Abstract:
To effectively plan for water resources and protect against watershed problems, it is necessary
to understand the quantity and quality in space and time through studies. The general objective
of this study is to model stream flow of Didessa River using SWAT model and to analyze the
related uncertainties. Digital elevation model, Land use classification map, Soil map and the
available weather data of 1980 to 2016 were used and the whole Didessa basin was separated
into 674 hydrological response units (HRU) in 112 sub-watersheds. The available flow data of
1997-2014 was used for calibration and validation at 2 hydro gauging stations. SUFI-2 and
GLUE program of SWAT CUP was used for calibration and uncertainty analysis and
performance of the two programs in calibrating SWAT model was also compared.
The SWAT model developed for the river basin evaluated and its performance is certain with
the statistical measures, coefficient of determination (R2) and Nash and Sutcliffe coefficient
(NS).The model performance was very good for monthly time steps. The obtained statistical
results of (R2,NS) by SUFI-2 at Dembi were (0.75 ,0.74) and (0.78,0.78) for calibration and
validation respectively. The obtained result at Arjo were (0.72,0.71) and (0.73, 0.72) for
calibration and validation respectively. The obtained result by GLUE were (0.77 ,0.75) and
(0.78,0.77) for calibration and validation at Dembi and (0.73,0.71) and (0.77, 0.72) for
calibration and validation at Arjo. The performance of the model for daily time steps were also
evaluated. The obtained result of (R2, NS) value for calibration and validation (0.72,0.69)
;(0.63,0.62) and (0.68,0.66) ;(0.62,0.62) for SUFI-2 and GLUE respectively at Dembi station.
The result of uncertainty analysis done by SUFI-2 shows 40-48% and 24-44% percent of
observed flow is bracketed by 95PPU for monthly and daily time steps respectively. GLUE
uncertainty analysis program brackets 25-34% and 28-29% of observed flow for monthly and
daily time steps respectively. GLUE uncertainty program able to obtain high value of R2 and
NS with small percent of p and r-factor which shows good parameter identification, this shows
that the overall associated uncertainty come from either conceptual or input or a combination
of them but not from parameter identification. So, Both SUFI-2 and GLUE performs well in
calibrating SWAT model and they were balanced predictive approach.
The calibrated model can thus use for futuristic prediction and as an input for decision making
in developing a better sufficient and integrated water resource management of the river basin
