• Global rainfall erosivity projections for 2050 and 2070

    Panagos, P.a, Borrelli, P.b, Matthews, F.a, Liakos, L.a, Bezak, N.c, Diodato, N.d, Ballabio, C.a, 2022. Global rainfall erosivity projections for 2050 and 2070. Journal of Hydrology 610, 127865. https://doi.org/10.1016/j.jhydrol.2022.127865

    aEuropean Commission, Joint Research Centre (JRC), Ispra, Italy
    bDepartment of Earth and Environmental Sciences, University of Pavia, 27100 Pavia, Italy
    cUniversity of Ljubljana, Faculty of Civil and Geodetic Engineering, Ljubljana, Slovenia
    dMet European Research Observatory – International Affiliates Program of the University Corporation for Atmospheric Research, Benevento, Italy

    The erosive force of rainfall (rainfall erosivity) is a major driver of soil, nutrient losses worldwide and an important input for soil erosion assessments models. Here, we present a comprehensive set of future erosivity projections at a 30 arc-second (∼1 km2) spatial scale using 19 downscaled General Circulation Models (GCMs) simulating three Representative Concentration Pathways (RCPs) for the periods 2041–2060 and 2061–2080. The future rainfall erosivity projections were obtained based on a Gaussian Process Regression (GPR) approach relating rainfall depth to rainfall erosivity through a series of (bio)climatic covariates. Compared to the 2010 Global Rainfall erosivity baseline, we estimate a potential average increase in global rainfall erosivity between 26.2 and 28.8% for 2050 and 27–34.3% for 2070. Therefore, climate change and the consequential increase in rainfall erosivity is the main driver of the projected + 30–66% increase in soil erosion rates by 2070.

    Our results were successfully compared with 20 regional studies addressing the rainfall erosivity projections. We release the whole dataset of future rainfall erosivity projections composed of 102 simulation scenarios, with the aim to support further research activities on soil erosion, soil conservation and climate change communities. We expect these datasets to address the needs of both the Earth system modeling community and policy makers. In addition, we introduce a modeling approach to estimate future erosivity and make further assessments at global and continental scales.

  • Exploring the possible role of satellite-based rainfall data in estimating inter- and intra-annual global rainfall erosivity

    Nejc Bezak1, Pasquale Borrelli2,3, and Panos Panagos4

    1University of Ljubljana, Faculty of Civil and Geodetic Engineering, Ljubljana, Slovenia
    2Department of Earth and Environmental Sciences, University of Pavia, Pavia, Italy
    3Department of Biological Environment, Kangwon National University, Chuncheon 24341, Republic of Korea
    4European Commission, Joint Research Centre (JRC), Ispra, Italy


    Despite recent developments in modeling global soil erosion by water, to date, no substantial progress has been made towards more dynamic inter- and intra-annual assessments. In this regard, the main challenge is still represented by the limited availability of high temporal resolution rainfall data needed to estimate rainfall erosivity. As the availability of high temporal resolution rainfall data will most likely not increase in future decades since the monitoring networks have been declining since the 1980s, the suitability of alternative approaches to estimate global rainfall erosivity using satellite-based rainfall data was explored in this study. For this purpose, we used the high spatial and temporal resolution global precipitation estimates obtained with the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for the estimation of rainfall erosivity on a global scale. Alternatively, the erosivity density (ED) concept was also used to estimate global rainfall erosivity. The obtained global estimates of rainfall erosivity were validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH estimates have a marked tendency to underestimate rainfall erosivity when compared to the GloREDa estimates. The most substantial underestimations were observed in areas with the highest rainfall erosivity values. At the continental level, the best agreement between annual CMORPH and interpolated GloREDa rainfall erosivity maps was observed in Europe, while the worst agreement was detected in Africa and South America. Further analyses conducted at the monthly scale for Europe revealed seasonal misalignments, with the occurrence of underestimation of the CMORPH estimates in the summer period and overestimation in the winter period compared to GloREDa. The best agreement between the two approaches to estimate rainfall erosivity was found for fall, especially in central and eastern Europe. Conducted analysis suggested that satellite-based approaches for estimation of rainfall erosivity appear to be more suitable for low-erosivity regions, while in high-erosivity regions (> 1000–2000 MJ mm ha−1 h−1 yr−1) and seasons (> 150–250 MJ mm ha−1 h−1 month−1), the agreement with estimates obtained from pluviographs (GloREDa) is lower. Concerning the ED estimates, this second approach to estimate rainfall erosivity yielded better agreement with GloREDa estimates compared to CMORPH, which could be regarded as an expected result since this approach indirectly uses the GloREDa data. The application of a simple-linear function correction of the CMORPH data was applied to provide a better fit to GloREDa and correct systematic underestimation. This correction improved the performance of CMORPH, but in areas with the highest rainfall erosivity rates, the underestimation was still observed. A preliminary trend analysis of the CMORPH rainfall erosivity estimates was also performed for the 1998–2019 period to investigate possible changes in the rainfall erosivity at a global scale, which has not yet been conducted using high-frequency data such as CMORPH. According to this trend analysis, an increasing and statistically significant trend was more frequently observed than a decreasing trend.

    Mean global rainfall erosivity map for the 1998–2019 period based on the CMORPH product (a) and ED concept using ERA5 (b).
  • Phosphorus plant removal from European agricultural land

    Total phosphorus removal per country and region

    Panagos, Panosa, Anna Muntwylera, Leonidas Liakosa, Pasquale Borrellib, Irene Biavettia, Mariia Bogonosc, and Emanuele Lugatoa. 2022. “Phosphorus Plant Removal from European Agricultural Land.” Journal of Consumer Protection and Food Safety, February. https://doi.org/10.1007/s00003-022-01363-3.

    a European Commission, Joint Research Centre (JRC), Ispra, Italy
    b Dipartimento di Scienze della Terra e dell’Ambiente, Università degli Studi di Pavia, Pavia, Italy
    c European Commission, Joint Research Centre (JRC), Seville, Spain

    Phosphorus (P) is an important nutrient for all plant growth and it has become a critical and often imbalanced element in modern agriculture. A proper crop fertilization is crucial for production, farmer profits, and also for ensuring sustainable agriculture. The European Commission has published the Farm to Fork (F2F) Strategy in May 2020, in which the reduction of the use of fertilizers by at least 20% is among one of the main objectives. Therefore, it is important to look for the optimal use of P in order to reduce its pollution effects but also ensure future agricultural production and food security. It is essential to estimate the P budget with the best available data at the highest possible spatial resolution. In this study, we focused on estimating the P removal from soils by crop harvest and removal of crop residues. Specifically, we attempted to estimate the P removal by taking into account the production area and productivity rates of 37 crops for 220 regions in the European Union (EU) and the UK. To estimate the P removal by crops, we included the P concentrations in plant tissues (%), the crop humidity rates, the crop residues production, and the removal rates of the crop residues. The total P removal was about 2.55 million tonnes (Mt) (± 0.23 Mt), with crop harvesting having the larger contribution (ca. 94%) compared to the crop residues removal. A Monte-Carlo analysis estimated a ± 9% uncertainty. In addition, we performed a projection of P removal from agricultural fields in 2030. By providing this picture, we aim to improve the current P balances in the EU and explore the feasibility of F2F objectives.

    Total phosphorus removal per country and region
    Total phosphorus removal per country and region. Green bars aggregate P crop removal per country and brown ones are the aggregated P removal with residues
  • Μοντέλα αστικής εξάπλωσης και παρακολούθηση με τηλεπισκόπηση νυκτερινών φώτων.

    Στα πλαίσια της δημοσιοποίησης των αποτελεσμάτων της διδακτορικής μου διατριβής με θέμα "Μοντέλα αστικής εξάπλωσης και παρακολούθηση με τηλεπισκόπηση νυκτερινών φώτων" έχει αναρτηθεί ειδική ενότητα στην διεύθυνση https://phd.geographer.gr/

    Η διατριβή είναι διαθέσιμη στο Εθνικό Αρχείο Διδακτορικών Διατριβών.

    Η διατριβή στοχεύει στην παρακολούθηση και ανάλυση της λειτουργίας των ελληνικών αστικών περιοχών μέσω της εκμετάλλευσης μιας ιδιαίτερης κατηγορίας δεδομένων τηλεπισκόπησης, των νυκτερινών φώτων. Τα νυκτερινά φώτα έχει παρατηρηθεί ότι αντικατοπτρίζουν σε σημαντικό βαθμό τα επίπεδα αστικής και οικονομικής ανάπτυξης και είναι άρρηκτα συνδεδεμένα με οικονομικά μεγέθη όπως το ΑΕΠ, ο πληθυσμός, η οικοδομική δραστηριότητα και η κατανάλωση του ηλεκτρικού ρεύματος.
    Η διεξαγωγή της διατριβής υποστηρίζεται από τα κυριότερα σύνολα δεδομένων νυκτερινών φώτων (DMPS/OLS, snpp-VIIRS, ISS) για την αποτύπωση της ελληνικής αστικής «πραγματικότητας». Τα δεδομένα νυχτερινών φώτων διατίθενται χωρίς να έχουν υποστεί την απαραίτητη προεπεξεργασία για την αφαίρεση παραγόντων που επηρεάζουν την ποιότητά τους και τα αποτελέσματατης έρευνας. Σημαντικοί εξωγενείς παράγοντες επιρροής της ποιότητας των δεδομένων, όπως η σεληνιακή ακτινοβολία, δεν έχουν αφαιρεθεί. Τεχνικά χαρακτηριστικά και περιορισμοί όπως η υπερλάμψη και ο κορεσμός των δεδομένων DSMP/OLS συνεχίζουν να απασχολούν την επιστημονική κοινότητα.
    Στα πλαίσια της διατριβής θα προταθούν μεθόδοι για την διόρθωση της σεληνιακής ακτινοβολίας
    από τα δεδομένα snpp-VIIRS και θα επιχειρηθεί η βελτίωση των δεδομένων DMSP/OLS με την
    συνδρομή δεδομένων SAR.
    Στόχος της διατριβής είναι να εκμεταλλευτεί τα δεδομένα νυχτερινών φώτων για να καταγράψει την εποχικότητα των αστικών περιοχών στον Ελλαδικό χώρο, να προσομοιώσει την αστική
    διάχυση που θα λάβει χώρα στο μέλλον, να προβλέψει την ένταση της αστικής δραστηριότητας,
    να εκτιμήσει σε λεπτομερή χωρική και χρονική κλίμακα οικονομικές μεταβλητές όπως το ΑΕΠ.
    Τα νυχτερινά φώτα θα χρησιμοποιηθούν για να αποτιμηθούν οι συνέπειες έκτακτων συνθηκών
    στην αστική δραστηριότητα, για να εκτιμηθεί η επιβάρυνση από την φωτορύπανση και για να
    περιγραφούν τα πρότυπα μετακίνησης στις σύγχρονες ελληνικές πόλεις.Από τα αποτελέσματα προκύπτουν αξιόλογες και καινοτόμες μέθοδοι για την βελτίωση των δεδομένων και την ανάλυση, πρόβλεψη και ερμηνεία των αστικών φαινομένων. Η χρήση νυχτερινών φώτων αποτελεί ένα σημαντικό εργαλείο στα χέρια του ερευνητή αλλά απαιτεί βαθειά επίγνωση των τεχνικών περιορισμών και των εξωγενών παράγοντων που επηρεάζουν τις δυνατότητές τους.

  • An in-depth statistical analysis of the rainstorms erosivity in Europe

    Gini coefficients (G) for the 1181 stations across Europe using all rainfall erosive events included in the REDES database. As a background map, the six K-means clusters as defined by Ballabio et al. (2017) are shown.

    Nejc Bezaka, Matjaž Mikoša, Pasquale Borrellibc, Leonidas Liakosd, Panos Panagosd. 2021. “An In-Depth Statistical Analysis of the Rainstorms Erosivity in Europe.” CATENA 206 (November): 105577. https://doi.org/10.1016/j.catena.2021.105577.

    a .University of Ljubljana, Faculty of Civil and Geodetic Engineering, Slovenia
    b. Department of Earth and Environmental Sciences, University of Pavia, Italy
    c. Department of Biological Environment, Kangwon National University, Chuncheon 24341, Republic of Korea
    d. European Commission, Joint Research Centre (JRC), Ispra, Italy

    Heavy rainstorms play a central role in the water-driving soil erosion processes. An in-depth knowledge about temporal and spatial erosivity of rainfall events is required to gain a better understanding of soil erosion processes and optimize soil protection measures efficiency. In this study, the spatiotemporal distribution of more than 300,000 erosive events measured at 1181 locations, part of the Rainfall Erosivity Database at European Scale (REDES) database, is studied to shed some new light on the rainfall erosivity in Europe. Rainfall erosive events are statistically investigated through the Lorenz curve and derived coefficients such as the Gini coefficient (G). Additionally, seasonal characteristics of the most and the less erosive events are compared to investigate seasonal characteristics of rainstorms across Europe. The G shows largest values of inequality of the inter-annual temporal distribution of the rainfall erosive events in the Alpine region, mostly due to the large number of rainfall events with smaller rainfall erosivity. While for other parts of Europe, the inequality described by the G is mostly due to a small number of high erosive events. The G slightly decreases from south to north while no clear regional patterns can be detected. Additionally, in Europe, on average 11% (ranging from 1 to 24%) of all erosive events contribute to form 50% of the total rainfall erosivity. Furthermore, higher erosive rainfall events tend to occur later in the year compared to less erosive events that take place earlier. To our knowledge, this study is the first one addressing event scale rainfall erosivity distribution using more than 300,000 rainfall erosivity events and covering almost a whole continent. Scientifically our findings represent a major step towards large-scale process-based erosion modelling while, practically, they provide new elements that can support national and local soil erosion monitoring programs.

    [Διαβάστε περισσότερα...]
  • Further adjustment of the Defense Meteorological Satellite Program-Operational Linescan System using radar data

    Fig. 2

    Stathakis, Demetris, and Leonidas Liakos. 2019. “Further Adjustment of the Defense Meteorological Satellite Program-Operational Linescan System Using Radar Data.” Journal of Applied Remote Sensing 13 (03): 034507/1-9. https://doi.org/10.1117/1.JRS.13.034507.

    University of Thessaly, Spatial Analysis/GIS and Thematic Mapping Laboratory,
    Department of Planning and Regional Development, Sekeri & Alamanas, Volos, Greece

    In the past, the Defense Meteorological Satellite Program—Operational Linescan
    System (DSMP/OLS) data have been successfully adjusted based on the presence of vegetation
    in each pixel. We propose a further adjustment based on radar data obtained by the SeaWinds
    scatterometer. It is shown that the adjustment results in substantial accuracy gains, measured as
    increased correlation with the more accurate Visible Infrared Imaging Radiometer Suite data as
    well as with the existing instances of radiance calibrated DSMP/OLS data. The proposed adjust-
    ment is easy to implement. The improvement of the proposed method is particularly significant
    in arid regions where vegetation is relatively sparse.

  • Median Shift Lunar Correction for VIIRS


    Stathakis, Demetris*, and Leonidas Liakos*. 2020. “Median Shift Lunar Correction for VIIRS.” IEEE Geoscience and Remote Sensing Letters, 1–5. https://doi.org/10.1109/LGRS.2020.3007965.

    * Planning and Development Department, University of Thessaly, 38334 Volos, Greece

    Visible Infrared Imaging Radiometer Suite (VIIRS) 24-h data are substantially affected by lunar illumination. A new method is proposed to correct this distortion and be able to form a consistent time series. The method has the advantage of being relatively simple in its deployment while at the same time effective. The results show that the proposed method removes periodical illumination noise due to the lunar circle while preserving meaningful information, in the test sites applied.

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  • Raster και Raster Stack στην R

    Η ανάρτηση αυτή περιλαμβάνει το περιεχόμενο από το workshop στο FOSSCOMM 2019 στην Λαμία. Για περισσότερες λεπτομέρειες ανατρέξτε στο github repository.

    Στόχος του εργαστηρίου είναι η εξοικείωση του χρήστη με το πακέτο raster της R το οποίο προσφέρει την δυνατότητα ανάγνωσης ψηφιδωτών δεδομένων (raster) και επεξεργασίας τους (cropreclassifyreprojectresample κτλ.).

    Επιπλέον, θα επικεντρωθούμε στην κλάση raster stack η οποία δημιουργεί συστοιχίες ψηφιδωτών δεδομένων, κατάλληλες για χρονοσειρές και πολυκαναλικές εικόνες.

    Η διεξαγωγή του εργαστηρίου θα γίνει μέσω παραδειγμάτων και με την χρήση δεδομένων νυκτερινών φώτων DMSP-OLS Nighttime Lights Time Series (Stable Lights Version 4). Θα προηγηθεί μια σύντομη παρουσίαση των βημάτων και της διαδικασίας ώστε οι χρήστες να αποκτήσουν μια σύντομη αλλά περιεκτική εικόνα του στόχου του εργαστηρίου και των δυνατοτητων που προσφέρει ο προγραμματισμός με την R.

    [Διαβάστε περισσότερα...]
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported
This work by Leonidas Liakos is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported.