Presentation is loading. Please wait.

Presentation is loading. Please wait.

Comparison between the United States Soil Conservation Service (SCS) curve number, the Pitman and Monarch models for estimating rainfall-runoff in South-Eastern.

Similar presentations


Presentation on theme: "Comparison between the United States Soil Conservation Service (SCS) curve number, the Pitman and Monarch models for estimating rainfall-runoff in South-Eastern."— Presentation transcript:

1 Comparison between the United States Soil Conservation Service (SCS) curve number, the Pitman and Monarch models for estimating rainfall-runoff in South-Eastern Botswana Rejoice Tsheko, PhD Faculty of Agriculture at B.C.A, Department of Agricultural Engineering and Land Planning, Private Bag 0027, Gaborone, Botswana WMO/FAO Training Workshop, Gaborone 14 – 18 November 2005

2 Structure of presentation Introduction Introduction Description of the study area Description of the study area Research methodology Research methodology Data sources for the SCS model Data sources for the SCS model Results Results Observations Observations

3 Introduction It is crucial that the watershed runoff or inflows, which are used as inputs for the modelling of water resources are accurate. It is crucial that the watershed runoff or inflows, which are used as inputs for the modelling of water resources are accurate. Erroneous values could have serious implications. Erroneous values could have serious implications. Because of the aridness of the country, the government of Botswana has invested heavily on studies to evaluate potential of water resources in the country (BWMP). Because of the aridness of the country, the government of Botswana has invested heavily on studies to evaluate potential of water resources in the country (BWMP). Two models namely Pitman and Monarch have been used extensively in the past to estimate potential reservoirs inflows in Botswana. Two models namely Pitman and Monarch have been used extensively in the past to estimate potential reservoirs inflows in Botswana. Deterministic models Deterministic models Pitman model ->Lumped parameter model Pitman model ->Lumped parameter model Monash model -> Distributed model Monash model -> Distributed model SCS curve number model -> Empirical model SCS curve number model -> Empirical model ->model parameters lacking in Botswana (BNWMP 1991) ->model parameters lacking in Botswana (BNWMP 1991)

4 Source: Botswana Atlas (1:9,460,000)

5

6 Shuttle Radar Topography Mission (NASA) Landsat ETM+ Imagery Landsat MSS and TM Imagery Rainfall charts (Botswana Department of Meteorological Services) Digital Elevation Model (FAO-SDRN) Land use / Land cover Database Soil Types Database (FAO+Botswana Ministry of Agriculture) Rainfall intensity Watershed delineation Basin characteristics Composite curve numbers Hydrologic soil group Land cover complex SCS 6-hour rainfall distributions Research Methodology SCS model inputs Digital Image processing GIS environment Mean Annual Runoff Volumes

7 Digital Elevation Models The Shuttle Radar Topography Mission (SRTM) DEMs data was acquired from FAO-SDRN The Shuttle Radar Topography Mission (SRTM) DEMs data was acquired from FAO-SDRN Watershed was delineateded from the DEMs using the drainage module of WMS. Watershed was delineateded from the DEMs using the drainage module of WMS.

8 NASA SRTM DEMs

9

10 Soil data Digital soil data was obtained from the Botswana Ministry of Agriculture. Digital soil data was obtained from the Botswana Ministry of Agriculture. This consisted of 1: shape and attribute data of the different soil types in Botswana (FAO/UNDP/Government of Botswana). This consisted of 1: shape and attribute data of the different soil types in Botswana (FAO/UNDP/Government of Botswana). The hydrologic soil type attribute was created in ArcView. The hydrologic soil type attribute was created in ArcView. This was based on the infiltration rates of the different soil types based on AG: BOT/85/011 Field Document Number 33 (Joshua, 1991) This was based on the infiltration rates of the different soil types based on AG: BOT/85/011 Field Document Number 33 (Joshua, 1991)

11 Soil types

12 Land use / Land cover data Landsat ETM+ data acquired from FAO-SDRN in Rome and the Regional Remote Sensing Unit (RRSU). Landsat ETM+ data acquired from FAO-SDRN in Rome and the Regional Remote Sensing Unit (RRSU). Channels 1, 3 and 4 of the Landsat ETM+ (Path172 Row ) image were used to create the land use / land cover database. Channels 1, 3 and 4 of the Landsat ETM+ (Path172 Row ) image were used to create the land use / land cover database. Manual and semi-automatic classification was carried out using the GeoVIS software (Terra Nova) Manual and semi-automatic classification was carried out using the GeoVIS software (Terra Nova)

13 Land use / land cover

14 Rainfall data In this study, the procedure outlined in McCuen 1984 was used to form a design storm using Gaborone rainfall data from the department of Meteorological Services (DMS). In this study, the procedure outlined in McCuen 1984 was used to form a design storm using Gaborone rainfall data from the department of Meteorological Services (DMS). Actual and generated rainfall (BNWMP, 1991) data from 8 stations in the Notwane, 10 stations in the Metsimotlhabe and 9 stations in the Thagale river systems were used to calculate the average rainfall data input for the model. Actual and generated rainfall (BNWMP, 1991) data from 8 stations in the Notwane, 10 stations in the Metsimotlhabe and 9 stations in the Thagale river systems were used to calculate the average rainfall data input for the model. From the long-term rainfall data (1925 – 1988), the average rainfall for the winter months (June, July and August) is less than 5 mm per month which is very little to produce any runoff in the SCS model. These months were excluded from the calculations. From the long-term rainfall data (1925 – 1988), the average rainfall for the winter months (June, July and August) is less than 5 mm per month which is very little to produce any runoff in the SCS model. These months were excluded from the calculations.

15

16 SCS model The land use and soil data was used to calculate composite curve number for the watersheds. The land use and soil data was used to calculate composite curve number for the watersheds. The shapefiles were mapped to WMS feature objects using the GIS module. The shapefiles were mapped to WMS feature objects using the GIS module. The soils coverage shapefile was mapped to HYDGRP (hydrologic soil group). The soils coverage shapefile was mapped to HYDGRP (hydrologic soil group). The land use coverage shapefile mapped to LUCODE (land use code). The land use coverage shapefile mapped to LUCODE (land use code). The mapping table were prepared and saved earlier in text mode. The mapping table were prepared and saved earlier in text mode. Finally the hydrologic modelling module was used to calculate the composite curve numbers. Finally the hydrologic modelling module was used to calculate the composite curve numbers. The model was then used to calculate runoff for the three sub basins. The model was then used to calculate runoff for the three sub basins.

17 Watershed delineation Using the DEMs to delineate watersheds gave km 2 for the Notwane river system, 3568 km2 for the Metsimotlhabe river system and 9686 km 2 for the Thagale river system. This compares very well with already established figures of 3983 km 2 and 3570 km 2 for both the Notwane and Metsimotlhabe river systems Using the DEMs to delineate watersheds gave km 2 for the Notwane river system, 3568 km2 for the Metsimotlhabe river system and 9686 km 2 for the Thagale river system. This compares very well with already established figures of 3983 km 2 and 3570 km 2 for both the Notwane and Metsimotlhabe river systems

18

19 Actual and predicted mean annual runoff

20 Observations and recommendations SCS model underestimate mean annual runoff for the Notwane drainage areas. SCS model underestimate mean annual runoff for the Notwane drainage areas. SCS model overestimate mean annual runoff for the Metsimotlhaba drainage areas. SCS model overestimate mean annual runoff for the Metsimotlhaba drainage areas. City of Gaborone runoff contributes to the Metsimotlhaba drainage area runoff. City of Gaborone runoff contributes to the Metsimotlhaba drainage area runoff. Is data available? Is data available? –Only the land use / land cover data has to be developed (FAO GLCN) –Other data are available –Processing of MET rainfall data is required This method is rapid, could be updated as required i.e. changes in land cover / land use. This method is rapid, could be updated as required i.e. changes in land cover / land use.

21 Mean monthly runoff volumes for the three watersheds


Download ppt "Comparison between the United States Soil Conservation Service (SCS) curve number, the Pitman and Monarch models for estimating rainfall-runoff in South-Eastern."

Similar presentations


Ads by Google