Environmental systems often span spatial and temporal scales covering different orders of magnitude. The session is oriented in collecting studies relevant to understand multiscale aspects of these systems and in proposing adequate multi-platform and inter-disciplinary surveillance networks monitoring tools systems. It is especially aimed to emphasize the interaction between environmental processes occurring at different scales. In particular, a special attention is devoted to the studies focused on the development of new techniques and integrated instrumentation for multiscale monitoring high natural risk areas, such as: volcanic, seismic, energy exploitation, slope instability, floods, coastal instability, climate changes and other environmental context.
We expect contributions derived from several disciplines, such as applied geophysics, geology, seismology, geodesy, geochemistry, remote and proximal sensing, volcanology, geotechnical, soil science, marine geology, oceanography, climatology and meteorology. In this context, the contributions in analytical and numerical modeling of geological and environmental processes are also expected.
Finally, we stress that the inter-disciplinary studies that highlight the multiscale properties of natural processes analyzed and monitored by using several methodologies are welcome.
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El Hierro (278 km2), the youngest, smallest and westernmost island of the Canarian archipelago, is a 5-km-high edifice constructed by rapid constructive and destructive processes in ~1.12 Ma, with a truncated trihedral shape and three convergent ridges of volcanic cones. It experienced a submarine eruption from 12 October, 2011 to 5 March 2012, off its southern coast that was the first one to be monitored from the beginning in the Canary Islands. As no visible emanations occur at the surface environment of El Hierro, diffuse degassing studies are a useful geochemical tool to monitor the volcanic activity in this volcanic island. Diffuse CO2 emission surveys have been performed at El Hierro Island since 1998 in a yearly basis, with much higher frequency during the period 2011-2012. At each survey, about 600 sampling sites are selected to obtain a homogeneous distribution. Measurements of soil CO2 efflux are performed in situ following the accumulation chamber method. During pre-eruptive and eruptive periods, the diffuse CO2 emission released by the whole island experienced significant increases before the onset of the submarine eruption and the most energetic seismic events of the volcanic-seismic unrest (Melián et al., 2014. J. Geophys. Res. Solid Earth, 119, 6976–6991). The most recent diffuse CO2 efflux survey was carried out in July 2019. Values ranged from non-detectable to 28.9 g m−2 d−1. Statistical-graphical analysis of the data shows two different geochemical populations; Background (B) and Peak (P) represented by 97.5% and 0.5% of the total data, respectively, with geometric means of 1.2 and 23.6 g m−2 d−1, respectively. Most of the area showed B values while the P values were mainly observed at the interception center of the three convergent ridges and the north-east of the island. To estimate the diffuse CO2 emission for the 2019 survey, we ran about 100 sGs simulations. The estimated 2019 diffuse CO2 output released to atmosphere by El Hierro was 214 ± 10 t d-1, value lower than the background average of CO2 emission estimated on 412 t d-1 and slightly higher than the background range of 181 t d-1 (−1σ) and 930 t d-1 (+1σ) estimated at El Hierro volcano during the quiescence period 1998-2010 (Melián et al., 2014, JGR). Monitoring the diffuse CO2 emission has proven to be a very effective tool to detect early warning signals of volcanic unrest at El Hierro.
How to cite: Hernández, P. A., Skeldon, C. A., Zhang, J., Rodríguez, F., Amonte, C., Asensio-Ramos, M., Melián, G. V., Padrón, E., and Pérez, N. M.: Spatial-temporal variations of surface diffuse CO2 degassing at El Hierro volcano, Canary Islands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11805, https://doi.org/10.5194/egusphere-egu2020-11805, 2020.
On 3 July 2019, Stromboli experienced a paroxysmal explosion without long-term precursors, as instead occurred before the last two effusive eruptions. In the following months, lava outpoured from a vent localized in the SW crater area, and sporadically from the NE one. On 28 August 2019, a new paroxysmal explosion occurred, followed by strong volcanic activity, culminating with a lava flow emitted from the SW-Central crater area. Subsequently, the eruptive activity decreased, although frequent instability phenomena linked to the growth of new cones on the edge of the crater terrace occurred. This contribution summarizes the measurements obtained through space-borne and ground-based InSAR sensors. The ground-based data allowed to detect pressurization of the summit area, as the instability of the newly emplaced material. The satellite data instead helped to identify the slope dynamics. The integration of the complementary systems strengthens the monitoring of both the eruptive activity and the instability phenomena.
This work is supported by the 2019-2021 Università di Firenze and Italian Civil Protection Department agreement, and by the 2019-2021 IREA-CNR and Italian Civil Protection Department agreement.
How to cite: Nolesini, T., Di Traglia, F., Casu, F., De Luca, C., Manzo, M., Lanari, R., and Casagli, N.: The 2019 Stromboli eruption: the space-borne and ground-based InSAR contribution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7307, https://doi.org/10.5194/egusphere-egu2020-7307, 2020.
The development of the satellite remote sensing technologies is providing a great contribution to monitor volcanic phenomena. Specifically, the large amount of the ground deformation field data (i.e., DInSAR measurements) holds information about the changes of physical and geometrical parameters of deep and shallow volcanic reservoirs; therefore, the exploitation of these data becomes an important task since they actively contribute to the hazard evaluation.
Currently, DinSAR measurements are mostly used for modeling the volcanic deformation sources through the optimization and the inversion procedures; although the latter provide a physical and geometrical model for the considered volcanic site, their results strongly depend on the availability of a priori information and on the considered assumptions about the physical settings; therefore, they do not provide a single solution and they unlikely guarantees a correct analysis for the multi-source cases.
In this scenario, we consider a new methodology based on the use of edge-detection methods for exploiting DInSAR measurements and characterizing the active volcanic sources. Specifically, it allows the estimation of the source geometrical parameters, such as its depth, horizontal position, morphological features and horizontal sizes, by using Multiridge, ScalFun and Total Horizontal Derivative (THD) methods. In particular, it has been proved the validity of Multiridge and ScalFun methods for modeling the point-spherical source independently from its physical features, such as the pressure variation, the physical-elastic parameters of the medium, such as the shear modulus, and low signal-to-noise ratio.
Now, we extend the proposed Multiridge and ScalFun methods from the hydrostatic-pressure point source to the tensile one, and then to the others (rectangular tensile-fault and the prolate spheroid analytical models) in order to investigate volcanic sources as sills, dikes and pipes.
Specifically, after the analysis of the physical and mathematical features of the considered models, we apply Multiridge and ScalFun methods to the synthetic vertical and E-W components of the ground deformation field. We carefully evaluate the advantages and the limitations which could characterize these cases, showing how to solve critical aspects. We especially focus on the sill-like source, for which the edge-detection methods provide very satisfying results. In addition, we perform a joint exploitation of the edge-detection methods to model the deformation source of Fernandina volcano (Galapagos archipelago) by analyzing COSMO-SkyMed acquisitions related to the 2012-2013 time interval.
In conclusion, this approach allows retrieving univocal information about the geometrical configuration of the analyzed deformation pattern. We remark that, although a subsequent analysis is required to fully interpret the ground deformation measurements, this methodology provides a reliable geometrical model, which can be used as a priori information to constrain the entire interpretation procedure during next analyzes.
How to cite: Barone, A., Castaldo, R., Fedi, M., Pepe, S., Solaro, G., and Tizzani, P.: Multiscale edge-detection methods for the geometrical constraint of deformation sources in the volcanic environment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-536, https://doi.org/10.5194/egusphere-egu2020-536, 2020.
We present an efficient tool for managing, visualizing, analysing, and integrating with other data sources, Earth Observation (EO) data for the analysis of surface deformation phenomena. In particular, we focused on specific EO data that are those obtained by an advanced-processing of Synthetic Aperture Radar (SAR) data for monitoring wide areas of the Earth's surface. More specifically, we refer to the SAR technique called advanced differential interferometric synthetic aperture radar (DInSAR) that have demonstrated its capabilities to detect, to map and to analyse the on-going surface displacement phenomena, both spatially and temporally, with centimetre to millimetre accuracy thanks to the generation of deformation maps and time-series. Currently, the DInSAR scenario is characterized by a huge availability of SAR data acquired during the last 25 years, now with a massive and ever-increasing data flow supplied by the C-band Sentinel-1 (S1) constellation of the European Copernicus program.
Considering this big picture, the Spatial Data Infrastructures (SDI) becomes a fundamental tool to implement a framework to handle the informative content of geographic data. Indeed, an SDI represents a collection of technologies, policies, standards, human resources, and related activities permitting the acquisition, processing, distribution, use, maintenance, and preservation of spatial data.
We implemented an SDI, extending the functionalities of GeoNode, which is a web-based platform, providing an open-source framework based on the Open Geospatial Consortium (OGC) standards. OGC makes easier interoperability functionalities, that represent an extremely important aspect because allow the data producers to share geospatial information for all types of cooperative processes, avoiding duplication of efforts and costs. Our implemented GeoNode-Based Platform extends a Geographic Information System (GIS) to a web-accessible resource and adapts the SDI tools to DInSAR-related requirements.
Our efforts have been dedicated to enabling the GeoNode platform to effectively analyze and visualize the spatial/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. We demonstrate the high performance of implemented GeoNode-Based Platform, showing DInSAR results relevant to the acquisitions of the Sentinel-1 constellation, collected during 2015-2018 over Italy.
This work is supported by the 2019-2021 IREA CNR and Italian Civil Protection Department agreement; the H2020 EPOS-SP project (GA 871121); the I-AMICA (PONa3_00363) project; and the IREA-CNR/DGSUNMIG agreement.
How to cite: Fusco, A., Buonanno, S., Zeni, G., Manunta, M., Marsella, M., Carrara, P., and Lanari, R.: An extended GeoNode-Based Platform for Detailed Analysis of the Spatial/Temporal DInSAR Information Contents, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17569, https://doi.org/10.5194/egusphere-egu2020-17569, 2020.
During a volcanic eruption, one of the most relevant threat for civil aviation is the dispersion of volcanic ash in the atmosphere. All the aircraft are susceptible to suffer damages from volcanic ash even at low concentrations. The economy of Canary Islands (Spain) strongly depends on tourism, so it is of fundamental importance to estimate the consequences of a possible eruptive scenario of the air traffic in the archipelago and consequently on the tourism. We made an exhaustive study about the impact of volcanic ashes on aviation for one of the most important islands in the archipelago: Tenerife.
We developed a large set of numerical simulations of small-magnitude eruptions in Tenerife, which are the most probable eruptive scenario in this island. Our main goal is to develop a probabilistic approach to evaluate the airports most affected by dispersion and fallout of volcanic ash. We carried out more than a thousand simulations with the software FALL3D using supercomputing facilities of Teide-HPC from the Instituto Tecnológico y de Energías Renovables (ITER). In order to model the small-magnitude eruptions, we calculated datasets of total mass of volcanic ash erupted and eruption lengths using a bivariate empirical probability density function obtained using Kernel Density Estimation (KDE) from data of historical eruptions in Tenerife. The vent positions were selected following the density of vents related to Holocene eruptions. Granulometries were chosen following Bi-Gaussian distribution of particle size ranging from Φ=-1 to Φ=12, where Φ=-log2d (diameter in mm). The number of eruptive phases within each eruption is selected randomly. We have split equally the total eruptive duration into these eruptive phases and we set a gaussian distribution in the centre of each division. After that, the intersection between each eruptive phase is chosen taking into account these gaussian distributions to have eruptive phases with different duration.
All the simulations are coupled with ERA-Interim meteorological reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). We have implemented a probabilistic procedure to map in 3D the hazard associated to volcanic ash. For this purpose, we calculated concentration percentiles (P25, P50 and P75) and time intervals of high concentrations of volcanic ash to evaluate the hazard of suspended ash in the volume surrounding the major airports in Tenerife.
How to cite: Prieto, A., D'Auria, L., Macedonio, G., Hernández, P. A., and Hernández, W.: How will the next eruption in Tenerife affect aviation?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-251, https://doi.org/10.5194/egusphere-egu2020-251, 2020.
Recently, the SAR Meteorology technique has demostrated the advantages of assimilating Sentinel-1 maps of Precipitable Water Vapor (PWV) in high resolution Numerical Weather Models (NWP) when forecasting extreme weather events . The impact of Sentinel-1 information on NWP forecast depends on the acquisition parameters of Sentinel-1 images and the physical status of atmosphere . Besides meteorological applications, enhancing NWP forecast could have an impact also on the mitigation of atmospheric artifacts in SAR interferometry applications based on NWP simulations .
This work describes a methodology to provide measurements of microwave propagation delay in troposphere. The proposed methodology is based on the processing of Sentinel-1 and GNSS data. In particular, Sentinel-1 images are processed by means of SAR Interferometry technique to get measurements of propagation delay in troposphere over land assuming that phase contribution due terrain displacements can be neglected. To fulfil this condition, the interferometric processing is carried out on Sentinel-1 images having the shortest temporal baseline of six days. Interferometric coherence is used to select portions of the interferogram where to estimates of PWV and the corresponding precision are provided. GNSS measurements of propagation delay in atmosphere are used to validate the Sentinel-1 measurements and derive a quality figure of PWV maps. A procedure is presented to concatenate PWV maps in time in order to derive a time series of spatially dense PWV measurements and the corresponding precisions.
Furthermore, Radio Occultation (RO) profiles are obtained by processing GNSS data. Profiles will be used to derive an estimate of propagation delay in troposphere over sea. In such a way, maps of propagation delay in atmosphere over both land and sea, even though characterized by a different spatial density of measurements, will be provided.
The study area includes the Basilicata, Calabria and Apulia regions and the Gulf of Taranto, southern Italy.
This work was supported by the Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR), Italy, under the project OT4CLIMA.
 P. Mateus, J. Catalão, G. Nico, “Sentinel-1 interferometric SAR mapping of precipitable water vapor over a country-spanning area”, IEEE Transactions on Geoscience and Remote Sensing, 55(5), 2993-2999, 2017.
 P.M.A. Miranda, P. Mateus, G. Nico, J. Catalão, R. Tomé, M. Nogueira, “InSAR meteorology: High‐resolution geodetic data can increase atmospheric predictability”, Geophysical Research Letters, 46(5), 2949-2955, 2019.
 G. Nico, R. Tome, J. Catalao, P.M.A. Miranda, “On the use of the WRF model to mitigate tropospheric phase delay effects in SAR interferograms”, IEEE Transactions on Geoscience and Remote Sensing, 49(12), 4970-4976, 2011.
How to cite: Nico, G., Vespe, F., Masci, O., Mateus, P., Catalão, J., and Rosciano, E.: New meteorological products based on Sentinel-1 and GNSS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19142, https://doi.org/10.5194/egusphere-egu2020-19142, 2020.
Environmental monitoring often requires the observation of phenomena at different spatial and temporal scales. For example, to study the anthropic impact on natural ecosystems, it is necessary both to evaluate its effects on a large scale and to detect and recognize the environmental criticalities that, locally, determine these effects. These needs impose tight requirements on data temporal, spatial and spectral resolution that a single aerospace platform can hardly satisfy. Therefore, it is necessary to develop new collaborative paradigms between different platforms to improve their observation capabilities, exploiting interoperability between heterogeneous platforms and sensors. However, even multi-platform approaches, due to the limitations of the individual platforms currently available in terms of revisit time and sensor spatial resolution, cannot fully comply with the requirements imposed by some specific environmental issues at acceptable costs.
In this paper, HAPS (High Altitude Pseudo-Satellite) use is proposed as a tool to overcome these limitations and to extend the applicability of the multi-platform paradigm.
In environmental monitoring context, one of the main advantages offered by HAPSs consists in the possibility of providing data with higher spatial resolution than satellites, and at lower cost compared to aerial platforms. Moreover, HAPSs offer a larger field of view than UAVs and can provide data types such as fluorescence or hyperspectral ones that, because of sensor weight and cost, rarely could be acquired by UAVs. Finally, HAPS platforms, thanks to their station-keeping capability on a desired area, offer the possibility of having data with a high temporal resolution to monitor the temporal evolution of phenomena at a rate currently not possible with other platforms.
Different HAPS configurations have been proposed, based on aerostatic or aerodynamic forces. CIRA is designing a HAPS that, thanks to its hybrid configuration, is able to generate aerodynamic and aerostatic forces. It could fly at an altitude of 18-20 km, from this altitude range, the field of view has a diameter length of about 600 km. Maintenance and updating of its equipment and payload is also possible because the platform can land and take-off again.
CIRA is also designing the platform payload. The design goal is to define a new wide-area sensor based on visible, thermal, or hyperspectral cameras, with a better resolution than satellites. In this way, it will be possible to detect environmental anomalies in persistence in order to alert the other platforms. A very high focal second-reading sensor will also be used to avoid false-positive alerts.
In this paper, we will present the main characteristics of HAPS platforms and how they, in synergy with other ones, would lead to considerable advantages in environmental monitoring. In particular, we will discuss the multi-platform paradigm, the current platform limits and their influence on the paradigm effectiveness in the context of environmental monitoring, characteristics of the HAPS platform that CIRA is currently conceptually designing in the context of the OT4clima Project and the main issues relative to its payload design.
How to cite: Persechino, G., Baraniello, V., Parrilli, S., Tufano, F., and Rianna, G.: HAPS role of in Earth Observation Multi-platform Paradigm for Environmental Monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21962, https://doi.org/10.5194/egusphere-egu2020-21962, 2020.
In the valley of the Alto Magdalena, Colombia, intensive agriculture and inefficient soil and water management techniques have generated a within field yield spatial variability, which have increased the production costs for the rice-based cropping system (rice, cotton and maize crops rotation field). Crop yield variations depend on the interaction between climate, soil, topography and management, and it is strongly influenced by the spatial and temporal availabilities of water and nutrients in the soil during the crop growth season. Understanding why the yield in certain portions of a field has a high variability is of paramount importance both from an economic and an environmental point of view, as it is through the better management of these areas that we can improve yields or reduce input costs and environmental impact. The aim of this study was 1) to predict rice yield using on farm data set and machine learning and 2) to compare delimited management zones (MZ) for rice-based cropping system with physiological parameters and within field variation yield.
A 72 sampling points spatially distributed were defined in a 5 hectares plot at the research center Nataima, Agrosavia. For each sampling point, physical and chemical properties, biomass and relative chlorophyll content were determined at different vegetative stages. A multispectral camera mounted to an Unmanned Aerial Vehicle (UAV) was used to acquire multispectral images over the rice canopy in order to estimate vegetation indices. Five nonlinear models and two multilinear algorithms were employed to estimate rice yield. The fuzzy cluster analysis algorithm was used to classify soil data into two to six MZ. The appropriate number of MZ was determined according to the results of a fuzziness performance index and normalized classification entropy.
Results of the rice yield prediction model showed that the best performance was obtained by K-Nearest Neighbors (KNN) regression algorithm with an average absolute error of 10.74%. Nonetheless, the performance of the other algorithms was acceptable except the Multiple Linear regression (MLR). The MLR showed the highest RMSE with 2712.26 kg.ha-1 in the testing dataset, while KNN regression was the best with 1029.69 kg.ha-1. These findings show the importance of machine learning could have for supporting decisions in agriculture processes management.
The cluster analyses revealed that two zones was the optimal number of classes based on different criteria. Delineated zones were evaluated and revealed significant differences (p≤0.05) in sand, apparent density, total porosity, pH, organic matter, phosphorus, calcium, magnesium, iron, zinc, cover and boron. The relative chlorophyll content of cotton and maize crops showed a similar spatial distribution pattern to delimited MZ. The results demonstrate the ability of the proposed procedure to delineate a farmer’s field into zones based on spatially varying soil and crop properties that should be considered for irrigation and fertilization management.
How to cite: Ouazaa, S., Barrero, O., Quevedo Amaya, Y. M., Chaali, N., and Montenegro Ramos, O.: Site-specific management zones delineation and Yield prediction for rice based cropping system using on-farm data sets in Tolima (Colombia), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2466, https://doi.org/10.5194/egusphere-egu2020-2466, 2020.
To improve nitrogen fertilization is well known that vegetation indices can offer a picture of the nutritional status of the crop. In this study, field management information (maize sowing and harvesting dates, tillage, fertilization) and estimated vegetation indices VI (Sentinel 2 derived Leaf Area Index LAI, Normalized Difference Vegetation Index NDVI, Fraction of Photosynthetic radiation fPAR) were analysed to develop a batch-mode VIs routine to manage high dimensional temporal and spatial data for Decision Support Systems DSS in precision agriculture, and to optimize the maize N fertilization in the field. The study was carried out in maize (2017-2018) on a farm located in Mantua (northern Italy); the soil is a Vertic Calciustepts with a fine silty texture with moderate content of carbonates. A collection of Sentinel 2 images (with <25% cloud cover) were processed using Graph Processing Tool (GPT). This tool is used through the console to execute Sentinel Application Platform (SNAP) raster data operators in batch-mode. The workflow applied on the Sentinel images consisted in: resampling each band to 10m pixel size, splitting data into subsets according to the farm boundaries using Region of Interest (ROI). Biophysical Operator based on Biophysical Toolbox was used to derive LAI, fPAR for the estimation of maize vegetation indices from emergence until senescence. Yield data were acquired with a volumetric yield sensing in a combine harvester. Fertilization plans were then calculated for each field prior to the side-dressing fertilization. The routine is meant as a user-friendly tool to obtain time series of assimilated VIs of middle and high spatial resolution for field crop fertilization. It also overcomes the failures of the open source graphic user interface of SNAP. For the year 2018, yield data were related to the 34 LAI derived from Sentinel 2a products at 10 m spatial resolution (R2=0.42). This result underlined a trend that can be further studied to define a cluster strategy based on soil properties. As a further step, we will test whether spatial differences in assimilated VIs, integrated with yield data, can guide the nitrogen top-dress fertilization in quantitative way more accurately than a single image or a collection of single images.
How to cite: Schillaci, C., Tomasoni, E., Acutis, M., and Perego, A.: Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15941, https://doi.org/10.5194/egusphere-egu2020-15941, 2020.
With population growth and a rising demand for meat based diets and energy, the global water demand will grow significantly over the next two decades. Agriculture is the largest global consumer of the available water resources, responsible for almost 70% of annual water withdrawals. Therefore, a pivotal step in addressing the alarming water-scarcity problem is improving water use efficiency in agriculture. This is a complex problem that many farmers face yearly: how to distribute available water optimally to maximize seasonal yield, while considering the uncertainty in future water resources (eg. seasonal rainfall).
In our work, we consider the general problem of optimal soil moisture regulation of multiple fields (e.g., a plantation) for a full growth season, where allocating water optimally over the growth season is considered together with daily irrigation scheduling for multiple fields. This adds complexity to the control problem, as operational constraints need to be included (such as a limited number of fields that can be irrigated in a day) and trade-offs need to be made between irrigation and potential yield of the different fields. Furthermore, the growth stages of the fields can be different, as often not all fields can be planted and harvested at the same time.
We propose a methodology to reduce this complex problem into two separate optimisation problems, which are solved using a two-level structure consisting of a scheduler for seasonal allocation and a model predictive controller for daily irrigation. In this approach, the scheduler determines the optimal allocation of water over the fields for the entire growth season to maximize the summation of each field’s crop yield, by considering a linear approximation of the multiplicative crop productivity function. In addition, the model predictive controller minimizes the daily water stress by regulating the soil moisture of the fields within a water-stress-free zone. This requires a model of the interaction between the soil, the atmosphere, and the crop. A simple water balance model is created for which the saturation dynamics are modeled explicitly using conditionally switched depletion dynamics to improve model quality. To further improve the controller's performance, we create an evapotranspiration model by considering the expected development of the crop over the season using remote-sensing-based measurements of the canopy cover. The presented methodology can handle resource and hydraulic infrastructure constraints. Therefore, our approach is generic as it is not restricted to a specific irrigation method, crop, soil type, or local environment. The performance of the two-level approach is evaluated through a closed-loop simulation in AquaCrop-OS of a real sugarcane plantation in Mozambique. Our optimal control approach boosts water productivity by up to 30% compared to local heuristics and can respect water use constraints that arise in times of drought.
How to cite: Kassing, R., de Schutter, B., and Abraham, E.: Optimal seasonal water allocation and model predictive control for precision irrigation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11270, https://doi.org/10.5194/egusphere-egu2020-11270, 2020.
This work describes the practical application on commercial wheat plots of the methodology developed and evaluated in Albacete, Spain, in the framework of the project FATIMA (http://fatima-h2020.eu/). The application considers two different methodologies for the prescription of nitrogen management prior to the flowering season, based on the diagnosis of crop nitrogen status based on nitrogen nutrition index (NNI) maps and the yield forecast spatially distributed. The NNI is the ratio between the actual nitrogen concentration (Na) over the critical nitrogen concentration (Nc) for the crop analysed (Justes et al 1997). The nitrogen uptake was determined from relationship between Nc and biomass, where biomass was estimated by a crop growth model based on the water productivity. The Na was derived from the relationship between the amount of nitrogen in the canopy, estimated from spectral vegetation index based on the Red-edge and the biomass. The knowledge about the NNI allows fertilizing at critical moments throughout the wheat campaign. The NNI maps for the analysed plots, were obtained throughout wheat development to flowering, of eight dates in the study campaign. The yield forecast is calculated through the relationship between biomass and the harvest index. The spatially distributed yield relies in the use of management zone maps (MZM) based on temporal series of remote sensing data. The MZMs were calculated for pre-flowering state to estimate yield, and capture the within-field variability of wheat production. Thus, the classical N balance model is used to calculate the N requirements at pixel scale, varying the target yield according to the MZM. The practical application was made in wheat commercial plots in the study area, analysing the performance of the proposed nitrogen fertilization strategies. The results indicated the possible optimization of the N application, maintaining or increasing the wheat productivity and reaching the higher levels of protein content in the area.
Keywords: Remote sensing, wheat, biomass, nitrogen nutrition index (NNI), fertilization.
How to cite: Plaza, C., Calera, M., Campoy, J., Osann, A., Calera, A., and Bodas, V.: Commercial wheat fertilization based on nitrogen nutrition index and yield forecast, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22608, https://doi.org/10.5194/egusphere-egu2020-22608, 2020.
Successful conservation strategies and adaptive management require frequent observations and assessments of ecosystems. Depending on the conservation target this is commonly achieved by monitoring schemes carried out locally by experts. In general, these expert surveys provide a high level of detail which however is traded-off against the limited spatial coverage and repetition with which they are commonly executed. Thus, it is common practice to spatially expand these observations by remote sensing techniques. For a resilient monitoring both the expert observations and the spatio-temporal upscaling have to be extended by automated measurements and reproducible modelling. Therefore, Nature 4.0 is developing a prototype of a modular environmental monitoring system for spatially and temporally high-resolution observations of species, habitats and key processes. This prototype system is being developed in the Marburg Open Forest, an open research, education and development platform for environmental observation methods. Here, we present the experiences and challenges of the first year with a focus on the conceptual design and the first implementation of the core observation subsystems and their comparison with the data collected by classical field surveys and remote sensing. The spatially distributed acquisition of abiotic and biotic environmental parameters is based on self-developed as well as third party sensor technology. This includes an automated area-wide radiotracking system of bats and birds and sensor units for measurements of microclimatic conditions and tree sap flow as well as spectral imaging and soundscape recording. The backbone of the automated data collection and transmission is an autonomous LoRa and WiFi mesh network, which is connected to the internet via radio relay. By utilizing powerful data integration and analysis methods, the system will enable researchers, conservationists and the public to effectively observe landscapes through a set of diverse lenses. Here, we present first results as well as an outlook for future developments of intelligent networked systems for ecosystem monitoring.
How to cite: Friess, N., Ludwig, M., Reudenbach, C., and Nauss, T. and the Nature 4.0-Team: Nature 4.0 – Intelligent networked systems for ecosystem monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22058, https://doi.org/10.5194/egusphere-egu2020-22058, 2020.
Surface temperature retrieval through thermal infrared imaging (TIR) is currently being applied in a multitude of natural science disciplines (e.g. agriculture and ecology). Enormous progress in sensor design, electronics, and computer science render TIR systems as easily applicable and accessible for research, industry and even private use. However, despite the existence of factory-set theoretical calibration models that are supposed to facilitate accurate conversion of digital pixel values into temperature values, complex environmental noise and handling parameters hamper the reliable and stable collection of temperature data. Here, we present an image-by-image calibration method that can potentially account for such environmental noise.
We used custom-built thermal calibration panels, a close-range thermal camera and a thermocouple to collect thermal images and ground truth temperatures of peanut plants (Arachis hypogaea L.) in an open and sheltered agricultural setting. Linear models were trained and tested to investigate whether an image-by-image calibration approach improves the accuracy of digital value to temperature conversion over calibrating once before an experiment, as well as before and after an experiment. For both, the sheltered and open setting, we collected data on multiple days.
Our data indicate that there are marked differences in calibration model stability and accuracy between the open and sheltered setting. For the open setting the image-by-image calibration resulted in lower mean absolute temperature errors (MAE = 0.9°C) compared to the sheltered setting (MAE = 4.37°C). We also found that the intercept and slope of the image-by-image calibration models varied substantially under open conditions. Between two images, both captured less than two minutes apart, the digital number to temperature conversion (intercept) could vary by up to 15°C. By contrast, the intercepts derived from the sheltered scenario rarely varied by more than 5 °C.
Our results show that an image-by-image calibration can be preferable to obtain reliable and accurate temperature data. Such data can be crucial to monitor and detect abiotic and biotic stress in animal and plant food production systems where differences in temperature can be very subtle. A reduction of stressors in such systems is often coupled with an increase in yield. At the EGU 2020, we would like to share our research, and some extensions of it, to receive constructive feedback to drive future research on how to reliably and accurately collect sensitive surface temperatures in industry and research.
How to cite: Heim, R., Guo, X., Zare, A., and Rowland, D.: Image-by-image calibration of thermal infrared data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5700, https://doi.org/10.5194/egusphere-egu2020-5700, 2020.
Sentinel-2 Multi-Spectral Instrument (MSI) (S-2) images have been used for mapping burned areas within the borders of the Vesuvio National park, Italy, severity affected by fires during summer 2017. A fuzzy algorithm, previously developed for Mediterranean ecosystems and Landsat data, have been adapted and applied to S-2 images. Major improvements with respect to the previous algorithm characteristics are i) the use of S-2 band reflectance in post-fire images and as temporal difference (delta pre- and post-fire) and ii) the definition of fuzzy membership function based on statistics (percentiles) of reflectance as derived from training areas.
The following input bands were selected based on their ability to discriminate burned vs. unburned areas: post-fire NIR (Near Infrared, S-2 band 8), post-fire RE (Red Edge, S-2 bands 6 and 7) and temporal difference (delta post-pre fire) of the same bands and additionally of SWIR2 (ShortWave Infrared, S-2 band 12).
For each input, a sigmoid function has been defined based on percentiles of the unburned and burned histogram distributions, respectively, derived from training data. In this way, and with respect to previous formulation of the algorithm, membership function can be defined in an automated way when ancillary layer are provided for extracting statistics of burned and unburned surfaces.
Input membership degrees for the selected bands have been integrated to derived pixel-based synthetic scores of burned likelihood with Ordered Weighted Averaging (OWA) operators. Different operators were tested to represent different attitudes/needs of the stakeholders between pessimistic (the maximum extent of the phenomenon to minimise the chance of underestimating) and optimistic (minimise the chance of overestimating).
Output score maps provided as continuous values in the [0,1] domain have been segmented to extract burned/unburned areas; the performance of the combined threshold and OWA operator has been evaluated by comparison with Copernicus fire damage layers from the Emergency Management Service (EMS) (https://emergency.copernicus.eu/). Error matrix, F-score and omission and commission error metrics have been analysed.
Finally, the correlation between fuzzy score derived by applying OWA operators has been analysed by comparison with Copernicus EMS fire damage layers as well as fire severity computed as temporal difference of the NBR index. Results show satisfactory accuracy is achieved for the identification of the most severely affected areas while lower performance is observed for those areas identified as slightly damage and probably affected by fires of lower intensity. Moreover, some discrepancies have been observed between different layers of fire severity due to the non-unique definition of the criteria used for assessing the impact of fires on the vegetation layer.
How to cite: Piaser, E., Sona, G., Sali, M., Boschetti, M., Brivio, P. A., Bordogna, G., and Stroppiana, D.: Sentinel 2 data and fuzzy algorithm for mapping burned areas and fire severity in the Vesuvio National Park, Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21611, https://doi.org/10.5194/egusphere-egu2020-21611, 2020.
Different studies evidenced an anticorrelated pattern behavior the activity of Mauna Loa and Kilauea volcanoes. We quantitatively demonstrate the existence of this pattern by using DInSAR SBAS time series, areal strain of horizontal GPS components and the spatial distribution of hypocenters. The DInSAR time series have been studied by using the Independent Component Analysis (ICA) statistical algorithm revealing an anticorrelated ground deformation pattern between sources located at shallow depths beneath Mauna Loa and Kilauea. Furthermore, ICA showed another independent source beneath Kilauea alone, being located at greater depth. A similar pattern was observed in the time series of areal strain of GPS data as well as by spatial distribution of earthquakes depths.
The anticorrelated behaviour of both volcanoes, has been explained by the crustal-level interaction of pulses of magma that cause pressure variations in shallow magma system . Another explanation for this peculiar behaviour is due to the interaction by pore pressure diffusion in a thin accumulation layer of the asthenosphere . Geochemical and petrological studies  however, points at the existence of separate reservoirs for Mauna Loa and Kilauea.
The aim of this work is to explain the mechanism that allows the crustal-level relationship between shallow ground deformation sources of both volcanoes. We applied inverse modelling to determine the geometries of the magmatic reservoirs beneath Mauna Loa and Kilauea and their dynamics. This method revealed to be a useful tool to better understand the dynamics and represent the interaction between Mauna Loa and Kilauea.
Our results indicate that the interaction between ground deformation sources of Mauna Loa and Kilauea occurs at shallower depths, therefore we excluded a direct interconnection between their magmatic systems and, instead, we postulate a stress transfer mechanism that explain this interaction. This mechanism has been postulated by several authors to explain the intrusions along rift zones and the interaction between earthquakes and eruptions in these two volcanoes [3, 4]. The magma ascent in Mauna Loa edifice creates a stress field in Kilauea which makes more difficult for the magma to ascent into its shallower reservoir. The same mechanisms could act in an opposite scenario.
 A. Miklius and P. Cervelli, “Interaction between Kilauea and Mauna Loa,” Nature, vol. 421, no. 6920, pp. 229–229, 2003.
 H. M. Gonnermann, J. H. Foster, M. Poland, C. J. Wolfe, and B. A. Brooks, “Coupling at Mauna Loa and Kilauea by stress transfer in an asthenospheric melt layer,” Nat. Geosci., vol. 5, no. 11, pp. 826–829, 2012.
 P. Amelung, F., Yun, S.H, Walter, T. and Segall, “Stress Control of Deep Rift Intrusion at Mauna Loa Volcano, Hawaii,” Science (80-. )., vol. 316, no. MAY, pp. 1026–1030, 2007.
 D.A. Swanson, W. A. Duffield, and R.S. Fiske, “Displacement of the south flank of Kilauea Volcano: the result of forceful intrusion of magma into the rift zones,” U.S. Geol. Surv. Prof. Pap. 963, p. 39 p.1976.
 J.M. Rhodes and S. R. Hart, “Episodic trace element and isotopic variations in historical mauna loa lavas: Implications for magma and plume dynamics,” Geophys. Monogr. Ser.,vol. 92, pp. 263–288,1995.
How to cite: Przeor, M., D'Auria, L., Pepe, S., and Tizzani, P.: Geodetical and seismological evidences of stress transfer between Mauna Loa and Kilauea , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-259, https://doi.org/10.5194/egusphere-egu2020-259, 2020.
Conservation of agriculture soils is a topic of major concern, namely through the increase of soil organic matter. SoilCare project (https://www.soilcare-project.eu/) aims to enhance the quality of agricultural soils in Europe, through the implementation and testing of Soil Improving Cropping Systems in 16 study sites. In Portugal, the application of urban sewage sludge amendments in agriculture soils has been investigated. However, this application is a sensitive topic, due to the risk of long term accumulation of heavy metals and consequent contamination of the soil. The recent Portuguese legislation (Decret-Law 103/2015) is more restrictive than the precedent one (Decret-Law 276/2009) in terms of maximum concentrations of heavy metals in agricultural soils. The analytical quantification of heavy metals, however, raises some methodological questions associated with soil sample pre-treatment, due to some imprecisions in standard analytical methods. For example, the ISO 11466 regarding the extraction in Aqua Regia provides two pre-treatment options: (i) sieve the soil sample with a 2 mm mesh (but if mass for analyses is <2g, mill and sieve the sample <250µm is required), or (ii) mill and sieve the soil sample through a 150µm mesh. On the other hand, the EN 13650 requests soil samples to be sieved at 500µm. Since heavy metals in the soil are usually associated with finer particles, the mesh size used during the pre-treatment of soil samples may affect their quantification.
This study aims to assess the impact of soil particle size on total heavy metal concentrations in the soil. Soil samples were collected at 0-30cm depth in an agricultural field with sandy loam texture, fertilized with urban sludge amendment for 3 years. These samples were then divided in four subsamples and sieved with 2mm, 500µm, 250µm and 106µm meshes (soil aggregates were broken softly but soil wasn’t milled). Finer and coarser fractions were weighted and analyzed separately. Heavy metals were extracted with Aqua Regia method, using a mass for analyze of 3g, and quantified by atomic absorption spectrophotometer with graphite furnace (Cd) and flame (Cu, Ni, Pb, Zn and Cr).
Except for Cu, heavy metals concentrations increase linearly with the decline of the coarser fraction. This means that analyzing heavy metals content only in the finest fractions of the soil leads to an over estimation of their concentrations in the total soil. Results also show that coarser fractions of soil comprise lower, but not negligible, concentrations of heavy metals. Calculating heavy metal concentrations in the soil based on the weighted average of both fine and coarse fractions and associated concentrations, provide similar results to those driven by the analyses of heavy metals in the <2mm fraction. This indicates that milling and analyzing finer fractions of the soil did not influence the quantification of heavy metals in total soil. Clearer indications on analytical procedures should be provided in analytical standards, in order to properly assess heavy metal concentrations and compare the results with soil quality standards legislated.
How to cite: Boulet, A. K., Veiga, A., Ferreira, C., and Ferreira, A.: Quantification of heavy metals in agricultural soils: the influence of sieving in standard analytical methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1487, https://doi.org/10.5194/egusphere-egu2020-1487, 2020.
Millions of small farmers rely on maize (Zea mays L.) produced in the Loess Plateau of China. However, little has been reported on the effects of plastic mulch and maize cultivar on crop yield in the check dam environment. The objectives of this experiment were to determine the effects of maize cultivar and plastic mulch on photosynthetic characteristics and grain yield when grown in the check dam environment. Three maize cultivars were assessed with and without plastic mulch in 2016 and 2017 in Ansai County, Shaanxi Province, China. Results showed that mulch increased grain yield by 10.5% in 2016 and 11.3% in 2017 across all cultivars. Among all cultivars, ‘Xianyu335’ had the highest grain yield under both mulch and no mulch. Grain yield was significantly correlated with soil water content in the 0-20 cm layer. Soil temperature under mulch decreased with increasing soil depth. Averaged over soil depths, mulch increased soil temperature from 0.2 to 1.9 °C over the entire growing season. Maize cultivar directly determined photosynthetic characteristics. Grain yield was more closely related to photosynthetic rate in July than in August, and was significantly associated with stomatal conductance and transpiration rate. Our findings suggest that photosynthetic characteristics is an important index affecting maize grain yield for small farmers using check dams in the Loess Plateau.
How to cite: Wang, X., Gao, Y., and Xing, Y.: Exploring options for improving maize productivity for small farmers in the Loess Plateau, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4318, https://doi.org/10.5194/egusphere-egu2020-4318, 2020.
The Canary Islands archipelago, due to their recent volcanism, are the only Spanish territory with high enthalpy geothermal resources. However, there is no evidence in the islands of endogenous fluids manifestations with the exception of the Teide fumaroles, in Tenerife. Although some efforts have been made to investigate the geothermal resources from the 1970s to the 1990s and later during the past decade, the final goal has not yet been achieved, which is to locate and define the size, shape and structure of the geothermal resource, and determine their characteristics and capacity to produce energy (Rodríguez et al. 2015). For this reason it is extremely important to use new tools that allow a better understanding of the geothermal resource. In this work we describe a probabilistic evaluation of the geothermal potential of the island of Tenerife using Geographical Information Systems (GIS) through a collection of geological, geophysical and geochemical data.
The Play Fairway Analysis (PFA) was used, as illustrated by Lautze et al. (2017) in a similar study for an environment having similar characteristics: the Hawaiian Archipelago. The PFA approach consists of joining information coming from multidisciplinary datasets within a probabilistic framework. Basically, the probabilities related to the presence of heat (H), fluids (F) and permeability (P) are computed quantitatively from the starting datasets and combined to obtain the probability of presence of geothermal resources and its confidence.
In the present study this probabilistic method have been implemented using GIS geoprocessing tools and raster image analysis using geological (Holocene vents, volcano-tectonic structures), geophysical (seismicity, resistivity data, gravity data) and geochemical (hydrogeochemistry, soil gas emission and geochemistry, etc…).
The main result of this work is a cartographic set that allow showing the areas of Tenerife with the greatest potential for geothermal exploration. Furthermore, using the statistical framework of PFA analysis, we obtained also confidence intervals on the retrieved probability maps.
How to cite: Morales González, F. A., D'Auria, L., Rodríguez, F., Padrón, E., and Pérez, N.: Using GIS tools for the Play Fairway Analysis in geothermal exploration, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-264, https://doi.org/10.5194/egusphere-egu2020-264, 2020.
Tenerife Island (2034 km2) is the largest of Canarian archipelago and is characterized by three main volcano-tectonic axis: the NS, NE and NW dorsals and a central caldera, Las Cañadas, hosting the twin stratovolcanoes Pico Viejo and Teide. Although Teide volcano shows a weak fumarolic system, volcanic gas emissions observed in the summit cone consist mostly of diffuse CO2 degassing. The first continuous automatic geochemical station in Canary Islands was installed at the south-eastern foot of summit cone of Teide volcano in 1999, with the aim of improving the volcanic monitoring system and providing a multidisciplinary approach to the surveillance program of Teide volcano. The 1999-2011 time series show anomalous changes of the diffuse CO2 emission with values ranging between 0 and 62.8 kg m-2d-1, with a mean value of 4.7 kg m-2d-1. The CO2 efflux increases remained after filtering the time series with multiple regression analysis (MRA), were soil temperature, soil water content, wind speed and barometric pressure explained 16.7% of variability.
We analysed the CO2 efflux time series by using the Continuous Wavelet Transform, with the Ricker wavelet, to detect relevant time-frequency patterns in the signal. The wavelet analysis showed, at low frequencies, quasi-periodical oscillations with periods of 3-4 years. Moreover, during the intervals of highest levels of CO2 efflux the analysis evidenced also oscillations with a period of about 6 months.
Our data show in 2002 a marked peak of the filtered CO2 signal. The beginning of this increase is nearly coincident with a similar signal on the data of CO2 emission, coming from periodic surveys performed yearly on the area of Teide summit cone since 1997. We interpret these signals as an “early warning” associated to the 2004 seismo-volcanic unrest in Tenerife. A similar coincidence was observed also for the interval 2006-2009, which was followed by an increase in the local seismicity of Tenerife as well, characterized both by an increasing number of small earthquakes occurring, respectively, mostly along the NW dorsal and in the southern part of the NE dorsal of Tenerife.
Our study reveals that wavelet analysis on the continuous CO2 efflux measurement could help to detect anomalous degassing periods, possibly indicating impending seismo-volcanic unrest episodes and/or eruptions. Finally, it is important to remark that the data presented in this work, constitute one of the longest time series of continuous CO2 efflux measurements in an active volcanic area, hence providing an important benchmark for similar measurements worldwide.
How to cite: Padilla, G. D., D'Auria, L., Peréz, N. M., Hernández, P. A., Padrón, E., Barrancos, J., Melián, G., and Asensio-Ramos, M.: Wavelet analysis of geochemical time series: continuous CO2 flux measurements at the summit cone of Teide volcano, Tenerife, Canary Islands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4764, https://doi.org/10.5194/egusphere-egu2020-4764, 2020.
Financial markets specialists often use multiscale analysis on different kind of time series. Many tools have been developed for these tasks. Two of them, widely used, are: candlestick charts and technical indicators. Our approach consists in using both tools to analyze geophysical and geochemical time series.
In this work we represent signals using candlesticks at user selected time scales. In our case we use four summary quantities of the signal: the amplitude of the first sample, the maximum amplitude within the candle, the minimum amplitude and the amplitude of the last sample used in the candle. We show how the graphical candlestick representation alone is able to emphasize representative changes within the time-series in a multiscale fashion.
On the other hand, many technical indicators have been defined to extract further information from such type of charts. Among the most commonly used technical indicators are: Simple Moving Average (SMA), Exponential Moving Average (EMA) and Moving Average Convergence/Divergence (MACD). EMA is a temporal smoothing with an exponential weighting determined by a time scale factor. MACD is the difference between EMA realized at a short scale with another EMA at a larger scale. For instance, a commonly used MACD in financial markets is computed using scales of 12 and 26 days. In the case of actual geophysical and geochemical datasets, such scales should be selected on the basis of the time scales of interest.
Using tests realized on synthetic datasets we demonstrate that MACD is a proxy for the derivative of time-series, event with a very high noise level. This is of course of great interest when analyzing geophysical and geochemical time series, with the aim of detecting changes in their trends. We applied candlestick analysis to various seismological and geochemical datasets, in particular we show an example application to recent 2011-2012 eruption of the island of El Hierro in the Canary Islands, highlighting the capability of this method to detect changes in the trend of time-series earlier and better that other simpler techniques.
How to cite: Salehiozoumchelouei, R., Rajaeitabrizi, Y., Sánchez de la Rosa, J. L., D'Auria, L., and Pérez, N. M.: Moving Average Convergence/Divergence analysis of geophysical and geochemical time series: application to the 2011-2012 El Hierro eruption, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4899, https://doi.org/10.5194/egusphere-egu2020-4899, 2020.
The 2011 eruption of Nabro volcano, situated at the southeast end of the Danakil Alps in Eritrea, has been the first historical on record and one of the largest eruptions of the last decade. Due to the remote location of the Nabro volcano and the lack of data from ground monitoring networks at the time of the eruption, satellite remote sensing gives the first global view of the event, providing insights on its evolution over time. Here we used numerical modeling and high spatial resolution satellite data (i.e. EO-ALI, ASTER, PlanetScope) to track the path and velocity of lava flows and to reconstruct the pre- and post-eruptive topographies in order to quantify the total bulk volume emitted. High temporal resolution images (i.e. SEVIRI and MODIS) were exploited to estimate the time-averaged discharge rate (TADR) and assess the dense rock equivalent (DRE) lava volumes constrained by the topographic approach. Finally, satellite-derived parameters were used as input and validation tags for the numerical modelling of lava flow scenarios, offering further insights into the eruption and emplacement dynamics. We found that the total volume of deposits, calculated from differences of digital elevation models (DEMs), is about 580 × 106 m3, of which about 336 × 106 m3 is the volume of the main lava flow that advanced eastward beyond the caldera. Multi-spectral satellite observations indicate that the main lava flow had reached its maximum extent (∼16 km) within about 4 days of the eruption onset on midnight 12 June. Lava flow simulations driven by satellite-derived parameters allow building an understanding of the advance rate and maximum extent of the main lava flow showing that it is likely to have reached 10.5 km in one day with a maximum speed of ~0.44 km/h.
How to cite: Del Negro, C., Ganci, G., Cappello, A., Bilotta, G., and Corradino, C.: Analyzing the 2011 eruption of Nabro volcano using satellite remote sensing and numerical modeling of lava flows, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5750, https://doi.org/10.5194/egusphere-egu2020-5750, 2020.
Lava flows represent the greatest threat to exposed population and infrastructure on Mt Etna volcano (Italy). The increasing exposure of a larger population, which has almost tripled in the area around Mt Etna during the last 150 years, has resulted from poor assessment of the volcanic hazard, allowing inappropriate land use in vulnerable areas. We present a new methodology to quantify the lava flow risk on Etna’s flanks using a GIS-based approach that integrates the hazard with the exposure of elements at stake. The hazard, showing the long-term probability related to lava flow inundation, is obtained by combining three different kinds of information: the spatiotemporal probability of the future opening of new flank eruptive vents, the event probability associated with classes of expected eruptions, and the overlapping of lava flow paths simulated by the MAGFLOW model. Data including all exposed elements have been gathered from institutional web portals and high-resolution satellite imagery, and organized in four thematic layers: population, buildings, service networks, and land use. The total exposure is given by a weighted linear combination of the four thematic layers, where weights are calculated using the Analytic Hierarchy Process (AHP). The resulting risk map shows the likely damage caused by a lava flow eruption, allowing rapid visualization of the areas subject to the greatest losses if a flank eruption were to occur on Etna.
How to cite: Cappello, A., Bilotta, G., Corradino, C., Ganci, G., Hérault, A., Zago, V., and Del Negro, C.: Lava flow risk assessment on Mount Etna through hazard and exposure modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4408, https://doi.org/10.5194/egusphere-egu2020-4408, 2020.
Chat time: Friday, 8 May 2020, 10:45–12:30
El Hierro is, together with La Palma, the youngest island of the Canarian Archipelago. Both islands are in the shield stage of their volcanic growth, which implies a high volcanic activity during the Holocene period. The submarine eruption occurred in October 2011 in the SSE rift of El Hierro evidenced the active volcanic character of the island. Even so, despite the numerous scientific works published following the submarine eruption (most of them centered to understand such volcanic event), there is still a lack of precise knowledge about the Holocene subaerial volcanism of this island. The LAJIAL Project focuses on solving this knowledge gap.
The Holocene subaerial volcanism of El Hierro generates fields of monogenetic volcanoes linked to the three systems of rifts present on the island. Its eruptive mechanisms are typically Strombolian although there are also phreato-Strombolian events. The most recent eruptions frequently form lava on coastal platforms, which are considered after the last glacial maximum (approx. 20 ka BP). The most developed coastal platforms in El Hierro are at the ends of the rifts and in the interior of the El Golfo depression. This geomorphological criterion shows that more than thirty subaerial eruptions have taken place in El Hierro since approx. 20 ka BP. In addition, there are many apparently recent volcanic edifices far from the coast.
The research of the most recent volcanism of the island, the last 11,700 years of the Holocene, covers a long enough period whereas it is close to the present day. Thus, this period is the best to model the eruptive processes that will allow us to evaluate the future scenarios of the eruptive dynamics in El Hierro. The Project LAJIAL combines methodologies of geological mapping, geomorphology, GIS, chronostratigraphy, paleomagnetism, petrology and geochemistry to solve the Holocene eruptive recurrence rate in El Hierro, and to constrain the rift model of intraplate ocean volcanic islands.
Financial support was provided by the Project LAJIAL (ref. PGC2018-101027-B-I00, MCIU/AEI/FEDER, EU). This study was carried out in the framework of the Research Consolidated Groups GEOVOL (Canary Islands Government, ULPGC) and GEOPAM (Generalitat de Catalunya, 2017 SGR 1494).
How to cite: Rodríguez-González, A., Aulinas, M., Perez-Torrado, F. J., Criado Hernández, C., Cabrera, M. C., and Fernandez-Turiel, J.-L.: The Holocene volcanism of El Hierro, Canary Islands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7667, https://doi.org/10.5194/egusphere-egu2020-7667, 2020.
Shoreline change analysis has been deployed across a range of spatio-temporal scales. Accordingly, shoreline change studies have sought to capture shoreline dynamics at a variety of scales, ranging from the local impacts of individual storms to global trends measured over multiple decades. The scale at which we can approach the issue of shoreline change is, to a large extent, determined by the availability of data over time and space. With existing threats from the interactions between accelerated sea level rise, changing storminess and human intervention, shoreline change analysis has never been more relevant or challenging. Historic, centennial-scale shoreline change analysis relies on historic maps where there is normally just a single proxy indicator for consistent shoreline position; the mean water level of ordinary tides on UK Ordnance Survey maps, for example. Occasionally where there are specific coastal landforms that can be mapped, there might be a second proxy such as cliff top position. Shoreline change rates can be determined by extracting these proxies from sequential map surveys, provided the survey dates (ie: not the map publication date) are known.
Shoreline change quantification for more recent decadal-scale periods has been greatly enhanced by increased data availability. This is exemplified by analyses that use widespread coverage available from aerial photographs (past 3 decades). Even more recently on near-annual scales Light Detection and Ranging (LiDAR) data are becoming the norm for capturing storm impacts and shoreline change, enabling volumetric assessments of change in addition to the more traditional linear approaches. LiDAR is enhanced by ground survey Real Time Kinematic (RTK) Instrumentation that can be timed to coincide with storms. As the frequency of dataset capture has increased so has the spatial scale of coverage. Hence the latest shoreline change assessments are global in scale and use Landsat images to focus on hotspots of shoreline change (advance as well as retreat) over the past 30 years. Considering all scales together raises three central questions for shoreline change analysis and these are addressed in this paper.
Firstly, what methodological approach is most suitable for delimiting shorelines and generating the underpinning digitised shorelines for shoreline change assessment?
Secondly, what lessons can be learnt from using an approach that combines both proxy-based (visually discernible signatures) and datum-based (related to a particular water level) shorelines that change differentially with respect to different process-drivers?
Thirdly, given the current state-of-the-art around data availability, what is the most appropriate scale to approach shoreline change assessments?
How to cite: Brooks, S., Pollard, J., and Spencer, T.: Contemporary challenges for Shoreline Change Analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8153, https://doi.org/10.5194/egusphere-egu2020-8153, 2020.
Temperature evaluation of PDCs has been recently performed using optical analysis of charred wood (Reflectance analysis - Ro%) embedded within the pyroclastic deposits.
The validity of this proxy for the emplacement temperature assessment, has been established in different case studies (Fogo Volcano, Laacher See volcano, Merapi Volcano, Colima Volcano, Doña Juana Volcano, Ercolano-Vesuvius Volcano), resulting comparable with the already well know paleomagnetic analysis (pTRM).
Due to its not retrograde nature, the process of carbonification records over time the maximum temperatures experienced by the wood fragment/tree trunk/furniture. This peculiarity has great importance in terms of timing of charring events, as the charred wood can record the possible temperature fluctuations in case of multiple pulse events. This allows us to reconstruct the thermal and dynamic of PDCs history at different steps.
Reflectance analysis (Ro%) results display samples with homogeneous charring temperature (same Ro% values) from rim to core and others with different charring temperatures throughout the sample. Ro% of the latter usually infer higher temperature on the edge of the fragment/tree trunk than in the inner part. This bimodal reflectance distribution can be attributable to multiple temperature exposure, occurred during diachronous events of flow and deposition. Therefore, within the same fragment/tree trunk we can extrapolate PDCs temperature information related not only to equilibrium (emplacement) condition but, more importantly, to dynamic (flow) regime.
This study constitutes a pioneering attempt for the indirect estimation of the temperature of the PDCs not only for volcanic hazard estimation, but also in the archaeological field. In fact, the numerous remains of charred wooden artefacts found in the archaeological sites of Pompeii, Herculaneum and in the Meurin quarry (Eiffel-Germany), allowed the reconstruction of temperature variation based on the vent distance and the presence of buildings which may have interacted with the depositional processes of pyroclastic flows. This study opens a promising new frontier to evaluate the maximum temperature of the PDCs, based on the degree of carbonization of the organic matter incorporated during volcanic events. Estimating the temperature of the dynamic temperature of the PDC has important implications in terms of volcanic risk assessment.
How to cite: Pensa, A., Corrado, S., and Giordano, G.: Assessment of maximum flow and emplacement temperatures reached by PDCs using charred wood fragments , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8632, https://doi.org/10.5194/egusphere-egu2020-8632, 2020.
Mitigating hazards when lava flows threaten infrastructure is one of the most challenging fields of volcanology, and has an immediate and practical impact on society. Lava flow hazard is determined by the probability of inundation, and essentially controlled by the topography of the area of interest. The most common actions of intervention for lava flow hazard mitigation are therefore the construction of artificial barriers and ditches that can control the flow direction and advancement speed. Estimating the effect a barrier or ditch can have on lava flow paths is non-trivial, but numerical modelling can provide a powerful tool by simulating the eruptive scenario and thus assess the effectiveness of the mitigation action. We present a numerical method for the design of optimal artificial barriers, in terms of location and geometric features, aimed at minimizing the impact of lava flows based on the spatial distribution of exposed elements. First, an exposure analysis collects information about elements at risk from different datasets: population per municipality, distribution of buildings, infrastructure, routes, gas and electricity networks, and land use; numerical simulations are used to compute the probability for these elements to be inundated by lava flows from a number of possible eruptive scenarios (hazard assessment) and computing the associated economic loss and potential destruction of key facilities (risk assessment). We then generate several intervention scenarios, defined by the location, orientation and geometry (width, length, thickness and even shape) of multiple barriers, and compute the corresponding variation in economic loss. Optimality of the barrier placement is thus considered as a minimization problem for the economic loss, controlled by the barrier placement and constrained by the associated costs. We demonstrate the operation of this system by using a retrospective analysis of some recent effusive eruptions at Mount Etna, Sicily.
How to cite: Bilotta, G., Cappello, A., Centorrino, V., Corradino, C., Ganci, G., and Del Negro, C.: Optimizing barrier placement for lava flow hazard and risk mitigation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8735, https://doi.org/10.5194/egusphere-egu2020-8735, 2020.
The Etna flank eruption started on 24 December 2018 lasted a few days and involved the opening of an eruptive fissure, accompanied by a seismic swarm and shallow earthquakes, and by large and widespread ground deformation especially on the eastern flank of the volcano. Lava fountains and ash plume from the uppermost eruptive fissure have accompanied the opening stage causing disruption of Catania international airport, and have been followed by a quiet lava effusion within the barren Valle del Bove depression until 27 December. This is the first flank eruption occurring at Etna in the last decade, during which eruptive activity was confined to the summit craters and resulted in lava fountains and lava flow output from the crater rims. In this paper we use ground and satellite remote sensing techniques to describe the sequence of events, quantify the erupted volumes of lava, gas and tephra, and assess volcanic hazard.
How to cite: Calvari, S., Bilotta, G., Bonaccorso, A., Caltabiano, T., Cappello, A., Corradino, C., Del Negro, C., Ganci, G., Neri, M., Pecora, E., Salerno, G. G., and Spampinato, L.: The VEI 2 Christmas 2018 Etna Eruption: A small but intense eruptive event or the starting phase of a larger one?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9120, https://doi.org/10.5194/egusphere-egu2020-9120, 2020.
Lettuce (Lactuca sativa L.) is a popular leafy vegetable, widely grown and consumed throughout the world. Growing Lettuce plants in controlled environment, it is useful to increase the yield and obtain production year-round. In CEA (Controlled Environment Agriculture), computer technology is an integral part in the production and different sensors used to monitor environmental parameters and activate environmental control, are necessary. With the advent of technology, proximal sensors and plant phenotyping (in terms of physiological measurements of plant status) can help farmers in crop management. However, these kinds of tools are often expensive or inaccessible for stakeholders. The application of these tools to small-scale cultivation trials, could provide data for the implementation of mathematical models capable of predicting changes possibly happening during the cultivation. These models could then be applied at larger scales, as extensive farm production and be used to help in the cultivation management.
In this study, green and red cultivars of Lactuca sativa L. ‘Salanova’ were grown in a growth chamber under controlled environmental condition (T, RH, light intensity and quality) in two trials under different vapour pressure deficit (VPD) : 1) VPD of 0.70 kPa (Low VPD; nominal condition) and 2) VPD of 1.76 (High VPD; off nominal condition). Plants were irrigated to field-capacity and weighted every-day in order to record daily ET; infra-red measurements were carried out to record leaf temperature and pictures were taken to monitor growth during the cultivation. Furthermore, after 23 days, on fully developed leaves, eco-physiological analyses (gas exchange and chlorophyll “a” measurements) were performed to assess the plant physiological behaviour in response to the different environmental conditions. Environmental data, were used as inputs in an energy cascade model (MEC) to predict changes in the plant daily growth, photosynthesis and evapotranspiration. The original model, was implemented with a few variations: leaf temperature (T) was used in place of air T for computing the stomatal conductance (gs) and the model parameters maxCUE and maxQY, were differentiated for the nominal and off-nominal scenarios and for green and red lettuce cultivars. After the validation against experimental data, this model appears to be a promising tool that can be implemented for forecasting variations triggered by anomalies in the environmental control. However, a next step will be to add a few parameters that will consider the intrinsic morpho-physiological variability of plants during leaf development.
How to cite: Amitrano, C., Chirico, G. B., Rouphael, Y., De Pascale, S., and De Micco, V.: Using explanatory crop models to help decision support system in controlled environment agriculture (CEA), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9672, https://doi.org/10.5194/egusphere-egu2020-9672, 2020.
La Palma Island (708.32 km2) is located at the north-western end of the Canary Archipelago and is one of the youngest of the archipelago. In the last 123 ka, volcanic activity has taken place exclusively at Cumbre Vieja, the most active basaltic volcano in the Canaries, which is located at the southern part of the island. Since no visible geothermal manifestations occur at the surface environment of this volcano, during the last 20 years there has been considerable interest in the study of diffuse degassing as a powerful tool in the volcano monitoring program. In this study we have used two different geochemical approaches for volcano monitoring from October 2017 to November 2019. First, we have developed a network of 21 closed static chambers to determine soil CO2 effluxes. Additionally, we have monitored physical-chemical parameters (temperature, pH, electrical conductivity -EC-) and chemical/isotopic composition and dissolved gases in the water of two galleries (Peña Horeb and Trasvase Oeste) and one water well (Las Salinas). Soil CO2 effluxes for the alkaline traps showed an average value of 7.4 g·m-2·d-1 for the entire Cumbre Vieja volcano. The gas sampled on the head space of the traps can be considered as CO2-enriched air, showing an average value of 1,942 ppmV of CO2. Regarding the CO2 isotopic composition (δ13C-CO2), most of the stations exhibited CO2 composed by different mixing degrees between atmospheric and biogenic CO2 with slight contributions of deep-seated CO2, with an average value of -19.3‰. The results of the physical-chemical parameters measured in waters showed mean temperature values of 23.7ºC, 19.6ºC and 22.1ºC, 7.40, 6.27 and 6.60 for the pH and 1,710 µS·cm-1, 411 µS·cm-1 and 41,100 µS·cm-1 for the EC, for Peña Horeb, Trasvase Oeste and Las Salinas, respectively. The δ13C-CO2 composition of the dissolved gas has a mean value of -7.8‰, -10.2‰ and -3.8‰ vs. VPDB for Peña Horeb, Trasvase Oeste and Salinas, respectively. The highest values of CO2 efflux coincided with the stations showing highest CO2 concentration values located at the southern end of Cumbre Vieja, where the most recent volcanic eruption took place, and also on the northwest flank. This is in accordance with the results obtained for Las Salinas well, located in the south of the island, which show a high concentration of dissolved CO2 and δ13C-CO2 values with a strong deep-seated CO2 contribution. This study represents an interesting contribution to detect early warning signals of future unrest episodes at Cumbre Vieja.
How to cite: Amonte, C., Mulliss, A., Sampson, E., Martín-Lorenzo, A., Rodríguez-Pérez, C., Di Nardo, D., Melián, G. V., Santana-dLeón, J. M., Hernández, P. A., and Pérez, N. M.: Groundwater and soil CO2 efflux weekly monitoring network for the surveillance of Cumbre Vieja volcano, Canary Islands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11456, https://doi.org/10.5194/egusphere-egu2020-11456, 2020.
Differential Synthetic Aperture Radar Interferometry (DInSAR) is one of the key methods to investigate, with centimeters to millimeters accuracy, the Earth surface displacements, as those occurred during natural and man-made hazards.
Nowadays, with the increasing of SAR data availability provided by Sentinel-1 (S1) constellation of Copernicus European Program, the radar Earth Observation (EO) scenario is moving from the historical analysis to operational functionalities. Indeed, the S1 mission, by using the Terrain Observation by Progressive Scans (TOPS) technique, has been designed with the specific aim of natural hazards monitoring via SAR Interferometry guaranteeing a very large coverage of the illuminated scene (250km of swath). These characteristics sum up with the free & open access data policy, the global scale acquisition plan and the high system reliability thus providing a set of peculiarities that make S1 a game changer in the context of operational EO scenario.
By taking benefit of the S1 characteristics, an unsupervised and cloud-based tool for the automatic generation of co-seismic ground displacement maps has been recently proposed. The tool is triggered by the significant (i.e. bigger than a defined magnitude) seismic events reported in the online catalogues of the United States Geological Survey (USGS) and the National Institute of Geophysics and Volcanology of Italy (INGV). The system permits to generate not only the co-seismic displacement maps but also the pre- and post- seismic ones, up to 30 days after the monitored event.
Although it was conceived to generate displacement maps relevant to the upcoming earthquakes, as an operational service for the Civil Protection departments, the implemented tool has also been applied to the study of historical events imaged by the S1 data. This allowed us to generate a global data-base of DInSAR-based co-seismic displacement maps.
Accordingly, the implementation of such data-base will be presented, with particular emphasis on the exploited computing infrastructure solutions (namely the AWS Cloud Computing environment), the used algorithmic strategies and the achieved interferometric results.
Moreover, the whole data-base of DInSAR products will be made available through the European Plate Observing System (EPOS) Research Infrastructure, thus making them freely and openly accessible to the European and international solid Earth community.
The implemented global data-base will be helpful for investigating the dynamics of surface deformation in the seismic zones around the Earth. Indeed, it will contribute to the study of global tectonic earthquake activity through the integration of DInSAR information with other geophysical parameters.
This work has been partially supported by the 2019-2021 IREA-CNR and Italian Civil Protection Department agreement, the EPOS-IP and EPOS-SP projects of the European Union Horizon 2020 R&I program (grant agreement 676564 and 871121) and the I-AMICA (PONa3_00363) project.
How to cite: Monterroso, F., Bonano, M., De Luca, C., De Novellis, V., Lanari, R., Manunta, M., Manzo, M., Onorato, G., Valerio, E., Zinno, I., and Casu, F.: Global data-base of co-seismic interferograms generated via unsupervised Sentinel-1 DInSAR processing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11929, https://doi.org/10.5194/egusphere-egu2020-11929, 2020.
The reliable monitoring and location of the seismic activity at a local and regional scale is a key factor for hazard assessment. The exploitation of a geothermal field can be affected by natural and induced seismicity, hence optimal planning of a seismic network is of great interest for geothermal development.
Seismic monitoring depends on two main aspects: i) sensitivity of the seismic network and ii) the effectiveness of detection and location methods.
In this study, we focus on the first aspect proposing and improvement of an algorithm for the optimization of seismic networks designed for monitoring the seismic activity related to the injection test that will be performed in a geothermal well.
The algorithm is based on the method proposed by Tramelli et al. (2013) that tries to find the optimal station positions minimizing the volume of the error ellipsoid of the location for synthetic events using the D-criterion (Rabinowitz and Steinberg, 2000).
In this version of the program we improve the algorithm to find an optimal seismic network considering several prior information such as: 1) maps of seismic noise levels at different frequency bands, 2) three-dimensional seismic models and 3) topographic gradient of the study region. This information is usually produced during the exploration stage of a geothermal site and available prior an injection test.
We applied the methodology to the Acoculco geothermal field (Mexico) where an injection test is planned. In this work, we show a comparison between the standard approach that uses 1D seismic models, constant values of noise levels, and no topographic effects, with the new one showing how important is to consider these parameters for a more suitable optimization of the seismic network.
This work is performed in the framework of the Mexican European consortium GeMex (Cooperation in Geothermal energy research Europe-Mexico, PT5.2 N: 267084 funded by CONACyT-SENER : S0019, 2015-04, and of the joint agreement between UNAM and INGV on the development of seismological research of volcanic and geothermal field (N:44753-1023-22-IV-16/1).
How to cite: Esquivel Mendiola, L. I., Calò, M., and Tramelli, A.: Design of an optimal seismic network for monitoring geothermal fields, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12259, https://doi.org/10.5194/egusphere-egu2020-12259, 2020.
In recent years, Coastal video monitoring methods have been widely accepted tools for continuous monitoring of complex coastal processes. In this paper, the progress made on a new python based coastal video monitoring system, PI-COSMOS (Portuguese Indian COaStal MOnitoring System) which is being developed and tested jointly in India and Portuguese coasts is presented. PI-COSMOS system aims at providing open source, high speed video monitoring toolboxes for the coastal community that can be used anywhere in the world. PI-COSMOS is camera independent system and comprises four modules viz. PI-Calib for camera calibration, RectiPI for video imagery rectification, PI-ImageStacks for image product and pixel product generation and PI- DB for efficient database management. The applicability of PICOSMOS system under different coastal environment conditions has been tested using the data collected from the India as well as the Portugal coast. The results from one of the Indian stations installed at Kozhikode beach, Kerala, India situated at 11°15'14.12" N, 75° 46'15.40" E are presented here to demonstrate the capabilities of the newly developed PI-COSMOS system. the performance of PI-COSMOS is evaluated by conducting a comparative study among PICOSMOS and existing video monitoring toolboxes like UAV processing toolbox provided by Coastal Research Imaging Network and RectifyExtreme provided by the University of Lisbon and it is found that the processing speed of PI-COSMOS is very much high i.e. more than 5 times when compared to UAV processing toolbox and RectifyExtreme. The high speed performance, camera independent nature and easiness in the operation made PI-COSMOS as the simplest and advanced open source video monitoring system.
How to cite: Madipally, R., Nair L, S., and Taborda, R.: PI-COSMOS: An Open Source Python based High Speed Coastal Video Monitoring System , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12879, https://doi.org/10.5194/egusphere-egu2020-12879, 2020.
We explore the use of graph theory to assess short-term hazard of lava flow inundation, with Mt Etna as a case study. In the preparation stage, we convert into a graph the long-term hazard map produced using about 30,000 possible eruptive scenarios calculated by simulating lava flow paths with the physics-based MAGFLOW model. Cells in the original DEM-based representation are merged into graph vertices if reached by the same scenarios, and for each pair of vertices, a directed edge is defined, with an associated lava conductance (probability of lava flowing from one vertex to the other) computed from the number of scenarios that reach both the start and end vertex. In the application stage, the graph representation can be used to extract short-term lava flow hazard maps in case of unrest. When a potential vent opening area is identified e.g. from monitoring data, the corresponding vertices in the graph are activated, and the information about lava inundation probability is iteratively propagated to neighboring vertices through the edges, weighted according to the associated lava conductance. This allows quick identification of potentially inundated areas with little computational time. A comparison with the deterministic approach of subsetting and recomputing the weights in the long-term hazard map is also presented to illustrate benefits and downsides of the graph-based approach.
How to cite: Centorrino, V., Bilotta, G., Cappello, A., Ganci, G., Corradino, C., and Del Negro, C.: Fast short-term lava flow hazard assessment with graph theory, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13218, https://doi.org/10.5194/egusphere-egu2020-13218, 2020.
The OT4CLIMA project, funded by the Italian Ministry of Education, University and Research, within the PON 2014-2020 Industrial Research program, “Aerospace” thematic domain, aims at developing advanced Earth Observation (EO) technologies and methodologies for improving our capability to better understand the effects of Climate Change (CC) and our capability to mitigate them at the regional and sub-regional scale. Both medium-to-long term impacts (e.g. vegetation stress, drought) and extreme events with rapid dynamics (e.g. intense meteorological phenomena, fires) will be investigated, trying a twofold (i.e. interesting both “products” and “processes”) technological innovation: a) through the design and the implementation of advanced sensors to be mounted on multiplatform EO systems; b) through the development of advanced methodologies for EO data analysis, interpretation, integration and fusion.
Activities will focus on two of the major natural processes strictly related to Climate Change, namely the Carbon and Water Cycles by using an inter-disciplinary approach.
As an example, the project will make it possible the measurements, with an unprecedented accuracy of atmospheric (e.g. OCS, carbon-sulphide) and surface (e.g. soil moisture) parameters that are crucial in determining the vegetation contribution to the CO2 balance, suggesting at the same time solutions based on the analysis and integration of satellite, airborne and unmanned data, in order to significantly improve the capability of local communities to face the short- and long-term CC-related effects.
OT4CLIMA benefits from a strong scientific expertise (14 CNR institutes, ASI, INGV, CIRA, 3 Universities), considerable research infrastructures and a wide industrial partnership (including both big national players, i.e. E-Geos and IDS companies and well-established italian SMEs consortia, i.e. CREATEC, CORISTA and SIIT, and a spin-off company, Survey Lab) specifically focused on the technological innovation frontier.
This contribution would summarize the project main objectives and show some activities so far carried out.
How to cite: Pergola, N., Serio, C., Ripullone, F., Marchese, F., Naviglio, G., Tizzani, P., and Donvito, A. and the OT4CLIMA Team: A public-private collaboration initiative for innovative Earth Observation (EO) technologies and methodologies for investigating climate change impacts by means of an inter-disciplinary approach: the OT4CLIMA project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13672, https://doi.org/10.5194/egusphere-egu2020-13672, 2020.
We present a probabilistic quantification of multiple volcanic hazards in an assessment of risk to visitors and assets in Egmont National Park, New Zealand. The probability of impact to proposed park infrastructure from volcanic activity (originating from Mt. Taranaki) is quantified using a combination of statistical and numerical techniques. While single (volcanic) hazard assessments typically follow a methodology where the hazard source (e.g. pyroclastic flow, ashfall, debris avalanche) is the focus and defines an area of impact, our multi-volcanic hazard assessment uses a location-centred methodology where critical locations are used to define the range of hazard sources that affect risk over park asset lifetimes. Key to this process is creating fast (i.e. linear/functional) mappings between hazard source parameters such as volume and impact parameters such as depth. These mappings can then be combined with stochastic models to find the probability of input parameters and the probability of eruptions generating these input parameters. For some hazards, such as ash fall, statistical models are available to map intensity to probability. However, mass flow hazards required the use of Gaussian process emulation to develop a computationally cheap surrogate to numerical simulations that can be efficiently sampled for probabilistic hazard assessment. This was a suitable alternative when statistical models for the hazard are unavailable. Our study demonstrates the use of these techniques to integrate stochastic and deterministic models for probabilistic volcano multi-hazard assessment.
How to cite: Procter, J., Mead, S., and Bebbington, M.: Quantifying asset and visitor risk at Mt. Taranaki, New Zealand from multiple volcanic hazards, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14473, https://doi.org/10.5194/egusphere-egu2020-14473, 2020.
This paper attempts to provide a contribute to electric field monitoring on soil by Self-potential (SP) survey utilizing matrix determinant and eigenvalues. SP is connected to the electrical conductivity of soil that is an indirect measurement and correlates very well with several physical and chemical properties. The purpose of this method is to map the electrical potential to reveal one or several polarization mechanisms at play in the ground. In some cases, the self-potential signals are monitored with an electrodes network which provides the possibility to discriminate between various sources.
Our study provides synthetic and experiment cases that carried out a semi-quantitative method to estimate the variation of the electric field vector in the soil starting from the measurement of the SP. The experimental case is referred to a site located in the Campania Region in southern Italy. SP measurements can be performed by array dipoles oriented N-S, E-W and vertical direction. In this way, we can define the contribution of the electric field in both time and spatial domain. Now, if we denote with V(t) the potential difference between two electrodes, for each dipole, we will have 3 values Vx(tn), Vy(tn) and Vz(tn) relative to the dipoles in the three directions. Assuming that the electric currents associated with the potentials are continuous or in any case at very low frequency, we can with good approximation assume that the resulting electric field associated with such currents is conservative. Remembering that in the case of a conservative field the electric field vector, we can be expressed as a gradient of the scalar potential V. The SP data were obtained using an Arduino acquisition system with internal voltmeter impedance of 10 MOhm and resolution of 0.1 mV. In order to provide reliable SP measurements, the impedance of the voltmeter needs to be substantially higher than the impedance of the soil between the electrodes because of the small bias current used to measure the voltage. The electric potentials were measured between each electrode and a reference electrode was connected by ground. The electrode consisted of 8 electrodes spaced 1m and arranged in a cross array which form 6 dipoles. The cross array was orientated in N-S and E-W direction.
Because the SP depend on the electrical conductivity of the soil and therefore on the sources and the medium, any variation of the chemical-physical soil variation implies a variation of the SP. We calculated the determinant and the eigenvalues of the matrix whose columns consist of the components of the measured electric field and therefore, by such parameters it was possible to observe the variations of the electric field in the time domain.
How to cite: Di Fiore, V., Bianco, F., Cavuoto, G., Milano, G., Pelosi, N., Punzo, M., Tarallo, D., Tizzani, P., Iavarone, M., Scotto di Vettimo, P., and Di Gregorio, C.: Self-potential (SP) soil monitoring tool by numbers and vectors characteristic, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17991, https://doi.org/10.5194/egusphere-egu2020-17991, 2020.
The deployment of multi-station and multi-parameter networks is considered fundamental in view of the investigation of Earth’s internal processes from which volcanic and seismic activity originate. The different changes often observed before the occurrence of strong earthquakes or eruptions (anomalies in sub-soil gas emission, hydrothermal discharge, chemical composition of groundwaters, Earth’s electromagnetic field) highlight the key role of fluids in the generation of these natural phenomena. Since they transfer from the underground to the surface messages about how the natural systems work, geochemistry can actively interact in a multidisciplinary context for investigating natural processes. While observational seismology has witnessed tremendous advances in the last twenty years, thanks to the development of very dense networks of stations measuring ground displacement, deformation and acceleration, the system of geochemical observations did not follow the same growth. The creation, ten years ago, of the Italian Radon mOnitoring Network (IRON) was motivated by the need for a permanent and dense network of stations aimed to make radon time series analysis a complement to traditional seismological tools. In fact, its radioactive nature makes radon a powerful tracer for fluid movements in the crust. The further step was the integration of IRON into a nationwide multi-parameter monitoring network, consisting so far of 10 homogenous sites including velocimeters, accelerometers, GPS sensors, and instruments measuring the Earth’s electromagnetic field. The potential of IRON as a tool to study the relationship between radon variability and the preparation process of earthquakes is discussed by means of two practical applications: to the 2016 Amatrice-Visso-Norcia seismic sequence and to the shorter sequence following the Ml 4.4 earthquake of 7 November 2019 in the Frusinate region.
How to cite: Soldati, G. and the IRON working team: IRON: a permanent dense nationwide radon network to approach the challenge of monitoring seismic regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18497, https://doi.org/10.5194/egusphere-egu2020-18497, 2020.
In this study we show an 2D Electrical Resistivity Tomography (ERT) survey acquired in Agnano site pre (Dec 5th, 2019) and post (Dec 12th, 2019) earthquake events occurred in Pisciarelli-Solfatara areas. This earthquake swarm consisted of sequence of 34 earthquakes with Magnitude (Md) -1.1≤Md≤2.8 at depths between 0.9 and 2.3 km. In particular, the earthquake of Dec 06th, 2019 at 00:17 UTC with Md = 2.8 (depth 2 km) was the maximum recorded event since bradyseismic crisis began in 2005.
The ERT survey allow us to identify the main structural boundaries (and their associated fluid circulations) defining the shallow architecture of the Agnano volcano. The hydrothermal system is identified by very low values of the electrical resistivity (<20 Ω m). Its downwards extension is clearly limited by the lava and pyroclastic fragments, which are relatively resistive (>100 Ω m). The resistivity values are increased after the main shock. This increase in resistivity may have been caused by a change in the state of stress and a decrease in pore pressure (subsequent depressurization). Previously to the earthquake, an increase in pressurized fluids has been observed which have reduced the resistivity values. The present observation suggests that the temporal variation of the resistivity values is related to the variation of the pore fluid pressure in the source area of the swarm, facilitated by earthquake and the subsequent fluid diffusion. The combination of these qualitative results with structural analysis leads to a synthetic model of magmatic and hydrothermal fluids circulation inside the Agnano area, which may be useful for the assessment of potential hazards associated with a renewal of fluid pressurization, and a possibly associated partial flank-failure.
How to cite: tarallo, D., Cavuoto, G., Di Fiore, V., Pelosi, N., Punzo, M., Milano, M., Contiero, M., Iavarone, M., and Iorio, M.: Electrical resistivity variation connected to volcanic earthquake in the Campi Flegrei, Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18692, https://doi.org/10.5194/egusphere-egu2020-18692, 2020.
Understanding causal effect relationships between the different variables in dynamical systems is an important and challenging problem in different areas of research such as attribution of climate change, brain neural connectivity analysis, psychology, among many others. These relationships are guided by the process generating them. Hence, detecting changes or new patterns in the causal effect relationships can be used not only for the detection but also for the diagnosis and attribution of changes in the underlying process.
Time series of environmental time series most often contain multiple periodical components, e.g. daily and seasonal cycles, induced by the meteorological forcing variables. This can significantly mask the underlying endogenous causality structure when using time-domain analysis and therefore results in several spurious links. Filtering these periodic components as preprocessing step might degrade causal inference. This motivates the use of time-frequency processing techniques such as Wavelet or short-time Fourier transform where the causality structure can be examined at each frequency component and on multiple time scales.
In this study, we use a parametric time-frequency representation of vector autoregressive Granger causality for causal inference. We first show that causal inference using time-frequency domain analysis outperforms time-domain analysis when dealing with time series that contain periodic components, trends, or noise. The proposed approach allows for the estimation of the causal effect interaction between each pair of variables in the system on multiple time scales and hence for excluding links that result from periodic components.
Second, we investigate whether anomalous events can be identified based on the observed changes in causal relationships. We consider two representative examples in environmental systems: land-atmosphere ecosystem and marine climate. Through these two examples, we show that an anomalous event can indeed be identified as the event where the causal intensities differ according to a distance measure from the average causal intensities. Two different methods are used for testing the statistical significance of the causal-effect intensity at each frequency component.
Once the anomalous event is detected, the driver of the event can be identified based on the analysis of changes in the obtained causal effect relationships during the time duration of the event and consequently provide an explanation of the detected anomalous event. Current research efforts are directed towards the extension of this work by using nonlinear state-space models, both statistical and deep learning-based ones.
How to cite: Shadaydeh, M., Guanche García, Y., Mahecha, M., and Denzler, J.: Understanding changes in environmental time series with time-frequency causality analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18727, https://doi.org/10.5194/egusphere-egu2020-18727, 2020.
An intense volcano-tectonic crisis affected a part of Etna volcano from 24 to 27 December 2018; such event was analyzed and monitored by using the DInSAR technique, taking advantage from Sentinel-1 constellation and COSMO-SkyMed measurements to retrieve the observed deformation pattern.
In particular, we used Sentinel-1 datasets acquired by ascending and descending orbits by with a 6 days revisit time and with the Interferometric Wide Swath (IWS) acquisition mode, referred to as Terrain Observation with Progressive Scans (TOPS). This volcano-tectonic crisis generated an intrusion dyke with an intense eruptive activity at summit craters accompanied by explosions and lava flows, modifying also part of the volcanic edifice and on 26 December 2018 an earthquake with M=4.9 localized on the lower part of the southeastern flank. We generated long-term deformation mean deformation velocity maps and the corresponding time series relevant to pre-event (April 2015-24 December 2018) and post-event (28 December 2018-March 2020). We exploited for the crisis two ascending and one descending orbits interferograms acquired from Sentinel-1 undergo to a multilook operation (5 and 20 pixels along the azimuth direction and range, respectively) to finally lead to a ground pixel size of about 70 by 70 meters. Furthermore, we combined ascending and descending orbits to obtain the East-West and Vertical components of the volcano edifice displacements.
The main results show that the deformation on Etna summit craters and on eastern flank of edifice astride the eruptive event, causing a vertical deformation of about 50 cm and a jump of about 40 cm on horizontal component. These evidences are confirmed by East-West interferogram, whose maximum values exceed 30 cm towards the West and 40 towards the East on the summit of the volcano. Instead In the area in correspondence of the 26 December main shock, a maximum eastward and westward displacement of 12-14 cm and 15-17 cm is observed, respectively. In general, after December 27th the velocity map vertical and horizontal show a progressive attenuation of movements over time. For the eastern flank, horizontal displacements (eastwards) until to 10 cm were achieved in the months following the seismic-volcanic events of December 2018. In the region south-west of the Fiandaca structure, affected by the 4.9 MW earthquake, there is an almost stationary trend of the movement in the post-event period with a small movement of 1.5 cm towards the west in the last month. Finally, even the deformation of the area around the Elachea island currently shows a positive stationary trend (towards the east). On the western side, the trend of post-event displacement showed increases compared to the period preceding the event, although with generally smaller entities than on the eastern side. he progressive Tattenuation of the extent of the movement towards the west with time reaching about 7 cm in the last year is highlighted.
This analysis allowed to Italian Civil Protection to follow the evolution in the last two years of volcano - tectonic crises and the scientific community to take relevant decisions about the level of emergency for the local population.
How to cite: Pepe, S., Bonano, M., Castaldo, R., Casu, F., De luca, C., De novellis, V., Lanari, R., Manunta, M., Manzo, M., Solaro, G., Tizzani, P., Valerio, E., Zeni, G., and Zinno, I.: Monitoring system at etna volcano during seismo-volcanic crisis of december 2018 based on multiorbits SBAS-DInSAR analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18739, https://doi.org/10.5194/egusphere-egu2020-18739, 2020.
Data-driven approaches applied to to large and complex data sets are intriguing, however the results must be revised with a critical attitude. For example, a diagnostic tool may provide hints for a serious disease, or for anomalous conditions potentially indicating an impending natural risk. The demand of a high score of identified anomalies – true positives - comes together with the request of a low percentage of false positives. Indeed, a high rate of false positives can ruin the diagnostics. Receiver Operation Curves (ROC) allows us to find a reasonable compromise between the need of accuracy of the diagnostics and robustness with respect to false alerts.
In multiclass problems success is commonly measured as the score for which calculated and target classification of patterns matches at best. A high score does not automatically mean that a method is truly effective. Its value becomes questionable, when a random guess leads to a high score as well. The so called “Kappa Statistics” is an elegant way to assess the quality of a classification scheme. We present some case studies demonstrating how such a-posteriori analysis helps corroborate the results.
Sometimes an approach does not lead to the desired success. In thes cases, a sound a-posteriori analysis of the reasons for the failure often provide interesting insights into the problem, Those problems may reside in an inappropriate definition of the targets, inadequate features, etc. Often the problems can be fixed just by adjusting some choices. Finally, a change of strategy may be necessary in order to achieve a more satisfying result. In the applications presented here, we highlight the pitfalls arising in particular from ill-defined targets and unsuitable feature selections.
The validation of unsupervised learning is still a matter of debate. Some formal criteria (e. g. Davies Bouldin Index, Silhouette Index or other) are available for centroid-based clustering where a unique metric valid for all clusters can be defined. Difficulties arise when metrics are defined individually for each single cluster (for instance, Gaussian Model clusters, adaptive criteria) as well as using schemes where centroids are essentially meaningless. This is the case in density based clustering. In all these cases, users are better off when asking themselves whether a clustering is meaningful for the problem in physical terms. In our presentation we discuss the problem of choosing a suitable number of clusters in cases in which formal criteria are not applicable. We demonstrate how the identification of groups of patterns helps the identification of elements which have a clear physical meaning, even when strict rules for assessing the clustering are not available.
How to cite: Langer, H., Falsaperla, S., and Hammer, C.: A-posteriori Analyses of Pattern Recognition Results, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19751, https://doi.org/10.5194/egusphere-egu2020-19751, 2020.
In order to assess the building portfolio composition for a particular natural hazard risk assessment application, it is necessary to classify the built environment into schemas containing building classes. The building classes should also address the attributes which may control their vulnerability towards the different hazards associated with their failure mechanisms, which along with their respective fragility functions are representative of a particular study area. In the case of volcanic risk, former efforts have been carried out in developing volcanic related fragility functions, this has been done mostly for European, Atlantic islands and South Asian building types (SEDIMER, MIA VITA, VOLDIES, EXPLORIS, SAFELAND projects). However, in other parts of the globe, particular construction practices, materials, and even occupancies may describe very diverse building types with different degrees of vulnerability which may or not be compatible with the existing schemas and fragility functions (Spence et al. 2005, Zuccaro et al. 2013, Mavrouli et al. 2013, Jenkins et al. 2014, Torres-Corredor et al. 2017).
As highlighted by Zuccaro et al. 2018, since in the case of volcanic active areas, the built environment will not only be exposed to a single hazard but to several compound or cascading hazards (e.g. tephra fall, pyroclastic flows, lahars), with different time intervals between them, a dynamic vulnerability with cumulated damage on the physical assets would be the baseline upon a multi-risk- volcanic framework should be described. In this similar context, single- hazard but still multi-state fragility functions have been very recently used in order to set up damage descriptions independently on the reference building schema. We propose to generalize this novel approach and further extend it in the volcanic risk assessment context. To do so, the very first step was to generate a multi-hazard- building- taxonomy containing a set of exhaustive mutually exclusive building attributes. Upon that framework, a probabilistic mapping across single- hazards- building- schemas and damage states has been achieved.
This methodological approach has been tested under the RIESGOS project over a selected study area of the Latin American Andes Region. In this region, cities close to active volcanos have been experienced a non-structured grow, which is translated into a significantly vulnerable population living in non- engineering buildings that are highly exposed to volcanic hazards. The Cotopaxi region in Ecuador has been chosen in order to explore the ash falls and lahars damage contributions with several scenarios in terms of volcanic explosivity index (VEI). Local lahars simulations have been obtained at different resolutions. Moreover, probabilistic ash- fall maps have been recently obtained after exhaustive ash fall and wind direction measurements. Lahar flow- velocity and ash- fall load pressure were respectively used as intensity measures. Furthermore, local and foreign building schemas that define the building exposure models have been constrained through ancillary data, cadastral information, and remote individual building inspections, to then been associated with a multi-state fragility function. These ingredients have been integrated into this novel methodological scenario-based- multi-risk- volcanic assessment.
How to cite: Langbein, M., Gomez- Zapata, J. C., Frimberger, T., Brinckmann, N., Torres- Corredor, R., Andrade, D., Zapata- Tapia, C., Pittore, M., and Schoepfer, E.: Scenario- based multi- risk assessment on exposed buildings to volcanic cascading hazards, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19861, https://doi.org/10.5194/egusphere-egu2020-19861, 2020.
Seismic oceanography (SO) has been widely used on the inversion of physical oceanographic properties due to its higher lateral resolution up to 10m, compared to conventional oceanographic measurement methods. Normally, the inversion process requires seismic data and in-situ hydrographic data, and the latter is acquired by deploying XBTs/XCTDs. Recently, due to the advantage of providing quantifiable uncertainties of the inverted parameters, a Markov chain Monte Carlo (MCMC) algorithm has been used for the temperature and salinity inversion from SO data. Based on the MCMC inversion method, this study investigates the effect of the lateral density of XBT deployments on the resultant uncertainties of inverted temperature and salinity. We analysed the seismic data acquired in the Gulf of Cadiz (SW Iberia) in 2007 in the framework of the Geophysical Oceanography project. A nonlinear Temperature-Salinity relation is modelled using a Genetic Algorithm from CTD casts collected in the research area. Combining the temperature data from XBTs with the T-S relation, smoothed temperature and salinity prior distributions are derived. Then the posterior distributions of temperature and salinity are estimated using the prior information and the field reflectivity data. In this study, priors are changed by controlling the amount of XBTs used, after which the corresponding uncertainties of the inverted temperature and salinity are calculated. The result quantifies the impact of the prior models with different XBT deployment densities on the uncertainties of inverted results. It is proposed that the acquisition of a reasonable temperature starting model is the prior consideration when deciding the XBT deployment strategy along the seismic oceanography survey.
How to cite: Xiao, W., Sheen, K., Tang, Q., Hobbs, R., Shutler, J., and Browse, J.: Uncertainty quantification during seismic oceanography inversion with start Temperature-Salinity models of different lateral resolutions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20931, https://doi.org/10.5194/egusphere-egu2020-20931, 2020.