Cloud Computing for Water and Climate Big Data Analysis on the African Continent From 1958 To 2021.

The main objective of this paper is to use Google Earth Engine (GEE) as a cloud computing platform to analyze the water and climate big data of Africa from January 1958 to December 2021. The TerraClimate dataset of monthly climate and water balance for African terrestrial surfaces from 1958–2022 with 1/24° spatial resolution (about 4 km) is targeted to be analyzed. Both primary Climate Variables, i.e., Maximum temperature (Tx), minimum temperature (Tm), vapor pressure (vap), precipitation accumulation (pr), downward surface shortwave radiation (srad), and wind speed (vs), and Derived Variables, i.e., reference evapotranspiration (pet), runoff (ro), actual evapotranspiration (aet), climate water deficit (def), soil moisture (soil), snow water equivalent (swe), Palmer Drought Severity Index (pdsi), and vapor pressure deficit (vpd). TerraClimate uses climatically assisted interpolation to produce a monthly dataset. TerraClimate also generates surface water balance information every month based on pet, pr, Tx, Tm, and estimated plant extractable soil water capacity. An annual Tx, Tm, pr, and pet were used to confirm TerraClimate's spatiotemporal features. Compared to coarser resolution gridded datasets, TerraClimate datasets have lower overall mean absolute error and higher spatial fidelity. The GEE platform was used to analyze the TerraClimate dataset parameters. All parameters have been analyzed except swe; Africa is the highest continent in temperature and solar radiation; after all, snow areas in Africa are not expected. The entire dataset's minimum, maximum, and mean analyses are processed, and maps for all mentioned parameters are produced to represent the African continent's lack, potential, and standard conditions, respectively.