Upscaling plot-based measurements of RUSLE C-factor of different leaf-angled crops in semi-arid agroecosystems.

Several models have been used to assess temporal cover change trends by using remote and proximal sensing tools. Particularly, from the point of hydrologic and erosional processes and sustainable land and soil management, it is crucial to determine and understand the variation of protective canopy cover change within a development period. Concordantly, leaf angle distribution (LAD) is a crucial parameter when using the vegetation indices (VIs) to define the radiation reflected by the canopy when estimating the cover-management factor (C-factor). This research aims to assess the C-factor of cultivated lands with sunflower and wheat that have different leaf orientations (planophile and erectophile, respectively) with the help of reduced models of NDVI and LAI for estimating crop-stage SLR values with the help of a stepwise linear regression. Those equations with R-squared values of 0.85 and 0.93 were obtained for sunflower and wheat-planted areas, respectively. The Normalized Difference Vegetation Index (NDVI), one of the two plant indices used in this study, was measured by remote and proximal sensing tools. At the same time, the Leaf Area Index (LAI) was obtained by a proximal hand-held crop sensor alone. Soil loss ratio (SLR) was upscaled for the establishment period (1P) of sunflower and the maturing period (3P) of wheat to present different growth stages simultaneously with plant-specific equations that can be easily adapted to those aforementioned crops instead of doing field measurements with conventional techniques in semi-arid cropping systems.

Monitoring seasonal and phenological variability of cover management factor for wheat cropping systems under semi-arid climate conditions.

In modeling studies, the use of spatial data derived from geographic information systems and remote sensing applications to simulate the impact of phenological and seasonal changes on soil loss has a promising effect on the accuracy of predictions. The objective of this work was to estimate the C-factor (cover management) as a dynamic-factor RUSLE (revised universal soil loss equation) model based on an NDVI (Normalized Difference Vegetation Index) approach derived from high-resolution Landsat 8 and Landsat ETM7 satellite images for 140 different rain-fed wheat parcels in terms of seasonal and phenological-based by the integrated use of remote sensing and GIS. Overall, it was found that the highest C values, an average of 0.70, were estimated for the emergence period of the wheat, while the lowest value of 0.06 was found in the booting period. Seasonally, the estimated average C values in these parcels were 0.69, 0.63, 0.13, and 0.44 for the autumn, winter, spring, and summer, respectively. Corresponding soil losses for those seasons were 1.70, 1.55, 0.28, and 1.13 t ha-1 year-1 respectively. Comparatively, without considering the phenological growing periods of wheat, the annual predicted soil loss rate was 11.5% higher than the conditions considered. The present study concluded that an assessment of seasonal and phenological changes in the C-factor for fragile ecosystems with weak crop-cover development could significantly improve the accuracy of the RUSLE model predictions and effectively manage limited soil and water resources.

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.

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.

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.

Ecosystem services studies in Turkey: A national-scale review.

The concept of "Ecosystem Services (ES)" has gained global importance since the 1990s. Today its link to sustainable development and human welfare is well documented. However, the level of know-how and the scale and effectiveness of practices differ significantly around the globe. The Ecosystem Services Partnership (ESP) National Network of Turkey aims to fill gaps in ES research and foster collaboration among experts in the public and academic sectors and non-governmental organizations. Therefore, a comprehensive review of ES studies was carried out with rigorous literature research. The review of 247 publications showed that ES research has advanced in the last two decades principally as a result of academia's impetus but increasing efforts in the science-policy interface have also supported its integration into diverse policy sectors. Among all ES, regulating ES were studied more intensely due to the growing effects of climate change on leading economic sectors such as agriculture, forestry, and water management. Monetary valuation and trade-off knowledge have remained low, based on the difficulties in data availability and assessment methods. Although protected areas are critical to biodiversity conservation, the ES concept has not been integrated into protected area management. Therefore, the ES knowledge in Turkey needs to be scaled up to cover the national level, with higher stakeholder engagement and more focused implementation driven by political will.

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.

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.

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.

Assessing the effects of land use changes on soil sensitivity to erosion in a highland ecosystem of semi-arid Turkey.

There has been increasing concern in highlands of semiarid Turkey that conversion of these systems results in excessive soil erosion, ecosystem degradation, and loss of sustainable resources. An increasing rate of land use/cover changes especially in semiarid mountainous areas has resulted in important effects on physical and ecological processes, causing many regions to undergo accelerated environmental degradation in terms of soil erosion, mass movement and reservoir sedimentation. This paper, therefore, explores the impact of land use changes on land degradation in a linkage to the soil erodibility, RUSLE-K, in Cankiri-Indagi Mountain Pass, Turkey. The characterization of soil erodibility in this ecosystem is important from the standpoint of conserving fragile ecosystems and planning management practices. Five adjacent land uses (cropland, grassland, woodland, plantation, and recreational land) were selected for this research. Analysis of variance showed that soil properties and RUSLE-K statistically changed with land use changes and soils of the recreational land and cropland were more sensitive to water erosion than those of the woodland, grassland, and plantation. This was mainly due to the significant decreases in soil organic matter (SOM) and hydraulic conductivity (HC) in those lands. Additionally, soil samples randomly collected from the depths of 0-10 cm (D1) and 10-20 cm (D2) with irregular intervals in an area of 1,200 by 4,200 m sufficiently characterized not only the spatial distribution of soil organic matter (SOM), hydraulic conductivity (HC), clay (C), silt (Si), sand (S) and silt plus very fine sand (Si + VFS) but also the spatial distribution of RUSLE-K as an algebraically estimate of these parameters together with field assessment of soil structure to assess the dynamic relationships between soil properties and land use types. In this study, in order to perform the spatial analyses, the mean sampling intervals were 43, 50, 64, 78, 85 m for woodland, plantation, grassland, recreation, and cropland with the sample numbers of 56, 79, 72, 13, and 69, respectively, resulting in an average interval of 64 m for whole study area. Although nugget effect and nugget effect-sill ratio gave an idea about the sampling design adequacy, the better results are undoubtedly likely by both equi-probable spatial sampling and random sampling representative of all land uses.

Use of USLE/GIS methodology for predicting soil loss in a semiarid agricultural watershed.

The Universal Soil Loss Equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices in agricultural watersheds by the effective integration of the GIS-based procedures to estimate the factor values in a grid cell basis. This study was performed in the Kazan Watershed located in the central Anatolia, Turkey, to predict soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Rain erosivity (R), soil erodibility (K), and cover management factor (C) values of the model were calculated from erosivity map, soil map, and land use map of Turkey, respectively. R values were site-specifically corrected using DEM and climatic data. The topographical and hydrological effects on the soil loss were characterized by LS factor evaluated by the flow accumulation tool using DEM and watershed delineation techniques. From resulting soil loss map of the watershed, the magnitude of the soil erosion was estimated in terms of the different soil units and land uses and the most erosion-prone areas where irreversible soil losses occurred were reasonably located in the Kazan watershed. This could be very useful for deciding restoration practices to control the soil erosion of the sites to be severely influenced.