Modeling Rainfall-runoff using SWAT model in data scarce area: in - PowerPoint PPT Presentation

Esri Eastern Africa Education GIS Conference Creating a World of Opportunities Modeling Rainfall-runoff using SWAT model in data scarce area: in the Weybo River Catchment of southwestern Ethiopia By Mathewos Muke (MSc : in Geo -Info & Earth Obs. Science)

Presentation outlines 1. Introduction 2. Methods and Materials 3. Results and Discussions 4. Conclusions

Introductions  The main slogan of Ethiopia in the new millennium is to pull the country out of pulverize poverty and leading the country to development.  Several indicators of poverty with focus on alleviations are related to the water sector (EAH, 2007 and Tesfamichael, 2009) .  But this sector is challenged or disturbed by climate change ( Bronstert et al., 2002 ; Kim et al., 2011 and Ayalew (2012)) and the impact is happening now and will be expected for the future.

Cont…  Because of the global warming, the water cycle will be  accelerated,  shifting seasonal patterns and  increasing extreme events in different regions.  Land management problem in addition to climate change also brings significant effect ( LI and Ishidaira, 2010 and Homdee et al., 2011)

Cont…  Land use land cover changes (LULC) on the other hand can bring variation on flow or increase runoff because of the conversion of particular kind of fram land from one type to other (Menzel et al., 2009; LI and ISHIDAIRA, 2010; Homde et al., 2011; Rientjes et al., 2011).  Run off variability not only attributed to the above case only but also associated with catchment level mainly catchment size.

Cont…  Assessing the relationship of the spatial and temporal distribution of rainfall with runoff and observing its future trends are fundamental input parameters for securing sustainable agricultural production as Ethiopia in general and the area under study in particular depends on rain-fed agriculture.  To this end, therefore; the SWAT model was applied to model the amount of water converted to runoff as a result of biophysical controlling factors dynamics in the catchment.

Cont…  The overall objective of this study was to model spatial and temporal variations of rainfall runoff relationship in data scarce area of Weybo River catchment. And specifically :  to identify the catchment’s sensitive parameters  to determine the ability of SWAT to closely simulate daily surface flow from excess precipitation.  to asses rainfall-runoff relationships in Weybo river catchments

Location  The Figure 3-1 depicts the location of the basin in general and the Weybo catchment (538.3km2). Figure 3-1 Location Map of the study area

 Physiographically, the study area is located within western rift margin at the NW of mount Damota with the highest and lowest elevation of 2946m and 845m.a.s.l respectively.  In general, the average elevation of the area is 1879m and its standard deviation is +193 and so that, the area can be described as less ruggedness in its physiographic setting. Figure – shows the relief of the area

Methods Data collection Both primary and secondary data were used for this research studies.  In primary data, GCPs by using GPS were collected for identification of outlet of the catchment, location and elevation of the respective meteorological stations. Where as,  Secondary data like:  long period of recorded metrological and discharge data,  soil and land use land cover data and  digital elevation model were collected from the respective offices and websites.

Data setting method Figure : schematic diagram that show the overall data sources of the model and its process

 All the above inputs (Raster, shapefiles and database file formats) for model were prepared as SWAT dataset in a manner which is compatible to ArcGIS. NB:  Spatial input data sets and were prepared based on the  ArcSWAT data bases or procedure recommended by Neitsch et al. (2002).  lookup tables for each inputs  Unlike to other hydrological models, SWAT helps to estimate the missing data.  So that, a negative 99.0 (-99.0) value was inserted for missing value since this value tells SWAT to generate meteorological data for that day.

Sensitivity analysis  For the selection of hydrologic parameters, the sensitivity analysis was carried out for the period of 11 years so as to identify the parameters that have the greatest influence on model results.  The years include both:  the warm-up period (Jan. 1 st 1992 – Dec 31, 1994) and  the calibration period (Jan 1 st , 1995-Dec 31, 2002) with keeping out validation year (4).

 Summary statistics are usually applied as a measure of how well the model hydrograph fits, or agrees with, the observed hydrograph.  A wide range of statistics like Coefficient of determination (R 2 ), the Nash-Sutcliffe model efficiency (NSE) coefficient and the Relative Volume error (RVe) has been used to evaluate SWAT hydrologic predictions.

2         n        , ,  O i O Si i S                           2 i 1 3 . 1 R 2 2       n n         , , O i O Si i S       i 1 i 1 n     2 Si , i O , i            1 NSE i 1 3 . 2 2 n         O , i O     i 1   n n      , , si i o i                      i 1 i 1 100 . 3 . 3 RVe x   n    oi    i 1

Cont…

3. RESULTS AND DISCUSSION Rainfall  As indicated in Figure 4-1, the graph reveals that the area receives a bi-modal rainfall distribution which extends over the period of April to September with its peak in April and August. 8 7 6 5 Rainfall (mm) 4 3 2 1 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Months Figure 4-1 Graphical representation of the mean monthly rainfall from 1992-2006  Total amount of annual rainfall and distribution influence the nature of surface runoff and volume of its flow.  The highest rainfall of the area is recorded in April, July, August and September whereas the minimum rainfall is exhibited in November, December, January and February.

The figure here reveals, its natural trends and its hydrological pattern in the Figure 4-2 Daily time series graphical representation of observed 9/1/2006 5/1/2006 1/1/2006 9/1/2005 catchment by making use of daily based discharge values for its plotting. 5/1/2005 1/1/2005 9/1/2004 5/1/2004 1/1/2004 9/1/2003 5/1/2003 1/1/2003 9/1/2002 5/1/2002 1/1/2002 9/1/2001 5/1/2001 1/1/2001 9/1/2000 5/1/2000 1/1/2000 Years 9/1/1999 5/1/1999 1/1/1999 9/1/1998 5/1/1998 1/1/1998 9/1/1997 5/1/1997 1/1/1997 discharge value (1992-2006) 9/1/1996 5/1/1996 1/1/1996 9/1/1995 5/1/1995 1/1/1995 9/1/1994 5/1/1994 1/1/1994 9/1/1993 5/1/1993 1/1/1993 9/1/1992 5/1/1992 1/1/1992 35 30 25 20 15 10 5 0 Discharge (m-3/s)

 The hydrograph below shows a fluctuating stream pattern in response to the rainfall .  They start to rise from April to reach the maximum in August ( rainy season ) and shows a decreasing trend in flow during dry season , which continuous to achieve its minimum in December. Discharge (m-3/s) Rainfall (mm) 40 0 Rainfall (mm/day) 30 20 Discharge (m 3 /s) 20 40 10 60 0 80 1/1/1992 7/1/1992 1/1/1993 7/1/1993 1/1/1994 7/1/1994 1/1/1995 7/1/1995 1/1/1996 7/1/1996 1/1/1997 7/1/1997 1/1/1998 7/1/1998 1/1/1999 7/1/1999 1/1/2000 7/1/2000 1/1/2001 7/1/2001 1/1/2002 7/1/2002 1/1/2003 7/1/2003 1/1/2004 7/1/2004 1/1/2005 7/1/2005 1/1/2006 7/1/2006 Years Figure 4-3 Daily time series comparison of observed discharge and Rainfall (1992-2006)

Sensitivity Analysis  From the total stream flow parameters, the table below shows the top five ranked parameters based on their degree of sensitivity. Table 4-3 Top 5 sensitive parameters applied for optimization Parameters Definitions Rank Mean 6.94E-01 Surlag Surface runoff lag coefficient 1 4.86E-02 Alpha_Bf Baseflow alpha factor (days) 2 2.75E-02 Cn2 Initial SCS runoff Curve Number for 3 moisture condition 2 4 1.17E-02 Ch_K2 channel effective hydraulic conductivity (mm/hr) 5 1.07E-02 Ch_N2 Manning coefficient for channel

 To calibrate the model, nine parameters were varied individually within the SWAT model (Table 4-4). Table 4-4 Default values, optimized parameter values and process Parameters Minimum Maximum Optimized Value Process Surlag 0 150 1 Surface runoff Alpha_Bf 0 1 0.05 Groundwater Cn2 35 98 35 Surface runoff Ch_K2 -0.01 500 0 Pertain to peak flow Ch_N2 0.01 0.3 0.01 Pertain to peak flow Additional parameters OV_N 0.01 30 30 Surface runoff Sol_K 0 2000 30 Groundwater Slsubbsn 10 150 100 Surface runoff HRU_SLP 0 0.6 0.1 Surface runoff  Of course, variations of other parameters were also attempted but variations in them showed no significant effect on daily streamflow simulations for this watershed.