Risk, Climate Change and Sustainable Development

Theme  1: Impacts of drought on plant biodiversity, ecosystem functioning and resilience in the Mediterranean Basin

Reference:  Luca Ferraris (DIBRIS), Mara Baudena (CNR-ISAC), Antonello Provenzale (CNR-IGG), Maria J Santos (Univ. of Zurich, CH), Edoardo Cremonese (CIMA), Francesco Avanzi (CIMA), Lauro Rossi (CIMA)

Funding: funded within D.M. 117 del 02.03.2023 (cofunded by CIMA  Research Foundation), under condition to the approval of Ministerial funding; the annual gross amount of the grant, including social security expenses to be paid by the recipient, is € 16.500,00

Abstract: Drought is a recurring natural extreme, triggered by lower-than-normal precipitation and often exacerbated by a strong evaporative demand due to high temperatures and strong winds (Avanzi et al. 2020, Massari et al. 2022). Drought events can occur in all climates and in most parts of the world, affecting millions of people across the globe each year. Droughts become a major concern when ecosystem functioning and service provision to humans are lost, with large economic and ecological costs. Based on the parts of the water cycle impacted, a drought event can be classified as meteorological (lack of precipitation), hydrological (low runoff, streamflow and reservoir storage) and agricultural or ecological (prolonged periods with drier than usual soils negatively affecting vegetation growth and crop production). Under most scenarios of climate change, intensity and frequency of droughts are expected to increase in many regions of the world, including the Mediterranean Basin (Douville et al 2021) and, in particular, northern-central Italy (Baronetti et al 2022). Predicting the risks for human activities and welfare of such drought events is important, as it is necessary to adapt to and mitigate them.

A meteorological drought is most often the trigger for hydrological and ecological droughts, which are closely interrelated. Biodiversity can increase ecosystem resilience to drought, through species interactions (Haberstroth and Werner, 2022), but also through its functional diversity (Serra-Maluquer et al 2022, Anderegg 2015, Anderegg et al 2016X[GU1] [GU2] [GU3] [GU4] ).  Functional diversity, i.e., the diversity of plant functions enabled by the traits characteristic to species, measured with trait-based approaches, is especially relevant in terms of ecosystem (ecohydrological) functioning (Anderegg et al. 2016), but is still understudied also in the Mediterranean area (Tramblay et al 2020).[GU5]  This PhD thesis will investigate whether plant functional diversity confers ecosystem resilience to drought events, with the aim of understanding how ecosystem functioning actively influences ecological and hydrological [GU6] droughts and whether it has the potential to mitigate them.

In this context, remotely sensed Earth observations are a fundamental and powerful tool, capable of providing spatially explicit information on vegetation properties for large areas. Recent developments indicate that, besides the commonly used vegetation indices, which already show indications of drought (Sturm et al. 2021[GU7] ), more detailed information on ecosystem processes can be inferred from measurements of functional diversity using spectral properties (Schweiger et al 2018, Pacheco-Labrador et al 2022). Plants exhibit what we call optical types, i.e., characteristic responses to light across the electromagnetic spectrum. For example, photosynthesis can be measured by the absorption in the blue and red wavelengths, in the transition between the red and the NIR, as well as through measures of fluorescence at 685 nm and 730 nm.  These techniques allow determining ecosystem properties, including some that are particularly relevant for the hydrological cycle such as what are now designated as hydraulic traits (Anderegg et al. 2016). For instance, imaging spectroscopy data enables the best measurements to date on water absorption features, photosynthesis driven absorption, leaf structure and water content. All of these are important for ecological drought measurements as they directly relate to plant water status and can also inform about hydrological droughts through their effects on how much water is released to runoff and flow into streams and reservoirs. More directly these traits measure how plants affect soil moisture content, and how plants respond to available water within their rooting area and drive hydraulic redistribution.

The goals of this PhD project are:

- determining the impacts of drought on vegetation and ecosystems functioning in the Mediterranean basin

- identifying the connection between ecosystem resilience to drought and ecosystem functioning

- analyzing how ecosystem functioning is related to the different components of the hydrological cycle

The project will also contribute to validation of innovative remote sensing approaches that link imaging spectroscopy data with ground based, plant trait data to develop predictive models for traits customized to the Mediterranean vegetation.

Theme  2: Drought stress and aquifers dynamics in Mediterranean environments 

Reference:  Luca Ferraris (DIBRIS), Antonello Provenzale (CNR-IGG), Matia Menichini (CNR-IGG), Giulia Ercolani (CIMA)

Funding:  funded within D.M. 117 del 02.03.2023 (cofunded by CIMA  Research Foundation), under condition to the approval of Ministerial funding; the annual gross amount of the grant, including social security expenses to be paid by the recipient, is € 16.500,00

Abstract: The Doctorate project will focus on exploring the effects of drought stress on aquifer dynamics in Mediterranean environments, through the implementation, use and development of computational codes for the numerical simulation of aquifer systems, including the dynamics in the unsaturated zone and evapotranspiration fluxes. Specific to this work will be the combination of classical aquifer simulation approaches with ecohydrological modelling, also considering the interplay of water and carbon fluxes. Specifically, the codes to be used will be based on the MODFLOW family (Harbaugh, 2005 and Langevin et al., 2017). MODFLOW, a modular finite-difference ground-water flow model developed by the U.S. Geological Survey, has been widely used for simulating groundwater and its interaction with surface-water. Traditionally, near-surface hydrology and regional groundwater flow models have been developed separately, with limited integration. Watershed-runoff models focus on surface hydrology and shallow groundwater flow, while regional groundwater models simplify near-surface processes by using external calculations for recharge independent of groundwater levels. However, there has been a recent shift towards coupled models that integrate near-surface hydrology and regional groundwater flow (Niswonger et al., 2006)4. Focusing on the upper part of the Critical Zone (the layer between the bottom of the surface aquifer and the top of vegetation canopy), the combination of MODFLOW with other specific codes, like the Unsaturated-Zone Flow (UZF1) Packages for the simulation of vertical unsaturated flow, as well as the development of additional codes, will allow to simulate particular aspects of hydrology processes and dynamics taking place in the context of climate change impact and, specifically, drought-induced stresses. The activities will include a comparison of the simulation results with the data obtained in two main test sites, characterized by active research infrastructures and representative of hydrological processes occurring within the Mediterranean climate hotspot: the small island of Pianosa in the Tuscan Archipelago National Park, where a research base and fixed instrumentation provide a large amount of data (www.brp.cnr.it), and a small catchment on the Ligurian coast. Comparison with simplified, conceptual models of vertical moisture fluxes in water-limited environments (Baudena et al., 2007) and their potential role to expand the unsaturated zone modules of MODFLOW will complete the study.

Theme 3: Probabilistic Wildfire Risk Scenarios under Climate Change: from wildfire susceptibility to risk including cascading and compounding effects 

Reference:  Luca Ferraris (DIBRIS), Paolo Fiorucci (CIMA), Andrea Trucchia (CIMA), Roberto Rudari (CIMA)

Funding: funded within D.M. 117 del 02.03.2023 (cofunded by CIMA Research Foundation), under condition to the approval of Ministerial funding; the annual gross amount of the grant, including social security expenses to be paid by the recipient, is € 16.500,00

Abstract: Probabilistic wildfire risk scenarios under climate change play a crucial role in understanding and managing the evolving land use/land cover and fire regime (Bryant et al. 2014). By considering wildfire susceptibility, risk assessment, and the cascading effects of wildfires, decision-makers can make informed choices and develop effective strategies to mitigate the impact of wildfires on ecosystems, communities, and economies. Continuous research, improved data collection, and enhanced modeling techniques are essential for refining these scenarios and strengthening our resilience in the face of changing wildfire dynamics. Climate change exacerbates wildfire impacts, introducing new challenges and uncertainties. This research proposal explores the concept of probabilistic wildfire risk scenarios under climate change, encompassing various aspects such as wildfire susceptibility, risk assessment, and the cascading and compounding effects.

Climate change and wildfires alters environmental conditions, leading to changes in vegetation and weather patterns, as well as fuel availability, all of which influence the susceptibility of an area to wildfires. By integrating climate projections, land cover data, and historical fire records, scientists and researchers can develop models to assess the likelihood (susceptibility) of wildfire occurrence in different regions. These models consider factors such as temperature, precipitation, wind patterns, fuel moisture, and topography to estimate the potential ignition and spread of wildfires (Tonini et al. 2020, Trucchia et al. 2022a, Trucchia et al. 2022b)

Probabilistic risk assessment quantifies the potential impact and likelihood of extreme events. In the context of wildfires, it involves analyzing the interaction between fire occurrence (and fire susceptibility patterns), fire behavior, and the values at risk, such as human lives, infrastructure, and natural resources. By considering historical fire data, climate projections, and other relevant factors, risk models can estimate the probability and potential consequences of wildfires in a given area. These assessments aid in prioritizing resources and developing risk reduction strategies. Moreover, wildfires can trigger a series of cascading and compounding effects, exacerbating the overall risk and impacting multiple interconnected systems. These effects include among others:

      Ecological Impacts: Large-scale wildfires can disrupt ecosystems, leading to habitat destruction, loss of biodiversity, and long-term ecological changes. The loss of vegetation can result in soil erosion, affecting water quality and availability.

      Infrastructure and Socioeconomic Impacts: Wildfires can damage critical infrastructure, including power lines, transportation networks, and residential areas. The resulting economic losses may include property damage, business interruptions, and increased demands on emergency services.

      Water Resources: Wildfires can alter the hydrological cycle by affecting watersheds and increasing the risk of post-fire floods and debris flows. The loss of vegetation cover reduces water absorption, leading to faster runoff and increased erosion.

The goals of this PhD project are:

A highly motivated and talented PhD researcher is sought to join a research team to conduct research on the probabilistic wildfire risk scenarios described above. This position offers an excellent opportunity for the candidate to contribute to cutting-edge research in the field of wildfire risk assessment, also advancing our understanding of the cascading and compounding effects of wildfires in the context of climate change.

Theme  4: Improving hydrological modeling for impact-based flood forecasting in Africa

Reference:  Luca Ferraris (DIBRIS), Lorenzo Alfieri (CIMA), Giulia Ercolani (CIMA), Andrea Libertino (CIMA), Marco Massabò (CIMA), Ahmed Amdihum (ICPAC), Abdu Ali (CILSS)

Funding: funded within D.M. 117 del 02.03.2023 (cofunded by CIMA  Research Foundation), under condition to the approval of Ministerial funding; the annual gross amount of the grant, including social security expenses to be paid by the recipient, is € 16.500,00

Abstract: Hydrological models are effective tools to reconstruct seamlessly the components of the water balance and reproduce continuous datasets to be used for applications ranging from water resources management, drought monitoring, flood forecasting, as well as multi-decadal climatic projections. In the past few decades, continuous efforts have been made to achieve higher-resolution and physically based modeling of all the natural and human-induced processes influencing the water cycle, which are now witnessing a new surge with the Digital Twin Earth and the Destination Earth (https://digital-strategy.ec.europa.eu/en/policies/destination-earth) initiatives. The calibration of spatially distributed hydrological models is a complex and multi-dimension problem, due to the scarcity of reliable data, uncertainty in representing the physical features of a river catchment, and the inevitable simplifications of hydrological processes in a simulation model (Immerzeel and Droogers, 2008). These challenges are amplified in the large portion of global land where in-situ data are not shared, nonexistent or of poor quality. Calibration combining Earth observations and in situ measurements is a promising solution to address the limitations of the traditional streamflow-only calibration. Satellite data including evaporation, soil moisture and terrestrial water storage have already been tested in parameter calibration giving satisfactory results (Dembélé et al., 2020; Jiang et al., 2020). Satellite estimates of river levels also show promising applications in the field. These were tested in the calibration of hydrological (Getirana et al., 2013; Dhote et al., 2021) and hydraulic (Domeneghetti et al., 2021) models. Alfieri et al. (2022) investigated hydrological modelling fully relying on satellite data, by calibrating and subsequently running the model using satellite precipitation and evaporation as forcing, and satellite-based estimates of river discharge as benchmark data for the calibration. However, with increasing number of input data and calibration parameters, the model calibration results in several degrees of freedom, leading to several possible parameter combinations leading to the same skills, yet sometimes with values far from reality. For instance, Kouchi et al. (2017) tested the sensitivity of calibration parameters to 1) different objective functions and 2) optimization algorithms. They found that most combinations of the two considered sets can achieve skillful results, though resulting in different configurations of the parameter values.

The aim of this PhD program is to explore the field of hydrological modeling and parameter calibration in the context of an impact-based flood forecasting system in the Greater Horn of Africa (see Alfieri et al., 2023). The candidate will perform a set of experiments to identify and quantify the sensitivity of the calibration parameters, objective function, hydrological regime, number and configuration of benchmark data in multi-site calibration, with the objective to maximize the information content of available data and optimize the performance of simulated variables, particularly with reference to river discharges. The effect of different model parameterizations will be evaluated on the resulting impact estimates to inform the assessment of uncertainty and as a means of model validation.

The candidate will spend between 6 and 12 months at ICPAC in Nairobi, Kenya (https://www.icpac.net/) to acquire expertise on the implementation region and strengthen the inter-institute collaboration.

Theme  5: Methodologies to support the Public Administration in decisions regarding the management of natural risks and adaptation to climate change: responsibility, compliance, transparency and accountability in the “risk society”.

Reference:  Luca Ferraris (DIBRIS ), Marina Morando (CIMA), Marco Altamura (CIMA), Francesca Munerol (CIMA), Davide Amato (Università Cattolica), Chiara Scolobig (Università di Ginevra).

Funding: grant funded within D.M. 118 del 02.03.2023 (Ricerca PA  cofunded by CIMA Research Foundation), under condition to the approval of Ministerial funding; the annual gross amount of the grant, including social security expenses to be paid by the recipient, is € 16.500,00.

Abstract: The PhD will focus on the development of a methodology that supports the Public Administration in decisions regarding the management of natural risks and adaptation to climate change, with particular reference to the relationship between the expert evaluation provided by the scientific community and the decisions taken by the Authorities, in terms of legal and social responsibility, compliance, transparency and accountability. The research activity will have to reconstruct and interpret the national and supranational legal framework of reference also by comparing the policies adopted in other disciplinary sectors. This methodology will therefore have to support the definition of policies that strengthen both the governance of risk and adaptation to climate change, as well as the objectivity and independence of studies and scientific cooperation, developing a complete chain of risk communication without neglecting a logic of collaboration with citizens and the duty of self-protection of citizens. The doctorate includes a period at the National Department of Civil Protection.

Theme 6: Developing an innovative framework for the integration of situational awareness and Early Warning to Early Action strategies in the Disaster Risk Management

Reference:  Luca Ferraris (DIBRIS), Lorenzo Massucchielli (Italian Red Cross), Davide Miozzo (CIMA Foundation), Eva Trasforini (CIMA Foundation), Antonio Gioia (CIMA Foundation).

Funding:  grant funded by CIMA Research Foundation with Italian Red Cross, the annual gross amount of the grant, including social security expenses to be paid by the recipient, is € 16.500,00.

Abstract: The establishment of Early Warning to Early Action (EWEA) strategies in disaster management is a crucial area of focus. Authoritative institutions such as the World Meteorological Organization (WMO) and the United Nations Office for Disaster Risk Reduction (UNDRR) have developed guidelines and frameworks that address the need to introduce such measures into modern civil protection planning schemes. The executive action plan 2023-2027 of the “Early Warning for All” UN initiative determines the 4 pillars of EWEA:

Efficient EWEA strategies cannot be implemented without an understanding of these four pillars. To effectively reduce disaster risks and implement targeted disaster risk reduction (DRR), risk transfer, and climate change adaptation (CCA) policies, it is crucial to understand the drivers, patterns, and dynamics of the risk associated with hazards. This necessitates the collection, analysis, and utilization of risk-informed data (Hagenlocher et al., 2020).

The interaction between hazard, exposure, vulnerability, and socioeconomic, political, and ecological factors contributes to the complex nature of disaster and climate risks. To better understand and manage these risks, comprehensive risk assessment and mapping are essential. These activities serve as central components in the broader process of identifying capacities and resources for risk reduction, planning risk mitigation measures, monitoring hazards and vulnerabilities, and communicating findings (EC, 2010). This approach emphasizes the need for risk-informed decision-making and appropriate methodologies (Fakhruddi et al., 2022).

Civil protection systems have developed over the last decades high competences and datasets to support the study of hazard and risk knowledge. This was also made possible through the adoption of international frameworks urging for the adoption of institutionalized systems. Humanitarian interventions, contrariwise, have been characterized by response-oriented actions. Part of this Ph.D. study will thus revolve around the study of possible stragies that could fill the gap and support humanitarian interventions to coordinate themselves into effective DRR systems by creating a framework for the acquisition and use of existing risk information. This will thus support a more efficient and rapid planning of the response strategy.

The PhD supports planning approaches aimed to enhance capacities in humanitarian intervention, working on existing data, used in a synergetic way, to support the creation of civil protection approaches and a more structured situational awareness. The integration of science-based analysis for risk analysis and integrated coordination - for understanding capacities and response actions - will be used to adapt humanitarian aid analysis framework (like MIRA or the IFRC analysis framework) and crisis management framework (e.g. ASCOPE-PMESII). The development of those capacities will be used to support and strengthen emergency management and humanitarian aid systems to be more structured and apply civil protection-like procedures and approaches.

The partnership between players belonging to the CP system and players coming from the world of humanitarian intervention could be also beneficial for improving the characterization of risk and ots components. In fact, the baseline for the development of such crucial information is the availability of data. There are numerous situations where there is a limited number of structured datasets, in which case the use of open data can play a crucial role in civil protection planning, particularly in the context of disaster risk management. Using open data contributes to reducing vulnerability and enhancing resilience by providing open access to valuable information. Aitsi-Selmi et al. (2016) emphasize the importance of open data in reducing vulnerability, stating that it enables the availability of data that can inform decision-making processes and support targeted interventions. Cinnamon et al. (2018) highlight the role of big data throughout the disaster management cycle. They underscore the potential of diverse data sources and analytics in enhancing situational awareness, identifying patterns, and predicting risks.

 Over the last decades, the international community of disaster risk managers made important progress in the direction of granting access to the necessary technological tools needed for the implementation of basic early warning systems. Nevertheless, there is a need to develop a systemic approach capable of determining a baseline upon which to build effective EWEA solutions. To this end, the Red Cross movement has de facto created one of the most widespread monitoring networks leveraging the biggest trained volunteer pool across the globe. Coupling this capacity with other available resources provides the basis for the deployment of effective risk assessment and early warning strategies prior to and during an emergency.

 The overall aim of the Ph.D. program is hence that of addressing the intrinsic complexity of EWEA and formalizing an innovative approach to plan, design, and implement EWEA strategies in contexts with low availability of data and supporting legal and institutional frameworks. The candidate will analyze existing early warning and Situational Awareness systems, identify best practices, and address gaps in the use of effective EWEA in crisis management, with special focus on humanitarian intervention The integration of technology, such as data analytics and artificial intelligence, will also be explored to enhance the  effectiveness of the proposed approach.

The candidate will be involved in projects and activities of the Italian Red Cross and its operational partner to ensure the relevance and applicability of the research.

Theme 7: Rural and environmental archeology and history (historical rural landscape, environmental resources management, abandonment as an historical process)

Reference:  Anna Maria Stagno (DAFIST, UniGE), Roberta Cevasco (UniSG, Pollenzo) 

Funding: grant funded within D.M. 118 del 02.03.2023 (Ricerca TDA cofunded by DAFIST), under condition to the approval of Ministerial funding; the annual gross amount of the grant, including social security expenses to be paid by the recipient, is € 16.500,00.

Abstract: With case studies carried out through a micro-analytical historical approach, the PhD research projects will focus on methodologies to develop an historical-environmental approach to the ecological transition and the environmental risk. Research projects have to present a possible case study, addressing the study of agro-sylvo-pastoral practices and systems in relation to population dynamics and the organisation of settlement patterns, considering also the process of abandonment in its historical dimension and the close relationships between social, environmental and economical processes, and need to consider these interrelation to build a sustainable development of rural and mountain areas. The research activity has to consider as (but not exclusive)at least one of the following themes: the changes in practices and management of environmental resource and how it is possible to learn sustainability from the pasts;  the historical trajectories of commons and collectives domains and the intimate social dimension of the landscape; the relationship between the changes in environmental resources management practices and the organisation of local communities between oral history and archaeology; the cycles of environmental resources management practices and their historical transformations (including abandonment) and environmental effects.

Research should include the collaboration to the preparation of policy briefing  on the addressed topics and the close collaboration with Città Metropolitana di Genova (Direzione Patrimonio e Sviluppo Sostenibile) and Regione Liguria  (Dipartimento Agricoltura, Turismo, Formazione e Lavoro).

The doctorate includes a secondment of at least 3 months at Charta snc (Society of applied environmental history), and a research period at, at least one, of the following institutions:  WSL Istituto federale di ricerca per la foresta, la neve e il paesaggio (ref. Mark Conedera), Rotterdam School of Management (RSM), Erasmus University Rotterdam, Professor of Social Enterprise and Institutions for Collective Action (ref. Prof. Tine De Moore),  Karlastad University, Svezia (ref. Prof. Eva Svensson); Universidad de Oviedo (ref.  Prof. Margarita Fernández Mier).