Natural disasters related to water resources as a result of climate change are becoming a major concern for the entire world. The majority of people in India work in agriculture. Droughts, floods, cyclones, and other natural disasters are all linked to uncertainty in meteorological parameters. The primary goal of this research is to investigate and forecast various meteorological parameters in the Bhavnagar district of the Gujarat region. The statistical downscaling technique uses Global Climate Model (GCM) data for analysis, while the dynamic downscaling technique uses Regional Climate Model (RCM) data. RCM data are prepared for important places of the Earth. GCM data are freely available for all parts of the Earth. So, in this study, GCM data are used. RCM data and dynamic downscaling make the process complicated. Using GCM data and the statistical downscaling technique, meteorological parameters i.e. maximum temperature, minimum temperature, and rainfall can be accurately predicted in this study. To predict meteorological parameters, RCP 8.5 scenario and SPI are used to identify the various types of drought years of the study area. Flood hazard maps and drought contingency plans can be prepared based on predicted meteorological parameters. According to SPI analysis, Bhavnagar will experience 26 mild, 8 moderate, 2 severe, and 3 special droughts up to the year 2100.

  • Forecasting the precipitation and temperature for Bhavnagar district.

  • Finding out the characteristic of meteorological parameters of Bhavnagar.

  • Preparation of model to predict drought years using multi-linear regression.

  • Identification of drought years up to the year 2100 by statistical downscaling technique.

  • Classifying predicted drought years according to its severity to guide policy makers.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Uncertainty in climate

Climate is typically thought of as dirty bridge weather in terms of temperature, precipitation, wind speed, and other factors over time and a specific location (Du et al. 2021; Patnani et al. 2021). Climate change is one of the world's most pressing issues; it is a top strategic priority for the international community. Climate change has been widespread, rapid, and intensifying over the last few decades. Human activities are undeniably causing climate change, increasing the frequency, and severity of extreme climate events such as heatwaves, heavy rainfall, and drought (Bacanlı & Tanrıkulu 2017; Zhou et al. 2020; Du et al. 2021). There is no turning back, and some changes in the climate system are inevitable. Climate change is inextricably linked to environmental and economic concerns, as well as modelling and simulation concerns, and social and management concerns. During the last century, the rise in global temperature has resulted in a widespread decline in water restoration on the continent (Bacanlı & Tanrıkulu 2017; Arnone et al. 2020; Marcinkowski & Mirosław-Świątek 2020). According to the IPCC's sixth assessment report (AR6), the global mean surface air temperature has increased by 1 °C. It increases 5 °C over the last 20 years, with a prediction of 2.7 °C by the end of the century (Abbas & Abdulateef 2020). Water is one of the most important natural resources for human and animal life, as it is required for almost all social and economic activities (Tiwari & Singh 2016). As a result of climate change, there has been a decrease in precipitation in many parts of the world, resulting in rainfall deficits and drought (BenKhalfallah & Saidi 2018; Arnone et al. 2020; Shah et al. 2021).

Hydrological extremes

Drought, flood, earthquake, landslide, tsunami, environmental degradation, mining disaster, cyclone, and other natural disasters all have devastating effects on various activities/properties on the planet. Droughts and floods (results of water deficit/excess) are known as hydrologic extremes (Tiwari & Singh 2016; Peltonen-Sainio et al. 2021b). The total amount of water on the planet remains constant, but its distribution over time at a given location is highly variable, resulting in hydrologic extremes (Bacanlı & Tanrıkulu 2017; Bartczak et al. 2021; Du et al. 2021). It can also vary in space so that when one part of a country suffers from drought, another suffers from floods almost simultaneously. Droughts and floods of this magnitude present a significant challenge for both the people and the government to deal with at different times. Flood is distinguished from the other two extremes by its rapid onset, rapid growth, and obvious spread, all of which culminate in disastrous consequences. Drought, on the other hand, is a slow-moving phenomenon (Genthon et al. 2009; Jafarzadeh et al. 2021). Its onset is subtle and undetectable, its spread is insidious and deceptively slow, and the consequences can be devastating. It can begin at any time and progress to various degrees of severity. The indeterminacy of onset, as well as the ambiguity of its spread and severity, has made these phenomena even more dangerous. As a result, drought research is critical for humanity in general and the economy of the country in particular.

Effect of climate change in India

According to a Christian Aid report titled 2021: A Year of Climate Breakdown, a total of 12.7 lakh crores was lost globally last year in ten major disasters, four of which occurred in Asia and two in India, including the Tauktae and Yash cyclones. According to the ministry of Jal Shakti, between 2021 and 2022, approximately 2022 people died in India as a result of natural disasters such as floods, cyclones, heavy rainfall, and landslides, among which Gujarat suffered the most economic loss and lost 165 people. Under the leadership of union Home minister Amit Shah, a relief fund of 3,063.33 crores was given from the national disaster response fund, of which 1,133.35 crores was allotted to Gujarat. To prepare for disasters caused by climate change, it is necessary to use GCM and RCM to study variations in meteorological parameters such as rainfall, temperature, humidity, and wind speed (Shukla et al. 2015; Arora et al. 2016; Quraishi 2020). We can use meteorological parameters that are related to climate change, its impact, and climate change forecasting. GCM is used on a global scale to cover large areas and grid cells. We must first analyse GCM Data because it is required for the statistical downscaling approach (Sarkar et al. 2015; Egbebiyi et al. 2020; Goodarzi et al. 2021).

Downscaling techniques

Downscaling is a technique for extracting high-resolution data from low-resolution data (Sarkar et al. 2015). This method is based on statistical or dynamical approaches. Meteorology, climatology, and remote sensing are the three main approaches (Goodarzi et al. 2021; Patnani et al. 2021). Statistical downscaling is the application of relationships between local climate variables (e.g., surface air temperature and precipitation) and large-scale predictors to the output of current and future global climate (Arora et al. 2016; Jafarzadeh et al. 2021). The use of high-resolution regional simulations to dynamically indicate and analyse the effects of large-scale climate processes on regional or local scales of interest is known as dynamical downscaling (Sarkar et al. 2015; Arora et al. 2016; Jafarzadeh et al. 2021).

Global climate modal

This is the most advanced tool, currently available for simulating the global climate system and its response to natural forces. A GCM is composed of many grid cells that represent horizontal and vertical areas on the Earth's surface. GCMs are used for weather forecasting, understanding the climate, and forecasting climate change (Shukla et al. 2015; Mo et al. 2017; Goodarzi et al. 2021). As shown in the diagram below, there is a better understanding of GCM and RCM (Figure 1).
Figure 1

Understanding of GCM and RCM.

Figure 1

Understanding of GCM and RCM.

Close modal

GCM's spatial resolution is too coarse to capture the local aspects of topography and it is limited by the computational model (Arora et al. 2016; Jafarzadeh et al. 2021). The sub-grid physical processes have to be parameterized. Parameterization is a way of describing the aggregated effect of sub-grid processes over a larger scale (Kavya Shree 2016). There are two main terms used when GCM data is collected: predicted and predictors (Goodarzi et al. 2021).

Representation concentration pathway

Climate variability and uncertainty are exacerbated by greenhouse gas emissions. Based on the volume of greenhouse gas concentrations, the IPCC has created various Representative Concentration Pathways (RCPs) that describe various climate futures (Shukla et al. 2015; Feldman et al. 2021). The IPCC uses four different pathways to model and research climate change. RCP 2.6, RCP 4.5, RCP 6, RCP 8.5, and new RCP 1.9, RCP 3.4, and RCP 7 were given as part of the IPCC's fifth assessment report (AR5-2014). Different CO2 emission scenarios, such as A2a, B2a, A1, A2, B1, and B2, can be seen (Mo et al. 2017; Najafi & HessamiKermani 2017; Feldman et al. 2021). Different climate conditions are found in different RCPs. Where RCP 8.5 A2a emissions show the worst-case scenario for climate change at a specific location, RCP 8.5 is the carbon concentration that causes average global warming of 8.5 degrees (Ostad-Ali-Askari et al. 2020). RCP 8.5 is the carbon concentration that causes global warming of 8.5 Watts per square metre on average across the globe (Ostad-Ali-Askari et al. 2020; Quraishi 2020). The Bhavnagar region of Gujarat was chosen for this study because it is severely impacted by climate change. To study climate change in that area and prepare for future catastrophic events, a statistical downscaling technique was used to analyse and forecast various methodological parameters of that area.

Objectives

The objective of this research work is to predict various meteorological parameters, i.e. rainfall, minimum temperate, maximum temperature, across the study area and to observe the effects of climate change on those meteorological variables. This study will also develop a model for forecasting these variables using a statistical downscaling technique.

Study areas

Due to climate change, floods and droughts are more common in Gujarat, which is located in the west corner of India's coast and has the longest coastline of 1,600 km. Gujarat has a variety of climatic conditions in different parts of the state. Gujarat is divided into five sections based on geography: Kutch region, Saurashtra region, North region, South region, and Central region. The agriculture and co-operation department of the government of Gujarat divides Gujarat into eight climatic zones based on rainfall patterns: lower southern Gujarat, upper southern Gujarat, middle Gujarat, northern Gujarat, Bhal and coastal area, southern Saurashtra, northern Saurashtra, and north-western zone. Climate, soil, and rainfall differ from one zone to the next. The study area for this research is the Gujarat district of Bhavnagar. It is situated in Gujarat's Saurashtra region. Figure 2 depicts a map of the study area. Q-GIS software can be used to create this map.
Figure 2

Map of Bhavnagar.

Figure 2

Map of Bhavnagar.

Close modal

Uncertainty in rainfall patterns, changes in east-west temperature patterns, and an increase in cyclonic activity in the Arabian Sea in coastal areas may be the reasons for increased rainfall in the Saurashtra region. In the Saurashtra region, the water level in the river will rise, hence an increase in submerging and flooded areas.

Bhavnagar is a district in Gujarat's Saurashtra region. At an elevation of 24 m, above mean sea level, it is located at 21.5092 °N latitude and 71.8571 °E longitude. It has a total area of 10,034 km2. It has a population of 2,880,365 people, making it Gujarat's fifth-most populous city. Bhavnagar has a maximum temperature of 47.47 °C, a minimum temperature of 6.49 °C, and average precipitation of 585 mm. The difference in temperature between maximum and minimum indicates climate change uncertainty.

Data collection

Rainfall and temperature data for the Bhavnagar district were obtained from the NASA Power Access View website for the period (1982–2020), i.e. for 39 years. Monthly rainfall and temperature data were discovered, with a 39-year historical trend observed. GCM data for grid of Bhavnagar was obtained from the Climate and Scenarios website in Canada for the period (1961–2099) for downscaling purposes. For this work, Hadley Centre Couple Global Climate Model Generation-3 data are used with the RCP 8.5 scenario (H3A2a_1961-2099). It has a spatial resolution of 2.5° × 3.75° (latitude by longitude). This data is presented in a daily format. As a result, it should be converted to a monthly format using PHP coding. The ‘Nested FOR Loop HTML code’ has been used in the study.

Methodology

Figure 3 shows a flowchart of the methodology. Observed precipitation, minimum temperature data, and maximum temperature data are collected in the first step. In a Microsoft Excel spreadsheet, enter monthly meteorological data from 1982 to 2020, such as precipitation and temperature. GCM data have been used as Predictant data in this study. For the years 1961–2099, this data can be found on the Climate and Scenarios website of Canada for the RCP 8.5 A2 scenario. Data from the GCM for the years 1961 to 1999 is also entered into the same MS Excel sheet. The study's observational data is on a monthly time scale. As a result, using PHP programming, GCM data are converted to a monthly scale. As a result, daily data should be converted to a monthly format using PHP coding. The ‘Nested FOR Loop HTML code’ is used in this study. The observed data and the GCM data are now correlated to determine the most correlated parameter with the observed data. For the regression model, choose the first three parameters. Microsoft Excel can now be used to create the multi-linear regression model. After performing a multi-linear regression analysis, the values of the intercept (regression constant) and regression coefficients can be used to create an equation. The model should be calibrated and validated using meaningful meteorological data. Now, the regression model is ready to run. The multi-linear regression model's equation is as follows. The results of this method are graphically represented.
where is the dependent parameters (modelled parameters); is the intercept (Regression constant); is the independent variables; and is the regression coefficient.
Figure 3

Flowchart of methodology.

Figure 3

Flowchart of methodology.

Close modal

In a time-series graph, the results of the modelled data are examined. From 1982 to 2020, 2021 to 2060, as well as 2061 to 2099, the rainfall data has been divided into three-time series cycles. Then, using R-Studios, prepare the Excel spreadsheet for SPI analysis. Determine the Standardized Precipitation Index (SPI) value using the proper R script. Using SPI values, droughts can be classified into different types.

Graphical outputs of meteorological parameters

Figure 4 depicts the graphical result of the normal rainfall for the calibration–validation process. Up until December 2099, that model can be used to forecast normal rainfall. This is illustrated in Figure 5.
Figure 4

Rainfall analysis for the years 1982–2020.

Figure 4

Rainfall analysis for the years 1982–2020.

Close modal
Figure 5

Statistical downscaling of rainfall for the years 1982–2099.

Figure 5

Statistical downscaling of rainfall for the years 1982–2099.

Close modal

India is a tropical country with a wide distribution of rainfall that is non-uniform and discontinuous. So, there is always a chance of error. Statistical downscaling methods cannot be used to predict floods, but can be used to predict droughts, so in this study statistical downscaling methods are used to predict droughts (Gagnon et al. 2005; Table 1).

Table 1

Results of the normal rainfall in Bhavnagar

Bhavnagar
Normal rainfall
Time seriesYearModelled (mm/day)
1982–2020 Jul-98 5.96 
2021–2060 Aug-43 5.91 
2061–2099 Aug-95 6.75 
Bhavnagar
Normal rainfall
Time seriesYearModelled (mm/day)
1982–2020 Jul-98 5.96 
2021–2060 Aug-43 5.91 
2061–2099 Aug-95 6.75 

The graphical result of the minimum temperature for the calibration–validation process is shown in Figure 6. Minimum temperature forecasting can be done using that model up to December 2099. Figure 7 illustrates this point.
Figure 6

Minimum temperature analysis for the years 1982–2020.

Figure 6

Minimum temperature analysis for the years 1982–2020.

Close modal
Figure 7

Statistical downscaling of minimum temperature for the years 1982–2099.

Figure 7

Statistical downscaling of minimum temperature for the years 1982–2099.

Close modal
The results of Bhavnagar's minimum temperature are shown in Table 2. In the table, the term highest-min refers to the lowest temperature value. The lowest-min temperature value is also referred to as the lowest minimum temperature value. Table 3 shows the results of Bhavnagar's maximum temperature. In the table, the term highest-max denotes the highest maximum temperature values. Similarly, the lowest-max temperature values refer to the lowest maximum temperatures (Figures 8 and 9).
Table 2

Results of the minimum temperature in Bhavnagar

Bhavnagar
Minimum temperature (°C)
Time seriesMinimum temperature (°C)YearObservedModelled
1982–2020 Highest-min Jun-91 27.49 24.60 
Lowest-min Dec-94 5.45 8.47 
2021–2060 Highest-min Jun-53 – 27.91 
Lowest-min Jan-35 – 7.07 
2061–2099 Lowest-min Jun-77 – 28.53 
Lowest-min Dec-67 – 8.03 
Bhavnagar
Minimum temperature (°C)
Time seriesMinimum temperature (°C)YearObservedModelled
1982–2020 Highest-min Jun-91 27.49 24.60 
Lowest-min Dec-94 5.45 8.47 
2021–2060 Highest-min Jun-53 – 27.91 
Lowest-min Jan-35 – 7.07 
2061–2099 Lowest-min Jun-77 – 28.53 
Lowest-min Dec-67 – 8.03 
Table 3

Results of the maximum temperature in Bhavnagar

Bhavnagar
Maximum temperature (°C)
Time seriesMaximum temperature (°C)YearObservedModelled
1982–2020 Highest-max Jun-91 48.83 44.04 
Lowest-max Dec-10 29.84 33.19 
2021–2060 Highest-max Jun-46 – 47.07 
Lowest-max Dec-34 – 32.84 
2061–2099 Highest-max May-88 – 47.77 
Lowest-max Dec-69 – 33.79 
Bhavnagar
Maximum temperature (°C)
Time seriesMaximum temperature (°C)YearObservedModelled
1982–2020 Highest-max Jun-91 48.83 44.04 
Lowest-max Dec-10 29.84 33.19 
2021–2060 Highest-max Jun-46 – 47.07 
Lowest-max Dec-34 – 32.84 
2061–2099 Highest-max May-88 – 47.77 
Lowest-max Dec-69 – 33.79 
Figure 8

Maximum temperature analysis for the years 1982–2020.

Figure 8

Maximum temperature analysis for the years 1982–2020.

Close modal
Figure 9

Statistical downscaling of maximum temperature for the year 1982–2099.

Figure 9

Statistical downscaling of maximum temperature for the year 1982–2099.

Close modal

Analysis of drought year

Table 4 depicts the SPI classification system based on the severity of droughts. The severity of different droughts that have occurred in the area can be determined using Table 4.

Table 4

SPI values and the modification associated with drought categories

GradeSPI valuesDrought categories
−0.5 < SPI No drought 
−1.0 < SPI ≤ −0.5 Mild drought 
−1.5 < SPI ≤ −1.0 Medium drought 
−2.0 < SPI ≤ −1.5 Severe drought 
SPI ≤ −2.0 Special drought 
GradeSPI valuesDrought categories
−0.5 < SPI No drought 
−1.0 < SPI ≤ −0.5 Mild drought 
−1.5 < SPI ≤ −1.0 Medium drought 
−2.0 < SPI ≤ −1.5 Severe drought 
SPI ≤ −2.0 Special drought 

R-Studio and the SPI software package can be used to identify the drought. In R-studios, it is a readily available software package. SPI results are available in both graphical and tabular formats. The graphical results are depicted in the diagram. Figure 10(a) depicts SPI values from 1982 to 2020. Figure 10(b) and 10(c) depicts future SPI values for the years 2021–2060 and 2061–2099, respectively. These findings can be used to predict how severe future droughts will be.
Figure 10

Analysis of drought categories in Bhavnagar with the help of SPI value for time series 1982–2020 (a), 2021–2060 (b), and 2061–2099 (c).

Figure 10

Analysis of drought categories in Bhavnagar with the help of SPI value for time series 1982–2020 (a), 2021–2060 (b), and 2061–2099 (c).

Close modal

Table 5 indicates drought years according to their severity. Droughts are classified into mild droughts, moderate droughts, severe droughts, and special droughts.

Table 5

Analysis of various drought years in Bhavnagar

BhavnagarDrought categories
MildMediumSevereSpecial
1982–2020 1985, 1988, 1990, 1992, 1993, 1996, 2001, 2002 1984, 1988, 2007 1993 – 
2021–2060 2023, 2025, 2026, 2030, 2033, 2039, 2051 2029, 2038, 2050 – 2021, 2022, 2040 
2061–2099 2061, 2062, 2063, 2064, 2066, 2072, 2081, 2084, 2086, 2087, 2088 2074, 2082 2068 – 
BhavnagarDrought categories
MildMediumSevereSpecial
1982–2020 1985, 1988, 1990, 1992, 1993, 1996, 2001, 2002 1984, 1988, 2007 1993 – 
2021–2060 2023, 2025, 2026, 2030, 2033, 2039, 2051 2029, 2038, 2050 – 2021, 2022, 2040 
2061–2099 2061, 2062, 2063, 2064, 2066, 2072, 2081, 2084, 2086, 2087, 2088 2074, 2082 2068 – 

From this research work, it is concluded that the statistical downscaling technique is very useful to predict meteorological parameters i.e. rainfall, maximum temperature and minimum temperature. According to these predicted meteorological parameters, it was found that hot days and coldest nights will become more common in Bhavnagar in the future up to the year 2100. From the results of maximum and minimum temperature, it is clearly visible that Bhavnagar's maximum temperature will climb roughly in proportion to the third part of the decrease in minimum temperature, which is why the number of hottest days is somewhat increasing in comparison to the number of coldest nights. The global warming is clear, which is the sole cause of climate change. SPI is a very useful and handy tool to analyse and predict the droughts using precipitation data. From this study, it was found that Bhavnagar will experience 26 mild droughts, 8 moderate droughts, 2 severe droughts, and 3 special droughts up to the year 2100. As a result of this research work, it can be concluded that Bhavnagar will face mild droughts in the years 1985, 1988, 1990, 1992, 1993, 1996, 2001, 2002, 2023, 2025, 2026, 2030, 2033, 2039, 2051, 2061, 2062, 2063, 2064, 2066, 2072, 2081, 2084, 2086, 2087, and 2088. Bhavnagar will face moderate droughts in 1984, 1988, 2007, 2029, 2038, 2050, 2074, and 2082. Bhavnagar will face severe droughts in years 1993 and 2068. Similarly, it will face three special droughts in the years 2021, 2022, and 2040. This analysis will be helpful to policy makers to prepare policy and setting out priorities for planning. Using these data drought contingency plans can be prepared.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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