•The new model, developed to project future changes in rainfall erosivity, incorporates the ratio of modeled precipitation from future to historical periods as the dependent variable.•Under the ...Shared Socio-Economic Pathway (SSP) 5–8.5 scenario, there’s an expectation of a 2.5% rise in annual rainfall erosivity during the mid-century and 6.4% towards the end, relative to the 2000–2010 baseline.•CMIP6 models present a more significant increase in future rainfall erosivity and demonstrate a higher lever of agreement in multi-model projections, compared to CMIP5 models.
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.
•The linkage between the El Niño/La Niña and rainfall erosivity over the contiguous United States is examined.•Five core regions were detected based on composite and harmonic analysis, etc.•The El ...Niño/La Niña-REI relationships show the opposite sign, positive and negative.•The teleconnection between the ENSO and mid-latitude rainfall erosivity is identified over the contiguous United States.
A comprehensive investigation of the contiguous Unites States rainfall erosivity patterns in relation to the warm and cold phases of El Niño/southern Oscillation (ENSO) was described using a set of empirical and statistical analyses, such as harmonic analysis, annual cycle composites, and cross-correlation analysis. Monthly rainfall erosivity index (REI) composites for the first harmonic, covering 24-month ENSO events, are formed for all climate divisions over the United States spanning up to 29 ENSO episodes. From the harmonic vectorial maps plotted on the study area, each vector reveals both intensity and temporal phase of the ENSO-related REI teleconnection, and the corresponding candidate and core regions are determined using a machine learning technique of Gaussian Mixture Model (GMM) based on magnitude and temporal phase of climate signal, and Köppen climate classification. As a result of vectorial mapping, five core regions were designated as the northwest (NW), the north central (NC), the northeast coastal (NEC), the southeast (SE), and the southwest/middle-inland (SWM) regions. During fall (0) to spring (+) seasons, the results of this analysis show negative (positive) rainfall erosivity response to the El Niño events at the NW and NC regions (NEC, SE, and SWM regions), while the opposite patterns are detected for the cold phase of ENSO. The temporal consistency values were 0.62 to 0.86 (0.73 to 0.82), and spatial coherence values ranged from 0.93 to 0.98 (0.94 to 0.97) for the El Niño (La Niña) events. Comparative analyses of rainfall erosivity responses to both warm and cold ENSO events reveal the high significance level of the ENSO-REI correlation with an opposite tendency in monthly rainfall erosivity anomalies. Above normal rainfall erosivity anomalies during the El Niño thermal forcing are more significant than below normal rainfall erosivity departures during the La Niña events. Consequently, middle latitude rainfall erosivity responses to the El Niño and La Niña phenomena are detectable over the contiguous United States.
Extreme rainfall erosivity, the capacity of intense rainfall to induce soil erosion, is vital for anticipating future impacts on soil conservation. Despite extensive research, significant differences ...persist in terms of understanding influencing mechanisms, potential impacts, estimation models and future trends of extreme rainfall erosivity. Quantitatively describing extreme rainfall erosivity remains a key issue in existing research. In this study, we comprehensively reviewed the literature to assess the relationships between extreme rainfall characteristics and rainfall erosivity, between extreme rainfall erosivity and soil erosion, estimation models and trend prediction. The aim was to summarize previous related research and achievements, providing a better understanding of the generation, impacts and future trends of extreme rainfall erosivity. Future research directions should include identifying the thresholds of extreme rainfall events, increasing research attention on tropical cyclones in terms of rainfall erosivity, considering on the impact of extreme rainfall erosivity on soil erosion, and improving rainfall erosivity estimation and simulation prediction methods. This study could contribute to adapting to global climate change and aiding in formulating soil erosion prevention and environmental protection recommendations.
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•Extreme rainfall characteristics profoundly affect extreme rainfall erosivity.•Soil erosion is more severely affected by extreme rainfall erosivity.•Optimized rainfall erosivity models are good for estimation efficiency.•Future rainfall erosivity exhibits varied upward and downward trends.
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.
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•A revised rainfall erosivity model is established based on the high-resolution precipitation data from the Third Pole grid precipitation dataset.•The new reconstructed rainfall erosivity datasets enhances the ability and accuracy of spatial heterogeneity characterization of rainfall erosivity over the Tibetan Plateau.•Rainfall erosivity over the Tibetan Plateau has shown an increasing trend in recent 40 years
•Three models estimating erosivity by daily rainfall are calibrated and validated.•The models effectively estimate average annual, yearly and half-month erosivity.•A model using daily and maximum ...60-min amounts effectively predicts daily erosivity.
The rainfall erosivity factor (R) represents the multiplication of rainfall energy and maximum 30min intensity by event (EI30) and year. This rainfall erosivity index is widely used for empirical soil loss prediction. Its calculation, however, requires high temporal resolution rainfall data that are not readily available in many parts of the world. The purpose of this study was to parameterize models suitable for estimating erosivity from daily rainfall data, which are more widely available. One-minute resolution rainfall data recorded in sixteen stations over the eastern water erosion impacted regions of China were analyzed. The R-factor ranged from 781.9 to 8258.5MJmmha−1h−1y−1. A total of 5942 erosive events from one-minute resolution rainfall data of ten stations were used to parameterize three models, and 4949 erosive events from the other six stations were used for validation. A threshold of daily rainfall between days classified as erosive and non-erosive was suggested to be 9.7mm based on these data. Two of the models (I and II) used power law functions that required only daily rainfall totals. Model I used different model coefficients in the cool season (Oct.–Apr.) and warm season (May–Sept.), and Model II was fitted with a sinusoidal curve of seasonal variation. Both Model I and Model II estimated the erosivity index for average annual, yearly, and half-month temporal scales reasonably well, with the symmetric mean absolute percentage error MAPEsym ranging from 10.8% to 32.1%. Model II predicted slightly better than Model I. However, the prediction efficiency for the daily erosivity index was limited, with the symmetric mean absolute percentage error being 68.0% (Model I) and 65.7% (Model II) and Nash–Sutcliffe model efficiency being 0.55 (Model I) and 0.57 (Model II). Model III, which used the combination of daily rainfall amount and daily maximum 60-min rainfall, improved predictions significantly, and produced a Nash–Sutcliffe model efficiency for daily erosivity index prediction of 0.93. Thus daily rainfall data was generally sufficient for estimating annual average, yearly, and half-monthly time scales, while sub-daily data was needed when estimating daily erosivity values.
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•A review of the current status of rainfall erosivity development is provided.•The accurate estimation of rainfall erosivity poses a challenge to quantitative analysis of soil ...erosion.•The differences in climate and environmental conditions result in variations in the estimated rainfall erosivity models.•The characteristics of rainfall itself influence the development of standards for rainfall erosion.•Rainfall erosivity can be estimated using either kinetic energy models or simplified estimation models.
Rainfall erosivity (R) is commonly used to measure water and soil loss by representing the degree of rainfall-induced soil erosion. However, methods for calculating rainfall erosivity vary significantly regarding regional climatic and precipitation characteristics. How to quantitatively illustrate rainfall erosivity remains a key issue for soil erosion monitoring. In this paper, we summarize the basic principles in calculating rainfall erosivity, as well as the relationships and differences among mainstream methods. By referring to experiences gained from previous studies, this paper aims to better summarize and analyze the current rainfall erosivity estimation models and space–time distribution, so as to avoid the confused use of each estimation model as well as to proposes future researches. Currently, there is a widespread utilization of simple algorithms for rainfall erosivity estimation, and statistical methods like machine learning are also seen in such applications. Besides, while many have proposed to quantify local-scale rainfall erosivity, significant limitations are recognized for large-scale estimations. Future researches that emerge recently developed technologies such as remote sensing are expected to further improve rainfall erosivity estimation.
•Rainstorms erosivity is investigated through Lorenz curve and derived coefficients.•The largest values of inter-annual inequality is observed for the Alpine region.•No clear regional pattern in the ...derived Gini coefficients could be detected.•On average, 11% of all erosive events contribute to the 50% of the total erosivity.•Higher erosive events tend to occur later in the year then less erosive events.
Heavy rainstorms play a central role in the water-driving soil erosion processes. An in-depth knowledge about temporal and spatial erosivity of rainfall events is required to gain a better understanding of soil erosion processes and optimize soil protection measures efficiency. In this study, the spatiotemporal distribution of more than 300,000 erosive events measured at 1181 locations, part of the Rainfall Erosivity Database at European Scale (REDES) database, is studied to shed some new light on the rainfall erosivity in Europe. Rainfall erosive events are statistically investigated through the Lorenz curve and derived coefficients such as the Gini coefficient (G). Additionally, seasonal characteristics of the most and the less erosive events are compared to investigate seasonal characteristics of rainstorms across Europe. The G shows largest values of inequality of the inter-annual temporal distribution of the rainfall erosive events in the Alpine region, mostly due to the large number of rainfall events with smaller rainfall erosivity. While for other parts of Europe, the inequality described by the G is mostly due to a small number of high erosive events. The G slightly decreases from south to north while no clear regional patterns can be detected. Additionally, in Europe, on average 11% (ranging from 1 to 24%) of all erosive events contribute to form 50% of the total rainfall erosivity. Furthermore, higher erosive rainfall events tend to occur later in the year compared to less erosive events that take place earlier. To our knowledge, this study is the first one addressing event scale rainfall erosivity distribution using more than 300,000 rainfall erosivity events and covering almost a whole continent. Scientifically our findings represent a major step towards large-scale process-based erosion modelling while, practically, they provide new elements that can support national and local soil erosion monitoring programs.
Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate ...rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, droughts and floods. This study aims to predict soil erosion probability using the deep learning approach: long short-term memory neural network model (LSTM) and revised universal soil loss equation (RUSLE) model. Daily rainfall data were gathered from five agro-meteorological stations in the Central Highlands of Sri Lanka from 1990 to 2021 and fed into the LSTM model simulation. The LSTM model was forecasted with the time-series monthly rainfall data for a long lead time period, rainfall values for next 36 months in each station. Geo-informatics tools were used to create the rainfall erosivity map layer for the year 2024. The RUSLE model prediction indicates the average annual soil erosion over the Highlands will be 11.92 t/ha/yr. Soil erosion susceptibility map suggests around 30 % of the land area will be categorised as moderate to very-high soil erosion susceptible classes. The resulted map layer was validated using past soil erosion map layers developed for 2000, 2010 and 2019. The soil erosion susceptibility map indicates an accuracy of 0.93 with the area under the receiver operator characteristic curve (AUC-ROC), showing a satisfactory prediction performance. These findings will be helpful in policy-level decision making and researchers can further tested different deep learning models with the RUSLE model to enhance the prediction capability of soil erosion probability.
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•The soil erosion susceptibility is important for sustainable land management.•A new method was introduced using RUSLE and LSTM for soil erosion susceptibility.•The LSTM was able to predict rainfall for 36 months (3 years) period.•Around 3503.17 km2 (33 %) land area are under moderate to very-high erosion classes.
•European-scalable method to simulate rainfall erosivity from gridded rainfall data.•Monthly power-law models link rainfall depth to erosivity with European coverage.•EMO-5 shows the best potential ...to simulate rainfall erosivity events.•Gridded datasets perform better in Northern over Southern Europe.•Future upscaling and regional bias correction may overcome current limitations.
Soil erosion is time compressed into a number of episodic erosive rainfall events with an associated potential to detach and transport soil particles (rainfall erosivity), each possessing unique spatial and temporal characteristics. Rainfall erosivity events in Europe follow extreme value distributions in which a limited number of rainstorms dominate the long-term budget of available erosive energy. To combat soil erosion in Europe in a targeted manor, timely erosion mitigation measures should derive from dynamic model simulations that incorporate spatially and temporally distributed estimations of rainfall erosivity. Rain gauge measurements from singular points are typically used to quantify rainfall erosivity, however the spatial uniqueness of rainfall presents a key limitation to dynamically model rainfall across broad spatial scales with a limited number of point measurements. Discretised gridded precipitation datasets with a widespread (e.g. continental) spatial coverage potentially offer an opportunity to adequately replicate the dynamics of rainfall erosivity events, however their performance remains poorly tested in the pan-European context.
This study builds upon the comprehensive Rainfall Erosivity Database at European Scale (REDES) archive of over 300,000 events from 1181 gauge stations to develop a two-step modelling process: 1) firstly, optimal monthly models were fitted and evaluated between gauge-recorded rainfall depth and rainfall erosivity (EI30) across European climatic regions to develop a European-scale parameter surface, 2) secondly, three datasets (EMO-5 (6-hr), E-OBS (24-hr), UERRA MESCAN-SURFEX (24hr)) were directly evaluated via a grid-to-point analysis based on their ability to simulate the station-specific event rainfall erosivity timeseries at a random selection of 32 locations. EMO-5 (Nash-Sutcliffe model efficiency mean = 0.24) outperformed other tested gridded datasets, showing the capability to adequately replicate the event number, timing, and their average magnitude. A higher model performance in Northern compared with Southern European climatic regions, in which characteristically higher and spatially-complex event rainfall erosivity magnitudes are found, was symptomatic of a poor ability of grid-based simulations to replicate the magnitudes of events in the outer extents of the frequency-magnitude spectrum. The absence of a clear global systematic predictive bias amongst simulated locations suggests the need for future upscaling of this analysis to the entire European REDES dataset to fully understand and correct for the method-derived bias in a climate region-specific way.
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