Spatial Distribution Of Rainfall Erosivity Research Articles

Unravelling the future changes in rainfall erosivity over India under shared socio-economic pathways

The rainfall erosivity index or R-factor is a climate-driven parameter affecting soil erosion. We studied the spatial distribution of rainfall erosivity over India for the historical (1951 – 2014) and two future periods (near future: 2015–2040; far future: 2041–2070) under two shared socio-economic pathways (SSP245 and SSP585). We used 15-minute rainfall datasets from 70-gauge stations to establish a relationship between rainfall erosivity estimated from 15-minute rainfall data and those estimated from 30 and 60-minute rainfall data. Further, we used 30,203 erosive storm rainfall data from 329 stations in India to estimate the daily rainfall erosivity model parameters. After that, annual rainfall erosivity values of 329 stations, nine bio-climatic variables, and a Gaussian Process Regression (GPR) model were used to prepare rainfall erosivity maps for the historical and two future periods. The bias-corrected Coupled Model Intercomparison Project-6 (CMIP6) dataset of 13 models was used for deriving bioclimatic variables and rainfall erosivity estimation for the historical and future periods. We used the GPR model to find the historical period's mean annual rainfall erosivity value as 5225 MJ.mm.ha−1.h−1.year−1. The result shows a +13 % and +15 % increase in the mean rainfall erosivity over India in the near future under SSP245 and SSP585 projected pathways compared to the historical period. The increase in erosivity values in the far future is even higher at +22 % and +31 % under SSP245, and SSP585 projected pathways, respectively. The northwestern and central India is likely to have a pronounced increase in rainfall erosivity in the far future. The change in rainfall erosivity due to climate change might substantially impact India's land and water resources, which is mainly an agrarian economy. The outcome of the present study could help in better planning and management of land and water resources in the upcoming decades.

Study on multidimensional changes of rainfall erosivity during 1970–2017 in the North–South Transition Zone, China

China is one of the world’s most seriously affected regions by water and soil erosion. Soil erosion is a major cause and an important component of land degradation, which has a negative impact on ecological protection and sustainable socioeconomic development. Rainfall erosivity (RE) is one of the key parameters to assess the degree of soil erosion. Quantifying the content of RE and the formation mechanism is important to accurately measure the degree of soil erosion and provide a theoretical basis for soil erosion management. Here, this study analyzed the spatial and temporal characteristics of RE and their driving mechanisms in the Qinba Mountains from 1970 to 2017 using a daily rainfall model. Furthermore, geographical detector methods were used to quantitatively identify the dominant factors affecting RE and the dominant factors affecting RE on different topographic reliefs. The results showed that the RE between 1970 and 2017 averaged 4,197.85 MJ mm hm−2 h−1 a−1, with a mutation coefficient of 0.16. The spatial distribution of RE is high in the southeast and low in the northwest, and the mean annual RE declines with the increase in latitude in longitude and increases with the reduction in longitude in latitude. In addition, precipitation and temperature are the main factors affecting the spatial distribution of RE. Among these, precipitation can explain about 97% of the RE and temperature can explain about 65% of the RE. These findings should be essential for managing soil and water loss in the North–South Transition Zone, China.

Spatial and temporal variations of rainfall erosivity in the middle Yellow River Basin based on hourly rainfall data

Understanding spatial and temporal variations in rainfall erosivity is essential for estimating the variability in soil erosion. However, rainfall erosivity in the middle Yellow River Basin is usually calculated based on daily rainfall data, which fails to consider the impact of the changing rainfall intensity at the subdaily scale. In this study, we collected hourly rainfall data from 355 meteorological stations within and around the region to calculate rainfall erosivity during 1960–2017. We found that the rainfall erosivity calculated based on hourly data had much larger variability than that calculated based on daily data, and the calculated spatial and temporal variability were substantially different in periods when the rainfall intensity was highly variable. Based on our new estimation, we found that the spatial distribution of rainfall erosivity was highly heterogeneous, with high erosivity values in the Loess Regions and Rocky Mountain Regions with high soil erodibility and low erosivity values in the Aeolian Regions and Soft Sandstone Regions with low soil erodibility. This heterogeneity was mainly due to the distinct spatial distribution patterns of rainfall amount and rainfall intensity. Although the long-term increasing trend of rainfall erosivity was statistically insignificant from 1960 to 2017, its rate of increase was higher than that of the annual rainfall amount. Decadal changes showed a slight decrease from the 1960 s to the 1980 s and an increase since the 1990 s, which was substantially different from the decadal changes in the annual rainfall amount. These differences in temporal variability were mainly caused by the contribution of changing rainfall intensity, which was greater in the Aeolian Regions and Soft Sandstone Regions than in the other subregions. Our study highlighted the importance of considering the changing rainfall intensity at a subdaily scale in semiarid and semihumid climate zones and provided the first estimation of the long-term variability in rainfall erosivity based on hourly rainfall data in this region.

An update of the spatial and temporal variability of rainfall erosivity (R-factor) for the main agricultural production zones of Austria

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.

Database of rainfall erosivity factor for 141 locations in Brazil.

Soil erosion is a serious agricultural and environmental problem considered as a threat to sustainable development around the world. Rainfall is the primary cause of soil erosion, what leads the knowledge of its potential to cause soil erosion (rainfall erosivity – R-factor) to be a valuable tool for the design of land conservation best practices. As Brazil has a lack of information about rainfall erosivity, the present paper has determined the R-factor of 141 pluviographic stations distributed over Brazilian territory. Initially, erosive rainfalls were identified, and then the EI30 erosivity index was used to obtain the rainfall erosivity values. Regression models for the estimation of rainfall erosivity using daily rainfall data were established based on the correlation between the monthly average values of erosivity and the modified Fournier index. Results showed that the annual rainfall erosivity in the Brazilian stations analyzed ranged from 368.7 to 16,850.6 MJ mm ha-1 h-1 year-1. The results presented help to expand information about the spatial distribution of rainfall erosivity in Brazil, contributing to better conservation planning of land use.

Temporal and spatial variation of rainfall erosivity in the Loess Plateau of China and its impact on sediment load

Rainfall erosivity is one of the key dynamic factors leading to water erosion, which causes widespread soil erosion worldwide. This study calculated the rainfall erosivity from 1965 to 2019, based on daily precipitation data, for 17 watersheds on the Loess Plateau. The data were also used to analyze the temporal and spatial variation of rainfall erosivity, and assess the impact of rainfall erosivity changes on sediment load in these typical watersheds. The possible causes of rainfall erosivity and sediment load changes are also discussed. The results of the study revealed that on different time scales, the spatial distribution of rainfall erosivity showed a pattern of decreasing from southeast to northwest in the Loess Plateau. Moreover, the rainfall erosivity measured by some weather stations increased significantly in May, June, and September (p < 0.05). Additionally, the changes brought on by ENSO and sunspots had a specific influence on the changes of rainfall erosivity in the Loess Plateau. Furthermore, the sediment load in the typical watersheds of the Loess Plateau showed a significant decreasing trend in yearly and monthly time scales (p < 0.05). Before 1980, the change in rainfall erosivity was an important reason for the change of sediment load and the construction of backbone check dams also intercepted a large amount of sediment. However, from 1980 to 1998, the interception effect of backbone check dams and the increase in vegetation together caused sediment load changes. After 1999, the restoration of vegetation was the main factor instigating a further reduction in sediment load. Studying the changes in the rainfall erosivity will provide a useful reference for future ecological construction and soil erosion control in the Loess Plateau.

Spatiotemporal Variation in Rainfall Erosivity and Correlation with the ENSO on the Tibetan Plateau since 1971.

Soil erosion is a serious ecological problem in the fragile ecological environment of the Tibetan Plateau (TP). Rainfall erosivity is one of the most important factors controlling soil erosion and is associated with the El Niño southern oscillation (ENSO). However, there is a lack of studies related to the spatial distribution and temporal trends of rainfall erosivity on the TP as a whole. Additionally, the understanding of the general influence of ENSO on rainfall erosivity across the TP remains to be developed. In this study, long-term (1971–2020) daily precipitation data from 91 meteorological stations were selected to calculate rainfall erosivity. The analysis combines co-kriging interpolation, Sen’s slope estimator, and the Mann–Kendall trend test to investigate the spatiotemporal patten of rainfall erosivity across the TP. The Oceanic Niño Index (ONI) and multivariate ENSO Index (MEI) were chosen as ENSO phenomenon characterization indices, and the relationship between ENSO and rainfall erosivity was explored by employing a continuous wavelet transform. The results showed that an increasing trend in annual rainfall erosivity was detected on the TP from 1971 to 2020. The seasonal and monthly rainfall erosivity was highly uneven, with the summer erosivity accounting for 60.36%. The heterogeneous spatial distribution of rainfall erosivity was observed with an increasing trend from southeast to northwest. At the regional level, rainfall erosivity in the southeastern TP was mainly featured by a slow increase, while in the northwest was more destabilizing and mostly showed no significant trend. The rainfall erosivity on the whole TP was relatively high during non-ENSO periods and relatively low during El Niño/La Niña periods. It is worth noting that rainfall erosivity in the northwest TP appears to be more serious during the La Niña event. Furthermore, there were obvious resonance cycles between the rainfall erosivity and ENSO in different regions of the plateau, but the cycles had pronounced discrepancies in the occurrence time, direction of action and intensity. These findings contribute to providing references for soil erosion control on the TP and the formulation of future soil conservation strategies.

Spatial and Temporal Patterns of Rainfall Erosivity in the Tibetan Plateau

The Tibetan Plateau is influenced by global climate change which results in frequent melting of glaciers and snow, and in heavy rainfalls. These conditions may increase the risk of soil erosion, but prediction is not feasible due to scarcity of rainfall data in the high altitudes of the region. In this study, daily precipitation data from 1 January 1981 to 31 December 2015 were selected for 38 meteorological stations in the Tibetan Plateau, and annual and seasonal rainfall erosivity were calculated for each station. Additionally, we used the Mann–Kendall trend test, Sen’s slope, trend coefficient, and climate tendency rate indicators to detect the temporal variation trend of rainfall erosivity. The results showed that the spatial distribution of rainfall erosivity in the Tibetan Plateau exhibited a significant decreasing trend from southeast to northwest. The average annual rainfall erosivity is 714 MJ·mm·ha−1·h−1, and varies from 61 to 1776 MJ·mm·ha−1·h−1. Rainfall erosivity was mainly concentrated in summer and autumn, accounting for 67.5% and 18.5%, respectively. In addition, annual, spring, and summer rainfall erosivity were increasing, with spring rainfall erosivity highly significant. Temporal and spatial patterns of rainfall erosivity indicated that the risk of soil erosion was relatively high in the Hengduan mountains in the eastern Tibetan Plateau, as well as in the Yarlung Zangbo River Valley and its vicinity.

Variation characteristics of rainfall erosivity in Guizhou Province and the correlation with the El Niño Southern Oscillation.

Rainfall erosivity is an important indicator that can be used to measure the ability of rain to cause erosion and is connected with the El Niño Southern Oscillation (ENSO) through the transmission of rainfall. This work aimed to explore the characteristics of rainfall erosivity in Guizhou Province and determine its correlation with ENSO. Rainfall erosivity was calculated from daily rainfall data from January 1960 to December 2017. The analyses were conducted using a daily rainfall erosivity model, inverse distance weighted (IDW) interpolation, linear regression analysis, Mann-Kendall test and correlation analysis. The long-term (1960-2017) average rainfall erosivity was 5825.60 MJ·mm·ha-1·h-1 in the study area and showed a high temporal variability with the estimates from the linear trend line ranging from -449.5 MJ·mm·ha-1·h-1/10a to 496.8 MJ·mm·ha-1·h-1/10a. According to rainfall and erosive rainfall, an uneven spatial distribution of rainfall erosivity was observed with an increasing trend from south to north. Temporal distribution of monthly rainfall erosivity was consistent with that of seasonal rainfall erosivity, and concentrated in the summer months (June to August). As the representation indices of ENSO phenomena, the Oceanic Niño Index (ONI), Southern Oscillation Index (SOI) and multivariate ENSO Index (MEI) were selected for correlation analysis with rainfall erosivity. During El Niño events, the ONI, SOI and MEI showed significant correlations (>95% confidence level) with rainfall erosivity, while during La Niña events, only the ONI and MEI were significantly correlated with rainfall erosivity, but no significant correlation was detected during the neutral period or for the entire study period. The degree of rainfall erosion is proportional to the ENSO duration; the longer the ENSO duration, the greater the rainfall erosivity. These findings could help predict soil erosion and be used to develop further adaptation measures to prevent water and soil loss.

Determination of erosion rainfall criteria based on natural rainfall measurement and its impact on spatial distribution of rainfall erosivity in the Czech Republic

Rainfall erosivity is the main factor of the USLE or RUSLE equations. Its accuracy depends on recording precision and its temporal resolution, number of stations and their spatial distribution, length of recorded period, recorded period, erosion rainfall criteria, time step of rainfall intensity and interpolation method. This research focuses on erosion rainfall criteria. A network of 32 ombrographic stations, 1-min temporal resolution rainfall data, 35.6-year period and experimental runoff plots were used. We analysed 8951 rainfalls from ombrographic stations, 100 rainfalls and caused soil losses and runoffs from experimental runoff plots. Main parameter which influenced the number of erosion rainfalls was the precondition AND/OR which determines if conditions of rainfall total (H) have to be fulfilled simultaneously with rainfall intensity (I&lt;sub&gt;15&lt;/sub&gt; or I&lt;sub&gt;30&lt;/sub&gt;) or not. We proved that if parameters I&lt;sub&gt;15 &lt;/sub&gt;&amp;gt; 6.25 mm/15 min AND H &amp;gt; 12.5 mm were fulfilled, then 84.2% of rainfalls caused soil loss &amp;gt; 0.5 t/ha and 73.7% ≥ 1 t/ha. In the case of precondition OR only 44.6% of rainfalls caused soil loss &amp;gt; 0.5 t/ha and 33.9% ≥ 1 t/ha. If the precondition AND was fulfilled, there were on average 75.5 rainfalls, average R factor for each rainfall was 21 MJ/ha·cm/h (without units below in the text, according international unit: 210 MJ/ha·mm/h) and average annual R factor was 45.4. In the case of precondition OR there were on average 279 rainfalls but average R factor for each rainfall was only 9.1 and average annual R factor was 67.4. Therefore if the precondition OR is used, R factor values are overestimated due to a high number of rainfalls with no or very low erosive potential. The resulting overestimated soil losses calculated using USLE/RUSLE subsequently cause an overestimation of financial expenses for erosion-control measures.

Spatial–temporal distribution of rainfall erosivity, erosivity density and correlation with El Niño–Southern Oscillation in the Huaihe River Basin, China

Rainfall erosivity (RE) is the ability of rainfall to cause soil or regolith erosion. Understanding the spatial distribution and temporal trends of RE is critical for assessing soil erosion risk and improving upon soil conservation planning. The aim of this paper is to study the temporal and spatial changes in RE in the Huaihe River Basin, China. This will be based on daily precipitation data from 67 meteorological stations in the Huaihe River Basin for the period 1971 to 2016. The assessment of the resulting RE values involved inverse distance-weighted (IDW) interpolation, Sen's slope estimation, and the Mann-Kendall nonparametric test. In addition, the possible influence of the El Niño – Southern Oscillation (ENSO) on RE in the Huaihe River Basin will be examined by the use of Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC), where the relationship between RE and the multivariate ENSO index (MEI) are analyzed. The results showed that the spatial distribution of RE is characterized by it increasing from west to east and north to south. In most areas, the annual RE has increased slightly, while this upward trend in the southeastern part of the study area was more significant. The rainfall erosivity of rainstorms (RE-SM) in the Huaihe River Basin played a leading role in the annual RE, and 97% of the stations displayed the order of importance of the different categories of rainfall as rainfall erosivity of rainstorms (RE-SM) > rainfall erosivity of heavy rain (RE-HR) > rainfall erosivity of moderate rain (RE-MR). Over monthly time scales, the RE was the highest in July, while it was lowest in December, with the monthly differences being apparent. The RE in the Huaihe River Basin were relatively large during non-El Niño/La Niña periods and relatively small during El Niño/La Niña periods. Finally, correlations between RE and the MEI in various parts of the basin showed different characteristics over time and space, with both displaying similarities and differences.

Land-use Assessment and its Influence on Spatial Distribution of Rainfall Erosivity: Case Study of Cameron Highlands Malaysia

Land-use Assessment and its Influence on Spatial Distribution of Rainfall Erosivity: Case Study of Cameron Highlands Malaysia

Spatial and temporal variations of rainfall-runoff erosivity (R) factor in Kakheti, Georgia

Abstract Soil erosion is a very complicated process. Rainfall erosivity is one of the main factors affecting on soil erosion. The erosive power of precipitation is accounted for by the rainfall erosivity factor (R-factor). Rainfall erosivity (R-factor) itself is a very important factor in soil erosion modeling. R-factor is a product of rainfall kinetic energy and rainfall intensity. Rainfall intensity change is one of the main indicators of climate change. It has a great influence on agriculture as one of the main factors causing soil erosion. Information of rainfall erosivity is rarely available with good spatial and temporal coverage. Accurate estimation of rainfall erosivity requires continuous rainfall data. Because many parts of the world still do not have detailed rainfall intensity data available, many studies have been performed to estimate R-factor based on available rainfall data. There are several alternative methods cited in science literature. This study aims to evaluate the temporal as well as the spatial distribution of rainfall erosivity and to calculate average annual rainfall erosivity for three study periods (1936–1962; 1963–1989; 1990–2016) in Kakheti, east Georgia. As far as Kakheti is the agrarian region, frequency and intensity of the rain are very important factors in agriculture point of view. Our study provides the assessment of rainfall erosivity potential with use of modern research methods for five weather stations (Telavi, Gurjaani, Sagarejo, Dedoplistskaro and Lagodekhi) in Kakheti. Rainfall erosivity potential was determined for every weather stations in Kakheti region from literature and records from meteorological stations. Then the same factor was determined by the selected methods (for each method separately), and the outcomes was compared, which allows us to determine the validity of a particular method for the study area. From the three methods used in the study process, method by Loureiro & Cautinho was finally used for the assessment rainfall erosivity during three study periods.

Spatio-Temporal Variation in Rainfall Erosivity over Jordan Using Annual and Seasonal Precipitation

The objective of this research is to estimate the annual and seasonal rainfall erosivity over Jordan based on three different regression models. Readily available annual and seasonal precipitation data with long records (40 - 53 years) pertaining to 40 weather stations were utilized to estimate rainfall erosivity. The spatial distribution of rainfall erosivity over Jordan is controlled largely by morphological (relief) and climatic factors. The lowest R-values (28 MJ mm.ha-1.h-1.yr-1) are found in the arid zone, where the average annual rainfall is below 100 mm, whereas the highest R-values are found in the northern highlands (505 MJ mm.ha-1.h-1.yr-1) where the average annual rainfall approaches 650 mm. The correlation between annual and seasonal precipitation (mm) and annual erosivity exhibits a very strong relationship (R varies from 0.964 to 1.0, and all correlations are significant at 0.01 level [2-tailed test]). Moderate positive correlations were achieved between latitude (N) and the mean annual/seasonal precipitation (R ranges from 0.407 to 0.642, and all correlations are significant at 0.01 level [2-tailed test]). Spatial differences observed in erosivity, afforded a substantial source of information and maps for predicting erosion in Jordan. According to the present analysis, two parameters proved to be useful to predict rainfall erosivity on a national level. These parameters are the average annual precipitation, and latitude.

Evaluation of discrepancies in spatial distribution of rainfall erosivity in the Czech Republic caused by different approaches using GIS and geostatistical tools

The study presents all approaches of rainfall erosivity factor (R) computation and estimation used in the Czech Republic (CR). A lot of distortions stem from the difference in erosive rainfall criteria, time period, tipping rain gauges errors, low temporal resolution of rainfall data, the type of interpolation method, and inappropriate covariates. Differences in resulting R values and their spatial distribution caused by the described approaches were analyzed using the geostatistical method of Empirical Bayesian Kriging and the tools of the geographic information system (GIS). Similarity with the highest temporal resolution approach using 1-min rainfall data was analyzed. Different types of covariates were tested for incorporation to the cokriging method. Only longitude exhibits high correlation with R and can be recommended for the CR conditions. By incorporating covariates such as elevation, with no or weak correlation with R, the results can be distorted even by 81%. Because of significant yearly variation of R factor values and not clearly confirmed methodology of R values calculation and their estimation at unmeasured places we recommend the R factor for agricultural land in the Czech Republic R = 40 MJ/ha·cm/h +/– 10% depends on geographic location.

Spatial distribution and temporal trends of rainfall erosivity in mainland China for 1951–2010

Rainfall erosivity is an important factor for estimating soil erosion rates. Understanding the spatial distribution and temporal trends of rainfall erosivity is especially critical for soil erosion risk assessment and soil conservation planning in mainland China. However, reports on the spatial distribution and temporal trends of rainfall erosivity for China, especially of its eight soil erosion regions, are still lacking, which reduces the accuracy of predicting soil losses, assessing soil erosion risks and evaluating the effects of soil conservation measures. Additionally, the lack of the most suitable spatial interpolation method in mainland China, to some degree, has reduced the applicability and reliability of the interpolation results. In this study, long-term (1951–2010) daily rainfall data from 756 national weather stations were assembled to characterize the spatial and temporal patterns of annual rainfall erosivity across mainland China. Sixteen spatial interpolation methods were compared to select the most suitable one for accurately mapping the spatial distribution of rainfall erosivity, and the Mann-Kendall test was employed to detect the temporal trends. The results indicated that 1) the universal co-kriging method with the aid of elevation was superior to the other spatial interpolation methods; 2) long-term average rainfall erosivity increased from the northwest to the southeast, ranging from 31 to 30,051MJmmha−1h−1a−1; 3) overall, rainfall erosivity across China and water erosion regions experienced an insignificant increasing trend over the study period. Significant decreasing trends were observed in the northwest Loess Plateau region (0.01 level), the northeast black soil region and the north earth and gravel mountain region (0.05 level). Significant increasing trends (0.05 level) were found in the southern red soil hilly region and the southwest Karst region; and 4) two lines were identified according to the temporal trends of rainfall erosivity from the east to the west. In total, this study offers useful information both for soil erosion prediction and land management practices of mainland China.

Rainfall erosivity in catchments contaminated with fallout from the Fukushima Daiichi nuclear power plant accident

Abstract. The Fukushima Daiichi nuclear power plant (FDNPP) accident in March 2011 resulted in the fallout of significant quantities of radiocesium over the Fukushima region. After reaching the soil surface, radiocesium is quickly bound to fine soil particles. Thereafter, rainfall and snowmelt run-off events transfer particle-bound radiocesium downstream. Characterizing the precipitation regime of the fallout-impacted region is thus important for understanding post-deposition radiocesium dynamics. Accordingly, 10 min (1995–2015) and daily precipitation data (1977–2015) from 42 meteorological stations within a 100 km radius of the FDNPP were analyzed. Monthly rainfall erosivity maps were developed to depict the spatial heterogeneity of rainfall erosivity for catchments entirely contained within this radius. The mean average precipitation in the region surrounding the FDNPP is 1420 mm yr−1 (SD 235) with a mean rainfall erosivity of 3696 MJ mm ha−1 h−1 yr−1 (SD 1327). Tropical cyclones contribute 22 % of the precipitation (422 mm yr−1) and 40 % of the rainfall erosivity (1462 MJ mm ha−1 h−1 yr−1 (SD 637)). The majority of precipitation (60 %) and rainfall erosivity (82 %) occurs between June and October. At a regional scale, rainfall erosivity increases from the north to the south during July and August, the most erosive months. For the remainder of the year, this gradient occurs mostly from northwest to southeast. Relief features strongly influence the spatial distribution of rainfall erosivity at a smaller scale, with the coastal plains and coastal mountain range having greater rainfall erosivity than the inland Abukuma River valley. Understanding these patterns, particularly their spatial and temporal (both inter- and intraannual) variation, is important for contextualizing soil and particle-bound radiocesium transfers in the Fukushima region. Moreover, understanding the impact of tropical cyclones will be important for managing sediment and sediment-bound contaminant transfers in regions impacted by these events.

Characteristics of rainfall erosivity based on tropical rainfall measuring mission data in Tibet, China

Rainfall erosivity in Tibet from 2000 to 2010 was estimated based on simplified erosion prediction model using daily rainfall data derived from the Tropical Rainfall Measurement Misssion (TRMM) 3B42 rainfall measurement algorithm. Semimonthly erosive rainfall and rainfall erosivity were validated using weather station data. The spatial distribution of annual rainfall erosivity as well as its seasonal and annual variation in Tibet was also examined. Results showed that TRMM 3B42 data could serve as an alternative data source to estimate rainfall erosivity in the area where only data from sparsely distributed weather stations are available. The spatial distribution of rainfall erosivity in Tibet generally resembles the distribution of multi-year average of annual rainfall. Annual rainfall erosivity in Tibet decreased from the southeast to the northwest. The concentration degree of rainfall erosivity shows an increasing trend from the southeast to the northwest. High rainfall erosivity accompanies low rainfall erosivity concentration degree and vice versa. Rainfall erosivity increased in the middle and western Tibet and decreased in the southeastern Tibet during the 11 years of this study.

Spatial variability of the rainfall erosivity in southern region of Minas Gerais state, Brazil

Rainfall erosivity and its spatial variability were studied for 54 pluviometric stations in Southern Minas Gerais State (48º00' - 44º00'W; 23º50' - 20º00'S), aiming to plan the land-use strategies. Therefore, erosivity factor was determined for the pluviometric stations, using long-term rainfall data sets obtained along with the Brazilian National Water Agency- ANA, which varied from 15 to 40 years. The monthly and annual erosivity indexes were generated using Fournier equation for Lavras, MG and the spatial distribution of rainfall erosivity was studied on the basis of geostatistical approaches considering only the distance which separates them, developing the isotropic experimental semivariogram. The semivariogram adjustment was done based on the Weighted Least Squares method and the spatial dependence degree. Once the structure and the semivariogram adjustment were defined, the ordinary kriging maps were created, providing erosivity spatial behavior in Southern Minas Gerais. It was observed that the Southern Minas Gerais presents high erosivity patterns, ranging from 5,145 to 7,776 MJ mm ha-1 h-1 year-1, in Ijaci (north of region) and Itajubá (southern region), respectively. Besides, it was verified that the erosivity indexes are intensely influenced by the topography, associated with climatic conditions. Higher erosivity is connected to areas with a higher altitude, such as along the Mantiqueira Range Mountain, and on high plateaus and mountain ranges in the North-Central part of the region. The geostatistical approach using long-term rainfall data in Southern region of Minas Gerais state, which is a relatively heterogeneous region in terms of altitude, soil depth and slope, showed to be adequate to the proposal of this study.

Spatial and temporal variations in rainfall erosivity during 1960–2005 in the Yangtze River basin

Water resources and soil erosion are the most important environmental concerns in the Yangtze River basin, where soil erosion and sediment yield are closely related to rainfall erosivity. The present study explores the spatial and temporal changing patterns of the rainfall erosivity in the Yangtze River basin of China during 1960–2005 at annual, seasonal and monthly scales. The Mann–Kendall test is employed to detect the trends during 1960–2005, and the T test is applied to investigate possible changes between 1991–2005 and 1960–1990. Meanwhile the Rescaled Range Analysis is used for exploring future trend of rainfall erosivity. Moreover the continuous wavelet transform technique is using studying the periodicity of the rainfall erosivity. The results show that: (1) The Yangtze River basin is an area characterized by uneven spatial distribution of rainfall erosivity in China, with the annual average rainfall erosivity range from 131.21 to 16842 MJ mm ha−1 h−1. (2) Although the directions of trends in annual rainfall erosivity at most stations are upward, only 22 stations have significant trends at the 90 % confidence level, and these stations are mainly located in the Jinshajiang River basin and Boyang Lake basin. Winter and summer are the seasons showing strong upward trends. For the monthly series, significant increasing trends are mainly found during January, June and July. (3) Generally speaking, the results detected by the T test are quite consistent with those detected by the Mann–Kendall test. (4) The rainfall erosivity of Yangtze River basin during winter and summer will maintain a detected significant increasing trend in the near future, which may bring greater risks to soil erosion. (5) The annual and seasonal erosivity of Yangtze River basin all have one significant periodicity of 2–4 years.