Integrating WaPOR and GEE for IGwA

6. Chapter 5: Model Validation -Spatial and Seasonal Analysis per field

Subtitles:
5.1 Data Integration Techniques
5.2 Model Validation for Groundwater Analysis
5.3 Ensuring Data Reliability

Content:

• Integration of spatial and observational data for consistent modeling.
• Techniques to validate groundwater recharge and abstraction models.
• Methods for improving model reliability and accuracy.

Challenges and Future Directions

While this model provides a robust framework for estimating irrigation groundwater abstraction and recharge, certain limitations warrant further exploration:

1. Integration of AETI and Crop Classification: A direct link between AETI values and classified crop types can improve abstraction estimates.

2. Spatial and Temporal Variability: High-resolution data on irrigation practices and crop phenology can enhance temporal accuracy.

3. Irrigation Efficiency: Incorporating region-specific irrigation practices and efficiencies could refine the model further.

4. Soil Parameter Variability: Using global datasets introduces uncertainties due to regional variations in soil properties. More localized soil data could improve model accuracy.

5. Temporal Resolution: The iterative nature of the Thornthwaite-Mather procedure relies on monthly data, which may not capture finer temporal recharge dynamics. Higher-resolution temporal data would enhance precision.

6. Climate Variability: Recharge patterns are sensitive to changing precipitation and evapotranspiration trends under climate change scenarios. Adapting the model to account for these shifts is essential for future studies.

7. Interplay Between Abstraction and Recharge: High abstraction rates can reduce the soil moisture available for recharge. Coupling abstraction and recharge models more tightly could yield a more holistic understanding of groundwater dynamics.