Rainfall Variability Research Articles

Watershed rehabilitation with forest gardens in Moneragala District, Sri Lanka

Forests in mountainous watersheds sustain water flows, providing a valuable lifeline in times of no rain. The clearance and destruction of forests, for plantations and modern chena cultivation, degrades watersheds, resulting in the loss of these ecosystem services, impoverishing people and diminishing watershed sustainability. The Neo Synthesis Research Centre (NSRC) has been involved in rehabilitating Sri Lankan watersheds for over 30 years, which has included implementing forest gardens (FGs) in Maragalakanda. The article describes the rehabilitation of one farmer's land (referred to as Rani), which resulted in increased household livelihood security, reversed forest loss, and sustained watershed health. The rehabilitation efforts followed a successional process, using regenerative agriculture, analog forestry, and conservation forestry. The landscape design divided Rani’s landholding into several land uses. In addition to the forest gardens which were modelled on the adjacent forest, there were paddy fields, chena and cash crop areas, and a buffer zone between the disturbed natural forest and the landholding. Comprised of 175 different species, the forest gardens provide various regulatory ecosystem services, and over half provide food, medicine, timber, firewood, ornamentals and biopesticides, which are either used for household consumption or sold to generate income. Monitoring and evaluation revealed positive impacts on biodiversity, soil fertility, and food and income security. By 2013, Rani's landholding had become a biodiversity corridor, attracting more birds and increasing her total income to USD 32 241, mainly thanks to her forest gardens. As a result, Rani achieved financial stability, enabling her to invest in and educate all five of her children, eliminating any need to clear forests for livelihood. Despite an average FG profit of USD 24 413, challenges of increased rainfall variability, animal pests, and rising costs of purchases stress her newfound livelihood. Nonetheless, watershed rehabilitation with forest gardens succeeded in reversing forest loss, restoring ecosystem services, and increasing livelihood security. To ensure more households benefit as Rani’s did, policymakers and planners must consider population growth and allocate new lands for expanding watershed populations while implementing laws to prevent forest destruction.

Rainfall variability increased with warming in northern Queensland, Australia, over the past 280 years

Floods and droughts are hydrological extremes that impact ecosystems, agriculture, and human well-being. These extremes are expected to intensify in a warmer world, although many regions lack the observations needed to place current trends in the context of long-term variability. Here we present a new multi-century record of tropical rainfall based on a multi-proxy approach from northern Great Barrier Reef coral. The robust calibration with instrumental rainfall allows us to quantitatively estimate summer rainfall in northern Queensland back to 1746 CE. We find that as global climate warmed, wet-season rainfall in this region has increased by ~10% since 1750 and the standard deviation (21-yr 1-σ) of wet-season rainfall more than doubled, as rainy years became much wetter, while dry years remained dry. Reconstructed rainfall correlates with El Niño indices and the link to Pacific temperature variability has strengthened as climate warmed, consistent with expected intensification of La Niña-related rainfall.

Synoptic Analysis of Flood-Causing Rainfall and Flood Characteristics in the Source Area of the Yellow River

The source area of the Yellow River (SAYR) in China is an important water yield and water-conservation area in the Yellow River. Understanding the variability in rainfall and flood over the SAYR region and the related mechanism of flood-causing rainfall is of great importance for the utilization of flood water resources through the optimal operation of cascade reservoirs over the upper Yellow River such as Longyangxia and Liujiaxia, and even for the prevention of flood and drought disasters for the entire Yellow River. Based on the flow data of Tangnaihai hydrological station, the rainfall data of the SAYR region and NCEP-NCAR reanalysis data from 1961 to 2020, three meteorological conceptual models of flood-causing rainfall—namely westerly trough type, low vortex shear type, and subtropical high southwest flow type—are established by using the weather-type method. The mechanism of flood-causing rainfall and the corresponding flood characteristics of each weather type were investigated. The results show that during the process of flood-causing rainfall, in the westerly trough type, the mid- and high-latitude circulation is flat and fluctuating. In the low vortex shear type, the high pressures over the Ural Mountains and the Okhotsk Sea are stronger compared to other types in the same period, and a low vortex shear line is formed in the west of the SAYR region at the low level. The rain is formed during the eastward movement of the shear line. In the subtropical high southwest flow type, the low trough of Lake Balkhash and the subtropical high are stronger compared to other types in the same period. Flood-causing rainfall generally occurs in areas with low-level convergence, high-level negative vorticity, low-level positive vorticity, convergence of water vapor flux, a certain amount of atmospheric precipitable water, and low-level cold advection. In terms of flood peak increment and the maximum accumulated flood volume, the westerly trough type has a long duration and small flood volume, and the low vortex shear type and the subtropical high southwest flow type have a short duration and large flood volume.

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.

Multiple-Time-Scale Variations of the Record-Breaking Pre-Summer Rainfall over South China in 2022

Abstract South China encountered an exceptionally heavy pre-summer rainy season in 2022 with the regional precipitation ranking first in the past 44 years. This study aims to analyze the multiple-time-scale variations of precipitation in this pre-summer rainy season to shed light on the complex dynamics influencing pre-summer precipitation over South China. The findings reveal that the variation of precipitation was dominated by the 10–20-day oscillation during April–May, while interannual variation and trend during May–June. The 10–20-day oscillation of precipitation in pre-summer rainy season in South China demonstrates a strong association with cold-air activity, which can be traced back to the propagation of disturbances along a teleconnection, which represents the dominant mode of intraseasonal atmospheric circulation over Eurasia in high latitudes during April–May. This teleconnection plays a crucial role in facilitating cold-air invasion and triggering precipitation over East China and South China. The interannual component of abnormal precipitation is strong during May–June of 2022. It is primarily attributed to the abnormal highs in the lower troposphere over the subtropical western North Pacific and Japan. These abnormal highs are likely stimulated by the combined influences of Eurasian teleconnection propagation and cooling sea surface temperature anomalies (SSTAs) over the tropical central and eastern Pacific in the third year of a consecutive La Niña event. However, the universality of the impact of Eurasian teleconnection propagation on the abnormal high over Japan on interannual scale necessitates further investigation. Furthermore, there is a significant upward trend in pre-summer rainfall over South China, accounting for 38% of the total anomaly observed in 2022.

Quantifying ENSOs Impact on Australia's Regional Monthly Rainfall Risk

AbstractThe El Niño‐Southern Oscillation (ENSO) is considered an important driver of rainfall variability in Australia, amongst many other global locations. Despite knowledge of the expected modulation of seasonal rainfall by ENSO, there is no consistently used method to quantify the role that specific ENSO events play in driving the observed anomalous rainfall. In this manuscript we adapt the Fraction of Attributable Risk (FAR) method, commonly used to identify the anthropogenic impact on a particular event, to quantify the impact of ENSO on the occurrence of monthly rainfall anomalies. We also explicitly calculate the ENSO induced change in risk and the FAR for all observed spring rainfall rates for our eastern Australian regions. A prominent role for ENSO in driving the large spring 2022 rainfall anomalies is identified. Though we choose to focus on ENSOs impact on rainfall in various Eastern Australian regions, the results are applicable to other climate modes, regions and climatic variables.

Temporal Rainfall Variations Induce Forecast Errors in Rainfed Agriculture in the Brazilian State of Ceará, Brazil

The research aims to: a) assess the instabilities associated with rainfall and the variables that define the production of rice, beans, cassava and corn in the state of Ceará between 1945 and 2020; b) estimate models that can be used to make projections of harvested areas, yields and prices for these crops between 1945 and 2020; c) assess the impact of rainfall on the estimated forecasting models; d) assess how rainfall affects the likelihood of farmers making forecasts of the variables that define agricultural production. Rainfall data was obtained from the National Centers for Environmental Information (NOAA). Crop yield data came from the Brazilian Institute of Geography and Statistics (IBGE). Instabilities were measured by the coefficients of variation. ARIMA models (autoregressive, integrated and moving average model) were used to make the forecasts. The hypothesis that the residuals generated by the models are influenced by annual rainfall was tested. The results showed high instabilities in annual rainfall, which spread to the variables that define crop yields. Parsimonious and robust adjustments were obtained from a statistical point of view and it was shown that the errors generated, including their magnitudes, in the models used to forecast all the variables that define bean and corn yields, harvested areas and rice yields, as well as cassava yields, are influenced by annual rainfall in Ceará between 1945 and 2020.

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.

Influence of snowmelt on increasing Arctic river discharge: numerical evaluation

Snow is the most important component of the Arctic climatic and hydrological system and is directly vulnerable to climate change. In recent decades, observations have indicated significant decreases in the Arctic snow cover and snowfall rate, whereas water discharge from circumpolar Arctic river basins into the Arctic Ocean has increased. To evaluate the contribution, not well quantified, of snow to the river discharge increase, we conducted sensitivity simulations with surface air temperature and precipitation as climatic treatment variables, combining a land surface model and a distributed discharge model. Variables were treated assuming higher climate variations in the Arctic cold season in 1979–2018. The surface and subsurface runoffs simulated by the land surface model were set as inflows in the discharge model to estimate river discharge. Snowmelt mostly converted to surface runoff, accounting for 73.6% of the anomalous surface runoff increase and inducing the simulated peak discharge in spring and early summer. This relationship was enhanced by the winter precipitation increase. Snow loss induced by higher air temperature contributed to the decrease in the peak and annual discharges, but caused the peak discharge to occur earlier. Additionally, warmer temperature increased the proportion of rainfall in the partitioning of precipitation, causing more subsurface runoff, particularly in autumn and winter. These results provide a first separate evaluation of factors influencing Arctic water discharge, including seasonal hydrographs, and illustrate the influence of climate warming-induced snowfall and rainfall variations on the circumpolar Arctic river discharge.

Vietnamese farmers’ perception of climate change effects on water resources and determinants of climate change

ABSTRACT This study investigates crop farmers’ perception on climate change effects on water resources and factors that influence their perceptions of it in Vietnam. A sample of 253 was randomly selected from a total population of 685 crop farmers and a structured questionnaire was created to gather data. Descriptive and inferential statistics were used. The research results showed that climate change effects on water resources varied by landform. A majority of farmers perceived that: shifts in rainfall amount and variability affect the operation of runoff water and groundwater recharge; shifts in temperature level and variability considerably influence evapotranspiration; and changes in runoff amount and runoff variability greatly influence soil water storage and groundwater. Mixed cropping systems, use of soil conservation, extension contacts, farming practices, non-agricultural activities, credit programme participation, information and communication technology (ICT) owned, and education level are the important determinants of farmers’ perceptions on climate change.

Remote Sensing & GIS Integration Bases Evaluation of Ground Water Quality in Telangana State, India

About 85% of drinking water needs in rural and 40% drinking water needs in urban areas are met from groundwater resources in Telangana state. There is a huge demand of ground water for different uses like agriculture, drinking and industrial purposes as the command area is very low in the state and it is evidenced by the fact that around 70% irrigation is dependent on ground water sources as compared to other surface sources like Tanks/Canals/Reservoirs etc. Climate change has affected the hydrological cycle with uncertain and increased variability in rainfall followed by with high rainfall in a short span of time resulting more surface runoff which ultimately decreases there charge to groundwater. Decreased recharge to the groundwater and excess withdrawal of groundwater has accelerated the depletion of groundwater resources there by resulting in stress on groundwater regime in terms of quality as well as quantity. The groundwater quality evaluation of Telangana state is carried out by using legacy quality data (2012-14) collected from Rural Water Supply & Sanitation Department, Government of Telangana. Contamination spread of groundwater is one of the most important concerns that have received attention at regional, local and global levels because of their importance on public health and its further impact in ecosystems. The spatial distribution of chemical parameters like pH, Total Dissolved Solids (TDS), Total Hardness (TH), Total Alkalinity (TA), Fluoride (F), Chloride (Cl), Iron (Fe), Nitrate (NO3) and Sulfate (SO4) are examined with respect to its contamination level. The spatial distribution and concentration of chemical elements is carried out by using spatial interpolation technique namely Inverse Distance Weightage (IDW) method from the point source data. The distribution maps reveal important information to understand the hot spot areas of the groundwater systems and for identification of potential areas for providing safe drinking water supply. Site specific water conservation and artificial recharge measures are needed to be taken up with scientific lines by using Remote Sensing & GIS techniques, for enhancing the groundwater storage and also improving its quality.

The performance of Climate Information Service in delivering scientific, local, and hybrid weather forecasts: A study case in Bangladesh

Access to reliable and skillful Climate Information Service (CIS) is crucial for smallholder farmers in Bangladesh to mitigate the impacts of rainfall variability and extremes. This study aims to systematically evaluate the performance of CIS in providing Scientific Forecast (SF) and Local Forecast (LF) to smallholders in Bangladesh. The results were then compared with farmers’ perceptions of the forecast accuracy. Additionally, the skill of a simple hybrid forecast (HF), which is an integrated system of SF and LF, was assessed using the ERA5 and ground observation datasets as benchmarks. The SF and LF data were obtained from the meteoblue hindcast and from the interview, respectively. The results indicate that, overall, LF exhibits slightly higher skill compared to SF when evaluated against the ERA5 dataset. The forecast performance, however, declines by almost half when the ground-based observations are used, associated with high false alarms. Farmers, on the other hand, perceived SF to possess superior performance compared to LF. This study demonstrates that combining the SF and LF into a simple HF yields higher forecast skill than either individual forecast, highlighting the importance of HF to deliver a reliable and trustworthy weather forecast.

Host sweet host: Rodent communities support similar ectoparasite diversity regardless of anthropogenic disturbance

AbstractRodents are important hosts for ectoparasites, such as fleas, ticks, and mites, which means they are also important intermediate hosts for many zoonotic diseases. As anthropogenic environments bring humans and rodents into closer contact, an understanding of host–ectoparasite ecology is essential to predict and manage disease spillover risks. We aimed to understand how disturbances in vegetation cover affect rodent ectoparasite diversity, prevalence, spatial segregation, host (i.e., sex, genus, size, and habitat domain), and environmental (i.e., vegetation structure, forest cover, and rainfall) variables in the state of Michoacan, Mexico. We investigated these relationships by trapping rodents in five paired disturbed (reduced vegetation cover and regular human activities) and undisturbed (no reduction in vegetation cover) sites in the summer and autumn of 2022. From 110 trapped rodents, we collected 138 ectoparasites on 38 individuals. We found no difference in rodent diversity, ectoparasite diversity, or ectoparasite prevalence between disturbed and undisturbed sites. However, arboreal and male rodents had a higher probability of carrying ectoparasites than ground‐dwelling and female rodents. Rodents with ectoparasites were not spatially clustered; rather, they were randomly distributed across trapping grids. We also identified two rodent genera (Rattus and Sigmodon) that carry ectoparasites of medical importance and that are in close contact with humans. Our results highlight the necessity of constant monitoring of rodents, ectoparasites, and their associated transmittable diseases. Assessing these interactions and how they are affected by anthropogenic disturbance could better inform management decisions and support the need for rodent conservation programmes in the area.

Temperature impacts on dengue incidence are nonlinear and mediated by climatic and socioeconomic factors: A meta-analysis

Temperature can influence mosquito-borne diseases like dengue. These effects are expected to vary geographically and over time in both magnitude and direction and may interact with other environmental variables, making it difficult to anticipate changes in response to climate change. Here, we investigate global variation in temperature–dengue relationship by analyzing published correlations between temperature and dengue and matching them with remotely sensed climatic and socioeconomic data. We found that the correlation between temperature and dengue was most positive at intermediate (near 24°C) temperatures, as predicted from an independent mechanistic model. Positive temperature–dengue associations were strongest when temperature variation and population density were high and decreased with infection burden and rainfall mean and variation, suggesting alternative limiting factors on transmission. Our results show that while climate effects on diseases are context-dependent they are also predictable from the thermal biology of transmission and its environmental and social mediators.

Power Data Access Viewer-Based Meteorological Drought Analysis and Rainfall Variability in the Nile River Basin

Meteorological drought poses a frequent challenge in the Nile River basin, yet its comprehensive evaluation across the basin has been hindered by insufficient recorded rainfall data. Common indices like the standard precipitation index, coefficients of variation, and precipitation concentration index serve as pivotal tools in gauging drought severity. This research aimed to assess the meteorological drought status in the Nile River basin by using the Power Data Access Viewer product rainfall data. Bias correction procedures were implemented to refine the monthly rainfall data for Bahirdar, Markos, Nekemt, and Muger stations, resulting in notable improvements in the coefficient of determination (R2) that were increased from 0.74 to 0.93, 0.72 to 0.89, 0.71 to 0.96, and 0.69 to 0.84, respectively. The average spatial distribution of drought in the Nile basin was classified as extremely wet (3.81%), severely wet (9.01%), moderately wet (7.36%), near normal (9.97%), moderately drought (21.20%), severely drought (17.11%), and extremely drought (31.54%). Approximately 10.33% of the Nile River basin was situated in regions characterized by high rainfall variability, while around 21.17% was located in areas with a notably irregular precipitation concentration index. Overall, this study sheds light on the prevailing meteorological drought patterns in the Nile River basin, emphasizing the significance of understanding and managing these phenomena for the sustainable development of the region.

Exploring Climate Patterns in Karnataka, India: A Comprehensive Examination of Rainfall and Temperature Variability Over Time and Space

Exploring Climate Patterns in Karnataka, India: A Comprehensive Examination of Rainfall and Temperature Variability Over Time and Space

Analysis of Rainfall as an Agroclimatological Element on Production and Determining the Planting Season of Shallots (Allium Cepa L.) in South Tapanuli Regency

The amount of rain that falls affects the availability of water for planting. For planting scheduling purposes, farmers still use conventional techniques. In our opinion, this decision is not the best. This research aims to determine the relationship between rainfall and the suitability of sweet potato plants by analyzing the impact of rainfall in South Tapanuli Regency on shallot production using rainfall data. Literature review and survey approach were the research methods used. This research uses a survey methodology using descriptive and correlational methodology to ascertain the relationship between rainfall and precipitation. The weakness of this positive influence is shown by the results of regression analysis and correlation of rainfall variables with the magnitude of productivity variables from research conducted a decade earlier. This information was conveyed by the South Tapanuli Regency Central Statistics Agency.

Comprehensive Analysis of Rainfall Variability in Urban Maiduguri, Nigeria: Implications for Climate Resilience and Sustainable Development

Climate variability, including its effects on precipitation and atmospheric conditions, is a critical aspect of Earth’s dynamic climate system. The increasing frequency of extreme weather events worldwide necessitates a thorough understanding of climatic changes. To address the scarcity of localized climatic studies, this research focuses on Maiduguri, the capital of Borno State in northeastern Nigeria. This study aimed to assess the rainfall variability of urban Maiduguri, with the objectives of investigating rainfall patterns, examining trends, and evaluating variability. The study utilized 31 years of rainfall data from the Nigerian Meteorological Agency, spanning from 1992 to 2023. The data was statistically analyzed using the Coefficient of Variation, Rainfall Anomaly Index (RAI), Sen slope, and the Mann-Kendall trend test. The findings indicate that the minimum annual rainfall in Maiduguri is 292.7 mm, the maximum is 838.2 mm, and the mean annual rainfall is 519.34 mm. These values provide essential insights into the range and central tendency of Maiduguri’s annual rainfall, contributing to a comprehensive understanding of the region’s climatic conditions. Moreover, August stands out as the month with the highest mean value of 196.66 mm, indicating the peak of the rainy season. The Total Annual Rainfall Trend in Maiduguri shows a significant positive Sen’s Slope value obtained from the Mann-Kendall test (z-statistic = 2.773, p-value = 0.005), suggesting an increasing trend in monthly rainfall. The calculated Sen’s Slope of 172.000 highlights the peak rainfall during the core months of the rainy season. This indicates a moderate level of rainfall variability (CV = 28.3%), accompanied by notable fluctuations in the Rainfall Anomaly Index (RAI) during critical months. The Mann-Kendall trend test suggests potential shifts in precipitation patterns. This research serves as a foundation for informed decision-making by local authorities, urban planners, and environmental stakeholders.

Investigation of the Variability of Dry / Wet Meteorological conditions using the SPEI / SPI Index in the Selo Watershed, Period 1981-2020.

The significant impact of climate change is high variability in rainfall and an increase in extreme rainfall events. The impacts resulted in severe dry/wet conditions. Severe dry conditions can lead to crop failure due to lack of water availability, while severe wet conditions can indicate flooding. This paper aims to investigate the variability of dry/wet meteorological conditions in the Selo watershed. The investigation was carried out using the SPEI and SPI. The results show that wet/dry conditions do not have a clear pattern variability in the long term. Seasonal wet/dry conditions show a changing trend towards wetter conditions in all seasons. Spatially, the changes occur throughout the study area, with the most significant changes occurring in the upstream and downstream parts of the watershed. In the last two decades, wet conditions are more dominant to replace dry conditions that were more prominent in the previous two decades. Other analysis results conclude that there is no influence of air temperature in creating wet/dry conditions in the Selo watershed area. So that the characteristics of wet/dry conditions are very useful for considering the selection of agricultural adaptation strategies in dealing with variability and climate change in the Selo watershed area.

Impact of Climate Change Assessments on Horticultural Crop Dynamics in Tamil Nadu, India

Aim: This study aims to analyze the dynamics of horticultural crop production in Tamil Nadu amidst climate change, focusing on trends, challenges, and opportunities across diverse agro-climatic zones, primarily considering variations in rainfall.
 Data: The research utilizes comprehensive datasets spanning a decade (2012-13 to 2021-22) encompassing records of rainfall, horticulture cultivation area, and production. The study focuses on major horticultural crop categories, including fruits, vegetables, plantation crops, spices and condiments, medicinal and aromatic crops, and flowers.
 Objective: (1) analyze compound annual growth rates of area, production, and productivity of key horticultural crops in Tamil Nadu, (2) conduct decomposition analysis over a decade for horticultural crops, and (3) assess climate change impact on agricultural production in Tamil Nadu focusing on rainfall patterns.
 Methodology: The study utilizes various analytical tools, including Compound Annual Growth Rate (CAGR) analysis, percentage analysis, decomposition analysis, and the Cobb-Douglas production function. Secondary data from government publications, including the Season and Crop Report of Tamil Nadu and the Department of Horticultural and Plantation Crop Report of Tamil Nadu, are utilized for analysis.
 Results: The analysis reveals intricate patterns influenced by climate variability, with certain zones demonstrating harmonious growth, while others contend with challenges related to cultivation and productivity. The decomposition analysis highlights the respective contributions of changes in cultivation area and crop yield to variations in production, underscoring the impact of climate change on agricultural dynamics. Additionally, the Cobb-Douglas production function analysis indicates significant effects of both rainfall and cultivation area on horticultural production, emphasizing the multifaceted nature of factors influencing production trends.