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Continuous simulation modelling for design flood estimation in South Africa: Preliminary investigations in the Thukela catchment
Physics and Chemistry of the Earth 30 (2005) 634–638 www.elsevier.com/locate/pce
Continuous simulation modelling for design flood estimation in South Africa: Preliminary investigations in the Thukela catchment Kershani Chetty *, Jeff Smithers School of Bioresources Engineering and Environmental Hydrology, University of KwaZulu-Natal, Pietermaritzburg, Private Bag X01, Scottsville 3209, South Africa Accepted 15 August 2005 Available online 23 September 2005
Abstract Several recent literature reviews highlight the need for improvements in procedures for design flood estimation [Cordery, I., Pilgrim, D.H., 2000. The state of the art of flood prediction. In: Parker, D.J. (Ed.), Floods. vol. II. Routledge, London, UK, pp. 185–197; Smithers, J.C., Schulze, R.E., 2001. Design runoff estimation: a review with references to practices in South Africa. In: Tenth South African National Hydrological Symposium. SANCIAHS, Pietermaritzburg, RSA]. In general, these reviews indicate that internationally the trend is to adopt a continuous simulation modeling approach for design flood estimation. The continuous simulation modelling (CSM) approach to design flood estimation has many advantages and has the potential to overcome many of the limitations of the often used design event approach. A pilot study into the development of a continuous simulation modelling system for design flood estimation is being undertaken in the Thukela catchment in South Africa. Preliminary studies using the ACRU agrohydrological modelling system are detailed in this paper and include investigations into the appropriate scale and levels of soil and land cover information required for use in a CSM approach for design flood estimation. Results indicate that CSM with the ACRU model requires quaternary catchments to be further divided into sub-quaternary catchments, and that using area weighted soils and land cover information gives better results than using modal soils information or single dominant soil or land cover information. 2005 Elsevier Ltd. All rights reserved. Keywords: Design flood estimation; Continuous simulation modelling
1. Introduction Design floods estimates are often required by engineers, hydrologists and agriculturalists for the design of hydraulic structures such as dams, bridges or culverts (Smithers and Schulze, 2000). Under or over-design of even small hydraulic structures can result in a considerable waste of resources and it is therefore important to have accurate design flood estimates. The potential loss of life, property and infrastructure caused by floods also gives incentive to improve the ability to predict the magnitude and frequency of floods. According to Cordery and Pilgrim (2000) research in design flood estimation is on the decline and there is a *
Corresponding author. Tel.: +27 33 2606027; fax: +27 33 2605818. E-mail address: [email protected] (K. Chetty).
1474-7065/$ - see front matter 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2005.08.002
large gap between design flood research and practice. This needs redress if improvements to design flood estimation practice is to be made. In general approaches to design flood estimation vary according to data availability as illustrated in Fig. 1. Where adequate streamflow data are available methods based on the analysis of flood data are used. These methods include empirical equations, and at site or regional statistical analyses. Regional analysis methods may be used to estimate design floods at locations with inadequate data. In this approach it is frequently assumed that the standardised variate has the same distribution at every site in the selected region and that data from a region can thus be combined to produce a single regional flood or rainfall frequency curve which is applicable anywhere in the region with appropriate site specific scaling (Cunnane, 1989; Hosking and
K. Chetty, J. Smithers / Physics and Chemistry of the Earth 30 (2005) 634–638
635
Design Flood Estimation Methods Analysis of Stream flow Data Empirical Methods
Flood Frequency Analysis Site
Regional
Rainfall Based Methods Flood Envelopes
Historical/ Stochastic Rainfall
Design Rainfall Deterministic/ Probabilistic
Continuous Simulation Frequency Analysis
Design Event Model Gradex
Rational
SCS
Unit Hydrograph
Run off Routing
Fig. 1. Methods for estimating design floods (Smithers and Schulze, 2001).
Wallis, 1997). Numerous reviews of flood frequency studies have shown that regional analyses are advantageous (Cunnane, 1989; Hosking and Wallis, 1997). Adequate streamflow data is however seldom available due to the sparsity of recorded flow data, or flow records which are too short for frequency analyses or are of dubious quality and rainfall-runoff models are often used instead as there is generally a larger more dense network of raingauges than gauging weirs. The design event rainfall runoff models are often utilised for design flood estimation and these event based models make several simplifying assumptions which are significant in design flood estimation. 2. Continuous simulation modelling The continuous simulation modelling (CSM) rainfallrunoff approach to design flood estimation has many advantages and has the potential to overcome many of the limitations of the design event approach. According to Rahman et al. (1998), continuous simulation models aim to represent the major processes responsible for converting the input catchment rainfall into streamflow. Hydrographs are generated over long periods of time from the input of historical rainfall series, potential evaporation and other climatological and catchment attributes. An important characteristic of these models is the use of a continuous water budget model for the catchment so that antecedent conditions for each storm are known (Rahman et al., 1998). A relatively sophisticated hydrological model capable of simulating the entire hydrological cycle is required by the method. Rahman et al. (1998) summarised the views of various authors and concluded that CSM is regarded as having the potential for solving the limitations of the current single event approach to design flood estimation. Firstly the need for using synthetic storms is eliminated, by using actual storm records, secondly, subjectivity in selecting antecedent conditions for the land surface is not required as a water budget accounts for, in each time step of the simulation, antecedent moisture conditions (AMC). The duration of the storm is also not an issue as AMC and the water budget are explicitly modelled for each time step and the assumption that the return period of the output streamflow is the same as the return period of the input rainfall no longer has to be made. According to Rahman et al. (1998) some of the
disadvantages of CSM include the loss of ‘‘sharp’’ events if the modelling time scale is too large, the extensive data requirements, which result in significant time and effort to obtain and prepare the input data and, the expertise required to determine parameter values such that historical hydrographs are adequately simulated. Taking into account the limitations according to Rahman et al. (1998) CSM may prove to be the most powerful means of estimating flood frequency from rainfall. This type of analysis, according to the ASCE (1997), is receiving increasing interest and use in the USA. Calver and Lamb (2000) express the opinion that CSM may form the basis for the next generation of flood frequency estimation in the UK. In South Africa event based rainfall runoff models are generally used for design flood estimation but with the international shift towards CSM for design flood estimation the Water Research Commission commissioned the School of Bioresources Engineering and Environmental Hydrology at the University of KwaZulu-Natal to complete a pilot study on the development of a continuous simulation modeling system for design flood estimation in South Africa. The preliminary results of this pilot study are discussed in this paper. 2.1. Case study: preliminary investigations in the Thukela catchment The main objectives of the preliminary investigations are to investigate • the appropriate scale at which CSM should take place; • the appropriate levels of soil and land cover information required for CSM; and • the effects of the spatial variability of rainfall within a catchment on runoff depths.
3. The study area The Thukela catchment (Fig. 2) extends latitudinally from 27E25 0 to 29E24 0 S and longitudinally from 28E58 0 to 31E26 0 E and it covers an area of approximately 29,000 km2 in the KwaZulu-Natal province of South Africa. The Thukela river has its source in the Drakensberg mountain and flows right through into the Indian Ocean. The major tributaries of the Thukela river are the Little
636
K. Chetty, J. Smithers / Physics and Chemistry of the Earth 30 (2005) 634–638
Fig. 2. Location of the Thukela catchment within South Africa and location of QC27 within the Thukela Catchment.
Thukela, Mooi and BushmanÕs Rivers which join from the southwest, and the Klip, Sundays and Buffalo Rivers flowing from the north. The mean annual precipitation (MAP) in the Thukela ranges from approximately 2000 mm in the Drakensberg to as low as 550 mm in the drier central regions and most of the rainfall is received during the mid-summer months from December to February. 4. Methodology The model selected for the development of the CSM system is the ACRU model (Schulze, 1995). Two previous studies on continuous simulation modeling for design flood estimation in South Africa demonstrated by Smithers et al. (1997) and Smithers et al. (2001) also made use of the ACRU model with reasonable results. The ACRU model is a physical-conceptual agrohydrological model which operates on a daily time step. The model simulates all major processes of the hydrological cycle which affect the soil water budget and is capable of simulating, inter alia, streamflow volume, peak discharge and hydrograph, reservoir yield, sediment yield, crop yield for selected crops and irrigation supply and demand. ACRU can operate at a point, as a lumped catchment model, or as a distributed cell-type model in order to account for spatial variability in climate, land use and soils. The ACRU modelling system is linked to the quaternary catchments database. South Africa has been divided into several primary catchments, each of which have been further divided into secondary, then tertiary and finally quaternary catchments, by the Department of Water Affairs and Forestry (DWAF) of South Africa, for planning and management purposes. There are 1946 quaternary catchments (QCÕs) and the Thukela catchment is made up of 86 such QCÕs. The QC database developed at the school of Bioresources Engineering and Environmental Hydrology, houses information such as location, rainfall, soils and land cover information for each quaternary catchment
which is used for modelling purposes. The soils information in the database was obtained from the ISCW soils maps ( SIRI, 1987) at the 1:50,000 resolution and has been translated into ACRU variables by Schulze and Pike (1995). The land cover information was obtained from the National Land Cover Database (CSIR, 1999) at a resolution of 1:250,000 and has been translated into ACRU variables by Schulze (2001). In order to meet the objectives set out above the following methodology was used: • A 253 km2 quaternary catchment was selected and was sub-divided into 9 sub-catchments according to topography. • The Calc pptcor (Schulze and Pike, 2004) suite of programmes was used to determine the closest, most representative rainfall station termed the driver rainfall station and the precipitation correction factors that needed to be applied to the rainfall at the station. Daily rainfall values were used. • All the other input variables for ACRU were obtained from the QC database. The model was run from 1950 to 2000. The scenarios were set up to according to Tables 1 and 2 in order to meet the various objectives of the investigations.
Table 1 Scenarios to investigate appropriate scale, levels of soils and land cover information for use in the model Mode of simulation
Soils information
Land cover information
Catchment modelled as one entity (lumped) Catchment divided into 9 sub-catchments Catchment divided into 9 sub-catchments
Modal
Modal
Modal
HRU
Area weighted
HRU
K. Chetty, J. Smithers / Physics and Chemistry of the Earth 30 (2005) 634–638 Table 2 Scenarios to investigate the spatial variability of rainfall on runoff depths within a catchment Driver rainfall stations per sub-catchment
Soils information
Land cover information
1 1 9 9
Modal Area weighted Area weighted Modal
HRU HRU HRU HRU
driver driver driver driver
station station stations stations
5. Results The results obtained from modelling the different scenarios with the emphasis on runoff depths are presented in Figs. 3 and 4. Fig. 3 shows the comparisons between the simulated streamflow depths for the scenarios which are aimed at investigating scale and levels of soil and land cover information required and the observed streamflow depth. From this graph it is evident that: (a) The simulated streamflow from a lumped catchment is much larger than the simulated streamflow from having divided the catchment into sub-catchments. Graph of daily simulated vs Observed Flows for QC 27 14 12
Daily Flow (mm)
10 8 6 4 2 0 06-Sep-82 26-Oct-82 15-Dec-82 03-Feb-83 25-Mar-83 14-May-83 03-Jul-83
area weighted soils
modal soils
lumped
22-Aug-83 11-Oct-83 30-Nov-83
observed streamflow
Fig. 3. Simulated streamflow vs observed streamflow for scenarios investigating the scale, levels of soil and land cover information.
Graph of daily simulated vs Observed Flows for QC 27 14
Daily Flow (mm)
12 10 8
637
(b) The simulated streamflow from the scenario which used modal soils deviates more from the observed values, than the scenario where area weighted soils are used. This implies that using area weighted values derived for sub-catchments soil and land cover information, results in improved simulations. Fig. 4 shows the comparisons between the simulated streamflow depths for the scenarios, which are aimed at investigating the spatial variability of rainfall within the catchment and the appropriate scale of modelling and, the observed streamflow depth. From this graph it is evident that: (a) The simulated streamflow from a lumped catchment is much larger than the simulated streamflow from having divided the catchment into sub-catchments. (b) Using nine driver rainfall stations (one for each subcatchment) gives better simulations that using one rainfall station to drive the entire catchment.
6. Discussion, conclusions and recommendations The results obtained indicate that simulated streamflow depths from lumped quaternary scale catchments were much larger than the simulated streamflow depths obtained from having divided the catchment into sub-catchments. This implies that the quaternary catchment scale of modelling is not adequate and quaternary catchments need to be divided into sub-catchments for realistic results. The results also show that area weighting the soils and land cover information gave the best simulated streamflow depth results when compared with the observed streamflow depths in comparison with using the modal soils and land cover information. This is further emphasised in Table 3 where the coefficient of determination (R2) values for the different scenarios show that the highest R2 is obtained when area weighted soils information are used. Results from investigating the spatial variability of rainfall within the catchment indicate that the best results are obtained when each sub-catchment is assigned a driver rainfall station that represents that sub-catchments rainfall after rainfall correction factors are applied. It is important to note that the results obtained are specific to the area in which the study was done. Further investigations are necessary in a range of sizes of quaternary catchments in the different climatic regions of South Africa,
6
Table 3 Table showing the coefficient of determination (R2) values when comparing simulated vs observed streamflow depths for the different levels of soil/cover information
4 2 0 06-Sep-82 26-Oct-82 15-Dec-82 03-Feb-83 25-Mar-83 14-May-83 03-Jul-83 22-Aug-83 11-Oct-83 30-Nov-83 area weighted soil, 9driver stations modal soil, 1 driver station
modal soil, 9 driver stations lumped
area weighted soil, 1 driver station observed streamflow
Fig. 4. Simulated streamflow vs observed streamflow for scenarios investigating the spatial variability of rainfall.
Scenario
R2
Modal soils information Area weighted soils information Single dominant soils information
0.71 0.81 0.59
638
K. Chetty, J. Smithers / Physics and Chemistry of the Earth 30 (2005) 634–638
to investigate the spatial resolution at which continuous simulation modelling should be applied. In addition further investigations should be extended to include the simulation of peak discharge. References ASCE, 1997. Flood-Runoff Analysis. ASCE Press, New York, USA, p. 176. Calver, A., Lamb, R., 2000. Generalised flood frequency estimation using continuous simulation. In: Bronstert, A., Bismuth, C., Menzel, L. (Eds.), PIK Report 65: Proceedings of the European Conference on Advances in Flood Research, vol. 1. Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany, pp. 412–421. Cordery, I., Pilgrim, D.H., 2000. The state of the art of flood prediction. In: Parker, D.J. (Ed.), Floods, vol. II. Routledge, London, UK, pp. 185–197. CSIR, 1999. South African National Land Cover (NLC) Database Project. CSIR, Environmentek, Pretoria, South Africa. Cunnane, C., 1989. Statistical distributions for flood frequency analysis. WMO Report No. 718. World Meteorological Organization, Geneva, Switzerland. Hosking, J.R.M., Wallis, J.R., 1997. Regional Frequency Analysis: An Approach Based on L-Moments. Cambridge University Press, Cambridge, UK, p. 224. Rahman, A., Hoang, T.M.T., Weinmann, P.E., Laurenson, E.M., 1998. Joint probability approaches to design flood estimation: a review. Report 98/8. Cooperative Research Centre for Catchment Hydrology, Monash University, Clayton, Victoria, Australia, p. 70. Schulze, R.E., 1995. Hydrology and agrohydrology: a text to accompany the ACRU 3.00 agrohydrological modelling system. Report TT69/95. Water Research Commission, Pretoria, RSA. Schulze, R.E., 2001. National Land Cover Classification. Development of a decision support system for the ACRU model, Internal Report,
School of Bioresources Engineering and Environmental Hydrology, University of Natal, Pietermaritzburg, South Africa, p. 25. Schulze, R.E., Pike A., 1995. AUTOSOILS VERSION 3.0. A soils decision support system for South African soils, Department of Agricultural Engineering, University of Natal, Pietermaritzburg, South Africa. Schulze, R.E., Pike, A., 2004. Development and evaluation of an installed hydrological modelling system. WRC Report No. 1155/1/04. Water Research Commission, Pretoria, South Africa, p. 179. SIRI, 1987. Land Type Series. Department of Agriculture and Water Supply, Pretoria, Soil and Irrigation Research Institute. Memoirs on the Agricultural Natural Resources of South Africa. Smithers, J.C., Schulze, R.E., Kienzle, S.W., 1997. Design flood estimation using a modelling approach. In: Rosbjerg, D., Boutayeb, N., Gustard, A., Kundzewicz, Z.W., Rasmussen, P.F. (Eds.), Sustainability of water resources under increasing uncertainty. IAHS Publication 240, pp. 365–376. Smithers, J.C., Schulze, R.E., 2000. Long duration design rainfall estimates for South Africa. WRC Report No. 811/1/00. Water Research Commission, Pretoria, RSA, p. 69. Smithers, J.C., Schulze, R.E., 2001. Design runoff estimation: a review with references to practices in South Africa. In: Tenth South African National Hydrological Symposium. SANCIAHS, Pietermaritzburg, RSA. Smithers, J.C., Schulze, R.E., Pike, A., Jewitt, G.P.J., 2001. A hydrological perspective of the February 2000 floods: a case study in the Sabie River catchment. Water SA 27 (23).
Further reading Cameron, D.S., Beven, K.J., Tawn, J., Blazkova, S., Naden, P., 1999. Flood frequency estimation by continuous simulation for a gauged upland catchment (with uncertainty). Journal of Hydrology 219, 169– 187.
Continuous simulation modelling for design flood estimation in South Africa: Preliminary investigations in the Thukela catchment Kershani Chetty *, Jeff Smithers School of Bioresources Engineering and Environmental Hydrology, University of KwaZulu-Natal, Pietermaritzburg, Private Bag X01, Scottsville 3209, South Africa Accepted 15 August 2005 Available online 23 September 2005
Abstract Several recent literature reviews highlight the need for improvements in procedures for design flood estimation [Cordery, I., Pilgrim, D.H., 2000. The state of the art of flood prediction. In: Parker, D.J. (Ed.), Floods. vol. II. Routledge, London, UK, pp. 185–197; Smithers, J.C., Schulze, R.E., 2001. Design runoff estimation: a review with references to practices in South Africa. In: Tenth South African National Hydrological Symposium. SANCIAHS, Pietermaritzburg, RSA]. In general, these reviews indicate that internationally the trend is to adopt a continuous simulation modeling approach for design flood estimation. The continuous simulation modelling (CSM) approach to design flood estimation has many advantages and has the potential to overcome many of the limitations of the often used design event approach. A pilot study into the development of a continuous simulation modelling system for design flood estimation is being undertaken in the Thukela catchment in South Africa. Preliminary studies using the ACRU agrohydrological modelling system are detailed in this paper and include investigations into the appropriate scale and levels of soil and land cover information required for use in a CSM approach for design flood estimation. Results indicate that CSM with the ACRU model requires quaternary catchments to be further divided into sub-quaternary catchments, and that using area weighted soils and land cover information gives better results than using modal soils information or single dominant soil or land cover information. 2005 Elsevier Ltd. All rights reserved. Keywords: Design flood estimation; Continuous simulation modelling
1. Introduction Design floods estimates are often required by engineers, hydrologists and agriculturalists for the design of hydraulic structures such as dams, bridges or culverts (Smithers and Schulze, 2000). Under or over-design of even small hydraulic structures can result in a considerable waste of resources and it is therefore important to have accurate design flood estimates. The potential loss of life, property and infrastructure caused by floods also gives incentive to improve the ability to predict the magnitude and frequency of floods. According to Cordery and Pilgrim (2000) research in design flood estimation is on the decline and there is a *
Corresponding author. Tel.: +27 33 2606027; fax: +27 33 2605818. E-mail address: [email protected] (K. Chetty).
1474-7065/$ - see front matter 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2005.08.002
large gap between design flood research and practice. This needs redress if improvements to design flood estimation practice is to be made. In general approaches to design flood estimation vary according to data availability as illustrated in Fig. 1. Where adequate streamflow data are available methods based on the analysis of flood data are used. These methods include empirical equations, and at site or regional statistical analyses. Regional analysis methods may be used to estimate design floods at locations with inadequate data. In this approach it is frequently assumed that the standardised variate has the same distribution at every site in the selected region and that data from a region can thus be combined to produce a single regional flood or rainfall frequency curve which is applicable anywhere in the region with appropriate site specific scaling (Cunnane, 1989; Hosking and
K. Chetty, J. Smithers / Physics and Chemistry of the Earth 30 (2005) 634–638
635
Design Flood Estimation Methods Analysis of Stream flow Data Empirical Methods
Flood Frequency Analysis Site
Regional
Rainfall Based Methods Flood Envelopes
Historical/ Stochastic Rainfall
Design Rainfall Deterministic/ Probabilistic
Continuous Simulation Frequency Analysis
Design Event Model Gradex
Rational
SCS
Unit Hydrograph
Run off Routing
Fig. 1. Methods for estimating design floods (Smithers and Schulze, 2001).
Wallis, 1997). Numerous reviews of flood frequency studies have shown that regional analyses are advantageous (Cunnane, 1989; Hosking and Wallis, 1997). Adequate streamflow data is however seldom available due to the sparsity of recorded flow data, or flow records which are too short for frequency analyses or are of dubious quality and rainfall-runoff models are often used instead as there is generally a larger more dense network of raingauges than gauging weirs. The design event rainfall runoff models are often utilised for design flood estimation and these event based models make several simplifying assumptions which are significant in design flood estimation. 2. Continuous simulation modelling The continuous simulation modelling (CSM) rainfallrunoff approach to design flood estimation has many advantages and has the potential to overcome many of the limitations of the design event approach. According to Rahman et al. (1998), continuous simulation models aim to represent the major processes responsible for converting the input catchment rainfall into streamflow. Hydrographs are generated over long periods of time from the input of historical rainfall series, potential evaporation and other climatological and catchment attributes. An important characteristic of these models is the use of a continuous water budget model for the catchment so that antecedent conditions for each storm are known (Rahman et al., 1998). A relatively sophisticated hydrological model capable of simulating the entire hydrological cycle is required by the method. Rahman et al. (1998) summarised the views of various authors and concluded that CSM is regarded as having the potential for solving the limitations of the current single event approach to design flood estimation. Firstly the need for using synthetic storms is eliminated, by using actual storm records, secondly, subjectivity in selecting antecedent conditions for the land surface is not required as a water budget accounts for, in each time step of the simulation, antecedent moisture conditions (AMC). The duration of the storm is also not an issue as AMC and the water budget are explicitly modelled for each time step and the assumption that the return period of the output streamflow is the same as the return period of the input rainfall no longer has to be made. According to Rahman et al. (1998) some of the
disadvantages of CSM include the loss of ‘‘sharp’’ events if the modelling time scale is too large, the extensive data requirements, which result in significant time and effort to obtain and prepare the input data and, the expertise required to determine parameter values such that historical hydrographs are adequately simulated. Taking into account the limitations according to Rahman et al. (1998) CSM may prove to be the most powerful means of estimating flood frequency from rainfall. This type of analysis, according to the ASCE (1997), is receiving increasing interest and use in the USA. Calver and Lamb (2000) express the opinion that CSM may form the basis for the next generation of flood frequency estimation in the UK. In South Africa event based rainfall runoff models are generally used for design flood estimation but with the international shift towards CSM for design flood estimation the Water Research Commission commissioned the School of Bioresources Engineering and Environmental Hydrology at the University of KwaZulu-Natal to complete a pilot study on the development of a continuous simulation modeling system for design flood estimation in South Africa. The preliminary results of this pilot study are discussed in this paper. 2.1. Case study: preliminary investigations in the Thukela catchment The main objectives of the preliminary investigations are to investigate • the appropriate scale at which CSM should take place; • the appropriate levels of soil and land cover information required for CSM; and • the effects of the spatial variability of rainfall within a catchment on runoff depths.
3. The study area The Thukela catchment (Fig. 2) extends latitudinally from 27E25 0 to 29E24 0 S and longitudinally from 28E58 0 to 31E26 0 E and it covers an area of approximately 29,000 km2 in the KwaZulu-Natal province of South Africa. The Thukela river has its source in the Drakensberg mountain and flows right through into the Indian Ocean. The major tributaries of the Thukela river are the Little
636
K. Chetty, J. Smithers / Physics and Chemistry of the Earth 30 (2005) 634–638
Fig. 2. Location of the Thukela catchment within South Africa and location of QC27 within the Thukela Catchment.
Thukela, Mooi and BushmanÕs Rivers which join from the southwest, and the Klip, Sundays and Buffalo Rivers flowing from the north. The mean annual precipitation (MAP) in the Thukela ranges from approximately 2000 mm in the Drakensberg to as low as 550 mm in the drier central regions and most of the rainfall is received during the mid-summer months from December to February. 4. Methodology The model selected for the development of the CSM system is the ACRU model (Schulze, 1995). Two previous studies on continuous simulation modeling for design flood estimation in South Africa demonstrated by Smithers et al. (1997) and Smithers et al. (2001) also made use of the ACRU model with reasonable results. The ACRU model is a physical-conceptual agrohydrological model which operates on a daily time step. The model simulates all major processes of the hydrological cycle which affect the soil water budget and is capable of simulating, inter alia, streamflow volume, peak discharge and hydrograph, reservoir yield, sediment yield, crop yield for selected crops and irrigation supply and demand. ACRU can operate at a point, as a lumped catchment model, or as a distributed cell-type model in order to account for spatial variability in climate, land use and soils. The ACRU modelling system is linked to the quaternary catchments database. South Africa has been divided into several primary catchments, each of which have been further divided into secondary, then tertiary and finally quaternary catchments, by the Department of Water Affairs and Forestry (DWAF) of South Africa, for planning and management purposes. There are 1946 quaternary catchments (QCÕs) and the Thukela catchment is made up of 86 such QCÕs. The QC database developed at the school of Bioresources Engineering and Environmental Hydrology, houses information such as location, rainfall, soils and land cover information for each quaternary catchment
which is used for modelling purposes. The soils information in the database was obtained from the ISCW soils maps ( SIRI, 1987) at the 1:50,000 resolution and has been translated into ACRU variables by Schulze and Pike (1995). The land cover information was obtained from the National Land Cover Database (CSIR, 1999) at a resolution of 1:250,000 and has been translated into ACRU variables by Schulze (2001). In order to meet the objectives set out above the following methodology was used: • A 253 km2 quaternary catchment was selected and was sub-divided into 9 sub-catchments according to topography. • The Calc pptcor (Schulze and Pike, 2004) suite of programmes was used to determine the closest, most representative rainfall station termed the driver rainfall station and the precipitation correction factors that needed to be applied to the rainfall at the station. Daily rainfall values were used. • All the other input variables for ACRU were obtained from the QC database. The model was run from 1950 to 2000. The scenarios were set up to according to Tables 1 and 2 in order to meet the various objectives of the investigations.
Table 1 Scenarios to investigate appropriate scale, levels of soils and land cover information for use in the model Mode of simulation
Soils information
Land cover information
Catchment modelled as one entity (lumped) Catchment divided into 9 sub-catchments Catchment divided into 9 sub-catchments
Modal
Modal
Modal
HRU
Area weighted
HRU
K. Chetty, J. Smithers / Physics and Chemistry of the Earth 30 (2005) 634–638 Table 2 Scenarios to investigate the spatial variability of rainfall on runoff depths within a catchment Driver rainfall stations per sub-catchment
Soils information
Land cover information
1 1 9 9
Modal Area weighted Area weighted Modal
HRU HRU HRU HRU
driver driver driver driver
station station stations stations
5. Results The results obtained from modelling the different scenarios with the emphasis on runoff depths are presented in Figs. 3 and 4. Fig. 3 shows the comparisons between the simulated streamflow depths for the scenarios which are aimed at investigating scale and levels of soil and land cover information required and the observed streamflow depth. From this graph it is evident that: (a) The simulated streamflow from a lumped catchment is much larger than the simulated streamflow from having divided the catchment into sub-catchments. Graph of daily simulated vs Observed Flows for QC 27 14 12
Daily Flow (mm)
10 8 6 4 2 0 06-Sep-82 26-Oct-82 15-Dec-82 03-Feb-83 25-Mar-83 14-May-83 03-Jul-83
area weighted soils
modal soils
lumped
22-Aug-83 11-Oct-83 30-Nov-83
observed streamflow
Fig. 3. Simulated streamflow vs observed streamflow for scenarios investigating the scale, levels of soil and land cover information.
Graph of daily simulated vs Observed Flows for QC 27 14
Daily Flow (mm)
12 10 8
637
(b) The simulated streamflow from the scenario which used modal soils deviates more from the observed values, than the scenario where area weighted soils are used. This implies that using area weighted values derived for sub-catchments soil and land cover information, results in improved simulations. Fig. 4 shows the comparisons between the simulated streamflow depths for the scenarios, which are aimed at investigating the spatial variability of rainfall within the catchment and the appropriate scale of modelling and, the observed streamflow depth. From this graph it is evident that: (a) The simulated streamflow from a lumped catchment is much larger than the simulated streamflow from having divided the catchment into sub-catchments. (b) Using nine driver rainfall stations (one for each subcatchment) gives better simulations that using one rainfall station to drive the entire catchment.
6. Discussion, conclusions and recommendations The results obtained indicate that simulated streamflow depths from lumped quaternary scale catchments were much larger than the simulated streamflow depths obtained from having divided the catchment into sub-catchments. This implies that the quaternary catchment scale of modelling is not adequate and quaternary catchments need to be divided into sub-catchments for realistic results. The results also show that area weighting the soils and land cover information gave the best simulated streamflow depth results when compared with the observed streamflow depths in comparison with using the modal soils and land cover information. This is further emphasised in Table 3 where the coefficient of determination (R2) values for the different scenarios show that the highest R2 is obtained when area weighted soils information are used. Results from investigating the spatial variability of rainfall within the catchment indicate that the best results are obtained when each sub-catchment is assigned a driver rainfall station that represents that sub-catchments rainfall after rainfall correction factors are applied. It is important to note that the results obtained are specific to the area in which the study was done. Further investigations are necessary in a range of sizes of quaternary catchments in the different climatic regions of South Africa,
6
Table 3 Table showing the coefficient of determination (R2) values when comparing simulated vs observed streamflow depths for the different levels of soil/cover information
4 2 0 06-Sep-82 26-Oct-82 15-Dec-82 03-Feb-83 25-Mar-83 14-May-83 03-Jul-83 22-Aug-83 11-Oct-83 30-Nov-83 area weighted soil, 9driver stations modal soil, 1 driver station
modal soil, 9 driver stations lumped
area weighted soil, 1 driver station observed streamflow
Fig. 4. Simulated streamflow vs observed streamflow for scenarios investigating the spatial variability of rainfall.
Scenario
R2
Modal soils information Area weighted soils information Single dominant soils information
0.71 0.81 0.59
638
K. Chetty, J. Smithers / Physics and Chemistry of the Earth 30 (2005) 634–638
to investigate the spatial resolution at which continuous simulation modelling should be applied. In addition further investigations should be extended to include the simulation of peak discharge. References ASCE, 1997. Flood-Runoff Analysis. ASCE Press, New York, USA, p. 176. Calver, A., Lamb, R., 2000. Generalised flood frequency estimation using continuous simulation. In: Bronstert, A., Bismuth, C., Menzel, L. (Eds.), PIK Report 65: Proceedings of the European Conference on Advances in Flood Research, vol. 1. Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany, pp. 412–421. Cordery, I., Pilgrim, D.H., 2000. The state of the art of flood prediction. In: Parker, D.J. (Ed.), Floods, vol. II. Routledge, London, UK, pp. 185–197. CSIR, 1999. South African National Land Cover (NLC) Database Project. CSIR, Environmentek, Pretoria, South Africa. Cunnane, C., 1989. Statistical distributions for flood frequency analysis. WMO Report No. 718. World Meteorological Organization, Geneva, Switzerland. Hosking, J.R.M., Wallis, J.R., 1997. Regional Frequency Analysis: An Approach Based on L-Moments. Cambridge University Press, Cambridge, UK, p. 224. Rahman, A., Hoang, T.M.T., Weinmann, P.E., Laurenson, E.M., 1998. Joint probability approaches to design flood estimation: a review. Report 98/8. Cooperative Research Centre for Catchment Hydrology, Monash University, Clayton, Victoria, Australia, p. 70. Schulze, R.E., 1995. Hydrology and agrohydrology: a text to accompany the ACRU 3.00 agrohydrological modelling system. Report TT69/95. Water Research Commission, Pretoria, RSA. Schulze, R.E., 2001. National Land Cover Classification. Development of a decision support system for the ACRU model, Internal Report,
School of Bioresources Engineering and Environmental Hydrology, University of Natal, Pietermaritzburg, South Africa, p. 25. Schulze, R.E., Pike A., 1995. AUTOSOILS VERSION 3.0. A soils decision support system for South African soils, Department of Agricultural Engineering, University of Natal, Pietermaritzburg, South Africa. Schulze, R.E., Pike, A., 2004. Development and evaluation of an installed hydrological modelling system. WRC Report No. 1155/1/04. Water Research Commission, Pretoria, South Africa, p. 179. SIRI, 1987. Land Type Series. Department of Agriculture and Water Supply, Pretoria, Soil and Irrigation Research Institute. Memoirs on the Agricultural Natural Resources of South Africa. Smithers, J.C., Schulze, R.E., Kienzle, S.W., 1997. Design flood estimation using a modelling approach. In: Rosbjerg, D., Boutayeb, N., Gustard, A., Kundzewicz, Z.W., Rasmussen, P.F. (Eds.), Sustainability of water resources under increasing uncertainty. IAHS Publication 240, pp. 365–376. Smithers, J.C., Schulze, R.E., 2000. Long duration design rainfall estimates for South Africa. WRC Report No. 811/1/00. Water Research Commission, Pretoria, RSA, p. 69. Smithers, J.C., Schulze, R.E., 2001. Design runoff estimation: a review with references to practices in South Africa. In: Tenth South African National Hydrological Symposium. SANCIAHS, Pietermaritzburg, RSA. Smithers, J.C., Schulze, R.E., Pike, A., Jewitt, G.P.J., 2001. A hydrological perspective of the February 2000 floods: a case study in the Sabie River catchment. Water SA 27 (23).
Further reading Cameron, D.S., Beven, K.J., Tawn, J., Blazkova, S., Naden, P., 1999. Flood frequency estimation by continuous simulation for a gauged upland catchment (with uncertainty). Journal of Hydrology 219, 169– 187.