Projections of meteorological drought severity-duration variations based on CMIP6.

This research utilized the outputs from three models of the Coupled Model Intercomparison Project Phase 6 (CMIP6), specifically CanESM5, GFDL-ESM4, and IPSL-CM6A-LR. These models were used under the SSP1-2.6 and SSP5-8.

Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems.

Soil temperature is a key meteorological parameter that plays an important role in determining rates of physical, chemical and biological reactions in the soil. Ground temperature can vary substantially under different land cover types and climatic conditions. Proper prediction of soil temperature is thus essential for the accurate simulation of land surface processes.