No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Apparent geographic and atmospheric influences on raindrop sizes and rainfall kinetic energy
Abstract
Median raindrop diameters in North Carolina, New Jersey, and the Marshall Islands tended to be less than those observed in Panama, Indonesia, Washington, D.C., and Zimbabwe. Calculated rainfall kinetic energies for Panama and Indonesia were within 10% of that predicted by the universal soil loss equation (USLE) rainfall energy equation. Calculated rainfall energies for New Jersey, the Marshall Islands, and North Carolina ranged from 5% to 28% less than that predicted by the USLE rainfall energy equation. Increasing the rainfall energy estimate by 7% for each 1000 m (3280 feet) of elevation above sea level is suggested to account for increased raindrop velocity under reduced atmospheric pressure. -from Author
... The calculation of the rainfall kinetic energy requires drop-size, drop-velocity, and drop-volume measurements as well as drop-size distribution (DSD). DSD data has been obtained using various techniques such as filter paper, flour pellet, camera, optical array, and meteorological radar [1][2][3][4][5][6][7]. The rainfall kinetic energy can be calculated by the measured DSD combined with empirical ( ) laws [8][9][10], direct measurement using a pressure transducer or acoustic devices [11,12] or using an Optical Spector Pluviometer allowing the real time measurement of drop-size and drop-velocity [7]. ...
... The existing empirical equations between rainfall kinetic energy and rainfall intensity showed significantly different coefficients according to geographical location and measurement technique. Therefore, it is not easy to apply in other countries or regions having different types of rainfall [4,20,28]. In particular, power law in calculation of rainfall kinetic energy shows larger deviation compared to exponential and logarithmic functions [26,29]. ...
... In general, the relationships between rainfall kinetic energy and rainfall intensity are presented in the form of exponential [4,17,24,28,[30][31][32][33][34], logarithmic [3,13,28,[34][35][36][37], linear [24,28,[38][39][40], and power-law [26,28,29,33,41,42] functions. ...
Rainfall kinetic energy has been linked to linear, exponential, logarithmic, and power-law functions using rainfall intensity as an independent variable. The power law is the most suitable mathematical expression used to relate rainfall kinetic energy and rainfall intensity. In evaluating the rainfall kinetic energy, the empirical power laws have shown a larger deviation than other functions. In this study, universal power law between rainfall kinetic energy and rainfall intensity was proposed based on the rainfall power theory under an ideal assumption that drop-size is uniformly distributed in constant rainfall intensity. An exponent of the proposed power law was 11/9 and coefficient was estimated at 10.3 from the empirical equations of the existing power-law relation. The rainfall kinetic energy calculated by universal power law showed >95% concordance rate in comparison to the average values calculated from exponential and logarithmic functions used in soil erosion model such as USLE, RUSLE, EUROSEM, and SEMMA and
... A number of researchers have proposed various constants in their empirical equations for different geographical locations. This can be due to methodological differences and errors in measurements, the range of rainfall intensities, problems with interpretation, or differences in rainfall characteristics (Kinnell, 1981;McIsaac, 1990;Van Dijk et al., 2002;Fornis et al., 2005). For these reasons, Fornis et al. (2005) warned that a relationship between kinetic energy and rainfall intensity that performs well in one location may estimate poorly in another location. ...
... The Parsivel disdrometer has the advantage of providing more accurate measurements of raindrop size and speed, and has more convenient operation and better data accessibility than previous methods, such as the flour-pellet method (Bentley, 1904;Laws and Parsons, 1943;Carter et al., 1974), filter-paper or stain method (Wiesner, 1895;Marshall et al., 1947;Hall, 1970;Brandt, 1988;Cerda, 1997), electromechanical spectrometer (Joss and Waldvogel, 1967;Kinnell, 1976;Azevedo Coutinho and Pereira Tom as, 1995), optical spectro-pluviometer (Wang et al., 1979;Illingworth and Stevens, 1987;Salles and Poesen, 1999), and photographic methods (Kinnell, 1981;McIsaac, 1990). The measuring principle of the Parsivel disdrometer is that a laser sensor emits a horizontal laser beam, and a receiver detects the light decrease caused by a particle passing through the beam. ...
... The underestimation of the exponential equation at higher intensities downplayed the potential soil loss when soil erosion was estimated from the KE mm eI relationship, which could be problematic. In cases where the exponential form is used based on a preference for its functional characteristics, using the following equation, which does not consider intensities <3 mm/h, is another way to avoid underestimation of kinetic energy content at higher intensities, although the coefficient of determination (R 2 ) was decreased from 0.465 to 0.38: (1986), McIsaac (1990), and Azevedo Coutinho and Pereira Tom as (1995). The values of the c parameter in the data set ranged from 24.6 to 35.9 J m À2 mm À1 . ...
The kinetic energy and momentum of rainfall are widely used as erosivity indicators for estimating soil detachment (erosion) induced by the impact of raindrops. Because direct measurements of the force or kinetic energy of rainfall on ground surfaces are not widely available, many empirical relationships have been derived to link the kinetic energy and intensity (I) of rainfall, the factor that has the most control over soil erosion and is readily accessible. This study considered three rainfall erosivity indices: kinetic energy expenditure (KEtime, J m−2 h−1), kinetic energy content (KEmm, J m−2 mm−1), and momentum (M, kg m s−1 m−2 s−1 or N m−2). The relationships between these rainfall erosivity indices (KEtime, KEmm, and M) and rainfall intensity were established by fitting to an existing functional model based on measurements of the number of drops by size and terminal velocity made from January 2010 to July 2011 using a laser optical disdrometer in Daejeon City, Korea. The best fit for the relationship between the three kinetic energy indices and rainfall intensity was obtained with a power law (KEtime-I, and M−I) and an exponential model (KEmm-I). Validation results for two different events indicated good performance for the KEtime-I and M−I equations, with a similar distribution of observed data and power-law curve fitting. However, the rainfall momentum index produced much lower uncertainty as determined from the measured terminal velocity when the equipment was exposed to in situ changeable conditions. Therefore, we suggest that the power-law relationship between momentum and rainfall intensity is the most suitable equation for the prediction of rainfall erosivity.
... Whilst erosion occurs primarily by riverbed erosion and wind erosion, physical erosion of the sediment surface by rain is largely dependent on the kinetic energy of rain drop numbers, sizes and velocity [132]. Drop velocity increases approximately by the inverse square root of air pressure (higher for low air pressure which correlates with warmer air), but kinetic energy increases by the square of the velocity, so kinetic energy (and weathering potential) increases approximately linearly with decreasing air pressure [132]. ...
... Whilst erosion occurs primarily by riverbed erosion and wind erosion, physical erosion of the sediment surface by rain is largely dependent on the kinetic energy of rain drop numbers, sizes and velocity [132]. Drop velocity increases approximately by the inverse square root of air pressure (higher for low air pressure which correlates with warmer air), but kinetic energy increases by the square of the velocity, so kinetic energy (and weathering potential) increases approximately linearly with decreasing air pressure [132]. Small drops get filtered out in drier or warmer air since they evaporate before reaching the ground leaving large erosive drops [132]. ...
... Drop velocity increases approximately by the inverse square root of air pressure (higher for low air pressure which correlates with warmer air), but kinetic energy increases by the square of the velocity, so kinetic energy (and weathering potential) increases approximately linearly with decreasing air pressure [132]. Small drops get filtered out in drier or warmer air since they evaporate before reaching the ground leaving large erosive drops [132]. Increased aridity and increased temperatures both have the effect of increasing drop size, and both of these characteristics will generally increase under anthropogenic climatechange. ...
Human activity could be changing the Earth’s foundations themselves, as we affect multiple systems interacting in feedback mechanisms changing the atmosphere, hydrosphere, cryosphere, biosphere, and even the lithosphere (solid surface) and asthenosphere (deformable semi-molten rock layer beneath). Anthropogenic movement of ice, water and sediment alters viscosity and movement of the asthenosphere; this induces earthquakes, tsunamis, volcanism and rifting, and may induce plate-tectonic-change. These processes may account for the timing of unexplained contemporary Icelandic, New Zealand, Chilean, Japanese and Indonesian seismicity, volcanism and magma movement. Climate-change and sea-level rise are creating: slip-planes from differential water pore-pressures and/or weakening of previous fault-planes; sediment-change and altered hydrology and reservoir-mass, inducing isostasy and further change in pore-pressure. Loss of plant biomass and diversity alter hydrology, precipitation and transpiration, causing isostasy and further sediment- and climate-change. Increased ocean-mass, temperatures and acidity, reduced oceanic oxygenation, and increased transport of (organic) sediments elevate the production and destabilisation of gas-hydrates, causing slumps and tsunamis. Isostasy and altered viscosity of the asthenosphere increase seismicity, slope and faulting, which are the prime triggers for slumping and tsunamis. Altered asthenosphere flows hasten subduction and rifting landward of subduction, enhancing volcanism. All of these processes predominantly coincide, temporally and spatially, in the coasts and continental margins, and the Pacific ring-of-fire, although response times and extents may vary from immediate to multi-millennial scales and from negligible to catastrophic. Contemporary Icelandic seismic and volcanic activity is explained by depleted magma reserves on the north-western side of the mid-ocean ridge as asthenosphere moves from the constructive boundary under deglaciating and rising Greenland.
... Some researchers reported that kinetic energy can be used a parameter of rainfall to detach soil, but, rainfall kinetic energy cannot be measured directly from meteorological parameters without disdrometers which is very expensive system, and it's usually estimated from rainfall intensity, therefore some kinetic energy formulas have been developed to calculate the kinetic energies of rainfall according to rain intensity (Salles et al., 2002;Petan et al., 2010). In this study, the objective was (1) to determine simulated rainfall intensities, uniformity coefficients, median drop diameters and kinetic energy ratios, using a ½ HH-50 WSQ type nozzle at different pressures (30, 40, 50, 60 and 70 kPa) and 2.00 m height, and (2) to compare kinetic energies calculated using formulas given by Wischeimer & Smith (1958), Hudson (1965), Carter et al. (1974), McGregor & Mutchler (1976, Park et al. (1980), Zanchi & Torri (1980), Kinnell (1981), Bollinne et al. (1984) Rosewell (1986, Onaga et al. (1988), Brandt (1990), McIsaac (1990), Sempere-Torres et al. (1992), Smith & De Veaux (1992), Coutinho & Tomas (1995), Renard et al. (1997), Cerro et al. (1998), Uijlenhoet & Stricker (1999), Jayawerdena & Rezaur (2000, Steiner & Smith (2000), Uson & Ramos (2001), Petan et al. (2010) with the ones calculated according to Rose's (reference) formula for this nozzle, and (3) to reveal the kinetic energy formulas that give the similar results according to the drop diameter and precipitation intensity measured in laboratory conditions. ...
... According to deviation from the reference (%), Wischmeier & Smith (1958), Hudson (1965), Carter et al. (1974), McGregor &Mutchler (1976, Kinnell (1981), 1 st Rosewell (1986), McIsaac (1990), Renard et al. (1997), and Petan et al. (2010) formulas showed very good performances within the range of ±5% ( Figure 2). According to this range, whereas the lowest KE was calculated from Renard et al. (1997) formula, the highest KE was calculated from McGregor & Mutchler (1976) formula. ...
Objective: The objective of this study was to compare kinetic energies calculated by different formulas with Rose’s (reference) formula, using Fulljet type nozzle (½ HH-50 WSQ) at different pressures. Material and Methods: A platform in the dimension of 1x1 m was used to place 17 cups (250 cm3) and inclined at a slope of 9%. Then, artificial rainfalls (at pressures of 30, 40, 50, 60 and 70 kPa) was applied with a ½ HH-50 WSQ nozzle for 5 minutes and each experiment was triplicated. Drop diameter, rainfall intensities, terminal velocities were determined and kinetic energies were calculated with different equations. Results: In this study, it was found that rain intensities varied between 85- and 109 mm h-1, Christiansen coefficients (CU) (%) were 83-87 %, drop diameter (D50) were 1.77-2.05 mm, and terminal velocities were 6.08-6.67 m s-1. Average kinetic energies were also calculated between 9.94-46.59 J m-2 mm-1, respectively. Conclusions: The results obtained from this study (±5 %) were found to be in good agreement with the Rose (1960) formula and some kinetic energy formulas.
... This has motivated researchers to develop individual KE-I equations for different geographic locations. Moreover, the reasons for different KE-I equations include errors in measurements, methodological differences, different ranges of rainfall intensities, and problems with interpretation (McIsaac 1990;Van Dijk et al. 2002). For these reasons, Fornis et al. (2005) warned that a relationship between KE and I that performs well in one location may work poorly in another. ...
... Therefore, measurement of the DSD is crucial, because the other rainfall characteristics (e.g., depth, I, KE) that affect erosivity derive from the properties of individual drops, i.e., their mass, volume, and velocity. In the past, several techniques have been used to measure raindrop size, such as using filter paper or the stain method (Hall 1970;Meshesha et al. 2014), the flour pellet or stained paper methods (Laws and Parsons 1943;Wischmeier and Smith 1958), electronic devices such as high-speed video cameras (Kinnell 1981;McIsaac 1990), electromechanical spectrometers (Joss and Waldvogel 1977;Kinnell 1976), highly sensitive piezoelectric force transducers (Jayawardena and Rezaur 2000), and optical disdrometers (Angulo-Martínez et al. 2012;Meshesha et al. 2016). All of these methods provide reasonable measurements of raindrop size, but only optical disdrometers and video recorders can accurately measure raindrop velocity. ...
Rainfall kinetic energy (KE) is an important factor in soil erosion models. Particle detachment from the soil surface depends on raindrop KE. Therefore, it is essential to evaluate the drop-size distribution (DSD) and KE of rainfall to understand soil erosion and runoff generation. In this study, we used an optical disdrometer to investigate the influence of DSD on the intensity (I), KE, and erosivity of rainfall at Bahir Dar, in northwestern Ethiopia. We recorded 1-min rainfall observations during 42 events, with I ranging from 0.82 to 46.27 mm h⁻¹. The median raindrop diameter (D50), which ranged between 1.14 and 4.33 mm, was significantly correlated with I (R² = 0.96; P < 0.001). We developed indices of rainfall KE as a function of time (KEtime) and of the KE content (KEcon). The best-fit relationships between KEtime and I were equally strong: R² = 0.96 (P < 0.001) for both a linear function and a polynomial function. KEcon and I were most strongly related for a logarithmic function (R² = 0.98; P < 0.001), followed by power (R² = 0.95; P < 0.001) and polynomial (R² = 0.93; P < 0.001) functions. The KEcon measured at Bahir Dar ranged from 7.4 to 32.43 J m ⁻² mm⁻¹, whereas KEtime ranged from 38.34 to 1992.64 J m⁻² h⁻¹ for the observed range of I. The potential erosivity of rainfall events was found to be well correlated to smaller rainfalls depths (R² = 0.60, P < 0.05). Our results suggest that, though empirical models are easy to use since they require readily available rainfall data, KE rather than rainfall depth should be used to estimate erosivity in the study area and regions of northwestern Ethiopia with similar characteristics. Moreover, the reasons why different measuring methods in the same area and similar methods in different areas provide different kinetic energy results are analyzed and discussed.
... These comparably low values are a consequence of rather low precipitation intensities in extensive rain events with low mean precipitation intensities, which are typical for the climate. Arguably the Rfactor might be unsuitable for those climatic conditions and there is doubt if functions from (R)USLE established in the United States are valid for climatic conditions at differing locales (Kinnell, 1980;McIsaac, 1990). For example antecedent soil moisture indices might locally be better predictors to describe imminent soil erosion events. ...
... But due to various site specific factors like altitude, temperature, and air pressure affecting drop size many studies hint to the need of functions from local measurements (Laws and Parsons, 1943;Hinkle et al., 1987). Establishment of such local functions is complicated by a lack in standardized methodology (Kinnell, 1980;McIsaac, 1990) and results vary depending on the time scale of measurements . ...
To estimate the impact of highly erosive precipitation on existing and planned forest infrastructure we deem the Forest Service WEPP Interfaces, based on the Water Erosion Prediction Project (WEPP), feasible.
As a first step towards testing WEPP and especially the implemented weather generator CLIGEN for conditions in Germany we evaluated the application of CLIGEN to calculate rain erosivities from generated time series. CLIGEN parameters were taken from time series of up to 17 years with 10-minute resolution from three sites in the Northern Black Forest, southwestern Germany. We assessed a rain kinetic energy function for this region from field measurements with a laser disdrometer to compare CLIGEN performance using the local function to using common kinetic energy functions.
We showed that running CLIGEN with unaltered input parameters is not suitable to model climatic conditions and erosivity indices in the Northern Black Forest. R-factors from unaltered model runs deviated extremely from observed R-factors, resulting in just one third of observed values. Model performance and parameter uncertainties do not benefit much from the use of a site specific kinetic energy function. Differences in model errors and sensitivities compared to a well-established kinetic energy function remain negligible. However, model output was improved by empirical calibration of input data. Virtual best parameter sets for best model results could be identified.
To reproduce observed rain erosivities the input parameters of CLIGEN have to be manipulated to model a precipitation regime where daily precipitation amounts and maximum precipitation intensities are higher at a lower number of rainy days.
We also identified the input parameters to which the model is most sensitive to when manipulated, i.e. precipitation amount and frequency, and maximum peak precipitation. These parameters are especially important when implementing future climate change scenarios.
... These comparably low values are a consequence of rather low precipitation intensities in extensive rain events with low mean precipitation intensities, which are typical for the climate. Arguably the Rfactor might be unsuitable for those climatic conditions and there is doubt if functions from (R)USLE established in the United States are valid for climatic conditions at differing locales ( Kinnell, 1980;McIsaac, 1990). For example antecedent soil moisture indices might locally be better predictors to describe imminent soil erosion events. ...
... But due to various site specific factors like altitude, temperature, and air pressure affecting drop size many studies hint to the need of functions from local measurements ( Laws and Parsons, 1943;Hinkle et al., 1987). Establishment of such local functions is complicated by a lack in standardized methodology ( Kinnell, 1980;McIsaac, 1990) and results vary depending on the time scale of measurements ( Panagos et al., 2016). In agreement to van Dijk et al., 2002 who tested various mathematical representations of E k (MJ ha − 1 mm − 1 ), we decided on using a continuous, exponential equation as described in Kinnell (1980) and used in RUSLE ( Brown and Foster, 1987;Renard et al., 1997). ...
Soil deformation and compaction is a widespread problem in today’s highly mechanized forestry. In German forests extensive areas are affected by soil compaction, hence regeneration strategies for the management of these sites have to be developed. The formation of bio
pores by roots of compaction tolerant plant species is assumed to improve aeration of compacted soils. To test this assumption, we investigated the effect of soft rush (Juncus effusus L.) and seagrass (Carex brizoides L.) growth on soil aeration at two skid trail sites in South- West Germany. Additionally, the microtopography of skid trail sections and vegetation cover was determined to assess the spatial distribution of target species. Gas diffusivity and bulk density of the soils were measured using soil ring samples from different parts of the skid trail. J. effusus formed dense stocks only in the most com-
pacted wheel ruts, whereas C. brizoides was present over the whole width of the skid trail. We did not observe plant-induced changes of gas diffusivity and bulk density. By implication, growth of both plants
seems to have no positive effects on regeneration, respectively the aeration status of compacted forest soils. Nonetheless, pore formation by root penetration of J. effusus and C. brizoides took place. We assume that plant-induced aeration improvements occur only after plants die-off and pores are released by the roots. Fur-
ther experiments have to be conducted to quantify the release of root pores.
... where m is mass (g), ρ is the density of water (1 g cm −3 ), v is velocity (m s −1 ), and D is diameter (mm). Many methods can be used to measure raindrop size and velocity, such as the flour pellet or stain paper methods (Wischmeier and Smith, 1958), electronic devices such as high-speed video cameras (Kinnell, 1980;McIsaac, 1990), acoustic disdrometers (Rosewell, 1986), and optical disdrometers (Cerro et al., 1998;Petan et al., 2010;Angulo-Martínez et al., 2012). These methods have certain limitations, including: (i) the sample interval at which measurements are taken, which can be configured if the instrument is automatic (cameras or disdrometers), but is unknown in other cases (Salles et al., 2002); (ii) difficulties in measuring raindrop velocity (Randeu et al., 2002); and (iii) uncertainties of instrument accuracy (Angulo-Martínez and Barros, 2015). ...
... Altitude may also affect the distribution of raindrop size and velocity. Blanchard (1953) and McIsaac (1990) reported that lower KE is expected at higher elevations. Blanchard (1953) suggested that small drops evaporate as they fall over progressively longer distances, so that only larger drops reach the soil at lower elevations and leading to higher observed KE at lower rain rates. ...
Determination of rainfall kinetic energy (KE) is required to calculate erosivity, the ability of rainfall to detach soil particles and initiate erosion. Disdrometers can measure rainfall KE by measuring raindrop size and velocity. In the absence of such devices, KE is usually estimated with empirical equations that derive KE from measured rainfall intensity (I). We evaluated the performance of 14 different KE–I equations to estimate the 1 min KE and event total KE, and compared these results with 821 observed rainfall events recorded by an optical disdrometer in the inner Ebro Basin, NE Spain. We also evaluated two sources of bias when using such relationships: bias from use of theoretical raindrop terminal velocities instead of measured values; and bias from time aggregation (recording rainfall intensity every 5, 10, 15, 30, and 60 min). Empirical relationships performed well when complete events were considered (R2 > 0.90), but performed poorly for within-event variation (1 min resolution). Also, several of the KE-I equations had large systematic biases. When raindrop size is known, estimation of terminal velocities by empirical laws led to overestimates of raindrop velocity and KE. Time aggregation led to large under-estimates of KE, although linear scaling successfully corrected for this bias.
... Por ejemplo, la diferencia entre frentes fríos y cálidos se atenúa en regiones costeras y se incrementa en zonas continentales, lo cual hace necesario establecer dos relaciones eC(i) diferentes, (Rosewell, 1986). La comparación de valores publicados por diversos autores confirma el incremento general de los valores de eC promedio desde la costa hacia el interior: Brisbane (26.4 J m -2 mm -1 ) en la costa australiana, en oposición a Gunnedah (28.2 J m -2 mm -1 ) en el interior de Australia, (Rosewell, 1986); Carolina del Norte (24.6 J m -2 mm -1 ) en comparación con New Jersey (25.1 J m -2 mm -1 ), (McIsaac, 1990); Japón (23.7 J m -2 mm -1 ), (Mihara, 1952); Trinidad (24.7 J m -2 mm -1 ), (Ker, 1954). ...
... Los estudios experimentales enfocados a cuantificar el efecto de la orografía en el tamaño de las gotas reflejan un descenso en el tamaño de las mismas con la altitud (Blanchard, 1953;McIsaac, 1990). Esto se ha explicado por el hecho de que a medida que las gotas de lluvia pasan de capas atmosféricas altas y frías a otras más cálidas próximas a la superficie, las gotas más pequeñas tienden a evaporar-se prevaleciendo por tanto las gotas de mayor tamaño que proporcionan valores mayores de eC (Blanchard, 1953;Beard, 1977). ...
This article reviews previous studies related with the ability of rainfall to erode soil. Precipitation is one of the main agents of soil erosion, which started to be quantified since the beginning of the 20th century. The quantification of soil erosion and related processes allowed the development of empirical models for soil erosion determination and, more specifically, for describing rainfall erosivity. This last is based on raindrop kinetic energy, a fundamental variable for estimating splash erosion. Empirical studies found a relationship between kinetic energy and rainfall at high resolution time scales, allowing the estimation of kinetic energy from mathematical formulae using rainfall intensity. These relationships are related with the atmospheric, climatic and geographical characteristics of every location. Finally, recent works incorporating instrumental developments allowing more direct and real rainfall energy measurements, are reviewed. Finally, a short summary about Spanish research on this topic is included.
... Kinnell (1973) related soil loss to the rainfall intensity. Park et al., (1983) and McIsaac (1990) calculated the rainfall energy with the rainfall intensity distributions. A study underlying the mathematical correlation between erosion and rainfall physics was done by Meyer (1981). ...
... After this, Park et al., (1983) and Gilley and Finkner (1985) showed that physical impact parameters (size, impact frequency and impact velocity) and splash erosion rates of rainfall were closely linked. McIsaac (1990) suggested rainfall intensity (I) and kinetic energy (KE) equations by using drop size distribution of rainfalls for any area. Especially, Gilley and Finkner (1985) intended to determine functionality of the erodibility equations statistically, defining an erodibility equation by using raindrops at terminal velocity and conclusively proposed a "rainfall detachment factor" under the natural conditions as a function of rainfall intensity. ...
Soil erodibility is an important parameter to determine the sensibility of soil to the erosion and there are many methods to specify the erodibility. Until today, many methods were improved and the “Universal Soil Loss Equation (USLE), which has the most common use in worldwide, is one of them. In this prediction technology, the soil susceptibility to the water erosion is represented by a multiplier factor together with those for climate, topography, vegetation and conservation practices. This study aimed to determine a soil erodibility factor by the laboratory simulated rainfall tests under the specified kinetic energy and rainfall intensity values using the splash cups. For test soils, a total of 256 surface samples were taken from the fallow-crop system in the Asartepe Dam Basin and the splash erosion rate was found with the units compatible with the USLE. However, since the USLE predicts soil losses from not only splash erosion but also sheet and rill erosions, the measured splash values should be mathematically related to the erodibility equations commonly employed in the model in order to meet the model requirement.
... The rain kinetic energy is obtained from the combined effects of the fall velocity (V t ) and drop size distribution (DSD) of raindrops (Kinnell 1981). Methods developed to collect DSD information from natural rain showers to determine the kinetic energy include the flour pellet technique (Laws and Parsons 1943) and the use of electromechanical distrometers (Joss and Waldvogel 1967), cameras (Kinnell 1980, McIsaac 1990) and optical pluviospectrometers (Illingworth andStevens 1987, Salles andPoesen 1999). ...
... Rosewell (1986) used the same electromechanical distrometer to measure rainfall kinetic energy at two locations in eastern Australia and obtained two statistically different relationships. Similarly, McIsaac (1990) used the same high-speed camera at various locations to measure kinetic energy and reported notable differences between locations. Therefore, the rainfall generating mechanism (stratiform, conventional, or orographic) is also a major contributing factor to the variations; for example, rainfall in semi-arid and tropical areas includes large thunderstorms that cause larger drop sizes and kinetic energy than that in temperate zone areas where frontal rainfall types are dominant. ...
Knowledge of rainfall characteristics is important for estimating soil erosion in arid areas. We determined basic rainfall characteristics (raindrop size distribution, intensity and kinetic energy), evaluated the erosivity of rainfall events, and established a relationship between rainfall intensity I and volume-specific kinetic energy KEvol for the Central Rift Valley area of the Ethiopian highlands. We collected raindrops on dyed filter paper and calculated KEvol and erosivity values for each rainfall event. For most rainfall intensities the median volume drop diameter (D 50) was higher than expected, or reported in most studies. Rainfall intensity in the region was not high, with 8% of rain events exceeding 30 mm h-1. We calculated soil erosion from storm energy and maximum 30-min intensity for soils of different erodibility under conditions of fallow (unprotected soil), steep slope (about 9%) and no cover and management practice on the surface, and determined that 3 MJ mm ha-1 h-1 is the threshold erosivity, while erosivity of >7 MJ mm ha-1 h-1 could cause substantial erosion in all soil types in the area. Editor Z.W. Kundzewicz; Associate Editor Q. Zhang
... Recently, many soil erosion studies incorporate the use of disdrometers to capture place-based DSDs which can then be used to estimate kinetic energy. McIsaac (1990) compared DSD data collected on the eastern slopes of the Southern Appalachians using a photographic technique with a lower measurement accuracy of 0.4 ± 0.2 mm and minimum diameter reported of 0.5 mm (Mueller and Sims, 1967) with other sites around the world, and pointed out that for a given rainfall rate the median diameter of DSDs in the Southern Appalachians was significantly smaller yielding rainfall erosivity estimates about 28% lower than RUSLE estimates using consistent time-scales of storm energy integration. McIsaac (1990) hypothesized that the smaller median diameter could be attributed to increased effectiveness of drop break up at higher elevations due to the higher KE of drops interaction with each other. ...
... McIsaac (1990) compared DSD data collected on the eastern slopes of the Southern Appalachians using a photographic technique with a lower measurement accuracy of 0.4 ± 0.2 mm and minimum diameter reported of 0.5 mm (Mueller and Sims, 1967) with other sites around the world, and pointed out that for a given rainfall rate the median diameter of DSDs in the Southern Appalachians was significantly smaller yielding rainfall erosivity estimates about 28% lower than RUSLE estimates using consistent time-scales of storm energy integration. McIsaac (1990) hypothesized that the smaller median diameter could be attributed to increased effectiveness of drop break up at higher elevations due to the higher KE of drops interaction with each other. This contrasts with the enhanced collection and coalescence hypothesis in complex terrain (Bergeron, 1960;Barros et al., 2008;Prat and Barros, 2010a,b;Wilson and Barros, 2014). ...
... where m is mass (g), ρ is the density of water (1 g cm −3 ), v is velocity (m s −1 ), and D is diameter (mm). Many methods can be used to measure raindrop size and velocity, such as the flour pellet or stain paper methods (Wischmeier and Smith, 1958), electronic devices such as high-speed video cameras (Kinnell, 1980;McIsaac, 1990), acoustic disdrometers (Rosewell, 1986), and optical disdrometers (Cerro et al., 1998;Petan et al., 2010;Angulo-Martínez et al., 2012). These methods have certain limitations, including: (i) the sample interval at which measurements are taken, which can be configured if the instrument is automatic (cameras or disdrometers), but is unknown in other cases (Salles et al., 2002); (ii) difficulties in measuring raindrop velocity (Randeu et al., 2002); and (iii) uncertainties of instrument accuracy (Angulo-Martínez and Barros, 2015). ...
... Altitude may also affect the distribution of raindrop size and velocity. Blanchard (1953) and McIsaac (1990) reported that lower KE is expected at higher elevations. Blanchard (1953) suggested that small drops evaporate as they fall over progressively longer distances, so that only larger drops reach the soil at lower elevations and leading to higher observed KE at lower rain rates. ...
The estimation of soil loss is an important issue for human activities. It generally relies on the use of (R)USLE empirical model. In this article we evaluate the reliability of three rainfall erosivity equations included in the RUSLE proposal with measured rainfall erosivity data by an optical disdrometer. Two sources of bias were evaluated: i) the influence of time aggregation data and ii) the influence in the use of theoretical terminal raindrops velocity instead of measured values. The results showed positive bias in the estimated rainfall erosivity values related to the use of theoretical terminal raindrops velocity, while time aggregation produce little subestimation. These results stress the relevance of monitoring raindrops velocity at high time resolution in order to obtain reliable estimation of rainfall kinetic energy.
... To date, many researchers have used a variety of techniques to measure some drop specifications with further focus on size and velocity, such as the disdrometer (Illingworth and Stevens 1987, Sheppard and Joe 1994, Bringi et al. 2003, the flour method (Laws and Parsons 1943, Kohl 1974, Eigel and Moor 1983, Kohl and De Boer 1984, Kincaid et al. 1996, the momentum method (Scheleusener 1967), the optical method (Swithenbank 1977, Chigier 1991, Sheppard and Joe 1994, the photography method (Kinnell 1980, McIsaac 1990, the radar technique (Sekhon and Srivastava 1971, Sheppard and Joe 1994, Ulbrich and Atlas 1997, Yuter and Houze 1997, Uijlenhoet and Stricker 1999, Chandrasekar and Bringi 2001, Zhang et al. 2001, Bringi et al. 2003, Hazenberg et al. 2011, the stain method (Seginer 1963, Hall 1970, Solomon et al. 1985, Kincaid et al. 1996, the sterbuscope technique (Wang et al. 1979, Illingworth and Stevens 1987, Lavergnat and Gole 1998, Salles and Poesen 1999 and the submersion technique (Eigel andMoore 1983, Kincaid et al. 1996). However, differences in methods and subsequent calculations, geographical locations, or in weather systems and changes in the raindrop sizes over time, have led to considerably varied results (Kinnell 1980, McIsaac 1990. ...
... To date, many researchers have used a variety of techniques to measure some drop specifications with further focus on size and velocity, such as the disdrometer (Illingworth and Stevens 1987, Sheppard and Joe 1994, Bringi et al. 2003, the flour method (Laws and Parsons 1943, Kohl 1974, Eigel and Moor 1983, Kohl and De Boer 1984, Kincaid et al. 1996, the momentum method (Scheleusener 1967), the optical method (Swithenbank 1977, Chigier 1991, Sheppard and Joe 1994, the photography method (Kinnell 1980, McIsaac 1990, the radar technique (Sekhon and Srivastava 1971, Sheppard and Joe 1994, Ulbrich and Atlas 1997, Yuter and Houze 1997, Uijlenhoet and Stricker 1999, Chandrasekar and Bringi 2001, Zhang et al. 2001, Bringi et al. 2003, Hazenberg et al. 2011, the stain method (Seginer 1963, Hall 1970, Solomon et al. 1985, Kincaid et al. 1996, the sterbuscope technique (Wang et al. 1979, Illingworth and Stevens 1987, Lavergnat and Gole 1998, Salles and Poesen 1999 and the submersion technique (Eigel andMoore 1983, Kincaid et al. 1996). However, differences in methods and subsequent calculations, geographical locations, or in weather systems and changes in the raindrop sizes over time, have led to considerably varied results (Kinnell 1980, McIsaac 1990. Furthermore, some techniques, such as high-speed CCTV, have been introduced very recently and can be easily applied by researchers. ...
Knowledge of the relationship between rainfall intensity and kinetic energy and its variations in time and space is important for the prediction of erosion hazard. Kinetic energy and erosivity are also strongly controlled by raindrop size. However, studies on raindrop measurement and different practical techniques have been rarely documented. The current study therefore aimed to apply existing raindrop-size measurement techniques—the photographic, flour-pellet and stain methods, as well as an innovative flour-stain method—and to evaluate their applicability at several intensities in Mazandaran Province, Iran. The distribution of raindrop size obtained by the different methods was recorded and compared with those obtained through applying a high-speed imaging technique. All the analyses were made with the help of a SPSS software package. The results showed that the raindrop diameters ranged from 0.2 to 5.16 mm at different rainfall intensities. Statistical comparison of the methods using the Duncan test showed that the flour-pellet method presented similar results to the photographic technique; it was concluded that this can be used as a practical and inexpensive method to estimate a wide range of raindrop sizes.
Key words: flour-pellet method; raindrop size distribution; median drop size; photographic method; stain method; Mazandaran Province, Iran
... Over the last decades, a large number of studies aimed at measuring rain drop size distributions (DSD) at the ground have been conducted for two basics reasons: (i) for assessing rain erosivity (e.g. Bubenzer and Jones, 1971;Gilley and Finkner, 1985;McIsaac, 1990); or (ii) for quantifying the rain intensity from radar measurements (e.g. Stout and Mueller, 1968;Battan, 1977;Sauvageot, 1988). ...
... In natural rain conditions the time variation is another source of fluctuation of the DSD. McIsaac (1990) considers that temporal variations of the DSD in natural conditions are much more important than DSD variations according to the geographic location. ...
Knowledge of drop size distributions is important for deriving various rain erosivity parameters. This study investigates the potential of an optical spectro pluviometer (OSP) to measure drop size distributions. Particular attention is paid to the impact of drop sample size and derived erosivity parameters. An experimental setup using a rainfall simulator and an OSP is described. The OSP allows a continuous real-time sampling of the drops. Results on drop size distributions and sampling effects are discussed. A simulation aimed at reproducing the sampling made with the widely used flour-pellet or filter-paper method is described. From this simulation, recommendations on the sample size of the collected drops needed for an accurate determination of median drop size and kinetic energy are given. Past studies reporting drop size characteristics have often used too small a sample for an adequate description of rain erosivity.
... According to Montero-Martínez et al., altitude may also affect the distribution of raindrop size and velocity, thereby affecting the overall findings of each equation [52]. McIsaac noted that a decrease in KE was predicted at higher altitudes [61]. However, the greater the elevation of a place, the higher the rate of KE values, although scientists were stumped as to why this disparity existed [19]. ...
Calculating rainfall erosivity, which is the capacity of rainfall to dislodge soil particles and cause erosion, requires the measurement of the rainfall kinetic energy (KE). Direct measurement of KE has its own challenges, owing to the high cost and complexity of the measuring instruments involved. Consequently, the KE is often approximated using empirical equations derived from rainfall intensity (Ir) inputs in the absence of such instruments. However, the KE-Ir equations strongly depend on local climate patterns and measurement methods. Therefore, this study aims to compare and evaluate the efficacy of 27 KE-Ir equations with observed data. Based on a re-analysis, we also propose an exponential KE-Ir equation for the entire Korean site, and the spatial distribution of its parameter in the equation is also discussed. In this investigation, we used an optical disdrometer (OTT Parsivel 2) to gather data in Sangju City (Korea) between June 2020 and December 2021. The outputs of this study are shown as follows: (1) The statistically most accurate estimates of KE expenditure and KE content in Sangju City are obtained using power-law equations given by Sanchez-Moreno et al. and exponential equations published by Lee and Won, respectively. (2) The suggested KE-Ir equation applied to the entire Korean site exhibits a comparable general correlation with the observed data. The parameter maps indicate a high variance in geography.
... Rainfall kinetic energy depends on the drop size, drop velocity, drop volume, and the drop-size distribution (DSD) of rain. The relationships between the rainfall kinetic energy and the rainfall intensity based on the data of drop-size and drop-velocity measurements have been proposed as an empirical expression of logarithmic, exponential, linear, and power-law functions [68][69][70][71][72][73][74][75][76][77][78][79]. A general exponential equation based on published data for the relationship between rainfall intensity and kinetic energy was presented by Van Dijk et al. [80]. ...
A large wildfire occurred due to strong winds and dry climates in the Gangwon province of South Korea. Thereafter, floods and sediment damage were caused by Typhoon Mitag in the burned areas. This study was an attempt to quantitatively evaluate the risk of soil erosion in wildfire areas using the Soil Erosion Model for Mountain Areas (SEMMA) based on GIS, which was developed in South Korea. The model required the integration of maps of the main factors involved, i.e., rainfall erosivity, vegetation index, soil erodibility, and slope length and steepness. According to the model simulation results, high erosion rates of over 100 t/ha were concentrated within the wildfire areas. Sediment yields from the study watershed, including the wildfire areas, were estimated to be 40.33 t/ha for the 30-year frequency of rainfall, which is similar to those of the typhoon. The high risk of erosion was predominantly observed in the upper mountains, which are characterized by steep slopes, silt loam, and shallow soil depths within the wildfire areas. Urgent and excessive logging of burned trees further increased the risk of erosion. However, various treatment strategies were implemented to control soil erosion and sediment transport from the post-fire watershed. This study confirmed that temporal and spatial BMPs should be selected and enforced to reduce sediment disasters in wildfire areas.
... After that, various researches have been using many different techniques to measure properties of raindrops. These techniques include the stain method (Sadeghi et al., 2013;Seginer, 1963;Hall, 1970;Solomon et al., 1985), flour method (DeBoer et al., 2001;Eigel and Moore, 1983;Kathiravelu et al., 2016;Laws and Parsons, 1943;Sadeghi et al., 2013), the photography method (King et al., 2014;Lima et al., 2015;McIsaac, 1990;Sadeghi et al., 2013;Salvador et al., 2009;Sudheer and Panda, 2000), the radar technique (Bringi et al., 2003;Tang et al., 2014;You et al., 2016;Zhang et al., 2001), the oil immersion techniques (Eigel and Moore, 1983), the stroboscope technique (Illingworth and Stevens, 1987;Lavergnat and Gole, 1998;Salles and Poesen, 1999), the optical array probe (Jash et al., 2019;Lilley et al., 2006;Wang et al., 2021) and disdrometers (Angulo-Martínez et al., 2016;Bringi et al., 2003;Das et al., 2017;Illingworth and Stevens, 1987;Jwa et al., 2020;Serio et al., 2019;Sheppard and Joe, 1994;Sulochana et al., 2016). The stain method, flour, and oil immersion techniques are time-consuming and record data temporarily (Sadeghi et al., 2013), while the photographic method can capture a scaled image of drops in flight and provide a direct measurement to determine the size of individual drops (Cruvinel et al., 2017;Chang et al., 2019;Frank et al., 1994;Sijs et al, 2021;Steinmann et al., 2021). ...
Kinetic energy and corresponding erosive force of rainfall are strongly influenced by raindrop. The present paper aims to explore the raindrop size variation during rainfall events with different intensities in northern Iran by applying the processes of camera-taken photographs. Five rainfall intensities of 1 to 10 mm h–1 that occur frequently in the study area were analyzed. A camera with a very short exposure time was used to record the distribution of raindrops size. The raindrops diameters of the rain events ranged from <0.2 to 5.1 mm while the majority of them were between 1 and 2 mm. The results also showed that the variation of rainfall intensity significantly influenced (P< 0.05) raindrops size. Image processing was proven as an accurate technique of translation between the human visual system and digital imaging devices. The findings of the study can be practically utilized by researchers who work in the field of soil erosion and meteorology.
Keywords: Raindrop Size Distribution; Image processing; Rainfall Intensity; Rain Erosivity.
... Many measurements can be used to obtain these two parame-Published by Copernicus Publications on behalf of the European Geosciences Union. 5408 Q. Dai et al.: Estimation of rainfall erosivity based on WRF-derived raindrop size distributions ters, including the stained paper or flour pellet methods (Marshall and Palmer 1948;Wischmeier and Smith, 1958), high speed cameras (Jones, 1959;Kinnell, 1981;McIsaac, 1990), and disdrometers (Petan et al., 2010;Angulo-Martinez et al., 2012). Accurate measurements of raindrop size can be provided in all their methods, and terminal velocity of raindrops can be further measured by video cameras and disdrometers. ...
Soil erosion can cause various ecological problems, such as land
degradation, soil fertility loss, and river siltation. Rainfall is the
primary water-driven force for soil erosion, and its potential effect on
soil erosion is reflected by rainfall erosivity that relates to the raindrop
kinetic energy. As it is difficult to observe large-scale dynamic
characteristics of raindrops, all the current rainfall erosivity models use
the function based on rainfall amount to represent the raindrops' kinetic
energy. With the development of global atmospheric re-analysis data,
numerical weather prediction techniques become a promising way to estimate
rainfall kinetic energy directly at regional and global scales with high
spatial and temporal resolutions. This study proposed a novel method for
large-scale and long-term rainfall erosivity investigations based on the
Weather Research and Forecasting (WRF) model, avoiding errors caused by
inappropriate rainfall–energy relationships and large-scale interpolation.
We adopted three microphysical parameterizations schemes (Morrison, WDM6,
and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall
kinetic energy, and rainfall erosivity, with validation by two disdrometers
and 304 rain gauges around the United Kingdom. Among the three WRF schemes,
Thompson aerosol-aware had the best performance compared with the
disdrometers at a monthly scale. The results revealed that high rainfall
erosivity occurred in the west coast area at the whole country scale during
2013–2017. The proposed methodology makes a significant contribution to
improving large-scale soil erosion estimation and for better understanding
microphysical rainfall–soil interactions to support the rational
formulation of soil and water conservation planning.
... This study proposed a novel method for 22 large-scale and long-term rainfall erosivity investigations based on the Weather Research and 23 Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall-energy relationships 24 and large-scale interpolation. We adopted three microphysical parameterizations schemes can be used to obtain these two parameters, including the stain paper or flour pellet methods 54 (Marshall and Palmer 1948;Wischmeier and Smith, 1958), high speed cameras (Jones, 1959; 55 Kinnell, 1981;McIsaac, 1990), and disdrometers (Petan et al., 2010; Angulo-Martinez et al., 56 2012). Accurate measurements of raindrop size can be provided in all their methods, and 57 terminal velocity of raindrops can be further measured by video cameras and disdrometers. ...
Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driving force for soil erosion and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy (KE). As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops KE. With the development of global atmospheric re-analysis data, numerical weather prediction (NWP) techniques become a promising way to estimate rainfall KE directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware [TAA]) to obtain raindrop size distributions, rainfall KE and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, TAA had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.
... (2) Soil erodibility, Rainfall intensity, Slope steepness, Slope length Rainfall intensity and runoff rate equations Kinnell (1991Kinnell ( , 1993 (Ferro, 1998;Schmidt, 1993;Gabet and Dunne, 2003 (Kinnell, 1991;De Veaux, 1992, Madden et al., 1998;Steiner and Smith, 2000 (Wischmeier and Smith, 1958;Zanchi and Torri, 1980;Rosewell, 1986;Brown and Foster, 1987;Brandt, 1990;McIsaac, 1990;Smith and De Veaux, 1992;Coutinho an Tomás, 1995;Uijlenhoet and Stricker, 1999;Steiner and Smith, 2000;Salles et al., 2002;Van Dijk et al., 2002;Fornis et al., 2005;Lee and Won, 2013;Shin et al., 2016). 몇몇의 연구자들은 자연강우 사상에는 강 우운동에너지의 상한 값이 존재함으로 제한된 값에 수렴하는 지수함수를 사용할 것을 제안했다 (Hudson, 1963;Baruah, 1973;Wishchmeier and Smith, 1978;Carter et al., 1974;Kinnell, 1981;Rosewell, 1986;Brown and Foster, 1987 (Bagnold, 1966;Chang, 1979 (Moss et al., 1979), 상향 경사보다는 하향 경사로 더 많은 입자들이 더 멀리 이동하는 것이 사실이다 (Moss et al., 1979). ...
Interrill erosion on a hillslope results from the combined action of the detachment of soil particles by raindrop impact and the sediment transport of surface runoff. This study newly defined the rainfall power which detaches soil particles and the sheet-flow power contributed to sediment transport in terms of energy expenditure rate of soil erosion and presented the effective power equation for interrill erosion by rainfall-induced sheet flow. The rainfall and sheet-flow power was evaluated by factors related with rainfall, slope, and runoff and coefficients of the power equation were analyzed based on references. Futhermore it was confirmed that the relative scales between the rainfall power and the sheet-flow power according to rainfall intensity reflect on the hydrological response and physical process of interrill erosion. From application of the field data for surface runoff and soil erosion it was verified that the rainfall and sheet-flow power is an appropriate equation to estimate a interrill erosion.
... In spite of the difficulties posed by temporal and spatial variability of raindrops, it is possible to derive general relations between kinetic energy (e kt ) and rainfall intensity (I ). The kinetic energy of rainfall have been simply estimated by exponential, logarithmic, and power-law functions of relationship with rainfall intensity (Wischmeier and Smith, 1958;Zanchi and Torri, 1980;Rosewell, 1986;Brown and Foster, 1987;Brandt, 1990;McIsaac, 1990;Smith and De Veaux, 1992;Coutinho and Tomás, 1995;Uijlenhoet and Stricker, 1999;Steiner and Smith, 2000;Salles et al., 2002;Van Dijk et al., 2002;Fornis et al., 2005;Lee and Won, 2013;Shin et al., 2016). ...
The process of interrill erosion is complex by interaction of raindrop impact and sheet flow. Their relative contribution to interrill erosion is difficult to be evaluated even on bare soil. This study presents the new erosivity factor to evaluate the interrill erosion on steep vegetated hillslope with the more relevant understanding of the physical processes. The effective energy, the erosivity factor, is defined as the sum of the effective kinetic energy of rainfall and effective potential energy of surface runoff based on the energy balance. The effective kinetic energy of rainfall is determined by the horizontal component for slope of kinetic energy deducting energies dissipated by structure of vegetation canopies and a litter layer. The effective potential energy of surface runoff is equal to potential energy of the available surface water following rain-mass allocations of interception and infiltration. The data from experimental field plots with various vegetation coverage after wildfire were used to verify the effective energy equation. On densely vegetated slopes sediment yield depended greatly on effective kinetic energy of rainfall, while they from hillslopes having sparse coverage were dominated by effective potential energy of surface runoff. The dissipated energy due to interrill erosion showed the highest correlation coefficient with the effective energy under various cover conditions. The kinetic energy of raindrops was greatly reduced by the litter layer and the potential energy of rainwater decreased predominantly due to infiltration. The ratio of effective potential energy of surface runoff to total effective energy was the highest at 71.2% in the plots with low vegetation coverage. The energy efficiency for interrill erosion increased with decreasing vegetation coverage and reached maximum 1.35% in extreme rainfall event under low vegetation coverage. The constant and exponent of power-law functions between the effective energy and the soil erosion work were strongly correlated with gravel ratio and litter coverage, respectively. The results indicate that the effective energy is useful erosivity factor to evaluate the interrill erosion occurred by the complicated interaction of rain splash and sheet flow on vegetated hillslopes.
... The widespread scatter in the KE mm ±I plots used to obtain the empirical equations is a typical feature (e.g. Bollinne et al., 1984;Rosewell, 1986;Kinnell, 1987;McIsaac, 1990;Coutinho and Toma Âs, 1995). These scatterplots have two characteristic features. ...
... Various raindrop measurement techniques and tools broadly categorized into manual and automated techniques to determine simulated raindrop size distributions and mean drop size (Kathiravelu et al., 2016). Manual techniques were used in early studies (Wiesner, 1895), the stain method (Hall, 1970;Kincaid et al., 1996;Sadeghi et al., 2013), the flour method (Laws and Parsons, 1943;Kohl and DeBoer, 1984;Ries and Langer, 2001;Parsakhoo et al., 2012;Sadeghi et al., 2013), the momentum method (Scheleusener, 1967), the submersion technique (Gunn and Kinzer, 1949;Eigel and Moore, 1983), the photography method (Hoffman, 1977;Kinnell, 1984;Mc Isaac, 1990), the optical array probe (Swithenbank, 1977;Chigier, 1991), the image processing technique (Sudheer and Panda, 2000;Salvador et al., 2009;Abudi et al., 2012), the radar technique (Chandrasekar and Bringi, 2001;Bringi et al., 2003) and the disdrometer (Nystuen, 1999;Salmi and Ikonen, 2005;De Moraes Frasson et al., 2011). Are all examples of methods that provide measurements of the number and size of raindrops. ...
The size of the drops determines soil erosion and runoff rates, and then the fate of ecosystems. Various raindrop measurement techniques and tools have been developed to determine natural and simulated raindrop size distributions and mean drop size. There is a need to improve the procedure to determine the raindrop properties, and this is why we develop a new technique to analyze drop size distribution and fall velocity. For this purpose a rainfall simulator with two oscillating Veejet 80100 nozzles in laboratory condition, and high speed imaging technique and edge detection approach in image processing was applied to identify and measure drop size and calculate drop velocity. The results showed that the rainfall simulator was able to create drops with diameter in the range from 0.2 to 9.9 mm. Fall velocity ranged from 0.8 to 9.2 m/s for different diameter classes in the height of 0.5 m above the ground. The results indicate that the low-cost technique developed in this paper had high ability to automatically and rapidly identify raindrops characteristics with high accuracy. This technique can help to calibrate other rainfall simulators, but also to characterize natural rainfall events in different regions, which is a worldwide need due to the lack of information, and the importance of the raindrop characteristic to characterize and model the soil erosion processes.
... In some of the kinetic energy-rainfall intensity results obtained from different researchers in different countries, the empirical constants differed from one place to another, and these differences were notable. The differences can be attributed not only to the errors introduced during measurements and interpretation but also to differences in rainfall characteristics inherent to the various geographic locations (Fornis et al., 2005;Kinnell, 1981;McIsaac, 1990;Van Dijk et al., 2002). For these reasons, Fornis et al. (2005) warned that a relationship between kinetic energy and rainfall intensity that performs well in one location may perform poorly in another location. ...
Rubber is usually grown as a monoculture but there have been recent attempts to encourage rubber-based agroforestry systems to reduce adverse environmental impacts, including the reduction of soil erosion in Xishuangbanna, SW China. To estimate the influence of different types of rubber-based agroforestry systems on soil erosion processes, we measured the throughfall kinetic energy (TKE) under different vegetation types by using 640 sand-filled Tübingen splash cups. This study was conducted in Xishuangbanna Tropical Botanical Gardens under natural rainfall conditions. Our results indicated that in both rubber-based agroforestry systems and rubber monocultures, a significant linear positive correlation exists between TKE and rainfall amount. Rainfall amount is a critical factor that contributes to soil detachment in rubber plantations in this region. TKE under rubber plantation conditions was found to be notably higher than under open field conditions (ranging from 1.84 to 2.32 times greater). However, there was no significant difference under multiple canopies compared to monoculture. TKE values under the different rubber-based agroforestry systems were closely related to the canopy structure, and TKE and leaf area index were significantly negatively correlated. The spatial variability of TKE was higher in rubber-based agroforestry systems than in rubber monocultures. In addition, TKE was usually concentrated in 3-4. m bands that did not have the protection of a sub-canopy. The fact that the erosion by TKE under rubber-based agroforestry was still high highlights the importance of selecting intercrops when constructing rubber-based agroforestry systems and of improving planting patterns.
... Hence, empirical relations between E and I, which were developed in different regions, most notably in the United States, are used. The influence of different rain patterns, which are influenced by particularities of the local climate, can cause uncertainty in regards to extrapolation in different regions, especially among temperate, tropical and subtropical regions (Kinnell, 1981;McIsaac, 1990;Van Dijk et al., 2002). According toMorgan, (2005), there are also seasonal differences led by frontal or convective rainfall that determine important differences in the characteristics of the drops. ...
... The influence of different rain patterns, which are influenced by particularities of the local climate, can cause uncertainty in regards to extrapolation in different regions, especially among temperate, tropical and subtropical regions (Kinnell, 1981;McIsaac, 1990;Van Dijk et al., 2002). According to Morgan, (2005), there are also seasonal differences led by frontal or convective rainfall that determine important differences in the characteristics of the drops. ...
... This means that the two terms should be computed separately and, then, merged into the calculation of R factor. With respect to the impact energy (equation 2), the relationships between rainfall kinetic energy and rainfall intensity are presented in the form of exponential (Blanchard, 1953;Rosewell, 1986;Brown and Foster, 1987;McIsaac, 1990;Azevedo Coutinho and Pereira Tomá s, 1995;Jayawardena and Rezaur, 2000;Fornis et al., 2005;Lim et al., 2012;Lee and Won, 2013), logarithmic (Wischmeier and Smith, 1958;Zanchi and Torri, 1980;Kinnell, 1981;Onaga et al., 1988;Brandt, 1990), linear (Sempere-Torres et al., 1992;Usón and Ramos, 2001), and power-law (Smith and de Veaux, 1992;Uijlenhoet and Stricker, 1999;Steiner and Smith, 2000) functions. The relation between e and the precipitation intensity have been determined in order to use ordinary raingauges. ...
The theoretical and the instrumental metrological basis for computation of rainfall impact in storm erosivity as defined in the Revised Universal Soil Loss Equation, version 2 (RUSLE2) are considered. The present determination of rainfall erosivity is based on two factors: E, representing the rainfall kinetic energy, and I30, the maximum 30-minutes intensity for a given precipitation event. The present short review evidences some of the exist-ing metrological limitations: (1) the non separation between the impact of falling rain and shallow flow of water; (2) the use of a non-universal semi-empirical approach, (3) the absence of a clear model with respect to rain flow, runoff and soil wetting; (4) the use of a hybrid measure unit; (5) the intrinsic limitation of measuring technologies. The improvement of the existing parameter calculation techniques or a transition from a prevailing semi-empirical to a mainly physical-based approach would be desirable, even if this transformation shouldn’t affect the usability of the developed tools for practitioners.
... For example, cyclonic rain in the temperate zone is mainly composed of small and average size raindrops whereas high-intensity tropical thunderstorms have a greater proportion of large drops. Thus, the rain energy of the two storm types differs even at the same rainfall intensity (McIsaac, 1990;Meshesha et al., 2013). Therefore, in this study, we investigated the kinetic energy and erosivity of rainstorms using simulated rainfall. ...
Rainfall kinetic energy is a widely recognized indicator of a raindrop's ability to detach soil particles in rainsplash erosion. However, it is challenging to estimate the kinetic energy (KE) of a given rain event, because it involves analysis of the terminal velocity and drop size distribution (DSD) of raindrops. A preferred alternative is to relate KE to rainfall intensity. Therefore we sought to characterize simulated rainfall, establish a relationship between kinetic energy and intensity as a function of both time (KEt, J m(-2) h(-1)) and volume (KEvol, J m(-2) mm(-1)), and examine the erosivity potential of each event. A rainfall simulator and Laser Precipitation Monitor (optical disdrometer) were used to characterize raindrop size, terminal velocity and ME at different rainfall intensities (1.5 to 202 mm h(-1)). Values of KEt ranged from 26.67 to 5955 J m(-2) h(-1) and KEvol ranged from 16.10 to 34.85 J m(-2) mm(-1), which is comparable to values determined from natural rain of similar intensity ranges. A power-law function and a polynomial function between KEt and rainfall intensity had coefficients of determination (R-2) of 0.99 and 0.98 (P < 0.0001), respectively. The best-fitting relationship between KEvol and intensity was a power-law function (R-2 = 0.95; P < 0.001). We found that erosivity had a very strong correlation with rainfall depth (R-2 = 0.99; P < 0.0001) in power-law function. Furthermore, regardless of rainfall intensity, ME is more strongly correlated with raindrop size than volume of raindrop.
... 수문학적 침식과정이 상이한 기존 모형을 비경작지에 적용하는 것 은 한계가 있음을 의미한다. 다수 연구진들은 20여년에 걸 쳐 야대지의 유출 및 토양침식 과정에 대해 많은 연구를 수행하였고 (Wilcox, 1994;Tongway and Ludwig, 1997;Pierson et al., 2002;Chartier and Rostagno, 2006;Bartley et al., 2006), 최근 야대지만을 대상으로 하는 토 양침식 예측모형인 RHEM (Rangeland Hydrology and Erosion Model)을 개발하였다 (Nearing et al., 2011 (Park et al., 2005;Park et al., 2012 (Morgan, 1996;Kinnell, 2005 (Smith and De Veaux, 1992;Steiner and Smith, 2000;Shin et al., 2015), 지수함수 (Rosewell, 1986;McIsaac, 1990;Lee and Won, 2013), 로그함수 (Wischmeier and Smith, 1958;Kinnell, 1981;Fornis et al., 2005), 선형함수 (Hudson, 1965;Sempere-Torres et al., 1992;Fornis et al., 2005) (Bagnold, 1966;Yang, 1972)을 고려해야 한다. 특히 단위수류력 (Yang, 1972(Yang, , 1973 3.3 단위수류력 Yang(1973)의 유량함수 단위수류력 산정결과와 강우 강도의 관계를 나타내었다 (Fig. 9) ...
Interrill erosion by the rainfall is divided into a detachment of soil particles by raindrop splash when raindrops having kinetic energy strike on the surface soil and a sediment transport by sheet flow of surface runoff. Rainfall kinetic energy is widely used as an indicator expressing the potential ability to separate the soil particles from soil mass. In this study, the soil erosion experiments of rainfall simulation were operated to evaluate the effects of rainfall kinetic energy on interrill erosion as using the strip cover to control raindrop impact. The kinetic energy from rainfall simulator was 0.58 times to that of natural rainfall. Surface runoff and subsurface runoff increased and decreased respectively with increase of rainfall intensity. Surface runoff discharge from plots of non-cover was 1.82 times more than that from plots with cover. The rainfall kinetic energy influenced on the starting time of surface and subsurface runoff. Soil erosion quantity greatly varied according to existence of the surface cover that can intercept rainfall energy. Sediment yields by the interaction between raindrop splash and sheet flow increased 3.6∼5.9 times and the increase rates of those decreased with rainfall intensity. As a results from analysis of relationship between stream power and sediment yields, rainfall kinetic energy increased the transport capacity according to increase of surface runoff as well as the detachment of soil particles by raindrop splash. Ke ywor ds : rainfall kinetic energy, sheet flow, interrill erosion, rainfall simulation, strip cover .
... However, by the mid-1980s a number of workers, both within the US (e.g. McIsaac, 1990) and elsewhere (e.g. Bolline, 1985), had begun to question the extent to which the USLE could usefully be applied under conditions which differ from those areas of the US for which the model was developed 1 . ...
... The relationship between kinetic energy and rainfall intensity varies with the drop size distribution of the rainfall. Different relationships have been reported for different geographical regions (Laws and Parsons 1943;Hudson 1965;Carter et al. 1974;Wischmeier and Smith 1978;Kinnell 1981;Zanchi and Torri 1980;Rosewell 1986;Brown and Foster 1987;McIsaac 1990). In this study, a relationship developed for Indian conditions has been used (Kinnell 1981), as given in Eq. (4) ...
In this study, the Morgan and Duzant version of the modified Morgan-Morgan-Finney (MMF) model coupled with geographical information system (GIS) is used for sediment yield estimation from the Gamber watershed, Satluj basin, Himachal Pradesh, India. The model incorporates particle size selectivity in the process of erosion, transport, and deposition, i.e., it simulates these processes for clay, silt, and sand separately. This modified MMF model also allows for surface runoff and sediment routing, which improves sediment yield estimation accuracy. It also determines the watershed contributions to the total sediment yield at the basin outlet. The present research fetches the MMF model in a geospatial environment and develops a system that can be used to estimate the actual sediment yield. Generally, it has been observed that the detachment of soil is greatly affected by raindrop impact. Therefore, the estimation of kinetic energy of erosive rainfall is a very important factor. Moreover, it has been noticed that the major contribution to kinetic energy of rainfall comes from direct throughfall, as compared to leaf drainage. Therefore, the relationship to estimate kinetic energy of direct throughfall developed for Indian conditions has been used rather than the traditional relationship developed for the United Kingdom. The model parameters were calibrated for the years 1998 and 2002. The results were validated for the years 1995 and 1999. The efficiency coefficient the model could achieve was 0.91. It was concluded that the model can estimate sediment yield from a catchment with reasonable accuracy.
... For example, cyclonic rain in the temperate zone is mainly composed of small and average size raindrops whereas high-intensity tropical thunderstorms have a greater proportion of large drops. Thus, the rain energy of the two storm types differs even at the same rainfall intensity (McIsaac, 1990;Meshesha et al., 2013). Therefore, in this study, we investigated the kinetic energy and erosivity of rainstorms using simulated rainfall. ...
Land degradation in many Ethiopian highlands occurs mainly due to high rainfall erosivity and poor soil conservation practices. Rainfall erosivity is an indicator of the precipitation energy and ability to cause soil erosion. In Central Rift Valley (CRV) of Ethiopia, where the climate is characterized as arid and semiarid, rainfall is the main driver of soil erosion that in turn causes a serious expansion in land degradation. In order to evaluate the spatial and temporal variability of rainfall erosivity and its impact on soil erosion, long-term rainfall data (1980-2010) was used, and the monthly Fournier index (FI) and the annual modified Fournier index (MFI) were applied. Student's t test analysis was performed particularly to examine statistical significances of differences in average monthly and annual erosivity values. The result indicated that, in a similar spatial pattern with elevation and rainfall amount, average annual erosivity is also found being higher in western highlands of the valley and gradually decreased towards the east. The long-term average annual erosivity (MFI) showed a general decreasing trend in recent 10 years (2000-2010) as compared to previous 20 years (1980-1999). In most of the stations, average erosivity of main rainy months (May, June, July, and August) showed a decreasing trend, whereby some of them (about 33.3 %) are statically significant at 90 and 95 % confidence intervals but with high variation in spatial pattern of changes. The overall result of the study showed that rainfall aggression (erosivity) in the region has a general decreasing trend in the recent decade as compared to previous decades, especially in the western highlands of the valley. Hence, it implies that anthropogenic factors such as land use change being coupled with topography (steep slope) have largely contributed to increased soil erosion rate in the region.
... The widespread scatter in the KE mm ±I plots used to obtain the empirical equations is a typical feature (e.g. Bollinne et al., 1984;Rosewell, 1986;Kinnell, 1987;McIsaac, 1990;Coutinho and Toma Âs, 1995). These scatterplots have two characteristic features. ...
The rain kinetic energy is a widely used indicator of the potential ability of rain to detach soil. Empirical laws linking the rain kinetic energy to the more easily available rain intensity have been proposed based on drop-size and drop-velocity measurements. This study reports on the various mathematical expressions used to relate rain kinetic energy and rain intensity available from the literature. Kinetic energy is expressed as the rain kinetic energy expended per volume of rain or volume-specific kinetic energy (KEmm) or as the rain kinetic energy rate or time-specific kinetic energy (KEtime). Using statistical and micro-physical considerations we show that KEtime is the most appropriate expression to establish an empirical law between rain kinetic energy and rain intensity. Finally, a discussion based on the existing drop-size distribution models from the literature shows that the power law is the most suitable mathematical function to link kinetic energy and rain intensity.
... [59] Many studies have examined the different sources of error in USLE and RUSLE and suggested possible improvements. These include changes in model structure [Tran et al., 2002;Sonneveld and Nearing, 2003], changes in specific parameters [Kinnell and Risse, 1998;Kinnell, 2005], and ways to extend USLE or RUSLE to new geographic areas [McIsaac, 1990;Liu et al., 2000;Cohen et al., 2005;Hammad et al., 2005]. The use of RUSLE in undisturbed forests is troublesome because overland flow is so uncommon [Dunne and Leopold, 1978], but the predominance of overland flow after high-severity burns [Shakesby and Doerr, 2006] means that RUSLE should be much more applicable. ...
High-severity wildfires can increase hillslope-scale sediment yields by several orders of magnitude. Accurate predictions of postfire sediment yields are needed to guide management decisions and assess the potential impact of soil loss on site productivity and downstream aquatic resources. The Revised Universal Soil Loss Equation (RUSLE) and Disturbed WEPP are the most commonly used models to predict postfire sediment yields at the hillslope scale, but neither model has been extensively tested against field data. The objectives of this paper are to (1) compare predicted sediment yields from RUSLE and Disturbed WEPP against 252 plot years of data from nine fires in the Colorado Front Range; and (2) suggest how each model might be improved. Predicted and measured sediment yields were poorly correlated for RUSLE (R2 = 0.16) and only slightly better correlated for Disturbed WEPP (R2 = 0.25). Both models tended to over-predict sediment yields when the measured values were less than 1 Mg ha-1 yr-1 and to under-predict higher sediment yields. Model accuracy was not improved by increasing the soil erodibility (K) factor in RUSLE and was only slightly improved by slowing the vegetative recovery sequence in Disturbed WEPP. Both models much more accurately predicted the mean sediment yields for hillslopes grouped by fire and severity (R2 = 0.54 to 0.66) than for individual plots. The performance of RUSLE could be improved by incorporating an erosivity threshold and a nonlinear relationship between rainfall erosivity and sediment yields. The performance of WEPP could be improved by reducing the effective hydraulic conductivity in sites that have recently burned at high severity. The results suggest that neither model can fully capture the complexity of the different controlling factors and the resultant plot-scale variability in sediment yields.
Rainfall erosivity factor (R) is the key part of both the Universal Soil Loss Equation (USLE) and the Revised Universal Soil Loss Equation (RUSLE). Its accuracy is related to the rainfall kinetic energy equation, the number of rain gauge stations, spatial distribution of rain gauge stations network, type of rain gauges, recording temporal resolution, the time step of rainfall intensity used, used time period, type of interpolation method, used covariates for determination of R values at unmeasured places, and way of regionalisation of R values or its determination for a given locality. These aspects are described, compared and discussed, including several common discrepancies and distortions. All approaches to R factor computation and estimation are presented with a focus on central Europe. The approaches were divided into two groups: the first low temporal resolution approach (yearly - daily rainfall totals), and the second high temporal resolution approach (1–60 min rainfall totals). The relationship between the spatio-temporal distribution of R- and cover-management factor (C) values is described, including other methods of C-factor estimation. Several methods of R- and C-factor estimation and calculation were developed due to the lack of optimal data required by the original methodology or due to incorrect interpretation of given parameters or criteria. Also, inappropriate integration with the geographical information system (GIS) tools and Remote Sensing (RS) data may cause many simplifications and distortions of the original principles of the USLE/RUSLE. This review should help to choose the appropriate methodology of R- and C- factor calculation in the field of water erosion risk assessment and help to reach a more appropriate allocation of financial expenses of erosion control measures.
L’érosion du sol est un phénomène naturel qui rend compte de certains processus
(détachement des particules, transport et dépôt) induits par différents agents érosifs. Dans
les zones semi-arides, les précipitations sont souvent intenses sur les sols secs à végétation
pauvre, ce qui peut entraîner une forte susceptibilité à l'érosion des sols. Par conséquent,
une évaluation appropriée de l'érosivité des précipitations revêt une importance particulière
en raison des effets négatifs causés par l'épuisement de la couche superficielle du sol et la
charge excessive de sédiments dans les eaux réceptrices des réservoirs.
La présente étude a été menée sur le bassin versant de l’Oued K'sob situé au Nord-Est
de l'Algérie, dans le but d’étudier l’influence la variabilité climatique sur la production des
sédiments. Le bassin draine une superficie de 1480 km² située entre les altitudes 585 et
1888 m. L’Oued principal s’écoule sur une longueur 73 km et alimente le barrage K’sob
d’une capacité initiale de 29.5 Mm3, mis en service en 1940. La région d’étude présente un
climat semi-aride à tendance continentale avec un hiver relativement pluvieux et un été sec
et chaud, avec une pluie moyenne interannuelle est de 340 mm. La construction des
courbes IDF a permis de déterminer un exposant climatique de la région, b=0.75.
Pour l’érosivité des précipitations, le calcul d'un tel indice est basé sur les pluies dépassant
un seuil spécifique et nécessite des données pluviométriques avec une fine résolution
temporelle, qui, souvent, sont rares ou difficiles à acquérir. L'examen des pluies
quotidiennes survenant avant une inondation portant une charge sédimentaire a montré une
variabilité spatiale des seuils d'érosivité des précipitations. Les valeurs saisonnières
des seuils sont faibles et se situent entre 2 mm en été et 6 mm en hiver, ce qui met
en évidence un processus d'érosivité typiquement élevé dans les régions semi-arides.
Des relations empiriques, établies à l'échelle saisonnière, ont été proposées comme solution
alternative au calcul de l'indice R dérivé de l'équation révisée des pertes en sol.
Les modèles déterminés ont permis de simuler l'érosivité des événements pluvieux
en fonction de la pluie quotidienne. Entre 68 et 78 % de la variance de l'érosivité
des précipitations s'explique par la pluie quotidienne donnant lieu à un événement pluvieux
érosif. Ensuite, la moyenne spatiale de l'indice d'érosivité annuel a fluctué entre 228 et 386
MJ mm ha-1 h-1 an-1 avec une moyenne interannuelle de 302 MJ mm ha-1 h-1 an-1,
ce qui a sous-estimé de 6% l'indice d'érosivité quantifié selon l'équation universelle révisée
de perte de sol.
L'érosivité des précipitations est le facteur déterminant du rendement en sédiments avec un
degré de fixation différent au cours de l'année. En automne, 68 % de la variance de la
production de sédiments s'explique par l'érosivité des pluies, contre seulement 42 % au
printemps en raison des changements des conditions du sol, notamment la présence d'une
couverture végétale qui protège le sol contre l'érosivité des pluies.
Face au risque d`inondation, la notion de scénarios hydrologiques de référence est déterminante pour l`analyse hydraulique des conséquences d`une crue. Ils sont construits à partir d`hydrogrammes de projet respectant certaines propriétés fréquentielles à l`échelle spatiale et temporelle. Dans l`approche proposée, ces scénarios de référence sont définis à partir d`un hydrogramme élémentaire associé à une caractéristique de crue de période de retour donnée. Respectant des propriétés d`invariance de forme avec l`intensité de la crue, l`hydrogramme de référence est obtenu par la moyenne d`un échantillon de crues normées. Les facteurs aggravants de montée des eaux sont pris en compte par l`étude des gradients maximaux de montée. De tels scénarios hydrologiques sont utiles dans le cas du dimensionnement d`ouvrages. Pour la détermination de zones inondables, des scénarios de référence mono-fréquents sont établis. Ils possèdent une caractéristique montée réaliste tenant compte de la variabilité de forme de montée de crue tandis que leur décrue est construite pas à pas à l`aide des courbes débit-durée-fréquence. L`application de ces méthodes à plus de 150 bassins versants français en a permis la validation. Dans le cas de grands bassins versants aux apports différenciés, le scénario de référence à l`aval d`une confluence peut être défini soit par composition de lois de probabilités, soit par combinaison d`hydrogrammes de référence. Les deux méthodes tiennent compte du degré de liaison entre les crues amont. Associés à des facteurs hydro-météorologiques, des scénarios de crue probables peuvent être construits sur de grands bassins comme celui du Rhône. Enfin, la méthode permet de considérer la notion de laminage des crues lors de son transfert sur un cours d`eau par simulation hydraulique.
Rainfall characteristics such as total amount and rainfall intensity (I) are important inputs in calculating the kinetic energy (KE) of rainfall. Although KE is a crucial indicator of the raindrop potential to disrupt soil aggregates, it is not a routinely measured meteorological parameter. Therefore, KE is derived from easily accessible variables, such as I, in empirical laws. The present study examines whether the equations which had been derived to calculate KE of natural rainfall are suitable for the calculation of KE of simulated rainfall. During the experiment presented in this paper, the measurement of rainfall characteristics was carried out under laboratory conditions using a rainfall simulator. In total, 90 measurements were performed and evaluated to describe the rainfall intensity, drop size distribution and velocity of rain drops using the Thies laser disdrometer. The duration of each measurement of rainfall event was 5 minutes. Drop size and fall velocity were used to calculate KE and to derive a new equation of time-specific kinetic energy (KE<sub>time</sub> – I). When comparing the newly derived equation for KE of simulated rainfall with the six most commonly used equations for KE<sub>time</sub> – I of natural rainfall, KE of simulated rainfall was discovered to be underestimated. The higher the rainfall intensity, the higher the rate of underestimation. KE of natural rainfall derived from theoretical equations exceeded KE of simulated rainfall by 53–83% for I = 30 mm/h and by 119–275% for I = 60 mm/h. The underestimation of KE of simulated rainfall is probably caused by smaller drops formed by the rainfall simulator at higher intensities (94% of all drops were smaller than 1 mm), which is not typical of natural rainfall.
Erosive rainfall events can cause significant problems in agriculture and other fields because of fertile soil losses. Therefore, high-frequency measurements of rainfall data are useful in order to improve our knowledge about this issue. This study presents rainfall measurements in years 2013, 2014 and 2015 at two locations in central Slovenia, namely Ljubljana and Črni vrh nad Polhovim Gradcem, which are located in temperate-continental climate. 1-minute rainfall data analysed in this study was measured using optical disdrometer. Results indicate large variability in rainfall erosivity at relatively short distances. Some specific extreme events can lead to rainfall erosivity values up to average annual rates. Large seasonal variability of erosivity was also observed in the measured data. Moreover, several KE-I equations were tested and the results show that locally developed equations are more suitable to estimate rainfall erosivity in cases where no disdrometer data is available.
In the present paper, the rainfall erosivity factor (R factor) for the area of the Czech Republic is assessed. Based on 10 min data for 96 stations and
corresponding R factor estimates, a number of spatial interpolation methods are applied and cross-validated. These methods include inverse distance
weighting, standard, ordinary, and regression kriging with parameters
estimated by the method of moments and restricted maximum likelihood, and a
generalized least-squares (GLS) model. For the regression-based methods,
various statistics of monthly precipitation as well as geographical indices
are considered as covariates. In addition to the uncertainty originating from
spatial interpolation, the uncertainty due to estimation of the rainfall
kinetic energy (needed for calculation of the R factor) as well as the
effect of record length and spatial coverage are also addressed. Finally, the
contribution of each source of uncertainty is quantified. The average
R factor for the area of the Czech Republic is
640 MJ ha−1 mm h−1, with values for the individual stations
ranging between 320 and 1520 MJ ha−1 mm h−1. Among various
spatial interpolation methods, the GLS model relating the R factor to the
altitude, longitude, mean precipitation, and mean fraction of
precipitation above the 95th percentile of monthly precipitation performed
best. Application of the GLS model also reduced the uncertainty due to the
record length, which is substantial when the R factor is estimated for
individual sites. Our results revealed that reasonable estimates of the
R factor can be obtained even from relatively short records (15–20 years), provided sufficient spatial coverage and covariates are available.
In the present paper, the rainfall erosivity factor (R-factor) for the area of the Czech Republic is assessed. Based on 10-minute data for 96 stations and corresponding R-factor estimates, a number of spatial interpolation methods are applied and cross-validated. These methods include inverse distance weighting, standard, ordinary and regression kriging with parameters estimated by the method of moments and restricted maximum likelihood and a generalized least-squares (GLS) model. For the regression-based methods, various statistics of monthly precipitation as well as geographical indices are considered as covariates. In addition to the uncertainty originating from spatial interpolation, also the uncertainty due to estimation of the rainfall kinetic energy (needed for calculation of the R-factor) as well as the effect of record length and spatial coverage are addressed. Finally, the contribution of each source of uncertainty is quantified. The average R-factor for the area of the Czech Republic is 64 MJ ha−1 cm h−1, with values for the individual stations ranging between 32 and 152 MJ ha−1 cm h−1. Among various spatial interpolation methods, the GLS model relating R-factor to the mean altitude, longitude, mean precipitation and mean excess above the 95th percentile of monthly precipitation performed best. Application of the GLS model also reduced the uncertainty due to the record length, which is substantial when the R-factor is estimated for individual sites. Our results revealed that reasonable estimates of the R-factor can be obtained even from relativelly short records (15–20 years), provided sufficient spatial coverage and covariates are available.
Iso-erodent maps can be used in soil conservation planning to identify regions with high rainfall erosive potential. This study was conducted to determine the significance of elevation in predicting the rainfall erosivity index (R) in addition to the average annual precipitation and to develop an iso-erodent map for Honduras. With previously calculated R-factor values for eight climatic stations in Honduras, a regression relationship was established for estimating the rainfall erosivity index as a function of average annual precipitation and elevation with R2 of 0.972. This regression model was used to estimate the rainfall erosivity index for each of the 344 Honduran climatic stations without calculated rainfall erosivity indices. Due to the limited number of data points and their geographic clustering, the best estimates of mean rainfall erosivity indices were for stations with average annual precipitation in the range from 831 to 1313 mm and elevation between 360 and 1080 m. A provisional iso-erodent map of Honduras at a scale 1:1 000 000 was compiled in Arc/Info format, using a basemap obtained from the digital chart of the world. Iso-lines for the 95% prediction intervals for new rainfall erosivity indices are displayed on the map to show the accuracy of the new estimates. Elevation was found to be highly significant in predicting the rainfall erosivity in addition to the average annual precipitation. Data from Costa Rica, Sri Lanka, and the southeastern USA supported this finding.
This paper presents a method of using GIS and public domain remote sensed data to perform a preliminary soil erosion risk analysis aggregated into 1,000 m sections for onshore pipeline corridors. The results obtained using this method correspond well with the soil erosion risk assessment carried out in the field, with over 69% in agreement and 95% of the results obtained being within ±1 erosion classification identified by the field data. The areas where this method fails to correctly classify the soil erosion risk are identified and are largely confined to major river crossings and areas of seismic activity, which would require field verification irrespective of the results obtained for these sections using this method. The limitations of the proposed method due to the lack of detailed soil data and strategies to mitigate poor soil data are discussed. Using this method it is possible to identify areas along the pipeline corridor where there is potential for soil erosion risk early on in the project design; this enables the route selection process to consider this important environmental aspect, as well as providing a basis for focusing any subsequent field investigation. The proposed method enables the erosion risk to be quickly reassessed to compare different route options or to revise the proposed pipeline route.
The international trade in agricultural commodities at the same time
constitutes a trade with water in virtual form. Water in external areas
has been used to produce the food and feed items that are imported.
The water footprint of a good or a service is the total amount of water,
external and internal, that is required to produce it. The concept can be
used to calculate and compare the strain on water resources resulting
from different options. It can also be extended to provide water budgets
for whole nations or continents.
ResearchGate has not been able to resolve any references for this publication.