Gxokwe, SiyamthandaDube, TimothyMazvimavi, DominicGrenfell, Michael2023-06-152023-06-152022Gxokwe, S., Dube, T., Mazvimavi, D. and Grenfell, M., 2022. Using cloud computing techniques to monitor long-term variations in ecohydrological dynamics of small seasonally-flooded wetlands in semi-arid South Africa. Journal of Hydrology, 612, p.128080.https://doi.org/10.1016/j.jhydrol.2022.128080http://hdl.handle.net/10566/9098Wetlands in drylands have high inter- and intra-annual ecohydrological variations that are driven to a great extent by climate variability and anthropogenic influences. The Ramsar Convention on Wetlands encourages the development of frameworks for national action and international cooperation for ensuring conservation and wise use of wetlands and their resources at local, national and regional scales. However, the implementation of these frameworks remains a challenge. This is mainly due to limited availability of high-resolution data and suitable big data processing techniques for assessing and monitoring wetland ecohydrological dynamics at large spatial scales, particularly in the sub-Saharan African region. The availability of cloud computing platforms such as Google Earth Engine (GEE) offers unique big data handling and processing opportunities to address some of these challenges. In this study, we applied the GEE cloud computing platform to monitor the long-term ecohydrological dynamics of a seasonally flooded part of the Nylsvley floodplain wetland complex in north-eastern South Africa over a 20-year period (2000–2020).enArtificial intelligenceDryland wetlandDryland wetlandMachine learning algorithmWetland conditionArticle