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Addressing undesirable changes associated with the driving forces of land use cover change are critical to sustainable land management, and the future modelling of land use systems in developing countries. The study ascertains local drivers of land-use cover change in Southwestern Ghana using a mixed-method approach. The approach aided in identifying key land-use drivers, using different research strategies for comparisons through confidence level analysis and Analytic Hierarchy Process. We used expert interviews, existing literature and geostatistical tools to ascertain the driving forces triggering such unprecedented changes. Landsat imagery 5 MSS, 4 and 5 TM, 7 ETM+ and 8 OLI/TIRS were acquired from the United States Geological Survey’s website. Land use analysis revealed a decline in forests (-334.8 km2yr−1.) and areas covered by waterbodies (-4.79 km2yr−1.). A remarkable increase in built-up (+137.93 km2yr−1.) and farmlands/shrubs (+131.97 km2yr−1.) areas were also observed. The contribution rate of change analysis revealed built-up areas and increasing population contributed the most to surface temperature and land use change. A steady increase in surface temperature can be attributed to the undesirable changes associated with land-use systems over the past 50 years. Socio-economic development in Southwestern Ghana is fuelling interest in studies related to land use cover change. Biophysical, cultural and technological factors are considered key drivers despite the “medium-to-very low confidence” in results generated. They could potentially impact climate-sensitive sectors that significantly modify land-use systems from the pessimists and optimists’ perspectives. Standpoints established through this study will enrich basic datasets for further studies at the continental level.

Addressing undesirable changes associated with the driving forces of land use cover change are critical to sustainable land management, and the future modeling of land use systems in developing countries. The study accentuates local drivers of land use cover change in Southwestern Ghana using a mixed-method approach. The approach aided in identifying key land-use drivers, using different research strategies for comparisons through confidence level analysis and Analytic Hierarchy Process. We used expert interviews, existing literature and geostatistical tools to ascertain the driving forces triggering such unprecedented changes. Landsat imagery 5 MSS, 4 and 5 TM, 7 ETM + and 8 OLI/TIRS were acquired from the United States Geological Survey's website. Land-use analysis revealed a decline in forests (- 82.41%) and areas covered by waterbodies (- 27.39%). A fundamental drift in built-up (+ 1288.36%) and farmlands/shrubs (+ 369.81%) areas were also observed. The contribution rate of change analysis revealed built-environment and increasing population contributed the most to surface temperature and land-use change. A steady increase in surface temperature can be attributed to the undesirable changes associated with land-use systems over the past 50 years. Socio-economic development in Southwestern Ghana is fuelling interest in studies related to land use cover change. Biophysical, cultural and technological factors are considered key drivers despite the "medium-to-very low confidence" in results generated. They could potentially impact climate-sensitive sectors that significantly modify land-use systems from the pessimists' and optimists' perspectives. Standpoints established through this study will enrich basic datasets for further studies at the continental level.The online version contains supplementary material available at 10.1007/s12665-022-10481-y.

This study aims to evaluate the performance of the Climate Hazards Group Infrared Precipitation with Station observation Version 2 (CHIRPS v2.0), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network‐Climate Data Record (PERSIANN‐CDR) and the Climate Research Unit Time Series Version 4 (CRU) products to identify which product delivers reliable rainfall caption over Burundi. The station data with long‐term records have been used as a reference to evaluate the performance of the three products for 34 years ranging from 1983 to 2016. Statistical metrics and precipitation detection capability have been used to measure the accuracy of each product over spatial and time scales. The result analysis carried out that the CHIRPS product has good performance compared to CRU and PERSIANN‐CDR products. It performs well over annual, monthly, and seasonal scales, and it strongly agrees with ground observation over the study domain with the lowest correlation coefficient (CC) = 0.78. The PERSIANN‐CDR product performs poorly over the study domain, though it seems to correlate with in situ rain gauge data in some regions where the CC > 0.7. It highly underestimates the rainfall amount for both short rains and long rains. The CRU shows a good performance in most of the regions with CC ≥ 0.74; however, it slightly overestimates rainfall amount over the less wet area including the north and the east regions and highly underestimates the heavy rain over mountainous region. The evaluation at daily scale shows that both CHIRPS and PERSIANN‐CDR moderately perform in most of the regions except the southern and western parts where CC < 0.3. The two products exhibit a good detection ability of light rainfall (20 mm/day). The CHIRPS product can deliver reliable and useful information for monitoring meteorological hazards over Burundi.

This study aims to identify the main causes of extreme rainfall events specifically recent ones that occurred in Central East Africa, which seriously affected the western part of Burundi causing 77 deaths, destruction of infrastructures, loss of properties and hectares of lands in February, 2014. To understand underlying issues about extreme rainfall occurrence and its key drivers, the present study investigated two distinct extreme rainfall events that caused massive losses in the western part of Burundi on November 11th, 2009 and February 9th, 2014. Different datasets have been used to depict which one captured reliable information of the two extreme rainfall events investigated. Analysis of different weather parameters was conducted using the European Centre for Medium-Range Weather Forecast (ECMWF) datasets. The Weather Research and Forecasting (WRF) Model was also employed to analyse different parameters over different periods, thus prior, during, and after the occurrence of an extreme rainfall event. It was found that the two distinct rainfall events were induced by the combination of local convection enhanced by moist air from the Congo basin and abnormal conditions in the mid-upper layer. Instabilities in the lower and mid-upper layers mainly emanated from the changes in geopotential height and wind circulation. Local physical factors especially the topography and the presence of the Lake Tanganyika localized in the Western Rift Valley of Africa enhanced local convection which triggered rapid condensation that induced a heavy rainfall over the western part of Burundi. Weather conditions vary depending on the time or season of occurrence of the extreme rainfall events. This study will propel future studies on historical diagnosis and forecasting of extreme weather events over Burundi. It will also guide policy-makers to mitigate negative impacts expected from future occurrences of extreme weather events.