1.      Introduction

The Upper Awash Basin in Ethiopia is a major agricultural zone, supplying food to urban centers and supporting national economic growth. However, the region faces increasing pressure from climate change, water scarcity, and inefficient irrigation practices. Smallholder farmers often lack access to modern water management tools, leading to crop losses and resource conflicts. This project aims to address these challenges by introducing climate-smart, remote sensing–based irrigation solutions. By combining field data with satellite observations, it seeks to optimize water use, improve crop yields, and support sustainable livelihoods. The study aligns with national priorities in food security, climate resilience, and sustainable development.

2.      Problem Statement

Despite the recognized benefits of irrigation and the availability of water resources, the Upper Awash area of Ethiopia continues to face significant challenges in adopting and implementing efficient irrigation practices, resulting in poor agricultural outcomes and threatening food security. These challenges are intensified by climate change, which increases water demand and reduces availability, yet existing studies lack comprehensive analysis on how climate change specifically affects irrigation systems in the region. Farmers predominantly rely on traditional, inefficient water management methods, with limited access to updated irrigation scheduling parameters and inadequate use of remote sensing technologies that could optimize water use under changing climatic conditions. Persistent knowledge gaps and data limitations hinder the development of climate-resilient irrigation strategies. Without targeted interventions, the region will remain vulnerable to reduced productivity, climate shocks, and ongoing food insecurity.

3.      Objectives

The objective of this study is:

1.      Assess impact of climate change on irrigation and improve irrigation efficiency in Upper Awash using Remote sensing applications

2.      Test, develop, customize and promote climate-smart, data-driven irrigation practices to optimize water use for smallholder farmers

3.      Support sustainable water management practices to support food security and climate resilience in the area

4.      Study Area Description

The Upper Awash Basin is located in central Ethiopia, extending from the highlands of the Oromia region towards the Rift Valley. It plays a crucial role in agricultural production, particularly small-scale irrigation. According to (1) the basin experiences a bimodal rainfall pattern, with annual precipitation ranging from 800 mm in the lowlands to over 1,200 mm in the highlands. Temperature varies between 10°C and 30°C. with respect to water resources, The Awash River and its tributaries provide the primary water sources for irrigation, supplemented by groundwater extraction(2). The irrigation Systems is dominated by small-scale and fragmented irrigation mainly relies on surface water diversions, traditional canals, and some modernized irrigation schemes which utilizes pumps.

5.      Methodology

5.1.Research Design method and sampling.

A mixed research design that combines quantitative data analysis with qualitative data will be employed in the study. The design will help for capturing both objective data and subjective (stakeholders') perspectives for better insight and acceptability of the option proposed. Besides experiments, Surveys Interviews/focus group discussion Observation Literature review, correlation and descriptive methods will be employed.

With respect to sampling, there are four catchments in Upper Awash area. For successful identification of irrigation water management problems, stratified sampling technique will be employed. Accordingly, in each of the four catchments, each scheme will be grouped into three categories depending on the intensity of the irrigation practice considering geographical representation, in to highly intensive > =200% medium intensive 199%>=II>=100% and low intensive <=99%. Field and satellite data will be collected from these specific schemes for further investigation.

For this study the following areas will be investigated

5.2.Impact of climate change on water management and water resources

Reference evapotranspiration (ET0) accurate estimation is a fundamental requirement of agricultural water management (3). Hence climate change impact of irrigation water management will be investigated using ETO in this study. This will be estimated from long term grided climatic data from 1985 to 2023 collected from Ethiopian Meteorological Institute (EMI) using CROPWAT 8 and remote sensing techniques. CROPWAT 8 is the most widely utilized technique in the study area as indicated above and remote sensing using the model SEBAL will be used for comparative assessment. Using this data, the dynamics of ETO will be examined using linear regression analysis to identify significant patterns.

5.3.Investigate factors that contribute to inefficient water management in the study area.

To investigate the presence of inefficient water management the performance of the practice will be examined as follows,

Performance evaluation

To evaluate the existing irrigation water management practices and its efficiency of water use with respect to the proposed water management, the following steps will be employed.  For comparison this analysis will also be performed between the actual practice, using available average climate data and remote sensing data of the same period and place.

The following parameters will be used to evaluate the actual water management of the system practiced in the area.

Determination of irrigation water requirement

Using the data collected the IWR is calculated as follows:

Reference crop evapotranspiration (ETo)

Depending on the available climate data the best method will be employed for the estimation of ETO for this study. Besides ETO will also be estimated using remote sensing data using SEBAL Modell for similar periods as follows.

A.    Met data for ETO

Using this software ETO is calculated by leveraging the collected climatic data, Temperature (Both Max and Mini), RH, sunshine hour, and wind speed from EMI.

B.     Remote sensing procedures.

For estimating ETO, SEBAL model will be employed, it is the most successful method to estimate ETO for the study area according to the literature examined.

Crop evapotranspiration.

The crop evapotranspiration (ET_crop) will be estimated daily for crop monitoring by the equation below:

ET_crop = ETo × Kc

Where:

ET_crop = crop evapotranspiration [mm/period]

Kc = crop coefficient

where Kc is the crop coefficient, both dual and single crop coefficient values will be considered in this study and FAO 56 steps will be followed.

Soil Moisture deficit (SMD)

SMD(RAW) = TAW (FC – PWP) – (1- p) x TAW

Amount of water to be applied.

IWR= R_d x (FC-PWP) x p

Where:

IWR = irrigation water to be applied

R_d = root depth (mm)

FC = field capacity (%)

PWP = permanent wilting point (%)

p = depletion fraction (fraction)

Net irrigation water to be applied.

Net irrigation is the difference between IWR and effective rainfall occurring in similar days. It is estimated as follows:

Net _irr = IWR − P_eff

Where:

Net _irr  = Net irrigation   [mm/period]

IWR = irrigation water to be applied [mm/period]

ETc=crop evapotranspiration [mm/period]

P_eff= effective rainfall

Several methods could be employed to estimate P_eff from the available rainfall, and for this study the USDA method will be considered because of its success in selected irrigation sites in Ethiopia.

Peff =f x (1.253P^0.824 – 2.935) x 10^0.001ETp

Where:

Peff = effective rainfall [mm/period] 

P = Total precipitation [mm/period] ETp= total crop evapotranspiration [mm/period]] f = a correction factor which depends on the depth of the irrigation water application per turn the factor f equals 1.0

Gross irrigation water demand

G_irr = Net _irr/eff

G_irr = gross irrigation requirement (mm

eff = efficiency of irrigation practices

Efficiency will be calculated using the data collected above.

Evaluate Irrigation Frequency and Timing

At this step the irrigation schedule will be compared to crop water requirements to assess the adequacy of irrigation timing. The intervals between irrigation events will also be evaluated if they match the crop's growth stage and water needs.

Irrigation interval

II =  G_irr/ETc

II = Irrigation interval (days)

The type of scheduling practiced in the area is of two types depending on the experience of the practitioners, according to the survey conducted.

a.       Crop Coefficient-Based Methods

b.      Plant indicator-Based Methods:

Dependability ratio:

·         It is used to evaluate the scheduling practice in the area and will be evaluated.

DR (Dependability ratio) =   ( Actual irrigation interval Planned)/ (required irrigation interval)

·         The relative water supply (RWS)                  

RWS =)Volume of irrigation water supplied)/(Volume of irrigation water demand

Crop Water Stress:

To evaluate whether sufficient water is supplied to the irrigated crop water stress will be calculated to see whether the actual practice provides sufficient water to meet crop needs. As it can give information about the existing practices’ performance. The WDI will be used estimated as follows.

WDI = 1 – Eta/Etc

Where:

WDI = water deficit index                                                                                                                                                                                                                

Irrigation efficiency

The following irrigation efficiency parameters influencing the performance of water management will be assessed during the study time:

a)      Distribution Uniformity (DU): Distribution uniformity measures the uniformity of water application across the irrigated area and calculated as follows.

Distribution uni(DU) = (Average depth infiltrated in the lowest one quarter of the area)/(Average depth of water infiltrated)

b)      Application Efficiency (AE): Application efficiency measures how effectively the irrigation system delivers water to the crop's root zone. It is determined by the percentage of applied water that is available for plant uptake. Factors that affect application efficiency include system design, operating pressure, nozzle selection, and maintenance practices. Advance-Cutoff-recession curve required and will be prepared.

Application efficiency (Ea.) =   (Volume of water added to the root zone)/(    Volume of water applied to the field)

c)      Water application efficiency,

eff = DU x Ea

Yield response factor (Ky)

1- (ya/ym) = Ky x [1 – (Eta/Etm)]

Where:

Ya = actual harvested yield

Ym = maximum harvested yield

Ky = yield response factor

Eta = actual evapotranspiration

ETm = maximum evapotranspiration.

Seek Stakeholder Feedback

Feedback will be gathered from farmers, irrigation managers, or other stakeholders involved in the irrigation process to understand their perspectives on the effectiveness and practicality of the scheduling practices proposed.

5.4.Calibrate and validate climate-resilient water management options.

ETO estimated from remote sensing data using SEBAL model will be calibrated using the most accurate and internationally accepted method which will be selected depending on the available data in parallel time from actual daily meteorological data of the study area. The statistical procedures of correlation and trend analysis will be employed to see the trend data obtained using the alternative option in reference to Penman-Monteith. Linear regression analysis will be applied to assess the relationship between these two methods over the study area in parallel time from actual daily meteorological data and remote sensing data. The comparison will be analyzed spatially and graphically using diagrams over the study area. Finally, RMSE, PBIAS and R² will be calculated to see whether there is correlation and similarity in the performance between SEBLE and Penman,

5.5.Developing regression model for ETO prediction

Using the ETO data from remote sensing application and the actually estimated data using the method described above a regression model will be developed for predicting ETO for a specific period of irrigation water management activity. This time will be fixed considering safe period of insignificant climate change.

5.6.Data Analysis

• Quantitative: Statistical analysis of efficiency and productivity differences (before/after or between systems); climate trend analysis.
• Qualitative: Thematic analysis of interview and Focus Group Discussion data.

6.      Timeline

SN	Phase	Duration
1	Preparation	Month 1
1.1.	Literature Review & Planning
1.2.	Materials purchase
1.3.	Enumerator’s training
1.4.	Field Data Collection (Climate, soil, water and stakeholders’ interview)	Month 2
2	Field study
2.1.	Climate Modeling & Analysis	Month 3 - 5
2.2.	Evaluation data collection (Performance data and satellite images)
2.3.	Data analysis
3	Final phase
3.1.	Validation & Stakeholder Input	Month 6
3.2.	Reporting & Dissemination

Total Duration: 6 months

7.      Ethical Considerations

• Informed consent will be obtained from all human participants.
• Ethical clearance will be secured from the relevant institutional review board.
• Data will be anonymized and used strictly for academic and developmental purposes.
• Results will be shared transparently with all stakeholders.

8.      Budget Summary (Aligned with €10,214 Goal)

SN	Category	Currency	Amount
1	Data collection	USD	1254.00
2	Materials purchase	USD	2,853.00
3	Travel and accommodation	USD	1,825.00
4	Labor cost	USD	1,531.00
5	Publication cost	USD	2,000.00
6	Unforeseen expenses	USD	751.00
Total	10,214.00

9.      Expected Outputs

1. A clear identification and assessment of the specific climate change impacts and challenges faced by the Upper watersheds,
2. A comprehensive understanding of the current water management practices, including irrigation water management techniques, in the Upper watersheds of the Awash Basin.
3. In-depth insights into the factors that contribute to inefficient irrigation water management that hinder the adoption of sustainable and climate-resilient practices in the study area. 
4. Identification and evaluation of the available climate-resilient water management options, including irrigation techniques (Remote sensing) suitable for the Upper watersheds in the context of the Awash Basin. 
5. Developing regression model for estimating water management variables, specifically ETO, leveraging the data obtained using Remote sensing information and the actual meteorological data. 
6. Context-specific recommendations will be evaluated and recommended for climate change resilient irrigation in the Upper watersheds.
7. Contribution to the existing body of knowledge on water management, climate change adaptation, sustainable development, and policy recommendation in the context of the Awash Basin and similar regions facing similar challenges.

10.  Impact and Relevance

This study contributes to the Sustainable Development Goals (SDGs):

1. SDG 2: Zero Hunger – by improving agricultural productivity
2. SDG 6: Clean Water – by enhancing water-use efficiency
3. SDG 13: Climate Action – by building local adaptation strategies

The results will support both grassroots farmers and national policymakers in scaling sustainable irrigation practices under climate uncertainty.

11.  References

1.        Taye MT, Dyer E, Hirpa FA, Charles K. Climate change impact on water resources in the Awash basin, Ethiopia. Water (Switzerland). 2018;10(11):1–16.

2.        Gedefaw M, Wang H, Yan D, Qin T, Wang K, Girma A, et al. Water resources allocation systems under irrigation expansion and climate change scenario in Awash River Basin of Ethiopia. Water (Switzerland). 2019;11(10):1–15.

3.        Zhang Z, Gong Y, Wang Z. Accessible remote sensing data based reference evapotranspiration estimation modelling. Agric Water Manag [Internet]. 2018;210(July):59–69. Available from: https://doi.org/10.1016/j.agwa...