Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution

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.

Soil erosion modelling: A global review and statistical analysis

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

Soil erosion modelling: A bibliometric analysis

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

Spatial and quantitative assessment of wind erosion from adjacent dunes with different surface cover ratios

Direct measurements, models, and risk maps play significant roles in assessment and monitoring of wind erosion cases. Although active and passive traps allow researchers to measure point sediment transports directly, it is also possible to make geostatistical analysis of wind erosion with grid and random sampling at multiple points. Geostatistical models can be used in multi-sample eolian researches to improve model success and update model parameters. The present study was conducted for case-based geostatistical analysis of sediment transport rates (STRs) over two adjacent dunes (plot A and B) with different vegetation cover rates between 22 May and 15 June 2011. The plot A has a vegetative cover ratio of 30%, while the plot B has a vegetation cover ratio of 2% and sand content of the plots is 88%. Actual mass transports were measured with BEST sediment traps. A total of 19 BEST sediment trap assemblies were placed randomly over the plot A and 21 were placed over the plot B. A climate station was installed over the research site to record climate data throughout the experimental period. There were two wind erosion cases during the research period. U test indicated that differences in sediment transport rates of the plots for each case were significant (p < 0.00). Spatial analyses of STRs (kg m^-1 h^-1) also exhibited case-based differences. While nugget effect was observed in case 1 of the plot B, the other case in both plots were modeled with spherical model. Maximum likelihood distances in plot A and B were respectively identified as 61 m and 1 m in the first case and as 13 m and 30 m in the second case. Total mass transport was measured as 112 kg m^-1 in plot A and as 2162 kg m^-1 in plot B. Consequently, it was found that 30% vegetation cover reduced the total mass transport dramatically.

Global rainfall erosivity assessment based on high-temporal resolution rainfall records

The exposure of the Earth's surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha^-1 h^-1 yr^-1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

Potential Use of BEST® Sediment Trap in Splash - Saltation Transport Process by Simultaneous Wind and Rain Tests

The research on wind-driven rain (WDR) transport process of the splash-saltation has increased over the last twenty years as wind tunnel experimental studies provide new insights into the mechanisms of simultaneous wind and rain (WDR) transport. The present study was conducted to investigate the efficiency of the BEST® sediment traps in catching the sand particles transported through the splash-saltation process under WDR conditions. Experiments were conducted in a wind tunnel rainfall simulator facility with water sprayed through sprinkler nozzles and free-flowing wind at different velocities to simulate the WDR conditions. Not only for vertical sediment distribution, but a series of experimental tests for horizontal distribution of sediments was also performed using BEST® collectors to obtain the actual total sediment mass flow by the splash-saltation in the center of the wind tunnel test section. Total mass transport (kg m-2) were estimated by analytically integrating the exponential functional relationship using the measured sediment amounts at the set trap heights for every run. Results revealed the integrated efficiency of the BEST® traps at 6, 9, 12 and 15 m s-1 wind velocities under 55.8, 50.5, 55.0 and 50.5 mm h-1 rain intensities were, respectively, 83, 106, 105, and 102%. Results as well showed that the efficiencies of BEST® did not change much as compared with those under rainless wind condition.

Spatial and temporal variation of moisture content in the soil profiles of two different agricultural fields of semi-arid region

Modeling spatio-temporal variation of soil moisture with depth in the soil profile plays an important role for semi-arid crop production from an agro-hydrological perspective. This study was performed in Guvenc Catchment. Two soil series that were called Tabyabayir (TaS) and Kervanpinari (KeS) and classified as Leptosol and Vertisol Soil Groups were used in this research. The TeS has a much shallower (0-34 cm) than the KeS (0-134 cm). At every sampling time, a total of geo-referenced 100 soil moisture samples were taken based on horizon depths. The results indicated that soil moisture content changed spatially and temporally with soil texture and profile depth significantly. In addition, land use was to be important factor when soil was shallow. When the soil conditions were towards to dry, higher values for the coefficient of variation (CV) were observed for TaS (58 and 43% for A and C horizons, respectively); however, the profile CV values were rather stable at the KeS. Spatial variability range of TaS was always higher at both dry and wet soil conditions when compared to that of KeS. Excessive drying of soil prevented to describe any spatial model for surface horizon, additionally resulting in a high nugget variance in the subsurface horizon for the TaS. On the contrary to TaS, distribution maps were formed all horizons for the KeS at any measurement times. These maps, depicting both dry and wet soil conditions through the profile depth, are highly expected to reduce the uncertainty associated with spatially and temporally determining the hydraulic responses of the catchment soils.

Land degradation assessment by geo-spatially modeling different soil erodibility equations in a semi-arid catchment

Land degradation by soil erosion is one of the most serious problems and environmental issues in many ecosystems of arid and semi-arid regions. Especially, the disturbed areas have greater soil detachability and transportability capacity. Evaluation of land degradation in terms of soil erodibility, by using geostatistical modeling, is vital to protect and reclaim susceptible areas. Soil erodibility, described as the ability of soils to resist erosion, can be measured either directly under natural or simulated rainfall conditions, or indirectly estimated by empirical regression models. This study compares three empirical equations used to determine the soil erodibility factor of revised universal soil loss equation prediction technology based on their geospatial performances in the semi-arid catchment of the Saraykoy II Irrigation Dam located in Cankiri, Turkey. A total of 311 geo-referenced soil samples were collected with irregular intervals from the top soil layer (0-10 cm). Geostatistical analysis was performed with the point values of each equation to determine its spatial pattern. Results showed that equations that used soil organic matter in combination with the soil particle size better agreed with the variations in land use and topography of the catchment than the one using only the particle size distribution. It is recommended that the equations which dynamically integrate soil intrinsic properties with land use, topography, and its influences on the local microclimates, could be successfully used to geospatially determine sites highly susceptible to water erosion, and therefore, to select the agricultural and bio-engineering control measures needed.

The effects of land use changes on some soil properties in Indaği Mountain Pass--Cankiri, Turkey

Understanding spatial variability of dynamic soil attributes provides information for suitably using land and avoiding environmental degradation. In this paper, we examined five neighboring land use types in Indagi Mountain Pass--Cankiri, Turkey to spatially predict variability of the soil organic carbon (SOC), bulk density (BD), textural composition, and soil reaction (pH) as affected by land use changes. Plantation, recreational land, and cropland were the lands converted from the woodland and grassland which were original lands in the study area. Total of 578 disturbed and undisturbed soil samples were taken with irregular intervals from five sites and represented the depths of 0-10 and 10-20 cm. Soil pH and BD had the lower coefficient of variations (CV) while SOC had the highest value for topsoil. Clay content showed greater CV than silt and sand contents. The geostatistics indicated that the soil properties examined were spatially dependent to the different degrees and interpolations using kriging showed the dynamic relationships between soil properties and land use types. The topsoil spatial distribution of SOC highly reflected the changes in the land use types, and kriging anticipated significant decreases of SOC in the recreational land and cropland. Accordingly, BD varied depending on the land use types, and also, the topsoil spatial distribution of BD differed significantly from that of the subsoil. Generally, BD greatly decreased in places where the SOC was relatively higher except in the grassland where overgrazing was the more important factor than SOC to determine BD. The topsoil spatial distributions of clay, silt, and sand contents were rather similar to those of the subsoil. The cropland and grassland were located on the very fine textured soils whereas the woodland and plantation were on the coarse textured soils. Although it was observed a clear pattern for the spatial distributions of the clay and sand changing with land uses, this was not the case for the silt content, which was attributed to the differences of dynamic erosional processes in the area. The spatial distribution of the soil pH agreed with that of the clay content. Soils of the cropland and grassland with higher amounts of clay characteristically binding more cations and having higher buffering capacities had the greater pH values when compared to the soils of other land uses with higher amounts of sand naturally inclined to be washed from the base cations by the rainwater.