Regional Regression Model of Mean Annual Streamflow For Ungauged Catchments
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Date
2019-10
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
Water resource developers and modelers focus on sustainable water resources
management and availability of watercourses into account during design of hydraulic
structures such as small dams for use at present and the future. Since most of the rivers in
Ethiopia are ungauged, knowledge of predicting annual streamflow to be used for water
resource development and other multiple purposes requires a study on stream flow of
ungauged catchments. In this study an attempt was made to develop multiple linear
regression model of mean annual stream flow for ungauged catchments of Awash River
Basin using some statistical tests to secure the performance of the model. First the annual
rainfall homogeneity test, trend analysis, and monthly and annual rainfall characteristics
and variation were analyzed, based on this study the mean monthly rainfall of Awash
River basin for the 19 stations varied from 3.15 mm to 411.8 mm in the period of 19812013
G.C
and
the
mean
annual
rainfall
distribution
varies
from
352.9
mm
to
1766.4
mm,
the
mean
annual
flow
varies
from
36.68
MCM
to
2026.82
MCM.
Second
development
of
equation for each gauged catchments based on different forms of equations (i.e.
power, polynomial and linear functions) were performed and only one form of equation
was chosen depending on model performance indices like R
2
, NSE and RMSE results.
The majority of the results show remarkably good fits with R
2
-values ranging from 0.51
to 0.72 and NSE ranging from 0.53 to 0.7. The third and main task of this thesis work
was developing a Regression model that could be used for estimation of mean annual
flow at ungauged catchments, which was trained for Region-I using 9 gauging stations
and for Region-II using 8 gauging stations. The performance and model fit was checked
by statistical indicators and graphical methods. For Region-I it is found that the model
with prediction variables Catchment area, mean annual rainfall and mean slope result the
best fit giving (R
2
=0.89), (NSE=0.94) and (RMSE=107.24), Similarly For Region-II it is
found that the model with prediction variables Catchment Area, mean annual Rainfall,
mean Elevation and mean Slope result the best fit giving (NSE=0.88) and
(RMSE=48.51). These model evaluation indicators were under a Very Good
performances for Region-I and Good performances for Region-II.
Description
Keywords
Ungauged Catchments, River basin, regression analyses, multiple linear regression (MLR)