This study examined the trends in annual rainfall and temperature extremes over the Vea catchment for the period 1985–2016, using quality-controlled stations and a high resolution (5 km) Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. The CHIRPS gridded precipitation data’s ability in reproducing the climatology of the catchment was evaluated. The extreme rainfall and temperature indices were computed using a RClimdex package by considering seventeen (17) climate change indices from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI). Trend detection and quantification in the rainfall (frequency and intensity) and temperature extreme indices were analyzed using the non-parametric Mann–Kendall (MK) test and Sen’s slope estimator. The results show a very high seasonal correlation coefficient (r = 0.99), Nash–Sutcliff efficiency (0.98) and percentage bias (4.4% and −8.1%) between the stations and the gridded data. An investigation of dry and wet years using Standardized Anomaly Index shows 45.5% frequency of drier than normal periods compared to 54.5% wetter than normal periods in the catchment with 1999 and 2003 been extremely wet years while the year 1990 and 2013 were extremely dry. The intensity and magnitude of extreme rainfall indices show a decreasing trend for more than 78% of the rainfall locations while positive trends were observed in the frequency of extreme rainfall indices (R10mm, R20mm, and CDD) with the exception of consecutive wet days (CWD) that shows a decreasing trend. A general warming trend over the catchment was observed through the increase in the annual number of warm days (TX90p), warm nights (TN90p) and warm spells (WSDI). The spatial distribution analysis shows a high frequency and intensity of extremes rainfall indices in the south of the catchment compared to the middle and northern of part of the catchment, while temperature extremes were uniformly distributed over the catchment.

The need for a detailed investigation of the Vea catchment water balance components cannot be overemphasized due to its accelerated land cover dynamics and the associated impacts on the hydrological processes. This study assessed the possible consequences of land-use change scenarios (i.e. business as usual, BAU, and afforestation for the year 2025) compared to the 2016 baseline on the Vea catchment’s water balance components using the Soil and Water Assessment Tool (SWAT) model. The data used include daily climate and discharge, soil and land use/land cover maps. The results indicate that the mean annual water yield may increase by 9.1% under the BAU scenario but decrease by 2.7% under the afforestation scenario; actual evapotranspiration would decrease under BAU but increase under afforestation; and groundwater recharge may increase under both scenarios but would be more pronounced under the afforestation scenario. These outcomes highlight the significance of land cover dynamics in water resource management and planning at the catchment.

The changes in land use and land cover (LULC) contribute to carbon emissions that lead to climate change and global warming. Obtaining information on LULC change is of necessity to ensure land transformation planning, and anthropogenic and natural impact evaluation. The objective of this study is to analyze the historical changes in LULC in the Tano River Basin of Ghana, to provide scientific information for decision-making on achieving sustainable development. A supervised classification of Landsat images (1986, 2010, and 2020) using the Random Forest algorithm was performed, as well as a post-classification comparison of LULC maps in terms of area and size. A "from-to" matrix was used to identify the changes in LULC that occurred between the periods 1986-2010, 2010-2020, and 1986-2020. The results show an overall classification accuracy of 88.9, 88.5, and 88% for LULC maps for the years 1986, 2010, and 2020, respectively. Between the period 1986 and 2020, there is a conversion of the greater part of dense forest to open forest, and subsequently to settlement and cropland as a major historical LULC change pattern in the Tano basin. Cropland and Settlement increased at the rate of 24.8 km/yr and 1.5 km/yr respectively, while Dense forest and Open forest decreased at the rate of 298.4 km/yr and 173.9 km/yr over the period 1986 to 2020. The study outputs are not only useful for formulating and implementing national policies and programs but also, can contribute to assessing and monitoring progress towards attaining Sustainable Development Goal 13 (climate action).

HIGHLIGHTS. Variations in the mean annual rainfall projection over the catchment were found, with 66 and 33% of the models indicating an increase and decrease,

This study analyzed the trends and spatio-temporal variability in rainfall and temperature, and the length of the rainy season (LRS) in the Lake Manyara catchment, Tanzania, covering a period between 1988 and 2018 using stations and satellite climate product. The Mann-Kendall statistical test, Sen's slope estimator, and inverse distance weighting interpolation techniques were used to detect the trends, magnitude of trends and spatial distribution of rainfall and temperature. A modified Stern's method and water balance concept were used for rainfall onset, cessation and LRS analysis, while a standardized precipitation index (SPI) was used to investigate the wetness or dryness of the area. The results showed high variability and decreasing trend (4 mm/y) in annual rainfall, and non-significant increasing trend for minimum and maximum temperature. Rainfall increased from the western to the northern part of the catchment whereas a reversal pattern was noticed for temperature. The SPI shows a signal of normal condition (about 65%) for all stations – with few years showing evidence of wetter and drier conditions. The LRS showed a decreasing trend indicating a potential negative influence on rain-dependent activities. There is a need, therefore, for adaptation measures such as improving water productivity and irrigation at the farm and catchment level.