Average Annual Rainfall Erosivity Research Articles

Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin

Rainfall erosivity is a crucial factor in the evaluation of soil erosion, significantly influencing the complex relationships among water, soil, and the environment. Understanding its attributes and variations in space and time is essential for effective water resource management, erosion mitigation, and land-use planning. This paper utilizes daily precipitation data from 123 grid points in the Niyang River Basin, spanning from 2008 to 2016, to calculate rainfall erosivity using a straightforward algorithmic model. Ordinary Kriging was used to examine the spatial and temporal variations in rainfall erosivity, while Spearman’s correlation analysis was employed to examine the relationships between annual rainfall erosivity and various factors, including multi-year average precipitation, erosive rainfall, dry-season rainfall, wet-season rainfall, temperature, and elevation. The results indicate a year-by-year increase in rainfall erosivity in the basin, with a trend towards stabilization. The average annual rainfall erosivity over the years is 711 MJ·mm·hm−2·h−1, peaking at 1098 MJ·mm·hm−2·h−1 in 2014. A significant 93.9% of rainfall erosivity is concentrated in the wet season, with a maximum of 191 MJ·mm·hm−2·h−1 in July. The left bank of the mainstream, especially the central and lower sections of the main river and its tributaries, experiences the highest rainfall erosivity. Rainfall factors predominantly influence erosivity, with erosive rainfall showing the strongest correlation (rho = 0.93), while temperature and elevation have relatively minor effects. This study enhances the understanding of rainfall erosive forces in the plateau region and provides a scientific basis for predicting soil loss, developing effective erosion control measures, and ensuring sustainable land use.

Evaluation of GPM IMERG-FR Product for Computing Rainfall Erosivity for Mainland China

Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and GPM_3IMERGDF (daily and 0.1°), in estimating two erosivity indices in mainland China: the average annual rainfall erosivity (R-factor) and the 10-year event rainfall erosivity (10-yr storm EI), by comparing with that derived from gauge-observed hourly precipitation (Gauge-H). Results indicate that GPM_3IMERGDF yields higher accuracy than GPM_3IMERGHH, though both products generally underestimate these indices. The Percent Bias (PBIAS) is −55.48% for the R-factor and −56.38% for the 10-yr storm EI using GPM_3IMERGHH, which reduces to −10.86% and −32.99% with GPM_3IMERGDF. A bias-correction method was developed based on the systematic difference between SSPs and Gauge-H. A five-fold cross validation shows that with bias-correction, the accuracy of the R-factor and 10-yr storm EI for both SPPs improve considerably, and the difference between two SSPs is reduced. The PBIAS using GPM_3IMERGHH decreases to −0.06% and 0.01%, and that using GPM_3IMERGDF decreases to −0.33% and 0.14%, respectively, for the R-factor and 10-yr storm EI. The rainfall erosivity estimated with SPPs with bias-correction shows comparable accuracy to that obtained through Kriging interpolation using Gauge-H and is better than that interpolated from gauge-observed daily precipitation. Given their high temporal and spatial resolution, and timely updates, GPM_3IMERGHH and GPM_3IMERGDF are viable data products for rainfall erosivity estimation with bias correction.

Rainfall Erosivity Characteristics during 1961–2100 in the Loess Plateau, China

Rainfall erosivity, which signifies the inherent susceptibility of soil erosion induced by precipitation, plays a fundamental role in formulating a comprehensive soil loss equation (RUSLE). It stands as a crucial determinant among the foundational factors considered in a comprehensive soil loss equation’s establishment. Nonetheless, the prediction and quantification of future alterations in rainfall erosivity under the influence of global warming have been relatively limited. In this study, climate change was widely evaluated and 10 preferred global climate models in the Loess Plateau were selected by using the data sets of 27 models simulating climate change and the CN05.1 data set provided by the latest CMIP6. The monthly precipitation forecast data were obtained by using the delta downscaling method. Combined with trend analysis, significance test, and coefficient of variation, the annual rainfall erosivity during 1961–2100 under four SSP scenarios was analyzed and predicted. Among the 27 GCM models used in this paper, the most suitable climate models for simulating monthly precipitation in the Loess Plateau were CMCC-CM2-SR5, CMCC-ESM2, TaiESM1, EC-Earth3, EC-Earth-Veg-LR, INM-CM4-8, CAS-ESM2-0, EC-Earth-Veg, ACCESS-ESM1-5, and IPSL-CM6A-LR. In comparison to the base period (1961–1990), during the historical period (1961–2014), the average annual rainfall erosivity on the Loess Plateau amounted to 1259.64 MJ·mm·hm−2·h−1·a−1, showing an insignificant downward trend. In the northwest of Ningxia, Yulin City and Yanan City showed a significant upward trend. In the future period (2015–2100), the annual rainfall erosivity continues to constantly change and increase. The potential average increase in rainfall erosivity is about 13.48–25.86%. In terms of spatial distribution, most areas showed an increasing trend. Among these regions, the majority of encompassed areas within Shanxi Province, central Shaanxi, and Inner Mongolia increased greatly, which was not conducive to soil and water conservation and ecological environment construction. This study offers a scientific reference for the projected future erosivity characteristics of the Loess Plateau.

Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020)

In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (RE), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (ED), which relates RE to precipitation. The RE requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for RE estimation. This study evaluates the performance of a new gridded dataset of RE and ED in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000–2020. By using this method, a correlation of 0.94 was found between PISCO_reed and RE obtained by the observed data. An average annual RE for Peru of 7840 MJ · mm · ha−1· h−1 was estimated with a general increase towards the lowland Amazon regions, and high values were found on the North Pacific Coast area of Peru. The spatial identification of the most at risk areas of erosion was evaluated through a relationship between the ED and rainfall. Both erosivity datasets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision.

Estimation of rainfall erosivity on the Chinese Loess Plateau: A new combination of the ERA5 dataset and machine learning

Determining the characteristics of rainfall erosivity at different times and locations is significant for global and regional soil erosion prevention and control. However, accurate quantification of rainfall erosivity remains a challenge due to the limitations of high-precision and long-time series precipitation data. Here, compared with the rainfall erosivity of 291 meteorological stations on the Chinese Loess Plateau, we evaluated the distribution characteristics and accuracy of rainfall erosivity on different temporal and spatial scales calculated by the ERA5 precipitation dataset. A random forest model was constructed to generate a bias dataset to correct the annual rainfall erosivity. Besides, the relationship between erosivity density (ED) and rainfall patterns was quantified to clarify the characteristics of rainfall erosivity. Conducted analysis suggested that the rainfall erosivity calculated from the ERA5 precipitation dataset has high accuracy on average annual, seasonal and monthly scales, and the R2 were 0.62, 0.86, and 0.85, respectively. The addition of the random forest model further optimized the result of average annual rainfall erosivity of ERA5, with root mean square error (RMSE) and absolute value of relative bias (BIAS) decreasing to 185.7 MJ mm ha−1h−1 a−1 and 2.17%, respectively. Meanwhile, ERA5 can well capture the average annual rainfall erosivity in different spaces of the study area. The high correlation between erosivity density and rainstorms (R2 = 0.87) indicated that a larger proportion of rainstorms in erosive rainfall will lead to a greater potential risk of soil erosion. These findings provide a reliable approach to accurately obtain the variable rainfall erosivity in areas with complex terrain and sparse stations, and have important implications for soil sustainability.

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.

Linkages between soil erosion and long-term changes of landscape pattern in a small watershed on the Chinese Loess Plateau

Understanding the response of soil erosion to the changes of land use and landscape pattern is valuable for the optimization of soil and water conservation measures in a watershed. This study selected a typical hilly-gully watershed of the Chinese Loess Plateau, the Zhifanggou watershed, to assess its long-term land use changes from 1938 to 2020 and their impacts on sediment yield by using the spatially distributed soil erosion model WaTEM/SEDEM with an average annual rainfall erosivity of 2000–2010. The results indicated that the main land use types in the Zhifanggou watershed were cropland, forest and grassland, and experienced two changing periods: rapid agricultural reclamation during 1938–1958 and vegetation restoration during 1958–2020. The average annual sediment yields varied consistently with land use changes, showing evident changes from 51.84 t ha−1 y−1 in 1938 to 254.1 t ha−1 y−1 in 1958 and then to 4.27 t ha−1 y−1 in 2020. Correlation analysis suggested that sediment yield was closely related to the landscape indices of interspersion and juxtaposition index and Perimeter-area fractal dimension. The results indicated that increased vegetation cover and high spatial heterogeneity of landscapes could alter sediment transport pathways, reduce sediment connectivity, and induce less sediment discharged into the river. Our findings could provide a good reference for optimizing soil and water conservation measures in the watershed.

A Global Rain-Driven Soil Erosion Investigation Based on Simulated Breakpoint Precipitation

HighlightsAn international dataset of simulated breakpoint precipitation climate stations was used to overcome the limitations of fixed-interval precipitation in global soil erosion applications.The international simulated breakpoint dataset was validated against collocated high-quality, high-resolution precipitation data from a ground network and other data sources.The process-based Rangeland Hydrology and Erosion Model (RHEM) was used to predict erosion based on global climate classifications and soil properties.Critical precipitation factors in RHEM scenarios were identified based on their ability to predict erosion rates.Abstract. Recent research has highlighted problems with erosion modeling applications that use coarser fixed-interval precipitation data as opposed to breakpoint precipitation data, which better preserves precipitation characteristics such as intensity and duration. Due to their wider availability, erosion modeling applications and risk assessments are typically based on fixed-interval data. However, these applications could be subject to substantial erosion underestimation bias related to time-averaged precipitation. Alternatively, this manuscript presents a novel approach to global-scale erosion assessment based on simulated breakpoint precipitation data. A point-scale stochastic weather generator, CLIGEN, was used to generate precipitation events with characteristics more similar to breakpoint precipitation data than fixed-interval alternatives. An international CLIGEN dataset of climate parameters from more than 10,000 long-term climate stations in numerous countries was evaluated for use in parameterizing CLIGEN simulations. CLIGEN-simulated event characteristics and derived statistics such as annual rainfall erosivity were compared to high-quality, high-resolution NOAA-ASOS precipitation data where available. The Rangeland Hydrology and Erosion Model (RHEM) was used to predict runoff and soil loss based on the same CLIGEN inputs for simple bare soil scenarios with site-specific soil textures taken from the global 250m SoilGrids product. Erosion results were analyzed according to climate type, revealing that predicted distributions of sediment yield and runoff were statistically unique for most global climate types. A multivariate regression model was developed to explore and understand the importance of various precipitation input factors. Peak precipitation intensity was the most critical climate factor for determining sediment yield, and combined CLIGEN precipitation factors had approximately as much predictive power as soil texture. Average annual rainfall erosivity values were calculated for each location and were 25% and 20% greater than values from RUSLE2 and Panagos et al. (2017) estimates, respectively. This finding is in agreement with the latest research on the topic. Keywords: ASOS, CLIGEN, Erosivity, Global soil erosion, Machine learning, RHEM, Simulated breakpoint precipitation.

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.

Rainfall erosivity in South America: Current patterns and future perspectives

Rainfall erosivity is the driving factor for soil erosion and can be potentially affected by climate change, impacting agriculture and the environment. In this study, we sought to project the impact of climate change on the long-term average annual rainfall erosivity (R-factor) and mean annual precipitation in South America. The CanESM2, HadGEM2-ES, and MIROC5 global circulation models (GCMs) and the average of the GCMs (GCM-Ensemble) downscaled by the Eta/CPTEC model at a spatial resolution of 20 km in the representative concentration pathway (RCP) 8.5 were applied in this study. A geographical model to estimate the R-factor across South America was fitted. This model was based on latitude, longitude, altitude, and mean annual precipitation as inputs obtained from the WorldClim database. Using this model, the first R-factor map for South America was developed (for the baseline period: 1961–2005). The GCMs projected mean annual precipitation for three 30-year time periods (time slices: 2010–2040; 2041–2070; 2071–2099). These projections were used to run the R-factor model to assess the impact of climate change. It was observed that the changes were more pronounced in the Amazon Forest region (namely, the North Region, NR, and the Andes North Region, ANR) with a strong reduction in the mean annual precipitation and R-factor throughout the century. The highest increase in the R-factor was projected on the Central and South Andes regions (CAR and SAR) because of the increase in the mean annual precipitation projected by the GCMs. The GCMs pointed contradictory projections for the Central-South Region (CSR), indicating greater uncertainty. An increase in the R-factor was projected for this region, eastern Argentina, and southern Brazil, whereas a decrease in the R-factor was expected for southeastern Brazil. In general, the GCMs projected reductions in the R-factor and annual precipitation for South America, with the highest changes projected from the baseline to the 2010–2040 time slice.

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 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.

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.

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

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

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.

Impacts of Climate Change on Rainfall Erosivity in the Huai Luang Watershed, Thailand

This study focuses on the impacts of climate change on rainfall erosivity in the Huai Luang watershed, Thailand. The multivariate climate models (IPCC AR5) consisting of CCSM4, CSIRO-MK3.6.0 and MRI-CGCM3 under RCP4.5 and RCP8.5 emission scenarios are analyzed. The Quantile mapping method is used as a downscaling technique to generate future precipitation scenarios which enable the estimation of future rainfall erosivity under possible changes in climatic conditions. The relationship between monthly precipitation and rainfall erosivity is used to estimate monthly rainfall erosivity under future climate scenarios. The assessment compared values of rainfall erosivity during 1982–2005 with future timescales (i.e., the 2030s, 2050s, 2070s and 2090s). The results indicate that the average of each General Circulation Model (GCM) combination shows a rise in the average annual rainfall erosivity for all four future time scales, as compared to the baseline of 8302 MJ mm ha−1 h−1 year−1, by 12% in 2030s, 24% in 2050s, 43% in 2070s and 41% in 2090s. The magnitude of change varies, depending on the GCMs (CCSM4, CSIRO-MK3.6.0, and MRI-CGCM3) and RCPs with the largest change being 82.6% (15,159 MJ mm ha−1 h−1 year−1) occurring under the MRI-CGCM3 RCP8.5 scenario in 2090s. A decrease in rainfall erosivity has been found, in comparison to the baseline by 2.3% (8114 MJ mm ha−1 h−1 year−1) for the CCSM4 RCP4.5 scenario in 2030s and 2.6% (8088 MJ mm ha−1 h−1 year−1) for the 2050s period. However, this could be considered uncertain for future rainfall erosivity estimation due to different GCMs. The results of this study are expected to help development planners and decision makers while planning and implementing suitable soil erosion and deposition control plans to adapt climate change in the Huai Luang watershed.

Spatio-temporal variation in rainfall erosivity during 1960–2012 in the Pearl River Basin, China

Accelerated soil erosion is an undesirable process that adversely affects the conservation of water and soil. Rainfall erosivity is used to measure the potential ability of rain to cause erosion. This study explores spatio-temporal variations in rainfall erosivity in the Pearl River Basin of China during 1960–2012, at annual and seasonal scales based on a daily model proposed by Yu. Analysis methods including linear regression, Mann-Kendall and the Kriging interpolation technology were applied to determine fluctuating spatial and temporal patterns. Yu's model was proven suitable for the Pearl River Basin by comparison with another model proposed by Zhang. Computation results show an average annual rainfall erosivity of 9918.98MJ·mm/(hm2·h) and that the Pearl River Delta region, most regions of the Dongjiang River Basin, and the Beijiang River Basin were all under considerable threat from rainfall erosion. The annual rainfall erosivity of the basin does not show significant or abrupt changes, and has fluctuated moderately over the past few decades. The rainfall erosivity of the basin shows an upward trend, and the probability of water and soil erosion caused by rainfall was increasing overall. The problem of water and soil loss in the Pear River Basin remains severe due to aggravating vegetation deterioration, rocky desertification, and climate change. Relevant response strategies and appropriate measures must be considered to guard against further deterioration of the basin.

Evaluation of Rainfall Erosivity Index for Abuja, Nigeria Using Lombardi Method

Rainfall erosivity index is one of the important factors influencing soil erosion. Erosivity index for Abuja, Nigeria was evaluated using the Lombadi method. Twelve (12) years rainfall data (2001 – 2012) used was obtained from Nigerian Meteorological Agency (NIMET) Abuja. Daily kinetic energy – intensity interaction was computed using EI = 1.03Vd1.51. The results showed that the average annual rainfall erosivity index for the city for the period of study was 1131.86 MJmm/hr. The correlation between annual erosivity index and average annual precipitation was expressed as Y = 8.2209X + 34.659. The coefficient of Determination R2 was 0.5011. During this period, the month of August (in all the years) had the highest erosivity index except for 2001, 2002, 2005 and 2008. The analysis of rainfall seasonal distribution showed that the most intensive erosion menace in the area can be expected in August, especially in the areas that are not protected by vegetation cover, which also depends on the climatic change. It is recommended that soil surfaces should not be left bare.http://dx.doi.org/10.4314/njt.v34i1.7

Projected Rainfall Erosivity Changes under Future Climate in the Upper Nan Watershed, Thailand

This study aims to estimate the potential changes in rainfall erosivity under future climatic conditions in the Upper Nan watershed, Thailand. The multi-climate model and multi-emission scenario approach for the estimation of climate change impacts used in the study consists of PRECIS: ECHAM4, GFDLR-30, HadCM3 and NCAR CCSM3. The change factor or the delta change method is used as a downscaling technique to generate future precipitation. The relationship between monthly precipitation and rainfall erosivity can be used to estimate monthly rainfall erosivity under future climate. Results indicate that the average annual precipitation for all three future time slices increases from a baseline value of 1250 mm by between 2.14% (1277 mm) in 2011-2040 and 7.00% (1337 mm) in 2071-2099. Moreover, the mean of each GCM and emission scenario combination illustrates an increase in average annual rainfall erosivity for all three future time slices; from a baseline value of 5503 MJ mm ha-1 h-1 yr-1, the amount increases by between 5.02% (5779 MJ mm ha-1 h-1 yr-1) in 2011−2040 to 14.20% (6284 MJ mm ha-1 h-1 yr-1) in 2071-2099. According to RUSLE, a 1% increase in rainfall erosivity will lead to a 1% increase in soil erosion, if other factors remain constant. Therefore, soil erosion in the Upper Nan watershed is projected to be more serious in the coming decades.

A Comparison of Spatial Interpolation Models for Mapping Rainfall Erosivity on China Mainland

Rainfall erosivity is an essential factor to reveal the response of water erosion to precipitation changes, and its spatial variation reveals erosion regional difference and water conservation regionalization. In this research, average annual rainfall erosivity in 1951 -2008 on China mainland is calculated through daily precipitation data from 711 meteorological stations. Precisions of 29 spatial interpolation models are quantitative compared including inverse distance weighting (IDW), radial basis function (RBF), kriging, cokriging (CK) and thin plate smoothing spline (TPS). Three variables cubic TPS is confirmed the optimum spatial interpolation model to rainfall erosivity on a large scale.