Trend Analysis of Temperature and rainfall in Kashmir Valley

Abstract

Weather is the key source of uncertainty affecting crop yield especially in the context of climate change. Among variables relevant to weather, rainfall and temperature are two important factors which have a large effect on crop yield. Typically, temperature affects the length of the growing season and rainfall affects plant production. Rainfall and temperature are important climatic inputs for agricultural production, especially in the context of climate change. The interdependence of rainfall and temperature has a direct influence on the output of agriculture, occurrence and design of hydraulic structures. Earlier the correlation of temperature and rainfall could not be studied with precision but the developments in the meteorology science have however rendered the study possible and precise. The study of temporal and spatial variation of temperature and rainfall of six stations of Kashmir valley for the period of thirty years which has been used for determination of various rainfall-temperature graphs. The rainfall-temperature curve has been further used to determine the trend or pattern in temporal and spatial variation and to determine periods of maximum and minimum rainfall and temperature and also the stations of maximum and minimum rainfall and temperature. The study also computes correlations between temperature and rainfall in Kashmir valley for each station and for each month of the corresponding stations. Modern statistical methods were used to test the statistical significance of our results.

Introduction

I. INTRODUCTION

The rapid growth of the human population and strained resources particularly as regards to food production and water supply for people and for agriculture, has imparted the importance of study of correlations among different physical variables of climate such as rainfall and temperature over a region. Agriculture is one of the most important activity engaging more than 70% of the population in Kashmir. In order to increase agricultural production, effective utilization of water resources is of prime importance. The relationship between rainfall and temperature is the major parameter influencing agricultural activity and its analysis is thus an important prerequisite for agricultural  planning in Kashmir valley.

The pattern and amount of rainfall are among the most important factors that affect agricultural systems. Along with temperature, the occurrence and variability of precipitation, to a large extent determine which crops can be grown in different regions throughout the world. The influence of temperature on rainfall has been incorporated in an indirect, or sometimes a direct way in a number of studies. Temperature influences rainfall in many ways; such that in some cases high temperatures may result in exceedingly high rates of potential evaporation and low precipitation. This results in an area being dominated by an arid or semi-arid landscape. In other cases, high temperatures lead to more evaporation and consequently increased condensation leading to high rainfall.

The characteristics of rainfall are of considerable interest to farmers, water resource managers and other user groups. Rainfall is a key factor in shaping the vegetation, hydrology, and water quality throughout the Earth. Since temperature and rainfall are critical determinants of crop yield, accurate simulation of temperature and rainfall is  important not only for meteorology but also for agricultural economics. However, in reality it is difficult to simulate rainfall and temperature simultaneously due to the interdependence (correlation) between them. Spatially, it is generally believed that there exists significant correlation between rainfall and temperature over tropical oceans and land. Temporally, it is generally believed that the correlation between rainfall and temperature changes between months.

II. METHODOLOGY

The methodology has been divided into four major parts namely:

A. Data collection

The monthly rainfall and average temperature of six stations of the Kashmir valley is used as the data set to determine correlation between the temperature and rainfall.

The data for the study was collected from Agromet Field Unit (AMFU) Srinagar, a unit of Agrometeorology in the Division of Agronomy, SKUAST-K. The data was collected for a period of 1991-2020. The World Meteorological Organization (WMO) suggests using 30 year rainfall and temperature data for analysis, but when analyzing

variations over time, data for shorter periods (10 or 20 years) can also be used. The data set of the study contains monthly rainfall and temperature with record length of 30 years.

B. Preparation of data

Before using the rainfall records of a station, it is necessary to first check the data for continuity and consistency. The continuity of a record may be broken with missing data due to many such reasons like fault in a rain guage or a thermometer during a period. The missing data may be estimated by using the data of the neighbouring stations. In these calculations, the normal data is used as a standard of calculation. The term normal annual precipitation at a station means the average precipitation based on a specified 30 years of record.

2) The missing temperature data can be estimated by choosing any one among the following seven methods:

a) Method of Ignoring Instances with Unknown Feature Values: This method is the simplest: just ignore the instances, which have at least one unknown feature value.

b) Most Common Feature Value: The value of the feature that occurs most often is selected to be the value for all the unknown values of the feature.

c) Concept Most Common Feature Value: This time the value of the feature, which occurs the most common within the same class is selected to be the value for all the unknown values of the feature.

d) Mean substitution: Substitute a feature’s mean value computed from available cases to fill in missing data values on the remaining cases. A smarter solution than using the “general” feature mean is to use the feature mean for all samples belonging to the same class to fill in the missing value

e) Regression or classification methods: Develop a Regression or classification model based on complete case data for a given feature, treating it as the outcome and using all other relevant features as predictors.

f) Hot deck imputation: Identify the most similar case to the case with a missing value and substitute the most similar case’s Y value for the missing case’s Y value.

g) Method of Treating Missing Feature Values as Special Values: Treating “unknown” itself as a new value for the features that contain missing values.

C.  Data Analysis And Interpretation

Analysis of the collected data has been divided into three stages and these are:

1. Temporal Variation Of Rainfall And Temperature

In this stage, the monthly variation of rainfall at each station is studied with respect to time. In monthly rainfall analysis, the months of max. and min.

rainfall are determined and also a trend in rainfall pattern is determined for each station and also for the mean rainfall of the six stations.

For temperature variation, the monthly variation of temperature at each station is studied with respect to time. In monthly temperature analysis, the months of max. and min. temperature are determined and also a trend in temperature fluctuation is determined for each station and also for the mean temperature of the six stations.

2. Spatial Variation Of Rainfall And Temperature

In spatial variation of rainfall, the monthly variation of rainfall is studied with respect to space, i.e., from station to station. In monthly rainfall analysis, the station of max. and min. rainfall intensities are determined and also a trend in rainfall pattern for each month is determined.

For temperature variation, the monthly variation of temperature is studied with respect to space, i.e., from station to station. In monthly temperature analysis, the station of max. and min. temperature are determined and also a trend in temperature fluctuation for each month is determined.

3. Interdependence Of Rainfall And Temperature

In this stage, the monthly average rainfall variation is studied with respect to monthly average temperature i.e., correlation graphs are plotted for each month and for each station and also for the mean of six stations. Correlation coefficients and corresponding p-values are determined for each graph.

D. Data Presentation And Output

Finally, the data has been presented by preparing various charts and diagrams using Microsoft Excel and various results are obtained which are discussed in next chapters.

III. COORDINATES OF SIX STATIONS

IV. DATA ANALYSIS

A. Descriptive Statistics

Descriptive statistics are brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire population or a sample of a population. Descriptive statistics are broken down into measures of central tendency and measures of variability (spread). Measures of central tendency include the mean, median, and mode, while measures of variability include standard deviation, variance, minimum and maximum variables.

Although descriptive statistics may provide information regarding a data set, they do not allow for conclusions to be made based on the data analysis but rather provide a description of the data being analysed. Descriptive statistics can be useful for two purposes:

1. to provide basic information about variables in a dataset, and
2. to highlight potential relationships between variables

B. Descriptive Statistics For Monthly Rainfall For Six Stations

The average monthly rainfall of 30 years period (1991-2020) calculated at the six stations have been used for preparing various diagrams. The descriptive statistics of the six stations and also for Kashmir valley (mean of six stations) is shown in the successive tables:

Table 1. Descriptive Statistics for monthly rainfall in mm pooled data from 1991- 2020 at Srinagar

Table 2. Descriptive Statistics for monthly rainfall in mm pooled data from 1991- 2020 at Gulmarg

Table 3. Descriptive Statistics for monthly rainfall in mm pooled data from 1991- 2020 at Pahalgam

Table 4. Descriptive Statistics for monthly rainfall in mm pooled data from 1991- 2020 at Qazigund

Table 5. Descriptive Statistics for monthly rainfall in mm pooled data from 1991- 2020 at Kupwara

Table 6. Descriptive Statistics for monthly rainfall in mm pooled data from 1991- 2020 at Kokernag

Table 7. Descriptive Statistics for monthly rainfall in mm pooled data from 1991- 2020 at Kashmir valley (mean of six stations)

1. Descriptive Statistics for monthly temperature for six stations

The average monthly temperature of 30 years period (1991-2020) calculated at the six stations have been used for preparing various diagrams. The descriptive statistics of the six stations and also for Kashmir valley (mean of six stations) is shown in the successive tables:

Table 8. Descriptive Statistics for monthly average temperature in pooled data from 1991-2020 at Srinagar

Table 9. Descriptive Statistics for monthly average temperature in pooled data from 1991-2020 at Gulmarg

Table 10. Descriptive Statistics for monthly average temperature in pooled data from 1991-2020 at Pahalgam

Table 11. Descriptive Statistics for monthly average temperature in pooled data from 1991-2020 at Qazigund

Table 12. Descriptive Statistics for monthly average temperature in pooled data from 1991-2020 at Kupwara

Table 13. Descriptive Statistics for monthly average temperature in pooled data from 1991-2020 at Kokernag

Table 14. Descriptive Statistics for monthly average temperature in pooled data from 1991-2020 at Kashmir valley (mean of six stations)

2. Correlation Of Temperature And Rainfall

The relationship of one or more variables to one or more other variables is often called correlation. There are several procedures to obtain some idea of this correlation. The Pearson Correlation Coefficient (PCC) is a statistical measure of the strength of a linear relationship between two data sets. PCC was employed to measure the linear dependence between temperature and rainfall. Monthly averages of rainfall for each year (from 1991 to 2020) were correlated with monthly average temperatures for each year (from 1991 to 2020). One of the most significant features determines the strength or weakness of the monotonic association between variables through positive or negative correlation which ranges between –1.00 to + 1.00  (Table 4.15)

PCC is expressed by the following statistic:

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Copyright

Copyright © 2023 Dr. Bashir Ahmad Pandit. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.