Seasonal Rainfall Variability Research Articles

Spatiotemporal climate variability and extremes in Middle Awash Afar region Ethiopia: implications to pastoralists and agro-pastoralists food security

Purpose This study aims to examine the phenomenon of climate variability and its implications for pastoralists and agro-pastoralists food security in Amibara and Awash Fentale districts of the Afar region, Ethiopia. Design/methodology/approach The study relied on meteorological records of temperature and rainfall in the study area between 1988 and 2018. Besides, literature on the topic was reviewed to make caveats on the literal picture that comes from quantitative data, and that is the contribution of this study to the existing debate on climate change and variability. The spatiotemporal trend was determined using the Mann–Kendall test and Sen’s slope estimator, while variability was analyzed using the coefficient of variation and standardized anomaly index, and standardized precipitation index/standardized precipitation evapotranspiration index were applied to determine the drought frequency and severity. Findings The result reveals that the mean seasonal rainfall varies from 111.34 mm to 518.74 mm. Although the maximum and minimum rainfall occurred in the summer and winter seasons, respectively, there has been a decrease in seasonal and annual at the rate of 2.51 mm per season and 4.12 mm per year, respectively. The study sites have been experiencing highly seasonal rainfall variability. The drought analysis result confirms that a total of nine agricultural droughts ranging from moderate to extreme years were observed. Overall, the seasonal and annual rainfall of the Amibara and Awash Fentale districts showed a decreasing trend with highly temporal variations of rainfall and ever-rising temperatures, and frequent drought events means the climate situation of the area could adversely affect pastoral and agro-pastoral households’ food security. However, analysis of data from secondary sources reveals that analyzing precipitation just based on the meteorological records of the study area would be misleading. That explains why flooding, rather than drought, is becoming the main source of catastrophe to pastoral and agro-pastoral livelihoods. Practical implications The analysis of temperature and rainfall dynamics in the Afar region, hence the inception of all development interventions, must take the hydrological impact of the neighboring regions which appears to be useful direction to future researchers. Originality/value The research is originally conducted using meteorological and existing literature, and hence, it is original. In this research, we utilized a standardized and appropriate methodology, resulting in insights that augment the existing body of knowledge within the field. These insights serve to advance scholarly discourse on the subject matter.

Association between exposure to ambient air pollution, meteorological factors and atopic dermatitis consultations in Singapore—a stratified nationwide time-series analysis

Atopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 20% of children globally. While studies have been conducted elsewhere, air pollution and weather variability is not well studied in the tropics. This time-series study examines the association between air pollution and meteorological factors with the incidence of outpatient visits for AD obtained from the National Skin Centre (NSC) in Singapore. The total number of 1,440,844 consultation visits from the NSC from 2009 to 2019 was analysed. Using the distributed lag non-linear model and assuming a negative binomial distribution, the short-term temporal association between outpatient visits for AD and air quality and meteorological variability on a weekly time-scale were examined, while adjusting for long-term trends, seasonality and autocorrelation. The analysis was also stratified by gender and age to assess potential effect modification. The risk of AD consultation visits was 14% lower (RR10th percentile: 0.86, 95% CI 0.78–0.96) at the 10th percentile (11.9 µg/m3) of PM2.5 and 10% higher (RR90th percentile: 1.10, 95% CI 1.01–1.19) at the 90th percentile (24.4 µg/m3) compared to the median value (16.1 µg/m3). Similar results were observed for PM10 with lower risk at the 10th percentile and higher risk at the 90th percentile (RR10th percentile: 0.86, 95% CI 0.78–0.95, RR90th percentile: 1.10, 95% CI 1.01–1.19). For rainfall for values above the median, the risk of consultation visits was higher up to 7.4 mm in the PM2.5 model (RR74th percentile: 1.07, 95% CI 1.00–1.14) and up to 9 mm in the PM10 model (RR80th percentile: 1.12, 95% CI 1.00–1.25). This study found a close association between outpatient visits for AD with ambient particulate matter concentrations and rainfall. Seasonal variations in particulate matter and rainfall may be used to alert healthcare providers on the anticipated rise in AD cases and to time preventive measures to reduce the associated health burden.

Spatio-Temporal Trend Analysis of Annual and Seasonal Rainfall in Ho Chi Minh City

Spatio-temporal variations and trends in climatic variables play an essential role in the adaptation and mitigation of climate change policy. This study analyzed spatial and temporal variability of annual and seasonal rainfall in Ho Chi Minh City based on non-parametric statistical trend tests and spatial interpolation techniques. In particular, Sen’s Slope Estimator and Inverse Distance Weighting (IDW) methods were applied to construct the spatial distribution of annual and seasonal rainfall over the city. The outcomes show that annual and seasonal rainfall experienced significant increasing trends. Generally, these findings draw out an overall picture of rainfall variability in terms of space and time, which makes it possible for local decision-makers to promote more promising action plans in order to respond to natural disasters in the context of climate change.

A Spatiotemporal Assessment of the Precipitation Variability and Pattern and an Evaluation of the Predictive Reliability of Global Climate Models over Bihar

Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these changes in precipitation patterns on a regional scale is essential to enhance resilience against the adverse effects of climate change. The present study, focused on the state of Bihar in India, uses a long-term (1901–2020) gridded precipitation dataset to analyze the effect of climate change. Change point detection tests divide the time series into two epochs: 1901–1960 and 1961–2020, with 1960 as the change point year. Modified Mann–Kendall (MMK) and Sen’s slope estimator tests are used to identify trends in seasonal and annual time scales, while Centroidal Day (CD) analysis is performed to determine changes in temporal patterns of rainfall. The results show significant variability in seasonal rainfall, with the nature of pre-monsoon and post-monsoon observed to have flipped in second epoch. The daily rainfall intensity during the monsoon season has increased considerably, particularly in north Bihar, while the extreme rainfall has increased by 60.6 mm/day in the second epoch. The surface runoff increased by approximately 13.43% from 2001 to 2020. Further, 13 Global Climate Models (GCMs) evaluate future scenarios based on Shared Socioeconomic Pathways (SSP) 370 and SSP585. The suitability analysis of these GCMs, based on probability density function (PDF), monthly mean absolute error (MAE), root mean square error (RMSE) and percentage bias (P-Bias), suggests that EC-Earth3-Veg-LR, MIROC6, and MPI-ESM1-2-LR are the three best GCMs representative of rainfall in Bihar. A Bayesian model-averaged (BMA) multi-model ensemble reflects the variability expected in the future with the least uncertainty. The present study’s findings clarify the current state of variability, patterns and trends in precipitation, while suggesting the most appropriate GCMs for better decision-making and preparedness.

Unraveling weak and short South Asian wet season in the Early Eocene warmth

AbstractThe timing and causative factors underlying the Asian summer monsoon initiation remain contentious as recent proxy data and modeling studies suggested the existence of a wet-dry monsoon-like climate from the Cretaceous period. Leveraging an ensemble of deep-time climate simulations focused on the early Eocene (DeepMIP-Eocene), we show that the early Eocene Asian wet season was notably weaker and shorter than present-day in the absence of an elevated heat source like the Tibetan Plateau. The deficient upper-tropospheric meridional temperature gradient was insufficient to propel the seasonal northward migration of the rainband over South Asia. The weaker cross-equatorial moisture flow encountered obstruction of Gangdese mountain along the southern edge of Asia, leading to significant South Asian aridity. The enhanced greenhouse effect was inadequate to augment the seasonal circulation and rainfall variability to current levels. The altered wet and dry seasonality across South Asia during the early Eocene does not meet the criteria to be classified as a monsoon, suggesting that South Asian monsoon initiation is likely contingent upon a specific level of Tibetan Plateau upliftment.

Utilizing Multi-Temporal ALOS/PALSAR Backscatter Data to Detect Deforestation in the Indonesian Tropical Forest

Deforestation detection mapping has been analyzed using Synthetic Aperture Radar (SAR) data from the ALOS PALSAR satellite. SAR is a promising tool for deforestation mapping in tropical forest regions because microwave energy can penetrate clouds. This study examined the effect of SAR polarization on the accuracy of deforestation mapping and the seasonal variation of radar backscattering values in Riau Province, Indonesia. Image differencing was used to determine the threshold value for distinguishing between forest and non-forest areas. The difference between HV (horizontal transmit-vertical receive) and HH (horizontal transmit-horizontal receive) polarization was used as the discriminant variable. The correlation between radar backscattering value and accumulated rainfall was also analyzed to assess the effect of seasonal change. The findings demonstrated that the accuracy of deforestation detection mapping using HV polarization outperforms that achieved with HH polarization. Moreover, seasonal variations in rainfall were observed to influence radar backscatter signatures in bare soil, acacia, and oil palm, though no such influence was detected in forested areas. The accuracy of deforestation detection mapping, employing the thresholding method, reached a rate of 92.21%. During the period from November 2007 to July 2009, forested areas in Riau experienced a reduction, decreasing to 6.77%, and further decreasing to 6.08% by October 2010.

ADOPTION OF SORGHUM NATIONAL INTEGRATED SOLUTIONS TECHNICAL PACKAGE BY RAINFED FARMERS, GEZIRA, SUDAN

The effect of climate change on African agriculture represents a major challenge to continental agricultural development including food security, nutrition and management. In Sudan, the yield of rainfed crops is characterized by high variability in the yield due to the high variability in seasonal rainfall and does not exceed two sacs/feddan because the rainfed farmers were adopted low input rainfed agriculture as a risk management option which led to reduce in the yield per unit of land and water. The Federal Ministry of Agriculture has designed and financed a special programme for the traditional the rainfed sector of the country called Integrated Solutions Programme as the main adaptation strategy for the 2014/ 2020 growing seasons to diversify and increase production and productivity of crops cultivated in traditional rainfed areas in each State of the country such as sorghum, sesame, millet and sunflower. This programme was implemented by the Administration of Agricultural Extension and Technology Transfer in each State in collaboration with Agricultural Research Corporation (ARC) and the Agricultural Bank of Sudan. Field survey was used to collect data from 50 rainfed farmers participated in the programme and an equal number from non- participant farmers from the study area were selected (for comparison) using the simple random sampling technique. A close ended questionnaire was constructed and the personal interview technique was used to administer the questionnaire. The collected data were coded, fed to computer and statistically analyzed using (SPSS), discussed interpreted using descriptive statistics and chi-square test. The results showed that the participant farmers adopted the programme components. The majority of chi-square test results revealed no significant association between farmer`s adoption of the programme components and participation in the programme and also no significant association between farmer`s adoption of the programme components and the selected socio-economic characteristics of the respondent farmers. From this study, it can be concluded that the NISP are very effective agricultural extension policy that can be applied in the rainfed sector of the country, which will help rainfed farmers to increase their income through their participation in various activities of the programme. The study recommends that NISP should become the main national policy specially designed for the agricultural rainfed sector of the country, the needed agricultural inputs should be available at a reasonable price, the curriculum of NISP should be updated and developed regularly to solve the constraints that might face the programme, in-service training should be organized for agricultural extension officers on various aspects related to the rainfed sector, various means for conduction the programme should be available, Further agricultural extension research should be conducted

Evaluating groundwater resources trends through multiple conceptual models and GRACE satellite data.

In this research, three numerical groundwater flow models, developed and calibrated from three equally plausible conceptual models over the Nasia Basin, have been used to assess groundwater resources variations over a transient period. The use of multiple numerical models reduces the effect of uncertainties in conceptual model formulation. All the three calibrated numerical models indicate an increasing trend of groundwater recharge and storage over the period of the groundwater level monitoring. This suggests that the prevailing erratic climatic conditions in the area are conducive for increasing groundwater recharge and storage in the terrain. The high-intensity, short duration rainfall patterns, attending climate change in the basin, enhance high levels of infiltration and percolation, leading to steadily increasing groundwater recharge. Groundwater recharge estimates from each of the models over the transient period appear to reflect the pattern of seasonal variations in rainfall in the region. Data from the models indicates a significant role of baseflow in sustaining perennial streamflow in the area. This presents a significant development in terms of groundwater-based adaptation projects, especially in agriculture. The trend of groundwater recharge in the Nasia Basin is in sync with regional groundwater storage variations estimated from the Gravity Recovery and Climate Experiment (GRACE) satellite data collected and processed over the Volta Basin. At the Volta Basin level, groundwater storage variations indicate a strong positive trend of increasing groundwater recharge from 2002 (beginning of the GRACE mission) to 2022 (end point of the data used for this research). Analysis of the GRACE data suggests that there is a cumulative increase in groundwater storage by 30cm, representing approximately 120 km3 of groundwater over the period in the basin. This translates into approximately 15mm/year of groundwater storage increase. Thus, at both the regional and local levels, groundwater appears to be responding positively to the impacts of erratic rainfall patterns observed in the area recently. The high-intensity, short duration rainfall patterns appear to favor significant groundwater recharge, resulting in a strong positive groundwater storage signal. The high positive groundwater storage signal suggests increasing groundwater resources potential in the area, indicating promising opportunities for groundwater-based climate change adaptation interventions.

Long-Term Spatiotemporal Investigation of Various Rainfall Intensities over Central India Using EO Datasets

This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfall variability, trends of rainy days with different intensities, decadal percentage deviation in long-term rainfall patterns, and decadal percentage deviation in rainfall events along with their respective intensities. The central region of India was meticulously divided into distinct subparts, namely, Gujarat, Daman and Diu, Maharashtra, Goa, Dadra and Nagar Haveli, Madhya Pradesh, Chhattisgarh, and Odisha. The experimental outcomes represented the disparities in rainfall distribution across different districts of central India with the spatial distribution of mean rainfall ranges during winter (2.08 mm over Dadra and Nagar Haveli with an average of 24.19 mm over Odisha), premonsoon (6.65 mm over Gujarat to 132.89 mm over Odisha), monsoon (845.46 mm over Gujarat to 3188.21 mm over Goa), and post-monsoon (30.35 mm over Gujarat to 213.87 mm over Goa), respectively. Almost all the districts of central India displayed an uneven pattern in the percentage deviation of seasonal rainfall in all three decades for all seasons, which indicates the seasonal rainfall variability over the last 30 years. A noticeable variation in the percentage deviation of seasonal rainfall patterns has been observed in the following districts: Rewa, Puri, Anuppur, Ahmadabad, Navsari, Chhindwara, Devbhumi Dwarka, Amreli, Panch Mahals, Kolhapur, Kandhamal, Ratnagiri, Porbandar, Bametara, and Sabar Kantha. In addition, a larger number of rainy days of various categories occurred in the monsoon season in comparison to other seasons. A higher contribution of trace rainfall events was found in the winter season. The highest contributions of very light, light rainfall, moderate, rather high, and high events were found in the monsoon season in central India. The percentage of various categories of rainfall events has decreased over the last two decades (2001–2020) in comparison to the third decade (1991–2000), according to the mean number of rainfall events in the last 30 years. This spatiotemporal analysis provides valuable insights into the rainfall trends in central India, which represent regional disparities and the potential challenges impacted by climate patterns. This study contributes to our understanding of the changing rainfall dynamics and offers crucial information for effective water resource management in the region.

A comprehensive analysis of surface water changes in Dau Tieng Lake, Vietnam, across dry and wet seasons

This study investigates the seasonal dynamics of Dau Tieng Lake near Tay Ninh, Vietnam, employing remote sensing technology, specifically the Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI). Focused on the period from 2016 to 2020, the research aims to detect changes in the water surface area during dry and wet seasons. As the largest reservoir for water supply and irrigation in Vietnam, Dau Tieng Lake faces operational challenges due to seasonal rainfall variations and increasing water demands. The results reveal significant differences in water body areas between wet and dry seasons, with MNDWI proving more effective in capturing these variations compared to NDVI. The study concludes that the seasonal change in Dau Tieng Lake is 21.76 km2 (NDVI) and 25.55 km2 (MNDWI), emphasizing the latter's superiority in accurately detecting water body changes. These findings contribute valuable insights for improved reservoir management and sustainable utilization of water resources in the region.

Seasonal Variability of Rainfall and Thunderstorm Patterns in Kenya

This paper presents an analysis of spatial and temporal variation of rainfall and thunderstorm occurrence over Kenya from January 1987 to December 2017. The meteorological data used were obtained from the Kenya Meteorological Department (KMD) for the same period. This included the monthly thunderstorm occurrences and rainfall amounts of 26 synoptic stations across the country. The characteristics of monthly, seasonal and annual frequency results were presented on spatial maps while Time series graphs were used to display the pattern for annual cycle, seasonal variations and the inter-annual variability of rainfall amounts and thunderstorm occurrences. A well-known non-parametric statistical method Mann Kendall (MK) trend test was used to determine and compare the statistical significance of the trends. Thunderstorm frequencies over the Eastern, Central and Coast regions of the country showed a bimodal pattern with high frequencies coinciding with March-April-May (MAM) and October-November-December (OND) rainy seasons. Very few thunderstorm days were detected over June-July-August (JJA) season. The areas to the western part of the country, near Lake Victoria, had the highest thunderstorm frequencies in the country over the three seasons: MAM, JJAS and OND. The annual frequency showed a quasi-unimodal pattern. These places near Lake Victoria showed significantly increasing thunderstorm trends during the MAM and OND seasons irrespective of the rainfall trends. This shows the effects of Lake Victoria over these areas, and it acts as a continuous source of moisture for thunderstorm formation. However, most stations across the country showed a reducing trend of thunderstorm frequency during MAM and JJA seasons. The importance of these findings is that they could support various policy makers, and users of climate information, especially in the agriculture and aviation industries.

Contributions of the vegetation index (NDVI) in water quality prediction models in a semi-arid tropical watershed

In this work, a new approach to using NDVI as a predictor of water quality parameters in arid environments is proposed. Our focus was the Araçuaí river basin, which has a predominance of native cerrado vegetation and is subject to seasonal variations in rainfall and vegetation cover. MODIS images (MOD13Q1) from 2000 to 2018 were used to calculate the NDVI of the contributing areas of the water quality monitoring stations and to analyze its relationship with fourteen water quality parameters. The NDVI showed significant seasonality, with high values in the rainy season, and temporal trends of increase in stretches related to the main river. A strong and new relationship was observed between NDVI and six water quality parameters: color, total dissolved solids, total suspended solids, total solids, nitrate and turbidity, this being stronger at the local scale, with better performance at from models that use incremental NDVI, capturing local variations in vegetation cover, instead of regional NDVI, which reflects the general state of vegetation. The results demonstrate the potential of using these indices to develop and improve prediction models of water quality parameters in river basins and to expand the spatial and temporal coverage of water quality monitoring.

Monthly and Seasonal Variation of Cloud Cover, Humidity and Rainfall in Lagos, Nigeria

The study of atmospheric variables such as cloud cover, humidity, and rainfall is needed to forecast/predict the weather to enhance policies implemented by the government concerning agriculture, water resources, and other relevant industries in Lagos State (6.45°N, 3.39°E), Southwest Nigeria. There is a need to ascertain the variability in cloud cover with other meteorological parameters in Lagos State which is fastgrowing with a total land mass of 1,171.28 square kilometers. Eleven years (2011-2021) ground data obtained from Visual Crossing a leading provider of weather data were analyzed on a monthly and seasonal basis using statistical tools. The results show that there is a significant rise in the extent of cloud cover in Lagos during July to September, with September being the peak month due to about sixty-three percent (63%) of the sky being cloudy in September. However, there is the minimum amount of cloud cover observed between December and February, with January being the least month about forty-one percent (41%) of the sky cloud-covered in the average year. Lagos experiences a yearly average humidity of 83.5% from June to October, peaks in September (87.88%), while the lowest value (77.26%) occurs in January. The annual average rainfall accumulation for the eleven (11) years is recorded to be 1611.30 mm. In June, September, and October, the rainfall rate is recorded to be very high with values that range between 242.53 mm, 227.25 mm, and 233.86 mm respectively, while December and January is observed to record the lowest accumulation of rainfall with values that ranges between 27.26 mm & 27.97 mm respectively. Finally, the comparison of the linear regression trend and the estimated Pearson correlation coefficient reveals a substantial, positive relationship exists between cloud cover and humidity, although cloud cover has a minor influence on rainfall. According to the study’s findings, it is advised that rainfall awareness programs be expanded and that government policies relating to agriculture, water resources, and other relevant sectors take into account the rising nature of rainfall in recent years.

Climate change increases the number of landslides at the juncture of the Alpine, Pannonian and Mediterranean regions

During the next few decades, changes in rainfall frequency and magnitude are expected to have major impacts on landscape evolution, social, and economic aspects of human society. We focus on seasonal rainfall variations by the end of the twenty-first century to define affected landslide-prone areas, future landslide alerts and the impact of landslides on landscape development in the juncture of the Alpine, Pannonian, and Mediterranean region. A moderate and a worst-case climate scenario from CMIP5 global climate simulations were considered to determine the impact of rainfall on the two most common types of landslides in region, shallow and deep-seated landslides. The observed changes in the occurrence of shallow landslides are significant, especially in the winter months, where we can expect more landslide-prone areas compared to the baseline period. Shallow landslides will have a greater impact on the landscape in spring and summer than deep-seated landslides, especially in vineyards.

Erratic Rainfall and its Upshot on Rice Production in Kerala

In recent years, rainfall irregularities have affected crop production in Kerala. Rice, a water-reliant crop, is the staple food of people in Kerala. It is challenging to revive paddy if the soil loses even 10 to 20 per cent of its moisture. Due to erratic rains and frequent dry spells, rain-dependent paddy cultivation is nearly impossible in Kerala. Among the three seasons of paddy cultivation in Kerala, the autumn season entirely depends on rainfall. According to 2020-21 Kerala agriculture statistics, the majority of the rice is cultivated under irrigated conditions in the state. This loss in rain-fed paddy cultivation was due to the decline in autumn rice cultivation after the mid-1990s. The study analysed the annual and seasonal rainfall variability and their relationships with autumn rice production in Kerala. The variability and concentration in the annual and seasonal rainfall were analysed using the coefficient of variation and the precipitation concentration index, respectively. The results revealed that the overall rainfall distribution pattern in Kerala was moderate to irregular. The seasonal index was estimated for the monthly rainfall data, and it was found that in June and July, the rainfall was the maximum. Production and productivity of autumn rice showed a high correlation with rains in June and July. The variability in rice production was positively influenced by rainfall and negatively influenced by the standard deviation of precipitation.

Fresh water management in coastal Bangladesh: preparedness and adaptation

Water management is very significant because of changing the pattern of rainfall, seasonal variation of rainfall, sea level rising, water consumption pattern, acceleration of water quality and unavailability of fresh water in the coastal areas of Bangladesh due to adverse impacts of natural calamities. The aims were to investigate fresh water management with preparedness and adaptation strategies. Household data were collected from respective union parisad and total 338 households (out of 2789) were determined considering 95% significance level from three shoreline (Bagerhat), interim (Khulna) and inland (Satkhira) coastal regions of Bangladesh which distributed proportionately. Data were collected with self-administrated semi-structured questionnaire and three FGDs and analyzed with SPSS. Map was drawn by Arc GIS software. The study resulted that plastic bottle was dominantly used as conservation system for drinking (58.28%) and rainwater (44.08%). Besides, plastic drum, silver pots, earthen and ferro cement container were also used to conserve water. The mean weighted average index of preparedness of household and homestead agriculture is moderate (0.332) in inland and poor in shoreline (0.313) and interim (0.309) area. About 25.15% households satisfied with current water supply system. Individually, about 65.76% respondents adopted pond sand filter technology in the shoreline, followed by 16.90% (interim) and 37.35% (inland) area. Other adaptation strategies (rainwater harvesting, pond water conservation, potable jar water use, re-excavation of pond, boiling pond/canal water) were adopted by households and institutions with seasonal variations. Proper adaptation strategies should be taken with involving the local stakeholders for better performance in the study area.

Mathematical analysis of a time-delayed model for cocoa yield

Abstract Cocoa is an important crop that is predominantly grown in the western part of Africa. However, there have been fluctuations and declining trends in production and several factors have been identified to be responsible for this. A significant factor is the effect of climate variation, which could result in a low farm-level yield. Therefore, to understand the contribution of climate variability on the farm-level yield, we construct and analyse a time-delayed model to capture the effect of rainfall on cocoa production. This work uses a system of differential equations to model the crop transition from the flowering stage to pod formation, pod ripening and then to harvesting. We introduce a periodic forcing function into the model of flowering to account for the impact of seasonal rainfall variations. This leads to a novel nonlinear parametrically forced ODE for the flowering with periodically varying coefficients, which is coupled to a time-delayed model for the ripened pod formation and then harvesting. We perform an analysis of all parts of the system proving that it has a periodic solution when (parametrically) forced periodically, and we then conduct an asymptotic analysis on this periodic solution to show how its rich behaviour depends on the parameters of the climatic forcing in the model.

Proximate cues of flowering in a subtropical rain forest

AbstractPlants have evolved mechanisms to track seasonal variation in environmental resources, enabling them to time key life‐history events to appropriate seasons. While the proximate cues for flowering initiation are well documented in the temperate region, it is still unclear what the flowering cues are in the tropics, especially in the subtropics. Our study compared first flowering dates (FFDs) predicted by eight hypothesized proximate cues concerning photoperiod, mean and directional changes in solar irradiance and warm/cool temperature, and rainfall with flowering dates observed over 19 years of weekly monitoring for 16 species in a subtropical rain forest. We observed considerable interannual variation in the median FFDs for the study species, ranging from 21 to 101 days. The early‐spring flowering species tended to have greater interannual variation in FFDs than the summer flowering species. For 13 study species, temperature cues best explained interannual variation in FFDs. Cool temperatures in the previous fall/winter and warm temperatures in the current spring (or previous summer) might trigger the onset of flowering in these 13 species. Cues associated with photoperiod and irradiance also predicted interannual variation in FFDs with small root mean square error (<1.5 census intervals) for 12 species but generally had higher prediction errors than temperature‐related cues. Cues associated with seasonal variation in rainfall failed to predict flowering times in any species. Our results suggest that future changes in temperature may alter flowering times for most species in subtropical forests, leading to changes in ecosystem processes and biosphere feedback to the climate system.Abstract in Chinese is available with online material.

Topography Effects on Rainfall Characteristics in Bandung City and Cilacap Regency for the 2016-2020 Period

Topography and rainfall correlate with each other. Therefore, this study aims to determine the effect of topography on rainfall characteristicss in the Cilacap and Bandung areas. The research method used is a descriptive statistical analysis of average monthly, seasonal, and annual rainfall. In addition, time series and regression tests were carried out using the SPSS application. The data used is the daily rainfall data period of 2016–2020. The results of the descriptive analysis show that the variability of monthly rainfall in Bandung is higher than in Cilacap, with a value range that is 113.6–643.82 mm/month for Bandung and 92.1-355.56 mm/month for Cilacap. Global climate dynamics for instance apparent sun motion, Monsoon, ITCZ, ENSO, and IOD, as well as local weather systems like valley winds, also affect seasonal and annual rainfall variability in Bandung and Cilacap. The rainfall time series test produces values that are not stationary concerning the variance but stationary concerning the average. A simple linear regression test shows the effect of a topography of 55.2% on rainfall. The purpose of simple linear regression is to understand and model the relationship between two variables: the independent variable (topography) and the dependent variable (rainfall characteristics).

Seasonal Variation of Rainfall in Indonesia under Normal Conditions without ENSO and IOD Events from 1981-2021

This study explores the seasonal rainfall variation in Indonesia under normal conditions, excluding the influence of El-Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events. Monthly rainfall data is sourced from the CHIRPS dataset, and wind and atmospheric movement data are obtained from the ECMWF ERA-5 dataset. Identification of normal conditions relies on the ONI and DMI indices. Results reveal that the seasonal rainfall patterns during these normal conditions align with previous studies encompassing all atmospheric conditions. Indonesia typically witnesses one or two peaks in rainfall annually, notably in December-January-February (DJF), May-June (MJ), and March-April-May (MAM), consistent with established rainfall zone classifications in earlier research. A nuanced discrepancy emerges in the timing of the May-June peak, previously noted in June-July. The congruence in peak rainfall patterns during normal conditions, as observed in prior comprehensive research, implies a negligible influence of ENSO and IOD on the timing of seasonal rainfall peaks. Although ENSO and IOD may impact the amplitude of seasonal variation, particularly in Indonesia's eastern and western regions, they do not alter the temporal occurrence of rainfall peaks. Consequently, the timing of rainfall peaks in Indonesia predominantly reflects the combined influence of the Asian and Australian monsoons.