Annual Rainfall Data Research Articles

Assessment of rainfall trend over Periyar Vaigai Command area of Tamil Nadu

Rainfall trend analysis is critical for studying the effects of climate change. The most crucial aspect of understanding climate change at the basin level is, how it will affect water usage, planning, and development. The present study has been attempted to analyze the trend of annual and seasonal rainfall data over Periyar Vaigai Command area from 1982 – 2021 (40 years) employing different trend analysis methods i.e., Mann-Kendall test for trend significance, Sequential Mann-Kendall test for assessment of start and end of trend, Sen’s slope test for trend magnitude, Innovative Trend Analysis for combination of trends and linear regression analysis for trend detection. Statistically positive trend was detected in summer season using all methods whereas, annual and SWM rainfall showed a statistically significant decreasing trend. Winter and NEM rainfall resulted with no significant trend. The results from this study helps to adopt various water management practices and useful for policy makers to prepare appropriate mitigation strategies.

ANALISIS HUJAN MAKSIMUM PADA DAERAH ALIRAN SUNGAI BABAK KABUPATEN LOMBOK BARAT

Hydrological analysis is the most important measurement in determining runoff in order to find ways to overcome flooding. The flow rate that is the basis for calculating flood control and protection is the planned flood flow rate obtained from the sum of the volume of rain runoff and the flow rate of the water catchment area in question during a certain return period. This research is a case study using rainfall data for 10 years, namely 2014-2023. Rainfall prediction is an estimate of the amount of rain that will fall in a river basin. There are several methods for determining annual rainfall, the Gumbel distribution method, Pearson log type III, and the normal method. After analyzing the annual rainfall data obtained at the West Lombok Regency Meteorology, Climatology and Geophysics Agency office, using three methods, namely the EJ Gumbel method, the Log Person Type III method and the Normal method, where from the three methods the maximum rainfall difference was quite large, In the following table you can see the difference in the amount of maximum rainfall in the return period T years.

Characteristics of Overburden Damage and Rainfall-Induced Disaster Mechanisms in Shallowly Buried Coal Seam Mining: A Case Study in a Gully Region

Shallow coal mining in gully regions has resulted in significant subsidence hazards and increased the risk of surface water inflow into mining panels, compromising the sustainability of surface water management and underground resource exploitation. In this study, the chain disaster process caused by shallow coal seam mining and heavy rainfall is quantitatively analyzed. The findings reveal that shallow coal seam mining leads to the formation of caved and fractured zones in the vertical direction of the overlying rock. The fractured zone can be further classified into a compression subsidence zone and a shear subsidence zone in the horizontal direction. The shear subsidence zone is responsible for generating compression and shear deformations, intercepting rainfall runoff, and potentially triggering landslides, necessitating crack landfill treatments, which are critical for promoting sustainable mining practices. The HEC-RAS program was utilized to integrate annual maximum daily rainfall data across different frequencies, enabling the establishment of a dynamic risk assessment model for barrier lakes. Numerical simulations based on unsaturated seepage theory provide insights into the infiltration and seepage behavior of rainfall in the study area, indicating a significant increase in saturation within lower gully terrain. Precipitation infiltration was found to enhance the saturation of the shallow rock mass, reducing matric suction in unsaturated areas. Finally, the disaster chain is discussed, and recommendations for managing different stages of risk are proposed. This study offers a valuable reference for the prevention and control of surface water damage under coal mining conditions in gully regions.

Using USLE, GIS and remote sensing for the soil loss assessment in the National Park of Theniet El Had, Algeria

Soil water erosion is one of the problems that affect the environment, agriculture and social life by threatening several land surfaces. The objective of this study is to use the USLE model, GIS and remote sensing (RS) to estimate the annual rate of soil loss by water erosion in the Theniet El Had National Park (THNP) which belongs to the mountainous ecosystem of Djebel El Meddad, located in the northwest of Algeria. The use of the USLE model takes into account the five factors controlling water erosion, namely: the rain erosivity (R) determined from the annual rainfall data, the soil erodibility (K) developed from soil survey data, the slope lengths (LS) generated by using DEM, the vegetation cover (C) by the use of RS data and erosion control management practices (P) by field trips. The integration of these factors made it possible to establish the quantitative map of the annual rate of soil loss varying between 0.02 and 55.10 (t/ha.year), with an average of around 6.64 (t/ha.year). Five erosion aggressiveness classes are used; very weak, weak, moderate, strong and very strong which represent a rate respectively of 23.70, 44.65, 22.72, 4.41 and 4.52 % of the study area surface. The areas with high and very high erosion rates are located in the north having a very rugged relief and low vegetation cover. This study can be used in the mountainous ecosystems and it will make it possible to set up priority intervention zones to combat the risk of water erosion.

Generalized fiducial inference for the GEV change-point model

Generalized extreme value (GEV) distribution is used to analyse the maximum from a block of data. It is very useful to describe the unusual event rather than the usual event. In this paper, we propose a change-point detection procedure for GEV distribution based on generalized fiducial inference. The fiducial distribution of the change-point location is constructed. Meanwhile, Markov Chain Monte Carlo method combined with Gibbs sampling and the Metropolis–Hastings algorithm is utilised to estimate the location of the change point and its confidence interval. In addition, the generalized fiducial factor is used to test whether there is a change point. Simulation results show that the proposed generalized fiducial method performs better in accuracy, robustness and the length of confidence intervals. Finally, we apply the proposed method to the annual maximum rainfall data in Beijing.

Use of multivariate techniques to regionalize rainfall patterns in semiarid Botswana

Monthly precipitation data from 58 synoptic stations throughout Botswana, spanning 1981–2016, were used in this study. The data were examined using multivariate analysis to determine regions exhibiting distinct precipitation variability patterns and regimes. To accomplish this, the T-mode of principal component analysis was applied to the correlation matrix of the data. Based on the maximum loading values of the rotational principal component scores, the T-mode indicated three separate subregions with varying precipitation patterns over time. Four clusters with distinct rainfall patterns were identified when cluster analysis was performed on the principal component scores. An assessment of the homogeneity of the clusters was performed using L-moment’s heterogeneity measure (H). Statistical analysis was employed to model annual rainfall data using five commonly used rainfall analysis probability distribution functions: normal, lognormal, gamma, Weibull, and Gumbel. The probability distributions with the greatest fit were determined based on the maximum overall score, which was calculated by adding the individual point scores of three chosen goodness-of-fit tests. Each cluster exhibited distinct probability distribution functions, with the gamma, Gumbel, lognormal, and Weibull distributions providing the most accurate descriptions.

Analysis of rainfall erosivity factor (R) on prediction of erosion yield using USLE and RUSLE Model’s; A case study in Mayang Watershed, Jember Regency, Indonesia

The Rainfall erosivity has a relatively high effect on soil erosion, in addition to being very difficult to predict and control. The Universal Soil Loss Equation (USLE) and The Revised Universal Soil Loss Equation (RUSLE) model are commonly used to predict erosion yield in Indonesia. However, these models have several erosivity formulations that give different results. In this sense, identifying the sensitivity of different erosivity formulations in both models above is important. The aim of this study is to analyze soil erosion yield prediction influenced by the difference in erosivity equation on the same rainfall data used in the models while other parameters used are the same. The monthly rainfall and annual rainfall data were tested using the erosivity formulas. The (1) Bols and (2) Utomo equations were tested using monthly rainfall data, while the (3) Bols and (4) Hurni equations were tested using annual rainfall data. The results show that the prediction of soil erosion yields estimates using monthly rainfall data in both models have no significant differences. On the other hand, soil erosion estimates using annual rainfall data in the models have significant differences, whereas the USLE model estimation results in 63% erosion yield on low classification (0-15 ton ha<sup>-1 </sup>year<sup>-1</sup>). Meanwhile, the RUSLE model estimates only 59% erosion yield on low classifications. Another result is that the USLE model estimates lower erosion yield than the RUSLE model when the models use annual rainfall data, which may give significantly different recommendations for soil conservation in Indonesia, especially in reducing erosion yield at the Watershed level.

Establishing rainfall intensity-duration-frequency curves for Baghdad, Iraq using satellite data

Abstract Rainfall intensity-duration-frequency (IDF) curves were generated for Baghdad by utilising three satellite precipitation datasets: Global Satellite Mapping of Precipitation Near Real-Time (GSMaP NRT), gauge-corrected (GSMaP GC) and Global Precipitation Measurement Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG). Maximum annual rainfall data was fitted using several probability distribution methods. The calculated coefficients from the best-fit distribution were used as fitting parameters to generate IDF curves for return periods of 2, 5, 10, 25, 50 and 100 years using the Sherman equation. To address discrepancies between the satellite-derived IDF curves and observed data, bias correction was performed based on the differences. The analysis revealed that the Generalized Extreme Value Distribution model accurately described the hourly rainfall distribution. GSMaP GC exhibited the highest correlation with the observed data, making it the preferred option for generating IDF curves. The study highlighted the importance of gauge correction for satellite rainfall data to minimise the underestimation or overestimation of rainfall. GSMaP GC demonstrated reasonable accuracy in estimating rainfall in Iraq’s mainly arid climate area. By assisting in the creation of efficient methods for dealing with rainstorm events, the created IDF curves are a major step towards advancing sustainable urban stormwater management in the country.

Analysing Spatial and Temporal Rainfall Variability of Southern Rajasthan using GIS Approach

This study focuses on analysing the behavioral patterns of rainfall in southern Rajasthan, particularly in reference to its amount and variation over time. The study made use of standard meteorological weeks (SMW) and historic weekly rainfall records. IDW (Inverse Distance Weighting) interpolation technique within the ArcGIS platform was used for spatial analysis, creating spatial variation maps. It was found that significant rainfall occurred from Standard Meteorological Week (SMW)-22 to SMW-42. The weekly mean rainfall across the study area during the monsoon period ranged from 2.1 to 68.4 mm, with corresponding standard deviations ranging from 9.3 to 79.7 mm. Notably, the standard deviation was often higher than the mean, indicating substantial variability in weekly rainfall. Trend analysis using the Mann-Kendall test revealed that most stations in the study area exhibited no significant trend in annual rainfall data. Similarly, weekly rainfall data showed a trend in SMW-30 for 44 stations, while all other weekly data series did not exhibit significant trends, except for SMW-30. Stationarity was generally accepted for most weekly data series, except for SMW-30 and SMW-32, which showed non-stationary behavior. However, homogeneity tests indicated homogeneity in weekly rainfall data series for almost all selected stations. Overall, the study highlighted the significant variability in rainfall patterns in southern Rajasthan, with no significant trend in annual rainfall, except during the peak monsoon period (SMW-30). These findings contribute to a better understanding of regional rainfall behavior, which is crucial for water resource management and agricultural planning in the region.

Assessment of climate change induced rainfall trend and variability with non-parametric and linear approach for Sirajganj district, Bangladesh

The monthly and annual trends and variance of rainfall have been studied for five stations in an economically important Bangladeshi district named Sirajganj since 1965 to 2021. Natural disasters have prevalent in Sirajganj which is indispensable to assess. But, several researchers have been normally focused on river bank management and flood risk assessment. However, no extensive research has been conducted on Sirajganj based on non-normally distributed time series meteorological data such as rainfall time series so the current study is very important. In this study, the non-parametric Mann-Kendall and Sen's methods have been used to determine the statistical significance of a positive or negative trend in rainfall data. Also, cumulative sum charts and bootstrapping, one-way ANOVA, Tukey's range tests, and linear regression have been used to discover the incidence of abrupt changes, compare the significant difference in monthly and annual rainfall data, multiple comparisons amidst mentioned stations to find changes, and to investigate the changeover on dry and rainy days, respectively. The analysis showed a statistically significant decreasing trends in monthly and annual rainfall series. As well, changes from positive to negative direction have been recognized in the February, May, July, September, and annual rainfall time sequence. Besides, ANOVA and Tukey's range tests revealed a statistically substantial difference in all monthly and annual rainfall volume excluding January, March, and June. Additionally, these two tests demonstrated momentous differences in all monthly and annual frequency of rainfall categories excepting January and April. However, Linear regression analysis revealed that the number of dry days gradually reduced at the end of the dry winter, though the number of rainy days decreased during the rainy season. As in, the number of rainy days replaces the number of dry days during the dry season and vice versa during the rainy season. Even though, with very few exceptions, the volume of rainfall decreases throughout the year. The outcomes of this research might helpful for implementing the planning and evaluating hydrological projects on Sirajganj district.

APPLICATION OF GENERALIZED EXTREME VALUE DISTRIBUTION TO ANNUAL MAXIMUM RAINFALL

Generalized Extreme Value Distributions was used to model annual maximum rainfall data in Akure, Ondo State, Nigeria from 1981-2019. The parameters of the distribution are estimated using the maximum likelihood estimation method. The model fit indicated that the shape parameter is negative this suggests that Fréchet distribution is the appropriate model for describing annual maximum rainfall in Ondo-state Nigeria. The estimated return levels for different return periods revealed an increase in the value over the years.

Analysis of Rainfall Trend in North Interior Karnataka (NIK) Meteorological Subdivision of Karnataka by Non-Parametric Methodologies

The present study analyzed the trend in the month-wise, seasonal and annual rainfall employing three non-parametric tests viz. Mann-Kendall (M-K), Modified Mann-Kendall (MM-K) tests and Sen’s slope estimator for North Interior Karnataka (NIK) meteorological subdivision of Karnataka using 60 years (1960-2019) rainfall data. The M-K test results revealed that there is no monotonic trend in all data sets which indicates that there may exist the influence of serial correlation in the rainfall data. The MM-K test was applied to rainfall data of the NIK subdivision, the result of the test revealed that January, February, March and August months have a monotonic increasing trend whereas April, May, July and September months have a monotonic decreasing trend whereas June, October, November and December have no monotonic trend in monthly rainfall data. The winter season has a monotonic increasing trend, monsoon and post-monsoon seasons have a monotonic decreasing trend, and the pre-monsoon season has no monotonic trend in seasonal rainfall data. The MM-K test statistic (Tau) value for the NIK (-0.12) subdivision was found to be significant and negative indicating that there is a monotonic decreasing trend. Larger negative Sen’s slope values for NIK (-1.40) subdivision indicate the high decreasing rate of change of rainfall for annual rainfall data. Therefore, to know the trend in rainfall data most accurately in the presence of outliers and serial correlation, the Modified Mann-Kendall (MM-K) test and Sen’s slope estimator are recommended instead of Mann-Kendall (M-K) and Sen’s slope estimator.

Assessing lake shoreline change and prediction for 2030 by physical drivers: A Case Study from Lake Batur, Batur UNESCO Global Geopark, Bali

Over ten years, the water level of Lake Batur has increased. Agriculture area and settlements around Lake Batur are threatened by rising water level. This study aims to analyze the shoreline change of Lake Batur, located in the Batur UNESCO Global Geopark (BUGG), during the period of 2007 – 2018 and design a prediction for 2030. Understanding the shoreline change is very important for lake management and planning. Shoreline changes were analyzed in Geographic Information System (GIS) application. The data were obtained based on Remote Sensing (RS) data, Landsat ETM+ imagery on September 24, 2007, and Landsat OLI imagery on October 24th, 2018. Predictions of lake shoreline in 2030 result from modelling by integrating ASTER-GDEM V2 data, lake water level data for 2007–2018, annual average rainfall data for 2007–2018, and bathymetry data for 2013 and 2015. The results of the satellite imagery analysis show that there has been a change in the length of the shoreline, which has increased from 20.47 km in 2007 to 21.28 km (3.96%) in 2018. The lake surface area changed from 15.34 km2 in 2007 to 16.16 km2 in 2018 (5.35%). The prediction of lake shoreline changes in 2030 showed that Lake Batur will increase to 26.90 km (26.41%), and lake surface area is predicted to increase by 17.67 km2 (9.34%) from 2018. This is because of the morphological change of Lake Bottom.

Hydrological Modelling with HEC-HMS in Krueng Peudada Sub-Watershed Bireuen Regency

The flood that occurred in Krueng Peudada was influenced by several factors, such as watershed conditions. Heavy rains caused Krueng Peudada to overflow so that a number of villages adjacent to the river were submerged. Based on these problems, to minimize the occurrence of flooding in Krueng Peudada building, namely by carrying out occurrence of flooding in Krueng Peudada to be used to obtain peak discharge as a reference in building planning in Krueng Peudada using HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modelling System). The benefit of the research is that the resulting model can be useful as a guide in calculating flood discharge considerations. The scope of this research includes the research location at Krueng Peudada using annual maximum daily rainfall data, calculating the return period of rainfall using of Gumbel probability distribution, Normal Log, Normal, and Pearson III Log as the basis for calculating the discharge and continuing the calculation of the hours with Manonobe and ABM. The results of calculations of the Nakayasu Synthetic Unit Hydrograph Method and running from HEC-HMS found that the flow that occurred in the Krueng Peudada two sub-watershed was 2903m3/second and 2723,0 m3/second, it can be concluded that in determining watershed management the flood discharge obtainded has error as much as 6,2%. The difference in the results obtained is due to the number of sub-watersheds in the watershed based on different flows and the value of the curve number for each area of the sub-watershed.

Potential of Irrigated Agricultural Production of Corn (Zea mays L.) in Semi-Arid Region using Geoprocessing

This work aimed to evaluate and map the potential of irrigated agricultural production for corn for the watershed of the region of the middle course of the Paraíba River. Using SPRING, a map of soil irrigation potential and soil retention capacity was prepared and a partial map was generated through matrix crossing. Using average annual rainfall data, the climate condition map was prepared. After using LEGAL, the agricultural production potential map was generated through the matrix crossing between the partial map and the climate scenario. For maize, a Full climatic condition (C1) was identified in 2.02% of the basin's total; the full climatic condition with prolonged rainy season (C2) and the climatic condition moderated by excess water (C3) were not identified; the climatic condition Moderate by water deficiency (C4) in 0.86%; the unfit condition due to severe water deficit (C5) in 97.12% of the basin. The potential of irrigated agricultural production for maize did not show Very High (MA), High (A), Medium (M) and Low (B) potential, only the Very Low (MB) potential in 100% of the basin.

Estimating Soil Erosion and the Effect of Changing Land Use and Land Cover in the Bringi Catchment’s Devalgam Watershed Using RS and GIS

Abstract: One of the main factors contributing to soil degradation worldwide is soil erosion. The geographical variability of erosion at the Devalgam watershed was evaluated using the Revised Universal Soil Loss Equation (RUSLE) model in a GIS environment. DEM ASTER (30 m × 30 m). The model was fed with annual rainfall data from 1990, 2000, 2010, and 2020 as well as soil and LULC maps. In the Devalgam watershed, the mean annual soil loss ranged from 0 to 127.32, 140.34, 146.49, and 187.23 t/ha/yr for the years 1990, 2000, 2010, and 2020. The mean annual sol loss was calculated to be 16.6, 18.3,19.1, and 22.08 t/ha/yr. According to a zonal statistical analysis of soil erosion for various types of land cover, open forests and barren areas were more likely to experience erosion with the least susceptible to erosion were vegetation, built-up areas, orchards, and agriculture, with estimates of 85.12 and 52.35 t/ha/yr, respectively. According to the current study, managing natural resources is increasingly dependent on the LULC change in the Devalgam Watershed. GIS and remote sensing technologies have shown to be useful tools for analyzing LU/LC changes on a watershed-by-watershed basis. Six LU/LC classes were used to categorize the study area: built-up, barren terrain, vegetation, farmland, woodland, and orchards. and it was discovered that, out of the total area, or 2058.618 hectares, the least area was covered by orchards (0.5%), while the highest area was covered by forest (30.5%), followed by barren ground (25.7%). The examination of the overlay of analysis of the changes from 1990 to 2020 was conducted using LANDSAT-5 1990 over LANDSAT-8 OLI 2020. The findings also indicate that whereas the area under agricultural, builtup areas, barren land, and orchards expanded by 123%, 36.5%, 0.5%, and 36.4%, respectively, the area under forests and vegetation dropped by 17.3% and 48%. With a kappa coefficient of 0.84, 0.71, 0.75, and 0.87, respectively, the overall accuracy for the categorized imageries LANDSAT-5 (1990), LANDSAT-7 (2000), LANDSAT-7 TM (2010), and LANDSAT-8 OLI (2020) was determined to be 86.6%, 76.6%, 80%, and 90%.respectively

Long term (1901−2021) trends and prediction of climatic variability in selected agro‐ecological zones of Himachal Pradesh using coupled statistical and machine learning approaches

This study analyses the trends of changing climatic elements in the hydrological regime of the Indian Himalayan Region with specific focus on Agro‐ecological zone II & III of Himachal Pradesh for the period of 1901−2021. The upper, middle, and lower catchment areas of Sutlej River Basin were studied to reveal regional trends in climatic parameters. The Mann‐Kendall test and Sen Slope analysis were used to estimate annual and seasonal trends and their magnitude. The results showed a significant decreasing trend in the lower catchment area, with a break point year estimated to be 1953 for the entire basin. Two blocks of analysis, 1901−1953 and 1954−2021, showed significant variations. Annual rainfall data revealed a statistically significant decreasing trend at different stations. The lower catchment area received a significant increase in rainfall compared to higher altitude stations. In terms of seasonal variation, the pre‐monsoon season showed a significant decrease, while the entire basin recorded a significant increase in average monthly temperature. The study concludes by generating future time series predictions using an ANN model for the period of 2022−2050. Overall, the study's findings indicate a significant change in climatic variables with signs of increasing monthly temperatures and decreasing annual rainfall.

Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania

This study focuses on modeling and predicting extreme rainfall based on data from the Southern Highlands region, the critical for rain-fed agriculture in Tanzania. Analyzing 31 years of annual maximum rainfall data spanning from 1990 to 2020, the Generalized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Three estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. The Bayesian MCMC approach demonstrated superior performance by incorporating noninformative priors to ensure that the prior information had minimal influence on the analysis, allowing the observed data to play a dominant role in shaping the posterior distribution. Furthermore, return levels for various future periods were estimated, providing guidance for flood protection measures and infrastructure design. Trend analysis using p value, Kendall’s tau, and Sen’s slope indicated no statistically significant trends in rainfall patterns, although a weak positive trend in extreme rainfall events was observed, suggesting a gradual and modest increase over time. Overall, the study contributes valuable insights into extreme rainfall patterns and underscores the importance of L-moments in identifying the best fit distribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and infrastructure planning in the region.

Temporal variations of Rainfall over Konkan & Goa during 1901-2020

This study examines the temporal variation of rainfall on monthly, seasonal, annual and decadal scale over Konkan & Goa, India during 1901-2020. Trend analysis of rainfall data is carried out by using Man-Kendall and t-test. A significant increasing trend has been observed in annual rainfall data. A significant increasing trend of 32mm/year is present in annual rainfall. Southwest monsoon showed significant increasing rainfall trends over Konkan & Goa during the last 120 years. On the monthly scale, rainfall indicate significant increasing trend during the month of June, August, September and October showed and significant decreasing trend during January & February. During the period of 120 year rainfall is highest in period of 1931-1960. Decadal rainfall analysis shows total 18 excess years and 15 deficit years observed annually over the period of study.

Homogenizing Monthly Rainfall and Temperature Data Series in Maharashtra & Goa

Annual rainfall and temperature data series of all climate stations in Maharashtra & Goa are statistically tested for data homogeneity. To inspect homogeneity of a station, a two-step approach is followed. First, four homogeneity tests Standard normal homogeneity test, Pettit’s test, Buishand’s range test and Von Neumann ration test at 5% level of significance are used to determine test hypothesis for homogeneity on testing parameters of annual rainfall and temperature. Second, results from all these four tests aggregated together into three different classes as ‘useful’, ‘doubtful’ and ‘suspect’. Here 30 rainfall, 29 maximum and minimum temperature climate stations were tested. The results showed 80% stations as ‘useful’, 7% as ‘suspect’ and 13% as ‘doubtful’ for rainfall, for maximum temperature series these results are 17% as ‘useful’, 7% as ‘suspect’ and 76% as ‘doubtful’, while for minimum temperature series these results are 21% as ‘useful’, 10% as ‘suspect’ and 69% as ‘doubtful’. Further, in this study an attempt is also made to correct the monthly rainfall and temperature data series for homogeneity. Stations categorised as ‘useful’ are used as reference series to remove inhomogeneities from ‘suspect’ and ‘doubtful’ stations. To correct rainfall series ratio’s method is used while for temperature series addition method is used. Correction results showed significant improvement in ‘suspect’ category stations. After correction of inhomogeneous series, the results shows all 100% of rainfall stations and more than 65% of temperature stations are now in ‘useful’ category. The corrected stations may be included in further climate research studies.