ACRU HYDROLOGICAL MODELLING OF THE MUPFURE CATCHMENT

Transcription

1 ACRU HYDROLOGICAL MODELLING OF THE MUPFURE CATCHMENT Table of Contents: 1 INTRODUCTION CONFIGURATION OF ACRU RAINFALL DATA SOILS LAND COVER INFORMATION STREAM FLOW DATA EVAPORATION TEMPERATURE DATA WIND SPEED DATA WATER ABSTRACTIONS AND STORAGE MODELLING RESULTS CONCLUSION REFERENCES Introduction It was decided that modelling of the Mupfure Catchment using ACRU should be confined to the upper catchment up to C70 flow measuring station (Fig 13). The catchment area up to this point is 1215 sq km. The main aim of modelling was to configure the ACRU model, derive model parameters, and then apply this model to assess the effects of land use change and climate change on water resources 1

2 Figure 13 Location of the catchment where the ACRU model was applied Subcatchment Delineation Mupfure Catchment W N E S Subcatchments Kilometers P N O K L J M D I C A H G E B F 2 Configuration of ACRU The ACRU model should be operated in a semi-distributed mode. This requires sub-division of the catchment into linked sub-catchments. The various sub-basins occurring in the catchment to be modelled were identified using stream networks and altitude digitized from 1: topographic maps. The catchment was then sub-divided taking into account that the resulting subcatchments had almost uniform land cover. As a result 16 sub-catchments were identified and these are shown in Fig 14 and in Table 9. Some of the sub-catchments are greater than the recommended maximum catchment area of 50 sq km when using ACRU model. It was decided that having all sub-catchments smaller than 50 sq km was not practically since some of the input data such as rainfall is available at a coarse spatial resolution. 2

3 Figure 14 Sub-catchment Configuration The flow of water from one subcatchment to the other as indicated above, is as follows: 1 to 3, 2 to 3, 3 to 4, 4 to 9, 5 to 4, 6 to 7, 7 to 4, 8 to 9, 9 to 13, 10 to 12, 11 to 12, 12 to 13, 13 to 15, 14 to 15, and 15 to 16. Table 9 Catchment Areas of the Delineated Sub-catchments Sucatchment ID X (Longitude) Y (Latitude) Area in km 2 Average Altitude (m) Subcatchment A Subcatchment B Subcatchment C Subcatchment D Subcatchment E Subcatchment F Subcatchment G Subcatchment H Subcatchment I Subcatchment J Subcatchment K Subcatchment L Subcatchment M Subcatchment N Subcatchment O Subcatchment P

4 2.1 Rainfall data Rainfall data used for modelling were derived from 6 stations (Carnock, Mahusekwa, Beatrice, Dunolly, Ensidale and Poltimore.) which are within or close to the study area (Fig 15). Figure 15 Location of the rainfall stations used in ACRU Modelling: Table 10 shows that Carnock has the highest mean annual rainfall of 800 mm, while Ensidale has the lowest, 637 mm. Table 12 Mean Annual Rainfall of Stations Used During ACRU Modelling STATION Beatrice Carnock Dunolly Ensidale Mahusekwa Poltimore Average Rainfall (mm) The rainfall within the modelled area has a high inter-annual variability (Fig 16). 4

5 Figure 16 Annual Rainfall Annual Rainfall (Beatrice, Mahusekwa) Precipitation (mm) Time (years) Mahusekwa Beatrice One of the characteristic feature of the rainfall within the area selected for ACRU modelling is that it comes mostly in the form of convective thunderstorms. Such storms are often isolated leading to a poor correlation of daily rainfall between the six rainfall stations. Table 11 below is a correlation matrix of daily rainfall for the 6 selected stations. There is practically no relationship of the daily rainfall at the various stations. Table 11 Correlation Matrix of Daily Rainfall Beatrice Cannock Dunolly Ensidale Mahusekwa Poltimore Beatrice Cannock Dunolly Ensidale Mahusekwa Poltimore The relationship between monthly rainfall at the various stations is rather strong showing that all the various parts of the catchment are affected by the same rainfall causing weather systems. Table 12 shows the correlation matrix of monthly rainfall. 5

6 Table 12 Monthly Correlation Coefficeints Monthly Correlation Coefficient ( r ) BEATRICE ONTH Chibero Dunolly Ensidale Mhondoro Mahusekwa Carnock Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec The Thiessen method was used to estimate daily rainfall for the various sub-catchments. The Thiessen polygons used are shown in Fig 17. Figure 17 Theissen Polygons for the Mupfure Upper Catchment area. # Dunolly N Beatrice # # Mahusekwa Poltimore # Ensidale # Kilometers # Rainfall Stations Theissen Polygons Beatrice Dunolly Ensidale Mahusekwa Poltimore 6

7 2.2 Soils A soil survey was undertaken in order to derive soil characteristics that are required for ACRU modelling. Soil samples were collected from augered and excavated pits at sites shown in Fig 18 Figure 18 Soil Sampling Sites The samples collected in the field were analysed in the laboratory. Table 13 shows that the soils in the sub-catchments are sand, sandy loam, and loamy sand. 7

8 Table 13 Soil Texture Subcatchment ID Soil Texture Subcatchment A 1 Sandy Loam Subcatchment B 2 Loamy Sand Subcatchment C 3 Sand Subcatchment D 4 Sand Subcatchment E 5 Sandy Loam Subcatchment F 6 Loamy Sand Subcatchment G 7 Loamy Sand Subcatchment H 8 Sandy Loam Subcatchment I 9 Sandy Loam Subcatchment J 10 Sand Subcatchment K 11 Sand Subcatchment L 12 Sandy Loam Subcatchment M 13 Sand Subcatchment N 14 Sand Subcatchment O 15 Loamy Sand Subcatchment P 16 Sand The various soil parameters which are required for ACRU modelling were derived from laboratory analysis of soil samples, and these are given in Table 14 below. Table 14 Soil Parameters Subcatchment ID Top Soil Average Depth (cm) Top Soil Field Capacity (% V/V) Top Soil Wilting Point (% V/V) Top Soil Saturation (% V/V) Sub Soil Average Depth (cm) Sub Soil Field Capacity (% V/V) Sub Soil Wilting Point (% V/V) Sub Soil Saturation (% V/V) Subcatchment A Subcatchment B Subcatchment C Subcatchment D Subcatchment E Subcatchment F Subcatchment G Subcatchment H Subcatchment I Subcatchment J Subcatchment K Subcatchment L Subcatchment M Subcatchment N Subcatchment O Subcatchment P The depths of the soils vary from 40 to 120 cm, while the top soil has depths of 10 to 18 cm. 8

9 2.3 Land Cover Information Land-cover information was derived from a classification of the Landsat TM image of 26 April Table 15 below shows the different land cover types used in ACRU. Table 15. Subcatchment Land cover used in ACRU Subcatchment Land cover used % Land cover from satellite image in ACRU A Communal Land (53.3%), Bushland (17.3%) Maize all areas B Bushland (65.84%), Communal Farming (9.03%) Woodland C Settlement (51.74%), Communal Land (17.35%) Dam D Communal Land (49.62%), Bushland (11.88%) Maize all areas E Bushland (63.53%), Bare Soil (9.98%) Woodland F Bushland (82.26%), Miombo Forest (6.82%) Woodland G Bushland (70.03%), Bare Soil (11.65%) Woodland H Bushland (62.93%), Miombo Forest (14.03%) Woodland I Bushland (61.81%), Bare Soil (14.91%) Woodland J Communal Land (45.97%), Bushland (17.77%) Maize all areas K Communal Land (55.05%), Bushland (15.96%) Maize all areas L Bushland (61.39%), Communal Land (13.00%) Woodland M Bushland (70.75%) Miombo Forest (14.14%) Woodland N Communal Land (59.76%), Bushland (14.93%) Maize all areas O Bushland (56.60%), Bare Soil (11.87%) Woodland P Bushland (66.27%), Miombo Forest (12.05%) Woodland 2.4 Stream flow Data Stream flow data was collected for stations C70, C107, and C108. The flow data were available for the periods shown in Table 16 below. Table 16 Stream flow Stations Station Name Period Of Data C70 01/07/70 to 01/09/97 C107 01/10/89 to 10/09/96 C108 01/10/88 to 01/09/95 Station C70 is the most downstream point of the area to be modelled. Station C107 is located upstream of Mahusekwa Dam and station C108 is downstream of the same dam. Flows for station C70 were used to verify the ACRU model. Table 17 summarises some of the statistical characteristics of flows at C70. 9

10 Table 17 C70 Flows (mm) For The Period 1969 To 1996 Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept Mean Maximum Minimum Variance Evaporation In Zimbabwe evaporation is in most cases measured by pans, the standard pan being the screened American Weather Bureau Class A Pan. In this study monthly totals of A-Pan evaporation values were used. Since there is no evaporation data within the catchment, the records of the closest stations, Chibero and Kutsaga Tobacco Research Station were used to estimate evaporation rates for the modelled catchment. Table 17 gives the mean monthly evaporation figures used during ACRU modelling. Table 18. Mean Monthly Evaporation (mm) OCT NOV DEC JAN FEB MAR APR MAY JUN SUL AUG SEP Temperature Data The monthly average maximum and minimum temperatures were estimated using records at nearby Mhondoro. These are given in Table 18 below. Table 19 Minimum and Maximum Temperatures ( 0 C) Month Jan Feb Mar Apr May Jun July Aug Sept Oct Nov Dec Monthly Average (maximum) Monthly Average (minimum) Wind Speed Data Wind speed data was derived from records of Mhondoro Meteorological Station. The average monthly wind speed are given in Table 20 below. 10

11 Table 20 Average Wind Speed Month Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec Average Wind Speed {Knots/hour } Average Wind Speed {m/s} 2.C C Water Abstractions and Storage There are no records of the actual amounts of water abstracted by various farmers and other water users within the modelled catchment. The amount of water abstracted can be estimated from water rights that have been granted to water users. Water rights will only indicate the maximum amount of water that a holder is allowed to abstract and/or store. In reality water right holders may not use their full allocations, or exceed illegally these allocations. Table 21 shows the water abstraction rates estimated from water rights that have been granted in the sub-catchments shown. Table 21 Water Abstraction for each Subcatchment (10 3 m 3 ) Sub-catchment Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

12 3 Modelling Results Initially the model was run with only changes being made to the coefficient of initial abstraction with the other model parameters being kept constant. According to Schulze (1995) the coefficient of initial abstraction ranges from 0.05 in urban areas, arid areas, and frozen conditions to 0.5 in ploughed and forested areas. The initial values used were 0.3 for the rainy season (October to April) and 0.1 for the dry season (May to September). A comparison of the simulated and observed flows is presented in Fig 19. Figure 19 Initial Simulation Initial Simulation 1985 to 1990 (With changes only to the Coefficient of Initial Abstraction) Flow (mm) Nov-84 Mar-85 Jul-85 Nov-85 Mar-86 Jul-86 Nov-86 Mar-87 Jul-87 Nov-87 Mar-88 Jul-88 Nov-88 Mar-89 Jul-89 Nov-89 Mar-90 Jul-90 Nov-90 Observed Streamflow Simulated Streamflow The simulated flows do closely mimic the observed flows, but there is a general overestimation of high flows during the wet seasons. Schulze (1995) states that the coefficient of initial abstraction can increase to say 0.3 or 0.4 immediately after ploughing when the surface roughness is high, or under forested conditions, and reduces to (say) 0.05 to 0.15 under conditions of soil compaction or in peri-urban areas. The coefficient of initial abstraction was therefore increased to 0.4 for the wet season and decreased to 0.09 for the dry season. The results obtained are given in Fig 20 below. 12

13 Fig 20: Effects of Changes in the Coefficient of Initial Abstraction Initial Simulation 1980 to 1985 (With changes to the Coefficient of Initial Abstraction) Flow (mm) Nov-84 Feb-85 May-85 Aug-85 Nov-85 Feb-86 May-86 Aug-86 Nov-86 Feb-87 May-87 Aug-87 Nov-87 Feb-88 May-88 Aug-88 Nov-88 Feb-89 May-89 Aug-89 Nov-89 Feb-90 May-90 Aug-90 Nov-90 Observed Streamflow Simulated Streamflow The changes made to the coefficient of initial abstraction caused a decrease in the simulated high flows which is particularly evident during the periods January 1989 to April 1989, and January 1990 to April The best result was obtained with the rainy season coefficient of initial abstraction of 0.39, dry season coefficient of 0.09, a pan coefficient of 0.8 and the streamflow response fraction equal to 0.6. Fig 21 shows a comparison of the simulated and observed daily flows. Figure 21 Final Simulation Monthly Final Simulation (1985 to 1990) Flow (mm) Jan-85 May-85 Sep-85 Jan-86 May-86 Sep-86 Jan-87 May-87 Sep-87 Jan-88 May-88 Sep-88 Jan-89 May-89 Sep-89 Jan-90 May-90 Sep-90 Observed Streamflow Simulated Streamflow A comparison was also made of the cumulative daily and monthly flows (Fig 21 and 22). The difference between the mean of the observed daily values (0.207 mm) and simulated values (0.224) is 8.4% which is less than 15% that Schulze (1995) indicated as what one should aim for 13

14 during modelling (Table 22). Similarly the difference between the standard deviation of the observed and simulated values is 5% which is the ideal situation. Table 22 Comparison of Observed and Simulated Daily Flows Conservation Statistics Mean of observed values Mean of simulated values % difference between means t Statistic for comparing means Standard deviation of observed values Standard deviation of simulated values % difference between standard deviations Coefficient of skewness of observed values Coefficient of skewness of simulated values % difference between skewness coefficient Total root mean square error Regression Statistics Correlation coefficient (Pearson's r) Slope of the regression line Y intercept of the regression line Coefficient of determination (SSR/SST) Coefficient of efficiency Coefficient of agreement Daily Daily The correlation coefficient between observed and simulated daily flows is 0.8 which is high. The slope (0.8) is close to the 1:1, while the intercept (0.07) is also close to the desirable 0.0. A visual comparison of the observed and simulated daily flows shows that the model does over-estimate flows in some years (e.g. 1985), but it also under-estimate flows in other years (e.g. 1986). The are some days with extremely high flows that were not modelled correctly. Fig 22 and 23 below compare the flow duration curves of the simulated and observed daily and monthly flows. These show that the model general under-estimates daily flows less than 1 mm, while it over-estimates flows greater than this value. One of the problems which was encountered during modelling is that observed daily flows have to be expressed in ACRU in millimetres to 1 decimal place. Thus in the rounding of flow values any flow less than 0.05 mm or m 3 /s is rounded off to 0.0 mm, and yet 42% of the flows are between greater than 0.0 and less than m 3 /s. This affects estimation of model parameter values during recession flows. 14

15 Exceedance probability (%) FIG 22 COMPARISON OF SIMULATED AND MODELLED DAILY FLOWS Simulated 30.0 Observed Flows (mm) FIG 23 COMPARISON OF MONTHLY FLOWS Exceedance probability (%) Simulated Observed Monthly flow (mm) 4 Conclusion This experienced gained in this study shows that the ACRU model is sensitive to land cover and soil characteristics as these affect storm flow formation, redistribution of soil moisture. Some of the vegetation characteristics required by the model are not readily available, e.g. leaf area index, and rooting depths. However, the ACRU expert system used during menu building does assist in overcoming these difficulties. Rainfall is the most important input in all rainfall-runoff models. The rain gauge network in most southern African countries, and within the Mupfure Catchment does not enable an accurate assessment of daily rainfall due to the dominance of thunderstorms. In this study, there were several days which had high flows which could not be explained by the available catchment rainfall. Such days were never modelled properly. Regression statistics showed that there was a close agreement between observed and simulated flows. This is partly due the fact that the greater part of the year has zero flows which are accurately simulated, but wet season flows may not necessarily be accurately simulated. 15

16 The development of methods for estimating catchment rainfall from satellite data is likely to assist in overcoming the problem of coarse spatial resolution of rainfall data. Bonifacio and Grimes (1998) observed that the rain gauge network in most African countries was not likely to improve, but rather deteriorate. They merged rain gauge data with rainfall estimates derived from METEOSAT thermal infrared images in order to estimate catchment rainfall for the Kafue River in Zambia. The derived catchment rainfall was used in modelling for flood warning purposes. The results showed that the use of satellite data did improve the accuracy of the model. 5 References - Bonifacio, R. and D.I.F. Grimes, (1998), Drought and flood warning in southern Africa. IDNDR Flagship Programme - Forecasts and Warnings, UK National Coordination Committeed for the IDNDR, Thomas Telford, London. - John C Rodda (1985) Facets of Hydrology John Wiley and Sons Ltd, London UK. - Kienzle, S. W., Lorentz, S. A., and Schulze, R. E. (1997) Hydrology and Water Quality of the Mgeni Catchment Water Research Commission, Pretoria, Report TT87/97 - Mazvimavi, D. (1998) Water Resources Management in the Water Catchment Board Pilot Areas, Phase 1: Data Collection, CASS, University of Zimbabwe. - Nyamapfene, K. (1991) Soils of Zimbabwe Nehanda Publishers, Harare,Zimbabwe - Schulze, R. E, Smithers, J.C., Lynch, S.D and Lecler, N.L (1995) ACRU Agrohydrological Modelling System: User Manual Version Water Research Commission, Pretoria, Report TT70/95 - Schulze, R. E (1995) Hydrology and Agrohydrology: A Text to Accompany the ACRU 3.00 Agrohydrological Modelling System. Water Research Commission, Pretoria, Report TT69/95. 16