Impact of Time Resolution of Rainfall Measurement on Erosivity Factor in Arid Region of India

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.

• Elevation should be an auxiliary variable to aid the mapping of rainfall erosivity. • The relationship between R-factor and elevation is non-stationary. • The number of gauges has an impact on spatial interpolation result. • Fewer samples amplify accuracy variance with distribution shifts. The rainfall erosivity factor (R-factor) is an important parameter in the universal soil loss equation (USLE), the revised universal soil loss equation (RUSLE) and several other soil erosion prediction models. It is necessary to choose suitable methods to map the R-factor at the basin and regional scales to effectively apply erosion prediction models and establish precise soil and water conservation measures. When rain gauges are sparsely and unevenly distributed on high and steep terrain, it is often difficult to obtain ideal results from traditional spatial interpolation methods. To optimize the interpolation method for the R-factor in mountainous areas and explore the impact of the number and distribution pattern of rain gauges on the interpolation results, the Longchuan River Basin in the Hengduan Mountain region in Southwest China was selected as the study area. Four methods, namely, inverse distance weighting (IDW), ordinary kriging (OK), global regression kriging (GRK) and geographically weighted regression kriging (GWRK), were selected to conduct a comparative analysis under various scenarios of gauge number and distribution. Compared with traditional univariate methods, GRK and GWRK, which incorporate elevation, yield more accurate and detailed results, with GWRK exhibiting the best performance. The accuracy and value ranges of the interpolation results are influenced by the coupling of the number and distribution of gauges. With fewer gauges, the impact of the distribution pattern is more obvious. This study provides insights into optimizing R-factor mapping under future climate scenarios, enabling precise soil erosion risk assessment and targeted prevention in mountainous regions.

Core Ideas The R factor was developed in the various versions of the USLE. Research on rainfall erosivity estimation methods, mapping, and temporal trends is summarized. The RUSLE underestimates R factor values by about 10%. Three approaches for developing erosivity maps are identified. The rainfall erosivity factor ( R factor) is one of six erosion factors in the Universal Soil Loss Equation (USLE), which together reflect the combined effects that cause soil loss by rill and interrill erosion on hillslopes by precipitation. It is defined as the summation of event EI 30 (the product of kinetic energy and maximum 30‐min intensity) over a year and calculated based on rainfall hyetograph data. The R factor was developed in the various versions of the USLE, including the definition of the individual event and the criterion for selecting events used in the calculation, the equation used to estimate the unit kinetic energy from the rainfall intensity, the estimation of erosivity from the snowmelt and thaw, and erosivity mapping. Most research on rainfall erosivity deals with any of three aspects: developing estimation methods for deriving erosivity from courser resolution rainfall data (such as daily, monthly, and annual) but with greater spatial and temporal coverages than those from hyetograph data; preparing erosivity maps including those for annual average, monthly, and 10‐yr recurrence erosivity; and documenting temporal trends in erosivity. Rainfall erosivity research on these three aspects is summarized to provide a greater understanding of the R factor.

<p>Indian is worst affected by soil erosion, especially due to erosion induced by rainfall. A factor of Universal Soil Loss Equation (rainfall erosivity factor) needs to be estimated throughout the country to assess the soil erosion in the country. Indian climate is dominated by monsoons, and their intensity and distribution vary significantly throughout the country. Rainfall erosivity is solely derived from the rainfall intensity, which is a function of climatic properties. In this study, the distribution and variability of the rainfall erosivity factor (R factor) had been analyzed in different regions and sub-divisions of India as classified by India Meteorological Department (IMD). For estimation of rainfall erosivity, the widely adopted principle of kinetic energy and rainfall intensity had been used. A well-known precipitation index, Modified Fournier Index (MFI), was also calculated to check its influence on the R factor. Regression equations in the form of power-law are derived for all regions of the country to establish the relationship between the R factor and MFI. Further, an analysis at the sub-divisional level was also performed to visualize the spatial variability of the R-factor throughout the nation. South peninsula India with the lowest average R factor of 615.61 MJ-mm/ha/h/yr, was recognized as least vulnerable to rainfall erosivity while the East and Northeast India was recognized as most susceptible with a highest R factor of 3312.39 MJ-mm/ha/h/yr. About 36% of the entire subdivisions of the country were spotted with an average rainfall erosivity factor higher than the national average rainfall erosivity factor, and hence they are more prone to erosion induced by rainfall. Estimating rainfall erosivity factors at sub-divisional and regional levels will help policymakers and watershed experts prioritize the watershed management practices to counter soil erosion induced by rainfall erosivity.</p><p>Keywords – Rainfall erosivity, IMD, Spatial variability, Climate, Precipitation index</p>

THIS paper reports a first-generation adaptation, based on limited data, of the Universal Soil Loss Equation to the Dryland Grain-growing Region of the Pacific Northwest. A preliminary analysis of the data in-dicated the Wischmeier-Smith slope length and steep-ness relationships overpredicted erosion for the steep slopes of the region. Revised relationships were pro-posed. The rainfall erosivity factor, based on rainfall intensity and energy, did not adequately account for erosive forces during the winter months when surface runoff from rain-fall and snowmelt dominates the erosion process. Field data were used to determine an equivalent winter R fac-tor to add to the spring, summer, and fall EI. A com-bined relationship based on the winter precipitation and the 2-yr return interval, 6-h duration precipitation was used to determine the areal distribution of a rainfall and runoff erosivity factor. The rainfall and runoff erosivity factor was distributed throughout the year so that cover and management fac-tors could be calculated for given rotations. Approxi-mately 85 percent of the erosion hazard occurs during the winter months when the fields planted to winter wheat are often bare and unprotected. The resulting adaptation of the Universal Soil Loss Equation is considered to be applicable to the Pacific Northwest Dryland Grain Region and extendable to the Intermountain Dryland Grain Region and other non-mountainous areas east of the Cascade ranges in Wash-ington, Oregon, and Idaho.

Rainfall erosivity is one of the key parameters influencing the degree of soil erosion. Due to the high spatiotemporal variability of rainfall erosivity and the influence of a changing climate it is crucial to use spatially well-distributed and temporally current rainfall data. Rainfall erosivity in Austria has been estimated by previous studies with varying rainfall data amounts. This study aimed to create an updated R-factor map for Austria and its main agricultural production zones based on a larger number of rainfall stations and a recent time series. As well as, compare R-factors from previous studies to identify differences in erosivity estimation. Rainfall data from 171 stations throughout Austria were gap-filled and corrected to improve data quality. Rainfall erosivity was calculated for 1995–2015 for the vegetation period and annually and used to establish two linear regressions describing rainfall erosivity as a function of mean rainfall amount. The regressions were applied to the 1 km2 daily rainfall grids from the SPARTACUS dataset to create the spatially distributed rainfall erosivity maps. Differences in the temporal and spatial distribution of rainfall erosivity, erosion index and erosivity density between the main agricultural production zones showed areas at risk of soil erosion and timing of vulnerability. The highest rainfall erosivities were found in the agriculturally important eastern regions of Austria during the summer months. Compared to previous studies, considerable differences in local R-factor estimation were found. The significantly larger number of rainfall stations and an updated time series increased the representativeness of rainfall erosivity estimation in Austria, which can contribute to a more precise soil erosion risk assessment.

Soil loss is estimated by different models in which the soil erodibility factor, K, is one of the important parameters, especially in soils with rock fragments. The objective of this study was to determine the soil erodibility factor by the Universal Soil Loss Equation (USLE) and USLE-M models by direct soil loss measurements for six soil series (three of them contained high amounts of rock fragments) under a rainfall simulator with rainfall intensities of 26–55 mm h−1. The erodibility factor of the six soil series varied between 0.0053 and 0.0125 t ha h (ha MJ mm)−1, in which the three gravelly soils (Bamoo, Loamy-skeletal over fragmental, carbonatic, mesic, Typic Xerorthents; Kuye-asateed, Loamy-skeletal over fragmental, carbonatic, mesic, Typic Xerorthents; and Shekarbany, Fragmental, mixed, mesic, Typic Xerorthents) had medium K values [0.006 t ha h (ha MJ mm)−1]. Other soils (Pump-namazi, Fine, mixed, mesic, Fluventic Haploxerepts; Ramjerdi, Fine, mixed, mesic, Fluventic Haploxerepts; and Daneshkadeh, Fine, mixed, mesic, Typic Calcixerepts) had high K values [0.01 t ha h (ha MJ mm)−1]. Using Wischmeier–Smith nomograph, the estimated K factor (Ku) for the six soil series were by average 5.1 times that of measured K values by the USLE model. The K factor from Wischmeier–Smith nomograph (Ku) was modified for gravel contents for Bamoo, Shekarbany, and Kuye-asateed soil series, therefore, its average value was about 3.5 folds of the amount determined by the USLE model. This modification decreased the Ku/K ratio about 33%. The erodibility factor obtained by USLE-M (Kum) for the six soil series ranged from 0.0184 to 0.0509 t ha h (ha MJ mm)−1. Kum for the six soil series was on the average 5.7 times of that measured K values by USLE models. Ratio of the modified Ku to Kum for the six soil series was on the average 1.39. Therefore, it was concluded that the USLE-M model and the Wischmeier–Smith nomograph after modification for gravel content closely determined the soil erodibility factor.

Great efforts have been made to curb soil erosion and restore the natural environment to Inner Mongolia in China. The purpose of this study is to evaluate the impact of returning farmland to the forest on soil erosion on a regional scale. Considering that rainfall erosivity also has an important impact on soil erosion, the effect of land use and land cover change (LUCC) on soil erosion was evaluated through scenario construction. Firstly, the universal soil loss equation (USLE) model was used to evaluate the actual soil erosion (2001 and 2010). Secondly, two scenarios (scenario 1 and scenario 2) were constructed by assuming that the land cover and rainfall-runoff erosivity are fixed, respectively, and soil erosion under different scenarios was estimated. Finally, the effect of LUCC on soil erosion was evaluated by comparing the soil erosion under actual situations with the hypothetical scenarios. The results show that both land use/cover change and rainfall-runoff erosivity change have significant effects on soil erosion. The land use and land cover change initiated by the ecological restoration projects have obviously reduced the soil erosion in this area. The results also reveal that the method proposed in this paper is helpful to clarify the influencing factors of soil erosion.

Spatio-temporal average rainfall erosivity factor map has been generated for India. This study on rainfall erosivity makes use of 101 years monthly rainfall data and 52 spatial points. The results presented here provide the much needed guidance to remove ambiguities regarding the rainfall erosivity factor in the Indian context. This study has a variety of applications in erosion prediction technology, such as Universal Soil Loss Equation or Revised Universal Soil Loss Equation, or in rainfall-data-deficient regions. In-depth observations can develop a deeper understanding of rainfall variation to estimate the erosivity factor. Rainfall erosivity factor map can facilitate agriculturists and soil conservationists to identify rainfall erosivity potential at diverse locations, and thereby apply obligatory safety measures to minimize soil erosion. The rainfall erosivity factor map has been used to provide a more rational value of the average rainfall erosivity factor covering India, in regions where rainfall distribution patterns is of the same order.

Present study aimed at estimating soil erosion potential and identification of critical areas for soil conservation measures in an ungauged catchment situated in Aravalli hills of Udaipur district, Rajasthan (India). Also, impact of rainfall on soil erosion is evaluated. The soil erosion is estimated for 10 year period (2001–2010) by Universal Soil Loss Equation (USLE) model using Geographical information system (GIS) and remote sensing techniques for every 12 m × 12 m area. Thematic maps of six USLE model parameters, i.e., rainfall erosivity (R-factor), soil erodibility (K-factor), slope length (L-factor), slope steepness (S-factor), crop and management (C-factor), and support practice (P-factor), were prepared in GIS platform. The R-factor ranged from 1,522.93 to 10,225.88 MJ mm ha−1 h−1 year−1 in the years 2006 and 2008, respectively, when the annual rainfall was 984.3 and 572.2 mm, and number of rainy days were 58 and 47, respectively. The K-factor was highest for fine loam soil covering 56 % area, while the lowest value was for coarse loamy sand in 22 % area. The lowest value of the L-factor (0.736) was in accordance with the high slopes nearby catchment boundary; whereas the highest value (0.832) was for almost zero slopes in 34 % area nearby waterbodies. Opposite to the L-factor, the S-factor values were high (>4) for the higher slopes nearby catchment boundary and the lowest values for the zero slopes. The C-factor value in 170.36 km2 or 48.91 % of the area is 0.1 while the value is zero for waterbodies and builtup lands. The P-factor value in 250.36 km2 or 71.87 % of the area is 0.8. The mean annual soil erosion in the major portion of the catchment (231.13 km2 or 66.38 %) exceeds 10 t ha−1 year−1 indicating high to very severe soil erosion conditions prevailing in the catchment. It is apparent that vast quantities of the soil are getting eroded from the catchment, and the annual rainfall amount and rainfall intensity have the profound effect on the soil erosion potential. This study emphasizes that USLE model coupled with GIS and remote sensing techniques are promising and cost-effective tools for mapping critical areas of soil erosion in ungauged catchments especially in developing countries.

The rainfall erosivity factorRof the Universal Soil Loss Equation is a good indicator of the potential of a storm to erode soil, as it quantifies the raindrop impact effect on the soil based on storm intensity. TheR‐factor is defined as the average annual value of rainfall erosion index,EI, calculated by cumulating theEIvalues obtained for individual storms for at least 22 years. By definition, calculation ofEIis based on rainfall measurements at short time intervals over which the intensity is essentially constant, i.e. using so‐called breakpoint data. Because of the scarcity of breakpoint rainfall data, many authors have used different time resolutions (Δt = 5, 10, 15, 30, and 60 min) to deduceEIin different areas of the world. This procedure affects the real value ofEIbecause it is strongly dependent on Δt. In this contribution, after a general overview of similar studies carried out in different countries, the relationship betweenEIand Δtis explored in Calabria, southern Italy. The use of 17 139 storm events collected from 65 rainfall stations allowed the calculation ofEIfor different time intervals ranging from 5 to 60 min. The overall results confirm that calculation ofEIis dependent on time resolution and a conversion factor able to provide its value for the required Δtis necessary. Based on these results, a parametric equation that givesEIas a function of Δtis proposed, and a regional map of the scale parameterathat represents the conversion factor for converting fixed‐interval values of (EI30)Δtto values of (EI30)15is provided in order to calculateRanywhere in the region using rainfall data of 60 min. Copyright © 2015 John Wiley & Sons, Ltd.

The Rainfall erosivity has a relatively high effect on soil erosion, in addition to being very difficult to predict and control. The Universal Soil Loss Equation (USLE) and The Revised Universal Soil Loss Equation (RUSLE) model are commonly used to predict erosion yield in Indonesia. However, these models have several erosivity formulations that give different results. In this sense, identifying the sensitivity of different erosivity formulations in both models above is important. The aim of this study is to analyze soil erosion yield prediction influenced by the difference in erosivity equation on the same rainfall data used in the models while other parameters used are the same. The monthly rainfall and annual rainfall data were tested using the erosivity formulas. The (1) Bols and (2) Utomo equations were tested using monthly rainfall data, while the (3) Bols and (4) Hurni equations were tested using annual rainfall data. The results show that the prediction of soil erosion yields estimates using monthly rainfall data in both models have no significant differences. On the other hand, soil erosion estimates using annual rainfall data in the models have significant differences, whereas the USLE model estimation results in 63% erosion yield on low classification (0-15 ton ha<sup>-1 </sup>year<sup>-1</sup>). Meanwhile, the RUSLE model estimates only 59% erosion yield on low classifications. Another result is that the USLE model estimates lower erosion yield than the RUSLE model when the models use annual rainfall data, which may give significantly different recommendations for soil conservation in Indonesia, especially in reducing erosion yield at the Watershed level.

Soil erosion is one of the major cause of land degradation and is a serious threat to food security and agricultural sustainability. Revised Universal Soil Loss equation (RUSLE) model using remote sensing (RS) and Geographical Information Systems (GIS) inputs was employed to estimate soil erosion risk in a watershed of mid-Himalaya in Uttarakhand state, India. Spatial distribution of soil erosion risk area in the watershed was estimated by integrating various RUSLE factors (R, K, LS, C, P) in raster based GIS environment. RUSLE model factor maps were generated using remote sensing satellite data (IRS LISS III and LANDSAT-8) and Digital elevation model. Agriculture (59%) was found to be the dominant land use system followed by scrub land (20%) in the watershed. Rainfall erosivity (R) factor was estimated using past 23 years rainfall data. SRTM DEM was used to generate slope length –steepness (LS) factor in this highly rugged terrain. Nearly 70% of the watershed is having steep to moderately steep slope (>40%). Satellite data was interpreted to prepare physiographic map at 1:50,000 scale. Surface soil samples collected in each physiograpohic unit was analyzed to generate soil erodibility (K) map. Soil erodibility factor ranged from 0.033 to 0.077 in the watershed. Soil erosion risk analysis showed that 36.25%, 9.31%, 15.80%, 15.27%, 11.46% and 11.89% area of watershed falls under very low, low, moderate, moderate high, high and very high erosion risk classes respectively. The average annual erosion rate was predicted to be 65.84 t/ha/yr. The soil erosion rates were predicted to vary from 3.24 t/ha/yr in dense mixed forest cover to 87.98 t/ha/yr in open scrub land. The soil erosion map thus generated employing remote sensing and GIS techniques, can serve as a tool for deriving strategies for effective planning and implementation of various management and conservation practices for soil and water conservation in the watershed.

Land erosion is regarded as one of the most important phenomenon causes land degradation. In revised universal soil loss equation (RUSLE) erosion model, rainfall erosivity factor (R) is one of the important parameters. Ideally, the calculation of EI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sub> (R factor) uses breakpoint rainfall intensity data which is calculated manually from graphical charts that are generated by continuously recording rain gauges. However, due to limited availability of breakpoint rainfall data, many simple methods for estimating EI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sub> have been developed by using yearly, monthly and daily rainfall data. In this research, due to limited data availability, pluviograph data at 10 minute interval from eight stations in Hitotsuse basin were used to compute EI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sub> (R factor) for RUSLE. The approach used in this research is based on storm rainfall and duration data from 1990 to 2009. This method is based on the calculation of rainfall energy per unit depth of rainfall, total storm kinetic energy (E), rainfall intensity for a particular increment of a rainfall, and maximum 30 minute rainfall intensity. Furthermore, EI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">30</sub> were computed, and then GIS method was used to create rainfall erosivity maps. The annual rainfall erosivity values prediction model was developed based on MFI values.

Quantitative evaluation of soil erosion rate is an important basic to investigate and improve land use system, which has not been sufficiently conducted in Indonesia. The Universal Soil Loss Equation (USLE) and Erosion Three Dimension (E3D) in Surfer were used to identify characteristic of dominant erosion factors in Sumani Watershed in West Sumatra, Indonesia using data soil survey and monitoring sediment yield in outlet watershed. Climatology data from three stations were used to calculate Rainfall erosivity (R) factor. As many as101 sampling sites were used to investigate soil erodibility (K-factor) with physico-chemical laboratory analysis. Digital elevation model (DEM) of Sumani Watershed was used to calculate slope length and Steepness (LS-factor). Landsat TM imagery and field survey were used to determine crop management (C-factor) and conservation practices (P-factor). Calculating soil loss and map of USLE factor were determined by Kriging method in Surfer 9. Sumani Watershed had erosion hazard in criteria as: severe to extreme severe (26.23%), moderate (24.59%) and very low to low (49.18%). Annual average soil loss for Sumani watershed was 76.70 Mg ha-1 y-1 in 2011. Upland area was designated as having a severe to extreme severe erosion hazard compared to lowland which was designated as having very less to moderate. On the other land, soil eroded from upland were deposited in lowland. These results were verified by comparing one year’s sediment yield observation on the outlet of the watershed. Land use (C-factor), rainfall erosivity (R- factor), soil erodibility (K-factor), slope length and steepness (LS-factor) were dominant factors that affected soil erosion. Traditional soil conservation practices were applied by farmer for a long time such as terrace in Sawah. The USLE model in Surfer was used to identify specific regions susceptible to soil erosion by water and was also applied to identify suitable sites to conduct soil conservation planning in Sumani Watershed.[How to Cite : Aflizar, R Afrizal, T Masunaga. 2013. Assessment Erosion 3D Hazard with USLE and Surfer Tool: A Case Study of Sumani Watershed in West Sumatra Indonesia. J Trop Soils, 18 (1): 81-92. doi: 10.5400/jts.2013.18.1.81][Permalink/DOI: www.dx.doi.org/10.5400/jts.2013.18.1.81]