Soil erosion modelling using ‘answers’ and geographical information systems

EARTH SURFACE PROCESSES AND LANDFORMS, VOL. 14, 517-532 (1989)

SOIL EROSION MODELLING USING ‘ANSWERS’ AND GEOGRAPHICAL INFORMATION SYSTEMS

A. P. J. DE ROO, L. HAZELHOFF, AND P. A. BURROUGH Instilute of Geographical Research, University of Utrecht, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands

Recevied I5 July 1988 Revised 26 January 1989

ABSTRACT

The linkage of the model ANSWERS and a Geographical Information System to simulate surface runoff and soil erosion is described. The GIS can be used to provide (such as slope and aspect), store, change, and display data needed for simulation models. Conservation scenarios can be designed within the CIS and evaluated by the model. The results can be compared with other scenarios and displayed in the CIS.

Model output is very sensitive to small changes of several input variables, such as infiltration, antecedent soil moisture, and soil roughness. Detailed information about rainfall intensities during an event is needed. Sensitivity and insufficient input data make the validation of ANSWERS difficult. The major constraint for accurate modelling is the degree to which reliable location-specific estimates of the input variables can be made. As models are made increasingly realistic, they need more and better data, which are not always available.

KEY WORDS Simulation model ANSWERS Soil erosion GIS Validation Scenarios

INTRODUCTION

On the soils developed on loess in the Dutch Province of Limburg the problems of soil erosion and excess runoff are becoming increasingly acute. People living in the valleys are frequently troubled with mudflows in their houses and on public roads, which cause serious financial damage both for civilians and the local government. Farmers sometimes lose a part of their seedbed, suffer reduced crop yields, and lose the fertile topsoil. Ecologically valuable areas on slopes and in valleys are affected by the penetration of partly polluted water and sediment.

At present, there is much interest in quantitative techniques to estimate the amount of runoff and soil erosion, not only in the scientific world, but also from the regional and local government. Information is needed by planners about the kinds and the best possible locations for erosion control measures. One of the most useful tools that can be employed when planning control measures for soil erosion is an accurate, comprehensive watershed model, capable of simulating all effects of proposed and/or applied control measures (Beasley, 1986).

This article presents the results of a study using the original version of the ANSWERS model, a recently developed distributed parameter model which simulates surface runoff and erosion. In addition the results of a sensitivity analysis of the model are presented.

DEVELOPMENTS IN EROSION MODELLING

In modelling soil erosion there has been a shift from models calculating soil erosion on a single slope, such as the basic USLE (Wischmeier and Smith, 1978) towards models estimating soil erosion and sedimentation within watersheds.

0197-9337/89/0605 17-16$08.00 0 1989 by John Wiley & Sons, Ltd.

518 A. P. J. DE ROO, L. HAZELHOFF AND P. A. BURROUGH

The Universal Soil Loss Equation (USLE) is a frequently used empirical soil erosion model. The equation is based upon statistical analysis of soil erosion data collected from small erosion plots (‘Wischmeier plots’) in the United States. It has become clear that, because of different climatic conditions and soil properties, the rainfall factor R and the K-nomographs developed in the U.S.A. to determine the erodibility factor K, are not applicable to the European loess-soils without fundamental modifications (De Ploey, 1983, 1986a, 1986b).

Besides these theoretical aspects, other disadvantages of the USLE are that the process of sedimentation is not represented in the equation and that soil losses and gains over neighbouring areas are not taken into account.

Recently there has been a shift from empirical models, such as the USLE and SLEMSA (Stocking, 1981), towards more analytical deterministic models, such as CREAMS (Knisel, 1980) and ANSWERS (Beasley and Huggins, 1982). Empirical models are based on a statistical analysis of important factors in the soil erosion process and yield only approximate and probable outcomes. Deterministic models describe the erosion process with physical-mathematical relationships which should, in principle, give more exact results (Hammond and McCullagh, 1980).

Deterministic models can be separated into ‘lumped’ and ‘distributed’ models. ‘Lumped’ models, such as CREAMS, describe an overall or average response of the watershed (Beasley, 1986). Because of the spatial variability of the erosion process, these models often do a less than adequate job of describing the physical situation.

A distributed parameter model attempts to increase the accuracy of the resulting simulation by preserving and utilizing information concerning the areal distribution of all spatially variable, non-uniform processes incorporated into the model. These models have a potential for providing a more accurate simulation of natural catchment behaviour. They have an ability to simulate conditions at all points within a watershed simultaneously. They have the capability of forecasting the spatial pattern of hydrological conditions within a catchment as well as simple outflows and bulk storage volumes (Beven, 1985). In recent years, the distributed approach has become practicable as a result of the introduction of Geographical Information Systems (GIS) and the continuing rapid improvements in the size, speed, and general availability of modern computers.

THE ANSWERS MODEL

The distributed parameter models ANSWERS (Areal Nonpoint Source Watershed Environment Response Simulation), developed by Beasley et al. (Beasley and Huggins, 1982), was used in this study for modelling surface runoff and soil erosion. It has been fully integrated within a GIS by writing interfacing programs to input data from the GIS to the model and to display the results. The ANSWERS model is designed to simulate the behaviour of watersheds that have agriculture as their primary landuse, during and immediately following a rainfall event. Its primary application is in planning and evaluating various strategies for controlling pollution from intensively cropped areas. A watershed to be modelled is assumed to be composed of square elements. Values of variables are defined for each element, e.g. slope, aspect, soil variables (porosity, moisture content, field capacity, infiltration capacity, erodibility factor), crop variables (coverage, interception capacity, USLE C/P factor), surface variables (roughness and surface retention) and channel variables (width and roughness).

A rainfall event is simulated with time increments of one minute, taking into account spatial and temporal variability of rainfall. The continuity equation is used to compute the composite response of the single elements. The output of up-slope elements becomes the input of downslope elements. Several physically- based mathematical relationships are used to describe interception, infiltration, surface retention, drainage, overland flow, channel flow, subsurface flow, detachment by rainfall, and/or overland flow and sediment transport by overload flow (interrill erosion). A summary of the relationships used in ANSWERS is given (Table I). When water and sediment reach an element with a channel they are transported to the watershed outlet. Sedimentation within a channel appears when the transport capacity has been exceeded.

The original element files for the input of spatially varying parameters to ANSWERS made by Beasley et al. had to be created by hand. Runoff and soil erosion processes and parameters vary over the catchment. If

SOIL EROSION MODELLING 519

Table I. Component relationships used in the ‘ANSWERS’ model (after Beasley and Huggins, 1982) ~

5. Detachment of soil particles by raindrop impact (Meyer and Wischmeier): DETR=0.108 * C * K * Ai * R2 where DETR = rainfall detachment rate (kg min ~ I),

C =cropping and management factor, C * P from the Universal Soil Loss Equation, K =soil erodibility factor, K from the USLE, A i = area increment (m’), R =rainfall intensity during a time interval (mm min I) .

6. Detachment of soil particles by overland flow (Meyer and Wischmeier, Foster): DETF=0.90* C * K * Ai* S L * Q where DETF= overland flow detachment rate (kg min - I ) ,

SL = slope steepness (%). Q=flow rate per unit width (m’ min-I). C =cropping and management factor, C * P from the Universal Soil Loss Equation, K =soil erodibility factor, K from the USLE, A i = area increment (m’).

TF=161 *SL*QO” if Q50.046m’ min-’ TF=16320*SL*QZ if Q>0.046mZ min-’ where TF=potential transport rate of sediment (kg min-’ m-I),

7. Transport capacity (Yalin, Meyer and Wischmeier, Foster and Meyer, Curtis):

Q = flow rate per unit width (m’ min- ‘), SL = slope steepness (%).

Continuity equation: dS I - Q = - dt

where I =inflow rate to an element from rainfall and adjacent elements, Q =outflow rate, S=volume of water stored in an element, t = time.

Surface storage potential (Huggins and Monke):

D E P = H U * R C * ~

[H:]IiRc where DEP = volume of stored water (mm),

H = height above datum (mm), H U =height of maximum microrelief (mm), RC = a surface characteristic parameter.

Infiltration (Holtan, Overton):

F M A X = F C + A * ~ ripv]’ where FMAX =infiltration capacity with surface inundated,

F C = final infiltration capacity,

TP= total volume of pore space within the infiltration control depth, A=maximum infiltration capacity in excess of FC,

PIV=volume of water that can be stored within the control volume prior to its becoming saturated, P = dimensionless coefficient relating the rate of decrease in infiltration rate with increasing soil moisture

content. Drainage (when soil moisture exceeds field capacity):

where DR =drainage rate of water from control zone, G WC=gravitational water capacity of the control zone (total porosity minus field capacity).

520 A. P. J. D E ROO, L. HAZELHOFF A N D P. A. BURROUGH

DIGITAL TERRAIN MODEL WATERSHED CATSOP

\ 0

Figure 1. Digital elevation model of the Catsop watershed (Limburg-NL)

data about these parameters are stored in geographical information systems they can easily be extracted for this type of erosion modelling. Not all data are equally available, however. At present most detailed information is available about the terrain form (Kirkby, 1985). To make the best possible use of distributed topographic data a watershed should be modelled by a raster-based digital elevation model (DEM). In many cases this DEM will have several thousands of elements, so that entering data by hand is no longer feasible.

A DEM can be constructed by digitizing contour maps (Figure 1). Within the GIS maps of altitude, slope and aspect, which are all input for the ANSWERS model can be computed (Burrough, 1986). Also, maps of concavity/convexity and potential stream channels can be derived from the DEM. Geostatistical inter- polation techniques, incorporated in the GIs, can be used to produce maps from point observations of the soil etc., collected during field experiments. Using a method such as block kriging, point observations are interpolated to blocks of the same size as the elements used for simulation.

When there are no sufficient field measurements available, the distribution of a desired input variable can be derived from digitized soil or landuse maps (Bouma and Bregt, 1989).

A large element file has to be created containing the information for all single elements. The original version of the ANSWERS model allowed only 20 soil and landuse types to be used in the simulation under the assumption that they were spatially homogeneous. This small number of units results in lumping occurring in what was meant to be a distributed model. Therefore the large spatial variability of many soil- physical variables, which may be different for each variable, necessitated a modification of the model.

SOIL EROSION MODELLING

altitude slope aspect

porosity field capacity soil moisture sat. infiltration-capacity initial infiltration control zone depth K-factor (USLE K) potential interception percentage cover C-factor (USLE C* P) soil roughness coefficient ~

maximum roughness height - Manning’s n

-

521

ANSWERS

I Digital Elevation Model 1

I Field measurements 1 I

The latest version of the ANSWERS model, as modified by the authors, uses the geographical database (with the maps of the input variables) to produce an Element file of the ANSWERS input format. A specially developed computer program transforms the maps of input variables to an input file (Figure 2). With the modified version of ANSWERS the number of soil and landuse types is limited only by the number of square elements. The only remaining source of lumping is determined by the size of the square elements.

These changes influence the output of the model. The same data set was used for a simulation of a rainfall event on 13 May 1987. In the distributed case (4275 units/grids) total runoff was 46 per cent higher than in the lumped case when only 20 units were possible; the soil loss was 36 per cent higher, and peak runoff 42 per cent. From this it is clear that distributed parameter models such as ANSWERS cannot be optimally utilized without the use of a GIS to supply data at correct spatial resolution.

Further advantages of using a GIS are the possibilities of rapidly producing modified element files, with different landuse patterns or conservation measures, to simulate alternative scenarios, the ability to use very large element files, so the watershed can be simulated with more detail and the facility to display the results as maps. The output of the modified version of ANSWERS is stored in the GIs, and maps can be produced showing the variation with time of spatial patterns of soil erosion (Figure 3), sedimentation (Figure 4), and runoff over the catchment (Figure 5). These maps can be compared by subtraction to yield maps indicating how erosion or sedimentation might be affected by certain control measures within the watershed (Figures 7 and 8). Runoff can also be displayed as an overlay on the landform surface (digital elevation model).

SENSITIVITY ANALYSIS O F THE ANSWERS MODEL

A sensitivity analysis of a quantitative model is used to examine the effects of variations in model input and parameter values upon model behaviour and output (Howes and Anderson, 1988). A deterministic sensitivity analysis was used here.

In essence the procedure used for a sensitivity analysis is to run a set of experiments on a computer in which the value of a system parameter or state variable is changed by a fixed amount in each experiment.

A set of experiments was run with the ANSWERS model to identify to which state variables the model is most sensitive. It is possible that the model is so sensitive to a particular variable that even with accurate field measurements (which always have some error) it is not possible to get reliable results. In such a case a model needs modification.


SOIL LOSS FIFTER 1 8 . 1 MM RAINFALL, MAY 13th 1987 CFITSOP WFITERSHED, SOUTH LIMBURG (THE NETHERLFINDS)

Figure 3. Soil erosion after 18.1 mm rainfall (May 13th 1987), Catsop (Limburg), simulated with ANSWERS

A rainfall event of 20 mm within 20 minutes was simulated on a part of the Catsop watershed (see below) with average values of soil and landuse variables which are assumed to be realistic for the loess area in South Limburg. The model output, in this case the total runoff and soil loss, was computed and will be identified by RES6. For each state variable, 10 values were chosen, 5 above and 5 below the average just used. These values did not exceed known possible values for the soils and vegetation of the area. The output is identified by RESl/RESS and RES7/RES11.

The relationships between input and output are not linear, and cannot be described by one type of function (Figure 6a-d). The sensitivity of input variables changes with magnitude of the variable. Several curve fitting


SEDIMENTATION AFTER 18.1 MM RAINFALL, MAY 13th 1987 CATSOP WATERSHED. SOUTH LIMBURG (THE NETHERLANDS)

Figure 4. Sedimentation after 18.1 mm rainfall, May 13th 1987, Catsop (South Limburg), simulated with ANSWERS

methods were used, but none provided accurate results for all variables. Standardization of sensitivity using uniform functions is not possible. Another problem is that sensitivity changes with the magnitude of the rainfall, different rainfall-intensity distributions, and probably also with different topography within the simulated watershed.

A simple index which describes the sensitivity of a variable within the defined range of a variable is introduced here:

RES, , - R E S , - R E S , S W I , V l * , -


SURFRCE RUNOFF - HRY 1 3 t h 1987, 6:50 PM CRTSOP WRTERSHED. SOUTH LIMBURG ( T H E NETHERLANDS)

Figure 5. Surface runoff after 18.1 mm rainfall (May 13th 1987), Catsop (Limburg), simulated with ANSWERS

with S,,, ,ygl)=index of sensitivity within range of variables V, and V,, RES, =result of simulation no. 1 RES, = result of the initial simulation

RES, =result of simulation no. 11

This index describes the output variation (RES, - RES,) around an average output. Table I1 gives sensitivity indices (runoff and soil loss) for soil, landuse, and channel variables.

Table I1 shows that the model is very sensitive to the infiltration variables (FC, A, and DF) and the initial soil moisture content (ASM). Table I1 also shows a positive correlation between the sensitivity indices of runoff and soil loss. In general, when the ANSWERS model is sensitive to variables that affect estimates of runoff, estimates of soil loss are also affected.

Table I11 describes the changes of output due to 10 per cent changes in input, positive and negative, in each variable one at a time. The variation in values of some variables over the catchment area is greater than 10

SOIL EROSION MODELLING

2 5 :

2 0 1

n :

€ 1 5 ; E v -

+ + 0

525

Sensitivity of total runoff t o infi l tration (FC)

2 5 :

2 0 1

n :

€ 1 5 ; E w -

Y- + 0

3 DL

0 5 0 0 0 3 8

Sensitivity of total runoff to poroslty (TP)

-Oi::::_ 040 0 4 2 0 4 4 0 4 6 0 4 8 0 5 0 0 5 2

Sensitivity of soil-loss to the K-faktor

(r

0 5 :

.- v, 0 I O O j

400 -

-300 - 0 I

0 - Y w -

200 -

\ :

(I] m 0 I -

0 0 30 0.40 0.50 0.60 0 70

K-faktor

-300 - 0 1

400/ Sensitivity of soil-loss to Manning's n (N)

0.00 0.10 0.20 0.30 0 40 0 50 Manning's n

Figure 6. Sensitivity of model output to input variables

per cent. From Table I11 one can see that it is desirable to be able to measure the infiltration variables, such as saturated infiltration capacity, initial soil moisture content, total porosity and soil roughness parameters as accurately as possible.

Detailed information about rainfall intensity during a rainfall event is also very important. The event of 20 mm rainfall in 20 min was simulated with several intensities (Table IV). Large errors can occur when no detailed rainfall information is available.

An alternative method of sensitivity analysis uses a stochastic approach (Howes and Anderson, 1988). Model input variables are randomly selected from probability distributions, characterized by a mean and standard devation. The results of this analysis will be published in a future paper.

Table 11. Results of a sensitivity analysis of the ANSWERS model

Variable Range Average Sensitivity index value Runoff Soil loss

TP (%) 40-50 FP (Yo) 5&70 FC (mm/h) 2-1 8 A (mm/h) 20-300 D F (mm) 5-155 p (-) 0 . 6 0 . 7 0 ASM (Yo) 40-90 K (-1 0 .40 .70 PIT (mm) 0-3 PER (%) 0-100 RC (-1 0.3W.80 HU (mm) 30-180 N (-1 0.05-0.37 c (-1 0-1

WIDTH (m) 0 . 5 4 5 MAN. (-) 0.01-0.16 GRF (-) 0 6 0 1

TP =total porosity; FP =field capacity; FC =saturated infiltration cap.; A =initial minus saturated

infiltration capacity; DF =infiltration control zone depth; P =infiltration constant; ASM =antecedent soil moisture; K =soil erodibility (USLE K);

45 - 1.89 - 1.79 60 0.24 0.26 10 - 3.98 - 5.88

160 - 10.83 - 26.23 80 -11.37 -25.21

0.65 0.39 0.67 65 10.15 22.32

0.55 0 0.44 1.5 - 1.67 -2.18

50 - 1.05 - 1.56 0.55 -2.11 -4.81 105 - 0.49 - 1.19 0.21 - 2.62 - 4.02 0.50 0 1.79

2.5 - 0.06 - 0.77 0.085 - 0.09 - 0.07 0.0005 0.94 0.1 1

PIT =potential interception capacity; PER =crop coverage; RC =soil roughness coefficient; HU =maximum roughness height; N =Manning's n soil surface; C WIDTH =channel width; MAN. =Manning's n channel; GRF =groundwater release fraction.

=crop factor (USLE C * P);

Table 111. Sensitivity of variables in the ANSWERS model. Output changes due to 10 per cent input changes are listed. Input changes increasing output are indicated as high, changes

decreasing output are indicated as low

v a r i a d 1 e Initial Runoff Soil loss value High Low High Low

TP (%) 45 - 55.9 78.6 - 59.6 103.5

FC (mm/h) 10 - 14.9 21.2 - 15.8 32.5 A (mm/h) 1 60 -9.7 14.4 - 17.0 24.1 DF (mm) 80 -43.6 92.2 - 44.9 134.7 p ( -1 0.65 19.0 - 19.5 35.1 -31.6 ASM (%) 65 174.3 - 80.8 282.2 - 84.7 K (-1 0-55 0 0 8.0 - 9.6

FP (Yo) 60 7.5 - 6.7 7.0 - 7.0

PIT (mm) 1.5 - 7.8 9.3 - 7.9 16.7 PER (%) 50 0 - 0.3 0 - 0.9 RC (-1 0.55 - 42.2 40.4 - 58.9 83.9 HU (mm) 105 -4.2 4.2 -6.1 16-0 N (-) 0.210 - 10.2 13.8 - 12.0 16.6 c (-1 0.50 0 0 7.9 - 8.8 WIDTH (m) 2.5 - 0-3 3.8 - 1.8 1.8 MAN. (-) 0.085 -0.5 5.4 - 1.0 0.0 GRF( -) 0.0005 9.4 0.0 1.1 0.0


Table IV. Effect of rainfall intensity on model output. Total rainfall is 20 mm in 20 minutes

Baseline Case 1 Case 2 Case 3 Time Intens. Time Intens. Time Intens. Time Intens. (min) (mm h- l ) (min) (mm h- ' ) (rnin) (mm h-') (min) (mm h-')

0-20 60.00 0-1 30.00 0-1 30.00 0-15 4-00 1 4 60.00 1-10 126.67 15-20 228.00

4 1 3 100~00 10-20 3.00 13-20 12.86

runoff 0.522 mm 0.941 mm 1.490 mm 2.623 mm soil loss 57 kg ha- ' 223 kg ha- ' 381 kg ha-' 743 kg ha- '

PROBLEMS OF GETTING BETTER DATA

Estimating or measuring soil moisture conditions prior to a rainfall event are difficult and mostly insufficient; these data can be a serious source of error. Infiltration and total porosity change during the growing season, due to tillage operations, surface sealing, and crusting. Because measurements of infiltration, soil moisture, and porosity during the year will be insufficient to fill all but a few of the thousands of elements, these data need to be interpolated in space and time to fill the 'element file'.

Another problem is the sensitive variable DF in the Holtan equation, the equation used for infiltration (see Table I): the infiltration control zone depth. The DF describes the volume of soil (depth) that influences the infiltration rate at the surface of the soil. Experimental data and simulation experience have lead to the conclusion that the control zone depth varies with time (Beasley and Huggins, 1982). The sensitivity of this variable and the difficulties of measuring or estimating it make this a very weak variable.

VALIDATION OF THE ANSWERS MODEL

Models of the type described above are of limited value unless they have been validated with independent data. The validation of the ANSWERS model for loess-soils in the Netherlands is presently being carried out in two watersheds (Catsop and Etzenrade) in the Dutch Province of Limburg. Input variables (precipitation, soil, landuse, and channel characteristics) are measured, and the simulated output (runoff and sediment leaving the watershed) is compared with measured runoff and sediment concentration. Because of practical problems however, several input variables, such as Manning's n for overland flow, have to be estimated using the tables in the ANSWERS user's manual, or by using research data from different areas.

At the time of writing, the complete results of the validation of ANSWERS are not yet available. They will be published in future papers. Preliminary results of the validation show that rainfall events with low rainfall intensities give very poor results. During those simulations the sensitivity of rainfall intensity and infiltration variables and the problem of insufficient data make modelling very difficult. The main problems with validation are with insufficient infiltration data. Also the process of crusting during a large rainfall event needs to be taken into account.

For larger rainfall events the amount of simulated runoff is of the same order of magnitude as the measured runoff. The simulated pattern of soil erosion and sedimentation resembles the observations in the field. Figures of sediment yield from watersheds are not yet available. Detailed measurements of the K-factor are not yet available. A constant K-factor has been used in the simulations so far, leading to unknown errors in simulated soil erosion and sediment yield. The simulation results for grassland correspond with observed runoff and erosion.


APPLICATIONS OF ANSWERS

The primary application of ANSWERS is for planning and evaluating various strategies for controlling pollution from intensively cropped areas. In 1987, a study was carried out in cooperation with the Department of Hydrology at the Agricultural University in Wageningen in a watershed of 42.7 hectares near Catsop (Stein, Limburg, the Netherlands). The Catsop research project is designed to provide information on possible erosion control measures and at the same time to provide data for the validation of the ANSWERS model. The impact of possible erosion control measures was assessed during several simulations.

The drainage area has a gently to moderately sloping topography with an average slope of 5.7 per cent. Approximately 0.5 per cent of the area is steeper than 15 per cent. The soils are developed on loess and consist mainly of silt loam. 98.7 per cent of the landuse in the area consists of agriculture, with 10.7 per cent grassland and 88.0 per cent row crops. The row crops are predominantly wheat (44 per cent in 1987), potatoes (20 per cent), and sugarbeet (12 per cent).

The watershed was modelled by constructing 4275 elements of 10 m square (0.01 ha). The Map Analysis Package (MAP) (Tomlin, 1983) was used as an initial geographical database. Some processing was also done using the Autometric Moss GIs. Recent work was done using the Deltamap GIs.

A Digital Elevation Model (DEM) (Figure 1) was constructed by digitizing a contour map, which was rastered to cells of 10 * 10 m. Maps of slope and aspect were derived from it. The landuse map was digitized and converted to a 10 * 10 m grid as were maps of soil, landuse, management, and channel descriptions. For the first simulations only two soil types were defined: soils under row-crops and soils under grassland or forest. The resulting database described the topography, soils, and landuse of the watershed as it existed in March 1987, which was taken as the baseline condition. Data on rainfall and channels were entered in the PREDATA file. The data used for the simulations are listed in Table V.

In order to evaluate some erosion control measures and to determine the best possible locations for them, several landuse scenarios were developed. The following scenarios were used for the Catsop watershed:

1. BASELINE Landuse and management as in March 1987. 88,O per cent of the surface was fallow with crop residuals, 10.7 per cent as grassland and 0.5 per cent as lynchets, which are forested terrace borders on slopes. The grassland is converted to fallow land with crop residuals and the lynchets are removed. All fallow land is converted to grassland.

2. FALLOW

3. GRASSLAND

Table V. Soil and landuse variables used for simulation

Variable Code Fallow

Total porosity (YO) Field capacity (YO) Antecedent soil moisture (%) Steady state infiltration (mm h- I )

Maximum infiltration-FC (mm h - ’) Exponent in inf. equation (-) Infiltration control zone (mm) Soil erodibility USLE ‘K’ (-)

T P FP

AS M FC A P

DF K

45 60 70 7.0

45.0 0.65

90 0.50

Value for Grassland

45 60 70 30.0

100.0

100 0.65

0.50

Forest

45 60 70 30.0

100.0

100 0.65

0.50 Potential interception (mm) PIT 0.0 0.4 2.0

Maximum roughness height (mm) HU 60 45 100 Manning’s n land-surface (-) N 0.08 0.12 0.15 Crop factor (USLE C * P) (-) C 0.76 0.01 0.01

Crop coverage (YO) PER 0 95 90 Roughness coefficient (-) RC 0.40 0.50 0.55

4. 5.

6.


CONTOURING LYNCHETS

CONTOUR-GRASS- STRIPS

The landuse in the scenarios is listed in Table VI.

The fallow land is ploughed on the contour. At several locations, lynchets (Dutch: graften) are constructed as flat forested terraces. Strips of grass, constructed on the contour, are left unploughed between bands of cropped land. This erosion control measure is a standard option in ANSWERS: the Best Management Practice no. 4.

The ANSWERS model was run several times with the following rainfall events of different magnitude, with different recurrence times:

1. 28.5 mm rain within 36 hours (once a year); 2. 17.0 mm rain within 20 minutes (once in 5 years); 3. 19.9 mm rain within 20 minutes (once in 10 years); 4. 23.8 mm rain within 20 minutes (once in 25 years); 5. 26.8 mm rain within 20 minutes (once in 50 years); 6. 29.8 mm rain within 20 minutes (once in 100 years).

By simulating these rainfall events it can be determined how the watershed might possibly react under normal and extreme conditions. The results of the simulations of the 28.5 mm event are summarized in Table VII.

The results for the simulations under more extreme conditions-a rainfall event once in 25 years-are summarized in Table VIII. From Figure 7 it can be concluded that the CONTOURING scenario is effective in reducing soil erosion. The CONTOUR-GRASS-STRIPS scenario is effective both for runoff and soil erosion. The maximum runoff rate decreases spectacularly. Under extreme conditions, the effectiveness of this scenario decreases. The LYNCHETS scenario is not as effective as expected, mainly due to the ineffective choice of the locations. The locations of the lynchets were chosen close to or on present field boundaries, because of present tillage restrictions. Planning lynchets within present fields, along the contour, will

Table VI. Landuse within the different scenarios

Scenario % Fallow % Grassland % Lynchets

BASELINE 88.0 10.7 0.5 FALLOW 100.0 0 0 GRASSLAND 0 98.7 0.5 CONTOURING 88.0 10.7 0.5 LYNCHETS 86.1 10.2 2.9 GRASS-STRIPS 83.8 14.9 0.5

Table VII. Simulation results for several scenarios for the Catsop watershed for event no. 1

Scenario Runoff Average soil Max. runoff (m3) % loss (kg ha-') % (1s-I) %

- 92 - BASELINE 1300 - 194 FALLOW 1498 +15.2 316 +62.9 101 +9.5 GRASSLAND 813 -37.5 0 - 100.0 5 -94.1 CONTOURING * 160 - 17.5 * LYNCHETS 1253 -3.6 188 - 3.1 78 -15.4 GRASS-STRIPS 914 -29.7 56 -71.1 28 -69.2


Table VIII. Simulation results for several senarios for the Catsop watershed for event no. 4

Scenario Runoff Average soil Max. runoff

(m') % loss (kg ha-') % (1s-1) Yo

- BASELINE 4083 - 1403 709 ~

FALLOW 4518 +10.7 1899 +35.4 731 +3.1 GRASSLAND 1014 -75.2 2 - 99.9 31 -95.7 CONTOURING * 1061 - 24.4 * LYNCHETS 3962 -3.0 1352 - 3.6 * GRASS-STRIPS 3330 -18.4 728 -48.1 493 -30.5

SOIL EROSION Catsup

soi lloss (tonnes/ha) 4

3

2

1

0 1 5 10 25 50 100

recurrence time (year) Baseline -I- Fallow * Contouring -

+ Grass-strlps -+- Grassland + Lynchnte

Figure 7. The effectiveness of soil erosion scenarios in the Catsop watershed (South Limburg, the Netherlands)

probably give better results. The GRASSLAND scenario reduces soil erosion to zero and minimizes the surface runoff.

Maps of soil erosion and sedimentation of the scenarios were compared by subtraction, as described above. These simulations indicated where possible control measures would have the greatest positive and negative consequences. Figure 8 shows the effect of the LYNCHETS scenario on soil loss. Planners can decide to combine the positive elements from several scenarios and leave out the negative elements and develop new scenarios. The advantage of using ANSWERS in combination with the GIS is that this operation can be done very quickly: combining maps in a GIS is a standard operation. The database can easily be updated with new data (such as field and laboratory measurements), necessitating new inter- polations to produce maps.


SOIL EROSION REDUCTION BY THE LYNCHETS SCENFlRIO DURING FI RFIINSTORM OF 28.5 MM., CFITSOP WRTERSHED

Figure 8. Soil erosion reduction by the lynchets scenario, simulated with ANSWERS

CONCLUSIONS

With the ANSWERS model it is possible to simulate surface runoff and soil erosion from watersheds having agriculture as their primary landuse. Because of the distributed character of the model, Geographical Information Systems are useful for preparing the data required. Maps of input variables, stored in a geographical database, are converted to input files for the model. Several tools of GIS, such as digitizing, rasterizing, storage, and display of spatial data are indispensable while using ANSWERS, or, in general, distributed parameter models, especially when they are used for planning.

1. The potential increased accuracy for predicting runoff and erosion; 2. The use of physically-based mathematical relationships;

ANSWERS has several advantages over older models such as the USLE. Some of these are:


3. The capacity to incorporate newly developed relationships; 4. The incorporation of information about the spatial variability of land characteristics; 5. The potential ease of validation because it deals with individual rainfall events; 6. The detailed spatially displayed output of the model, useful for planners because the effectiveness of

potential control measures can be evaluated.

The main disadvantages of ANSWERS are theoretical weaknesses (processes such as subsurface flow, gully erosion, and infiltration), the quantity and required quality of necessary input data and the related costs of data acquisition.

The sensitivity of the model to the variables infiltration, soil moisture content and soil roughness is a major problem. Field data for these variables must be very carefully collected. With insufficient data, which will be the case when thousands of elements have to be ‘filled’, there is a serious risk of substantial errors occurring. Using geostatistical methods such as block kriging, used to interpolate point data to blocks of the same size as the elements used for simulation, this problem can partly be handled.

The major problem when attempting accurate modelling is the degree with which reliable location-specific estimates of the attributes (input variables) can be made. As models get increasingly realistic they need more and better data. Although a GIS can provide some of these (such as slope, aspect), many of them are not always available.

The use of a GIS linked to the ANSWERS model provides not only new possibilities for planning, but it also provides new possibilities in the process of developing a useful model that predicts runoff and erosion at the scale of the landscape.

ACKNOWLEDGEMENTS

The authors wish to thank Ir. L. Eppink et al. from the Agricultural University of Wageningen for their cooperation in the Catsop project. The ‘Landinrichtingsdienst’, the ‘Staatsbosbeheer’ Limburg, the ‘Waterschap Roer en Overmaas’, and the Province of Limburg are thanked for financing the research and for giving permission to publish material from the Catsop study.

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Soil erosion modelling using ‘answers’ and geographical information systems