Multi Index Climate Drought Assessment (MICDA) Using Hybrid ML Weightings – A Spatio-temporal analysis for the Semi-Arid Watershed in Eastern Ghats, India (2000–2024)
DOI:
https://doi.org/10.54386/jam.v28i3.3484Keywords:
Combined Drought Index (CDI), Remote sensing, Principal component analysis (PCA), RF, MLR, Google Earth Engine, Geo-informatics frameworkAbstract
Drought is a severe climatic hazard that affects the ecology and environment. It occurs when there is a prolonged period of low precipitation, increased evapotranspiration demand, poor vegetation health, low soil moisture, and limited surface water availability. Similar observations were noticed in the watersheds of Eastern Ghats, India. A comprehensive Combined Drought Index (CDI) for the ARG watershed (1,186.23 km²) using Principal Component Analysis (PCA), ML models, and multi-decadal remote-sensing data (2000-2024) was carried out. Six drought indicators, SPI3, ETA, VHI, SMAI, MNDWI, and FAPAR were used for CDI generation. CDI weights were calculated using PCA driven Random Forest (RF) and Multiple Linear Regression (MLR) model. RF showed superior performance (R = 0.830; r² = 0.701). Spatiotemporal analysis shows 6 of 25 years with drought conditions, associated with high temperatures (34-36 °C) and unpredictable monsoon precipitation (<1000 mm). The drought analysis in the catchment and command area along with trend analysis shows recovery patterns. The long-term satellite data was used for scalable drought monitoring for Multi Indicator Climate Drought Assessment (MICDA). The PCA–RF-based CDI supports Sustainable Development Goals SDG-6, SDG-13, and SDG-15 by utilizing data to plan for watershed resilience and monitor drought in real time.
References
Bera, D., Dutta, D., Poddar, S., & Kundu, A. (2025). Meteorological drought dynamics and climatic interactions in the arid and semi-arid regions of western India. Journal of Environmental Management, 387, 125836. https://doi.org/10.1016/j.jenvman.2025.125836
Berhail, S., & Katipoğlu, O. M. (2024). Exploring hydrological and meteorological drought trends in Northeast Algeria: Implications for water resource management. Theoretical and Applied Climatology, 155, 9689–9712. https://doi.org/10.1007/s00704-024-05207-y
Bhuiyan, C., & Kogan, F. N. (2010). Monsoon variation and vegetative drought patterns in the Luni Basin in the rain-shadow zone. International Journal of Remote Sensing, 31, 3223–3242. https://doi.org/10.1080/01431160903159332
Burka, A., Biazin, B., & Bewket, W. (2024). Spatial drought occurrences and distribution using VCI, TCI, VHI, and Google Earth Engine in Bilate River Watershed, Rift Valley of Ethiopia. Geomatics, Natural Hazards and Risk, 15. https://doi.org/10.1080/19475705.2024.2377672
Cabello-Solorzano, K., Ortigosa de Araujo, I., Peña, M., et al. (2023). The impact of data normalization on the accuracy of machine learning algorithms: A comparative analysis. Lecture Notes in Networks and Systems, 750, 344–353. https://doi.org/10.1007/978-3-031-42536-3_33
Chuphal, D. S., Kushwaha, A. P., Aadhar, S., & Mishra, V. (2024). Drought atlas of India, 1901–2020. Scientific Data, 11. https://doi.org/10.1038/s41597-023-02856-y
Dakhil, F. T., Sabtu, N. B., & Khalaf, A. G. (2026). Assessment of agricultural and meteorological drought in Southern Iraq’s wetlands using vegetation condition and drought indices. Journal of Agrometeorology, 28(1), 32–37. https://doi.org/10.54386/jam.v28i1.3161
Giridharan, N., & Sivakumar, R. (2024). Estimation and monitoring on fraction of absorbed photosynthetically active radiation (FPAR) changes in Sathyamangalam Reserve Forest. Lecture Notes in Civil Engineering, 398, 215–226. https://doi.org/10.1007/978-981-99-6229-7_18
Gumma, M. K., Nelson, A., & Yamano, T. (2019). Mapping drought-induced changes in rice area in India. International Journal of Remote Sensing, 40, 8146–8173. https://doi.org/10.1080/01431161.2018.1547456
Hao, C., Zhang, J., & Yao, F. (2017). Multivariate drought frequency estimation using copula method in Southwest China. Theoretical and Applied Climatology, 127, 977–991. https://doi.org/10.1007/s00704-015-1678-5
Kanji, S., & Das, S. (2025). Assessing predictability of spatio-temporal soil-moisture heterogeneity with satellite-data using GeoDetector and ensemble machine learning models considering environmental and climatic factors. Journal of Hydrology, 663, 134312. https://doi.org/10.1016/j.jhydrol.2025.134312
Kannan, P. R. G., Govindasamy, R., & Radhika Panchabikesan, V. A. (2025). Hydrological drought assessment using drought indices in semi-arid zones of Vaippar river sub basins, Tamil Nadu, India. Theoretical and Applied Climatology, 156, 519. https://doi.org/10.1007/s00704-025-05766-8
Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15, 91–100. https://doi.org/10.1016/0273-1177(95)00079-T
Kulkarni, S. S., Wardlow, B. D., Bayissa, Y. A., Tadesse, T., Svoboda, M. D., & Gedam, S. S. (2020). Developing a remote sensing-based combined drought indicator approach for agricultural drought monitoring over Marathwada, India. Remote Sensing, 12. https://doi.org/10.3390/rs12132091
Liu, W. T., & Kogan, F. N. (1996). Monitoring regional drought using the vegetation condition index. International Journal of Remote Sensing, 17, 2761–2782. https://doi.org/10.1080/01431169608949106
Mckee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the Eighth Conference on Applied Climatology, (pp. 17–22).
Mondal, S., Arun Prasad, K., Balasubramani, K., & Kaliraj, S. (2024). Assessment of agricultural drought severity in the Southern Agroclimatic Region of Tamil Nadu using remote sensing-based combined drought index. In 2024 IEEE India Geoscience and Remote Sensing Symposium (InGARSS 2024). Institute of Electrical and Electronics Engineers.
Parthsarthi Pandya, N., Gontia, N. K., & Parmar, H. V. (2022). Development of PCA-based composite drought index for agricultural drought assessment using remote sensing. Journal of Agrometeorology, 24(4), 384–392. https://doi.org/10.54386/jam.v24i4.1738
Patil, P., Jagtap, M. P., & Dakhore, K. K. (2024). Drought severity estimation using NDWI index in Parbhani district of Maharashtra. Journal of Agrometeorology, 26(2), 225–227. https://doi.org/10.54386/jam.v26i2.2540
Prasad, A., Singh, R. K., Ramana Rao, K. V., & Saxena, C. K. (2025). Applicability of machine learning models for drought prediction using SPI in Kalahandi, Odisha. Journal of Agrometeorology, 27(2), 216–220. https://doi.org/10.54386/jam.v27i2.2906
Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23, 1696–1718. https://doi.org/10.1175/2009JCLI2909.1
Wang, Q., Zhao, L., Wang, M., Wu, J., Zhou, W., Zhang, Q., & Deng, M. (2022). A random forest model for drought monitoring and validation for grassland drought based on multi-source remote sensing data. Remote Sensing, 14, 4981. https://doi.org/10.3390/rs14194981
Yu, H., Li, L., Zhu, W., Piao, D., Cui, G., Kim, M., Jeon, S., W., & Lee, W. K. (2019). Drought monitoring of the wetland in the Tumen River Basin between 1991 and 2016 using Landsat TM/ETM+. International Journal of Remote Sensing, 40, 1445–1459. https://doi.org/10.1080/01431161.2018.1524604
Ssembajwe, R., Twah, A., Kagezi, G. H., Loytty, T., Kobusinge, J., Gidudu, A., Arinaitwe, G., Du, Q., & Voda, M. (2025). Assessment and validation of FAPAR, a satellite-based plant health and water stress indicator, over Uganda. Remote Sensing, 17(20), 3501. https://doi.org/10.3390/rs17203501
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