Analysis Of Rainfall Patterns Research Articles

PyMTRD: A Python package for calculating the metrics of temporal rainfall distribution

Temporal rainfall distribution facilitates the understanding of rainfall patterns at various time scales, extreme events, and corresponding water resources implications. Researchers have developed various metrics of temporal rainfall distribution but there exist no easy-to-use software packages for calculating these metrics. To address this gap, we developed the PyMTRD package, which can be conveniently used to calculate the metrics of temporal rainfall distribution and conduct rainfall pattern analysis. The metrics calculated in the package included rainfall intensity, rainfall frequency, consecutive dry days, Gini index, unranked Gini index, wet-day Gini index, precipitation concentration index, dimensionless seasonality index, and seasonality index. This paper documented our Python software development, which included the architecture design, the Application Programming Interfaces design and algorithms for calculating each metric, and also the point and global scale applications.

Spatio-Temporal Variations of Indonesian Rainfall and Their Links to Indo-Pacific Modes

The analysis of rainfall patterns in the Indonesian region utilized the Empirical Orthogonal Function (EOF) method to identify spatial and temporal variations. The study evaluated the dynamic influence of the Tropical Indian Ocean (TIO) and the Tropical Pacific Ocean (TPO) on Indonesian rainfall using monthly data from the Southeast Asian Climate Assessment and Dataset (SACA&D) spanning from January 1981 to December 2016 and encompassing three extreme El Niño events in 1982/1983, 1997/1998 and 2015/2016. Using combined reanalysis and gridded-observation data, this study evaluates the potential impact of the two primary modes in the tropical Indo-Pacific region, namely the Indian Ocean Dipole (IOD) and the El Niño-Southern Oscillation (ENSO) on Indonesian rainfall. The analysis using the EOF method revealed two main modes with variances of 35.23% and 13.07%, respectively. Moreover, the results indicated that rainfall in Indonesia is highly sensitive to sea surface temperatures (SST) in the southeastern tropical Indian Ocean and the central Pacific Ocean (Niño3.4 and Niño3 areas), suggesting that changes in SST could significantly alter rainfall patterns in the region. This research is useful for informing government policies related to anticipating changes in rainfall variability as part of Indonesia’s preparedness for hydrometeorological disasters.

Prediction of Rainfall Trends using Mahalanobis-Taguchi System

Full comprehension of precipitation patterns is crucially needed, especially in Pekan, a district in Pahang, Malaysia. The area is renowned for its elevated levels of precipitation, making it imperative to precisely categorize and enhance the analysis of rainfall patterns to facilitate effective resource allocation, agricultural productivity, and catastrophe readiness. The variability of rainfall patterns is contingent upon geographical location, necessitating the collection of a comprehensive data set that includes several characteristics that influence precipitation to make reliable predictions. Data were collected from the Vantage Pro2 weather station, which is located on the UMP Pekan campus. This study used the RT method to classify rainfall and T-Method 1 to determine the degree of contribution of each parameter. Significant parameters were validated using a data set from the same type of weather station but in a different district. The results showed that the Mahalanobis-Taguchi Bee Algorithm (MTBA) is more effective than the Mahalanobis-Taguchi System (MTS) in finding the significant parameters, but the parameters were a subset of MTS Teshima. Finally, the validation with T mean-based error (Tmbe) using Mean Absolute Error (MAE) revealed a pattern of errors to provide insight to find the significant parameters of MTS.

Multiscale teleconnection analysis of rainfall patterns over Calicut, India using wavelet coherence

Multiscale teleconnection analysis of rainfall patterns over Calicut, India using wavelet coherence

Trend Analysis of Rainfall Patterns in Rajnandgaon District, Chhattisgarh, India

Climate is a crucial determinant of an area's vegetation, wildlife, and livability, influenced by long-term atmospheric characteristics and fluctuations. Precipitation, particularly rainfall, plays a vital role in the Earth's hydrological cycle, connecting weather, climate, and water resources. This study focuses on Rajnandgaon district in Chhattisgarh, India, with an average annual precipitation of 1274mm. Analyzing trends, we identify changes in certain properties over time. The findings indicate that rainfall variability, measured by the coefficient of variation, is lowest during June to September (CV < 76%). Monsoon rainfall displays the least variability (CV of 30.88%), while pre-monsoon rainfall and winter rainfall exhibit higher variability (CV of 33.83% and 208.48%, respectively). The analysis also reveals a significant decreasing trend in annual, monsoon, and September rainfall. However, May rainfall shows a significant increase at a 5% level of significance. The study utilizes the non-parametric Mann-Kendall and Sen's Slope Estimator tests, employing monthly rainfall data from 1988 to 2018 for Rajnandgaon district, Chhattisgarh.

Long-term climatic trends in rainfall pattern analysis over Madhya Pradesh

Long-term climatic trends in rainfall pattern analysis over Madhya Pradesh

Exploring hidden patterns in rainfall in Mula-Mutha and Bhima sub-basin of Upper Krishna Valley using statistical trend analysis for water resources planning

In-depth information on rainfall patterns is invaluable for planning and development of water resources. Trend analysis of rainfall datasets in Mula-Mutha and Bhima sub-basin characterised by strong presence of seasonality, serial correlation and skewed distribution carried out using robust non-parametric methods showed statistically significant rising trend in the plateau region and no or falling trend in the Western Ghat region. An interesting semi-circular shaped cyclic pattern, with period around 11-18 years was visualised during the comprehensive rainfall pattern analysis. This rudimentary pattern could possibly be linked to the 'solar cycles'. The outcome can credibly be used in framing local and region-specific medium-term plans of 11-15 years duration for water resources planning to adapt and cope up with rain-deficient and flood years. Development of robust trend analysis methods to address likely cyclic patterns could be taken up further for research.

Rainfall pattern analysis in 24 East Asian megacities using a complex network

Abstract. Concurrent floods in multiple locations pose systemic risks to the interconnected economy in East Asia via supply chain disruptions. Despite these significant economic impacts, understanding of the interconnection between rainfall patterns in the region is still currently limited. Here, we analyzed the spatial dependence of the rainfall patterns of 24 megacities in the region using complex analysis theory and discussed the technique's applicability. Each city and rainfall similarity were represented by a node and a link, respectively. Vital-node identification and clustering analysis were conducted using adjacency information entropy and multiresolution community detection. The results of vital-node identification analysis show that high-ranking nodes are cities that are located near main vapor providers in East Asia. Using multiresolution community detection, the groups were clustered to reflect the spatial characteristics of the climate. In addition, the climate links between each group were identified using cross-mutual information considering the delay time for each group. We found a strong bond between Northeast China and the southern Indochinese Peninsula and verified that the links between each group originated from the summer climate characteristics of East Asia. The results of the study show that complex network analysis could be a valuable method for analyzing the spatial relationships between climate factors.

Extreme Rainfall Pattern Analysis for Drought Prone Shali Reservoir Area in West Bengal of India

Rainfall is one of the major sources of needed water. Rainfall amount also determines the agricultural and industrial activities in the region. The present study has been conducted to examine the rainfall trend in the Shali reservoir area of the Gangajalghati block in Bankura district in West Bengal. The Shali reservoir was made to provide irrigation water in the Gangajalghati block. Water insufficiency is a habitual threat to the people of this area. Here the planting and production of crops highly depend on the constricted period of inconsistent rainfall. About 80-90 % of rainfall usually occurs in this area from June to September until the onset of heavy rains. Therefore, in this drought-prone area, the analysis of rain patterns is very vital to understand the change in rainfall at all times. The monthly and annual rainfall data have been taken from field surveys and secondary surveys for almost the last 40 years (1980 to 2020). The Mann Kendall (MK) and Sen’s Slope estimate (SSE) tests have been applied to recognize the standing trend and the SSE test has been used to discover the existing trend and extent of change in rainfall over time. The rainfall data has been taken from ten rain gauge stations surrounding the study area. The relevant maps were prepared using Remote Sensing and the rainfall distribution pattern was analyzed with the help of the kriging interpolation technique of GIS. The annual MK test shows a positive trend but the season-wise MK test is showing a negative trend during monsoon season. SPI result describes that the study area is not getting normal rainfall during the monsoon season, it may create harmful effects on agricultural productivity. The study suggests taking proper drought management programs to combat this situation.

Decadal trend analysis of rainfall patterns of past 115 years & its impact on Sikkim, India

Fluctuations or variations in climatic parameters is a recurring phenomenon in India. The effects of climate variability exacerbate existing social and economic encounters across the area. Over the last 115 years, the rainfall patterns in India have changed significantly. The most significant change is the shifting of the monsoon season altogether. This study aims to discuss the significant changes of rainfall pattern and their influence in Sikkim state, India. Daily rainfall data of 115 years from 1901 to 2015 has been processed in the study to find out the monthly variability of rainfall for which Mann-Kendall (MK) Test together with the Sen's Slope Estimator for the determination of trend and slope magnitude. Monthly precipitation trend has been identified here to achieve the objective, which has been shown with 115 years of data. The Mann–Kendall test and Sen's estimation tests indicated significant changing patterns in the frequency and intensity of the rainfall across the Sikkim in an intra-decadal time series domain. It has been noted in this study that the Sikkim region observed drastic rainfall pattern changes after 2001, because of which there was an increase in disasters like landslides and floods., This study also used inverse distance weighted (IDW) interpolation method and reclassify tool to produce the maps for identifying the pattern and region that caused more rainfall. NDVI (Vegetation Index) has also shown the drastic changes in south Sikkim of vegetation due to decreased rainfall, urbanisation. NDVI values gradually risefrom spring to rainy season and fall steadily from autumn to winter.

Extending seasonal malaria chemoprevention to five cycles: a pilot study of feasibility and acceptability in Mangodara district, Burkina Faso

BackgroundSeasonal malaria chemoprevention (SMC) involves administering antimalarial drugs at monthly intervals during the high malaria transmission period to children aged 3 to 59 months as recommended by the World Health Organization. Typically, a full SMC course is administered over four monthly cycles from July to October, coinciding with the rainy season. However, an analysis of rainfall patterns suggest that the malaria transmission season is longer and starting as early as June in the south of Burkina Faso, leading to a rise in cases prior to the first cycle. This study assessed the acceptability and feasibility of extending SMC from four to five cycles to coincide with the earlier rainy season in Mangodara health district.MethodsThe mixed-methods study was conducted between July and November 2019. Quantitative data were collected through end-of-cycle and end-of-round household surveys to determine the effect of the additional cycle on the coverage of SMC in Mangodara. The data were then compared with 22 other districts where SMC was implemented by Malaria Consortium. Eight focus group discussions were conducted with caregivers and community distributors and 11 key informant interviews with community, programme and national-level stakeholders. These aimed to determine perceptions of the acceptability and feasibility of extending SMC to five cycles.ResultsThe extension was perceived as acceptable by caregivers, community distributors and stakeholders due to the positive impact on the health of children under five. However, many community distributors expressed concern over the feasibility, mainly due to the clash with farming activities in June. Stakeholders highlighted the need for more evidence on the impact of the additional cycle on parasite resistance prior to scale-up. End-of-cycle survey data showed no difference in coverage between five SMC cycles in Mangodara and four cycles in the 22 comparison districts.ConclusionsThe additional cycle should begin early in the day in order to not coincide with the agricultural activities of community distributors. Continuous sensitisation at community level is critical for the sustainability of SMC and acceptance of an additional cycle, which should actively engage male caregivers. Providing additional support in proportion to the increased workload from a fifth cycle, including timely remuneration, is critical to avoid the demotivation of community distributors. Further studies are required to understand the effectiveness, including cost-effectiveness, of tailoring SMC according to the rainy season. Understanding the impact of an additional cycle on parasite resistance to SPAQ is critical to address key informants’ concerns around the deviation from the current four-cycle policy recommendation.

Developing a Formula to Estimate Precipitation at Ungauged Location and Analysis of Rainfall Pattern: Case Study for Rastra Bank Chowk, Pokhara, Nepal

Pokhara receives the second highest amount of rainfall in the country, according to Department of Hydrology and Meteorology. The rainfall distribution within this small region is random. The precipitation in Pokhara is measured from limited rain gauge stations. For understanding the spatial variation, it is necessary to access the daily rainfall data at the gauged as well as ungauged stations. In view of this problem, this study is aimed at developing the formula to estimate the rainfall value at an ungauged station, Rastrabank Chowk Pokhara using the calibration and validation method. For this purpose, the daily rainfall data of the sample station was measured using standard rain gauge for a period of four months from May 13, 2020 to September 15, 2020. The analysis of the spatial distribution and variation of daily rainfall at meteorological stations as well as sample station was done to select the data for the development of the formula. Calibration and validation of formula was performed for equal period using the statistical indices NSE, R2 and PBIAS. Calibration was performed using rainfall data of available meteorological stations of Pokhara in the first two consecutive months. From the numerous hit and trial procedures, the equation developed with rainfall data of Airport and Malepatan stations comprising of three constant values were obtained with the highest NSE and R2. PBIAS value was then calculated for maximum NSE and R2. In the next phase, validation of the equation for other two months (14 July- 15 September,2020) was performed to obtain maximum possible value of NSE and R2 and a smaller value of PBIAS. The equation was obtained with 0.980 NSE, 0.980 R2 and 6.437 % PBIAS value which indicated good agreement between observed and predicted rainfall value. Spatial rainfall pattern analysis using SURFER model demonstrated that there was a homogenous distribution of rainfall in Malepatan, Airport and sample stations. The temporal trend showed that the month July of 2020 had a higher amount of precipitation at Pokhara valley. From the HYSPLIT model, it was observed that the rainfall events in Pokhara valley was due to westerlies dominance. This study would provide the rainfall estimation techniques and hence would significantly contribute to meteorological investigations and water resource planning.

Rainfall Pattern Analysis in the Three Major Valley Systems of Bengaluru

Bengaluru has three major valley systems, i.e., Vrishabhavathi Valley (V-Valley), Hebbal Valley and Kormangala-Challaghatta Valley (K-C Valley) which are the repositories of many lakes. Due to the increasing population, urbanization and industrialization, the lakes and their drainage areas are being encroached. Bengaluru is facing the problem of flooding. Hence, the rainfall analysis is very important to understand the rainfall pattern, trend as well as its quantity. Rainfall analysis helps to predict urban floods. In the present study, the Taluk level station rainfall data was collected from Karnataka State Natural Disaster Monitoring Cell (KSNDMC) for the period of 50 years from 1971 to 2020 and the same was analyzed for monthly, seasonal and annual periods using the Thiessen polygon method. Based on the monthly rainfall analysis carried out in the valley systems, it is observed that the highest rainfall occurs during the month of September, i.e., nearly 20% of the total annual rainfall, whereas the January month contributes the least amount of rainfall. Based on the seasonal rainfall analysis, it is known that the South-West Monsoon contributes to the highest percentage of rainfall when compared to other two season. The South-West Monsoon contributed to nearly 54.7%, 55% and 55.4% of the total annual rainfall in the Hebbal Valley, K-C Valley and Vrishabhavathi Valley respectively. The normal average annual rainfall is observed to be 830.1mm, 890.2mm and 1002.2mm respectively for Hebbal Valley, K-C Valley and Vrishabhavathi Valley when calculated from the past 50 years data (1971 to 2020). Hebbal Valley has received a lowest rainfall of 455mm during 2012 and Vrishabhavathi Valley has recorded the highest rainfall of 1556.2mm during 2005. When analyzed annually, it was observed that the highest rainfall of 1321.2 mm (2005) and lowest rainfall of 455mm (2012) was recorded in the Hebbal Valley. In the K-C Valley, the maximum rainfall observed was 1225.3mm (2004) and the minimum rainfall of 623.9mm was observed (1985). In the Vrishabhavathi Valley, the highest rainfall of 1556.2mm (2005) was recorded and the least rainfall of 551.3mm (2012) was recorded. Since all the valleys are located spatially adjacent to each other, they experience similar kinds of monthly and seasonal rainfall pattern. The rainfall pattern in the three valleys shows an increasing trend.

Analysis of rainfall patterns in Kalimantan using fast fourier transform (FFT) and empirical orthogonal function (EOF)

Rainfall patterns in Kalimantan are generally divided into 2 types, namely monsoonal and equatorial. The pattern can be determined by analyzing the 6-month frequency of rainfall signal. This analysis has been carried out on general data in Indonesia, but no one has yet examined it in detail in Kalimantan. Therefore, this study will analyze the 6-month frequency signal and rainfall patterns spatially and temporally in Kalimantan using TRMM 3B42RT as the main data. The Fast Fourier Transform (FFT) method is applied to analyze the 6-month frequency of rainfall signal, while the Empirical Orthogonal Function (EOF) method is applied to reduce data and obtain the main pattern of rainfall in Kalimantan. The results of FFT analysis in 15 cities of Kalimantan show that the rainfall pattern in Samarinda, Sendawar, Tarakan, Tanjungselor, Malinau, Pangkalanbun, Pontianak, Ketapang, and Sintang are an equatorial type, while a monsoonal type appear in Balikpapan, Palangkaraya, Purukcahu, Banjarmasin, Kotabaru and Barabai. Moreover, based on the results of FFT and EOF analysis, most areas in West, East and North Kalimantan have an equatorial rainfall pattern. Meanwhile, most areas in Central and South Kalimantan have a monsoonal rainfall pattern.

The Influence of Tropical Cyclones to the Plant Productivity Indices along the Coast of Tanzania

The study investigated the influence of Tropical cyclone (TCs) to the plant productivity indices along the coast of Tanzania using both field observations and change detection methods. These indices are normally designed to maximize the sensitivity of the vegetation characteristics and are very crucial in monitoring droughts intensity, yield and biomass amongst others. The study used three types of satellite imageries including the 16 days Moderate Resolution Imaging Spectroradiometer (MODIS) of 250 × 250 m resolution; 8 days Landsat 7 enhanced thematic mapper (ETM) with resolution of 30 × 30 m composites, and 5 Landsat 8 (LC8) images, to determine the patterns and the variability of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) and TCs impacts on vegetation. Moreover, we used Tropical Rainfall Measuring Mission (TRMM) data and the daily to monthly rainfall data from Tanzanian Meteorological Authority (TMA). The change detection between the pre and post storm (TCs) conditions was used to analyse inter annual variability of EVI over Chwaka, Rufiji and Pugu— Kazimzumbwi. The changes in NDVI and EVI and monthly rainfall at the coastal stations were calculated, plotted and analyzed. The results revealed that, highest EVI values over coastal Tanzania were observed during March and April, and minimum (low) values in November. The results for EVI changes based on pre and post storm conditions revealed that most observed stations and most TCs led to significant EVI changes which ranged from -0.05 to 0.19, and -0.3 to 0.22, for MODIS and L7 ETM data, respectively. As for the spatial changes in NDVI results revealed that, TCs (Besija and Fobane) were associated with positive NDVI changes i.e. (enhancement) of >0.51 and >0.31, and NDVI reduction (i.e. negative changes) of -0.19 for Chwaka and Rufiji, respectively. Besides the results revealed that, TCs episodes have induced a land cover changes from i.e. water covered areas changed to be vegetation covered especially over the shorelines and inter tidal areas. Indeed, these results were consistent with the analysis of rainfall patterns which indicated that low rainfall occurred in low NDVI areas and vice versa.

The focussed information criterion for generalised linear regression models for time series

SummaryThe present paper proposes the focussed information criterion (FIC) to tackle the model selection problems pertinent to generalised linear models (GLM) for time series. As a first step towards constructing the FIC, we formally discuss the local asymptotic theory of quasi‐maximum likelihood estimation for time series GLM under potential model misspecification. The general FIC formula is derived subsequently that is useful for the simultaneous selection of the order of the autoregressive response as well as a subset of important covariates. We also develop the average FIC (AFIC) that is instrumental in selecting an overall good model for a range of covariates and time regions and establish the equivalence of the AFIC with the classical Akaike's information criterion (AIC). We demonstrate our theory with the analysis of rainfall patterns in Melbourne by means of the logistic and Gamma regression models.

Rainfall and runoff trend analysis in the Limpopo river basin using the Mann Kendall statistic

An in-depth understanding of rainfall and runoff patterns of a catchment is central to sustainable water resources management and enhanced disaster risk reduction. This makes it important for studies in rainfall and runoff analysis to be carried out in developing countries like Zimbabwe. This study endeavoured to understand the rainfall patterns and the resultant runoff downstream along the Limpopo River. The study focused on the lower part of Mzingwane Catchment; which is a sub basin of the Limpopo River. Monthly observed data for four rainfall and two runoff stations were used for the study. Trend analysis of the data for the period 1949–2015 was conducted using Mann Kendal and Sen Slope Tests. The Seasonal Kendal test was applied to observe the influence of seasonal variations in rainfall and runoff. Trend analysis of rainfall patterns carried out for West Nicholson, Beitbridge and Chiredzi rainfall stations revealed an insignificant decreasing trend in rainfall activity at p < 0.05. However, Gwanda recorded an increasing rainfall trend though insignificant. Runoff activity at B62 Doddieburn station upstream of the catchment showed a declining trend while the B35 Limpopo River station exhibited an increase in river runoff. The overall p-value (of 0.03) for B35 station was below 0.05 thereby suggesting that there was a significant trend observed in the dataset. The increase in runoff at B35 could be attributed to managed releases from dams upstream. The study recommends measurement of all dam water releases and enhanced environmental management upstream and within the catchment in general.

A Data-Driven Probabilistic Rainfall-Inundation Model for Flash-Flood Warnings

Owing to their short duration and high intensity, flash floods are among the most devastating natural disasters in metropolises. The existing warning tools—flood potential maps and two-dimensional numerical models—are disadvantaged by time-consuming computation and complex model calibration. This study develops a data-driven, probabilistic rainfall-inundation model for flash-flood warnings. Applying a modified support vector machine (SVM) to limited flood information, the model provides probabilistic outputs, which are superior to the Boolean functions of the traditional rainfall-flood threshold method. The probabilistic SVM-based model is based on a data preprocessing framework that identifies the expected durations of hazardous rainfalls via rainfall pattern analysis, ensuring satisfactory training data, and optimal rainfall thresholds for validating the input/output data. The proposed model was implemented in 12 flash-flooded districts of the Xindian River. It was found that (1) hydrological rainfall pattern analysis improves the hazardous event identification (used for configuring the input layer of the SVM); (2) brief hazardous events are more critical than longer-lasting events; and (3) the SVM model exports the probability of flash flooding 1 to 3 h in advance.

Adopting the &amp;lt;i&amp;gt;I&amp;lt;/i&amp;gt;&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;–&amp;lt;i&amp;gt;R&amp;lt;/i&amp;gt;&amp;lt;sub&amp;gt;24&amp;lt;/sub&amp;gt; rainfall index and landslide susceptibility for the establishment of an early warning model for rainfall-induced shallow landslides

Abstract. Rainfall-induced landslides number among the most devastating natural hazards in the world and early warning models are urgently needed to reduce losses and fatalities. Most landslide early warning systems are based on rainfall thresholds defined on the regional scale, regardless of the different landslide susceptibilities of various slopes. Here we divided slope units in southern Taiwan into three categories (high, moderate and low) according to their susceptibility. For each category, we established separate rainfall thresholds so as to provide differentiated thresholds for different degrees of susceptibility. Logistic regression (LR) analysis was performed to evaluate landslide susceptibility by using event-based landslide inventories and predisposing factors. Analysis of rainfall patterns of 941 landslide cases gathered from field investigation led to the recognition that 3 h mean rainfall intensity (I3) is a key rainfall index for rainfall of short duration but high intensity; in contrast, 24 h accumulated rainfall (R24) was recognized as a key rainfall index for rainfall of long duration but low intensity. Thus, the I3–R24 rainfall index was used to establish rainfall thresholds in this study. Finally, an early warning model is proposed by setting alert levels including yellow (advisory), orange (watch) and red (warning) according to a hazard matrix. These differentiated thresholds and alert levels can provide essential information for local governments to use in deciding whether to evacuate residents.

A NOVEL APPROACH TO ANALYSIS DISTRICT LEVEL LONG SCALE SEASONAL FORECASTING OF MONSOON RAINFALL IN ANDHRA PRADESH AND TELANGANA

India is a nation which purely relies around agriculture, so rain fall prediction is very important for agriculture to make crop management decisions. Long scale forecast of rainfall during monsoon season (southwest and northeast), at spatial firmness of a district, could serve as an important comment to the agricultural community to take better decisions in yield management. Such forecasts are not producing efficient results which are available now. In this paper rain fall, crops and soil data of Andhra Pradesh (AP) & Telangana (TS) states are gathered to analyze rain fall patterns based on soil for crop management. Variety of crops needs adequate rain fall based on their different categories. In this paper, we are proposing a model which relates the analysis of rain fall patterns, soil types and different crops grown in two states on seasonal wise. We are experimenting with last 12 years of rain fall data, variety of crops grown in different seasons and identifying the average rain fall needed for different crops. DB SCAN clustering algorithm used to identify the rain fall patterns as low and high density. The proposed system serves as a tool to explore the rain fall patterns. The statistical results shows that proposed model could enhance those effectiveness and exactness.