Agroclimatic Zonning of Nigeria Based on Rainfall Characteristics and Index of Drought Proneness

Nigeria, a country in sub-Saharan West Africa that depends largely on rainfall distribution for its agricultural practices has been categorised into three major climatic zones based on its rainfall characteristics and drought-proneness analysis. The data used comprises of daily rainfall of thirty years (1983 to 2012) for the thirty-eight climatic stations spread over the country. Rainfall characteristics such as onset dates, cessation dates, length of rainy season and rainfall amount within the seasons for thirty years were extracted over each of these stations for the analysis. Rainfall distribution during the rainy season was also investigated by using two-state Markov chain analysis of order one and two. The result is useful in making some pre-sowing decisions such as site selection for a particular crop and specie selection for a particular zone. The first zone has earliest rainfall onset dates, latest cessation dates and hence, having longest length of rainy season in the country. It also has the highest (lowest) Markovian probability of a wet (dry) week after a previously wet week and hence least prone to drought occurrence. Therefore, this zone is tagged “rain-forest” (Guinea). Followed closely is the zone II which is the “Savannah” and lies on the north of the zone I. On the northern part of zone II is the zone III with the shortest length of rainy season termed “Sahel”. Despite the fact that Sahel zone has the latest onset, earliest cessation and hence shortest length of rainy season, it is most prone to drought occurrence, while Savana has moderate values between those of zones I and III. Keywords: Rainfall onset, rainfall cessation, length of rainy season, drought-proneness, zones

Evaluation of the Flexural Strength, Sorption, Rheological and Thermal Properties of Corncob Plastic Composites

Plastic composites were made from corncobs and high density polyethylene (HDPE) by extrusion and evaluated. The composites were manufactured using two different screened corncob particle size fractions

Understanding the response of sorghum cultivars to nitrogen applications in the semi-arid Nigeria using the agricultural production systems simulator

The Agricultural Production Systems simulator (APSIM) model was calibrated and evaluated using two improved sorghum varieties conducted in an experiment designed in a randomized complete block, 2014–2016 at two research stations in Nigeria. The results show that the model replicated the observed yield accounting for yield differences and variations in phenological development between the two sorghum cultivars. For early-maturing cultivar (ICSV-400), the model indicated by low accuracy with root means square error (RMSE) for biomass and grain yields of 20.3% and 23.7%. Meanwhile, Improved-Deko (medium-maturing) cultivar shows the model was calibrated with low RMSE (11.1% for biomass and 13.9% for grain). Also, the model captured yield response to varying Nitrogen (N) fertilizer applications in the three agroecological zones simulated. The N-fertilizer increased simulated grain yield by 26–52% for ICSV-400 and 19–50% for Improved-Deko compared to unfertilized treatment in Sudano-Sahelian zone. The insignificant yield differences between N-fertilizer rates of 60 and 100 kgha−1 suggests 60 kgNha−1 as the optimal rate for Sudano-Sahelian zone. Similarly, grain yield increased by 23–57% for ICSV-400 and 19–59% for Improved Deko compared to unfertilized N-treatment while the optimal mean grain yield was simulated at 80 kgNha−1 in the Sudan savanna zone. In the northern Guinea savanna, mean simulated grain yield increased by 8–20% for ICSV-400 and 12–23% for Improved-Deko when N-fertilizer was applied compared to unfertilized treatment. Optimum grain yield was obtained at 40 kgha−1. Our study suggests a review of blanket recommended fertilizer rates across semi-arid environments for sorghum to maximize productivity and eliminate fertilizer losses, means of adaptation strategies to climate variability

Seasonality and trends of drivers of mesoscale convective systems in southern West Africa

Mesoscale convective systems (MCSs) are the major source of extreme rainfall over land in the tropics and are expected to intensify with global warming. In the Sahel, changes in surface temperature gradients and associated changes in wind shear have been found to be important for MCS intensification in recent decades. Here we extend that analysis to southern West Africa (SWA) by combining 34 years of cloud-top temperatures with rainfall and reanalysis data. We identify clear trends in intense MCSs since 1983 and their associated atmospheric drivers. We also find a marked annual cycle in the drivers, linked to changes in the convective regime during the progression of the West African monsoon. Before the peak of the first rainy season, we identify a shear regime where increased temperature gradients play a crucial role for MCS intensity trends. From June onward, SWA moves into a less unstable, moist regime during which MCS trends are mainly linked to frequency increase and may be more influenced by total column water vapor. However, during both seasons we find that MCSs with the most intense convection occur in an environment with stronger wind shear, increased low-level humidity, and drier midlevels. Comparing the sensitivity of MCS intensity and peak rainfall to low-level moisture and wind shear conditions preceding events, we find a dominant role for wind shear. We conclude that MCS trends are directly linked to a strengthening of two distinct convective regimes that cause the seasonal change of SWA MCS characteristics. However, the convective environment that ultimately produces the most intense MCSs remains the same

Advances in the application and utility of subseasonal-to-seasonal predictions

The joint WWRP–WCRP Subseasonal to Seasonal Prediction Project (e.g., Robertson et al. 2014) created a global repository of experimental or operational near-real-time S2S forecasts and reforecasts (hindcasts) from 11 international meteorological institutions, cohosted by ECMWF and CMA (Vitart et al. 2017). These data are publicly accessible by researchers and users (https://apps.ecmwf.int/datasets/data/s2s and http://s2s.cma.cn/index). With the exception of the fourth case study, which uses GloSea5 forecasts (MacLachlan et al. 2015), all case studies use selected S2S forecasts and reforecasts that are available from this repository, providing a consistent basis for S2S forecast skill assessment and evaluation of their utility.The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.DD gratefully acknowledges support from the Swiss National Science Foundation through project PP00P2_170523. For case study 1, ACP and WTKH were funded by the U.K. Climate Resilience Programme, supported by the UKRI Strategic Priorities Fund. RWL was funded by NERC Grant NE/P00678/1 and by the BER DOE Office of Science Federal Award DE-SC0020324. TS was funded by NERC Independent Research Fellowship (NE/P018637/1). CMG and DB were funded by the Helmholtz Young Investigator Group “SPREADOUT” Grant VH-NG-1243. Case study 2 was supported by the U.K. Global Challenges Research Fund NE/P021077/1 (GCRF African SWIFT) and the Tertiary Education Trust Fund (TETFUND) of Nigeria TETFund/DR&D/CE/NRF/STI/73/VOL.1. EO thanks Adrian Tomkins of ICTP, Italy, for his contribution. Case study 3 was undertaken as part of the Columbia World Project, ACToday, Columbia University (https://iri.columbia.edu/actoday/). Case study 4 was supported by the ForPAc (Towards Forecast-based Preparedness Action) project within the NERC/FCDO SHEAR Programme NE/P000428/1, NE/P000673/1, and NE/P000568/1. Case study 5 was undertaken as part of the International Research Applications Project, funded by the U.S. National Oceanic and Atmospheric Administration. EO thanks IRAP project colleagues at The University of Arizona, Indian Meteorological Department, Regional Integrated Multi-Hazard Early Warning System for Africa and Asia, and two of Bihar’s State Agricultural Universities for their contributions. For case study 6, CASC thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico Process 305206/2019-2 and Fundação de Amparo à Pesquisa do Estado de São Paulo Process 2015/50687-8 (CLIMAX Project) for their support. For case study 7, DW’s contributions were carried out under contract with the National Aeronautics and Space Administration. Case study 8 was funded by the EU Horizon 2020 Research and Innovation Programme Grant 7767874 (S2S4E). We also acknowledge the Subseasonal-to-Seasonal Project’s Real-Time Pilot Initiative for providing access to real-time forecasts. For case study 9, TIC-LCPE Hydro-04 was funded by the University of Strathclyde’s Low Carbon Power and Energy program. JB was supported by EPSRC Innovation Fellowship EP/R023484/1. We thank Andrew Low and Richard Hearnden from SSE Renewables for their input. Case study 10 was supported by the Earth Systems and Climate Change Hub under the Australian Government’s National Environmental Science Program, and the Decadal Climate Forecasting Project (CSIRO). Case study 11 was funded by the Technologies for Sustainable Built Environments Centre, Reading University, in conjunction with the EPSRC Grant EP/G037787/1 and BT PLC. Case study 12 was funded through the framework service contract for operating the EFAS Computational Center Contract 198702 and the Copernicus Fire Danger Computations Contract 389730 295 in support of the Copernicus Emergency Management Service and Early Warning Systems between the Joint Research Centre and ECMWF.Peer Reviewed"Article signat per 60 autors/es: Christopher J. White, Daniela I. V. Domeisen, Nachiketa Acharya, Elijah A. Adefisan, Michael L. Anderson, Stella Aura, Ahmed A. Balogun, Douglas Bertram, Sonia Bluhm, David J. Brayshaw, Jethro Browell, Dominik Büeler, Andrew Charlton-Perez, Xandre Chourio, Isadora Christel, Caio A. S. Coelho, Michael J. DeFlorio, Luca Delle Monache, Francesca Di Giuseppe, Ana María García-Solórzano, Peter B. Gibson, Lisa Goddard, Carmen González Romero, Richard J. Graham, Robert M. Graham, Christian M. Grams, Alan Halford, W. T. Katty Huang, Kjeld Jensen, Mary Kilavi, Kamoru A. Lawal, Robert W. Lee, David MacLeod, Andrea Manrique-Suñén, Eduardo S. P. R. Martins, Carolyn J. Maxwell, William J. Merryfield, Ángel G. Muñoz, Eniola Olaniyan, George Otieno, John A. Oyedepo, Lluís Palma, Ilias G. Pechlivanidis, Diego Pons, F. Martin Ralph, Dirceu S. Reis Jr., Tomas A. Remenyi, James S. Risbey, Donald J. C. Robertson, Andrew W. Robertson, Stefan Smith, Albert Soret, Ting Sun, Martin C. Todd, Carly R. Tozer, Francisco C. Vasconcelos Jr., Ilaria Vigo, Duane E. Waliser, Fredrik Wetterhall, and Robert G. Wilson"Postprint (author's final draft

GCRF African SWIFT Testbed 1 Report

This document describes the activities and outcomes of the GCRF African Science for Weather Information and Forecasting Techniques (SWIFT) Weather Forecasting Testbed 1. Testbed 1 was conducted in the first part of 2019, from an operational forecasting office at IMTR Nairobi, at the Kenya Meteorological Department (KMD). Other centres connected to the Testbed by video-conference. The Testbed was designed to support SWIFT’s programme of research capability-building in the science of weather prediction. New forecasting and evaluation products were tested. The outcomes of the Testbed will be used to steer the research and development of these tools, as well as to provide meteorological case studies and to stimulate new hypotheses. Successes of Testbed 1 include the real-time use of satellite-based Nowcasting products (NWC SAF products), convection-permitting model ensembles from the UK Met Office and systematic forecast evaluation. Testbed 1 also devised and refined an effective programme of work for operational synoptic forecasting, nowcasting and evaluation, which could form the basis for new Standard Operating Procedures

Economic value and latent demand for agricultural drought forecast: Emerging market for weather and climate information in Central-Southern Nigeria

Provision of weather and climate services are expected to improve the capacity for rural households’ preparedness and response plans to weather shocks. With increase in public investments in developing and communicating weather information on local scale in Nigeria, uncertainty in timescales that meet farmers’ needs and economic value of the information is still poorly understood. It is now a policy concern on whether farmers’ preferences and demands might increase its uptake. This study analyzed the economic value, latent demand, and emerging market of weather and climate information in Central-Southern Nigeria. Farm-level cross-sectional data reveals that 76% of the respondents were willing to pay for improved weather information and early warnings in taking climate smart decisions. Within farmers who showed positive responses, 86% would pay for sub-seasonal to seasonal weather information while 38% would pay for medium and short range weather information respectively. The economic value of sub-seasonal to seasonal weather information was estimated at N1600 ($3.60) per year per capita with total aggregated value of N1.3 billion ($2.9 m) yearly for the derived savannah area. Predictive total market value of N17.43billion ($39 m) would be obtained from improved weather information in Nigeria. Simulated results of 5$193,360) for service providers. Large farm size, good farm-income, mobile phone dissemination channels, and location-specific information were drivers of farmers’ uptake decisions of weather information in the dry savannah area. The huge emerging market for improved weather information should be developed into a public–private market to efficiently facilitate uptake and use in Nigeria

Verification of satellite and model products against a dense rain gauge network for a severe flooding event in Kumasi, Ghana

Floods as a result of severe storms cause significant impacts on lives and properties. Therefore, timely and accurate forecasts of the storms will reduce the associated risks. In this study, we look at the characteristics of a storm on 28 June, 2018 in Kumasi from a rain gauge network and satellite data, and reanalysis data. The storm claimed at least 8 lives and displaced 293 people in Kumasi, Ghana. The ability of satellite and reanalysis data to capture the temporal variations of the storm was assessed using a high temporal resolution (accumulation per minute) rain gauge data. We employed the observation data from the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) rain gauges to assess the storm's onset, duration, and cessation. Subsequently, the performance of the ERA5 reanalysis and Global Precipitation Measurement (GPM) satellite precipitation estimates in capturing the rainfall is assessed. Both GPM and the ERA5 had difficulty reproducing the hourly pattern of the rain. However, the GPM produced variability that is similar to the observed. Generally, the region of maximum rainfall was located in the southern parts of the study domain in ERA5, while GPM placed it in the northern parts. The study contributes a verification measure to improve weather forecasting in Ghana as part of the objectives of the GCRF African Science for Weather Information and Forecasting Techniques (SWIFT) project

Exploring the need for developing impact-based forecasting in West Africa

While conventional weather forecasts focus on meteorological thresholds for extreme events, Impact-Based Forecasts (IBF) integrate information about the potential severity of weather impacts with their likelihood of occurrence. As IBF provides an indication of local risk, there is an increasing uptake of this approach globally. Despite the vulnerability of West Africa to severe weather, and the potential benefits of such a risk-based approach for informing disaster risk reduction, IBF remains rarely used in this region. To meet this need, three national workshops were held in Ghana, Nigeria and Senegal with forecasters, project researchers and users of Climate Information Services (CIS) from key sectors (e.g. agriculture, water resources, disaster risk reduction). In addition, a more localised district level workshop was held in Northern Ghana to explore needs at a subnational scale in Tamale District. The objectives of these workshops were to evaluate the current use of forecast products provided by National Meteorological and Hydrological Services (NMHSs) and to explore the potential for applying IBF. Findings indicate a recognition that the quality of forecast products provided by NMHSs in West Africa has substantially improved in recent years. However, challenges remain related to user understanding, clarity about forecast uncertainty, insufficient spatial and temporal resolution of forecasts leading to limited trust in forecasts. The workshops identified high demand for weather information related to storms, droughts and heatwaves in all the three countries. Dust storms were identified as having strong potential for IBF application in both Nigeria and Senegal. To increase the uptake of CIS by users in West Africa, NMHSs will need to develop and implement user-tailored IBF in their normal weather forecast approaches and improve communication channels with user communities. There is an urgent need for governments in West Africa to enhance the capacity of NMHSs to incorporate IBF as a routine forecast activity by first establishing a National Framework for Climate Services with user engagement as a key first pillar