Landslide risk management : a multidisciplinary approach to define a Decision Support System for rainfall induced landslides

It is generally observed that direct impact of natural hazards, irreversible losses of human and physical capital and economic activities, is stronger on already poor economies. Moreover, the indirect adverse impact on the wealth growth of their ex-ante strategy to mitigate risk may even outweigh ex-post direct impact of a catastrophic event. The reason is that already poor economies have scarce resources to cope with natural hazard, weaker risk management capacity and higher degree of risk aversion. Therefore, in trying to coping ex-ante with risk, they choose a lower risk-return portfolio of assets (Elbers et al., 2007). If this is true, we can conclude that both effects of catastrophic events exacerbate inequalities and stuck economies in ‘poverty traps’ due to huge economic losses. Avoiding those ‘traps’ must be a common worldwide object just as improving environmental security. Areas and populations involved are not limited to those directly hit by the catastrophic event but even those indirectly involved by forced raising of funds, expropriation of property rights and immigration (forced socialization of risk consequences). Unfortunately, then, even in more developed countries, the level of resources devoted to the prevention against natural hazards were, often, extremely scarce and badly allocated. Worries about free-riding in raising funds from governments and taxpayers; rent–seeking and shifting of the responsibility of experts or politicians are the main causes of this misallocation. Things get worse due to bad, incomplete and biased information. In this paper we try to understand the economic and financial impact of natural disasters such as landslide. This is one of the major worldwide natural hazard. One of the problem we deal with is that landslide risk assessment methodologies were mainly qualitative and subjective. Qualitative methods, for example, are essentially based on the assumption that landslides will occur in the same geological, geomorphological, hydrogeological and climatic conditions as in the past. We propose a multidisciplinary approach to landslide risk management. This will be a useful DSS (Decision Support System) able to remove much of the uncertainty in dealing with rainfall landslides Risk Assessment Methodologies. This approach consists of a ‘simulation chain’ to link forecast rainfall (input) to the effects in terms of infiltration, slope stability up to definition of vulnerability and risk assessment. This ‘simulation chain’ is developed at CMCC (Euro–Mediterranean Centre for the Climate Change) (Meteoroligical Models) and at Geothecnical Laboratory of the Second University of Naples (Geothecnical Models), both partners in SafeLand (7th Framework Programme, Cooperation Theme 6 Environment including climate change Sub-activity 6.1.3 Natural Hazards), and at the Department of Economics of the University of Naples “Federico II” (Economics and Finance). Multidisciplinary groups of experts will gain enough resources to devote to the prevention against natural hazards if they are self-selected to be: - able to manage multi-skills involved, - able to manage the main inputs of the provision of environmental security, that is, sophisticated techniques and huge amount of continuous data from ‘early-warning monitoring-webs’, - they don’t escape from their professional responsibility, - able to be independent from politics and objectively trustable. If people trust in them and understand the objectivity of science and its limits, whatever is the national or local government, the only problem to raise funds from taxpayers and borrowers will be that imposed by the EU-27 ability to exploit Lisbon Treaty and the new Stability Pact

MATISSE: an ArcGIS tool for monitoring and nowcasting meteorological hazards

Abstract. Adverse meteorological conditions are one of the major causes of accidents in aviation, resulting in substantial human and economic losses. For this reason it is crucial to monitor and early forecast high impact weather events. In this context, CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug-in able to detect and forecast meteorological aviation hazards over European airports, using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models data). MATISSE presents a graphical interface allowing the user to select and visualize such meteorological conditions over an area or an airport of interest. The system also implements different tools for nowcasting of meteorological hazards and for the statistical characterization of typical adverse weather conditions for the airport selected

A "simulation chain" to define a Multidisciplinary Decision Support System for landslide risk management in pyroclastic soils

Abstract. This paper proposes a Multidisciplinary Decision Support System (MDSS) as an approach to manage rainfall-induced shallow landslides of the flow type (flowslides) in pyroclastic deposits. We stress the need to combine information from the fields of meteorology, geology, hydrology, geotechnics and economics to support the agencies engaged in land monitoring and management. The MDSS consists of a "simulation chain" to link rainfall to effects in terms of infiltration, slope stability and vulnerability. This "simulation chain" was developed at the Euro-Mediterranean Centre for Climate Change (CMCC) (meteorological aspects), at the Geotechnical Laboratory of the Second University of Naples (hydrological and geotechnical aspects) and at the Department of Economics of the University of Naples "Federico II" (economic aspects). The results obtained from the application of this simulation chain in the Cervinara area during eleven years of research allowed in-depth analysis of the mechanisms underlying a flowslide in pyroclastic soil

Evaluation of the hydro-meteorological chain in Piemonte Region, north western Italy - analysis of two HYDROPTIMET test cases

International audienceThe HYDROPTIMET Project, Interreg IIIB EU program, is developed in the framework of the prediction and prevention of natural hazards related to severe hydro-meteorological events and aims to the optimisation of Hydro-Meteorological warning systems by the experimentation of new tools (such as numerical models) to be used operationally for risk assessment. The object of the research are the Mesoscale weather phenomena and the response of watersheds with size ranging from 102 to 103 km2. Non-hydrostatic meteorological models are used to catch such phenomena at a regional level focusing on the Quantitative Precipitation Forecast (QPF). Furthermore hydrological Quantitative Discharge Forecast (QDF) are performed by the simulation of run-off generation and flood propagation in the main rivers of the interested territory. In this way observed data and QPF are used, in a real-time configuration, for one-way forcing of the hydrological model that works operationally connected to the Piemonte Region Alert System. The main hydro-meteorological events that interested Piemonte Region in the last years are studied, these are the HYDROPTIMET selected test cases of 14-18 November 2002 and 23-26 November 2002. The results obtained in terms of QPF and QDF offer a sound basis to evaluate the sensitivity of the whole hydro-meteorological chain to the uncertainties in the numerical simulations. Different configurations of non-hydrostatic meteorological models are also analysed

Brief communication "A prototype forecasting chain for rainfall induced shallow landslides"

Although shallow landslides are a very widespread phenomenon, large area (e.g. thousands of square kilometres) early warning systems are commonly based on statistical rainfall thresholds, while physically based models are more commonly applied to smaller areas. This work provides a contribution towards the filling of this gap: a forecasting chain is designed assembling a numerical weather prediction model, a statistical rainfall downscaling tool and a geotechnical model for the distributed calculation of the factor of safety on a pixel-by-pixel basis. The forecasting chain can be used to forecast the triggering of shallow landslides with a 48 h lead time and was tested on a 3200 km2 wide area

Climate change adaptation cycle for pilot projects development in small municipalities: The northwestern Italian regions case study

More than half of the European population live in small and medium size municipalities, where climate adaptation planning is an under-researched topic within the climate change field. Many constraints might hinder the implementation of adaptation pilot projects due to lack of economic, knowledge, and technical available resources. Local institutions find difficulties in building a coherent local adaptation planning and design processes with international and national frameworks. In this context, this article proposes a methodology based on the available international frameworks to support the small communities with the aim to implement adaptation pilot projects within different sectors. In doing so, this paper tests a climate change adaptation cycle for pilot projects development in small municipalities; the first in Italy for small municipalities under 20.000 inhabitants. The proposed methodology could lead local adaptation initiatives in climate change risk assessment by supporting the research communities in developing a coherent vision for the local territories and to identify proper oriented measures to enhance demonstrative pilot projects and to increase the level of resilience in small municipalities, avoiding maladaptation

Social inequalities in heat-attributable mortality in the city of Turin, northwest of Italy: a time series analysis from 1982 to 2018

Background: Understanding context specific heat-health risks in urban areas is important, especially given anticipated severe increases in summer temperatures due to climate change effects. We investigate social inequalities in the association between daily temperatures and mortality in summer in the city of Turin for the period 1982–2018 among different social and demographic groups such as sex, age, educational level, marital status and household occupants. Methods: Mortality data are represented by individual all-cause mortality counts for the summer months between 1982 and 2018. Socioeconomic level and daily mean temperature were assigned to each deceased. A time series Poisson regression with distributed lag non-linear models was fitted to capture the complex nonlinear dependency between daily mortality and temperature in summer. The mortality risk due to heat is represented by the Relative Risk (RR) at the 99th percentile of daily summer temperatures for each population subgroup. Results: All-cause mortality risk is higher among women (1.88; 95% CI = 1.77, 2.00) and the elderly (2.13; 95% CI = 1.94, 2.33). With regard to education, the highest significant effects for men is observed among higher education levels (1.66; 95% CI = 1.38, 1.99), while risks for women is higher for the lower educational level (1.93; 95% CI = 1.79, 2.08). Results on marital status highlighted a stronger association for widower in men (1.66; 95% CI = 1.38, 2.00) and for separated and divorced in women (2.11; 95% CI = 1.51, 2.94). The risk ratio of household occupants reveals a stronger association for men who lived alone (1.61; 95% CI = 1.39, 1.86), while for women results are almost equivalent between alone and not alone groups. Conclusions: The associations between heat and mortality is unequal across different aspects of social vulnerability, and, inter alia, factors influencing the population vulnerability to temperatures can be related to demographic, social, and economic aspects. A number of issues are identified and recommendations for the prioritisation of further research are provided. A better knowledge of these effect modifiers is needed to identify the axes of social inequality across the most vulnerable population sub-groups

Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

Smart climate hydropower tool: A machine-learning seasonal forecasting climate service to support cost–benefit analysis of reservoir management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets