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International Journal of Oceanography & Aquaculture Research Article 19 min read

Effect of Climate Change on CPEC Route due to Precipitation Variation

Aamir E*, Fatima N and Naz F
* Corresponding author
ISSN: 2577-4050  10.23880/ijoac-16000299  Received: February 28, 2024  Published: March 27, 2024
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 44 references
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Keywords
Pakistan Economic Corridor (CPEC) Precipitation Trend Detection Climate Indices Mann–Kendall (MK) SDG
Abstract

China Pakistan Economic Corridor (CPEC) the game changer not only for Pakistan but also for Asia is nominated for the study area of this research. The maximum portion of CPCE passes through Baluchistan province. Baluchistan province has plains, valleys, mountains, rugged terrain, coastline, and an extreme climate that addresses most of the determinants that affect precipitation. Precipitation in Baluchistan mostly turns into flash floods due to its topography. Flash floods are proven to be more catastrophic and devastating in nature. This study focuses the one of the major aspects of climate change that is variation and trend of precipitation pattern CPEC route. The study investigates the trend of precipitation using nonparametric statistical analysis known as Mann Kendall because when 45 years of precipitation data (1977-2022) was analyzed by four different normality tests it was found to be not normally distributed data. Leading us to the conclusion that a nonparametric test will be applicable to find trend. The monthly precipitation data for selected weather stations along the CPEC route is taken from Pakistan Meteorological Department (PMD). The study provides benefits to the CPEC route in many ways, firstly it provides a trend analysis of precipitation on a monthly, yearly, and seasonal basis, by two methods i) the conventional graphical method and ii) the nonparametric technique known as the Mann-Kendall test. Secondly, the study selected the 9 most common climate indices that affect Pakistan’s climate and precipitation trends by literature review and studying the impact of all the climate indices by applying Person’s correlation. Person’s correlation investigates the linear association of Climatic Indices with Precipitation, and lastly Partial Mann–Kendall for observing the impact of teleconnections on precipitation patterns. This study is the first statistical analysis of the CPCE route and the effect of climate indices on CPEC routes to due precipitation variation and climate indices. This research will provide a baseline study for future comparative studies tracing the effects of CPEC on Precipitation patterns. The research addresses 2 SDGs namely SDG # 11.5 (natural disasters) and SDG#13 (climate action).

Introduction

Baluchistan host a substantial portion of the Western and Central portion of CPEC. It is the future hub of all financial activity for the next century. It is the region with vibrating, socioeconomic activity, financial growth, and trade center for Asia, therefore, it’s very important to examine the trend of precipitation, factors causing variation in precipitation, and the future weather pattern of this financial hub. Baluchistan’s topography, mountains, rugged terrain, coastline, extreme climate, relative continental position, atmospheric current, orographic barriers, surface condition, and natural or anthropogenic activity address most of the determinants that affect precipitation. However, the author through a meticulous literature review has carefully developed the innovation of this research article which lies in observing the impact of climate indices/teleconnection on the precipitation pattern as well as the effect of climate change on the CPEC and international trade route to boots the deying economy of pakistan.

Literature Review

Climate indices, also accepted as teleconnections, are predominantly liable for variations in precipitation, causing droughts or flooding in the region. Teleconnections can influence close areas to far-flung regions of the globe. Precipitation and droughts all over the globe are sturdily controlled by atmospheric circulations and climate indices. Hence, different climate indices have different effects. This explains the reason why the different region has different climates; for illustrate, some parts have more precipitation compared to others; some parts of the world are hotter than other [1]. The key parameters governing precipitation variation are ocean circulations, air circulations, wind speed, vapor movement, wind direction, thermal fluxes, and so on. The majority of ocean circulation investigations and research have been conducted under the banner of teleconnections [2, 3]. Investigation of teleconnections and their impacts, on precipitation patterns can be very obliging to comprehend future climate patterns [4]. Many Asian countries like Pakistan, India, and Bangladesh, have suffered unfamiliar flooding in the past decades because of unprecedented precipitation trends [5, 6]. A thorough investigation of precipitation is essential for identifying hazards such as droughts, flooding, landslides, infrastructural and environmental damage, etc [7].

Global Climate Risk Index (GCRI) emphasizes that climate studies and research are eminent for Pakistan [8]. Pakistan was placed among the top vulnerable countries to climate change in 2012 [9]. The province of Baluchistan has experienced disastrous flooding and devastating droughts as a result of unprecedented precipitation patterns. Furthermore, the region’s current role is even more vital due to CPEC and Gawadar Port [1]. Climate indices such as the Arctic Oscillation (AO), El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), and North Atlantic Oscillation (NAO) are frequently used to assess variations in precipitation throughout Pakistan [10, 11]. Studies carried out by Krishnamurthy L, et al. [12], Liess A, et al. [13] and Afzal M, et al. [14], report that there exists a significant association between precipitation variations and climate indices in Pakistan. Research carried out by Iqbal MF, et al. [15], 2018 applied Pearson’s correlation to find the impacts of teleconnections, like NAO, AO, AMO, IOD, PDO, QBO, and ENSO on precipitation.

Study Area

The western route of CPCE passes through the major cities in Baluchistan as shown in Figure 1 and is designated as the research area of this study. Baluchistan has tough, dry, rocky terrain, including both plains and mountain ranges. Baluchistan accounts for over 44% of Pakistan’s total landmand mass [16, 17]. The region is arid and humid, with high and frigid temperatures [18, 19]. Western disturbances are mainly responsible to impact the weather of Baluchistan in the spring, and winter months. Monsoons have minimum influence during the summer. Also, during the autumn season, few tropical storms are reported along the Baluchistan coastal areas [20, 21, 22, 23].

Figure 1: The Western and central route of the CPEC passes through Baluchistan.
Click to enlarge
Figure 1: The Western and central route of the CPEC passes through Baluchistan.

Data Source and Formulation

Data PMD was approached to get the monthly precipitation data (in millimeters). Six cities in Baluchistan as shown in Figure 2 were carefully chosen with the authenticity, accuracy, inclusivity, and readiness of data for the 45-year of analysis. Six stations such as: Barakhan, Panjgur, Kalat, Khuzdar, Quetta, and Zhob were selected for the study. The precipitation data of Gawadar weather stations do not spread over the 45 years and thus are not included in the analysis. There is also one exclusive international research paper published on the weather and climate of Gwadar deep sea port in by the author in 2022. Data gathered from PMD were on a monthly basis (in mm/month) for six stations. Monthly data were then converted to annual precipitation data for the analysis.

Data characteristics (Kurtosis and skewness) were also checked for the precipitation statistics used in the analysis to highlight outliers, missing values, distribution types, changes, cycles, and homogeneity, and the results are shown in Appendix A (Table 1).

Sr.NoStationsMeanStandard DeviationKurtosisSkewness
1Barakhan421.376140.0470.7690.455
2Kalat200.413170.91710.052.612
3Khuzdar265.875118.2151.1470.994
4Panjgur94.88456.8813.951.641
5Quetta270.502152.869.3652.542
6Zhob281.85992.734-0.1010.193

Table 1: Data characteristics.

Figure 2: Map showing the selected six weather stations along the Western route of (CPEC) passing through Baluchistan.
Click to enlarge
Figure 2: Map showing the selected six weather stations along the Western route of (CPEC) passing through Baluchistan.

Precipitation data was checked for its normality by implementing some tests, such as the Shapiro–Wilk W test, Lilliefors test, Anderson–Darling test, and the Jarque–Bera test. The results of the test are presented in Appendix B (Table 2).

StationsShapiro-WilkAnderson-DarlingLillieforsJarque-Bera
1BarakhanPassedPassedPassedPassed
2Kalatfailedfailedfailedfailed
3Khuzdarfailedfailedfailedfailed
4Panjgurfailedfailedfailedfailed
5Quettafailedfailedfailedfailed
6ZhobPassedPassedPassedPassed

Table 2: Normality tests on the annual time series.

Normality test results indicated that the precipitation data of only two stations, namely Barakhan and Zhob, were normally distributed; and in the other four stations, the data were not normally distributed. Table 3 illustrates the mean precipitation (monthly / annually) for the analysis duration (from 1997 to 2022) in the 06 stations of Baluchistan.

The table indicates that Barakhan, Kalat, Khuzdar, and Zhob get the majority of their precipitation during the monsoon season. Panjgur is located in western Baluchistan and receives the majority of its precipitation during the winter season as a result of the western disturbance. Quetta station was excluded since the majority of its precipitation fell during the winter season.

StationsWinterSpring/Pre-
monsoon		MonsoonPost-
monsoon		Annual
precipitation
DecJanFebMarAprMayJunJulAugSepOctNov
Barakhan6.513.121.131.334.624.748.2108.484.6359.14.9421.5
Kalat30.334.737.831113.96.716.313.44.655.8200.5
Khuzdar14.716.530.929.316.314.116.551.256.69.16.54.3266
Quetta30.853.851.755.5267.5412.511.13.15.78.8270.5
Zhob9.217.126.943.529.114.817.756.244.811.15.85.6281.8
Panjgur1012.81515.18.33.5512.17.71.72.11.694.9
Monthly
Avg.		13.718.72223.712.17.411.828.6246.73.93.4
Seasoal Sum54.343.271.17.3

Table 3: Average precipitation (mm) from 1977 to 2022.

Monthly and Annual Precipitation Data

To detect and demonstrate the monthly trend of precipitation, data obtained from PMD, was used for the selected six stations all over Baluchistan.

Seasonal Precipitation Data

The five most common seasons of Baluchistan namely winter, spring, summer, monsoon, and autumn are selected for seasonal investigation. The seasonal data was prepared by adding two to three monthly values. Table 4 show the five selected seasons for the seasonal analysis and the respective months fall in all of the seasons.

S.NoSeasonsMonths
1WinterDecember, January, February
2SpringMarch, April
3SummerMay, June
4MonsoonJuly, August, September
5AutumnOctober, November

Table 4: Five most common Seasonal and their respective months

Climatic Indices

Climatic indices nine (09), namely the Arctic Oscillation (AO), North Atlantic Oscillation (NAO), Atlantic Multi-decadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO- MEI), Equatorial Indian Ocean Zonal Wind Index (EQWIN), and ENSO Modoki Index (EMI), identified through studies to affect the precipitation in the study area were selected for the analysis [11, 17, 24]. Nonetheless, the effects of climate indices, such as the Equatorial Indian Ocean Zonal Wind Index (EQWIN) and ENSO Modoki Index (EMI), have never been explored in Baluchistan [25]. This is also one of the novelties of this research.

Methodology

Precipitation pattern analyses were conducted using two different methods as discussed below.

  • Simple graphical method
  • Mann–Kendall (Statistical) tests Simple graphical method ii) Mann–Kendall (Statistical) tests were applied on a monthly interval for the investigation of 45 years on six stations in Baluchistan [26, 27, 28]. All 12 months from January to December (1977 to 2020) were analyzed for trend detection. To minimize lengthy, irrelevant repetition, only stations and months with substantial and noticeable trends were selected for further research. The correlation between climate indices and precipitation was established by using Pearson’s correlation.

The impacts of teleconnection as influencing variables on precipitation patterns were studied by applying the Partial Mann–Kendall technique [29, 30, 31]. Teleconnection or climatic indices were selected as covariate independent variables (whereas precipitation was selected as the dependent variable). PMK and Pearson’s correlation was used to evaluate the effect of climatic indices on precipitation [32]. PMK is a widely used technique due to its authentic result. PMK is used in several analyses containing the recent climate studies performed by Libiseller C, et al. [33], Oldenborgh GJ, et al. [34], Hajani E, et al. [35] and Yadav RK, et al. [36].

Finally, the study and results were completed utilizing the combined technique, with the influencing variables (climate indices) in tabular form. The table is shown, and the findings are discussed in the results and discussion section. The table is only for the substantial and noteworthy precipitation patterns and stations. These important precipitation connections have mild, moderate, and strong influences on precipitation [37].

Results and Discussions

Linear Association of Climatic Indices with Precipitation

To check the correlation between the climate indices and precipitation data of the individual PMD stations. Pearson’s correlation was performed. A two-tailed test at a 5% significance level) is considered significant having a correlation coefficient value of 0.316 (−0.316) or higher (lower) for 45 years of precipitation station data. Stations and months having prominent trends in precipitation, as shown in Table 2 are selected to avoid repetition and monotonous calculation. Table 5 displays the correlation between the precipitation (dependent variable) and climatic indices (independent variables).

MonthNAOAOIODPDOENSO-EMIEQWINEMI-MODOKIOLR
BarakhanJanuary-15.83%-0.02%-1.22%-1.57%1.77%-14.05%21.46%-44.79%
BarakhanJune-6.05%-6.594.74%31.85%6.40%3.94%-0.34%-2.81%
BarakhanNoember11.61%25.03%40.10%1.08%37.93%59.39%-42.18%-3.97%
KhuzdarDecember-0.17%12.84%9.50%-18.98%22.22%30.97%-20.08%14.76%
PanjgurJuly6.10%-15.01%2.24%1.52%-24.26%21.98%11.83%6.50%
PanjgurDecember14.53%19.09%8.45%-5.95%24.03%37.27%-21.87%12.53%
QuettaJanuary-0.93%14.93%3.70%-12.46%3.66%13.20%-23.98%19.27%
QuettaJune-17.37%-11.43%16.90%7.14%-3.48%27.02%17.05%-21.23%
QuettaSeptember-11.23%9.12%33.63%-33.02%0.48%25.04%1.42%15.25%
ZhobJanuary-28.68%-18.96%11.70%-11.04%14.91%23.30%-20.99%-9.06%

Table 7: Correlation of precipitation at individual stations with climatic indices.

The significant correlations at a confidence level of 0.05 (5%) are shown in bold font. The influence of climatic indices or teleconnection on precipitation was evaluated by Pearson’s correlation analyses are displayed in Table 5 above. It is intimated again noticeable trends are shown by Barakhan, Khuzdar, Zhob, and Quetta, stations, hence, only these stations were counted.

Numerous research articles and findings have highlighted that climate indices ENSO and NAO impact the precipitation pattern in Pakistan both locally and regionally [38]. For instance, Yadav RK, et al. [36] reported the influence of ENSO on the weather of Pakistan has grown as compared to NAO. Rashid A [37] investigated the influence of ENSO and reported that ENSO had a negative impact on the winter precipitation pattern in Pakistan. The precipitation in the winter in Pakistan remains below normal under the influence of the ENSO (-ve) phase (i.e., La Niña condition). Researchers Abraham A, et al. [39] and Khan AH [40] reported that ENSO’s impact on the precipitation pattern of Pakistan is not very significant.

To summarize the finding reported in Table 5 author can conclude that climate indices namely NAO, AO, IOD, PDO, and ENSO-MEI are correlated with the precipitation pattern of December and January, and climate indices namely EQWIN, EMI, and OLR are correlated with June precipitation.

Results of PMK and Correlation representing the impact of climatic Indices—A Combined Approach.

Analysis performed, findings, and results reported in Table 3 to Table 5 were combined in Table 6 for the ease of the reviewer and reader. One can easily conclude the statistical analysis and draw the crips conclusion that will be helpful for future planning, policy-making, and visualizing the effect of climate change on precipitation trends. Pointing the noteworthy correlations of climate indices with the precipitation along with their influences (classified as weak, moderate, or strong). Table 6 clearly states that the climate indices namely NAO, AO, IOD, PDO, and ENSO-MEI were significantly correlated but had a weak influence on precipitation in December and January. Similarly, IOD, EMI, EQWIN, and OLR were significantly correlated but had a moderate to strong influence on precipitation in June.

VariablesMonthsStationsMann-KendallPartial Mann-Kendall
with Covariate		% Change in
MK Statistics
due to
Addition of
Covariate		Influence
Type as
per PMK		Person
Correlation
MKPMK
p-ValueStatisticsp-valueStatistics
NAOJanuaryQuetta0.0119-2240.0121-223.40.27%Weak-0.93%
AOBarakhan0.0462-1770.0397-1791.13%Weak-0.02%
IODBarakhan0.0462-1770.0338-1886.21%Weak-1.22%
Quetta0.01192240.0086-233.44.20%Weak3.70%
PDOBarakhan0.0462-1770.0325-185.95.03%Weak-1.57%
ENSO-MEIBarakhan0.0462-1770.0437-182.53.11%Weak1.77%
Quetta0.0119-2240.0161-2134.91%Weak3.66%
IODJuneBarakhan0.04931750.0485175.60.34%Weak4.74%
EMIBarakhan0.04931750.1076140.9-19.49%Moderate-0.34%
EQWINBarakhan0.04931750.008210.920.50%Strong3.94%
OLRBarakhan0.04931750.0527171.2-2.17%Weak-2.81%
NAODecemberKhuzdar0.0401-1780.0574-1628.99%Weak-0.17%

Table 8: Influence of climatic index on precipitation trends.

Conclusions and Policy Implications

Precipitation trend analysis of selected stations along the western route having major cities along it route like, Barakhan Quetta, Zhob Khuzdar, and Kalat was studied by both simple graphical and statical methods. Similar trends are observed by both the linear graphs method and statistical techniques the MK method. Trend detection was applied to monthly, annual, and seasonal time series confirming that Baluchistan has received less precipitation in the past few decades and the precipitation pattern is decreasing. This finding is also endorsed by many other studies [16]. The influence of climate indices on precipitation was found to be significant. It was observed that the influences of climate indices are weak in the winter months and moderate to strong in the monsoon months. It was also noted that the climate indices namely MEI, EQWIN, and OLR have noticeable correlations varying from strong to moderate influences on the precipitation trends in the monsoon months (e.g., June). The study also indicates that the climate indices namely MEI and EQWIN are significantly correlated and have a strong influence; thus, they are more appropriate and relevant climate indices than IOD and ENSO-MEI for measuring the influence on precipitation CPEC route [41, 42].

This study is beneficial to National Disaster Management Authority (NDMA), Provincial Disaster Management Authority (PDMA), and Pakistan Metrological Department (PMD). The analysis will give basic information to local inhabitants, provincial government, administration, National Disaster Management Authority (NDMA), Provincial Disaster Management Authority (PDMA), and Pakistan Metrological Department (PMD) to issue early flash flood warnings, take proactive measures in June due to unprecedented precipitation and dryness or drought alert in January due to less rain. This information can be helpful in planning crops or going for re-cropping (dual cropping), ridge farming, and re-sowing technique. As 90% of the grasslands, shrubs, and bushes in Baluchistan are rain-fed. Livestock mostly depends on these grasslands. A decrease in rainfall in the month of January will have a direct impact on this grassland and on the livestock. The model will help them to look for an alternative or plan accordingly by storing food for the livestock, which is the second most common occupation of local dwellers. The livestock contributes 20% of the provincial Gross Domestic Product (GDP). The research paper output information will not only be helpful for the livestock business but to other related industries like leather, carpet, and pharmaceutical which are the leading consumer of livestock products. Baluchistan also fulfills the need and demand for leather tannery and carpet industries in Pakistan by providing a good and constant supply of hiding, skin, and wool. Policymakers can use this research and findings to comprehend the recent conditions, from the perspective of climate change. This research will be a model study for future comparative studies tracing the effects of CPEC and anthropogenic activity on climate change. The research addresses 2 SDGs namely SDG # 11.5 (natural disasters) and SDG#13 (climate action) [43, 44].

Funding

This project is funded by China Study Center, National University of Sciences and Technology, Pakistan.

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@article{aamir2024,
  title   = {Effect of Climate Change on CPEC Route due to Precipitation
Variation},
  author  = {Aamir E, Fatima N and Naz F},
  journal = {International Journal of Oceanography & Aquaculture},
  year    = {2024},
  volume  = {8},
  number  = {1},
  doi     = {10.23880/ijoac-16000299}
}
Aamir E, Fatima N and Naz F (2024). Effect of Climate Change on CPEC Route due to Precipitation
Variation. International Journal of Oceanography & Aquaculture, 8(1). https://doi.org/10.23880/ijoac-16000299
TY  - JOUR
TI  - Effect of Climate Change on CPEC Route due to Precipitation
Variation
AU  - Aamir E, Fatima N and Naz F
JO  - International Journal of Oceanography & Aquaculture
PY  - 2024
VL  - 8
IS  - 1
DO  - 10.23880/ijoac-16000299
ER  -