PL2

Plinius17

This session aims at bringing together scientists working on the use of remote sensing observations
and in situ measurements, and physical or statistical/machine learning models, for the definition,
characterization, and pre- and post-event monitoring of natural hazards and extreme events in the
Mediterranean area. The goal of the session is to stimulate the discussion and to contribute to the understanding of climate change interconnection and feedback mechanisms with extreme events occurrence and trends, and with the natural hazards associated to them. Studies related to the use of long-term data record and new methodologies able to describe and identify patterns and parameters of natural disasters and to define anomalous and rare features of extreme events are encouraged. Some examples include, but are not limited to, heavy precipitation systems, tornadoes and Medicanes, strong winds, droughts and forest fires, floods, debris-flows and landslides, subsidence phenomena, volcanic events, earthquakes, coastal erosion, and glaciers.

Public information:

Earth Observation data and techniques for the definition, characterization and monitoring of natural hazards

Conveners: Giulia Panegrossi, Emmanouil Anagnostou, Riccardo Lanari
Orals
| Tue, 18 Oct, 14:30–16:30|Sala degli Svizzeri, Wed, 19 Oct, 14:30–16:30|Sala degli Svizzeri, Thu, 20 Oct, 12:45–15:00|Sala degli Svizzeri
Posters
| Attendance Thu, 20 Oct, 15:00–16:30 | Display Wed, 19 Oct, 09:00–Thu, 20 Oct, 17:00|Poster gallery
Public information:

Earth Observation data and techniques for the definition, characterization and monitoring of natural hazards

Orals: Tue, 18 Oct | Sala degli Svizzeri

Chairpersons: Giulia Panegrossi, Emmanouil Anagnostou
Weather and hydrological hazards: monitoring and applications
14:30–14:45
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Plinius17-56
Luca Ciabatta, Stefania Camici, Paolo Filippucci, Christian Massari, Leo Pio D'Adderio, Giulia Panegrossi, Hamidreza Mosaffa, and Luca Brocca

Accurate precipitation estimates are paramount for the activities related to water management and risk assessment. Satellite-based rainfall estimates are generally obtained by an inversion of the atmospheric signals reflected or radiated by atmospheric hydrometeors, i.e., a “top-down” approach. The main drawback of this retrieval technique is related to the number of satellite overpasses, that may lead to a general underestimation of rainfall.

To overcome these issues, recently, some studies have investigated the possibility to integrate the state-of-the-art rainfall products with rainfall estimates obtained by a consolidated “bottom-up” approach, SM2RAIN (Brocca et al., 2014) exploiting satellite soil moisture observations for obtaining accumulated rainfall estimates. The integration between top-down and bottom-up estimates can produce a more reliable rainfall product for hydrological applications, characterized by better estimation of rainfall amounts and timing. 

On this basis, the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) has started the development and the sharing of integrated datasets of top-down satellite-based precipitation and soil moisture-derived rainfall estimates.

During the Continuous Development and Operational Phase (CDOP) 3 and 4, the H SAF consortium planned to develop and provide several integrated products to the users for hydrological applications, also by taking advantages of the next EPS-SG instruments with enhanced retrieval capabilities.

In this study, the usefulness of integrated products for river discharge simulation is assessed in Italy. More in details, the integrated product between SM2RAIN-derived estimates and Passive Microwave auxiliary product H67 (H64), the gauge corrected version (H84) and the SM2RAIN-only derived product (H87), along with the parent products will be use to force a semidistributed rainfall-runoff model (MISDc) during the period 2016-2019. The obtained river discharge timeseries have been compared with observed ones in order to evaluate the skill of the investigated products showing confirming the added value of using an integrated rainfall product for hydrological applications. Moreover, the results will provide useful insight that will help in improving the integrated products.

The analysis provided good results, confirming the added value of using an integrated rainfall product for hydrological applications, allowing to overcome some of the limitations of the state-of-the-art precipitation datasets.

How to cite: Ciabatta, L., Camici, S., Filippucci, P., Massari, C., D'Adderio, L. P., Panegrossi, G., Mosaffa, H., and Brocca, L.: Satellite soil moisture-derived rainfall for flood modelling in italy, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-56, https://doi.org/10.5194/egusphere-plinius17-56, 2022.

14:45–15:00
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Plinius17-68
Arnaud Cerbelaud

Pluvial floods (PFs) caused by extreme overland flow inland account for half of all flood damage claims each year, equally with fluvial floods (FFs). However, most remote sensing-based flood detection techniques only focus on the identification of degradations and/or water pixels in the close vicinity of overflowing streams. Geomatics hydrological models have been developed to easily and widely map susceptibility towards the occurrence of intense surface runoff without physics-based modelling. However, in order to increase confidence in such methods, they need to be comprehensively evaluated using PF observations from past events. For this, a generalized remote sensing fusion method called FuSVIPR (Fusion of Sentinel-2 & Very high resolution Imagery for Pluvial flood detection in Runoff prone areas) is developed. Based on 10 m change detection (from Sentinel-2) and sub-metric optical imagery (from Pléiades satellites and airborne sensors), machine learning (ML) and deep learning (DL) techniques are used to locate PF footprints on the ground at 0.5 m spatial resolution following heavy weather events. Post processing involving land use, soil type and topography allows accounting for runoff production processes to induce PFs downstream. In this work, six watersheds in the Aude and Alpes-Maritimes departments in the South of France are investigated over more than 3000 km2 of rural and periurban areas during three flash-flood events between 2018 and 2020. With a unique learning sample from the Aude flash-floods of October 2018, overall detection accuracies greater than 86% and false detection rates below 7% are reached independently on all three distinct events. These results emphasize the high generalization capability of this method to locate PFs at any time of the year and over diverse regions worldwide. The resulting damage proxy maps have high potential for helping precipitation downscaling and thorough evaluation and improvement of surface water inundation models at very high spatial resolution.

How to cite: Cerbelaud, A.: Multi-sensor optical remote sensing for generalized sub-metric detection of pluvial flood damages using U-net CNN and Random Forest., 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-68, https://doi.org/10.5194/egusphere-plinius17-68, 2022.

15:00–15:15
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Plinius17-1
Stergios Emmanouil, Andreas Langousis, Efthymios I. Nikolopoulos, and Emmanouil N. Anagnostou

The accurate quantification of hydroclimatic risk requires detailed knowledge on the spatiotemporal characteristics of extreme rainfall. In engineering design, Intensity-Duration-Frequency (IDF) curves are fundamental tools that encompass information on rare precipitation events over a wide range of characteristic temporal scales and exceedance probability levels. Inspired by physical evidence and laws of thermodynamics, researchers widely suggest that the rapidly changing climate instigates more frequent and intense precipitation-related natural hazards. Based on the foregoing implication, current protection standards may be systematically threatened in the upcoming years. Under this non-stationary setting, several IDF estimation approaches have been proposed that allow for distribution parameter estimates to vary (in most cases linearly) with time. Yet, the introduction of additional model parameters increases the estimation uncertainty of rainfall intensity quantiles, especially for rare events. As a potential solution to limitations related to non-stationarity, Emmanouil et al. (2022) proposed an elaborate parametric approach founded on multifractal (MF) scaling arguments (see Langousis et al., 2009), which assumes that the statistical structure of rainfall at interannual scales can be approximated by sequential realizations of a stationary multifractal process with parameters that vary slowly across (not within) realizations. The suggested framework is particularly robust when describing the intensity and frequency of extreme rain rates from small precipitation samples (i.e., down to 2 years; see Emmanouil et al., 2020) and, therefore, it can be effectively applied to adequately short sequential segments of data, allowing for climatic variations to be revealed. Given the above, we attempt to expand the analysis of Emmanouil et al. (2022) toward evaluating the effects of future climate pathways on extreme rainfall, under a wide spectrum of topographical and climatological conditions. To do so, we derive IDF curves based on statistically downscaled estimates of multiple climate model outputs (e.g., Mearns et al., 2017) that cover a 120-year period (i.e., from 1979 to 2099) over the Contiguous United States (CONUS). The yielded outcomes for both historical data and climate model hindcasts exhibit that, on average, extreme rainfall displays similar trends over the study domain. However, it is shown that the dependence structure and variability of rare precipitation events vary significantly across data sources, and should be scrupulously delineated when assessing how existing risk considerations could actually be affected.

References

  • Emmanouil, S., A. Langousis, E.I. Nikolopoulos, and E.N. Anagnostou (2020) Quantitative assessment of annual maxima, peaks-over-threshold (PoT) and multifractal parametric approaches in estimating intensity-duration-frequency (IDF) curves from short rainfall records, Journal of Hydrology, 589, 125151, doi: 10.1016/j.jhydrol.2020.125151.
  • Emmanouil, S., A. Langousis, E.I. Nikolopoulos, and E.N. Anagnostou (2022) The spatiotemporal evolution of rainfall extremes in a changing climate: A CONUS-wide assessment based on multifractal scaling arguments, Earth’s Future, 10 (3), e2021EF002539, doi: 10.1029/2021EF002539.
  • Langousis, A., D. Veneziano, P. Furcolo, and C. Lepore (2009) Multifractal rainfall extremes: Theoretical analysis and practical estimation, Chaos, Solitons and Fractals, 39 (3), 1182–1194, doi: 10.1016/j.chaos.2007.06.004.
  • Mearns, L.O., S. McGinnis, D. Korytina, R. Arritt, S. Biner, M. Bukovsky, et al. (2017) The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway. Boulder (CO): The North American CORDEX Program, 10.

How to cite: Emmanouil, S., Langousis, A., Nikolopoulos, E. I., and Anagnostou, E. N.: A multifractal framework to evaluate extreme rainfall trends across scales under a changing climate, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-1, https://doi.org/10.5194/egusphere-plinius17-1, 2022.

15:15–15:30
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Plinius17-27
Leo Pio D'Adderio, Daniele Casella, Stefano Dietrich, Paolo Sanò, and Giulia Panegrossi

Mediterranean hurricanes (Medicanes) are meso-scale cyclones typical of the Mediterranean area which during their lifetime may show some dynamical features with tropical cyclones: the presence of a quasi-cloud-free calm eye, spiral-like cloud bands elongated from the center, strong winds close to the vortex centre and a warm core. They are often associated to heavy rainfall and flooding, intense wind, and high waves and storm surge, and can be serious threats to human life and infrastructure. Recent studies highlighted that extra-tropical and tropical-like cyclone (TLC) characteristics can alternate or even coexist in the same cyclonic system, and that only in some cases strong diabatic forcing leads to tropical-like transition (i.e., purely barotropic structure) associated to shallow or deep warm core. In this study a comparative analysis among the Medicanes occurred during the Global Precipitation Measurement (GPM) era (i.e. since March 2014), is carried out. The goal is to extract common features from passive MW measurements to identify and characterize the transition to TLC phase during the Medicane evolution. Passive microwave measurements from the GPM constellation radiometers are used to characterize the precipitation structure and warm core properties throughout the Medicane evolution. In particular, the NASA/JAXA GPM Core Observatory (GPM-CO) active and passive microwave (MW) sensors are used in conjunction with ground-based LIghtning NETwork (LINET) measurements to analyse the rainband structure and infer microphysics processes and convection strength. On the other hand, MW temperature sounding channels available from AMSU-A and ATMS radiometers are used to identify the warm core and infer its properties (e.g., depth and symmetry) around the cyclone center.  The most intense Medicane on record, named Ianos, which swept across the Ionian Sea between 14 and 18 September 2020, is anlaysed in detail. The GPM-CO Dual-frequency Precipitation Radar (DPR) overpass, available for the first time during a medicane TLC phase, provides key measurements and products to analyze the 3D precipitation structure in the rainbands, offering further evidence of the main precipitation microphysics processes inferred from the passive MW measurement analysis. Moreover, the GPM-CO overpasses highlight a significant change in deep convection features between Ianos development and mature phases, which explain the substantial drop in lightning activity during Ianos TLC phase. The study demonstrates the value of satellite MW measurements in the GPM era to provide evidence of Medicanes' transition to TLC phase and to characterize its precipitation structure and microphysics processes.

How to cite: D'Adderio, L. P., Casella, D., Dietrich, S., Sanò, P., and Panegrossi, G.: Satellite-based characterization of Medicanes in the GPM era, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-27, https://doi.org/10.5194/egusphere-plinius17-27, 2022.

15:30–15:45
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Plinius17-6
Mario Montopoli, Errico Picciotti, Luca Baldini, Saverio Di Fabio, Frank Marzano, Marcello M. Miglietta, Alessandro Tiesi, Simone Mazzà, and Gianfranco Vulpiani

In recent years, the Mediterranean area has been affected by a continuous and significant increase in the intensity of violent weather events resulting in floods, hailstorms and tornadoes and an increasing impact on human activities, infrastructure and agricultural production.

Among these extreme events, a particularly intense phenomenon occurred on July 10, 2019 affecting much of the central Adriatic coast. In particular, the Pescara area was affected by a supercell that produced heavy rainfall and an exceptional hailstorm, with hailstones even larger than 10 cm in diameter, causing extensive damage.

This contribution documents, for the first time in Italy, the dynamics, morphology and main characteristics of the Pescara supercell [1] which was simultaneously observed, by two C-band meteorological radars of the national Department of Civil Protection (DPC). The results obtained highlight the irreplaceable role of dual-polarization Doppler weather radars in monitoring the evolution of hail, identifying the mesocyclone initiation and the related updraft and downdraft zones as well as their vertical extension, and highlighting the current limitations in determining the size of hail particles from radar measurements. Numerical simulations with the WRF model, using the HAILCAST module to simulate the evolution of hail, were carried out in order to evaluate the capabilities of an operational model in the simulation of such a particular event.

In the context of the intensification of extreme events, this work is also a food for thought on the main aspects to be addressed in the near future to improve the chain of alerting and modelling of extreme events for prevention and civil protection.

[1] M.Montopoli, E.Picciotti, L.Baldini, S.Di Fabio, F.S.Marzano, G.Vulpiani, "Gazing inside a giant-hail-bearing Mediterranean supercell by dual-polarization Doppler weather radar", Atmospheric Research,  Vol. 264, 15 Dec. 2021, 105852, https://www.sciencedirect.com/science/article/pii/S0169809521004087?dgcid=author

How to cite: Montopoli, M., Picciotti, E., Baldini, L., Di Fabio, S., Marzano, F., Miglietta, M. M., Tiesi, A., Mazzà, S., and Vulpiani, G.: Analysis of a hail bearing Mediterranean supercell through weather radars, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-6, https://doi.org/10.5194/egusphere-plinius17-6, 2022.

15:45–16:00
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Plinius17-73
Moshe Armon, Andries de-Vries, Francesco Marra, Nadav Peleg, and Heini Wernli

The scarcity of rainfall in the Sahara, the largest desert in the world, turns almost every rainstorm into an “extreme” event. The desert is situated to the south of the Mediterranean’s storm track and north of the equatorial-monsoonal rain belt, at the subsiding branch of both the Hadley and Walker circulation cells. The meager rainfall is observed by just a few rain gauges, recording few rainy days almost every year, occasionally triggering flash floods. Given the low amount of rainfall and the low number of observations, the characteristics of rainfall during such events were seldomly analyzed, especially at the scale of the whole Sahara. In this study, we (a) use high-resolution satellite remote sensing rainfall data (IMERG), to identify thousands of heavy precipitation events over the past 20 years, (b) characterize rainfall properties during these events, and (c) identify the governing atmospheric conditions on days of heavy precipitation using meteorological reanalysis (ERA5) data.

Heavy precipitation events occur throughout the Sahara, except for a small portion of its core. Southern Sahara events are the most frequent and happen mainly in summer. During winter, events occur primarily in the north and west parts of the desert. Preliminary analyses indicate that the events with the largest volume of rainfall (with volumes ≥ roughly the volume of Lake Chad) are characterized by much higher than normal upper-tropospheric temperatures over the eastern Mediterranean and lower temperatures over the southern Sahara.

The small number of events at each location is compensated in our analysis by the huge area of the desert with events occurring on average every second day. The high-resolution datasets we use enable us to characterize small-size events, with substantial implications at the local scale, which can help to cope with natural hazards.

How to cite: Armon, M., de-Vries, A., Marra, F., Peleg, N., and Wernli, H.: Heavy precipitation events where there’s no rain: Saharan rainfall climatology, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-73, https://doi.org/10.5194/egusphere-plinius17-73, 2022.

16:00–16:15
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Plinius17-53
Imane EL Martili, Khalid Barkouki, Jihane Ahattab, and Najat Serhir

Over the last twenty years, remote monitoring of rainfall has become a fact. Thus, techniques to measure rainfall by analyzing satellite data is increasingly integrated in hydrology fields helping to tackle some of scientists’ biggest challenges such as the lack of rain gauges or the difficulty to access their data. 

This work is a study of the reliability and performance of precipitation observation products like PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System) with a temporal resolution of one hour (https://chrsdata.eng.uci.edu/) and like IMERG-GPMv6 (Integrated Multi-satellitE Retrievals for GPM version6) with a temporal resolution of 30 minutes (https://search.earthdata.nasa.gov/search) by using the programming language Python. These satellite-derived data are then compared to precipitation observed from rain gauges in the watershed Bouregreg-Chaouia in Morocco.   

The first procedure was to create a database of hourly rainfall collected from PERSIANN-CCS and IMERG-GPMv6 and based on measured rainfall events received from three rain gauges located in the study area. Afterwards, the comparison between the two data sources was executed through calculation of several statistical parameters such as the Pearson’s R correlation coefficient in addition to an analysis of the dimensionless values of rain (instantaneously observed precipitations from PERSIANN-CCS and IMERG-GPM).

Obtained results show a good correlation between values deduced from satellite imagery and those observed in rain gauges. Quantitively, the “R” correlation coefficients varied from 0.69 to 0.98 in the case of PERSIANN-CCS and from 0.86 to 0.99 in the case of IMERG-GPMv6. Furthermore, analysis of the dimensionless rainfall curves showed that they represent very comparable patterns, especially for the case of IMERG-GPMv6, it was also remarked that the latter dataset overestimated precipitation levels while the first underestimated them in comparison with ground station values. 

We concluded that, it’s possible to use instant rainfall data from satellites in contexts like hydrology for purposes like flood risk assessment. But, a correction of these products is necessary to improve the results and the quality of this data.

How to cite: EL Martili, I., Barkouki, K., Ahattab, J., and Serhir, N.: Comparison of instantaneous satellite rainfall data and observations from rain gauges network in the Bouregreg-Chaouia region in Morocco, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-53, https://doi.org/10.5194/egusphere-plinius17-53, 2022.

16:15–16:30
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Plinius17-38
Roberto Nebuloni, Greta Cazzaniga, Cristina Deidda, Michele D'Amico, and Carlo De Michele

The use of non-conventional rainfall sensors can help to close the gap on rainfall characterization, which is one weak link in the modelling of the Earth’s water cycle. This has been an hot topic in hydro-meteorology for the last decade. In this frame, the EU COST ACTION named OPENSENSE (Opportunistic precipitation sensing network) has been recently approved and is running since October 2021 [1].

Specifically, the point-to-point wireless links massively used by the cellular networks for backhauling, namely, commercial microwave links (CML) have some unique features that render them attractive for rainfall detection [2]. The ubiquitous deployment of CML, the relatively high density of sensors, especially in urbanized areas, and the availability of raw data as outputs of the link quality control process are a strong plus. On the other hand, CML data are owned by mobile operators, hence they are not of public domain, and they are usually not optimized for rainfall measurements. It is therefore important to assess the capability of CMLs to detect the temporal and spatial patterns of precipitation and to quantify precipitation intensity by validation against conventional rainfall sensors where the latter are present and sufficiently dense [3].

In this work, we investigate the capability of CMLs to detect extreme rainfall events analyzing a case study in a large area North of Milan, where a mesh of more than 200 links is present. The region is covered by an operational network of rain gauges owned by ARPA Lombardia and by MeteoSwiss weather radar. Due to the different spatial sampling of CML, rain gauge and radar observations, specific procedures must be envisaged to carry out a fair data comparison. Even though individual CMLs may return large discrepancies in rainfall intensity values with respect to nearby rain gauges, especially in the case of short high-frequency links, it is possible to obtain a good reconstruction of the rainfall patterns of extreme events, without an in-advance calibration through ground truth data.

References:

[1] OPENSENSE COST ACTION official site, https://www.cost.eu/actions/CA20136/ (last accessed on May 2, 2022)

[2] Messer, H. Rainfall monitoring using cellular networks [in the spotlight]. IEEE Signal Processing Magazine 2007, 24, 144–142.

[3] Nebuloni, R.; Cazzaniga, G.; D’Amico, M.; Deidda, C.; De Michele, C. Comparison of CML Rainfall Data against Rain Gauges and Disdrometers in a Mountainous Environment. Sensors 2022, 22, 3218. https://doi.org/10.3390/s22093218

How to cite: Nebuloni, R., Cazzaniga, G., Deidda, C., D'Amico, M., and De Michele, C.: Detection of extreme rainfall events by a network of microwave links in the area of Milan, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-38, https://doi.org/10.5194/egusphere-plinius17-38, 2022.

Orals: Wed, 19 Oct | Sala degli Svizzeri

Chairpersons: Emmanouil Anagnostou, Giulia Panegrossi
Weather and hydrological hazards: new observing systems and techniques
14:30–14:45
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Plinius17-39
Alessandra Mascitelli, Stefano Federico, Gianfranco Vulpiani, Mattia Crespi, and Stefano Dietrich

Over the years, there has been an increase in extreme weather events which encouraged the scientific community to employ ever more different techniques for their studies. In this context, GNSS (Global Navigation Satellite System) find its place. Over the last thirty years, this technique has shown increasing applicability and reliability in the field of weather forecasting and analysis. However, there are points that it is critical to continue to investigate; one of the most discussed and noteworthy is the behavior of the GNSS-PWV (Precipitable Water Vapor from GNSS) time course during severe weather events. The relation between GNSS-PWV pattern and weather event evolution appears to be non-constant, sometimes showing a PWV peak at maximum convection, sometimes an advance and sometimes a delay. In this study we try to identify the causes of this unevenness of behavior using the number of lightning as a reference for the trend of convection and the VIL (Vertical Integrated Liquid content) obtained from radar as a term of comparison for the validation of GNSS-PWV.

How to cite: Mascitelli, A., Federico, S., Vulpiani, G., Crespi, M., and Dietrich, S.: GNSS-PWV time evolution in extreme weather events: comparison analysis with lightning and radar-VIL, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-39, https://doi.org/10.5194/egusphere-plinius17-39, 2022.

14:45–15:00
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Plinius17-49
Simone Lolli, Jasper R. Lewis, Gemine Vivone, Michael Sicard, Ali Tokay, and Ellsworth J. Welton

Lidar measurements can detect exceptionally light precipitation, such as drizzle or virga. This kind of precipitation is really hard to detect by other remote sensing techniques such as radars because a very short longwave (in the visible) is needed due to the small size of raindrops. For those reasons, lidar instruments are well suited to fill a gap in detecting light precipitation. In this study, we show the intercomparison results between the ground-based disdrometer observations and lidar precipitation algorithm detection at Goddard Space Flight center for future precipitation calibration/validation of the next European Space Agency (ESA) Earthcare mission, which is expected to be launched in 2023.

How to cite: Lolli, S., Lewis, J. R., Vivone, G., Sicard, M., Tokay, A., and Welton, E. J.: NASA MPLNET precipitation detection algorithm validation by ground-based disdrometers in the frame of future ESA Earthcare mission, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-49, https://doi.org/10.5194/egusphere-plinius17-49, 2022.

15:00–15:15
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Plinius17-74
Alessandro Battaglia, Frederic Tridon, Antonio Parodi, Martina Lagasio, Vincenzo Mazzarella, and Anthony Illingworth

The WIVERN (WInd VElocity Radar Nephoscope, www.wivern.polito.it) mission, one of the four ESA Earth Explorer 11 mission candidates, currently in Phase-0, promises to complement the ADM-Eolus Doppler wind lidar measurements by globally observing, for the first time, vertical profiles of winds in cloudy areas. This work aims to determine the potential of the new cutting edge WIVERN W-band polarization-diversity Doppler radar for sampling Mediterranean hurricanes and monitoring their internal structure. It builds on the recently developed end to end simulator of the WIVERN dual-polarization Doppler conically scanning 94 GHz radar (Battaglia et al., Atmos. Meas. Tech., 15, 3011–3030, 2022, https://doi.org/10.5194/amt-15-3011-2022). The simulator is applied to: 1) the long-term CloudSat observation dataset of intense cyclones in the Mediterranean basin; 2) a Weather Research and Forecasting (WRF) Model very high horizontal resolution (333 m) run for Medicane Apollo occurred in October 2021. The analysis of the CloudSat results provides statistics for understanding which part of the cyclones can actually be seen by the W-band radar and where line of sight winds can be accurately measured. The high resolution WRF simulation provides insight into wind errors introduced by non-uniform beam filling and small-scale convective motions for the WIVERN observing system.

How to cite: Battaglia, A., Tridon, F., Parodi, A., Lagasio, M., Mazzarella, V., and Illingworth, A.: The Potential of the W-band polarization diversity Doppler radar envisaged for the WIVERN mission for looking into the internal structures of Mediterranean cyclones, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-74, https://doi.org/10.5194/egusphere-plinius17-74, 2022.

15:15–15:30
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Plinius17-81
Romain Husson, Alexis Mouche, Olivier Archer, Henrick Berger, Aurélien Colin, Charles Peureux, and Gaël Goimard

Spaceborne observations over extreme atmospheric events at global scale such as Tropical Cyclones, Extra-Tropical Cyclones, Polar Lows and Medicanes are a key component in extreme events monitoring and in anticipating appropriate risk mitigation and emergency response at landfall. In particular, meteorological forecasters and numerical modelers need to access various sources of conventional and specialized data/products including remote sensing observations to refine their analysis or adjust their models. 

Recent progresses in SAR processing have shown the potential of C-band SAR data acquired in dual-polarization for estimating at high-resolution (1 km) an ocean surface wind field [1, 2], including extreme events such as major hurricanes (category -3 to -5) [3]. Comparison with SFMR for yield to high correlation (R > 0.90), small bias ( < 0.5 m.s-1) and RMSE ( < 5 m.s-1) [4], including at highest wind speeds (80 m.s-1). 

C-band SAR signal can also be impact by non-wind related signatures. This is particularly true over Mediterranean Sea where strong convective events, associated with heavy precipitations are often met. These deep convections lead to SAR signatures through several processes, either surface and/or volume scattering and can significantly bias the wind estimates if not well delineated. A combined estimation of the wind vector and rain signature is therefore mandatory. 

The present work shows the ability of SAR measurements to provide accurate wind vector estimation, by providing independent wind speed and wind direction estimates [6] as well as a complementary rain rate regression based on Deep Neural Network architecture [5]. This is illustrated over several medicanes or extra-tropical storm use cases and described statistically. 

The provision of these measurements is made possible through CYMS project (Cyclone and Storm Monitoring Service based on Sentinel-1), an ESA funded project since 2020, aiming at monitoring ocean extremes with SAR, in view of its potential integration as part of a Copernicus Service.  

[1] Zhang, B. and W. Perrie, 2012: Cross-Polarized Synthetic Aperture Radar: A New Potential Measurement Technique for Hurricanes. Bulletin of the American Meteorological Society, 93 (4), 531–541 

[2] Mouche Alexis et al. Combined Co- and Cross-Polarized SAR Measurements Under Extreme Wind Conditions. IEEE Transactions On Geoscience And Remote Sensing, 55(12), 6746-6755.  (2017).  

[3] Mouche Alexis et al. Copolarized and Cross‐Polarized SAR Measurements for High‐Resolution Description of Major Hurricane Wind Structures: Application to Irma Category 5 Hurricane. Journal Of Geophysical Research-oceans, 124(6), 3905- 3922. 

[4] Combot Clement et al. “Extensive high-resolution Synthetic Aperture Radar (SAR) data analysis of Tropical Cyclones: comparisons with SFMR flights and BestTrack". Monthly Weather Review, 148(11), 4545–4563. (2020b). 

[5] A. Colin et al. ”Segmentation of rainfall regimes by machine learning on a colocalized Nexrad/Sentinel-1 Dataset”, Living Planet Symposium, May 2022 

[6] Husson R. et al. “Wind Direction Estimation and Accuracy Retrieval from Sentinel-1 SAR Images Under Thermal and Dynamical Unstable Conditions.” In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 7588–91, 2021. 

How to cite: Husson, R., Mouche, A., Archer, O., Berger, H., Colin, A., Peureux, C., and Goimard, G.: Combined high-resolution rain/wind measurements over extreme wind events using Synthetic Aperture Radar , 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-81, https://doi.org/10.5194/egusphere-plinius17-81, 2022.

15:30–15:45
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Plinius17-52
Andrea Cecilia, Giampietro Casasanta, Igor Petenko, Alessandro Conidi, and Stefania Argentini

Provided that the population living in cities is increasing, projected to reach 5.2 billion in 2030, and that heat waves are getting more intense and lasting as a consequence of the global warming, the urban heat island (UHI) phenomenon is leading increasingly to extremely high temperatures within cities. It is therefore important to find reliable and simple methods for estimating and characterizing at a high resolution the UHI.

In this work we characterize the urban heat island (UHI) of Rome, Italy, during summer, through a dense weather station network. Measurements were collected in summers 2019-2020. We calculate the UHI intensity using a method that relates the air temperature to imperviousness (IMP), which quantifies the presence of artificially sealed surface in a radius around each station using Copernicus Land Monitoring Service satellite data. To assess the reliability of this method we made a comparison with the LCZ-based approach, finding compatible daily trends of UHI intensity, with a fixed bias during night. Our method both simplifies the measurement area classification and allows to determine the UHI intensity even when measurements in totally urban and totally rural areas are not available. The correlation coefficient values between IMP and daily maximum, minimum and mean temperatures were 0.17, 0.81 and 0.82, respectively, evidencing the nighttime UHI peak observed in other cities. The UHI intensity diurnal cycle pattern showed, starting from its minimum of -0.1°C at 10:00 (CET), a progressive increase which intensifies after sunset, reaching a maximum of 3.4°C at midnight. During the night a slight decrease is observed, which exacerbates after sunrise. We did not find a relevant correlation between UHI and heat waves.

How to cite: Cecilia, A., Casasanta, G., Petenko, I., Conidi, A., and Argentini, S.: Measuring the urban heat island of Rome through a dense weather station network and imperviousness Copernicus Land Monitoring Service data, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-52, https://doi.org/10.5194/egusphere-plinius17-52, 2022.

Fire monitoring
15:45–16:00
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Plinius17-57
Annalina Lombardi, Gabriele Pizzi, Valentina Colaiuda, Fabio Ferrante, Paolo Tuccella, Ludovico Di Antonio, Raffaele Lidori, Daniela Di Fazio, Thomas Malatesta, Jose Maria Costa Saura, Donatella Spano, Vincenzo Rizi, Frank Silvio Marzano, Francesco Luigi Rossi, Silvio Liberatore, Mauro Casinghini, Giuliano Rossi, and Barbara Tomassetti

About 10000 hectares of forest, corresponding to the 12% of the national forestry heritage, are lost each year in Italy due to arson or negligent fires. Consequences on ecosystem and natural equilibrium are relevant, since the time for the natural restoration process may take several decades. Climate extremes exacerbates Mediterranean area fire risk, due to prolonged drought conditions. On the other hand, hydrogeological risk is also expected to increase over burnt slopes, where surface runoff is incremented due vegetation loss. According to the current legislation, fire risk management is in charge of the Italian Regional Civil Protection, therefore the development of user-oriented tools, able to prevent the fire hazardous conditions, is key element to ensure the forest-fire risk management. In the proposed model, the atmospheric conditions preceding a forest fire are estimated thought the combination of air temperature and relative humidity, as reference of atmospheric parameters.The approach assesses how many times the observed air temperature and RH of the previous 12 days area above the critical conditions (i.e., >25°C and < 50%, respectively). The model validation is carried out by using a three-years dataset of forest fires, that hit the Abruzzo region from 2018 to 2020, combined with meteorological data from civil protection gauges’ network. The developed index identified fire-precursors in the 80% of selected case studies. The missing 20% is manly related to the meteorological uncertainty in poorly gauged areas. Starting from the index validation, a pre-operational tool forced with ECMWF analyses is also described.

How to cite: Lombardi, A., Pizzi, G., Colaiuda, V., Ferrante, F., Tuccella, P., Di Antonio, L., Lidori, R., Di Fazio, D., Malatesta, T., Costa Saura, J. M., Spano, D., Rizi, V., Marzano, F. S., Luigi Rossi, F., Liberatore, S., Casinghini, M., Rossi, G., and Tomassetti, B.: Atmospheric Precursor of fire hazard: development of a fire-sentinel index for risk management in Abruzzo Region (Central Italy)., 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-57, https://doi.org/10.5194/egusphere-plinius17-57, 2022.

16:00–16:15
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Plinius17-76
Patrícia Páscoa, Tiago Ermitão, and Célia M. Gouveia

Mediterranean European countries are considered fire-prone regions, being affected by fire events every summer, and Portugal is among these countries. Moreover, Portugal has been recording large burned areas over the last 20 years. Catastrophic fire season occurrence, associated with hot and dry conditions and high fuel availability in forests, has been recurrently destroying several ecosystems. Furthermore, the Mediterranean basin has been stated with high potential to be one of the most disturbed areas due to climate change, which strongly promotes the increase of fire weather conditions and fire risk and, thereby, the occurrence of more extreme fire seasons.

During the last years, Portugal has been implementing new effective policies regarding the prevention of fires during pre-fire season months, improving the investment in combat strategies. In this context, our study contributes to identify the regions with more potential to burn in a specific fire season. Through satellite-based data and reanalysis products, with large temporal extent and moderate to high spatial resolution, we combine a wide range of variables linked, directly or indirectly, with fire, in order to identify the most exposed regions to burn.

The application of Principal Component Analysis (PCA) to our range of climatological, ecological and biophysical parameters allowed to assess six different regions with more susceptibility to fire events. The central and the southernmost regions of the country presented a stronger signal on PCA analysis, indicating a higher exposure to future fire events. Fuel accumulation during several months, in conjunction with topography, land cover and fire weather conditions were the terms that explained the most variability of the first six PCAs. Therefore, with these results, our work addresses the key trigger parameters of fires, and the most susceptible areas to burn in Portugal, contributing to enhancing the effectiveness of fire prevention policies.

Acknowledgements: This study was supported by FCT (Fundação para a Ciência e Tecnologia, Portugal) through national funds (PIDDAC) – UIDB/50019/2020, and under the projects FlorestaLimpa (PCIF/MOG/0161/2019) and FIRECAST (PCIF/GRF/0204/2017).      

How to cite: Páscoa, P., Ermitão, T., and M. Gouveia, C.: Identification of the most fire susceptible areas in Portugal , 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-76, https://doi.org/10.5194/egusphere-plinius17-76, 2022.

16:15–16:30
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Plinius17-43
Elisabetta Fiori, Giuseppe Squicciarino, and Luca Pulvirenti

The damages generated by fire events on vegetation structure and its evolution and the economic impacts on human activity, life and infrastructures have led the scientific interest to develop tools and algorithms able to support the detection and monitoring of burned areas (BAs).

The possibility of monitoring the fire evolution and mapping the BAs has been strongly promoted in last decades by the opportunity to use a significant quantity of satellite observations. Earth observation (EO) data represent one of the key components in supporting both government agencies and local decision-makers in monitoring natural disasters such as wildfires. Among EO instruments, multispectral sensors have demonstrated their suitability for BA mapping, because fire has significant effects on vegetation reflectance. The Copernicus Sentinel-2 (S2) with 20-m spatial resolution and a 5-day return period is a good candidate for near real-time (NRT) monitoring of the fire situation throughout the fire season.

Pulvirenti et al. (2020) proposed an automatic NRT BA mapping approach based on S2 data. They developed the AUTOmatic Burned Areas Mapper (AUTOBAM) tool to respond the need of the Italian Department of Civil Protection in monitoring spatial distribution and numerousness of the BAs during the fire season (June- September) over the Italian territory. It is used, in pre-operational mode, since summer 2019. The atmospherically corrected Level-2A(L2A) surface reflectance products from S2 are used by AUTOBAM: the automatic chain downloads and processes the most updated L2A products available on Copernicus Open Access Hub over the studied area. Then, a change detection approach is applied to the three spectral indices chosen to map BA (Normalized Burn Ratio, the Normalized Burned Ratio 2, and the Mid-Infrared Burned Index). AUTOBAM compares the values of these indices acquired at current time with the values derived from the most recent cloud-free S2 data. The procedure for BA mapping is based on different sequential image processing techniques such as clustering, automatic thresholding, region growing that conduce to a final BA map with grid pixel size of 20m. Finally, a quality flag is included for each AUTOMAB BA to certify a temporal and spatial correspondence with ancillary data, such as active fire products derived from MODIS and VIIRS, as well as national fire notifications.

This processing chain has been tested for the fire seasons of years 2019-2021, and the AUTOBAM-derived BAs have been compared with the burned perimeters compiled by Carabinieri Command of Units for Forestry, Environmental and Agri-food protection. A validation procedure in fact has been realized to verify a-posteriori the ability of AUTOBAM to detect the actual BAs mapped by Carabinieri after local surveys. Timing and spatial criteria are adopted to validate AUTOBAM mapping, and a threshold of 20% overlapping is fixed to make an AUTOMBAM BA classified as a reliable detection. Results indicate that the proposed method has potential for NRT mapping of BAs.

How to cite: Fiori, E., Squicciarino, G., and Pulvirenti, L.: An automatic algorithm for near real-time burned area mapping from Sentinel-2 data: validation results for 2019-2021 fire seasons over Italy, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-43, https://doi.org/10.5194/egusphere-plinius17-43, 2022.

Orals: Thu, 20 Oct | Sala degli Svizzeri

Chairpersons: Emmanouil Anagnostou, Francesco Casu
Coastal and land hazards monitoring
12:45–13:00
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Plinius17-71
Laura Candela, Alessandro Coletta, Maria Girolamo Daraio, Ettore Lopinto, Deodato Tapete, Monica Palandri, Daniele Pellegrino, Massimo Zavagli, Angelo Amodio, Antonio Vecoli, Simone Mantovani, and Claudia Giardino

Coastal areas are increasingly becoming more vulnerable due to economic overexploitation and pollution. The Italian Space Agency (ASI) supports the research and development of technologies aimed at the use of multi-mission EO data, in particular of the national COSMO-SkyMed Synthetic Aperture Radar and PRISMA hyperspectral missions, as well as Copernicus Sentinels, through the development of algorithms and processing methodologies in order to generate products and services for coastal risk management.

In this context, ASI has promoted the development of the thematic platform costeLAB as a tool dedicated to monitoring, management and study of coastal areas (sea and land). This platform was developed in the frame of the “Progetto Premiale Rischi Naturali Indotti dalle Attività Umana - COSTE", n. 2017-I-E.0 (http://costelab.asi.it/en/homepage-en/), funded by the Italian Ministry of University and Research (MUR), coordinated by ASI and developed by e-GEOS and Planetek Italia with the participation of National Research Council of Italy (CNR), Meteorological Environmental Earth Observation (MEEO) and Geophysical Applications Processing (G.A.P.) s.r.l. The aim of the project was to define, develop and run in a pre-operational context, an integrated system that exploits Earth Observation data to support the management of coastal areas environmental processes and risks. The platform is addressed to the institutional, scientific and industrial users and allows the study, experimentation and demonstration of new downstream pre-operational services for the monitoring of the coastal area environment and in support to risk management.

The costeLAB platform provides a common entry point for several web-based EO data processing in the field of coastal zone monitoring and emergency management, to generate and visualize products by means of consolidated algorithms that users can utilize for their duty tasks.

The rationale of the platform is to “keep applications close to the data”, i.e. allowing users to access huge amount of EO data relieving them of demanding tasks for big data download and processing in local computers. Users are therefore able to generate reliable products by means of validated algorithms with reduced processing times.

Of the thirty consolidated products that users can generate through the platform (Candela et al., 2021), the paper will showcase in particular those of main relevance for coastal risk management: Coastline change map, Coastal subsidence rate, Landslide activities, Hydrocarbon beaching, Flooding maps, Flood exposure, Erosion exposure, Coastal pollution at national scale, Pollution at coastal scale, under different application scenarios.

Finally, the paper will present experimental scientific products that Researcher Users from CNR generated over selected Mediterranean sites via testing the “collaborative virtual laboratory” namely “Virtual Lab”, i.e. the ad hoc costeLAB facility for researchers and developers to share, test and demonstrate innovative algorithms in order to build new processing chains. These experiments within the platform followed on from dedicated research activities that were carried out on the various components of the marine-coastal environment (land-sea interface) during the costeLAB project. The breadth and novelty of these activities towards an improved understanding of Mediterranean coastal hazards will be presented.

How to cite: Candela, L., Coletta, A., Daraio, M. G., Lopinto, E., Tapete, D., Palandri, M., Pellegrino, D., Zavagli, M., Amodio, A., Vecoli, A., Mantovani, S., and Giardino, C.: costeLAB platform: a prototype collaborative environment for research and applications in support to coastal risk management, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-71, https://doi.org/10.5194/egusphere-plinius17-71, 2022.

13:00–13:15
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Plinius17-23
Luca Cenci, Valerio Pampanoni, Giovanni Laneve, Carla Santella, Valentina Boccia, and Clément Albinet

Earth Observation (EO) data characterised by a spatial resolution in the range of 10-30 m (e.g., Sentinel 1 – S1, Sentinel 2 – S2, Landsat series), systematically acquired and freely distributed by national and international space agencies/institutions (e.g., ESA, EU, NASA), are a valuable tool for analysing shoreline evolution trends. These data can be used for supporting coastal erosion hazard and risk management strategies (Cenci et al., 2018). However, the accuracy of such trends is not often quantified because of the difficulties in finding systematic and freely available EO data at Very High Resolution (VHR) concurrently acquired over the same target areas to use as reference.

Within this context, this work was conceived for taking advantage of the Copernicus VHR optical datasets (spatial resolution: 2-4 m) to use as reference data to validate the shoreline evolution trends obtained by exploiting S1 and S2 images. The abovementioned analysis was carried out for a short-term scenario (i.e., 3 years: from 2015 to 2018) in an exemplifying littoral of the Mediterranean Sea characterised by both urbanised and natural coastal areas: i.e., Lido di Ostia (Rome, Italy). Importantly, the shoreline extraction method used in this case study was based on a methodological approach that allowed to map the shoreline positions with sub-pixel precision (Bishop-Taylor et al., 2019; Cenci et al., 2021).

Preliminary results showed that the shoreline evolution trends based on the S2 Visible Near-InfraRed (VNIR) spectral bands (spatial resolution: 10 m) retain an accuracy of 4.5 m (in term of Root Mean Squared Error - RMSE), if compared against the corresponding trends acquired by using Copernicus VHR data with a spatial resolution of 2 m. At the conference, the results of the analysis based on S1 data will be also presented, as well as a thorough interpretation and discussion of the S1 and S2 -based results that take into account the characteristics of the coastal area under assessment (e.g., presence or absence of defence structures) and the relationship between the magnitude of the shoreline advance/retreat trends and the corresponding accuracy. The overall objective of this work is to show the potentialities of the Copernicus EO data for the management of the coastal erosion hazard/risk in the Mediterranean area.

References:

  • Bishop-Taylor R., Sagar S., Lymburner L., Alam I. and Sixsmith J. Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra. Remote Sensing. 2019; 11(24):2984. https://doi.org/10.3390/rs11242984
  • Cenci L., Disperati L., Persichillo M.G., Oliveira E.R., Alves F.L. and Michael Phillips. Integrating remote sensing and GIS techniques for monitoring and modeling shoreline evolution to support coastal risk management. GIScience & Remote Sensing. 2018. 55(3), pp. 355-375.https://doi.org/10.1080/15481603.2017.1376370
  • Cenci L., Pampanoni V., Laneve G., Santella C. and Boccia V. Evaluating the Potentialities of Copernicus Very High Resolution (VHR) Optical Datasets for Assessing the Shoreline Erosion Hazard in Microtidal Environments. AIT Series: Trends in earth observation. 2021. Volume 2, pp. 81-84. ISSN: 2612-7148. ISBN: 978-88-944687-0-0. Published on behalf of the Associazione Italiana di Telerilevamento (AIT) https://aitonline.org/wp-content/uploads/2021/10/PlanetCarefromSpace.pdf DOI: 10.978.88944687/00

How to cite: Cenci, L., Pampanoni, V., Laneve, G., Santella, C., Boccia, V., and Albinet, C.: Assessing the Accuracy of Shoreline Evolution Trends Obtained by Using Copernicus Earth Observation Data. Case Study: Mediterranean Coastal Areas, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-23, https://doi.org/10.5194/egusphere-plinius17-23, 2022.

13:15–13:30
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Plinius17-78
Francesco Casu, Paolo Berardino, Manuela Bonano, Sabatino Buonanno, Federica Casamento, Claudio De Luca, Carmen Esposito, Adele Fusco, Riccardo Lanari, Michele Manunta, Mariarosaria Manzo, Fernando Monterroso, Antonio Natale, Giovanni Onorato, Stefano Perna, Yenni Roa, Pasquale Striano, Muhammad Yasir, Giovanni Zeni, and Ivana Zinno

Surface displacement is one of the main parameters to assess the natural hazard in volcanic and seismic regions, as well as in areas affected by landslides and subsidence.

Differential Synthetic Aperture Radar Interferometry (DInSAR) is becoming one of the key techniques to measure ground deformation in any atmospheric conditions, with continuous day and night imaging capabilities and a high accuracy level, thanks to its capability to provide dense measurements at large spatial scale and at relatively low cost.

The increasing diffusion of the use of DInSAR is also due to the large availability of huge and easily accessible SAR data archives, as those acquired, since late 2014, by the Copernicus Sentinel-1 constellation, which is globally and routinely providing C-band SAR data with a defined repeat-pass frequency. Therefore, with such a constant and reliable availability of data, it is possible to use the DInSAR technique for monitoring purposes, such as those related to the measurements of ground motion in natural hazard prone areas.

In this work, we present the operative services and tools that have been developed at CNR-IREA, in the framework of its cooperation with the Italian Department of Civil Protection (DPC), for detecting and monitoring large scale surface deformation through the use of the DInSAR technique.

A first service is focused on seismic areas and relies on the publicly accessible earthquake catalogues. Once an earthquake that likely produces ground deformation occurs, it triggers an automatic DInSAR processing that generates the co-seismic induced displacement maps, by retrieving the relevant pre- and post-seismic Sentinel-1 acquisitions. While being focused on the Mediterranean region the system works at global scale.

A second service is devoted to volcano displacement monitoring. The designed system is fully automatic and the process is triggered by the availability, for every monitored volcano site, of a new SAR data in the Sentinel-1 catalogues acquired from both ascending and descending passes. The data, per each orbit, are automatically ingested and then processed through the well-known Parallel Small BAseline Subset (P-SBAS) DInSAR technique that allows generating the corresponding displacement time series and mean displacement velocity maps. The so-retrieved Line of Sight (LOS) measurements are then combined to compute the Vertical and East-West components of the deformation, which are straightforward understandable by the end user. This service is currently operative for the main active Italian volcanoes (Campi Flegrei caldera, Mt. Vesuvius, Ischia, Mt. Etna, Stromboli and Vulcano), but it can be easily extended to include other volcanic areas on Earth.

Finally, a third tool is based on the use of an airborne platform which is equipped with a X-band and L-band SAR sensor, and that is used in conjunction with the already mentioned systems to provide further information on the areas under study.

Retrieved deformation results and their implication in the understanding of the analyzed phenomena will be discussed at the conference.

 

This work is supported by the CNR-IREA and Italian DPC agreement, the CNR-IREA/MiTE-DGISSEG agreement, the H2020 EPOS-SP (GA 871121), the ASI DInSAR-3M project, and the I-AMICA (PONa3_00363) project.

How to cite: Casu, F., Berardino, P., Bonano, M., Buonanno, S., Casamento, F., De Luca, C., Esposito, C., Fusco, A., Lanari, R., Manunta, M., Manzo, M., Monterroso, F., Natale, A., Onorato, G., Perna, S., Roa, Y., Striano, P., Yasir, M., Zeni, G., and Zinno, I.: DInSAR-based monitoring services for ground deformation retrieval on active volcanoes and seismic regions through spaceborne and airborne radar sensors, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-78, https://doi.org/10.5194/egusphere-plinius17-78, 2022.

Chairpersons: Emmanouil Anagnostou, Francesco Casu
14:30–14:45
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Plinius17-98
Leslie Gale and Gabriella Scarpino

Earth Observation (EO) data plays an important role in understanding how climate change impacts our environment. However, when considering the ensuing disaster as the result of heavy precipitation for instance, we observe that anthropogenic contributions such as urbanisation and land use change contribute significantly to the risk of a hazard influenced by external factors becoming a disaster (man-made disaster).

The H2020 EOPEN Project (https://eopen-project.eu/) demonstrated possibilities to fuse Sentinel data with multiple, heterogeneous, and big data sources, to improve the monitoring and analysis capabilities of the future EO downstream sector2. Additionally, the involvement of mature ICT solutions in the EO sector shall address major challenges in effectively handling and disseminating Copernicus-related information to the wider user community. A reasonable level of automation was achieved making it possible to establish workflows to implement systematic processing of multiple data sources.

EOPEN3 components are a framework core, a Dashboard environment, application specific extensions and three Pilot Use Cases which demonstrate its usage focused, respectively, on flood risk assessment and prevention, food security and climate change; moreover, the Crop Water Demand module implemented in the H2020 MOSES4 was run through the platform to demonstrate its interoperability.

Some achievements with regards to Mediterranean natural risks are:

PUC1 - Flood risk assessment and prevention - in this use case stakeholders, from offices, from local scale (municipality of Vicenza) to government scale, with specific roles during flood emergencies, have been involved to specify the needed information elements and presentation features to support their current operations; also, the early warning system (EWS) Flood Forecasting System to predict water level of the Bacchiglione river in Vicenza, implemented by the Eastern Alps River Basin District Authority (AAWA), was empowered through access to several additional input data for their hydrological model, such as those available from Copernicus Global Land Services (CGLS) as well as an additional weather forecast, provided by the Finnish Meteorological Service, and maps of the flooded areas generated in the platform.

PUC3 - Climate Change - this use case focused on challenges that climate change brings to the local reindeer herding livelihoods and to the infrastructure and transportation in Finland, which can be a reference for Mediterranean countries facing similar problems in mountainous areas.

While EOPEN achieved a level of processing autonomy, not all aspects of the operational workflow were included. Adding interconnection and feedback mechanisms would significantly reduce human intervention needed to compliment the data and connect the systems that eventually lead to delivering the service in support of decision makers, also interfacing currently used DSS. For example, in the case of PUC1, the system supported the preparedness and partly the response (weather forecast included) phases whereas recovery and prevention actions were not included.

Anticipating future Horizon calls, future EOPEN improvements will address the quality of modelling in particular risk assessment and compounding cascading events, and the connectivity of the various steps of operational workflows thus reducing the need for human intervention simply to create a continuum of the workflow to deliver services.

How to cite: Gale, L. and Scarpino, G.: Federated platforms in support of risk assessment and cascading effects: EOPEN1 - one step done, still more to go , 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-98, https://doi.org/10.5194/egusphere-plinius17-98, 2022.

14:45–15:00
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Plinius17-99
Exceptionally low susceptibility of aerosol cloud-mediated radiative forcing over the Mediterranean Sea
(withdrawn)
Daniel Rosenfeld and Yang Cao
Display time: Wed, 19 Oct 09:00–Thu, 20 Oct 17:00

Posters: Thu, 20 Oct, 15:00–16:30 | Poster gallery

Chairpersons: Giulia Panegrossi, Emmanouil Anagnostou, Francesco Casu
P6
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Plinius17-4
Sarah Vigoureux, Pierre Brigode, Maria-Helena Ramos, Pierre Javelle, Julie Poggio, Stan Nomis, Raphaëlle Dreyfus, Olivier Delestre, Emmanuel Moreau, Christophe Laroche, and Emmanuel Tric

The French Riviera, located in the eastern part of the French Mediterranean coast, has experienced devastating flash floods, in particular during the last decade. These floods were generated by localized and intense or severe rainfall events, leading to significant material and human losses, especially on small coastal catchments. Flood forecasting is still challenging on those catchments  because they often have sparse rain gauge networks and are mostly ungauged in terms of streamflow. For example, the October 2015 event affected the Riou de l’Argentière, Frayère and Brague coastal catchments (respectively 47 km², 21 km² and 72 km²) with rain intensities (up to 200 mm in only two hours) characterized by a significant spatial variability (up to two times more precipitation on the downstream part of the catchments). This study investigates whether todays’ operational precipitation forecasts are effective on the French Riviera to accurately predict the episodes of intense Mediterranean precipitation.

We evaluate the performance of three rainfall prediction methods on 47 French Riviera coastal catchments. The NOVIMET method is based on a “simple” advection of observed radar rainfall fields and provide predictions at a horizontal resolution of 1 km and up to 2 hours of lead time. An “elaborated” advection method using a machine learning algorithm which applies radar image analysis to provide predictions up to six hours at the same spatial and temporal resolutions. Finally, we also evaluate predictions based on a blended product based on the aggregation of radar extrapolation and atmospheric numerical model predictions: PIAF. PIAF, provides forecasts at a horizontal resolution of 1 km and shorter lead times (0-3h). Rainfall forecasts are provided either as a single estimation or as an ensemble of equiprobable forecasts.

We evaluate the methods on their ability to reconstruct historical precipitation events. Forecasts are evaluated against the hourly, 1km x 1km gridded COMEPHORE radar precipitation product of Météo-France, available from 1997 to 2020. We calculate metrics of forecast quality that capture the spatio-temporal characteristics of the precipitation events. The results are discussed from the point of view of users, who assist municipalities in flood risk forecasting and mobilizes teams when it is needed to monitor events and take appropriate actions to anticipate the risk of flooding.

How to cite: Vigoureux, S., Brigode, P., Ramos, M.-H., Javelle, P., Poggio, J., Nomis, S., Dreyfus, R., Delestre, O., Moreau, E., Laroche, C., and Tric, E.: Spatio-temporal evaluation of three rainfall prediction methods on French Riviera coastal catchments, 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-4, https://doi.org/10.5194/egusphere-plinius17-4, 2022.

P7
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Plinius17-60
Alessandra Tiberia, Enrico Arnone, and Stefano Dietrich

Typical features of lightning distribution in the mountain area of Mt. Cimone (2165 m a.s.l. - Northern-Central Italy) have been studied through detections provided by the ground-based LIghtning NETwork data (LINET) and the Lightning Imaging Sensor (LIS) onboard the International Space Station (ISS-LIS).  The study was performed within the context of lightning implications as natural hazard, and its role in a changing climate. Of particular interest are mountain regions because of their orographic impact, which determine most lightning hotspots around the globe. LINET VLF/LF radio measurements allowed the characterization of both cloud-to-ground (CG) and intra-cloud (IC) strokes' geographical distribution and altitude of occurrence over 2012 through 2020. The lightning distribution showed a remarkable clustering of CGs at the mountain top in contrast to a homogeneous distribution of ICs, highlighting the likely impact of orography. IC strokes peaked around 4 to 6 km altitude, consistency with the observed typical cloud range. The joint exploitation of LIS-ISS optical observations of LINET detections extended the study to further features of flashes not seen in radio wavelengths and stands as cross-validation of the two detection methods over such a complex orography. These results give an example of mountain-driven changes in lightning occurrence. The clustering at the Cimone mountain top induced by the orography replicates a general feature of the dependence of global lightning hot-spots from elevation and is of great interest in the understanding of the lighting-climate relationship, considering known effects of elevation-depedent climate change.

How to cite: Tiberia, A., Arnone, E., and Dietrich, S.: Combined ground and space-borne lightning detection over a mountainous region , 17th Plinius Conference on Mediterranean Risks, Frascati, Rome, Italy, 18–21 Oct 2022, Plinius17-60, https://doi.org/10.5194/egusphere-plinius17-60, 2022.