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Splash erosion under natural rainfall on three soil types in NE
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Spain
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M. Angulo-Martínez *, S. Beguería, A. Navas, J. Machín.
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Department of Soil and Water, Estación Experimental de Aula Dei, Consejo Superior de
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Investigaciones Científicas (EEAD–CSIC), 1005 Avda. Montañana, 50080 Zaragoza (Spain)
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Correspondence to: marta.angulo24@gmail.com
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Abstract
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Splash detachment and transport of soil particles by raindrops impacting the soil surface is the
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initiating mechanism of water erosion. The amount of splash depends on the rainfall characteristics
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(mainly kinetic energy and intensity) and the soil properties. Experimental results of rainfall and
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splash monitoring in three soil types under natural rainfall in NE Spain are presented. Some 27
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rainfall events were evaluated, during which high rates of soil splash were measured (≤6.06 g per
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splash cup), stressing its importance as an erosion process on bare soils. Sources of variation of soil
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splash were analysed by a linear mixed-effect model. Significant relationships were found with the
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rainfall erosivity index EI30. No significant differences were found between the soil types analysed.
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The LME model explained 55% of variance, and most of the residual variability (≤ 74%) was due to
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differences between splash cups within a single soil type and event (i.e. to random effects).
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Keywords: Rainfall erosivity, EI30 index, splash erosion, erodibility, Linear Mixed-Effects models,
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NE Spain
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1 Introduction
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Splash erosion is a complex process composed by the detachment of soil particles by raindrops
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hitting the surface followed by splash transport of (a part of) the detached particles. Splash is
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responsible for initiating water erosion, since it is the first erosion to occur when an erosive rainfall
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event takes place (Sempere Torres et al., 1994; Hudson, 1995; Morgan, 2005). Rainfall erosivity,
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i.e. the capacity of rain to erode soil, depends on the kinetic energy of rain, which depends in turn
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on the mass and velocity of raindrops hitting the soil surface, and on rainfall intensity, which
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determines the number of drops per unit surface. During a rainfall event these parameters are highly
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variable in time and space, and so is splash erosion. The detachment of soil particles by splash
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depends not only on the energy of rain drops, but also on soil erodibility, which relies on soil
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physico-chemical characteristics, such as the soil crust, infiltration capacity, the nature of soil
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aggregates, organic matter content, texture, cohesion and porosity, capacity of ionic interchange and
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clay content (Poesen and Torri, 1988). The transport of detached particles depends mainly on the
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kinetic energy of raindrops and on the mass of the particles.
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Measuring both (rainfall erosivity and soil erodibility) during natural rainfall events requires
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considerable instrumental effort and prolonged experiments to ensure a representative number of
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events. Consequently, scientists have concentrated in measuring rainfall erosion under simulated
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rainfall conditions. Most studies on splash erosion under simulated rain do not reflect the properties
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of natural rainfall, because usually the soil is exposed to intense, steady rainfall rates during the
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experiment, while in nature rainfall is characterized by very high frequency variation of intensity. In
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addition, little variation of drop size distribution is possible, and often the largest drops found in
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natural rainfall are missing (Navas et al., 1990; Navas, 1993). These experiments often result in soil
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loss rates higher than those produced under natural rainfall (Dunkerley, 2008). However, the results
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of these experiments are summarized in mathematical expressions used as erosion models and
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applied to natural rainfall.
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The classical method for quantifying splash erosion relies on the use of splash cups, or small traps
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that collect the soil particles detached and transported by splash (Ellison, 1947; Morgan, 1978;
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Poesen and Torri, 1988; Salles and Poesen, 1999; Van Dijk et al., 2003; Legout et al., 2005). The
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data obtained by these measurements allowed the development of empirical formulae, such as the
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erosivity models proposed by Ellison (1944), Bisal (1960) and Meyer (1981). These early empirical
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models estimated soil loss as a power function of rainfall energy or rainfall intensity with a
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modulating multiplying coefficient determined by soil erodibility and an exponent (Park et al.,
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1983; Mouzai and Bouhadef, 2003). Later work showed that a certain energy threshold must be
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passed to initiate soil detachment, since initial energy is focused in breaking the soil crust or
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infiltration (Sharma and Gupta, 1991).
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One of the most extensively used indices for quantify rainfall erosivity, EI30; (Renard et al., 1997)
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requires knowledge of the kinetic energy of rain (E), precipitation volume per unit of time, together
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with the maximum intensity in 30 minutes as a measure of the saturation of the soil and starting of
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runoff. Since the 1960s the scientific community has developed increased interest in the size and
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velocity of hydrometeors, especially in relation with the development of radar methodology. This
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motivated the development of instruments such as optical disdrometers and laser precipitation
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monitors. Lately these instruments have been integrated into soil erosion studies (Salles and Poesen,
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1999; Fernández-Raga et al., 2010; Scholten et al., 2011), but studies are still scarce and spatial and
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temporally constrained.
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The purpose of this study is to evaluate and analyse the relationship between rainfall erosivity and
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soil erodibility under natural rainfall conditions in three soil types of the inner Ebro valley, NE
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Spain. Rainfall characteristics were determined using an optical disdrometer, and splash erosion
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was monitored in three plots using splash cups.
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2. Materials and methods
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2.1. Experimental design
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In order to evaluate the relationship between rainfall erosivity parameters and the amount of soil
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particles detached and transported by splash, we designed an experiment to monitor rainfall
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characteristics under natural conditions and splash erosion produced by natural rainfall events over
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three typical soils types commonly found in the Ebro valley (NE Spain). The monitoring period
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was 04/03/2010−<30/10/2011. The experiment was located at 41º43’30’’N, 0º48’39’’O., 230 m.
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a.s.l. Rainfall characteristics were monitored using a THIES Clima Laser Precipitation Monitor,
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which had a very good performance during the experiment.
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The Laser Precipitation Monitor (LPM, also known as Optical Spectro Pluviometer) was designed
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to measure the size and fall velocity of every raindrop ≥ 0.16 mm diameter at ground level. Initially
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developed by Donnadieu et al. (1969), the LPM derives fall velocity and diameter of hydrometeors
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from the duration and amplitude of obscurations in the path of an infrared laser beam, between a
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light emitting diode and a receiver, with a sampling area of 0.00514 m2. The geometry of the beam
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limits the estimation of fall velocity to the vertical component (Salles and Poesen, 1999), so
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velocity measures can be overestimated with strong wind. The size and velocity of measured drops
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are grouped into 22 and 20 classes, respectively (Table 1). From these data several rainfall variables
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are integrated every minute. For each rainfall period we calculated the cumulative time rainfall (P,
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mm); effective duration (Deff, minutes); maximum intensity in 30 minutes (I30, mm h-1) and kinetic
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energy per minute (er, J m2 mm-1), (Table 2). We considered the beginning of every event since the
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moment when splash cups were placed at the experimental site, and the end of it, once splash
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sediment was found and splash cups were removed. Hence the total duration of the event
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corresponds to the time during which splash cups were in the field, and effective duration was
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calculated from the period in which actual rainfall was recorded.
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The kinetic energy kei,j (J) of a drop pertaining to diameter class i and velocity class j was estimated
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following the formula:
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kei , j 
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where mi is the mean mass corresponding to drop diameter class i (g); ρ is the density of water (1 g
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cm-3); vj is the mean speed for the velocity class j (m s-1); and Di is the mean diameter for class i
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(mm). The mass of raindrops was calculated from the diameter measured by the LPM, by assuming
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a spherical drop shape. Total kinetic energy kesum per minute was determined by multiplying kei,j by
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the number of drops registered in each size and velocity class. Finally, the unit energy (er) was
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obtained by dividing the sampling area of the device (a) (in our case 0.00514 m2) by rainfall amount
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per minute (pr) for obtaining energy rates per unit surface and precipitation amount (J m-2 mm-1),
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and then transformed into MJ ha-1 mm-1:
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kesum
2
-1 10
er 
 er (J m mm ) 4  er (MJ ha 1 mm -1 ) ,
a pr
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where er and vr are, respectively, the unit rainfall energy (MJ ha-1 mm-1), obtained from eq. (2), and
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the rainfall amount (mm) during a time period r (one minute), and I30 is the maximum rainfall
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intensity during a period of 30 consecutive minutes in the event (mm h-1).
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The event’s rainfall erosivity EI30 (MJ mm ha-1 h-1) (Renard et al., 1997) was obtained as follows:
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 0

EI 30    er vr  I 30 ,
 r 1

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mi v j  10 3 v 2j Di3 ,
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(1)
(2)
(3)
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2.2. Soil characteristics
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The three types of soils used in this study were Cambisol, Gypsisol and Solonchak (FAO, 1989).
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They are representative of the central Ebro valley in NW Spain, and they are subject to accelerated
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erosion rates because they are either occupied by agricultural lands that remain bare during several
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months every year (Machín and Navas, 1998) or else they sustain low-coverage plant communities
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due to their restrictive conditions for vegetation and semi-arid climate (Guerrero et al., 1999; Pueyo
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et al., 2007).
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The soils were brought to the experimental site from a nearby location taken from the upper 30 cm
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of the topsoil horizon. Cambisols are developed over glacis and terraces from fluvial deposits and
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marls. Its texture is silty with 25% pebbles, alkaline pH and low salinity. They show good drainage,
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low organic matter content (< 2%) and low gypsum content (2.5%), and 35.4% carbonate content.
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Gypsisols are located in colluvial-alluvial valley areas developed over deposits from nearby
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gypsiferous hills. They have a sandy-loam texture, alkaline pH and higher salinity than Cambisols.
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They have a low organic matter and carbonate content and high gypsum content. Solonchaks are
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found in depressions or level areas. Their texture is clay-loam, and they have poor drainage.
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Detailed descriptions of the soils are given by Bermúdez (1997). Their main properties are
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summarized in Table 3. Data on this table was based on one sample of the upper 20 cm of soil for
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each soil type. The samples were air-dried, grinded, homogenized and quartered, to pass through a 2
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mm sieve. The following properties were determined for each sample: i) bulk (considering the soil
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pores) and real (considering only the solid phase) density; ii) porosity; iii) fractions of sand (coarse
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sand: 250-2000 m, medium sand: 100-250 m, and fine sand: 50-100 m), silt (50 to 2 m) and
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clay (< 2 m) particles and texture classification according to USDA (1973); iv) pH; v) electric
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conductivity; vi) cation exchange capacity; vii) organic matter, Carbon and Nitrogen content,
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Carbon / Nitrogen ratio; vii) carbonates and gypsum content. The properties were measured
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following standard techniques. Grain size was determined by a Coulter LS 230 equipment after
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chemical elimination of the organic matter. The pH (1:2.5 soil:water) was measured using a pH-
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meter. Electric conductivity was determined by a Crison 522 conductivimeter. Organic matter was
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determined by titration. Carbonates were measured using a pressure calcimeter. Total nitrogen was
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measured using the Kjeldhal Method. The cation exchange capacity was determined by a Mg
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(NO3)2 solution was followed by ICP-OES analysis.
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2.3. Splash monitoring
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Splash erosion was monitored with Morgan’s splash cups (Morgan 1981). This simple device
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consists in a circle with another smaller circle inside in contact with the soil, with a soil sampling
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area of 0.0085 m2. Soil particles detached by raindrop impacts need to jump over a rim of 2.5 cm,
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and accumulate inside the splash cup. To avoid sediment loss, some drainage is allowed with small
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holes at the edges of cups. A porous membrane was used that let the water slowly drain from the
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cups but prevented the sediment from escaping.
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The experimental design is shown in Fig. 1. The three soils were arranged side to side in three plots
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of 14x1 m. The soils were kept bare during the monitoring period by manual removing of the new
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seedlings. The three plots are completely level to avoid slope differences. Apart from that, the soils
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were kept undisturbed and as close to their natural condition as possible.
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Five splash cups were deployed in each plot. Splash cups were checked after every rain event, and
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if sediment was present they were replaced by clean ones and sediment was sieved and weighed. If
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no significant sediment was registered (<0.0012g per splash cup), the cups were cleaned and placed
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again. In order to maintain randomness and avoid sediment exhaustion effects, splash cups were
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placed each time in a different site within a corresponding rectangle.
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2.4. Statistical analysis
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Previous to modelling the relationship between rainfall erosivity and splash erosion, an exploratory
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analysis was carried out. ANOVA tests between the amount of soil particles eroded per event by
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soil type was performed in order to check the statistical significance of differences between soil
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types. Secondly, we modelled the relationship between the EI30 and soil splash by soil type. The
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relationship between the response variable (soil splash erosion) and the covariate (EI30 index) takes
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the form of an exponential function. Therefore, we took the logarithms of all variables and
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evaluated their relationship with linear models.
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A linear mixed-effects model (LME) was used to account for pseudo-replication. Unlike standard
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linear models, mixed-effects models allow incorporating both fixed-effects and random-effects in
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the regression analysis (Pinheiro and Bates, 2000). The fixed-effects in a linear model describe the
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values of the response variable in terms of explanatory variables that are considered to be non-
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random, whereas random-effects are treated as arising from random causes. Random effects can be
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associated with individual experimental units sampled from the population. Hence, mixed-effects
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models are particularly suited to experimental settings where measurements are made on groups of
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related experimental units. If the classification factor is ignored when modelling grouped data, the
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random (group) effects are incorporated in the residuals, leading to an inflated estimate of within-
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site variability. In our case, relationships were explored between the response variable (splash
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erosion) and the rainfall erosivity covariate EI30, on a data set grouped according to soil type. Five
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measurements (pseudo-replicates) were available for each rain event and soil type. Hence, the
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mixed-effects model allows exploring relationships between the response variable and the
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covariates that are general to the soil type, regardless of local differences given by the pseudo-
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replicates, which are considered a random effect. The mixed-effects model combines a linear
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regression model with a random-effects Analysis of Variance. The mathematical formula takes the
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form:
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y ji  1   2 x ji  b j   ji
(4)
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b j ~ N (0, 2b j ),  ji ~ N (0, 2j )
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where yji is the ith observation in the jth group of data and xji is the corresponding value of the
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covariates, β1 is a global intercept, bj is a random effect on the intercept for given soil type j, and εji
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is a random error allowing for different variance between the soil type, σj2. In our case, we counted
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with three soil types, i.e. j = 1,…,3, and 15 observations (five pseudo-replicates per soil type, i.e. i =
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1,…,15).
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The LME model in Eq. (4) was fitted by generalized least squares (GLS). Function lme from the R
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library nlme (Pinheiro and Bates, 2011) was used for the linear mixed effects modelling.
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Minimization of the Akaike’s Information Criterion, AIC, was used for selecting the best model,
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and for comparing between homoscedastic (equal error variances) and heteroscedastic (unequal
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error variances for different soil types) models. The AIC is a measure of goodness of fit that
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penalizes the complexity of the model, and hence is much suited for model selection than statistics
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that only measure goodness of fit such as the R2. The fitting process started with the most complex
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formulae that included all interactions between the factor (soil type) and the model parameters
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(intercept and slope), and heterocedasticity. No significant factors were progressively eliminated
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until a minimum model was attained, in which all factors were significant.
(5)
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3. Results
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During the monitoring period 45 rainfall events were registered. Quality control of the rainfall
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events and of the corresponding soil splash allowed us to select 27 events from the total in which
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rainfall erosivity parameters and splash erosion were perfectly recorded (Table 4). The other rainfall
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events were rejected, due to problems with any of the monitoring devices, i.e. due to data loss
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during earlier stages of the experiment (thirteen events), or due to splash sediment lost by water
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washing or strong wind blowing after the erosive event (five events). From the twenty-seven
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rainfall events, twelve events did not register substantial sediment. During the monitoring period the
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highest splash sediment collected per rainfall event was 6.06 g per splash cup for Gypsisols. The
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same event mobilized 4.99 g per splash cup for Solonchaks, and 2.87 g per splash cup for
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Cambisols
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Variability of soil splash between events was high, while differences between soil types were lower
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(Fig. 2). The variability between pseudo-replicates (i.e. within a single event and soil type) was high
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and increased with the amount of sediment mobilized. The “low” events (10, 19, 22, 23, 27, 40 and
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45) showed smaller variability between pseudo-replicates for all soil types. Most of them had low
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I30 values ranging between 1.08>–<20.79 mm h-1, had a long effective duration, and high
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cumulative rainfall amounts were reached. Most of these events occurred during winter, early
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spring or autumn, corresponding to atmospheric dynamics typical of frontal systems. Rainfall
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energy, precipitation amount, and EI30 were relatively low during these events, although high
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variability was found. Energy ranged between 0.25>–<3.79 MJ ha-1 mm-1 and EI30 ranged between
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0.27>–<45.50 MJ mm-1 ha-1 h-1. The events with the highest energies were 19, 23 and 40. Highest
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amounts of splash were found in most cases in Solonchack.
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The events registering higher amounts of soil splash (2, 5, 6, 11, 13, 32, 33 and 38) showed more
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coincidence with rainfall parameters, but more variability between pseudo-replicates. Soil splash
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ranged between 0.97>–<6.06 g per splash cup. These events registered high precipitation amounts,
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between 8.2≥–<61.3 mm, with a short effective duration, with the exception of event nº 2 that
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lasted for 20.8 h. All these events occurred during late spring and summer. They were characterized
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by intense showers in which rain fell at high intensity rates during a short period. Energy ranged
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between 1.67–11.70 MJ ha-1 mm-1, showing variability between the events. I30 ranged between
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11.5–92.9 mm h-1 with more dispersion between events. Consequently, EI30 also showed high
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variability, between 19.24>–<1086.7 MJ mm ha-1 h-1.
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Event 6 can be considered an exception, since rainfall was moderate (8.2 mm) and energy, I30 and
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EI30 were low, but soil splash was high. In this group no differences in splash erosion were found
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between soil types.
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A relationship between splash and rainfall erosivity was found (Fig. 3), although the high variability
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between the pseudo-replicates masked the global effect. LME analysis incorporated EI30 as the
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significant covariate explaining soil splash in all soil types (Table 5). The soil type was not
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significant. Heterocedasticity was included in the model, meaning that within group errors had a
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different variance, depending on soil type. The model explained 55% of variance of the measured
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variable (r2 coefficient). Variability between splash cups was very high, accounting for 72.4, 55.6,
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and 55.6% of the random variance for Cambisols, Solonchaks and Gypsisols, respectively. The
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remaining 27.6, 44.4, and 44.5% corresponded to random errors.
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The results of the linear mixed-effects model yielded the following equation, in which splash
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erosion for soils with similar characteristics as the ones included in the study depend upon the EI30 :
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S  0.32 ( EI 30 ) 2.19 ,
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where, S is soil splash (g per splash cup).
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Events with zero soil splash were not included in the LME analysis, due to the logarithmic
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transformation required. However, a plot of rainfall erosivity for events with and without splash
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helps in defining a threshold value for splash for the soils analysed (Fig. 4). Such value appears to
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be around 1 MJ mm ha-1 h-1. Events 10, 22, 27 and 45 with EI30 values of 2.02, 0.27, 0.63, and 2.49
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MJ mm ha-1 h-1, respectively registered splash sediment, although events 7 and 34 with EI30 values
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of 2.21 and 1.46 MJ mm ha-1 h-1, respectively, did not. However, considering the small number of
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events such threshold should be taken as an approximate value.
(6)
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4. Discussion
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There are few similar studies evaluating the effects of natural rainfall on splash erosion, and the
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ones available in the scientific literature deal mostly with extreme rainfall events (Dunkerley,
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2008). Most soil splash studies used simulated rainfall, and it has been argued that soil splash
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erosion rates obtained are highly overestimated (Poesen and Torri 1988; Dunkerley 2008). Another
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difference between previous studies and this one is that we were able to directly measure rainfall
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energy, instead of estimating it from rainfall intensity. Longer monitoring periods in different
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geographical regions using similar experimental designs are needed for attaining more general
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results. Fernandez-Raga et al. (2010) performed a similar experiment in Soutelo, northern Portugal.
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They found good agreement between kinetic energy and soil detachment, although they found large
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sources of uncertainty when undertaking experiments under natural conditions, such as the effects
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of rain washing or wind, leading to uncertain estimates of splash erosion. We found similar
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difficulties and a relative high number of events that had to be discarded due to problems of rain or
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wind sediment washing. Splash amounts collected in the experiment of Fernandez-Raga et al.
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(2010) were relatively low compared with our results; this could be explained by the large
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contribution of small drops and low rainfall intensities.
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As common in splash experiments under natural rain, the sampling intervals include several days,
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and in some cases more than one event occurred before the cups were removed (i.e. Shakesby et al.,
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1993, Terry, 1989). In our experiment we dealt with similar situations, resulting in several events
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that had to be rejected due to sediment wash by subsequent rains before the cups could be removed.
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It is possible that better results would be obtained by collecting splash cups more frequently, to
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isolate individual rain events, but this can be some problematic.
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It has been argued that the soil conditions previous to the rainfall event and properties changes
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produced during the event may control splash conditions (Wainwright, 1996). In our case, the high
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splash amount recorded in event 6 could be explained by splash detachment prone conditions
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regarding soil crust and porosity. Inspection revealed higher levels of moisture and porosity,
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although no measurements were taken. The study of Singer and Le Bissonnais (1998) in 17
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Mediterranean soils allowed them to distinguish two groups of soils. They found seal formation to
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be the main factor controlling splash and wash erosion. Other authors also pointed to large
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aggregate size and high organic matter content (O.M.) as being significant for protecting against
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splash detachment (Luk, 1979; Ekwue, 1991). In our case differences in O. M. between the three
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soils were relatively low, but differences in soil texture did not lead to differences in splash.
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The results of our analysis suggested that whilst I30 is an important parameter controlling splash
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erosion (Moldenhauer and Kemper, 1969; Mohammad and Kohl, 1987; Torri et al., 1999), E exerts
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a very important role as well. Both must be contained in EI30 index. A Linear-Mixed effects model
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between soil splash and the three rainfall erosivity covariates (EI30 and its components E and I30)
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yielded no significance for E and I30 alone, whereas EI30 was significant at the 99% confidence
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level. This supports the usefulness of the EI30 index in its original formulation. Another relevant
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result of our analysis is the high variability found between pseudo-replicates within the same soil
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type and rainfall event. This demonstrates that splash erosion is highly variable in space, and
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recommends the use of many splash cups per sampling unit in further experimental studies. Since
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these were pseudo-replicates (i.e. the samples from different splash cups within the same soil type
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were not statistically independent), ordinary regression was a sub-optimal model. The mixed-effects
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model, however, provided a convenient framework for analysing such an experimental design, since
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it allows incorporating fixed effects (soil type and rainfall erosivity index) and random effects
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(variability between splash cups within one soil type).
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Several issues not accounted for in our experimental design could have had an influence on the
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results. We assumed that rainfall energy measurements from one single disdrometer were
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representative for the whole experimental site (approx. 60 m2). However, rainfall erosivity can be
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expected to vary in space so our assumption represents a source of random variability that might
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reduce the power of the statistical analysis. Despite that, significant results were found for the
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relationship between splash erosion and rainfall erosivity. Another source of uncertainty could be
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possible changes in the soil surface such as crust formation. We did not appreciate signs of crust
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activity and sealing did not take place during the experiment, but we did not strictly monitor it. It is
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possible that small differences in soil surface processes existed between soils that could affect the
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comparison between them. This is an issue that should be taken into consideration for further
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studies.
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5. Conclusions
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In this study, we conducted an experiment to determine relationships between rainfall erosivity
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parameters and soil splash erosion in three soil types under natural conditions. Linear Mixed-Effects
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analysis allowed us to explain most variability. Soil type did not significantly affect the amount of
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soil splash. Monitoring the rainfall properties relevant for soil splash erosion (kinetic energy,
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maximum intensity, effective storm duration and rainfall amount falling above a certain intensity
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threshold) and relating them to the amount of sediment mobilized is still needed in order to better
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understand the role of rainfall properties and soil characteristics in soil splash. Rainfall monitoring
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at high time resolution (e.g. every minute) is important to determine these properties, since more
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aggregated data (e.g. hourly or daily) are unable to capture these properties (Dunkerley, 2008,
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2010).
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This work has presented the experimental results of rainfall and splash erosion monitoring in three
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soil types under natural rainfall in NE Spain. During the measuring period (04/03/2010–
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30/10/2011), 27 events were evaluated. High rates of soil detachment were measured (≤ 6.06 g per
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splash cup per event), which stress the importance of soil splash as an erosion process in bare soils.
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We analysed the sources of variation of splash rates, and found significant relationships with the
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EI30 index, while no significant differences were found between the analysed soil types. The LME
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model explained 55% of variance, and the largest part of the residual variability (≤ 74%) was due to
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differences between splash cups within a single soil type and event (i.e. to random effects). This
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result has implications for further studies, since it makes clear that many pseudo-replicates (splash
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cups) need to be analysed to assess relationships between splash erosion, rainfall and soil
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characteristics. This also implies that the linear mixed-effects model, which includes both fixed and
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random effects, need to be used to analyse data generated by this experiment setup.
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348
Acknowledgements
349
This work has been supported by the research projects CGL2011–24185/BTE, CGL2008-
350
00831/BTE, and CGL2008-01189/BTE, funded by the Spanish Ministry of Science and Innovation.
351
Research of M. A.-M. is supported by a JAE-Predoc Research Grant from the Spanish National
352
Research Council (Consejo Superior de Investigaciones Científicas, CSIC).
353
354
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19
439
Table 1. Laser Precipitation Monitor classification of drop diameter and velocity
Particle diameter class
Class
Particle speed class
Diameter
from (mm)
To (mm)
Class width
Class
Speed from
1
0.125
0.250
0.125
1
0.00
0.20
0.20
2
0.250
0.375
0.125
2
0.20
0.40
0.20
3
0.375
0.500
0.125
3
0.40
0.60
0.20
4
0.500
0.750
0.250
4
0.60
0.80
0.20
5
0.750
1.000
0.250
5
0.80
1.00
0.20
6
1.000
1.250
0.250
6
1.00
1.40
0.40
7
1.250
1.500
0.250
7
1.40
1.80
0.40
8
1.500
1.750
0.250
8
1.80
2.20
0.40
9
1.750
2.000
0.250
9
2.20
2.60
0.40
10
2.000
2.500
0.500
10
2.60
3.00
0.40
11
2.500
3.000
0.500
11
3.00
3.40
0.40
12
3.000
3.500
0.500
12
3.40
4.20
0.80
13
3.500
4.000
0.500
13
4.20
5.00
0.80
14
4.000
4.500
0.500
14
5.00
5.80
0.80
15
4.500
5.000
0.500
15
5.80
6.60
0.80
16
5.000
5.500
0.500
16
6.60
7.40
0.80
17
5.500
6.000
0.500
17
7.40
8.20
0.80
18
6.000
6.500
0.500
18
8.20
9.00
0.80
19
6.500
7.000
0.500
19
9.00
10.00
1.00
20
7.000
7.500
0.500
20
10.00
∞
∞
21
7.500
8.000
0.500
-
-
-
-
22
8.000
∞
∞
-
-
-
-
(m s-1)
(mm)
Speed to Class width
(m s-1)
(m s-1)
440
441
20
442
Table 2. Laser Precipitation Monitor variables recorded in real time every minute
Name
Unit
Description
Synop code 4677
-
Hydrometeor code
Rain intensity
mm h-1
Amount of drops falling in a minute
Precipitation amount
mm
Water content
g m-3
Kinetic energy
J m-2 mm-1
Estimated from the LPM classes.
443
444
445
Table 3. Soil type properties
Parameters
Units
Cambisol
Gypsisol
Solonchak
Real density
g cm-3
2.52
2.01
2.52
Apparent density
g cm-3
1.31
1.18
1.31
%
47.88
41.26
47.94
Coarse Sand (250-2000 μm)
%
2.4
7.2
3.2
Medium Sand (100-250 μm)
%
13
16.6
13
Fine Sand (50-100 μm)
%
11.1
9.9
12.4
Silt (2-50 μm)
%
59.2
55.2
55.4
Clay (< 2 μm)
%
14.3
11.1
16
Silt
Sandy loam
Clay loam
8.63
8.35
8.13
0.37
2.4
2.33
3.84
5.92
Total porosity
Granulometry
Texture
pH
-1
EC 1/5
dSm
EC (es)
‘’
C.I.C.
meqL-1
149.4
119.88
155.99
Carbon
%
1.02
0.49
1.03
Organic matter
%
1.73
0.84
1.78
Nitrogen
%
0.11
0.07
0.06
9.19
7.54
17.76
C/N
CO3=
%
35.41
15.72
35.7
Gypsums
%
2.5
61.79
3.81
446
447
21
448 Table 4. Properties of rainfall events registered and corresponding soil splash: nº of event; was it erosive
449 (Y) or not (N: splash was negligible, less than 0.25 g m-2); D: total time during which splash cups were
450 deployed (hours); Deff: total time of rain registered at the monitoring site (hours); P: total rainfall (mm);
451 E: total kinetic energy (MJ ha-1 mm-1); I30: maximum intensity in 30 minutes (mm h-1); EI30: rainfall
452 erosivity index (MJ mm ha-1 h-1); mean soil splash by soil type (g per splash cup): CA = Cambisol, GA=
453 Gypsisol, SA = Solonchak. Events eliminated due to loss of data are not shown.
454
Event
Erosive
D
Deff
P
E
I30
EI30
2
4
5
6
7
10
11
13
16
19
21
22
23
25
26
27
31
32
33
34
35
37
38
40
42
44
45
Y
N
Y
Y
N
Y
Y
Y
N
Y
N
Y
Y
N
N
Y
N
Y
Y
N
N
N
Y
Y
N
N
Y
430.6
19.5
16.3
175.3
3.5
9.8
29.8
25.7
160.4
741.9
1.9
12
90.2
23.5
4.1
35.4
10.5
26.3
79.1
15.6
155.1
0.9
8.5
42.8
16509
21.0
9.6
20.8
12.0
4.9
8.3
2.8
8.3
3.8
3.4
7.7
56.5
1.4
11.8
26.6
2.7
3.5
6.2
7.0
7.6
8.9
5.1
1.8
0.6
3.2
0.4
2.8
3.0
8.3
26.4
4.6
24.3
8.2
3.6
6.7
44.4
61.3
2.76
20.9
0.2
3.2
30.0
1.1
1.2
3
3.14
27.0
37.2
3.4
0.3
0.9
30.4
10.4
0.55
0.5
6.1
4.92
0.53
4.34
1.67
0.40
0.87
10.57
11.70
0.32
2.79
0.02
0.25
3.79
0.11
0.11
0.33
0.36
4.85
6.40
0.54
0.00
0.20
7.86
2.19
0.06
0.07
0.88
15.85
1.55
24.25
11.53
5.50
2.34
56.11
92.90
1.31
11.65
0.30
1.08
7.31
1.13
0.75
1.93
1.23
26.18
61.61
2.71
0.10
1.64
35.57
20.79
0.53
0.34
2.84
77.92
0.81
105.21
19.24
2.21
2.02
592.94
1086.70
0.41
32.55
0.00
0.27
27.73
0.12
0.08
0.63
0.44
126.94
394.23
1.46
0.00
0.33
279.45
45.50
0.03
0.02
2.49
Soil splash by soil
CA
GA
SA
1.48
0.00
3.09
2.87
0.00
0.61
2.25
2.28
0.00
0.78
0.00
0.34
0.87
0.00
0.00
0.17
0.00
0.97
1.52
0.00
0.00
0.00
3.88
0.87
0.00
0.00
0.57
1.22
0.00
1.96
6.06
0.00
0.33
3.65
2.52
0.00
0.45
0.00
0.15
1.40
0.00
0.00
0.07
0.00
1.95
2.11
0.00
0.00
0.00
3.24
1.18
0.00
0.00
0.16
2.45
0.00
2.61
4.99
0.00
0.63
3.39
2.81
0.00
0.94
0.00
0.65
0.92
0.00
0.00
0.36
0.00
1.24
2.31
0.00
0.00
0.00
2.98
1.19
0.00
0.00
0.46
455
456
22
457
Table 5. Linear Mixed-Effects analysis summary. Only significant covariates are shown.
Fixed effects
value
std error
df
t-value
p-value
Intercept
-0.49
0.07
178
-7.12
<0.001
Log(EI30)
0.34
0.04
43
9.18
<0.001
Akaike Information Criterium (AIC)
27.92
2
Variance explained (r )
0.55
Correlation coefficient
0.74
Error variance components for soil type:
Cambisol
Solonchak
Gypsisol
Splash cups variability
72.42
55.62
55.55
Other variability
27.58
44.38
44.45
458
459
23
460
Figure captions
461
Fig. 1. Experimental scheme at the Experimental Station of Aula Dei-CSIC (41º43’30’’N,
462
0º48’39’’O. 230 m. a.s.l.). Soil plots dimensions: 14x1 m. The circles indicate the placement of the
463
Morgan’s splash cups. LPM is the Laser Precipitation Monitor recording rainfall properties every
464
60 seconds.
465
466
24
467
Fig. 2. Soil splash (g per splash cup) boxplots by soil type sorted by the amount collected. The
468
boxes indicate the 25th and 75th percentiles, the thick line indicates the median (50th percentile), the
469
whiskers are extreme observations (highest/lowest observation which is not more/less than 1.5
470
times the interquartile range from the box), and the circles indicate outlier observations
471
(observations which are higher/lower than 1.5 times the interquartile range from the box).
472
25
473
Fig. 3. Scatter plot of soil splash (g per splash cup) vs. rainfall erosivity index EI30 (MJ mm ha-1 h-
474
1
475
Solonchak (cross) and Gypsisol (triangle).
); Both variables are log-transformed. Soil types are indicated by symbols: Cambisol (circle),
476
477
26
478
Fig. 4. Rainfall erosivity (EI30) for events with soil splash sediment (T) and events without it (F).
479
27