Seasonal Rainfall Research Articles

Relative contribution of dynamic and thermodynamic components on Southeast Asia future precipitation changes from different multi-GCM ensemble members

To address the gap in understanding precipitation changes in Southeast Asia and to enhance the reliability of climate projections for the region through moisture budget analysis, this study examines the differences among six multi-model ensembles of CMIP6 simulated precipitation in term of moisture budget analysis. It investigates the relative contributions of thermodynamic and dynamic components to seasonal precipitation changes over Southeast Asia under the highest emission scenario, SSP5-8.5. The comparison between ensembles indicates that Good performance model ensembles slightly outperform the combination of all resolution and all category ensembles in reducing the biases. There is no strong evidence showing that good category ensembles outperform the combination of all model ensemble groups in simulating the spatial pattern of historical seasonal precipitation. From the perspective of moisture budget, regions receiving seasonal high rainfall intensity are mainly influenced by the moisture convergence during the monsoon seasons: northeast monsoon (December‒January‒February) and southwest monsoon (June–July–August). By the late 21st century (2081–2100), all model ensemble projections show an increase in December‒January‒February precipitation over the northern Southeast Asia and decreased June‒July‒August rainfall in the southern regions. The moisture budget analysis explained that the seasonal mean rainfall change in Southeast Asia is largely influenced by evaporation and followed by moisture flux convergence. The changes in moisture flux convergence are contributed by both the dynamic and thermodynamic components. Greater inter-model uncertainty was found in the precipitation dynamic component compared to the thermodynamic component suggesting the existence of large discrepancy between the various approaches used by GCMs in describing atmospheric dynamics. The study highlights that the Good model ensemble with middle to low resolution is able to narrow the inter-model uncertainties in terms of the moisture budget analysis compared to the combination of all Good model ensembles.

Variação das características pluviométricas na Planície de Reeds sob os impactos das mudanças climáticas

ABSTRACT Globally, there is enough evidence to affirm that climate change is one of the main factors causing the changing trends in rainfall features (CTRFs). The aim of this study, therefore, is to assess the CTRFs across the rice cultivation regions (RCRs) of the Plain of Reeds in Vietnam. Vietnam is a prime example of the impacts of climate change (ICC) on agricultural activities. Daily rainfall data series from 16 observation stations during the period 1984-2015 were appraised for quality and then the CTRFs were detected by applying the Mann_Kendall test and Sen’s slope estimator. The quality of rainfall data at all observation stations is homogeneous with a significance level of 0.05. Over a year, the trends in rainfall features slightly increased at most obsrvation stations, except for Hong Ngu and Moc Hoa. Additionally, the trends in rainfall features slightly decreased in rainny season rainfall (RSR) at 10 out of 16 observation stations, while insignificant to significant increasesing in dry season rainfall (DSR) were detected at all observation stations.

Determining the onset and cessation of seasonal rains in Shire Valley, Southern Malawi

Background: Malawi's agricultural activities rely heavily on rainfall. Rain signals the start of a distribution of rain that is unsuitable for plant development, whereas lighting marks the end of rain's ability to support plants. Malawi's tropical savanna climate has a rainy season from November to April, accounting for 95% of total annual rainfall, and a dry season from May to October, with milder temperatures and occasional showers at higher elevations from May to July. Methods: This study examines the commencement and cessation of seasonal rainfall in the Shire Valley, southern Malawi, between 1990 and 2019. The objectives include identifying rainfall characteristics, examining trends in the start, closure, and length of the rainy season, and categorizing onset and narrative dates. The Department of Climate Change and Meteorological Services provided daily rainfall data, and dates were determined using R-Instat. Findings: Rain often begins in Nsanje on November 28, followed by Ngabu on December 7, and Nchalo on December 9. The advertising dates are comparable across stations: April 3 for Nchalo and Ngabu, and April 5 for Nsanje. While most years fall within the typical range, global phenomena like La Niña and El Niño can cause early or late results. Conclusion: These insights are crucial for sustainably managing and developing socioeconomic activities at the national and district levels in Malawi. Novelty/Originality of this Study: This study provides a comprehensive analysis of the onset and cessation dates of the rainy season, utilizing long-term rainfall data spanning from 1990 to 2019. This localized analysis addresses existing gaps in the understanding of regional rainfall characteristics, which is essential for enhancing agricultural planning and mitigating resource wastage in an area heavily reliant on rain-fed agriculture.

New records for the Middle Siwalik flora of eastern Nepal and their climatic significance

The Siwalik plant fossils are important in understanding the climate change caused by the uplift of the Himalaya. The plant fossil assemblages are abundantly known from Nepal's central and western Siwalik sediments. However, they are poorly known from the Siwalik of the eastern region from where the modern Indian Summer Monsoon (ISM) enters Nepal. That is why fossils of this region are important in understanding the evolutionary history of ISM in Nepal. In the present communication, we report four new fossil leaf impressions/ compressions, namely Thelypteridaceophyllum tertiarum Joshi and Mehrotra of the family Thelypteridaceae, Calophyllum suraikholaensis Awasthi and Prasad of the Calophyllaceae, Albizia siwalica Prasad of the family Fabaceae, and Daphnogene makumensis Mehrotra, Dilcher et Lott of the family Lauraceae from the well-dated Middle Siwalik (∼9.5 Ma) sediments of eastern Nepal. The aforementioned and previous fossil records suggest that seasonal or monsoonal rainfall was present during the deposition of the sediments.

Long-Term Seasonal and Annual Rainfall Trend Analysis of Giridih District, Jharkhand, India

Long-Term Seasonal and Annual Rainfall Trend Analysis of Giridih District, Jharkhand, India

Riverine seasonal rainfall event tracing of organic pollution sources using fluorescence fingerprint difference spectrum

Elucidating the dynamics of dissolved organic matter (DOM) transport and transformation under seasonal rainfall events is essential for the conservation of riverine ecosystems, for mitigating the effects of climate change, and for crafting informed water management strategies. Therefore, this study aimed to investigate the evolutionary characteristics of organic pollution sources during consecutive rainfall events in early spring and to quantify their relative contributions to the process of surface water pollution. The results showed seasonal rainfall induces water quality exceedances in rivers due to the combined impacts of terrestrial inputs and endogenous releases. Humic acid (HA) (region V) and fulvic acid (FA) (region III) emerged as the predominant organic matter in the water column, with their fluorescence intensity altering as rainwater flushed the riverbed. Sources of pollution include agricultural and urban domestic sources (AS + DS) (72.29 %), industrial and urban domestic and microbial sources (IS + DS + MS) (37.71 %), and agricultural and industrial sources (AS + IS) (63.32 %), indicating that agricultural surface pollution discharges contribute significantly. The gas–chromatography–mass spectrometry (GC–MS) further confirmed that exogenous inputs were predominantly comprised of particulate pollutants. This study underscores the efficacy of fluorescence difference spectrometry in delineating the migration and transformation of river pollution sources during seasonal rainfall and facilitating the implementation of targeted management strategies for river ecosystems.

The distribution and drivers of tree cover in savannas and forests across India

The distribution of forest and savanna biomes and the role of resources (climate and soil) and disturbances (fire and herbivory) in determining tree-grass dynamics remains elusive and variable across geographies. This is especially problematic in Indian savannas which have been historically misclassified as degraded forests and are targeted for tree-planting. Here, we examine biome distribution and determinants through the lens of tree cover across India. Our analyses reveal four distinct zones of differing tree cover, with intermediate zones containing savanna vegetation. Rainfall seasonality determines maximum possible tree cover non-linearly. Once rainfall seasonality is factored out, soil sand fraction and topography partially explain residual variation of tree cover. High domestic livestock herbivory and other anthropogenic pressures reduce tree cover. Lastly, lack of detectable fires precludes robust conclusions about the relationship between fire and tree cover. By considering these environmental drivers in restoration planning, we can improve upon simplistic tree planting initiatives that may be detrimental to Indian savannas.

Loss and recovery of vegetation productivity in response to extreme drought during 2022 across China

Extreme droughts can severely impact terrestrial vegetation productivity. In the summer of 2022, approximately 32.7% of China’s land area experienced extreme drought conditions, marked by reduced rainfall both in terms of amount and intensity, prolonged consecutive dry days, minimal rainy days, and accompanied by heat waves. While previous studies have reported productivity losses in over half of China’s vegetated areas in 2022, how vegetation productivity recover from extreme drought remains to be clarified. In this study, we utilized the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and meteorological data to evaluate the extent and pace of vegetation productivity change from loss in 2022 to post-drought recovery in 2023. We also assessed the contributions of seven meteorological variables and two drought-related variables to variations in vegetation productivity in regions affected by extreme drought using machine-learning techniques. Our findings revealed that, in 2022, the average EVI and GPP in regions affected by extreme drought were 2.3% and 8.0% lower, respectively, compared to the baseline period from 2018 to 2021. Soil water content (SWC) emerged as the primary factor contributing to the decline in EVI/GPP, followed by the number of rainy days for EVI and vapor pressure deficit for GPP. In 2023, we observed a significant vegetation recovery, with the average EVI increasing by 2.6% and the GPP rising by 14.9% within the study area compared to 2022. SWC played a dominant role in this recovery, and both EVI recovery and GPP recovery displayed a non-linear response to SWC. Additionally, we identified a threshold effect for SWC on recovery; once SWC increased above a certain threshold, EVI and GPP would recover largely and rapidly. Furthermore, rainfall intensity and rainfall amount emerged as the second most important factors influencing the recovery of EVI and GPP, respectively. Our study underscores the significance of soil moisture and rainfall seasonality in influencing plant growth.

Drivers of seasonal rainfall variability over the Angolan and Namibian plateaus

AbstractSouthern Africa has been strongly affected by ongoing climate change in recent decades. Rainfall variability is modulated by regional patterns of moisture advection and convergence in the lower troposphere. Using reanalysis, ground and satellite‐based rainfall products and observations from 10 weather stations, we perform a synoptic and climatological analysis focussing on atmospheric circulation, moisture transport and their relationship with rainfall anomalies over the Angolan and Namibian Plateau region. Results clearly show that a stronger (weaker) Zambezi low level jet (LLJ) magnitudes are associated with above (below) normal rainfalls over the main Angolan and Namibian plateaus. With lower confidence, a stronger Limpopo LLJ may also lead to enhanced rainfall over Namibia and southeast Angola. The Zambezi LLJ moisture fluxes are moderately controlled by Mozambique Chanel Trough and Angola Low intensities, while the Limpopo LLJ intensities have very low influence from the Mozambique Chanel Trough and Angola Low, respectively. The Angola Low in its tropical phase is associated with deeper moisture convergence and stronger vertical velocities, leading to higher amounts of precipitable water within the air column, thus enhancing precipitation over the region. It is shown that the major moisture source of rainfall, which is advected to via the Zambezi LLJ, is the Indian Ocean. Meanwhile, the Atlantic Ocean plays a minor role. Given the current lack of observations and projected climate change, further research and investments are urgently needed in the region, for example, regarding the expansion of the surface data network.

Seasonal Effects of Wildfires on the Physical and Chemical Properties of Soil in Andean Grassland Ecosystems in Cusco, Peru: Pending Challenges

Soils are a valuable renewable resource on human timescales, and they interact with distinctive grassland ecosystems characterized by unique biodiversity and essential provision of ecosystem services, such as water supply and carbon sequestration. However, knowledge of the effects of wildfires on soil properties and nutrient availability in the Andes remains limited. Andean grasslands are currently one of the ecosystems of the Peruvian Andes most affected by wildfires. Our objective is to analyze the effect of fire activity on the physicochemical properties of soil and analyze its social context in Cusco, in the southern Andes of Peru. Soil samples were collected during five periods, spanning both the dry and rainy seasons, to characterize changes in soil properties and monitor vegetation recovery post-fire in two local communities dedicated to livestock activities. The vegetation restored after the wildfire was measured by the “step transect” method. Post-fire changes in soil properties indicate slight increases in pH, electrical conductivity, organic matter, nitrogen, phosphorus, and potassium during the onset of the rainy season; thereafter, a gradual reduction in these values was observed. This reduction can be attributed to leaching associated with the seasonal rainfall and runoff regime. Our findings indicate that one-year post-fire, the biomass in burned areas is reduced to 30–46% of the biomass in unburned areas. A complete regeneration is likely to occur in up to 4 years; this assertion is supported by the perceptions of the affected population, as expressed in interviews conducted in the two farming communities. These results are significant for decision-makers formulation of policies and regulations regarding grasslands and their seasonal restoration.

Deep learning-based bias correction of ISMR simulated by GCM

Simulated data of atmospheric variables like precipitation is very important for climate science research, especially for understanding future scenarios. Such data is generated by global or regional climate models (GCM/RCM), for both past and future periods under different initial conditions. Unfortunately, these models frequently display biases in simulated precipitation data compared to ground observations, due to their inability to incorporate the entire physics of the process accurately. It is imperative to mitigate such biases using contemporary techniques to make the simulated data a valuable end product. Our aim in this research is to correct seasonal (from June to September) Indian Summer Monsoon Rainfall (ISMR) data as simulated by the Climate Forecast System (CFS) over the Indian subcontinent, with the Global Precipitation Climatology Project (GPCP) data serving as the ground truth reference. This period is important as more than one-third of India's annual rainfall occurs during these months. This study presents a novel Deep Learning (DL) based architecture known as the Convolutional Neural Network for Bias Correction (CNNBC) that aims to calibrate the model-simulated data with past observations, to address these biases while preserving the statistical properties and spatial correlations relationships among grid points and enhancing the spatial mean of precipitation estimates. To evaluate the effectiveness of our proposed method, we compare its performance to that of three other statistical and DL techniques: quantile mapping (QM), quantile delta mapping (QDM), and the SRDRN model. The comparative analysis demonstrates that the CNNBC model outperforms the other in terms of our task.

Effect of Lime Source, Fineness and Granulation on Soil Permeation with Contrasting Textures under Simulated Mediterranean Climate Rainfall Conditions

ABSTRACT Granulated micro-fine limes have recently been introduced as liming materials which are purportedly more effective than conventional agricultural limes in terms of rapidly correcting subsoil acidity in no-tillage systems. Hydrated limes are known to react more quickly in soils than calcitic limes, yet their soil permeability has not been evaluated under Mediterranean climate growing conditions. Therefore, the aim of this controlled study was to compare soil permeation and pH neutralization of different liming materials (hydrated lime, calcitic agricultural lime, micro-fine lime and molasses-granulated micro-fine lime) in two contrasting (sand and sandy loam) acid (pHKCl < 4.1) topsoils under simulated Mediterranean climate conditions. Limes were surface applied to 40 cm soil columns and then rainfall (350 mm) was simulated. Subsequently, soil chemical properties (pH, exchangeable cations, available P) were measured in 5 cm increments. Application of all liming materials increased soil pH 1–2 pH units above target pHKCl of 5.5 in the top 5 cm of both soils. Only hydrated lime was able to increase soil pHKCl (5.7–6.8) below 5 cm up to a depth of 15 cm on the sand soil. Hydrated lime, however, resulted in substantial over-liming (pHKCl 7.3–8.7) of the top 5 cm of soil compared to the other materials. Granulated micro-fine lime reacted more slowly than the agricultural limes and is therefore not a suitable quick-fix for subsoil acidity in rain-fed no-till systems under Mediterranean climate rainfall conditions. Whereas hydrated lime showed potential in ameliorating subsoil acidity in the sand soil within one season of rainfall.

Indigenous Knowledge Collection in NICRA-AICRPAM Domain Districts for Weather Events

Indigenous Technical Knowledge (ITK) is the experience of local people gained from past generation and forefathers. Knowledge base is gained through tradition and culture. Prevailing knowledge is used in making farming operational management for gain in crop production. Crop and irrigation cycles are also being followed through traditional knowledge base and advanced techniques are available now. However, importance of the treasure of knowledge gained from forefathers and inherited to local people in present situation can't be ignored. Indigenous technical knowledge (ITK) is the assemblage of awareness and understanding of various facts which people have developed over a large span of time and continue to expand it. Social participation gives an idea of the respondent's participation in social activities. The NICRA-AICRPAM project has been carried out since 2011 in Chhattisgarh state. Main objective of the project is developing climate resilient technologies in agriculture. This ITK collection can be one effective mean to achieve this objective. The study was conducted to identify indigenous knowledge prevailing among the village side in NICRA domain districts Mahasamund and Kanker (Chhattisgarh state). The study was to examine traditional knowhow of farmers in Kanker and Mahasamund districts of Chhattisgarh during 2021 and their scope of application in farming activities. The objective of survey was to collect and document traditional knowledge base from the local farmers so as to work towards climate resilience in agriculture. As rainfall is the main factor in rainfed agriculture, stress was on collection of knowledge regarding rainfall prediction. The study was conducted under CRIDA, Hyderabad sponsored NICRA-AICRPAM project entitled “National Innovations on Climate Resilient Agriculture”. Project has been implemented in Department of Agrometeorology, Indira Gandhi Krishi Vishwavidyalaya, Raipur, (C.G.) since 2011. Here in this paper the astronomical, environmental and biological indicators perceived by local masses in forecasting rainfall have been collected, identified and examined. This knowledge will help its utilization in agricultural operations and practices. Interviews were conducted by Field Information Facilitators by contacting masses and peasants. Various indigenous technical knowledge of the project domain area were collected and analyzed. Experience of seasonal rainfall forecasting can be strengthened by using this knowledge base. ITK techniques of rainfall prediction may serve as supplemental knowledge base to advanced scientific technologies.

Trend and Variability Analysis of Annual Maximum Rainfall Using Observed and Remotely Sensed Data in the Tropical Climate Zones of Uganda

Understanding rainfall variability and trends is crucial for effective water resource management and disaster preparedness, particularly in tropical regions like Uganda. This study analyzes the trends and variability of the Annual Maximum Series (AMS) and seasonal rainfall data across four rainfall stations in Uganda, comparing observed data with various Remotely Sensed Rainfall (RSR) products. The key methods used in this study include the Mann–Kendall test and Sen’s slope estimator for trend analysis, AMS rainfall variability analysis using statistical performance metrics such as the Nash–Sutcliffe Coefficient of Efficiency (NSE) and Percent Bias (PBIAS), and data distribution comparisons based on goodness-of-fit evaluation using the Kolmogorov–Smirnov (KS) test. The results indicate that most trends in the seasonal rainfall and AMS data are statistically insignificant. However, the September to November (SON) observed rainfall at the Gulu station shows a statistically significant increasing trend of 7.68 mm/year (p-value = 0.03). Based on the PBIAS metric, GPCC and NOAA_CPC products outperform other RSR data products. At the Jinja station, NOAA_CPC has a PBIAS value of −12.93% and GPCC, −14.64%; at Soroti, GPCC has −9.66% and NOAA_CPC, −14.79%; at Mbarara, GPCC has −5.93% and NOAA_CPC, −11.63%; and at Gulu, GPCC has −3.05% and NOAA_CPC, −19.23%. The KS test results show significant differences in the distribution of RSR data and observed rainfall data, though GPCC shows significant agreement at the Gulu (p-value = 0.60) and Mbarara (p-value = 0.14) stations. Additionally, NOAA_CPC outperforms other RSR data products at the Mbarara station, with a KS p-value of 0.24. This study highlights the limitations of current RSR datasets in replicating observed AMS rainfall data. Based on KS test results, GPCC is identified as a better product for hydrological applications at the Gulu, Jinja, and Soroti station areas compared to other RSR products. For the Mbarara station, NOAA_CPC outperforms other RSR products.

Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania

The Southern Highlands region of Tanzania has witnessed an increased frequency of severe flash floods. This study examines rainfall data of four stations (Iringa, Mbeya, Rukwa, and Ruvuma) spanning 30 years (1991–2020) to investigate drivers of extreme rainfall and non-stationarity behavior. The Generalized Extreme Value (GEV) model, commonly used in hydrological studies, assumes constant distribution parameters, which may not be true due to climate variability, potentially leading to bias in extreme quantile estimation. Recent studies have introduced a technique for constructing non-stationary Intensity-Duration-Frequency (IDF) rainfall curves. The method incorporates trends in the parameters of the GEV distribution, only using time as a covariate. However, uncertainty exists about whether time is the most suitable covariate, highlighting the need to explore all potential covariates for modeling non-stationarity. The aim of this study is to assess the influence of other time-varying covariates on extreme daily rainfall events, considering seasonality and climate change in the rainfall data. Specifically, five processes (i.e., local temperature changes (LTC), urbanization, annual Global Temperature Anomaly (GTA), the Indian Ocean Dipole (IOD), and the El Niño-Southern Oscillation (ENSO) cycle) were studied as drivers of extreme rainfall events. Sixty two non-stationary GEV models are developed based on these covariates and their combinations, alongside two non-stationary GEV models using the time covariate to capture the seasonality of the unimodal rainfall in the region, and one stationary GEV model (S0). With the use of corrected Akaike Information Criterion (AICc), the best model for each duration (i.e., 1-, 3-, and 5-days) of rainfall series is chosen. Results indicate that local processes (i.e., LTC and urbanization) are the optimal covariates for 1 day-duration rainfall, while global processes (i.e, IOD, ENSO cycle, and GTA) are identified as the most suitable covariates for 3, and 5 day-duration rainfall across all stations. The identified best non-stationary model (with their best covariates) are then used to develop non-stationary rainfall IDF curves for all stations. According to the analysis of non-stationary extreme values, the return periods of extreme rainfall events concluded a notable decrease in comparison to the stationary approach. The study also revealed strong correlations between global climate indices (ENSO, IOD, GTA) and long-duration extreme rainfall in Tanzania’s Southern Highlands. Local factors like Urbanization and temperature changes also show significant associations with 1-day duration events. These findings emphasize the need for integrated climate forecasting to inform effective adaptation strategies. Finally, the study addresses associated uncertainties in our predictions of forthcoming extreme rainfall events through rigorous analysis. The study demonstrated that return levels for extreme rainfall events exhibit a rising trend with increasing return period, indicating heightened intensity over longer time spans, whereas, a relative uncertainty analysis illustrate escalating uncertainty with increasing return periods, emphasizing challenges in long-term prediction.

Indian Agriculture Under Climate Change: The Competing Effect of Temperature and Rainfall Anomalies

AbstractThe latest generation of global climate models robustly projects that monsoon rainfall anomalies in India will significantly increase in the 21st century due to global warming. This raises the question of the impact of these changes on the agricultural yield. Based on annual district data for the years 1966-2014, we estimate the relationship between weather indices (amount of seasonal rainfall, number of wet days, average temperature) and the most widely grown kharif crops, including rice, in a flexible non-parametric way. We use the empirical relationship in order to predict district-specific crop yield based on the climate projections of eight evaluated state-of-the-art climate models under two global warming scenarios for the years 2021-2100. We find that the loss in rice yield by the end of the 21st century lies on average between 3 - 22% depending on the underlying emission scenario. For the sustainable scenario impacts range from an increase of 3.2% to a decrease of 12.1% for individual districts. In the worst-case scenario, all districts are negatively affected, with a predicted decrease in rice yield ranging from 34% to a decrease of 11.5% in the long run. Potential gains due to increasing rainfall are more than offset by the negative impacts of increasing temperature. Adaptation efforts in the worst-case global warming scenario would need to cut the negative impacts of temperature by 50% in order to reach the outcome of the sustainable scenario.

Tree size partially mediates the influence of climate on fruit and seed production in Ricinodendron heudelotii (Baill.) Heckel

AbstractUnderstanding environmental drivers of fruit and seed production in wild edible fruit species is essential for their valorisation. Ricinodendron heudelotii is an oilseed plant whose kernels are particularly treasured in cosmetic and medical industries. This study assessed fruits and seeds production patterns of R. heudelotii. Data were collected for three consecutive years on 30 trees in the phytodistricts of Plateau, Pobè and South Borgou where it naturally occurs. Tree dbh, total height, crown diameter, number of fruits and seed mass were measured. Climatic data were obtained from NASA Power Database. Data were analysed using linear (seed mass) and generalised linear (number of fruits) mixed models and structural equation modelling. Fruit and seed production were significantly higher in the South‐Borgou phytodistrict (1433 ± 1144 fruits, 4.26 ± 3.39 kg of Dry matter per tree) and Plateau phytodistrict (1.66 ± 0.35 g DM/seed). Tree dbh further significantly mediated the effect of minimum temperature, and relative humidity on seed mass. Tree dbh, irradiance and dry season rainfall were identified as the best‐predictors of seed mass production (kg DM). These results improved current knowledge of the fruit and seed production of R. heudelotii and are of significant importance for the exploitation of the species.

Analysis of Rainfall Variability Using Precipitation Concentration Index for a Catchment

The PCI is a critical metric to assess the distribution and concentration of precipitation over a specified period. By analyzing historical rainfall data, this research aims to identify trends and patterns in precipitation concentration, shedding light on potential implications for water resource management and hydrological systems. The methodology involves calculating the PCI for different temporal scales and regions, allowing for a comprehensive assessment of rainfall variability. The findings contribute to a deeper understanding of precipitation dynamics, facilitating informed decision-making in climate-sensitive sectors. There are simple rainfall measures that can be used to provide information on variability and, therefore, on the climate state. These mainly include annual and monthly rainfall data and averages, which can be used to assess rainfall seasonality, variability, and the frequency of extreme events.

Alpine Catchments’ Hazard Related to Subaerial Sediment Gravity Flows Estimated on Dominant Lithology and Outcropping Bedrock Percentage

Sediment gravity flows (SGFs) cause serious damage in the Alpine regions. In the literature, several methodologies have been elaborated to define the main features of these phenomena, mainly considering the rheological features of the flow processes by laboratory experiments or by flow simulation using 2D or 3D propagation models or considering a single aspect, such as the morphometric parameters of catchments in which SGFs occur. These very targeted approaches are primarily linked to the definition of SGFs’ propagation behavior or to identify the predisposing role played by just one feature of catchments neglecting other complementary aspects regarding phenomena and the environment in which SGFs can occur. Although the research aimed at the quantification of some parameters that drive the behavior of SGFs provides good results in understanding the flow mechanisms, it does not provide an exhaustive understanding of the overall nature of these phenomena, including their trigger conditions and a complete view of predisposing factors that contribute to their generation. This paper presents a research work based on the collection and cross-analysis of lithological, geomechanical, geomorphological and morphometrical characteristics of Alpine catchments compared with sedimentological and morphological features of SGF deposits, also taking in to account the rainfall data correlation with historical SGF events. A multidisciplinary approach was implemented, aiming at quantifying SGF causes and characteristics starting from the catchments’ features where the phenomena originate in a more exhaustive way. The study used 78 well-documented catchments of Susa Valley (Western Italian Alps), having 614 historical flow events reported, that present a great variability in geomorphological and geological features. As the main result, three catchment groups were recognized based on the dominant catchment bedrock’s lithology characteristics that influence the SGFs’ rheology, sedimentological and depositional features, triggering rainfall values, seasonality, occurrence frequency and alluvial fan architecture. The classification method was also compared with the catchments’ morphometry classification, demonstrating that the fundamental role in determining the type of flow process that can most likely occur in a given catchment is played by the bedrock outcropping percentage, regardless of the results provided by the morphometric approach. The analysis of SGF events through the proposed method led to a relative estimate of the hazard degree of these phenomena distinguished by catchment type.

SARIMA Model-Based Monte Carlo Simulation of Option Contract Design for Maize Seasonal Heavy Precipitation in Shenyang

Agricultural production is highly dependent on weather conditions such as temperature, light, water and heat. The huge loss caused by extreme disaster weather makes the global insurance and reinsurance market overwhelmed, and seriously affects the enthusiasm of agricultural investment and the development of agricultural economy. With unique topography and vast territory, China is vulnerable to complex and diverse climate disasters. In recent years, the annual average economic loss caused by extreme weather disasters has reached about 200-300 billion yuan, among which floods caused by extreme heavy rainfall are the main agricultural disasters in China. In order to enhance the "farmer - insurance company - government" interest association to resist the risk of flood disaster caused by extreme heavy rainfall, financial hedging derivatives of weather disaster risk have gradually become a new hedging tool besides traditional insurance and reinsurance. This paper takes Shenyang, the main grain producing area in northeast China, as the sample of the study area, and uses the SARIMA model to obtain the distribution characteristics of seasonal rainfall time series. Different seasonal rainfall index option products (call option and put option contracts) are designed respectively, and the final option pricing is obtained by Monte Carlo stochastic simulation. The seasonal rainfall call option and put option contracts have opened up a new hedging model of agricultural extreme weather catastrophe outside the insurance and reinsurance market, which has enriched the varieties of weather financial derivatives market in China, reduced the severe impact of extreme weather disasters on agriculture, and enhanced the ability of agricultural stakeholders to resist the risk of extreme weather disasters.