Comparison of future and base precipitation anomalies by SimCLIM statistical projection through ensemble approach in Pakistan

Accepted Manuscript Comparison of future and base precipitation anomalies by SimCLIM statistical projection through ensemble approach in Pakistan

Asad Amin, Wajid Nasim, Muhammad Mubeen, Dildar Hussain Kazmi, Zhaohui Lin, Abdul Wahid, Syeda Refat Sultana, Jim Gibbs, Shah Fahad PII: DOI: Reference:

S0169-8095(17)30002-9 doi: 10.1016/j.atmosres.2017.05.002 ATMOS 3952

To appear in:

Atmospheric Research

Received date: Revised date: Accepted date:

3 January 2017 31 March 2017 2 May 2017

Please cite this article as: Asad Amin, Wajid Nasim, Muhammad Mubeen, Dildar Hussain Kazmi, Zhaohui Lin, Abdul Wahid, Syeda Refat Sultana, Jim Gibbs, Shah Fahad , Comparison of future and base precipitation anomalies by SimCLIM statistical projection through ensemble approach in Pakistan, Atmospheric Research (2016), doi: 10.1016/ j.atmosres.2017.05.002

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ACCEPTED MANUSCRIPT Comparison of Future and Base Precipitation Anomalies by SimCLIM Statistical Projection through Ensemble Approach in Pakistan Asad Amin1, Wajid Nasim1,2,3,*, Muhammad Mubeen1, Dildar Hussain Kazmi4, Zhaohui Lin5, Abdul Wahid6, Syeda Refat Sultana1, Jim Gibbs7, Shah Fahad8* 1

Department of Environmental Sciences, COMSATS Institute of Information Technology (CIIT), Vehari-61100, Pakistan

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CIHEAM-InstitutAgronomiqueMéditerranéen de Montpellier (IAMM), 3191 route de Mende, 34090 Montpellier, France 3

National Agromet Centre, Pakistan Meteorological Department, Islamabad, Pakistan

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CSIRO Sustainable Ecosystems, National Agricultural Research Flagship, Toowoomba, QLD 4350, Australia

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International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. Department of Environmental Sciences, Bahauddin Zakariya University, Multan, Pakistan

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Livestock Health and Production, Department of Animal Science, Lincoln University Lincoln

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7647, Christchurch, New Zealand 85084

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College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China

*Correspondence: Wajid Nasim ([email protected]): Shah Fahad

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([email protected] or [email protected])

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ACCEPTED MANUSCRIPT ABSTRACT Unpredictable precipitation trends have largely influenced by climate change which prolonged droughts or floods in South Asia. Statistical analysis of monthly, seasonal, and annual precipitation trend carried out for different temporal (1996-2015 and 2041-2060) and spatial scale (39 meteorological stations) in Pakistan. Statistical downscaling model (SimCLIM) was used for future precipitation projection (2041-2060) and analyzed by statistical approach.

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Ensemble approach combined with representative concentration pathways (RCPs) at medium

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level used for future projections. The magnitude and slop of trends were derived by applying

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Mann-Kendal and Sen’s slop statistical approaches. Geo-statistical application used to generate precipitation trend maps. Comparison of base and projected precipitation by statistical analysis

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represented by maps and graphical visualization which facilitate to detect trends. Results of this study projects that precipitation trend was increasing more than 70 % of weather stations for

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February, March, April, August, and September represented as base years. Precipitation trend was decreased in February to April but increase in July to October in projected years. Highest

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decreasing trend was reported in January for base years which was also decreased in projected years. Greater variation in precipitation trends for projected and base years were reported in

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February to April. Variations in projected precipitation trend for Punjab and Baluchistan highly accredited in March and April. Seasonal analysis shows large variation in winter, which shows

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increasing trend for more than 30 % of weather stations and this increased trend approaches 40 % for projected precipitation. High risk was reported in base year pre-monsoon season where 90

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% of weather station shows increasing trend but in projected years this trend decreased up to 33 %. Finally, the annual precipitation trend has increased for more than 90 % of meteorological

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stations in base (1996-2015) which has decreased for projected year (2041-2060) upto 76 %. These result revealed that overall precipitation trend is decreasing in future year which may prolonged the drought in 14 % of weather stations under study.

Keywords: GCM, Climate change, Mann-Kendall, Future projections, RCPs, Sen’s slop, Meteorological stations. 1. Introduction

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ACCEPTED MANUSCRIPT Climate change is a vital issue, has its effectives all over the Globe. Different climatologist reported that change in climate may cause variation in temperature (Abbas et al., 2013; IPCC,2014; Subash and Sikka, 2014; Jhajharia et al., 2014; Iqbal et al., 2016) and precipitation (Basit et al., 2012; Abbas et al., 2013; IPCC, 2014; Khattak and Ali, 2015; Fahad and Bano, 2012; Fahad et al. 2013; Fahad et al.2014a,b; Fahad et al.2015a,b,c; Fahad et al.2016a,b,c,d ). Fifth assessment report (AR5) (IPCC,2013) also indicated that increase in temperature by 0.75 o

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C caused global warming for 1951-2011 published by Intergovernmental Panel on Climate

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Change (IPCC) have also effects on global precipitation change. This report showed significant

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precipitation increase in 1900 to 2005 for northern Europe, Asia, North and South America but decrease observed in southern regions of Africa and Asia (IPCC,2013, 2014). These studies

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confirm risk associated with the climatic variation in different climatic regions of the world. Several studies were conducted to observe the variations in precipitation in different areas at

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regional, national and global level over the last decade (Ying et al.,2016) Eastern China (Wang et al.,2015), the Northwest Pacific around the area of the Kuroshiocurrent (Ying et al., 2013; Ying

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et al., 2015), the tropical Indian Ocean (Wu et al., 2012; He et al.,2015) and the North Atlantic Ocean (Linderholm et al., 2011; Wu et al., 2012; Zuo et al., 2013), and (Ghummanet al.,2012) in

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Mediterranean. Climatic extremes (annually) like temperature variations, floods, and droughts were threatening the agricultural activities in South Asia (Zahid and Rasul, 2011; Websteret

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al.,2011; Amin et al., 2016). Different studies have been conducted to find out the unpredicted climatic factors cause calamities. Different rainfall patterns have been displaced which was

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normal routine in Bangladesh and north-eastern India (Houze et al., 2011; Rasmussen et al., 2014; Priya et al., 2015). Extreme rainfall caused due to interruption of mid latitude weather

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system adjacent to northwest India-Pakistan (Lau and Kim,2012; Ullah and Shouting,2013). Worldwide large variability in temperature and precipitation has been recorded over the globe including Pakistan (Sohail and Farkhunda,2013). High rainfall in 2010 caused floods in China, India, and Pakistan, in 2011 floods in Sindh (province of Pakistan), and 2012 flood were reported in Southern Punjab, Sindh, and Baluchistan due to increased rainfall in monsoon, these climatic variations in Pakistan may have adverse effects on ecosystem in future (Houze et al., 2011; Hong et al., 2011; Lau and Kim, 2012;Ding and Ke, 2013; Martius et al., 2013; Abbas et al., 2013; Kumar et al., 2014; Rasmussen et al.,2014;Amin et al., 2015;Nai et al., 2016). Ahmad et al., (2015) concluded that entire Swat River basin was statistically insignificant for annual 3

ACCEPTED MANUSCRIPT precipitation. The current variations in this region are due none significant precipitation anomalies, although climate trends may be varied with the area and the study period. These climatic variations are caused seasonal and annual rainfall pattern change in different regions of Pakistan. That is why; annual variability cause difficulties to access rainfall trend distribution for these regions. Seasonal analysis of precipitation revealed that monsoon system of South Asia greatly affected the Pakistan monsoon in summer (Ding and Ke, 2013; Ahmad et al., 2015).

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Northern areas of Pakistan has largely affected by uneven precipitation during summer and

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monsoon seasons (Hanif et al., 2013). Different studies revealed significant increasing trend in

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precipitation in northern areas of Pakistan (Imran et al., 2013), and province Punjab of Pakistan (Cheema and Hanif, 2013; Amin et al., 2017) similar increasing trends were also reported for

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Mountain regions (Hindu Kush and Sulaiman), and in the areas of Indus River basin (Himalayas) (Khattaket al., 2011; Hartmann and Andresky,2013; Hartmann and Buchanan, 2014; Ahmad et

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al.,2014). During monsoon 2010, 2011, and 2012 unpredicted rainfall in this entire region caused the worst floods which resulted a large number of deaths; lose in 2010 floods exceeded $40

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billion (U.S. dollar) (Webster et al., 2011; Nie et al., 2016). It is hard to justify the results concluded by different studies of precipitation trend analysis depend upon the spatial and

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temporal scale. Nevertheless, most of the studies shown large variations with other studies (Del R´ıoet al., 2012). If rainfall projection had been available in Pakistan, then high risk of floods

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could be forecast by future projection of precipitation using general circulation models (GCMs), and the proper adaptation could be adopted to lower the damage (Ding and Ke,2012).

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Future precipitation adoption can be improved by climate projection techniques by using statistical downscaling models (SimCLIM) to mitigate the future climatic extremes (Warrick et

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al., 2009;Moss et al., 2010; Yin et al., 2013).Precipitation is most vital variable for climate projections used by dynamic GCMs (Yousuf et al., 2016). This approach is used basically involves numerical modeling (Warrick, 2007). Coupled Model Inter comparison Project, Phase 5 (CMIP5) used for temperature and precipitation future simulation up to 2100 for global and regional level with 31 GCMs (Chan et al., 2016). SimCLIM was used for precipitation projection with representative concentration pathways (RCPs) (RCP-2.6, RCP-4.5, RCP-6.0 and RCP-8.5 W m−2) were developed and incorporated with 40 GCMs “ensemble” to future precipitation values for specific region (Yin et al., 2013) and also used by AR5 (Taylor et al., 2012; IPCC, 2014; Alexander, 2016; Yousuf et al., 2016). 4

ACCEPTED MANUSCRIPT This study is carried out to achieve these objectives: -To update the precipitation trends (sign and magnitude) in Pakistan from 1996 to 2015 on a monthly, seasonal and annual timescale to identify trend of precipitation in different agro climatic regions. -To generate future projection for precipitation trends (sign and magnitude) for 2041 to 2060 on

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a different temporal and spatial scale to indicate the risk associated with rainfall.

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-To generate surface precipitation maps by applying geo statistical techniques in order to

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analyses the spatial distribution patterns of rainfall trends by statistical method to for different temporal. Study indicates the future precipitation pattern in different agro climatic regions of the

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country enable us for adoptive measure for sustainable water management. 2. Material and Methods

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2.1 Selected study area and methods

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This study was carried out to analyse the monthly, seasonal, and annual precipitation for the base period 1996-2015 and future projections for2041-2060. Geographically, Pakistan holds versatile

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National

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climate from one region to another; the study used data form 39 weather stations for precipitation Aeronautics

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(NASA)

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(http://power.larc.nasa.gov) as shown in Fig. 1.These stations were preferred due to area, availability of data, and homogeneity, so that complete area of country could be represented by

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these records. We have selected the weather stations with less than 3% of precipitation missing values for the whole study period for monthly data. The average for monthly values was used to

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substitute to remove the gap (Del R´ıo et al., 2012). Climate projections (2041-2060) were derived from SimCLIM by using ensemble of 40 GCMs with median RCP-6.0 to eradicate the very high or low variations. To find the trend for seasonal analyses seasons were considered in following way winter = January and February; Pre-monsoon = March, April, and May; Monsoon= June, July, August, and September; Post monsoon = October, November, and December recommended by Pakistan Meteorological Department (PMD) (Rasul, 2012; Iqbal et al., 2016). Furthermore, non-parametric Mann–Kendall test was applied to verify the homogeneity of the series (Del R´ıo et al., 2012; Abbas et al., 2013; Iqbal et al., 2016). Mann– Kendall test used to estimate the magnitude and Sen’s slop to find the sign (positive, negative or 5

ACCEPTED MANUSCRIPT no sign) of climate trend distribution for precipitation by using a combined Microsoft Excel template (MAKESENS) (Salmi et al., 2002; Del R´ıo et al., 2012; Abbas et al., 2013; Iqbal et al., 2016). Mann- Kendall test is used to find out the relative trends in the data series better to represent the individual values (Gilbert, 1987). According to this test each data value in the time series is

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compared to the other data values can be assessed by selected statistical model.

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2.2 Statistical model approach

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Data analysis of precipitation was carried out for spatial scales with different temporal series observed base (1996-2015) data and future projections (2041-2060). Mann-Kendall test is used

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to find out the relative trends in the data series better to represent the individual values (Gilbert, 1987). According to this test each data value in the time series is compared to the other data

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values and this value is termed as S can be assessed by equation (i);

(i)

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Where x1, x2 , . . . xn are n data points with xj represent the point at j time and xkat K time. Mann- Kandall statistic (S) gives the values of S which used for normal approximation test to

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calculate the variance (VAR(S)) in equation (ii) to find out the magnitude and significant level

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(95% general) (Sen, 1968).

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(ii)

Where n (number of locations), g is the number of groups with similar values, and tp is the data location in Pth group are used to find out the distribution trend (Z) as given in equation (iii), (iv) and (v) for different selected sites at different temporal scales.

if S> 0

(iii)

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ACCEPTED MANUSCRIPT Z= 0

if S = 0

if S< 0

(iv)

(v)

This statistical analysis shows that distribution trend is said to be decreasing if Z < 0 (negative) and distribution trend is said to be increasing if Z > 0 (positive), which shows that statistical

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probability is greater than the level of significance for both cases, but if statistical probability is

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less than level of significance then there is no trend (Salmi et al., 2002; Khambhammettu, 2005).

2.3 Regional description

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Pakistan is a developing country located in southern part of continental Asia between latitude 24° and 37° North and longitude 62° and 75° East (Kazmi et al., 2014; Iqbal et al., 2016). It is

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surrounded by Iran on the West, Afghanistan (north and northwest), China (northwest to northeast), and India in the East (Ghumman et al., 2012; Abbas et al., 2013). Pakistan is highly versatile in climate, its ranges from coastal areas of Arabian Sea in South to the high mountain

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ranges in North (Kazmi et al., 2014). Landscape of the Pakistan diversifies with plains,

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mountains, glaciers, and deserts (Shahid et al., 2016). Main streamline of water resources in Pakistan is due to the snowmelt in the mountain system of north-eastern Himalayan and rainfall

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to the Indus River (Del R´ioet al., 2012). Indus River have major role as water resource for plain areas of Pakistan including Khyber PakhtonKhwa, Punjab, Baluchistan, and Sindh provinces.

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Sindh lies in northwest, Baluchistan in south west and regions of southern Punjab mostly under the drought conditions due to its arid and semi-arid climate (Mubeen et al., 2013; Nasim et al.,

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2015;Iqbal et al., 2016; Nasim et al., 2016a). The rainfall regime of the Pakistan is differentiated by climatic variations in both spatial and temporal scales due to its diverse geographical locations (Abbas et al., 2013). In South coastal strip caused to lower the annual monsoon rainfall in the northern areas due to its moderate climate variations and higher values ‘130 mm’ for Indus plains and 890 mm for Himalaya (Ghumman et al., 2012). Heavy rainfall was reported in monsoon season followed by pre-monsoon and post-monsoon. This was studied that almost 70% annual rainfall contributed by monsoon season in major rainfall regions of Pakistan and almost no rainfall or very low rainfall in months of October and November (Shahid et al., 2016). These climatic variations has been observed since the early 1970s (Shahid et al., 2016), which shows 7

ACCEPTED MANUSCRIPT spring monsoon either very low rainfall causes drought or has high rainfall that caused floods (Sohailand Farkhunda, 2013; Mubeen et al., 2016; Nasim et al., 2017). This seasonal and annual rainfall variation also varies with spatial and temporal scales with its unpredicted occurrences. 2.4 Statistical downscaling approach for climate projection

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The fundamental approaches for spatial and temporal downscaling are the possible techniques to

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find reliable past and future trends for global to regional or local climate conditions (Ghumman

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et al., 2012). Statistical downscaling develop a numerical relationship between past climate trends to project the future climatic variables (Kazmi et al., 2014; Kazmi et al., 2015; Kazmi et al., 2016; Hasson et al., 2016; Nasim et al., 2016b). SimCLIM is a compatible computer

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application has been developed to implement statistical downscaling methods to project the different climate variables for temporal and spatial scales (Bao et al., 2015; Amin et al., 2016). It

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has easily customizable features to adjust the specific spatial and temporal model resolution with impact models (Yin et al., 2013). SimCLIMutilized for decision making by using different

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scenarios for baseline and future projections of climatic variability and extremes (Warrick, 2009; SimCLIM, 2011; Sharma et al., 2014). It contain scenario generator which allows to future

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projection with pattern scaling (Rogelj et al., 2012; Yin et al., 2013) that have the ability standardized the climate change patterns from GCMs. Data of 40 GCMs used in SimCLIM

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retrieved from the CMIP5 and Earth System Grid (ESG) for global climate projection with more improvements (e.g. varying complexity of interactive ocean and land carbon cycles; indirect

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effect of aerosols) (Taylor et al., 2012; Yin et al., 2013). Different emission scenarios were used to support the GCMs for more realist future projection. Representative Concentration Pathways

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(RCPs) (emission scenarios) are four greenhouse gas concentration trajectories with radiating forces (RCP2.6, RCP4.5, RCP6.0, and RCP8.5 Wm-2) (Moss et al.,2010; Rojeliet al., 2012; Yin et al., 2013; IPCC, 2014). Fifth Assessment Report (AR5) published by IPCC used these RPCs for future climate trajectories (IPCC, 2013, 2014). To project the future climate more precisely for specific location or region, so GCM selected should be more appropriate to predict the current climate accurately (Bao et al., 2015). Use of single GCM limited the scope for accurate climate projections of spatial and temporal scale (Coquard et al., 2004). To eradicate this deficiency more than one GCM patterns should be used as “ensemble” to incorporate the future projection more accurately (Yin et al., 2013; Amin et al., 2016). 8

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3. Result and Discussion 3.1 Precipitation trend analysis for base and projected temporal scale

The results of the current study for monthly rainfall are shown in Figure 4, 5, 6,&7. These

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figures show the spatial distribution of precipitation trends in Pakistan for base (1996-2015) and

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projected (2041-2060) by using geo-statistical techniques. The Fig. 2 shows highest monthly and

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seasonal precipitation trend for April and pre-monsoon for base line year (1996-2015), but the lowest precipitation trend for this temporal scale was observed in January and winter. However for projection year (2041-2060) shows(Fig. 3) highest increasing trend in monthly and seasonal

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precipitation for September and monsoon season and lowest precipitation was reported in month of April and pre-monsoon season. Monthly Z trend analysis shows that for month of January

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large negative trend was reported in the whole country for base but for projected year positive trend was increased for January and larger negative trend for most of the study locations (Fig. 4).

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Larger trend fluctuations observed for February and March between the base (1996-2015) and projected (2041-2060) temporal scales. Highest positive trend was reported in these months for

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base but negative trend increased in projected year for most of the spatial scales of the study sites (Fig. 4). In upper Punjab and Baluchistan most of the sites reported to increase the negative trend

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according to the projection of the model similar results was reported by Basit et al., (2012).Khattak and Ali, (2015) reported the similar increasing trend in Punjab for monsoon

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season justifies the findings of current study as monsoon season reported increasing trends (more than 85 %) for under study meteorological stations.

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Figure 5for base and projected months (April, May, and June) show that rate of precipitation become more extensive in future and rate of negative trend was increased for most of the understudy meteorological stations. Mostly negative trend was observed for April with more extreme negative precipitation trend than May and June. Positive trend was reported for base and projected data for the months of July, August, and September with high rate of precipitation in projected year as shown in Fig. 6. Precipitation analysis by Imran et al., (2013) justifies these results over northern Pakistan. Highest negative trend was reported for all temporal and spatial scales in the month of December followed by November shown in Fig. 7. Spatial distributions for these months over upper parts of northern areas show negative trends for projected period 9

ACCEPTED MANUSCRIPT only in the whole country. These results confirmed by Dimri, (2006), Ghaffar and Javid, (2011), and Rasul, (2012) which found the statistically significant increasing trends for precipitation in northern areas of Pakistan. Monthly, seasonal, and annual percentages for the total understudy areas with significant increasing, decreasing, and no trend for base line data (1996-2015) are shown in Fig. 2 and for projected data Fig. 3. A number of meteorologically stations were studied by analysis of

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monthly, seasonal, and annual precipitation trends. More the 40 % of precipitation decreasing

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trend was reported in January, June, November, and December. While for the month of

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February, March, April, August, and September shows more increasing trend almost larger the 60%. Lowest decreasing trend was reported for monthly analysis for February, March,

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September, and August. In seasonal analysis large positive trend was observed for pre-monsoon and monsoon more than 70 % of weather stations. While decreasing trend was reported for

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winter and post-monsoon. Annual trends for base (1996-2015) showed almost 3% decreasing trend, 90% increasing trend and 7% for no trend in precipitation for all spatial scales. These

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results also verified by Ahmad et al., (2015) which shows significant positive trend for annual precipitation in Pakistan.

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While the overall results of projected (2041-2060) trends shows decreasing trend was increased for monthly, seasonal, and annual precipitation shown in Fig.3. While discussing the monthly

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trend lowest increasing trend was reported for April and highest for October. Almost 5 months (July to October) shows increasing trend for more than 80% of the weather stations. Decreasing

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trend was reported more than 50 % for January, March, April, and December for projected period (2041-2060). Seasonal analysis shows highest increasing trend was observed in monsoon

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followed by post-monsoon as shown in Fig. 3while highest decreasing trend was reported for winter and pre-monsoon. Similar results also reported by Abbas et al., (2013) that monsoon had reported wettest season which causes floods. Fig. 3 shows that projected (2041-2060) annual increasing trend for precipitation was decreased as compared to base (1996-2015) and decreasing trend is almost doubled in future. According to the Mann-Kendal seasonal and annual trend estimator (Z statistics) are shown in Fig. 8. Which reflects that projected (2041-2060) was reported high variations in precipitation increasing as compared to base (1996-2015). Similar statistical trend was also reported by Hartmann and Andresky, (2013) for most of the northern meteorological station of Pakistan. 10

ACCEPTED MANUSCRIPT According to the spatial trends Khairpur, Jacobabad, Lasbabela, Dadu, and Mithi meteorological stations shows highest increase in precipitation while Chitral showed lowest increase for annual and seasonal analysis expect winter as shown in Fig. 8. Sen’s Slop trend estimate at 95 % shows variation in trend for seasonal and annual analysis. Base (1996-2015) trends show very low variations with scattered trend for spatial scales but these values are quite close and high for projected (2041-2060) as shown in Fig. 9. Similar statistical analysis was also carried out in

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Pakistan by Ahmad et al., (2015) which show the similar increasing trend in precipitation.

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Conclusion

Objective of this study was to analyze the precipitation trend for different spatial and temporal

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scales in Pakistan. In order to find out the monthly, seasonal and annual precipitation data for 39 metrological stations has been analyzed by different statistical tools. Statistical down scaling

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method has been utilized for future precipitation projection. This study revealed the following

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results:

Monthly precipitation trend analysis in January and December shows larger negative

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trend for 80% of weather stations understudy and positive trends were reported in July for base (1996-2015) and Projected (2041-2060). At seasonal timescale, the decreasing trend in precipitation increased in pre-monsoon and

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monsoon for projected year. While for the base period, pre-monsoon and monsoon show

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increasing trend over 80 % of weather stations, and lowest increasing trend was reported in winter which was increased in projected years. Annual trend shows 3 - 6 % decreasing and 90 - 80 % increasing precipitation trend for

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base and projection years respectively for all spatial scales. 

Uneven precipitation trends in future may enhance the risk of serious prolonged floods in 76 % of under study weather stations and 14 %prone to droughts in projected precipitation trend (2041-2060).

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Our finding conform that different statistical trend analysis techniques combined for results representation of spatial and temporal interpolation has improved the authentication of trend detection. Which enable us for adoptive measures to sustainable

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ACCEPTED MANUSCRIPT agricultural activities and water management due to uneven behavior of precipitation in future. Acknowledgement First author is grateful to the International Global Change Institute (IGCI) Hamilton, New Zealand for providing software (SimCLIM 2013), required climatic dataset for future projections

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of Pakistan. Furthermore, the Second author (Wajid NASIM) is also thankful to "Government of

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Australia for Endeavour Research Fellowship, 4915_2015" and staff of Endeavour Fellowship

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especially case manager (Chris Tope), and staff (Jeremy Whish, Perry Poulton, John Hargreaves, Peter Thorburn and Dean Holzworth) of CSIRO, Toowoomba QLD, Australia for entire support

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during stay in Australia during 2015-2016.Morever, the author (Wajid NASIM) is also grateful to Higher Education Commission (HEC) and Pakistan Science Foundation (PSF) Pakistan, for

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partial funding.

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Reference

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Abbas, F., Ahmad, A., Safeeq, M., Ali, S., Saleem, F., Mohkum, H.F., Farhad, W., 2013. Changes in precipitation extremes over arid to semiarid and subhumid Punjab, Pakistan.

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Theor. Appl. Climatol. DOI 10.1007/s00704-013-0988-8. Ahmad, I., Tang, D., Wang, T.F., Wang, M., Wagan, B., 2015.Precipitation Trends over Time

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Using Mann-Kendall and Spearman’s rho Tests in Swat River Basin, Pakistan.Advances in

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Meteorology. http://dx.doi.org/10.1155/2015/431860. Ahmad, W., Fatima, A., Awan, U.K., Anwar, A., 2014. Analysis of long term meteorological trends in the middle and lower Indus basin of Pakistan a non-parametric statistical approach. Glob. Planet Change. 122, 282–291. http://dx.doi.org/10.1016/j.gloplacha. 2014.09.007. Alexander, L.V., 2016. Global observed long-term changes in temperature and precipitation extremes: A review of progress and limitations in IPCC assessments and beyond. Weather and Climate Extremes. 11, 4–16.

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ACCEPTED MANUSCRIPT Amin, A., Mubeen, M., Hammad, H.M., Nasim, W.,2015. Climate Smart Agriculture-a solution for sustainable future. Agric. Res. Commun. 2, 13–21. Amin, A., Nasim, W., Mubeen, M., et al., 2016. Regional climate assessment of precipitation and temperature in Southern Punjab (Pakistan) using SimCLIM climate model for different temporal scales.Theor. Appl.Climatol. DOI 10.1007/s00704-016-1960-1.

T

Amin, A.,Nasim, W., Mubeen, M., Nadeem, M., Ali, L., Rehman, M.H., Hammad, H.M., Wajid,

IP

S.A., Fahad, S., et al., 2017. Evaluating the Performance of CSM-CROPGRO-Cotton

CR

Model for Phosphorus Use in Cotton Crop Under Climatic Conditions of Southern Punjab, Pakistan. Environ. Sci. Pollut. Res. 24, 5811–5823. DOI 10.1007/s11356-016-8311-8.

US

Bao, Y., Hoogenboom, G.,McClendon, R., Urich, P., 2015.Soybean production in 2025 and 2050 in the southeastern USA based on the SimCLIM and the CSM-CROPGRO-Soybean

AN

models. Clim. Res. 63, 73–89. doi:10.3354/cr01281.

M

Basit, A., Shoaib, S.R., Irfan, N., Avila, R., 2012. Simulation of monsoon precipitation over South-Asia using RegCM3. ISRN Meteorology. 12, 754902. doi:10.5402/2012/754902.

ED

Chan, D., Wu, Q.G.,Jiang, G.X.,Dai, X.L.,2016. Projected shifts in K¨oppen climate zones over China and their temporal evolution in CMIP5 multi-model simulations. Adv. Atmos. Sci.

PT

33(3), 283–293. doi: 10.1007/s00376-015-5077-8.

CE

Cheema, S.B., Hanif, M., 2013. Seasonal precipitation variation over Punjab province.Pakistan Journal of Meteorology. 10(9), 61–82.

AC

Coquard, J., Duffy,P.B., Taylor,K.E.,Iorio, J.P.,2004. Present and future surface climate in the western USA as simulated by 15 global climate models. Clim. Dyn. 23, 455−472. DOI 10.1007/s00382-004-0437-6. Del, R´ıo. S., Iqbal, M.A., Cano-Ortiz, A., Herrero, L., Hassan, A., Penas, A., 2012.Recent mean temperature trends in Pakistan and links with teleconnection patterns.Int. J. Climatol. DOI: 10.1002/joc.3423. Dimri, A.P., 2006. Surface and upper air fields during extreme winter precipitation over the western Himalayas.Pure and AppliedGeophysics. 163(8), 1679–1698. 13

ACCEPTED MANUSCRIPT Ding, T.,Ke, Z., 2013. A Comparison of Statistical Approaches for Seasonal Precipitation Prediction. American Meteorological Society 28, 1116-1132. DOI: 10.1175/WAF-D-1200112.1. Fahad, S., Bano, A., 2012. Effect of salicylic acid on physiological and biochemical characterization of maize grown in saline area. Pak J Bot. 44,1433–1438 Saud, S., Wang, K., Xiong, D., Chen, C., Wu, C., Shah, F., Nie, L.,

T

Fahad, S., Chen, Y.,

IP

Huang, J., 2013. Ultraviolet radiation effect on photosynthetic pigments, biochemical

CR

attributes, antioxidant enzyme activity and hormonal contents of wheat. J Food, Agri Environ. 11 (3&4) , 1 6 3 5 - 1 6 4 1 .

US

Fahad, S., Hussain, S., Bano, A., Saud, S., Hassan, S., Shan, D., Khan, F.A., Khan, F., Chen, Y., Wu, C., Tabassum, M.A., Chun, M.X., Afzal, M., Jan, A., Jan, M.T., Huang, J., 2014a.

AN

Potential role of phytohormones and plant growth-promoting rhizobacteria in abiotic stresses: consequences for changing environment. Environ Sci Pollut Res. doi:10.1007

M

/s11356-014-3754-2

ED

Fahad, S., Hussain, S., Matloob, A., Khan, F.A., Khaliq, A., Saud, S., Hassan, S., Shan, D., Khan, F., Ullah, N., Faiq, M., Khan, M.R., Tareen, A.K., Khan, A., Ullah, A., Ullah, N.,

PT

Huang, J., 2014b. Phytohormones and plant responses to salinity stress: a review. Plant Growth Regul. DOI 10.1007/s10725-014-0013-y

CE

Fahad, S., Hussain, S., Saud, S., Hassan, S., Chauhan, B.S., Khan. F., et al. 2016a. Responses of rapid Viscoanalyzer profile and other Rice grain qualities to exogenously applied plant

AC

growth regulators under high day and high night temperatures. PLoS One 11(7), e0159590. doi:10.1371/journal.pone.0159590 Fahad, S., Hussain, S., Saud, S., Hassan, S., Ihsan, Z., Shah, A.N., Wu, C., Yousaf, M., Nasim, W., Alharby, H., Alghabari, F., Huang, J., 2016c. Exogenously applied plant growth regulators enhance the Morphophysiological growth and yield of Rice under high temperature. Front Plant Sci. 7, 1250. doi:10.3389/fpls.2016.01250 Fahad, S., Hussain, S., Saud, S., Hassan, S., Tanveer, M., Ihsan, M.Z., Shah, A.N., Ullah, A., Nasrullah, K.F., Ullah, S., Alharby, H., Nasim, W., Wu, C., Huang, J., 2016d. A combined 14

ACCEPTED MANUSCRIPT application of biochar and phosphorus alleviates heat-induced adversities on physiological, agronomical and quality attributes of rice. Plant Physiol Biochem. 103,191–198 Fahad, S., Hussain, S., Saud, S., Khan, F., Hassan, S., Jr, A., Nasim, W., Arif, M., Wang, F., Huang, J., 2016b. Exogenously applied plant growth regulators affect heat-stressed Rice pollens. J Agron Crop Sci. 202,139– 150.

T

Fahad, S., Hussain, S., Saud, S., Tanveer, M., Bajwa, A.A., Hassan, S., Shah, A.N., Ullah, A.,

IP

Wu, C., Khan, F.A., Shah, F., Ullah, S., Chen, Y., Huang, J., 2015a. A biochar application

CR

protects rice pollen from high-temperature stress. Plant Physiol Biochem. 96,281–287 Fahad, S., Nie, L., Chen, Y., Wu, C., Xiong, D., Saud, S., Hongyan, L., Cui, K., Huang, J.,

US

2015c. Crop plant hormones and environmental stress. Sustain Agric Rev 15:371–400

AN

Ghaffar, A., Javid, M., 2011. Impact of global warming on monsoon variability in Pakistan.The Journal of Animal andPlant Sciences21(1), 107–110.

M

Ghumman, A.R., Hassan, I., Khan, Q.U.Z., Kamal, M.A., 2012. Investigation of impact of environmental changes on precipitation pattern of Pakistan. Environ.Monit. Assess. DOI

ED

10.1007/s10661-012-2911-7.

PT

Gilbert, R.O.,1987. Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold, New York. ISBN 0-442-23050-8.

Pakistan.

CE

Hanif, M., Hayyat, A.K., Adnan, S., 2013. Latitudinal precipitation characteristics and trends in Journal

of

Hydrology.

492,

266–

AC

272.http://dx.doi.org/10.1016/j.jhydrol.2013.03.040. Hartmann, H., Andresky, L., 2013. Flooding in the Indus River basin—a spatiotemporal analysis of precipitation records.Global and Planetary Change. 107, 25–35. Hartmann, H., Buchanan H.,2014. Trends in extreme precipitation events in the indus River Basin and flooding in Pakistan. Atmosphere-Ocean. 52 (1), 77–91. Hasson, S., Pascale, S., Lucarini, V., B¨ohner, J., 2016. Seasonal cycle of precipitation over major river basins in South and Southeast Asia: A review of the CMIP5 climate models 15

ACCEPTED MANUSCRIPT data for present climate and future climate projections.Atmospheric Research. doi:10.1016/j.atmosres.2016.05.008. He, Z., Wu, R., Wang, W., 2015. Signals of the South China Sea summerrainfall variability in the Indian Ocean.Clim. Dyn. doi:10.1007/s00382-015-2760-5. Hong, C.C., Hsu, H.H., Lin,N.H.,Chiu, H.,2011. Roles of European blocking and tropical

T

extratropical interaction in the 2010 Pakistan flooding.Geo. Phys. Res. Lett.38, L13806

IP

doi:10.1029/2011GL047583.

CR

Houze, Jr. R.A., Rasmussen, K.L.,Medina, S.,Brodzik, S.R.,Romatschke, U.,2011. Anomalous atmospheric events leading to the summer 2010 floods in Pakistan. Bull. Amer. Meteor.

US

Soc.92, 291–298.doi:10.1175/2010BAMS3173.1.

AN

Imran, A.,Zaman, Q., Afzal, M.,2013. Temporal Trends in the Peak Monsoonal Precipitation Events over Northeast Pakistan. Pakistan Journal of Meteorology. 10(19), 19–30.

M

IPCC,2013. Climate Change 2013. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In:

ED

T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley, (Eds.), Cambridge University Press, Cambridge, United

PT

Kingdom and New York, NY, USA. Pp, 1535.

CE

IPCC,2014. Climate Change: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate

AC

Change. Cambridge, UK and New York, USA, Cambridge University Press.Pp, 1–32. Iqbal, M.A., Penas,A.,Cano-Ortiz, A.,Kersebaum, K.C.,Herrero, L.,Del Rio, S.,2016. Analysis of recent changes in maximum and minimum temperatures in Pakistan. Atmos. Res. 168, 234–249.http://dx.doi.org/10.1016/j.atmosres.2015.09.016. Jhajharia, D., Dinpashoh, Y., Kahya, E., Choudhary, R.R., Singh, V.P., 2014. Trends in temperature over Godavari River basin in Southern Peninsular India. Int. J. Climatol. 34 (5), 1369–1384. http://dx.doi.org/10.1002/joc.3761.

16

ACCEPTED MANUSCRIPT Kazmi, D.H., Li, J., Ruan, C., Sen, Z., Li, Y., 2016.A statistical downscaling model for summer rainfall over Pakistan Clim. Dyn. DOI 10.1007/s00382-016-2990-1. Kazmi, D.H., Li, J.P., Rasul, G., Tong, J., Ali, G., Cheema, S.B., Liu, L.L., Gemmer, M., Fischer, T., 2015. Statistical downscaling and future scenariogeneration of temperatures for Pakistan region. Theor. Appl. Climatol. 120, 341–350. doi:10.1007/s00704-014-1176-1.

T

Kazmi, D.H., Rasul, G., Li, J.P., Cheema, S.B., 2014. Comparative study forECHAM5 and

IP

SDSM in downscaling temperature for a geo-climaticallydiversified region, Pakistan. Appl.

CR

Math. doi:10.4236/am.2014.51016.

Khambhammettu, P., 2005. Mann-Kendall Analysis for the Fort Ord Site.Hydro Geo Logic,

US

Inc.–OU-1 2004 Annual Groundwater Monitoring Report – Former Fort Ord, California.Pp

AN

1-6.

Khattak, M.S., Ali, S., 2015. Assessment of temperature and rainfall trends in Punjab province of

M

Pakistan for the period 1961-2014. Journal of Himalayan Earth Sciences.48(2), 42–61. Khattak, M.S., Babel, M.S., Sharif, M., 2011. Hydro-meteorological trends in the upperIndus

ED

River basin in Pakistan. Clim. Res. 46, 103–119. http://dx.doi.org/10.3354/cr00957.

PT

Kumar, A., Houze,Jr.R.A., Rasmussen, K.L., Peters-Lidard, C.,2014. Simulation of a flash flooding storm at the steep edge of the Himalayas. J. Hydro. Meteorol. 15, 212–228.

CE

Lau, W.K., Kim, K.M., 2012. The 2010 Pakistan Flood and Russian Heat Wave: Teleconnection of Hydrometeorologic Extremes. Journal of Hydro meteorology.13, 392–403. doi:

AC

10.1175/JHM-D-11-016.1. Linderholm, H.W., Ou, T., Jeong, J-H., Folland, C.K., Gong, D., Liu, H., Liu, Y., Chen, D., 2011. Interannualteleconnections between thesummer North Atlantic Oscillation and the summer East AsianMonsoon. J.Geophys. Res. 116, D13107. Martius, O., Sodemann, H., Joos, H.,Pfahl, S., Winschall, A., Croci-Maspoli, M., Graf,M., Madonna, E., Mueller,B., Schemm,S., Sedláček,J., Sprenger,M., Wernli,H.,2013. The role of upper-level dynamics and surface processes for the Pakistan flood of July 2010.Quart. J. Roy. Meteor. Soc.139 (676), 1780–1797. DOI: 644 10.1002/qj.2082. 17

ACCEPTED MANUSCRIPT Moss, M.et al., 2010. The next generation of scenarios for climate change research and assessment. Nature. doi:10.1038/nature08823. Mubeen, M., Ahmad, A., Khaliq, T., Hammad, H.M., Sultana, S.R., Ahmad, S., Nasim, W., 2016. Application of CSM-CERES-maize model in optimizing irrigated conditions. Outlook on Agri. 45(3), 01–12.

T

Mubeen, M., Ahmad, A., Wajid, A., Khaliq, T., Bakhsh, A., 2013. Evaluating CSM-CERES-

IP

maize model for irrigation scheduling in semi-arid conditions of Punjab, Pakistan. Int. J.

CR

Agri. Bio. 15(1), 1–10.

Nasim, W., Ahmad, A., Ahmad, S., Nadeem, M., Masood, N., Shahid, M., Mubeen, M.,

US

Hoogenboom, G., 2017. Response of sunflower hybrids to nitrogen application grown

AN

under different Agro-environments. J.Plant Nutrition 40(1), 82–92. Nasim, W., Ahmad, A., Belhouchette, H., Hoogenboom, G., 2016a. Evaluation of the

M

OILCROP-SUN model for sunflower hybrids under different agro-meteorological conditions of Punjab-Pakistan. Field Crops Res. 188, 17–30.

ED

Nasim, W., Belhouchette, H., Ahmad, A., Rahman, M.H., Jabran, K., Ullah, K., Fahad, S.,Shakeel, M., Hoogenboom, G., 2016b. Modelling climate change impacts and adaptation

PT

strategies for sunflower in Punjab- Pakistan. Outlook on Agri. 45(1), 39–45.

CE

Nasim,W., Belhouchette, H., Tariq, M., Fahad, S., Hammad, H.M.,Mubeen, M., Abbas, T., 2015. Correlation studies on nitrogen for sunflower crop across the agroclimatic variability.

AC

Environ. Sci.Pollut. Res. 23(4), 3658–3670. Nie, J., Shaevitz, D.A.,Sobel, A.H., 2016.Forcings and feedbacks on convection in the 2010 Pakistan flood: Modeling extreme precipitation with interactive large-scale ascent. J. Adv. Model Earth Syst. doi:10.1002/2016MS000663. Priya, P., Mujumdar, M.,Sabin,T., Pascal,T., Krishnan,R.,2015. Impacts of Indo-Pacific sea surface temperature anomalies on the summer monsoon circulation and heavy precipitation over northwest India-Pakistan region during 2010. J. Climate. doi:10.1175/JCLI-D-1400595.1. 18

ACCEPTED MANUSCRIPT Rasmussen,

K.L.,

Hill,

A.J.,Toma,

V.E.,Zuluaga,M.D.,

Webster,

P.J.,Houze,Jr.R.A.,

2014.Multiscale analysis of three consecutive years of anomalous flooding in Pakistan.Q.J.R. Meteorol. Soc. 141, 1259–1276. Rasul, G., 2012. Climate Data and Modelling Analysis of the Indus Region, Pakistan Meteorological Department (PMD), 2012, http://wwfpak.org/ccap/pdf/Climate Data

T

Modelling%20Analysis% 20of%20the%20Indus%20Ecoregion.pdf.

IP

Rogelj, J., Meinshausen, M., Knutti, R., 2012. Global warming under old and new scenarios

CR

using IPCC climate sensitivity range estimates. Nature Climate Change. DOI: 10.1038/NCLIMATE1385.

US

Salmi, T., Maatta,A.,Anttila, P.,Ruoho-Airola, T.,Amnell, T.,2002. Detecting trends of annualvalues of atmospheric pollutants by the Mann–Kendall test and Sen’s slope

Rep.

AN

estimates—The Excel template application MAKESENS. Finnish Meteorological Institute 31,

35

pp.http://cdn.fmi.fi/legacy-fmi-fi-

M

content/documents/MAKESENS_MANUAL.pdf.

Assoc.63, 1379–1389.

ED

Sen, P.K.,1968. Estimates of the regression coefficient based on Kendall'stau. J. Am. Stat.

PT

Shahid, M.A., Boccardo, P., Cámaro, G.W., Albanese, A.,Cristofori, E., 2016. Evaluation of TRMM satellite data for mapping monthly precipitation in Pakistan.Imagining cultures of

CE

cooperation: Universities networking to face the new development challenges:Proceedings

AC

of the III CUCS Congress. 146–154. Sharma, L.K., Kanchan, B., Sharma, N., 2014.Towards computational model for climate change.International

Journal

of

Environmental

Sciences.

4(5),

816–

822.doi:10.6088/ijes.2014040404521. SimClim, 2011.SimCLIM Essentials, CLIMsystems Ltd., Hamilton New Zealand. Sohail, M.G., Farkhunda, B., 2013. Climate Change and Precipitation in Pakistan A Meteorological Prospect. Int. J. econ. Environ. geol. 4(2), 10–15.

19

ACCEPTED MANUSCRIPT Subash, N., Sikka, A.K.,2014. Trend analysis of rainfall and temperature and its relationship over India.Theor. Appl. Climatol. 117 (3-4), 449–462. http://dx.doi.org/10.1007/s00704013-1015-9. Taylor, K.E., Stouffer, R.J.,Meehl,G.A., 2012. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498.

T

Ullah, K., Shouting, G.,2013. A diagnostic study of convective environment leading to heavy

IP

rainfall during the summer monsoon 2010 over Pakistan. Atmospheric Research.120-121,

CR

226–239.

Wang, B., Lee, J.Y., Xiang, B., 2015. Asian summer monsoon rainfall predictability:a

US

predictable mode analysis. Clim. Dyn. 44, 61–74.doi:10.1007/s00382-014-2218-1.

AN

Warrick, R.A., 2007.SimCLIM: Recent developments of an integrated model for multi-scale, risk-based assessments of climate change impacts and adaptation. The 2007 ANZSEE Zealand

Society for

Ecological

Economics.

M

Conference Papers, Australia-New

www.anzsee.org/ 2007 conference/conference_papers.asp (accessed on 21 Jan 2015)

ED

Warrick, R.A., 2009. Using SimCLIM for modelling the impacts of climate extremes in a changing climate: a preliminary case study of household water harvesting in

PT

Southeast Queensland, 18th World IMACS / MODSIM Congress, Cairns, Australia 13-

CE

17 July, pp 2583–89.

Webster, P.J., Toma, V.E.,Kim, H.M.,2011. Were the 2010 Pakistan floods predictable.Geophys.

AC

Res. Lett. 38, L04806.doi:10.1029/2010GL046346. Wu, R.G., Yang, S., Wen, Z., Huang, G., Hu, K., 2012.Interdecadalchangein the relationship of southern China summer rainfall withtropical Indo-Pacific SST.Theor. Appl. Climatol. 108, 119–133.doi:10.1007/s00704-011-0519-4. Yin, C., Li, Y., Urich, P., 2013.SimCLIM 2013 Data Manual.CLIMsystems Ltd. 9 Achilles Rise, Flagstaff, Hamilton 3210 New Zealand. www.climsystems.com. Ying, K., Zhao, T., Quan, X.W., Zheng, X., Frederiksen, C.S., 2015. Interannualvariability of autumn to spring seasonal precipitation ineastern China. Clim. Dyn. 45(1–2), 253–271. 20

ACCEPTED MANUSCRIPT Ying, K., Zheng, X., Quan, X.W., Frederiksen, C.S., 2013. Predictable signalsof seasonal precipitation in the Yangtze-Huaihe river valley.Int. J.Climatol. 33, 3002–3015. doi:10.1002/joc.3644. Ying, K., Zheng, X., Zhao, T., Carsten, S., Frederiksen,C.S., Xiao‑ Wei, Q., 2016. Identifying the predictable and unpredictable patterns of spring‑ to‑ autumn precipitation over eastern

T

China.Clim. Dyn. DOI:10.1007/s00382-016-3258-5.

IP

Yousuf, I., Razzaq, A.G., Nisar, H.H., 2016. Optimally Sizing Small Hydropower Project Under

CR

Future Projected Flows. KSCE Journal of Civil Engineering. 00(0), 1-15. DOI 10.1007/s12205-016-1043-y.

US

Zahid, M., Rasul, G., 2011. Thermal classification of Pakistan. Atmos. Climate Sci. 1, 206–213.

AN

Zuo, J., Li, W., Sun, C., Xu, L., Ren, H., 2013. Impact of the North AtlanticSea surface temperature Tripole on the East Asian summerMonsoon. Adv. Atmos. Sci. 30(4), 1173–

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CE

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Fig.1 Graphical representation and regional description of study stations.

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Fig.2 Percentages of weather stations with increasing, decreasing, and no trends at a confidence level of 95% for Base (1996-2015). Projected (2041-2060) Precipitation Trend

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Fig.3 Percentages of weather stations with increasing, decreasing, and no trends at a confidence level of 95% for Projected (2041-2060).

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Fig. 4 Monthly (January to March) trend (Z) distribution (sign and magnitude) of precipitation showed for 1996-2015 (Base) and 2041-2060 (future projection) with 39 metrological stations. Labels refer to the slope of positive (Blue arrow) and negative (Red arrow) trends at confidence levelof 95%.

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0.07

-0.60

4.87

4.87 4.87

4.87

4.87

0.35

-0.35

-0.42

-0.28

-0.28

0.07

4.87

-4.87

4.87

- 4.87

-0.12

4.87

-4.87

4.87

1.47

-0.53

-0.53

4.87

-3.63

4.87

ED

0.46

1.40

AN

-0.88

0.46

0.51

May-Projected

1.19

0.77

1.19

0.12

0.51

4.87

0.77

1.12

4.87

4.87

1.86

May-Base

1.30

4.87

4.87

CR

1.61

4.87

US

0.98

4.87

4.87

2.31

T

-2.81

2.17

3.15

-4.87

-4.87

IP

1.89

-4.87

-4.87 -4.87

1.65

-3.94

-4.87 -4.87

1.61

1.89

1.47

-4.87

1.54

1.61

2.21

-4.87 0.84

1.51

-3.41

-4.87

4.87

-1.16

4.87

4.22

4.87

3.19

4.87 4.87

4.87

Fig. 5 Monthly (April to June) trend (Z) distribution (sign and magnitude) of precipitation showed for 1996-2015 (Base) and 2041-2060 (future projection) with 39 metrological stations. Labels refer to the slope of positive (Blue arrow) and negative (Red arrow) trends at confidence levelof 95%.

25

ACCEPTED MANUSCRIPT July-Base

1.05

0.00

0.21

0.00

0.00 -0.35

4.87

4.87

4.87

0.14

0.91

4.87

4.87

4.87

-3.63

0.49

4.87

4.87 4.87

0.63

4.87

4.87 4.87

4.87

0.28

0.46

4.87

4.87 4.87

0.14

0.91

-1.62

0.15

4.87

4.87

0.14

0.07 -0.07

0.56

4.78

0.98

0.28

0.91

0.28

4.33

4.87

4.87

0.98

0.00

-1.14

4.87

0.35

0.98

0.07

July-Projected 0.42

4.87

4.87

0.91

0.49

4.87

4.87

0.98

2.10

-0.49 -0.49

2.03

Aug-Projected

1.12

-0.77 -0.49

2.45

0.42 1.26

1.16

2.03

1.86

AN

2.31

2.24

2.24

1.19

1.05

1.02

1.12

0.00

-1.60

0.56

0.74

0.21

0.63 1.05

Sept-Base

2.80

3.08

1.33

1.02

0.49

-0.96

2.10

2.79

1.69

AC

3.15

1.05

1.16

0.98

0.98

CE

1.26

1.05

0.67

1.09

1.37

1.05

4.87

4.87

4.87 4.87

4.87 4.87

4.87

4.87

4.87

4.87

4.87

4.87 0.51

4.87

Sept-Projected

- 4.87

-4.87

4.87

4.87

1.89

1.47

4.87

0.98

4.87

4.87 4.87

4.87

4.87

4.87 4.87

4.87

0.98

4.87 2.31

4.87

1.96

4.87 4.87

4.87

4.87

2.10

4.87

4.87

4.87

4.87

4.87

4.87

4.87

4.87

4.87

4.87 4.87

-0.39

4.87

1.72 2.03

4.87

1.60

4.87

1.19

1.02

4.87 4.87

4.87

4.87

0.88

1.89

4.87

-3.63

4.87

4.87

1.79

1.51

4.87 4.87

4.87

2.87

PT

3.08

-4.22

4.87

4.62

4.87

4.87

4.87

0.77

- 4.87

4.87

4.87

4.87

ED

1.33

M

0.77

0.74

3.05

CR

1.19

1.68

1.75

0.81

4.87 0.51

1.68

1.51

4.87

4.87

1.26

Aug-Base

1.16

4.87

0.91 0.56

1.71

4.87

4.87

0.49

1.12

US

0.21

2.17

IP

1.33

T

4.87

4.87

1.47

4.87

4.87

4.87

4.87

4.87 0.51

4.87

Fig. 6 Monthly (July to September) trend (Z) distribution (sign and magnitude) of precipitation showed for 1996-2015 (Base) and 2041-2060 (future projection) with 39 metrological stations. Labels refer to the slope of positive (Blue arrow) and negative (Red arrow) trends at confidence levelof 95%.

26

ACCEPTED MANUSCRIPT Oct-Base

2.03

0.91

1.75

0.91

0.91 1.16

0.84

4.87 4.87

0.88

4.87

4.87

4.87

4.87

4.87

4.87

4.33

-0.58

4.87

4.87 4.87

4.87

-0.15

-0.32

4.87

4.87

4.87

0.07

0.59

4.87

4.87

0.88

0.99

0.64

4.87

0.11

1.40 0.95

-0.28

-2.95

4.87

4.87

0.84

0.98

0.63

4.22

- 4.87

1.16

-0.04

2.41

-4.87

1.54

0.84

1.40

Oct-Projected 1.51

4.22

4.87

0.21

4.87

4.22

4.87

-0.35

-0.46 4.87

-1.26

4.87

4.87

4.87

IP

0.88

4.87

-1.19

4.22

3.05

CR

0.50

-1.27

0.00

-0.35

T

4.87

Nov-Projected

1.05

-0.11

0.56

-0.11

0.07 0.70

-0.81 -0.81

-0.49

0.07

US

Nov-Base

-0.11 -0.81

-0.21

-0.18 -0.49 -0.21

-1.02

0.42

-1.23

-0.21

0.07

-0.07

AN

-0.53

0.25

-0.48

-0.11

-3.05

-0.35

-0.04

-1.27

-0.93 -0.14

-0.93

-0.11

0.00

-0.46

Dec-Base

PT

-0.67

-0.98

-0.67

-0.88

CE

-0.84

-0.42

-1.37

-0.91

-0.81

AC

-1.10

-0.94

-0.84

-0.67

-0.53

-0.15

4.87

1.98

4.33 4.87

4.87 4.87

4.87 4.87

4.87

4.87

4.87

4.87 4.87

4.87

Dec-Projected

1.78

4.87

4.22

-4.87

4.87

-1.47 - 4.87

-0.32 - 4.87

-0.67

- 4.87 3.05

- 4.87

- 4.87

- 4.87

- 4.87

- 4.87

-0.49

-0.56 -0.88

- 4.87

4.87

4.22

1.05

-1.12 -1.12

-0.47

-1.60

-0.12

4.33

- 4.87

4.87 - 4.87

3.63

4.87 4.87

0.54

4.87

-3.63

-4.87

0.21

2.95

- 4.87

4.22

2.06

ED

0.32

M

-1.50 0.50

1.03

3.94

-4.87

- 4.87

-1.23

0.84

- 4.87

-0.49 -0.91

- 4.87

- 4.87

- 4.87

-4.87

- 4.87

4.33

4.87 2.21

-0.61

- 4.87

- 4.87

0.16

4.87

-0.64

-0.83

-0.74

-1.05

-1.16

-0.71

4.87 -4.87

-0.67

3.23

-0.74

-0.98 -0.67

-4.87

-4.87

-4.87

4.87

4.87 4.87

4.87

-0.21

Fig. 7 Monthly (October to December) trend (Z) distribution (sign and magnitude) of precipitation showed for 1996-2015 (Base) and 2041-2060 (future projection) with 39 metrological stations. Labels refer to the slope of positive (Blue arrow) and negative (Red arrow) trends at confidence levelof 95%.

27

ACCEPTED MANUSCRIPT Base (1996-2015) Annual

Post-Monsoon

IP

T

Monsoon

US

CR

Pre-monsoon

Winter Annual

AN

Projected Projection(2041-2060) (2041-2060)

M

Post-Monsoon

ED

Monsoon

CE

AC

Chitral Bunji Skardu Peshawar Murree Kotli Muzaffarabad Mianwali Lahore Sargodha Multan Bahawalnagar Bahawalpur Barkhan Quetta Sibbi Kalat Khairpur Jacobabad Panjgur Hyderabad Karachi Hunza Islamabad Chakwal Gujranwala Toba Tek Singh Rahim Yar Khan Zhob Nok Kundi Lasbela Uthal Ormara Gwadar RCW Rohri Dadu Mithi Dir Bannu

Winter

PT

Pre-monsoon

Fig. 8 Comparison of Mann-Kendal trend estimator (Z statistic) seasonal and annual analysis for Base (1996-2015) and Projected (2041-2060) temporal and different spatial scales.

28

ACCEPTED MANUSCRIPT

2

Base 1996-2015

T

0

IP

-1

-2

CR

Sen's trend at 95 %

1

US

-3

-4

AN

Projected 2041-2060

M ED

0.0

PT

-0.2

-0.6

-0.8

CE

-0.4

AC

Sen's trend at 95 %

0.2

Winter

Pre-monsoon

Monsoon

Post-monsoon

Annual

Fig. 9 Comparison of Sen’s trend estimator (S statistic) seasonal and annual analysis for Base (1996-2015) and Projected (2041-2060) temporal and different spatial scales.

29

ACCEPTED MANUSCRIPT Highlights 1- Statistical downscaling model (SimCLIM) has used for future precipitation projection (2041-2060). 2- Ensemble approach combined with representative concentration pathways (RCPs) at medium level used for future projections.

T

3- Geo-statistical application used to generate precipitation trend maps.

IP

4- To generate future projection for precipitation trends (sign and magnitude) for 2041 to

CR

2060 on a different temporal and spatial scale to indicate the risk associated with rainfall. 5- To generate surface precipitation maps by applying geo statistical techniques in order to analyses the spatial distribution patterns of rainfall trends by statistical method to for

US

different temporal. Study indicates the future precipitation pattern in different agro climatic regions of the country enable us for adoptive measure for sustainable water

AC

CE

PT

ED

M

AN

management.

30