Seasonal Rainfall Time Series Research Articles

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.

Optimization of waterproofing and drainage measures for open-pit mines based on seasonal rainfall time series prediction

Influenced by seasonal rainfall, the total groundwater inflow at the bottom of open-pit mines exhibits seasonal fluctuations. However, effective methods are lacking for forecasting groundwater inflow because groundwater inflow mechanisms remain unclear, thus hindering targeted seasonal adjustments of drainage measures. Consequently, the drainage capacity may be underutilized and emergency response capabilities may be insufficient. Addressing the aforementioned issues, this study investigated the intrinsic relationship between rainfall and groundwater inflow using seasonal time series analysis and proposes a groundwater inflow prediction model, aiming to explore the inherent correlation between precipitation and runoff. Moreover, seasonal adjustment strategies for drainage measures were explored. The proposed model can enhance the economic viability of drainage projects while ensuring safety. It addresses the key challenge of adapting measures to seasonal variations and provides a conceptual framework for optimizing drainage measures in open-pit mining.

Regionalization of rainfall in the upper Madeira basin based on interannual and decadal variability: A multi‐seasonal approach

AbstractIdentifying rainfall regions associated with specific modes of variability is of practical interest for water resources management, seasonal forecasting, and mitigation of weather‐related risks. This study aims to identify homogeneous rainfall regions within the ~1 million km2 Upper Madeira River basin—southwestern Amazon—by their interannual and decadal variability and relates this variability to ocean indices. An observed dataset of 146 ground‐based rainfall stations, distributed throughout the Andes and the Amazon, and homogenized at the monthly time‐step for the period 1980–2016, was used for the analysis. With no spatial constraints, hierarchical cluster analysis and principal component analysis (PCA) optimally grouped stations into 10 rainfall homogenous regions. The value of the regionalization for interpreting the rainfall variability was evaluated by relating the seasonal rainfall time series of the regions with ocean indices. Then, by applying PCA to seasonal rainfall series and linking the principal components to sea surface temperature and ocean indices, an insight into the main large‐scale drivers of the rainfall spatio‐temporal variability in this basin at interannual and decadal scales is provided. This analysis identified differences in the year‐round influences of the tropical Pacific and/or Atlantic oceans on the 10 homogenous regions.

Persistence of Rainfall Time Series: Kırşehir Case Study

This study examines the persistence and long-term correlation of monthly and seasonal rainfall time series of Kırşehir for the period of 1960-2019, with widely used Hurst exponent and Detrended Fluctuation Analysis (DFA) analyses. Both Hurst exponent and DFA analyses could be used to detect the long-term memory and correlation that can be assessed as a reference of predictability. To support the analyses results Augmented Dickey Fuller and Mann-Kendall tests also applied to time series. Within various rainfall series, evidence of persistence and long-term correlation was identified. According to H exponent values of simple R/S and corrected R/S methods, 10 out of 12 months and winter, spring (only simple R/S), summer (only corrected R/S) and autumn season and according to DFA scaling exponent values 4 out of 12 months and winter and autumn seasons exhibit long term correlation. On the other hand, when the H exponent and DFA scaling exponent values compared only four monthly and two seasonal rainfall series concluded to be consistent with both H exponent and DFA scaling exponent results.

Application of wavelet artificial neural networks in forecasting seasonal rainfall time series in Queensland, Australia

Predicting accurate future rainfall plays an essential role in managing the water resources as well as controlling the unforeseen phenomena like flooding or drought. In this study, an Artificial Intelligence (AI) method called wavelet artificial neural network is used to predict one-year ahead seasonal rainfall of Queensland, Australia. The technique used in this study is based on wavelet transform and artificial neural networks (ANN). Over the wavelet transforms analysis, the primary signal is split into some sub-time series called approximation and detail. The outcome of this process is some meticulous and valuable information that cannot be easily captured in the main time series. To assessment the skillfulness of the proposed method, five input sets combined of rainfall and three climate indices are defined. The comparisons showed that the wavelet-ANN (WANN) presented the lower root-mean-square-error (RMSE) with 64.8mm compared to ANN with 88.9mm, and it outperforms the ANN forecast with respect to statistical measures. Since fewer data in predicting seasonal rainfall is available compared to monthly rainfall data, the application of WANN provides promising insights into using this AI method for predicting seasonal rainfall one year in advance in Australia.

Variability and Trend Analysis of Rainfall Data of Shillong and Agartala Stations of North East India

This paper discusses the variability in rainfall and trend analysis of annual and seasonal rainfall time series of Shillong and Agartala stations located in the north-east region of India. Commonly used non-parametric statistical methods namely Mann-Kendall and Sen’s slope estimator was used to analyse the seasonal and annual rainfall time series. Statistical analysis showed less variation in annual and south-west monsoon rainfall for both Shillong and Agartala stations. In the total annual rainfall, the share of south-west (SW) monsoon, north-east (NE) monsoon, winter season and summer season rainfall was observed 64.60%, 13.22%, 1.40% and 20.80%, respectively for Shillong station of Meghalaya state. However, the contribution of SW monsoon, NE monsoon, winter season and summer season rainfall in the total annual rainfall was 59.59%, 9.55%, 1.14% and 29.72%, respectively for Agartala station of Tripura state. Non-significant increasing trends of rainfall was observed by 4.54 mm/year, 2.80 mm/year and 2.54 mm/year for annual, SW monsoon, and summer season, whereas, non-significant decreasing trends in rainfall for NE monsoon and winter season was observed with a magnitude of 1.83 mm/year and 1.63 mm/year for Shillong, Meghalaya during 1992 to 2017. Results also revealed that rainfall increased by 1.07 mm/year and 0.18 mm/year in SW monsoon and winter season whereas, rainfall decreased by 7.64 mm/year, 2.58 mm/year and 1.29 mm/year during annual, NE monsoon and summer season non-significantly during 1995 to 2019 in case of Agartala. The findings of present study will be useful for water management and crop planning in hill agriculture of Meghalaya and Tripura state of India.

Seasonal and Annual Rainfall Variability and Their Impact on Rural Water Supply Services in the Wami River Basin, Tanzania

In some parts of Africa, rainfall variability has resulted in widespread droughts and floods, thus posing a substantial challenge to water availability in rural areas, especially drinking water. Therefore, due to increasing water demands, increases in the population, and economic development, water supply systems are under constant stress. One of the critical uncertainties surrounding the effects of rainfall variability in Africa is the significant impact that it imposes on rural water supply services. The present study analyzes the trends in annual and seasonal rainfall time series in the Wami River Basin to see if there have been any significant changes in the patterns during the period 1983–2017 and how they affect the access to water supply services in rural areas. The study analyzes the trends of rainfall series of three stations using simple regression, Mann–Kendal Test and Sen’s Slope Estimator. The water point mapping datasets were analyzed considering seasonal variation. The analysis showed a statistically significant positive trend in annual rainfall at Kongwa and March–April–May (MAM) seasonal rainfall at Dakawa. The maximum increase in annual rainfall occurred at Kongwa (5.3 mm year−1) and for MAM seasonal data at Dakawa (4.1 mm year−1). Water points were found to be significantly affected by seasonal changes, both in terms of availability and quality of water. There also exists a strong relationship between rural water services and seasons.

Evaluation of district-level rainfall characteristics over Odisha using high-resolution gridded dataset (1901–2013)

In the present global warming era, exploring the regional/district-level changes in rainfall patterns/events is highly essential for understanding the diverse characteristics of rainfall, which will be value added for the policy planning for rainfed agricultural-based states like Odisha, a region that is also highly prone to flood and droughts. Since most of the global and continental scale climate studies rarely contribute for such planning of agro-economic states of India like Odisha, there is a colossal necessity for the analysis of rainfall characteristics at regional/district level. In the present study, the rainfall characteristics at district level over Odisha are extensively evaluated for winter (JF), pre-monsoon (MAM), monsoon (JJAS) and post-monsoon (OND) using India Meteorological Department high-resolution gridded rainfall analysis dataset for the period 1901–2013. The El Nino Southern Oscillation (ENSO) influence on the district-level seasonal rainfall during four distinctive seasons has also been studied. The results suggest that the frequency of various categorical rainfall events and associated seasonal rainfall patterns over Odisha is highly heterogeneous and varies from season to season in magnitude and similar statements can be highlighted for inter-annual variability and coefficient of variation. Although the seasonal rainfall time series is significantly persistent in most of the districts during all the seasons, it is manifested to the normal distribution in majority of districts only for JJAS. Statistical analysis infers that most of the districts possess reasonable abrupt changes in seasonal rainfall during mid- and late twentieth century. A significant increasing (decreasing) trend in the frequency of high-intensity (low-intensity and wet days) rainfall events over most parts of Odisha is observed for all the seasons. A notable decreasing trend in seasonal rainfall is perceived over southern districts during MAM (7–21 mm) and OND (23–49 mm), western districts during MAM (2–41 mm) and JJAS (6–309 mm) and most of the districts during JF (6–46 mm), while increasing trend is prominent over coastal and northern Odisha during MAM (7–46 mm), northern coastal Odisha during JJAS (25–175 mm), northern and northern coastal Odisha during OND (2–64 mm). It has been noticed that the ENSO influence on seasonal rainfall is insignificant during summer monsoon, while it is notable during the other seasons. This study is very useful in determining the effects and assists the risk management for various sectors in acclimating innovative technologies for a sustainable future in the present global warming era.

Analysis of Long-Term Trends of Annual and Seasonal Rainfall in the Awash River Basin, Ethiopia

With climate change prevailing around the world, understanding the changes in long-term annual and seasonal rainfall at local scales is very important in planning for required adaptation measures. This is especially true for areas such as the Awash River basin where there is very high dependence on rain- fed agriculture characterized by frequent droughts and subsequent famines. The aim of the study is to analyze long-term trends of annual and seasonal rainfall in the Awash River Basin, Ethiopia. Monthly rainfall data extracted from Climatic Research Unit (CRU 4.01) dataset for 54 grid points representing the entire basin were aggregated to find the respective areal annual and seasonal rainfall time series for the entire basin and its seven sub-basins. The Mann-Kendall (MK) test and Sen Slope estimator were applied to the time series for detecting the trends and for estimating the rate of change, respectively. The Statistical software package R version 3.5.2 was used for data extraction, data analyses, and plotting. Geographic information system (GIS) package was also used for grid making, site selection, and mapping. The results showed that no significant trend (at α = 0.05) was identified in annual rainfall in all sub-basins and over the entire basin in the period (1902 to 2016). However, the results for seasonal rainfall are mixed across the study areas. The summer rainfall (June through September) showed significant decreasing trend (at α ≤ 0.1) over five of the seven sub-basins at a rate varying from 4 to 7.4 mm per decade but it showed no trend over the two sub-basins. The autumn rainfall (October through January) showed no significant trends over four of the seven sub-basins but showed increasing trends over three sub-basins at a rate varying from 2 to 5 mm per decade. The winter rainfall (February through May) showed no significant trends over four sub-basins but showed significant increasing trends (at α ≤ 0.1) over three sub-basins at a rate varying from 0.6 to 2.7 mm per decade. At the basin level, the summer rainfall showed a significant decreasing trend (at α = 0.05) while the autumn and winter rainfall showed no significant trends. In addition, shift in some amount of summer rainfall to winter and autumn season was noticed. It is evident that climate change has shown pronounced effects on the trends and patterns of seasonal rainfall. Thus, the study contribute to better understanding of climate change in the basin and the information from the study can be used in planning for adaptation measures against a changing climate.

Spatiotemporal variability of annual and seasonal rainfall time series in Ho Chi Minh city, Vietnam

AbstractThis study analyzed spatial and temporal patterns of rainfall time series from 14 proportionally distributed stations in Ho Chi Minh City for the period 1980–2016. Both parametric and nonparametric approaches, specifically, linear regression, the Mann–Kendall test and Sen's slope estimator, were applied to detect and estimate the annual and seasonal trends after using original and notched boxplots for the preliminary interpretation. The outcomes showed high domination of positive trends in the annual and seasonal rainfall time series over the 37-year period, but most statistically significant trends were observed in the dry season. The results of trend estimation also indicated higher increasing rates of rainfall in the dry season compared to the rainy season at most stations. Even though the total amount of annual rainfall is mainly contributed by rainfall during the rainy season, the pronounced increment in the dry season can be a determining factor of possible changes in annual rainfall. Additionally, the interpolated results revealed a consistently increasing trend in the southeastern parts of the study area (i.e., Can Gio district), where annual rainfall was by far the lowest intensity compared to other regions.

Time–frequency characterization of sub-divisional scale seasonal rainfall in India using the Hilbert–Huang transform

Time–frequency characterization is useful in understanding the nonlinear and non-stationary signals of the hydro-climatic time series. The traditional Fourier transform, and wavelet transform approaches have certain limitations in analyzing non-linear and non-stationary hydro-climatic series. This paper presents an effective approach based on the Hilbert–Huang transform to investigate time–frequency characteristics, and the changing patterns of sub-divisional rainfall series in India, and explored the possible association of monsoon seasonal rainfall with different global climate oscillations. The proposed approach integrates the complete ensemble empirical mode decomposition with adaptive noise algorithm and normalized Hilbert transform method for analyzing the spectral characteristics of two principal seasonal rainfall series over four meteorological subdivisions namely Assam-Meghalaya, Kerala, Orissa and Telangana subdivisions in India. The Hilbert spectral analysis revealed the dynamic nature of dominant time scales for two principal seasonal rainfall time series. From the trend analysis of instantaneous amplitudes of multiscale components called intrinsic mode functions (IMFs), it is found that both intra and inter decadal modes are responsible for the changes in seasonal rainfall series of different subdivisions and significant changes are noticed in the amplitudes of inter decadal modes of two seasonal rainfalls in the four subdivisions since 1970s. Further, the study investigated the links between monsoon rainfall with the global climate oscillations such as Quasi Bienniel Oscillation (QBO), El Nino Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multidecadal Oscillation (AMO) etc. The study noticed that the multiscale components of rainfall series IMF1, IMF2, IMF3, IMF4 and IMF5 have similar periodic structure of QBO, ENSO, SN, tidal forcing and AMO respectively. As per the seasonal rainfall patterns is concerned, the results of the study indicated that for Assam-Meghalaya subdivision, there is a likelihood of extreme rare events at ~0.2 cycles per year, and both monsoon and pre-monsoon rainfall series have decreasing trends; for Kerala subdivision, extreme events can be expected during monsoon season with shorter periodicity (~2.5 years), and monsoon rainfall has statistically significant decreasing trend and post-monsoon rainfall has a statistically significant increasing trend; and for Orissa subdivision, there are chances of extremes rainfall events in monsoon season and a relatively stable rainfall pattern during post-monsoon period, but both monsoon and post-monsoon rainfall series showed an overall decreasing trend; for Telangana subdivision, there is a likelihood of extreme events during monsoon season with a periodicity of ~4 years, but both monsoon and post-monsoon rainfall series showed increasing trends. The results of correlation analysis of IMF components of monsoon rainfall and five climate indices indicated that the association is expressed well only for low frequency modes with similar evolution of trend components.

Trend Analysis of Drought Events Over Upper Krishna Basin in Maharashtra

Drought is a significant natural disaster which leads to food, fodder and water shortages along with destruction of vital ecological system. Drought is acknowledged as a phenomenon associated with scarcity of water due to reduction in the amount of precipitation over an extended period of time, usually a season, a year or more in length. Droughts are frequently occurring in Upper Krishna basin in Maharashtra where about 80 percent of agriculture land is rain fed. Also this region is witnessing rapid urbanization due to industrial growth where thirst for water for drinking as well as industrial use is demanding. The study of drought trends is extremely important as it is related with food security and management of scarce water resource, which becomes critical in case of drought events. In this work, the drought trend of Standardized precipitation index (SPI) at 1-, 6-, 12-, 24- and 48- month time scales, Percent of Normal Precipitation (PNP) at Annual and Water-Year time scales and Seasonal rainfall (winter, pre-monsoon, monsoon and post monsoon time scales) are computed using long time series (1960-2012) of monthly precipitation data at 59 stations in the study area. The statistical significance at 95% confidence level as per Mann-Kendall and Sen's slope estimator are used for drought trend analysis over the Upper Krishna basin in Maharashtra. The PNP drought trend analysis at both annual and water-year time scale is able to detect significant trend at only 5 stations out of all rain gauge stations, SPI based drought trend analysis is found to be more sensitive to multiple time scales. As the SPI-1 time scale is able to detect significant trend at only 4 stations, number of stations having significant trend increases as the SPI time scale increases up to SPI-48 at 50 stations. Drought trend of Seasonal rainfall time series vary as per the classes. Pre-monsoon time scale detects significant negative trend at 41 stations with no significant positive trend at any of the stations. Monsoon time scale detects only 2 positive and 1 negative significant trends at total stations. Post-monsoon time scale is not able to detect any significant trend at all stations. The results indicate that, there is negative trend of pre-monsoon rainfall at over 63 percent of area. SPI- 48 time scale is able to detect significant trend at over 84 percent of area. This analysis may help to solve problems associated with floods, droughts and allocation of water for agriculture, industry, hydro-power generation, domestic and industrial use.

Trend analysis of annual and seasonal rainfall time series in the Mediterranean area

AbstractPrecipitation trend analysis, on different spatial and temporal scales, has been of great concern during the past century because of the attention given to global climate change by the scientific community. According to some recent studies, the Italian territory has been suffering a precipitation decrease, especially in the last 50 years, and the southern areas seem to be more affected. The aim of the present study is to analyse rainfall time series over a wide time interval and a wide area, detecting potential trends and assessing their significance. For this purpose, 211 gauged stations, mainly located within the Campania region, southern Italy, have been analysed for the period 1918–1999. An accurate database has been set up through a data quality and time series homogeneity process. Statistical analysis of the database highlight that (1) the trend appears predominantly negative, both at the annual and seasonal scale, except for the summer period when it appears to be positive; (2) over the whole reference period, positive and negative trends are significant respectively for 9 and 27% of total stations and (3) over the last 30 years, a negative trend is instead significant for 97% of the total stations. Copyright © 2009 Royal Meteorological Society

Testing homogeneity, stationarity and trend in climatic series at Udaipur – a case study

Three seasonal climatological time series, i.e., rainfall, minimum temperature, and maximum temperature of Udaipur in three seasons, i.e. summer, rainy and winter were tested for the presence of homogeneity, stationarity, and trend components. Box plot indicated normality in the rainy season rainfall. Minimum temperature series of rainy season was more uniform and normal than summer and winter season time series. Maximum temperatures during rainy season follow normal distribution. Homogeneity of seasonal time series was tested by using von-Neumann, Cumulative Deviations and Bayesian tests, which indicated that all seasonal rainfall and maximum temperature time series were homogenous. Homogeneity was present only in summer and rainy season minimum temperature time series. Stationarity and trends in the time series were detected by using Mann-Whitney test and Mann-Kendall test, respectively, which suggested that stationarity was present in all seasonal maximum and minimum temperature, and winter season rainfall time series and absent in summer and rainy season rainfall time series. Based on the results of Mann-Kendall tests, all the time series under study were free from any kind of trend.