Modelling of event-based soil erosion in Costa Rica, Nicaragua and Mexico: evaluation of the EUROSEM model

Catena 44 Ž2001. 187–203 www.elsevier.comrlocatercatena

Modelling of event-based soil erosion in Costa Rica, Nicaragua and Mexico: evaluation of the EUROSEM model A. Veihe a,) , J. Rey b,1, J.N. Quinton c,2 , P. Strauss d,3, F.M. Sancho e,4 , M. Somarriba f,5 a

Department of Geography and International DeÕelopment Studies, Roskilde UniÕersity, Hus 19.2, PO Box 260, 4000 Roskilde, Denmark b Departamento de Suelos, UniÕersidad Autonoma Chapingo, C.P. 56230 Chapingo, Estado de Mexico, Mexico c Institute of Water and EnÕironment, Cranfield UniÕersity at Silsoe, Bedfordshire MK45 4DT, UK d Institute for Land and Water Management, Federal Agency for Water Management, A-3252 Petzenkirchen, Austria e Centro de InÕestigaciones Agronomicas, UniÕersidad de Costa Rica, Ciudad UniÕersitaria Rodrigo Facio, Cod. Postal 2060, San Pedro, Costa Rica f UniÕersidad Nacional Agraria, Facultad de Recursos Naturales y del Ambiente, Km 12 1r 2 carretera norte, Managua, Nicaragua Received 16 February 2000; received in revised form 17 August 2000; accepted 18 October 2000

Abstract This study was undertaken as part of a larger project to evaluate the impact of soil erosion on soil productivity in Costa Rica, Nicaragua and Mexico. An important part of the overall project consists of the use of the event-based EUROSEM model ŽEuropean Soil Erosion Model. to predict soil erosion rates. This paper evaluates the use of the model both for single event and yearly soil loss estimations using erosion plot data from Nicaragua and data obtained through rainfall

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Corresponding author, fax: q45-4674-3032. E-mail addresses: [email protected] ŽA. Veihe., [email protected] ŽJ. Rey., [email protected] ŽJ.N. Quinton., [email protected] ŽP. Strauss., [email protected] ŽF.M. Sancho., Matilde – [email protected] ŽM. Somarriba.. 1 Fax: q52-595-48076. 2 Fax: q44-1525-863300. 3 Fax: q43-7416-52108-3. 4 Fax: q506-234-1627. 5 Fax: q505-233-1208. 0341-8162r01r$ - see front matter q 2001 Elsevier Science B.V. All rights reserved. PII: S 0 3 4 1 - 8 1 6 2 Ž 0 0 . 0 0 1 5 8 - 2

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simulator experiments in Costa Rica and Mexico. EUROSEM was calibrated based on the hydrographs followed by the sedigraphs in Costa Rica and Mexico and this was followed by a model validation. In Nicaragua, model calibration was done using total soil loss values for 1993 and the model was consecutively validated using plot data for 1994 and 1995. The study stresses the importance of calibrating the model for individual catchments, and that the total area of plant stems and soil cohesion are crucial calibration parameters when modelling grassland with cover percentages above 60%. EUROSEM generally did not perform well on single event simulations in terms of simulating hydrographs and sedigraphs. Whereas the difference between observed and simulated total soil loss was between 0.0% and 100.0%, differences in total discharge, peak run-off rate and peak soil loss ranged between 2.0% and 326.5%. The difference was attributed to the model’s inability to model crusting. The application of the model for yearly soil loss predictions looks promising with simulated and observed total soil loss values in Nicaragua differing by between 2.5% and 5.0%. q 2001 Elsevier Science B.V. All rights reserved. Keywords: Soil erosion modelling; Model evaluation; EUROSEM; Costa Rica; Nicaragua; Mexico

1. Introduction Soil erosion by water and its associated detrimental effects on soil productivity is an important problem over the greater part of North and Central America. In Mexico as much as 85% of the land is affected ŽMoreno, 1998. and there has been an increase in the percentage of areas seriously affected by soil erosion over the last decade ŽBecerra et al., 1993.. Soil loss rates of 10–30 trha have, for instance, been reported for natural tepetate soils in Mexico ŽPrat et al., 1996.. Soil loss rates ranged from 2.3 to 3.7 trha on volcanic ash soils with tepetate 6 years after they had been rehabilitated ŽFechter-Escamilla et al., 1996.. In Nicaragua yearly soil loss rates of between 16 and 71 trha have been measured on Universal Soil Loss Equation ŽUSLE. erosion plots ŽMendoza and Somarriba, personal communication., whereas soil erosion rates in coffee plantations in Costa Rica range from 2 to 42 trha depending on the weeding practices ŽSancho, 1991.. Rainfall erosivity in Central America is high, though only a few events during the year generate high soil erosion rates ŽVahrson, 1990.. Soil erosion affects soil productivity by changing soil properties, and particularly by destroying topsoil structure, reducing soil volume and water holding capacity, reducing infiltration, increasing run-off and washing away plant nutrients such as nitrogen, phosphorous and organic matter ŽMeyer et al., 1985; Fetwi, 1993; Oyedele, 1996.. This can have devastating effects on crop yields. However, depending on soil properties, the same soil loss may impact soil productivity differently. For example, small soil losses on shallow soils may cause a greater relative reduction in available water capacity than a larger soil loss from a deep soil. The development of appropriate decision support tools that can be used for identifying high risk areas with respect to erosion and its effect on soil productivity is therefore of paramount importance. The SPIES ŽSoil Productivity Indices and their Erosion Sensitivity. project is a cooperation between universities in Austria, the UK, Costa Rica, Nicaragua and Mexico. The project’s primary aim is to develop a decision support system within a GIS

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ŽGeographical Information System. framework ŽArcView., which enables users to create scenarios reflecting different land use and conservation practices and to evaluate the impact of these practices on soil productivity ŽVeihe et al., 1999; Folly et al., 2000; Magagna et al., 2000.. The SPIES application consists of the event-based EUROSEM Model ŽEuropean Soil Erosion Model. ŽMorgan et al., 1998a. linked with a semi-empirical soil productivity index model SOFIC ŽSoil Fertility Index Calculator. ŽWaldingbrett, 1998. that operates on a monthly basis and which has been developed particularly for Central America. In order to model the long-term effects of soil loss on soil productivity, synthetic rainstorms are generated by using stochastic techniques which seek to reproduce the most important characteristics of erosive rainfalls ŽStrauss et al., 1999.. Before using applications such as SPIES, it is crucial to evaluate the performance of the models being used to ensure that model predictions are realistic. The evaluation procedure also helps in parameterising the model, especially when choosing table values from the EUROSEM User Manual ŽMorgan et al., 1998b. and helps in choosing parameter values that may not easily be obtained in the field or which exhibit a high spatial variability. A number of more empirically based models have been evaluated in Central America based on erosion plot studies. The USLE has been modified and validated for Mexican conditions with the view to use it in areas for which no experimental data on soil loss exist. Equations were developed for the estimation of rainfall erosivity based on annual rainfall in 14 different regions and tables were created for the estimation of soil erodibility, slope steepness and the effect of crop and soil management ŽFigueroa et al., 1991.. With the purpose of modelling soil loss from small catchments Ž1–2 ha. on an event-basis, the MOPEAU model ŽModele ` de Production d’EAU. was evaluated where soil loss is estimated using the USLE. The lowest efficiency coefficient obtained was 88% indicating that the model to a large extent was able to reflect the observed variation ŽOropeza, 1993.. The SWRRB model ŽA Basin Scale Simulation Model for Soil and Water Resources Management. was validated using 22 plots Ž2 = 22 m. representing seven different soil and crop management practices. It was concluded that the model sufficiently was able to describe run-off and soil loss ŽOropeza et al., 1996.. EUROSEM evaluations in Latin America have only been carried out in Bolivia, where the effect of buffer strips was simulated ŽQuinton and Rodriguez, 1999.. However, the evaluation was hampered by a lack of high-resolution rainfall and erosion data and no firm conclusions about model performance were drawn. Meanwhile, an earlier and the present version of EUROSEM have been evaluated several places in Europe and the US both at the plot and the catchment scale with good results ŽQuinton, 1994; Albaledejo et al., 1994; Quinton and Morgan, 1998; Folly et al., 1999.. This paper evaluates the EUROSEM model in Central America using erosion plot data from Nicaragua and data obtained through rainfall simulator experiments in Costa Rica and Mexico. The overall model performance is assessed and guidelines for parameterising the model in the region are provided. The implications of using data supplied from erosion plots and rainfall simulator experiments when validating a model to be used at a catchment scale are discussed and future developments of EUROSEM are identified which will ensure an optimum model performance in Central America.

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2. The EUROSEM model The EUROSEM model is a dynamic distributed model, which is able to predict soil erosion by water from individual fields and small catchments. The model is based on a physical description of the erosion processes and operates for short time steps of approximately 1 min ŽMorgan et al., 1998a.. EUROSEM has a modular structure that simulates erosion by linking it to the water and sediment routing structure of the KINEROS model where water and sediment are routed over the land surface as a series of interlinked uniform slope planes and channel elements ŽWoolhiser et al., 1990.. Rainfall is first intercepted by the plant canopy, which is split into direct throughfall and leaf drainage, and the volume of stemflow. After determining the kinetic energy of these components, soil splash detachment is calculated. Infiltration is then modelled and after subtracting the volume of surface depression storage, run-off is routed over the soil surface using the kinematic wave equation accompanied by the modelling of soil erosion as a continuous exchange of particles between the flow and the soil surface. Rill and interrill flow is simulated explicitly and soil loss is computed as a sediment discharge defined as the product of the volume of run-off and the sediment concentration in the flow, to give a volume Žor mass. of sediment passing a given point in a given time. This is based on a numerical solution of the dynamic mass balance equation ŽMorgan et al., 1998a.. When working at a catchment scale, the catchment is divided into channel elements with contributing sideslopes. The sideslopes are then divided into planes or elements which are uniform with respect to soil, land use and slope characteristics ŽMorgan et al., 1998a..

3. Research methodology 3.1. Costa Rica Rainfall simulator experiments were the basis for the EUROSEM evaluation in the Santa Ana catchment of Salitral, Costa Rica. This catchment has been used as a test catchment for the SPIES application in the SPIES project. Rainfall simulations were carried out on eight plots representing different types of land use Žsee Table 1.. The experimental plots were 2.5 m long and 1.5 m wide with well-demarcated boundaries made from strips of metal set on edge in the soil and with a covered collecting trough at the bottom of the plot. A single nozzle rainfall simulator was used for the experiment. The nozzle ŽLechler 460 848 5ECE. was mounted 3 m above the surface on a bracket which was attached to a frame made out of aluminium. The simulator was surrounded by ‘shade netting’ to minimize the effect of the wind and water was supplied at a pressure of 130 kPa. Because run-off could not be generated at low rainfall intensities, a nozzle providing 132 mmrh was used. The uniformity of the rainfall expressed by the Christiansen’s coefficient was 81.3% ŽChristiansen, 1942. and the median volume drop size was 1.06 mm as determined with a Particle Measuring System ŽPMS. laser probe Žtype OAP260X.. Fall velocities were calculated based on the raindrop distribution and using these fall velocities, kinetic energy was estimated to be 8.7 Jrm2rmm. EU-

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Table 1 Characteristics of sites used for rainfall simulator experiments in Costa Rica, Nicaragua and Mexico Land use

Soil classification

Soil texture ŽUSDA.

Hydraulic conductivity Žmmrh.

Moisture content Ž%.

Canopy cover Ž%.

Ground cover Ž%.

Canopy height Žm.

Costa Rica Bare Maize Coffee Grass

Dystrustept Dystrustept Dystrustept Usthortent

Clay loam Clay loam Clay loam Sandy clay loam

56 26 12 60

26–36 26 28–30 32

40 90

25 70 90

1.5 0.6

Nicaragua Bare Cambisol

Sandy clay loam

7

30

Mexico Bare

Sandy clay loam

2–13

Vitrand

3–26

ROSEM was calibrated using data from one of the plots with maize, a plot on grassland and one on coffee. The simulated hydrograph was first fitted to the observed hydrograph followed by a fit of the sedigraphs. The performance of the model was then validated using the results of four of the remaining plots. To assess the uncertainty of model output, the coefficient of variation of some of the main input parameters was calculated based on field measurements in the Santa Ana catchment. Information for the determination of variability was available for three different soil types being Inceptisols, Alfisols and Andosols. A series of model runs was then carried out using the data from the rainfall simulator plots. Input parameters were increased and decreased in accordance with standard deviations calculated for each of the parameters and its effect on total soil loss observed. 3.2. Nicaragua The model evaluation for Nicaragua was carried out using erosion plot data collected during the period 1993–1995 ŽPfeffer et al., 1997.. The standard erosion plots measuring 22.8 = 2 m on a 15% slope were situated approximately 10 km south of Managua on sandy clay loam soils classified as vertic Cambisols ŽTable 1.. The organic matter content was 2.5% and soils were characterised by a layer of Tepetate Žindurated volcanish ash. 20–30 cm below the soil surface ŽMentler and Strauss, 1994.. The climate is classified as tropical savanna with a distinct rainy season from May to November. Run-off and sediment from the bare plots was collected with Coshocton wheels and after each event, crusts were broken using a hoe. In 1994, rills were measured in order to estimate the soil volume lost due to rill erosion in particular ŽMentler and Strauss, 1994.. Model calibrations were first done based on total run-off followed by the calibration of total soil loss using the data set from 1993. The parameter file resulting from the calibration was then used for the validation exercise using data from 1994 and 1995.

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3.3. Mexico The model evaluation in the Coatlinchan ´ catchment northeast of Mexico City, was carried out using data from rainfall simulator experiments. The mean annual precipitation in the area is 600 mm falling from March to October. The soil texture is sandy clay loam ŽTable 1. and the average soil depth is 40 cm though in many parts of the catchment soil depth is as low as 25–30 cm because of tepetate. The area is under intensive agriculture with maize, wheat, barley and pumpkin as the dominant crops and with average slopes of 10%. Rainfall simulator experiments were carried out with an ORSTOM type rainfall simulator having an oscillating nozzle placed 3 m above the surface. Water was applied at 50 kPa pressure and an intensity of 31 mmrh. The experimental plots measured 1 = 1 m and were selected so that they represented the land use types mentioned above though plots were bare during the experiments. The model was first calibrated based on the hydrographs from three different plots followed by a calibration of the sedigraphs. Subsequently the model was validated based on results obtained through the remaining four rainfall simulator experiments.

4. Results and analysis 4.1. Costa Rica Selected calibration and validation results are shown in Fig. 1 representing characteristic successful and unsuccessful simulation results. For the calibration plot with maize, net capillary drive had to be increased by 40%, soil cohesion by 100% and Manning’s n was increased to 0.18 ŽTable 2. which gave a good fit of both the hydrograph and the sedigraph ŽFig. 1., and the static output parameters total discharge, total soil loss, peak run-off and peak soil loss ŽTable 3.. When validating the model for the bare plots, total soil loss values were predicted quite accurately and to some extent peak soil loss whereas simulated discharge was too high ŽTable 4 and Fig. 1.. On the other hand, poor simulation of both run-off and in particular soil loss was observed for the validation plot with maize ŽTable 4.. The poor validation results for the maize plot can be attributed to two factors, one of which is the difference in slope Ž17% for the calibration plot and 13.5% for the validation plot.. Previous sensitivity analysis of EUROSEM ŽVeihe et al., 2000. has shown that both run-off and soil loss estimations are very sensitive to changes in interrill slope which influences the calculation of transport capacity ŽGovers, 1990.. Secondly, local spatial variations in hydraulic properties may influence rainfall simulation results. With respect to the simulations on grassland, it proved extremely difficult to calibrate the model ŽFig. 1.. Whereas run-off to some extent could be simulated by EUROSEM, it proved impossible to obtain a good calibration in terms of the sedigraph ŽFig. 1. keeping input parameter values within physically realistic limits. Simulated soil loss values were therefore far exceeding observed values. The most important parameter used for the calibration was the total area of plant stems that adjusts the saturated hydraulic conductivity in the model based on an exponential function ŽMorgan et al., 1998a.. The

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Fig. 1. A sample of calibrated and validated hydrographs and sedigraphs from Salitral, Costa Rica.

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Table 2 Calibration required Žpercentage wise increase or decrease in parameter values. in order to fit EUROSEM to observed data Calibration parameter

Costa Rica Maize

Hydrologic parameters Saturated hydraulic conductivity Porosity Maximum initial moisture content Net capillary drive Soil detachability Manning’s n Soil parameters Soil cohesion

Nicaragua Grass y100

Plots 24bq27 q185 q11 q11

0.5

q100

Max. TV

Other parameters Rill depth and width Total area of plant stems Ž%.

Plot 10b

q200

q40 Set to 0.18

Mexico

y20

q50

q50 TV

TV

TV

q100 q20

TVs Table values and refers to the EUROSEM User manual ŽMorgan et al., 1998b..

total area of plant stems was increased by 20%. However, the model proved very sensitive to changes in this parameter, particularly when the area of plant stems was above 70% where a 1% change effected a change in soil loss of up to 76%. Saturated hydraulic conductivity was further reduced by 100%, Manning’s n was set to 0.5 and the maximum table value for soil cohesion indicated in the EUROSEM User Manual ŽMorgan et al., 1998b. was used. Although the total discharge could be fitted fairly well, it was not possible to fit the hydrograph. Consequently, the validation results ŽTable 4.

Table 3 Model results for the calibration storms with respect to discharge, soil loss, peak discharge and peak sediment discharge ŽNrA s not available. Location and Discharge Žm3 rha. Soil loss Žtrha. Peak run-off Žlrmin. Peak soil loss Žgrmin. storms Simulated Observed Simulated Observed Simulated Observed Simulated Observed Costa Rica Maize Grass Coffee

1686.9 140.0 302.0

1775.5 125.3 648.7

3.0 0.2 NrA

2.2 0.0 NrA

4.8 2.5 4.3

5.3 1.6 7.9

9.6 14.5 NrA

9.0 0.1 NrA

Nicaragua 1993

2038.0

1485.5

69.3

59.0

NrA

NrA

NrA

NrA

171.8 234.6 159.5

124.0 142.7 124.0

1.9 0.7 1.7

2.1 0.8 2.3

0.6 0.7 0.6

0.8 1.0 0.8

7.6 2.2 0.7

3.7 4.3 1.9

Mexico Plot 10b Plot 24b Plot 27

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Table 4 Model results for the validation storms with respect to discharge, soil loss, peak discharge and peak sediment discharge ŽNrA s not available. Location and Discharge Žm3 rha. Soil loss Žtrha. Peak run-off Žlrmin. Peak soil loss Žgrmin. storms Simulated Observed Simulated Observed Simulated Observed Simulated Observed Costa Rica Bare1 Bare2 Maize Grass

478.8 154.2 443.5 140.0

266.1 51.0 914.7 1043.7

3.9 0.1 0.0 0.2

3.1 0.1 1.3 0.2

Nicaragua 1994 1995

1446.9 689.0

892.9 727.1

56.9 28.3

55.5 29.7

115.1 165.8 135.8 353.9

100.7 91.7 91.7 101.9

2.7 0.1 0.2 0.3

3.9 1.7 0.1 0.4

Mexico Plot 10c Plot 24a Plot 45a Plot 45b

3.7 2.3 4.8 2.5

2.8 1.7 7.8 7.7

30.6 8.5 0.0 14.5

32.8 4.0 12.9 3.4

NrA NrA

NrA NrA

NrA NrA

NrA NrA

0.6 0.6 0.6 0.8

0.8 0.5 0.3 0.3

14.3 0.5 1.0 0.7

21.5 4.3 0.8 1.4

are not good. The situation is made worse by the fact that although the initial field conditions for both grass plots were identical and the plots were situated next to each other, observed run-off and soil loss differed significantly. Possible explanations may be as outlined above for the maize plot. The attempt to calibrate the model for one of the plots with coffee proved unsuccessful ŽTable 3.. Simulated discharge was only half of observed discharge even when all input parameters had been set to generate maximum run-off though within the recommended range indicated by the EUROSEM User Manual. The explanation should be sought in the fact that the measuring plot was installed along the slope, i.e. cutting across the terraces. This caused ponding behind terrace risers and concentrated run-off once water broke through the riser, processes that were not simulated by EUROSEM. The experiment should ideally have been carried out with the plot installed along the contours, following the drainage ways from the terraces, but this would have required a modification of the rainfall simulator, which was not possible at the time the experiment was carried out. Because of the calibration results, it was decided not to validate the coffee plot as the validation plot had a very similar response to the calibration plot. The variability of some of the main input parameter values such as saturated hydraulic conductivity, cohesion and surface roughness is high with coefficient of variations ranging from 61% to 105% depending on soil type ŽTable 5.. On the other hand, estimates of bulk density and porosity have low coefficient of variations. Of the three input parameters exhibiting a high variability, changes in soil surface roughness were found to have no effect on model output. When using extreme values of saturated hydraulic conductivity, i.e. decreasing to lowest measured value for a particular soil type measured in the field, it was for example found to increase estimated soil loss from 0.1 to 4.1 trha for one of the bare plots. However, the most interesting result was the fact

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Table 5 Average values Ž x . and coefficient of variation ŽCV. for some of the main input parameters in Costa Rica Input parameters Bulk density Žgrcm3 . Porosity Ž%. Hydraulic conductivity Žcmrday. Cohesion ŽkPa. Roughness Žcmrm.

Inceptisols

Alfisols

Andosols

x

CV

x

CV

x

CV

1.06 59.90 108.78 305.91 2.70

0.14 0.10 0.94 0.63 0.76

1.09 59.03 89.25 251.87 3.06

0.12 0.08 0.55 0.81 0.91

0.90 65.89 59.13 81.58 2.00

0.17 0.09 0.63 1.05 0.61

that changes in soil cohesion affected total soil loss quite significantly on the bare plots and the plots planted with maize with maximum changes in total soil loss of up to 600%. For the grass plots, changes in input parameter values Žin this case, a decrease in soil cohesion. caused an increase in simulated total soil loss from 0.2 to 7.7 trha. 4.2. Nicaragua During the model calibration, the parameter saturated hydraulic conductivity required the largest modifications in order to give the correct run-off values though porosity and the maximum volumetric moisture content also had to be adjusted. Cohesion and rill dimensions were subsequently used to calibrate the model with respect to total soil loss ŽTable 2.. Whereas EUROSEM did not perform particularly well when looking at the individual rainstorms ŽFig. 2., the model performed very well when observed and simulated total run-off was compared, both in 1994 and in 1995 ŽTable 4.. When looking at the validation results ŽTable 4., simulated soil loss amounted to 56.9 trha in 1994 as compared to an observed soil loss of 55.5 trha and in 1995, soil loss was simulated to be 28.3 trha and the observed soil loss was 29.7 trha. Thus, predicted and observed values differed by not more than 5%, which means that the model is able to predict average yearly soil loss. 4.3. Mexico For the calibration of the model on plots with a clay loam soil Žplots 24b and 27., the model input value saturated hydraulic conductivity had to be increased by 200% and the net capillary drive parameter decreased by 50% ŽTable 2.. For plot 10b on a loamy soil, the value of the net capillary drive parameter was increased by 50%. EUROSEM table values ŽMorgan et al., 1998b. were chosen for soil detachability by raindrop impact. However, soil cohesion values measured in the field proved far too high Žand above recommended values given by the EUROSEM User Manual. when trying to fit the sedigraphs for the three calibration plots. Consequently, soil texture dependant table values indicated by the EUROSEM User Manual ŽMorgan et al., 1998b. were used. Simulations with calibrated files gave good results in terms of total soil loss whereas it proved difficult to obtain a good correlation between observed and simulated peak soil loss ŽTable 3.. This is also illustrated in Fig. 3 where the calibrated hydrograph and

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Fig. 2. Average run-off Žm3 rha. and soil loss Žtrha. for rainstorm events in Nicaragua 1994 and 1995 that produced run-off andror soil loss.

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Fig. 3. Simulated and observed hydrographs and sedigraphs for plot 27 Žcalibration. and plots 10c and 45b Žvalidation. in Coatlinchan, ´ Mexico.

sedigraph are shown for plot 27 showing a good trend, but a poor estimation of peak values. During the validation, it appeared that the calibration used for plot 10b was not applicable to the loamy textured validation plot. The calibration procedure applied to

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plots 24b and 25 was therefore used for all the validation plots. Simulated total discharge and soil loss corresponded quite well with observed values ŽTable 4. except for plot 24a where observed soil loss was much higher than simulated values and plot 45b where simulated run-off was 3.5 times higher than observed run-off. Simulated peak run-off was generally too high as compared to observed values while the simulated peak soil loss tended to be higher than observed values. Fig. 3 shows some examples of the validation results. A general problem for many of the simulations was the timing of the rise of the simulated hydrograph and sedigraph Žsee, in particular, plot 10c.. The observed run-off almost immediately after the beginning of the experiment reflects the crusting phenomenon common in the region resulting from the breakdown of aggregates less than 2 mm when impacted by raindrops ŽQuantin et al., 1993.. Crusting is a process EUROSEM currently is unable to model. 5. Discussion and conclusions There was no consistency with respect to the calibration of EUROSEM for the Central American region as a whole, since each area tested had its own characteristics. However, it appeared that values of soil cohesion should be chosen within the range indicated in the EUROSEM User Manual ŽMorgan et al., 1998b.. The model calibration of grassland in Costa Rica showed the importance of carefully choosing input values to characterize the total area of plant stems and soil cohesion as the model is particularly sensitive to changes in these parameters. This also supports findings using Monte Carlo simulation techniques to analyze the sensitivity of EUROSEM ŽVeihe and Quinton, 2000.. The current study therefore stresses the importance of model calibration combined with extensive field measurements of key input parameters if reliable results should be obtained and that single measurements of input parameters such as saturated hydraulic conductivity may lead to huge errors in model predictions of soil loss. Unfortunately, no data were available on the variability of input parameters in Nicaragua and Mexico, the reason why the uncertainty of model output could not be assessed. Looking at the overall performance of the model in single event simulations ŽMexico and Costa Rica., it proved difficult to fit the shape of the hydrographs and sedigraphs which can probably be attributed to EUROSEM’s inability to model crusting. This topic is now the subject of a 3-year research programme, Modelling Within Storm Erosion Dynamics ŽMWISED. funded by the European Commission which aims to develop the within storm changes in erosion processes within EUROSEM, including crusting. In spite of the problems experienced in fitting hydrographs and sedigraphs, simulated total soil loss values reasonably reflected the observed values though simulation results were often not well related to the measured run-off volume. The unexplained variability in soil loss from nearly uniform plots, particularly in Costa Rica, has also been observed in studies by Wendt et al. Ž1986. and Nolan et al. Ž1997., and shows the importance of carrying out a large number of replicate rainfall simulator experiments for purposes of model calibration. To this should be added significant, annual variations in observed run-off and soil loss from a given simulated rainstorm ŽMcIsaac and Mitchell, 1992.. Variations in soil loss from rainfall simulation experiments on grassland in Costa Rica may not only reflect spatial variations in saturated hydraulic conductivity as

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discussed earlier. Morgan et al. Ž1997. found that one of the most important factors determining soil loss from grassland is the amount of loose material on the soil surface. The material available for transport is controlled by raindrop impact, weathering between storm events, or any other disturbance of the soil surface. The plot size used in rainfall simulator experiments also affects results. Wischmeier and Mannering Ž1969. and Auerswald et al. Ž1992. observed an almost instantaneous run-off under natural rainstorms while there was a time lapse in rainfall simulator studies that increased with decreasing plot size. This is determined by the length of the border per unit plot area, as the border tends to restrict run-off at the beginning of a rainstorm ŽAuerswald et al., 1992.. The issue of plot size may be reflected in the data from the rainfall simulator experiments carried out in Mexico with a plot size of only 1 m2 . On the other hand, EUROSEM was unable to simulate crusting or the changes in surface characteristics during a storm, being a prevalent process on tepetate soils in Mexico ŽRivera and Oropeza, 1996.. Whereas a fast initiation of run-off could have been simulated by entering low values of saturated hydraulic conductivity, this would have been at the expense of total run-off amounts which would have been far in excess of observed values. Errors associated with the effect of wind should also be taken into consideration and may be more important for the smaller plots ŽAuerswald and Eicher, 1992.. This problem was faced in Costa Rica where wind was prevalent throughout the day due to the location of the test plots within a narrow valley. Another source of error that needs to be mentioned is the rainfall characteristics in the Costa Rican rainfall simulator experiment. Both kinetic energy and the mean volume drop diameter were less Žabout half. of what would have been expected for a rainstorm with the given intensity ŽHudson, 1989.. Furthermore, fall velocities of raindrops are not expected to have reached terminal velocity and this means that the raindrop impact may be less than a natural rainstorm. The rainfall energy estimated to reach the surface as direct throughfall in EUROSEM, assuming a drop size distribution as described by Marshall and Palmer Ž1948., is about three times higher than the measured kinetic energy in the rainfall simulator experiment. Nevertheless, simulated soil loss values are generally much lower than observed ones, indicating that detachment by run-off may be underestimated by the model. Furthermore, the drop size distribution in the Costa Rican experiment was more skewed to the right than the distributions shown by Hudson Ž1989. for similar rainfall intensities, meaning that raindrop impact will be less than under a natural storm. Assouline et al. Ž1997. point to the fact that this can influence experimental results dealing with soil–rainfall relationships affected by cumulative rainfall kinetic energy such as soil sealing and erosion. The application of the model for yearly soil loss predictions looks promising with simulated total soil loss values in Nicaragua differing 2.5% and 5.0% in 1994 and 1995, respectively, as compared to observed soil loss. The results are particularly promising when seen in the light of the variation in rill size and location observed through the year, particularly since rill dimensions have to be specified explicitly in EUROSEM prior to the model being run. It can be concluded from this study that although there are some problems associated with event-based simulations in Central America using EUROSEM, the model is able to

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predict yearly run-off and soil loss quite well. It is hence a suitable soil erosion modelling tool within the context of the SPIES project aiming at estimating long-term effects of soil erosion on soil productivity. However, this is conditioned to adequate model calibration to ensure proper parameterisation of the model. Further research into the modelling of erosion from grassland is required together with modelling of soil erosion for terraces as these land use types are widespread in the region. The monitoring of rill development both spatially and temporally is also of importance, particularly in Mexico and Nicaragua, as well as dynamic modelling of rills in EUROSEM.

Acknowledgements This research work was carried out as part of the SPIES project ŽSoil Productivity Indices and their Erosion Sensitivity., contract no. ERBIC 18CT960096, whose funding by the Commission of the European Community is gratefully acknowledged. Anita Veihe was employed at the Institute of Water and Environment, Cranfield University, while working on this project. We would like to thank the following persons who assisted during fieldwork: Alexander Waldingbrett ŽBOKU, Austria. and Mario Villatoro ŽCIA, Universidad de Costa Rica. and finally Dietmar Palmetzhofer ŽBOKU, Austria. for making data on soil cohesion available.

References Albaledejo, J., Castillo, V., Martinez-Mena, M., 1994. EUROSEM: preliminary validation on non-agricultural soils. In: Rickson, R. ŽEd.., Conserving Soil Resources: European Perspectives. CAB International, Wallingford, UK, pp. 314–325. Assouline, A., El Idrissi, A., Persoons, E., 1997. Modelling the physical characteristics of simulated rainfall: a comparison with natural rainfall. J. Hydrol. 196, 336–347. Auerswald, K., Eicher, A., 1992. Comparison of German and Swiss rainfall simulators—accuracy of measurement and effect of rainfall sequence on runoff and soil loss rates. Z. Pflanzenernaehr. Bodenkd. 155, 191–195. Auerswald, K., Kainz, M., Wolfgarten, H.J., Botschek, J., 1992. Comparison of German and Swiss rainfall simulators—influence of plot dimensions. Z. Pflanzenernaehr. Bodenkd. 155, 493–497. Becerra, M.A., Tovar, S.J.L., Ojeda, T.E., Ortiz, S.M.L.M., 1993. La erosion y su tasa de cambio en Coatlinchan, Estado de Mexico. In: Figueroa, J.F.R. ŽEd.., Manejo y Conservacion del Suelo y Agua, Primera Reunion Nacional, 12–15 August 1992. Unidad de Congresos del Colegio de Postgraduados, Montecillo, Estado de Mexico, pp. 377–380. Christiansen, J.E., 1942. Irrigation by sprinkling. Agric. Exp. Stn., Univ. Calif., Bull. 670. Fechter-Escamilla, U., Vera, A., Werner, G., 1996. Erosion endurecido Žtepetate ´ hidrica en un suelo volcanico ´ t3. en el bloque de Tlaxcala, Mexico. Memorias del III Simposio Internacional Sobre Suelos Volcanicos ´ ´ Endurecidos, pp. 351–358. Fetwi, F.G., 1993. The Impact of Erosion on Soil Productivity: Model Development and Validation with special reference to low input agriculture. PhD Thesis, Cranfield University at Silsoe, UK. Figueroa S.B., Amante O.A., Cortes O.J.M., Morales F.F.J., ´ T.H.G., Pimentel L.J., Osuna C.E.S., Rodrıguez ´ 1991. Manual de prediccion de perdidas de suelo por erosion. Secretaria de Agricultura y Recursos Hidraulicos ŽSARH., Morelia, Michocan, 150 pp. ´ Mexico, ´ Folly, A., Quinton, J.N., Smith, R.E., 1999. Evaluation of the EUROSEM model using data from the Catsop watershed, The Netherlands. Catena 37 Ž3–4., 507–519.

202

A. Veihe et al.r Catena 44 (2001) 187–203

Folly, A., Magagna, B., Muhar, A., Quinton, J., Sancho, F., 2000. The integration of an event-based soil erosion model with a geographic information system for the prediction of soil productivity changes. In: Conese, C., Falchi, M.A. ŽEds.., Proceedings from 7th ICCTA 1998, Computer Technology in Agricultural Management and Risk Prevention. Accademia dei Georgofili, Florence, Italy, pp. 122–129. Govers, G., 1990. Empiral relationships on the transporting capacity of overland flow. Int. Assoc. Hydrol. Sci. Publ. 189, 45–63. Hudson, N., 1989. Soil Conservation. Batsford, London, UK. Magagna, B., Folly, A., Honninger, K., Muhar, A., Quinton, J., Sancho, F., Strauss, P., 2000. The SPIES ¨ model: data flow and GIS linkage between a soil erosion and a soil productivity model. In: Fullerton, K. ŽEd.., Proceedings 5th EC-GIS Workshop 1999, Space Applications Institute, European Commission, Joint Research Centre, Stresa, Italy, pp. 259–268. Marshall, I.S., Palmer, W.M., 1948. Relation of raindrop size to intensity. J. Meteorol. 5, 165–166. McIsaac, G.F., Mitchell, J.K., 1992. Temporal variation in run-off and soil loss from simulated rainfall on corn and soybeans. Am. Soc. Agric. Eng. 25 Ž2., 465–472. Mentler, A., Strauss, P., 1994. Beitrag zur Erfassung des diffusen Stoffeintrages im Einzugsgebiet des ManaguaseesrNicaragua. Report to Bundesministerium fur ¨ Wissenschaft, Forschung, Transport und Verkehr, Vienna, Austria, 32 pp. Meyer, L.D., Bauer, A., Heil, R.D., 1985. Experimental approaches for quantifying the effect of soil erosion on productivity. In: Follett, R.F., Stewart, B.A., Ballew, I.Y. ŽEds.., Soil Erosion and Crop Productivity. American Society of Agronomy, Crop Science Society of America and Soil Science Society of America Publishers, Madison, WI, USA, pp. 213–234. Moreno, A.B., 1998. Conservacion de suelos y desarrollo sustentable, ¿Utopia o posibilidad en Mexico ? TERRA 16 Ž2., 181–187. Morgan, R.P.C., McIntyre, K., Vickers, A.W., Quinton, J.N., Rickson, R.J., 1997. A rainfall simulation study of soil erosion on rangeland in Swaziland. Soil Technol. 11, 291–299. Morgan, R.P.C., Quinton, J.N., Smith, R.E., Govers, G., Poesen, J.W.A., Auerswald, K., Chisci, G., Torri, D., Styczen, M., 1998a. The European Soil Erosion Model ŽEUROSEM.: a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surf. Processes 23, 527–544. Morgan, R.P.C., Quinton, J.N., Smith, R.E., Govers, G., Poesen, J.W.A., Auerswald, K., Chisci, G., Torri, D., Styczen, M., Folly, A.J.V., 1998b. The European Soil Erosion Model ŽEUROSEM.: Documentation and User Guide. Silsoe College, Cranfield University, UK. Nolan, S.C., van Vliet, L.J.P., Goddard, T.W., Flesch, T.K., 1997. Estimating storm erosion with a rainfall simulator. Can. J. Soil Sci. 77, 669–676. Oropeza, M.J.L., 1993. Evaluacion de la relacion erosion—escurrimiento en pequenas ˜ cuencas agricolas a partir del modelo de simulacion MOPEAU. In: Figueroa, J.F.R. ŽEd.., Manejo y Conservacion del Suelo y Agua, Primera reunion nacional, 12–15 August 1992, Unidad de Congresos del Colegio de Postagraduados, Montecillo, Estado de Mexico, pp. 110–118. Oropeza, M.J.L., Rios, B.J.D., Salazar, L.J., 1996. Uso de modelos matematicos de erosion para la ´ ´ hıdrica ´ optimizacion ´ de la rehabilitacion ´ de tepetates con fines agricolas. Memorias del III Simposio Internacional Sobre Suelos Volcanicos Endurecidos, Quito, December, pp. 384–396. ´ Oyedele, J.D., 1996. Effects of erosion on the productivity of selected southwestern Nigerian soils. PhD thesis, Department of Soil Science, Obafemi Awolowo University, Ile-Ife, Nigeria. Pfeffer, M., Mentler, A., Strauss, P., 1997. Bodenabtrag und Nahrstoffverluste unter einem typischen ¨ Fruchtfolgesystem Mittelamerikas, am Beispiel eines dreijahrigen Parzellenversuches in Managuar ¨ Nicaragua. Mitt. Dtsch. Bodenkundlichen Ges. 85, 1457–1460. Prat, C., Baex, A., 1996. Erosion ´ A., Marquez, ´ ´ y escurrimiento en parcelas de tepetate t3 en Texcoco, Mexico. ´ Memorias del III Simposio Internacional sobre Suelos Volcanicos Endurecidos, pp. 371–383. ´ Quantin, P., Arias, A., Etchevers, J., Ferrera, R., Oleschko, K., Navarro, A., Werner, G., Zebrowski, C., 1993. Tepetates de Mexico: caracterizacion y habilitacion para la agricultura. Terra 11, ŽInforme Cientıfico Final ´ del Proyecto TS2-A 212-C CEErORSTOM., Special Issue. Quinton, J.N., 1994. The validation of physically-based erosion models—with particular reference to EUROSEM. PhD thesis, Cranfield University at Silsoe, Bedford, UK. Quinton, J.N., Morgan, R.P.C., 1998. EUROSEM: an evaluation with single event data from the C5 Watershed, Oklahoma, USA. In: Boardman, J., Favis-Mortlock, D. ŽEds.., Global Change: Modelling Soil

A. Veihe et al.r Catena 44 (2001) 187–203

203

Erosion by Water, NATO ASI series 1: Global Environmental Change. Springer-Verlag, London, UK, pp. 65–74. Quinton, J.N., Rodriguez, F., 1999. Modelling the impact of live barriers on soil erosion in the Pairumani sub-catchment, Bolivia. Mt. Res. Dev. 19, 292–299. Rivera, R.P., Oropeza, M.J.L., 1996. Evaluacion y de la produccion ´ del potencial hidrologico ´ ´ de sedimentos en tepetates tipo t3 con lluvia simulada. Memorias del III Simposio Internacional sobre Suelos Volcanicos ´ Endurecidos, Quito, December, pp. 412–419. Sancho, F.1991. Medicion de perdidas de suelos a traves ´ ´ del empleo de parcelas de escurrimiento. Memorias, Taller de Erosion de Suelos, Heredia, Universidad Nacional, Costa Rica, pp.102–115. Strauss, P., Konecny, F., Blum, W.E.H., 1999. A rainfall generation procedure for the European Soil Erosion Model ŽEUROSEM.. Hydrol. Earth Syst. Sci. 3 Ž2., 213–222. Vahrson, W., 1990. El potencial erosivo de la lluvia en Costa Rica. Agron. Costarric. 14 Ž1., 15–24. Veihe, A., Quinton, J.N., 2000. Sensitivity analysis of EUROSEM using Monte Carlo simulation I: hydrological, soil and vegetation parameters. Hydrol. Processes 14, 915–926. Veihe, A., Magagna, B., Muhar, A., Quinton, J., Sancho, F., Strauss, P., Waldingbrett, A., 1999. Modelling soil productivity changes: the use of the SPIES application in Costa Rica. In: Musy, A. ŽEd.., Emerging Technology for sustainable Land and Water Management. Presses Polytechniques et Universitaires Ronandes, Lausanne, Switzerland. Veihe, A., Quinton, J.N., Poesen, J. et al., 2000. Sensitivity analysis of EUROSEM using Monte Carlo simulation II: the effect of rills and rock fragments. Hydrol. Processes 14, 927–939. Waldingbrett, A., 1998. SOFI Model Description. Institute of Soil Science, University of Agricultural Sciences, Vienna, Austria. Wendt, R.C., Alberts, E.E., Hjelmfeldt Jr., A.T., 1986. Variability of run-off and soil loss from fallow experimental plots. Soil Sci. Soc. Am. J. 50, 730–736. Wischmeier, W.H., Mannering, J.V., 1969. Relation of soil properties to its erodibility. Proc. Soil Sci. Soc. Am. J. 33, 131–137. Woolhiser, D.A., Smith, R.E., Goodrich, D.C., 1990. KINEROS: A kinematic and erosion model: documentation and user manual. USDA Agricultural Research Service ARS-77.