Analysis of extreme weather events over Kolhapur district of Western Maharashtra using RClimDex model

Introduction

The primary objective of developing climate extreme indices by the Expert Team on Climate Change Detection and Indices (ETCCDI) was to monitor changes in the general climate and support detection investigations. According to the IPCC Sixth Assessment Report, an "extreme weather event" is defined as an event that is rare for a particular place and time of year. While definitions of "rare" vary, such events are typically as rare as or rarer than the 10th or 90th percentile of a probability density function derived from observations. Therefore, analyzing extreme events is critical for understanding climate change. Extreme weather refers to unusual events that deviate significantly from the historical distribution for a given area. These events can be unexpected, unusual, severe, or unseasonal. Climate change, which involves significant variations in average weather conditions, presents one of the most pressing challenges for human society. As the climate changes, the frequency and intensity of extreme weather events are expected to increase. Given the importance of weather variables and climate in agriculture, particularly in light of the changing climatic conditions, it becomes essential to analyze long-term weather data and identify trends. This allows for the recording, evaluation, and application of this data by various stakeholders for different purposes. The RClimDex model was used for decadal trend analysis of extreme weather events. The study conducted for the analysis of extreme weather events in the Kolhapur district, located in the Western Maharashtra region, serves this purpose.

Materials and Methods

This chapter deals with description of study area, data collected for the study and their sources and methodologies adopted for research work of "Analysis of extreme weather events of Western Maharashtra region using RClimDex model" using temperature and precipitation data from 1982-2022 are briefly presented. The data was first homogenized, and then quality control was performed in RClimDex model.

Location and Extent

Western Maharashtra is division of Maharashtra state also called as "Desh" is the region adjacent to Western Ghats between the Godavari River and Krishna River, a part of Deccan Plateau. It is bordered by the Konkan region to the West and the states of Karnataka and Goa to South. The Kolhapur district is Southwest Maharashtra district which is a part of Deccan table land with an average height of 550 meters. There is only one main mountain range and hills that is the Sahyadri range. In contrast the eastern part of the district is plain. The district experiences tropical monsoon climate. The climate of Kolhapur district is generally pleasant, with general dryness except during the southwest monsoon season. On average there are 75 rainy days (i.e. days with rainfall of 2.5 mm or more) in a year in the district. This number varies from 122 mm at Gaganbawada to 43 mm at Kurundwad near the eastern border. The heaviest rainfall in 24 hours recorded at any station in the district was 499.0 mm at Gaganbawada in 1989. The highest maximum temperature ever recorded at Kolhapur was 42.3 °C on 9 th May 1988 while the lowest minimum ever recorded was 8.6 °C in 1968. (Climate of Maharashtra, GOI, IMD, Pune).

Information about RClimDex

Detection of climate change in the observed record refers to the identification of the statistically significant changes in weather variables for the concerned regions. In 2003, it was discovered that the methods used for computing percentile-based temperature indices in ClimDex model and other programs would produce inhomogeneity in indices series. A permanent solution for this was development of the software packages that would perform data homogenization (RHtestsV4) and calculation of the indices (RClimDex) based on a very robust and freely available (open-source) statistical package 'R' which is able to perform under both Microsoft Windows as well as Linux/Unix. A "user-friendly Graphical User Interface" (GUI) in "R" is offered by the RClimDex library, which facilitates the computation of the 27 (fundamental) core indices of extreme climatic events as recommended by the ETCCDI: Expert Team on Climate Change Detection and Indices. RClimDex includes a simple-procedure for available meteorological data quality control that was provided in ClimDex, as in it, the quality-controlled data is required before the indices can be computed and analysed. The software modifies the following aspects: 1. The deviation (eccentricity) in the daily precipitation, daily average temperature, daily minimum temperature and daily maximum temperature data were identified. 2. The unreasonable data (outliers) mainly included data that the daily minimum temperature is greater than the daily maximum temperature and the daily precipitation is negative.

~ Exit from R by entering (q) in the R console under both Windows and Unix. Under Windows, you may also click "File" menu and then "Exit".

Loading of RClimDex

Within the R console prompt ">" enter source ("rclimdex.r"). This will load RClimDex into R environment. You may need to include the full path before the filename rclimdex.r or you may download the most recent version. Under windows, RClimDex can also be loaded from drop down menu. Choose the "File" from the RGui menu, and then select "Source R code". This will bring a new pop-up window within which you can select our R source code "rclimdex.r" from the directory where the program was saved or type http://cccma.seos.uvic.ca/ETCCDMI/RClimDex/rclimdex.r to download the latest version from the website.

Indices calculation

RClimDex is capable of computing all 27 core indices listed in the following table. Users may, however, compute only those indices they require. After selecting "Indices Calculation" from the main menu, a user is asked to set up some parameters for the indices calculation. The "Set Parameter Values" window allows the user to enter the first and last years of the base period for the threshold calculation, the station latitude (Southern Hemisphere is negative) to determine in which hemisphere the station is located, a user defined daily precipitation threshold, P (in mm), to compute the number of days when daily precipitation amounts exceed this threshold (the Rnn indicator), and 4 user defined temperature thresholds. Once this step is completed, a window will appear to allow the user to select their desired indices for calculation. All indices are selected by default. [20,5,14] . This study highlights shifting pattern of temperature and precipitation over Kolhapur district as well as the regional variations in frequency and intensity of extreme weather events.

Using the RClimDex model, extreme weather events were assessed. The significance of the variations that arise from the RClimDex model's computation was investigated more thoroughly. The p-value and a comparison of the estimate and error slopes were used to test the significance. This test was used to calculate the magnitude of trends. If the p-value is less than 0.05, significant changes will be identified. Significant changes were recognized when p-value was less than 0.05 and slope estimate was larger than the slope error value. According to the data, there are no frost days or ice days in the Kolhapur district (Table 3). The number of cold days (FD15) showed significant decreasing trend (p=0.021). The decadal trend for the annual number of summer days (SU25) and the number of very hot days (SU38) indicated non-significant increasing trend which illustrated decrease in frequency of occurrence of heat wave in Kolhapur region more predominantly. There existed significant increasing trend in annual count of number of days when TN (daily minimum) temperature was recorded greater than 20° C i.e. number of tropical nights (TR20) and annual count of number of hot nights (TR23) over Kolhapur district from 1982-2022. TR23 index showed considerable increase from thirteen days in 1990 to about 45 days in 1998 and from about 15 days in 2000 to about 60 days in 2010. From 1982-2022 TR23 increased exponentially.

Kolhapur District

The decadal trend for number of cool nights over Kolhapur district during the period of 1982-2022 has found to be (p=0) decreasing significantly with slope estimate = -0.285 whereas the decadal trend for annual number of warm nights (TN90p) was found to be significantly increasing in Kolhapur district (p=0). The number of cool days (TX10p) and number of warm days (TX90p) have indicated non-significant increasing trend with p=0.199 and p=0.266 respectively. Decadal trend for diurnal temperature range (DTR) that represented the mean difference between TX and TN over time period of 1982-2022 indicated significantly decreasing trend over Kolhapur district (p=0.001). The DTR for 1992 was about 13° which decreased to about 5 ° C in the year 2021 and 2022. The similar results were found by Frich et al (2002) [8] . The extreme temperature index cold spell duration indicator (CSDI) where annual count of days with at least 6 consecutive days when TN (minimum temperature) < 10th percentile was found to be decreasing significantly (p=0.001) over Kolhapur district during the period of 1982-2022 whereas the other temperature extreme named warm spell duration indicator (WSDI) where the annual count of days with at least 6 consecutive days when TX (maximum temperature) > 90th percentile was found to follow nonsignificant increasing trend over period from 1982-2022 over Kolhapur district (p= 0.46).The decadal trend for the warmest daily maximum temperature (TXx) have indicated decreasing trend over the period of 1982-2022 for Kolhapur district with p value = 0.121 while the decadal trend for the warmest daily minimum temperature (TNx) has showed significantly increasing trend over the Kolhapur district (p=0.026). The coldest daily maximum air temperature (TXn) for Kolhapur district showed non-significantly increasing trend over the period of 1982-2022 (p=0.533). The monthly minimum values of daily minimum temperature (TNn) have showed nonsignificant increasing trend. [24] discovered the same results. The annual number of very wet days (R95p) where annual total precipitation when RR > 95th percentile, extremely wet days (R99p) and annual total wet-day precipitation (PRCPTOT) indicated significantly increasing trend over the period of 1982-2022 for Kolhapur district with p=0.011; p=0.001 and p=0.019 respectively whereas the simple daily intensity index obtained when the annual total precipitation is divided by the number of wet days in the year (SDII) showed increasing trend over Kolhapur district for the period of 1982-2022 but nonsignificantly (p=0.194) with slope estimate=0.035. This study aided climatic analysis of the study area and made it possible to comprehend the climatic characteristics of the region concerned. The findings in the study can be used by scientists for future investigations and also can be used by policy makers.

Conclusion

Acknowledgement

AcknowledgementI place my deep sense of gratitude to Department of Agriculture, Government of Maharashtra, India Meteorological Department, Pune and Department of Agricultural Meteorology, College of Agriculture, Pune for their valuable help, meticulous guidance and cooperation.

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