Mainstreaming of Nature-Based Solutions for the mitigation of hydro-meteorological hazard: governance analysis of a socio- technical change

The Nature-Based Solutions (NBS) concept and approach were developed to simultaneously face challenges such as risk mitigation and biodiversity conservation and restoration. NBSs have been endorsed by major International Organizations such as the EU, the FAO and World Bank that are pushing to enable a mainstreaming process. However, a shift from traditional engineering “grey” solutions to wider and standard adoption of NBS encounters technical, social, cultural, and normative barriers that have been identified with a qualitative content analysis of policy documents, reports and expert interviews. The case of the region Emilia-Romagna was studied by developing an analytical framework that brought together the social-ecological context, the governance system and the characteristics of specific NBSs.

Landslide activity in contrasting land-cover settings constrained by multi-temporal inventorying

This doctoral thesis focuses on the study of historical shallow landslide activity over time in response to anthropogenic forcing on land use, through the compilation of multi-temporal landslide inventories. The study areas, located in contrasting settings and characterized by different history of land-cover changes, include the Sillaro River basin (Italy) and the Tsitika and Eve River basins (coastal British Columbia). The Sillaro River basin belongs to clay-dominated settings, characterized by extensive badland development, and dominated by earth slides and earthflows. Here, forest removal began in the Roman period and has been followed by agricultural land abandonment and natural revegetation in recent time. By contrast, the Tsitika-Eve River basins are characterized by granitic and basaltic lithologies, and dominated by debris slides, debris flows and debris avalanches. In this setting, anthropogenic impacts started in 1960’s and have involved logging operation. The thesis begins with an introductory chapter, followed by a methodological section, where a multi-temporal mapping approach is proposed and tested at four landslide sites of the Sillaro River basin. Results, in terms of inventory completeness in time and space, are compared against the existing region-wide Emilia-Romagna inventory. This approach is then applied at the Sillaro River basin scale, where the multi-temporal inventory obtained is used to investigate the landslide activity in relation to historical land cover changes across geologic domains and in relation to hydro-meteorological forcing. Then, the impact of timber harvesting and road construction on landslide activity and sediment transfer in the Tsitika-Eve River basins is investigated, with a focus on the controls that interactions between landscape morphometry and cutblock location may have on landslide size-frequency relations. The thesis ends with a summary of the main findings and discusses advantages and limitations associated with the compilation of multi-temporal inventories in the two settings during different periods of human-driven, land-cover dynamics.

Analysis and developments of uncertainty processors for real time flood forecasting

The hydrologic risk (and the hydro-geologic one, closely related to it) is, and has always been, a very relevant issue, due to the severe consequences that may be provoked by a flooding or by waters in general in terms of human and economic losses. Floods are natural phenomena, often catastrophic, and cannot be avoided, but their damages can be reduced if they are predicted sufficiently in advance. For this reason, the flood forecasting plays an essential role in the hydro-geological and hydrological risk prevention. Thanks to the development of sophisticated meteorological, hydrologic and hydraulic models, in recent decades the flood forecasting has made a significant progress, nonetheless, models are imperfect, which means that we are still left with a residual uncertainty on what will actually happen. In this thesis, this type of uncertainty is what will be discussed and analyzed. In operational problems, it is possible to affirm that the ultimate aim of forecasting systems is not to reproduce the river behavior, but this is only a means through which reducing the uncertainty associated to what will happen as a consequence of a precipitation event. In other words, the main objective is to assess whether or not preventive interventions should be adopted and which operational strategy may represent the best option. The main problem for a decision maker is to interpret model results and translate them into an effective intervention strategy. To make this possible, it is necessary to clearly define what is meant by uncertainty, since in the literature confusion is often made on this issue. Therefore, the first objective of this thesis is to clarify this concept, starting with a key question: should be the choice of the intervention strategy to adopt based on the evaluation of the model prediction based on its ability to represent the reality or on the evaluation of what actually will happen on the basis of the information given by the model forecast? Once the previous idea is made unambiguous, the other main concern of this work is to develope a tool that can provide an effective decision support, making possible doing objective and realistic risk evaluations. In particular, such tool should be able to provide an uncertainty assessment as accurate as possible. This means primarily three things: it must be able to correctly combine all the available deterministic forecasts, it must assess the probability distribution of the predicted quantity and it must quantify the flooding probability. Furthermore, given that the time to implement prevention strategies is often limited, the flooding probability will have to be linked to the time of occurrence. For this reason, it is necessary to quantify the flooding probability within a horizon time related to that required to implement the intervention strategy and it is also necessary to assess the probability of the flooding time.