Rainfall Erosivity Model Research Articles

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

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

Improvement of satellite‐based rainfall product CHIRPS in estimating rainfall erosivity on the Loess Plateau

AbstractSatellite‐based rainfall products have great potential for estimating rainfall erosivity, as they can provide continuous spatiotemporal distribution of precipitation estimates over large areas, especially for monitoring in areas with complex terrain and extreme climates. This paper uses the daily rainfall derived from the satellite‐based CHIRPS product on the GEE platform and Zhang's daily rainfall erosivity model to calculate the rainfall erosivity on the Loess Plateau during the period of 1981–2020. The accuracy of rainfall erosivity for the CHIRPS product is evaluated by comparing the results to estimates from national meteorological stations, and then the calculation formula of rainfall erosivity is optimized to improve the accuracy of rainfall erosivity based on CHIRPS products. The results show that the annual average rainfall of the CHIRPS product at the locations of national meteorological stations in 1981–2020 is 473.7 mm, which is 6.8% higher than that observed by the meteorological stations. The multi‐year average value of daily rainfall and the yearly average rainfall both for days with rainfall larger than or equal to 12 mm are 15.9% and 18.2% greater than those of the meteorological stations, respectively, and eventually resulting in an overestimation of rainfall erosivity on the Loess Plateau by 44.0%. The annual mean rainfall erosivity interpolated based on meteorological stations and calculated from the gridded CHIRPS product in 1981–2020 is 1344.2 MJ·mm/(hm2·h) and 2013.7 MJ·mm/(hm2·h), respectively. This result suggests that rainfall erosivity estimated by gridded CHIRPS product is overestimated by 49.8%, of which 46.1% is due to the overestimation of erosive rainfall in gridded CHIRPS and 3.7% is caused by site density and interpolation. Satellite‐based CHIRPS product is similar to the observations of meteorological stations in total rainfall and trends, but differs in rainfall frequency and intensity, which is an important reason for the difference in rainfall erosivity between satellite‐based rainfall products and meteorological stations. The CHIRPS‐based rainfall erosivity calculated using the optimized parameters reduces the overestimation from 49.8% to 3.2%, greatly reducing the satellite‐based rainfall erosivity estimation error.

Variation characteristics of rainfall erosivity in tropical China and its impact on river sediment load

Rainfall erosivity is one of the important factors leading to soil and water erosion, affecting the intensity of soil erosion and the variation of river sediment load. This work aimed to explore the distribution and variation characteristics of rainfall erosivity in the Nandu River Basin in tropical China and to clarify the effect of rainfall erosivity on sediment load variation. The daily rainfall data of 13 meteorological stations in the Nandu River Basin during 1971–2020 were used to calculate the rainfall erosivity of the river basin by using the rainfall erosivity model based on the daily rainfall. The analyses were conducted using the Mann-Kendall non-parametric trend/abrupt test, wavelet analysis, inverse distance weighted interpolation and other research methods to determine the temporal and spatial fluctuation patterns of rainfall erosivity and sediment load. The annual mean rainfall erosivity in the Nandu River Basin during 1971–2020 was 16,497.67 MJ mm·ha−1·h−1, which showed an upward trend. The distribution of rainfall erosivity was concentrated from July to September. Except spring, the variation of rainfall erosivity in summer, autumn and winter showed an upward trend. Spatially, rainfall erosivity showed a decreasing trend from south to north. The variation coefficient of each meteorological station ranged from 0.24 to 0.43, showing high regional variability. The correlation between rainfall erosivity and erosive rainfall in the basin was the highest, followed by altitude and latitude. From 1980 to 2020, the sediment load in the Nandu River Basin showed an overall upward trend, with obvious periodic changes. The main change period was 25 years, and the sediment load had a large inter-annual variation. Taking the period before the inflection point of the double accumulation curve of sediment load and rainfall erosivity as the base period, since 2009, the river sediment load increased by 46.36% compared with the base period. The influence of the variation of rainfall erosivity on sediment load is −22.23%, and that of human activities on sediment load is 122.23%. These findings can provide a scientific reference for soil erosion model prediction, soil and water conservation and environmental management in tropical basins of China.

Effects of Land Use Change on Rainfall Erosion in Luojiang River Basin, China

This paper, based on daily rainfall erosivity model, ArcGIS, trend analysis and Kriging interpolation method, analyzed the spatial and temporal distribution characteristics of rainfall erosivity in the Luojiang River Basin of China, and then explored the influence relationship between land use change types and rainfall erosivity potential. The results showed the following: (1) from 1980 to 2019, the distribution range of multi-annual rainfall erosivity in the Luojiang River Basin was 14,674–15,227 MJ·mm/ (hm2·h), with an average value of 14,102 MJ·mm/(hm2·h), showing an overall increasing trend; (2) the spatial distribution of rainfall erosivity value tends to be consistent with the multi-year average rainfall, showing a decreasing trend from the middle to the periphery of the basin; (3) land use change is an important factor affecting the spatial and temporal distribution characteristic of rainfall erosivity value in the basin. The increase in rainfall erosivity will undoubtedly increase the potential of soil erosion. This study can provide theoretical reference for future basin land use planning and put forward preventive suggestions according to the distribution characteristics of rainfall erosivity.

Shear Strength Characteristics of Clay-Gravel Layer and Its Slope Failure Law and Mechanism

The Anqing Formation clay-gravel layer is widely distributed in the Anqing area of the upper reaches of the Wanjiang River. It is a typical soil-rock mixture composed of gravel and clay. The poor engineering properties of the clay-gravel layer are the main factors for the failure of the slope. On the basis of regional geological survey, the clay-gravel threshold, clay mineral composition and cementation characteristics of clay-gravel layer were studied. The in-situ horizontal push-shear tests under different gravel content, water environment and overlying load were designed and carried out, and its shear failure mode and strength were analyzed. The study found that the shear zone was in the shape of a broken line and was controlled by the particle size and distribution of the gravel. The shear strength of the clay-gravel layer is relatively high in the natural state due to the structure of the gravel and the friction and cementation force between the clay and the gravel. Water had the greatest influence on the shear strength of the clay-gravel layer, and the overlying load affected its shear strength within a certain range. The indoor rainfall erosion model test of the clay-gravel slope was carried out, and the failure and instability modes and mechanisms of the slope with different gravel contents were studied. When the gravel content was 30%, the failure of the slope surface was the development of gullies. The erosion damage process was similar to the soil slope. When the gravel content was 50%, affected by the distribution of gravel, the slope failure was manifested as the development of erosion pits, followed by local slumps, and finally the overall sliding in layers. When the gravel content was 70%, the clay particles on the slope were quickly washed away by the water flow, and the gravel skeleton of the slope formed a temporary relatively stable structure. After being disturbed, the gravel layer collapsed as a whole.

The changing pattern of rainfall erosivity and its impact on sediment load in the Loess Plateau, China: A case study of a typical watershed

The widespread occurrence of soil erosion reflects the accelerated deterioration of the surface ecological environment. The spatiotemporal variation of rainfall erosivity is the key driving factor for soil erosion by water, especially in the Loess Plateau in China. The Fenhe River basin in the Loess Plateau was selected as a case study based on the daily rainfall data from 1962 to 2019. Using a rainfall erosivity model based the daily rainfall, we studied the changing pattern. Furthermore, the impact of rainfall erosivity on sediment load was evaluated, and the possible causes for changes in sediment load were discussed. The results showed that the annual rainfall erosivity of the 12 weather stations experienced an insignificant change (<i>p</i> &gt; 0.05). Moreover, before the 1980s, the decrease in annual watershed average rainfall erosivity may be the main reason for the decrease in annual sediment load, while after the 1980s, the construction of check dams may be the main factor leading to annual sediment load reduction, especially around 1993. However, the increase of vegetation was a main reason for the reduction of annual sediment load after 1999. Given Fenhe River basin as a typical watershed of the Loess Plateau, the study of rainfall erosivity and its impacts on the sediment in this area can provide a useful reference for further ecological construction and soil erosion control in the Loess Plateau.

A millennium-long climate history of erosive storms across the Tiber River Basin, Italy, from 725 to 2019 CE

Rainfall erosivity drives damaging hydrological events with significant environmental and socio-economic impacts. This study presents the world’s hitherto longest time-series of annual rainfall erosivity (725–2019 CE), one from the Tiber River Basin (TRB), a fluvial valley in central Italy in which the city of Rome is located. A historical perspective of erosive floods in the TRB is provided employing a rainfall erosivity model based on documentary data, calibrated against a sample (1923–1964) of actual measurement data. Estimates show a notable rainfall erosivity, and increasing variability, during the Little Ice Age (here, ~ 1250–1849), especially after c. 1495. During the sixteenth century, erosive forcing peaked at > 3500 MJ mm hm–2 h–1 yr–1 in 1590, with values > 2500 MJ mm hm–2 h–1 yr–1 in 1519 and 1566. Rainfall erosivity continued into the Current Warm Period (since ~ 1850), reaching a maximum of ~ 3000 MJ mm hm–2 h–1 yr–1 in the 1940s. More recently, erosive forcing has attenuated, though remains critically high (e.g., 2087 and 2008 MJ mm hm–2 h–1 yr–1 in 1992 and 2005, respectively). Comparison of the results with sediment production (1934–1973) confirms the model’s ability to predict geomorphological effects in the TRB, and reflects the role of North Atlantic circulation dynamics in central Italian river basins.

Climate Patterns in the World’s Longest History of Storm-Erosivity: The Arno River Basin, Italy, 1000–2019 CE

Rainfall erosivity causes considerable environmental damage by driving soil loss. However, the long-term evolution of erosive forcing (over centennial to millennial time-scales) remains essentially unknown. Using a rainfall erosivity model (REMARB), this study simulates the variability of rainfall erosivity in Arno River Basin (ARB), Italy, a Mediterranean fluvial basin, for the period 1000–2019 CE resulting in the world’s longest time-series of erosivity. The annual estimates show a noticeable and increasing variability of rainfall erosivity during the Little Ice Age (∼1250–1849), especially after c. 1490, until the end of 18th century. During this cold period, erosive forcing reached ∼1600 MJ mm hm−2 h−1 yr−1once every four years, and ∼3000 MJ mm hm−2 h−1 yr−1once every 20 years. The extremes of rainfall erosivity (the 98th percentile) followed a similar increasing trend, with an acceleration of the hydrological hazard (erosivity per unit of rainfall) during the 20th century. The comparison ofREMARBoutput with the sediment yield of the basin (1951–2010) confirmed the model’s ability to predict geomorphological effects in the ARB. Thus, our methodology could be applied to simulate erosivity in environmentally similar basins. A relationship has been identified between the Atlantic Multidecadal Variation and erosivity patterns, suggesting a role of North Atlantic circulation dynamics on the hydrology of central Italy’s fluvial basins.

Laboratory Model Tests on Flow Erosion Failure Mechanism of a Slope Consisting of Anqing Group Clay Gravel Layer

The Anqing group clay gravel layer is a special geological body composed of gravel and clay. In excavation projects, involving this soil, such a gravel layer, is prone to slope collapse and instability under the influence of rainfall. To clearly understand the failure mechanism and influencing factors of clay gravel slopes, an indoor artificial rainfall erosion model testing was carried out to analyse the effect of various slope ratios, gravel contents, and rainfall intensities. The slope erosion damage form, runoff rate, infiltration rate, scoured material, and slope stability of the clay gravel slope were studied. The test results show that sloping surfaces of the gentle slope were mainly damaged by erosion, and the degree of damage gradually increased from the top to the bottom of the sloping surface; however, the stability of the surface was good. In the case of the sloping surface layer of the steep slope, large-scale landslides occurred, and the stability of the surface was poor. When the gravel content was small, the surface failure was manifested as a gully failure. When the gravel content was large, it was manifested as a “layer-by-layer sliding” failure. The degree of influence of different conditions on the stable runoff rate was as follows: rainfall intensity&gt;slope ratio&gt;gravel content. The degree of influence of the parameters on the stable infiltration rate was as follows: slope ratio&gt;rainfall intensity&gt;gravel content. On gentle slopes, the total mass of the scoured material was inversely proportional to the gravel content and directly proportional to the rainfall intensity; on a steep slope, the total mass of the scoured material increased with an increase in the rainfall intensity and gravel content. Moreover, the slope ratio was the key influencing factor to decide whether there was gravel in the scoured material.

Relationship between the periodicity of soil and water loss and erosion-sensitive periods based on temporal distributions of rainfall erosivity in the Three Gorges Reservoir Region, China

More variable or extreme rainfall caused by global climate change is likely to cause much uncertainty in soil erosion. Exploring the effects of changes in the timing and magnitude of rainfall erosivity on soil erosion allows to better understand the sensitivity of soil erosion to rainfall. The aim of this study was to investigate the relationships between the periodicity of soil and water loss and erosion-sensitive periods based on the temporal distributions of rainfall erosivity in the Three Gorges Reservoir Region, China. A daily rainfall erosivity model and field runoff plot monitoring were used to achieve these goals. We found that extreme rainfall has a great effect on the distributions of rainfall erosivity. The average annual erosive rainfall frequency accounts for only 21.2% of the average annual total rainfall frequency, but the average annual erosive rainfall amount accounts for 70.4% of the average annual total rainfall amount. The monthly runoff yields and sediment yields show synchronization with variations in rainfall erosivity to some extent, but the former are closer to the changes in monthly rainfall erosivity. The annual runoff yields and soil loss rates do not show synchronization, but the changes in annual soil loss rates are closer to the interannual variations of rainfall erosivity. The annual soil erosion in the study area can be divided into three periods, namely, highly sensitive periods, moderately sensitive periods and slightly sensitive periods, which indicate the erosion risks from high to low, respectively. Additionally, low vegetation coverage in highly sensitive periods and human activities in moderately sensitive periods can aggravate soil erosion.

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.

Global rainfall erosivity changes between 1980 and 2017 based on an erosivity model using daily precipitation data

Rainfall erosivity (R) is the most important factor affecting soil erosion. It is crucial to estimate it on a global scale to identify the potential soil erosion risk area and to develop mitigation strategies. On a large scale, R is often estimated based on available precipitation data, such as annual, monthly and daily rainfall. Compared with the rainfall erosivity model driven by monthly and annual rainfall data, the daily erosivity model can provide more information about intense erosive events, producing more accurate results. Given the climate variability in different regions, regional parameters are needed when applying the daily erosivity model on a global scale. However, regional parameter estimation is a challenge, and few studies have addressed this.Here, we present a method for parameterising the daily erosivity model for different regions of the world by generating equations for estimating the required parameters of the model in each climate zone. This was based on a literature review to collect the data sites with estimated parameter values globally. We then applied the model to compare the global average annual and monthly rainfall erosivity changes between 1980–1999 (P1) and 2000–2017 (P2). The results showed that globally, average annual R decreased in all climate zones in P2, except for a substantial increase in Df (Cold without dry season), located in high latitudes of the northern hemisphere. At the monthly scale, except for the region 40-70°N, all regions experienced a considerable decrease. Even though the global total annual and monthly rainfall erosivity was reduced between 1980 and 2017, the risk of extreme daily erosivity is high. The acceptable agreements between the values estimated in our model, estimated from high-resolution rainfall data and retrieved from the literature, indicate that the present approach could assess the effects of climate change on soil erosion on a global scale, particularly regarding extreme rainfall events.

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

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

CLIGEN as a weather generator for RUSLE2

RUSLE2 is a rainfall erosion model that, in its standard mode, predicts long term soil losses as an aid to making decisions related to soil conservation in the USA. In recent times, RUSLE2 has developed a capacity to predict event soil losses using user entered data for event rain and EI30. WEPP is a more process based model which uses a stochastic weather generator, CLIGEN, to generate events that are stochastically distributed in time. CLIGEN does not estimate event EI30 values for the rainfall events it generates but it is possible to do so through use of erosivity densities (EI30 per unit quantity of rainfall) data available in RUSLE2. This enables CLIGEN to be used as a climate generator for RUSLE2 and provides the capacity to use RUSLE2 in roles similar to the ones where WEPP is being applied.

Reply to Comment on “determining soil erodibility for the USLE-MM rainfall erosion model by P.I.A. Kinnell”

In Kinnell (2018), I proposed that the soil erodibility factor KUMM used in the USLE-MM model could be replaced by the product of the soil erodibility factor KUM and a coefficient a1 that was given by the ratio of between KUMM and KUM. The theory was developed based on the well-known practice that for USLE based models, the erodibility factor is calculated as the ratio between the total soil loss for a set of erosion producing events and the total value of the erosivity index associated with those events. In their comment, Pampalone et al. (2018) used data for KUMM and KUM values for 12 plots at Sparacia determined by regression analysis to calculate a1 using a different method to show that the exponential equation I proposed in Kinnell (2018) between the power of the erosivity term and the a1 was demonstrated to be site dependent. In this reply, the approach adopted by Pampalone et al. is challenged for not using the well-known method for determining soil erodibilities in USLE based models and the consequences of power increasing with slope length while the runoff ratio decreases with slope length on the values of a1 is illustrated. At Sparacia, soil loss varies with KUM, slope length, and slope gradient, and, in addition, in the context of the USLE-MM, consideration has to be given to the fact that both b1 and the runoff ratio vary with slope length.

Comment on “Determining soil erodibility for the USLE-MM rainfall erosion model by P.I.A. Kinnell”

The measurements units of the USLE-MM soil erodibility factor are dependent on the exponent of the erosivity term. This circumstance prevents to compare soil erodibility values of sites differing by the value of this exponent. To overcame this problem, Kinnell (2018) suggested to relate the soil erodibility factor of the USLE-MM with that of USLE-M by a linear relationship with the objective to obtain a soil erodibility factor independent of the power of the erosivity term. The USLE-MB, which is a recently proposed model, has also a soil erodibility factor having measurement units common to USLE modelling environment.Kinnell (2018) also showed that the relationship between the power of erosivity term and the coefficient of proportionality of the linear relationship between soil erodibility of USLE-MM and USLE-M is site independent.In this comment, using the database of Sparacia experimental area, both this geographical independence and the linearity hypothesis between soil erodibilities were tested. The linearity hypothesis was not completely consistent for Sparacia experimental area while it was more satisfied for the comparison between soil erodibility of USLE-MB and USLE-M. The exponential equation proposed by Kinnell (2018) between the power of erosivity term and the coefficient of proportionality was demonstrated to be site-dependent.

Determining soil erodibilities for the USLE-MM rainfall erosion model

The USLE-MM is a rainfall erosion model which predicts soil loss using an erosivity index which is given by the QREI30 index to a power >1.0. In the USLE modelling environment, soil erodibility is defined as the soil loss per unit quantity of the erosivity index. With the USLE-MM model where event soil loss=KUMM(QREI30)b1, using KUMM as the erodibility factor does not conform with the requirement that the erodibility factor has units that are consistent between soils and locations. The alternative is to use the model event soil loss=KUMa1 (QREI30)b1 where KUM is determined from dividing the total amount of soil loss from a large number of events by the total value of QREI30 associated with those events because this model isolates the soil erodibility effect from the effect of b1 on KUMM. The effect of b1 on a1 is demonstrated in this paper for bare fallow plots at 15 locations in the USA and plots at Sparacia, Sicily.

Development of daily rainfall erosivity model for Kelantan state, Peninsular Malaysia

Abstract The study was conducted to develop a rainfall erosivity model for tropical climates to estimate daily rainfall erosivity and determine the most effective power law relationship between rainfall erosivity and daily precipitation. Thirty minute resolution rainfall data recorded in 55 stations of a state in Peninsular Malaysia were analysed. Using three precipitation limits of 0.1, 5.0 and 12.7 mm, the behaviour of rainfall on average annual rainfall erosivity gave ranges of 10,264–54,284, 8,151.5–48,301 and 4,958–39,938 MJ mm ha−1 h−1 y−1 respectively. It was found that the number of individual events, occurring within a day, were more in the area compared other types of events for all precipitation limits (0.1, 0.5 and 12.0 mm). Spatio-temporal variation of monthly coefficient values of power law relationship was found with the highest R2 in the range of 0.93–0.94 for 0.1 mm precipitation limit. Out of 55 stations, 15 were selected for model development and assessment. On the basis of importance of smaller events, a 0.1 mm precipitation limit was selected for the proposed model. The proposed model was found good for monthly and annual rainfall erosivity estimation apart from few limitations. Furthermore, the validity of the proposed model was checked for different parts of the area.

Predicting Changes of Rainfall Erosivity and Hillslope Erosion across New South Wales, Australia

New South Wales and Australia Capital Territory Regional Climate Modelling (NARCliM) project has produced a suite of 12 regional climate projections for south-east Australia spanning the range of likely future changes in climate. The aim of this study was to model and predict the impacts of climate change on rainfall erosivity and hillslope erosion risk across New South Wales using the NARCliM projections to assist the long-term climate change adaptation and regional planning. We developed a daily rainfall erosivity model for Southeast Australia to calculate monthly and annual rainfall erosivity values from the projected daily rainfall data for the baseline (1990-2009) and future periods (2020-2039 and 2060-2079). We produced monthly and annual hillslope erosion maps for these three periods using the Revised Universal Soil Loss Equation (RUSLE) with spatial interpolation to finer scale (100 m). Automated scripts have been developed in a geographic information system (GIS) to calculate the time-series rainfall erosivity and hillslope erosion so that the processes of large quantity climate projections are realistic, repeatable and portable. The model performance was assessed by comparing with data from Bureau of Meteorology for the baseline period and the overall coefficient of efficiency reached 0.9753 (RMSE 13.2%). Both rainfall erosivity and hillslope erosion risk are predicted to increase about 7% in the near future, and about 19% increase in the far future compared with the baseline period. The change is highly uneven in space and time, with the highest increase occurring in the Far-west in autumn. The rainfall erosivity is generally higher in summer and lower in winter, with about 10 times difference between February (highest) and July (lowest).

Spatiotemporal variation and trends in rainfall erosivity in China's dryland region during 1961–2012

This study evaluated rainfall erosivity and its changes in China's dryland region during 1961–2012, using a daily rainfall erosivity model, ArcGIS based spatial interpolation tools, and the Mann–Kendall and Sen's methods. It was found that mean rainfall erosivity was 2–4098MJmmha−1h−1a−1 in the region, with the regional average of 794MJmmha−1h−1a−1, showing a significant increase trend accordingly with an increase in rainfall from northwest to southeast. Rainfall erosivity varied annually with the CV of 32.4–491.3%, and varied seasonally with about three-quarter distributed in summer. During 1961–2012, a statistically significant increase/decrease trend in rainfall erosivity was detected only for 24/10 stations or about 4.6/0.4% of the region, and a non-significant increase/decrease trend was observed for 32/45 stations or 16.5/10.0% of the region at the 50 to 90% confidence level. No statistically confidential trend was found for other 126 stations or 68.5% of the region. At regional level, mean annual erosivity of the arid zone indicated an upward trend, while the sub-humid zone a downward, but the semi-arid zone generally had no evidential trend. Comparing the 26year's averages during 1987–2012 and 1961–1986, mean erosivity of the arid zone increased by 17.4%, the sub-humid zone decreased by 6.1%, and the semi-arid zone reduced slightly by 0.45%. The results suggest that change in the rainfall characteristics during the past half century had no significant influence on water erosion in the majority of the region, but it could probably induce an increase of water erosion risk in parts of the drier west and a slight decrease in parts of the wetter east including the Loess Plateau.