Amba Shalishe Shanka* and Yoseph Arba
Faculty of Meteorology and Hydrology, Ethiopia
*Corresponding author: Amba Shalishe Shanka, Faculty of Meteorology and Hydrology, Ethiopia
Submission: May 07, 2020Published: March 18, 2021
ISSN 2578-0336 Volume8 Issue1
Climate change has become a major concern and receiving serious attention at global, National, regional and local levels. The purpose of this study is to evaluate maize crop water requirement under climate change scenario in konso woreda, Southern Ethiopia. This was conducted based on near-term 2019-2048 projected climate variables from CORDEX-Africa data for RCP 4.5 and RCP 8.5 scenarios. The projected data was quality checked, biased corrected and model performance was evaluated. Mann Kendall trend detection and Sen’s slope estimator were used by XLSTATA software to evaluate the trends of projection and to detect changes. The Cropwat model was used to estimate crop water requirement of maize. Projected rainfall pattern shows decrease by 1.312mm/annual, 1.5mm/annual and 0.803 under RCP 2.6, RCP 4.5 and RCP 8.5 respectively. Maximum temperature projected to increase 0.004, 0.004 and 0.005̊C/annual for near term under RCP 2.6, RCP4.5 and RCP 8.5 scenarios respectively. Minimum temperature is projected to increase significantly along with calculated evapotranspiration from projected temperature under three Scenarios. Overall result of this research shows that adaptation measures and water harvesting technologies are mandatory and recommended for cropping season to have sustainable production to feed increasing population.
Keywords: Climate change; Evapotranspiration; CROPWAT
The global climate change, climate variability and associated weather extremes
challenging both the developed and developing countries. While significant debate remains
over the extent to which humans have induced climate change, it has generally been accepted
that the effect of climate change is manifested in terms of increased weather variability,
higher frequency of extreme weather events and decreased predictability . Climate change
impacts on agricultural crop production vary from place to place and from crop to crop .
Higher temperatures can reduce crop production in parts of the world  although crop yield
could increase with warm-wet climate change in some areas .
A country like Ethiopia whose economy is heavily dependent on rain fed agriculture, climate variability is one of the more important factors in explaining various socioeconomic problems such as yield reduction and food insecurity. Agricultural sector in the country provides employment to more than 80% of the population and contributes around 43 % to the overall GDP. Yet, Climate variability and change affect mainly the agricultural sectors through the emission of Greenhouse Gas (GHG) from different farming practice . Climate change in the form of higher temperature, reduced rainfall and increased rainfall variability reduces crop yield and threatens food security in low income and agriculture-based economies. Adverse climate change impacts are considered to be particularly strong in countries located in tropical Africa that depend on agriculture as their main source of livelihood . Modeling the effect of climate change on crop water requirement is usually achieved either by direct use of climate model data in crop models or by changing existing climate data series with expected changes.
Water is important for plant for its growth as well as for food production. There is a competition between municipal, industrial and agriculture users for the water available in reservoirs. Estimating irrigation water requirements is prerequisite for water project planning and management. For better production a medium matured maize crop requires between 500 to 800mm of water depending on environment . Crops usually differ for their water demand and this is an important subject of concern. Crop water requirement is mainly dependent on the supply of water from the soil, the demand of water by the crop, and the manner in which the crop is able to use the limited water supply.
Maize constitutes one of the most widely consumed food sources and a basic raw material for feed mill and beverage industries. Its sustainable production promotes adequate food supply, job opportunities, increased family income and foreign exchanges. Maize is a Tropical crop largely grown in various parts of Southern- Ethiopia. Climate is one of the main environmental determinants influencing crop yields and could be used to estimate maize water requirement . Estimates of Maize water requirement is essential in order to curtail excessive application of water than needed, which could cause crop damage, poor traffic ability, soil erosion, excessive leaching and the wastage of water, labor and energy Hudson 2004. Therefore, study was undertaken to evaluate the maize of crop water requirement under climate change scenario in Konso.
Konso Woreda is located in southern part of Ethiopia, (5° 15′ 0″ N, 37° 29′ 0″ E) 600km away from Addis Ababa. It is bordered on the south by the Oromia Region, on the west by the Weito River which separates it from the Debub Omo Zone, on the north by the Dirashe woreda, on the northeast by Amaro woreda, and on the east by Burji woreda (Figure 1).
Figure 1:Map of the study area.
Climatic conditions of the area are mostly characterized under ‘kola’ weather condition. Rainfall distribution over the area is in monthly distribution is bimodal even if there is different in different area. The annual rainfall variation is between 400 and 1000mm. The rain follows a bimodal pattern there are two rainy seasons’ i.e., Belg big rains with the period starting mid- February and lasts to April and the small rain period Maher occurring around October and November. From the long-term temperature data obtained from National Metrological Agency, the mean maximum temperature is 33 °C and minimum temperature is 12 °C.
Research design, data collection and analysis
Daily rainfall, minimum and maximum temperature, wind, relative humidity, and sunshine hours were collected from National Meteorological Agency of Ethiopia. Daily climate parameters such as precipitation, maximum and minimum temperature were obtained from regional climate datasets with the recently developed scenarios collected from Canadian climate center. The climate data used corresponded to the Canadian Earth System Model2 (HadGEM2) climate model which statistically downscaled outputs by CORDEX Africa at a grid resolution of 0.5O by 0.5O (approximately 55km). The future climate scenarios simulation was conducted to determine the impact of two specific IPCC climate change emission scenarios, RCP 4.5 and RCP 8.5. The used RCP data corresponds to a Grid point lies near the selected representative meteorological stations. Also, agronomic data specially type of crop, harvesting and planting date and its area of coverage were taken from Ministry of Agriculture.
Before the analysis, some pre-analysis activities such as filling of the missing years from Archives of the stations, adjustment of incomplete data and adjustment of outliers were employed for quality data. The appropriate rainfall, minimum and maximum temperature data were captured into Microsoft Excel spreadsheet following the days of a year entry format. Data quality control was done by careful inspection of the completeness, spatial and temporal consistency of the rainfall records in the study areas. Monthly rainfall and temperature data sets were subjected to detail analyses using sequences of statistical packages. All records are analyzed, and the missing data is calculated.
Filling of the missed data
Some of the meteorological stations have short breaks in their records because of absences of the observer and instrumental failures. Therefore, normal ratio method, inverse distance method and inverse distance methods were used.
Data quality control
Double mass curve is a simple, visual and practical method, and it is widely used of the consistency and long-term trend test of hydro_ meteorological data Mu et al. 2010. The double-mass technique plots the accumulated rainfall data against the mean value of all neighborhood stations. Therefore double-mass curve was used to adjust inconsistent precipitation data.
For statistical analysis rainfall data from a single series should ideally possess property of homogeneity properties or characteristics of different portion of the data series do not vary significantly. Therefore, Homogeneity test is required for validation purposes and there is a shared need for such tests with other climatic variables. Rainbow is a software package for hydrometeorological frequency analysis and testing the homogeneity of historical data sets. Therefore, Rainbow software packages was used to check the homogeneity of rai fall data.
Mann Kendall trend test
The Mann Kendall Trend Test (sometimes called the M-K test) is used to analyze data collected over time for consistently increasing or decreasing trends.
According to FAO , for analyzing the crop water requirement, irrigation scheduling and scheme design, metrological and agronomic data of the study area could be analyzed by using Crop watt software. For the Calculation of the CWR can be carried out by calling up successively the appropriate climate and rainfall data sets, together with the crop files and the corresponding planting dates. The crop water requirement module includes calculations, producing the irrigation water requirement of the crop daily and over the total growing season as variance between the crop evapotranspiration under standard conditions.
ET0 = K0 × ET0 (1)
Where, K0 is crop coefficient and ETO is reference evapotranspiration (mm/day).
ETO Values measured or calculated at different locations or in different seasons are comparable as they refer to the ET from the same reference surface. The only factors affecting ETO are climatic parameters. So, ETO is a climatic parameter and can be calculated from weather data.
Since the exiting data are the downscaled precipitation and minimum and maximum temperature, the potential evapotranspiration for future time horizon is calculated by using Hargreaves method.
PET= 0.0023(Tm+ 17.8)( Tmax -Tmin )Ra (2)
Where, PET Hargreaves potential evapotranspiration; Ra Extraterrestrial radiation (calculated from latitude and time of year); Tm Mean temperature; Tmin Minimum temperature; Tmax Maximum temperature.
Statistical Downscaling Model (SDSM)
Statistical downscaling involves the establishment of empirical
relationships between historical large-scale atmospheric and local
climate characteristics. Once a relationship has been determined
and validated, future large-scale atmospheric conditions projected
by GCMs are used to predict future local climate characteristics.
It is the one of the best models in the classification of various
downscaling methods. The basis of this method is designing
multiple regression models. So that in this method, in order to
simulate climatic parameters for any time scale period (monthly,
seasonally or annually), a multivariate linear regression model
is developed between large-scale predictors (as independent
variables) and predicted climatic variables at station scale (as
dependent variable) and through following steps [9,10].
The SDSM model categorizes the task of downscaling into a series of discrete processes such as quality control and data transformation, screening of predictor variables, model calibration and weather generator, summary statistics, frequency analysis, scenario generation, compare results and time series analysis.
Model performance evaluation
In order to check the goodness of fit between the simulated and observed data and to decide the best parameter values, the model performance should be evaluated. Many different test criteria have been developed to assess the efficiency of a climate model. For this study, Statistical techniques such as Bias, Coefficient of Variation (CV), and Correlation Coefficient (Correl.) were used to evaluate the model simulation outputs of rainfall data. Bias is used to evaluate the systematic error of rainfall amount. The positive value of bias indicates overestimation while negative bias shows underestimation. There is no systematic difference in between observed and simulated model outputs as the value of bias close to zero.
Trends of observed meteorological data
The long-term trends of observed rainfall, as well as maximum and minimum temperature, were evaluated in terms of annual time series using non-parametric Kendall trend test at significance level of alpha equal to 0.05. Normalized P-values was used for significance test in this study. As shown in figure below the observed annual rainfall variation during the period 1987-2012 in both stations of the study area. While the annual rate of change was increased by 1.675mm/annual in Konso and 7.765mm/annual in Gato (Figure 2).
Figure 2: Observed annual rainfall distribution trends of both stations used for this study during (1987-2012).
The summary of Mann-Kendall (MK) trend test results is shown in the Table below. It illustrates that the observed annual rainfall is statistically significant and shows increasing trend in Gato. Whereas Insignificant increasing trend was observed in Konso station with alpha equal to 0.05 significant level (Table 1). The rate change of maximum temperature increased by 0.032 °C/annual in Konso station, whereas decreased in Gato station by 0.009 °C/annual rate (Figures 3 & 4).
Figure 3: The trends of maximum and minimum temperature of both stations of the study area (1987-2012).
Table 1:Mann-Kendall test results for annual rainfall during 1987-2012.
The table below shows statically insignificant increasing trend at Konso station, where its maximum temperature is significant and shows decreasing trend at alpha equal to 0.05 significant level. The Mann- Kendall statistical test result of minimum temperatures Table 2 showed statically insignificant and increasing trend for both stations (Table 2).
Table 2:Mann-Kendall test results for annual maximum and minimum temperature during1987-2012.
Predictor variables selection
The strongest correlation was obtained between the predictands and each predictor variables for each Month. The precipitation showed a better correlation with 500hPa geopotential height, 850hPa airflow strength and Surface specific humidity. The correlation of maximum temperature with the predictor variables was correlated strongly with Mean sea level pressure, Mean temperature at 2m, 500hPa vorticity and 500hPa meridional velocity. The minimum temperature was strongly correlated with all selected predictor variables except 500hPa vorticity and 500hPa zonal velocity.
Before using weather, it is dynamically or statically downscaled climate datasets, for any hydrological or meteorological model, it is important to check its performance whether the historical or observed conditions can be replicated or not. The mean annual measured rainfall amount and dynamically downscaled model simulation output for (1970- 2000) period was compared for this study (Table 3).
Table 3:Model performance evaluation.
Projected changes in seasonal and annual rainfall
Evaluation of Seasonal rainfall distribution is very important for rain-fed agriculture because agricultural activity highly depends on the rainfall distribution. The average seasonal rainfall computed for (2018-2047) from the baseline period. Projected percentage of average seasonal rainfall change in the Figures below shows that future rainfall distribution over the study area decreases for Autumn (SON) in near-term under Three RCP scenarios and it is expected to increase for winter (DJF), spring (MAM) and summer (JJA) under the Three RCP scenarios. The magnitude of positive change is better projected for summer (JJA) and spring (MAM) under Three RCP scenarios compared with other two seasons and negative change is projected for autumn (SON) under the three scenarios in near-term from the baseline period. This is due to better rainfall distribution expected in summer and spring in near-term (Figure 5).
Figure 5: Change in seasonal and annual precipitation in the near-term future.
Projected changes in maximum and minimum temperature
The average seasonal maximum temperature change is computed for (2018-2047) from the base line period. Projected percentage of average seasonal maximum temperature change in the Figures below shows that future maximum temperature over the study area increases for all seasons except for spring (MAM) in near-term under the Three RCP scenarios. The peak positive change is projected for autumn (SON) which is 0.54, 0.33 and 0.46 oC under RCP 2.6, RCP 4.5 and RCP 8.5 scenarios respectively. The peak negative change in maximum temperature is projected for spring (MAM) which is -0.26, -0.35 and -0.29 under RCP2.6, RCP 4.5, and RCP 8.5 scenarios respectively in near-term from the baseline period (Figure 6).
Figure 6: Change in seasonal and annual maximum and minimum temp in the near-term future.
The average seasonal minimum temperature change is computed for (2021-2050) from the baseline period. Projected percentage of average seasonal minimum temperature changes in the Figures below shows that future minimum temperature over the study area increases for all seasons in near-term under the Three RCP scenarios. The peak positive change is projected for summer (JJA) which is 1.88, 1.96 and 2.2 under RCP 2.6, RCP 4.5 and RCP 8.5 scenarios respectively.
Observed ETO and projected changes in evapotranspiration.
The monthly 26 years average of evapotranspiration is analyzed
and described to explain its monthly fluctuation in the study area.
As shown in the Figure below high evapotranspiration is recorded
in January, February and March which is 4.43, 4.80 and 4.89mm/
day respectively. On the other hand, low evapotranspiration is recorded in May, June and July which is 3.93, 3.84 and 3.79mm/day
The projected evapotranspiration is analyzed and described to explain its seasonal change in the study area. The figure below shows projected seasonal average variation of evapotranspiration change from the baseline period to near-term projection (2021- 2050) under RCP scenarios. It illustrates that the projected evapotranspiration over the study area decreases for all seasonal for RCP 4.5 and RCP 8.5 scenario except in autumn (SON) for RCP 2.6 scenario. The peak negative change in evapotranspiration is projected for summer (JJA) which is -1.11, -1.28 and -1.19 under RCP2.6, RCP 4.5, and RCP 8.5 scenarios respectively in near-term from the baseline period (Figure 7).
Figure 7: Projected changes in season a land annualet for near-termunder the RCP2.6,RCP4.5 and CP8.5scenarios.
Water requirements for maize under current conditions
As stated in Figure below the crop water requirement for maize is maximum at mid stage. Since effective rainfall is low and the relative water deficits is high thus crop need additional water to fulfill the required amount water. The total crop water requirement at mid stage is 274mm/dec and the effective rainfall is 50.3mm/ dec. due to satisfy the required amount of water, the total irrigation requirement at mid sage is 223.5mm/dec. As the table below shows the total irrigation requirement is very low at initial stage, because of the effective rainfall is greater than crop water requirement and also the peak rainfall observed in April. In the case crop water requirement is low which is not needed the irrigation (Table 4).
Table 4: Mann-Kendall test results for annual rainfall during 1987-2012.
Water requirements for maize under future condition
As the Figure below shows the ETc for maize crop in different
growth stage for near-term scenarios. First peak values of ETc are
projected for mid-stage is of crop development in near-term and
the low values projected for of ETc is for initial stage. This shows
crop canopy development play a significant role to determine the
crop water requirement during mid-stage. High ETc during midstage
and late stages are associated to shortage of precipitation
amount under all scenarios and air temperature is also projected
to increase that enhances high rate of ETO Future projection of water requirement and ETO shows a need of additional water for
cropping to compensate the loss of water at different stage of crop
development to sustain maize cropping in the area. Thus, more
irrigation schedule needs over the study area in the future climate
change scenario at different development stage of maize for summer
season (June to August). The projected monthly maize crop water
requirement change is decreasing for the near-term projection.
The magnitude of decreasing low under RCP 8.5scenario compared
with 4.5 and RCP.
2.6 Scenarios. This is because, more rapid socioeconomic and population growth, with limited climate change mitigation and adaptation under the RCP8.5 climate change scenarios with high emission of CO2 resulting more atmospheric forcing. As shown in table below the total Crop water requirements for maize for 2021- 2050 is expected to decrease by a low percentage, 1.3-1.6% under three scenarios (Table 5).
Table 5:Result shows that the average crop water use and irrigation water requirement.
The pattern of rainfall distribution in konso shows bimodal
with peak rainfall in April and October. The Observed annual
rainfall for 1987-2012 shows decreasing trend for both Konso and
Gato stations. The mann-kendall trend test result shows warming
trend for both maximum and minimum temperatures. The annual
rainfall is projected to decrease slightly for future under RCP 2.6,
RCP 4.5 and RCP
8.5 scenarios. The seasonal change of projected rainfall distribution over the study area decreases for Autumn (SON) and it is expected to increase for winter (DJF), spring (MAM) and summer (JJA) under RCP 2.6, RCP 4.5 and RCP 8.5 scenarios. Monthly mean of projected peak distribution of rainfall for 2018-2047 is expected in the same spring (MAM) season as observed.
Air temperature is projected to increase under three future scenarios with statistically significant increasing trend. The projected Temperature change is also increasing for the nearterm projection in both maximum and minimum temperature. Magnitude of temperature change projected is high under RCP 8.5 scenario compared with 4.5 and RCP 2.6 scenarios. This is because, more rapid socioeconomic and population growth, with limited climate change mitigation and adaptation under the RCP8.5 climate change scenarios with high emission of CO2 resulting more atmospheric forcing.
However, the evapotranspiration calculated from minimum and maximum temperature is projected to decrease under the three RCP scenarios. The change in climate variables such as precipitation and temperature there by decrease in evapotranspiration which is very sensitive parameter that can be affected by changing climate scenarios and likely to have significant impact on maize cop water requirement.
Seasonal crop water requirement by Crop Watt from projected rainfall data shows that the water needed for crop growth is expected high during summer (JJA) for near term. Therefore, irrigation water required is for summer. However, the maize crop water requirement for spring is low and irrigation water is not required for this season. Due to the Scarcity of effective rainfall, irrigation water required is for summer. However, the maize crop water requirement for spring is low and irrigation water is not requiring for this season. Finally, water requirements for maize under climate change for 2018-2047 is expected to decrease by a low percentage, 1.3-1.6%. Thus, it is very important to revise and fix the production system for maize.
This study utilized single climate model, CANESM2 outputs of Representative concentration Pathways of low, medium and high emission scenario (RCP 2.6, 4.5 and 8.5) to show climate change. Further, investigation extended different climate model outputs with considering all the entire emission scenario of RCP 6.0 joining with Land use land cover change to enhance full understanding and minimize climate model ambiguity. Hence, the crop water requirements for maize under climate change for 2018-2047 is expected to decrease by a low percentage, 1.3-1.6%. Thus, it is very important to revise and fix the production system for maize.
© 2021 Amba Shalishe Shanka. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and build upon your work non-commercially.