Mapping monthly rainfall erosivity in Europe Ballabio, Cristiano; Borrelli, Pasquale; Spinoni, Jonathan ...
Science of the total environment,
02/2017, Volume:
579
Journal Article
Peer reviewed
Open access
Rainfall erosivity as a dynamic factor of soil loss by water erosion is modelled intra-annually for the first time at European scale. The development of Rainfall Erosivity Database at European Scale ...(REDES) and its 2015 update with the extension to monthly component allowed to develop monthly and seasonal R-factor maps and assess rainfall erosivity both spatially and temporally. During winter months, significant rainfall erosivity is present only in part of the Mediterranean countries. A sudden increase of erosivity occurs in major part of European Union (except Mediterranean basin, western part of Britain and Ireland) in May and the highest values are registered during summer months. Starting from September, R-factor has a decreasing trend. The mean rainfall erosivity in summer is almost 4 times higher (315MJmmha−1h−1) compared to winter (87MJmmha−1h−1).
The Cubist model has been selected among various statistical models to perform the spatial interpolation due to its excellent performance, ability to model non-linearity and interpretability. The monthly prediction is an order more difficult than the annual one as it is limited by the number of covariates and, for consistency, the sum of all months has to be close to annual erosivity. The performance of the Cubist models proved to be generally high, resulting in R2 values between 0.40 and 0.64 in cross-validation. The obtained months show an increasing trend of erosivity occurring from winter to summer starting from western to Eastern Europe. The maps also show a clear delineation of areas with different erosivity seasonal patterns, whose spatial outline was evidenced by cluster analysis. The monthly erosivity maps can be used to develop composite indicators that map both intra-annual variability and concentration of erosive events. Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year.
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•Rainfall erosivity is mapped intra-annually for the first time at European scale.•The modelling is based on a developed monthly Rainfall Erosivity Database at European Scale (REDES).•REDES data is modelled with WorldClim covariates using Cubist regression trees.•Using Cubist erosivitiy is effectively spatially estimated over Europe for each month.•Seasonal patterns of erosivity are further analyzed using clustering techniques.
Floods are one of the most devastating natural disasters that can cause large economic damage and endanger human lives. Flood forecasting is one of the flood risk mitigation measures serving to ...protect human lives and social estate. The Danube River Basin (DRB) is the world's most international river basin, flowing through the territory of 19 countries, covering more than 800,000 km2. The frequency of floods in the DRB increased in the last decades, urging the need for a more effective and harmonized regional and cross‐border cooperation in the field of flood forecasting. Reliable and comprehensive hydrologic data are the basis of flood forecasting. This paper provides an overview of the national flood forecasting systems in the DRB. Detailed information about meteorological and hydrological measurements, flood modelling, forecasting, and flood warnings is provided for 12 countries that cover almost 95% of the total DRB area. Notably, significant differences exist among the countries in terms of the measuring network density, the models used as well as forecasting and warnings methodology. These differences can be attributed to the geographical and climatological setting, political situation, historical forecasting development, etc. It can be seen that there is still much room left for improvements of measurement networks (e.g., density, measured parameters) and models used that could be improved to enhance the flood forecasting in the DRB.
•Reconstruction of the rainfall erosivity for Europe for the period 1961–2018.•Annual trend for 1675 stations is positive for 15% and negative for 7% stations.•Station-wise and regional Mann-Kendall ...tests are conducted.•The rainfall erosivity trend map shows increase in major part of EU.•Rainfall erosivity in Europe has, on average, increased for about 4%.
Rainfall erosivity is the driving force of soil erosion and it is characterized by a large variability in space and time. In order to obtain robust estimates of rainfall erosivity, long series of high-frequency rainfall data are needed, which are often not available for large study areas. In this study we reconstructed past rainfall erosivity in Europe for the period 1961–2018, with the aim to investigate temporal changes in rainfall erosivity. As input data, we used the Rainfall Erosivity Database at European Scale (REDES) and Uncertainties in Ensembles of Regional Reanalyses (UERRA) rainfall data. Using a set of regression models, which we derived with the application of the k-fold cross-validation approach, we computed the annual rainfall erosivity for the 1675 stations forming the REDES database. Based on the reconstructed data, we derived a rainfall erosivity trend map for Europe where the results were qualitatively validated. Among the stations showing a statistically significant trend, we observed a tendency towards more positive (15%) than negative trends (7%). In addition, we also observed an increasing tendency of the frequency of years with maximum erosivity values. Geographically, large parts of regions such as Eastern Europe, Scandinavia, Baltic countries, Great Britain and Ireland, part of the Balkan Peninsula, most of Italy, Benelux countries, northern part of Germany, part of France, among others, are characterized by a positive trend in rainfall erosivity. By contrast, negative trends in annual rainfall erosivity could be observed for most of the Iberian Peninsula, part of France, most of the Alpine area, Southern Germany, and part of the Balkan Peninsula, among others. The new dataset of rainfall erosivity trends reported in this study scientifically provides new information to better understand the impacts of the ongoing erosivity trends on soil erosion across Europe, while, from a policy perspective, the gained findings provide new knowledge to support the development of soil erosion indicators aiming at promoting mitigation measures at regional and pan-European level.
Rainfall erosivity is one of the most important parameters that influence soil erosion rates. It is characterized by a large spatial and temporal variability. For example, in Slovenia, which covers ...around 20,000 km
2
, the annual rainfall erosivity ranges from less than 1,000 MJ mm ha
−1
h
−1
to more than 10,000 MJ mm ha
−1
h
−1
. Drop size distribution (DSD) data are needed to investigate rainfall erosivity characteristics. More than 2 years of DSD measurements using optical disdrometers located at six stations in Slovenia were used to investigate the spatial and temporal variability in rainfall erosivity in Slovenia. Experimental results have indicated that elevation is a poor predictor of rainfall erosivity and that erosivity is more strongly correlated to the mean annual precipitation. Approximately 90% of the total kinetic energy (KE) was accounted for in about 35% of 1 min disdrometer data. The highest 1 min intensities (I) and consequently also KE values were measured in summer followed by autumn and spring. The local KE-I equation yielded an acceptable fit to the measured data in case of all six stations. The relatively large percentage of 1 min rainfall intensities above 5 mm/h can at least partially explain some very high annual rainfall erosivity values (i.e., near or above 10,000 MJ mm ha
−1
h
−1
). Convective and large-scale precipitation events also result in various rainfall erosivity characteristics. The station microlocation and wind impacts in case of some stations yielded relatively large differences between the data measured using the optical disdrometer and the pluviograph. Preliminary conclusions have been gathered, but further measurements are needed to get even better insight into spatial and temporal variability in rainfall erosivity under Alpine climate in Slovenia.
Soil degradation is a major environmental problem in many parts of the world, including Slovenia. The spatially distributed WATEM/SEDEM model can be used to identify the most critical parts of the ...catchment with regard to soil erosion. Five Slovenian (Central Europe) catchments with inhomogeneous topography, land use, geological conditions, hydro-meteorological properties and sizes (catchment areas between 1 and 2000 km²) were modeled with calibrated parameters, while the WATEM/SEDEM model was calibrated with an automatic parameter estimation procedure, which is model independent. Both direct and indirect information regarding sediment yields, including turbidity measurements, daily suspended sediment concentration observations and bed load observations, were used for the WATEM/SEDEM model’s calibration. A detailed rainfall erosivity (R) factor map, which was constructed from 5-min rainfall data from 31 pluviographic meteorological stations, was used as one of the inputs for the WATEM/SEDEM model. The calculated mean annual soil loss was between 0.3 and 7.4 t/ha/year, and the sediment delivery ratio (SDR) ranged from 0.07 to 0.22 for 5 modeled catchments. The results indicate that the SDR decreases with increasing catchment area; however, the ratio between the average sediment yield and mean soil erosion obviously depends on many other factors, e.g., topography, climatic and geological conditions. The parcel trap efficiency parameter for forests had the greatest influence on the WATEM/SEDEM model’s outputs in all five case studies.
As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity ...has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall erosivity and presents the spatial patterns of monthly rainfall erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall erosivity.
Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was ...compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk.
In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling.
The Slovenian Environmental Agency (SEA) has started a project entitled "Upgrading the system for monitoring and analysing the state of the water environment", which is partly financed through the ...European Cohesion Fund. The project was named "BOBER" (Better Observation for Better Environmental Response). The wider purpose of the project is to increase the capacity of the SEA to monitor, examine and forecast water cycle factors. The Flood Forecasting System (FFS) has been developed in the framework of the BOBER project. The system provides an overview over forecasts of river discharges and water levels for six days ahead. The FFS is becoming a basic operational tool for the preparation and issuing of daily hydrological forecasts and flood warnings by the Flood Forecasting Department of the SEA. To issue effective flood warnings to the general public, the Hydroalarm system has been implemented. The setup of the FFS is strongly connected to sufficient and available spatial data sources. The FFS combines different tasks; data management, model calibration, decision and management support systems, which are combined through a graphical user interface. During the project, few data sources appropriate for modelling purposes were found. Quite frequently the gathered data were available in different usable formats and needed transformation to usable forms. The discussion in the article focuses on the required data sources needed to develop flood forecasting systems and future spatial and other data requirements for the upgrading and further development of water management sector.
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