DefinePK

Research Objective

To explore the feasibility of using cloud computing and open data sources, specifically Google Earth Engine (GEE) and the Soil Conservation Service Curve Number (SCS CN) method, to estimate runoff in the Mangla watershed.

Methodology

The study developed an algorithm in Google Earth Engine to integrate precipitation data (CHIRPS, GPM, TRMM), MODIS land use/land cover (LULC) data, and USDA soil texture data to classify hydrological soil groups. The SCS CN method was applied to estimate runoff, considering antecedent moisture conditions (AMC). The model was applied to the Mangla watershed from 2005 to 2015. Model accuracy was validated using meteorological data from the Pakistan Meteorological Department (PMD), Water and Power Development Authority (WAPDA), and Climate Forecast System Reanalysis (CFSR). A sensitivity analysis of SCS CN model parameters was also conducted.

Methodology Flowchart

                        graph TD
    A[Data Collection: Precipitation CHIRPS, GPM, TRMM, LULC MODIS, Soil Texture USDA] --> B[GEE Algorithm Development: Integrate Datasets];
    B --> C[Classify Hydrological Soil Groups];
    C --> D[Generate Curve NumberCN Map];
    D --> E[Assess Antecedent Moisture ConditionAMC];
    E --> F[Estimate Daily Runoff using SCS CN Method];
    F --> G[Apply to Mangla Watershed 2005-2015];
    G --> H[Validate Results with Meteorological Data: PMD, WAPDA, CFSR];
    H --> I[Perform Sensitivity Analysis of SCS CN Parameters];
    I --> J[Analyze and Interpret Findings];
    J --> K[Draw Conclusions and Recommendations];                    

Discussion

The study highlights the effectiveness of Google Earth Engine in processing large geospatial datasets for hydrological modeling, overcoming limitations of traditional computing. The integration of multiple precipitation datasets (CHIRPS, GPM, TRMM) provided a comprehensive view of rainfall-runoff dynamics in the monsoon-affected Mangla watershed. Validation against ground-based meteorological data confirmed the reliability of the GEE-based SCS CN model, with CHIRPS showing superior accuracy. The sensitivity analysis underscored the importance of accurate CN values for reliable runoff estimation. Recommendations are made to improve the model's accuracy by adjusting CN values for snow-covered areas using temperature data.

Key Findings

- The developed GEE algorithm successfully integrated multiple datasets for hydrological modeling.
- Over the 2005-2015 period, significant fluctuations in average rainfall and runoff were observed, with notable seasonal patterns.
- In 2015, the highest average precipitation was 1412.194 mm, resulting in an average runoff of 215.021 mm.
- In 2009, the lowest average precipitation was 672.808 mm, with an average runoff of 78.476 mm.
- CHIRPS consistently demonstrated better accuracy in 2008, 2009, and 2010 compared to GPM and TRMM, with accuracies of 90%, 79%, and 86% respectively.
- The SCS CN model is more sensitive to changes in Curve Number (CN) values than to changes in initial abstraction (Ia) values.

Conclusion

This research successfully demonstrated the integration of multiple datasets in Google Earth Engine for advanced hydrological modeling using the SCS CN method. The developed model provides valuable insights into hydrological processes in monsoon-affected regions and offers a scalable approach for water resource management.

Fact Check

- The study period for rainfall-runoff analysis in the Mangla watershed was from 2005 to 2015. (Confirmed in text)
- In 2015, the highest average precipitation recorded was 1412.194 mm, resulting in an average runoff of 215.021 mm. (Confirmed in text)
- In 2008, CHIRPS demonstrated 90% accuracy in runoff estimation compared to ground data. (Confirmed in text)

Mind Map