Trends In Rainfall Erosivity Research Articles

Evaluating rainfall erosivity on the Tibetan Plateau by integrating high spatiotemporal resolution gridded precipitation and gauge data

High-precision rainfall erosivity mapping is crucial for accurately evaluating regional soil erosion on the Tibetan Plateau (TP) under the backdrop of climate warming and humidification. Although high spatiotemporal resolution gridded precipitation data provides the foundation for rainfall erosivity mapping, the increasing spatial heterogeneity of rainfall with decreasing temporal granularity can lead to greater errors when directly computing rainfall erosivity from gridded precipitation data. In this study, a site-scale conversion coefficient was established so that rainfall erosivity calculated using hourly data can be converted to rainfall erosivity calculated using per-minute data. A revised model was established for calculating the rainfall erosivity based on high-resolution hourly precipitation data from the Third Pole gridded precipitation dataset (TPHiPr). The results revealed a notable underestimation in the original calculation results obtained using the TPHiPr, but strong correlation was observed between the two sets of results. There was a significant improvement in the Nash–Sutcliffe coefficient of efficiency (from −0.39 to 0.80) and the Percent Bias (from −63.95 % to 0.37 %) after model revision. The TPHiPr effectively depict the spatial characteristics of rainfall erosivity on the TP. It accurately reflected the rain shadow area on the northern flank of the Himalayas and the dry-hot valley in the Hengduan Mountains. It also showed high rainfall erosivity values in the tropical rainforest area on the southern flank of the eastern Himalayas. The overall trend of rainfall erosivity has increased on the TP during the period 1981 to 2020, with 65.91 % of the regions exhibiting an increasing trend and 22.25 % showing significant increases, indicating an intensified risk of water erosion. These findings suggest that the 40-year-high spatial resolution rainfall erosivity dataset can provide accurate data support for a quantitative understanding of soil erosion on the TP.

Monitoring soil erosion in support of achieving SDGs: A special focus on rainfall variation and farming systems vulnerability

The Sustainable Development Goals (SDGs) and soil-related activities are closely intertwined. Healthy soil ecosystems promote the survival of life on Earth and uplift agricultural productivity. One of the issues that is putting healthy soil ecosystems at risk is soil erosion. The Central Highlands of Sri Lanka were chosen because it is a humid, tropical nation that is particularly susceptible to climate threats. Soil erosion was tracked and predicted in connection to rainfall variation in order to reduce soil erosion in farming systems and in supporting the achievement of the SDGs. A revised universal soil loss equation (RUSLE) was executed to determine soil loss. Rainfall variance was calculated using trend analyses of extreme rainfall indices and rainfall erosivity. Soil erosions for the year 2040 under the Representative Concentration Pathways (RCP) 2.6 and 8.5 scenarios were predicted using a variety of machine learning techniques, including artificial neural networks (ANN), support vector machines (SVM), and adaptive network-based fuzzy inference system (ANFIS). Results indicated a positive trend in rainfall erosivity and several other extreme indices. In addition, soil erosion rates increased, with October recording the highest monthly erosion rates. Based on 2020 soil erosion rates, soil erosion model forecasts suggest that erosion may range from 4 % to 22 % in 2040. Under the RCP 2.6 and 8.5 climate scenarios, the rate of soil erosion can be raised from 10.5 t/ha/yr to 12.4 t/ha/yr, respectively. These facts indicate that rainfall-induced soil erosion will increase the vulnerability of the Central Highlands and pose a severe danger to the achievement of the SDGs. In order to ensure the well-beingof the soil ecosystem, this research offered strategies to monitor and anticipate soil erosion in support of achieving the SDGs to protect the healthy soil ecosystem. This methodology can be replicated to minimize soil erosion in farming systems elsewhere.

Characteristics and Projection of Rainfall Erosivity Distribution in the Hengduan Mountains

This study examines the spatiotemporal variations of rainfall erosivity in the Hengduan Mountains, known for their rugged terrain and high potential for soil erosion risks, over the past 30 years. Additionally, it investigates the changing trends of rainfall erosivity between 2025 and 2040 under the Sustainable Development Pathway 2–4.5 (SSP2–4.5), using four Global Climate Models (GCMs) based on the Coupled Model Intercomparison Project phase 6 (CMIP6). The results indicate: (1) The annual distribution of rainfall erosivity in the Hengduan Mountains exhibited significant seasonal variations, ranking in the order of summer > autumn > spring > winter on a seasonal scale. (2) Over the past 30 years, there has been a slight decrease in annual precipitation and a corresponding slight increase in rainfall erosivity. Periodic extreme values occur every 6–8 years. (3) Spatially, rainfall erosivity demonstrates a decreasing gradient from southeast to northwest. There is a significant positive correlation between rainfall erosivity and precipitation, while a significant negative correlation exists with elevation in the vertical direction. Furthermore, the northeastern part of the Hengduan Mountains exhibits an increasing trend of rainfall erosivity, while the southern region experiences a decreasing trend. (4) Considering the joint driving forces of increased precipitation and erosive rainfall events, rainfall erosivity is expected to significantly increase in the future, posing a more severe risk of soil erosion in this region.

Long-term trends of precipitation and erosivity over Northeast China during 1961–2020

Northeast China (NEC) is one of the vital commercial grain bases in China and it has suffered from soil erosion due to prolonged cultivation and lack of protection. To determine long-term trends of precipitation and rainfall erosivity over NEC during the latest decades, daily precipitation for the entire year during 1961–2020 and hourly precipitation for the warm season (May to September) during 1971–2020 were collected for 192 and 126 stations, respectively. Three seasons, including the cold season (October to April), early warm season (May to June), and late warm season (July to September) were divided according to the combination of precipitation and vegetation. Results demonstrate: (1) Daily precipitation reveals total precipitation and rainfall erosivity in the cold season and early warm season increase significantly at relative rates of 3.1% ∼ 6.1% compared with the average during 1961–2020, and those in the late warm season decrease insignificantly. (2) Hourly precipitation reveals storms occurring in the early and late warm seasons have undergone significant increasing changes, which shift towards longer storm duration, larger amount, peak intensity, kinetic energy, and rainfall erosivity during 1971–2020. Moreover, the frequency of extreme storms increased. (3) Rainfall erosivities estimated from daily precipitation during 1971–2020 increase insignificantly for the early and late warm season, whereas those from hourly precipitation increase significantly (6.1% and 5.5%, respectively), which indicates daily precipitation may not be able to capture the trend fully under the warming background, and precipitation at higher resolutions than the daily scale is necessary to detect trends of rainfall erosivity more accurately.

Detection Framework of Abrupt Changes and Trends in Rainfall Erosivity in Three Gorges Reservoir, China

Rainfall erosivity is commonly used to estimate the probability of soil erosion caused by rainfall. The accurate detection of temporal changes in rainfall erosivity and the identification of abrupt changes and trends are important for understanding the physical causes of variation. In this study, a detection framework is introduced to identify temporal changes in rainfall erosivity time series as follows: (i) The significance of time series variation of rainfall erosivity is assessed based on the Hurst coefficient and divided into three levels: None, medium, and high. (ii) The detection of abrupt changes (Mann–Kendall, Moving T, and Bayesian tests) and trends (Spearman and Kendall rank correlation tests) of variate series and the correlation coefficient between the variation component and the original series is calculated. (iii) The modified series is obtained by preferentially eliminating the variation component (trend or change point) with larger correlation coefficients. (iv) We substituted the modified series into steps i to iii until the correlation coefficient was not significant. This framework is used to analyze the variation of rainfall erosivity in the Three Gorges Reservoir, China. The results showed that by using traditional methods, both an increasing trend and an upward change point were observed in Zigui station. However, after the upward change point was deducted from the annual rainfall erosivity series R(t), the resultant Rm(t) showed no statistically significant trend. Trend analysis should be performed considering the existence of an abrupt change to assess the long-term changes in rainfall erosivity series; otherwise, it would result in the wrong conclusion. In addition, the change points detected in the Rm(t) varied with the methods. Compared with the single-test method, the proposed framework can effectively reduce uncertainty.

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.

Increasing trends in rainfall erosivity in the Yellow River basin from 1971 to 2020

Hourly precipitation data from 1971 to 2020, collected from 98 stations distributed across the Yellow River basin, were analyzed to detect changes in characteristics on rainfall and rainfall erosivity for all storms and storms with extreme erosivity (greater than 90th percentile). Results showed that over the past 50 years, rainfall erosivity at both event and seasonal scales over the whole basin increased significantly (p < 0.05) with rates of 5.46% and 6.86% decade-1, respectively, compared to the 1981–2010 average values. Approximate 80% of 98 stations showed increasing trends and 20% of stations had statistically significant trends (p < 0.1). The increase of rainfall erosivity resulted from the significant increasing trends of average storm precipitation (p < 0.1), duration (p < 0.1), rainfall energy (p < 0.05) and maximum 1-h intensity (p < 0.05). In addition, the total extreme erosivity showed significant upward trends at a relative rate of 6.05% decade-1 (p < 0.05). Extreme erosivity storms occurred more frequently and with higher rainfall energy during the study period (p < 0.05). Trends for seasonal total and extreme erosivity were also estimated based on daily rainfall data, and the changing magnitudes were similar to those based on hourly rainfall data, which suggested daily rainfall can be applied to detect interannual and long-term variations of rainfall erosivity in the absence of rainfall data with higher resolution. It was suggested that soil and water conservation strategies and vegetation projects conducted within the Yellow River basin should be continued and enhanced in the future.

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.

Fingerprint of climate change in precipitation aggressiveness across the central Mediterranean (Italian) area

Rainfall erosivity and its derivative, erosivity density (ED, i.e., the erosivity per unit of rain), is a main driver of considerable environmental damages and economic losses worldwide. This study is the first to investigate the interannual variability, and return periods, of both rainfall erosivity and ED over the Mediterranean for the period 1680–2019. By capturing the relationship between seasonal rainfall, its variability, and recorded hydrological extremes in documentary data consistent with a sample (1981–2015) of detailed Revised Universal Soil Loss Erosion-based data, we show a noticeable decreasing trend of rainfall erosivity since about 1838. However, the 30-year return period of ED values indicates a positive long-term trend, in tandem with the resurgence of very wet days (> 95th percentile) and the erosive activity of rains during the past two decades. A possible fingerprint of recent warming is the occurrence of prolonged wet spells in apparently more erratic and unexpected ways.

Historical predictability of rainfall erosivity: a reconstruction for monitoring extremes over Northern Italy (1500\u20132019)

Erosive storms constitute a major natural hazard. They are frequently a source of erosional processes impacting the natural landscape with considerable economic consequences. Understanding the aggressiveness of storms (or rainfall erosivity) is essential for the awareness of environmental hazards as well as for knowledge of how to potentially control them. Reconstructing historical changes in rainfall erosivity is challenging as it requires continuous time-series of short-term rainfall events. Here, we present the first homogeneous environmental (1500–2019 CE) record, with the annual resolution, of storm aggressiveness for the Po River region, northern Italy, which is to date also the longest such time-series of erosivity in the world. To generate the annual erosivity time-series, we developed a model consistent with a sample (for 1981–2015 CE) of detailed Revised Universal Soil Loss Erosion-based data obtained for the study region. The modelled data show a noticeable descending trend in rainfall erosivity together with a limited inter-annual variability until ~1708, followed by a slowly increasing erosivity trend. This trend has continued until the present day, along with a larger inter-annual variability, likely associated with an increased occurrence of short-term, cyclone-related, extreme rainfall events. These findings call for the need of strengthening the environmental support capacity of the Po River landscape and beyond in the face of predicted future changing erosive storm patterns.

Multi-time scale variability of rainfall erosivity and erosivity density in the karst region of southern China, 1960–2017

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.

Spatial-temporal trends of rainfall erosivity and its implication for sustainable agriculture in the Wei River Basin of China

Water and soil are important natural resources for agricultural development. Soil erosion is a worldwide problem threatening sustainable agriculture development. Rainfall is one of the major natural factors that cause soil erosion. The study of the spatial distribution and temporal trends of rainfall erosivity is practical and important for the assessment of soil erosion hazards, the planning of soil and water conservation measures, and the sustainable agriculture development. In this study, the data of daily rainfall in the Wei River Basin from 1969 to 2018 were collected, the spatial and temporal variation characteristics of rainfall and rainfall erosivity were analyzed. The results show that the regional mean annual rainfall varies from 315.0 to 807.3 mm, which shows an increasing spatial trend from northwest to southeast. The mean annual rainfall erosivity in the basin ranges from 751.4 to 2600.0 MJ⋅mm⋅hm-2⋅h-1, which has a similar pattern of spatial distribution as the rainfall. The monthly rainfall erosivity changing pattern is a single-peak type. High erosivity of rainfall was mainly observed from July to September. The peaks mostly occur in July and August. The temporal trend of annual rainfall erosivity ranges from −6.6–10.1 MJ⋅mm⋅hm-2⋅h-1 per year, the Loess Plateau and the Qinling Mountains in the southern basin show an increasing trend, and the surroundings of Xifeng, Changwu and Huanxian have the statistical significance of P < 0.05. In the midstream and downstream of the basin and the Guanzhong Plain, the rainfall erosivity shows an insignificant decreasing trend. The North Atlantic Oscillation obviously influences the rainfall erosivity in the recent two decades. The study results may helpful for the scientific management, soil erosion prevention and sustainable agricultural development of the Wei River Basin.

Reconstruction of past rainfall erosivity and trend detection based on the REDES database and reanalysis rainfall

Rainfall erosivity is the driving force of soil erosion and it is characterized by a large variability in space and time. In order to obtain robust estimates of rainfall erosivity, long series of high-frequency rainfall data are needed, which are often not available for large study areas. In this study we reconstructed past rainfall erosivity in Europe for the period 1961–2018, with the aim to investigate temporal changes in rainfall erosivity. As input data, we used the Rainfall Erosivity Database at European Scale (REDES) and Uncertainties in Ensembles of Regional Reanalyses (UERRA) rainfall data. Using a set of regression models, which we derived with the application of the k-fold cross-validation approach, we computed the annual rainfall erosivity for the 1675 stations forming the REDES database. Based on the reconstructed data, we derived a rainfall erosivity trend map for Europe where the results were qualitatively validated. Among the stations showing a statistically significant trend, we observed a tendency towards more positive (15%) than negative trends (7%). In addition, we also observed an increasing tendency of the frequency of years with maximum erosivity values. Geographically, large parts of regions such as Eastern Europe, Scandinavia, Baltic countries, Great Britain and Ireland, part of the Balkan Peninsula, most of Italy, Benelux countries, northern part of Germany, part of France, among others, are characterized by a positive trend in rainfall erosivity. By contrast, negative trends in annual rainfall erosivity could be observed for most of the Iberian Peninsula, part of France, most of the Alpine area, Southern Germany, and part of the Balkan Peninsula, among others. The new dataset of rainfall erosivity trends reported in this study scientifically provides new information to better understand the impacts of the ongoing erosivity trends on soil erosion across Europe, while, from a policy perspective, the gained findings provide new knowledge to support the development of soil erosion indicators aiming at promoting mitigation measures at regional and pan-European level.

Spatio-temporal dynamics of rainfall erosivity due to climate change in Cameron Highlands, Malaysia

Rainfall erosivity is one of the main factors of soil erosion that is expected to change as climate variables alter and impact soil conservation policies significantly. It is important to understand the potential trends in rainfall erosivity and its impacts on the environment, especially in tropical areas where severe rainfall is expected to continue to rise. This study aimed to project spatial and temporal rainfall erosivity factor using ensemble Global Circulation Models (GCMs). The study employed 20 GCMs, four RCPs scenarios, and two projection timescales (2050s and 2080s). The erosivity factor was determined by integrating baseline rainfall intensity with Modified Fournier Index (MFI). The values of erosivity were then interpolated using GIS software, and thematic maps were generated for these variables. The result shows 92% coefficient of determination between observed and simulated rainfalls. The ensemble model revealed that the MAE, SE and RMSE are 0.1487, 1.3692 and 1.2499, respectively. The relative increment of rainfall erosivity ranged from 4.7% to 122% with the highest value of 6292 MJmmha−1 hr−1 yr−1 by 2080s at Kg. Raja sub-basin located at North-Western part of the watershed. Similarly, the peak flow response through Ringlet river was expected to increase in the range of 4.72–35.8% with peak discharge in December by 2080s under RCP8.5 emission scenario. This study disclosed a potential increase in rainfall erosivity, and availability of water resources influenced by climate change which require appropriate conservation strategies.

Assessing rainfall erosivity and erosivity density over a western Himalayan catchment, India

The present study aims to assess rainfall erosivity and erosivty density both in space and time over the Suketi River catchment of western Himalayan region in India during 1971–2015. The data used comprises of daily rainfall measurements at three rain gauge stations, which are sparsely distributed over the catchment. Rainfall erosivity was assessed by employing Wischmeier and Smith algorithms, whereas erosivity density was estimated by applying Kinnell’s algorithm. The spatial distribution of both algorithms was analyzed through Kriging method based on geographical information system. The obtained results indicate remarkable year-to-year, seasonal and monthly variations in average annual rainfall erosivity and erosivity density. Apart from this, individual cases of high and very high rainfall erosivity and erosivity density were noticed. The long-term average annual rainfall erosivity and erosivity density revealed a general decreasing trend. This decreasing trend in rainfall erosivity was found to be statistically significant at 0.05 significance level, whereas it was found to be non-significant for erosivity density. The highest values of both indices were observed in the month of July followed by August and June particularly in northern parts. These results indicate that July month followed by August and June are the most susceptible months for soil erosion over the Suketi River catchment with lower reaches (northern) being the most vulnerable one. Finally, results of this study will be valuable for farmers, agronomists and regional planners in chalking out best management practices for reducing water erosion in vulnerable areas of the catchment.

Spatial Distribution and Temporal Trends of Rainfall Erosivity in Three Gorges Reservoir Area of China

Rainfall erosivity is a key factor to predict soil erosion rate in universal soil loss equation (USLE) and revised USLE (RUSLE). Understanding rainfall erosivity characteristics, especially its spatial distribution and temporal trends, is essential for soil erosion risk assessment and soil conservation planning. In this study, the spatial-temporal variation of rainfall erosivity in the Three Gorges Reservoir Area (TGRA) of China during 1960–2010, at annual and seasonal scales, was explored based on daily rainfall data from 40 stations (26 meteorological stations and 14 hydrologic stations). The Mann–Kendall test and Co–kriging interpolation method were applied to detect the temporal trends and spatial patterns. The results showed that TGRA’s annual rainfall erosivity increased from west, south, and east to the north-central, ranging from 3647.0 to 10884.8 MJ·mm·ha−1·h−1 with an average value of 6108.1 MJ·mm·ha−1·h−1. The spatial distribution of summer and autumn rainfall erosivity was similar to the pattern of annual rainfall erosivity. Summer is the most erosive season among four seasons, accounting for 53% of the total annual rainfall erosivity, and winter is the least erosive season. July is the most erosive month with an average of 1327.3 MJ·mm·ha−1·h−1, and January is the least erosive month. Mean rainfall erosivity was 5969.2 MJ·mm·ha−1·h−1 during 1960–2010, with the lowest value of 3361.0 MJ·mm·ha−1·h−1 in 1966 and highest value of 8896.0 MJ·mm·ha−1·h−1 in 1982. Mann–Kendall test showed that the annual rainfall erosivity did not change significantly across TGRA. Seasonal rainfall erosivity showed a significant decrease in autumn and insignificant decrease in summer and winter. Monthly rainfall erosivity in TGRA showed insignificant increases from Jun to Jul and then underwent decreases from Aug to Nov. and from Dec to Feb and it rose again in Feb reaching a 0.01 level significance. The daily rainfall data of supplemental stations is very useful to interpolate rainfall erosivity map, which could help to find the credible maximum and minimum value of TGRA. In total, the findings could provide useful information both for soil erosion prediction, land management practices, and sediment control project of TGRA.

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.

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.

Spatial and temporal variations in rainfall erosivity and erosivity density in South Korea

Rainfall erosivity is one of the key parameters in Universal Soil Loss Equation (USLE), which has been used to predict the amount of soil loss by water for 50 years. Investigating spatial and temporal trends in rainfall erosivity is important for soil and water conservation planning. Rainfall erosivity (the R factors) in many regions is expected to be altered due to changes in rainfall patterns related to rainfall intensity and the frequency and spatial distribution of storm events that may occur with climate change. In South Korea, some researchers have studied temporal variation in meteorological and hydrologic phenomena with climate change, particularly temperature and precipitation trends. The purpose of this study is to investigate spatial and temporal variations in rainfall erosivity and erosivity density and to improve our understanding of the evolution of rainfall erosivity in South Korea. First, we calculated rainfall erosivity at 46 stations for 1961–2015 using 5-min precipitation data. Second, we examined spatial and temporal variability in the rainfall erosivity; trends and change points of R factor time series and analyzed the relationships between rainfall erosivity and climate indices (such as the precipitation amount, number of effective events, and duration). Four trend tests such as the t-test, MK test, modified version of the MK test, and BBS-MK test, were used to detect trends in the annual R factor, total duration, number of effective events, total depth, mean maximum 30-min intensity, and total kinetic energy time series for all employed stations. The results provide insights into the evolution of rainfall erosivity and the effects of large-scale climatic circulation on rainfall erosivity.

Climatology of rainfall erosivity during 1961–2012 in Jiangsu Province, southeast China

Based on daily precipitation records at 52 meteorological stations in Jiangsu Province, southeast China, the space and time changes of characteristics in rainfall erosivity (RE) during 1961–2012 were analyzed. The meteorological and topographical diagnosis methods including Mann–Kendall trend test, Ensemble Empirical Mode Decomposition (EEMD), and K-means cluster analysis (KCA) were used. The result showed that the average annual RE in Jiangsu was 8621.8 (MJ mm ha−1 h−1 a−1), and more than 50% of the annual RE was concentrated in summer. In contrast, the intra-annual distribution of RE in north Jiangsu presented higher concentration. There was an obvious seasonal difference in temporal trends of RE at the scale of station, and most of stations were characterized by increasing RE in winter and summer. Especially all the stations were dominated by significantly increasing RE in winter. By using KCA, Jiangsu could be divided into three sub-regions with different temporal variations in annual RE such as northern, central and southern area, and the southern area was dominated by a significant increasing tendency. Furthermore, EEMD for regional series of annual RE indicated that a 2.5–2.6 year periodical feature was noticeable in each sub-region.