model (S3M and Continuum, see Avanzi et al. 2021a and Silvestro et al 2013);

  • Develop a data-driven approach to model calibration that fully relies on remote sensing and cloud computing.

Link to the group or personal webpage

References:

  1. Avanzi, F., Rungee, J., Maurer, T., Bales, R., Ma, Q., Glaser, S., and Conklin, M.: Climate elasticity of evapotranspiration shifts the water balance of Mediterranean climates during multi-year droughts, Hydrol. Earth Syst. Sci., 24, 4317–4337, https://doi.org/10.5194/hess-24-4317-2020, 2020.

  2. Avanzi, F., Gabellani, S., Delogu, F., Silvestro, F., Cremonese, E., Morra di Cella, U., Ratto, S., and Stevenin, H.: S3M 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-92, in review, 2021a.

  3. Avanzi, F., Ercolani, G., Gabellani, S., Cremonese, E., Pogliotti, P., Filippa, G., Morra di Cella, U., Ratto, S., Stevenin, H., Cauduro, M., and Juglair, S.: Learning about precipitation lapse rates from snow course data improves water balance modeling, Hydrol. Earth Syst. Sci., 25, 2109–2131, https://doi.org/10.5194/hess-25-2109-2021, 2021b.

  4. Beven, Keith, and Jim Freer. "Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology." Journal of hydrology 249.1-4 (2001): 11-29.

  5. Tessa Maurer, et al. "Optimizing Spatial Distribution of Watershed-scale Hydrologic Models Using Gaussian Mixture Models." Environmental modelling & software, v. 142,. pp. 105076. doi: 10.1016/j.envsoft.2021.105076

  6. Pagano, T. C., Wood, A. W., Ramos, M., Cloke, H. L., Pappenberger, F., Clark, M. P., Cranston, M., Kavetski, D., Mathevet, T., Sorooshian, S., & Verkade, J. S. (2014). Challenges of Operational River Forecasting, Journal of Hydrometeorology, 15(4), 1692-1707. Retrieved May 10, 2022, from https://journals.ametsoc.org/view/journals/hydr/15/4/jhm-d-13-0188_1.xml

  7. Piazzi, G., Thirel, G., Campo, L., and Gabellani, S.: A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment, The Cryosphere, 12, 2287–2306, https://doi.org/10.5194/tc-12-2287-2018, 2018.

  8. Savenije, H. H. G.: HESS Opinions "The art of hydrology", Hydrol. Earth Syst. Sci., 13, 157–161, https://doi.org/10.5194/hess-13-157-2009, 2009.

  9. Silvestro, F., Gabellani, S., Delogu, F., Rudari, R., and Boni, G.: Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the Continuum model, Hydrol. Earth Syst. Sci., 17, 39–62, https://doi.org/10.5194/hess-17-39-2013, 2013.

  10. Silvestro F., Ercolani G., Gabellani S., Giordano P., Falzacappa M. Improving real-time operational streamflow simulations using discharge data to update state variables of a distributed hydrological model. Hydrology Research nh2021162 (2021) https://doi.org/10.2166/nh.2021.162

Title: Exploring Multi-platform integrated approaches based on remote and proximal sensing techniques to support forest fire risk assessment

Proposer(s): Luca Ferraris (DIBRIS), Paolo Fiorucci (CIMA), Umberto Morra di Cella (CIMA), Luca Pulvirenti (CIMA)

Curriculum: Risk, Climate Change and Sustainable Development

Description:

Wildfires impact environments and communities around the planet by changing vegetation composition, altering soil characteristics after the fire, modifying hydrologic regimes by increasing runoff and decreasing soil infiltration and reducing the ability to perform ecosystem services. Wildfires also cause losses in human life and property. While some of these changes to local environments are desirable from an ecological perspective, the destructive consequences of wildfires are generally considered undesirable and require mitigation (Szpakowski et al., 2019)

Due to climate change, it is expected that fire regimes will be altered in the future: generally increasing areas prone to wildfires, increasing intensity of fires, and increasing in fire occurrence are expected in the future (Hurteau et al., 2014 and Alarcón et al., 2015).

Remote sensing provides a means for analyzing conditions and monitoring changes over large geographic extents and provides biophysical measurements of the ground conditions prior to and post-fire. These measurements have been used to assist in fire risk mapping (Yu et al., 2017), fuel mapping (D’este et al., 2021), active fire detection, burned area estimates (Pulvirenti et al., 2020), assessing burn severity (Fernandez-Manso et al., 2016), and monitoring vegetation recovery (Veraverbeke et al., 2012). Recent advancements in remote sensing technology have facilitated new approaches to study fire ecology: LIght Detection And Ranging (lidar) has rapidly increased in popularity in nearly every type of remote sensing research: lidar data are used to create highly accurate digital terrain models (DTMs), model forest stands in 3-D (Gordon et al., 2017), and support traditional multispectral methods in classification. Of particular interest for fire managers, lidar has been incorporated into the mapping of surface fuels, assessing burn severity, and monitoring vegetation recovery, due to its ability to measure vegetation structural properties.

Unmanned aerial system (UAS) technologies have recently been used to acquire hyperspatial imagery, construct orthomosaics, and generate 3-D models using structure-from-motion (SfM) methodology or LIDAR sensors. UAS technology provides the means for rapid and cost-effective data acquisition necessary for fire ecology research by providing timely multispectral measurements and 3-D models of terrain and vegetation structure.

New technologies provides a huge volume of data where cloud computing, and Artificial Intelligence techniques play a key role to improve environmental sciences, including forest fire prevention, fire behavior modelling and burned area mapping.

The objectives of the PhD will be:

  • Perform an in-depth state of the art of existing data-driven approaches and their use in fire ecology;

  • Analysis of multisource datasets to identify key factors to improve current fuel maps and vegetation indexes;

  • Development of a modeling chain for the simulation of vegetation cover dynamics and vegetation recovery after wildfires;

  • Evaluate the threat posed by wildfires to ecosystem services focusing on cascading effects.

Link to the group or personal webpage

References:

  1. Alarcón, A.V.; Climent, J.M.; Casais, L.; Nieto, J.R.Q. Current and future estimates for the fire frequency and the fire rotation period in the main woodland types of peninsular Spain: A case-study approach. For. Syst. 2015, 24, 10.

  2. D’Este, M.; Elia, M.; Giannico, V.; Spano, G.; Lafortezza, R.; Sanesi, G. Machine Learning Techniques for Fine Dead Fuel Load Estimation Using Multi-Source Remote Sensing Data. Remote Sens. 2021, 13, 1658. https://doi.org/10.3390/rs13091658.

  3. Fernández-Manso, A.; Fernández-Manso, O.; Quintano, C. SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. Int. J. Appl. Earth Obs. Geoinf. 2016, 50, 170–175.

  4. Gordon, C.E.; Price, O.F.; Tasker, E.M. Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR. Ecol. Appl. 2017, 27, 1618–1632.

  5. Hurteau, M.D.; Bradford, J.B.; Fulé, P.Z.; Taylor, A.H.; Martin, K.L. Climate change, fire management, and ecological services in the southwestern US. For. Ecol. Manag. 2014, 327, 280–289.

  6. Pulvirenti, L.; Squicciarino, G.; Fiori, E.; Fiorucci, P.; Ferraris, L.; Negro, D.; Gollini, A.; Severino, M.; Puca, S. An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data. Remote Sens. 2020, 12, 674. https://doi.org/10.3390/rs12040674.

  7. Szpakowski, D. M., Jensen, J. L. R.: A Review of the Applications of Remote Sensing in Fire Ecology, Remote Sensing, 11, 2072-4292 (2019), https://doi.org/10.3390/rs11222638.

  8. Yu, B.; Chen, F.; Li, B.; Wang, L.; Wu, M. Fire Risk Prediction Using Remote Sensed Products: A Case of Cambodia. Photogramm. Eng. Remote Sens. 2017, 83, 19–25.

  9. Veraverbeke, S.; Gitas, I.; Katagis, T.; Polychronaki, A.; Somers, B.; Goossens, R. Assessing post-fire vegetation recovery using red–near infrared vegetation indices: Accounting for background and vegetation variability. ISPRS J. Photogramm. Remote Sens. 2012, 68, 28–39.

Social and environmental vulnerability and sustainability of internal and mountain territories

Proposer(s): Antonella Primi (DAFIST)

Curriculum: Risk, Climate Change and Sustainable Development

Description:

As recently highlighted, in Italy the pursuit of the sustainable development goals set out in the 2030 Agenda cannot neglect the inland areas (hills and mountains), often fragile from the environmental and socio-economic point of view (Working Group on Goal 11, 2022). This fragility is often linked to the effects connected to the persistent depopulation and ageing, to the partial or total abandonment of rural activities and the consequent scarcity of basic services. In particular, the National Strategy of Internal Areas 2014-2020 highlighted the gaps between Italian municipalities in access to services relating to education, health and transport (Barca, Casavola, Lucatelli, 2014; De Rossi, 2019; Vendemmia et al. 2021).

Geographic research can extensively deal with many of these phenomena of social and environmental vulnerability, also from a perspective oriented towards sustainable territorial planning (Macchi Janica, Palumbo, 2019; Ciervo, 2014). In fact, if on the one hand, these vulnerabilities and gaps represent slowing down factors for local development, on the other, the internal and mountain territories preserve a natural and cultural heritage, material and intangible, of know-how and management practices that reflect their propensity to resilience and which can be a boost to innovation. It is no coincidence that the UN has declared 2022 the International Year for the sustainable development of the mountains.

The same conformation and altimetric extension of the internal hilly and mountain territories often favors a wide climatic, biotic and landscape variety which is sometimes associated with the establishment of parks and protected areas. In particular, ecosystem and landscape services are configured as aspects of particular importance to be investigated also from the point of view of their perception by stakeholders (Termorshuizen, Opdam 2009) and through participatory practices involving local communities (Primi, Dossche, 2021 ).

Furthermore, we must remember the potential for sustainable tourism linked to the numerous environmental, cultural, historical-artistic and architectural, food and wine and agri-food resources, know-how and traditions that the internal territories have preserved and whose usability is increasingly requested by urban and metropolitan communities (as was evident during the 2020 lockdown for Covid-19). Among the various processes that contribute to mitigating the social and economic vulnerability of internal territories, we can mention, for example, rural gentrification, i.e. the process of transferring middle-upper classes to rural areas (Richard et al., 2014; Marengo, 2019), and amenity migrations or lifestyle migrations, i.e the tendencies widespread mostly among northern European inhabitants who choose to move for long periods of the year to places of higher environmental quality and cultural differentiation (Glorioso et al., 2007; Moss, 2006; Moss et al. 2014).

Where these multiple forms of territorial protection and sustainable usability can find a correct and adequate design and implementation, they can also help to convey a concept of rural and mountain space that is not only productive (sometimes in decline), but also that of a welcoming and inclusive network of social and cultural relations.

In this regard, the objectives of the PhD project will consist of

  1. studying and assessing the environmental and social vulnerabilities of internal territories (hilly and / or mountainous) starting from the national and international state of the art;

  2. studying, in the light of the 2030 Agenda sustainable development goals, the virtuous cases of environmental, economic, cultural and lasting population recovery conditions in Italy and abroad, to highlight the processes that can mitigate environmental and socio-economic vulnerability;

  3. identifying and analyzing the potential of internal territories through the collection and processing of quantitative and qualitative data;

  4. highlighting and systematizing territorial resources with a view to their offer and usability of slow and sustainable tourism, aimed at highlighting the peculiarities and identity of the territories and enhancing the territorial and social heritage;

  5. outlining sustainable local development strategies with local communities and arouse sensibilities shared by all the users of the territories (residents, tourists, visitors).

These objectives will be pursued through various methods and investigation tools, of a quantitative and qualitative nature; through spatial analyzes conducted with GIS (geographical information system), PPGIS and historical GIS elaborations (Brown, Fagerholm, 2015; Burini, 2016; Grava et al. 2020); and with a participatory approach (questionnaires, focus groups, workshops, interviews with privileged witnesses) to involve all the stakeholders of the territories.

Link to the group or personal webpage

References:

  1. BARCA F., CASAVOLA P., LUCATELLI S. (2014), Strategia nazionale per le aree interne. Definizione, obiettivi, strumenti e governance, Materiali Uval, 31.

  2. BROWN, G., FAGERHOLM, N. (2015). Empirical PPGIS/PGIS mapping of ecosystem services: A review and evaluation, in «Ecosystem services», 13, 119-133.

  3. BURINI F. (2016), Cartografia partecipativa. Mapping per la governance ambientale e urbana, FrancoAngeli, Milano.

  4. CIERVO M. (2014), Un approccio geografico per una pianificazione territoriale sostenibile, in «Bollettino della Società Geografica Italiana», Roma - Serie XIII, vol. VII (2014), pp. 559-572.

  5. DE ROSSI A. (2019). Riabitare l'Italia: le aree interne tra abbandoni e riconquiste. Donzelli editore.

  6. GLORIOSO R.S., MOSS L.A.G. (2007), Amenity migration to mountain regions: Current knowledge and a strategic construct for sustainable management, in «Social Change», 37, 1, pp. 137-161.

  7. GRAVA M., BERTI C., GABELLIERI N. E GALLIA A. (2020), Historical GIS. Strumenti digitali per la geografia storica in Italia, EUT Edizioni Università di Trieste, Trieste,2020.

  8. GRUPPO DI LAVORO SUL GOAL 11 (2022), Le aree interne e la montagna per lo sviluppo sostenibile, Position paper 2022, ASviS - Alleanza Italiana per lo Sviluppo Sostenibile, ISBN 979-12-80634-05-4, https://asvis.it/public/asvis2/files/Pubblicazioni/Position_Paper_ASviS_2022_MontagnaAreeInterneGoal11.pdf

  9. MACCHI JÁNICA G., PALUMBO A. (a cura di) (2019), Territori spezzati. Spopolamento e abbandono nelle aree interne dell'Italia contemporanea, CISGE, Roma.

  10. MARENGO M. (2019), Diversamente migranti: il ruolo delle lifestyle migrations nelle dinamiche di gentrification rurale contemporanee. Il caso della Vallesanta (Casentino), in «Geotema», 61, pp. 107-115.

  11. MOSS L.A.G., GLORIOSO R.S. (2014), Global Amenity Migrations. Transforming Rural, Culture, Economy & Landscape, The New Ecology Press, Kaslo.

  12. PRIMI A., DOSSCHE R. (2020) Mappatura partecipata e analisi spaziale della percezione del rischio alluvionale in Val Bisagno (GE), in «Bollettino dell’Associazione Italiana di Cartografia», n. 169, pp. 128-144. https://www.openstarts.units.it/handle/10077/32233

  13. RICHARD F., DELLIER J., TOMMASI G. (2014), Migration, environnement et gentrification rurale en Montagne limousine, in «Journal of Alpine Research», 102, 4.

  14. TERMORSHUIZEN J. W., OPDAM P. (2009), Landscape services as a bridge between landscape ecology and sustainable development, in «Landscape ecology», 24(8), 1037-1052.

  15. VENDEMMIA B., PUCCI P., BERIA P. (2021), An institutional periphery in discussion. Rethinking the inner areas in Italy, in «Applied Geography», 135, 102537.