Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios

Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment

Global warming upsets the environmental balance and leads to more frequent and severe climatic events. These extreme events include floods, droughts, and heatwaves. These widespread extreme events disrupt various sectors of ecosystems directly. However, among all these events, drought is one of the most prolonged climatic events that significantly destroys the ecosystem. Therefore, accurate and efficient assessment of droughts is necessary to mitigate their detrimental impacts. In recent years, several drought indices based on global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) have been proposed to quantify and monitor droughts. However, each index has its advantages and limitations. As each index ensembles different models by using different statistical approaches, it is well known that the margin of error is always a part of statistics. Therefore, this study proposed a new drought index to reduce the uncertainty involved in the assessment of droughts. The proposed index named the Ridge Ensemble Standardized Drought Index (RESDI) is based on the innovative ensemble approach termed ridge parameters and distance-based weighting (RDW) scheme. And the development of this RDW scheme is based on two types of methods i.e., ridge regression and divergence-based method. In this research, we ensemble 18 different GCMs of CMIP6 using the RDW scheme. A comparative analysis of the RDW scheme is performed against the simple model average (SMA) and Bayesian model averaging (BMA) schemes at 32 locations on the Tibetan plateau. The comparison revealed that RDW has less mean absolute error (MAE) and root-mean-square error (RMSE). Therefore, the developed RESDI based on RDW is used to project drought properties under three distinct shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, across seven different time scales (1, 3, 7, 9, 12, 24, and 48). The projected data is then standardized by using the K-components Gaussian mixture model (K-CGMM). In addition, the study employs steady-state probabilities (SSPs) to determine the long-term behavior of drought. The outcome of this research shows that "normal drought (ND)" has the highest probability of occurrence under all scenarios and time scales.

Spatial Assessment of Land Suitability Potential for Agriculture in Nigeria

From land cover analysis, cropland expansion was a major driving factor for land use land cover changes in Nigeria from 2000 to 2020. This further highlights the food production needs in the country. While this land use change indicates a significant alteration in land cover, it was exigent to assess land suitability using a Multi-Criteria Decision Analysis (MCDA) combined with geospatial techniques to identify areas with agricultural suitability potential and to analyze cropland suitability. The results showed that the country had 8% of very high suitability, high suitability (25%), moderate suitability (29%), and marginal suitability (25%) croplands. However, low suitability accounts for 14% of the entire cropland. The spatial distribution of cropland suitability shows that most areas in the South East, South South, and South West, respectively, have the most suitable cropland as they meet the biophysical conditions for crop production, followed by the North Central regions, while most places in the North (North East and North West) have a higher share of moderate to low suitability. This study highlights the potential of the country to target localized self-sufficiency. Therefore, this study recommends using the cropland suitability map to launch food security programs across the six geopolitical zones to maximize their inherent environmental potentials to alleviate the country's food production needs.

Selecting the optimal gridded climate dataset for Nigeria using advanced time series similarity algorithms

Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.

Impacts of Temperature Trends and SPEI on Yields of Major Cereal Crops in the Gambia

Variations in the climate constitute a significant threat to the productivity of food crops in the Gambia. A good understanding of the influence of climate variability on crop production is vital for climate resilience and improved food security. This study examined the trends, relationships, and the extent to which growing season temperatures and the SPEI (Standardized Precipitation and Evapotranspiration Index) impacted sorghum, millet, maize, and rice yields in three agro-ecological regions of the Gambia during 1990–2019. Mean temperatures and the SPEI exhibited increasing trends while observed yields showed a decline across all regions. The SPEI had a significant positive relationship with yields, and temperatures were negatively associated with yields. Though yield response to climate variability differs among regions, 20% to 62% of variations in the four crop yields were due to climate trends. The combined effect of the SPEI and temperatures decreased yields from 3.6 kg ha−1 year−1 to 29.4 kg ha−1 year−1, with the most severe decline observed in rice and maize yields in the Sahelian zone. Although uncertainties might arise from not considering related extreme climate events, this study highlights how past climate trends affect cereal yields in the Gambia; thus, any unfavorable change in the local climate could have severe repercussions on the country’s food security. There is a need for concerted efforts to increase investments in adaptation strategies to lessen the effects of the climate for improved crop productivity.

Research progress on the response of tea catechins to drought stress

Drought stress (DS) is the most important abiotic stress affecting yield and quality of tea worldwide. DS causes oxidative stress to cells due to the accumulation of reactive oxygen species (ROS). As non-enzymatic antioxidants, tea catechins can scavenge excess ROS in response to DS. Further, catechin accumulation contributes to the formation of oxidative polymerization products (e.g. theaflavins and thearubigins) that improve the quality of black tea. However, there are no systematic reports on the response of tea catechins to DS. First, we reviewed the available literature on the response of tea plants to DS. Second, we summarized the current knowledge of ROS production in tea leaves under DS and typical antioxidant response mechanisms. Third, we conducted a detailed review of the changes in catechin levels in tea under different drought conditions. We found that the total amounts of catechin and o-quinone increased under DS conditions. We propose that the possible mechanisms underlying tea catechin accumulation under DS conditions include (i) autotrophic formation of o-quinone, (ii) polymerization of proanthocyanidins that directly scavenge excess ROS, and (iii) formation of metal ion complexes and by influencing the antioxidant systems that indirectly eliminate excess ROS. Finally, we discuss ways of potentially improving black tea quality using drought before picking in the summer/fall dry season. In summary, we mainly discuss the antioxidant mechanisms of tea catechins under DS and the possibility of using drought to improve black tea quality. Our review provides a theoretical basis for the production of high-quality black tea under DS conditions. © 2021 Society of Chemical Industry.

Environmental and socioeconomic impacts of pipeline transport interdiction in Niger Delta, Nigeria

Over the years, pipelines have been the most economic medium for transporting crude oil to production and distribution facilities in the Niger Delta area of Nigeria. However, damages to the pipelines in this area by interdiction have hampered the continuous flow of crude oil to the facilities. Consequently, the revenue of the government dwindles, and the environment is severely degraded. This study assesses the economic and environmental impacts of pipeline interdiction in the Niger Delta region. Data from National oil spills detection and response agency, Nigeria is used to map spatial distribution of oil spills using Kernel Density Estimation with Geographic Information System. Literature was assessed to synthesize the historical, socioeconomic, and environmental impacts of oil spills and pipeline interdiction. Soil samples were collected from study area to determine the types of hydrocarbon pollutants and their concentrations in comparison with uncontaminated sites in the area. Results show that the range of concentrations of total petroleum hydrocarbon (TPH) for the impacted soil (IMP) was 17.27–58.36 mg/kg; remediated soil (RS) was 11.73–50.78 mg/kg which were higher than the concentrations of 0.68 mg/kg in the control samples (CS). Polycyclic aromatic hydrocarbons (PAH) concentrations were in the range of 0.43–77.54 mg/kg for IMP, 0.42–10.65 mg/kg for RS, against CS value of 0.49 mg/kg while BTEX ranged between 0.02 – 0.38 mg/kg for IMP, 0.01–2.7 for RS against CS value of 0.01. The values of the PAH were higher than the limits of the Department of Petroleum Resources, Nigeria. This study also revealed that pipeline interdiction has affected the livelihood of the inhabitants of the study area and the revenue of the Nigerian government. The major hotspots for oil spills in the Niger Delta region are Bayelsa, Rivers and Delta states.

Assessing the use of a drought-tolerant variety as adaptation strategy for maize production under climate change in the savannas of Nigeria

The Decision Support System for Agricultural Technology Transfer (DSSAT) was used to quantify the impact of climate change on maize yield and the potential benefits of the use of drought-tolerant maize variety over non-drought tolerant variety in savanna ecological zones of Nigeria. Projections of maize yields were estimated for three locations representing different agro-climatic zones and soil conditions, in the mid-century (2040-2069) and end-century (2070-2099) under representative concentration pathways scenarios (RCP 4.5 and 8.5) against the baseline period (1980-2009). Relative to the baseline period, the ensemble Global Circulation Models (GCMs) predicted significant increase in minimum and maximum temperatures and seasonal rainfall across the sites. In the mid-century, ensemble GCMs predicted temperatures increase between 1.7-2.4 °C for RCP4.5 and 2.2-2.9 °C for RCP8.5. By end-century, the temperature increases between 2.2-3.0 °C under RCP4.5 and 3.9-5.0 °C under RCP8.5. Predicted seasonal rainfall increase between 1.2-7% for RCP4.5 and 0.03-10.6% for RCP8.5 in the mid-century. By end of century, rainfall is expected to increase between 2-6.7% for RCP4.5 and 3.3-20.1% for RCP8.5. The DSSAT model predictions indicated a negative impact on maize yield in all the selected sites, but the degree of the impact varies with variety and location. In the mid-century, the results showed that the yield of the non-drought tolerant maize variety, SAMMAZ-16 will decline by 13-19% under RCP4.5 and 19-28% under RCP8.5. The projection by end-century indicates a decline in yield by 18-26% under RCP4.5 and 38-47% under RCP8.5. The yield of the drought-tolerant variety is projected to decline by 9-18% for RCP4.5 and 14-25% for RCP8.5 in the mid-century and 13-23% under RCP4.5 and 32-43% under RCP8.5 by the end-century. The higher temperatures by both emission scenarios (RCP 4.5 and 8.5) were primarily shown to cause more yield losses for non-drought-tolerant variety than that of the drought-tolerant variety. There will be 1-6% less reduction in yield when drought-tolerant variety is used. However, the higher yield reductions in the range of - 13 to - 43% predicted for the drought-tolerant variety by the end of the century across the study areas highlighted the need to modify the maize breeding scheme to combine both tolerances to drought and heat stresses in the agro-ecological zones of northern Nigeria.

Evaluation of the TRMM product for monitoring drought over Paraíba State, northeastern Brazil: a trend analysis

Droughts are complex natural phenomena that influence society's development in different aspects; therefore, monitoring their behavior and future trends is a useful task to assist the management of natural resources. In addition, the use of satellite-estimated rainfall data emerges as a promising tool to monitor these phenomena in large spatial domains. The Tropical Rainfall Measuring Mission (TRMM) products have been validated in several studies and stand out among the available products. Therefore, this work seeks to evaluate TRMM-estimated rainfall data's performance for monitoring the behavior and spatiotemporal trends of meteorological droughts over Paraíba State, based on the standardized precipitation index (SPI) from 1998 to 2017. Then, 78 rain gauge-measured and 187 TRMM-estimated rainfall time series were used, and trends of drought behavior, duration, and severity at eight time scales were evaluated using the Mann-Kendall and Sen tests. The results show that the TRMM-estimated rainfall data accurately captured the pattern of recent extreme rainfall events that occurred over Paraíba State. Drought events tend to be drier, longer-lasting, and more severe in most of the state. The greatest inconsistencies between the results obtained from rain gauge-measured and TRMM-estimated rainfall data are concentrated in the area closest to the coast. Furthermore, long-term drought trends are more pronounced than short-term drought, and the TRMM-estimated rainfall data correctly identified this pattern. Thus, TRMM-estimated rainfall data are a valuable source of data for identifying drought behavior and trends over much of the region.

Future global socioeconomic risk to droughts based on estimates of hazard, exposure, and vulnerability in a changing climate

A consistent and equitable global drought risk assessment for multiple regions, populations, and economic sectors at the gridded scale under future diverse climate change scenarios has been the subject of scarce research. Climate change is projected to increase the future hazard of drought and cause consequential damages to socioeconomic systems. The risk assessment of drought caused by climate change can be a bridge between impacts and adaptation. To assess the socioeconomic risk to droughts in a base period and two future periods (2016 to 2035 and 2046 to 2065), the projections of five general circulation models and population and gross domestic product (GDP), land cover, and water resources data were used to analyze the socioeconomic risk under three scenarios combining representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs). The socioeconomic risk was calculated as the product of three determinants: hazard, exposure, and vulnerability. The risk of the global population to drought was projected to be highest in 2046 to 2065 under scenario RCP8.5-SSP3, with up to 1.45 × 10⁹ persons affected, a 63% increase compared with the base period. The highest risk to GDP (4.29 × 10¹³ purchasing power parity $) was possibly in 2046 to 2065 under scenario RCP2.6-SSP1, with the risk increasing 5.64 times compared to the base period. Regions with high socioeconomic risk were primarily concentrated in the East and South Asia, Midwestern Europe, eastern US, and the coastal areas of South America. With climate change, the inequality in future socioeconomic risk to drought among countries is predicted to increase. The ten countries with the highest risks to population and GDP accounted for nearly 70% of the global risk.

Comparison of Ensembles Projections of Rainfall from Four Bias Correction Methods over Nigeria

This study compares multi model ensemble (MME) projections of rainfall using general quantile mapping, gamma quantile mapping, Power Transformation and Linear Scaling bias correction (BC) methods for representative concentration pathways (RCPs) 4.5 and 8.5 of the Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs). Using the Global Precipitation Climatology Centre historical period (1961–2005) rainfall data as the reference, projection was conducted over 323 grid points of Nigeria for the periods 2010–2039, 2040–2069 and 2070–2099. The performances of the different BC methods in removing biases from the GCMs were assessed using different statistical indices. The computation of the MME of the projected rainfall was conducted by aggregation of 20 GCMs using random forest regression method. The percentage differences in the future rainfall relative to the historical period were estimated for all BC methods. Spatial projection of the percentage changes in rainfall for Linear scaling, which was the best performing BC method, showed increases in rainfall of 5.5–6.9% under RCPs 4.5 and 8.5, respectively, while the decrease range was −3.2–−4.2% respectively during the wet season. The range of annual increases in precipitation was 5.7–7.3% for RCP 4.5 and 8.5, respectively, while the decrease range was −1.0–−4.3%. This study also revealed monthly rainfall within the country will decrease during the wet season between June and September, which is a significant period where most crops need the water for growth. Findings from this study can be of importance to policy makers in the management of changes in hydrological processes due to climate change and management of related disasters such as floods and droughts.