"The angel within the devil": COVID-19 silver linings

Coronavirus disease (COVID-19) has impacted every aspect of human existence in a variety of ways. However, depending on how we interpret the impact of the pandemic, we may either despair or embrace challenges with hope. Several empirical findings and expert opinions have highlighted the significant negative impact of COVID-19 on economy, health and wellbeing, education, ecosystem and governance around the world. Amid all these negative effects on human existence, we claim that there are some silver linings across several domains such as health and wellbeing, education, eco-system and social connectedness, with the main benefit being adherence to public health measures which will be retained beyond the pandemic

Impact of climate change on groundwater recharge in the lake Manyara catchment, Tanzania

This research article is published in scientific Africa volume 15Groundwater account for about 60 to 80% of water supply to the population of Tanzania's semi-arid regions for domestic and agriculture uses. Despite the importance of groundwater resource in semi-arid areas, limited information exists on the recharge amount and potential recharge zones in Tanzania in the context of climate change which could result in unsustainable withdrawals. This study aimed to estimate the potential impact of climate change on groundwater recharge and identify potential recharge zones in the Lake Manyara catchment using Water and Energy Transfer between Soil, Plants and Atmosphere under the quasi-steady State (WetSpass) model. The WetSpass model was setup and calibrated using hydro-meteorological data (rainfall, temperature, wind speed, potential evapotranspiration, and groundwater depth) and biophysical data (soil, land use, topography, and slope). Simulated rainfall, temperature and potential evapotranspiration from an ensemble of four CORDEX-Africa regional climate models for the period 2021–2050 under the Representative Concentration Pathway (RCP 8.5) scenario (hereafter referred as business-as-usual scenario) were used as input in the WetSpass model for the climate change impact assessment. WetSpass model calibration using the water balance equation showed a coefficient of determination (R2) value of 0.9 and Root-Mean-Square Error (RMSE) of 0.49 mm/yr between the simulated and calculated recharge. It was determined that the mean annual recharge of 53.9 mm/year (149 MCM/year) for the period 1989–2018 would increase by 7.9% in the future (2021–2050) under the business-as-usual climate scenario, due to the increase in rainfall. Seasonality and spatial differences in recharge amount were observed, with recharge projected to increase in the dry season and at areas that receive high amount of rainfall. Potential recharge zones in the catchment were found mostly around the northern part near Ngorongoro, the south-western part, and around Mbulu region. Findings from this study would help policymakers, and local stakeholders in planning and management of the groundwater resources for sustainable development

Rainfall and temperature changes under different climate scenarios at the watersheds surrounding the Ngorongoro Conservation Area in Tanzania

This research article published by Elsevier, 2022Considering the high vulnerability of Northern Tanzania to climate change, an in-depth assessment at the local scale is required urgently to formulate sustainable adaptations measures. Therefore, this study analyzed the fu- ture (2021-2050) changes in rainfall and temperature under the representative concentration pathways (RCP4.5 and RCP8.5) for the watersheds surrounding the Ngorongoro Conservation Area (NCA) at a spatio-temporal scale relative to the observed historical (1982-2011) period. The climate change analysis was performed at monthly and annual scale using outputs from a multi-model ensemble of Regional Climate Models (RCMs) and statistically downscaled Global Climate Models (GCMs). The performance of the RCMs were evaluated, and the downscaling of the GCMs were performed using Statistical Downscaling System Model (SDSM) and LARS-WG, with all the models indicating a higher accuracy at monthly scale when evaluated using statistical indicators such as corre- lation (r), Nash-Sutcliff Efficiency (NSE) and percentage bias (PBIAS). The results show an increase in the mean annual rainfall and temperature in both RCPs. The percentage change in rainfall indicated an increase relative to historical data for all seasons under both RCPs, except for the June, July, August and September (JJAS) season, which showed a decrease in rainfall. Spatially, rainfall would increase over the entire basin under both RCPs with higher increase under RCP4.5. Similar spatial increase results are also projected for temperature under both RCPs. The results of this study provide vital information for the planning and management of the studied watershed under changing climatic conditions

Local climate change projections and impact on the surface hydrology in the Vea catchment, West Africa

Water security has been a major challenge in the semi-arid area of West Africa including Northern Ghana, where climate change is projected to increase if appropriate measures are not taken. This study assessed rainfall and temperature projections and its impact on the water resources in the Vea catchment using an ensemble mean of four bias-corrected Regional Climate Models and Statistical Downscaling Model-Decision Centric (SDSM-DC) simulations. The ensemble mean of the bias-corrected climate simulations was used as input to an already calibrated and validated Soil and Water Assessment Tool (SWAT) model, to assess the impact of climate change on actual evapotranspiration (ET), surface runoff and water yield, relative to the baseline (1990–2017) period. The results showed that the mean annual temperature and actual ET would increase by 1.3 °C and 8.3%, respectively, for the period 2020–2049 under the medium CO$_{2}$ emission (RCP4.5) scenario, indicating a trend towards a drier climate. The surface runoff and water yield are projected to decrease by 42.7 and 38.7%, respectively. The projected decrease in water yield requires better planning and management of the water resources in the catchment

Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems

In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications

Rainfall Projections from Coupled Model Intercomparison Project Phase 6 in the Volta River Basin: Implications on Achieving Sustainable Development

Climate change has become a global issue, not only because it affects the intensity and frequency of rainfall but also because it impacts the economic development of regions whose economies heavily rely on rainfall, such as the West African region. Hence, the need for this study, which is aimed at understanding how rainfall may change in the future over the Sahel, Savannah, and coastal zones of the Volta River Basin (VRB). The trends and changes in rainfall between 2021–2050 and 1985–2014 under the Shared Socioeconomic Pathway (SSP2-4.5 and SSP5-8.5) scenarios were analyzed after evaluating the performance of three climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as observation. The results show, in general, a relatively high correlation and low spatial biases for rainfall (r > 0.91, −20% < Pbias < 20%) over the entire Volta Basin for the models’ ensemble mean. An increasing trend and projected increase in annual rainfall under the SSP2-4.5 scenario is 6.0% (Sahel), 7.3% (Savannah), and 2.6% (VRB), but a decrease of 1.1% in the coastal zone. Similarly, under SSP5-8.5, the annual rainfall is projected to increase by 32.5% (Sahel), +22.8% (Savannah), 23.0% (coastal), and 24.9% (VRB), with the increase being more pronounced under SSP5-8.5 compared to the SSP2-4.5 scenario. The findings of the study would be useful for planning and designing climate change adaptation measures to achieve sustainable development at the VRB

An increase in temperature under the shared socioeconomic scenarios in the Volta River Basin, West Africa: implications for economic development

This study examined the temperature variations in West Africa's Volta River Basin (VRB) from 2021 to 2050 in comparison to the historical period (1985–2014) under two Shared Socioeconomic Pathway Scenarios (SSP2-4.5 and SSP5-8.5). Datasets from three Global Climate Models (GCMs) of the sixth Coupled Model Intercomparison Project (CMIP6) were used. The GCMs and their ensemble were evaluated on a monthly scale. The study used the ensemble mean to analyse the changes in annual and monthly temperature over the Sahel, Savannah, Guinea Coast, and the entire Volta basin. The results demonstrate the individual GCMs reproduced the observed temperature pattern at the VRB, though with some overestimations, but the ensemble mean indicated a better representation of the observed temperature. A warming trend in the basin is projected under both climate scenarios, with higher temperatures projected under SSP5-8.5 compared to SSP2-4.5 in all three zones. The mean annual temperature is projected to increase by 0.8 and 1.0 °C, with a statistically increasing trend under SSP2-4.5 and SSP5-8.5, respectively. Without a doubt, high temperatures, if unchecked, can erupt into resource conflict among the competing interest groups, thereby affecting the achievement of economic development at the VRB. HIGHLIGHTS The study contributes to understanding climate change impacts on economic development.; Temperature in VRB to rise up to 1 °C by 2050.; Temperature rise will lead to water shortages, extinction of fish species, and livelihood loss.; Temperature rise could lead to droughts, pest invasion, reduced crop yields, food security, and economic growth.; VRB is home to large hydropower dams, which could be affected by temperature rise.