This document presents the progress of a research thesis evaluating the impacts of climate change on irrigated agriculture in the North Gojjam Sub-basin of Ethiopia. The study aims to assess climate trends, estimate current and future crop water demand under climate scenarios, and quantify climate change impacts on evapotranspiration, temperature, and rainfall. Methods include analyzing observed meteorological data, bias-correcting future climate projections, evaluating climate model performance, and using the CROPWAT model to estimate reference evapotranspiration and crop water requirements. Preliminary results show increasing temperature trends but decreasing rainfall trends in historical data, and future projections also indicate potential decreases in precipitation under climate change scenarios.

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BAHIR DAR UNIVERSITY
BAHIR DAR INSTITUTE OF TECHNOLOGY
SCHOOL OF RESEARCH AND GRADUATE STUDIES
FACULTY OF CIVIL AND WATER RESOURCES ENGINEERING
Department of Irrigation Engineering and Management
Research Thesis progress:
Evaluating Impacts Of Climate Change On Irrigated Agriculture
A Case Of North Gojjam Sub-basin, Blue Nile Basin, Ethiopia
By: Enyew Belayneh
Advisor: Abebech Abera (PhD) BAHIR DAR, ETHIOPIA
DECEMBER, 2023

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Presentation outline
 Introduction
 Statement of the problem
 Objectives
 Scope of the study
 Significance of the study
 Materials and Methods
 Result and discussion
 Conclusion
 Recommendation

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1. Introduction
1.1 background
 Climate change refers to a systematic change in the long-term statistics
of climate elements over several decades. By changing the average
weather conditions over time, such as rainfall, temperature, streamflow,
groundwater storage, and wind, it alters the composition of the earth’s
atmosphere (IPCC, 2007, 2014).

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Introduction …
 If the climate change phenomenon occurs frequently, future water
availability for crop water demand (CWD), domestic water consumption,
and agricultural production will become more uncertain. Depending on
the crop type and crop characteristics, CWD can be increased by up to
250% by the end of the twenty-first century (Gondim et al., 2012). In
developing regions, irrigation water requirement (IWR) can be increased
by 50% for each degree of global warming, while in developed regions, it
is expected to increase by 16% (Fischer et al., 2007).

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1.2 Statement of the problem
 Higher temperatures due to climate change will result in increased
evapotranspiration, affecting hydrological systems and agriculture. The
investigation of climate change impacts on various sectors, particularly
agriculture, is critical for decision-makers; however, most developing
countries, including Ethiopia, have not done that well.

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 Despite the fact that the effects of climate change and variability are particularly
devastating in the North Gojjam sub-basin, the study of climate change
impacts on crops and irrigation work due to water stress has not been yet
investigated, and also its impact at local level is not quantify rather global, In
addition to this, the majority of small scale irrigation systems, as commonly
found in developing African countries, including Ethiopia particularly in north
gojjam sub- basin directly affected by unpredictable temperature and
rainfall variations, crop water demand and irrigation water requirements
resulting in yield reduction or crop failure. In order to fill this gap, this study
was held.

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1.3 Research question
 How is climate change is expected to trigger changes in rainfall and temperature series?
 How alarming is the future rainfall and temperature change in the study area? Is there a
warming trend?
 What percent (%) changed in precipitation and temperatures from the baseline period?
 Will the future (rainfall and temperatures) affect the irrigation water requirement in the
study area?
 What will be the future CWD and IWR look like compared to the base line period?

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1.4 Objective of the study
General objective
 Assess and evaluate the impacts of climate change on irrigated agriculture.
Specific objectives
 To assess climate change trends of hydro climatic variables (rainfall and temperature)
in the Study area.
 To estimate the current and future crop water demand of selected major crops grown in
the study area under various climate change scenarios.
 To quantifying the climate change parameters (evapotranspiration, temperature &
rainfall) in the projected time period.

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Scope of the study
 Explores the impact of climate change on irrigated agriculture under various climate
change scenarios, in the North Gojjam Sub Basin of Abay Basin.
 Limited to predict rainfall, temperature, CWR and IWR.
Significance of the study
 It is critical for preparing and planning adaptation to climate change effects on water
resources, IWR, and agricultural sustainability.
 It is also critical for proper water management to investigate and resolve risks
associated with available irrigation water resources based on future variability in
climatic variables.

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Cont.…
In general, this study will assist water managers, decision-makers, and producers in
understanding the impact of climate change and planning appropriate adaptation
measures that must be implemented ahead of time.
 Further it increases the scientific community's and other stakeholders' awareness and
knowledge of the effects of climate change on water use for irrigation at the local
level

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2. Methodology
Description of the study area
 Geographically located between 37.3° and 39.6° E longitude and 10.8° and 11.9° N latitude
 Located on its elevation approximately between 1030 and 4090 m a.s.l and has coverage
area of 1,431,360 ha.
 The average maximum and minimum temperatures of the sub-basin range from 24.6 °C to
28.1 °C and 11.0 °C to 14.5 °C, respectively, and the mean annual temperature is 19.41 °C.

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Maps of the study area

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Data source/collection
The data downloaded or received from the appropriate sources
Data source
Observed metrological data Ethiopian National Meteorological Agency (ENMA)
Future metrological data WCRP-CMIP6 (https://esgf-node.llnl.gov/projects/cmip6/)
soil and crop data From FAO drainage paper 56 and local observation
soil map Amhara Design and Supervision office, Bahirdar
Event methods
Filling missing data XLSTAT trial version 2018
consistency test Double mass curve
Bias correction projected data CMhyd (Climate Model data for hydrologic modeling) tool
Performance Evaluation of CMIP6 Model R studio (hydroGOF" R package)
Trend analysis Mann–Kendall (MK) test
CWR CROPWAT 8.0 software
GIS and Microsoft office 16 are also used in this research

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CROPWAT model
Estimation of reference evapotranspiration (ETo)
Rn is the net radiation (MJ/m2/day);
G is the soil heat flux density (MJ/m2 per day);
T is the average daily air temperature at standard height(°C);
u2 is the wind speed at 2 m height (m/s);
es is the saturation vapor pressure (kPa);
FAO Penman Monteith equation ea is the actual vapor pressure (kPa);
es-ea is the saturation vapor pressure deficit (kPa);
Δ is the slope vapor pressure curve (kPa °C-1)
γ is the psychometric constant (kPa °C-1)

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Estimation of Crop Water
Requirement
ETc = Kc × ETo
Where
ETc is crop evapotranspiration (mm/day),
ETo is the reference crop
evapotranspiration (mm/day), and
Kc is the crop coefficient.
Estimation of Effective Rainfall
 By using USDA Soil Conservation Service
 Pe = TR × (125 − 0.2 × TR)/125
When total rainfall is > 250 mm, effective
rainfall is given as:
 Pe = 125 + 0.1 × TR

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Flowchart of CWR
Flowchart of CWR


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Flowchart of the methodology adopted in this study

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4 Result and discussion
 Climatic model bias correction
The climatic model bias was corrected with Delta change method
using CMhyd tool, and observed, corrected and uncorrected
values are shown in the figure below

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0.0
100.0
200.0
300.0
400.0
500.0
600.0
700.0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
RF
in
mm/month
Month
Observed
Corrected
Uncorrected
a)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
Tmax(oC)
Month
Obseved Tmax
Corrected
Uncorrected
b)
Figure. 4.1: Observed, corrected, and uncorrected rainfall (a) Figure. 4.1: Observed, corrected, and uncorrected Tmax (b)

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 The difference between the uncorrected and observed mean
climate variables is significant.
 After bias adjustments, the observed and model-generated data
showed good agreement.
 The difference in temperature between observed and predicted
amounts is greater than the rainfall difference.
 The difference between the observed and predicted minimum
temperatures is larger than the difference between the observed
and predicted maximum temperatures.
 The corrected mean maximum and minimum temperature
values show good agreement with the observed mean
maximum temperatures.
 The corrected mean Tmax of the study sub-basin is nearly 28
°C while the mean daily Tmin is 11 °C.
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
Tmin(oC)
Month
Min temp
corrected
uncorrected
Figure. 4.1: Observed, corrected, and
uncorrected minimum temperature (c)

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Performance Evaluation of CMIP6 models at regional
scale
The efficiency criteria found in the "hydroGOF" R package were used
to evaluate the performance of CMIP6 models under different
shared socio-economic pathways (SSPs)
 Root-mean-square errors (RMSE),
 percent of bias (PBIAS),
 Nash-Sutcliffe efficiency (NSE),
 Coefficient of Determination (𝑹𝟐) are among the efficiency criteria.

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Performance Evaluation of models …
For both the observed (1990-2019) and two future periods, near-term (2026–2055)
and mid-term (2056–2085), seven models were used to estimate the climate variables
Climate variables Precipitation (mm/month)
Model R2 RMSE (mm) NSE PBIAS %
BCC-CSM-2MR 0.92 23.01 0.90 15.15
CAMS-CSM1-0 0.82 55.07 0.71 -37.2
CanESM5p1 0.90 30.76 0.83 10.25
CanESM5p2 0.90 30.32 0.83 9.55
FGOALS-g3 0.88 47.15 0.75 20.95
MIROC6 0.90 110.65 −0.44 83.32
MIROC-ES2L 0.90 46.12 0.61 37.57
MRI-ESM2-0 0.92 33.42 0.64 25.15

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Performance evolution of models …
Climate
variables
temperature(°C)
Model R2 RMSE (mm) NSE PBIAS %
BCC-CSM2MR 1.42 4.98 0.5 0.9
CanESM5p1 2.26 8.06 0.03 0.92
CanESM5p2 1.65 6.48 0.36 0.88
FGOALS-g3 0.68 0.87 0.82 0.9
MIROC6 1.54 5.06 0.64 0.90
MIROC-ES2L 1.69 −6.05 0.23 0.85
MRI-ESM2-0 0.8 −0.92 0.75 0.92
Table 4.1: Statistics used to evaluate performance climate models

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Performance Evaluation of models…
From the above table BCC-CSM-2MR for precipitation and MRI-ESM2-0 for
temperature models were found to perform better than another model.
This is also in line with other studies that employed an ensemble mean of climatic model outputs
(Alaminie et al., 2021). According to some studies, using the ensemble mean of simulated rainfall is more
accurate than using the output of a single model (Kim et al., 2014). It has also been discovered that using
an ensemble mean instead of individual model rainfall simulations improves the correlation between
simulated and gauged data (Alemseged & Tom, 2015). For example,Foyhirun et al. (2019) used 15
GCMs at various weather stations and proposed the MRI-CGCM3 and GFDL-ESM2M climate models
for predicting wave energy in the future.

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Trends of climate variables
Rainfall observed Temperature observed
y = -4.1326x + 9535.4
R² = 0.0343
600.0
800.0
1000.0
1200.0
1400.0
1600.0
1800.0
1990 2000 2010 2020
Rainfall
(mm)
year
Rainfall(Obseved)
Rainfall(Obseved)
y = 0.0539x - 83.003
R² = 0.4663
1990 1995 2000 2005 2010 2015 2020
23.0
23.5
24.0
24.5
25.0
25.5
26.0
26.5
year
Average
Tmax
Temprature (Observed)
tempmax
Linear (tempmax)
Figure 4.2: Plot of annual rainfall for observed period (1990-2019) Figure 4.3: Plot of average temperature trend for observed period (1990-2019)

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Episode Climatic
variables
Kendall’s
tau
Z S Sen’s
slope
P value Trend type
Baseline
period
Rain falls -0.12 -0.9 -11 -4.1 0.35 Decreasing
Temperature 0.49 3.82 259 0.05 0.0001 Increasing
Table 4.2: Trends of climate variables (rainfall and temperature) for baseline periods

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Future Precipitation and Temperature Trend Analysis
800.0
900.0
1000.0
1100.0
1200.0
1300.0
1400.0
1500.0
1600.0
1700.0
2026 2031 2036 2041 2046 2051 2056 2061 2066 2071 2076 2081 2086
precipitation
(mm)
SSP2-4.5(2026-2055) SSP5-8.5(2026-2055)
y = 0.0195x - 12.376
R² = 0.2952
y = 0.0473x - 68.081
R² = 0.7493
25.0
26.0
27.0
28.0
29.0
30.0
31.0
32.0
2026 2032 2038 2044 2050 2056 2062 2068 2074 2080
Mean
Max
Temp(oC)
Year
Simulated temprature (2026-2085)
SSP2-4.5 SSP5-8.5 Linear (SSP2-4.5) Linear (SSP5-8.5)

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Episode Scenario Climatic
variables
Kendall’s
tau
Z S Sen’s
slope
P value Trend type
2026-
2055
SSP2-4.5
Rain
fall
-0.16 -1.24 -71 -5.07 0.211 Decreasing
SSP5-8.5 -0.19 -1.49 -85 -6.18 0.134 Decreasing
2056-
2085
SSP2-4.5 -0.22 -1.71 -97 -5.18 0.086 Decreasing
SSP5-8.5 -0.47 -3.6 -95 -4.21 0.002 Decreasing
2026-
2055
SSP2-4.5
Temperature
0.24 1.86 105 0.02 0.0628 Increasing
SSP5-8.5 0.77 5.9 332 0.09 0.000 Increasing
2056-
2085
SSP2-4.5 0.06 0.44 26 0.001 0.65 Increasing
SSP5-8.5 0.55 4.23 238 0.05 0.00 Increasing

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 In relation to this study Bekele et al. (2019) the MK tested showed that there is
decreasing trend in annual rainfall of three station that found in birr watershed
(Adet, Dembecha, and Lay birr). The change in rainfall magnitude was 8.19, 2.20,
and 3.90 mm/ year in Adet, Dembecha, and Lay birr station respectively

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20
22
24
26
28
30
32
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Maximum
temprature
oC
SSP5-8.5(2056-2085) SSP2-4.5(2056-2085)
SSP5-8.5(2026-2056) SSP2-4.5(2026-2056)
North gojam areal-Averaged (observed)
a)
Mean monthly temperature
0
50
100
150
200
250
300
350
400
450
500
550
600
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mean
Precipitation
(mm/month)
month
SSP5-8.5(2056-2085) SSP2-4.5(2056-2085)
SSP5-8.5(2026-2056) SSP2-4.5(2026-2056)
North gojam areal-Averaged (observed)
b) Mean monthly precipitation
As shown in figure 4.5(b) the monthly average precipitation is decrease in month December, January, February,
and May i.e., irrigation season for the study area, this less amount of rain fall has direct effect on effective rain
fall and consequently it has effect on crop water demand and irrigation water requirements while the
temperature is high during march

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Comparative change in climate variables
RCM Runs Time BCC-CSM-2MR Scenarios
Mean Precipitation (mm/30 years) SSP2-4.5 SSP5-8.5
Baseline 1251.6 1251.6
2026-2055 1224.8 1183.8
2056-2085 1260.8 1218.7
2026-2085 1242.8 1201.2
Change of Near-term (%) -2.1 -5.4
Change of Mid-term (%) 0.7 -2.6
Change of 21th Century (%) -0.7 -4.0
MRI-ESM2-0 Max. Temperature (◦C) SSP2-4.5 SSP5-8.5
Baseline 25.1 25.1
2026-2055 27.3 28.5
2056-2085 28 29.7
2026-2085 27.6 29.1
Change of Near-term (◦C) 2.2 3.4
Change of mid-term (◦C) 2.9 4.6
Change of 21th Century (◦C) 2.5 4

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4.4. Crop water demand and irrigation water requirements
570
580
590
600
610
620
630
640
650
660
CWR(SSP2-4.5) IWR(SSP2-4.5) CWR(SSP5-8.5) IWR(SSP5-8.5)
CWD
and
IWR
(mm/growth
period
CWD and IWR under SSP2-4.5 & SSP5-8.5(Maize)
Baseline 2020 2050
430
440
450
460
470
480
490
500
CWR(SSP2-4.5) IWR(SSP2-4.5) CWR(SSP5-8.5) IWR(SSP5-8.5)
CWD
and
IWR
(mm/growth
period
CWD and IWR under SSP2-4.5 & SSP5-8.5(wheat)
Baseline 2020 2050

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4.4. Crop water demand and irrigation water requirements
potato and onion
490
500
510
520
530
540
550
560
CWR(SSP2-4.5) IWR(SSP2-4.5) CWR(SSP5-8.5) IWR(SSP5-8.5)
CWD
and
IWR
(mm/growth
period
CWD and IWR under SSP2-4.5 & SSP5-8.5(potato )
Baseline 2020 2050
450
460
470
480
490
500
510
520
CWR(SSP2-4.5) IWR(SSP2-4.5) CWR(SSP5-8.5) IWR(SSP5-8.5)
CWD
and
IWR
(mm/growth
period
CWD and IWR under SSP2-4.5 & SSP5-8.5(Onion)
Baseline 2020 2050

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5. Conclusion
From this study
 Results from the Mann-Kendall trend test generally showed that climate change and
variability are expected to trigger changes in temperature and precipitation series that
would affect the irrigation water availability in the study area.
 The areal average precipitation may decrease under all SSP scenarios.
 The rise in IWR and CWD is found in all future scenarios. The maximum incremental
CWD and IWR are expected during the developmental growth stage. This increment in
future CWD and IWR of crops may be attributed due to increase in average
temperature and evapotranspiration.

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6. Recommendation
 The study points to an upcoming increase in irrigation water requirements that will
raise problems so that mitigation and adaptation strategies can be made ahead of time.
The study will also assist policymakers in making decisions on irrigation development
in the sub-basin and watersheds.
 Policymakers, water managers, and users should be curious about the proper use and
implementation of water projects. Appropriate irrigation schedules, reservoir operation
rules, and environmental protection procedures shall be in place.

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