Monitoring of Land Subsidence in the Po River Delta (Northern Italy) Using Geodetic Networks

Cenni, Nicola; Fiaschi, Simone; Fabris, Massimo

doi:10.3390/rs13081488

Cited by 23 publications

(24 citation statements)

References 67 publications

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“…The most exposed are those of Egypt (12.879 km 2 ), Italy (10.060 km 2 ) and France (3.681 km 2 ). In these countries, the river deltas of Nile (Egypt, about 12.879 km 2 ), Po (Italy, about 4.000 km 2 ) and Rhone (France, about 2.000 km 2 ), which are undergoing high rates of land subsidence due to natural (soil compaction) and anthropogenic (ground fluid exploitation) processes (Syvitski et al 2009, Bohannon 2010, Cenni et al 2021, Pont et al 2002, are at high risk of flooding in 2050-2100-2150, as shown by the revised SL projections show in figure 6 It is worth noting that most of the population living along the coasts of the Mediterranean is not aware of SL rise, land subsidence and related coastal hazard that impact on the environment, coastal infrastructures, and human activities (Loizidou et al 2023). In addition, the current resident population in the Mediterranean countries is about 450 million with a possible rise to 700 million by 2100.…”

Section: Exposed Zonesmentioning

confidence: 99%

Sea level rise projections up to 2150 in the northern Mediterranean coasts

Vecchio,

Anzidei,

Serpelloni

2023

Environ. Res. Lett.

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Vertical land movements (VLM) play a crucial role in affecting the sea level rise along the coasts. They need to be estimated and included in the analysis for more accurate Sea Level (SL) projections. Here we focus on the Mediterranean basin characterized by spatially variable rates of VLM that affect the future SL along the coasts. To estimate the VLM rates we used geodetic data from continuous global navigation satellite system stations with time series longer than 4.5 years in the 1996–2023 interval, belonging to Euro-Mediterranean networks and located within 5 km from the coast. Revised SL projections up to the year 2150 are provided at 265 points on a geographical grid and at the locations of 51 tide gauges of the Permanent Service for Mean Sea Level, by including the estimated VLM in the SL projections released by the Intergovernmental Panel on Climate Change (IPCC) in the AR6 Report. Results show that the IPCC projections underestimate future SL along the coasts of the Mediterranean Sea since the effects of tectonics and other local factors were not properly considered. Here we show that revised SL projections at 2100, when compared to the IPCC, show a maximum and minimum differences of 1094 ± 103 mm and −773 ± 106 mm, respectively, with an average value that exceeds by about 80 mm that of the IPCC in the reference Shared Socio-economic Pathways and different global warming levels. Finally, the projections indicate that about 19.000 km2 of the considered Mediterranean coasts will be more exposed to risk of inundation for the next decades, leading to enhanced impacts on the environment, human activities and infrastructures, thus suggesting the need for concrete actions to support vulnerable populations to adapt to the expected SL rise and coastal hazards by the end of this century.

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Section: Exposed Zonesmentioning

confidence: 99%

Sea level rise projections up to 2150 in the northern Mediterranean coasts

Vecchio,

Anzidei,

Serpelloni

2023

Environ. Res. Lett.

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“…Cenni et al [22] highlighted the spatial and temporal evolution of the land subsidence in the Po River Delta (PRD) area (Italy) analyzing time-series obtained from CGNSS stations using a moving window approach temporally overlapping with both the surveys of a new GNSS network (PODELNET-PO DELta NETwork) (46 non-permanent sites measured in 2016 and 2018), and InSAR data (SBAS processing of Sentinel-1A/B images from 2014 to 2017). The authors investigated the integration between these data: since the InSAR technology does not perform well in high vegetated areas or areas with high temporal variability, the PODELNET sites represent an important improvement for the monitoring of the land subsidence in the PRD.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Editorial for Special Issue “Monitoring Land Subsidence Using Remote Sensing”

Fabris

Cenni²,

Fiaschi

2021

Remote Sensing

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“…Due to the increased concern, many recent scientific articles addressing this subject have been published in recent years. Studies include those from Australia [10], France [11], Italy [12], Poland [13], Turkey [14], USA [15], Mexico [16], Argentina [17], Japan [18], China [3,19], Thailand [20], Indonesia [21], India [22], and Iran [23,24]. A repeating message from these investigations is that due to the extensive distribution of LS and its potentially devastating consequences for the economy and environment, there is an urgent need for LS sensitivity assessments, LS sensitivity zone assessments, and identification of the leading causes of LS.…”

Section: Introductionmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Gharechaee

Samani

Sigaroodi

et al. 2023

Land

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Most published studies identify groundwater extraction as the leading cause of land subsidence (LS). However, the causes of LS are not only attributable to groundwater extraction. Other land-use practices can also affect the occurrence of LS. In this study, radar interferometric techniques and machine learning (ML) models were used for the prediction, susceptibility zoning, and prioritization of influential variables in the occurrence of LS in the Bakhtegan basin. The LS rate was characterized by applying an interferometric synthetic aperture radar (InSAR). The recursive feature elimination (RFE) method was used to detect and select the dominant combination of indicators to prepare an LS susceptibility map. Three ML models, including random forest (RF), k-nearest neighbors (KNN), and classification and regression trees (CART), were used to develop predictive models. All three models had acceptable performance. Among the ML models, the RF model performed the best (i.e., Nash–Sutcliffe efficiency, Kling–Gupta efficiency, correlation coefficient, and percent bias metrics of 0.76, 0.78, 0.88, and 0.70 for validating phase, respectively). The analysis conducted on all three ML model outputs showed that high and very high LS susceptibility classes were located on or near irrigated agricultural land. The results indicate that the leading cause of land LS in the study region is not due to groundwater withdrawals. Instead, the distance from dams and the proximity to anticlines, faults, and mines are the most important identifiers of LS susceptibility. Additionally, the highest probability of LS susceptibility was found at distances less than 18 km from synclines, 6 to 13 km from anticlines, 23 km from dams, and distances less than 20 to more than 144 km from mines. The validated methods presented in this study are reproducible, transferrable, and recommended for mapping LS susceptibility in semiarid and arid climate zones with similar environmental conditions.

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Monitoring of Land Subsidence in the Po River Delta (Northern Italy) Using Geodetic Networks

Cited by 23 publications

References 67 publications

Sea level rise projections up to 2150 in the northern Mediterranean coasts

Sea level rise projections up to 2150 in the northern Mediterranean coasts

Editorial for Special Issue “Monitoring Land Subsidence Using Remote Sensing”

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

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