TY  - JOUR
AU  - Hamarash, Hushiar Raheem
AU  - Rasul, Azad
AU  - Hamad, Rahel
PY  - 2026
DA  - 2026/06/12
TI  - A Systematic Review of Methods Used to Monitor and Predict Agricultural, Hydrological, and Meteorological Droughts
JO  - Journal of Geoscience and Earth Observation
T2  - Journal of Geoscience and Earth Observation
JF  - Journal of Geoscience and Earth Observation
VL  - 1
IS  - 1
SP  - 69
EP  - 84
DO  - 10.62762/JGEO.2026.930918
UR  - https://www.icck.org/article/abs/JGEO.2026.930918
KW  - drought monitoring
KW  - drought prediction
KW  - drought indices
KW  - machine learning
KW  - remote sensing
KW  - ENSO
KW  - climate variability
AB  - Droughts are among the most economically and ecologically destructive natural hazards, affecting more than half of the world's land surface annually and increasing in frequency and severity under anthropogenic climate change. Unlike episodic disasters such as earthquakes or hurricanes, droughts develop gradually and their full impacts may not be evident until months or years after peak conditions. This systematic review synthesises current knowledge of drought monitoring and prediction methods, encompassing approximately 27 drought indices—spanning meteorological, agricultural, hydrological, and remote sensing categories—and six major classes of prediction model: regression, time series, probabilistic, hybrid, machine learning, and deep learning. A structured literature search was performed across the Scopus, Web of Science, and Google Scholar databases. Following PRISMA-aligned screening, 95 primary studies were included. Results indicate that no single index performs optimally across all drought types, climates, or timescales. Multi-variable indices that integrate precipitation, evapotranspiration, and remote sensing data outperform univariate indices in complex settings. Among prediction models, hybrid architectures—most notably ARIMA-LSTM and wavelet-ANN combinations—consistently outperform individual models on both short- and long-term forecasting tasks, while deep learning approaches show strong potential but require large training datasets. Key research gaps include the limited integration of climate teleconnection indices into operational forecasting systems, the absence of standardised benchmarking across drought prediction studies, and the need for improved spatio-temporal modelling frameworks. This review provides a foundation for researchers and practitioners developing next-generation drought early warning systems.
SN  - pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Hamarash2026A,
  author = {Hushiar Raheem Hamarash and Azad Rasul and Rahel Hamad},
  title = {A Systematic Review of Methods Used to Monitor and Predict Agricultural, Hydrological, and Meteorological Droughts},
  journal = {Journal of Geoscience and Earth Observation},
  year = {2026},
  volume = {1},
  number = {1},
  pages = {69-84},
  doi = {10.62762/JGEO.2026.930918},
  url = {https://www.icck.org/article/abs/JGEO.2026.930918},
  abstract = {Droughts are among the most economically and ecologically destructive natural hazards, affecting more than half of the world's land surface annually and increasing in frequency and severity under anthropogenic climate change. Unlike episodic disasters such as earthquakes or hurricanes, droughts develop gradually and their full impacts may not be evident until months or years after peak conditions. This systematic review synthesises current knowledge of drought monitoring and prediction methods, encompassing approximately 27 drought indices—spanning meteorological, agricultural, hydrological, and remote sensing categories—and six major classes of prediction model: regression, time series, probabilistic, hybrid, machine learning, and deep learning. A structured literature search was performed across the Scopus, Web of Science, and Google Scholar databases. Following PRISMA-aligned screening, 95 primary studies were included. Results indicate that no single index performs optimally across all drought types, climates, or timescales. Multi-variable indices that integrate precipitation, evapotranspiration, and remote sensing data outperform univariate indices in complex settings. Among prediction models, hybrid architectures—most notably ARIMA-LSTM and wavelet-ANN combinations—consistently outperform individual models on both short- and long-term forecasting tasks, while deep learning approaches show strong potential but require large training datasets. Key research gaps include the limited integration of climate teleconnection indices into operational forecasting systems, the absence of standardised benchmarking across drought prediction studies, and the need for improved spatio-temporal modelling frameworks. This review provides a foundation for researchers and practitioners developing next-generation drought early warning systems.},
  keywords = {drought monitoring, drought prediction, drought indices, machine learning, remote sensing, ENSO, climate variability},
  issn = {pending},
  publisher = {Institute of Central Computation and Knowledge}
}