Design flood estimation using a modelling approach - Hydrologie.org

Sustainabilityof Water Resources under Increasing Uncertainty (Proceedings of the Rabat Symposium SI, April 1997). IAHS Publ. no. 240, 1997. 365

Design flood estimation using a modelling approach: a case study using the ACRU model

JEFF SMITHERS, ROLAND SCHULZE & STEFAN KIENZLE Department of Agricultural Engineering, University of Natal, Pietermaritzburg, Bag X01, Scottsville 3209, South Africa

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Abstract The potential of using streamflow simulated by a continuous model to estimate design floods at ungauged sites in KwaZulu, South Africa is investigated. The ACRU model was selected and simulated daily volume and peak discharge was verified against observed data at two sites. Design floods computed from the simulated output compared well with those computed from the observed data. The model was used to investigate the effect of farm dams and river reaches on the design flood. The advantages of using a continuous simulation model which models the flood attenuating effects of dams and reaches, as compared to single event type models, are highlighted. INTRODUCTION The economic cost, and potential loss of life, resulting from the failure of hydraulic structures can be enormous (e.g. Dawdy & Letttenmaier, 1987; Wallis, 1988) and highlights the importance of obtaining the "best possible" design flood estimate. The best estimate is usually limited by the available reliable data. Furthermore a number of approaches are possible when estimating design floods. In cases where long records of measured streamflow data are available, a direct statistical analysis of the data may be feasible. However, the streamflow data series are often short and assumptions of consistency, homogeneity and stationarity are often not valid. In practice, the most frequently occurring case is that no measured streamflow data are available at or near the site of interest. Under such conditions an estimate of the design flood has to be based on some hydrological model, with the range of possible models extending from the rational method to dynamic, continuous simulation modelling. In South Africa, as is the case in many countries in the world, the density and length of record of rainfall stations far exceeds that of runoff gauging stations. The designer thus frequently has to resort to estimating a design flood from the available rainfall data. An event-based model may be used to estimate the designfloodfrom the design rainfall. Schulze (1989) examines the validity of these types of models which generally assume that the T-year design rainfall event produces the J-year flood event, and which generally do not take cognisance of antecedent soil moisture conditions prior to rainfall events. The merits of using a continuous simulation model to simulate streamflow are listed by Schulze (1989) and include the use of a longer data series for analysis, the ability to simulate streamflow under future land use/climate conditions which may be present during the design life of the structure and the explicit modelling of the effect of soil moisture conditions on streamflow, thus not assuming a direct transformation of a design rainfall to a design flood.

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The objective of this paper is to investigate and assess the potential of using streamflow simulated by a continuous simulation model to estimate design floods at ungauged sites. The peak discharge simulated by the ACRU model (Schulze, 1995) is verified at two sites in KwaZulu-Natal, South Africa, and comparisons are made between design floods estimated from observed and simulated data. The combined effect of numerous relatively small farm dams and the attenuation of hydrographs in river reaches is illustrated. During the course of the investigation it became apparent that at some sites where observed streamflow data were available, gauging structure limitations and other problems associated with the data made the use of simulation modelling in the estimation of design floods the preferred approach.

THE ACRU AGROHYDROLOGICAL MODEL The model selected for the simulation was the ACRU model (Schulze, 1995), which has been developed in South Africa over the past 15 years and is still undergoing further development and refinement. ACRU is a physical conceptual agrohydrological model which generally operates with 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 variability in climate, land use and soils. Where automatically recorded rainfall data are not available, the model is capable of using synthetic rainfall distributions to disaggregate the daily rainfall into shorter time increments which enables the generation of hydrographs displaying intra-daily variations in peak discharge. The lagging and attenuation of the hydrograph as it passes through a river reach or reservoir is also modelled with time increments of less than one day.

Simulation of streamflow volume In ACRU the stormflow depth is simulated using a modified SCS approach where the soil moisture deficit, computed from a daily water budget of the soil profile, is used as a surrogate for a curve number. The baseflow is added to the stormflow to compute the volume of streamflow for each day.

Simulation of peak discharge The ACRU model simulates peak discharge from the individual sub-catchments using the SCS triangular-shaped unit hydrograph approach. Where automatically recorded rainfall data with time increments of less than one day are not available, synthetic design rainfall distributions, as developed for southern Africa by Weddepohl (1988), may be used to disaggregrate daily rainfall totals into shorter time intervals, thus allowing incremental storm hydrographs to be generated for each time interval. These are then aggregated to form the stormflow hydrograph at the outlet to each sub-catchment. The

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hydrographs are routed from the outlet of a sub-catchment through the next downstream sub-catchment to the outlet of the downstream sub-catchment, and are added to the hydrographs generated for both the downstream sub-catchment and all other upstream sub-catchments which flow directly into the downstream catchment. This procedure is detailed by Smithers & Caldecott (1993, 1995).

Flow routing in river reaches The flow routing options in the ACRU model are based on the Muskingum method which is detailed in many standard hydrology texts (e.g. Chow et al., 1988; Fread, 1993). Details on the manner in which is it implemented in ACRU are reported by Smithers & Caldecott (1993, 1995).

Flow routing through reservoirs The storage indication method for level pool routing of flows through dams has been implemented in the ACRU model in a manner similar to that explained by Chow et al. (1988). Details on the method and the manner in which is it implemented in ACRU are reported by Smithers & Caldecott (1993, 1995). The ACRU model is structured such that dams may be located at the outlet of a subcatchment, as "external" dams, or as "internal" dams within the sub-catchment, with the latter to enable the modelling of the cumulative effect of many small dams which may be present within a sub-catchment. When modelling external dams, the entire streamflow from the sub-catchment, including all upstream contributions, are assumed to flow into the dam. In the case of internal dams, only the streamflow simulated from that portion of the sub-catchment in which the dam is located is assumed to flow into the dam. For internal dams, the hydrographs generated on a daily basis at the sub-catchment outlet are apportioned such that the ratio of the volume of water flowing into the internal dams to the total volume of the hydrograph for a particular day is the same as the ratio of the catchment area contributing to the internal dam to the total catchment area. The volume of water not flowing into the dam is assumed to flow directly into the downstream sub-catchment. Hence, when the flood routing options are invoked, internal dams which may be off-channel dams are assumed to be in-channel dams, and are assumed to be located at the outlet to the sub-catchment, and only a user specified fraction of the streamflow, simulated only from the sub-catchment in which the dam is located, flows into the dam.

METHODOLOGY The model was applied to the Lions and Mfofana catchments which have a combined area of 760 km2 and which form part of the upper reaches of the Mgeni River catchment (4353 km2) in KwaZulu-Natal, South Africa (Fig. 1). The mean annual precipitation in these two catchments ranges from 870 to 1040 mm. The major land cover classifications

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Fig. 1 Location, catchment discretization and schematic flow path.

in the catchments are natural grassland, bushveld, dryland and irrigated agriculture (maize and pastures) and commercial afforestation. The Schmidt-Schulze option in ACRU for estimating catchment lag time, which was developed for natural catchments in sub-humid areas, was used in this study (Schmidt & Schulze, 1984; Smithers & Schulze, 1995). Unfortunately, automatically recorded rainfall data with time increments of less than one day were not sufficiently abundant in the study area to warrant their use in this application of the model. Therefore, the synthetic design rainfall distributions developed for southern Africa by Weddepohl (1988) were used to disaggregrate daily rainfall totals into selected shorter time intervals, thus allowing incremental storm hydrographs to be generated for each time interval. The multiple reach routing with varying parameters option was invoked to estimate the Muskingum parameters (Smithers & Schulze, 1995). This option is based on the Muskingum-Cunge method of flow routing, and requires physically-based parameters such as channel dimensions and shape of the cross-sectional area of the river, the slope and length of the reach and Manning's roughness coefficient. These values were derived inter alia from field surveys (Kienzle et al., 1995; Gôrgens et al., 1994). Daily streamflow volume and discharge simulated by ACRU was verified against observed data at two sites in the Mgeni catchment. Gauging station U2H013, with an upstream area of 296 km2, is located on the Mpofana River and for the purposes of modelling has been delineated into seven sub-catchments with sub-catchment areas ranging from 5.9 to 85.8 km2. Gauging weir U2H007 is located on the Lions River and

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has an upstream area of 362 km2 and has been subdivided into 10 sub-catchments which have areas ranging from 2.3 to 109.8 km2. With the exception of sub-catchment 15, all sub-catchments have either an internal or external dam with the modelled full supply capacities of the dams ranging from 14.0 X 103 to 5.5 x 106 m3 . The individual and combined effect of farm dams and river reaches on design floods was assessed by comparing design floods estimated from streamflow simulated with, and without, attenuation of the hydrograph through the dams and reaches.

SIMULATION RESULTS A 33-year period (1960-1993) of observed daily rainfall from seven stations was used to simulate streamflow with the model. The land cover information used in the simulations was obtained from satellite imagery flown in 1986, and is therefore not representative of the catchment for the earlier part of the observed data. Tarboton & Schulze (1992) showed that the simulation of streamflows is highly sensitive to changes in land use over time. Thus, for the purposes of verifying the daily volumes and peak discharges simulated by the model, a 7-year period on either side of the date of the land cover survey (i.e. from 1979 to 1993) was used in computing the statistics of performance of the model.

Daily totals of streamflow volume The simulated vs observed daily streamflow volume is depicted in Fig. 2 and selected statistics of performance of the model with respect to the simulation of streamflow volume are contained in Table 1. These results are considered to be highly acceptable.

Table 1 Observed vs simulated statistics for daily flows (mm) for the period 1979-1993. U2H013 Sample size

U2H007

5413

5077

3325 3173 2.366 1.674 25.093 14.654

2440 2362 0.550 0.476 3.676 3.741

0.841 0.708 0.151

0.876 0.815 0.074

Conservation statistics: Sum of observed values Sum of simulated values Variance of observed values Variance of simulated values Skewness coefficient of observed values Skewness coefficient of simulated values Regression statistics: Correlation coefficient (Pearson's r) Slope of the regression line 7 intercept of the regression line

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