Rainfall erosivity is a crucial factor influencing soil erosion, which results from the detachment, transportation, and deposition of soil particles caused by rainfall and runoff. Rainfall erosivity ...assessment is a fundamental process aimed at quantifying the erosive power of rainfall events in a given region. The paper provides an overview of rainfall erosivity assessment in several heterogeneous regions of the Republic of North Macedonia based on rainfall data from meteorological stations characterized by different climatic and geographical conditions. Having into consideration that rainfall erosivity can be quantified by using various indices, this study uses two common ones: Fournier Index (FI) and the Modified Fournier Index (MFI) based on monthly and annual precipitation totals for the period 1951-2020. The results imply that in general, the studied stations belong to a low class of erosivity. It is found that FI has greater sensitivity and provides more details, also showing years with high and even very high rainfall erosivity while MFI gives more information for moderate rainfall erosivity.
•Climate shift-erosion dynamics in shaping landscape evolution is emphasised.•Erosivity density and particulate sediment in Rhône prodelta coevolved over 1858–2022.•Rising rainfall erosivity unveils ...mounting erosive events in France after 1964.•These findings inform landscape management and conservation strategies.
Understanding the historical context of rainfall erosivity enhances comprehension of climate and environmental changes, guiding strategies to address global environmental challenges. However, the understanding of the interplay between modern-era environmental hydrology, climate change, and storm evolution remains limited, despite the pivotal role of storm disasters in global environmental change, especially in inland-basin areas. To address this knowledge gap, we present the longest interannual rainfall erosivity history in France, with a specific focus on the area of Clermont-Ferrand, covering the period 1858–2022. Using the Integrated Rainfall Erosivity Model (IREM) approach, refined with process-based insights and expert knowledge, we analyzed the erosivity data and correlated them with environmental and climatic changes. Our findings indicate a rapid and abrupt change in rainfall erosivity around 1964, which we attribute to the negative phase of the North Atlantic Oscillation and a positive phase shift of the Dipole Mode Index. Moreover, the Mann-Kendal statistic demonstrates a significant positive trend in extreme rainfall erosivity (95th percentile) over the entire time-series. A notable finding is the strong coevolution observed between the erosivity density indicator and particulate organic carbon, modulated by multidecadal environmental hydrology in the Rhône River Basin (RRB). These results shed light on the complex interactions among storms, climate, and sediment organic matter, offering insights for environmental management and conservation strategies in the RRB and other regions with similar hydrological characteristics.
•The rainfall erosivity increased in the karst region of SW China from 1901 to 2020.•There are significant differences in the spatial distribution of annual, seasonal and monthly rainfall ...erosivity.•The trend of increasing rainfall erosivity is more obvious in January in winter.•More variability is found in the monthly scale study.
Global warming changes global and regional patterns of precipitation, which will inevitably lead to more severe soil erosion risks in fragile karst ecosystems. Understanding the variation pattern of rainfall erosivity is vital for preventing and controlling soil erosion in the karst region of southwestern (SW) China. In this study, the spatiotemporal dynamic evolution pattern of the rainfall erosivity throughout the year in the karst region of SW China was explored using regression analysis and the Mann–Kendall test based on the monthly rainfall data for 1901–2020 in the CRU_TS 4.05 dataset. The results indicate that: (1) The rainfall erosivity in the karst region of SW China varies with latitude and is more significant in the south, and its interannual variations are linear and positive. (2) The interannual linear increase in rainfall erosivity was the greatest in the peak forest-plain rocky desertification control zone (FLPY). (3) The rainfall erosivity exhibited an increasing trend in spring, summer, and winter, with the most significant increase in January. (4) More variability was observed on the monthly scale. In summary, future investigations and control of soil erosion in the karst region of SW China under climate change should focus on the critical times when (e.g., January) and areas where (e.g., peak forest-plain rocky desertification control zone) rainfall erosivity changes significantly. Identifying the shift in the rainfall erosivity patterns caused by climate change is critical for assessing soil erosion risks in karst areas and for formulating countermeasures.
Three studies used empirical equations to calculate the rainfall erosivity factor R, and all three equations appeared to be incorrect. All of the studies were published in the journal Science of the ...Total Environment, and none of them accurately cited the sources of the incorrect equations that were used in them. We were able to track down the original equation as well as the source of the equation. Additionally, it was discovered that the original equation contained an incorrect conversion factor, which needs to be corrected.
•Daily historical CFSR rainfall data was bias corrected by observed data.•Future soil erosion is mapped for the first time at Iran scale by five CMIP6 GCMs.•Present soil erosion in Iran is estimated ...at 6.2 ton/ha/year.•The highest soil erosion is expected in western and northern regions.•The lowest future soil erosion is projected by GFDL-ESM4 model.
In the past few decades, there has been increasing interest in studying various environmental phenomena derived from climate change and its social, environmental, and economic serious impacts. It is particularly critical to developing countries where resources are limited, and conditions may worsen in the midst of environmental disasters caused by soil erosion, leading to indispensable costs in resources and human lives. Since no studies have investigated these implications on a regional scale for the case of Iran, here we assessed the impact of climate change on soil erosion across the country. The RUSLE model was employed to estimate potential soil erosion based on historical climate records and projected future data to assess climate change implications. Daily rainfall data (1987–2006) from 103 meteorological stations were used to bias correct the Climate Forecast System Reanalysis (CFSR) rainfall data. Furthermore, CFSR data, as the historical record period data (1987–2006), was used for bias correcting five General Circulation Models (GCMs) to develop rainfall erosivity values for the future climate period (2046–2065) under SSP2-4.5 and SSP5-8.5 scenarios. Based on these data sets, rainfall erosivity maps were developed to compare the variations with projected climate results across Iran. Results show an overall decrease in rainfall erosivity and soil erosion in most provinces. Considering the average outputs of five GCMs and under both SSPs, the most affected provinces with projected soil erosion decrease are P.11 and P.14, whereas the highest increase in soil erosion is likely to occur in P.13 by SSP5-8.5. In the northern provinces, particularly in P.7, the highest increase in soil erosion is expected. It was also noted that the highest range of soil erosion change among the five selected GCMs occurs at P.27, with the maximum increase of above 135% and the minimum decrease of 71% from the historical period. The present study represents the first assessment of soil erosion at a large scale in Iran, providing an overview of soil erosion that may be useful for soil and water conservation planning, hazard mapping, agriculture, and other activities that are subject to soil erosion.
•The parameters of the empirical models for rainfall erosivity estimation vary with month.•The new reconstructed rainfall erosivity estimates closely correlated with the observed values.•The rainfall ...erosivity over the TP presents a significant increasing trend over the past 40 years.
As a typical fragile ecological plateau area, the risk of water erosion on the Tibetan Plateau (TP) in China continues to increase with climate change. Rainfall erosivity is one of the most widely used indicators to assess the potential impact of rainfall events on water erosion. However, limited by the scarcity of historical in situ precipitation observations with sufficient spatiotemporal resolution, the estimates of rainfall erosivity over the TP have much larger biases than those of other regions in China. To accurately investigate the spatiotemporal evolution of rainfall erosivity, empirical models were first established to estimate monthly rainfall erosivity using 1-minute in situ precipitation observations from 1711 meteorological stations on the TP. The independent assessment showed that the correlation correction values between the observed and estimated monthly values were greater than 0.81 for all months. The annual rainfall erosivity data were then produced with a 0.1° spatial resolution for the 1979–2018 period based on the China Meteorological Forcing Dataset (CMFD) precipitation data using newly established estimation models. Our results show that the CMFD-based estimates successfully captured the decreasing spatial pattern of the multiyear average annual rainfall erosivity from the southeast to northwest of the TP. In addition, the CMFD-based annual rainfall erosivity had a good linear relationship with the observed values but with a certain overestimation. Therefore, standardized annual rainfall erosivity values were used to detect the changes in rainfall erosivity. For most regions on the TP, annual rainfall erosivity values have exhibited significant increasing trends over the last 40 years. This study provides a theoretical basis and reference for controlling water erosion and climate adaptation on the TP.
Geodetic landmarks (GLs) are essential for obtaining precise height, horizontal coordinates, and the Earth's gravity field. This study aims to assess the soil susceptibility of GLs for past, present, ...and future scenarios, considering the projected anthropogenic effect from Coupled Model Intercomparison Project, Version 6 (CMIP6). Therefore, the soil loss estimations were made for all the GLs in the southern region of Santa Catarina state in Brazil using the Revised Soil Loss Equation (RUSLE). Our results showed average soil loss decreasing from 1985 to 2020. There was an increase in GLs in the null class (soil loss = 0 t/ha/year), mainly caused by urban growth. A decrease occurred in the low (0 t/ha/year < soil loss ≤10 t/ha/year), very severe (soil loss >200 t/ha/year), severe (50 t/ha/year < soil loss ≤200 t/ha/year), and moderate classes (10 t/ha/year < soil loss ≤50 t/ha/year). In addition, most future scenarios projected an increase in soil loss susceptibility, which also increased the GLs' susceptibility to soil loss from 2020 to 2100, albeit with lower values than the historical series. The soil loss remained stable in ssp126, slightly increased in ssp245 and ssp370, and increased in ssp585. The future scenarios only take into account changes in rainfall. Thus, the land cover change forecast would also be necessary for better analysis for future studies. Therefore, climate simulations can be used to understand the effects of climate change on soil erosion to support decision-making regarding GLs maintenance and the construction of new ones.
•Soil erosion can affect the physical integrity of a geodetic network;•Landmarks can be destabilized and become useless;•Rainfall, soil types, relief, land use and land cover are important factors;•The RUSLE equation allows to determine soil loss in the past, present, and future;•Understanding future climate impacts can be important in preserving landmarks.
•We made updated R-factor maps for Austria’s main agricultural production zones.•Significant differences in spatial R-factor distribution were found between studies.•Spatiotemporally distributed ...R-factors revealed areas at risk of soil erosion.
Rainfall erosivity is one of the key parameters influencing the degree of soil erosion. Due to the high spatiotemporal variability of rainfall erosivity and the influence of a changing climate it is crucial to use spatially well-distributed and temporally current rainfall data. Rainfall erosivity in Austria has been estimated by previous studies with varying rainfall data amounts. This study aimed to create an updated R-factor map for Austria and its main agricultural production zones based on a larger number of rainfall stations and a recent time series. As well as, compare R-factors from previous studies to identify differences in erosivity estimation. Rainfall data from 171 stations throughout Austria were gap-filled and corrected to improve data quality. Rainfall erosivity was calculated for 1995–2015 for the vegetation period and annually and used to establish two linear regressions describing rainfall erosivity as a function of mean rainfall amount. The regressions were applied to the 1 km2 daily rainfall grids from the SPARTACUS dataset to create the spatially distributed rainfall erosivity maps. Differences in the temporal and spatial distribution of rainfall erosivity, erosion index and erosivity density between the main agricultural production zones showed areas at risk of soil erosion and timing of vulnerability. The highest rainfall erosivities were found in the agriculturally important eastern regions of Austria during the summer months. Compared to previous studies, considerable differences in local R-factor estimation were found. The significantly larger number of rainfall stations and an updated time series increased the representativeness of rainfall erosivity estimation in Austria, which can contribute to a more precise soil erosion risk assessment.
•Erosivity can be calculated well by a power law equation with cosine relation.•The magnitude of rainfall erosivity varies unevenly among seasons.•Increasing trend of annual and seasonal rainfall ...erosivity was observed.•Erosivity density shown a significant high correlation with rainfall erosivity.•The gravity centre of rainfall erosivity has shifted across the three provinces.
Rainfall erosivity, a measure of the potential for soil erosion by water, is an important factor for estimating soil loss. Understanding the variation tendency of rainfall erosivity is especially critical for soil and water conservation in the fragile ecological environment of the karst region in southern China. This study analysed the rainfall erosivity at multiple spatial and temporal scales based on daily rainfall data observed at 223 meteorological stations during the period of 1960–2017. A daily rainfall erosivity model, co-kring interpolation, regression analysis, gravity model and the Mann-Kendall test were applied in the analysis process. The results indicate the following: (i) The mean annual rainfall erosivity is 5130.00 MJ·mm·ha−1·h−1 in southern China, with a range of 3964.24 to 6425.87 MJ·mm·ha−1·h−1, and varies greatly among different provinces. (ii) The magnitude of rainfall erosivity varies unevenly among seasons, with the mean rainfall erosivity in summer being almost 15 times higher than that in winter. (iii) The annual and seasonal rainfall erosivity has increased in the karst region of southern China over the past 58 years, whereas at the province scale, the seasonal trend in rainfall erosivity is more complex, and the trends are not necessarily linear and positive. Furthermore, at the interdecadal scale, there is no regular trend, and the data exhibit considerable variation. (iv) The temporal variation characteristics of erosivity density are basically consistent with those of rainfall erosivity, and the two show a significant high correlation. (v) The gravity centre of annual rainfall erosivity is located in Tongdao County, while the monthly gravity centre has shifted across Guangxi, Hunan and Guizhou. In summary, knowledge of rainfall erosivity patterns is valuable for assessing the risk of soil erosion and formulating countermeasures.
Optical disdrometers can be used to estimate rainfall erosivity; however, the relative accuracy of different disdrometers is unclear. This study compared three types of optical laser-based ...disdrometers to quantify differences in measured rainfall characteristics and to develop correction factors for kinetic energy (KE). Two identical PWS100 (Campbell Scientific), one Laser Precipitation Monitor (Thies Clima) and a first-generation Parsivel (OTT) were collocated with a weighing rain gauge (OTT Pluvio
2
) at a site in Austria. All disdrometers underestimated total rainfall compared to the rain gauge with relative biases from 2% to 29%. Differences in drop size distribution and velocity resulted in different KE estimates. By applying a linear regression to the KE-intensity relationship of each disdrometer, a correction factor for KE between the disdrometers was developed. This factor ranged from 1.15 to 1.36 and allowed comparison of KE between different disdrometer types despite differences in measured drop size and velocity.
PDF