How can we transform water management in Upper Awash through remote sensing?

Methods

Summary

This project employs a mixed-method approach combining quantitative analysis of climatic and irrigation performance data with qualitative inputs from stakeholders to develop climate-resilient irrigation water management solutions in Ethiopia’s Upper Awash Basin.

Techniques and Protocols:

1. Data Collection and Sampling:

• Stratified sampling of irrigation schemes based on intensity (≥200%, 100–199%, ≤99%)
• Long-term climatic data from the Ethiopian Meteorological Institute (EMI)
• Field observations, stakeholder interviews, and remote sensing imagery

2. ET₀ Estimation and Climate impact analysis:

• Depending on the available climate data FAO-recommended methods will be employed for reference evapotranspiration (ET₀) calculation
• SEBAL (Surface Energy Balance Algorithm for Land) for satellite-based ET₀ estimation
• Trend analysis and linear regression to assess climate variability impact

3. Irrigation Performance Evaluation:

• Parameters include ETcrop, soil moisture deficit, net irrigation requirement, and effective rainfall (USDA method)
• Evaluation of irrigation interval, application efficiency, distribution uniformity, and yield response factor (Ky)
• Water Deficit Index (WDI) and Relative Water Supply (RWS) calculated to assess sufficiency of irrigation

1. Stakeholder Engagement:

• Farmers and irrigation managers will be consulted via surveys and focus groups to validate and adapt proposed schedules

4. Model Calibration and Validation:

• SEBAL-based ET₀ calibrated against Penman-Monteith method using RMSE, PBIAS, and R²
• Spatial validation with ArcPro for mapped analysis

5. Predictive Model Development:

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.

Reproducibility

This project integrates well-documented tools and publicly available datasets:

Acceptable ETo estimation methods depending on the available climate data, which will be collected from Ethiopian Meteorological Institute (EMI)

• SEBAL model implementation guides: SEBAL Technical Info (WUR)
• Climate Data: Ethiopian Meteorological Institute will be expected to provide the long-term climate data.
• Remote Sensing Tools: Landsat 8 & 9 imagery via USGS Earth Explorer will be considered.

Challenges

Anticipated project challenges and risk mitigation strategies

1. Data Availability and Quality
2. Limited Stakeholder Engagement or Participation
3. Climatic and Environmental Variability
4. Institutional and Logistical Constraints
5. Model Calibration and Validation Errors

Mitigation strategies:

1. Use of multiple data sources (e.g., EMI, satellite platforms such as Landsat and MODIS), fill data gaps using interpolation techniques and cross-validation with station-based records, leverage open-access remote sensing databases to supplement missing ground data.
2. Engage stakeholders early and continuously, explaining the benefits of the study, Coordinate with local extension offices and development agents for smoother facilitation.
3. Include climate resilience indicators to strengthen the robustness of proposed irrigation practices.
4. Plan well in advance and obtain institutional permissions early, establish partnerships with local universities, irrigation authorities, and regional bureaus and maintain a flexible work plan and include contingency resources for travel and equipment.
5. Use RMSE, PBIAS, and R² for model validation and ensure parallel temporal datasets are used and conduct sensitivity analysis and incorporate expert reviews to refine model outputs.

By proactively identifying these challenges and preparing mitigation strategies, the project aims to ensure reliable, scientifically sound, and practically useful outcomes for improving irrigation water management in Ethiopia's Upper Awash Basin.

Pre Analysis Plan

This pre-analysis plan outlines how the project data will be analyzed to assess climate change impacts on irrigation water management and evaluate the performance of existing practices in Ethiopia’s Upper Awash Basin. The aim is to develop data-driven, climate-resilient irrigation management strategies.

1. Hypotheses to Be Tested 
   1. H1: Climate change has significantly influenced reference evapotranspiration (ET₀) trends over the past four decades.
   2. H2: Existing irrigation water management practices in the study area are inefficient and do not meet crop water requirements.
   3. H3: SEBAL remote sensing models provide reliable estimates of ET₀ comparable to ground-based Penman-Monteith methods.
   4. H4: Improved irrigation scheduling, based on accurate ET₀ and crop water demand, increases water use efficiency and reduces water stress.
2. Data Analysis Methods
   1. ET₀ Trend Analysis
      1. Estimate ET₀ using both with method suitable according to available climate and SEBAL (with satellite data).
      2. Use trend analysis tool using R considering Mann-Kendall tests to detect long-term trends in ET₀ and assess seasonal and annual changes.
   2. Irrigation Performance Evaluation
      1. Calculate key performance indicators including:
         1. Net Irrigation Requirement (NIR)
         2. Effective Rainfall (Peff) using the USDA method
         3. Water Deficit Index (WDI)
         4. Application Efficiency (Ea) and Distribution Uniformity (DU)
         5. Relative Water Supply (RWS) and Yield Response Factor (Ky)
      1. Evaluate scheduling practices using:
         1. Irrigation Interval Analysis
         2. Dependability Ratio (DR) comparing planned vs. actual intervals
   3. Calibration and Validation
      1. Validate SEBAL ET₀ estimates against the estimated values with climate data:
         1. Root Mean Square Error (RMSE)
         2. Percent Bias (PBIAS)
         3. Coefficient of Determination (R²)
      2. Perform regression analysis and plot scatter diagrams to assess the level of agreement. 
   4. Predictive Modeling 
      1. Develop a regression model to predict ETa using available historical climate variables (temperature, humidity, wind, sunshine) from ET₀ obtained from real time analysis using SEBAL.
3. Managing Multiple Outcomes
   1. Use stratified analysis across different irrigation intensity zones (high, medium, low).
   2. Apply multivariate techniques (Principal Component Analysis or stepwise regression) to identify the most influential variables affecting irrigation efficiency. 
   3. Present results disaggregated by region, method, and crop type to manage data complexity. 
4. Addressing Variance and Uncertainty
   1. Anticipate data gaps and inconsistencies in meteorological records and address them through: 
      1. Interpolation, where applicable 
      2. Cross-validation with satellite datasets
   2. Conduct sensitivity analysis to test robustness of key findings under different assumptions. 
   3. Use confidence intervals and error bars to communicate uncertainty in estimates.

1. Outputs and Visualization
   1. Use GIS mapping tools (ArcPro) to spatially present irrigation performance and ET₀ variability. 
   2. Create charts and dashboards for visual comparison of remote sensing vs. ground-based methods. 
   3. Report all findings following international best practices (considering, FAO, IPCC standards) to ensure transparency and reproducibility.

This pre-analysis plan ensures that the study results will be analytically sound, transparent, and practically useful for improving irrigation water management in climate-stressed environments. Let me know if you would like this formatted into a Word document or presentation slide.