Spatial variability of rainfall trends in Iran

The main objective of this paper is to analyze the spatial variability of rainfall trends using the spatial variability methods of rainfall trend patterns in Iran. The study represents a method on the effectiveness of spatial variability for predicting rainfall trend patterns variations. In rainfall trend analysis and spatial variability methods, seven techniques were used: Mann–Kendall test, Sen’s slope method, geostatistical tools as a global polynomial interpolation and the spatial autocorrelation (Global Moran’s I), high/low clustering (Getis-Ord General G), precipitation concentration index, generate spatial weights matrix tool, and activation functions of semiliner, sigmoid, bipolar sigmoid, and hyperbolic tangent in the artificial neural network technique .For the spatial variability of monthly rainfall trends, trend tests were used in 140 stations of spatial variability of rainfall trends in the 1975–2014 period. We analyzed the long and short scale spatial variability of rainfall series in Iran. Spatial variability distribution of rainfall series was depicted using geostatistical methods (ordinary kriging). Relative nugget effect (RNE) predicted from variograms which showed weak, moderate, and strong spatial variability for seasonal and annual rainfall series. Moreover, the rainfall trends at each station were examined using the trend tests at a significance level of 0.05. The results show that temporal and spatial trend patterns are different in Iran and the monthly rainfall had a downward (decreasing) trend in most stations, and the trend was statistically significant for most of the series (73.5% of the stations demonstrated a decreasing trend with 0.5 significance level). Rainfall downward trends are generally temporal-spatial patterns in Iran. The monthly variations of rainfall decreased significantly throughout eastern and central Iran, but they increased in the west and north of Iran during the studied interval. The variability patterns of monthly rainfall were statistically significant and spatially random. Activation functions in the artificial neural network models, in annual time scale, had spatially dispersed distribution with other clustering patterns. The results of this study confirm that variability of rainfall revealing diverse patterns over Iran should be controlled mainly by trend patterns in the west and north parts and by random and dispersed patterns in the central, southern, and eastern parts.     

<Emphasis>H</Emphasis>ydrological variability of the <Emphasis>S</Emphasis>oummam watershed <Emphasis>(N</Emphasis>ortheastern <Emphasis>A</Emphasis>lgeria<Emphasis>)</Emphasis> and the possible links to climate fluctuations

The long-term variability of rainfall in the Soummam watershed (NE Algeria) has been analysed over the past 108 years using continuous wavelet method in order to identify the interannual modes controlling the rainfall variability. Statistical analyses of rainfall timeseries have shown its distribution following five periods of time, limited by a series of discontinuities around 1935, 1950, 1970 and 1990. The continuous wavelet transform have demonstrated different low frequency modes: 2–4, 4–8, 8–16 and 16–32 years.The annual band is expanded during the full study period with some pics around 1905, 1920–1935 and 1960; it shows a negative long-term trend, in particular since the period 1970–1990 when a major change has been identified. Then, the relationships between climate patterns of North Atlantic Oscillation (NAO) and Southern Oscillation Index (SOI) and the hydrological variability in the frequency domain have been investigated; they have shown a mean explained variance of 40 and 24 %, respectively. Such variances are less obvious for the annual mode and increase for the interannual frequencies. The coherence suffer from high perturbations since the period 1970–1990 when the NAO (SOI) shifts from negative (positive) phases to positive (negative) ones. Such anomalies are responsible for significant changes of rainfall variability, emphasising the global warming effects.     

Analysis of seasonal and annual rainfall trends for Ranchi district,Jharkhand, India

The aim of this study was to investigate temporal variation in seasonal and annual rainfall trend over Ranchi district of Jharkhand, India for the period (1901–2014: 113 years). Mean monthly rainfall data series were used to determine the significance and magnitude of the trend using non-parametric Mann–Kendall and Sen’s slope estimator. The analysis showed a significant decreased in rainfall during annual, winter and southwest monsoon rainfall while increased in pre-monsoon and post-monsoon rainfall over the Ranchi district. A positive trend is detected in pre-monsoon and post-monsoon rainfall data series while annual, winter and southwest monsoon rainfall showed a negative trend. The maximum decrease in rainfall was found for monsoon (? 1.348 mm year^?1) and minimum (? 0.098 mm year^?1) during winter rainfall. The trend of post-monsoon rainfall was found upward (0.068 mm year^?1). The positive and negative trends of annual and seasonal rainfall were found statistically non-significant except monsoon rainfall at 5% level of significance. Rainfall variability pattern was calculated using coefficient of variation CV, %. Post-monsoon rainfall showed the maximum value of CV (70.80%), whereas annual rainfall exhibited the minimum value of CV (17.09%), respectively. In general, high variation of CV was found which showed that the entire region is very vulnerable to droughts and floods.     

Trend analysis of hydroclimatological variables in Urmia lake basin using hybrid wavelet Mann–Kendall and Şen tests

This paper investigates monthly, seasonal, and annual trends in rainfall, streamflow, temperature, and humidity amounts at Urmia lake (UL) basin and analyzes the interaction between these variables and UL’s water level fluctuation during the 1971–2013 period. Two new methods including nonparametric hybrid wavelet Mann–Kendall test and ?en’s methodology have been used to determine potential trends in the variables and their dominant periods. The results showed significant decreasing trends in the water level and streamflow series, moderate decreasing trend in the rainfall and relative humidity series, and increasing trends in the observed temperature data. The 8- , 12-month, and 2-year periods were detected as the dominant periods of the variables in monthly, seasonal, and annual timescales, respectively. The results from the interaction analysis revealed that the main factor influencing the water level at UL is decreasing trend in the streamflow series. Both the monthly series of UL’s water level and the streamflow series of the stations indicated two start points of significant decreasing trend in 1973 and 1998. Furthermore, a comparative analysis among the applied methods indicated a good agreement between the results of hybrid wavelet Mann–Kendall test and ?en’s trend analyzing method.     

Analyses of precipitation,temperature and evapotranspiration in a French Mediterranean region in the context of climate change

This study is focused on the western part of the French Mediterranean area, namely the Pyrénées-Orientales and Aude administrative departments. The water resources (surface and groundwater) in the region are sensitive to climate change. The study addresses the question of whether any trend in the annual and monthly series of temperature, rainfall and potential evapotranspiration (PET) already appears at the scale of this region. Two data sources have been used: (a) direct local measurements using the meteorological network; and (b) spatially interpolated data from the French weather service model SAFRAN for the period 1970–2006. The non-parametric Mann–Kendall test was applied to identify significant trends at the local scale and, because of the natural spatial variability of the Mediterranean climate, regional interpretation was also performed. The trends observed in the 13 catchments of interest are consistent with those observed at a larger scale. An increase in annual mean temperature and annual PET was observed throughout the study area, whereas annual precipitation has not exhibited any trend. The monthly scale has revealed strong seasonal variability in trend. The trend for an increase in monthly PET has been observed mainly in the spring, and has not been seen in the coastal areas. A trend for an increase in monthly temperature has been observed in June and in the spring throughout the entire area. Monthly rainfall has been found to decrease in June and increase in November throughout the area. The significant trends observed in rainfall and temperature seem to be consistent between the different data sources.     

Evaluation of rainfall erosivity and its temporal variation in the Yanhe River catchment of the Chinese Loess Plateau

The potential of rain to generate soil erosion is known as the rainfall erosivity (R), and its estimation is fundamental for a better understanding of the erosive ability of certain rainfall events. In this paper, we investigated the temporal variations of rainfall erosivity using common daily rainfall data from four meteorological stations during 1956 to 1989 and 2008 to 2010 periods in the Yanhe River catchment of the Chinese Loess Plateau. The adaptability of several simplified calculation models for R was evaluated and compared with the results of previous studies. An exponential model based on the modified Fournier index (MFI) was considered as the optimum for our study area. By considering the monthly distribution and coefficient of variation of annual precipitation, equations based on two indices, the MFI and its modification F _F , produced a higher calculation accuracy than mean annual precipitation. The rainfall erosivity in the Yanhe River catchment has a remarkable interannual difference, with a seasonality index ranging from 0.69 to 1.05 and a precipitation concentration index from 14.51 to 27.46. In addition to the annual rainfall amounts, the extreme wave of monthly rainfall distribution also has an effect on the magnitude and temporal variation of rainfall erosivity, especially interannual variation. For long time series of rainfall erosivity, a trend coefficient r of ?0.07 indicated a slight decline in erosivity in the Yanhe River catchment from 1956 to 2010.     

Extreme weather and economic well-being in rural Mozambique

Societies dependent on rain-fed agriculture are highly vulnerable to weather extremes; thus, linkages between rainfall variability and economic well-being merit close attention. The hypothesis of this paper is that rainfall patterns impact changes in income within our study region of central and northern Mozambique. Utilizing satellite-based estimates of rainfall analyzed within a GIS, we establish a 12-year rainfall climatology and calculate monthly rainfall anomalies for 419 villages during three growing seasons. We also approximate storm-total rainfall from tropical cyclones entering the Mozambique Channel. Hierarchical cluster analysis groups the villages according to the monthly rainfall anomalies and rainfall received from Cyclones Delfina and Japhet. Then, using data from the National Agricultural Survey of Mozambique conducted in 2002 and 2005, we relate rainfall and change in income through the calculation of Pearson’s correlation coefficients and independent-samples t tests using village-groups produced by the cluster analysis. We find that no season closely approximates the 12-year climatology and that rainfall varied among the three seasons. Although most villages experience income declines, those affected by Delfina exhibit the worst economic performance, indicating that heavy rainfall from some tropical cyclones can have long-lasting negative effects on income. Additionally, receiving above-normal rainfall may hinder economic well-being more than below-normal rainfall. Our study identifies patterns in sub-national rainfall variability and economic well-being that enable a more detailed understanding of weather-related effects on socio-economic outcomes.     

Relationship between northeast monsoon rainfall and near-surface atmospheric wind convergence over the North Indian Ocean using multisatellite data

The northeast monsoon rainfall (NEMR) contributes about 20–40 % of annual rainfall over the North Indian Ocean (NIO). In the present study, the relationship between the NEMR and near-surface atmospheric wind convergence (NSAWC) over the NIO is demonstrated using high-resolution multisatellite data. The rainfall product from the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis and near-surface wind product from the Cross-Calibration Multi-Platform available at 0.25° × 0.25° spatial resolution are used for the study. Large-scale NSAWC and divergence maps over the tropical Indian Ocean are generated at monthly scale from the wind product for the period of 1988–2010. A preliminary analysis is carried out for two consecutive anomalous Indian Ocean Dipole (IOD) years 2005 (negative) and 2006 (positive). The distinct spatial patterns of rainfall rate and NSAWC fields over the NIO clearly show the evolution of the anomalous IOD events in the south eastern equatorial Indian Ocean (EEIO). The spatially averaged time-series of pentad NSAWC over the south EEIO box suggests that the variability occurs in phase with rainfall rate during both the northeast monsoon years. Furthermore, the scatter plot between area-averaged pentad rainfall and convergence over the south EEIO box for the period of 1998–2010 shows statistically significant linear correlation which reveals that NSAWC plays a key role in regulating the NEMR.     

Statistical detection and no-detection of rainfall change trends and breaks in semiarid Tunisia—50+ years over the Merguellil agro-hydro-climatic reference basin

Rainfall variability is an important feature of semiarid climates with major effects on hydrology, and beyond on key water-dependent societal aspects. Eventual changes in rainfall variability are a strong driver of change of hydrological processes, resources, and hazards, up to catchment signatures and spatial arrangements. We deal with observed precipitations and subsequent statistical coefficients available from a network of 15 rainfall gauges over and around the Merguellil catchment (1175 km²), with series ranging up to the 1961–2013 period. We look for eventual annual trends and breakpoints with a set of methods: Mann Kendall test, Pettitt test, Hubert segmentation procedure, Buishand U statistic, and Lee Heghinian Bayesian procedure. The results underline oscillation of dry and wet periods; several studied rain gauges (Tella, Oueslatia forêt, Majbar, Kesra forêt, Henchir Bhima, and Haffouz DGRE) denote significant trends in annual precipitation. Some breaks are detected but they are not synchronous. These methods reveal the variability of rainfall regimes in the semiarid region and provide a synoptic view of detection and no-detection of symptoms of change. This work gives opportunities to water stakeholders and climate experts in understanding the relationships between climate variability and water availability.     

Analysis of rainfall and temperature time series to detect long-term climatic trends and variability over semi-arid Botswana

Arid and semi-arid environments have been identified with locations prone to impacts of climate variability and change. Investigating long-term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible inhomogeneities and years of intervention are analysed using four absolute homogeneity tests. Trends in the climatic variables were determined using Mann–Kendall and Sen’s Slope estimator statistics. Association of El Niño Southern Oscillation (ENSO) with local climate is also investigated through multivariate analysis. Results from the study show that rainfall time series are fully homogeneous with 78.6 and 50% of the stations for maximum and minimum temperature, respectively, showing homogeneity. Trends also indicate a general decrease of 5.8, 7.4 and 18.1% in annual, summer and winter rainfall, respectively. Warming trends are observed in annual and winter temperature at 0.3 and 1.5% for maximum temperature and 1.7 and 6.5% for minimum temperature, respectively. Rainfall reported a positive correlation with Southern Oscillation Index (SOI) and at the same time negative association with Sea Surface Temperatures (SSTs). Strong relationships between SSTs and maximum temperature are observed during the El Niño and La Niña years. These study findings could facilitate planning and management of agricultural and water resources in Botswana.     

中国大陆流域分区TRMM降水质量评价

根据中国境内2 257个气象站点1998-2013年逐日降水资料,结合流域分区,采用探测准确性、相关系数以及相对误差等指标,对热带降水测量（TRMM）降水精度和一致性进行系统评价。结果表明:① TRMM日降水准确性从东南沿海向西北内陆递减;② 气象站点年均降水日数显著大于TRMM年均降水日数;③ 西北片区以外气象站点降水量和TRMM降水量在月尺度和年尺度上均具有较好的相关关系;④ 各流域年均TRMM面降水量均高于气象站点面降水量,且TRMM面降水量相对误差雨季较小,枯季较大;⑤ 各流域TRMM面降水量与气象站点面降水量演变趋势基本一致,南方各流域年降水量均呈减少趋势,北方各流域年降水量均呈增加趋势,全国尺度上年降水量呈微弱的减少趋势。     

TRMM卫星降雨雷达观测的南海降雨空间结构和季节变化

利用热带降雨计划卫星(TRMM)获得的雷达降雨资料,对南海及其周边区域(简称南海地区)降雨的空间分布和季节特征进行了研究。结果表明：南海地区的降雨在空间上分布很不均匀,同时具有显著的季节变化。除了副高活动、季风潮、冬季冷涌和热带低压活动等天气过程,南海周边广泛分布的山地地形对该地区的降雨分布也产生强烈影响,降雨呈现南部高于北部、东部高于西部的分布特征?与CAMP和台站资料相比,PR观测具有更丰富的空间结构,能够更好地体现降雨随时间和空间变化的特征、反映高大的山地地形对降雨分布的影响。     

Homogeneous Indian Monsoon rainfall: Variability and prediction

The Indian summer monsoon rainfall is known to have considerable spatial variability, which imposes some limitations on the all-India mean widely used at present. To prepare a spatially coherent monsoon rainfall series for the largest possible area, fourteen subdivisions covering the northwestern and central parts of India (about 55% of the total area of the country), having similar rainfall characteristics and associations with regional/global circulation parameters are merged and their area-weighted means computed, to form monthly and seasonal Homogeneous Indian Monsoon (HIM) rainfall series for the period 1871–1990. This paper includes a listing of monthly and seasonal rainfall of HIM region. HIM rainfall series has been statistically analysed to understand its characteristics, variability and teleconnections for long-range prediction. HIM rainfall series isfound to be homogeneous, Gaussian distributed and free from persistence. The mean (R) rainfall is 757 mm (87% of annual) and standard deviation (S) 119 mm, with a Coefficient of Variation (CV) of 16%. There were 21 dry (K, -<R S) and 19 wet (R _i R + S) years during 1871–1990. There were clusters of frequent negative departures during 1899–1920 and 1965–1987 and positive departures during 1942–1961. The recent three decades show very high rainfall variability with 10 dry and 6 wet years. The decadal averages were alternatively positive and negative for three consecutive decades, viz., 1871–1900 (positive); 1901–1930 (negative); 1931–1960 (positive) and 1961–1990 (negative) respectively. Significant QBO and autocorrelation at 14th lag have been found in HIM rainfall series. To delineate the changes in the climatic regime of the Indian summer monsoon, sliding correlation coefficients (CCs) between HIM rainfall series and (i) Bombay msl pressure, (ii) Darwin msl pressure and (iii) Northern Hemisphere surface air temperature over the period 1871–1990 have been examined. The 31-year sliding CCs showed the systematic turning points of positive and negative CCs around the years, 1900 and 1940. In the light of other corroborative evidences, these turning points seem to delineate ‘meridional’ monsoon regime during 1871–1900 and 1940–1990 and ‘zonal’ monsoon regime during 1901–1940. The monsoon signal is particularly dominant in many regional and global circulation parameters, during 1951–1990. Using the teleconnections ofHIM series with 12 regional/global circulation parameters during the recent 36-year period 1951–86 regression models have been developed for long-range prediction. In the regression equations 3 to 4 parameters were entered, explaining upto 80% of the variance, depending upon the data period. The parameters that prominently enter the multiple regression equations are (i) Bombay msl pressure, (ii) April 500 mb Ridge at 75°E, (iii) NH temperature, (iv) Nouvelle minus Agalega msl pressure and (v) South American msl pressure. Eleven circulation parameters for the period 1951–80 were subjected to Principal Component Analysis (PCA) and the PC’s were used in the regression model to estimate HIM rainfall. The multiple regression with three PCs explain 72% of variance in HIM rainfall.     

TRMM卫星降雨雷达观测的南海降雨空间结构和季节变化

利用热带降雨计划卫星（TRMM）获得的雷达降雨资料，对南海及其周边区域（简称南海地区）降雨的空间分布和季节特征进行了研究。结果表明：南海地区的降雨在空间上分布很不均匀，同时具有显著的季节变化。除了副高活动、季风潮、冬季冷涌和热带低压活动等天气过程，南海周边广泛分布的山地地形对该地区的降雨分布也产生强烈影响，降雨呈现南部高于北部、东部高于西部的分布特征。与CAMP和台站资料相比，PR观测具有更丰富的空间结构，能够更好地体现降雨随时间和空间变化的特征、反映高大的山地地形对降雨分布的影响。     

18世纪南京、苏州和杭州年、季降水量序列的复原研究

利用清代宫廷档案“晴雨录”南京、苏州和杭州的逐日天气记录,复原了18世纪三地的年、季和月降水量序列;论述了将“晴雨录”的逐日降水时数和降水类型的记载转换成7级降水日数和逐步回归推算月降水量的方法及其科学依据;给出长江下游地区18世纪典型多雨、少雨年份的降水量值。18世纪南京等三地的年、季、月降水量序列的复原,对了解在寒冷小冰期中相对温暖时段我国主要农业区长江三角洲地区的降水特征有重要意义。     

Trends and the pole tide in Indian tidegauge records

This paper studies tidegauge records of stations on the Indian coastline. An analysis of trends did not reveal a monotonie trend. Trends were seen for limited periods at only five of the eight stations on the Indian coast. A spectral analysis of annual records produced evidence of long period cycles with shorter cycles riding on them. The shorter cycles had a period of 5.0 years. The spectra of monthly records revealed evidence of a pole tide and an annual cycle. The amplitude of the pole tide was estimated to be around 7.5 mm. This was larger than the equilibrium tide. A spectral analysis of monthly rainfall at Bombay, a station on the Indian west coast, also showed a 13.9 month cycle and a (3,1,0) autoregressive model. But the coherence between monthly rainfall and relative sealevel fluctuations was low.     

Identifying non-stationary groundwater level response to North Atlantic ocean-atmosphere teleconnection patterns using wavelet coherence

The first comprehensive use of wavelet methods to identify non-stationary time-frequency relations between North Atlantic ocean-atmosphere teleconnection patterns and groundwater levels is described. Long-term hydrogeological time series from three boreholes within different aquifers across the UK are analysed to identify statistically significant wavelet coherence between the North Atlantic Oscillation, East Atlantic pattern, and the Scandinavia pattern and monthly groundwater-level time series. Wavelet coherence measures the cross-correlation of two time series as a function of frequency, and can be interpreted as a correlation coefficient value. Results not only indicate that there are common statistically significant periods of multiannual-to-decadal wavelet coherence between the three teleconnection indices and groundwater levels in each of the boreholes, but they also show that there are periods when groundwater levels at individual boreholes show distinctly different patterns of significant wavelet coherence with respect to the teleconnection indices. The analyses presented demonstrate the value of wavelet methods in identifying the synchronization of groundwater-level dynamics by non-stationary climate variability on time scales that range from interannual to decadal or longer.     

Mapping heatwave vulnerability in Korea

Analysis of event-based soil erosion magnitude with special return periods is essential to appropriately design strategies and adopt soil conservation practices. However, the spatiotemporal variations of soil erosion with different return periods, especially at national level, have not been adequately considered. Therefore, the present study aimed to zone rainfall erosivity index (R factor) as the most dynamic factor affecting variability of soil erosion rate, with different return periods in monthly, seasonal and annual time scales in Iran. Toward this attempt, the kinetic energy and maximum 30-min intensity (I ₃₀) over 12,000 available and accessible events of 70 stations were calculated during the common period of 1984–2004 and the corresponding R factor of the Universal Soil Loss Equation was then computed. Subsequently, the best-fitted frequency distributions were determined in all stations in three time scales using the EasyFit Software. The R factor was accordingly estimated for 2-, 5-, 10-, 25- and 50-year return periods. In addition, the inverse distance weighting technique was employed to determine and analyze the spatial variability patterns of R factor in different time scales using geographic information system. The results indicated that the frequency distributions fitted to study data were different in study time scales due to variability of spatiotemporal patterns of R factor. In addition, no specific spatial pattern of R factor could be recognized for different return periods and time scales. The average annual R factor was also found 1.41 MJ mm ha^?1 h^?1, whereas the respective R factor for different respective return periods of 2, 5, 10, 25 and 50 years was obtained 1.47, 2.62, 3.35, 4.48 and 5.54 MJ mm ha^?1 h^?1. These findings can be used for suitable decision making and effective environmental planning for land management Iran countrywide.     

Image acquisition for detection of vegetation change based on long-term rainfall in an arid rangeland in Western NSW, Australia

Research on vegetation change, rangeland assessment or desertification modelling in drylands using remotely sensed image acquisition normally ignores long-term rainfall as a key criterion in image acquisition. This article will present a novel procedure for image acquisition to investigate vegetation change in a degraded rangeland located in Western New South Wales (Western NSW) Australia. Western NSW experienced an unusually prolonged period of rainfall deficit during the 2000s compared to the 1970, 1980 and 1990s. For this purpose, vegetation changes were assessed using Landsat images supported by field survey. The long-term rainfall variability (42-year) was regarded as a key element in image acquisition. Within the timeframe of the 2000s, 2 years with 25 % lower than the 42-year mean annual rainfall were selected. These images were then compared to an image captured in a year (1988) with rainfall closer to the 42-year mean annual rainfall. Two change detection techniques were used, namely univariate image differencing and GIS approaches. Classification of the produced images was pursued based on the digital numbers (supervised) of ground-checked points within the reference image whilst considering the histogram (unsupervised) of each digital number of the produced image. This research emphasized rainfall as a key variable in image acquisition for vegetation change analysis in rangelands. Image acquisition based on long-term rainfall data allowed for the assessment of changes in perennial plant cover by eliminating the effects of extreme rainfall variation on annual grass dynamics and removing extreme reflections caused by their temporary high photosynthetic activity.     

Spatial and temporal variability of rainfall: case of Bizerte-Ichkeul Basin (Northern Tunisia)

This study examines spatial and temporal variability of rainfall in Bizerte-Ichkeul Watershed. The basin, located in the extreme north of Tunisia, covers an area of 3084 km². Thirteen rainfall stations, with continuous monthly precipitation records over the period (1970–2011), were considered in the analysis. Two methods were used. In the first, the dimensionless standardized precipitation ratio is applied to examine precipitation temporal variation. The second method is represented by continuous wavelet analysis for the precipitation spatial analysis and the identification of the origin of its variability. The study of temporal variability of annual rainfall showed severe persistent and recurrent drought episodes over the period (1977–2001). Wavelet analysis resulted in detecting the modes and origins of precipitation variability. Three energy bands were clearly identified: (1, 2–4, and 4–8 years) for the entire watershed. The visualization of the power distribution showed that the observed modes of variability are different in their power distributions from one station to another. The approach adopted allowed the identification of two groups with the same precipitation frequency and temporal variation. These groups were defined according to the difference in occurrence of the frequency band for each station.