Analysis of extreme weather events over Solapur 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) is to monitor shifts in the overall climate and aid in climate change detection investigations. According to the IPCC Sixth Assessment Report, an "extreme weather event" is defined as a rare event for a specific place and time of year. While the definition of "rare" varies, such events typically fall at or beyond the 10 th or 90 th percentile of a probability distribution based on historical observations. Therefore, studying extreme weather events is crucial for comprehending climate change. Extreme weather refers to events that significantly deviate from the historical norms of a given area. These events are often unexpected, unusual, severe, or unseasonal. As climate changecharacterized by substantial alterations in average weather patterns-emerges as one of the most critical challenges facing human society, the frequency and intensity of extreme weather events are projected to increase. Analyzing long-term weather data and identifying trends are vital in this context, especially given the significance of weather variables in agriculture and the evolving climate conditions. To achieve this, the RClimDex model was employed for decadal trend analysis of extreme weather events. The study focuses on the Kolhapur district in the Western Maharashtra region providing valuable information into extreme weather patterns in the area.

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 West and the states of Karnataka and Goa to South. The Solapur district is situated on the south east fringe of Maharashtra state and lies entirely in the Bhima and Seena basins. Solapur has flat or undulating terrain in general, it has no important hill system. Only some spurs of Balghat range pass to the north of Barshi. Solapur district experiences tropical monsoon climate. In Solapur district on an average there are 38 rainy days (i.e. days with rainfall of 2.5 mm or more) in a year in the district. This number increases from 32 at Malsiras near the western border of the district to 45 at Solapur near the south eastern border. The highest maximum temperature ever recorded at Solapur was 46.0 °C in 1988 and the lowest minimum temperature ever recorded was 4.4 °C in 1945. The highest maximum temperature ever recorded at Jeur was 46.6 °C in 1989 and the lowest minimum temperature ever recorded was 3.2 °C on 2nd January 1991. Meteorological Department, Pune). 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.

How to run R

Under the Windows environment, double click the R icon on your desktop, or launch it through Windows "start" menu. This usually gets you into the R user interface. For some computers, you may need to first setup an environment variable called "HOME". Under a Unix environment, just run R to give you the R console. 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 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.

Results and Discussion

The primary objective of the research was to compute temperature and precipitation based extreme indices during the period from 1982 to 2022 for Kolhapur district from Western Maharashtra using RClimDex model. The indices have been utilized to monitor climate change and undertake investigations regarding extreme weather events. Khoir Aulia N et al. (2018) [10] , Kaur Baljeet et al. (2022) [8] , and Singh Waikhom et al.

(2021) [18] used similar indices for analysis. 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. The study indicated that there does not exists any frost days and ice days in Solapur district (Table 3) from which it is understood that the daily temperature range for Solapur district do not fall at considerable cold extremes that would lead to formation of frost and ice in the study area. The number of cold days (FD15) showed significant decreasing trend (p=0.052) over study area during the period of 1982-2022. The similar results were obtained by Deshmukh et al (2020) [5] . The annual number of summer days (SU25) showed a non-significant increase (p= 0.94), whereas the number of very hot days (SU38) showed a non-significant decrease (p=0.662) (Table 3). There was a 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) as well as annual count of number of hot nights (TR23) over Solapur district from 1982-2022 with p= 0.021 and p=0 respectively. The number of cool nights (TN10p) showed significant decrease over Solapur district (p=0) whereas number of warm nights (TN90p) showed significant increase over the period of 1982-2022 in Solapur district (p=0.004). Vincent et al (2005) [20] showed the similar results which indicated warming was majorly due to significant increase in frequency of warm nights. While the number of cool days (TX10p) increased but not significantly, the number of warm days (TX90p) decreased significantly (p=0.769) over the Solapur district from 1982 to 2022. The extreme temperature index cold spell duration indicator (CSDI) has been found to be decreasing non-significantly over the Solapur district between 1982 and 2022 (p=0.082) whereas the other temperature extreme named warm spell duration indicator (WSDI) was found to be increasing but nonsignificantly (p=0.267). The diurnal temperature range (DTR), which referred to the mean difference between TX and TN from 1982 to 2022, indicated a significantly decreasing trend over the Solapur district (p=0.001).

Solapur District

The warmest daily maximum temperature (TXx) showed a significantly decreasing trend for the Solapur district from 1982 to 2022, with a p value of 0.041, whereas the warmest daily minimum temperature (TNx) showed a non-significantly increasing trend (p=0.584) for the same period. The coldest daily maximum air temperature (TXn) for Solapur showed particularly significant increasing trend (Table 3) over the period of 1982-2022 (p=0.03) whereas the annually minimum values of daily minimum temperature (TNn) have showed non-significant decreasing trend with p=0.763 and slope estimate is negative 0.004. Panda et al (2014) obtained similar outcomes. This study observed a significant increase in the frequency and intensity of heat waves over the investigated area (Solapur). This study revealed that for the Solapur district annual maximum consecutive 1-day precipitation (RX1day) indicated increasing trend over the period of 1982-2022 (p=0.953) with slope estimate= 0.019 but non-significantly. Number of days having total rainfall of 10mm i.e number of heavy precipitation days (R10) with p=0.197 showed increasing trend but nonsignificantly and also the number of very heavy precipitation days (R20) showed increasing trend over Solapur district (p=0.582) but non-significantly. Hence it was found that most of the indices showed non-significant positive trend (Table 4). The consecutive dry days (CDD) where maximum number of consecutive days with RR<1mm was found to be increasing over period of 1982-2022 but non-significantly (p=0.493) whereas the consecutive wet days (CWD) where maximum number of consecutive days with RR≥1mm was found to be increasing over the period of 1982-2022 for Solapur district (p=0.051) significantly. The annual number of very wet days (R95p) where annual total precipitation when RR > 95 th percentile, extremely wet days (R99p) and annual total wet-day precipitation (PRCPTOT) indicated significantly increasing trend over the period of 1982-2022 for Solapur district with p=0.054; p=0.571 and p=0.204 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) indicated negative trend but non-significantly over Solapur district for the period of 1982-2022 (p=0.855) with slope estimate= -0.004.

Conclusion

The study on extreme weather events in Solapur district revealed that the temperature-based indices viz. Number of cold days (FD15), number of cool nights (TN10p), Diurnal Temperature Range (DTR) and warmest daily maximum temperature (TXx) showed significant decreasing trend whereas number of hot nights (TR23), annual count of number of tropical nights (TR20), number of warm nights (TN90p) and coldest daily maximum air temperature (TXn) has indicated significant increasing trend over Solapur district during the period of 1982-2022. Nine precipitation indices were analyzed; only one index showed a significantly increasing (positive) trend; the other seven indicated a positive but non-significant trend, and the other remaining index showed a significantly decreasing (negative) trend.

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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