Rainfall Patterns Research Articles

Southerly Surge Impact on Rainfall Patterns in Southern Indonesia during Winter Monsoon and Madden–Julian Oscillation (MJO)

The impact of the southerly surge’s interaction with the MJO on rainfall in this study was investigated using daily rainfall data from 2140 weather-observation stations. The southern surge, which coincided with the MJO, enhanced rainfall in the western research region, with Yogyakarta seeing the greatest increase at 4.69 mm/day. Meanwhile, the southern surge that occurred without the MJO increased rainfall in the eastern region, with West Nusa Tenggara seeing the greatest rise at 3.09 mm/day. However, the southerly surge has the effect of lowering rainfall in Jakarta, reaching −2.21 mm/day when the MJO is active and −1.58 mm/day when the MJO is inactive. The southerly surge causes extreme rainfall to only occur in a small part of certain areas, so it tends to significantly reduce the possibility of extreme rainfall. In the southern part of the Indonesian maritime continent, the southerly surge predominates over the MJO, supporting increased water vapor transport. Rainfall mostly increases in the afternoon and decreases in the morning when the southerly surge occurs, whether there is the MJO or not. Convective instability analysis indicates that SS increases precipitation, most likely by raising vertically integrated moisture flux convergence, with a correlation coefficient value of 0.82.

Climate vulnerability of agroecological and conventional smallholders in Mvomero district, Tanzania: using mixed-methods to uncover local experiences and motivations of farming for the future

Changing rainfall patterns make farmers increasingly vulnerable to crop failure, income loss and food insecurity. Agroecology is proposed to reduce climate vulnerabilities of farmers, as such practices and social movement aim to create more resilient farm and food systems. It is however fairly unknown if, and to what extent, agroecological farmers are better able to cope with climate induced exposures as compared to conventional smallholders. We conducted 194 surveys with agroecological and conventional smallholders to explore the three components of climate vulnerability: exposure, sensitivity and adaptive capacity. We combined this with field observations and interviews, and an analysis of long-term rainfall data. We also followed up the initial survey analysis with additional focus group discussions. Just as climate change occurs incrementally over time, we highlight modest, yet important differences between conventional and agroecological farmers. We find that agroecological farmers are less vulnerable to short-term dry spells, due to a combination of farming practices that improve soil water retention, like mulching and the use of cover crops. However, the use of botanicals might induce new vulnerabilities, as their processing requires additional labor, and sometimes expenditures, and may not protect the crops from pests and diseases. We also find limitations to agroecology in terms of scale, as most farmers are unable to use botanicals on all their farmland. Yet, agroecological farmers process and apply botanicals for health benefits both in production and consumption of foods, and they can occasionally sell their organic farm products for a higher price than conventional famers. With this study, we emphasize that farmers’ reasons to practice agroecology is not just to boost productivity and become more climate resilient, but rather for improving the long-term health of producers, consumers, soils and the environment.

The Nigeria climate zones: Variability, trends and analysis from era-interim data (2010-2015)

This study analyses diurnal weather patterns in the lower atmosphere across four climate domains in Nigeria from Era-Interim data for synoptic hours of 0000, 0600, 1200 and 1800 hours from 2010-2015. The climate domain includes: Port Harcourt (tropical maritime), Enugu (bi-modal tropical continental), Jos (montane), Kano (mono-modal tropical continental) and Maiduguri (semi-arid). Findings revealed that air temperature across the areas indicate strong latitudinal positions with drier and hotter weather pattern northwards. Average air temperature across diurnal hours were 23.2-26oC, 23-29oC, 26-32oC, 26-34oC and 26.6-34 oC for Port Harcourt, Enugu, Jos, Kano and Maiduguri. Average solar radiation peaked at 1200 noon with values 508 W/m2, 639 W/m2, 830 W/m2, 905 W/m2, 832 W/m2 for the areas. Average wind speed increases northwards, i.e. 0.9-1.3m/s, 2.0-2.5m/s, 2.1-2.5m/s, 2.4-3.0m/s and 2.9-3.5m/s respectively. The wind variation is due to the surface characteristics across the areas. Southwesterly winds dominated coastal zone while southeasterly/northeasterly winds dominated the arid zone except during periods of peak rainy season. Average cloud cover for the coastal and northern domains in oktas was: 6-7 and 2-4. Relative humidity shows that prevailing atmospheric conditions at any given time are stronger at the areas close to the sources of influence and the position of Intertropical discontinuity (ITD). Humidity values were: 67-93% and 31-71% for coastal and northern domains. Rainfall amount reduces northwards i.e. 200-46 mm. Rainfall pattern is significantly influenced by the air masses source regions. These regions include closeness to massive water bodies, proximity to Sahara Desert and effects of mountainous barriers.

Pemetaan Daerah Rawan Tanah Longsor di Daerah Desa Sanggang, Kecamatan Bulu, Kabupaten Sukoharjo

<p>Landslides are one of the frequent natural disasters occurring in various regions of Indonesia, including the Village of Sanggang in Bulu District, Sukoharjo Regency, Central Java. This area boasts diverse topography with numerous steep slopes, rendering it susceptible to landslides. The Bulu District is located in a hilly region with a high vulnerability to landslides, where from 2019 to 2020, there were recorded 11 landslide disaster events, with 10 of them occurring in the Bulu District. This study aims to map landslide-prone areas in the Village of Sanggang using a spatial analysis approach and relevant risk factors. The analytical methods include the use of topographical data, soil types, rainfall patterns, and historical landslide occurrences. The mapping results will provide a deeper understanding of locations vulnerable to landslides in this area. The outcomes of this research are expected to serve as a foundation for the development of more effective mitigation strategies, including recommendations for prevention measures and early warnings. A better understanding of these susceptible areas will assist local government authorities and stakeholders in safeguarding their communities and assets from the potential landslide hazards in the Village of Sanggang, Bulu District, Sukoharjo Regency.</p>

Use of multivariate techniques to regionalize rainfall patterns in semiarid Botswana

Monthly precipitation data from 58 synoptic stations throughout Botswana, spanning 1981–2016, were used in this study. The data were examined using multivariate analysis to determine regions exhibiting distinct precipitation variability patterns and regimes. To accomplish this, the T-mode of principal component analysis was applied to the correlation matrix of the data. Based on the maximum loading values of the rotational principal component scores, the T-mode indicated three separate subregions with varying precipitation patterns over time. Four clusters with distinct rainfall patterns were identified when cluster analysis was performed on the principal component scores. An assessment of the homogeneity of the clusters was performed using L-moment’s heterogeneity measure (H). Statistical analysis was employed to model annual rainfall data using five commonly used rainfall analysis probability distribution functions: normal, lognormal, gamma, Weibull, and Gumbel. The probability distributions with the greatest fit were determined based on the maximum overall score, which was calculated by adding the individual point scores of three chosen goodness-of-fit tests. Each cluster exhibited distinct probability distribution functions, with the gamma, Gumbel, lognormal, and Weibull distributions providing the most accurate descriptions.

Study of Drought Using the Theory of Run and Hydrological Drought Index and its Relationship to Reservoir Storage at the Batubai Dam and Pengga Dam

Drought is a frequent disaster in Indonesia, particularly in areas with long dry seasons. Several factors influencing drought in Indonesia include irregular rainfall patterns, climate change, and unsustainable water resource usage. This study was conducted to provide an overview of drought and its impact on the storage of the Batujai Dam and Pengga Dam. This research utilizes drought analysis and reservoir simulation analysis. The results of this study are as follows: a) The drought condition at the Batujai Dam based on the Theory of Run method has a maximum deficit value of -644.66 mm, and the drought based on the Hydrological Drought Index using inflow data from 2013 to 2022 has a maximum deficit value of -2.88 m³/s. Meanwhile, the drought condition at the Pengga Dam based on the Theory of Run method has a maximum deficit value of -407.86 mm, and the drought based on the Hydrological Drought Index using inflow data has a maximum deficit value of -2.371 m³/s. b) The impact of drought on the simulation of the Batujai Dam and Pengga Dam over specific periods shows that the Batujai Dam was full (overflowing) for 7 dekadal periods, whereas under existing conditions, it was never full. The Pengga Dam was full (overflowing) for 6 dekadal periods, while under existing conditions, it was only full once, with a K Factor (ratio of water supply to water demand) having a minimum value of 20% from October I to October III, 2018.

Auto-Machine-Learning Models for Standardized Precipitation Index Prediction in North–Central Mexico

Certain impacts of climate change could potentially be linked to alterations in rainfall patterns, including shifts in rainfall intensity or drought occurrences. Hence, predicting droughts can provide valuable assistance in mitigating the detrimental consequences associated with water scarcity, particularly in agricultural areas or densely populated urban regions. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applied an Auto-Machine-Learning approach to deploy Artificial Neural Network models, aiming to predict the Standardized Precipitation Index in four regions of Zacatecas, Mexico. Climatological time-series data spanning from 1979 to 2020 were utilized as predictive variables. The best models were found using performance metrics that yielded a Mean Squared Error, Mean Absolute Error, and Coefficient of Determination ranging from 0.0296 to 0.0388, 0.1214 to 0.1355, and 0.9342 to 0.9584, respectively, for the regions under study. As a result, the Auto-Machine-Learning approach successfully developed and tested Artificial Neural Network models that exhibited notable predictive capabilities when estimating the monthly Standardized Precipitation Index within the study region.

Influence of multi-stressor combinations of pCO2, temperature, and salinity on the toxicity of Heterosigma akashiwo (Raphidophyceae), a fish-killing flagellate.

Climate change and global warming have led to more frequent harmful algal blooms in the last decade. Among these blooms, Heterosigma akashiwo, a golden-brown phytoflagellate, is one of the 40 species with a high potential to form harmful blooms, leading to significant fish mortality. Climate change leads to rising atmospheric and ocean temperatures. These changes, along with altered rainfall patterns and meltwater input, can cause fluctuations in ocean salinity. Elevated atmospheric carbon dioxide (CO2) levels increase water acidity as oceans absorb CO2. This study investigated the effects of temperature, salinity, and CO2 levels on lipid production, hemolytic activity, and toxicity of H. akashiwo using the design of experiment approach, which can be used to investigate the effect of two or more factors on the same response simultaneously in a precise manner with fewer experiments and materials but in a larger region of the factor space. The lipid content was measured using a high-throughput Nile Red method, and the highest level of lipid content was detected at 25°C, a salinity of 30, and a CO2 concentration of 400 ppm. Hemolytic activity was assessed using rabbit blood erythrocytes in a 96-well plate, and the optimal conditions for achieving the highest hemolytic activity were determined at 15°C, a salinity of 10, and a CO2 concentration of 400 ppm. As the chemical structure of the toxin is not known, we used the toxicity against the cell line RTgill-W1 as the cell toxicity proxy. The maximum toxicity was identified at 15°C, a salinity of 10, and a CO2 level of 700 ppm.

How can dry tropical forests respond to climate change? Predictions for key Non-Timber Forest Product species show different trends in India.

The present study provides an assessment of the distribution of key Non-Timber Forest Product species in India, namely Aegle marmelos (L.) Correa, Buchanania lanzan Spreng., Madhuca longifolia (J. Koenig ex L.) J. F. Macbr., Phyllanthus emblica L. and Terminalia bellirica (Gaertn.) Roxb. The suitable habitat was analyzed under current climate scenarios and subsequently, the future distribution (2050s and 2070s) was mapped under RCP 2.6 and 8.5 scenarios, along with the past distribution (mid-Holocene, ~ 6000calyear BP) using the MaxEnt species distribution model. The distribution of all species is primarily driven by key bioclimatic factors, including annual precipitation (Bio_12), mean annual temperature (Bio_1), isothermality (Bio_3) and precipitation of the coldest quarter (Bio_19). The results indicate that the present distribution of these species is mainly centred in the Western Ghats regions, Central Highlands, North-eastern India and Siwalik hills. The current study suggests that under the future climate change, the suitable habitat for A. marmelos and T. bellirica is expected to increase while for B. lanzan, M. longifolia and P. emblica, it is projected to decline. A. marmelos and T. bellirica are anticipated to exhibit resilience to future climate changes and are expected to be minimally affected, while B. lanzan, M. longifolia and P. emblica are highly sensitive to high temperature and alteration in rainfall pattern expected under future climate changes. The projections of habitat suitability areas can be used as a valuable foundation for developing conservation and restoration strategies aimed at alleviating the climate change impacts on NTFP species.

Pre-processing satellite rainfall products improves hydrological simulations with machine learning

ABSTRACT A new pre-processing methodology for gridded Satellite Precipitation Products (SPPs) is developed to improve the performance of Machine Learning (ML) algorithms for runoff prediction. The developed approach was applied to capture the rainfall patterns, and to select relevant input data. This approach was tested using the FeedForward Neural Network (FFNN) and the Extreme Learning Machine (ELM) given their flexibility and ability in hydrological modelling. The methodology was tested in a semiarid transboundary watershed located in North Africa (Algeria, Tunisia) with the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPMIMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. The results demonstrate the effectiveness of the proposed approach using all employed SPPs. In terms of Nash-Sutcliffe efficiency, the suggested pre-processing technique improved the prediction ability of FFNN by 13%, and of ELM by 15%, which highlights how pre-processing techniques significantly enhance ML models with SPP data.

Satellite gravimetry observations on the state of groundwater level variability in the Arabian Peninsula Region and the associated socio-economic sustainability challenges

Groundwater is an important resource for the Arabian Peninsula Region. The population increase, rise in agricultural activities, and Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) inclination towards economic diversification and tourism promotion have heightened the freshwater demand. As a result of climate change and varying weather patterns, the situation has become more complicated. Due to arid conditions, recharge is mostly less than withdrawal which consequently results in underground water level decline over time. In the research, we have used Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) MASCON solutions, Global Land Data Assimilation System (GLDAS) soil moisture, and the Integrated Multi-satellite Retrievals for Global Precipitation Mission (IMERG) rainfall data to observe the Equivalent Water Thickness (EWT), and rainfall patterns in this region for the past two decades (2002–2023). The results indicate that in Saudi Arabia the water level is declining nearly at a linear rate and the linear regression model fits well with the data (R2 value, the coefficient of determination, for different cities of Saudi Arabia is ≥ 0.94). In the Al Jouf Area, the water decline is the highest at −1.69 cm/year which is 43% greater than the previous calculations. The lowest decline rate is in Sanaa (Yemen) which is −0.13 cm/year. Furthermore, all the other studied locations show a groundwater declining trend. In Saudi Arabia's Makkah, Madina, Riyadh, and Damam the reduction rate is −0.36, −0.48, −0.72, and −0.48 (cm/year) respectively. Kuwait, UAE's Dubai, and Al Ain show a similar groundwater reduction rate of −0.19 cm/year. In Oman's Masqat, the groundwater decline rate is −0.22 cm/year. Also, in the recent data, one can see the higher seasonal amplitudes that are indicative of greater fluctuations in EWT data in recent times. If water mining continues at the same pace, this important resource can become a rare commodity. Limited water supply can likely become a limiting factor for further social, agricultural, and industrial development. That's why major reviews and shifts are necessary in the current policies related to water resource management and conservation.

Investigation of the flood event under global climate change with different analysis methods for both historical and future periods

ABSTRACT Global climate change is a phenomenon resulting from the complex interaction of human influences and natural factors. These changes lead to imbalances in climate systems as human activities such as greenhouse gas emissions increase atmospheric gas concentrations. This situation affects the frequency and intensity of climate events worldwide, with floods being one of them. Floods manifest as water inundations due to factors such as changes in rainfall patterns, rising temperatures, erosion, and sea level rise. These floods cause significant damage to sensitive areas such as residential areas, agricultural lands, river valleys, and coastal regions, adversely impacting people's lives, economies, and environments. Therefore, flood risk has been investigated in decision-making processes in the Diyarbakır province using the Analytical Hierarchy Process (AHP) method and future disintegration of global climate model data. HadGEM-ES, GFDL-ESM2M, and MPI-ESM-MR models with RCP4.5 and RCP8.5 scenarios were used. Model data were disintegrated using the equivalent quantile matching method. The study reveals flood risk findings in the HadGEM-ES model while no flood risk was found in the GFDL-ESM2M and MPI-ESM-MR models. In the AHP method, flood risk analysis was conducted based on historical data across Diyarbakır and interpreted alongside future rainfall data.

Enhancing climate resilience in northern Ghana: A stochastic dominance analysis of risk-efficient climate-smart technologies for smallholder farmers

Northern Ghana is a semi-arid region characterised by a unimodal rainfall pattern, and hot and dry weather conditions. Heavy reliance on rain-fed agriculture and the lack of resources for irrigation, makes smallholder farmers in the region increasingly vulnerable to climate-related crop failures. In recent years, climate-smart technologies (CSTs) such as changing planting dates (PD), compartmental bunding (CB), mulching (M), and transplanting (TP) have been recommended to minimise yield losses. However, there is limited information on the most risk-efficient CSTs for crops cultivated in the region. This study used a stochastic dominance approach to identify the most risk-efficient CSTs for maize, rice, and sorghum. The stochastic modelling process employed the Aqua-crop model to simulate climate-related yield variability using Ghana climate data, and gross margin variability with crop budgets from literature sources. From the study's findings, changing planting date from April to May was the most risk-efficient choice for maize and sorghum under farmers' and recommended practices. In contrast, transplanting was the most risk-efficient technology for rice farming in the study area. The study also highlights the importance of considering the risk-averse nature of smallholder farmers when selecting CSTs. By identifying the most risk-efficient CSTs, the study can help improve the resilience of smallholder farmers. These findings have important implications for the development and adoption of CSTs in northern Ghana.

Emergence Pattern of Argemone mexicana, Brassica tournefortii, and Rapistrum rugosum in Eastern Australia

A study assessed the potential for using cumulative growing degree days (CGDD) to predict the weed emergence periodicity of three weed species: Argemone mexicana, Brassica tournefortii, and Rapistrum rugosum. Weed emergence was monitored regularly by placing 200 fresh seeds of each weed species on the soil surface. Weed emergence data was fit using a three-parameter sigmoidal Gompertz model. The CGDD required for 50% emergence of A. mexicana ranged from 3380 to 5302, depending upon the seasonal variation in temperature and rainfall. The majority of emergence appeared from March to June. The seeds of A. mexicana exhibited dormancy, as the majority of seeds germinated in the second season. The CGDD required for 50% emergence of B. tournefortii ranged from 824 to 2311, depending upon the seasonal variation in temperature and intensity of rainfall. Most cohorts of B. tournefortii appeared in the first season from February to June, indicating little dormancy in seeds. The CGDD required for 50% emergence of R. rugosum ranged from 2242 to 2699, depending upon weather parameters (temperature and rainfall). The main cohorts of R. rugosum appeared from February to June, and 60% of seeds germinated in the first season, while 40% germinated in the second season, indicating dormancy in seeds. The coefficients of determination for the model verification on the emergence pattern of three weeds were > 85%, suggesting that CGDD are good predictors for the emergence of these weeds. These results suggest that forecasting the emergence of three weed species on the basis of CGDD and rainfall patterns will help growers to make better weed management decisions.

Spatiotemporal variability identification and analysis for non-stationary climatic trends for a tropical river basin of India

The non-stationary behavior of climatic variables has been increasingly recognized as a challenge that disrupts the equilibrium of human-defined climate-based stationary processes, including hydrological and agricultural practices, and irrigation systems. This study aims to investigate long-term trends and non-stationarity in climatic variables across 23 stations of the Krishna River basin, India. Prominent trends in rainfall, temperature, and their extreme indices were identified using the Modified Mann-Kendall (MMK), Bootstrapped Mann-Kendall (BMK), and Sen's Slope Estimator tests, while the Innovative Trend Analysis (ITA) test uncovered hidden trends and potential shifts in climatic patterns. This study addresses a critical research gap by exploring both significant and hidden trends in climatic variables, providing a better understanding of future dynamics. Traditional methods like MMK and Sen's Slope were insufficient to reveal these hidden trends, but ITA offered a more comprehensive analysis. The findings revealed an increase in total annual rainfall for almost 50% of the basin, which aligns with rising maximum temperatures, suggesting enhanced evaporation rates and subsequent fluctuations in rainfall patterns. Seasonal analysis indicated a shift towards decreased rainfall during winter and pre-monsoon seasons, contrasted by increased precipitation during the monsoon and post-monsoon periods, highlighting a clear alteration in rainfall distribution. The Simple Daily Intensity Index (SDII) and other indices suggest intensified rainfall events despite a decrease in the number of rainy days, indicating fewer but more intense events. Temperature analysis showed an overall increase in maximum temperatures, with the Diurnal Temperature Range (DTR) significantly increasing across all stations, implying greater daily temperature variations and potential for intensified water cycles and extreme climatic events. Furthermore, the study simplifies these trends by classifying them into two attributes: intensity and frequency, aiding policymakers in site-specific management of water resources and planning for future climatic scenarios. The presence of non-stationarity in extreme rainfall was confirmed by the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. These findings are significant as they conclude how climate change is altering hydrological patterns at each station. The study emphasizes the necessity for adaptive management strategies to mitigate the adverse impacts on agriculture, infrastructure, and human safety.

Estimating future bathymetric surface of Kainji Reservoir using Markov Chains and Cellular Automata algorithms

The menace of sedimentation to reservoirs has a significant implication for water quality, storage capacity and reservoir lifetime. Rainfall patterns and other anthropogenic and environmental impacts alter the erosion rate and, by extension, directly affect sedimentation rates if left unchecked. This research focused on using the integration of Markov Chains and Cellular Automata (MC – CA) models to estimate and forecast the future bathymetric surface of the Kainji reservoir in Nigeria for the year 2050. The bathymetric datasets used for this research comprise two different epochs (1990 and 2020). The datasets were acquired using a Single Beam Echosounder at Low and High frequencies of 20 kHz and 200 kHz. The preliminary investigation revealed that sedimentation is exacerbating a greater danger to the reservoir functionality. The results show that the maximum observed depth is 71.2 m, indicating a 7.53% loss in depth from the 1990 archived data and a 16.24% depth loss to sedimentation from 1968 to 2020 and 22.35% depth loss in the year 2050 as shown on the projected surface. Consequently, the integrated model (MC and CA) efficiently predicted the future bathymetric surface of the Kainji reservoir for the year 2050 based on the data characteristics. However, the proven techniques for analysing spatial data, such as the Markov Chain and Cellular Automata, best suited for analysing categorical transition data, show some artefacts (black spots) on the projected generated map which is subject to further investigation.

Addressing the Effect of Intra-Seasonal Variations in Developing Rainfall Thresholds for Landslides: An Antecedent Rainfall-Based Approach

We developed a rainfall threshold model with the objective of limiting the effects of uncertainties typically associated with them, such as a lack of robust landslide database, the selection of the contributing rain gauge, seasonal variations in rainfall patterns, and the effect of extreme rainfall conditions. With the aid of gauge-corrected satellite precipitation data and a landslide database compiled from various sources, separate rainfall thresholds were developed for two waves of the monsoon season in the Western Ghats, India. The daily vs. antecedent rainfall distributions for different scenarios of antecedent rainfall were analyzed for landslide occurrence. The different scenarios considered included 1, 2, 3, 5, 10-, 20-, 30- and 40-day antecedent rainfalls along with the monsoon antecedent defined as the cumulative rainfall from the start of the monsoon to the day prior to landslide occurrence, and the event antecedent defined as the cumulative rainfall from the start of a rainfall event to the day prior to landslide occurrence. A statistically defined critical value was used to define the thresholds for extreme rainfall conditions, while ordinary least squares and quantile regression models were compared to identify the best-fit model for the non-extreme rainfall threshold. Receiver Operating Characteristic (ROC) analysis was performed on all these models and the best model was chosen based on the efficiency values. The daily vs. monsoon antecedent threshold was the best model for the first monsoon wave, and the daily vs. event antecedent model was the best model for the second monsoon wave. A separate rainfall threshold was defined for the entire monsoon without subdivision into separate waves, and corresponding ROC statistics were compared with the former approach to analyze the efficacy of intra-seasonal variations in rainfall threshold development. The results suggest that cumulative rainfall makes a significant contribution towards landslide initiation and that intra-seasonal variations should be necessarily considered in rainfall threshold modeling.

Crop planning using innovative trend analysis of 62-years rainfall data

The success of climate-smart agriculture in high rainfall zone lies in understanding the rainfall trend and planning or modifying the cropping system for maximum yield. Moisture stress in critical crop growth stages is detrimental to the crop and drastically reduces the yield. Udhagamandalam region in Western Ghats is a high rainfall area and is largely cultivated by vegetable crops. Rainfall trend based crop planning would enhance the crop yield without water stress. A study was carried out at ICAR-Indian Agricultural Research Institute, Dhemaji, Assam focused on assessing the long-term seasonal and monthly rainfall trends of Udhagamandalam region, Tamil Nadu using non-parametric tests and Innovative Trend Analysis (ITA). Daily rainfall of 62 years from 1960–2021 was analyzed with non-parametric tests, viz. Mann-Kendall and modified Mann-Kendall and ITA to find the seasonal rainfall characteristics. Mann- Kendall (3.055) and modified Mann-Kendall (3.055) tests showed a significantly increasing trend in the annual and seasonal monsoonal rainfall. ITA revealed either a significant positive or a negative trend in all the months except February, with the highest trend in June (2.625). In contrast to standard non-parametric tests, ITA detected a significant positive trend in all seasons and annual rainfall, except in cold winters where the trend is negative. The long-term trend analysis results suggest that the ITA is more precise for rainfall trend analysis than standard non-parametric tests and can be used to evaluate hidden variations of rainfall trends. Hence, ITA is recommended for analyzing rainfall trends for crop planning in high-rainfall regions. IT analysis of 62 years of rainfall data of Udhagamandalam suggested that vegetable crop planning can be done by farmers from August–November months as the rainfall trend during this period is assured as an increasing trend of rainfall pattern was observed.

Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A Case of Jimma Zone, Southwestern Ethiopia

Understanding climate variability and monitoring time-series trends of temperature and rainfall is crucial for the sustainable development of our planet. This study utilized historical data from the Global Historical Climatology Network-Monthly (GHCN-M) provided by the National Centers for Environmental Information (NCEI) to analyze the temperature and rainfall data from 2015 to 2022. The analysis was conducted using Python 3.1.1 on Anaconda Jupyter Notebook and the package matplotlib 3.2.1 was used for data visualization. The results revealed a pattern of maximum rainfall between March to May for the years 2020, 2021, and 2022, while for the years 2017, 2018, and 2019, the maximum rainfall was recorded in October, December, and November. Additionally, the annual maximum rainfalls were recorded in the years 2020 and 2022, and the annual maximum temperatures for all study years were recorded in January, February, and March months. On the other hand, the annual minimum temperatures for all study years occurred in June, July, August, and September months. Similarly, annual average temperatures were recorded in January, February, and March months. This study emphasizes the importance of monitoring climate change and its impacts on our planet. By understanding climate variability and time-series trends, we can better prepare for the future and work towards a sustainable world.

Bio-inoculant consortia modulated plantain (Musa × paradisiaca L.) growth, rhizosphere pH, acid phosphatase and urease activity

Fertilizers influence biochemical interactions in the rhizosphere that can affect crop performance. This study evaluated the interaction of plantain (Musa × paradisiaca) growth with soil pH, acid phosphatase and urease activities under different fertilizer amendments in Buea with mono-modal and Akonolinga with bi-modal rainfall patterns in Cameroon. The two experiments were setup in June 2021 following a randomized complete block design with NPK (nitrogen, phosphorus, and potassium), bio-inoculant consortia (plant growth-promoting bacteria – PGPB, Beauveria, Trichoderma, PGPB + Trichoderma), and untreated control, with four replications each. Soil samples were collected on 30th September 2021 and 30th April 2022 for analyses of soil physico-chemical and biological properties. Higher plantain performance occurred in Buea than Akonolinga across samplings, while treatments modulated plantain performance. Significant correlations occurred between plantain height or stem girth with rhizosphere pH, acid phosphatase and urease activities. The soil in Akonolinga is clayey with 38 % higher clay content than Buea, while Buea was clay-loamy with two times more silt than Akonolinga. The pre- and post-planting soil pH in Buea were strongly acidic as compared to extremely acidic soil in Akonolinga, with significant variation of post-planting pH across treatments, sampling time, and field sites. The positive correlation of soil pH, acid phosphatase and urease activities with plantain growth strongly indicate functional relationships that highlight fertilizer effects, sampling time or site-specific variations, and their interactions on plantain performance. Thereby, highlighting the role of locally formulated bio-inoculants in improving crop performance. These findings open up avenues for further studies on the role of bio-inoculants on crop nutrient uptake, and control of crop pests and diseases, in order to promote a holistic management approach that integrates bio-inoculants, crop type, and site-specific factors.