Developing a Combined Drought Index to Monitor Agricultural Drought in Sri Lanka
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Study Area
2.2. Data Used
2.2.1. Precipitation
2.2.2. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST)
2.2.3. Evaporative Demand Drought Index (EDDI)
2.2.4. Agricultural Land Use
2.2.5. Crop Yield
2.2.6. Streamflow Data
2.3. Method
- Standardize all the input variables based on long-term weekly mean and standardization values.
- Estimate the eigenvectors/eigenvalues used to transform each variable into separate orthogonal principal components (PCs).
- Determine the percentage contributions (weight) of each input variable in PC1 (a total of 52 grid-based maps of the percentage contribution of each input variable were developed and used as a weight to combine the input variables into a single combined drought index).
- Compute the weighted sum of the input variables using Equation (1) and generate the time series CDI maps, then normalize to minimize the higher degree of variability if it exists in some weeks.
- Assess the spatial and temporal patterns of drought.
- Evaluate the agricultural drought maps using independent datasets, including crop yield and streamflow-based SRI.
2.4. Spatial and Temporal Assessment of Drought
2.5. Evaluation of agCDI
3. Results and Discussions
3.1. Identifying the Historical Drought Years
3.2. Frequency of Occurrence of Drought
3.3. Spatial Patterns of Drought
3.4. Evaluation of Drought with Crop Yield and SRI
4. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Sno | Input Variables | Resolution | Data Length | Source | |
---|---|---|---|---|---|
Spatial | Temporal | ||||
1 | CHIRPS or other satellite or observed rainfall (SPI- at various time scales) | 5 km | daily | 1982-present | University of California, Santa Barbara |
2 | Normalized difference vegetation index (NDVI) | 4 km | weekly | 1982-present | NOAA STAR |
3 | Land surface temperature (LST) | 4 km | weekly | 1982-present | NOAA STAR |
4 | Evaporative demand drought Index (EDDI) | 12.5 km | Monthly | 1982-present | NOAA |
Land Use Type | Area Coverage (%) |
---|---|
Paddy | 13 |
Crop lands | 5 |
Vegetated area | 16 |
Perennial agriculture—rubber | 4 |
Perennial agriculture—tea | 3 |
Perennial agriculture—coconut | 6 |
Woody perennial crops | 17 |
Other land uses | 28 |
Water bodies | 7 |
SPI Values | Drought Category |
---|---|
−2.00 and less | Extreme drought |
−1.50 to −1.99 | Severe drought |
−1.00 to −1.49 | Moderate drought |
0 to −0.99 | Near normal or mild drought |
Above 0 | No drought |
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Bayissa, Y.; Srinivasan, R.; Joseph, G.; Bahuguna, A.; Shrestha, A.; Ayling, S.; Punyawardena, R.; Nandalal, K.D.W. Developing a Combined Drought Index to Monitor Agricultural Drought in Sri Lanka. Water 2022, 14, 3317. https://doi.org/10.3390/w14203317
Bayissa Y, Srinivasan R, Joseph G, Bahuguna A, Shrestha A, Ayling S, Punyawardena R, Nandalal KDW. Developing a Combined Drought Index to Monitor Agricultural Drought in Sri Lanka. Water. 2022; 14(20):3317. https://doi.org/10.3390/w14203317
Chicago/Turabian StyleBayissa, Yared, Raghavan Srinivasan, George Joseph, Aroha Bahuguna, Anne Shrestha, Sophie Ayling, Ranjith Punyawardena, and K. D. W. Nandalal. 2022. "Developing a Combined Drought Index to Monitor Agricultural Drought in Sri Lanka" Water 14, no. 20: 3317. https://doi.org/10.3390/w14203317