Monthly Precipitation Series Research Articles

Homogeneity tests and non-parametric analyses of tendencies in precipitation time series in Keszthely, Western Hungary

This study aims to investigate the precipitation trends in Keszthely (Western Hungary, Central Europe) through an examination of historical climate data covering the past almost one and a half centuries. Pettitt’s test for homogeneity was employed to detect change points in the time series of monthly, seasonal and annual precipitation records. Change points and monotonic trends were analysed separately in annual, seasonal and monthly time series of precipitation. While no break points could be detected in the annual precipitation series, a significant decreasing trend of 0.2–0.7 mm/year was highlighted statistically using the autocorrelated Mann-Kendall trend test. Significant change points were found in those time series in which significant tendencies had been detected in previous studies. These points fell in spring and winter for the seasonal series, and October for the monthly series. The question therefore arises of whether these trends are the result of a shift in the mean. The downward and upward shift in the mean in the case of spring and winter seasonal amounts, respectively, leads to a suspicion that changes in precipitation are also in progress in these seasons. The study concludes that homogeneity tests are of great importance in such analyses, because they may help to avoid false trend detections.

Climate change impact on water supply demands: case study of the city of Skopje

Abstract The aim of the paper is to present the climate change impact on drinking water supply, and to assess the availability of current water resources to meet the increasing demands in climate change conditions, for the city of Skopje. Series of monthly precipitation and temperature data are estimated up to 2050, using recorded data and data for predicted changes of air temperature and precipitation according to climate change scenarios. Climate change impact on drinking water supply is assessed using multi-regression models for the dependence of the water supply data on the main climate parameters: temperature and precipitation. Forecast of future water quantities includes implementation of multi-regression models defined with a set of independent variables: the number of the population for time sets (t, t-1, t-2, t-3), precipitation and air temperature including predicted changes of the data under climate change conditions for the same time sets. Two scenarios are analysed: the first predicts that the number of the population until 2050 remains nearly the same as nowadays, the second predicts an increase of the number of the population at an average annual rate of increase of 0.58%. Results indicate shortage of available water resources for population and industry in climate change scenarios (increase of temperatures and decrease of precipitation) and increase of population.

Assessing the history-based predictability of regional monthly precipitation data using statistical and fuzzy methods

This paper points out two procedures for verifying the statistical independence between the historical and actual data series. They are based respectively on the computation of an empirical distance correlation coefficient, and the use of Hamming distance between binary encoded phase signals corresponding to Log-Gabor filtered time-series. Both procedures are applied to the monthly precipitation series recorded during 480 successive months at 49 meteorological stations in Dobrogea region (Romania) and on 4 and 5-valued fuzzified versions of the initial data represented using linguistic labels. The results show that the crisp data and their fuzzified versions cannot support the hypothesis of history-based predictability from their history. Hence, the two statistical independence tests are robust with respect to the k-means fuzzification of data and cross-validate each other, being applicable to any long-term analysis of precipitation series, to any other signals in general, and to their fuzzified versions, as well.

A quality control approach to better characterize the spatial distribution of snow depth over New Brunswick, Canada

AbstractThe aim of this work is to develop, from a high‐resolution climate analysis, a quality control standard methodology applied to manual snow cover (HS) series managed by the Snow Survey Database in New Brunswick (Canada). The database collected snow depth data biweekly starting at the end of January until the end of April. A 30‐year (1981–2010) analysis of 60 weather stations belonging to two independent meteorological networks was performed. A quality control of the climatic series was performed to evaluate the homogeneity. Three snow depth climatic areas were defined by means of two geostatistical methods (Kriging and Cluster analyses) applied on monthly snow depth, precipitation and temperature data series. Then, for each cluster, the climatological thresholds that characterize a snow fall event during the cold months were detected. Subsequently, a quality control on the daily snow depth series recorded during the January to April period was performed. For each daily series, outlier values were identified by checking both the sudden day‐to‐day changes and extreme thresholds (95th percentile). The quality control was then carried out to the manual series and the observed doubtful events were compared with the snow depth values recorded in the nearby stations. The results show that for the daily snow depth series, the highest number of suspect events was recorded during the months of March and April, and the analysis also shows that there are rain‐on‐snow events. As for the manual records, questionable snow depth errors randomly distributed in the series were highlighted. Finally, in order to improve the spatial distribution of stations located in the Canadian territory, the results give evidence that, thanks to the high‐resolution climatic analysis, the proposed approach provides all the benchmarks required to conduct a quality control of snow depth series in absence of other auxiliary variables.

Exploring the multiscale changeability of precipitation using the entropy concept and self-organizing maps

Abstract This study utilized a spatio-temporal framework to assess the dispersion and uncertainty of precipitation in Iran. Thirty-one rain gauges with data from 1960 to 2010 were selected in order to apply the entropy concept and study spatio-temporal variability of precipitation. The variability of monthly, seasonal and annual precipitation series was studied using the marginal disorder index (MDI). To investigate the intra-annual and decadal distribution of monthly and annual precipitation values, the apportionment disorder index (ADI) and decadal ADI (DADI) were applied to the time series. The continuous wavelet transform was used to decompose the ADI time series into time-frequency domains. The decomposition of the ADI series into different zones helped to identify the dominant modes of variability and the variation of those modes over time. The results revealed the high disorderliness in the amount of precipitation for different temporal scales based on disorder indices. Based on the DI outcome for all rain gauges, a self-organizing map (SOM) was trained to find the optimum number of clusters (seven) of rain gauges. It was observed from the clustering that there was hydrologic similarity in the clusters apart from the geographic neighborhood.

Influence of solar activity changes on European rainfall

European hydroclimate shows a high degree of variability on every time scale. The variability is controlled by natural processes such as Atlantic ocean cycles, changes in solar activity, volcanic eruptions and anthropogenic factors. This contribution concentrates on the solar influence on European precipitation, a relationship which has been documented by a large body of published case studies. Here we are concentrating on the period 1901–2015 for which we compare sunspot data with monthly precipitation series of 39 European countries by calculating Pearson correlation coefficients for a multi-year cross-correlation window. The coefficients have been mapped out across Europe with the aim to identify areas in which solar activity may have influenced precipitation. Results show that February precipitation in Central and Western Europe yields the strongest solar response with coefficients reaching up to +0.61. Rainfall in June–July is equally co-driven by solar activity changes, whereby the solar-influenced zone of rainfall shifts from the British Isles towards Eastern Europe during the course of summer. Other months with noteworthy solar responses are April, May and December. On a decadal scale, the correlation between precipitation and solar activity in central Europe appears to be mostly positive, both statistically and by visual curve comparison. Yet, best positive correlations coefficients of February, June, July and December are typically reached when the solar signal lags rainfall by 1.5–2 years. Taking into account cause and effect, it is suspected that increases in Central European rainfall are actually triggered by the solar minimum some 3–4 years before the rainfall month, rather than the lagging solar maximum. Similar lags of a few years occur between solar activity and the solar-synchronized North Atlantic Oscillation (NAO) due to memory effects in the Atlantic. The literature review demonstrates that most multidecadal studies from Central Europe encountered a negative correlation between solar activity and rainfall, probably because short time lags of a few years are negligible on timescales beyond the 11 year solar Schwabe cycle. Flood frequency typically increases during times of low solar activity associated with NAO- conditions and more frequent blocking.

Temporal Variations in Tree-Ring Growth Response to Climate of European Larch (Larix Decidua Mill.): An Example from Sudetes, Poland

The temporal stability of climate-growth relationship is a basic assumption and requirement of climate reconstruction using tree rings. However, many recent studies showed change in tree-ring growth response to climate for the last decades. The temporal stability of relationship mentioned above can depend on the local factors, e.g. slope aspect. In presented study the tree-ring growth response to climate of larch from Polish part of Sudetes was analysed over time. The aim was to investigate its temporal variability, existence of trends and influence of local factors. The relation between tree-ring growth of larches from six sites located at comparable altitudes (about 700-750 m.a.s.l.) and climatic factors that mainly control the tree-ring growth of larches in the study area was analysed over time. The Pearson correlation between tree-ring width site chronologies and climatic data for the study area (the average monthly air temperature and total monthly precipitation series from high-resolution gridded dataset CRU TS v.4.01) was calculated in 40-years moving windows for the period 1901-2010. The results showed variations in the tree-ring growth response to climate over the analysed time period. Although in general, similar trends were observed for all sites, some differences in trends and magnitudes among sites were also spotted. Interesting is, that the response of sites became more consistent and similar for the last decades. The discussion of the received results with respect to currently observed climatic trends in the region and local factors that can explain the differences in response between particular sites is presented.

Manifestaciones de la sequía meteorológica en la provincia de Villa Clara (Cuba) en el período 2000-2017

El presente trabajo tiene como objetivo evaluar el comportamiento de la sequía meteorológica en Villa Clara, Cuba, en el período 2000-2017. Asimismo, se analizó el estado de la sequía meteorológica por períodos estacionales, seco (noviembre-abril) y lluvioso (mayo-octubre). Se caracterizó además la evolución del proceso de sequía 2014-2017 y por último se analizó la tendencia de la precipitación 1950-2017 y los procesos sinópticos asociados a las mismas. Para ello se utilizó el Índice de Precipitación Estandarizado (SPI) y el Test de Man-Kendall a las series mensuales y estacionales de precipitación. Como principales resultados se concluyó que en la provincia se han presentado 11 períodos estacionales con sequía, 4 correspondientes al período lluvioso y 7 al poco lluvioso. Además, el período estacional lluvioso más seco en la provincia se registró en mayo-octubre de 2004, mientras que el período estacional poco lluvioso más seco fue entre noviembre de 2004 y abril de 2005. La tendencia de la precipitación muestra que las mayores disminuciones se concentran en el bimestre mayo-junio, el más lluvioso del año, asociado al aumento de la influencia de las altas presiones.

Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning Algorithms: a Multiple-Case Study from Greece

We provide contingent empirical evidence on the solutions to three problems associated with univariate time series forecasting using machine learning (ML) algorithms by conducting an extensive multiple-case study. These problems are: (a) lagged variable selection, (b) hyperparameter handling, and (c) comparison between ML and classical algorithms. The multiple-case study is composed by 50 single-case studies, which use time series of mean monthly temperature and total monthly precipitation observed in Greece. We focus on two ML algorithms, i.e. neural networks and support vector machines, while we also include four classical algorithms and a naive benchmark in the comparisons. We apply a fixed methodology to each individual case and, subsequently, we perform a cross-case synthesis to facilitate the detection of systematic patterns. We fit the models to the deseasonalized time series. We compare the one- and multi-step ahead forecasting performance of the algorithms. Regarding the one-step ahead forecasting performance, the assessment is based on the absolute error of the forecast of the last monthly observation. For the quantification of the multi-step ahead forecasting performance we compute five metrics on the test set (last year’s monthly observations), i.e. the root mean square error, the Nash-Sutcliffe efficiency, the ratio of standard deviations, the coefficient of correlation and the index of agreement. The evidence derived by the experiments can be summarized as follows: (a) the results mostly favour using less recent lagged variables, (b) hyperparameter optimization does not necessarily lead to better forecasts, (c) the ML and classical algorithms seem to be equally competitive.

Alternative approaches for estimating missing climate data: application to monthly precipitation records in South-Central Chile

BackgroundOver the last decades interest has grown on how climate change impacts forest resources. However, one of the main constraints is that meteorological stations are riddled with missing climatic data. This study compared five approaches for estimating monthly precipitation records: inverse distance weighting (IDW), a modification of IDW that includes elevation differences between target and neighboring stations (IDWm), correlation coefficient weighting (CCW), multiple linear regression (MLR) and artificial neural networks (ANN).MethodsA complete series of monthly precipitation records (1995–2012) from twenty meteorological stations located in central Chile were used. Two target stations were selected and their neighboring stations, located within a radius of 25 km (3 stations) and 50 km (9 stations), were identified. Cross-validation was used for evaluating the accuracy of the estimation approaches. The performance and predictive capability of the approaches were evaluated using the ratio of the root mean square error to the standard deviation of measured data (RSR), the percent bias (PBIAS), and the Nash-Sutcliffe efficiency (NSE). For testing the main and interactive effects of the radius of influence and estimation approaches, a two-level factorial design considering the target station as the blocking factor was used.ResultsANN and MLR showed the best statistics for all the stations and radius of influence. However, these approaches were not significantly different with IDWm. Inclusion of elevation differences into IDW significantly improved IDWm estimates. In terms of precision, similar estimates were obtained when applying ANN, MLR or IDWm, and the radius of influence had a significant influence on their estimates, we conclude that estimates based on nine neighboring stations located within a radius of 50 km are needed for completing missing monthly precipitation data in regions with complex topography.ConclusionsIt is concluded that approaches based on ANN, MLR and IDWm had the best performance in two sectors located in south-central Chile with a complex topography. A radius of influence of 50 km (9 neighboring stations) is recommended for completing monthly precipitation data.

Occurrence Probabilities of Wet and Dry Periods in Southern Italy through the SPI Evaluated on Synthetic Monthly Precipitation Series

The present article investigates dry and wet periods in a large area of the Mediterranean basin. First, a stochastic model was applied to a homogeneous database of monthly precipitation values of 46 rain gauges in five regions of southern Italy. In particular, after estimating the model parameters, a set of 104 years of monthly precipitation for each rain gauge was generated by means of a Monte Carlo technique. Then, dry and wet periods were analyzed through the application of the standardized precipitation index (SPI) over 3-month and 6-month timespan (short-term) and 12-month and 24-month period (long-term). As a result of the SPI application on the generated monthly precipitation series, higher occurrence probabilities of dry conditions than wet conditions have been detected, especially when long-term precipitation scales are considered.

Complexity measurement of precipitation series in urban areas based on particle swarm optimized multiscale entropy

Entropy theory is commonly applied to study the complexity of hydrological systems. In view of the subjectivity and uncertainty of parameter selection in entropy theory research, this paper combines a particle swarm optimization (PSO) algorithm with entropy theory to improve the traditional parameter selection method and the accuracy and reliability of the complexity measurement results. We combined PSO and multiscale entropy (MSE) to analyze the complexity of monthly precipitation series from 11 stations in Harbin, Heilongjiang Province, China, and the complexity results were classified into three levels. Harbin, Shuangcheng, Yilan, and Fangzheng are level I areas; Wuchang, Tonghe, Yanshou, and Binxian are level II areas; and Bayan, Mulan, and Shangzhi are level III areas. We selected the mountainous area ratio, water area ratio, GDP, and grain production as indicators that influence the complexity of local precipitation. The results showed that the correlation between the precipitation complexity and terrain was obvious, the impact of the water area ratio on the precipitation complexity was small, and the GDP and grain production were mostly negatively correlated with precipitation. These results reveal the spatiotemporal characteristics of the precipitation complexity and the factors that potentially influence the complexity. This study provides a reference model for other complexity measures in the field of regional water resource systems and related studies.

Development of the homogenized monthly precipitation series during 1910–2014 and its changes in Hunan Province, China

Abstract Based on the statistical method and the historical evolution of meteorological stations, the precipitation time series for each station in Hunan Province of China during 1910–2014 are tested for their homogeneity and then adjusted. The missing data caused by war and other reasons at the eight meteorological stations which had records before 1950 is filled by interpolation using adjacent observations, and complete precipitation time series since the establishment of stations are constructed. After that, according to the representative analysis of each station in different time periods, the precipitation series of Hunan Province during 1910–2014 are built and changes are analyzed. The results indicate that the annual precipitation has no significant linear trend but has obvious inter-decadal fluctuation during 1910–2014 and a periodic oscillation of 20 years is the most significant. Precipitation in winter (DJF) and summer (JJA) shows a slight wetter trend, and a slight dryer trend in spring (MAM) and autumn (SON). Abrupt change test suggests that annual and seasonal precipitations except for MAM and SON have abrupt ascending changes in the recent 100 years.

EFFECT OF CLIMATE CHANGE ON GROUNDWATER AND SALTS STORAGE IN EL SHEIKH ZUWAID-RAFAH, NORTH SINAI, EGYPT

Identifying and quantifying future climate impacts on water resources has major economic and societal implications, rendering such studies extremely important for water planners. In this research, we integrate the outputs of the general global models GDFL-ESM2M (the suitable one of the three studied models; CNRM-CM5, GFDL-ESM2M and EC-EARTH) downscaled by regional climate model (RCA4) under representative concentration pathways RCPs scenarios 8.5, into two hydrological models to provide approximations of climate change impacts on water quantity and quality in El Sheikh Zuwaid-Rafah quaternary aquifer system which is the main source of water in the area. The projected changes of the rising scenario between the periods 1980-2005 and 2050-2075 from the three studied GCMs predicted an increase in the mean of July temperature between 4.2 - 4.8 K (kelvin degree), whereas January temperature was estimated to increase between 1.8 - 5.4 K (kelvin degree). The cumulative annual precipitation was estimated to decrease by a range varied between 30% to 51%. A Simple Bias correction (SBC) technique was implemented to correct the simulated monthly precipitation series of RCA4 (GDFL-ESM2M) over Sinai Peninsula. A three dimensional finite difference flow model (Processing MODFLOW -PM5), and the implemented solute transport model (MT3D) were used to predict the groundwater and the total dissolved solids storage. Results from running the raising scenario RCP 8.5 estimated that by the hydrological year 2074 groundwater recharge would be decreased by 2.73 M.m3, leading with the yearly exploitation to decrease groundwater storage to 11.83 M.m3 and decrease in dissolve salt storage 33.1 M. Kg.

Analysis of geographic and orographic influence in Spanish monthly precipitation

ABSTRACTPrecipitation is a major concern in water resources studies. Being a main variable, the inefficiency of historical ground network, particularly at higher altitudes where precipitation and runoff augmentations are expected, constitutes a major drawback. This work analyses the competence of Spanish historical precipitation network and explores the physiographic influence of elevation and orientation at a national scale. The usefulness of slopes is also explored. Nearly 12 000 monthly precipitation series recorded from the 19th century until the hydrological year 2004/2005 over a high‐resolution topographic map (200 m) of Spanish territory are used. Comparable statistics and precipitation lapse rates are managed once a completion of gaps is accomplished. Then, Spanish yearly rates range from 0.3 to 1.2 mm m−1, reaching 1.5 mm m−1 in Northern Iberian Peninsula, diminishing at highest altitudes.Conclusions address the inefficiency and uncertainties revealed by historical ground networks as well as the variable relationship existing between precipitation and physiographic parameters, such as altitude and orientation.

VARIABILIDADE ESPACIAL DOS PERCENTIS 75 DA PRECIPITAÇÃO PLUVIAL MENSAL NO ESTADO DO ESPÍRITO SANTO

Conhecer a quantidade, intensidade e a distribuição espacial da precipitação pluvial contribuem para o gerenciamento de ações em vários setores da atividade humana. Nesse trabalho objetivou-se estudar a variabilidade espacial da precipitação no estado do Espírito Santo para cada mês, utilizando postos de observações da Agência Nacional de Águas (ANA), com intervalos de observações de 25 a 70 anos. Ao analisar a série de precipitação pluvial mensal constatou-se a presença de elevada amplitude total nos dados. Sendo assim, optou-se em trabalhar com o valor dos percentis 75 da série em cada mês como a maior precipitação, evitando informar anomalias climáticas. Os dados foram analisados pela estatística clássica determinando as medidas de posição e dispersão. Na sequência utilizou a geoestatística para definição de semivariogramas e a construção de mapas da distribuição espacial da precipitação em cada mês, utilizando a krigagem ordinária. Os meses com menor ocorrência do percentil 75, definindo a estação de seca, foram de abril a setembro. Os percentis 75 da precipitação pluvial mensal apresenta forte dependência espacial, com exceção no mês de janeiro, sendo que os alcances de dependência espacial variam de 41,6 a 210,6 km entre os meses. A distribuição espacial dos percentis 75 da precipitação pluvial evidenciou diferentes microclimas no estado, mostrando que os municípios que compõem a região noroeste tem menor ocorrência de precipitação, principalmente no mês de junho. O mês de dezembro, novembro e janeiro apresentam maiores lâminas de precipitação pluvial.

Physical and geographical principles of tourist cluster’s formation in Kharkiv region

Formulation of the problem. The article considers the main physical and geographical factors (climate, water, forest, relief) forming a tourist cluster on the territory of Kharkiv region. The cluster approach allows us to determine priority directions for the development of tourism, to use natural conditions as much as possible and to carry out nature conservation measures.The purpose of the article. The purpose of this article is to study natural-geographical criteria for the formation of the tourist cluster in Kharkiv region, their evaluation and generalization.Methods. The study of climatic parameters has been carried out using a statistical method, analyzing time series of average monthly air temperature, precipitation and solar radiation for the period 2001-2015.Results. Given the fact that Kharkiv region is in the northeast of Ukraine, it is characterized by a peripheral location. In general, its area is approaching a circle, determining equivalent relations between the regional center and the outskirts.The relief of Kharkiv region is mostly undulated, it is a plain, cut by river valleys, valley floors and power ravines. The peculiarities of the terrain in the region are emphasized by burial mounds (graves) created by man in ancient times, which increases historical and cultural value of the region under study. Climatic resources of Kharkiv region are favorable for recreation, especially for summer types, such as beach, sports, cognitive recreation.According to N. Fomenko’s methodology of estimating the recreational territory, the climatic resources of the studied region correspond to the indicator good, as well as to the parameters of optimal climatic conditions for recreational purposes. The territory of Kharkiv region is located in two landscape zones - the forest-steppe (northern part of the region) and the steppe (southern part). Today steppe vegetation has almost disappeared, which is explained by excessive agricultural activity. Kharkiv region is one of the main centers of balneotherapy on the left bank of Ukraine.Scientific novelty and practical significance. Today, the tourist cluster is considered an innovative form of the regional economic development, since tourism is one of the territory’s socio-economic functioning factors. Tourist and recreational activities are also very important in international economic relations. The formation of the tourist cluster of the territory will make it possible to effectively use all available tourist and recreational resources of the territory. Thus, the main factors determining the organization of tourist and recreational activity on the territory of Kharkiv region are relief, climate, water and vegetation resources. The region under study has sufficient recreational and tourist resources and good potential for their development, primarily of cognitive, river recreational, green and ecological tourism.

A joint analysis of river runoff and meteorological forcing in the Karakoram, upper Indus Basin

AbstractSatellite‐geodetic altimetry investigations in the Karakoram have indicated slight mass gain or loss of the glaciers during the early part of 21st century. Equivalent discharge in the upper Indus Basin due to these mass changes has been estimated at 5 to 10% of mean annual flow. However, satellite altimetry and geodetic glacier mass estimates in the extreme topography of the Karakoram have not yet been counter‐validated by hydrological analysis. Therefore, we present a first cross validation of three to five decades of river flow data from the three major watersheds in the Karakoram, with matching series of monthly precipitation, temperature, and evaporation provided by six atmospheric reanalysis products for 1979–2014. The analyses suggest that in most cases river flows have been increasing steadily from the end of the 1960s and 1970s to the middle of the 1990s and have stabilized or are in decline since then. Hunza watershed in Karakoram West shows consistently declining flows over the first half of the analysis period and stable flows during the second half for most of the summer melting season, suggesting mass accumulation. Rising river flows in the Shyok and Shigar watersheds, followed by stabilizing or slightly declining flows from 1995 onward, can be explained by consistently increasing precipitation during the first half of the analysis period, and successive stabilization or minor decline thereof. Flow data do not necessarily suggest considerable loss or gain of glacial mass in the Karakoram during the late 90s and early 2000s as suggested by satellite‐based altimetry studies.

Changes in precipitation regime in the Baltic countries in 1966–2015

The aim of the study was to analyse trends and regime shifts in time series of monthly, seasonal and annual precipitation in the eastern Baltic countries (Lithuania, Latvia, Estonia) during 1966–2015. Data from 54 stations with nearly homogeneous series were used. The Mann-Kendall test was used for trend analysis and the Rodionov test for the analysis of regime shifts. Rather few statistically significant trends (p < 0.05) and regime shifts were determined. The highest increase (by approximately 10 mm per decade) was observed in winter precipitation when a significant trend was found at the large majority of stations. For monthly precipitation, increasing trends were detected at many stations in January, February and June. Weak negative trends revealed at few stations in April and September. Annual precipitation has generally increased, but the trend is mostly insignificant. The analysis of regime shifts revealed some significant abrupt changes, the most important of which were upward shifts in winter, in January and February precipitation at many stations since 1990 or in some other years (1989, 1995). A return shift in the time series of February precipitation occurred since 2003. The most significant increase in precipitation was determined in Latvia and the weakest increase in Lithuania.

Monotonic trends in spatio-temporal distribution and concentration of monsoon precipitation (1901–2002), West Bengal, India

This paper intended to investigate spatio-temporal monotonic trend and shift in concentration of monsoon precipitation across West Bengal, India, by analysing the time series of monthly precipitation from 18 weather stations during the period from 1901 to 2002. In dealing with, the inhomogeneity in the precipitation series, RHtestsV4 software package is used to detect, and adjust for, multiple change points (shifts) that could exist in data series. Finally, the cumulative deviation test was applied at 5% significant level to check the homogeneity (presence of historic changes by cumulative deviations test). Afterward, non-parametric Mann-Kendall (MK) test and Theil-Sen estimator (TSE) was applied to detect of nature and slope of trends; and, Sequential Mann Kendall (SQMK) test was applied for detection of turning point and magnitude of change in trends. Prior to the application of statistical tests, the pre-whitening technique was used to eliminate the effect of autocorrelation in precipitation data series. Four indices- precipitation concentration index (PCI), precipitation concentration degree (PCD), precipitation concentration period (PCP) and fulcrum (centre of gravity) were used to detect precipitation concentration and the spatial pattern in it. The application of the above-mentioned procedures has shown very notable statewide monotonic trend for monsoon precipitation time series. Regional cluster analysis by SQMK found increasing precipitation in mountain and coastal regions in general, except during the non- monsoon seasons. The results show that higher PCI values were mainly observed in South Bengal, whereas lower PCI values were mostly detected in North Bengal. The PCI values are noticeably larger in places where both monsoon total precipitation and span of rainy season are lower. The results of PCP reveal that precipitation in Gangetic Bengal mostly occurs in summer (monsoon season), and the rainy season arrives earlier in North Bengal than South Bengal, whereas the results of PCD also indicate that the precipitation in North Bengal was more dispersed within a year than that in South Bengal. The concentration characteristic of precipitation could be detected by fulcrum analysis, and significant concentration over most of West Bengal was obvious within July month band. Precipitation trend observed in West Bengal is compared with that in Central India (CI) region and comparison of precipitation departure with Indian monsoon and Gangetic Bengal can be explained by forecasting ensemble.