Sediment yield estimation using GIS

Transcription

1 Hydrological Sciences-Journal-des Sciences Hydrologiques, 42(6) December Sediment yield estimation using GIS UMESH C. KOTHYARI Department of Civil Engineering, University ofroorkee, Roorkee , UP, India SANJAY K. JAIN National Institute of Hydrology, Roorkee , India Abstract A method has been developed in the present study for the determination of the sediment yield from a catchment using a GIS. The method involves spatial disaggregation of the catchment into cells having uniform soil erosion characteristics. The surface erosion from each of the discretized cells is routed to the catchment outlet using the concept of sediment delivery ratio, which is defined as a function of the area of a cell covered by forest. The sediment yield of the catchment is defined as the sum of the sediments delivered by each of the cells. The spatial discretization of the catchment and the derivation of the physical parameters related to erosion in the cells are performed through a GIS technique using the Integrated Land and Water Information Systems (ILWIS) package. Estimation de l'exportation de sédiments par utilisation d'un SIG Résumé Dans cette étude une méthode d'estimation de l'exportation de sédiments d'un bassin utilisant un SIG a été développée. La méthode repose sur une dicrétisation du bassin en mailles dont les caractéristiques d'érosion du sol sont uniformes. L'érosion de surface de chacune de ces mailles est routée à l'exutoire du bassin en utilisant le concept de fraction sédimentaire exportée, définie comme fonction de la surface de maille recouverte par la forêt. L'exportation de sédiments du bassin est définie comme la somme des sédiments fournis par chacune des mailles. La discrétisation spatiale du bassin et la détermination des paramètres physiques relatifs à l'érosion dans les mailles sont réalisés grâce à une technologie fondée sur les SIG utilisant le logiciel ILWIS (Integrated Land and Water Information). INTRODUCTION Estimates of sediment yield are needed for studies of reservoir sedimentation, river morphology, soil and water conservation planning, water quality modelling and design of efficient erosion control structures. The process of soil erosion by rainfall and runoff mainly consists of the detachment and transport by raindrops and runoff. Models available in the literature for sediment yield estimation can be grouped into two categories: (i) physically-based models; and (ii) lumped models. In the physically-based models the ground surface is generally separated into inter-rill and rill erosion areas. Detachment over inter-rill areas is considered to be by the impact of raindrops because flow depths are shallow, while runoff is considered to be the dominant factor in rill detachment and sediment transport over both rill and inter-rill areas. The physically-based models include AGNPS (Young et al., 1987), ANSWERS (Beasley et al., 1980), WEPP (Hearing et al., 1989) and SHE (Abbott et al., 1986; Wicks & Bathurst, 1996). Physically-based models are expected to Open for discussion until 1 June 1998

2 834 Umesh C. Kothyari & Sanjay K. Jain provide reliable estimates for the sediment yield. However, these models require the coordinated use of various sub-models related to meteorology, hydrology, hydraulics and soil. As a result the number of input parameters for some of the models may be as high as 50, e.g. in the case of the WEPP model (Nearing et al., 1989). Therefore, the practical application of these models is still limited because of uncertainty in specifying model parameter values and also due to the difference between the scales of application i.e. a catchment vs a field (Hadley et al., 1985; Wu et al., 1993). Alternatively, lumped models such as the universal soil loss equation (USLE) (Wischmeier & Smith, 1978), modified universal soil loss equation (MUSLE) (Williams, 1978) or revised universal soil loss equation (RUSLE) (Renard et al., 1991), combine the erosion from all processes over a catchment into one equation. Rainfall characteristics, soil properties and ground surface conditions are represented by empirical constants in these methods. The lumped methods of sediment yield estimation are in frequent use in many parts of the world (Bogardi et al., 1986; Julien & Tanago, 1991; Kothyari et al, 1994, 1996). There is ample evidence that the USLE yields a good estimate of the amount of detached soil (surface erosion) at the plot scale (Wischmeier & Smith, 1978). However, in the case of a catchment, part of the eroded soil is deposited within the catchment before its outlet. Nevertheless, the catchment can be sub-divided into sub-areas for representing spatial heterogeneity. Surface erosion as computed using the USLE in the sub-areas can be routed to the catchment outlet using any appropriate procedure. Some studies have been carried out wherein a Geographic Information System (GIS) was used for the determination of the potential for soil erosion in different plot size areas (Bocco & Valenzuela, 1988; Omakupt, 1989; Jurgens & Fander, 1993; Jain & Saraf, 1995; Dutta et al., 1995). A GIS technique, however, is best suited for quantification of the heterogeneity in the topographic and drainage features of a catchment (Schumann, 1993; Schultz, 1994; Beven, 1989). Therefore in the present study a GIS technique has been utilized for the spatial discretization of a catchment into hydrologically homogeneous cells. The GIS technique is also utilized for the determination of those physical parameters of individual cells that are related to soil erosion. Surface erosion is then computed within the individual cells using the USLE. Next, the eroded sediment is routed to the catchment outlet by rationally accounting for the process of sediment delivery. Thus a GIS technique is used in the present study in conjunction with a method for prediction of sediment yield from a catchment. The results are reported below. HYDROLOGICAL DATA The data on sediment yield resulting from storm events used herein are from the Karso catchment in Bihar, India. The catchment area is 27.9 km 2. The catchment lies in a sub-humid tropical climate. On average it receives some 1240 mm of rainfall annually. The temperature in the catchment varies from 42.9 C (maximum) to 2.4 C (minimum). Its soil mainly consists of light sandy loam, while about 40% of its area is forested. Agriculture is practised on about 50% of the total area. The data for this

3 Sediment yield estimation using GIS 835 catchment include the variation of rainfall, runoff, sediment yield and various parameters of the USLE. In addition, the land contour map and the land-use map were also available. The catchment runoff was gauged by Damodar Valley Corporation (SWCD, 1991, vol. II). Rainfall was measured using recording raingauges. An automatic water level recorder was used to measure the stream stage and the runoff was derived using the rating curve. The sediment yield was measured using a Coshocton wheel silt sampler. Data for five storm events were compiled. Out of these, two storm events were used for verification of the present method after data for the remaining storm events had been used for the determination of coefficients (i.e. calibration). Dates of the storms were 3-4 August 1991; 4-5 August 1991; August 1991; August 1991; and August METHODOLOGY Apart from rainfall and runoff, the erosion rates from an area are also strongly dependent upon its soil, vegetation and topographic characteristics. These characteristics vary greatly within the various segments of a catchment. Therefore, a catchment should be discretized into smaller homogeneous units before making the computations for soil loss. A grid- or cell-based discretization is the most commonly used procedure both in physically-based models and in lumped models (Beven et al., 1984). For the present study, a cell-based discretization procedure was adopted. The cell size to be used for discretization should be small enough such that the cell encompasses a hydrologically homogeneous area. Keeping this in mind a cell size of 500 m x 500 m was used. There is ample evidence in the literature that the USLE produces acceptable estimates of surface erosion over small areas (Wischmeier & Smith, 1978). Therefore, soil erosion within each cell was estimated with the USLE. The USLE method was expressed by Williams & Berndt (1972) as: S' = R-KL-S-CP (1) in which 5' = computed soil loss per unit area, R = rainfall factor, K = soil erodibility factor, L = slope length, S = gradient factor, C = cropping management factor and P = erosion-control practice factor. Values of the USLE parameters were derived following Kothyari et al. (1994). Only the parameter R is found to vary with storm event. In each storm event, the soil loss was computed using equation (1) for each rain amount of duration 15 min. The algebraic sum of these losses over the storm duration became the computed soil loss for the storm event within that particular cell. The eroded sediment was routed from each cell to the catchment outlet using the concept of sediment delivery ratio as described below. Sediment delivery ratio In the case of a catchment, part of the eroded soil is deposited within the catchment before its outlet. The ratio of sediment yield to total surface erosion is termed the

4 836 Umesh C. Kothyari & Sanjay K. Jain sediment delivery ratio, D R. Empirical formulae have been developed for D R for a catchment, in terms of the catchment area and length of relief, by various investigators (Maner, 1958; Roehl, 1962; Williams & Berndt, 1972; Walling, 1983, 1988; Richards, 1993). In a cell-based approach for the determination of sediment yield the D R values for individual cells are first defined. King (Hadley et al., 1985) considered the D R for two adjacent cells to be the ratio of the average land slope of a given (draining) cell to that of the adjacent (receiving) cell. In an approach for sediment yield determination based upon the time-area segmentation of a catchment, Kothyari et al. (1994, 1996) considered the D R of two adjacent time-area segments to be a combined function of the slope ratio, the area ratio, and the ratio of percent forest cover of the corresponding segments. In the present method, the effects of area and slope on sediment delivery are considered by choosing uniform size cells and deciding on the drainage direction within the cells on the basis of steepest descent. The D R of the z'th cell is therefore defined as below: D J% =C,(l-F 4 ) (2) where C x is a coefficient and F A is the fraction of the area of the z'th cell covered by forest. Now let S' be the amount of erosion produced within the z'th cell estimated using equation (1). Then sediment yield S for the catchment during the storm event is then obtained via: s = I>;,s o) where N is the total number of cells in the catchment, and the term D' R is the fraction of S' t that ultimately reaches the catchment outlet. The value of D' R for the z'th cell is derived from: D' Ki^D R -D Ri -D Ri2...D Rix (4) GENERATION OF INFORMATION THROUGH A GIS The GIS package used in the present study was ILWIS (Integrated Land and Water Information System, ITC, 1992), which was developed at the International Institute of Aerospace Survey and Earth Sciences (ITC), Ensehede, The Netherlands (Biesheuvel & Hemker, 1993). The parameters needed for the estimation of surface erosion, sediment delivery ratio and sediment yield are generated and stored in ILWIS. The base map depicting drainage pattern and elevation contours of the study area were prepared using the Survey of India topographical map at a scale of 1: These maps were then converted into digital form with the help of a digitizer. The catchment was also discretized into a grid network, using ILWIS, as shown in Fig. 1 by adopting square cells having equal sides of 500 m. The elevation contour map was rasterized and, using interpolations from isolines, converted into a

5 Sediment yield estimation using GIS X V X 1 f -^ X w s v^ jl k h 71 k k X *-, it «- Fig. 1 Flow directions of the study area. Fig. 2 Elevation (DEM) map of the study area.

6 838 Umesh C. Kothyari & Sanjay K. Jain digital elevation model, DEM. This is depicted in Fig. 2 in the form of elevations for the cells. In IL WIS, water from any grid cell is permitted to flow to one of its eight nearest neighbouring cells. Using the grid of terrain elevations, a grid of flow directions was created for the study area with one direction for each cell which represents the direction of steepest descent amongst the eight permitted choices. The grid map which depicts the drainage directions finally obtained is shown in Fig. 1. As can be seen a unique drainage direction is obtained for each cell of the network. A land-use map of the study area was prepared and converted into digital form in ILWIS (Fig. 3). For computations regarding the extent of land use in each grid cell, Fig. 3 was reclassified into two categories viz. forest (vegetation) and others. Next, for each cell, the percentage of forest cover was computed after overlaying thus yielding a land-use map over the grid network. The D R value for each cell was then computed via equation (2). The D' R values were also determined for each cell from equation (4) and are depicted in Fig. 4. The parameter values of the USLE, viz. K, L, S, C and P were finally determined for each cell by overlaying the pertinent maps on the grid network. The combined term KLSCP does not vary with the storm events in a given cell of the catchment. Thus it represents the erosion potential of that cell. The term KLSCP was determined and is depicted in Fig. 5 for the grid network. This Figure indicates the potential for erosion in different segments of the catchment. Fig. 3 Land-use map of the study area.

7 Sediment yield estimation using GIS Fig. 4 Sediment delivery ratio values of the study area. Fig. 5 KLSCP values of the study area.

8 840 Umesh C. Kothyari & Sanjay K. Jain RESULTS First, the data selected for calibration were used to determine the optimum value of the coefficient C x in equation (2). The method of grid search was used for this purpose. In this method, different values were given to C, and the sediment yields for these storm events obtained using equations (l)-(4) for each value. The C, value giving minimum error as defined by equation (5) was considered as the optimum one. If Sj and SS, are the corresponding observed and estimated values of the sediment yield then the error of the estimate is defined as: E = P S S ^SS, (5) where m is the number of storm events. The C, value in this grid search was varied between zero and one at intervals of The optimum value of C, thus obtained was A close scrutiny of equation (2) reveals that a value of C x equal to 0.94, would indicate almost negligible delivery of eroded sediment from the forested cell areas. This result is realistic as the overland flow velocity is low in such cells because of the high frictional resistance offered to the flow by vegetation present in these areas (Beasley et al., 1980; Cooper et al, 1987). Likewise equation (2) indicates that, other conditions remaining the same, almost all the soil eroded in the non-forested cells is transported away by the flow. This is attributed to the larger overland flow velocity resulting from less frictional resistance (Henderson, 1966). Using this value of C, the sediment yield was computed for the storm events selected for calibration as well as for verification. The computed sediment yields were compared with the corresponding observed values as shown in Table 1. A study of Table 1 indicates that sediment yields were overestimated for events of smaller duration, i.e. storm events 1, 2 and 4, while underestimation occurred for storm events having larger duration viz. storm events 3 and 5. However, the R values for the latter storm events are smaller indicating an observed value for the delivery ratio to be more than 100 in these events. This can be attributed to remobilization of the sediment stored in the catchment during preceding storm events (Piest et al., 1975; Walling, 1983). Table 1 Computed and observed values of sediment yield. Event number Rainfall event date if-value Sediment yield (t): 3-4 August 1991 (C) 4-5 August 1991 (C) August 1991 (C) August 1991 (V) August 1991 (V) C = calibration; V = verification Observed Computed Ratio Obs.Comp

9 Sediment yield estimation using GIS 841 Table 1 also indicates that the present method produced sediment yields with a reasonable accuracy in two storm events selected for calibration and in one storm event selected for verification. The larger error for one of the storm events was attributed to the uncertainty of observations. Nevertheless, the prediction accuracy of the proposed method was reasonable particularly considering that predictions from some of the process-based models show still larger scatter in a plot between measured and computed sediment yields (Wu et al., 1993). The accuracy of the proposed method is considered to be satisfactory considering the given estimation, measurement and cartographic errors that are endemic in such analysis. It should be noted that the proposed method depends on calibration against a record of past and present conditions and hence it cannot be directly used to predict the impacts of future changes in catchment land use or climate. IDENTIFICATION OF SEDIMENT SOURCE AREAS Figures 4 and 5 were overlaid in ILWIS to identify the source areas for sediment reaching the outlet from within the catchment. Through such an overlaying the areas producing large sediment amounts in the catchment were identified and are indicated in Fig. 6. It may be emphasized that these regions would need special priority during the implementation of erosion control measures. A comparison of Fig. 6 with Figs 2 and 3 reveals that, as expected, the sources of sediment in the catchment coincide with the less vegetated and steep areas. Fig. 6 Sediment source areas in the study area.

10 842 Umesh C. Kothyari & Sanjay K. Jain CONCLUSIONS A grid- or cell-based approach has been used along with a GIS for the determination of the sediment yield from a catchment. The GIS technique was used for discretization of the catchment into a network of cells which possess unique drainage directions. Also, the physiographic parameters of the grids were determined using the GIS. Surface erosion in the individual cells was determined using the USLE. The eroded sediment was routed to the catchment outlet using the concept of sediment delivery ratio as defined by equation (2). Reasonable results were obtained when the proposed method was used for the determination of sediment yield for several storm events in one catchment in India. The method depends on calibration against a record of existing conditions and hence it can be used for the estimation of sediment yield in other such ungauged catchments which have similar hydrometeorological and land use conditions. Acknowledgement The authors wish to thank the anonymous reviewers whose comments greatly improved the quality of the paper. REFERENCES Abbott, M. B., Bathurst, J. C, Cunge, J. A., O'Connell, P. E. & Rammussen, J. (1986) An introduction to the European Hydrological System-System Hydrologique Européen, SHE.. Hydrol. 87, Beasley, D. B., Huggins, L. F. & Monke, E. J. (1980) ANSWERS: A model for watershed planning. Trans. ASCE 23, Beven, K. J. (1989) Changing ideas in hydrology the use of physically based models.. Hydrol. 105, Beven, K. J., Kirkby, M. J., Schofield, N. & Tagg, A. F. (1984) Testing a physically based flood forecasting model (TOPMODEL) for three UK catchments. J. Hydrol. 49, Biesheuvel, A. & Hemker, C. J. (1993) Groundwater modelling and GIS: integrating MICRO-FEM and ILWIS. Application of GIS in Hydrology and Water Resources Management, ed. by K. Kovar & P. Nachtnebel, (Proc. Vienna Conf., April 1993), IAHS Publ. no Bocco G., & Valenzuela, C. R. (1988) Integration of GIS and image processing in soil erosion studies using ILWIS. TTC Journal. 4, Bogardi, I., Bardossy, A., Fogel, M. & Duckstein, L. (1986) Sediment yield from agricultural watersheds. J. Hydraul. Engng. Proc. ASCE 112(HY 1), Cooper, J. R., Gilliam, J. W., Damsels, R. B. & Robarge, W. P. (1987) Riparian areas as filters for agricultural sediment. Soil Sci. Soc. Am. J. 51, Dutta, D., Das, S. N. & Sharma, J. R. (1995) Potential use of remote sensing and GIS for mapping of soil erosion through USLE. Proc. Nat. Symp. on Remote Sensing of Environment with Special Emphasis on Green Revolution, (22-24 November, Ludhiana, India), Hadley, R. F., Lai, R., Onstad, C. A., Walling, D. E., & Yair, A. (1985) Recent developments in erosion and sediment yield studies. Report UNESCO (IHP), Paris, France. Henderson, F. M. (1966) Open Channel Flow (Chapters 8 and 9). Macmillan, New York, ITC (1992) The Integrated Land and Water Information System (ILWIS) (3rd edn). Int. Inst, for Aerospace Survey and Earth Sciences, Enschede, The Netherlands. Jain, S. K. & Saraf, A. K. (1995) GIS for the estimation of soil erosion potential.. GIS, India 4(1), 3-6. Julien, P. Y. & Tanago, M. G. D. (1991) Spatially varied soil erosion under different climates. Hydrol. Sci. J. 36(6) Jurgens, C. & Fander, M. (1993) Soil erosion assessment and simulation by means of SGEOS and ancillary digital data. Int. J. Remote Sensing 14(15), Kothyari, U. C, Tiwari, A. K. & Singh, R. (1994) Prediction of sediment yield, J. Irrg. & Drain. Engng Div. ASCE 120(6), Kothyari, U. C, Tiwari, A. K. & Singh, R. (1996) Temporal variation of sediment yield. J. Hydrol. Engng Div. ASCE 1(4),

11 Sediment yield estimation using GIS 843 Maner, S. B. (1958) Factors affecting sediment delivery ratio in the Red Hill physiographic area. Trans. AGI] 39(4), Nearing, M. A., Foster, G. R., Lane, L. J. & Finkener, S. C. (1989) A process based soil erosion model for USDA water erosion prediction project technology. Trans. ASCE 32(5), Omakupt, M. (1989) Soil erosion mapping using remote sensing data and GIS. Proc. 10th ACRS, November, Kuatalampur, Malaysia. Piest, R. F., Kramer, L. A. & Heinemann, H. G. (1975) Sediment movement from loessial watersheds. In: Present and Prospective Technology for Predicting Sediment Yields and Sources, US Dept. of Agric. Publ. ARS-S- 40, Renard, K. G., Foster, G. R., Weesies, G. A. & Porter, J. P. (1991) RUSLE, Revised Universal Soil Loss Equation.. Soil and Water Conserv. January-February, Richards, K. (1993) Sediment delivery and the drainage network. In: Channel Network Hydrology, ed. by K. Beven & M. J. Kirkby, Wiley, Chichester, UK. Roehl, i. W. (1962) Sediment source areas, delivery ratios and influencing morphological factors. In: Symposium ofbari (1-8 October 1962), IAHS Publ. no. 59. Schultz, G. A. (1994) Meso-scale modelling of runoff and water balances using remote sensing and other GIS data. Hydrol. Sci. J. 39(2), Schumann, A. H. (1993) Development of conceptual semi-distributed hydrological models and estimation of their parameters with the aid of GIS. Hydrol. Sci. J. 38(6), SWCD (Soil and Water Conservation Division) (1991) Evaluation of the Hydrological Data (vol. I and II), 269. Ministry of Agriculture, Govt, of India, New Delhi, India. Walling, D. E. (1983) The sediment delivery problem. J. Hydrol. 65, Walling, D. E. (1988) Erosion and sediment yield some recent perspectives. J. Hydrol. 100, Wicks, J. M. & Bathurst, J. C. (1996) SHESED: A physically based, distributed erosion and sediment yield component for the SHE hydrological modelling system,. Hydrol. 175, Williams, R. & Berndt, H. D. (1972) Sediment yield computed with universal equation. J. Hydraul. Engng Div. ASCE 98(HY12) Williams, J. R. (1978) A sediment graph model based on instantaneous unit sediment graph. Wat. Resour. Res. 14(4), Wischmeier, W. H. & Smith, D. D. (1978) Predicting Rainfall Erosion Losses. Agriculture Handbook no. 537, USDA, Science and Education Administration, 58. Wu, T. H., Hall, J. A. & Bonta, J. V. (1993) Evaluation of runoff and erosion models. J. Irrig. Drain. Engng Div. ASCE 119(4), Young, R. A., Onstad, C. A., Bosch, D. D. & Anderson, W. P. (1987) AGNPS: An agriculture nonpoint source pollution model. Conservation Research Report 35, USDAARS, Washington, DC, USA. Received 26 July 1996; accepted 24 March 1997

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