Climate extreme indices analysis and spatiotemporal trend variation over Lake Tana sub-basin, upper Blue Nile basin, Ethiopia: under future climate change

Abstract

One of the world’s most challenging problems is climate change, which threatens many aspects of the social-ecological landscape of nature and human systems, particularly in the Horn of Africa. The study focuses on the spatiotemporal distribution of climate extremes indices under projected future climatic conditions over the Lake Tana sub-basin. Daily-time series in three socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the near, mid, and far future periods and CMIP6 models were chosen as the best-fit models. Additionally, the climate variability of the basin is represented by eleven climate extreme indicators. The result indicates that model MPI-ESM1-2-HR gives reasonable performance for precipitation which is 0.76 coefficient of determination (R2) and 0.75 for Nash and Sutcliffe efficiency (NSE) and for maximum temperature 0.76 for R2, 0.75 for (NSE) and minimum temperature 0.65 for R2, 0.64 for NSE. Scenario SSP5-8.5 in the far future period will experience the highest warm period TXx and TXn around 33–32 °C and 21–20 °C, respectively, at stations Zegie, Addis Zemen, and Maksegnt. The coldest extremes TNn discovered at station Dangla throughout the near timeframe under the SSP2-4.5 scenario were between 5.9 and 6.7 °C. Heavy rainfall (R10) occurred in the top section of the subbasin for 26–27 days in Scenario SPP1-2.6, but exceptionally heavy rainfall (R20) was discovered in a far future period on average for 0.83–0.94 days in Scenario SSP5-8.5. The highest Rsum found was 1100–1000 mm rainfall at SSP1-2.6 at station Maksegnt in the same period. In a scenario of SSP5-8.5, dry CDD occurred at station Debretabor during a mid-future period for an average of 130–120 days. CWD was discovered during the mid-period of SSP2-4.5 in the upper portion of the basin at Maksegnt and Addis Zemen stations for an average of 120–130 days. In general, it is crucial for the stakeholders to be aware of the facts to develop and plan future activities on the basin that are related to the climate, health, and other extremes for alerting future adaptation and mitigation planning techniques to battle the effects of excessive heating in the sub-basin.

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Vol.:(0123456789)
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Arabian Journal of Geosciences (2023) 16:660
https://doi.org/10.1007/s12517-023-11752-z
ORIGINAL PAPER
Climate extreme indices analysis andspatiotemporal trend
variation overLake Tana sub‑basin, upper Blue Nile basin, Ethiopia:
underfuture climate change
MikhaelG.Alemu1· MelsewA.Wubneh2
Received: 1 June 2023 / Accepted: 21 October 2023 / Published online: 21 November 2023
© Saudi Society for Geosciences and Springer Nature Switzerland AG 2023
Abstract
One of the world’s most challenging problems is climate change, which threatens many aspects of the social-ecological
landscape of nature and human systems, particularly in the Horn of Africa. The study focuses on the spatiotemporal distribu-
tion of climate extremes indices under projected future climatic conditions over the Lake Tana sub-basin. Daily-time series
in three socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the near, mid, and far future periods and CMIP6
models were chosen as the best-fit models. Additionally, the climate variability of the basin is represented by eleven climate
extreme indicators. The result indicates that model MPI-ESM1-2-HR gives reasonable performance for precipitation which
is 0.76 coefficient of determination (R2) and 0.75 for Nash and Sutcliffe efficiency (NSE) and for maximum temperature
0.76 for R2, 0.75 for (NSE) and minimum temperature 0.65 for R2, 0.64 for NSE. Scenario SSP5-8.5 in the far future period
will experience the highest warm period TXx and TXn around 33–32°C and 21–20°C, respectively, at stations Zegie,
Addis Zemen, and Maksegnt. The coldest extremes TNn discovered at station Dangla throughout the near timeframe under
the SSP2-4.5 scenario were between 5.9 and 6.7°C. Heavy rainfall (R10) occurred in the top section of the subbasin for
26–27days in Scenario SPP1-2.6, but exceptionally heavy rainfall (R20) was discovered in a far future period on average
for 0.83–0.94days in Scenario SSP5-8.5. The highest Rsum found was 1100–1000mm rainfall at SSP1-2.6 at station Mak-
segnt in the same period. In a scenario of SSP5-8.5, dry CDD occurred at station Debretabor during a mid-future period
for an average of 130–120days. CWD was discovered during the mid-period of SSP2-4.5 in the upper portion of the basin
at Maksegnt and Addis Zemen stations for an average of 120–130days. In general, it is crucial for the stakeholders to be
aware of the facts to develop and plan future activities on the basin that are related to the climate, health, and other extremes
for alerting future adaptation and mitigation planning techniques to battle the effects of excessive heating in the sub-basin.
Keywords Awash basin· CMIP6· Extreme indices· Socioeconomic pathways
Introduction
Climate change is one of humanity’s most pressing issues,
posing an existential threat to many parts of the current
social-ecological panorama of nature and human systems.
Currently, in Africa, such issues cause the continent on
significant catastrophic effects (Zielinski 2023; Gaisie and
Cobbinah 2023; Salih et al. 2020; Schilling etal. 2020).
And also according to a World Meteorological Organiza-
tion publication in 2022 (https:// public. wmo. int/ en/ media/
press- relea se/ state- of- clima te- africa- highl ights- water- stress-
and- hazar ds) between 1991 and 2021, the temperature on the
continent rose at an average rate of roughly + 0.3°C each
decade, which is faster than the warming observed between
1961 and 1990, considered the third or fourth warmest dec-
ade on record for Africa. Such variation in temperature and
precipitation has an impact on localized climate in addi-
tion to global saltwater and wind circulation (Yadav etal.
2023; Ibrahim and Samy 2022; Salameh etal. 2019; Rakib
etal. 2018). Several research on climate change analyses
in Africa, particularly in the Horn of Africa (East Africa),
Responsible Editor: Zhihua Zhang
* Mikhael G. Alemu
michaelgetu22@gmail.com
1 Action forHuman Rights andDevelopment, Po-Box1551,
Adama, Ethiopia
2 Department ofHydraulic andWater Resources Engineering,
University ofGondar, Gondar, Ethiopia

Arab J Geosci (2023) 16:660
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660 Page 2 of 27
suggest that there is a rise in climate anomalies (Alemu etal.
2023; Wainwright etal. 2021; Onyutha 2020; Haile etal.
2020). Additionally, according to IPCC’s (2021) 6th Report,
since 1973, there have been significantly more warm nights,
warm days, and warm spells over East Africa, increasing
to yearly maximum and minimum temperatures. The aver-
age annual surface temperature in the region increased by
0.7–1°C between 1973 and 2013.
Several studies were conducted for the analysis of extreme
indices over the globe. Khan etal. (2023) conducted extreme
indices over Pakistan and resulted in most of the climate
extremes having heterogeneous trends for the precipitation
under RCP4.5 and RCP8.5 and showing significant extreme
increasing trend is observed across the country. Similarly,
Sa’adi etal. (2023) and Zhang etal. (2023) also analyzed
the long-term trend of extreme indices using Mean Ken-
dall over the north-western coast and China, respectively,
using different extreme indices. Both show that a significant
increase in indices could signal an impending threat from
climate change, and research into these changes gives scien-
tists a critical understanding of how many extreme climate
variables behave and what effects they might have. Climate
extreme temperature indices have a direct or indirect impact
on crop output, human health, environment, energy demand,
and hydrology (Alidoost etal. 2019; Javadinejad etal. 2020;
Azam etal. 2021; Seo etal. 2019). Ethiopia is one of the
countries affected by hydro-climatological change, with an
increase in extreme temperatures (Lebeza etal. 2023; Gede-
faw 2023; Wubneh etal. 2023; Menna 2017). The implica-
tions of climate change are substantial, particularly in basins
where temperature extremes occur frequently (Belay etal.
2021; Teshome and Zhang 2019; Simane etal. 2016). Ethio-
pian basins are vulnerable to such extremes (Gebremichael
etal. 2022; Shawul and Chakma 2020). Both studies found
that the average anomalies of all temperature extreme indica-
tors for the top half of the basin showed warming tendencies,
and the MK trend test showed a noticeably rising trend in
maximum temperature using genuine meteorological data.
Due to the rarity and importance of this type of analysis
of extremes in the research region of the Lake Tana sub-
basin over the Abbay basin, in particular with the inclusion
of a climate scenario under future analysisfound out very
essential.
Analysis of climate extremes indices with an Integration
of Coupled Model Intercomparison Project (6 Phase) CMIP6
climate models take consideration in different study areas
which give a significant in-site consideration of upcoming
catastrophic events and results significant performance of
coefficient of determination (R2) and Nash and Sutcliffe
efficiency (NSE) on their perspective study area (Bhattarai
etal. 2023; Reddy and Saravanan 2023; Almazroui etal.
2021; Suman and Maity 2020; Ayugi etal. 2021). Over the
Abbay basin, there are limited articles analyzing climate
extreme indexes, particularly considering climate models it
has not yet analyzed. Unlike those the manuscript analysis
was conducted using the CMIP6 model under daily data and
also it conducted the best-fitted climate model for the basin.
Finally, it represents a spatial variation of climate indices
using the ArcGIS Inverse distance weighting (IDW) inter-
polation technique; and it also investigates the spatial dis-
tribution of eleven essential climate extreme indices under
the CMIP6 fitted climate models using SSP2.6, SSP4.5, and
SSP8.5 scenarios with long-term trend analysis at three con-
secutive periods (near, mid, and far) future periods.
Materials andmethodology
Study area
The research area is the Lake Tana sub-basin, which is situ-
ated in Ethiopia in the northeastern or east central Horn of
Africa, between latitudes 3° and 15° north and longitudes
33° and 48° east (Fig.1A). The sub-basin is found at Abbay
basin (Fig.1B) and covers about 15,000 km2 and Lake Tana
covers about 3000 km2 and lies between the highest eleva-
tion of 4090 and lowest elevation of 1672 in the northwest
highlands at Lake Tana, around 564km from Addis Ababa
(Fig.1C) (Wubneh etal. 2022). Lake Tana sub-basin is split
into several agro-climatic zones within the temperate zone,
including Woyna-Dega, Dega, Wurch, and Alphine Wurch
(Legese etal. 2016). Considering a combination of altitude
and rainfall, agro-climatic zones of the sub-basin are classi-
fied into different raster layers. According to meteorological
data from 1976 to 2000, the mean monthly maximum and
minimum temperatures range between 31 and 4.5°C.
Data collection
Baseline andscenario data availability
The National Meteorological Agency’s Bahir Dar branch is
the primary source of meteorological data (precipitation and
temperature). In this study, eight basin representative mete-
orological stations (Fig.1C) and daily rainfall and tempera-
ture data from 1976 to 2000 took an essential part for ana-
lyzing as an input to rectify the biasness of climate models
on the study area Fig.2 (A and B). As Table1 indicates 16
CMIP6 model scenario data were selected and used for the
analysis of future climate extremes under more influential
temperature extremes of socioeconomic pathways (SSP1-
2.6, SSP2-4.5, and SSP5-8.5) (Almazroui etal. 2021). The
data is available on the website https:// esgf- node. llnl. gov/
search/ cmip6/, and all the downloaded data were daily time-
series climate models.

Arab J Geosci (2023) 16:660
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Climate model bias correction method
There are a lot of bias correction methods which is used
effectively (Seo and Kim 2018). For instance; Quantile map-
ping (QM) (Reiter etal. 2018), Detrended quantile mapping
(DQM) (Cannon etal. 2015), and Quantile delta mapping
(QDM) (Xavier etal. 2022) are highly and frequently used
methods because of their ability to remove biases while tak-
ing high order moments. Seo and Kim (2018) analyzed the
best-performed bias correction methods, and finally, the
quantile mapping (QM) method (Eq.1) with Gamma distri-
bution (Eq.3) is suitable for better correction of precipitation
data and Normal Distribution Quantile mapping (Eq.2) for
temperature correction. Additionally, Wubneh etal. (2022)
and Alemu etal. (2022) also used similar methods over the
Lake Tana sub-basin and over Awash basin gives reasonable
result. Hence, in this study, such techniques were used. Thus,
the equation looks like as follows:
where
Qm(t)
and
Qs(t)
are tth bias-corrected data and simu-
lated data from the RCM during the reference period (also
known as the historical period); Fs and
F−1
o
are the cumula-
tive distribution function (CDF) of the raw data from the
RCM and the inverse CDF of the observed data, respec-
tively; and
Γ
and
Υ
are a gamma function and a lower incom-
plete gamma function, respectively.
(1)
Q
m(t)=
{
F
−1
o
[
Fs
[
Qs(t)
]]
,Qs≥Q
th
0, Q
s
≤Q
th
(2)
Qm
(t)=Q
S
F
−1
o[
F
s[
Q
s
(t)
]]
−F
−1
mh
(F
s
(Q
S
(t)
)
(3)
F
(x,𝛼,𝛽)=
Υ(k,
x
𝛼
)
Γ(𝛽)
Fig. 1 Location map of Lake Tana sub-basin (C), Abbay basin (B), and Ethiopia boundary map (A)

Arab J Geosci (2023) 16:660
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660 Page 4 of 27
CMIP6 models performance analysis
Daily time-series data were used to study and evaluate the
16 GCM models’ performance in relation to the observed
data over the Lake Tana sub-basin using four error cri-
teria: Nash and Sutcliffe efficiency (NSE) (Eq.5) (Nash
and Sutcliffe 1970), the coefficient of determination (R2)
(Eq.4) (Arnold etal. 2012), percent of bias (PBIAS)
(Eq.6) (Gupta etal. 1999), and the root mean square error
(RMSE) (Eq.7) (Draper etal. 2013) are employed for the
analysis of how well the model functions. Some academ-
ics employ those techniques in Ethiopian Basins, which
Fig. 2 Annual baseline mete-
orological station’s daily pre-
cipitation (A) and temperature
(B) data from 1976 to 2000 over
the Lake Tana sub-basin
0
500
1000
1500
2000
2500
3000
3500
)yad/mm(noitatipicerPlaunnA
Year
A) Precipitation
Dangla Zegie Bahir Dar Aykel
Gonder Maksegit Adiss Zemen Debretabor
0
5
10
15
20
25
30
35
(eruterpmetnaemlaunnA 0C)
Year
B)Tempreture
Dangla T-max Zegie T-maxBahir Dar T-maxAykel T-max
Gonder T-max Maksegit T-maxAdiss Zemen T-max Debretabor T-max
Dangla T-min Zegie T-minBahir Dar T-minAykel T-min
Gonder T-min Maksegit T-minAdiss Zemen T-min Debretabor T-min
Table 1 List of Candidate
CMIP6 climate models for the
analysis of climate extremes
over Lake Tna Sub-basin 
No CMIP6 model name Country Atmospheric resolution
(lon × lat in deg)
Key references
1 ACCESS-CM2 Australia 1.9° × 1.3° Rashid etal. (2022)
2 AWI-ESM-1–1-LR Germany 1.88° × 1.88° Makula and Zhou (2022)
3 BCC-ESM1 China 2.8° × 2.8° Klutse etal. (2021)
4 CanESM5 Canada 2.8° × 2.8° Shiru and Chung (2021)
5 CMCC-ESM2 Italy 1.3° × 0.9° Lovato etal. (2022)
6 CNRM-ESM2-1 France 1.4° × 1.4° Séférian etal. (2019)
7 EC-Earth3 Europe 0.7° × 0.7° Farhat etal. (2022)
8 FGOALS-g3 China 2° × 2.3° Klutse etal. (2021)
9 GFDL-ESM4 USA 1.3° × 1° Kamruzzaman etal. (2022)
10 IPSL-CM6A-LR France 2.5° × 1.3° Lurton etal. (2020)
11 MIROC6 Japan 1.4° × 1.4° Klutse etal. (2021)
12 MPI-ESM1-2-HR Germany 0.9° × 0.9° Kamruzzaman etal. (2022)
13 MPI-ESM1-2-LR Germany 1.9° × 1.9° Makula and Zhou (2022)
14 MRI-ESM2-0 Japan 1.1° × 1.1° Shiru and Chung (2021)
15 NESM3 China 1.9° × 1.9° Shiru and Chung (2021)
16 SAM0-UNICON South Korea 0.5° × 0.5° S. Park etal. (2019)

Arab J Geosci (2023) 16:660
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results in notable performance, on the climate model
analysis for example Wubneh etal. (2023) and Alaminie
etal. (2021) at Abbay Basin; Alemu etal. (2023), Alemu
etal. (2022), and Jilo etal. (2019) at Awash basin. In addi-
tion, a Taylor diagram was utilized to show how accurately
the patterns in the climate models match the observations
(Alaminie etal. 2021;C. Park etal. 2016). The diagram
illustrates the temporal performance of climate models to
the daily observation and comprises of correlation coef-
ficient, centered RMSE, and standard deviation (Park etal.
2016). To calculate the future climate extreme indices for
the basin, the best-performing bias-corrected CMIP6 mod-
els were chosen.
Trend analysis
Many statistical techniques are employed for trend analy-
sis, including slope-based tests (Swain etal. 2022), least
squares linear regression (Raposo 2016), Sen’s slope esti-
mator (Ali and Abubaker 2019), rank-based testing based
on the Mann–Kendall (MK) test and Spearman rank cor-
relation (SRC) test (Singh etal. 2021), among others. The
non-parametric MK test is the most often used method for
spotting trends in time series (Mann 1945; Kendall 1975)
because the Mann–Kendall (MK) statistical test is often
used to find trends in hydro-meteorological data series.
Additionally, Aamir and Hassan (2020, 2018) and Sala-
meh etal. (2022) use the MK statistical test for conducting
indices trend and depicts reasonable output in their study
area. Furthermore, the trend test can be especially success-
ful when combined with projects like flood risk assessment,
climate data analysis, and others(Wang etal. 2018). As a
result, the Mann–Kendall test was utilized in this study to
identify the high (0.01), medium (0.05), and low (0.1) levels
of significance, and the significant level p-value was studied
throughout time.
(4)
R
2=
n
i=1

xi−x

yi−y

(n−1)s
x
s
y
(5)
NSE =
1−
n
i=1(ZObs −Z
simu)2

n
i=1
(Z
Obs
−Z
mean
)2

(6)
PBIAS
=100 ∗
∑n
i=1(Qm−QS)i
∑
n
i=1
Q
m,i
(7)
RMSE
=




1
N
N

t=1
(yi−y)2
In this calculation, the time series xi is from i = 1, 2…
n − 1, and
Xj
from j = i + 1… n.
The normalized test statistic is calculated by the equation
given below:
The test statistic is
Zc
and when
|
|
Z
c|
|
>
Z
1
−
𝛂
∕2
, in which
Z1−
𝜶
∕2
are the standard normal variables and α is the sig-
nificance level for the test, H0 will be rejected. The extent
of the trend is given as follow:
where m and ki represent the number of time series and the
ties of the sample time series, respectively.
Climate extreme indices analysis
Currently, due to a change of climate variabilities (tempera-
ture and precipitation), the frequency, intensity, spatial exten-
sion, occurrence, and persistence of extremes become contro-
versial. Although there is not a universally accepted definition
of an extreme event, several definitions have been put out and
used in the past (Suman and Maity 2020; O’Gorman 2015).
The World Meteorological Organization (WMO) Commis-
sion for Climatology was included into the Expert Team of
Climate Change Detection and Indicators (ETCCDI), which
was tasked with improving the instrument for sector-specific
climate indicators and developed 27 extreme daily rainfall
and temperature indicators which span a variety of climates
(Cannon 2015). Such indices are used extensively to evaluate
excessive precipitation and temperature in Africa, the Middle
East, and other continents (Simanjuntak etal. 2023; Das etal.
2023; Onyutha 2020; Chou etal. 2020).
To describe the regional and temporal variability of tem-
perature and precipitation extremes for this study, 11 of the
most useful indicators were taken into account (Table2).
These indices were all calculated yearly for each independent
time series for the near-, mid-, and long-term future change.
Our study, which utilizes the best-fit CMIP6 climate models,
(8)
S
=
n−1
∑
i=
1
n
∑
j=i+
1
sign(Xj−Xi
)
(9)
sgn
Xk−Xi

=





1, if

Xj−Xi

>0
0, if

Xj−Xi

=0
−1if

Xj−Xi

<0





(10)
Z
c=
⎧
⎪
⎨
⎪
⎩
s−1
√Var (s)
s+1
√
Var (s)
,
S>
0
S=
0
S<
0
(11)
Var
(s)=
m(−1)(2m+5)−
∑n
k=1K1(K1−1)(2k1+5
)
18

Arab J Geosci (2023) 16:660
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660 Page 6 of 27
focuses on the spatial distribution of indices, in contrast to
earlier studies. Rsum is the annual precipitation of all days
over the year with precipitation > 1mm. (TXx, TXn, TNx,
and TNn) are indexes that represent the change in tempera-
ture over time. CWD is the length of total rainy days or wet
days where precipitation is > 1mm during a year, whereas
consecutive dry days (CDD) refers to the length of total
non-rainy where precipitation < 1mm within the day. R10
and R20 are the variations of heavy precipitation and very
heavy precipitation, respectively. Rx5 is the annual maximum
consecutive 5-day precipitation amount, and SDII stands for
the annual total precipitation divided by the number of wet
days in the year. The indices were calculated by R program-
ming integrated software “Climpact” which was made pos-
sible by the WMO Commission for Climatology (CCl) and
downloaded from the website https:// climp act- sci. org/. The
Expert Team on Climate Risk and Sector-Specific Climate
Indices (ET-CRSCI), which provides input for Climpact and
its related materials, is also used by the IPCC to analyze
extreme indices and provide conclusions for reasonable indi-
ces (Imran etal. 2023; Dow etal. 2022; Bautista etal. 2019).
Result
Model performance
The 16 daily time series CMIP6 models’ performance was
examined after bias correction in relation to the observed
meteorological station climatic daily data for temperature
and precipitation. As Tables3 and 4 indicated that model
MPI-ESM1-2-HR depicts reasonable performance for
precipitation and temperature over Lake Tana sub-basin.
Results were 0.79 for R2 and 0.78 for NSE for precipitation.
Whereas, for maximum and minimum temperatures, 0.76 for
R2, 0.74 for NSE, and 0.65 for R2, 0.64 for NSE, respectively.
Additionally, Fig.3 (A, B, and C) uses a Taylor diagram to
display the correlation, RMSE, and biasness of the candidate
model. As a result, best-fitted models are employed in this
study’s analysis of the prediction of climatic indices.
Figures4 and 5 exhibit the total annual data utilized for
this research from 2015 to 2090 of precipitation and temper-
ature, respectively, under socioeconomic pathways of SSP1-
2.6, SSP2-4.5, and SSP5-8.5 across the Lake Tan Sub-basin.
Table 2 Precipitation,
maximum and minimum
temperature, and extremes
indices
ID Description Unit
Maximum temperature
TXx  Maximum value of daily maximum temperature/year °C
TXn  Minimum value of daily maximum temperature/year °C
Minimum temperature
TNx  Maximum value of daily minimum temperature/year °C
TNn  Minimum value of daily minimum temperature/year °C
Precipitation
CWD  Length of total rainy days/wet days with PRCP > 1mm in the year Days
CDD  Length of total non-rainy days/dry days with PRCP < 1mm in the year Days
Rsum  Annual total precipitation > = 1.0mm mm
R10  The R10, PRCP > 10mm Days
R20  The R20, PRCP > 20mm Days
Rx5  R5day is the annual maximum consecutive 5-day precipitation amount mm
SDII  Annual total precipitation divided by the number of wet days (defined as
PRCP > = 1.0mm) in the year
mm/day
Table 3 Output precipitation of candidate models performance anal-
ysis for climate extreme analysis (“Bold” value indicates best per-
formed model)
Precipitation
Model name RMSE PBIAS % NSE R2
ACCESS-CM2 3.53 0.3 0.3 0.42
AWI-ESM-1–1-LR 3.09 3.4 0.46 0.5
BCC-ESM1 3.58 3.5 0.28 0.37
CanESM5 3.6 2.5 0.27 0.38
CMCC-ESM2 3.99 1.2 0.1 0.28
CNRM-ESM2-1 2.69 − 2 0.59 0.64
EC-Earth3 2.19 − 4.1 0.73 0.75
FGOALS-g3 3.81 0.4 0.18 0.34
GFDL-ESM4 3.28 0.3 0.39 0.47
IPSL-CM6A-LR 2.73 − 3.3 0.58 0.62
MIROC6 2.18 2.5 0.73 0.74
MPI-ESM1-2-HR 1.97 − 3 0.78 0.79
MPI-ESM1-2-LR 2.62 2.2 0.61 0.63
MRI-ESM2-0 4.16 2 0.02 0.24
NESM3 2.31 2.5 0.7 0.71
SAM0-UNICON 3.62 1.1 0.26 0.37

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Table 4 Output temperature of
candidate models performance
analysis for climate extreme
analysis(“Bold” value indicates
best performed model)
Maximum temperature Minimum temperature
Model name RMSE PBIAS % NSE R2RMSE PBIAS % NSE R2
ACCESS-CM2 1.78 0 0.5 0.56 1.23 0 0.12 0.31
AWI-ESM-1–1-LR 1.28 0 0.74 0.76 1.1 0 0.3 0.42
BCC-ESM1 2.34 0 0.13 0.32 1.22 0 0.14 0.33
CanESM5 2.22 0 0.22 0.37 1.15 0 0.24 0.38
CMCC-ESM2 3.81 0 0.4 0.49 1.23 0 0.12 0.31
CNRM-ESM2-1 2.77 0 0.56 0.61 1.11 0 0.29 0.41
EC-Earth3 1.55 0 0.62 0.66 1.25 0 0.09 0.3
FGOALS-g3 2.23 0 0.21 0.37 1.35 0 − 0.06 0.22
GFDL-ESM4 2.01 0 0.36 0.46 1.2 0 0.17 0.34
IPSL-CM6A-LR 1.97 0 0.39 0.48 1.52 0 − 0.33 0.11
MIROC6 3.88 0 0.39 0.48 1.2 0 0.17 0.34
MPI-ESM1-2-HR 1.27 0 0.74 0.76 0.98 0 0.64 0.65
MPI-ESM1-2-LR 1.36 0 0.71 0.73 1.09 0 0.32 0.43
MRI-ESM2-0 1.56 0 0.62 0.65 1.26 0 0.09 0.29
NESM3 1.25 0 0.32 0.45 1.13 0 0.26 0.4
SAM0-UNICON 1.89 0 0.44 0.52 1.18 0 0.2 0.36
Fig. 3 Taylor diagram for comparison of the sixteen CMIP6 models
with respect to the observed Meteorological station data and his-
torical GCM data from 1976 to 2000; “A” for precipitation, “B” for
maximum temperature, and “C” for minimum temperature over Lake
Tana sub-basin

Arab J Geosci (2023) 16:660
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And finally, the climate indices are conducted based on the
model outputs.
Climate data trend analysis
Following CMIP6 model bias adjustment, the trend of model
output was checked using the Mann–Kendall (MK) trend test
based on average yearly precipitation and temperature. The
analysis was carried out throughout three slice periods at
near (2015–2040), mid (2041–2065), and far (2066–2090)
future of the basin using the scenarios SSP1-2.6, SSP2-4.5,
and SSP5-8.5. The series revealed that the majority of the
climate meteorological station’s locations show a statistically
significant trend at the p < 0.05 significance level. Table5
shows that the precipitation variation of a trend over model
MPI-ESM1-2-HR under the socioeconomic pathways. The
result depicts that there is no significant trend change for
the entire meteorological stations around under each SSPs.
Considering the trend variation on temperature shows
there is a fluctuation in change on different stations and
scenarios (Tables6 and 7). The maximum temperature
(Table6) at near and mid period and scenario Debreta-
bor station show a significant trend increment. At the
near future period, Zs becomes 2.29 under SSP1-2.6,
Fig. 4 Annual precipitation of
entire data from 2015 to 2090
under SSP1-2.6 (A), SSP2-
4.5(B), and SSP5-8.5 (C) of
each meteorological station at
Lake Tana sub-basin
0
400
800
1200
1600
2015
2018
2021
2024
2027
2030
2033
2036
2039
2042
2045
2048
2051
2054
2057
2060
2063
2066
2069
2072
2075
2078
2081
2084
2087
2090
)yad/mm(noitatipicerPlaunnA
Year
A) SSP1-2.6
Dangla Zegie Bahir Dar Aykel
Gonder Maksegit Adiss Zemen Debretabor
0
400
800
1200
1600
2015
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
2051
2053
2055
2057
2059
2061
2063
2065
2067
2069
2071
2073
2075
2077
2079
2081
2083
2085
2087
2089
)yad/mm(noitatipicerPlaunnA
Year
B) SSP2-4.5
Gonder Maksegit Dangla Zegie
Bahir Dar AykelAdiss Zemen Debretabor
0
400
800
1200
1600
2015
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
2051
2053
2055
2057
2059
2061
2063
2065
2067
2069
2071
2073
2075
2077
2079
2081
2083
2085
2087
2089
)yad/mm(noitatipicerPlaunnA
Year
C) SSP5-8.5
Dangla ZegieBahir DarAykel
Gonder Maksegit Adiss Zemen Debretabor

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SSP2-4.5 becomes 3.29, and under SSP5-8.6 it becomes
2.42. And atmid future period, the station faces a 2.08
trend significantly at a scenario of SSP1-2.6, but under
SSP2-4.5, Debretabor and Gonder stations also show a
slight decrease; but there is a significant trend (around
2.45 and 2.41), respectively. At the far future period under
the SSP5-8.5 scenario at station Bahir Dar, Gonder, and
Debretabor depicts an increasing trend (around 2.64, 2.31,
and 3.39) respectively. However, there is a decreasing
trend at the station of Dangla and Zegie under SSP2-4.5
and under the scenario SSP5-8.5. And Zegie and Maksegnt
shows no trend (Table6).
The minimum temperature trend demonstrates that there
is a significant increase in temperature trend throughout the
period (Table7). The variation of a trend at the near period
under SSP1-2.6 and SSP5-8.5 both shows only atBahir Dar
station faces the same rise of trend (around 3.66) and under
SSP2-4.5 Bahri Dar and Aykel stations (around 3.53 and
2.47). Considering the mid future period, both scenarios
SSP1-2.6 and SSP5-8.5 show similar trend distribution at
stations Bahir Dar, Aykel, and Gonder which is about (3.85,
2.31, and 2.83), respectively. And under SSP2-4.5 only sta-
tion Bahir Dar shows an increment of trend bout 2.69. In the
final period of the far future, most of the stations under the
Fig. 5 Annual maximum and
minimum temperature of entire
data from 2015 to 2090 under
SSP1-2.6 (A), SSP2-4.5 (B),
and SSP5-8.5 (C) of the mete-
orological station over Lake
Tana sub-basin
0
5
10
15
20
25
30
2015
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
2051
2053
2055
2057
2059
2061
2063
2065
2067
2069
2071
2073
2075
2077
2079
2081
2083
2085
2087
2089
eruterpmeTnaemlaunnA 0C
Year
A) SSP1-2.6
Dangla T-max Zegie T-max Bahir Dar T-max Aykel T-max
Gonder T-max Maksegit T-max Adiss Zemen T-max Debretabor T-max
Dangla T-min Zegie T-min Bahir Dar T-min Aykel T-min
Gonder T-min Maksegit T-min Adiss Zemen T-min Debretabor T-min
0
5
10
15
20
25
30
2015
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
2051
2053
2055
2057
2059
2061
2063
2065
2067
2069
2071
2073
2075
2077
2079
2081
2083
2085
2087
2089
(eruterpmeTnaemlaunnA 0C)
Year
B) SSP2-4.5
Dangla T-max Zegie T-max Bahir Dar T-max Aykel T-max
Gonder T-maxMaksegit T-max Adiss Zemen T-max Debretabor T-max
Dangla T-min Zegie T-min Bahir Dar T-min Aykel T-min
Gonder T-min Maksegit T-min Adiss Zemen T-min Debretabor T-min
0
5
10
15
20
25
30
2015
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
2051
2053
2055
2057
2059
2061
2063
2065
2067
2069
2071
2073
2075
2077
2079
2081
2083
2085
2087
2089
(eruterpmeTnaemlaunnA 0C)
Year
C) SSP5-8.5
Dangla T-max Zegie T-maxBahir Dar T-max Aykel T-max
Gonder T-max Maksegit T-ma x Adiss Zemen T-max Debretabor T-max
Dangla T-min Zegie T-minBahir Dar T-min Aykel T-min
Gonder T-min Maksegit T-minAdiss Zemen T-min Debretabor T-min

Arab J Geosci (2023) 16:660
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scenario of SSP1-2.6 and SSP2-8.5 illustrate a significant
trend, but under the SSP2-4.5 scenario, there is no signifi-
cant trend encountered (Table7).
Historical extreme indices analysis
The historical indices over the Lake Tana sub-basin depict
that there is variation in climate extreme conducted, as Fig.6
depicts that the actual observed distribution experienced a
variation of temperature and precipitation indices. During
the 1976–2000 period, the maximum temperature as illus-
trated in Fig.6 the highest average annual TXx occurred
around 32.4–33.6°C over stations of Zegie, Addis Zemen,
and Gondar. Similarly, for the TXn, TNx, and TNn extremes
temperature occurred at Gondar, station around 22–23°C,
20°C, and 7.8–9.2°C, respectively, over the study area.
On the other hand, at station Zegie and Debretabor, the
maximum distribution of total precipitation (Rsum) was
found around 1600mm, heavy rainfall (R10) occurred
for 60–65days, and extreme heavy rainfall occurred for
25–26days. And at Gondar station, 5days maximum con-
secutive rainfall (Rx5) becomes 160mm (Fig.6). Such
variation leads the basin into the high intensity of SDII,
particularly, at station Debretabor between 11 and 13mm/
day and captured the highest consecutive wet day (CWD)
for 44–50days and lower CDD at the same station for
16–23days. However, the low wet days on the sub-basin
covered all the stations without including Debretabor and
Dangla Station (Fig.6). Such variation might be occurring
due to the high temperature at the station.
Temperature extremes analysis
Four extreme indices (TXx, TXn, TNx, and TNn) and three
pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5)
were used to model temperature extremes for the near-
term (2015–2040), mid-term (2041–2065), and far-term
(2066–2090) futures. The indicators’ spatial distribution
reveals a changing trend over the Lake Tana sub-basin.
On average, across the entire period and situation, such
Table 5 Mann–Kendall trend test of model MPI-ESM1-2-HR precipitation output at the near, mid, and far future period under the scenario of
SSP1-2.6, SSP2-4.5, and SSP5-8
NT, no trend; SP, significant/positive trend; SN, significant/negative trend and Zs is greater than alpha/2 which is Zs > (1.960); hence, we reject
the null hypothesis
SSP1-2.6 SSP2-4.5 SSP5-8.5
Period Stations Variance S Zcalculated Trend S Zcalculated Trend S Zcalculated Trend
Near period Dangla 2058 − 61 − 1.37 NT − 47 − 1.06 NT 13 0.26 NT
Zegie 2058 − 51 − 1.15 NT − 57 − 1.28 NT 17 0.35 NT
Bahir Dar 2058 − 59 − 1.32 NT − 63 − 1.41 NT 25 0.53 NT
Aykel 2058 − 51 − 1.15 NT − 51 − 1.15 NT 19 0.40 NT
Gonder 2058 − 61 − 1.37 NT − 39 − 0.88 NT 19 0.40 NT
Maksegnt 2058 − 55 − 1.23 NT − 39 − 0.88 NT 11 0.22 NT
Adiss Zemen 2058 − 63 − 1.41 NT − 35 − 0.79 NT 19 0.40 NT
Debretabor 2058 − 63 − 1.41 NT − 45 − 1.01 NT 17 0.35 NT
Mid period Dangla 1833 12 0.26 NT − 32 − 0.77 NT 13 0.28 NT
Zegie 1833 26 0.58 NT − 26 − 0.63 NT 2 0.02 NT
Bahir Dar 1833 22 0.49 NT − 32 − 0.77 NT 8 0.16 NT
Aykel 1833 12 0.26 NT − 24 − 0.58 NT 4 0.07 NT
Gonder 1833 6 0.12 NT − 30 − 0.72 NT 6 0.12 NT
Maksegnt 1833 16 0.35 NT − 34 − 0.82 NT 14 0.30 NT
Adiss Zemen 1833 17 0.37 NT − 40 − 0.96 NT 10 0.21 NT
Debretabor 1833 2 0.02 NT − 40 − 0.96 NT 8 0.16 NT
Far future period Dangla 1833 4 0.07 NT − 10 − 0.26 NT − 22 − 0.54 NT
Zegie 1833 6 0.12 NT − 16 − 0.40 NT − 10 − 0.26 NT
Bahir Dar 1833 − 4 − 0.12 NT 2 0.02 NT − 30 − 0.72 NT
Aykel 1833 6 0.12 NT − 16 − 0.40 NT − 16 − 0.40 NT
Gonder 1833 8 0.16 NT − 21 − 0.51 NT − 22 − 0.54 NT
Maksegnt 1833 14 0.30 NT − 12 − 0.30 NT − 26 − 0.63 NT
Adiss Zemen 1833 26 0.58 NT − 14 − 0.35 NT − 34 − 0.82 NT
Debretabor 1833 0 − 0.02 NT − 10 − 0.26 NT − 22 − 0.54 NT

Arab J Geosci (2023) 16:660
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increases were noticeable for TXx, TXn, TNx, and TNn.
Figure7 A and B indicate the spatial distribution of tempera-
ture indices of TXx and TXn illustrated a positive increased
trend of maximum temperature around the lower part of the
basin particularly at Zegie and Bahir Dar stations. At the
initial near period (2015–2040), the highest annual average
of the TXx becomes 32°C at the lower part of the basin
particularly at station Zegie and Adiss Zemen under scenario
of SSP2-4.5. Similar temperature but the highest increase
of spatial distribution on TXx encountered at mid period
of the basin at station Zegie, Bahir Dar, Adiss Zemen, and
Gonder station about 32°C under SSP1-2.6. When the time
increases the distribution of TXx shows a slight increment
till the far future period. Hence, at the far future period under
SSP2-4.5, a similar station depicts the same distribution of
TXx with a positive trend (Fig.7A). When considering TXn
(Fig.7B), there is a similar trend variation countered. At the
near period of the scenario, SSP2-4.5 shows the lowest tem-
perature which is 16°C at the upper part of the sub-basin,
particularly, at Debretabor, Dangla, and Aykel stations under
SSP1-2.6, and at mid period, all scenarios show a similar
distribution of TXn at the upper part of the sub-basin at
Debretabor, Dangla, and Aykel stations around 16–17°C
with positive trend distribution. At the far future period, the
lowest TXn lies between 16 and 17°C occurred at the upper
part of the basin under scenarios of SSP1-2.6 and SSP2-4.5
(Fig.7B).
The minimum temperature extremes (TNx and TNn)
show atFig.8 A and B and there is a slight similarity
between TXx and TXn values in terms of the trend varia-
tion as well as distribution of the temperature. Hence, the
distribution of TNx and TNn is highest at far future period
of the basin under all scenarios over the upper part of the
sub-basin. The trend variation increases when the time and
period rise. The highest TNx at the near period of the basin
lies 20°C at a scenario of SSP2-4.5 under the station of
Aykel, Gonder, and Maksegnt. At mid period, the high-
est annual average TNx was found at a scenario of SSP5-
8.5 in the same stations between 21 and 20°C. At the far
future period, the highest TNx shows a slight increase to
Table 6 Mann–Kendall trend test of model MPI-ESM1-2-HR maximum temperature output at the near, mid, and far future period under the
scenario of SSP1-2.6, SSP2-4.5, and SSP5-8
NT, no trend; SP, significant/positive trend; SN, significant/negative trend and Zs is greater than alpha/2 which is Zs > (1.960); hence, we reject
the null hypothesis
SSP1-2.6 SSP2-4.5 SSP5-8.5
Period Stations Variance S Zcalculated Trend S Zcalculated Trend S Zcalculated Trend
Near period Dangla 2058 3 0.04 NT 21 0.44 NT − 61 − 1.37 NT
Zegie 2058 − 29 − 0.66 NT 15 0.31 NT − 53 − 1.19 NT
Bahir Dar 2058 83 1.81 NT 149 3.26 SP 79 1.72 NT
Aykel 2058 1 0.00 NT 73 1.59 NT − 35 − 0.79 NT
Gonder 2058 73 1.59 NT 161 3.53 SP 77 1.68 NT
Maksegnt 2058 39 0.84 NT 51 1.10 NT 39 0.84 NT
Adiss Zemen 2058 23 0.48 NT 91 1.98 SP 35 0.75 NT
Debretabor 2058 105 2.29 SP 149 3.26 SP 111 2.42 SP
Mid period Dangla 1833 − 16 − 0.40 NT 24 0.54 NT 60 1.38 NT
Zegie 1833 − 72 − 1.70 NT − 14 − 0.35 NT 6 0.12 NT
Bahir Dar 1833 28 0.63 NT 76 1.75 NT 114 2.64 SP
Aykel 1833 − 10 − 0.26 NT 10 0.21 NT 40 0.91 NT
Gonder 1833 50 1.14 NT 104 2.41 SP 100 2.31 SP
Maksegnt 1833 28 0.63 NT 40 0.91 NT 54 1.24 NT
Adiss Zemen 1833 22 0.49 NT 48 1.10 NT 80 1.85 NT
Debretabor 1833 90 2.08 SP 106 2.45 SP 146 3.39 SP
Far future period Dangla 1833 − 30 − 0.72 NT − 84 − 1.99 SN 140 3.25 SP
Zegie 1833 − 62 − 1.47 NT − 108 − 2.55 SN 70 1.61 NT
Bahir Dar 1833 40 0.91 NT 6 0.12 NT 172 3.99 SP
Aykel 1833 − 34 − 0.82 NT − 62 − 1.47 NT 98 2.27 SP
Gonder 1833 48 1.10 NT 16 0.35 NT 174 4.04 SP
Maksegnt 1833 40 0.91 NT 6 0.12 NT 68 1.56 NT
Adiss Zemen 1833 18 0.40 NT 0 − 0.02 NT 106 2.45 SP
Debretabor 1833 66 1.52 NT 40 0.91 NT 148 3.43 SP

Arab J Geosci (2023) 16:660
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21°C under scenario SSP5-8.5 at stations Aykel, Gonder,
and Maksegnt. At the near period, the minimum TNn was
found at a scenario of SSP2-4.5 around 5.9–6.7°C at Dan-
gla station. At the mid period, there is a slight increase
in distribution occurred at station same stations under
SSP2-4.5 around 6.5–7.3°C with a positive trend. So, such
change increases when the time and scenario increase. The
minimum TNn at the far future period occurred at a sce-
nario of SSP2-4.5 at the upper part of the sub-basin at
station Dangla.
Precipitation extremes variability
Particularly for precipitation extremes (Rsum, R10, R20,
CWD, CDD, Rx5, and SDII) around the basin, it visual-
ized a recurring pattern of mixed trends. Three distinct
pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5)
were examined for the near (2015–2040), mid (2041–2065),
and far (2066–2090) time periods. The indices’ spatial
distribution reveals a changing trend throughout the Lake
Tana sub-basin.
Rsum, R10, andR20 analysis
We found that there is an overall spatially fluctuating pattern
of annual precipitation Rsum. At the initial period from the
overall basin, the lowest precipitation occurred at station
Dangla and Debretabor basin which is around 780–820mm
under SSP5-8.5, and the highest Rsum occurs at the upper
part of the basin at station Maksegnt and Addis Zemen
under SSP1-2.6 scenario between 990 and 1000mm, such
distribution shows similar change on the other scenarios.
Considering the mid period, the highest precipitation con-
centration occurs under the SSP2-4.5 scenario between 960
and 990mm at stations Addis Zemen and Maksegnt; the
distribution shows a slight negative trend, and the lowest
Rsum was found under the same scenario at station Dan-
gla and Debretabor around 750–800mm. At the far future
Table 7 Mann–Kendall trend test of model MPI-ESM1-2-LR minimum temperature output at the near, mid, and far future period under the sce-
nario of SSP1-2.6, SSP2-4.5, and SSP5-8
NT, no trend; SP, significant/positive trend; SN, significant/negative trend and Zs is greater than alpha/2 which is Zs > (1.960); hence, we reject
the null hypothesis
SSP1-2.6 SSP2-4.5 SSP5-8.5
Period Stations Variance S Zcalculated Trend S Zcalculated Trend S Zcalculated Trend
Near period Dangla 2058 − 1 − 0.04 NT − 5 − 0.13 NT − 1 − 0.04 NT
Zegie 2058 31 0.66 NT 63 1.37 NT 31 0.66 NT
Bahir Dar 2058 167 3.66 SP 161 3.53 SP 167 3.66 SP
Aykel 2058 57 1.23 NT 113 2.47 SP 57 1.23 NT
Gonder 2058 59 1.28 NT 79 1.72 NT 59 1.28 NT
Maksegnt 2058 11 0.22 NT 55 1.19 NT 11 0.22 NT
Adiss Zemen 2058 17 0.35 NT 11 0.22 NT 17 0.35 NT
Debretabor 2058 − 55 − 1.23 NT 5 0.09 NT − 55 − 1.23 NT
Mid period Dangla 1833 8 0.16 NT 0 -0.02 NT 8 0.16 NT
Zegie 1833 70 1.61 NT 28 0.63 NT 70 1.61 NT
Bahir Dar 1833 166 3.85 SP 116 2.69 SP 166 3.85 SP
Aykel 1833 100 2.31 SP 74 1.70 NT 100 2.31 SP
Gonder 1833 122 2.83 SP 64 1.47 NT 122 2.83 SP
Maksegnt 1833 68 1.56 NT 34 0.77 NT 68 1.56 NT
Adiss Zemen 1833 22 0.49 NT 4 0.07 NT 22 0.49 NT
Debretabor 1833 28 0.63 NT 8 0.16 NT 28 0.63 NT
Far future period Dangla 1833 86 1.99 SP − 106 − 2.5 NT 86 2.0 SP
Zegie 1833 122 2.83 SP 6 0.1 NT 122 2.8 SP
Bahir Dar 1833 180 4.18 SP 98 2.3 NT 180 4.2 SP
Aykel 1833 196 4.55 SP − 14 − 0.4 NT 196 4.6 SP
Gonder 1833 146 3.39 SP 12 0.3 NT 146 3.4 SP
Maksegnt 1833 110 2.55 SP 2 0.0 NT 110 2.5 SP
Adiss Zemen 1833 46 1.05 NT − 42 − 1.0 NT 46 1.1 NT
Debretabor 1833 140 3.25 SP − 72 − 1.7 NT 140 3.2 SP

Arab J Geosci (2023) 16:660
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period, there is an increasing trend of Rsum which is the
highest occurring under SSP1-2.6 at station Maksegnt and
Addis Zemen between 1100 and 1000mm, and the lowest
Rsum was found at station Dangla and Debretabor around
760–800mm under SSP2-4.5 (Fig.9).
Heavy rainfall and very heavy R10 and R20, respectively,
as shown on the (Fig.9A and B). Maksegnt station experi-
enced the maximum day of heavy rainfall during the near time
under scenario SSP1-2.6, which was between an average of
26–27days, and Debretabor and Dangla station experienced
the lowest under scenario SSP2-4.5, which was between
15–17days. At the mid period, scenario SSP1-2.6 shows the
highest heavy rainfall between 25 and 26days at Maksegnt
station, and the lowest was found at all stations between 15 and
17days. At the distant future period scenario SSP1-2.6, the
largest number of days of R10 is found at the upper part of the
basin, specifically stations Maksegnt and Addis Zemen, around
26–27days, while the lowest occurs around 14–17days at the
upper portion of the Sub-basin at Debretabor station.
Considering very heavy rainfall at the near period, the
highest R20 occurred at a scenario of SSP5-8.5 at Dan-
gla, Debretabor Gonder, and Maksegnt stations around
0.39–0.42days on average, and the lowest was found on
the same scenario at Bahir Dar and Addis Zemen stations
Fig. 6 Spatial distribution of annual average observed eleven extreme indices (TXx, TXn, TNx, and TNn) in “°C,” (CWD, CDD, R20, R10) in
“Days” and Rsum and Rx5 in “mm” and SDII in mm/day from 1976 to 2000 over Lake Tana sub-basin

Arab J Geosci (2023) 16:660
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Arab J Geosci (2023) 16:660
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around 0.19–0.24days. The maximum R20 was found at
the midpoint of the period under the SSP1-2.6 scenario at
the Dangla, Debretabor, Gonder, and Maksegnt stations,
between 0.47 and 0.48days, and the lowest R20 was found
under the SSP2-4.5 scenario at the Addis Zemen and Zegie
stations, between 0.12 and 0.14days. And at the final period,
the highest distribution of R20 was found under the sce-
nario of SSP2-4.5 around 0.47–0.48days at station Dangla,
Debretabor, and Gonder, and the least R20 was experienced
under a scenario of SSP5-8.5 at station Dangla, Zegie, and
Addis Zemen around 0.24–0.26days (Fig.10B).
Rx5, SDII, CDD, andCWD analysis
As a variation distribution on the upcoming periods, the
spatial shift in the values of absolute extremes like Rx5day
was discovered. At the beginning of the period, Maksegnt
station in the SSP5-8.5 scenario had the highest increment
of Rx5day extreme precipitation events with an average of
63mm, and Debretabor station in the SSP1-2.6 scenario
had the lowest increment with an average of 54–55mm. The
midpoint of the most Rx5day is similarly concentrated at
roughly 63mm at SSP1-2.6, but the distribution increases as
a positive trend at stations Maksegnt and Addis Zemen, and
the lowest Rx5day is discovered at scenario SSP2-4.5 with
an average of 56–58mm at station Debretabor. At station
Aykel, the far period of the sub-basin displays an average
of 90–97mm under SSP1-2.6, and the smallest Rx5day is
discovered at scenario SSP2-4.5, especially on Debretabor
and Dangla (Fig.10A). The highest Simple daily intensity
index (SDII) concentration was found at the upper part of the
sub-basin at stations Addis Zemen, Maksegnt, and Gonder.
At near period, maximum SDII occurred in scenario SSP2-
4.5 at station Maksegnt approximately 6.5–6.6mm/day,
and the lowest SDII was discovered in scenario SSP5-8.5 at
stations Addis Zemen and Gonder, where the average SDII
was found to be between 6.1 and 6.2mm/day. Mid-period
stations Addis Zemen and Maksegnt exhibit the most similar
intensity and distribution across all scenarios at approxi-
mately 6.5–6.6mm/day, whereas station Debretabor exhib-
its the lowest similar intensity and distribution at around
5.7–5.9mm/day for both SSP2-4.5 and SSP5-8.5. And at the
final period, an average of 6.6–6.7mm/day of intensity was
found at station Maksegnt under a scenario of SSP1-2.6, and
the minimum SDII was found at both SSP2-4.5 and SSP5-
8.5 around 5.7–5.9mm/day at station Debretabor (Fig.11B).
The distribution of consecutive dry days and consecu-
tive wet days for the basin are provided by the precipitation
extreme indices CDD and CWD, respectively. As Fig.12 A
and B show that the majority of the dry days occurred at the
upper and middle part of the Sub-basin (Fig.12A). The sta-
tion Debretabor experienced the maximum dry days (CDD)
in the SSP5-8.5 scenario, with an average of 120days in
the near time, and the stations Maksegnt and Addis Zemen
experienced the lowest dry days, which ranged from 81 to
87days. The mid-period of CDD is high at SSP5-8.5 for
130days at the same station Debretabor, whereas the low-
est dry days will occur at Maksegnt station between 82 and
88days under SSP1-2.6. In the far future, station Debretabor
suffers high dry days for 120days under scenario SSP5-8.5,
whereas station Maksegnt experiences the lowest CDD on
average for 76–81days with scenario SSP1-2.6. On other
hand, the highest CWD of the basin occurred at the sce-
nario of SSP2-4.5 at the near period for an annual average of
79–82days at station Maksegnt and Addis Zemen, whereas
the minimum wet day was found under scenario SSP5-8.5
for 55–59days in average at station Dangla, Zegie, and Bahir
dar. At mid period, only station Maksegnt lies under high
CWD under SSP2-4.5 scenario on average of 72–75, and
station Zegie and Debretabor days experience the least CWD
around 56–57days under SSP2-4.5. At the far future under
scenario SSP1-2.6, both Addis Zemen and Maksegnt faces
the highest CWD around 74–75days, and station Dangla,
Zegie, and Debretabor will experience minimum wet day
around 45.7–49.4days under SSP5-8.5 (Fig.12B).
Discussion
In this study, we utilized CMIP6 models to look at how
extremes in precipitation and temperature are expected to
alter throughout the Lake Tana sub-basin between 2015
and 2090. Eleven meteorological station data is used as
an input observed data for bias correction. Accordingly,
from the 16 most influential daily climate models MPI-
ESM1-2-HR, for precipitation R2 = 0.79 and NSE = 0.78,
for maximum temperature R2 = 0.76, NSE = 0.74, and for
minimum temperature for R2 = 0.65, NSE = 0.64 gives us
reasonable performance which gives different model out-
put by (Alaminie etal. 2021) over Abbay basin analysis
with climate GPCC data. So, selection of models based
on actual data gives precise model output (Adib etal.
2022). The historical climate extremes (1976–2000) over
the study area revealed that station Zegie, Adiss Zemen,
and Gondar experienced the highest temperature of TXx
around 32.4–33.6°C which makes the sub-basin vulnerable
to drought as expressed by Wubneh etal. (2023). The max-
imum precipitation (Rsum) is captured around 1600mm,
heavy rainfall (R10) occurred for 60–65days, and extreme
Fig. 7 Spatial distribution of TXx (A) and TXn (B) in “°C” under the
socioeconomic pathway of SSP1-2.6, SSP2-4.5, and SSP5-8.5 under
near (2015–2040), mid (2041–2065), and far (2066–2090) of Lake
Tana sub-basin. “N,” stands for a negative trend; “SN,” stands for a
significant negative trend; “P,” stands for a positive trend; and “SP,”
stands for a significant positive trend
◂

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Arab J Geosci (2023) 16:660
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heavy rainfall (R20) for 25–26days at Zegie and Debreta-
bor. Such variation over the study area can lead to a high
infiltration rate causing high morphometric characteristics
number of high order streams, structural disturbance, avail-
ability of erodible soils, and occurrence of high overland
flow and discharge; it also causes groundwater abstraction
(Tassew etal. 2023; Fantaye etal. 2023). We observed
that, across the whole basin, statistics on precipitation and
temperature reveal a considerable upward trend, notably
under the SSP5-8.5 scenario for the near (2015–2040) and
far (2066–290) futures. The spatial distribution of climate
extreme indices from the lower to upper part of the basin
showed an increase in extremes with same as Shawul and
Chakma (2020) in which data was taken from 26 meteoro-
logical stations in the basin.
This finding indicates that the overall distribution of tem-
peratures extreme over the basin have a similar distribution,
but at some scenarios, there is a slight fluctuation. Under
the SSP5-8.5 scenario, the lower portion of the sub-basin
at station Zegie, Addis Zemen, and Gonder saw the maxi-
mum frequency of temperature extremes, TXx and TXn,
at roughly 33–32°C and 21–20°C, respectively, in the far
future period. Such output makes the basin more suspected
for drought, and the significant warming climate at the basin
also supports the idea of Tenagashaw and Andualem (2022);
at the same sub-basin using CMIP5 models which concluded
the temperature in the basin increases by 0.83°C and vul-
nerable to hydrological drought. The minimum tempera-
ture TNx and TNn also show the same distribution found
at 21°C and 11°C, respectively, in the same scenario and
period. The temperature variation over the basin are con-
sistent with regional trends suggesting on decreasing water
Lake Tana water level in the sub-basin (Wubneh etal. 2022).
The annual average significant increases of dry days CDD
occurred mid future period over the upper part of the basin
about 130–120days under a scenario of SSP5-8.5, particu-
larly, at station Debretabor. Such dry days seem also related
with the decreasing of Rsum on basin found between 780 and
830mm and low Rx5day around 58–59mm of concentration
lead to dry day increment on Sub-basin. Such variation of
precipitation extremes which puts the lower basin under lack
of agricultural productivity as discussed by Taye (2021) in
the upper part of the Sub-basin crops like Tef and Maize is
common and the indexed value of agro-ecosystem sensitiv-
ity to climate change is high on those cash crops. Consid-
ering heavy rainfall, R10 and extreme heavy rainfall R20
occurred at the upper part of the sub-basin. Station Addis
Zemen and Maksegnt under SSP1-2.6 found out the highest
R10 encountered for 26–27days at the far future period,
whereas on the same period under SSP5-8.6 scenario, sta-
tion Dangla, Debretabor, and Maksegnt face extreme heavy
rainfall for an average of 0.83–0.84days. Such variation in
low R10 and R20 rainfall gives less concentration of wet day
(CWD) on the sub-basin, and it becomes the lowest wet day
found in the same scenario and period for 45.7–49.4days. It
seems that stations Dangla, Zegie, and Debretabor will face
a low number of wet days with a decreasing trend. Posi-
tive trends in total precipitation within the basin are often
associated with rising average precipitation frequency and
intensity. Lake Tana sub-basin is one agricultural sector of
the country, particularly in arid regions where most crops are
precipitation-dependent (Johnsgard 2014). Hence, the high-
est intensity on the sub-basin SDII found at station Addis
Zemen and Maksegnt under a scenario of SSP1-2.6 at far
future period around 6.6–6.7mm/day which is suitable for
agriculture and in station Dangla, Debretabor, and Maksegnt
faces maximum an extreme heavy rainfall R20 compared to
other scenarios around 0.83–0.84mm/day under SSP5-8.5
at far future period might be suspected for the high flood
and health issues as discussed by Lai etal. (2020). While
excessive rainfall causes flooding, rising heavy rainfall
increases soil moisture for cultivated lands, which will ulti-
mately contribute to productivity (Gedamu 2020). Similar
to this, some authors and the most recent IPCC indicate that
the variability of climatic extreme indices is growing over
eastern Africa (Almazroui etal. 2021; Iturbide etal. 2020).
However, we saw a slight decline in climate indicators for
the basins near future and a rise in extreme indices for its
mid- and long-term periods.
Conclusion
The current study concentrated on the spatiotemporal vari-
ation of projected climatic extreme indices of temperature
and precipitation under historical (1976–2000) and sce-
nario using the CMIP6 models under the scenarios of close
(2015–2040), mid (2041–2065), and distant (2066–2090)
time frames. The manuscript uses eleven influential pre-
cipitation indices (Rsum, R10, R20, Rx5day, SDII, CDD,
and CWD) and temperature extremes (TXx, TXn, TNx,
and TNn) to depict the basin variability distribution. For
the study of the best fitting GCM analysis with respect to
the observed meteorological data (precipitation and tem-
perature) under daily time-series data over the Lake Tana
sub-basin, 16 of the most significant CMIP6 models were
downloaded. Based on our research findings, at the sub-
basin model, MPI-ESM1-2-HR demonstrates good agree-
ment for temperature and precipitation. The current research
Fig. 8 Spatial distribution of TNx (A) and TNn (B) in “°C” under the
socioeconomic pathway of SSP1-2.6, SSP2-4.5, and SSP5-8.5 under
near (2015–2040), mid (2041–2065), and far (2066–2090) of Lake
Tana sub-basin. “N,” stands for a negative trend; “SN,” stands for a
significant negative trend; “P,” stands for a positive trend; and “SP,”
stands for a significant positive trend
◂

Arab J Geosci (2023) 16:660
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shows that there are substantial regional differences in the
frequency and intensity of heat extremes across the basin.
Additionally, compared to the lower portions of the sub-
basin, warming trends predominated in the upper regions.
On the other hand, cold extremes showed a tendency toward
a downward trend. In contrast to the cold season, we dis-
covered that the extremes were largely shifted during the
hot season. TXx, TXn, and TNx showed trends that were
increasing positively, which indicated further warming. The
implications of climate change had a direct impact on the
comparison of warming and cooling temperature extremes.
Particularly, when compared to the cooling tendency,
changes in the warming tendency were more sensitive. We
discovered that the distribution of cold, warm, and heavy and
very heavy rainfall precipitation, intensity, and variance in
the five-day precipitation extremes were strongly correlated
with significant changes in CDD and CWD on the basin. The
Fig. 9 Spatial distribution of Rsum in “mm” under the socioeco-
nomic pathway of SSP1-2.6, SSP2-4.5, and SSP5-8.5 under near
(2015–2040), mid (2041–2065), and far (2066–2090) of Lake Tana
sub-basin. “N,” stands for negative trend; “SN,” stands for significant
negative trend; “P,” stands for positive trend; and “SP,” stands for sig-
nificant positive trend

Arab J Geosci (2023) 16:660
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Page 19 of 27 660
annual average precipitation has significantly increased in
the upper portion of the sub-basin around the stations Addis
Zemen and Maksegnt, whereas it is trending downward in
the middle and lower basins. Also, temperature indicates
under all scenarios that there is a close similar distribution
of temperature increasing at the upper part of the sub-basin
seems to continue to rise in the Lake Tana sub-basin. There-
fore, it is crucial to boost capacity and reduce sensitivity to
extreme temperatures. Finding probable patterns and vari-
ances in future extreme temperature and precipitation events
is the main result of this investigation. This research was
essential to understanding how climate threats were chang-
ing. Future evaluations of climatic extremes for potential
climate-based hazards in the basin and warning future
adaptation and mitigation planning approaches to combat
the consequences of excessive warmth in the sub-basin will
surely benefit from the findings of this study.

Arab J Geosci (2023) 16:660
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Arab J Geosci (2023) 16:660
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Fig. 10 Spatial distribution of R10 (A) and R20 (B) in days under the
socioeconomic pathway of SSP1-2.6, SSP2-4.5, and SSP5-8.5 under
near (2015–2040), mid (2041–2065), and far (2066–2090) of Lake
Tana sub-basin. “N,” stands for a negative trend; “SN,” stands for a
significant Negative trend; “P,” stands for a positive trend; and “SP,”
stands for a significant positive trend
◂

Arab J Geosci (2023) 16:660
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Arab J Geosci (2023) 16:660
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Fig. 11 Spatial distribution of Rx5day (A) and SDII (B) in “mm and
mm/day” respectively, under the socioeconomic pathway of SSP1-
2.6, SSP2-4.5, and SSP5-8.5 under near (2015–2040), mid (2041–
2065), and far (2066–2090) of Lake Tana sub-basin. “N,” stands for
a negative trend; “SN,” stands for a significant negative trend; “P,”
stands for a positive trend; and “SP,” stands for a significant positive
trend
◂

Arab J Geosci (2023) 16:660
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Arab J Geosci (2023) 16:660
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Author contribution Contributors to the study include MGA and
MAW. MGA conceived the concept, wrote the first draft of the manu-
script, and edited it with MAW. The study’s authors all contributed,
and they all consented to its publication.
Data availability Upon request, all data produced or examined for this
research will be made available.
Declarations
Ethics approval We certify that the aforementioned authors have
reviewed the text and given their approval. We will say it again: the
Corresponding Author is the only one you should speak with about
the editorial process.
Consent to participate The decision to participate in this study was
made by the authors.
Consent for publication The work may be published with our permis-
sion as the authors.
Conflict of interest The authors declare no competing interests.
Ethical statement We affirm that the work is original, has never been
published, and is not currently being considered for publication else-
where in accordance with the authors’ promises that the study was
conducted ethically.
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