Distribution Of Monthly Rainfall Research Articles

Less concentrated precipitation and more extreme events over the Three River Headwaters region of the Tibetan Plateau in a warming climate

Widely known as the “China Water Tower”, changes in precipitation concentration and insufficient rainfall days over the Three River Headwaters region (TRH) may threaten food and water security in TRH and downstream areas. However, current research on rainfall occurrences in TRH lacks a comprehensive understanding of the spatiotemporal variations in precipitation concentration, the seasonal distribution of rainy days across different rainfall categories, and their correlations with global warming. In this study, we utilize the CN05.1 gridded daily rainfall dataset to address these gaps. Our analysis reveals a decreasing trend in both the precipitation concentration index (PCI) and precipitation concentration degree (PCD), accompanied by an earlier onset of the precipitation concentration period (PCP) in TRH over the past six decades. The distribution of monthly rainfall becomes more uniform throughout the year, with precipitation spreading across a greater number of days annually. Notably, the reduction in both the number of rainy days and the proportion of rainfall during the summer, along with the decrease in the frequency of light and moderate rainfall days and the increase in the frequency of moderately heavy and heavier rainfall days, will lead to a more extreme distribution of summer precipitation in TRH. The increase in the number of rainy days across all intensity categories and the proportion of precipitation during winter, has favorable implications for the sustainable development and water security in TRH. Additionally, the relative increase in the occurrence of heavy and extremely heavy rainfall days in spring demonstrates the most pronounced change, with a notable rise of 12.74% to 28.24% per decade. This provides a certain degree of alleviation for spring drought in TRH. Furthermore, we find that for every 1-degree Celsius increase in global temperature, both PCI and PCD decrease by 9.82% and 8.5%, and PCP advances by 6.41 days. With the 50th percentile of precipitation serving as a dividing line, stronger precipitation events beyond this threshold exhibit a higher positive response to global warming. Conversely, weaker precipitation events falling below the 50th percentile show a higher negative response to global warming. Light rainy days decrease by 16.2% per degree Celsius of warming, while very heavy and extremely heavy rainy days increase by 26.4% and 27.5%, respectively. These findings have important implications for mitigating droughts and floods, as well as for developing a comprehensive framework for the management of water resources and agricultural irrigation in TRH.

How does increasing temperature affect the sub-annual distribution of monthly rainfall?

This paper investigates the relationship between temperature and sub-annual rainfall patterns using long-term monthly rainfall and temperature data from 1920 to 2018 in Australia. A parameter (τ) is used to measure the evenness of temporal rainfall distribution within each year, with τ = 0 indicating a uniform pattern. The study examines the relationship between τ and temperature for each year, considering whether it was warmer or cooler than average across five climate zones (CZs) in Australia, including tropical, arid, and three temperate climate classes. This study discovered a considerable association between annual maximum temperature and the distribution of monthly rainfall, with high temperatures resulting in greater variation (as represented by larger τ values) in the sub-annual distribution of monthly rainfall throughout all CZs, particularly in arid regions with τ values ranging from 0.27 to 0.52. In contrast, regions with temperate climates without dry seasons had a lower and narrower range of τ, from 0.15 to 0.26. This variability in rainfall distribution makes managing water resources more challenging in arid regions in Australia.

Bivariate Time Series Analysis of Rainfall Pattern in Selected States in Nigeria

The issues concerning global rainfall distribution and warming/climate change cannot be over emphasized since it affects virtually every part of live. The study used rainfall pattern in two states of Nigeria. Data on the monthly rainfall distribution in Imo and Rivers state, for a period of 37 years was examined. The result showed a continuous increase in the pattern of rainfall for a period of thirty seven years within the period under study. However the pattern was inconsistence for the remaining years with some kind of fluctuations. The monthly mean series plots showed a clear presence of trend with the peak period being September annually in both series with coefficient of determination- R square value of 83% and 86% for Imo and Rivers series respectively. The yearly mean series plots showed a clear irregular variation over the years in both series. The irregularity in the pattern of rainfall calls for serious commitment in joining the force for climate change abatement process. Pearson correlation coefficient between the two series is 0.80 (substantial correlation), which is the same result with cross correlation between the two rainfall series (0.79988 approx. 0.80). The series reveal the following characteristics: high correlations appear seasonality of order 12 in the monthly mean plots (every September), irregular variation and trend curve which is quadratic trend. ARIMAX model with independent variable was employed to identify the best bivariate time series model for prediction purpose. The result reveal an increase in Rivers series (Yt ) would tend to a linear combination of some increase of the preceding Imo series (It ) values which is vise visa. SARIMAX (1, 2) and SARIMAX (2, 1) models are identified as the best model with respect to Rivers on Imo series rainfall; via Imo on Rivers series rainfall.

Rainfall Spatial-Temporal Variability and Trends in the Thamirabharani River Basin, India: Implications for Agricultural Planning and Water Management

Rainfall is critical to agricultural and drinking water supply in the Thamirabharani river basin. The upper catchment areas of the Thamirabharani basin are located in high-elevated forest regions, and rainfall variability affects dam inflow and outflow. The well-known methods for rainfall analysis such as the coefficient of variation (CV), the precipitation concentration index (PCI), and trend analysis by Mann-Kendall and Sen’s slope test, as well as the Sen’s graphical innovative trend method (ITA) recently reported in several studies, were used. Rainfall data from gauge stations and the satellite-gridded Multisource Weighted Ensemble Precipitation (MSWEP) dataset were chosen for analysis at the annual and four-season time scales, namely, the Southwest Monsoon, Northeast Monsoon, winter, and summer seasons from 1991 to 2020. The mean annual PCI value reflects irregular monthly rainfall distribution (PCI > 20) in all gauge stations. The spatial monthly rainfall distribution of PCI values remarkedly shows a moderate distribution in the western and an anomalous distribution in the eastern part of the basin. The annual mean rainfall ranges from 718.4 to 2268.6 mm/year, decreasing from the high altitude zone in the west to the low plains and coastal regions in the east. Seasonal rainfall contributes about 42% from the NEM, 30.6% from the SWM, 22.8% from summer, and 3.9% from winter, with moderate variability (CV less than 30%). Ground stations experienced extremely high interannual variability in rainfall (more than 60%). Trend analysis by the MK, TFPW-MK, and ITA methods shows increasing annual rainfall in the plains and coastal regions of the basin; particularly, more variations among the seasons were observed in the Lower Thamirabharani sub-basin. The NEM and summer season rainfall are statistically significant and contribute to the increasing trend in annual rainfall. The ITA method performed better in the annual and seasonal scale for detecting the rainfall trend than the MK and TFPW-MK test. The Lower Thamirabharani sub-basin in the eastern part of the basin receives more rain during the NEM than in other areas. To summarize, the low plains in the central and coastal regions in the southeast part experience an increase in rainfall with irregular monthly distribution. This study helps farmers, governments, and policymakers in effective agricultural crop planning and water management.

Prediction Map of Rainfall Classification Using Random Forest and Inverse Distance Weighted (IDW)

The amount of rainfall that occurs can affect natural disasters and even food production to economic activities. the factor of the area where the rain occurs is one of the main parameters for how the change occurs. So, it is necessary to have a rainfall prediction approach that aims to find out when and what type of rain will occur. Spatial classification and interpolation are two methods used to make predictions. Random Forest is a classification method that can be used to predict rainfall. and Inverse Distance Weighted is one of the stochastic interpolation techniques to calculate the estimated rainfall from the data points of rainfall that occur so that the distribution can be visualized. In the implementation of random forest, the model that is built on a daily basis gets the best level of accuracy in the 5D model sub model C with an accuracy of 0.8238 while the monthly model gets the best level of accuracy in the sub-model B 4M 0.9362. and the results of predictions and mapping using IDW show that daily predictions from June 1-4 2022 show that Most of Java Island will experience light rain, June 5-7 2022 most of Java Island will experience sunny cloudy days. And for monthly predictions, August and June 2022 show the distribution of monthly rainfall with predictions that most of Java is cloudy, while May, July, October, September have light rainfall in most of Java

Prediction of bimodal monsoonal rainfall in the central dry zone of Myanmar using teleconnections with global sea surface temperatures

In the central dry zone of Myanmar, the mean annual rainfall is less than 1000 mm. Although rainfed agriculture is commonly practiced there, the feasibility of rainfed farming is compromised by the large fluctuations of rainfall and the frequent occurrence of dry years. The monthly distribution of rainfall follows a bimodal pattern. The intensity of the monsoonal rainfall from May to October is characterized by two peaks, an early peak (May-June) and a late peak (August–October), separated by the inter-monsoon (July). The return times of dry and wet years make management of rainfed agriculture problematic. There is very little correlation between the early and late monsoonal rainfall (r=–0.257). However, monsoonal rainfall is teleconnected to sea surface temperatures (SSTs) in certain areas of the Pacific Ocean in real time. Furthermore, at lag times of 6–9 months, there are teleconnections between the early monsoonal, inter-monsoonal, and late monsoonal rainfall and SSTs in certain areas of the Indian Ocean and Atlantic Ocean. We used an Elman artificial neural network model to predict early monsoonal, inter-monsoonal, and late monsoonal rainfall based on teleconnections with SSTs in the Indian and Atlantic oceans 6–9 months before the rainfall occurred. The correlation coefficient between the predicted and observed rainfall exceeded 0.7 in all three cases.

Springshed protection planning for combating drought in Grobogan District, Central Java

Drought occurs almost every dry season in Grobogan District, Central Java. Climate change has resulted in an atypical monthly rainfall distribution, which, along with limestone geology, has made water scarce in some months. One of the efforts to mitigate drought disasters is to revitalize water resources from existing springs. The research aims to develop a spring protection plan through a geological-spatial and participatory approach. The research location is in Kradenan Sub-District, Grogoban District, Central Java Province. The research method starts with identifying springs, delineation of springshed area, formulation of spring and springshed management, and discussion and socialization with stakeholders. The identification results show that the springs are classified as “umbul wadon” type with water discharge ranging from 0.9 to 4.8 l/minute, forming a radial pattern. The area of the springshed is 3,347.7 ha, with more than 70% of the land covered by teak forest plantations. The recommendations for spring conservation activities are organized into three zones: Zones I, II, and III, which differ in range radius, function, and proposed management. In each zone and type of land uses, the potential risk of pollution, prohibited activities, protection strategies and programs, and interested parties as program implementers have been formulated. Several official institutions can aid spring protection management. Together with water users and forest area managers, all institutions agreed to carry out activities to protect and conserve springs and springshed areas.

​Rainfall Probability Distribution and Forecasting Monthly Rainfall of Navsari using ARIMA Model

Background: Reliable rainfall forecast could be helpful to farmers as major decisions regarding selection of crops and sowing time are based on the rainfall. The univariate time series ARIMA model requires only past data for model formulation and to simulate stochastic processes. The current study was aimed to obtain the probability distribution of monthly rainfall using the method of moments and to forecast rainfall using the ARIMA model. Methods: The method of moments was used to determine the parameters of distributions and the chi-square test was used as a goodness of fit test to obtain the best fit distribution for monthly rainfall of Navsari, Gujarat utilizing 36 years of rainfall data. Auto regressive moving average (ARIMA) model, popular owing to its simplicity and ability to simulate various stochastic processes was used in the study. Result: It was revealed that the Weibull distribution was the best fit distribution for June and September, whereas Gumbel was the best fit distribution for July. For simulating monthly rainfall, the seasonal ARIMA model (0,0,1) (0,1,1)12 was found to be the appropriate model based on its performance. The model had the least root mean square value and also the residuals were found to have no correlation.

Influence of atmospheric macroprocesses on the spatial distribution of spring precipitation within the territory of Ukraine

The article presents the results of a comprehensive statistical study of the spatial distribution of spring precipitation in Ukraine and its relationship with the main low-frequency oscillations of the Northern Hemisphere. The research focuses on the monthly amount of rainfalls in March, April and May at 40 long-range stations of Ukraine that are evenly distributed throughout its territory, and the indices of North Atlantic (NAO) and North Sea-Caspian (NCP) fluctuations over a 45-year period (1962-2006). The use of multiyear data allowed for objective clusterization and division of the Ukrainian territory into regions with different types of weather observed in the event of precipitation, with each of them being physically substantiated. To ensure clarity, the article presents regional statistical models in the form of schematic maps of probable relationships between distribution of spring precipitation in Ukraine and the North Atlantic and Euro-Mediterranean macroprocesses. In order to forecast the monthly rainfall in March, it is advisable to take into account the state of the North Atlantic fluctuations. The response of this fluctuation was found within Chernivtsi, Vinnytsia, Cherkasy, Kyiv Regions, northern part of Odesa Region, western districts of Chernihiv Region and southern part of Zhytomyr Region. In the western regions of Ukraine, as well as in Vinnytsia, Kyiv, Zhytomyr and Chernihiv Regions, the formation of precipitation in April depends on the state of both NAO and NCP; in the rest of Ukraine – only of NCP. The 90% probability linear statistical relationship between precipitation in May and the main climatic signals of the Northern Hemisphere under study was not found only in some northern regions of Ukraine, namely: Kyiv, Chernihiv, Sumy Regions and central (Poltava and northern part of Cherkasy Region) part of Ukraine. A study of the influence of North Atlantic and Euro-Mediterranean macroprocesses on the distribution of monthly rainfall in Ukraine, conducted using a statistical approach, shows its ambiguity during different spring months and within different regions of Ukraine, and requires further research to solve the general scientific problem – study of climate-conditioned natural resources required to ensure sustainable socio-economic development of Ukraine under conditions of global climate change.

Long-term changes in rainfed olive production, rainfall and farmer\u2019s income in Bail\xe9n (Ja\xe9n, Spain)

Economic, social, and climatic conditions affect agricultural production. Those changes are relevant to the rainfed agricultural areas of the Mediterranean Belt, including Spain—the largest producer of olive oil in the world. However, little is known about the effect of the climate on olive production and farmer income. In this study, the correlation between changes in rainfall and total olive production was examined using a long-term dataset (28 years) on conventional rainfed production and tillage soil management. The dataset focused on different olive groves in the municipality of Bailén (Jaén, Andalusia) that have been owned by the same farmer since 1966. The province of Jaén is the region of Spain with the highest production of olive oil and the largest area of olive groves. The data included annual rainfall, production per plot and the price of olives. After calculating missing data to complete the rainfall series, pairwise correlation analysis with nonparametric Spearman's rank coefficients and principal component analysis were used to process the data. The results showed that higher production coincided with increased rainfall during August and December. Therefore, we concluded that the impact of rainfall on olive production is variable and depends on drought intensity and the monthly rainfall distribution. An economic study showed that farmer income was highly dependent on the seasonal distribution of the rainfall among other factors such as the price of olives. Farmer income was low during drought periods, indicating that rainfed agriculture is perceived by farmers as unsustainable due to the resulting highly variable income. This study could help to prevent risks to food security in the future. We recognise that other key factors have also been important influences on the fluctuations in olive production over the years, such as soil properties and plant status. However, cultivating olives without irrigation—depending only on the total rainfall amount and rainfall intensity to supply all of the water consumed by the plants—is very risky too. This research demonstrates that the subsistence of Mediterranean rainfed olive farmers can be highly dependent on the rainfall conditions.

Climatologia da Chuva em Maceió: Aspectos Climáticos e Ambientais

The objectives of the study are: i) to evaluate the climatology of rain in Maceió based on observed data, with emphasis on climatic and environmental aspects and ii) to validate the precipitation product for the municipality. Data from 1979 to 2013 of the precipitation product CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) were validated by rainfall data from the National Water Agency (NWA) from 1960 to 2016. Statistical indicators showed a high coefficient of determination and linear correlation between CHELSA and observed data (R2 = 0.80; r = 0.89) and the smallest errors (SEE = 6.58 mm and RMSE = 18.76 mm), therefore the CHELSA product can be applied in the region. The time series presented a period 1 (P1) - (1960 to 1989) with rainfall above the historical average and a period 2 (P2) - (1990 to 2016) with a significant reduction in rainfall. Observed data versus climatological normals showed a significant decrease in normal 1 (1961-1990) in the rainy season, while in relation to normal 2 (1981-2010) there was an increase in the months of February, March and April (between 10 to 20%) and October and December (between 5 to 15%). The spatial distribution of monthly rainfall via the CHELSA product showed the formation of a pluviometric gradient between the coast and the upper part of Maceió. The topography influences the rainfall regime in neighborhoods belonging to the administrative regions (AR) - (R4, R5 and R6) with the highest rainfall records. The ENOS phases are directly responsible for the variability of interannual rain, while the decadal variability corresponded to the PDO phase change and changes in land use and occupation in Maceió.

Spatial and temporal characteristics of rainfall over a forested river basin in NW Borneo

The spatial and temporal patterns of rainfall over the Baram River Basin (BR) in Sarawak (Malaysian Borneo) were characterised through cluster analysis and multivariate statistics for the 25 year period from 1990 to 2014. Baram River Basin recorded an average annual rainfall of 3654 mm with high spatial and temporal variations. Ward’s method based analytical hieratical clustering (AHC) identified three homogeneous clusters (HC’s) of rain gauging stations (HC-I, HC-II, and HC-III). Rain gauges in HC-I are located in high rainfall domain, HC-II gauges are in moderate rainfall domain and HC-III gauges are in low rainfall domains. The moderate elevation regions in the Baram basin recorded the highest amounts of rainfall compared to lower and upper elevated regions. Multivariate statistics show variation in the distribution of monthly and annual rainfall within and between individual HC’s. Mean annual rainfall recorded at individual HC’s varies by around 1000 mm. Though the mean monthly rainfall distribution in HC’s varies by more than 70 mm, all clusters show a common pattern of high and low rainfall seasons related to monsoon characteristics of the region. The northeast monsoon (NEM) is the wettest period with highest mean rainfall falling in the months of November and December at all stations. In comparison, the southwest monsoon (SWM) is the driest season with the lowest mean rainfall falling in July. It was also noted that the inter-monsoon periods are actually wetter than the SWM season. Overall, rainfall in the Baram shows high inter-annual variability with dominant low rainfall domain in the lower (coastal) and upper (plateau) reach with highly localised high rainfall domain surrounded by fluctuating moderate rainfall areas in the middle reaches of the river basin. The localised nature of high rainfall domain in the central (south and southwest) part of the basin suggests that the surrounding elevated mountains and hills in that region influence the high rainfall recorded. The findings of the present research will aid in formulating water resource management plans, large scale plantation and agricultural practices and any other hydrological development projects in the region.

Statistical analysis of rainfall in Bihar

Availability of water is largely dependent on the amount of precipitation received by an area. Fluctuations in precipitation pattern exercises significant impact on the livelihood of the people especially in areas where rainfed agriculture has predominance. In the present study, temporal variation of rainfall in Bihar, India has been analysed. Trend analysis for monthly, seasonal as well as annual rainfall series for the duration of 1950–2016 was performed using non-parametric Mann–Kendall. Parametric linear regression test was also used to check the trends. Deficiency in yearly total rainfall was identified using Index of Wetness and standardized rainfall anomaly. Precipitation Concentration Index (PCI) was also estimated to check the monthly distribution of rainfall. Both parametric and non-parametric tests predict declining trend in rainfall for almost all the months except for the month of May, which is reflected as a decline in total annual rainfall in the study area. Analysis of rainfall data revealed very high intra and inter annual variability [average coefficient of variation (CV) = 91.26]. PCI values too revealed very high nonuniformity of rainfall. Analyses of data confirmed high concentration (84%) of rainfall in monsoon months. Rainfall depth with 90% dependability has been found to be over 180 mm and 160 mm for the months of July and August, respectively. Significant decline has been observed in total rainfall over the last three decades (1986–2016) highlighting the need of rainwater harvesting and management to avoid acute shortage of water in near future.

Precipitation trend and concentration in the Sardinia region

The Mediterranean region is an area potentially vulnerable to climatic changes. In fact, it is characterized by a significant precipitation variability resulting from synoptic dynamics of hazardous events moving and evolving along the Mediterranean basin. In this paper, the results of an investigation on rainfall trend in the Sardinia region are presented. The trend analysis has been performed by means of the Mann–Kendall test and of the Theil–Sen estimator applied on annual, seasonal, and monthly scales. To this aim, a monthly database of precipitation registered at 158 rain gauges, with an average observation of 84 years, was used for the analyses. The results mainly evidenced a rainfall amount reduction in the winter months and an increase during the summer months. The monthly rainfall distribution throughout the year was also investigated by means of the precipitation concentration index (PCI). A negative trend emerged and, more specifically, a tendency towards a more uniform rainfall distribution on the Sardinia region throughout the year.

Análisis comparativo del comportamiento de la escorrentía de tres microcuencas andinas con diferente régimen de precipitación y cobertura vegetal

The paper presents a descriptive comparative analysis of the precipitation and runoff of three Andean small watersheds (varying in size between 0.6 and 1 km2), using approximately 8 years of rainfall and discharge data, with the objective to relate differences in runoff behavior to the level of annual precipitation, the monthly distribution of rainfall and land use. The catchment covered with native forest receives on average the largest amount of rainfall (1,540 mm/year), whereas the two other catchments, respectively under grassland with extensive cattle breeding and pine trees, receive annually on average respectively 1,267 and 1,181 mm. Although the analysis did not permit to describe in detail the processes controlling the transformation of precipitation into runoff, the exhaustive breakdown of the daily rainfall and runoff data enabled to define the effect of climate and land cover on the runoff pattern of each of the studied micro-catchments. Furthermore, the detailed analysis allowed to accurately identify the type of events whereby the studied micro-catchments show a different response in runoff related to a difference in climate and land use, which would not have been possible to define using only the average monthly precipitation and runoff data of the catchments, a traditional approach in hydrologic consulting services.

TRANSFER FUNCTION MODELS FOR STATISTICAL DOWNSCALING OF MONTHLY PRECIPITATION

Three transfer function based statistical downscaling namely, linear regression model (LM), generalized linear model (GLM), generalized additive model (GAM) have been developed to assess their performance in downscaling monthly rainfall. Previous studies reported that performance of downscaling model depends on climate region and characteristics of climatic variable being downscaled. This has motivated to assess the performance of these three statistical downscaling models to identify most suitable model for downscaling monthly rainfall in the East coast of Peninsular Malaysia. Assessment of model performance using standard statistical measures revealed that LM model performs best in downscaling monthly precipitation in the study area. The Nash–Sutcliffe efficiency (NSE) for LM was found always greater than 0.9 and 0.7 with predictor set selected using stepwise multiple regression method during model calibration and validation, respectively. The finding opposes the general conception of better performance of non-linear models compared to linear models in downscaling rainfall. The near normal distribution of monthly rainfall in the tropical region has made the LM model much stronger compared to other models which assume that distribution of dependent variable is not normal.

Spatiotemporal variability of precipitation concentration in western Turkey

This paper addressed the spatial and temporal variations of precipitation concentration across western Turkey, by analyzing daily and monthly precipitation time series recorded at 28 stations over the period 1966–2011. Four indices: daily precipitation concentration index (DPCI), monthly precipitation concentration index (MPCI), precipitation concentration degree (PCD) and precipitation concentration period (PCP) were used. The results showed higher values of DPCI in northwestern and southern parts and lower values in western-central, central, eastern and northeastern parts, and the southern part was the most critical part of the study area, with the highest values of DPCI, MPCI, PCD and annual total precipitation, but the lowest number of rainy days. MPCI and PCD values showed a seasonal precipitation regime over the whole study area and exhibited a pattern decreasing from southwest to northeast, which implies a more uniform distribution of monthly rainfall in a year toward the northeast. PCP values showed that annual precipitation mainly falls in December and January. Trend analysis demonstrated mostly non-significant decreasing trends for all indices, implying a very slight tendency toward a more regular precipitation distribution in a year and a slightly earlier occurrence of precipitation over western Turkey.

Projected changes of rainfall seasonality and dry spells in a high greenhouse gas emissions scenario

In this diagnostic study we analyze changes of rainfall seasonality and dry spells by the end of the twenty-first century under the most extreme IPCC5 emission scenario (RCP8.5) as projected by twenty-four coupled climate models contributing to Coupled Model Intercomparison Project 5 (CMIP5). We use estimates of the centroid of the monthly rainfall distribution as an index of the rainfall timing and a threshold-independent, information theory-based quantity such as relative entropy (RE) to quantify the concentration of annual rainfall and the number of dry months and to build a monsoon dimensionless seasonality index (DSI). The RE is projected to increase, with high inter-model agreement over Mediterranean-type regions—southern Europe, northern Africa and southern Australia—and areas of South and Central America, implying an increase in the number of dry days up to 1 month by the end of the twenty-first century. Positive RE changes are also projected over the monsoon regions of southern Africa and North America, South America. These trends are consistent with a shortening of the wet season associated with a more prolonged pre-monsoonal dry period. The extent of the global monsoon region, characterized by large DSI, is projected to remain substantially unaltered. Centroid analysis shows that most of CMIP5 projections suggest that the monsoonal annual rainfall distribution is expected to change from early to late in the course of the hydrological year by the end of the twenty-first century and particularly after year 2050. This trend is particularly evident over northern Africa, southern Africa and western Mexico, where more than $$90\,\%$$ of the models project a delay of the rainfall centroid from a few days up to 2 weeks. Over the remaining monsoonal regions, there is little inter-model agreement in terms of centroid changes.

The spatial and temporal analysis of precipitation concentration and dry spell in Qinghai, northwest China

Understanding precipitation variations from various aspects is important for the assessment of drought risk and the utilization of water resources. The precipitation concentration index (PCI) and the concentration index (CI) were used to investigate/quantify the heterogeneity of the monthly and daily rainfall in Qinghai province that is part of northwestern China, respectively. The precipitation concentration in Qinghai shows a significant irregularity of the monthly rainfall distribution and highly homogeneous distribution of the daily rainfall. It is found that PCI and CI show negative trends at most stations. Meanwhile, the spatial and temporal variation of nine dry spell (DS) indices are analyzed. From the spatial perspective, drought in the northwestern area is much severer than that in other areas of Qinghai. According to the results of temporal analysis by using the Mann–Kendall test, the number of very long DSs, maximum length of DS, mean length of DSs, and the total dry days of extreme DS all decrease. All these results verify that the warm dry climatic pattern in Qinghai can be changed into the warm wet climatic pattern.

Spatio-temporal analysis of rainfall trends over a maritime state (Kerala) of India during the last 100 years

Kerala, a maritime state of India is bestowed with abundant rainfall which is about three times the national average. This study is conducted to have a better understanding of rainfall variability and trend at regional level for this state during the last 100 years. It is found that the rainfall variation in northern and southern regions of Kerala is large and the deviation is on different timescales. There is a shifting of rainfall mean and variability during the seasons. The trend analysis on rainfall data over the last 100 years reveals that there is a significant (99%) decreasing trend in most of the regions of Kerala especially in the month of January, July and November. The annual and seasonal trends of rainfall in most regions of Kerala are also found to be decreasing significantly. This decreasing trend may be related to global anomalies as a result of anthropogenic green house gas (GHG) emissions due to increased fossil fuel use, land-use change due to urbanisation and deforestation, proliferation in transportation associated atmospheric pollutants. We have also conducted a study of the seasonality index (SI) and found that only one district in the northern region (Kasaragod) has seasonality index of more than 1 and that the distribution of monthly rainfall in this district is mostly attributed to 1 or 2 months. In rest of the districts, the rainfall is markedly seasonal. The trend in SI reveals that the rainfall distribution in these districts has become asymmetric with changes in rainfall distribution.