A Portable Rainfall Simulator for Field Assessment of Splash and Slopewash in Remote Locations

2052 M. A. Clarke and R. P. D. Walsh

Copyright © 2007 John Wiley & Sons, Ltd. Earth Surf. Process. Landforms 32, 2052–2069 (2007)DOI: 10.1002/esp

Earth Surface Processes and LandformsEarth Surf. Process. Landforms 32, 2052–2069 (2007)Published online 22 June 2007 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/esp.1526

Technical communication

A portable rainfall simulator for field assessment ofsplash and slopewash in remote locations*Michelle A. Clarke1* and Rory P. D. Walsh2

1 NSRI, Cranfield University, Silsoe, Bedfordshire MK45 4DT, UK2 Department of Geography, University of Wales Swansea

AbstractThis paper describes the design, operation and performance of a field-portable ‘drip-type’simulator and erosion measurement system. The system was constructed specifically forsoil erosion research in the humid tropics and has been used extensively in MalaysianBorneo. The simulator is capable of producing replicable storms of up to 200 mm h−−−−−1

intensity and 20–30 minutes duration with a drop-size distribution close to that of naturalstorms of such intensity (D50 of simulated rainfall is 4·15 mm at 200 mm h−−−−−1 and 3·65 mm at160 mm h−−−−−1, D50 measured during natural rainfall ===== 3·25 mm). The simulator is portableand simply constructed and operates without a motor or electronics, thus making it parti-cularly useful in remote, mountainous areas. The erosion measurement system allowsassessment of: (1) rainsplash detachment and net downslope transport from the erosionplot; (2) slopewash (erosion transported by overland flow); and (3) infiltration capacityand overland flow. The performance of the simulator–erosion system compared withprevious systems is assessed with reference to experiments carried out in primary andregenerating tropical rainforest at Danum Valley (Malaysian Borneo). The system was foundto compare favourably with previous field simulators, producing a total storm kinetic energyof 727 J m−−−−−2 (over a 20-minute storm event) and a kinetic energy rate of 0·61 J m−−−−−2 s−−−−−1,approximately half that experienced on the ground during a natural rainfall event ofsimilar intensity, despite the shorter distance to the ground. Copyright © 2007 John Wiley& Sons, Ltd.

Key words: rainfall simulator; splash erosion; slopewash; rainforest; Danum Valley

*Correspondence to: Michelle A.Clarke, NSRI, Cranfield University,Silsoe, Bedfordshire MK45 4DT,UK. E-mail:[email protected]*This work has been undertakenas part of a PhD project at theUniversity of Wales Swansea,and has been funded by EUgrant ERBIC18CT960102,and grants from the DudleyStamp Memorial Fund ofthe Royal Society, the BritishGeomorphological ResearchGroup, the Quaternary ResearchAssociation, and the RoyalSociety SE Asia Rain ForestResearch Programme. This paperis publication number A400 ofthe Royal Society SE AsiaRainforest Research Programme.

Received 2 June 2006;Revised 8 March 2007;Accepted 26 March 2007

Introduction

Rainfall simulators have been used as a research tool in soil erosion studies since the 1930s (Ellison andPomerene, 1944; Mutchler and Hermsmeier, 1965; Moldenhauer, 1979). They vary greatly in mode of production anddelivery of raindrops, scale, complexity of operation, rainfall intensity and kinetic energy, raindrop size distribution,replicability, ease of control, and degree of suitability for field (as opposed to laboratory) use. This paper reportsthe development and performance of a portable rainfall simulator–erosion measurement system designed specificallyfor field use in remote humid tropical locations. First the performance and limitations of previous simulators arebriefly reviewed and the specifications for the new system are identified. The simulator and erosion assessmentsections of the system are then described. Finally the performance, advantages and limitations of the system areassessed with reference to laboratory tests and field experiments in primary and logged rain forest at Danum Valley inMalaysian Borneo.

A portable rainfall simulator 2053


Brief Review of Previous Field Rainfall Simulator Systems

Within soil erosion research, rainfall simulators are used to create controlled rainfall events, either in the laboratory orin the field. Whereas laboratory systems can be highly complex and sophisticated, there is a need for simple, portablesimulators that can be used to carry out experiments on soils in situ.

Advantages and disadvantages of rainfall simulatorsThe principal advantage of using a rainfall simulator in erosional and hydrological research involving rainfall is thegreater control it provides over the rainfall variable. Rainfall can be produced on demand, wherever necessary and ofthe character and for the duration required. A standard storm can, therefore, be replicated many times within a muchshorter time-scale than would be observed under natural rainfall (Rickson, not dated). In terms of rainsplash research,increased experimental control means that specific processes can be observed in isolation, and simulated rain has beenused on small plots (Ellison, 1944; Poesen, 1981; Bryan and De Ploey, 1983; Walsh et al., 1998; Fox and Bryan,1999) and/or with splash cups (Riezebos and Epema, 1985; Terry and Shakesby, 1993; Salles and Poesen, 2000) todetermine the effectiveness of the rainsplash process. Such work has demonstrated the efficiency of detachment byraindrop impact, in contrast to relatively low downslope movement, because much of the detachment is lateral orupslope (Moeyersons and De Ploey, 1976; Poesen and Savat, 1981; Reeve, 1982). By holding rainfall erosivityconstant, features related to soil detachability can be isolated and an index of erodibility of different soils can bedetermined (Poesen, 1985; Torri and Poesen, 1988). Laboratory rainfall simulation can allow further control and ismore replicable than a field experiment, as disruptive effects of wind, temperature and humidity can be reduced(Bubenzer and Meyer, 1965).

A major disadvantage of rainfall simulators, however, is the small spatial scale at which they operate. Althoughlarge-scale simulators exist (Moore et al., 1983), they are generally impractical, non-portable and, therefore, difficultto use in field research in remote areas.

Types of field rainfall simulatorAs is the case with laboratory studies, many different simulator designs exist where the design used depends largelyon the issue being studied and whether they are being used on flat or rugged terrain. Simulator size and sophisticationvaries greatly, from a simple one-person portable infiltrometer with a rainfall area of 15 cm diameter (Bhardwaj andSingh, 1992) to the complex Kentucky Rainfall Simulator, which covered a 4·5 m by 22 m plot (Moore et al., 1983).A basic separation is into (i) pressurized and (ii) non-pressurized types. The requirements that need to be met,however, are similar:

(1) rainfall intensity needs to be easily controllable and remain constant (or vary in a prescribed fashion) for thespecified length of the experiment;

(2) the drop size distribution and drop velocities of the simulated rainfall should ideally be as similar as possible tonatural rainfall, or at least be easily repeatable for comparative experiments;

(3) the spatial distribution of raindrops should be even and random, and hence devoid of concentrated drip-points(unless specifically required by the experimental design).

Simulators that use pressurized water produce raindrops through single or multiple nozzles. Theoretically, as thewater is released under pressure, satisfactory droplet velocities and kinetic energy values are produced at lower fallheights than for natural rainfall (Imeson, 1977) and rainfall from gravity-fed simulators. Because of the high pressureof the supplied water, however, drop intensities and velocities are usually exaggerated. Attempts to reduce velocitiesinclude forcing the drops to reach zero velocity before they fall to the ground by pointing the nozzle upwards toproduce an arc of water (Bryan, 1973; Bowyer-Bower and Burt, 1989). Allowing the nozzle to rotate is a moreefficient way of reducing total storm intensity (e.g. Swanson et al., 1965) and downward velocities; the greater thespeed of rotation, the greater will be the spatial spread of the water and reduction in intensity at any point on theground (Rickson, not dated). Alternatively, a rotating disc with apertures ranging from 5–40° can be used (e.g. Morinet al., 1967). This allows water to fall onto the ground whenever an aperture is aligned directly beneath the nozzle.

If pressurized water is not used, simulated rainfall can be produced using drop-formers that allow water drops to falland gather momentum by gravitational acceleration. Drop-formers include soaked woollen threads (Woodburn, 1948),glass capillaries (McIntyre, 1958), silicon rubber tubes (Imeson, 1977; Poesen and Savat, 1981) and hypodermicneedles (Farres, 1987). Drops fall when their weight overcomes surface tension forces (Gunn and Kinzer, 1949). Themain disadvantage with non-pressurized simulators is that for any given intensity the rainfall tends to have lower



kinetic energy than natural rainfall, as the fall height is usually insufficient for drops to reach their terminal velocities.For example, a 5 mm diameter raindrop requires a fall height of 12 m to reach its terminal velocity (Epema andRiezebos, 1983). Other disadvantages are that drop-formers of equal size produce an unrealistically narrow drop-sizedistribution and that fixed drop-formers lead to drip-points vertically beneath them.

Bowyer-Bower and Burt (1989) compared the attributes of the two types of simulator most commonly used (aspray-type nozzle simulator and a capillary tubing drip-type simulator), including the logistical difficulties involved intheir field use. They highlighted the difficulty of maintaining a constant drop-size distribution, and the large amountof water necessary to carry out replicated experiments, especially when using a spray-type simulator. Despite suchdifficulties, however, both nozzle and drip-type rainfall simulators have been widely used for soil erosion research.Despite concerns expressed over many years (Bryan, 1981; Rickson not dated), progress towards standardization oftest and analytical procedures in assessing simulator specifications and performance has been very limited.

There is, therefore, a need to assess and compare simulator performance more objectively. There is also a need for afully portable, easy-to-operate simulator that can be used in remote areas and on steep slopes. The simulator designdescribed below addresses the latter gap, and the test procedure adopted is designed to permit comparison with othersimulators.

Simulator Design

Specifications required for this researchThe simulator used during this research was needed specifically for relatively remote fieldwork in the tropics assessinginfiltration, overland flow, rainsplash and slopewash. The design therefore, had to be:

(1) simple, robust and easy to transport within rain-forest terrain on foot;(2) easy to maintain, with few or no mechanical parts that would be susceptible to breakage or malfunction in the heat

and humidity of the tropical rain forest;(3) able to provide a consistent, reproducible rainfall with realistic tropical intensities and drop-size distributions;(4) able to provide an even coverage of rainfall (low spatial variability).

The simulator water supply had to be capable of delivering enough rainfall at high intensities over the 0·3 × 0·3 mplot area for the chosen experiment time of 20 minutes, but was constrained by the logistics of carrying water to eachexperimental location. A 20-l water tank was the maximum size manageable in the field (as water had to be carried toeach site and left to de-gas for 24 hours before use), and the simulator design and capability is therefore related to thisfinite availability of water.

The overall designA drip-type simulator was selected as: (1) it is easier to control in the field; (2) it achieves a more consistentperformance in terms of the rainfall character simulated in replicate experiments (Bowyer-Bower and Burt 1989); (3)it avoids the problem of spatial and temporal rainfall variability associated with spray-type simulators; and (4) it doesnot require a power supply (to operate rotating parts and/or a water pump). Drop-formers were made using Teflontubing (rather than hypodermic needles) primarily to allow easy on-site replacement and to ensure the safety of theoperators in the field.

The simulator system comprises the simulator and the runoff/erosion reception plot. The simulator (Figures 1–3) ismade up of five principal components: (1) the structural frame; (2) the water supply system; (3) the droplet box; (4)the mesh droplet-randomizer; and (5) a removable rainfall intensity measurement trough (not shown, but explainedbelow). The reception plot on the ground comprises: (1) the metal plot enclosure; (2) the overland flow and slopewashrecorder; and (3) the plot surround and splash funnels. All these components are described below.

The structural frameMade of Dexion steel, the simulator framework structure provides support for a water tank (at 1·7 m height), thedroplet box (at 1·35 m), the randomizing gauze (at 1·2 m) and a rainfall intensity measurement device (at around0·50 m). The overall height of 1·7 m is the maximum height manageable in a field situation given the need to place areservoir of water on top of the frame. On slopes the frame can be roughly levelled by lengthening the front legs usingadditional, adjustable lengths of Dexion; finer adjustment of the drip tray is described later.



Figure 1. Rainfall simulator modular design.



Figure 2. Rainfall simulator in regenerating forest.

The water supply systemA 20-l water tank (sealed at the top with a rubber bung) is connected to the droplet box by two feed pipes. These pipesare encased in a hard plastic sheath and inserted into a support shelf in the middle of the droplet box, such that the endof one tube is fixed slightly below the other. The height of the lower tube above the droplet box base sets the‘constant-head’ level governing the rainfall intensity. As the water level drops below the longer tube, air is forced intothe supply tank and water is forced out until the original (upper tube) level is regained; this ensures rainfall intensityremains constant. Rainfall intensity can be adjusted by raising or lowering the height of the feed pipes. The range ofrainfall intensities able to be produced by the simulator is considered later.

Bryan et al. (1984) stated that the chemistry of water used in rainfall simulation should be considered, as theelectrolyte concentration of infiltrating water influences the infiltration process (Agassi et al., 1981). During thedevelopment of the simulator in the laboratory, distilled water was used. In the tropical field work, however, rainwatercollected from tanks containing roof runoff was used in preference, as it had a conductivity of only 3·7 μS cm−1.

The droplet boxA 0·2 m2 (0·45 × 0·45 m) Perspex box containing 181 holes (equally spaced over a 0·40 × 0·40 m grid, 2 cm apart) wasselected as the droplet box. Water drops from the holes are produced by 10-mm lengths of Teflon tubing (internaldiameter (ID) 0·5 mm, external diameter (ED) 1·6 mm) inserted inside 20 mm of silicon tubing (ID 1 mm, ED 3 mm).These drop-formers are sealed into the holes in the Perspex box with silicon glue. Together with the constant head inthe droplet box, the ED of the tubing determines the drop size created (median drop size at 160 mm h−1 = 3·75 mm, at200 mm h−1 = 4·15 mm); the ID of the tubing determines the rate of water-drop formation (Bowyer-Bower and Burt,1989). Although all drop-formers are of uniform size, for water drops to form equally from each drop-former, thedroplet box must be completely level. This is achieved by fixing the droplet box to the frame with a pivot and screwfacility, allowing it to be levelled in two dimensions (see Figure 3) with the aid of a spirit level.



Figure 3. Detail of droplet box, water delivery tube and pivot /screw facility.

Mesh droplet randomizerIn common with most gravity-fed simulators, the droplet box will deliver drops to the ground in fixed positions, as thebox’s position and hence the position of drop-formers are fixed. This is clearly undesirable, unless evidence suggeststhat the canopy is causing this in nature (e.g. below drip-tips of rain forest leaves). In order to vary the landingposition of drops, a wire mesh (1 mm diameter wire and 1 cm2 aperture) is suspended 0·5 m below the drop-formers.This mesh is swung constantly during experiments. This has the effect not only of scattering the drops, but also ofproducing a wider range of droplet sizes and a distribution of drop sizes closer to that of natural rainfall. Thedisadvantage is that it reduces the fall height of most of the drops to 0·85 m, with consequent reductions in groundimpact velocity and kinetic energy.

Rainfall intensity measurement troughThis comprises a sloping v-shaped trough that can be readily attached to the lower part of the Dexion frame so as tointercept rainfall and prevent it entering the plot below. It is inserted prior to and at the end of simulations so that (1)



the rainfall output can be accurately assessed over a set duration (usually a minute) and (2) the duration of simulatedrainfall onto the plot can be controlled.

Slope Plot Design

SpecificationThe runoff/erosion system used in conjunction with the simulator needed to meet three main objectives:

(1) to allow measurement of overland flow/infiltration rate throughout the simulated rainfall event;(2) to enable the collection of slopewash generated from within the plot;(3) to enable the collection of material splashed from inside the plot including an assessment of net downslope

transport.

The plot design includes two elements to meet these objectives.

Plot boundary wall and overland flow/slopewash collection systemThe plot boundary wall measuring 0·3 × 0·3 m is made from galvanized steel. The square shape, arranged in a diamondfashion downslope, was selected as the most appropriate for directing overland flow downslope (see Figure 4). Theplot boundary was split into two pieces (upper and lower) to ease insertion into hard, rooty or stony soils. The smallplot size means that there is a high edge-to-surface-area ratio and this could lead to enhanced infiltration of wateralong the lower edge of the plot. However, the soils at the experimental sites were moist and silty, and therefore, self-sealing. A small outlet pipe is built into the downslope apex of the lower plot boundary; a 500 ml bottle is attached tothis using a short length of plastic tubing to collect overland flow and slopewash sediment (this could be automated ifnecessary via a tipping bucket device and in-line turbidity sensors).

Splash assessmentIn order to ensure that rainsplash only occurs from the plot surface, a 0·1 m wide, plastic buffer surround wasconstructed to fit around the plot and protect the soil surface immediately outside it (see Figure 4). At the mid-point ofeach side of the plot, holes 72 mm in diameter were made in the surround to accommodate four splash funnels (96 mmin diameter) based on the design of Terry (1992) (Figure 5). Spacers placed between the funnel and the filter paperensure that funnels are able to drain freely without clogging. These funnels are set into the plot surround such thattheir tops are level with the top of the overland flow plot walls. Summing or averaging the catches of sediment of allfour funnels yields an index of splash detachment. The difference in splashed sediment between the upper and lowerfunnel pairs provides an index of net downslope splash.

Performance of the Rainfall Simulator

Rainfall intensityIn initial rainfall intensity tests in the laboratory it was found that water from a tapped supply introduced air bubblesinto the system that blocked some of the Teflon drop-formers and thus led to a progressive decline in rainfall intensity.This problem was avoided in field experiments by leaving the water to de-gas for 24 hours before use.

As intensity and drop-size distribution of simulated rainfall depend in part on temperature (Bowyer-Bower andBurt, 1989), intensities achieved with the simulator in the tropical field location were significantly higher than thoseproduced in the UK for the same constant-head level (Table I). In the tropics, the simulator proved capable ofproducing rainfall with intensities of 50 to over 200 mm h−1, although performance at the lower end of the intensityrange was not as consistent. The results presented here relate to a water depth of 2 cm, which in the tropical conditionsof Sabah (mean simulator water temperature 25 °C, mean below-canopy air temperature 27 °C) was the depth requiredto produce an intensity of around 200 mm h−1, but the simulator has been successfully used with lower intensities of80–100 mm h−1 over several field seasons at a variety of temperatures in a Mediterranean climate in Portugal (Leighton-Boyce et al., 2001; Doerr et al., 2003).



Figure 4. The arrangement of the plot boundary wall, overland flow/slopewash collection and splash assessment systems. The cupsshown within the inner plot are used only to test the spatial variability of rainfall, and are not in place during simulation experiments.

Spatial variability of rainfallA field experiment was carried out (with the swinging droplet randomizer operating) to determine spatial variabilityof rainfall intensity across the plot. Nine cups of 0·10 m diameter were arranged within the plot as shown inFigure 4. Amounts of water collected in each cup during six short periods of rainfall, three at 160 mm h−1 and three at200 mm h−1 were measured and recorded. For each run Christiansen’s uniformity coefficient (CU) (Christiansen,1942) was calculated, where CU = (1 − SD/mean) × 100. Average CU was 88 per cent at 160 mm h−1 and 91 per centat 200 mm h−1 (Table II). These CU values are comparable to those obtained for many more complex (e.g. Munn,1974; Greene et al., 1994) and expensive systems. For example Thomas and El Swaify (1989) produced a CU of92 per cent at 150 mm h−1 with a trailer-mounted nozzle/rotating disk assembly, and Dunne et al. (1980) achieved



Figure 5. Splash funnels, based on the design of Terry (1992).

Table II. Spatial variability of rainfall at two intensities using the simulator

Intensity RunCup number

Mean(mm hr−1) number 1 2 3 4 5 6 7 8 9 Mean SD CU* (%) CU (%)

200 1 32 31 37 35 40 38 37 35 31 52·78 4·60 91·282 39 31 40 37 47 36 41 39 38 35·19 3·07 91·273 32 25 30 35 40 30 37 40 36 38·56 3·09 91·98 91·51

160 4 35 33 35 33 42 29 38 37 36 33·89 5·04 85·125 34 30 35 33 42 29 38 38 34 35·33 3·64 89·696 30 27 37 31 41 28 33 37 34 34·78 4·09 88·24 87·68

(see Fig. 4 for locations of cups within the diamond plot). All values are in millimetres.* CU ===== Christiansen’s uniformity coefficient (Christiansen, 1942) ===== (1 − SD/mean) × 100 per cent

Table I. Intensities resulting from different depths ofwater in the simulator constant-head box in temper-ate and tropical locations

Intensities (mm hr−1)

Depth (cm) Temperatea Tropicalb

1 100 1502 150 2003 200 250

a Water temperature = 23 °C, air temperature = 18 °C (±1 °C).b Water temperature = 25 °C, air temperature = 27 °C (±1 °C).



CUs ranging from 80 to 90 per cent for intensities up to 114 mm h−1 with a large, frame-mounted nozzle spray. Alaboratory-based nozzle-type simulator in Plymouth University, tested in the same way, has a CU of 88 per cent at50 mm h−1 (C. Fitzjohn, pers. comm.). Other simple designs with comparative CU values include those of Battanyand Grismer (2000), who achieved a 91·7 per cent CU at 60 mm hr−1 with a simple drop-former construction, andCerda et al. (1997), who designed a simple nozzle-type simulator and reported a CU of 93·3 per cent at 55 mm h−1.

Drop-size distributionDrop-size distribution was determined three times under tropical conditions (i.e. at >27 °C) at Danum using the FlourPellet Method (Laws and Parsons, 1943, Chapman, 1948). Final mean droplet diameter within each class was deter-mined using the three sets of results obtained.

Trays containing approximately 2·5 cm of sifted flour were exposed to simulated rainfall of c. 200 mm h−1 for a fewseconds. The trays were then left in an oven overnight (at 110 °C) until the pellets formed by the raindrops hadhardened. The pellets were separated into seven size classes by passing them through a set of sieves. Pellets in eachsize class were then weighed and counted to give the mean and total pellet mass within each class. The mean pelletmass for each class was converted to a mean drop mass by using the mass ratio computed by Laws and Parsons(1943). Assuming drops are spherical, the diameters of mean droplets within each class were then determined.

Although most water drops produced by the simulator were found to have a diameter of less than 1 mm, both at 200and 160 mm h−1, larger drops of 4–5 mm diameter were volumetrically more important. According to Hudson (1971)the best index for drop distributions is the median drop diameter (D50). It is obtained from a plot of cumulative volume(determined from the percentage total mass of raindrops in each size class) against drop diameter. Median dropdiameter was calculated as 4·15 mm at 200 mm h−1 and 3·65 mm at 160 mm h−1 (Figure 6). However, D50 gives aweighted central value but gives no indication of the drop-size spread. If the spread from D25–D75 is considered, dropsizes at 200 mm h−1 range from 3·2 to 5·1 mm, and at 160 mm h−1 from 2·9 to 4·4 mm.

These drop-size distributions are particular to the size of drop-former and the type and size of gauze used. Differentdrop-size distributions, therefore, could be obtained if different tubing diameters or varying sizes of gauze were used.Without the gauze, the range of drop sizes produced is restricted and most droplets formed are close to 3 mm diameter(the ED of the Teflon tubing used as drop-formers). The consistency of the drop-size distributions within an experi-ment (often assumed erroneously to be insignificant in ‘spray-type’ simulators) have substantial influence on thehydrological and sedimentological response of soil surfaces (Bowyer-Bower and Burt, 1989). Repeated tests on drop-size distribution carried out during this research give a mean coefficient of variation of 17 per cent between simulatedrainfall events. Unfortunately data for other simulators are not available for comparison.

Data from an associated study (Payne, 2001), which used the same flour pellet method as described earlier, werefurther analysed to compare drop-size distributions of natural rainfall in the open with that produced by the simulatorin the field in Sabah (Table III). Payne’s data provide a ‘snap-shot’ of rainfall characteristics over a period of around

Figure 6. Cumulative drop-size distribution and D50 at two different rainfall intensities using the rainfall simulator.



Table III. Drop-size distributions of natural (open) rainfall for two tropical storm events at Danum Valley, Sabah, compared withrainfall simulator data. Raw natural rainfall data from Payne (2001)

(a) Storm 1. Mean intensity 26·2 mm h−1, 135 min duration, 56·4 mm total rainfall

Class Median size Mass of one Number Total mass Percentage(mm) (mm) drop (mg) of drops (mg) of rain mass

>6·70 – – 0 – 05·60–6·70 – – 0 – 04·00–5·60 – – 0 – 03·35–4·00 3·59 24 3 72 9·062·0–3·35 2·42 7 11 82 10·301·0–2·00 1·45 2 274 439 54·960·5–1·00 0·67 0·16 1264 205 25·68

(b) Storm 2. Mean intensity 16·7 mm h−1, 188 min duration, 52·5 mm total rainfall


>6·70 9·64 469 8 3755 18·185·60–6·70 5·74 99 15 1489 7·214·00–5·60 4·55 49 72 3563 17·253·35–4·00 3·87 30 44 1335 6·462·0–3·35 2·98 13 369 5130 24·841·0–2·00 1·61 2 1869 4098 19·840·5–1·00 0·69 0·17 7541 1284 6·22

(c) Rainfall simulator event mean intensity 200 mm h−1, 20 min duration, 66·7 mm total rainfall


>6·70 7·38 210 1 160 2·465·60–6·70 6·65 153 7 1070 7·614·00–5·60 5·25 76 26 1945 12·993·35–4·00 3·96 33 36 1147 21·012·0–3·35 3·15 17 57 914 33·661·0–2·00 1·59 2 190 364 19·240·5–1·00 0·83 0·3 492 130 3·04

10 seconds. The two storms analysed (which had mean storm intensities of 26 mm h−1 and 17 mm h−1 respectively)were characterized by very different drop size distributions. Storm 1 (Table IIIa) contained no raindrop larger than4 mm diameter, whereas in Storm 2 over 40 per cent of the total rain fell as raindrops greater than 4 mm (Table IIIb).In comparison, despite the much higher intensity (200 mm h−1), the distribution of raindrops from the simulator wasvery similar to that of Storm 2, with 55 per cent of the raindrops being greater than 4 mm in diameter. There were,however, fewer very large raindrops (3% > 6·70 mm, compared with 18% in Storm 2), possibly because the low fallheight of the simulator restricts coalescence of raindrops before they reach the ground (Brandt CJ, 1989).

Drop velocityThe relatively low heights of the droplet box and the randomizing gauze mean that most drops strike the ground atwell below their terminal velocity. This is true of most drip-type field simulators (e.g. Laws and Parsons, 1943; Epemaand Riezebos, 1983; Cerda et al., 1997) and most laboratory simulators unless located in purpose-built towers orstairwells (e.g. Miller, 2005). Utilizing data given by Epema and Riezebos (1983), the mean velocity at the ground forraindrops of 4·1 mm diameter falling from 0·85 m (the height of the randomizer) is 3·7 m s−1, compared with aterminal velocity for the same size of drop of 9 m s−1 (Laws, 1941). A larger median drop size is sometimes used for



Table IV. Kinetic energy rates and Total Storm KE for the natural and simulated rainstorms of Table 3 calculated using the drop-distribution and Hudson equation methods

KE rate per KE rate per Storm

Rainfallsecond unit rainfall KE

amount Intensity Storm(J m−−−−−2 s−−−−−1) (J m−−−−−2 mm−−−−−1) (J m−−−−−2)

Event (mm) (mm h−−−−−1) duration (min) Drops Equation Drops Equation Drops Equation

Simulator 66·7 200 20 0·61 1·62 10·91 29·16 727·49 1944Natural 52·5 16·67 188 0·15 0·10 32·56 21·94 1709·64 1151·88(Storm 2 –open rainfall)

simulations where fall height cannot be increased (e.g. for portable simulators in the field) to compensate for thisreduction in drop velocity. The median drop diameter of natural rainfall at intensities greater than 165 mm h−1 ispredicted to be 1·5 mm (Hudson, 1963), with a terminal velocity of 5·5 m s−1 (Laws, 1941). The D50 of 4·15 mmproduced by the simulator thus provides a reasonably high drop velocity (67% of that of natural rainfall), despite thelow fall height.

Kinetic energyAs kinetic energy (KE) of an individual droplet is given by 1/2 mv2, where m is the mass of the droplet and v isvelocity, and if the size distribution and velocity of raindrops are known, the KE of rainfall can be calculated. Inrainfall energy studies, the kinetic energy rate per unit time (in J m−2 s−1) and total storm kinetic energy (in J m−2) areusually calculated. The KE per unit rainfall during a storm is also sometimes assessed (in J m−2 mm−1).

Using the measured drop-size distribution and calculated impact velocities given in the preceding sections, a200 mm h−1 rainfall produced by the simulator is calculated to have a KE rate per unit time of 0·61 J m−2 s−1, whichover a 20-minute simulation is equivalent to a total storm KE of 727 J m−2 (Table IV). The smaller drops (<1 mmdiameter) are more abundant (61% of droplets), but generate together only 1 per cent of the total storm KE becausethey represent a much smaller mass (Table V). Raindrops of 1–5 mm diameter (38%) are responsible for most of theKE (75%) due to their magnitude and comparative frequency. Simulated raindrops larger than 5 mm diameter are rare(1%) but they contribute 24 per cent of the total KE because of their large mass. Ideally, therefore, KE should becalculated by integrating calculations derived from the drop-size distribution.

Repeating this process with the data from Payne (2001), a 16·7 mm h−1 natural tropical rainstorm event (Storm 2) pro-duced a KE rate per unit time of 0·15 J m−2 s−1, which over a 3-hour event is equivalent to a total KE of 1709 J m−2. Incomparison, the 20 minute simulated rainfall therefore delivers a KE rate four times as large, but a Total KE only halfas large as that experienced at ground level beneath a forest canopy during the 3-hour natural Storm 2 event. Thehigher KE rate of the simulator reflects the fact that the very high rainfall intensity used outweighs the impacts oflower raindrop fall height and velocities. The lower Total KE for the simulated event reflects the shorter duration ofthe storm as well as the reduced fall height. This demonstrates that the use of high rainfall intensities allows morerealistic KE rates and Total KEs to be achieved. There is an additional reason why very high intensities should beused: Seuffert et al. (1992) has demonstrated in Sardinia that short bursts of very high-intensity rainfall during intensestorm events are responsible for very high proportions of total splash erosion.

In situations where drop-size data are unavailable, storm KE is often calculated using Hudson’s (1965) empiricalequation,

KE per unit rainfall (in J m−2 mm−1) = 29·8 − 127·5/I

where I is rainfall intensity (mm h−1).Using rainfall totals and intensities from the Danum Valley Field Centre meteorological station, Sabah, Malaysia

(5°51′N, 90°6′E, 152 m altitude) records for the Storm 2 event, Table IV compares the two methods. Storm 2 (a rainfallof 52·5 mm over 3 hours with a mean rainfall intensity of 16·7 mm h−1) would produce a total KE of 1151 J m−2. Usingthe same equation, a 200 mm h−1 rainfall over 20 minutes (66·6 mm of rain) would produce a total KE of 1944 J m−2.Using Hudson’s equation appears to overestimate total KE, as its calculated values for the simulator are almost twicethat of the natural Storm 2 (i.e. four times higher than that calculated using the actual drop-size data). Values for KE



Table V. Overland flow, infiltration capacity and slopewash results obtained in 2002 using the rainfall simulator and erosionmeasurement system in (a) primary forest of contrasting slope angle and (b) different mosaic elements of regenerating forest thathad been selectively logged in 1988–89 in the Danum Valley area of Sabah, Malaysian Borneo

Sample size Mean rainfall Overland flow Mean infiltration Mean SSC Mean slopewashTerrain type (number) intensity (mm h−−−−−1) (l m−−−−−2/% of rain) capacity (mm h−−−−−1) (mg l−−−−−1) (g m−−−−−2)

Primary forest:<10° 9 197 13·3(20·0) 156·8 887 11·810–19° 7 204 17·9(26·8) 150·4 728 13·120–29° 12 201 24·8(37·1) 124·1 683 16·930–40° 12 188 19·4(29·2) 129·2 759 14·8

Logged forest:Slopes (8–20°) 4 206 5·5(8·3) 189·0 No data No dataSkid trails (4–25°) 20 181 26·8(40·2) 108·3 50 22·3Log-landings (0–7°) 12 199 31·2(46·9) 105·7 124 38·8Landslide scars (7–36°) 12 208 43·1(64·7) 73·6 335 137·4

therefore vary greatly depending on the method used to calculate them. Care must be taken when interpreting resultsderived from different methods and when comparing published values. Calculations using actual drop-size data areclearly preferable to use.

Performance of the Overland Flow, Slopewash and Splash Measurement System

The simulator was used in 2001–02 to assess overland flow, slopewash and rainsplash in a field programme ofsimulations of 20 minutes duration at 200 mm h−1 in primary forest and regenerating rain forest that had been selec-tively logged 13 years previously in 1988–89 in the Danum Valley area of eastern Sabah, Malaysia. The 200 mm h−1

intensity was selected for two reasons: (1) very high rainfall intensities are experienced at Danum relatively fre-quently, and (2) as mentioned earlier, most splash is caused by particularly intense pulses of rainfall within stormevents (Seuffert et al., 1992). Thus at Danum during the period 1997–2000, storms with a maximum 5 minute inten-sity of at least 50 mm h−1 occurred on average every 4·5 days and in excess of 100 mm h−1 every 15·8 days. These figuresyield much shorter return periods for intense rainfall than earlier suggested by Sherlock (1997) for Danum. This mayreflect more representative rainfall conditions in 1997–2000, as the period spanned both El Niño and La Niña phases.

Sample results are used here to demonstrate the performance of the measurement system. Overland flow, slopewashand splash were assessed after 5 minutes and after 20 minutes in each simulation experiment.

Overland flowAlthough average values of overland flow for primary forest and different logged forest disturbance categories (Table V)varied in logical fashion with degree of disturbance and subsequent recovery, percentage values were higher thanvalues recorded for larger areas of slope, even at such high rainfall intensities. Thus the primary forest values (mean29%) exceed values of 4–6 per cent obtained for larger bounded (10 × 2 m) and semi-bounded (60 m2) runoff/erosionplots in the same area obtained under natural rainfall in previous studies (Sinun et al., 1992; Chappell et al., 1999), buttally with the recording of frequent and widespread overland flow on the same slopes following natural rainfall usingnetworks of simple recorders (Clarke, 2002; Sayer et al., 2004). Larger runoff plots or sections of slope are morelikely to include root holes and other ‘infiltration sinks’ than the small microplots used with the simulator. The results,nevertheless, highlight the very high overland flow on the largely bare scars of post-logging landslides and still verycompacted log-landing areas on the one hand, and the low overland flow of the recovered soils of skid trails andlogged slopes, where percentages were sometimes lower than at primary forest sites (Table V).

SlopewashIn the field experiments (Table V, Figure 7) at Danum, the average slopewash (0–20 min) obtained for old skid trails(22·3 g m−2) is little different from that in primary forest (11·8–16·9 g m−2 depending on slope), but is twice as high onlog-landings (38·8 g m−2) and seven times as high on landslide sites (137·4 g m−2). The rank order of these differences



Figure 7. Mean overland flow and slopewash on different disturbance categories in regenerating forest 13 years after selectivelogging in 1988–89. (Primary forest values shown for comparison are averages of all primary experiments).

is similar to that derived from actual slopewash rates obtained using the repeat-measurement erosion bridge tech-nique (Clarke et al., 2002; Clarke and Walsh, 2006; Walsh et al., 2006). The representativeness, however, of thesesimulation-derived slopewash measurements is subject to some limitations imposed by both the equipment and theprocedure. The small size of the plot area reduces the depth and hence entraining power of overland flow generated.Also the splash feeder component is reduced compared with what occurs in nature because, although ‘outsplash’ fromthe plot can take place, ‘insplash’ from terrain outside the plot is prevented by the plot plastic surround (Figure 4).Thus the attempt to assess splash objectively conflicts somewhat with accurate measurement of slopewash.

Splash detachment and erosionThe splash measurement system appeared to be effective in isolating the splash component of slopewash and provid-ing measures of both splash detachment and net downslope splash erosion. Thus, for example, Figure 8 shows howmean detachment and net splash erosion varied with slope angle at primary forest locations. Whereas mean detach-ment remained constant with slope angle (Figure 8A), net erosion, as indexed by subtracting the mean catch of the twoupslope funnels from the mean of the two downslope funnels, increased progressively with slope angle from a meanof 8·5 mg m−2 at slopes of less than 10° to 33·4 mg m−2 at slopes of 30–40° (Figure 8B). For all slope categories, over



Figure 8. Mean splash detachment and net splash erosion on different disturbance categories in regenerating forest 13 years afterselective logging in 1988–89.

50 per cent of the total sediment splashed occurred during the first 5 minutes. This shows the importance of pre-simulation ground conditions in influencing splash results and hence emphasizes the need for care in planning a splashassessment programme and interpreting results between sites.

Discussion

The results obtained with the simulator and plot measurement system suggest that it is useful particularly for work inremote tropical situations. The simulator is cheap to construct (<£500) and easy to transport, assemble and operate insteep terrain in rain-forest locations. Its lack of motorized and electronic parts is also a distinct advantage in a remotearea. Furthermore, its performance compared well with more complex, but less portable systems, including manylaboratory models. The tests discussed in previous sections indicate that the combination of a 200 mm h−1 intensity, thedroplet randomizer, and the 1·35 m simulator fall-height yield a drop-size distribution and kinetic energy rates that areof the same order of magnitude to those experienced naturally within a tropical rain-forest environment. The 20-l tankprovides enough water to carry out a 20-minute experiment at a constant 200 mm h−1 rainfall intensity. This was foundto be a sufficient duration to produce meaningful amounts of overland flow and splash detachment within the small



runoff plot used. It takes approximately 1 hour to set up the simulator, complete a 20-minute simulation and take thenecessary accompanying measurements at each site.

The splash measurement system and experiment duration of 20 minutes, if used in an erosion programme with asufficient sample size of locations, appears to provide a potentially valuable way of assessing both detachment and netdownslope splash erosion. The results presented show that the erosion produced was of sufficient magnitude to detectdifferences between site types both within and between the primary forest and the regenerating forest mosaic. In termsof net splash erosion, all mean figures from the different disturbance categories showed a net movement of sedimentdownslope (except one for log-landings 5–20 min). This, together with the logical rank order of the category means(i.e. landslide scars > log-landings > logged slopes > skid trails), tends to indicate that the upslope/downslope cupapproach is capable of assessing differences in net downslope splash erosion.

The system is capable of further refinement. As with all simulators, results depend on how representative simulatedrainfall is of the target environment. Clearly in the study area rainfall drop-size distributions and ground impactvelocities in the rain forest will vary with canopy height and density (Payne, 2001), which vary with regenerationstage in the logged forest and both locally and between gap, building and mature phases within the primary forest. Thesimulator system could be adjusted to some extent to accommodate different canopy conditions by adjusting theintensity and the randomizer gauze height and mesh size; for example, increasing the height of the gauze to 3 m (andkeeping the rainfall characteristics of the storm event the same) increases the energy supplied to the ground surface to1·13 J m−2 s−1, twice that achieved in the work presented here. The drop size could also be varied by replacing thedrop-former tubes with ones of different internal and external diameters.

Conclusions

The rainfall simulator described in this paper has proved to be both robust and portable enough for use on slopes of upto 45° in a remote rain-forest location. Unlike other field simulators the simulator presented here does not require avehicle to transport it (e.g. Dunne et al., 1980), or a motor or pump (e.g. Thomas and El Swaify, 1989; Cerdà et al.,1997) to operate it when in the field. Any maintenance problems encountered during this research were readily solvedwhile in the field. The simulator proved capable of generating reproducible high (and constant) intensity rainfallevents of at least 20 minutes duration, and such events produced measurable and meaningful amounts of overlandflow and erosion. The drop-formers, in conjunction with the mesh randomizer, provide realistic tropical raindrop sizesand, unlike many nozzle-type simulators, acceptably low spatial variations in intensity across the experimental plot.The kinetic energy produced by the high intensity, short duration simulated storms was approximately half thatexperienced on the ground during a natural intense tropical rainfall event.

Although the microplot erosion and overland flow measurement system developed in conjunction with the simulatoris (as with all simulators) subject to scale problems, particularly as regards the representativeness of slopewash andoverland flow results, the upslope/downslope splash cup system appears to provide a useful way of assessing netdownslope splash transport as well rates of splash detachment. The equipment presented here can also be easily set upin a laboratory to allow more controlled experiments on key soil types away from the field site. The complete rainfallsimulation equipment is arguably, therefore, a useful addition to geomorphological techniques currently in use in thestudy of tropical erosion and hydrology.

AcknowledgmentsThe authors would like to thank Philip Bevan for his assistance with the design and construction of the simulator and Nicola Jonesand Anna Ratcliffe for drawing the diagrams. The Economic Planning Unit of the Prime Minister’s Department of Malaysia and theDanum Valley Management Committee are thanked for their permission to conduct research in the Danum Valley area of Sabah,Malaysia. The support of Glen Reynolds (RS Senior Scientist at DVFC), Hamzah Tangki (Deputy Senior Scientist), JohnnyLarenus, Muhammad Jamal Hanapi, and the other research assistants at Danum Valley Field Centre is gratefully acknowledged. Thispaper is number A/400 of the Royal Society SE Asia Rain Forest Research Programme.

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A Portable Rainfall Simulator for Field Assessment of Splash and Slopewash in Remote Locations

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