Rainfall Variability Research Articles

Effect of slope angle on fractured rock masses under combined influence of variable rainfall infiltration and excavation unloading

Intense precipitation infiltration and intricate excavation processes are crucial factors that impact the stability and security of towering and steep rock slopes within mining sites. The primary aim of this research was to investigate the progression of cumulative failure within a cracked rock formation, considering the combined effects of precipitation and excavation activities. The study was conducted in the Huangniuqian eastern mining area of the Dexing Copper Mine in Jiangxi Province, China. An engineering geological investigation was conducted, a physical model experiment was performed, numerical calculations and theoretical analysis were conducted using the matrix discrete element method (MatDEM), and the deformation characteristics and the effect of the slope angle of a fractured rock mass under different scenarios were examined. The failure and instability mechanisms of the fractured rock mass under three slope angle models were analyzed. The experimental results indicate that as the slope angle increases, the combined effect of rainfall infiltration and excavation unloading is reduced. A novel approach to simulating unsaturated seepage in a rock mass, based on the van Genuchten model (VGM), has been developed. Compared to the vertical displacement observed in a similar physical experiment, the average relative errors associated with the slope angles of 45°, 50°, and 55° were 2.094%, 1.916%, and 2.328%, respectively. Accordingly, the combined effect of rainfall and excavation was determined using the proposed method. Moreover, the accuracy of the numerical simulation was validated. The findings contribute to the seepage field in a meaningful way, offering insight that can inform and enhance existing methods and theories for research on the underlying mechanism of ultra-high and steep rock slope instability, which can inform the development of more effective risk management strategies.

A New Paradigm for Active–Break Cycle in the Indian Summer Monsoon

Abstract A multichannel singular spectrum analysis of 121 years of daily rainfall over India and 43 years of outgoing longwave radiation and horizontal wind has revealed two dominant intraseasonal oscillations (ISOs) that describe the space–time variability of monsoon rainfall. The two nonlinear oscillations are not perfectly periodic but exhibit broadband spectra centered at 45 and 28 days. These modes have coherent and near-regular spatial structure and temporal variations. This study posits that the two nonlinear oscillations provide a consistent and comprehensive framework to study the variability and predictability of intraseasonal rainfall variations. The active and break cycles of monsoon rainfall over India have been traditionally defined by using an arbitrarily chosen duration of persistence of arbitrarily chosen amount of rainfall averaged over an arbitrarily chosen area. This study shows that the phases of the two objectively derived modes of variability provide an objective method for defining the active and break cycles. The two ISOs are further shown to make negligible contribution to the seasonal mean rainfall and its interannual variability. This study proposes that the investigations of the origins of these nonlinear modes, and modulations of their amplitudes and phases, provide a new paradigm for future research on validation and predictability of intraseasonal variations in climate models.

Using the Observed Variations of the Start Date of the Rainy Season over Central America for Its Reliable Seasonal Outlook

Abstract We introduce a simple method to define the start and the end of the rainiest part of the year as the first and the last day of the year when the daily rain rate is more or less than the annual mean climatological rain rate for a region or at a given grid point of the rainfall analysis, respectively. A novelty of this work is the adoption of a perturbation technique to generate a total of 1001 ensemble members to account for observational and analysis uncertainties. This allows for a probabilistic estimate of the start and retreat dates of the rainy season at the granularity of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), version 6, rainfall analysis over Central America. The seasonal cycle of the IMERG rainfall analysis is also found to verify with in situ observations in the region. Many large-scale climate drivers affect regional rainfall, often with complex interactions that affect the onset date, retreat date, and magnitude of the seasonal rainfall cycle, making it difficult to predict the length or total quantity of seasonal rainfall using climate drivers alone. Once an onset date is established, however, this metric alone can be more indicative of both the length and total seasonal rainfall anomaly than predicting how the climate drivers will interact to affect the quantity and duration of upcoming seasonal rainfall. The local relationships of the start date with seasonal length and rainfall anomaly are leveraged to produce effective seasonal outlooks of the rainy season for the region by just monitoring the start date variations. Significance Statement The start date and retreat date of the rainy season in Central America are defined every year, precisely to a specific date from our proposed definition of it. This is possible because the region exhibits a strong seasonality of the rainfall. As a result, the year-to-year (interannual) variation of the seasonal rainfall during the rainy season is also determined by the variations in the length of the season that are usually overlooked in fixed calendar seasons. We define the start or retreat date of the rainy season as the first or the last day of the year when the daily rain rate exceeds or falls below the average daily rainfall from 2001 to 2022, respectively. We also find that both start and retreat date variations independently influence the length and total seasonal rainfall of the rainy season. Consequently, these relationships are leveraged to provide an outlook of the forthcoming rainy season from the diagnosis of the variations of its onset date, which is shown to be an effective predictor with significant useful seasonal prediction skills.

Urbanization and Suspended Sediment Transport Dynamics: A Comparative Study of Watersheds with Varying Degree of Urbanization Using Concentration-Discharge Hysteresis.

Suspended sediment is a critical water quality parameter and an indicator of geomorphic processes, but suspended sediment dynamics in urban streams may not conform to the first-flush model widely used for other pollutants. We analyzed discharge and turbidity data for 367 events from three urban watersheds (impervious cover 16-45%) in Cleveland, Ohio (USA). Less intensely urbanized watersheds exhibit higher turbidity compared to that of the most highly urbanized watershed. Proportionally, more counterclockwise hysteresis is observed in the two less urbanized watersheds, and more clockwise hysteresis occurs in the highly urbanized watershed. However, hysteresis patterns are driven by different mechanisms in each watershed, and geomorphic analysis was critical to identifying the underlying mechanisms. In the least urbanized watershed, spatial rainfall variability controls sediment hysteresis. In the intermediate watershed, the erosion of upstream weathered shale banks during dry periods plays a significant role in the sediment supply and shaping hysteresis. In the most urbanized watershed, high eroding banks in downstream reaches lead to more frequent clockwise hysteresis. Overall, we suggest that as the impervious surfaces increase, the availability of instream sediments (bed and banks) plays an increased role in suspended sediment dynamics, and geomorphology remains essential for guiding management decisions.

Untangling the environmental drivers of gross primary productivity in African rangelands

Precipitation variability is forecast to increase under climate change but its impacts on vegetation productivity are complex. Here, we use generalised additive models and remote sensing-derived datasets to quantify the effect of precipitation amount, distribution, and intensity on the gross primary productivity of dry rangelands across sub-Saharan Africa from 2000 to 2019 and differentiate these effects from other variables. We find that total precipitation is the primary driver of productivity, but that more variable rainfall has a small negative effect across vegetation types and rainfall regimes. Temperature and soil nitrogen also have strong effects, especially in drier rangelands. Shrublands and grasslands are more sensitive to environmental variability than savannas. Our findings support a model in which the main constraints on productivity are maintenance of soil moisture and minimisation of plant water stress. This highlights the risks of climate warming and increasing variability for productivity in water-limited grass and shrublands but suggests savannas may have greater resilience in Africa.

Interannual variation of summer southwest monsoon rainfall over the monsoon core regions of the eastern Bay of Bengal and its relationship with oceans

The study looked at how summer monsoon rainfall in the eastern Bay of Bengal area changes from year to year due to Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). Study used rainfall data and sea surface temperature data to see these variations. It's found that during ENSO positive phase, rainfall decreased in the eastern coastal region of the Bay of Bengal but increased in the northern Indo-Myanmar region. The opposite happened during ENSO negative phase. The study used a special analysis method called EOF and Morlet wavelet power-spectrum analysis to look for important patterns in the rainfall data and did correlation analysis to understand what causes abnormal rainfall in these regions. The study also found that the local convection and water vapor flux during ENSO positive phase are related to the anomalous rainfall in the Monsoon Core region. Rainfall is made stronger by the unusual anticyclone circulation in the upper troposphere. A strong/weak Mainland Indochina southwest monsoon (MSwM) in the positive or negative phase of ENSO can bring excess/less moisture to wet/dry the local southwest summer rainfall. In northern Indo-Myanmar, the anomalous rainfall is not only relied on the intensity of the MSwM but also the frequency of western disturbances also influences the regional rainfall, and further study need to develop.

Quantifying the internal and external drivers of Southeast Asian rainfall extremes on decadal timescales

Rainfall over mainland Southeast Asia experiences variability on seasonal to decadal timescales in response to a multitude of climate phenomena. Historical records and paleoclimate archives that span the last millennium reveal extreme multi-year rainfall variations that significantly affected the societies of mainland Southeast Asia. Here we utilize the Community Earth System Model Last Millennium Ensemble (CESM-LME) to quantify the contributions of internal and external drivers to decadal-scale rainfall extremes in the Southeast Asia region. We find that internal variability was dominant in driving both Southeast Asian drought and pluvial extremes on decadal timescales although external forcing impacts are also detectable. Specifically, rainfall extremes are more sensitive to Pacific Ocean internal variability than the state of the Indian Ocean. This discrepancy is greater for droughts than pluvials which we suggest is attributable to external forcing impacts that counteract the forced Indian Ocean teleconnections to Southeast Asia. Volcanic aerosols, the most effective radiative forcing during the last millennium, contributed to both the Ming Dynasty Drought (1637–1643) and the Strange Parallels Drought (1756–1768). From the Medieval Climate Anomaly to the Little Ice Age, we observe a shift in Indo-Pacific teleconnection strength to Southeast Asia consistent with enhanced volcanism during the latter interval. This work not only highlights asymmetries in the drivers of rainfall extremes but also presents a framework for quantifying multivariate drivers of decadal-scale variability and hydroclimatic extremes.

Late Holocene vegetation history and monsoonal climate change from the Core Monsoon Zone of India

To understand the vegetation response to climate change and the Indian Summer Monsoon rainfall (ISMR) variability during the Late Holocene (approx. 2.5 ka), pollen analysis was conducted on a 1.2-m-long lacustrine sediment profile from the Korba District of Chhattisgarh, central India, within the core monsoon zone (CMZ). The pollen evidence suggest that between approx. 2500 and 1950 cal yr BP, open tropical deciduous forest vegetation occupied the landscape under a warm and relatively less humid climate, coupled with decreased ISMR. Between approx. 1950 and 1035 cal yr BP, a warm and relatively more humid climate prevailed, along with moderate ISMR, leading to the transition of the existing vegetation to an open mixed tropical deciduous forest. Between approx.1035 and 220 cal yr BP (CE 915–1730), the mixed tropical deciduous forest became established under a warm and humid climate with increased ISMR, coinciding globally with the Medieval Climatic Anomaly (MCA). Mixed tropical deciduous forest expanded and transformed into a dense mixed tropical deciduous forest between approx. 220 cal yr BP to the present (CE 1730 onwards). This transformation occurred under a warm and relatively more humid climate, accompanied by intensified ISMR, aligning with the Current Warm Period (CWP). Gradual forest transformation from open vegetation to dense mixed tropical deciduous forests, as well as in the ISMR has been observed since the last approx. 2.5 ka in central India. Cereal-based agricultural practices and other anthropogenic activities have been documented in the study area since approx. 2.5 ka, with an accelerated pace observed from 1035 cal yr BP to the present.

Influences of Central and Eastern Atlantic Niño on the West African and South American summer monsoons

The rainfall variabilities of the West African and South American summer monsoons, pivotal for local and global climate systems, are strongly influenced by tropical Atlantic sea surface temperature anomalies. This study investigates the impacts of two recently identified Atlantic Niño types, central and eastern Atlantic Niño (CAN and EAN), on these monsoon systems using observational data and numerical experiments. During boreal summer, EAN events exhibit increased rainfall over West Africa compared to CAN events, indicating a strengthened West African summer monsoon. Enhanced moisture flux convergence from eastern Atlantic warming drives these wetting conditions during EAN events. Conversely, CAN events have a more pronounced influence on South American monsoon rainfall during austral summer, causing a rainfall anomaly dipole between the Amazon and eastern Brazil, suggesting an eastward shift in the South American summer monsoon rainfall belt. These rainfall changes are linked to cyclonic circulation anomalies over the southwest Atlantic Ocean, attributed to central Atlantic warming during CAN events. Furthermore, a statistical model assesses hindcast skills of rainfall variability in the two summer monsoon regions, affirming the benefits of separating Atlantic Niño into CAN and EAN events for improved seasonal climate predictions.

Determining dengue infection risk in the Colombo district of Sri Lanka by inferencing the genetic parameters of Aedes mosquitoes

BackgroundFor decades, dengue has posed a significant threat as a viral infectious disease, affecting numerous human lives globally, particularly in tropical regions, yet no cure has been discovered. The genetic trait of vector competence in Aedes mosquitoes, which facilitates dengue transmission, is difficult to measure and highly sensitive to environmental changes.MethodsIn this study we attempt, for the first time in a non-laboratory setting, to quantify the vector competence of Aedes mosquitoes assuming its homogeneity across both species; aegypti and albopictus and across the four Dengue serotypes. Estimating vector competence in relation to varying rainfall patterns was focused in this study to showcase the changes in this vector trait with respect to environmental variables. We quantify it using an existing mathematical model originally developed for malaria in a Bayesian inferencing setup. We conducted this study in the Colombo district of Sri Lanka where the highest number of human populations are threatened with dengue. Colombo district experiences continuous favorable temperature and humidity levels throughout the year creating ideal conditions for Aedes mosquitoes to thrive and transmit the Dengue disease. Therefore we only used the highly variable and seasonal rainfall as the primary environmental variable as it significantly influences the number of breeding sites and thereby impacting the population dynamics of Aedes.ResultsOur research successfully deduced vector competence values for the four identified seasons based on Monsoon rainfalls experienced in Colombo within a year. We used dengue data from 2009 - 2022 to infer the estimates. These estimated values have been corroborated through experimental studies documented in the literature, thereby validating the malaria model to estimate vector competence for dengue disease.ConclusionOur research findings conclude that environmental conditions can amplify vector competence within specific seasons, categorized by their environmental attributes. Additionally, the deduced vector competence offers compelling evidence that it impacts disease transmission, irrespective of geographical location, climate, or environmental factors.

On mapping urban community resilience: Land use vulnerability, coping and adaptive strategies in Ghana

Cities across the globe are prioritizing resilience in the wake of increasing climate change-related disasters. About 44% of these disasters are floods and their manifestation in cities is more pronounced, threatening urban social, ecological, and economic systems. This study draws on community resilience and participatory GIS, to examine land use vulnerability to flooding and local coping and adaptive strategies to achieve resilience. Using Ghana as a case study, the results show that participatory mapping offers community resilience benefits by providing context to community resilience challenges and potentials, enabling a deeper understanding of socio-environmental coupling that contributes to flood vulnerability and builds on community adaptive strategies through harnessing local community knowledge. We identified that topography, poor drainage and road network, rainfall variability, residents’ land use practices, and land use planning conundrum drive disparities in land use vulnerability to flooding. Participants underscored the necessity of critical urban infrastructure in facilitating community adaptability to floods. The findings indicate that socio-spatial inequities threaten urban community resilience, especially in increasingly cosmopolitan urban contexts, by putting the marginalized urban population in a more vulnerable position. We recommend the prioritization of recognitional equity in community resilience planning efforts to allow for the targeting of resilient interventions that reflect and respect social differentiation in the urban environment so that outcomes will not exacerbate or generate new urban socio-spatial inequalities.

Analysis of the Status of Irrigation Management in North Carolina

Farmers in North Carolina are turning to irrigation to reduce the impacts of droughts and rainfall variability on agricultural production. Droughts, rainfall variability, and the increasing demand for food, feed, fiber, and fuel necessitate the urgent need to provide North Carolina farmers with tools to improve irrigation management and maximize water productivity. This is only possible by understanding the current status of irrigated agriculture in the state and investigating its potential weaknesses and opportunities. Thus, the objective of this study was to perform a comprehensive analysis of the current state of irrigation management in North Carolina based on 15-year data from the Irrigation and Water Management Survey by the United States Department of Agriculture–National Agricultural Statistics Service (USDA-NASS). The results indicated a reduction in irrigation acres in the state. Also, most farms in the state have shifted to efficient sprinkler irrigation systems from gravity-fed surface irrigation systems. However, many farms in North Carolina still rely on traditional irrigation scheduling methods, such as examining crop conditions and the feel of soil in deciding when to irrigate. Hence, there are opportunities for enhancing the adoption of advanced technologies like soil moisture sensors and weather data to optimize irrigation schedules for improving water efficiency and crop production. Precision techniques and data-based solutions empower farmers to make informed, real-time decisions, optimizing water use and resource allocation to match the changing environmental conditions. The insights from this study provide valuable information for policymakers, extension services, and farmers to make informed decisions to optimize agricultural productivity and conserve water resources.

Soil Microbial Properties of Durum Grown after Camelina, Carinata, Cover Crops, and Fallow

Novel crop rotation is an important factor influencing the composition of soil microorganisms. However, the effect of introducing oilseed and cover crops (CCs) into monoculture durum rotations on soil microbial communities is not clear in the Northern Great Plains. The objective of this study was to evaluate the effects of 2-year durum (Triticum durum D.) rotations with Ethiopian mustard (Brassica carinata A.) or camelina (Camelina sativa L.) or a 10-species forage/CC mix planted to replace fallow rotations with wheat on soil microorganisms. This study was designed as a randomized complete block with three replications in a no-tillage system. The results showed that total bacterial proportion was significantly higher in durum following fallow and camelina compared to durum following CC and carinata. Total fungal proportion was significantly higher in durum following CC compared to durum following oilseed crops and fallow. The fungal-to-bacterial ratio was significantly higher in durum following CC compared to durum following camelina and fallow. The crop species, soil pH and highly variable rainfall influenced the microbial community dynamics. The abundance of specific microbial groups due to crop-related changes might play a key role in the yield of subsequent crops and soil biological health. This study provides valuable insights into the use of CC mix and oilseeds as an alternative crop for fallow in a wheat–fallow system under dryland farming conditions. Future work is required to elucidate the biological mechanisms and functions of the soil microbial communities in the preceding and subsequent crop relative to soil health and crop productivity.

Application of machine learning and fuzzy AHP for identification of suitable groundwater potential zones using field based hydrogeophysical and soil hydraulic factors in a complex hydrogeological terrain

The eastern section of West Bengal grapples with limited surface water availability in its hard rock terrain, compounded by a semi-arid climate, variable rainfall, and a plateau topography, prompting communities to adapt groundwater water-use practices, leading to unsustainable extraction and misuse. Thus, the novel objective of the present research was to produce groundwater potential maps by comparing machine learning techniques with a Fuzzy MCDM model using specific field-based conditioning factors. In the first step, 285 wells were identified, of which 70 percent were used for training and 30 percent for the validation of the models. Secondly, field-based conditioning factors including, longitudinal conductance (SC), longitudinal resistance (ρl), transverse resistance (TR), coefficient of electrical anisotropy (λ), resistivity of formation (ρm), fracture porosity (φf), reflection coefficients (r), hydraulic conductivity (K), transmissivity(Tr), bulk density, porosity, permeability, soil moisture content and water holding capacity were used to analyze the association between these conditioning factors and groundwater occurrences. In the following steps, the XGBoost, Random Forest, and Naïve Bayes models were executed using the training dataset, and factor weights were calculated using Fuzzy Analytical Hierarchy Process of Extent analysis method. To validate and compare the performance of four models, ROC curves, AUCs, MCAs, and correlation plots were used. In general, all four models were successful in evaluating the potential of groundwater occurrences. The predictive capability of the XGBoost techniques with the highest AUC values (0.79) and the highest correlation value (0.78) is superior to those of other machine learning and MCDM models. Geophysical survey revealed that transmissivity and hydraulic conductivity of the aquifer of the river basin range from 1.55 to 440.11 m/day and 10.15–2253 m2/day, indicating a moderate to good hydrodynamic potential. Planners and engineers can use such groundwater potential maps to manage water resources effectively.

Spatio-Temporal Mechanisms of Rainfall in Kalaburagi District: An Analysis of Trends and Variability

The study used data from 11 taluks to analyse the changes and variability in annual and seasonal rainfall in the Kalaburagi district between 2016 and 2022. Sen Slope and Mann-Kendall analyses were used to evaluate trends, while the coefficient of variation was used to measure rainfall variability. The mean rainfall was calculated using simple mathematical formulas. The findings showed minimal seasonal and yearly variability, with the exception of the southwest monsoon season. The district as a whole as well as all 11 taluks consistently saw a rise in rainfall, according to annual Sen slope research. However, during the northeast monsoon season, negative trends were observed in Aland and Sedam Taluks. In the Mann-Kendall test, the district did not exhibit any general pattern; nevertheless, several taluks, such as Yadrami and Kamalapur, demonstrated remarkable trends in the southwest and northeast seasons, respectively. Due to geographical and climatic fluctuations, the district of Kalaburagi experienced an annual mean precipitation of 797 mm. Higher mean rainfall was recorded in Sedam, Kalagi, Chincholi, and Kamalapur, while lower mean rainfall was recorded in Jevaragi and Yadrami Taluks. In the water-scarce Kalaburagi district, this work is important for policymakers addressing climate change and water resource management.

Opposing Changes in Indian Summer Monsoon Rainfall Variability Produced by Orbital and Anthropogenic Forcing

AbstractFuture projections indicate that Indian Summer Monsoon Rainfall (ISMR) faces a “wetter and more variable” climate. However, the reasons remain uncertain. The Last Interglacial (LIG) climate provides a potential analog for future warming. Investigating ISMR responses to these two warming scenarios could help understand the causes of ISMR changes. Using PMIP4 simulations, we find that ISMR became “wetter and more stable” during the LIG, contrasting the future climate. The opposing changes in ISMR variability are related to divergent changes in the El Niño‐Southern Oscillation (ENSO) amplitudes, ENSO‐ISMR relationships, and ENSO‐induced large‐scale atmospheric circulation anomalies. During the LIG, orbital forcing weakened ENSO variability and its impacts on ISMR. A westward positioning of ENSO shifted the atmospheric circulation anomalies westward, suppressing extreme ISMR anomalies. These processes are supported by atmospheric model simulations. Our results suggest that different warming patterns (dynamic effects) are more critical than moisture‐increasing effects in controlling regional climate variability.

Characterization of extreme rainfall changes and response to temperature changes in Guizhou Province, China

Different geographical zones have regional heterogeneity in underlying earth surface structure and microclimate which result in different evolution trends and their response to climate change varies in extreme rainfalls in these zones. In the Guizhou province of China, there are complex landforms, which lead to spatial redistribution of rainfall, frequent extreme rainfall, and disasters high risk of geologic disasters. Research on extreme climate in Guizhou mostly paid attention to its spatio-temporal characteristics and modeling, but lack of analysis on its characteristics of extreme rainfall variability and response to temperature changes under different subsurface conditions. This study investigated the characteristics of the extreme rainfall spatiotemporal and recurrence periods in Guizhou province and discussed the relationship between the response of extreme rainfall to temperature change. Daily rainfall data from 1990 to 2020 and 2021–2100 at 31 meteorological observation stations throughout the province were collected to calculate extreme precipitation. This research had the following results. (1) Both historical and future periods show an upward trend in extreme rainfall in Guizhou province, with a spatial distribution pattern of “high in the south and low in the north, high in the east and low in the west” and “high in the southeast and low in the northwest”, respectively; the spatial distribution of extreme rainfall under each recurrence period is consistent with the non-recurrence period. (2) Both historical and future periods show an upward trend in temperature in Guizhou province, with a spatial distribution consistent with that of the extreme rainfall in the corresponding period. (3) The change in extreme rainfall intensity with increasing temperature is almost always greater than the C–C rate for different periods and underlying earth surface structure; Extreme rainfall has a Hook response structure to temperature change, and the climate response structure shifts to the right with climate warming. The results of the study can provide a basis for decision-making on regional disaster prevention and mitigation in the context of temperature change.

Spatiotemporal variability and trends of intra-seasonal rainfall and temperature in the drought-prone districts of Northwestern Ethiopia

Agriculture in Ethiopia is highly dictated by spatial patterns and temporal distributions of climate variables. The analysis of these climate variables is crucial for understanding the impacts on agricultural productivity. This study aimed to analyze spatiotemporal variability and trends of intra-seasonal rainfall and temperature using site-specific daily data from the Ethiopian Meteorology Institute (1992–2021). Standardized methods explore variability, while Mann–Kendall tests identify trends, using the Modified version for data with autocorrelation. Inverse Distance Weighted interpolation was employed for spatial analysis of rainfall, length of growing season, and temperature. The findings identified that Kiremt dominated the mono-modal rainfall pattern, contributing 72%-86% of total annual rainfall. The study found that the season typically begins early on June 13 in Adiszemen, and July 6 in Arbgebiya and ends between October 6 and October 26. The duration of the season varied across locations, averaging 95 days at Ebenat and 148 days at Adiszemen. The seasonal rainfall anomaly index shows identical patterns between ENSO episodes and seasonal rainfall. These findings inform decision-making and adaptation strategies for ENSO-driven rainfall variability. Temperatures showed predictable seasonal patterns, but have significantly increased over time, with maximum and minimum temperatures rising 0.014 °C to 0.421 °C and 0.027 °C to 0.485 °C per year respectively. This warming trend is negatively impacting water, crops, and livestock, requiring adaptation measures to build regional resilience. This study underscores the critical impact of climate variability on agriculture in the study area. The findings reveal shifts in rainfall patterns and temperature trends, providing essential insights for adapting agricultural practices.

Understanding Climate Variability and Malaria Transmission in West Africa: Case Studies, Regional Insights, and Future Directions

Climate variability plays a crucial role in malaria transmission in West Africa. Variable temperature, rainfall, and humidity patterns in diverse climatic zones, such as tropical rainforests and semi-arid regions, significantly influence mosquito breeding sites and transmission rates. Tropical rainforests experience high transmission rates due to consistent rainfall and humidity, while savanna regions experience seasonal peaks. Semi-arid regions with limited rainfall and extreme temperatures show lower but variable transmission rates. Temperature fluctuations affect the development and survival of Anopheles mosquitoes and malaria parasites. Rising temperatures can extend transmission seasons and spread the disease to unaffected areas. Rainfall patterns affect breeding site availability, with heavy rains increasing breeding sites and droughts reducing mosquito populations. Humidity influences mosquito survival and parasite development. Extreme weather events, such as cyclones and floods, exacerbate malaria transmission by increasing breeding sites and damaging infrastructure. Socioeconomic factors, including urbanization, land use changes, and economic instability, also interact with climate variability to impact malaria dynamics. The review highlights the need for integrated malaria control strategies that incorporate climate considerations, including enhanced surveillance, climate-resilient infrastructure, and community engagement. Future research should address knowledge gaps regarding the interaction between climate variables and malaria transmission, and the socioeconomic impacts of climate change on malaria dynamics. By tailoring control strategies to specific climatic and socio-economic contexts, stakeholders can better manage malaria risks and improve health outcomes across West Africa. Keywords: Climate, Variability, Malaria, Transmission, West Africa, Case Studies.

Analysis of sea surface temperature pattern, variability and their teleconnection with rainfall dynamics over the Gulf of Guinea

Spatial pattern and variability of sea surface temperature SST and their teleconnections with rainfall dynamics in the Gulf of Guinea (GOG) were examined in this study. SST and rainfall data of 50 years (1970–2022) were obtained from ERA5 and NOAA CPC at 0.25° x × 0.25° and 0.5° × 0.5° spatial resolution from longitudes 10°W and 8°W, 6oW, 4oW, and 2oW and latitudes 15°N, 5oN, 3oN, 15oS, 5oS, and 3oS, distributed along the Gulf of Guinea (GOG) respectively. Analysis was further carried out on twelve rainfall gridded stations distributed along the Gulf of Guinea (GoG) for the characterization of the Rainfall-SST teleconnection across the region while the relationship between the rainfall-SST anomalies, seasonal, inter-annual, and decadal scales was carried out using correlation analyses and composites. Interpolation of the meteorological variables was carried out using Inverse Distance Weight (IDW) from the ArcGIS Spatial Analyst Tool, Ferret, and CDO which were further employed to generate the seasonal and decadal rainfall and SST maps and statistical analysis of the study area. The result of the decadal and seasonal analysis of SST variability from 1970–1980,1980–1990, 1990–2000,2000–2010,2010–2020 and 2022 indicate that SST was highest from 2010 to 2020 at 28.91 °C and fluctuated between (28.49 °C) in the 1970–1980 and (28.08 °C) for the 1980–1990 decade While the seasonal pattern of SST showed marked variability with March–April and May(MAM) recording 29.34 °C with the lowest being in June-July–August(JJA) at 28.7 °C. In terms of decadal analysis of rainfall, the period 2010–2020 recorded the highest amount of rainfall along the coast (3,145.5 mm-3,928.3 mm while 1970–1980 recorded the lowest amount of rainfall (2,650–3.310 mm. To investigate the teleconnection between of SST and rainfall dynamics, statistical analysis was used where the SST values were plotted against seasonal rainfall in 11 stations namely Abidjan, Banjul, Accra, Guinea, Conakry, Cotonou, Dakar Doula, Freetown, Lagos, Lome, and Monrovia. The outcome of the statistical analysis and Standardized Anomaly Index used indicate that Banjul, Cotonou, Dakar, and Doula exhibited statistically insignificant correlation at 0.05 confidence level while Abidjan, Accra, Lagos, Lome, Freetown, and Monrovia showed positive and statistically significant correlation. The spatial pattern of seasonal rainfall climatology categorized into DJF, MAM, JJA, and SON reveals that JJA and SON produced 80% of rainfall in the Coastal GOG followed by MAM. The study affirmed that warm and cold tongues exist in the GOG alongside positive teleconnection and that the spatial variability of SST observed in this study corresponds positively with the decadal and seasonal variability of rainfall.