Field Evaluation of Selected Soil Erosion Models for Catchment Management in Indonesia

ARTICLE IN PRESS Biosystems Engineering (2004) 88 (4), 491–506 doi:10.1016/j.biosystemseng.2004.04.013 SW}Soil and Water

Available online at www.sciencedirect.com

Field Evaluation of Selected Soil Erosion Models for Catchment Management in Indonesia H. Moehansyah1; B.L. Maheshwari1; J. Armstrong2 1

School of Environment and Agriculture, University of Western Sydney, Locked Bag 1797, Penrith South DC, NSW 1797, Australia; e-mail of corresponding author: [email protected] 2 NSW Department of Infrastructure, Planning and Natural Resources (DIPNR), PO Box 390 Goulburn, NSW 2580, Australia; e-mail: [email protected] (Received 26 April 2003; accepted in revised form 21 April 2004; published online 8 July 2004)

Three models, viz., areal non-point source watershed environment response simulation (ANSWERS), universal soil loss equation (USLE) and adapted universal soil loss equation (AUSLE) are evaluated for their performance under the field conditions of the Riam Kanan catchment in South Kalimantan province of Indonesia. While ANSWERS is evaluated for its accuracy to predict both runoff and soil loss, USLE and AUSLE are evaluated for soil loss only. The study was carried out in the context of sedimentation concerns for the Muhammad Nur Reservoir}an important source of drinking and irrigation water supply for the catchment. The models are evaluated using field data collected under four different land uses and during 2 years of field experiments. The land uses considered are cropland with minimum tillage, cropland with conventional tillage, grassland and areas reforested with rubber trees. The ANSWERS model in general has a tendency to overpredict runoff values. The ANSWERS model also was relatively better for predicting soil loss followed by the AUSLE and USLE models. Overall, the ANSWERS model proved superior for predicting soil loss in the Riam Kanan catchment. However, given that the AUSLE model produced sufficiently reliable results and is relatively easy to use, the AUSLE model would also appear to be a useful tool for predicting soil erosion in the catchment. # 2004 Silsoe Research Institute. All rights reserved Published by Elsevier Ltd

1. Introduction Soil erosion is an environmental concern that leads to decline in soil fertility and sedimentation of reservoirs. The relationships between rainfall and runoff and processes that result in erosion at a given location are generally complex. Prediction of runoff and soil loss is important for assessing soil erosion hazard and for determining suitable land uses and soil conservation measures for a catchment. In turn, this can help in deriving optimum benefit from the use of the land whilst minimising the negative impacts of land degradation and other environmental problems. There have been numerous models (both empirical and process-based) developed in the past to predict both runoff and soil loss at a field or catchment level. The models vary from very complex procedures requiring a 1537-5110/$30.00

range of input parameters [e.g., water erosion prediction project (WEPP), European soil erosion model (EUROSEM) and areal non-point source watershed environment response simulation (ANSWERS)], to reasonably simple requiring only a few key parameters [e.g., Morgan–Morgan and Finney (MMF), productivity erosion runoff functions to evaluate conservation techniques (PERFECT), universal soil loss equation (USLE) and revised universal soil loss equation (RUSLE)] to predict runoff and soil loss (Beasley et al., 1980; Fentie et al., 2002; Laflen et al., 1997; Littleboy et al., 1996; Morgan et al., 1984; Morgan, 1995; Morgan et al., 1998; Morgan, 2001; Renard et al., 1991; Williams et al., 1982). Some models, in spite of their strong theoretical base, may not be very suitable in the context of developing country situations such as those in Indonesia since the detailed rainfall, topographic and other input 491

# 2004 Silsoe Research Institute. All rights reserved Published by Elsevier Ltd

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Notation a a1 A Apt As b c C Cf Cs Ea Er K Ksat ‘ L m M N P R r2 r21 Rf

soil organic matter content, % intercept estimated soil loss, t ha1 y1 area of plot, ha specific catchment area, m2 structural code of the soil, dimensionless permeability class of the soil, dimensionless crop cover factor, dimensionless clay fraction in soil, % concentration of sediment in the runoff water, mg ‘1 average error comparison of observed and predicted values, % average error by regression analysis, % soil erodibility, dimensionless saturated hydraulic conductivity, mm h-1 slope length, m length of the slope factor, dimensionless exponent a parameter defined by ½ðSt  Svf Þ=100  Cf number of measurements factor for the effects of soil conservation practices, dimensionless erosivity of rainfall, mm ha1 h1 y1 coefficient of determination for Eqn (8) coefficient of determination for Eqn (9) total monthly rainfall, mm

data required to run them are often not available or difficult to collect due to resource constraints. Soil erosion models can play a critical role in addressing problems associated with land management and conservation, particularly in selecting appropriate conservation measures for a given field or catchment. They can also assist governmental agencies in developing suitable policies and regulations for agricultural and forestry practices. Two important considerations in selecting an appropriate model for field use are input data availability or whether data can be obtained within the constraints of the field resources available, and the prediction accuracy of the models. Therefore, an evaluation of potentially suitable models that can be used with readily available input data is an important step in using them for practical applications. The main objective of this study is to evaluate selected runoff and soil erosion models, using rainfall, runoff and soil loss data, collected over a 2-year period, for their prediction accuracy under a range of field conditions in the Riam Kanan catchment in Indonesia.

Rm Rn S Sc Sf Sp Srw St Svf Vr Xo Xp X# p X% p Ys a l l1 s s1

maximum rainfall during 24 h, the observed month, mm number of rainy days per month slope factor, dimensionless dry weight of sediment deposited on the chute, kg dry weight of sediment deposited on the flume, kg dry weight of sediment deposited in the sediment pond, kg dry weight of sediment present in the runoff water, kg silt fraction of soil, % very fine sand fraction in soil, % volume of the runoff water, m3 ha1 observer value of variable predicted value of variable predicted value of Xp from regression of Xp on Xo the mean of Xp values sediment yield of plot for a given rainfall event, kg ha1 slope, degrees coefficient coefficient standard error of the estimate for Eqn (8) standard error of the estimate for Eqn (9)

2. Study area and methods 2.1. The catchment The study was undertaken in the Riam Kanan catchment (RKC) in Indonesia. The RKC (latitude 28450 to 38450 S, longitude 114850 to 1158300 E) is located in the Banjar district of South Kalimantan province (Figs 1 and 2). The outlet of the catchment is about 30 km south-east of Martapura, the capital of the district, and about 65 km south-east of Banjarmasin, the capital of the province. The area of the catchment is 102 442 ha (SKRPB, 1991). The average rainfall for the catchment is 2277 mm. About 77% of the total rainfall occurs from October to April (rainy season) and the remaining (23%) from May to September (Fig. 3). The catchment has an important reservoir, the Muhammad Nur Reservoir (MNR), which was constructed in 1973. The MNR is the only multipurpose use reservoir in South Kalimantan and has a strategic value

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Rantau

TAPIN DISTRICT Tatakan

Barito river

Binuang

Belimbing

Kupang Rejo Sungkai

Alalak river

Pinang

Sungai Pengaron

Banjarmasin

NS

Riam kiwa river

Martapura

NT

AI

Martapura river

M

BANJAR DISTRICT

OU

Riam kanan river

Banjarbaru

US

RKC

M

ER

AT

Dam

N

Pleihari

TANAH LAUT DISTRICT SEA A

JAV

RKC 0

30

60

Riam Kanan Catchment Muhammad Nur Reservoir

kilometres

Fig. 1. Location of the Riam Kanan catchment and the Muhammad Nur Reservoir

for the region. The reservoir is used for flood control, hydroelectric power generation, irrigation, domestic water supply, transportation and recreation. Since the early 1980s, significant changes in vegetative cover and land use have increased the soil erosion hazard and may have affected the rainfall-runoff characteristics of the catchment. In turn, these changes may have affected the volume of water available from the MNR during

different times of year (Moehansyah et al., 2002), the extent of soil erosion and the useful life of the reservoir. The topography of the catchment varies: about 18% undulating, 35% rolling and 45% hilly to mountainous. Soil orders are dominantly Ultisols that cover about 71% of the catchment, followed by Entisols (17%) and Oxisols (12%) and the soils are often highly weathered and acidic. The vegetation and land use types of the

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Paau Rantaubalai

Paau Paau Ha

Ka

na

n

Rantaubujur

ja wa

Ri

am

Anawit

Bunglai

Hajawa

Hanaru

Manuaggul Artain

Aranio Banuariam

Ma

ba

tan

Tabatan

Tiwinganbaru

Tiwinganlama

na

lia

Ta

Kalaan

Ha

na ru

Apuai

la

ro

jan

ma

an

M

in nd

a

M

Ka

Tayup

an

Belangian

LEGEND Catchment boundary Subcatchment boundary

Kalaan

River Muhammad Nur Reservoir Village

Average rainfall, mm

Fig. 2. The Riam Kanan catchment with its subcatchments and the Muhammad Nur Reservoir

MNR. A network of seven major rivers, viz., Paau, Hajawa, Tabatan, Tuyup, Kalaan, Puliin and Maliana Rivers drains the catchment (SKRPB, 1993; MacKinnon et al., 1996; Indrabudi et al., 1998).

400 350 300 250 200 150 100 50 0

2.2. Location of field experiments

J

F

M

A

M

J J Month

A

S

O

N

D

Fig. 3. Average monthly rainfall depth in the Riam Kanan catchment

catchment are virgin forest 59%, reforested areas 6%, cropland 3% and alang–alang (Imperata cylindrica) grassland 23%. The remaining 9% is occupied by the

Field experiments were carried out in the Hanaru subcatchment near Kalaan village (Figs 2 and 4) over a period of 2 years, viz. 1994–1995 and 1995–1996 wet seasons (January to April). Hereafter, the field experiments during 1994–1995 and 1995–1996 are referred to as first year and second year field experiments, respectively. The Hanaru subcatchment was selected for the experiments because: (i) it represents the largest area (40%) of the catchment and includes the location of the MNR; (ii) the erosion in this subcatchment will

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LU4

LU2

N

LU3

500 m

Kalaan Village

LU1

400 m

Slope

(a) Earh bank

description of the land use types is given in Table 1 and plots 1, 2, 3 and 4 correspond to land uses LU1, LU2, LU3 and LU4 respectively. Four plots having area ranging from 2236 to 3895 m2, length from 84 to 92 m, width from 25 to 45 m and slope from 9 to 10% were used for the field experiments (Table 2). The plots were separated by about 300 to 500 m. A schematic diagram showing the layout of the plots and instrumentation is given in Fig. 4. A small earthen bank 04 m high and 05 m wide was constructed along

Table 1 Description of different land uses included in field experiments Land use

Standard rainguage 203 mm DRG1 raingauge and 392 data logger Rising stage sampler (b)

0.5 H-flume

Collection bank

LU1

Groundnut with minimum tillage: land was prepared without ploughing. Initial weeds (alang–alang) were killed using herbicide (Roundup 1%, at the rate of 600 ‘ ha1). Dead weeds were removed before planting. Lime (CaCO3) at the rate 2 ha1 was applied only for the first year. Local variety of groundnut (padi) was planted manually at a density of 120 000 plants ha1. The crop was fertilized using triple supper phosphate at the rate of 45 kg P2O5 ha1 at the time of planting. Weeding was done manually once between 6 and 7 weeks after planting. The crop was harvested after 100 days of planting.

LU2

Groundnut with conventional tillage: land was prepared by ploughing twice to 0.25 m depth with a tractor using mouldboard plough and was harrowed twice. Initial weeds (alang–alang) were incorporated in soil during ploughing and harrowing. Liming, planting, fertilization, weeding and harvesting were done as in land use types LU1. In the second year experiment land was prepared manually by hoeing. Other treatments were the same as those in the first year experiments.

LU3

Alang–alang grass with no tillage: the grass grew naturally without being planted. At the beginning of experiments, the grass was at the maximum growth and was about 08 m high. At maximum growth, the vegetative cover provided by plants was about 85% of the soil surface.

LU4

Reforested with rubber trees: the area is reforested with rubber trees along with existing native trees and alang–alang grass. About 85% of the trees reforested with rubber trees and rest with native forest and alang–alang grass. At the beginning of experiments rubber trees were 11 years old and were 7–10 m high. Native forest was between 5–10 years old and 5–9 m high. The under growth plants was alang–alang grass. The surface cover by rubber and native trees was about 65% and the undergrowth and litter was about 15%, so the total surface cover was 80%.

W.L. sensor and 392 data logger Tipping bucket runoff recorder and 392 data logger

Fig. 4. Schematic diagram showing (a) the layout of the plots, and (b) instrumentation used in one of the plots (LU4)

directly affect the operation and life of the MNR; (iii) the forest area in the subcatchment has been considerably affected by logging; and (iv) the subcatchment has the largest area under grassland and cropland of all the subcatchments of the RKC. The soil order of the experimental site is Ultisols, which represents the largest area (71%) in the RKC. 2.3. Field layout and instrumentation Field experiments involved four different land use types representing the major land uses in the catchment. These include cropland with minimum tillage (LU1), cropland with conventional tillage (LU2), grassland (LU3) and areas reforested with rubber trees (LU4). The

Description

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Table 2 Descriptions of plots used in the field experiment Plot Plot dimensions, number m

1 2 3 4

Width

Length

266 405 448 432

84 92 87 90

Average slope, %

9 10 95 10

Previous land use

Alang–alang Alang–alang Alang–alang Shrub and grass

grass grass grass alang-alang

the plot boundaries to prevent runoff into or out of the plots. At the outlet of each plot, a sediment pond was provided to collect sediment and a 05 m H-flume with depth sensor and tipping bucket was provided for measuring runoff. A chute and stilling basin were provided to facilitate the flow of water through the flume. The flume had a maximum capacity of 150 ‘ s1. A plastic bottle of 2 ‘ capacity was installed in each flume to collect water samples for determining sediment loads in the runoff water. The crop selected was groundnut, while areas with grassland had alang-alang grass (Imperata cylindrica). Apart from cropland with conventional tillage, the other three land uses in the catchment are important in the catchment because: (i) minimum tillage can be used as one of the soil conservation techniques to reduce the impact of land cultivation; (ii) the areas with grassland can meet the grazing needs for livestock, and the grass cover can function as a possible soil conservation measure in the catchment; and (iii) the areas planted with rubber trees can assist in the reforestation of degraded areas as the trees are well adapted in the catchment and can be a source of future income for the people.

2.4. Data collected The field data collected during the 2 years of the experiments included soil physical properties, rainfall, runoff, and soil loss in the plots. The details of methods used for collecting the rainfall, runoff and soil loss data are given below. 2.4.1. Physical properties Soil samples for surface (0–02 m) and profile (0–12 m) soils were collected separately for the determination of texture. A total of 12 samples from the surface soil were taken randomly in each plot for the description of the surface soil, while a total of eight samples were taken from a soil pit measuring 10 m  10 m  15 m for the description of the soil profile. The profile was divided

into four layers at a regular interval of 03 m depth, i.e., 0–03, 03–06, 06–09 and 09–12 m, and two soil samples were taken from each layer in the profile. Other soil physical properties measured included bulk density, porosity, saturated hydraulic conductivity, field capacity and long-term infiltration rate. Bulk density and porosity values were determined from 20 core samples taken randomly in each plot: 12 samples from the surface and eight (two in each layer) from the soil profile. The values of field capacity were determined in the laboratory by subjecting the core samples to 03 bar pressure for 24 h in a pressure plate apparatus. The saturated hydraulic conductivity and long-term infiltration rate values for the plots were determined in the field by using the CSIRO disc permeameter (White et al., 1992). The hydraulic conductivity measurements were made at 12 locations selected randomly in each plot. 2.4.2. Rainfall Rainfall data were collected from manual and automatic raingauges. An automatic raingauge equipped with a data logger and a manual raingauge were set up near plot 1 to measure the rainfall during the field experiments. The manual raingauge was used to calibrate the results from the automatic raingauge. Rainfall data from the manual raingauge were noted every day at 7 a.m. Data from the automatic raingauge were recorded continuously at an interval of 1 min by the data logger, and readings were retrieved from the logger once a week. 2.4.3. Runoff Runoff data for each plot were obtained in two ways: (i) from a hydrograph of flow through the H-flume recorded with a depth sensor and a data logger; and (ii) from a 10 ‘ tipping bucket equipped with automatic counter and data logger to record tips in real time. The tipping bucket was used to provide a backup runoff record in case the depth sensor failed. As it was desirable not to exceed one tip per second, the runoff from the Hflume to the tipping bucket was split. The maximum runoff from a plot was estimated to be 100 ‘ s1, and therefore the runoff was split by a factor of 10. This means 1/10th of runoff passed through the tipping bucket. The depth readings in the flume and tipping bucket event times were retrieved from the data loggers once a week, while the counter readings from the tipping bucket recorder were noted every time there was a rainfall event. The total runoff volume through the flume was obtained using a calibration curve, while that with the tipping bucket was obtained by noting the

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number of tips registered by the recorder and multiplying them by 10 and the volume of the bucket. 2.4.4. Soil loss Soil loss data for the plots were obtained from the sum of the sediment deposits in the sediment pond Sp , chute Sc , and flume Sf and those present in the runoff water Srw . To determine the value of Sp , Sc , and Sf , the moist weights of sediment deposits in the pond, chute and flume, respectively, were determined. Three samples (about 50 g each) were then taken from the sediment deposited at each location. The samples were then oven dried at 105 8C for 48 h to get the average moisture content. Sediment yield, on dry weight basis, from a plot for a given rainfall event was determined using the following equation: Sp þ Sc þ Sf þ Srw Ys ¼ ð1Þ Apt where: Ys is sediment yield of plot for a given rainfall event in kg ha1; Sp is the dry weight of sediment deposited in the sediment pond in kg; Sc is the dry weight of sediment deposited on the chute in kg; Sf is the dry weight of sediment deposited on the flume in kg; Srw is the dry weight of sediment present in the runoff water in kg; and Apt is the area of plot in ha. The sediment concentration of the runoff water Cs was obtained by knowing the average weight of dry sediment per unit volume of the runoff water. From the runoff collected in a plastic bottle, three samples, each 50 m‘, were taken and oven dried to obtain dry weights of the sediment. These weights were then used for calculating an average value of sediment concentration in the runoff water using the following equation: Cs Vr ð2Þ Srw ¼ 1000 where: Cs is the concentration of sediment in the runoff water in mg ‘1; and Vr is the volume of the runoff water from plot in m3. 2.5. Models selected for runoff and soil erosion As mentioned earlier, there are numerous models currently available to predict runoff and soil erosion in catchment but only those models that are widely available and can be used within the constraints of the field resources available in the RKC are selected for the purpose of this study. The ANSWERS was selected to evaluate its suitability for predicting both runoff and soil erosion under field conditions for the catchment. ANSWERS is a well documented and widely used event-based prediction model developed by Beasley et al. (1980).

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The USLE and a slight adaptation of the USLE (called Adapted USLE or AUSLE), along with the ANSWERS model, were also selected for predicting the level of soil erosion under the field conditions of the RKC. It should be noted that the USLE and AUSLE cannot predict runoff and the models predict average soil loss for a season or year. 2.5.1. ANSWERS model The ANSWERS model is a process-based distributed parameter model that can be used for predicting the spatial variation in erosion across a catchment. However, the model is not entirely physically-based since the erosion sub-model is semi-empirical as it uses components of the USLE. Also, the infiltration equation in the version of the model used in this study is an empirical equation. The model can be used to predict soil loss from an area of plot size to a small catchment. In this model, the area is divided into a regular grid where each grid cell can be allocated a given value for a catchment attribute. The model consists of two components, i.e., hydrologic and erosion. The hydrologic components simulate the processes of interception, infiltration, surface runoff and, to some extent, interflow and baseflow. A limitation of the version of the model used in this study is that Hortonian overland flow is the only runoff producing mechanism that is fully modelled (Armstrong & Mackenzie, 1995). The erosion component of the ANSWERS model consists of a modification of the USLE model. Two soil detachment processes are modelled: rainfall detachment and overland flow detachment. Channel erosion is assumed negligible, and only deposition is allowed in channel flow. Land use changes, tillage technique and management procedure are simulated in ANSWERS using the appropriate parameter values for the practise selected. Gully stabilization structures such as drop spillways or chutes may be simulated by reducing slope steepness of the associated channel segments. Diversion banks can be simulated by changing the direction of flow in the input data file of the elements representing the bank. Other structural measures such as dams, banks and grass waterways and field border strips are handled by a separate subroutine in the program. The original ANSWERS model, documented in Beasley and Huggins, (1982), was used in this study. The original model has since undergone several modifications, including improvements to the infiltration component and the development of a continuous simulation version (Bouraoui & Dilliha, 2000), however these later versions of the model were not available at the time this work was undertaken. Before ANSWERS can be run, a detailed input data file must be created. The input file contains five basic

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Table 3 Key soil physical properties for the ANSWERS model Particle size analysis (0–03 m soil profile), % Land use

Clay

Silt

Sand

Bulk density, kg m3

Porosity, %

Ksat, mm h1

LU1 LU2 LU3 LU4

540 543 544 550

194 184 175 185

267 274 281 265

1150 1020 1210 1050

570 621 548 609

106 175 102 257

Field capacity, %

Long-term infiltration rate, mm h1

315 366 315 312

330 342 309 512

Ksat, saturated hydraulic conductivity.

blocks of data: (i) rainfall information for up to four raingauges; (ii) soil information for up to 20 soil types; (iii) land use information (crop type); (iv) channel descriptions; and (v) individual element information. Some of the key input data, obtained from field measurements, for the ANSWERS model are summarised in Table 3. Runoff, sediment concentration and cumulative sediment yield is output for the grid cell located at the catchment outlet so that hydrographs and graphs of sediment production from the catchment can be produced for the selected storm event. Soil loss from each grid cell is output allowing the spatial distribution of erosion and deposition in the catchment to be plotted. Like all empirical and semi-empirical models, this model also has advantages and disadvantages. This model is useful for predicting soil erosion based on a limited climate data set. Soil loss is predicted on a rainfall event basis. There are some limitations in the use of this model. For example, Armstrong and Mackenzie (1995) pointed out that the ANSWERS model could not be used for predicting gully erosion. This particularly limits its usefulness in part of Australia, for example, where this type of erosion predominates. Armstrong et al. (1995) reported that the original ANSWERS also did not satisfactorily predict storm runoff for a small catchment in West Java, Indonesia, due to its poor representation of the infiltration processes operating in the catchment studied. Application of this model in different geographical and climatic conditions should be evaluated before it can be recommended as a suitable tool for erosion prediction at a given location. 2.5.2. USLE and AUSLE models The USLE model is an empirically based equation which is designed to predict long-term average soil loss, resulting from sheet and rill erosion from uniform hillslopes (Wischmeier & Smith, 1978). As such, it is ideally suited to providing a first approximation of the long-term erosion stability of uniform slope segments,

such as cropland and grassland areas. Soil loss is estimated using the following equation: A¼RK LSCP

ð3Þ

where: A is the estimated soil loss in t ha1 y1; R is the erosivity of rainfall in mm ha1 h1 y1; K is the inherent soil erodibility, dimensionless; L is length of the slope factor, dimensionless; S is slope factor, dimensionless; C is crop cover factor, dimensionless; and P is a factor that accounts for the effects of soil conservation practices, dimensionless. The advantage of the USLE model is that it has been widely tested over many years and the validity and limitations of this model is already known (Lal, 1990). The disadvantage of this model is that it was developed using data from the Midwest of the USA, and therefore significant adjustment may be required to the algorithms used to derive the key parameters before the model can be applied to other areas, such as the RKC. In Indonesia, the USLE has been applied but with a limited evaluation. One of the main applications has been in assessing the impact of changes in land use on erosion and selecting appropriate erosion control measures in transmigration areas (Sinukaban, 1989). The application of this model for Indonesia requires evaluation of the factors based on the local conditions. In general, rainfall erosivity R and soil erodibility K are the most important factors that need evaluation based on local conditions for successful application of the model (Armstrong et al., 1995). The USLE model is generally used for estimating the average annual soil loss, but the model can also be used for estimating the average soil loss for other periods or individual storms if appropriate values of rainfall erosivity R are used. In the present study, the USLE is used for calculating the soil loss for wet seasons, viz., January–April period. The erosivity index is calculated for the wet season as the sum of erosivity index values of the individual months for the season. Values for factors C and P are estimated using the procedure outlined in

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Wischmeier and Smith (1978) and the range of values suggested by Morgan et al. (1978) for the vegetative cover conditions considered in this study. Considering that the vegetative covers were generally similar during two seasons of experiments and there are difficulties in estimating the C values, it was assumed that the C values for corresponding months for the two seasons were similar. For computing the monthly value of the R factor, the following equation proposed by Bols (1978) for Indonesia was used: R ¼ 619ðRf Þ121 ðRn Þ047 ðRm Þ053

ð5Þ

where: M is given by ½ðSt  Svf Þ=100  Cf ; a is the percentage of soil organic matter content; b is the structural code; c is the permeability class of the soil; St is silt fraction of soil in %; Svf is very fine sand fraction in soil in %; and Cf is clay fraction in soil in %. Equation (5) is suitable when St does not exceed 70%. Slope length L and slope S factors are computed using the following equation (Wischmeier & Smith, 1978): LS ¼ ½‘=221m ð6541 sin2 a þ 465 sin a þ 0065Þ

ð6Þ

where: ‘ is the slope length in m; a is the slope in degrees; and m is the exponent that varies with slope as in given Table 4. Equation (6) was derived from the data with a slope between 3 and 18%. Extrapolation beyond this range is not recommended due to risk of significant error when applied to steeper slopes. The AUSLE model used in the present study is the USLE model with an adjustment for the LS factor. Other factors, viz., R, K, C and P are the same as those in the USLE model. This model was chosen to examine Table 4 Relation between slope S and exponent m used for LS computation in the USLE model Slope (S), deg 5057 057–517 17–5 29 >29 L, length of slope factor.

Exponent (m) 02 03 04 05

whether the change in the value of the LS factor can improve the model prediction. The value of the LS factor for the AUSLE model was computed using the equation derived by Moore and Wilson (1992): LS ¼ ðAs =2213Þ04 ðsin a=00896Þ13

ð7Þ

where: As is the specific catchment area in m2; and a is the slope in degree. Some of the key input data, obtained from field measurements, for the USLE and AUSLE models are summarised in Table 5.

ð4Þ

where: R is monthly erosivity; Rf is total monthly rainfall; Rn is number of rainy days per month; and Rm is the maximum rainfall during 24 h in the observed month. The value for factor K is computed using the following equation (Wischmeier & Smith, 1978): 100K ¼ 21M 1:14 ð104 Þð12  aÞ þ 325ðb  2Þ þ 25ðc  3Þ

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2.6. Evaluation procedure The models were evaluated by examining how the predicted values fit the observed values. The performance accuracy of the model was evaluated qualitatively and quantitatively. Qualitatively, it was evaluated by plotting the observed and predicted values on a graph and examining how these points fall in relation to a 458 line passing through the origin. The closer the points fall about the line, the more accurate the model will be. Quantitatively, the performance of the model was evaluated by fitting regression equations between the predicted values Xp and the observed values Xo. Two linear regression equations were selected for evaluation, viz., one passing through the origin and the other one with an intercept. The mathematical forms of the two equations are: X# p ¼ lXo ð8Þ X# p ¼ a1 þ l1 Xo

ð9Þ

where: X# p is predicted value of Xp from regression of Xp on Xo ; a1 is intercept, and l and l1 are coefficients. The goodness of fit of the above equations to the data is evaluated from the values of coefficient of determination and the standard error of estimate. Coefficient of determination is denoted, respectively, by r2 for Eqn (8) and r21 for Eqn (9). Standard error of the estimate is denoted, respectively, by s for Eqn (8) and s1 for Eqn (9). The greater the values of r2 and r21 , the better will be the fit of the data to the equations. Also, the values of l and l1 close to the unity means unbiased the prediction. When the value of either l or l1 is 51, it indicates under-prediction and when >1 it indicates over-prediction. Equation (9) can be used in two ways, (i) to test whether the Eqn (8) is reasonable or not and (ii) to see the trend of the model prediction. If the intercept a1 is very small and the fit is satisfactory then Eqn (9) will provide information about model performance. Conversely, when a1 is large and the fit is not satisfactory, then attempting to force the line through the origin will

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Table 5 Key input data used for the USLE and AUSLE models Land use type

Month

(i) First year experiments LU1 January February March April

Rainfall erosivity (R), Soil erodibility Length and slope mm ha1 h1 y1 factor (K) factor (LS)

Crop cover factor (C)

Soil conservation practices factor (P)

0 351 883 723

022 022 022 022

198 198 198 198

030 025 025 030

1 1 1 1

LU2

January February March April

0 351 883 723

021 021 021 021

207 207 207 207

055 035 025 030

1 1 1 1

LU3

January February March April

0 351 883 723

021 021 021 021

201 201 201 201

015 015 015 010

1 1 1 1

LU4

January February March April

0 351 883 723

021 021 021 021

204 204 204 204

0005 0005 0005 0005

1 1 1 1

1787 3847 3992 4252

022 022 022 022

198 198 198 198

030 025 025 030

1 1 1 1

(ii) Second year experiments LU1 January February March April LU2

January February March April

1787 3847 3992 4252

021 021 021 021

207 207 207 207

055 035 025 030

1 1 1 1

LU3

January February March April

1787 3847 3992 4252

021 021 021 021

201 201 201 201

015 015 015 010

1 1 1 1

LU4

January February March April

1787 3847 3992 4252

021 021 021 021

204 204 204 204

0005 0005 0005 0005

1 1 1 1

cause distortion in the fit and a negative value of r21 will result. This is statistically meaningless. However, when a1 is large but the fit is satisfactory, this must be due to a systematic error and imperfect description of one or several of the model components and their interaction. The value of r2 (for Eqn (8)) shows the closeness of the model. If the value of l is close to unity and followed by a high value of r2 , the performance of the model is satisfactory. The performance of the model is also tested by computing absolute value of average error based on regression fit Er and the average error based on direct comparison of observed and predicted values Ea using

the following equations: Er ¼ jð1  lÞj  100 Ea ¼

N jXoi  Xpi j 100 X N i¼1 Xoi

ð10Þ ð11Þ

where N is the number of measurements. The index Er shows error based on the overall trend of the majority of data. However, the meaning of Er will depend much on the values of r2 and s. If the values of r2 and s are small, the value of Er is not meaningful for the estimation of prediction error. For the condition where the predicted values are close to the observed

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values, the value of coefficient correlation r, which is equal to Or2 , is usually close to unity.

3. Results 3.1. General The soil texture at the experimental site is heavy clay and there is little variation between the plots (Table 3). The average bulk density of the surface soil ranges from 1010 to 1210 kg m3 under different land uses. Relatively low bulk densities for LU1 and LU4 are most likely due to the effect of cultivation in the case of LU1, while for LU4 it is most likely due to the effect of plant roots and soil macro-organisms (e.g., worms and termites), that are more active in forest soil. The higher bulk density under alang-alang grass (LU3) is most likely the result of past trampling by livestock resulting in soil compaction. Saturated hydraulic conductivity values Ksat (Table 3) reflect the differences in soil porosity, with the exception that Ksat was higher for forest compared to conventional tillage. This is explained by the presence of intact macropores in the forest soil (LU4) that act as a preferred flowpath for the transmission of water in the soil profile. This would more than compensate for the slightly lower surface porosity under forest compared to conventional tillage (LU3).

The ANSWERS model was used to predict runoff and soil erosion for four events during the first year and five events during the second year of field experiments. The other rainfall events, not included for analysis here, had either insufficient rainfall to generate runoff or the data logger failed to record all the required data for the event. Similarly, the USLE and AUSLE models were used to predict soil erosion for the wet season during both years of field experiments. The key data required for evaluating the models as to their prediction of runoff and soil erosion are summarised in Tables 6 and 7. Figure 5 shows a variation of observed and model predicted runoff values for the ANSWERS model, while Table 8 indicates the values of performance indices for the model. Since there were limited data sets available for each land use type, the data from all the land use types were combined for the calculations of model performance indices. Similarly, Figs 6 and 7 show a variation of observed and model predicted erosion values for the ANSWERS, USLE and AUSLE models, while Table 9 indicates the values of performance indices for the models. In general, the observed soil loss data and those predicted by the USLE and AUSLE models for the first year experiments are slightly different from those for the second year experiments (Fig. 7). This is mostly due to the differences in rainfall characteristics and runoff during the 2 years of experiments.

Table 6 Observed values of rainfall, rainfall intensity, runoff and soil erosion under four land use types during the first year experiments Date of rainfall event

Feb. 25 Feb. 27 March 3 March 5 March 10 March 12 March 15 March 18 March 21 March 23 March 25 March 26 March 28 March 31 April 2 April 3 April 5 April 6 April 9 April 11

Rainfall, mm

372 301 111 179 58 134 172 198 13 305 208 177 24 108 132 275 388 77 211 299

Average rainfall intensity, mm h1

519 212 133 113 66 102 101 112 35 261 100 142 46 154 226 317 358 92 162 408

Soil loss, kg ha1

Runoff, mm LU1

Land use type LU2 LU3

152 61 14 19 0 13 28 22 0 74 19 23 0 14 27 57 86 04 33 72

144 59 13 19 0 14 29 23 0 93 2 25 0 15 29 65 94 04 33 81

102 34 1 13 0 08 15 14 0 62 1 14 0 08 2 37 58 0 18 38

LU4

LU1

Land use type LU2 LU3

LU4

68 2 04 1 0 06 12 11 0 38 07 09 0 04 15 29 4 0 13 28

662 208 322 58 0 186 724 365 0 442 24 70 0 46 98 232 436 02 98 418

7868 2247 42 699 0 244 893 535 0 4902 31 816 0 54 110 2505 455 04 1065 433

36 102 13 25 0 1 28 35 0 20 18 25 0 15 58 112 18 0 5 8

132 46 95 164 0 35 15 11 0 98 6 15 0 9 26 56 85 0 26 58

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Table 7 Observed values of rainfall, rainfall intensity, runoff and soil erosion under four land use types during the second year experiments Date of rainfall event

Rainfall, mm

Average rainfall intensity, mm h1

Land use type LU2 LU3

LU1

Predicted runoff, mm

Jan. 8 Jan. 13 Jan. 22 Feb. 2 Feb. 5 Feb. 9 Feb. 13 Feb. 23 March 1 March 6 March 14 March 19 March 26 April 2 April 7 April 14

62 12 162 178 13 304 48 75 118 81 91 7 25 26 47 84

72 24 6 76 46 152 96 136 203 10 73 14 104 182 348 11

Soil loss, kg ha1

Runoff, mm

09 35 25 07 0 28 09 18 21 11 11 1 18 32 53 09

14 46 26 11 0 35 12 14 18 08 13 12 27 41 64 09

04 09 08 06 0 2 05 06 14 08 08 07 09 39 19 06

LU4

LU1

Land use type LU2 LU3

01 03 02 02 0 08 01 03 1 02 03 01 03 04 12 03

184 1227 1316 48 0 828 434 327 1398 448 312 223 432 814 150 379

25 116 212 24 0 158 84 686 2106 694 66 395 728 148 2045 686

LU4

4 382 304 4 0 384 58 65 204 36 22 24 36 142 298 28

06 58 4 08 0 45 08 24 34 06 08 1 18 43 64 08

25

Table 8 Values of performance indices for the ANSWERS model for runoff prediction

20

Performance index

15

10

5

0 0

5

10

15

20

25

Coefficient l Coefficient of determination r2 Standard error of estimate s Coefficient of variation s=X% p , % Intercept a1 Coefficient l1 Coefficient of determination r21 Standard error of estimate s1 Coefficient of variation s=X% p , % Average error based on regression analysis Er, % Average error based on the comparison of observed and predicted values Ea, %

Value of performance index 110 057 262 446 180 086 065 240 409 10 44

Observed runoff, mm Fig. 5. Observed and predicted runoff computed using the ANSWERS model for various land use (LU) types: ^, LU1; &, LU2; m, LU3; x, LU4; }, 1:1 line

3.2. Observed and predicted runoff There is a large difference between the observed runoff values and those predicted by the models (Fig. 5). For the ANSWERS model, the scatter of the points is much closer to the 1:1 line, and the number of data points above the 1:1 line are slightly higher than those scattered below the line. From the distribution of points along the 1:1 line, Fig. 5 shows that the ANSWERS model tends to overpredict the runoff. This is also confirmed by the

value for l of 1.1 (Table 8). The error, based on Er , is relatively small but the average absolute error Ea is 44%. Based on the value of r2 (057) and coefficient of variation (s=X% p ; where X% p is the mean of Xp values) (446%) (Table 8), there is some relatively high unexplained variation in the model predicted runoff values.

3.3. Observed and predicted soil loss 3.3.1. The ANSWERS model Figure 6 shows that the ANSWERS model tends to overpredict soil loss. This is also confirmed by the values

ARTICLE IN PRESS FIELD EVALUATION OF SELECTED SOIL EROSION MODELS

of l (112) that is greater than unity (Table 9). The values of Er and Ea are 12% and 105% respectively, and most of the data sets have the prediction error 550%. Although the model has a relatively higher value for r2 (080), the coefficient of variation (s=X% p ) (512%) is the highest among the three models used. Under forest conditions (LU4), the erosion is quite small (Figs 6 and 7) but even a relatively small error in erosion value (in kg ha1) contributes to a larger overall error, and therefore a high value for the coefficient of variation is slightly misleading here.

1000 900

Predicted soil loss, kg ha-1

800 700 600 500 400 300 200 100 0 0

200 400 600 800 Observed soil erosion, kg ha-1

1000

Fig. 6. Observed and predicted soil loss computed using the ANSWERS model for various land use (LU) type: ^, LU1; &, LU2; m, LU3;  , LU4; }, 1:1 line

Observed and predicted soil erosion, kg ha-1

3500 3000 2500 2000 1500 1000 500 0 LU1 (a)

LU2 LU3 Land use type

LU4

Observed and predicted soil erosion, kg ha-1

3500 3000

3.3.2. The USLE model As shown in Fig. 7, the USLE model slightly overpredicts soil loss. This is also confirmed by the values of l (102) that is greater than unity (Table 9). The values of Er are low (2%) but that of Ea is quite high (176%). The value of l is close to unity, but the values of r2 (059) and coefficient of variation (s=X% p ) (405%) indicate that the model is probably not highly reliable for predicting soil loss. The value of Er for the model is low because it overpredicts for some data sets and underpredicts for the others. The value of r21 (076) is relatively high and a1 is large but that of r2 (046) is relatively low. Also, the values of r21 , a1 and r2 indicate that there is a systematic error resulting from an imperfect description of one or more model components. 3.3.3. The AUSLE model The model overpredicts in four instances and underpredicts in three instances (see Fig. 7). The values of l (093) are smaller than unity (Table 9). The overprediction is mostly for the first year experiments. The value of Er is 7% while that of Ea is 48% (Table 9). From the values of r2 (068), coefficient of variation (s=X% p ) (423%) and data with error >50% (Table 9), the model performance is superior to the USLE model, but the values of r21 , a1 and r2 indicate that there is evidence of some systematic error similar to the USLE model.

2500 2000

4. Discussion

1500 1000 500 0 LU1

(b)

503

LU2 LU3 Land use type

LU4

Fig. 7. Observed and predicted soil loss computed using USLE and AUSLE models: (a) first year experiments, (b) second year experiments; , observed; , predicted by USLE model; , predicted by AUSLE model

Erosion prediction at the catchment levels is difficult due to the complexity of large heterogenous catchments. So far, only a few erosion models have been used satisfactorily to predict catchment sediment yields (Kothyari et al., 1994). This is because either the models failed to simulate the erosion process in the catchment or adequate sediment yield data from catchment are not readily available for testing the model accuracy. Due to variability and uncertainty of erosion data under field conditions, an error in model prediction up to 50% has

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Table 9 Values of performance indices for the ANSWERS, USLE and AUSLE models for soil loss prediction Performance index

Value of performance index Model

Coefficient l Coefficient of determination r2 Standard error of estimate s Coefficient of variation s=X% p , % Intercept a1 Coefficient l1 Coefficient of determination r21 Standard error of estimate s1 Coefficient of variation s=X% p , % Average error based on regression analysis Er, % Average error based on the comparison of observed and predicted values Ea, %

been considered acceptable by some researchers (Kothyari et al., 1994). Therefore, the important points that need to be considered while selecting erosion models for the RKC are that: (i) they reasonably fit the experimental data, (ii) the prediction error is 50%; and (iii) they are simple and can be used with limited input data. So far, the USLE model is probably the most widely used for erosion prediction. However, the application of this model is limited to the field plot level. The same is true for the AUSLE model. The ANSWERS model can be used either at farm or at catchment levels. The problem associated with the application of the USLE and AUSLE models at catchment level, in general, stem from the fact that they do not calculate soil deposition within the catchment (Hudson, 1995). Application of USLE and USLE with sediment delivery ratio (USLE+SDR) models for sediment yield prediction at catchment level was reported by Kothyari et al. (1994). They found that the USLE model overpredicts and USLE+SDR model underpredicts the sediment yield. However, they reported that the USLE+SDR model when applied based on time-area segments predicts sediment yield at catchment level satisfactorily. All models tend to overpredict the soil loss for the 2 years of experiments. The difference between the observed soil loss values and those predicted by the models varies considerably. The USLE and AUSLE models underpredict soil loss for some storm events during the first year of experiments. In general, the overall prediction error of the models to predict soil loss is in the order of ANSWERS5AUSLE5USLE, indicating the ANSWERS model is the most accurate and the USLE model is the least accurate among the three models considered in this study.

ANSWERS

USLE

AUSLE

112 080 1075 517 354 103 081 1053 507 12 106

102 059 6455 405 7026 073 086 4050 254 2 176

093 068 5825 423 4911 072 081 4883 355 7 48

It is difficult to determine which factors result in the variation of error. In general, soil erosion models calculate soil loss based on three components: splash detachment due to raindrops, flow detachment due to runoff and deposition within the area. Assuming there is no deposition due to a high flow velocity of runoff water in the catchment, the soil loss is the result of splash detachment and flow detachment. The values of Ea for the ANSWERS model for 2 years of experiments (Tables 8 & 9) show that Ea for soil loss is much greater than that for runoff data. This difference indicates that the error of prediction may be due to the error in computing flow detachment due to runoff and as well as in computing splash detachment from raindrops. For the ANSWERS model, Wu et al. (1993) reported that the error in the prediction of soil loss due to raindrops is related to antecedent moisture and crop cover and therefore those two aspects may also have affected the prediction with the ANSWERS model. The USLE and AUSLE models are lumped parameter models. Soil loss is calculated based on factors of rainfall erosivity, soil erodibility, slope and slope length, plant cover and soil conservation management factors. Among those factors, C is probably the greatest source of error for the models (Wischmeier & Smith, 1978). The higher values of predicted soil loss than those observed in the study might be related to values of C used in the two models. It should be noted that the evaluation of the models is based on only 2 years of field data, but the USLE and AUSLE models are meant for long-term predictions. This means the results of this evaluation mainly indicate the suitability of these two models as planning tools in the Indonesian context rather than relative accuracy of the models with the ANSWERS model which can predict soil loss for individual storms.

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The ANSWERS model has the best fit for the field data. However, the model prediction under forest conditions is not very satisfactory. About 60% of the RKC is under forest and therefore the overall suitability of the ANSWERS model for the catchment is questionable. Experience of Armstrong et al. (1995) in Citere (West Java, Indonesia) and Armstrong and Mackenzie (1995) in Australia indicate that the model is not completely satisfactory for predicting runoff at the catchment level. Furthermore, the application of this model for the catchment needs detailed and accurate soil, topography, vegetation and land use data of the catchment since its application is grid based. This means, its application requires considerable work for collecting data, and such a task is time consuming and requires considerable resources. Since the AUSLE model shows sufficiently reliable results, is relatively easy to use and the data of SDR can be computed from the data of sediment in the MNR, the study suggests that the AUSLE model is probably more suitable than the ANSWERS model for comparing the effect of land use and management practices on soil loss in the RKC.

5. Conclusions The main conclusions of this study are: 1. The ANSWERS model in general has a tendency to overpredict runoff in the RKC. 2. The performance of the ANSWERS, USLE and AUSLE models for predicting soil loss varies considerably and the models tended to overpredict soil loss in the catchment. The magnitude of overall prediction error of the models is in the order of ANSWERS5AUSLE5USLE, i.e. for this catchment and the land uses studied, the ANSWERS model is the most accurate and the USLE model is the least accurate among the models considered. 3. Overall, the ANSWERS model is superior for predicting soil loss in the RKC. However, considering that the AUSLE model showed sufficiently reliable results and since the input data required for the AUSLE are relatively easy to obtain in the catchment, the AUSLE model is probably more suitable for predicting soil erosion in situations where detailed catchment data is not readily available.

Acknowledgements The financial support of the Australian Centre for International Agricultural Research (ACIAR),

505

Canberra for fieldwork during this study is gratefully acknowledged.

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