The interactions between geo-environmental and anthropic processes are increasing due to the ever-growing population and its related side effects (e.g., urban sprawl, land degradation, natural resource and energy consumption, etc.). Natural hazards, land degradation and environmental pollution are three of the possible “interactions” between geosphere and anthroposphere. In this context, spatial and spatiotemporal data are of crucial importance for the identification, analysis and modelling of the processes of interest in Earth and Soil Sciences. The information content of such geo-environmental data requires advanced mathematical, statistical and geomorphometric methodologies in order to be fully exploited.

The session aims to explore the challenges and potentialities of quantitative spatial data analysis and modelling in the context of Earth and Soil Sciences, with a special focus on geo-environmental challenges. Studies implementing intuitive and applied mathematical/numerical approaches and highlighting their key potentialities and limitations are particularly sought after. A special attention is paid to spatial uncertainty evaluation and its possible reduction, and to alternative techniques of representation of spatial data (e.g., visualization, sonification, haptic devices, etc.).

In the session, two main topics will be covered (although the session is not limited to them!):
1) Analysis of sparse (fragmentary) spatial data for mapping purposes with evaluation of spatial uncertainty: geostatistics, machine learning, statistical learning, etc.
2) Analysis and representation of exhaustive spatial data at different scales and resolutions: geomorphometry, image analysis, machine learning, pattern recognition, etc.

Co-organized by ESSI2/GM2/SSS10
Convener: Caterina GozziECSECS | Co-conveners: Marco Cavalli, Sebastiano Trevisani
| Attendance Wed, 06 May, 10:45–12:30 (CEST)

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Chat time: Wednesday, 6 May 2020, 10:45–12:30

Chairperson: Caterina Gozzi, Marco Cavalli and Sebastiano Trevisani
D735 |
Sebastiano Trevisani, Dev Kumar Maharian, Denis Sandron, Surya Narayan Shrestha, Sarmila Paudyal, Franco Pettenati, and Massimo Giorgi

In this study a set of 39 single station passive seismic surveys conducted in the Kathmandu basin (Nepal), based on the horizontal to vertical spectral ratio methodology (HVSR), is analyzed by means of a geostatistical approach. The Kathmandu basin is characterized by a heterogeneous sedimentary cover and by a complex geostructural setting, inducing high spatial variability of the bedrock depth. In relation to the complex geological setting, the interpretation and analysis of HVSR data are challenging, both from the perspective of bedrock depth analysis as well as of seismic site effects detection. In order to maximize the broad range of information available, the HVSR data are analyzed according to a geostatistical approach. First, the spatial continuity structure of HVSR data is analyzed and interpreted taking into consideration the geological setting and available stratigraphic and seismic information. In addition, we test the possibility to integrate the analysis with potential auxiliary variables, derived from geomorphometric variables and considering the distance from outcropping bedrock. The explorative geostatistical analysis confirms the complexity of the geo-structural setting of the area. Finally, a mapping of HVSR resonance periods, with the evaluation of interpolation uncertainty, is obtained by means of ordinary kriging interpolation. The resulting map, even if characterized by a large interpolation support, is congruent with the geo-structural setting and the main lineaments of the area. The adopted approach is particularly useful in the context of micro-zonation studies based on HVSR methodology conducted in historical urban areas. Moreover, this work contributes to the geo-structural knowledge of the deep structure of the Kathmandu basin.


Paudyal YR, Yatabe R, Bhandary NP, Dahal RK, 2013. Basement topography of the Kathmandu Basin using microtremor observation. J Asian Earth Sci 62:627–637, doi.org/10.1016/j.jseaes.2012.11.011.

Nakamura Y., 1989. A method for dynamic characteristic estimation of subsurface using microtremors on the ground surface. Quart. Rep. Railway Tech. Res. Inst. 30, 25-33

Sandron D., S. .Maskey , M. Giorgi, D. V. Maharjan, S. N. Narayan, C. Cravos, F. Pettenati, 2019. Environmental and on buildings noise measures: Laliptur (Kathmandu). Earthquake Engineering. Vol. 60, n. 1, 17-38: March 2019, doi 10.4430/bgta0259.

Trevisani S., Boaga J., Agostini L., Galgaro A., 2017. Insights into bedrock surface morphology using low-cost passive seismic surveys and integrated geostatistical analysis. Science of the Total Environment, 578, 186-202, http://dx.doi.org/ 10.1016/j.scitotenv.2016.11.041.

How to cite: Trevisani, S., Maharian, D. K., Sandron, D., Shrestha, S. N., Paudyal, S., Pettenati, F., and Giorgi, M.: Geostructural complexity and passive seismic surveys: a geostatistical analysis in the Kathmandu basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7117, https://doi.org/10.5194/egusphere-egu2020-7117, 2020.

D736 |
Reka Pogacsas and Gaspar Albert

The Dorog Basin is a morphologically unique region of the Transdanubian Mountains revealing the combined work of tectonic forces and erosion. Overprinted by the forms of fluvial erosion, numerous NW-SE striking half-graben and horst structures are present. The surface is dominantly covered by lose 1–15 m thick Quaternary sediments (aeolian loess, and siliciclastic alluvial and coluvial formations), while the lithified bedrock consists of Mesozoic carbonates, Paleogene limestones, marls and sandstones and limnic coal sequences. The rheological difference of the Quaternary and pre-Quaternary formations is so pronounced that the morphological characteristics of the outcrops also differ significantly. The area was in the focus of geologists for many decades, due to its Eocene coal beds, and a renewal of the geological map of the region is in progress. The current research aims to assist the mapping with multivariate methods based on geomorphological attributes, such as slope angle, aspect, profile curvature, height, and topographic wetness index. We perform a random forest classification (RFC) using these variables, to predict the outcrops of pre-Quaternary formations in the study area.

Random forest is a powerful tool for multivariate classification that uses several decision trees, each one with a prediction, where the most popular one will be the overall result [1]. The reason why it is getting popular in spatial predictions is the high accuracy to classify raster-type objects [2]. We used raster-type spatial data as subject of RFC predicting a result for each pixel. The geology of the study area was known from previous geological mapping [3]. Morphological information was derived from the MERIT DEM.

Our model used a raster with multiple bands containing geomorphological variables, and training data from the digitalized geological map. The number of random samples of data was 2500. After testing several combinations of the bands, and several spacing of the study areas, the best prediction has cca. 80% accuracy. Model validation is based on the calculation of rates of well predicted pixels in the same rasterized geological map that was used for training. Our aim was to use exact data, which is completely true for remotely sensed images, but not for geological maps. That means the accuracy still can be improved by field perception, or from borehole data.



[1] Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.

[2] Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31.

[3] Gidai, L., Nagy, G., & Siposs, Z. (1981). Geological map of the Dorog Basin 1: 25 000. [in Hungarian] Geological Institute of Hungary, Budapest.

How to cite: Pogacsas, R. and Albert, G.: Predicting the outcrop of pre-Quaternary formations in the Dorog Basin (Hungary) using random forest classification, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7255, https://doi.org/10.5194/egusphere-egu2020-7255, 2020.

D737 |
Leonardo Disperati, Enrico D'Addario, Michele Pio Papasidero, Marco Pignatiello, Lorenzo Marzini, Michele Amaddii, and Nazariy Broda

Apart high elevation and arid regions, bedrock is generally covered by unconsolidated materials that result from recent or actual bedrock weathering and fracturing, consequent transport along the hillslope mainly by un-channelized flux or gravity-dominated processes and deposition. These slope deposits (SD) are largely affected by shallow landslides triggered during intense rainfall events, hence mapping SD spatial distribution and properties is a challenging task to perform accurate regionalized analysis of landslide hazard.

Nevertheless, geological and geomorphological maps typically represent the spatial distribution of SD following a 0/1 approach, instead of attempting to describe, in a more realistic perspective, the spatial variation of SD depth, even though this latter is a fundamental input parameter for landslide hazard estimation by physically based models.

In this work we present two different approaches to assess SD depth at regional scale, coupling field SD depth measurements, statistical analysis and then topographic-based regionalization: unsupervised clustering and multilinear regression analysis. Geo-environmental variables such as geology, land use and morphometric parameters, have been considered. The unsupervised clustering analysis has been based on some morphometric variables (eg. flow accumulation, slope and hillslope curvatures), derived from a digital elevation model with cell size of 10 m. These variables allowed us to extract, for homogeneous regions obtained by considering bedrock lithology, a set of morphometric units where the distribution of SD depth was assessed. The same variables were processed in the multilinear regression analysis in order to obtain equations estimating the spatial distribution of SD depth. The results of this work were then compared each other, as well as to the outputs obtained by implementing other methods of SD depth estimation known in the literature. Finally, the proposed methods were applied to evaluate SD depth in a test area, where field survey measurements were used as a check to assess the prediction capability. The results are analyzed and discussed in order to identify best solutions to evaluate and represent SD depth at regional scale.

How to cite: Disperati, L., D'Addario, E., Papasidero, M. P., Pignatiello, M., Marzini, L., Amaddii, M., and Broda, N.: Assessment of slope deposits depth at regional scale by means of morphometric clustering and multi-linear regression: a comparison, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18101, https://doi.org/10.5194/egusphere-egu2020-18101, 2020.

D738 |
Annamária Laborczi, Csaba Bozán, Gábor Szatmári, János Körösparti, and László Pásztor

Inland excess water (IEW), considered to be a typical Carpathian Basin land degradation problem, is an interrelated natural and human induced phenomenon, which causes several problems in the flat-land regions of Hungary covering nearly half of the country. The term ‘inland excess water’ refers to the occurrence of inundations outside the flood levee that originate from sources differing from flood overflow, it is surplus surface water forming due to the lack of runoff, insufficient absorption capability of soil or the upwelling of groundwater. There is a multiplicity of definitions, which indicate the complexity of processes that govern this phenomenon. Most of the definitions have a common part, namely, that inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources.
Identification of areas with high risk requires spatial modelling, that is mapping of the specific natural hazard. Various external environmental factors determine the behaviour of the occurrence, frequency of IEW. Spatial auxiliary information representing IEW forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency values. Two hybrid spatial prediction approaches, which combine machine learning and geostatistics, were tested to construct reliable maps, namely regression kriging (RK) and Random Forest with Ordinary Kriging (RFK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. Both methods divides the spatial inference into two parts. 
In Regression Kriging the target variable is modelled at first by multiple linear regression (MLR) of the environmental co-variables. Then ordinary kriging is applied on the difference between the reference and the modelled values (residuals). The prediction result map comes from the sum of the MLR model and the interpolated residuals. Random Forest combined with Kriging is a relatively new method applied in digital environmental mapping. In RFK, the deterministic component of spatial variation is modelled by random forest (RF).  RF algorithm builds lots of regression trees and the final model relies on averaging the result of the trees, which are grown independently from each other. In RFK the stochastic part of variation is modelled by kriging using the derived residuals. The final map is the sum of the two component predictions.
Comparing the results of the two approaches, we did not find significant differences in their accuracy in our pilot. However, both methods are appropriate for predicting inland excess water hazard, RFK is suitable for revealing non-linear and more complex relations than RK. Therefore, we suggest the usage of RFK in further predictions and investigations.

Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

How to cite: Laborczi, A., Bozán, C., Szatmári, G., Körösparti, J., and Pásztor, L.: Spatial assessment of inland excess water hazard using combined machine learning and geostatistical methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9063, https://doi.org/10.5194/egusphere-egu2020-9063, 2020.

D739 |
| Highlight
Elisie Jonsson, Navid Ghajarnia, Gustaf Hugelius, and Zahra Kalantari

The Arctic is warming twice as fast as the rest of the globe, causing changes to Arctic ecosystems. While wetlands in the Arctic provide many ecosystem services with both local and global importance, still more knowledge is needed on the location and state of Arctic wetlands to successfully focus adaptation and mitigation efforts. To understand the links between temperature changes and changes to Arctic wetlands, this study includes the use of spatial tools to map existing wetlands and model permafrost response to temperature changes, highlighting wetland areas with risks of future changes. Using available high-resolution wetland databases together with soil wetness and soil type data, a wetland map covering the Arctic was created. Based on existing relationships between climate and observed permafrost, future changes in permafrost were modeled using projected mean annual temperature from the HadGEM2-ES climate model outputs for the RCP2.6, 4.5 and 8.5 scenarios and for years 2050, 2075 and 2100. We found that the Arctic contains a large number of wetlands and a very significant number of these exist on permafrost. As substantial permafrost thaw is projected, the extent and properties of wetlands will shift, and local/regional increases or decreases in wetland extent will depend on variables such as soil type. These changes could lead to serious local consequences, such as threats to food and water security, changes in distribution and demographics of animal and plant species, and losses and disruptions of infrastructure. The findings of this study highlight vulnerable areas that need extra attention in terms of adaptation and mitigation efforts to limit the likely impacts of projected changes, given the current trends.

Keywords: Arctic wetland, spatial modeling, permafrost, climate change

How to cite: Jonsson, E., Ghajarnia, N., Hugelius, G., and Kalantari, Z.: Mapping of Arctic Wetlands with Threats of Future Permafrost Thaw, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18208, https://doi.org/10.5194/egusphere-egu2020-18208, 2020.

D740 |
Vasilică-Dănuț Horodnic, Vasile Efros, Dumitru Mihăilă, Luminița-Mirela Lăzărescu, and Petruț-Ionel Bistricean

Landscape fragmentation is the expression of patchiness and spatial heterogeneity of land cover pattern. After the breakdown of the socialism regime in 1989, Romania has undergone significant changes at the level of political, institutional and socio-economic profile, which determined researchers to consider this country an experimental territory for land use and landscape research.

The aim of present study is to detect hotspots of changes of forests landscape fragmentation patterns in the Romanian Carpathian Mountains over the last 28 years. In order to meet our demand we applied a holistic approach to assess the multiple teleconnections between forest cover changes and the degree of fragmentation at regional scale for two distinct periods that make up the 1990-2018 period: (1) 1990-2006 (land restitution period or transition period to the market economy) and (2) 2006-2018 (post-accession period to the European Union).

The analysis were carried out using freely available time series CORINE Land Cover data of 1990, 2006 and 2018 provided by Copernicus Land Monitoring Services. The initial spatial datasets were processed with the help of Geographic Information Systems (GIS), while GUIDOS, a free software toolbox dedicated to quantitative analysis of digital landscape images, was used to generate spatial and statistics data of the degree of forest landscape fragmentation.

Our findings indicate that the first period of analysis was more dynamic regarding forest cover changes with a gross area gain of 316 304 ha (7.59%) and a gross area loss of 147 496 ha (3.54%) leading to a net forest area change of 168 808 ha (4.05%) which reflects the level of forest recovery. The change pattern of fragmentation classes showed that 332 045 ha (71.47%) of fragmentation decrease is found for the transition of dominant forest in 1990 into the less fragmented class interior in 2006, while 67 418 ha (65.10%) of all fragmentation increase is found for transition from interior in 1990 to dominant in 2006. The other side, for the period from 2006 to 2018 we found a gross area gain of 127 146 ha (2.93%) and a gross area loss of 212 933 ha (4.91%) leading to a net forest area change of -85 787 ha (-1.98%) which emphasizes the level of forest disturbance. In the same time frame, the high values of fragmentation pattern have been registered for the same classes, 56.82% for fragmentation decrease and 70.60% for fragmentation increase, respectively. The results highlight the reversible impact of land use change on land cover pattern, spatially shaped through afforestation in the first period of analysis and through deforestation in the second period. The afforestation process were determined by high rate of external migration, while deforestation process is a consequence of land restitution laws (Law no. 247/2005), which caused considerable mutations in the ownership of land.

The study emphasizes the impacts of land use policies and land management practices on the pattern of forest landscape and the usefulness of Guidos Toolbox, a universal digital image object analysis, to detect hotspots of changes at regional scale.

How to cite: Horodnic, V.-D., Efros, V., Mihăilă, D., Lăzărescu, L.-M., and Bistricean, P.-I.: Detecting hotspots of changes in spatial pattern of forest fragmentation in the Romanian Carpathian Mountains, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-474, https://doi.org/10.5194/egusphere-egu2020-474, 2020.

D741 |
Luise Wraase, Victoria Reuber, Philipp Kurth, Nina Farwig, Georg Miehe, Lars Opgenoorth, Dana Schabo, and Thomas Nauss

Ecosystem engineers continuously shape and re-shape the spatial and temporal structure of the environment. Burrowing animals are an important group of ecosystem engineers, because of their ability to rework sediments and soils with consequences for e.g. soil formation and vegetation patterns. Simultaneous, burrowing animals depend on climate, local soil characteristics and vegetation. The endemic Giant Molerat (GMR) is a burrowing animal and important ecosystem engineer in the Bale Mountains. As part of the Bale Mountain Exile Hypothesis Project, the aim of this study is to investigate (1) the interlinkages between GMR, climate and vegetation patterns as well as (2) to upscale the influence of GMR on the vegetation pattern across the plateau with Sentinel satellite data. Field data comprise 47 paired plots of 5m x 5m with and without GMR activity. Additionally, 1.500 independent GMR burrow openings have been mapped. For investigating interlinkages, all parameters are first pre-analysed for correlations and their dependencies (1). In the following these results, the remote sensing data and the individual variables are implemented into the prediction model. To increase the accuracy, an error correction of the model is pursued. For this, the area is calculated into likelihoods of areas influenced by GMR, based on the vegetation survey pairs serving as training areas for the correction. The corrected results are used as final input model in a machine learning-based classification approach using Random Forest with forward-feature selection and leave-feature-out option (2). In the following the results of this ongoing upscaling approach used for the Sanetti Plateau, Ethiopia is presented.

How to cite: Wraase, L., Reuber, V., Kurth, P., Farwig, N., Miehe, G., Opgenoorth, L., Schabo, D., and Nauss, T.: Predicting the impact of Giant Molerat influenced vegetation on Sanetti Plateau, Bale Mountains, Ethiopia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18984, https://doi.org/10.5194/egusphere-egu2020-18984, 2020.

D742 |
Seongyun Kim, Craig Daughtry, Andrew Russ, and Yakov Pachepsky

Shallow restrictive soil layer may enhance plant growth and development in dry years due to the creation of subsurface-minipond-like water storages in dry years and may hamper plant development in wet years due to overwetting. These effects may manifest itself differently under different nutrient management. The objective of this work was to see there exist spatial patterns that are temporally stable (do not change over several years), can explain substantial proportions of spatiotemporal variation of maize yield, and can be related to subsurface restrictive layer topography and fertilizer application. Empirical orthogonal functions (EOFs) were good candidates to express such patterns. Data were collected with yield monitors across maize fields with manure applications, uniform chemical fertilization, and precision farming-based chemical fertilizer application over the six-year period. The subsurface restrictive layer was found at depths from one to three meters using the ground penetration radar. Three EOFs explained around 60, 30 and 10 % of interannual yield variation, respectively. As evidenced by semivariograms, the spatial structure was well pronounced in EOFs at the manured field and to a lesser extent at the chemical fertilizer fields. Little difference was observed in cumulative probability distributions of the first EOF across fields with different fertilizer applications. The topography of the restrictive layer was analyzed to determine the subsurface preferential flow lateral flow pathways that could provide water accumulation in dry years and enhanced drainage in wet years. The first EOF on average increased as the distance to the subsurface flow pathways decreased both at the manure and uniform chemical fertilized field, but not at the precision fertilization field where unfavorable water availability conditions could be compensated by the improved fertilizer availability. Differences in the soil surface topography could be reflected by the second EOF. Overall, the temporal stability in crop yields reveals the topography of the shallow vadose zone boundary as the powerful control of yield variation in space and time.  

How to cite: Kim, S., Daughtry, C., Russ, A., and Pachepsky, Y.: Using empirical orthogonal functions to interpret the spatiotemporal variability of crop yields in presence of shallow restrictive soil layer, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11684, https://doi.org/10.5194/egusphere-egu2020-11684, 2020.

D743 |
Katja Augustin, Michael Kuhwald, and Rainer Duttmann

The application “FiTraM” (Field Traffic Model) models the spatially explicit wheel tracks and the field traffic intensity of agricultural vehicles from recorded GPS points. The spatial location of traffic intensities are required to analyse the effect of field traffic on the soil structure, e.g. with regard to mitigate soil compaction. The modelling is based on geometrical and geodetical calculations. The application is written in python and uses PostgreSQL and PostGIS for data storing and calculation of statistics.
The results of FiTraM are the spatially mapped wheel tracks, wheel pass frequency, wheel load and the soil pressure induced by machines (optionally). With continuous route recording various operations (sowing, harvesting, soil tillage) can be analysed in terms of the intensity of travel and the complete process chain during single crops can be mapped. These results (e.g. amount of wheel passages, summed wheel load) can be related to further soil measurements to link field traffic intensities with loss of soil functionality or reduced yield.
This contribution intends to illustrate the process of modelling the field traffic intensity by means of different agricultural working processes - from data acquisition to the statistical evaluation of the spatial modelling results. Examples of different traffic operations are used to explain how driving behaviour needs to be taken into account for modelling, such as reversing and lifting equipment (e.g. during soil tillage). The difficulties, such as the evaluation of the positional accuracy in the field and the processing of the large data sets, will be addressed.

How to cite: Augustin, K., Kuhwald, M., and Duttmann, R.: Possibilities and challenges of modelling the agricultural tracks at field scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8226, https://doi.org/10.5194/egusphere-egu2020-8226, 2020.

D744 |
| Highlight
Małgorzata Jenerowicz, Anna Wawrzaszek, Wojciech Drzewiecki, Michał Krupiński, and Sebastian Aleksandrowicz

     Every year the total number of people who had been forcibly displaced (refugees, asylum seekers, and internally displaced persons) is constantly rising, a fact that is directly reflected in the area covered by IDP/refugee camps. Long-term humanitarian relief requires reliable and comprehensive information that is constantly delivered during a crisis. Very High Resolution (VHR) optical satellite data have been shown to be useful in monitoring IDP/refugee camps as it can provide an overview of the affected areas with a spatial resolution of up to 0.3 m within a matter of days.

     The aim of our research is to verify the usefulness of multifractal parameters as descriptors of IDP/refugee camps area, both in the context of their applicability and usability in the humanitarian related issues. In particular, we perform studies devoted to: (I) the complex terrain situation description with the division into compact and dispersed structures; and (II) the identification of IDP/refugee camps area extent aiming at distinguishing residential areas from other land use/land cover types. The analysis performed in two IDP/refugee camps, i.e. Ifo and Ifo 2 (Daadab) in Kenya and Al Geneina in Sudan, based on GeoEye-1 and Pléiades-1A VHR satellite data, gives a promising aspect of limited calculation time needed for the initial stage of image classification in respect to the spatial complexity of analysed terrain. Our results show that the degree of multifractality calculated for the selected images increases for compact areas with high-contrast structures (e.g., functional buildings and dwellings). Consequently, the extraction of the IDP/refugee camps extent by using only one feature, i.e., the degree of multifractality, proved to be an efficient way for initial image classification.

     We hope that our studies supplemented by further research, i.e. pre- and post-processing, the inclusion of multispectral bands, analyzing other areas of interest, and examining the added value of other multifractal measures, will help to develop an unsupervised classification approach providing results more quickly, with more frequent updates.


Research supported by the National Science Centre, Poland, under Grant 2016/23/B/ST10/01151.

How to cite: Jenerowicz, M., Wawrzaszek, A., Drzewiecki, W., Krupiński, M., and Aleksandrowicz, S.: Multifractality in Humanitarian Applications, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13262, https://doi.org/10.5194/egusphere-egu2020-13262, 2020.

D745 |
Peng Wei, Yang Xing, Li Sun, and Zhi Ning

Air quality and traffic-related pollutants in urban areas are major concerns especially in meg-cities. Current Air Quality Monitoring Station (AQMS) cannot sufficiently reveal these pollution conditions with limited point measurements and limited information cannot supply adequate insight on personal exposure in a complex urban environment. Land Use Regression (LUR) model provided a feasible solution for estimating outdoor personal exposure by adding multiple data sources. However, fixed-site passive monitoring still lacks enough spatial coverage or spatial flexibility to estimate pollutant distribution at the fine-scale level.

A Mobile Air Sensor Network (MASEN) project was deployed in the Hong Kong area, with electrochemical gas sensors installed on the routine buses to capture on-road NOx pollutant measurement, the data was collected by the integrated sensor system and transfer to the database for real-time visualization. Compared with previous mobile measurements used for LUR model building which limited to 1-2 routes, this measurement covered major roads in the Hong Kong area and get an overview of pollutant distribution at various ambient. Two main variables were introduced to improve the model performance: 1) Sky View Factor (SVF) which represented pollutant dispersion status were obtained from Google street view image, a deep learning model was used for scene parsing to recognized targets in this procedure, 2) a Real-time Traffic Congestion Index (RTCI) which represented traffic pollutants emission was obtained from Google map and merged with road network. A common LUR model will be built based on a distance-decay regression selection strategy for variables selection. Meanwhile, a spatial-temporal LUR model will be built which contained both diurnal variability and day-to-day variability. Finally, a high-resolution pollution map of the urban areas will illustrate NO2 pollutant distribution.

In this work, we aimed at estimating traffic-related pollutants in a complex city environment and identifying hotspots at both spatial and temporal aspects. Meanwhile, the novel data source which closely associated with traffic-related pollutant emission also gives a better understanding of guidance on urban planning.

How to cite: Wei, P., Xing, Y., Sun, L., and Ning, Z.: Building spatial-temporal NO2 land use regression models in complex urban environment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6478, https://doi.org/10.5194/egusphere-egu2020-6478, 2020.

D746 |
Giancarlo Ciotoli, Monia Procesi, MariaGrazia Finoia, Peter Bossew, Giorgia Cinelli, Tore Tollefsen, Javier Elìo, and Valeria Gruber

Radon generation, migration and exhalation into the atmosphere are natural processes that can lead to infiltration of radon into indoor environments, thus constituting a health risk. Analyses and models of these three processes can be used to create different maps depicting the potential of geological radon sources (GRS), geogenic radon migration (GRM), and radon exhalation (REX). The latter includes the first two processes, and be used to identify areas with increased radon levels in buildings (Radon Prone or Priority Areas, RPA). Here, we limit our analysis to the first two processes, and propose a spatial technique to map the contribution of some geological factors to the potential radon risk or geogenic radon potential (GRP) at European scale. The GRP can be understood as a measure of susceptibility of a location or of an area to increased indoor radon concentration for geogenic reasons.

The problem of estimating GRP has been examined over several years, using different multivariate statistical and spatial techniques. A number of direct and indirect models have been developed in order to create GRP maps (i.e., susceptibility maps) of a certain region by using both deterministic and probabilistic models. Direct models can be ascribed to multivariate regression of some predictors, but this was possible only at local scale where the response variables (i.e. soil gas radon and thoron) are available. The indirect mapping method integrates many factors and criteria and weighs the importance of the factors, based on subjective decision-making rules according to the experience of the geoscientists involved, or on multivariate statistical analysis.

In this work, we first propose to construct/create a GRP map at European level by using a GIS-based (spatial) multicriteria decision analysis (SMCDA) to quantify the geogenic contribution to indoor radon; and then, to create a European map of geogenic radon priority areas. SMCDA involves combining and handling of different criteria that determine the presence of a RPA, then uses the Analytical Hierarchy Process (AHP) to assess their importance and derives the relative weights for factors and criteria; finally it determines the overall final scores. The GRS map was derived by using a new lithological classification of the International Geological Map of Europe. Lithologies were ranked according to the mean content of uranium, thorium and potassium associated with each lithology. The GRS map was then coupled with maps of other parameters that serve as proxies for permeability, such as available water capacity and the fine fraction of the soil, the fault density and the map of the karst areas. All these maps were standardised by using the max score function and weighted by using AHP. A variance-based sensitivity analysis was conducted to define the uncertainty of the final map. In the absence of direct soil gas measurements, the final map was validated by using the indoor radon values collected by the JRC in the framework of the European Atlas of Natural Radiation. The work is conducted as a task within the framework of the European Metro RADON project (http://metroradon.eu/).

How to cite: Ciotoli, G., Procesi, M., Finoia, M., Bossew, P., Cinelli, G., Tollefsen, T., Elìo, J., and Gruber, V.: Spatial Multicriteria Decision Analysis (SMCDA) for the construction of the European Geogenic Radon Migration map, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7350, https://doi.org/10.5194/egusphere-egu2020-7350, 2020.

D747 |
Christian Schneider

In Germany a vast amount of spatial geo-environmental as well as climatic datasets is available. But anthropic data on land-use and agriculture are still very sparse making it difficult to assess the environmental impacts of different agricultural practices. Recently, some data on spatial pattern of crop production as well as livestock production was made publicly available. It opened up the opportunity to model the impact of agriculture on nitrate leaching into groundwater bodies.

A high share of groundwater bodies in Germany contains nitrate levels above the legal threshold of 50 mg l-1. Our study aims to answer the question: to what extend different types of agriculture are contributing to NO3 leaching into ground water bodies in relation to environmental factors.

We use the random forest (RF) machine learning algorithm to model and predict nitrate exceedance in ground water bodies. The advantage of the RF algorithm is that it has a high predictive accuracy, it is able to use metric as well as multi-level categorical datasets and it calculates a variable importance measure for each predictor used in a model. It therefore gives a measure to which extend each predictor contributes to the accuracy of the model. For this study we applied the RF classification as well as the RF regression algorithms on different spatial scales.

Out of 56 environmental predictor datasets which are of potential importance for NO3 transport into groundwater bodies 22 where chosen to model NO3-exceedance.
A recursive variable elimination scheme was applied to calculate minimum predictor sets based on variable importance. In the end the predictor set which resulted in the most accurate NO3 prediction was identified and used to model groundwater pollution.

RF-modeling proofed to be successful on all three scale levels with OBB accuracy between 0.82 and 0.95. At all scale levels environmental co-variables played a major role in predicting NO3-exceedance. But the RF variable importance measure could also be used to identify the contribution of agricultural predictors to NO3 exceedance and to quantitatively proof our hypotheses.

On main challenge was to identify the influence of data quality on the RF variable importance measure.

How to cite: Schneider, C.: Making use of open geo-environmental and agricultural datasets to model NO3 pollution in groundwater bodies, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22527, https://doi.org/10.5194/egusphere-egu2020-22527, 2020.

D748 |
Roberta Sauro Graziano, Renguang Zuo, Antonella Buccianti, Orlando Vaselli, Barbara Nisi, Marco Doveri, and Brunella Raco

Groundwater systems are typical dissipative structures and their evolution can be affected by non-linear dynamics. In this framework, geochemical and hydrological processes are often characterized by random components mixed with intermittency and presence of positive feedbacks between fluid transport and mineral dissolution. Therefore, in these cases, complex variability structures in the chemical signature of waters are recognized. Large fluctuations in intermittent processes are not rare as in normal and log-normal processes and significantly contribute to the statistical moments, thus moving the physicochemical data from the Euclidean geometry to fractals and multifractals.

Since the knowledge of dynamics in water systems has substantial implications in the management of the water resource, groundwater chemistry can better be understood by using innovative graphical and numerical methods in the light of the Compositional Data Analysis Theory (CoDA, Aitchison, 1986), which is particularly suitable to explore the whole composition and the relationships between its parts.

The whole compositional change, characterizing each sample with respect to some end-members (i.e. rain waters, pristine waters and sea water), is modeled by using the perturbation operator in the simplex geometry (Pawlowsky-Glahn and Buccianti, 2011). Perturbation factors are calculated and then analyzed by investigating their cumulative distribution function (Pr[X>=x]) with the aim of registering the presence of power laws (fractal and multifractal dynamics) and forecasting a possible spatial behavior.

Results obtained for some aquifers from Tuscany (central Italy) are presented and discussed in the framework of the GEOBASI project (Nisi et al., 2016). Preliminary evaluations indicate that perturbation factors are sensible tools to: 1) identify the different components (random, deterministic, fractal) contributing to the variability of the geochemical data, 2) discriminate the role of additive and multiplicative phenomena in time and/or space, 3) highlight the presence of non-linear dissipation with the energy exchanges between different scales.[Office1] 


Aitchison, J., 1986.  The statistical analysis of compositional data. Monographs on Statistics and Applied Probability (Reprinted in 2003 by The Blackburn Press), Chapman and Hall, 416 p.

Nisi, B., Buccianti, A., Raco, B., Battaglini, R., 2016. Analysis of complex regional databases and their support in the identification of background/baseline compositional facies in groundwater investigation: developments and application examples. Journal of Geochemical Exploration 164, 3-17

Pawlowsky-Glahn, V., Buccianti, A., 2011. Compositional Data Analysis: Theory and applications. Chichester, John Wiley & Sons, 378 p.

How to cite: Sauro Graziano, R., Zuo, R., Buccianti, A., Vaselli, O., Nisi, B., Doveri, M., and Raco, B.: Innovative graphical-numerical methods to investigate compositional changes in groundwater systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19388, https://doi.org/10.5194/egusphere-egu2020-19388, 2020.

D749 |
Petra Diendorfer, Caterina Gozzi, Anna Bauer, Antonella Buccianti, Gerd Rantitsch, Robert Scholger, Barbara Nisi, and Orlando Vaselli

The Tiber and the Arno river basins, represent the first (17,156 km2) and the second (8,228 km2) largest catchments in the peninsular Italy, respectively. The recent combined sampling (2017-2019)  of river waters and sediments in the heterogeneous geological environment of the Apennines enables the assessment of the geochemical and mineralogical interaction between bedrock, river sediments and water. The mineralogical and geochemical composition of the stream sediments are related to the corresponding lithological composition of the hydrological catchment, thus assessing physical weathering within the river basins. On the other hand, chemical weathering is assessed by the analysis of hydrochemical data from the Arno and Tiber rivers and their main tributaries. Locally, anthropogenic processes overprint the natural signature and the magnetic properties of the sediments provide effective data to map those areas. The application of multivariate robust statistical techniques on the combined dataset evaluates the water-sediment interaction and their spatial properties in central Italy. The main goal of this research is to investigate how the linkage between surface waters and steam sediments chemistry can be influenced by catchment-specific properties (e.g. landscape attributes, anthropic impact and climate) through an effective comparative analysis between two of the most important Italian watersheds.

How to cite: Diendorfer, P., Gozzi, C., Bauer, A., Buccianti, A., Rantitsch, G., Scholger, R., Nisi, B., and Vaselli, O.: Water-sediment interaction in the Arno- and Tiber river catchments (central Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21502, https://doi.org/10.5194/egusphere-egu2020-21502, 2020.