In many developing countries, soil moisture data is often limited or unavailable. Stakeholders may use soil moisture data to monitor, detect and sometimes forecast agricultural droughts. Therefore, modelling soil moisture to identify and monitor droughts can benefit many countries, especially those whose economies depend heavily on agriculture. This study aimed to investigate historical soil moisture in Guyana from 1962 to 2016 using the JULES Model. The model utilized in-situ precipitation data from Georgetown, along with data from the National Centers for Environmental Prediction (NCEP) for the study period. The results from the analysis of the historical soil moisture found that the soil moisture in the total column and at the various layers of soil was quite variable. Further, a clear relationship emerged between interannual climate variability and soil moisture trends. El Niño years were usually related with reduced soil moisture, indicating drier than average conditions, while the La Niña years displayed higher soil moisture content, reflecting wetter conditions. Based on the results obtained the model showed some skill in capturing soil moisture at different depths in the area of study. Hence, further studies using different soil types, vegetation structures, and land use practices should be considered to support Guyana’s agricultural sector.
Donessa L. David (Wed,) studied this question.