Deltaic systems are broadly recognized as vulnerable hot spots at the interface between land and sea and are highly exposed to harmful natural and manmade threats. The vulnerability to these threats and the interactions of the biological, physical, and anthropogenic processes in low-lying coastal plains, such as river deltas, requires a better understanding in terms of vulnerable systems and to support sustainable management and spatial planning actions in the context of climate change. This study analyses the potential of Bayesian belief network (BBN) models to represent conditional dependencies in vulnerability assessment for future sea level rise (SLR) scenarios considering ecological, morphological and social factors using Earth observation (EO) time series dataset. The BBN model, applied in the Po Delta region in the northern Adriatic coast of Italy, defines relationships between twelve selected variables classified as driver factors (DF), land cover factors (LCF), and land use factors (LUF) chosen as critical for the definition of vulnerability hot spots, future coastal adaptation, and spatial planning actions to be taken. The key results identify the spatial distribution of the vulnerability along the costal delta and highlight where the probability of vulnerable areas is expected to increase in terms of SLR pressure, which occurs especially in the central and southern delta portion.

Assessing Po River Deltaic Vulnerability Using Earth Observation and a Bayesian Belief Network Model

Taramelli, Andrea;Righini, Margherita
;
2020

Abstract

Deltaic systems are broadly recognized as vulnerable hot spots at the interface between land and sea and are highly exposed to harmful natural and manmade threats. The vulnerability to these threats and the interactions of the biological, physical, and anthropogenic processes in low-lying coastal plains, such as river deltas, requires a better understanding in terms of vulnerable systems and to support sustainable management and spatial planning actions in the context of climate change. This study analyses the potential of Bayesian belief network (BBN) models to represent conditional dependencies in vulnerability assessment for future sea level rise (SLR) scenarios considering ecological, morphological and social factors using Earth observation (EO) time series dataset. The BBN model, applied in the Po Delta region in the northern Adriatic coast of Italy, defines relationships between twelve selected variables classified as driver factors (DF), land cover factors (LCF), and land use factors (LUF) chosen as critical for the definition of vulnerability hot spots, future coastal adaptation, and spatial planning actions to be taken. The key results identify the spatial distribution of the vulnerability along the costal delta and highlight where the probability of vulnerable areas is expected to increase in terms of SLR pressure, which occurs especially in the central and southern delta portion.
coastal vulnerability; earth observation; delta hotspot areas; Bayesian belief network; coastal zone management; sea level rise
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12076/7286
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