Variable Rainfall Patterns Research Articles

Conservation agriculture enhances maize yields and profitability in Mexico’s semi-arid highlands

Rainfed agriculture in semi-arid regions is affected by variable rainfall patterns, resulting in low yields under conventional farming systems. To address this issue, cropping systems based on conservation agriculture were evaluated in 2 field experiments on 17 farmers’ fields in the semi-arid highlands of Queretaro, Mexico, from 2013 to 2020, to assess yields and profitability. Monocropped maize (Zea mays L.) under conventional tillage was compared to growing maize on permanent beds with soil mulch, either monocropped or in rotation with triticale (X Triticum Secale Wittmack), oats (Avena sativa L.), or common beans (Phaseolus vulgaris L.). In the San Juan del Rio field experiment, maize yields on permanent beds averaged from 2,475 to 3,517 kg ha− 1 over five years, exceeding 70% the yields under conventional tillage. In the Cadereyta field experiment, 4-year average maize yields on permanent beds ranged from 979 to 1,382 kg ha− 1 with no significant difference to those under conventional tillage. In farmers’ fields, maize on permanent beds yielded an average of 3,717 kg ha− 1, 70% higher than with conventional tillage. The most profitable system was permanent beds with a maize-bean rotation in field experiments and with maize monocropping in farmers’ fields. Overall, conservation agriculture can improve maize yields and profitability in Central Mexico’s semi-arid highlands.

Assessing climatic change impacts on mangrove structural dynamics on the northern coasts of the Persian Gulf

Abstract. Monitoring long-term trends in structural changes within mangrove ecosystems is essential for understanding their response to climate change and devising effective adaptation strategies in coastal regions. This study examines changes in mangrove patchiness within the Hara Biosphere Reserve (HBR) along the northern coasts of the Persian Gulf (PG) over a 31-year period (1986-2017), focusing on variations in rainfall patterns and drought occurrences. Employing a 35-year time series of monthly Standardized Precipitation Index (SPI) values alongside satellite imagery analysis, trends in both the number of patches (NP) and the Largest Patch Index (LPI) were assessed at the mangrove stand level. The analysis reveals a significant correlation between structural changes in mangroves and drought events. Pre-1998, characterized by wetter conditions, witnessed a decrease in both NP and LPI, indicating patch expansion and habitat extension. Conversely, post-1998, during drought periods, both indices increased, indicating habitat degradation due to heightened drought intensity. Pre-1998 structural changes in the HBR signify habitat expansion, with increased patch extent and core areas reflecting enhanced structural integrity. However, post-1998, a concerning trend of habitat degradation emerged, with increased NP and LPI attributed to intensified droughts. These findings highlight a transitional period marked by favourable conditions for mangrove growth followed by habitat degradation linked to increased drought intensity. This underscores the vulnerability of mangrove ecosystems to climate change impacts, particularly exacerbated droughts, necessitating urgent efforts for conservation and management to preserve biodiversity and ecosystem services in coastal regions.

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

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

Inter-annual variability in the phytoplankton abundance and community structure of a tropical monsoonal estuary in response to variable monsoonal patterns

The study elucidates the inter-annual and seasonal variability in the phytoplankton abundance and community structure concurrent to variable rainfall patterns in Chettuva, a tropical monsoonal estuary along the southwest coast of India. The phytoplankton community exhibited statistically significant inter-annual variation with a 1.2-fold increase in abundance (av. 19.44 ×104cellsL−1) in 2021 in comparison with 2019 (av. 15.30 ×104cellsL−1). A total of 87 phytoplankton species were observed during the study, among which Bacillariophyceae dominated with 52 species, followed by Dinophyceae (20 species), and to a lesser extent by Chlorophyceae (11 species) and Cyanophyceae (2 species). Phytoplankton community structure also showed variation, with 71 species contributing to the diversity in 2021 and 62 species in 2019. Though Bacillariophycea dominated during both years, the community was more diverse in 2021comparatively a more saline year Biddulphia sp. Coscinodiscus sp., Cyclotella striata, Melosira sulcata, Pleurosigma sp., and Skeletonema costatum were the diatoms common to both the years, though with varied abundance. Dinophyceae did not show a conspicuous change in community structure in both years, with mostly the same species contributing to the total phytoplankton population but showed slight variation in abundance. Cyanophycea was observed exclusively during the year 2021. The abundance and community structure of the species varied from year to year, but they also changed seasonally, with lower numbers observed during the monsoon compared to the post-monsoon period in both years. CCA analysis identified two groups of phytoplankton assemblages in relation to the prevailing environmental conditions. The Group I species preferred lower temperature and salinity and elevated nutrient concentrations and they dominated during the monsoon season. Group II species preferred high saline well oxygenated, nutrient-poor waters and they dominated during the post-monsoon. Furthermore, the trophic state index analysis indicated mesotrophic conditions during the monsoon, while eutrophic conditions were observed in the post-monsoon period.

Climate and Land Use Change Pressures on Food Production in Social-Ecological Systems: Perceptions from Farmers in Village Tank Cascade Systems of Sri Lanka

Climate and land use change pressures are critical to food production in Social-Ecological Systems (SESs). This study assessed farmers’ perceptions of the pressures of climate and land use changes alongside their impacts on food production in Mhahakanumulla Village Tank Cascade System (MVTCS), a SES maintained by traditional agricultural land use systems in the dry zone of Sri Lanka. This study used both rating and ranking scale questions to quantify farmers’ perceptions. The tobit regression model was employed to evaluate how farmer perception was influenced by socio-economic factors. The results showed that most of the farmers had experienced that the climate of the MVTCS area had changed over time, and they perceived variability of rainfall patterns as the most prominent and influential climate change. The increased cost of production, wildlife damage, and land degradation were ranked by the farmers as the most impactful factors of food production due to climate change. The farmers rated deforestation and land clearing as the most influential and impactful changes in land use, while wildlife damage and land degradation ranked as the highest impacts on food production due to land use changes. Among the socio-economic determinants, training and income/profit positively influenced farmer perceptions of the severity of both climate and land use change. The level of farmer’s adaptation to climate change had a negative association with their perception of the severity of climate change. Household size negatively influenced the perceptions of the severity of climate change while positively influencing perceptions of land use change impacts. Among the spatial determinants, farm size and downstream locations of MVTCS positively influenced perceptions of the severity of both climate and land use change. Thus, the effectiveness of adaptation strategies towards climate and land use change pressures depends on how well they are understood by the farmers. The study findings provide helpful insights for formulating localized land use policies and climate change adaptation strategies in these globally important landscapes with a combination of both top-down and bottom-up approaches.

Rainfall And Drought Characteristics For Crop Planning In Cuttack District Of Odishastate, India

The increasing frequency of agricultural droughts and variability in rainfall patterns over the past three decades prompted this study, which analyzed 34 years of block-wise daily rainfall data (1988-2021) from Cuttack district, Odisha. The aim was to determine the long-term average and temporal variability of rainfall on weekly, monthly, seasonal, and annual scales, with a focus on identifying strategies to mitigate production risks in the region. The average annual rainfall in Cuttack was 1597.6 mm, with August receiving the highest amount of rainfall (476 mm), followed by July (407 mm) during the monsoon season. A trend analysis on rainfall over the past 34 years showed an increasing pattern of an average 6.03 mm and 3.6 mm per year in annual and kharif season rainfall, respectively. Agricultural droughts were frequently observed during the early (22-28 Standard Meteorological Week) and late (36-42 SMW) stages of kharif crops. The study also revealed that the station is prone to mild-moderate meteorological droughts of different intensities, including mild,moderate, and severe. To minimize production risks, short duration, low water-requiring, high-value crops such as maize, pulses, oilseeds, and some vegetables are recommended for this region.

Detection of Dominant Rainfall Patterns in Indonesian Regions Using Empirical Orthogonal Function (EOF) and Its Relation with ENSO and IOD Events

Several studies indicate that Indonesia’s position on the equator, where winds from the northern and southern hemispheres converge, leads to year-round rainfall in Indonesia. Previous studies have also shown the presence of monsoon winds over Indonesia, which causes rain in Indonesia to follow the Asian and Australian monsoon wind patterns. Recently, variations in rainfall patterns and intensity in Indonesia have been highly volatile and difficult to predict. This study uses gridded data analysis of ocean-atmospheric rainfall for 69 years (1948–2016) to detect monsoonal, equatorial, and local signals and relate them to ENSO and IOD events. Rainfall analysis reveals that the first mode accounts for 38.57%, the second 13.92%, and the third 8.93% of the total variance. These results show that these variants are very variable, both in space and time. Monsoonal patterns were detected in the southern region of Indonesia, equatorial patterns in northern Sumatra and western Kalimantan, while local patterns were detected in Maluku and the Maluku Islands. Furthermore, the monsoonal and local rainfall patterns are strongly correlated (> 0.85) with local SST, but the equatorial rainfall pattern is weakly correlated (< 0.5) with local SST. Note that the equatorial pattern is associated with the Inter Tropical Convergence Zone (ITCZ) phase. The varied vector correlation map illustrates the asymmetric fluctuations in sea surface temperature and the simultaneous existence of winds coming from the west from the Indian Ocean and winds coming from the east from the Pacific Ocean during the period of heavy precipitation.

Differential responses of soil CO2 dynamics along soil depth to rainfall patterns in the Chinese Loess Plateau

Soil surface carbon dioxide (CO2) efflux not only originates from topsoils, but also significantly involves contributions from deeper soil layers. Soil surface CO2 efflux significantly fluctuated with rainfall patterns in arid and semiarid regions. However, how soil CO2 dynamics respond at different soil depths to varying rainfall patterns remains largely unclear. To address this gap, we continuously monitored soil CO2 concentrations, temperature, and moisture content at 10 cm, 50 cm, and 100 cm depths in situ under cropland and orchards located in the semiarid Loess Plateau over a full year. Rainfall events were meticulously recorded, categorizing them into light (<10 mm), moderate (10 mm–40 mm), and heavy (>40 mm) to discern their impact on soil CO2 dynamics. Specifically, soil CO2 flux was not affected during light rainfall. Moderate and heavy rainfall decreased soil CO2 flux at 0–10 cm by an average of 70% and 83%, respectively. This decrease was associated with reduced gas diffusivity across rainfall patterns. For instance, heavy rainfall reduced gas diffusivity by an average of 83% and 53% at 10 cm and 50 cm soil depths, respectively. Furthermore, soil CO2 concentrations slightly dropped as soil temperature decreased at 10 cm depth during light rainfall. Soil CO2 concentrations at 10 cm and 50 cm depths initially decreased by up to 15% and subsequently increasing by up to 52% during moderate and heavy rainfall. This response was likely influenced by temperature reductions and subsequent rises in moisture content, with a hysteretic response of soil CO2 concentrations to temperature. The rapid increase in soil CO2 concentrations was mainly due to a substantial decrease in gas diffusivity. Notably, heavy rainfall induced a delayed increase in soil moisture content at 50 cm depth and a significant decrease in CO2 concentration by 16% at 100 cm depth. A substantial decrease in soil CO2 concentrations in deep soil layers was primarily related to decreased soil temperature. Additionally, the observed soil CO2 dynamics were partly attributed to biotic factors (microbial biomass carbon and root density) mainly on cropland, but mainly abiotic factors (soil organic carbon and bulk density) under orchards. Overall, these results suggest that reduced gas diffusivity triggered by increased soil moisture content in topsoils and weakened biological processes caused by decreased soil temperature in deep soils typically drive the differential responses of soil CO2 dynamics to rainfall patterns.

Women’s coping and adaptation strategies to climate change: a case of the Dodoma Region

ABSTRACT Climate change is escalating in intensity, frequency, and complexity. Its related threats have profound implications for humanity, particularly women. Measures to cope with and adapt to it have been researched in general and does not focus on the role of women in the specific community, who bear the brunt of the strife associated with climate change. It is from this view that this research assesses the effects of women’s coping and adaptation mechanisms to climate change. Data were collected through household surveys, key informant interviews, observation, and documentary reviews. The collected data were descriptively analysed with the aid of SPSS version 20 and Microsoft Excel. Results indicate that communities understood climate change in terms of variability in rainfall patterns and amounts, water availability, increased incidences of drought, and decreased agricultural yields. Such events reduced agricultural productivit and led to the reoccurrence of food insecurity. Women grew drought-tolerant and early-maturing crop varieties, walking long distances to fetch water, crushing pebbles, and engaging in small vending businesses, which had some effects, including falling into family conflicts, diseases, and food insecurity. The study suggested that there is a need for upscaling awareness and capacity building on good agricultural practices, the provision of loans to small vending businesses, and the provision of inputs that assist women to cope with climatic changes smoothly.

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

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

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

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

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.

Observations of enhanced rainfall variability in Kenya, East Africa

Understanding local patterns of rainfall variability is of great concern in East Africa, where agricultural productivity is dominantly rainfall dependent. However, East African rainfall climatology is influenced by numerous drivers operating at multiple scales, and local patterns of variability are not adequately understood. Here, we show evidence of substantial variability of local rainfall patterns between 1981 and 2021 at the national and county level in Kenya, East Africa. Results show anomalous patterns of both wetting and drying in both the long and short rainy seasons, with evidence of increased frequency of extreme wet and dry events through time. Observations also indicate that seasonal and intraseasonal variability increased significantly after 2013, coincident with diminished coherence between ENSO (El Nino Southern Oscillation) and rainfall. Increasing frequency and magnitude of rainfall variability suggests increasing need for local-level climate change adaptation strategies.

Perspectives on total phosphorus response in rivers: Examining the influence of rainfall extremes and post-dry rainfall

Eutrophication is a significant environmental problem caused by nutrient loads from both point and non-point sources. Weather variables, particularly precipitation, affect the concentration of nutrients in water bodies, particularly those from non-point sources, in two contrasting ways. Heavy precipitation causes surface runoff which transports pollutants to rivers and increases nutrient concentration. Conversely, increased river flow can dilute the concentration, lowering it. This study investigates the impact of extreme precipitation, prolonged precipitation, and precipitation after a dry period on the total phosphorus concentration in the Moehne and Erft rivers in Germany, given the projected increase in frequency of extreme precipitation events and long drought periods due to climate change. The study comprises two parts: selecting extreme weather days from 2001 to 2021 and comparing observed Total Phosphorus concentrations with estimated concentrations derived from Generalized Additive Models and linear regression based on the discharge-concentration relationship. Changes in river TP concentration in response to continuous precipitation and precipitation after a dry period were also studied. Our results showed that during wet extreme and post-dry period rainfall events, TP concentration consistently surpassed expected values, underscoring the profound influence of intense rainfall on nutrient mobilization. However, we observed the impact of continuous rainfall to be non-unidirectional. Our work is distinguished by three key innovations: 1) addressing limitations in studying the effects of extreme weather on water quality due to limited temporal resolution, 2) incorporating both linear and non-linear modeling approaches for discharge-concentration relationships, and 3) performing a comprehensive analysis of temporal and spatial patterns of Total Phosphorus concentrations in response to varying rainfall patterns.

Analysing Spatial and Temporal Rainfall Variability of Southern Rajasthan using GIS Approach

This study focuses on analysing the behavioral patterns of rainfall in southern Rajasthan, particularly in reference to its amount and variation over time. The study made use of standard meteorological weeks (SMW) and historic weekly rainfall records. IDW (Inverse Distance Weighting) interpolation technique within the ArcGIS platform was used for spatial analysis, creating spatial variation maps. It was found that significant rainfall occurred from Standard Meteorological Week (SMW)-22 to SMW-42. The weekly mean rainfall across the study area during the monsoon period ranged from 2.1 to 68.4 mm, with corresponding standard deviations ranging from 9.3 to 79.7 mm. Notably, the standard deviation was often higher than the mean, indicating substantial variability in weekly rainfall. Trend analysis using the Mann-Kendall test revealed that most stations in the study area exhibited no significant trend in annual rainfall data. Similarly, weekly rainfall data showed a trend in SMW-30 for 44 stations, while all other weekly data series did not exhibit significant trends, except for SMW-30. Stationarity was generally accepted for most weekly data series, except for SMW-30 and SMW-32, which showed non-stationary behavior. However, homogeneity tests indicated homogeneity in weekly rainfall data series for almost all selected stations. Overall, the study highlighted the significant variability in rainfall patterns in southern Rajasthan, with no significant trend in annual rainfall, except during the peak monsoon period (SMW-30). These findings contribute to a better understanding of regional rainfall behavior, which is crucial for water resource management and agricultural planning in the region.

Prediction of Rainfall Trends using Mahalanobis-Taguchi System

Full comprehension of precipitation patterns is crucially needed, especially in Pekan, a district in Pahang, Malaysia. The area is renowned for its elevated levels of precipitation, making it imperative to precisely categorize and enhance the analysis of rainfall patterns to facilitate effective resource allocation, agricultural productivity, and catastrophe readiness. The variability of rainfall patterns is contingent upon geographical location, necessitating the collection of a comprehensive data set that includes several characteristics that influence precipitation to make reliable predictions. Data were collected from the Vantage Pro2 weather station, which is located on the UMP Pekan campus. This study used the RT method to classify rainfall and T-Method 1 to determine the degree of contribution of each parameter. Significant parameters were validated using a data set from the same type of weather station but in a different district. The results showed that the Mahalanobis-Taguchi Bee Algorithm (MTBA) is more effective than the Mahalanobis-Taguchi System (MTS) in finding the significant parameters, but the parameters were a subset of MTS Teshima. Finally, the validation with T mean-based error (Tmbe) using Mean Absolute Error (MAE) revealed a pattern of errors to provide insight to find the significant parameters of MTS.

Influence of Eastern, Central and Mix El Niño on the variability of rainfall in southeastern South America

El Niño/Southern Oscillation (ENSO) plays a very important role in modulating rainfall variability in South America. Although the effects of conventional ENSO are well known, there are significant variations in rainfall patterns when sea surface temperature anomalies manifest over the different regions along the equatorial Pacific, namely, Niño 3 (East), Niño 3 and Niño 4 (Mix), and Niño 4 (Central) ENSO. This work aims to analyze the variability of precipitation in southeastern South America associated with different types of El Niño (EN). For this, composite analyzes of precipitation, winds at low and high levels, vertical velocity and geopotential height were carried out. The results indicate that the rainfall pattern associated with Eastern Pacific (EP) El Niño shows the classic features expected from EN described in previous literature, i.e., a stronger and well-defined positive precipitation anomaly over southeast South America, while Central Pacific (CP) El Niño shows weaker and even an opposite sign over the region. Mix (MX) El Niño events present a spatial pattern of precipitation anomalies similar to EP El Niño events, but with lower intensity in the study area. The heterogeneity of EN-related precipitation effects results from a change in large-scale atmospheric circulation patterns: the Walker cell in the tropics, the Rossby waves in the extratropics, and the circulation of low and high-level jet streams. EP El Niño events feature a configuration similar to the South American Low-Level Jet, the strengthening of the subtropical jet, better structured Rossby waves and the Pacific-South America pattern. These characteristics are observed in MX El Niños, but in a weaker way compared to EP El Niños. CP El Niño events, on the other hand, present a weakening and spatial displacement of the aforementioned conditions, which is reflected in negative precipitation anomalies in most seasons. Thus, more significant rainfall variations are noted between the EP El Niños and CP El Niños, with a predominance of positive and negative rainfall anomalies in the study area, respectively.

Analysis of recent decade rainfall data with new exponential-exponential distribution: Inference and applications

This study introduces the New Exponential-Exponential Distribution (NEED) within the broader New Exponential-Generating (NE-G) family of distributions, targeting enhancements in rainfall data analysis. The significance of this research lies in addressing the need for sophisticated statistical models to accurately capture the complex variability of rainfall patterns, which are critical for effective environmental planning and disaster management. Aiming to refine rainfall data modeling, we employ the NEED model, emphasizing its application across diverse climatic conditions. Our methodology encompasses a comprehensive evaluation of seven distinct parameter estimation techniques, with a particular focus on the Anderson-Darling and maximum product spacing methods. These were selected based on their performance in minimizing bias and mean square error, assessed through a rigorous Monte-Carlo simulation study. Additionally, the study utilizes rainfall data from various geographical regions to validate the model's efficacy. The major conclusion of our investigation is the demonstrable superiority of NEED over traditional models in fitting rainfall data, as evidenced by its enhanced predictive accuracy. This outcome not only contributes to the theoretical advancements in statistical meteorology but also offers practical methodologies for improved weather forecasting. The integration of NEED with contemporary machine learning algorithms further suggests potential for groundbreaking applications in climate science and water resource management.

Stability analysis of a loess landslide considering rainfall patterns and spatial variability of soil

Accurate analysis of the stability of rainfall-induced landslides is an important and urgent task. Under complex geological conditions, traditional stability analysis methods cannot meet stability evaluation requirements. To study the impact of the rainfall pattern on the probability of instability of loess slopes, the intensity-frequency-duration curve and breakdown coefficient methods were introduced to analyse the effects of the rainfall extremum distribution and time scale, and bounded random cascade simulation were used to generate random rainfall patterns (RRPs). The obtained RRPs under several classical rainfall patterns were compared in terms of the safety factor, rainfall threshold (IaS/IaF), and annual failure probability (AFP) of loess slopes. Finally, soil preferential flow and spatial variability combined with the RRPs were introduced to analyse the change in the stability of rainfall-induced loess landslides under different theoretical frameworks. It was revealed that the use of a uniform rainfall pattern produced conservative stability evaluation results. Considering the RRPs, the calculation of IaS/IaF and AFP should include the common characteristics of various rainfall patterns. By comprehensively evaluating slope stability considering soil spatial variability and rainfall pattern variability, this study revealed that neglecting certain factors may result in incorrect slope stability estimates. Incorporating multiple assumptions in failure probability evaluation could provide more comprehensive slope safety assessment.

Impacts of recent rainfall changes on agricultural productivity and water resources within the Southern Western Ghats of Kerala, India.

Significant changes in rainfall patterns are critical to agriculture, and the dependency of cropping systems on rainfall variability would engender appropriate farming practices and agriculture policies for a climate-resilient agriculture system. This study analyses the significance of rainfall variability on agriculture productivity in the Wayanad district of Kerala (India) using time series data on rainfall (1989-2019) and crop yield (2000-2019). The spatial variability of rainfall patterns reveals a dichotomy between the rain gauge stations in the northern and southern parts of the region. Despite the absence of statistically significant trends in the monthly, seasonal and annual rainfall, based on the Mann-Kendall trend analysis, an increase in the yield of many crops (e.g., winter paddy, banana) is evident, which emphasises the critical role of irrigation in driving the crop productivity. As an adaptation strategy to changing rainfall patterns, irrigation would meet the additional crop water requirement for sustainable agricultural production under the varying rainfall distributions. However, the increase in the area under irrigation in recent years has had significant implications for both surface water and groundwater resources. The conclusive findings suggest that the region requires climate-resilient agriculture, focusing on optimising irrigation and developing sustainable agriculture and water conservation strategies.