Detection of trends and teleconnections of

stream flows with large scale climate signals in the Omo-Ghibe River Basin, Ethiopia

Mekonnen Adnew & Woldeamlak Bewket

Addis Ababa University

Department of Geography & Environmental Studies

Contents

Introduction and rationales

Objective

Data and Method

Findings

Conclusion

Introduction and rationales There is a general scientific consensus on the intensification of the earth’s

hydrological cycle due to global warming

The change is very likely on extreme hydrological variables

Climate change could alter the magnitude, frequency, timing, duration and the

natural variability of extreme hydrological events (Burn and Hag Elnur,

2002)

The natural river-flow regimes can be also altered due to anthropogenic

activities

The long-term changes could have significant effects in the designs of water

engineering structures and watershed development programs

Cont… Observational evidence is also very important to address the uncertainty in

climate model scenarios at the local scale (Hannaford and Marsh, 2006)

On the other hand, the lower part of the study area (Omo-Ghibe River

Baisn) one of the drought and flood prone areas in the country

There are also huge hydroelectric (5 hydro power plants) and sugarcane

irrigation projects (about 600, 000 ha) which are very sensitive to extreme

hydrological events

Very few studies covered this part of Ethiopia (Demissie et al., 2013 and

Worku et al., 2014)

Objectives The objectives of this study are:

to identify possible changes in the hydrological regimes with emphasis to

extreme events

to explore the streamflow associations with global SSTs

Fig. 1 The study area

Methodology

Study area

Data and Method Daily streamflow data for 15 gauging station for two time periods 1972-

2006 and1982-2007 was employed

Indices of important SST modes that are known to affect the climate of Africa (see Rowell, 2013) and Ethiopia in particular (Gissila et al., 2004; Korecha and Barnston, 2007; Segele et al., 2009a, b; Diro et al., 2011a)

Equatorial east Pacific (Nino/ENSO)

Central Indian Ocean (CindO)

Indian Ocean Dipole (IOD)

Equatorial east Atlantic (EqEAtl)

Tropical Atlantic Dipole (TAD)

All the daily flow data were cross-checked with the data from the nearby stations for any possible errors

Table 1. Hydrological indices used to represent extreme flow regimes

Index Description Unit

AMS Annual maximum daily flow series m3/s

MAX7day Kiremt maximum 7-day flow volume m3/s

MAX30day Kiremt maximum 30-day flow volume m3/s

POT1Mag Peak-over-threshold magnitude on average one event/year m3/s

POT3Mag Peak-over-threshold magnitude on average three event/year m3/s

POT1Fre Peak-over-threshold frequency on average one event/year day

POT3Fre Peak-over-threshold frequency on average three event/year day

MIN1day Annual minimum daily flow m3/s

MIN7day Annual minimum 7-day flow volume m3/s

MIN30day Annual minimum 30-day flow volume m3/s

MIN10P Number of days exceeded the 90th percentile of the record period days

ANNmean

Wet and dry

Annual mean flow volume

For wet and dry seasons

m3/s

m3/s

Cont…

Long-term changes were examined using the non-parametric Mann-

Kendall’s test and Sen’s slope estimator using MAKESENS

Pre-whiten method You et al. (2002b) and Petrow and Merz (2009) was

used to remove the serial correlation from a data set prior to apply MK

trend test

Results

Trend results for mean annual and seasonal flow

events

There are no clear and systematic trends both in annual and

seasonal flow volumes both for 1972-2006 and 1982-2006

Trends for high-flows/flood events

There is no clear emergent trends for indices of flood events

Fig. 3 Trends in high-flow events

Trend results for low-flow events

There is a tendency towards increasing trends

Streamflow-to-SSTs relations

Indices of SSTs from 5 Oceanic regions that are known to affect the climate

of the Ethiopian summer season were correlated against streamflow indices

for the period 1972-2006

Streamflow

index

Sing of

correlations

CindO OID ENSO EqEAtl TAD

July Positive 0 3 0 2 0

Negative 5 2 5 3 5

Agust Positive 2 0 0 2 0

Negative 3 5(1) 5(4) 3 5

September Positive 1 0 0 2 1

Negative 4(1) 5(2) 5(4) 3 4

JAS Positive 0 0 0 1 0

Negative 5 5(1) 5(4) 4 5

MAX7day Positive 2 3 2 3 1

Negative 3 2(1) 3(2) 2 4

MAX30day Positive 0 3 1 2 1

Negative 5 2(1) 4(3) 3(1) 4

POT3fre Positive 1 1 0 2 1

Negative 4(1) 4(1) 5(3) 3 4

There are reasonable associations between the streamflow-SSTs

Changes in the SSTs and associated changes in local and zonal atmospheric

circulations were reported (Segele et al., 2009; Jury and Funk, 2012)

There is a change zonal walker circulation towards low level northeast (cool

moist air) and upper level westerly (weakening the TEJ –with upper level

divergence and increase subsidence) over Ethiopia

An increase in warming and ascending air trends over Indian Ocean associated

with westward Rossbay wave and increase Subsidence

All these lead towards decreasing rainfall and streamflow

There is a tremendous change in land use and land cover in the study area

(Worku et al., 2014)

Overall forestland, woodland, woody grassland & shrub land decrease by 53%

Cropland and Grassland increase by 83%

LULC changes from FL, WL, WG & SL into CL &GL would be expected

contribute increase runoff due to reduced interception, less infiltration and

lower actual evapotranspiration

Conclusion There is no clear and systematic trends for most of the streamflow indices

Trends in streamflow indices did not reflect the effect exerted from changes

in atmospheric circulation and LULC

However, the findings of this study generally provide information for the

local scale climate change adaptation and watershed management activities

Thank you!

Thank you!