Changes In Rainfall Erosivity Research Articles

Future changes in global rainfall erosivity: Insights from the precipitation changes

Quantifying future changes in rainfall erosivity is essential to address potential water erosion risks. This study reveals a significant power function relationship between rainfall erosivity and precipitation both globally and across various climatic zones. Building on this foundation, new empirical models were developed to project relative changes in rainfall erosivity. These models, predicted on the understanding that General Circulation Models (GCMs) provide more accurate predictions of climate change trends than exact meteorological values, utilizes the ratio of modeled historical precipitation to future precipitation under various scenarios as the variable, formulated as a power function. Data from three GCMs runs from both the Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP6–including historical, mid-term, and long-term periods–were employed. The analysis suggests that by the end of the 21st century, global annual rainfall erosivity could increase by 2.6% under the Shared Socio-Economic Pathway (SSP)1–2.6 scenario, by 3.5% under SSP2-4.5, and by a significant 6.4% under SSP5-8.5, relative to the 2000–2010 baseline. Furthermore, over 76% of the global land area is projected to experience an increase in rainfall erosivity over the century. Regions with projected changes in rainfall erosivity, whether increase or decrease, are likely to face more pronounced changes from mid-century onwards. The CMIP6 exhibits improved model consistency over its predecessor, CMIP5, indicating a greater water erosion risk as global warming progresses. These projections offer insights for strategies to combat soil degradation due to climate changes.

Soil erosion-based sub-watershed prioritization through coupling various crop management and erosivity scenarios using game theory

The soil erosion of the watershed is different in temporal and spatial scales due to complex climatic and environmental conditions and their dynamic state over time. Accordingly, it is necessary to prioritize sub-watersheds based on their dynamic conditions to reduce the destructive effects of soil erosion. However, this issue has to be sufficiently reported on the scale of the watershed. Towards that, a game theory-based prioritization approach was planned concerning changes in vegetation cover and rainfall in the Shazand Watershed of the Markazi Province, Iran. To achieve the purpose of the study, the amount of soil erosion was first estimated in different scenarios resulting from the changes in rainfall erosivity and vegetation cover using the RUSLE model. Next, pairwise comparisons between 24 sub-watersheds in all scenarios were made based on the sub-watersheds' ranks in soil erosion in each scenario. Considering all scenarios, the priority of the sub-watersheds was assessed using the Condorcet approach. The results showed that the mean soil erosion in the watershed ranged from 5.4 and 36 t ha−1 y−1 while applying various scenarios. It is worth mentioning that the prioritization results obtained from the Condorcet algorithm and the current soil erosion rate are highly similar. On the other hand, sub-watersheds 2 and 9 were selected as the first and last priority, providing an appropriate roadmap for managers and relevant departments to adopt appropriate soil erosion inhibition measures.

Assessing rainfall erosivity changes over China through a Bayesian averaged ensemble of high-resolution climate models

Spatiotemporal variation in rainfall erosivity resulting from changes in rainfall characteristics due to climate change has implications for soil erosion in developing countries. To promote soil and water conservation planning, it is essential to understand past and future changes in rainfall erosivity and their implications on a national scale. In this study, we present an approach that uses a Bayesian model averaging (BMA) method to merge multiple regional climate models (RCMs), thereby improving the reliability of climate-induced rainfall erosivity projections. Our multi-climate model and multi-emission scenario approach utilize five RCMs and two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios for the baseline period (1986–2005) and future periods (2071–2090) to characterize the spatiotemporal projection of rainfall erosivity and assess variations in China. Our results indicate that the two models outperform other models in reproducing the spatial distribution and annual cycle of rainfall erosivity in China. Moreover, we found an increasing trend in the annual rainfall erosivity from the baseline climate up to the RCMs for all models, with an average change in erosivity of approximately 10.9% and 14.6% under RCP4.5 and RCP8.5, respectively. Our BMA results showed an increase in the absolute value of rainfall erosivity by 463.3 and 677.0 MJ·mm·hm−2·h−1, respectively, in the South China red soil region and the Southwest China karst region under the RCP8.5 scenario. This increase indicates that climate warming will significantly enhance the potential erosion capacity of rainfall in these regions. Additionally, our study revealed that the Southwest China karst region and the Northwest China Loess Plateau region are more sensitive to radiation forcing. To mitigate the risk of soil erosion caused by climate change, it is necessary to consider changes in rainfall erosivity, local soil conditions, vegetation coverage, and other factors in different regions and take appropriate soil and water conservation measures.

Temporal and Spatial Variation in Rainfall Erosivity in the Rolling Hilly Region of Northeast China

The Rolling Hilly Region of Northeast China (RHRNEC) is a critical grain production area in China, and soil erosion is a major problem in this region. To determine spatial and temporal changes in rainfall erosivity (RE) in the RHRNEC and generate information useful for soil and water conservation, agricultural management, and ecological protection efforts, a RE index consisting of nine erosivity indices based on normal and extreme precipitation was established. The normal RE index (NREI) comprised annual RE (ARE), wet season RE (WRE), dry season RE (DRE), typical wet-month RE (TWRE), and typical dry-month RE (TDRE), and the extreme RE index set (EREI) comprised maximum one-day RE (RE × 1 day), maximum five consecutive days RE (RE × 5 day), storm RE (RE50), and maximum continuous RE (CRE). ARE, WRE, and TWRE decreased at relative rates of 2.5%, 2.9%, and 4.1%, respectively. By comparison, DRE increased at a non-significant relative rate of 6.3%, and all extreme RE indices decreased at a non-significant rate relative to 1981–2015 mean values. The future trends for all RE indices were predicted to be opposite to historical trends. The future trends and historical trends of all indices exhibited opposite patterns. RE gradually increased from north to south, and WRE, DRE, and all extreme RE indices were significantly negatively correlated with longitude, latitude, and altitude (p < 0.05). ARE, WRE, and TWRE showed increasing trends in the north and south and decreasing trends in the center. The findings are useful for soil and water conservation, especially for agricultural management and ecological protection.

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.

Vegetation restoration dominated the attenuated soil loss rate on the Loess Plateau, China over the last 50 years

The Chinese Loess Plateau has experienced large-scale land use changes in the past few decades. Understanding how land use change affects soil erosion is critical in the region's ecological construction and land management. In this study, the Beiluo River Basin in the core area of the Loess Plateau was selected to investigate the effects of land use changes on soil loss rates over the last 50 years. Results show that the land use has changed considerably, mainly reflected in the upper reaches. From 1970 to 2020, cropland in the upper reaches decreased by 54 %, directly leading to a 9.1-fold increase in forested land. Landsat-NDVI shows vegetation coverage increased from 21.1 % to 69.9 % over time. The Chinese Soil Loss Equation (CSLE) was used and confirmed to be satisfactory with a high coefficient of determination (R2, 0.89) and a strong Nash–Sutcliffe efficiency coefficient (0.72), although an underestimation exists. The specific soil loss simulated in the upper reaches maintained a high rate of around 8,000 t·km−2·yr−1 from the 1970s to the 1990s, dramatically dropping to 3,058 t·km−2·yr−1 in the 2000s, then attenuated to 1,321 t·km−2·yr−1 in the 2020s. For the entire basin, soil loss rates dropped from 4,090 to 1,848 and 890 t·km−2·yr−1 from the 1970s to the 2000s and 2020s, respectively. Attribution analysis showed that the dominant factor of the change in soil loss rates in the 1980s and 1990s relative to the 1970s was the change in rainfall erosivity for the entire watershed. However, vegetation restoration rapidly converted to the dominant factor contributing 78.3 % to soil loss decrease in the 2000s. The shift is evident in each reach of the basin except the terrain-plain area with the majority of farmland. The findings are helpful for sustainable land use planning and socio-economic development on the Loess Plateau and in similar areas.

The possible role of fused precipitation data in detection of the spatiotemporal pattern of rainfall erosivity over the Tibetan Plateau, China

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.

Projected seasonal changes in future rainfall erosivity over the Lancang-Mekong River basin under the CMIP6 scenarios

Climate change is a driver of soil erosion, but the future projections of seasonal rainfall erosivity variability and spatial distribution over the Lancang-Mekong River Basin (LMRB) are still not well understood. Based on the bias-corrected precipitation data from five General Circulation Models (GCMs) in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), the impacts of future climate change on the seasonal rainfall erosivity over the LMRB were assessed using three widely applied empirical daily rainfall erosivity models under three combined scenarios of the Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP1-RCP2.6, SSP3- RCP7.0 and SSP5- RCP8.5). The results show that rainfall erosivity would generally increase in the near term (2030–2060) and far term (2070–2100), and more ensemble members agree with the increase in rainfall erosivity, especially under the high emission scenarios in the far term. In the near term, the ensemble mean of basin-wide rainfall erosivity would increase by 2.5%-8.7% compared to the baseline period (1980–2010), while in the far term, the ensemble mean would increase by 12.2%-31.0%. Seasonal variations in rainfall erosivity show that summer rainfall erosivity from June to August accounts for more than two-thirds of the total annual rainfall erosivity. Although the projected basin-wide average summer rainfall erosivity would increase, the mid-southern basin in Thailand and southern Lao PDR would experience a decrease. For rainfall erosivity from March to May, large areas except for the mountainous part of China would also experience a decrease in seasonal rainfall erosivity. The projected changes in rainfall erosivity can contribute to a better understanding of soil erosion risk under climate change across the LMRB.

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.

Future Rainfall Erosivity over Iran Based on CMIP5 Climate Models

Soil erosion affects agricultural production, and industrial and socioeconomic development. Changes in rainfall intensity lead to changes in rainfall erosivity (R-factor) energy and consequently changes soil erosion rate. Prediction of soil erosion is therefore important for soil and water conservation. The purpose of this study is to investigate the effect of changes in climatic parameters (precipitation) on soil erosion rates in the near future (2046–2065) and far future (2081–2100). For this purpose, the CMIP5 series models under two scenarios RCP2.6 and RCP8.5 were used to predict precipitation and the R-factor using the Revised Universal Soil Loss Equation (RUSLE) model. Rainfall data from synoptic stations for 30 years were used to estimate the R- factor in the RUSLE model. Results showed that Iran’s climate in the future would face increasing rainfall, specially in west and decreasing rainfall in the central and northern parts. Therefore, there is an increased possibility of more frequent occurrences of heavy and torrential rains. Results also showed that the transformation of annual rainfall was not related to the spatial change of erosion. In the central and southern parts, the intensity of rainfall would increase. Therefore, erosion would be more in the south and central areas.

Spatial-temporal dynamic impact of changes in rainfall erosivity and vegetation coverage on soil erosion in the Eastern Mediterranean.

In Syria, soil erosion (SoEr) by water is one of the major challenges for sustainability. Thus, the main goals of this research were to evaluate the spatial changes of SoEr between 2000 and 2018 in the whole coastal basin (CB) of Syria and to provide a soil water erosion risk map for the study area. For this purpose, monthly rainfall data, the SoilGrids dataset, satellite image derived NDVI layers, and Digital Elevation Model (DEM) were collected. Through the integration of these layers into the Revised Universal Soil Loss Equation (RUSLE), under a Geographic Information System (GIS), soil loss was assessed. Also, the contribution of land cover changes and R factor on SoEr were evaluated. The outcomes of this assessment illustrated that the R factor ranged from 800 to 2600MJmmha-1h-1yr-1, while the soil erodibility factor (K factor) ranged from 0.048 to 0.035 ton ha MJ-1mm-1. The C factor (vegetation coverage) values ranged between 0.07 and 1 with a spatial average value of 0.44 for the 2000-2009 period and 0.39 for the 2010-2018 interval. The output of RUSLE revealed that average annual SoEr was of 21.35 ton ha-1 y-1 (± 38) for 2000-2009 and 22.47 ton ha-1 y-1(± 41.8) for 2010-2018. Interestingly, the increased SoEr caused by the R factor was dominant (34.65%), followed by changes in both C factor and R factor (13.34%). However, decrease of SoEr rates is due to the increase of the C factor accounting for 36.82% of the CB. The outcome of this research can provide constructive spatial insights for rehabilitation plans for the post-war phase of Syria.

Changes in rainfall erosivity from combined effects of multiple factors in China’s Loess Plateau

Rainfall erosivity is a key factor leading to soil erosion, and it is thus critical to investigate its spatiotemporal variability and controlling factors. Despite that the effects of a single factor have been largely investigated, the combined effects of multiple factors have rarely been considered. This study first investigated the spatiotemporal variability of rainfall erosivity in the Loess Plateau for 1961–2020, and then examined its relations to rainfall amount, temperature, and large-scale atmospheric circulation patterns by the bivariate and multiple wavelet coherence analysis. Averaged over the Loess Plateau, the annual rainfall erosivity had an insignificant increase at the rate of 1.74 MJ mm·ha−1·h−1·a−1. However, the changes exhibited spatial variability with a downward trend in the southeast and southwest, while an upward trend in the west, northeast and central regions. The regional-scale controlling factor was found to be rainfall amount, rather than temperature. For global atmospheric circulation, ENSO (El Niño-Southern Oscillation) and ENSO–AO (Arctic Oscillation) control rainfall erosivity individually or simultaneously at different timescales. This study provides technical support on how to identify the combined effects of multiple factors, and the results are helpful to project rainfall erosivity and control soil erosion.

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.

Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya

Ongoing climate change poses a major threat to the soil resources of many African countries that mainly rely on an agricultural economy. While arid and semi-arid lands (ASALs) take up most of Kenya’s land mass, approximately 64% of its total croplands lie within mountainous areas with high rainfall, hence, areas highly vulnerable to water erosion. Flooding of the Great Lakes and increasing desertification of the ASALs are illustrative cases of the implications of recent precipitation dynamics in Kenya. This study applied the Revised Universal Soil Loss Equation (RUSLE) to estimate future soil erosion rates at the national level based on four Coupled Model Intercomparison Project v5 (CMIP5) models under two Representative Concentration Pathway (RCP) scenarios. Results showed the current soil loss rate to be at 4.76 t ha−1 yr−1 and projected an increase in average rainfall erosivity under the two scenarios, except for RCP-2.6 (2030s) and (2080s) for the MIROC-5 model. Future projections revealed an incremental change in rainfall erosivity from the baseline climate by a cumulative average of 39.9% and 61.1% for all scenarios by the 2030s and 2080s, respectively, while soil loss is likely to increase concomitantly by 29% and 60%, respectively. The CCCMA_CANESM2 model under the RCP 8.5 (2080s) scenario projected the highest erosion rate of 15 t ha−1 yr−1 over Kenya, which is a maximum increase of above 200%, with the Rift Valley region recording an increase of up to 100% from 7.05 to 14.66 t ha−1 yr−1. As a first countrywide future soil erosion study, this assessment provides a useful reference for preventing water erosion and improving ecosystem service security.

Changes in rainfall erosivity over mainland China under stabilized 1.5 ℃ and 2 ℃ warming futures

Soil erosion is one of the major threats to the environment and agriculture in the world and rainfall erosivity is the most active factor to lead changes in soil erosion. Here, we use statistically downscaled, bias-corrected Community Earth System Model (CESM) low-warming simulations to investigate the future changes in rainfall erosivity in mainland China under the Paris Agreement global warming targets. The downscaled simulations evidently outperform the original CESM simulations in capturing the spatial distribution, magnitudes and annual cycle of rainfall erosivity in China in the present day (1986–2005). The rainfall erosivity will be significantly increased in most regions of China under the 1.5 °C and 2 °C warming targets and the regional mean increases are approximately 33% and 40%, respectively. In addition, the corresponding increases are even larger than 60% and 75%, respectively, in a quarter of mainland China. The increase in rainfall erosivity is resulted from the joint contributions of increases in frequency and intensity of erosive rainfall. However, it is dominated by the increase in the frequency. Compared with annual rainfall amount, the future warming will bring a four times greater impact on the soil erosion potentially caused by rainfall. Limiting global warming to 1.5 °C instead of 2 °C would reduce 17% of the increase in rainfall erosivity in China. For grain producing areas like Sichuan Basin, the middle and lower reaches of Yangtze River and South China, the values are approximately 20%. The future warming will significantly increase the potential risk of soil loss in China, and it is beneficial to relief this risk if the global warming is limited to 1.5 °C rather than 2 °C.

Climate and Land Use Change Effects on Sediment Production in a Dry Tropical Forest Catchment

Understanding the natural and anthropogenic drivers that influence erosion and sediment transport is a key prerequisite for adequate management of river basins, where, especially in tropical catchments, there are few direct measurements or modeling studies. Therefore, this study analyzed the effect of human-induced land-use changes and natural ENSO (El Niño-Southern Oscillation) related changes in rainfall patterns on soil erosion and catchment-scale sediment dynamics with the SEDD (Sediment Delivery Distributed) model. In the 393 km2 Tonusco river basin, representative of tropical, mountainous conditions, daily rainfall data were used to quantify changes in rainfall erosivity and satellite images for the evaluation of cover factor changes between 1977 and 2015. The final model combined soil loss, calculated by RUSLE, with a sediment routing-based delivery ratio, that was calibrated and validated with data from the sediment load recorded at the basin outlet. The results detected a great reduction of the vegetation cover in the catchment during the last decade of from 79.5 to 29.5%, and the influence of important runoff and erosion events linked to La Niña episodes. Soil erosion rates were locally very high, of over 120 Mg ha−1yr−1, and sediment yields were estimated at the range of 6.17–8.23 Mg ha−1yr−1.

Spatiotemporal evolutionary analysis of rainfall erosivity during 1901-2017 in Beijing, China.

Rainfall erosivity is regarded as one of the main factors affecting soil erosion. Based on 117-year monthly precipitation data of Beijing from 1901 to 2017, the spatiotemporal evolutionary analysis of rainfall erosivity in Beijing were analyzed by using Theil-Sen median analysis (Sen), the Mann-Kendall (MK) trend test, R/S analysis method, cumulative anomaly method, MK mutation test method, Pettitt test, and wavelet analysis. The results showed that the average annual rainfall erosivity in Beijing ranged from 1080.6 to 6432.78 MJ • mm/(hm2 • h • a), with an average value of 3465.06 MJ • mm/(hm2 • h • a), showing a gradual decrease from the southeast to northwest. Regarding seasonal distribution, 86% of rainfall erosivity was mainly concentrated in summer. In the past 117 years, the annual rainfall erosivity in most areas of Beijing showed a downward trend, but its future trend also showed an increasing trend, indicating that Beijing, especially the northern part, was facing greater potential pressure from soil erosion. Through cross-validation of various methods, the abrupt change interval of rainfall erosivity in Beijing from 1901 to 2017 was from 1994 to 1997. The change in rainfall erosivity in Beijing had a strong oscillation in 32 years and a small periodic change in 15 and 7 years. The results will provide a decision-making basis for soil erosion control and water/soil conservation planning. Additionally, they will benefit and ensure national agricultural and food security.

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.

Reexamination of the Xie model and spatiotemporal variability in rainfall erosivity in mainland China from 1960 to 2018

Soil erosion has become one of the most serious global environmental and ecological crises. Rainfall erosivity measures the potential ability of precipitation to trigger soil erosion. This study estimated the degree of rainfall erosivity in mainland China from 1960 to 2018 using the Xie model and analyzed its spatiotemporal variability. The accuracy of the Xie model was reexamined and further verified with the use of data from densely distributed stations by comparing it with two typical daily models and an hourly model. The relationships between changes in rainfall erosivity and soil erosion were also discussed. Results showed that annual rainfall erosivity in mainland China ranged from 2 to 23,129 MJ⋅mm/(hm2⋅h⋅a) and decreased gradually from southeast to northwest. Annual rainfall erosivity also showed a significant increasing trend (P < 0.05) with a mean value of 3738 MJ⋅mm/(hm2⋅h⋅a) and a growth rate of 8 MJ·mm/((hm2·h·a)·a). The significant breakpoint (P < 0.05) occurred in 2002. Multiple oscillation periods were identified, but 3.5 years was identified as the main oscillation period. The Xie model was shown to be more accurate for estimating rainfall erosivity in mainland China because of its simple formula, which reduces computing time and the effort needed to make calculations. Therefore, the Xie model provides a robust daily model that could be widely used in mainland China. Although annual precipitation has reduced, the increasing concentration and intensity of precipitation appears to be the primary reason underlying the increase in rainfall erosivity. Efficient, economic, and environmental measures need to be taken to address some of the challenges of soil conservation in mainland China.

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.