Rainfall Variability Research Articles

Food insecurity, associated climate factors and intervention mechanisms: a theoretical and empirical analysis of Ethiopia’s case

Purpose In our world, nearly nine million people die every year from hunger, losing one person to hunger every few seconds. Ethiopia is among the leading countries in the total number of people facing hunger. Several actions have been taken to achieve food security globally and at the household level. However, the problem of food insecurity is still high in many parts of the world. Why are we failing to achieve food security? And where should we start? This study aims to answer these main questions. Design/methodology/approach Relevant quantitative and qualitative data were duly used to describe food insecurity and associated climate factors globally and nationally. For this, PRISMA review methodology was used to help the systematic review. More than 90 relevant empirical and theoretical literature in the field were reviewed in an integrated way with practical evidence from Ethiopia. Governmental technical reports, NGOs reviews and other relevant empirical data referring to Ethiopia are thematically analyzed. Findings The subjectivity, dynamism and complexity of the concept of food security are found to be some of the issues challenging the practice of achieving food security. The unresolved issues in the combination and interaction of the four pillars of food security (food availability, access to food, food utilization and stability) are affecting its measurement. In this study, it is emphasized that food insecurity in rural and urban areas should be treated differently, as food insecurity in rural areas is directly related to food production which depends on rainfall patterns. In rural Ethiopia, rainfall variability was among the main causes of food insecurity. More importantly, it is indicated that rainfall variability does not affect all pillars of food security equally; its effect is more pronounced on food availability and stability aspects. Deconstructing the concept of food security to make it more pragmatic, and understanding the factors behind the rainfall variability should be the starting tasks in achieving food security. Further, even though food aid is preferred to react to transitory food insecurity, focusing on long-lasting preventive measures that address the root causes of the recurring food insecurity in rural areas of Ethiopia would be an effective way of addressing the problem. Practical implications Policy makers and other governmental and humanitarian agencies working on food security can make an evidence-based decision, shape policies and programs if they have clear information about the features of food insecurity, the nature of rainfall variability and critiques of the reacting mechanisms to food insecurity. Shortages of food in some place and food price crises in the other places have been both referring to food insecurity. This makes the understanding of food security situations more difficult to explain and communicate. Originality/value This study primarily clarifies the conceptual complexity surrounding food security as it currently exists. Further, it provides a comprehensive and quantitative description of the state of global to national food security, along with the associated variability of rainfall patterns that are related to it. It also provides a brief overview of the intervention mechanisms for addressing food security, with a specific focus on Ethiopia. This study has set a clear demarcation to assess food security in rural and urban areas.

Quantifying the rainfall variability effects on crop growth and production in the intensified annual forage - winter wheat rotation systems in a semiarid region of China

Replacing summer fallow period (July to September, SF) with annual short-season forages in the traditional fallow-winter wheat (Triticum aestivum L.) system may maintain grain yield and improve productivity in the semi-arid environments. But the uneven and variability rainfall led to instable productivity of the annual forage–winter wheat cropping system. The aims of this study were to 1) quantifying rainfall variability effects on annual forage–winter wheat system crop growing process and productivity; 2) determine the optimal annual forage–winter wheat production system that will response better to future climate change. A four-year (2016–2020) field experiment was conducted to investigate the impact of replacing summer fallow period with annual forages including oat (FO, Avena sativa L.), soybean (SB, Glycine max L.), and vetch (FV, Vicia sativa L.) on plant height (H), leaf area index (LAI), and above-ground biomass (AByield) growth index dynamics under three different levels of rainfall manipulation i.e. 30 % of ambient rainfall exclusion (R-30 %), natural rainfall (CK), and 30 % of ambient rainfall increase (R+30 %). Additionally, we assessed the correlations between forage and winter wheat production with growing season precipitation across 12 rainfall scenarios. Average forage biomass values of oat, soybean, and vetch were 5.50, 4.29, and 2.82 t ha−1, respectively during summer fallow period. The average winter wheat grain yield values in SF, FO, SB, and FV were 3.78, 3.12, 4.02, and 3.18 t ha−1, respectively. Integrating oat into fallow period had negative effects on wheat growth and production, and the H, LAI, and AByield for FO were 63.7 %, 50.9 %, and 29.9 % lower than SF in dry year, but the wheat grain yield in SB were 18.2 % and 24.8 % greater than SF in normal and wet years. Across the four growing seasons, the forage and wheat yields were shown to be strongly related to precipitation, and increasing precipitation significantly enhanced the production. In 2016–2017 growing season, LAI of wheat in SF, FO, SB, and FV with R+30 % scenario was increased by 30.2 %, 21.7 %, 32.7 %, and 19.8 % and that with R-30 % scenario decreased by 23.2 %, 17.8 %, 24.7 %, 16.5 % compared CK, respectively. The traditional summer fallow practice had advantage for maintaining stability in wheat gain production, especially under dry years. In consideration of forage and wheat production to rainfall variability, integrating soybean into fallow season may be an efficient option to maintain wheat yield and produce high forage amount under future climate change on the Loess Plateau and similar semi-arid regions.

Spatial and temporal characterization of Aedes albopictus oviposition activity in candidate urban settings for sterile insect technique testing in La Reunion Island

BackgroundUnderstanding of mosquito spatiotemporal dynamics is central to characterize candidate field sites for the sterile insect technique (SIT) testing, and is critical to the effective implementation and evaluation of pilot sterile male release programs. Here, we present a detailed description of Aedes albopictus (Skuse) egg-laying activity over a 6-year period in urban areas identified as potential SIT testing sites on Reunion Island.MethodWeekly entomological collections using ovitraps were carried out in residential and adjacent uninhabited habitats in two urban areas, Duparc and Bois Rouge, in the municipality of Sainte Marie, Reunion Island. Time-series data incorporating the frequency of positive ovitraps and the total number of eggs/ovitrap recorded each time at each locality during the study period from May 2013 to December 2018 were analyzed with multifaceted statistical approaches including descriptive statistics and spatiotemporal analyses incorporating the role of climatic factors on overall ovitrap productivity.ResultsDuring the ovitrap survey, the proportion of egg-positive ovitraps differed among study sites (χ2 = 50.21, df = 2, P < 0.001), being relatively lower in Duparc (89.5%) than in Bois-Rouges (95.3%) and the adjacent buffer zone (91.2%). Within each neighborhood, Ae. albopictus egg abundance varied by month in a roughly seasonal pattern marked by a single peak occurring more regularly February each year, a decline at the onset of the austral winter in July, followed by a period of lower ovitrap productivity in August and September. Fluctuation in both positivity rate and eggs densities per ovitraps were related to annual and seasonal variations in local temperature and rainfall (P < 0.001 in all cases). The spatial analysis also captured substantial between- and within-habitats heterogeneity, whereby the overall ovitrap productivity was higher in residential areas than in the buffer zone.ConclusionsCollectively, these results reveal that the distribution of Ae. albopictus oviposition activity is shaped by local habitat heterogeneity and seasonal climatic factors. Overall, this study provides baseline insights into the reproductive dynamics of Ae. albopictus, which would assist in planning locally tailored SIT interventions, while addressing concerns related to focal areas of high egg-laying intensity and potential immigration of females from natural areas.

Effects of Rainfall Variability on Maize Production in Migori County, Kenya

Maize is the main staple food crop in Kenya and is of vital concern to agricultural policy decisions, food security, and the overall development of the sector and the economy. It is also the dominant staple food crop in Migori-County. However, there has been a declining trend in maize production among farmers in the study area threatening household food security. This study was conducted in Migori County using cross sectional survey research design. A sample size of 384 households was selected through stratified and systematic sampling techniques though just 310 households accepted to be interviewed. Data was collected using a structured questionnaire. Validated secondary weather data for the period 2011-2023 was obtained from the Migori Meteorological Station. The collected data was entered into SPSS software, version 20, and analysed using both descriptive and inferential statistical methods. The results indicated that 60% of households have an adequate food supply, while 40% face food shortages. Further, there was a strong positive and significant relationship between maize production (yield) and annual weather conditions from 2018 to 2023 (r = 0.845, p = 0.05). This indicates that changes in weather patterns, such as rainfall and temperature, had a significant influence on maize yields during the study period. Hence, farmers should be encouraged to use income from cash crops to purchase food stocks and engage in non-farm activities as alternative income sources to mitigate the effects of rainfall variability and climate change on food security.

Interdecadal Variations in the Seasonal Cycle of Explosive Growth of Southern Hemisphere Storms with Impacts on Southern Australian Rainfall

Interdecadal variations, since the middle of the 20th century, in the seasonal cycle of Southern Hemisphere extratropical synoptic scale weather systems, are studied and related to associated anomalies in Southern Australian rainfall over south-west Western Australia (SWWA) and southeast Australia (SEA). A data-driven method is employed in which atmospheric fluctuations, specified from 6-hourly lower-tropospheric reanalysis data, are spectrally analysed in space and time to determine the statistics of the intensity and growth rates of growing and decaying eddies. Extratropical storms, blocking and north-west cloud band weather types are investigated in two frequency bands, with periods less than 4 days and between 4 and 8 days, and in three growth rate and three decay rate bins. Southern Australian rainfall variability is found to be most related to changes in explosive storms particularly in autumn and winter. During the first 10 years of the Australian Millennium Drought (AMD), from 1997 to 2006, dramatic changes in rainfall and storminess occurred. Rainfall declines ensued over SEA in all seasons, associated with corresponding reductions in the intensity of fast-growing storms with periods less than 4 days. These changes, compared with the 20-year timespans of 1949 to 1968 and 1975 to 1994, also took place for the longer duration of 1997 to 2016, apart from summer. Over SWWA, autumn and winter rainfall totals have decreased systematically with time for each of the 10-year and 20-year timespans analysed. Southern Australian rainfall variability is also found to be closely related to the local, hemispheric or global features of the circulation of the atmosphere and oceans that we characterise by indices. Local circulation indices of sea level pressure and 700 hPa zonal winds are good predictors of SWWA and SEA annual rainfall variability particularly in autumn and winter with vertical velocity generally less so. The new Subtropical Atmospheric Jet (SAJ) and the Southern Ocean Regional Dipole (SORD) indices are found to be the most skilful non-local predictors of cool season SWWA rainfall variability on annual and decadal timescales. The Indian Ocean Dipole (IOD) and Southern Oscillation Index (SOI) are the strongest non-local predictors of SEA annual rainfall variability from autumn through to late spring, while on the decadal timescale, different indices dominate for different 3-month periods.

Hydrological classification of mine pit lakes using modelling experiments

With the growing global prevalence of open-pit mining activities, there is an increasing necessity for sustainable mine life cycle plans with an early outlook towards mine closure. A major consideration in mine closure planning is the potential formation of lakes in the mine void and how these “pit lakes” can be managed to minimise risks and, if possible, create benefits. Understanding the long-term interactions between pit lakes, groundwater, and surface water systems is essential for that purpose. While numerous site-specific studies have been undertaken, there have been no studies that aim to provide a general, broadly applicable understanding of how pit lake hydrology relates to geographical context, and no efforts to hydrologically classify pit lakes using geographical criteria. This research employs an integrated generic pit lake water balance model to examine mine pit lake interactions with the surrounding surface water and groundwater. Simulating 243 scenarios, the influence of five input factors (climate, hydraulic conductivity, regional groundwater level, catchment area and pit slope) on pit lake behaviour up to 6000 years beyond the closure of the mine was considered. The assessment focused on four pit lake hydrological attributes once the system reached equilibrium: water level, time to equilibrium, the fraction of days where the pit recharges groundwater, and the fraction of days where there is surface overflow from the lake. All scenarios were assigned to one of five hydrological classes based on the interactions between the pit lake and the surrounding surface water and groundwater. Our findings show that, in many contexts, general data on climate type and subsurface hydraulic conductivity can yield reliable predictions of a pit lake's long-term hydrological classification without having to develop a detailed, site-specific pit lake model. The classification needs improvements in non-arid climates where inter-annual variation in rainfall is pronounced. The pit lake classification is particularly valuable for first-pass risk assessments to determine whether site-specific modelling is required.

Establishing Climate Change on Rainfall Trend, Variation and Change Point Pattern in Umuahia, Nigeria

This study investigates the prevailing conditions of changing climatic trends and change point dates in Umuahia, Nigeria, using daily annual maximum series (AMS) rainfall data spanning 31 years. The Indian Meteorological Department (IMD) was employed to downscale the time series data into durations ranging from 5 minutes to 24 hours. Mann-Kendall (MK) trend and Sen's Slope Estimator (SSE) tests revealed a statistically significant increasing trend across all durations. The Sen's Slope magnitude ranged from 0.1373 mm/year for 5-minute duration to 0.9045 mm/year for 24-hour duration, corresponding to a variation rate of 9.05 mm/decade for the 24-hour duration. Trend change-point analysis, utilizing Distribution-free CUSUM and Sequential Mann-Kendall tests, identified 2002 as the initial rainfall change-point date. These results provide robust evidence of positive changing climatic conditions for rainfall in Umuahia, indicating that non-stationary Intensity-Duration-Frequency (IDF) modelling may be more appropriate for flood risk management and urban planning in this area.

Performance Evaluation of CMIP6 Climate Model Projections for Precipitation and Temperature in the Upper Blue Nile Basin, Ethiopia

The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias in the CMIP6 model data was adjusted using data from meteorological stations. Additionally, this study uses daily CMIP6 precipitation and temperature data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the near (2015–2044), mid (2045–2074), and far (2075–2100) periods. Power transformation and distribution mapping bias correction techniques were used to adjust biases in precipitation and temperature data from seven CMIP6 models. To validate the model data against observed data, statistical evaluation techniques were employed. Mann–Kendall (MK) and Sen’s slope estimator were also performed to identify trends and magnitudes of variations in rainfall and temperature, respectively. The performance evaluation revealed that the INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. The precipitation projections in all agro-climatic zones under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show a significant (p &lt; 0.01) positive trend. The mean annual maximum temperature over UBNB is estimated to increase by 1.8 °C, 2.1 °C, and 2.8 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5 between 2015 and 2100, respectively. Similarly, the mean annually minimum temperature is estimated to increase by 1.5 °C, 2.1 °C, and 3.1 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. These significant changes in climate variables are anticipated to alter the incidence and severity of extremes. Hence, communities should adopt various adaptation practices to mitigate the effects of rising temperatures.

Impacts of adaptation to climate change on farmers’ food security and level of sesame production in Western Ethiopia

The existence of multiple stresses and poor adaptive capacity make Africa most susceptible to climate change. In Ethiopia the potential adverse effects of climate change on the agricultural sector, the main stay of the country’s economy, are major concerns. To ensure food security, reducing the vulnerability of agricultural systems through different feasible adaptation strategies is one of the policy options in response to climate change impact This study is a first of its kind in examining the relative effectiveness of various adaptation strategies in ensuring farmers’ food security and enhancing level of sesame production in rural area of Western Ethiopia. In addition to data obtained from meteorological stations, cross-sectional data were collected interviewing 400 farm households. Descriptive statistics, two-stage least square (2SLS) and double-hurdle (D-H) models were used to analyze the data. The results of the study indicate that households are adapting using various strategies to the looming climate change in the area. The study also indicated that, though sesame production was negatively impacted by the climate hazards, smallholders have continued its production at minimum level due mainly to its high value crop character. 2SLS estimation results revealed that rainfall and temperature variability have negative impact on household’s food security. Moreover, the result indicates effectiveness of climate adaptation strategies namely agronomic practices, irrigation and soil and water conservation in reducing climatic risks and ensuring household food security. The result also implicitly indicated that farmers continued to adapt sesame production under risk climate and it is contributing to farmers’ food security. Further, the result revealed that climate change adaptation strategies have positively impacted the level of sesame production. Consequently, policy that augments households’ climate awareness and promotes adaptation decision and strategies could help reducing risks pertaining to climate and thereby improves farmers’ food security status and production of high value export potential crop—sesame.

Study on the Sand Reduction Effect of Slope Vegetation Combination in Loess Areas

Slope erosion in the Loess Plateau region has long been a concern, and vegetation plays an important role in slowing down erosion and controlling sedimentation. However, a single vegetation model shows some limitations when facing complex natural conditions and variable rainfall events. Therefore, this study investigated the influence mechanism of vegetation configuration on slope sand production at different slopes through theoretical analyses and indoor experiments. The results of the study showed that certain factors, such as vegetation configuration mode, flow rate, runoff power, runoff velocity, and runoff shear, are closely related to slope runoff sand production. The specific findings are as follows: (1) Under the condition of slope gradient of 2°, the sand reduction effect of the rigid–flexible single-row staggered configuration is the most significant, and the sediment production is reduced by 29.89%. (2) With the increase in the slope gradient and flow rate, the sand production on the slope surface rises significantly, and when the slope gradient is increased from 2° to 6°, the average sand production is increased from 1.43 kg to 2.51 kg.(3) The erosion reduction effects of different vegetation configurations were in the order of rigid–flexible single-row staggered combination &gt; flexible vegetation single combination &gt; rigid–flexible double-row staggered combination &gt; rigid vegetation single combination &gt; upstream rigid downstream flexible combination &gt; bare slope. This study provides a theoretical basis for optimizing the vegetation configuration for effective sand reduction and provides an important reference for the sustainable development of the Yellow River Basin.

Phenological Monitoring of Irrigated Sugarcane Using Google Earth Engine, Time Series, and TIMESAT in the Brazilian Semi-arid

Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the sugarcane varieties SP 79-1011 and VAP 90-212 observed from the NDVI time series over 19 years (2001–2020) from global databases. In addition, this research had the following specific objectives: (i) to estimate phenological parameters (Start of Season (SOS), End of Season (EOS), Length of Season (LOS), and Peak of Season (POS)) using TIMESAT software in version 3.3 applied to the NDVI time series over 19 years; (ii) to characterize the land use and land cover obtained from the MapBiomas project; (iii) to analyze rainfall variability; and (iv) to validate the sugarcane harvest date (SP 79-1011). This study was carried out in sugarcane growing areas in Juazeiro, Bahia, Brazil. The results showed that the NDVI time series did not follow the rainfall in the region. The sugarcane areas advanced over the savanna formation (Caatinga), reducing them to remnants along the irrigation channels. The comparison of the observed harvest dates of the SP 79-1011 variety to the values estimated with the TIMESAT software showed an excellent fit of 0.99. The mean absolute error in estimating the sugarcane harvest date was approximately ten days, with a performance index of 0.99 and a correlation coefficient of 0.99, significant at a 5% confidence level. The TIMESAT software was able to estimate the phenological parameters of sugarcane using MODIS sensor images processed on the Google Earth Engine platform during the evaluated period (2001 to 2020).

Analysis of seasonal rainfall variability with innovative graphical methods of Konya Closed Basin, Türkiye

This study looked into the variability of seasonal rainfall in the semi-arid Konya Closed Basin (KCB), a water-limited region with high exploitation demand for water resources due to agricultural activities. In the study, seasonal precipitation data of eleven stations in the basin for the period 1971–2020 were used and analyzed with four trend methods (Şen-ITA, CWTSD, IPTA, and TPSC). Visual and statistical findings from the analyses showed generally increasing trends in winter and summer precipitation and decreasing trends in spring precipitation in the basin. In autumn precipitation, according to Şen-ITA, there is an increasing trend in seven stations and a decreasing trend in four stations, while according to CWTSD, there is an increasing trend in six stations and a decreasing trend in five stations. Based on the arithmetic mean IPTA graphs, Winter-Spring transitions in all stations occur from a rising trend region to a falling trend region, but Spring-Summer transitions take the reverse direction. In the standard deviation IPTA graphs, similar transitions in the trend regions are predominant. To the mean and standard deviation TPSC graphs, most of the stations generally have similar behavior. Spring-Summer transition arrows are dominant in Zone III, while Summer-Autumn and Autumn-Winter transitions are dominant in Zone I. Although Winter-Spring transitions are variable, they are more intense in Zone I and III. CWTSD and Şen-ITA methods are also consistent with the results obtained from IPTA graphs, both visually and numerically.

An assessment of characteristics of rainfall and temperature variability in Dadinkowa Yamaltu Deba LGA OF Gombe State, Nigeria

An assessment of characteristics of rainfall and temperature variability in Dadinkowa Yamaltu Deba LGA OF Gombe State, Nigeria

Analysis of Extreme Daily Rainfall in Cotonou, Benin (West Africa)

Global warming has a global impact on the water cycle and in particular on precipitation. This work is devoted to the analysis of the variability of extreme rainfall in the economic capital of Benin. For this purpose, the 58-year daily rainfall data collected at the Meteo Benin synoptic station in Cotonou between 1960 and 2018 are used. The annual daily maxima was established for the analysis. The generalized distribution of extreme values ​​(GEV) is used to estimate the quantiles according to the defined return periods. The daily heavy rainfall had a normal character with return periods of less than six years in Cotonou, between 1960 and 2018. There was a marginal increase in frequency of extreme rainfall since the beginning of 21st century. Extreme rainfall events may lead to floods as seen in 2010. Therefore, pro-active short- and long-term measures are the need of the hour to cope up against rainfall variability under the global warming scenario.

The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

ABSTRACT Many cities around the world have faced water scarcity due to climate change, population growth, and urbanization. Accurate water supply and demand forecasting is critical for sustainable urban water management. Machine learning (ML) models provide new possibilities for forecasting compared with traditional models in handling non-linearity. This study aims to address the efficacy of ML models, long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM), and multilayer perceptron (MLP), for forecasting water supply and demand in Greater Melbourne, Australia. The ML modelling utilized daily water supply and demand, and climatic variables (rainfall and maximum temperature) recorded by Melbourne Water and the Bureau of Meteorology from 1990 to 2019. The stacked LSTM performs better than other models in forecasting with R2, RMSE, and MAPE values of 0.908, 335.74 ML, and 23.5% for water supply and 0.791, 94.88 ML, and 5.3% for water demand, respectively. The inclusion of climatic variables enhanced the accuracy of forecasting by improving R2 and reducing RMSE and MAPE. The results indicate effectiveness of ML models, particularly LSTM-based architectures, in forecasting water supply and demand. However, these models have limitations, particularly in forecasting extreme values, emphasizing the need to improve ML models for more reliable and accurate water management.

Effects of sea surface temperatures on monthly and dekadal Kiremt rainfall variability in the contrasting topographical regions of northern Ethiopia

Effects of sea surface temperatures on monthly and dekadal Kiremt rainfall variability in the contrasting topographical regions of northern Ethiopia

Spatiotemporal analysis of rainfall and temperature variability and trends for a mixed crop-livestock production system: its implications for developing adaptation strategies

Spatiotemporal analysis of rainfall and temperature variability and trends for a mixed crop-livestock production system: its implications for developing adaptation strategies

Assessing ecological risks and microplastic pollution in a tropical coastal ecosystem: Effects of rainfall variability in Southeast India

Microplastic pollution poses a significant threat to coastal ecosystems, prompting an in-depth investigation into its seasonal fluctuations and ecological impacts. This study examines microplastics in Cuddalore’s coastal waters and sediments across four seasons. Results show varying microplastic concentrations, peaking during the monsoon and pre-monsoon seasons, likely influenced by rainfall variability. Fibers dominate in both water (69–100 %) and sediment samples (66–92 %), with polyethylene and polyethylene terephthalate as the predominant polymer types in water. Sediments exhibit a more diverse polymer distribution, with polypropylene and polyethylene terephthalate being prevalent. Elevated polymer hazard index and potential ecological risk index raise concerns, reaching values of 108930 and 1125, respectively. This underscores the urgency of implementing effective management strategies to safeguard tropical coastal ecosystems from microplastic pollution. This study highlights the complex interplay between seasonal dynamics, rainfall variability, polymer types, and pollution levels, emphasizing the need for proactive environmental management measures.

Influence of mixed Rossby-Gravity waves on the North-East monsoon rainfall over south India

The northeast (NE) monsoon contributes considerable annual rainfall over southern peninsular India. Here, an attempt has been made to evaluate the role of convectively coupled mixed Rossby-Gravity (MRG) waves on the variabilities of NE monsoon rainfall. The gridded rainfall data from 1980 to 2021 from the India Meteorological Department shows large interannual variability in NE monsoon rainfall over southern peninsular India. Wavelet analysis of outgoing longwave radiation (OLR) data shows the dominance of short-period waves over the equatorial Indian Ocean during the years with above-normal NE monsoon rainfall compared to below-normal rainfall years. The space–time spectral analysis further confirms the dominance of short-period westward propagating convectively coupled equatorial waves (CCEW) during above-normal NE monsoon rainfall years. In contrast, the eastward component of CCEWs dominates during the below-normal rainfall years. The empirical orthogonal function (EOF) analysis of MRG waves filtered OLR data shows that the first two leading modes explain more than 14 % of variability over the Indian Ocean domain. Moreover, the vertically integrated moisture flux convergence indicates the role of MRG wave in triggering the extreme rainfall over southern peninsular India and also offers favorable conditions for the formation of cyclogenesis and, thereby, inducing heavy rainfall over the South Indian domain. Thus, the results suggest that the westward propagating MRG waves play a seminal role in modulating the NE monsoon rainfall over southern peninsular India.

Spatial-temporal seasonal and annual rainfall trends and variability assessment in the Pangani Basin, East Africa

Although in situ rainfall data remains the most accurate, the gauge network density in East Africa is sparse. It lacks continuity, thus making it inadequate to assess the spatial and long-term rainfall trend and variability accurately. As such, rainfall remote sensing data are normally used instead of ground station data. This study evaluates the capabilities and limitations of remote sensing data compared with ground-based observations in Tanzania's Pangani Basin in assessing the seasonal and annual rainfall trends and variability. Data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and twenty-three ground stations were analyzed, comprising a time series from 1990 to 2022. Trend analysis was conducted using the Mann-Kendall test, while the spatial distribution of precipitation was determined using Sen's slope method. CHIRPS annual rainfall showed good agreement with station rainfall data, with NSE, R2, slope, Pbias, MAE, and RMSE values of 0.84, 0.92, 0.92, 7.55%, 297.4, and 397.1, respectively. The coefficient of variation from station data indicated extreme variability, exceeding 30% for annual rainfall, while CHIRPS data showed moderate variability, ranging from 20% to 30%. Both station data and remote sensing data showed an increasing trend for annual and seasonal rainfall at least 10 stations. However, stations like Maji Moshi, Kiungu Primary School, Segera C. Tank, and Kibong'oto exhibited an increasing trend for vuli rainfall with CHIRPS data, while station data indicated a significant decreasing trend.The study highlights the necessity for calibration and validation to avert misinterpretations in climate trend analyses, especially at the basin level.