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P. O. Box No. 1214, Sector H-8/2, Pakistan Meteorological Department Journal of Meteorology Vol. 6; Issue 12 January, 2010 Pkit J l fMt l PAKISTAN JOURNAL OF METEOROLOGY Chief Editor: Dr. Qamar-uz-Zaman Chaudhry Editor: Dr. Ghulam Rasul
Board of Editors
Prof. Dr. Qin Dahe, Prof. Dr. Samuel Shen, Academician, Chinese Academy of Sciences, University of Alberta, Beijing, China. Canada.
Prof. Toshio Koike, Mr. Arif Mahmood, Tokyo University, Japan. Chief Meteorologist, PMD.
Prof. Gianni Tartari, Prof. S. K. Dube, CNR-IRSA, EV-K2-CNR Committee, Italy. Director IIT, India.
Dr. Zheng Guoguang, Administrator, China Dr.Muhammad Qaiser, Meteorological Administration (CMA). MSSP,PINSTECH, Islamabad. Dr. Bruce H. Raup, CIRES, University of Colorado, USA. Dr. Madan Lal Shrestha, Academician, Academy of Sciences, Prof. Dr. Shouting Gao, Nepal. Laboratory of Cloud-Precipitation Physics, IAP/CAS, China. Dr. Mona Lisa, Asst. Professor, Dept. of Earth Sciences, QAU, Dr. Muhammad Akram Kahlown, Islamabad. Chairman, PCRWR. Dr. Ghulam Rasul, Prof. Dr. Dev Raj Sikka, Chief Meteorologist, R&D, PMD. Chief Scientist, IMD, India.
Note: This is a six monthly journal of the Pakistan Meteorological Department which serves as a medium for the publication of original scientific research work in the field of Meteorology and related sciences. Views expressed in these papers are only those of the authors. Director General, Pakistan Meteorological Department or the Editor does not hold himself responsible for the statements and the views expressed by the authors in their papers.
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Pakistan Journal of Meteorology Vol. 6, Issue 12
Review of Advance in Research on Asian Summer Monsoon Ghulam Rasul1, Q.Z. Chaudhry1 Abstract The Asian monsoon is the most significant component of the global climate system. During recent two decades, concerned efforts have been made to investigate the Asian monsoon. A substantial achievement has been made in basic physical processes, predictability and prediction since the MONEX of 1978-79. The major advance in our understanding about of the variability of the Asian summer monsoon has been highlighted in this paper. The present paper is structured with four parts. The first part present the introduction, indicating the new regional division of the Asian monsoon system and significant events of the history of the Asian monsoon research. The second part discusses the annual cycle and seasonal march of the Asian monsoon as the mean state, with a special emphasis on the onset, propagation, active-break cycle and withdrawal of the Asian summer monsoon which takes place in the near-equatorial East Indian ocean-central and southern Indochina Peninsula. The third part deals with the multiple scale variability of the Asian summer monsoon, including the intraseasonal, interannual and inter-decadal variability. Their dominant modes such as 10-20 day and 30-60 day oscillations for the intraseasonal variability, the Tropospheric Bennial Oscillation (TBO), the Indian Ocean Dippole Mode (IODM) and teleconnection patterns for interannual variability and the 60-year oscillation for the inter-decadal variability, as well as related SST-monsoon relationship and land-monsoon relationship have been discussed in more details. The fourth part is the conclusion, summarizing the major findings and proposing future work.
Introduction The Asian monsoon is characterized with a distinct seasonal reversal of wind and clear partition between dry and wet season in the annual cycle, which is related with the seasonal reversal of the large-scale atmospheric heating and steady circulation features (Webster et al. 1998; Ding and Chan 2005; Trenberth et al. 2006). About two decades ago, the Asian monsoon was mainly viewed as the Indian or the South Asian monsoon (ISM) in the English literature. The summer monsoon over East Asia was just thought of as the northward extension of the Indian monsoon, on the one hand, and on the other hand, the summer monsoon over the Western North Pacific (WNPSM) was fully taken to be the eastward extension of the Indian monsoon (Ding 1994). However, now it has been increasingly realized that the Asian monsoon system should further extend to incorporate these two regions due to similar features of monsoon climate, as a large amount of literatures has been contributed to the study of the East Asian summer monsoon (EASM) (Chang 2004) and the WNPSM in recent two decades (Murakami and Matsumoto 1994; Ueda and Yasunari 1996; Wu and Wang 2001; Wu 2002; Wang and Lin 2002; Wang et al. 2005a; Wang et al. 2005c). Thus, the Asian-Pacific monsoon is demarcated into three sub-systems: the Indian summer monsoon, the western North Pacific summer monsoon and the East Asian summer monsoon The EASM domain defined by Wang and Lin (2002) includes the region of 20-45°N and 110-140°E, covering eastern China, Korea, Japan and the adjacent marginal seas. This definition does not fully agree with the conventional notion used by Chinese meteorologists (Tao and Chen 1987; Ding 1994; Ding and Chan 2005), who usually includes the South China Sea (SCS) in the EASM region. Wang and Lin (2002) believe that the ISM and WNPSM are tropical monsoons in which the low level winds reverse from winter easterlies to summer westerlies, whereas the EASM is a sub-tropical monsoon in which the low- level winds reverse primarily from winter northerlies to summer southerlies. However, if the SCS region is included in the EASM, the EASM should be a hybrid type of tropical and subtropical monsoon. In the ISM region, the monsoon or seasonal changes of winds and rainfall could be interpreted as a result of northward seasonal migration of the east-west oriented precipitation belt accompanied by the inter- tropical convergence zone (ITCZ or TCZ), while in the EASM region the seasonal march of the monsoon is displayed in the northward excursion of the planetary frontal zone or Meiyu-Baiu frontal system. So, one of main differences between these two monsoon sub-regions is the different effect of mid-and high
1 Pakistan Meteorological Department
1 Review of Advance in Research on Asian Summer Monsoon Vol. 6 latitude events. In the WNPSM region, the ITCZ is the dominating weather system which is the major birthplace of typhoons and tropical convective systems. At the upper level, the Asian monsoon region is dominated by the Tropical Easterly Jet (TEJ) to the south of the huge South Asian high. The major monsoon rainfalls are located in the right sector of the jet entrance zone where the upper-level divergence and low-level convergence, i.e. upward motion is observed (Fig. 1b,c) (Hoskins and Wang 2006; Chen et al. 2006). In short, the Asian monsoon system is a huge monsoon system and constitutes an essential part of the Asian-Australian monsoon system. On the other hand, it is closely related to the African monsoon system through the TEJ. Research on the Asian monsoon has a long history (Webster 2006), but the substantial progress has been made since 1960's through a number of international and regional monsoon projects and field experiments such as the International Indian Ocean Expedition (IIOE) in the mid-1960s and in 1975-1976, the FGGE Monsoon Experiments (MONEX) in 1978-1979 (Krishnamurti 1985), the Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE) in 1992-1993 (Webster and Lukas 1992), the GEWEX Asian Monsoon Experiment (GAME) in 1996-2000 (GAME ISP, 1998), the South China Sea Monsoon Experiment (SCSMEX) in 1996-2000 (Lau et al. 2000; Ding and Liu 2001; Johnson et al. 2004), the Bay of Bengal Monsoon Experiment (BOMEX) in 1999 (Bhat et al. 2001) and the Joint Air-Sea Monsoon Interactive Experiment (JASMINE) in 1999 and the Arabian Sea Monsoon Experiment (ARMEX) in 2002 (Webster et al.2002). At the same time, a number of books, monographs and review papers written in English summerizing the major scientific achievements of the Asian monsoon in different periods have been published, including Monsoon Meteorology (Ramage 1971), Southwest Monsoon (Rao 1976), Monsoons (Fein and Stephens 1987), Monsoon Meteorology (Chang and Krishnamurti 1987), Monsoon over China (Ding 1994), The East Asian Monsoon (Chang 2004), The Global Monsoon System (Chang et al. 2005) and The Asian Monsoon (Wang 2006). They provide international scientific communities with continuously updated input of knowledge of observations, processes, dynamics, prediction and socio- economic impact of the Asian monsoon. Now, it has been realized that the Asian monsoon can not only have significant disastrous events, but also can exert very important influences on the global climate system and the global climate prediction through, e.g., the tropical-extratropical interaction, the monsoon- ENSO relationship and the global teleconnection (Webster et al. 2005b; Ding and Wang 2005; Wang et al. 2005a). During recent two decades, a more and more attention has been paid to study the Asian monsoon. Recent studies on the Asian monsoon have been devoted to the following aspects: (1)
Annual cycle and seasonal march of the Asian monsoon The onset and the seasonal march of the Asian summer monsoon The Asian monsoon shows a strong annual cycle which distinguishes it from the other monsoon systems over the world that have much weaker annual cycles. Observations indicate that the largest amplitude of the annual cycle of precipitation, zonal and meridional winds at low-level occurs in the Asian monsoon regions where there is the strongest atmospheric heating or heat sources driving the monsoon. In the process of the annual cycle, the onset of Asian summer monsoon is a key indicator characterizing the abrupt transition from the dry season to the rainy season and subsequent seasonal change (Lin and Wang 2002; Qian et al. 2002; Wang and Ding 2006). Numerous investigators have studied this problem from the regional perspectives. It is to some extent difficult to obtain a unified and consistent picture of the climatological onset of the Asian summer monsoon in different regions due to differences in data, monsoon indices and definitions of monsoon onset used in these investigations. Ding (2004) has summarized the climatological dates of the onset of the Asian summer monsoon in different monsoon regions based on various sources, with dividing the whole onset process into four stages: Stage 1 (late in April or early in May): the earliest onset in the continental Asia is often observed in the central Indochina Peninsula late in April and early in May. Over the ocean, one may trace earlier onset in the near-equatorial East Indian Ocean in late April. Stage 2
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(from mid- to late May): this stage is characterized by the areal extension of the summer monsoon, advancing northward up to the Bay of Bengal and eastward down to the South China Sea. Stage 3 (from the early to the middle June): this stage is well known for the onset of the Indian summer monsoon and the arrival of the East Asian rainy season such as the Meiyu over the Yangtze River Basin and the Baiu season in Japan. At the same time, the summer monsoon over the western North Pacific extends from the SCS to the southwestern Phillipine Sea, accompanied by the increase in convective cloudiness and the eastward shift of the ITCZ (Wu and Wang 2001; Wu 2002). Stage 4 (the early and middle July): the summer monsoon at this stage advances up to Northwest India, Pakistan North China, the Korean Peninsula (so-called Changma rainy season) and even Central Japan. After mid-July, the WNPSM and associated ITCZ further marches northeastward as monsoon rainfall and active convection abruptly penetrate to the region of 25°N, 150°-160°E in Pentad 42 (July 25-29) (Ueda et al. 1995). This is the maturing process of the WNPSM which may maintain till late September, the major period of the typhoon season of the western North Pacific.Summer mons (Ueda and Yasunari 1996; Wu and Wang 2001;/ rasul et at.2004; Wang et al. 2005c, 2006). The earliest onset occurs in the end of April (the 24th pentad) in the near-equatorial East Indian Ocean and Sumatra. Then the onset process propagates northeastward and northwestward, respectively. In western coast of the Indian subcontinent, the onset begins first across the Kerala coast, normally by the end of May or early June (Ananthakrishnan and Soman 1988) when heavy rains lash the coastal state after the cross-equatorial low-level jet is established across the Somali jet into the near-equatorial Arabian Sea and a cyclonic vortex, so-called onset vortex (Krishnamurti et al. 1981) is often observed. Then the Indian summer monsoon gradually advances northward across the western Indian subcontinent and eventually merges with the summer monsoon simultaneously propagating northwestward from the Bay of Bengal. By the middle of July the whole of the Indian subcontinent comes under the grip of summer monsoon. Therefore, the onset of the Asian summer monsoon is one of most important events, as a singularity of annual cycle of the monsoon. Furthermore, the onset process of the summer monsoon in a certain location or region is very rapid or even abrupt, with taking a couple of days or one week to complete the dramatic change from dry season to rainy season. It clearly shows abrupt changes in rainfalls, OLR and 850hPa zonal wind around the onset dates for the South China Sea, the Indochina Peninsula and the Indian subcontinent, respectively. The rob of convective activity during the onset process is very significant The abrupt increase in precipitation after the summer onset was previously indicated by various investigators, e.g., Ananthakrishnan and Soman (1989) for south Kerala, Matsumoto (1997) for the central Indochina Peninsula and Tao and Chen (1987) for the SCS. The onset of the Asian summer monsoon brings with it a dramatic change in large-scale circulation features and weather situation. Numerous investigators have examined this problem from climatological and synoptic perspectives based on the large-scale wind, geopotential height, precipitation and OLR patterns (Lau and Yang 1997; Matsumoto 1997; Fong and Wang 2001; Ding and Liu 2001; Ding and Sun 2001; Wang and Lin 2002; Liu et al. 2002; Goswami 2005c; Zhang et al. 2004; Ding and Liu 2006a). Their detailed description will not be given, and instead, based on these studies, the sequence chain of significant events during the onset of the Asian summer wind may be identified below:
A. The development of a cross-equatiorial current in the equatorial East Indian Ocean (80-90°E) and off the Somali coast, and the rapid seasonal enhancement of heat sources over the Indochina Peninsula, South China, Tibetan Plateau, and neighboring oceanic areas, thus leading to seasonal reversal of sign of the tropospheric meridional temperature gradient in the Asian monsoon region. B. The acceleration of low-level westerly wind in the tropical East Indian ocean; C. Formation of a mid-tropospheric shear zone across the central Bay of Bengal to the Southeast
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Arabian Sea in which may be embedded a cyclonic vortex in most of cases, which may even intensify into a cyclonic storm either in the Bay of Bengal or the Southeast Arabian Sea (the onset vortex). D. The eastward and northward expansion of tropical monsoon from the tropical East Indian Ocean, initiating the arrival of the summer monsoon and associated rainy season in the regions of the Bay of Bengal and Indochina Peninsula with involvement of impacts from mid-latitudes; E. The significant weakening, breaking around the Bay of Bengal and eastward retreat of the main body of the subtropical high, and eventual onset of the SCS summer monsoon with convective clouds, rainfall, low-level southwesterly wind and upper-level northeasterly wind suddenly developing in this region. F. The ITCZ or TCZ usually used in the South Asian region and accompanying zonally oriented precipitation belt rapidly moves from a mean position about 5°S in winter and early spring to about 20°N in northern summer with the progress of the monsoon onset in the Indo-pak subcontinent. G. Significant development and northwestward movement of the South Asian high at 200hPa, thus allows the establishment of the tropical easterly jet on its southern flank and rapid northward jump of the upper-level westerly jet on its northern flank over the northern Tibetan Plateau. This signals the start of rainy season and noth eastern Pakistan as well as Kashmir nomally get first monsoon shower during first week of July (Rasu etat 2003). Numerous Scientists have studied the problem on generation and maintenance of deep tropospheric heat source in the north which is a central issue of the onset of the Asian summer monsoon. They have found that the heating over the Tibetan Plateau plays an important role in the seasonal evolution of the meridional gradient of heating and in triggering the onset of the Asian summer monsoon (Murakami and Ding 1982; Ding 1992a; Yanai et al. 1992; Wu and Zhang 1998; Ueda and Yasunari 1998; Zhang et al. 2004; Yanai and Wu 2006). Goswami (2005c) used the evolution of the mean temperature of the tropospheric layer between 200hPa and 700hPa averaged between 30°E and 110°E to illustrate the seasonal evolution of the meridional gradient of tropospheric heating. The change in the sign of meridional gradient of the tropopheric temperature ushers in the setting up of an off- equatorial large-scale deep heat source. The monsoon onset itself is a problem of internal atmospheric dynamic involving the instability of the basic flow in tropics. However, the change in the large-scale heat source is a necessary prerequisite. The atmospheric response to such a heat source leads to cross- equatorial flow and strengthening of the low-level westerlies. This causes the zero absolute vorticity line in lower atmosphere to move north of the equator to about 5°N facilitating symmetric inertial instability (Tomas and Webster 1997; Krishna Kumar and Lau 1998). The change in the sign of meridional gradient of tropospheric temperature thus makes the circulation conductive for symmetric instability that forces frictional boundary layer convergence, overcomes the inhibition from the subsidence above the planetary boundary layer over the Indian continent and the Bay of Bengal caused by compensating subsiding air of the convection over the Maritime Continent during April and May and capping effect of southward flow of dry and colder air on pre-existing moist and warm air, and leads to explosive development of off-equatorial convection over India and the Bay of Bengal and the subsequent onset of the Indian summer monsoon. The reversal of the meridional temperature gradient has a significant regional difference, with the earliest reversal taking place in the region from the eastern part of the Bay of Bengal to the Indochina Peninsula in the early May (Wu and Zhang 1998; Zhang et al. 2004; Ding and Liu 2006a). And this reversal process was first found in the upper troposphere and then it rapidly propagated downward, mainly through vigorous convective vertical transport of strong sensible and latent heat through
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(Yanai et al. 1973; He et al. 1987; Yanai et al. 1992; Li and Yanai 1996; He et al. 2006). The sensible heat driven air-pumping effect also plays a considerable role in vertical heat transport (Yanai and Wu 2006). Then, the reversal process extends eastward to the SCS (the 4th pentad of May) and westward to the Indian Peninsula (the 1st-2nd pentad of June). Timing of the large-scale reversal of the meridional temperature gradient is fully consistent with onset dates of the summer monsoon in these regions. The cause why the earliest reversal of the meridional gradient occurs in the Indochina Peninsula rather than the Indian Peninsula is attributed to earlier occurrence of sensible and latent heating and vigorous convective activity over the land of Indochina Peninsula under favorable effect of upward motion ahead of the Indian-Burma trough. In contrast, at this time the Indian Peninsula is located behind the trough where downward motion suppresses the development of convection (Wu and Zhang 1998; Zhang and Qian 2002; He et al. 2006; Ding and Liu 2006a). The summer monsoon progress over the Western North Pacific (WNP) cannot be explained by the change in the tropospheric heat source that reflects the land-sea thermal contrast. The results obtained by most of investigators suggest that the summer monsoon advance over the WNP may result from air-sea interaction. The key is to understand the role of SST change and SST gradient as well as related feedback processes. The zonal asymmetry in SST between western and eastern Pacific along 10°-20°N is thought to be a leading factor for the development of the WNP summer monsoon (e.g., Murakami and Matsumoto 1994). Ueda and Yasunari (1996) indicated the importance of the development of a warm SST tongue around 20°N and 150°-160°E in early July. The northeastward monsoon advance over the WNP seems to follow the northeastward extension of the warm SST tongue (Wu and Wang 2001) which is created by the cloud-radiation and wind-evaporation feedback processes. Monsoon-induced changes in cloudiness and surface wind produce contrasting changes in surface short-wave radiation, and latent heat flux between the convection and pre-convection region. The resulting SST tendency difference turns around the SST gradient east of the convection region in about one month, and induces the northeastward shift of the highest SST center. Under this condition, the convective instability and low-level moisture convergence increase, thus triggering convection and atmospheric destabilization and subsequent monsoon onset. After the onset of the Asian summer monsoon, the monsoonal rainfall, the TCZ or ITCZ and other
properties or variables (e.g., Psudo equivalent potential temperatureθse ) assume significant northward advection, but with quite marked regional differences. In India, the seasonal advance of the monsoon gradually proceeds northwards. However, the northward progress is not a fully smooth affair as well and takes place often in surge or in stages, sandwiiched by periods of weakening or stagnation of monsoon activity (Gadgil and Kumar 2006). For each surge or stage, the advance process is accompanied by a synoptic-scale disturbance pushing the leading boundary of monsoon either northward or westward. Along the Indo-Ganetic Plains the advance of the monsoon occurs from east to west and is associated with the formation of 2 or 3 monsoon depressions/lows (Ding and Sikka 2006). The monsoon advances takes nearly 3 to 4 weeks to cover the entire Gangetic Plains. Thus, the advance of the monsoon over the entire subcontinent is a rather slow process taking on average nearly 40-45 days from its start off Kerala around 31 May to its culmination by mid-July over central Pakistan. Pakistan receives summer monsoon associated with southeasterly current in the north which set up in late June or early July and southwesterly current in south established in middle of July (Rasul et at 2004). Onset over Kashmir and northeast Pakistan takes simultaneously on 1st July with a deviation error of 15 days.. Similarly on southern half of the country, rains start around 15 July with 16 days standard deviation( Rasul et at 2005). The ITCZ or monsoon trough and accompanying major rain belt in this region move farther northward compared to the ITCZ in the WNPSM region and reach the northernmost latitude (~25°N) in mid-July. The climatological pentad- mean maximum rainfall rate (10-12 mm day-1) occurs in early June. Note also that another rain belt is located at 5°S, and the Indian monsoon rain has a close linkage with the equatorial convection
5 Review of Advance in Research on Asian Summer Monsoon Vol. 6 throughout the entire summer. On the other hand, in the East Asian-West Pacific regions, the ITCZ or the tropical rainfall belt is located in the latitudinal range of 5~25°N, after the onset of the summer monsoon. There is no such near-equatorial convective band as in the Indian region. The maximum intensity and the northernmost location (~25°N) are observed in August when the subtropical high over the western North Pacific considerably moves northward. North of the ITCZ, there is another significant seasonal rain belt which is separate from the ITCZ and propagates northward in a stepwise way, not in continuous way. Numerous studies have demonstrated that it generally undergoes three standing stages and two stages of abrupt northward shifts (Ding 1992b). These stepwise northward jumps are closely related to seasonal changes in the general circulation in East Asia, mainly evolution of the planetary frontal zone or the Meiyu-Baiu frontal zone, the upper-level westerly jet stream and the subtropical high over the western North Pacific. The peak rainy seasons tend to occur primarily in three standing stages i.e., the first standing period from May to mid-June for the pre-summer rainy season in South China and Taiwan, the second standing period from mid-June to mid-July for the Meiyu-Baiu-Changma rainy season, and the third standing period from mid-July to mid-August for the rainy season in North and Northeast China and tropical western North Pacific. From the end of August to early September the monsoon rain belt rapidly moves back to South China, with most of the eastern part of China dominated by a dry spell, which symbolizes the termination of the East Asian summer monsoon. Therefore, in the EASM region, the monsoon rainy season has a shorter duration and weaker precipitation intensity than the ISM and the WNPSM which generally end in the late September and the late October, respectively (Li et al. 2005; Wang et al. 2005a,c). In addition, in the EASM region, three separate low-level westerly wind systems originating in the mid-latitude, subtropical and tropical regions, respectively, actually exist, and their interaction sets a large-scale stage for occurrence of many significant weather and climate events in this region that is very unique in the world monsoon climate zone. Active-break cycle and retreat of the Asian summer monsoon The active and break periods of the monsoon are characterized by precipitation maxima and minima over South Asia, depending on the season (Webster et al. 1998). These periods are thought to be associated with shifts in the location of the monsoon trough in India (Krishnamurti and Ardanuy 1980; Ding and Sikka 2006). During the active period of the monsoon, the trough is generally located in the central and northern Indian Peninsula. The break monsoon phenomenon is a reverse of the active monsoon spell over central and northern India. During the monsoon break the monsoon trough hugs the Himalayan rim, the low-level easterlies disappear entirely along the Indo-Gangetic plains and are replaced by west-northwest flow along the periphery of the Himalayas, resulting in decrease of rainfall over much of India, but enhanced rainfall in the far north and south. These anomalies are large-scale and extend across the entire South Asia. Active and break cycles vary in duration and may last for a few days and weeks. They are significant variations on the intra-seasonal scale to cause alterations between wet spells and dry spells. The phenomenon of break monsoon has been of interest because prolonged droughts often occur during intense break. For example, a prolonged break situation in the peak monsoon of July in 2002 not only resulted in rainfall deficit of the month of July, but also caused a seasonal-scale drought over the whole country in India (Gadgil and Joseph 2003; Gadgil and Kumar 2006). The break and active periods clearly demonstrate the two modes of the South Asian monsoon system at the intra-seasonal oscillation. The meridional shear of the low-level zonal wind and cyclonic vorticity at 850hPa are significantly enhanced (weakened) during an active (break) phase of the ISO. Hence, conditions for cyclone genesis are more favorable during an active phase compared to a break phase. Based on the analysis of genesis and tracks of low pressure systems (LPS) for 40 years (1954-1993), Goswami et al. (2003) show that genesis of the LPS is nearly 3.5 times more favorable during an active condition compared to a break condition of the monsoon (Fig. 5b). They also show that the LPS are spatially strongly clustered to be along the monsoon trough region during an active phase.
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The above result show that there is an association of active and break periods of the monsoon with the ISO. Numerous investigators have studied this problem, with a special emphasis on the role of 30-50 day oscillation (MJO mode) and 10-20 day oscillation in modulating the active-break cycle of the summer monsoon (Yasunari 1979; Sikka and Gadgil 1980; Krishnamurti and Surgi 1987; Goswami 2005c). Their observations have revealed that the active and break period of the Indian summer monsoon or the wet and dry spells over the Indian continent are manifestation of repeated northward propagation of the TCZ or the monsoon trough from the equatorial position to the continental position and results from superposition of 10-20-day and 30-60-day oscillations. Krishnamurti and Ardanuy (1980) examined the 10 to 20-day waves at 20°N, utilizing 30 years of sea level pressure. They noted that alternate passage of troughs and ridges of these west-propagating oscillations modulated the pressure of the monsoon trough over northern India, and there is a strong relationship between the arrival of the ridges of these waves over the central India and occurrence of breaks in the monsoon. On the other hand, the arrival of ridges of northward and eastward propagating 30-50-day mode over the region of a climatologiacal monsoon trough has been known to coincide with the periods of occurrence of breaks in the summer monsoon (Yasunari 1980; krishnamurti 1985). The nearly simultaneous arrival (phase-locking) of ridges of these two ISO modes appeared to modulate the pressure of the monsoon trough considerably. Recently, a number of investigators have found similarities in the ISO and the inter-annual oscillation of monsoon system (Krishnamurthy and Shukla 2000; Lawrence and Webster 2001). This is not surprising as it is due to the dominance of two modes of active and break of monsoon activity in the monsoon season (Ding and Sikka 2006). The breaks in the monsoon also occurs under mid-latitude interaction in which case a westerly trough in the upper-middle troposphere and the associated western disturbance in the lower troposphere greatly influence the activity of the monsoon trough over the Indo-Pakistan region. The western disturbances from mid-latitude can shift the monsoon trough to the foothills of the Himalayas, thus leading to the break in the monsoon when the monsoon has already remained active over central and northern India for 10-15 days and it is time for intra-seasonal reversal of the monsoon activity in terms of rainfall as the ridge phase of the ISO is approached over the northern Bay of Bengal (Ding and Sikka 2006). In the East Asian monsoon region, the similar active-break cycle of the summer monsoon is climatologically observed. After the pre-summer rainy season in May and early June in South China and the Meiyu-Baiu rainy season from the early June to mid-July in the Yangtze River basin and Japan are terminated, breaks of the monsoon rainy period occur, respectively. Breaks of different spans are observed in South China, Taiwan, central East China, Northeast China and Korea (Chen et al. 2004; Wang and Ding 2006). Therefore, the monsoon rainfall variation during the warm season in East Asia is generally characterized by two active rainfall periods separated by a break spell. The northward passage of the Meiyu-Baiu rain band is followed by a break spell (monsoon break) which also propagates northward. Then the monsoon rainfall revival after the break is clearly observed. Chen et al. (2004) has shown that the monsoon revival in East Asia is caused by different mechanism associated with the development of other monsoon circulation components including the ITCZ, typhoons, and weather systems in mid-latitudes. Therefore, the seasonal variations with the dual peak in South China and Southwest China and the triple peak in the Yangtze River basin can be observed. In the Yangtze River basin, in addition to two peaks associated with northward advance and southward retreat of the summer monsoon, respectively, the first peak occurring before the onset of the summer monsoon in East Asia (the 25th pentad) is produced by frontal systems (the spring rainy season). It seems that the active-break cycle of the monsoon is mainly observed in the region to the south of the Yangtze River where the rainfall events are greatly affected by tropical and subtropical summer monsoon. Farther northward, only single peak of rainfalls is observed in North China, implying that the active-break cycle is not very marked. For Changma rainy season in Korea, the peak rainfall occurs in the period of late June-mid-July. Afterwards, a short break is generally observed in the late July. Starting from mid-and late August, the revival of monsoon rainy period is also observed
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(Chen et al.2004). This second rain spell is not long and it maintains until early September, forming the autumn rainy season in Korea (Qian et al. 2002). In Japan, the autumn rainy season or the Shurin season (Akisama) generally takes place during the period of September 6-26 after the Baiu season (e.g., Matsumoto 1988). Previously, the Shurin frontal zone is regarded as the polar frontal zone with strong temperature gradient in both Japan and China, but according to the later study made by Matsumoto (1988), the Shurin frontal zone is characterized by a weak thermal gradient (~4℃/1000㎞) and a strong moisture gradient (~5g/㎏/1000㎞) to the west of 130°E along the southern coast of Japan like the Baiu frontal zone, with little sunshine observed (Inoue and Matsumoto 2003). However, the stagnating nature of the Shurin frontal zone was less pronounced than the Baiu frontal zone. During the Shurin season, the mid-latitude westerlies show a blocking pattern over Eastern Eurasia. In China, the autumn rainy season begins in early September and ends in mid-October, with pronounced occurrence over western China and the Yangtze River delta (Kao and Kuo 1958). This rainy season causes the second and third peaks of precipitation in southwest China and the Yangtze River basin, respectively. The end of the autumn season, accompanied by the southward shift of the subtropical jet stream over the Tibetan Plateau, is nearly simultaneous with the end of the Indian summer monsoon (Yeh et al. 1958). The retreat of the Asian summer monsoon has been earliest observed in East Asia, which generally starts from the 44th pentad (6-10 August). The process of the retreat is very rapid, taking only one month or even less to retreat from northern to southern China. Two pentads later, the low-level southwesterlies disappear in the region to the south of the Yangtze River basin. Early in September, the leading zone of the summer monsoon quickly withdraws southward to the northern part of the South China Sea and then is stationary there, making the end of the summer monsoon in East Asia. Matsumoto (1997) also indicated that the monsoon westerlies are already replaced by easterlies in the northern part of the South China Sea in early September, while monsoonal westerlies are still dominant over the Indochina Peninsula, until late October when the summer in East Asia fully ends over Southeast Asia. So, the life cycle of the activity of the East Asian summer monsoon is about four months from May to early September. Pakistan western border makes the western boundary of summer monsoon and withdrawal starts in the first weeks of September. In almost a decade monsoon rolls back to the northwestern India which is generally associated whith a quick shift of westrlies to the south of mid-latitudes (Rasul et at 2005) The retreat of the Indian summer monsoon begins in the western parts of the Northern Indian state of Rajasthan in early September (Gadgil and Kumar 2006). Thus, the effective duration of the southwest monsoon rains over this area is only about one and a half month. The southward retreat of the summer monsoon rains continues rapidly until about the middle of October, by which time it withdraws completely for the northern half of the Indian Peninsula. During the period of October and December, major parts of South Asia is generally dry, only with the south and southwest peninsular of Indian and Sri Lamka receiving significant amounts of rainfall. By the end of December when the northeast monsoon blows with its full strength over the northern Indian Ocean, the rainy season has practically ended (Matsumoto 1990; Ding and Sikka 2006). In the WNPSM region, the summer monsoon retreats latest, generally in the end of October. Therefore, the summer monsoon season has a life cycle of about five months from June to October in this region (Li et al. 2005; Wang et al. 2005c).
8 Issue 12 Ghulam Rasul, Q. Z. Chaudhry
References: Chang, C.-p. (Ed), 2004: The East Asian Monsoon. World Scientific, Singapore, 564pp. Chang, C.-P., B. Wang and N.-C.G. Lau (Eds), 2005: The Global Monsoon System: Research and Forecast. WMO/TD No.1266 (TMPR Report No. 70). 542pp. Chang, C.-P., and T.N Krishnamurti (Eds), 1987: Monsoon Meteorology (Oxford Monographs on Geology and Geophys. No. 7). Oxford University Press, 544 pp. Chen, H., Y.H. Ding, and J.H He, 2006: The structure and variations of tropical easterly jet and its relationship with the monsoon rainfall in Asia and Africa. Accepted by Chinese J.Atmos. Sci. Chen, T.C., S.Y. Wang, W.-R. Huang, and M.-C. Yen, 2004: Variations of the East Asian summer j.Climate, 17,2271-2290. Ding, Y.H., 1994: Monsoons over China, Kluwer Academic Publisher, Dordrecht/ Boston/London, 419pp. Ding, Y.H and C.L. Chan, 2005: The East Asian Summer monsoon: an overview. Meteor. Atoms Phys., 89, 117-142. Fein, J.S. and P.L. Stephens, (Eds), 1987:Monsoons. Wiley Interscience Publication. John Wiley and Sons, New York, 632. PP. Hoskins, B. and B. Wang , 2006 : Large-scale atmospheric dynamics. In: The Asian Monsoon. Ed. by Bin Wang, Praxis Publishing Ltd, Chi Chester, UK, 787pp. Johnson, R.H., H.p. Ciesielski, and T.D. Keenan, 2004: Oceanic East Asian monsoon convection: Results from the 1998 SCSMEX. In : The East Asian Monsoon. Ed. by C.-.P. Chang. World Scientific, Singapore, 436-459. Krishnamurti, T.N., 1985: Summer Monsoon Experiment-A review. Mon. Wea. Rev., 113, 1590-1626. Lau, K.-M., Y.H. Ding, J.T. Wang, R. Johnson, T.Keenan, R. Cifelli, J. Gerlach, O.Thiele, T.Rickenbach, S.C Tsay et al.,2000: A report of the field operation and early results of the South China Sea Monsoon Experiment (SCSMEX). Bull. Amer. Meteor. Soc.81, 1261-1270. Murakami, T., and J. Matsumoto, 1994: Summer monsoon over the Asian continent and western North Pacific. J . Meteor. Soc.Japan, 70,597-611. Ramage, C.S., 1971: Monsoon Meteorology (Int. Gephys. Ser. Vol.15), Academic Press, San Diego, California, 296 pp. Rao, Y.P., 1976: Southwest Monsoon (Meteorological Monograph, Synoptic Meteorology No.1),Indian Meteorological Department, New Delhi, 367 pp. Shouting Gao, Xingrong Wang, and Yushu Zhou, Generation of generalized moist potential vorticity in a frictionless and moist adiabatic flow, Geophys. Res. Lett., 31-(2004), L12113 doi: 10.1029/2003 GL019152 Tao, S.Y. and L.X. Chen, 1987: A review of recent research on the East Asian monsoon in China In: Monsoon Meteorology. Ed. by C.-P. Chang and T.N. Krishnamurti, Oxford University Press, London, 60- 92. Trenberth, K.E., J.W. Hurrell, and D.P. Stepaniak, 2006: The Asian Monsoon: Global perspectives. In: The Asian monsoon: Ed. by bin Wang, Praxis Publishing Ltd., Chichester, UK, 781pp. Ueda, H. and T. Yasunari, 1996: Maturing process of summer monsoon over the western North Pacific- A coupled Ocean/ Atmosphere system. J. Meteor . Soc. Japan, 74, 493-508 (JMSJ award paper).
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Wang, B. and Lin Ho, 2002: Rainy seasons of the Asian-Pacific monsoon. J. Climate, 15, 386-396. Wang, B., T. Li Y.H. Ding, R.H Zhang and H.J Wang, 2005: East Asian-Western North Pacific monsoon: A distinctive component of the Asian-Australian monsoon system. In: The Global Monsoon Systemt: Research and Forcast. Ed. by C.-P Chang, Bin Wang and N.-C.G Lau, WMO/TD No. 1266 (TMRP Report No. 70), 72-79. Webster, P.J., 2005: Oceans and monsoons, In The global Monsoon System: Research and Forecast. Ed. by C.P Chang, Bin Wang and N-C.G.Lau, WMO/TD NO.1266,253-298.
Webster, P.J., E.F. Bradley, C.W. Fairall, J.S Godfrey, P.Hacker, R.A Houze Jr, R. Lukas, Y. Serra, J.M Humman, T.D.M Lawrence et al., 2002: The JASMINE pilot study. Bull.Amer, Meteorol. Soc.83 1603-1630. Webster, P.J and R. Lukas, 1992: The Coupled Ocean-Atmospheric Response Experiment. Bull. Amer Meteor. Soc. 73, 1377-1416. Webster, P.J., V.O. Magana, T.N. Palmer, et al., 1998: Monsoon: Process , predictablility, and the prospects for forecast. J.Geophy. Res., 103, 14454-14510. Wu, R., 2002: Process for the northeastward advance of the summer monsoon over the western North Pacific.J. Meteor. Soc. Japan, 80, 67-83. Wu, R. and B. Wang, 2001: Multi-stage onset of the summer monsoon over the western North pacific. Clim. Dyn., 17,277-289.
10 Pakistan Journal of Meteorology Vol. 6, Issue 12
The Rainfall Activity and Temperatures Distribution Over NWFP during the Pre- Monsoon Season (April to June) 2009 Syed Mushtaq Ali Shah1, Alam Zeb1, Shahzad Mahmood1 Abstract In this report, changes in the rainfall activity, minimum, maximum and mean temperatures have been studied on monthly as well as on seasonal basis during the pre-monsoon season (April-June) 2009. The data was collected from 11 meteorological observatories located in NWFP. By comparing with the climatic normal values of 1971- 2000 , it has been found that the rainfall was largely above normal during April, slightly below normal during May and normal in June. As a whole, it remained slightly above normal during the pre monsoon season across the region. The minimum temperature remained slightly below normal during April and June and normal during May throughout the region. As a whole, it remained normal during the season in the area. The maximum temperature remained slightly below normal during April and normal during the months of May and June respectively. As a whole, it remained normal throughout the season across the region. As a result, the mean temperature remained normal during the study period across the region.
Introduction: Pre monsoon season (April-June) is a transition period from the winter circulation to the monsoon circulation in the region. During the season, westerly waves shift northwards and relatively the frequency of western disturbances become less. It remains active over the northern parts of the region with the decreasing frequency of occurrence as compared to the peak winter months. Sometimes, due to intense solar heating, mesoscale convective systems dominate over the plains and mountainous areas. As a result, heavy downpour associated with hailstorm and thunderstorms occur. Heat lows or troughs are a prominent climatologically features of many arid land areas of the country during the warmer months, especially in low latitudes where insulation is at its peak, for example in Balochistan and adjoining areas. They are shallow disturbances, generally confined below 700 mb. Heat troughs are important for day-to-day weather forecasting as they influence the location of regions of low- level convergence, which in turn are likely at places where convective storms will be triggered if the converging air happens to be sufficiently moist. The study has been conducted regarding the analysis of rainfall, minimum, maximum and mean temperature data for the pre monsoon period 2009. The data was collected from the 11 stations of meteorological observing network of Pakistan Meteorological Department (Chitral, Drosh, Mir Khani, Dir, Balakot, Kakul, Cherat, Peshawar, Kohat, Parachinar, Bannu and D.I.Khan) located in the North West Frontier Province (N.W.F.P.). Two meteorological observatories, Kalam and Saidu Sharif remained close due to law and order situation in the area. The climatic normal value for Bannu meteorological observatory was calculated on the basis of available data for 13 years (1997-2009).
Methods & Materials: Mean monthly rainfall data (Table-1), minimum, maximum and mean temperatures data (Tables-2, 3 and 4) for the months April through June from all observatories, situated in NWFP was collected and used for the analysis. Percentage departures of the rainfall and departures of minimum, maximum and mean temperatures were calculated for each month as well as for the season as a whole and the graphs were also drawn.
1 Pakistan Meteorological Department
11 The Rainfall Activity and Temperatures Distribution over NWFP …. Vol. 6
Result and Discussions: Monthly Features of Rainfall Distribution: 3.1.1. April, 09: During the month rainfall was in large excess at six meteorological observing stations (Kakul, Cherat, Peshawar, Parachinar, Kohat and Bannu); moderate excess at two stations (Chitral and Balakot); slightly excess at one station (Dir); normal at one station (Drosh) and slight deficit at one station (D.I.Khan). As a whole, the rainfall was largely above normal at a number of places across the region during the month. The heaviest amount of rainfall was recorded 71.0 mm on 17th April, 2009 at Dir. Figure 1 shows normal and actual whereas Figure 2 illustrates percentage departures from the normal.
ACTUAL & NORMAL RAIN FALL (APRIL, 2009) 250.0 ) m 200.0 (m
L 150.0 L
A 100.0 N F 50.0 RAI 0.0 t l r t t r r n h a a a o a s a r h n kul Di k o t w h i nnu era i a a a h la h K Ko Dr c h K B s C Ch a Ba DI
r
Actual Normal Pe
STATIONS Pa
Figure 1
% DPARTURE FROM NORMAL RAIN FALL (APRIL,2009) 200.0
150.0
p 100.0 e
% D 50.0
0.0 l t l t r t u h a u ar ar a an r n s Di t -50.0 n i n o er i ako oha aw l Kh Kak K Dr a h Ba Ch Ch DI s B ach r e a P % DEPARTURE STATIONS P
Figure 2
12 Issue 12 Mushtaq Ali Shah, Alam Zeb, Shahzad Mahmood
May, 09: The Rainfall was in large excess at one station ( Peshawar); slight excess at one station (Parachinar); normal at one station (Cherat); slight deficit at two stations ( Chitral and Dir ); moderate deficit at four stations (Drosh, Balakot, Kohat and Bannu) and in large deficit at two stations (Kakul and D.I.Khan). As a whole, the rainfall was slightly below normal throughout the region during the month. The heaviest amount of rainfall was recorded 41.0 mm on 5th May, 2009 at Dir. Figure 3 shows normal and actual whereas Figure 4 illustrates percentage departures from the normal.
ACTUAL & NORMAL RAIN FALL (MAY, 2009) 120.0
) 100.0 m
(m 80.0 L
L 60.0 A
F
N 40.0 I A 20.0 R 0.0 l t l r r t t r h a a o a an r ha n Di ku k os t w h i nnu era i a a a l h K Dr Ko h ch Ka B s C Ch Ba DI ra Actual Normal a STATIONS Pe P Figure 3
% DEPARTURE FRO M NO RMAL RAIN FALL (MAY,2009) 100.0 80.0 60.0 40.0
20.0
p e 0.0 t t l t r r r n h % D a o a a a s -20.0 a r h k kul o Di n h w t nnu era
i i a a a a h K -40.0 h Dr Ko h K c B C s Ch a Bal DI r
-60.0 Pe -80.0 Pa -100.0
STATIONS % DEPARTURE
Figure 4
June, 09 The Rainfall was in large excess at two meteorological stations (Chitral and Parachinar); moderate excess at two stations (Drosh and Kohat); normal at one station (Dir); slight deficit at two stations ( Balakot and Kakul); moderate deficit at one station ( Cherat) and in large deficit at three stations (Peshawar, Bannu and D.I.Khan). As a whole, the rainfall was normal during the month throughout the region. The heaviest amount of rainfall was recorded 25.0 mm on 2nd June, 2009 at Parachinar.
13 The Rainfall Activity and Temperatures Distribution over NWFP …. Vol. 6
Figure 5 shows normal and actual, whereas Figure 6 illustrates percentage departures from the normal.
ACTUAL & NORMAL RAIN FALL (JUNE, 2009)
120.0
m 100.0 m 80.0 60.0 FALL ( N 40.0
RAI 20.0 0.0 l t r t r n h a ar a a o s a r n h kul t k o Di nnu w h i erat a a a a l h K h Ko Dr K B Ch C s Ba rachi DI
Actual Normal a STATIONS Pe P Figure 5
% DEPARTURE FROM NORMAL RAIN FALL (JUNE, 2009) 200.0 150.0 100.0
p 50.0 e
% D 0.0 t t r t l n r u h a o a a ar a s n h k r n kul h Di o n t i era a i la aw K
-50.0 h Ko ch h K Dr Ba C s Ch DI Ba
e ra P -100.0 a P -150.0
STATIONS % DEPARTURE
Figure 6
Seasonal Rainfall (April-June, 2009): During the season, rainfall was in large excess at three meteorological observing stations (Peshawar, Cherat and Parachinar); moderate excess at one stations (Chitral); slight excess at four stations (Kakul, Kohat and Bannu); normal at three stations (Drosh, Dir and Balakot) and in moderate deficit at one station ( D.I.Khan). As a whole, Precipitation was slightly above normal over the region during the pre-monsoon season. The heaviest amount of rainfall was recorded 367.0 mm at Parachinar during the season. The mean monthly rainfall data with normal and percentage departures from the normal are shown in Figures 7 & 8.
14 Issue 12 Mushtaq Ali Shah, Alam Zeb, Shahzad Mahmood
Sesonal Rainfall Apr,09 - Jun,09 140.0 120.0 ) 100.0 m
(m 80.0 l l fa
n 60.0 i
a
R 40.0 20.0 0.0 April 09 May 09 June 09
Months ACTUAL NORMAL
Figure 7
Sesonal % Departure From Normal Rainfall Apr,09 - Jun, 09 60.0 50.0 40.0 30.0 p
e 20.0 10.0 % D 0.0 -10.0 April 09 May 09 June 09 -20.0 -30.0
Months % DEPARTURE
Figure 8
Monthly features of Minimum Temperature Distribution: April, 09: During the month, minimum temperature remained slightly below normal at one station (Parachinar), and normal at eleven stations (Chitral, Drosh, Dir, Balakot, Kakul, Cherat, Peshawar, Kohat, Bannu and D.I.Khan). As a whole, it remained normal almost at all places of the region during the month. The month’s lowest minimum temperature was 3.0° C recorded at Parachinar on 8th April, 2009. Figure 09 shows normal & actual, whereas figure 10 illustrates departures from the normal.
15 The Rainfall Activity and Temperatures Distribution over NWFP …. Vol. 6
ACTUAL & NORMAL MINTEMP (APRIL, 2009) 40.0 35.0
) 30.0 C º ( 25.0 MP
E 20.0
T
N 15.0 MI 10.0 5.0 0.0 l l t t r r u n h a a u ar a o at a s r n Di k h k t n o i n i er a l aw h Kh h Ka Ko Dr c Ba Ch C sh DI a Ba r e
P STATIONS Pa Actual Normal Figure 9
DEPARTURE FROM NORM AL MINTEMP (April 09) 3.0 2.0
) 1.0 P
M 0.0 l l t t r TE u n h a a at u ar ar N r a s n I Di
-1.0 k t h n i er o i n ako M aw l h Kh Ka Ko a Dr
-2.0 Ch C Ba p ( DI ach B e r esh D a
-3.0 P P -4.0 -5.0
STATIONS DEPARTURE
Figure 10 May, 09: During the month, minimum temperature remained normal at ten stations (Chitral, Drosh, Dir, Balakot, Kakul, Cherat, Peshawar, Bannu and D.I.Khan); slightly above normal at Kohat and appreciably above normal at Parachinar. As a whole, it remained normal almost at all places of the region during the month. The month’s lowest minimum temperature was 5.0 °C recorded at Chitral on 6th May, 2009. Figure 11 shows normal & actual, whereas figure 12 illustrates departures from the normal.
16 Issue 12 Mushtaq Ali Shah, Alam Zeb, Shahzad Mahmood
ACTUAL & NORMAL MINTEMP (MAY, 2009)
45.0 40.0 35.0 ) C º
( 30.0
25.0 MP E 20.0 T N 15.0 MI 10.0 5.0 0.0 l t l t r u n h a ar ar at a r n s Di t n w i n o er i aku ako oha a l
Kh h K K Dr a Ba Ch C DI B ach r esh a Actual Normal STATIONS P P
Figure 11
DEPARTURE FROM NORM AL MINTEMP May 09
3.5 3.0 2.5 )
P 2.0
M 1.5 TE N I
M 1.0
p ( e
D 0.5 0.0 r t l l t t h r u n a a a s a a r n Di r h
-0.5 t o n h i e n aku i ako awar l K h Dr K Ko a Ba Ch Ch -1.0 c DI B a esh r P Pa STATIONS DEPARTURE
Figure 12 June, 09: During the month, minimum temperature remained appreciably below normal at one station (Drosh); slightly below normal at seven stations (Chitral, Dir, Kakul, Cherat, Peshawar and Bannu), normal at three stations (Balakot, Kohat and D.I.Khan) and slightly above normal at one station (Parachinar). As a whole, it remained slightly below normal at a number of places across the region during the month. The month’s lowest minimum temperature was recorded 9.0 °C at Dir on 7th June, 2009. Figure 13 shows normal & actual, whereas figure 14 illustrates departures from the normal.
17 The Rainfall Activity and Temperatures Distribution over NWFP …. Vol. 6
ACTUAL & NORMAL MINTEMP (JUNE, 2009)
45.0 40.0
) 35.0 C º
( 30.0 25.0 MP
E 20.0 T N 15.0 10.0 MI 5.0 0.0 l t l t r u n h
a ar ar at a r s n Di r t n h i o e i ako oha aw K Kaku K Dr Ban Ch Ch
DI Bal ach r esh a Actual Normal STATIONS P P Figure 13
DEPARTURE FROM NORM AL MINTEMP (June,2009)
3.0
2.0
) 1.0 P
M
E 0.0 T l l t t r r
u n h a a u o ar a at a r N s n
-1.0 Di h I k t n i o n er i
a aw l Kh h h Kak Ko Dr Ba c
-2.0 Ch C DI Ba a r esh EP (M
P
D -3.0 Pa -4.0 -5.0 STATIONS DEPARTURE
Figure 14
Seasonal Minimum Temperature (April-June, 2009): During the season, minimum temperature remained normal at eleven stations (Chitral, Drosh, Dir, Balakot, Cherat, Peshawar, Kohat, Parachinar, Bannu and D.I.Khan) and slightly below normal at one station (Kakul). As a whole, it remained normal almost at all places across the region during the season. The season’s lowest minimum temperature was 3.0° C recorded at Parachinar on 8th April, 2009. Mean monthly minimum temperatures with normal & departures are shown in Figures 15 & 16.
18 Issue 12 Mushtaq Ali Shah, Alam Zeb, Shahzad Mahmood
Sesonal Minimum Temp Apr,09 - Jun,09 40.0
) 35.0 C º
( 30.0
p
m 25.0
Te 20.0 n i
M 15.0 n
a 10.0 e M 5.0 0.0 April 09 May 09 June 09
Months ACTUAL NORMAL
Figure 15
Sesonal Departure From Normal M inimum Temp Apr,09 - Jun,09 1.5
1.0 p
m 0.5 n Te i 0.0 M
n April 09 May 09 June 09 a -0.5 e
p M -1.0 e D -1.5
-2.0
Months DEPARTURE
Figure 16
Monthly features of Maximum Temperature Distribution: April, 09: During the month, maximum temperature remained slightly above normal at one station (Kohat); normal at four stations (Balakot, Kakul, Cherat and D.I.Khan); slightly below normal at four stations (Chitral, Dir, Peshawar and Bannu); appreciably below normal at two stations (Drosh and Parachinar). As a whole, it remained slightly below normal during the month throughout the region. The month’s highest maximum temperature was 38.5° C recorded at Kohat on 30th April, 2009. Figure 17 shows normal & actual, whereas figure 18 illustrates departures from the normal.
19 The Rainfall Activity and Temperatures Distribution over NWFP …. Vol. 6
ACTUAL & NORMAL MAX TEMP APRIL, 2009)
20.0 18.0 16.0 )
C 14.0 º (
P 12.0 M
E 10.0 8.0 AX T
M 6.0
4.0 2.0 0.0 t l l t t r r r n h u a a o u a a a s a n r r k Di o n h w t n e
i i a a l K Koh Dr h Kak ch Ba Ch s Ch Ba DI ra Pe a Actual Normal P STATIONS
Figure 17
DEPARTURE FROM NORMAL MAX TEMP (APRIL,2009)
1.0 0.5
) P 0.0 M l l t t r r t E n h a u ar a a a T s r r Di
k
t
-0.5 o i nnu in e ako oha aw l a Kh Ka K AX a Dr ch B Ch Ch sh DI a B M -1.0 r e
( a P P -1.5 P DE -2.0 -2.5
STATIONS DEPARTURE
Figure 18
May, 09: During the month, maximum temperature remained slightly above normal at four stations (Dir, Kakul, Cherat and Kohat); normal at seven stations (Chitral, Drosh, Balakot, Peshawar, Parachinar, Bannu and D.I.Khan). As a whole, it remained normal in the area during the month. The month’s highest maximum temperature was 44.5 °C recorded at D.I.Khan on 18th May, 2009. Figure 19 shows normal & actual, whereas figure 20 illustrates departures from the normal.
20 Issue 12 Mushtaq Ali Shah, Alam Zeb, Shahzad Mahmood
ACTUAL & NORMAL MAX TEMP (MAY, 2009) 30.0
25.0 ) C º
( 20.0 MP
E 15.0
T X 10.0 MA 5.0 0.0 l l t t r n h a at ar ar r a s Di t n i er o i nnu aku ako oha aw l h a
Kh K K a Dr Ch C B DI ach B r esh a P
P Actual Normal STATIONS
Figure 19
DEPARTURE FROM NORMAL MAX TEMP (MAY,2009)
4.0
3.0
) P
M 2.0 E T 1.0 AX M (
P 0.0 t r l l t h n a DE at s ar ar a r Di t o n h er i nnu aku -1.0 i ako oha aw a l K h Dr K K a B Ch C
DI B ach esh -2.0 r a P STATIONS P DEPARTURE
Figure 20
June, 09: During the month, maximum temperature remained slightly above normal at one station (Kohat); normal at six stations (Kakul, Cherat, Peshawar, Parachinar, Bannu and D.I.Khan); slightly below normal at two stations (Dir and Balakot) and appreciably below normal at two stations (Chitral and Drosh). As a whole, it remained normal throughout the region during the month. The month’s highest maximum temperature was recorded 47.5 °C at Kohat on 27th June, 2009. Figure 21 shows normal & actual, whereas figure 22 illustrates departures from the normal.
21 The Rainfall Activity and Temperatures Distribution over NWFP …. Vol. 6
ACTUAL & NORM AL MAX TEMP (JUNE, 2009) 30.0
25.0 ) C º
( 20.0 MP
E 15.0
T
X 10.0
MA 5.0 0.0 l t l t r u n h a a ar ar at a r n s Di h t n h i n o er i aku ako aw l K h K Ko Dr a Ba ch Ch C DI B a r esh
a STATIONS P Actual Normal P
Figure 21
DEPARTURE FROM NORMAL MAX TEMP (JUNE, 2009) 4.0
3.0
) 2.0 P
M 1.0 E
T 0.0 X l t l t r A u n h a a ar ar at
-1.0 a r n s Di M h t n
h i n o er i aku ( ako aw l K -2.0 h K P Ko Dr a Ba Ch C DI B ach DE r
-3.0 esh a P
-4.0 P
-5.0
STATIONS DEPARTURE
Figure 22
Seasonal Maximum Temperature (April-June, 2009): During the season, maximum temperature remained slightly above normal at one station (Kohat); normal at seven stations (Dir, Balakot, Kakul, Cherat, Peshawar, Parachinar, Bannu and D.I.Khan) and slightly below normal at two stations (Chitral and Drosh). As a whole, it remained normal across the region during the month. The season’s highest maximum temperature was 47.5 °C recorded at Kohat on 27th June, 2009. Mean monthly maximum temperatures with normal & departures are shown in Figures 23 & 24.
22 Issue 12 Mushtaq Ali Shah, Alam Zeb, Shahzad Mahmood
Sesonal Maximum Temp Apr,09 - Jun,09 25.0
) 20.0 C º (
p m 15.0 e T
ax 10.0
M an e 5.0 M
0.0 April 09 May 09 June 09 Months ACTUAL NORMAL
Figure 23
Sesonal Departure From Normal Maximum Temp Apr,09 - Jun, 09 1.0 0.5 ) p m
e 0.0
April 09 May 09 June 09 ax T
M -0.5
ean
M -1.0 ( p
e
D -1.5
-2.0 Months
DEPARTURE
Figure 24
Monthly Features of Mean Temperature Distribution: April, 09: During the month, mean temperature remained normal at six observing stations (Balakot, Kakul, Dir, Cherat, Peshawar, and Kohat); slightly below normal at five stations (Chitral, Drosh, Mir Khani, Parachinar and Bannu) and appreciably below normal at one station (D.I.Khan). As a whole, it remained slightly below normal throughout the region during the month. Figure 25 shows normal and actual whereas figure 26 illustrates departures from the normal.
23 The Rainfall Activity and Temperatures Distribution over NWFP …. Vol. 6
Mean Temp & Normal Mean April, 2009
30.0
25.0 C (º
P 20.0
M E 15.0 T n a 10.0 Me 5.0 0.0 t t r t l n r u h o a a ar a s n ha k r n kul h Di o n t
i era a i aw l K h Ko ch h Dr Ka Ba C s Ch DI Ba
Mean Temp Normal Mean e ra P STATIONS a P Figure 25
DEPARTURE FROM NORMAL MEAN (April, 2009) 2.0
1.0
0.0 l l t t t r n r u h a u
a a a ar n s r p r Di k t h n n o i e w i ako -1.0 oha l K De a Ka
K Dr a Ba Ch Ch DI B sh ach r -2.0 e a P P -3.0
-4.0 STATIONS DEPARTURE
Figure 26 May, 09: During the month, mean temperature remained slightly above normal at three observing stations (Cherat, Parachinar and Kohat); normal at eight stations (Chitral, Drosh, Dir, Balakot, Kakul, Peshawar, Bannu and D.I.Khan) and slightly below normal at one station (Mir Khani).As a whole, it remained normal throughout the region during the month. Figure 27 shows normal and actual whereas figure 28 illustrates departures from the normal.
24 Issue 12 Mushtaq Ali Shah, Alam Zeb, Shahzad Mahmood
Mean Temp & Normal Mean May, 2009
35.0 30.0
C º (
25.0 P 20.0
TEM
n 15.0 a e 10.0 M 5.0 0.0 t t r l t l r n r u
h a o a a u a a a s n h k r r n h o Di t w n i e a i a h K
Ko c h Dr Kak Ba Ch s Ch a DI Bal r Mean Temp Normal Mean STATIONS Pe Pa
Figure 27
DEPARTURE FROM NORMAL MEAN (May, 2009) 2.5
2.0
1.5
1.0 Dep 0.5 0.0 t t r t l l r n r u h a o a a a u a a s n k r n k
-0.5 h o Di n w t i er a
i a l K Koh ch h Dr Ka Ba Ch s Ch DI Ba
ra Pe a STATIONS P DEPARTURE
Figure 28 June, 09: During the month, mean temperature remained slightly above normal at one observing station (Parachinar); normal at five stations (Kakul, Cherat, Kohat, Bannu and D.I.Khan); slightly below normal at five stations (Chitral, Mir Khani, Dir, Balakot and Peshawar) and appreciably below normal at one station (Drosh). As a whole, it remained normal throughout the region during the month. Figure 29 shows normal and actual whereas figure 30 illustrates departures from the normal.
25 The Rainfall Activity and Temperatures Distribution over NWFP …. Vol. 6
Mean Temp & Normal Mean
June, 2009 40.0 35.0 C º 30.0 (
P 25.0
20.0 TEM
n
a 15.0 e
M 10.0 5.0 0.0 t
t r l l t n r r h a o a u a a a a s h k r r n k h Di o nnu w t i e a i l a a K Ko ch h Dr Ka B Ch s Ch DI Ba
ra Pe Mean Temp Normal Mean STATIONS a P Figure 29
DEPARTURE FROM NORMAL MEAN (June, 2009) 2.0 1.0 0.0 l t r r t t n u h a a a
o ar s a n r
-1.0 h n kul Di p k o t h i n
era i a aw la h h K De Dr Ko c h K Ba s -2.0 C Ch a Ba e DI r -3.0 P Pa -4.0
-5.0
STATIONS DEPARTURE
Figure 30
Seasonal Mean Temperature (April-June, 2009): During the season, mean temperature remained slightly above normal at one station (Kohat); normal at seven observing stations (Dir, Balakot, Kakul, Cherat, Peshawar, Parachinar and Bannu) and slightly below normal at four stations (Chitral, Drosh, Mir Khani and D.I.Khan). As a whole, it remained normal throughout the region during the season. Figure 31 shows normal and actual whereas figure 32 illustrates departures from the normal.
26 Issue 12 Mushtaq Ali Shah, Alam Zeb, Shahzad Mahmood
Sesonal Mean Temp
Apr, 09 - Jun, 09 30.0
25.0 )
C º
( 20.0 p m e 15.0 T n a 10.0 Me 5.0
0.0 April 09 May 09 June 09 Months Mean Temp Normal Mean
Figure 31
Sesonal Departure From Normal Mean Apr,09 - Jun,09
1.0
0.5
p m e T
0.0 n
a e April 09 May 09 June 09
M -0.5
p
De -1.0
-1.5 Months DEPARTURE
Figure 32 Conclusion During the month of April, moderate to heavy precipitation occurred in the region causing drop in the mean temperature during the month. As a result the month remained relatively cooler than normal. Below normal precipitation was received in most parts of the province during the month of May causing no change in the mean temperature and was remained normal across the region. Variable amount of rainfall was received over the region during the month of June and consequently mean temperature remained normal over most parts of the region. Consequently, during the pre-monsoon (April-June) 2009 season, NWFP received slightly above normal rainfall and also the mean temperature remained normal during the study period across the region.
27 The Rainfall Activity and Temperatures Distribution over NWFP …. Vol. 6
Recommendations Accurate prediction of weather during the pre-monsoon season (April-June) is essential for human activities such as construction, aviation and agriculture. For this purpose, it is recommended that further study is required to correlate the regional weather parameters with that of the global parameters for good prediction of weather.
References: Lutgens, F.K. and Edward J. Tarbuck, 2004. The Atmosphere, an introduction to Meteorology, Prentice Hall, USA. Shamshad, K.M., 1988. The Meteorology of Pakistan, Royal Book Company, Karachi, Pakistan,
28 Issue 12 Mushtaq Ali Shah, Alam Zeb, Shahzad Mahmood
Table 1: Station Wise Rain Fall (mm) for each month & Season as a whole (April to June, 2009) April, 2009 May, 2009 June, 2009 Season ( Apr – Jun ) Stations Actual Normal Dep % Actual Normal Dep % Actual Normal Dep % Actual Normal Dep %
Chitral 110.3 78.3 40.9 36.4 47.1 -22.7 26.8 9.4 185.1 173.5 134.8 28.7
Drosh 100.3 99.2 1.1 43.8 67.0 -34.6 26.4 19.7 34.0 170.5 185.9 -8.3
Dir 193.0 173.7 11.1 83.0 97.1 -14.5 46.0 50.0 -8.0 322.0 320.8 0.4
Balakot 159.0 119.4 33.2 58.8 79.8 -26.3 81.0 98.3 -17.6 298.8 297.5 0.4
Kakul 206.9 112.6 83.7 34.5 73.8 -53.3 78.9 101.4 -22.2 320.3 287.8 11.3
Cherat 137.5 57.3 140.0 27.0 29.9 -9.7 10.0 19.6 -49.0 174.5 106.8 63.4
Peshawar 96.1 50.4 90.7 42.7 23.8 79.4 2.1 12.1 -82.6 140.9 86.3 63.3
Kohat 76.0 47.9 58.7 23.0 34.1 -32.6 31.0 22.1 40.3 130.0 104.1 24.9
Parachinar 190.0 96.5 96.9 89.1 73.5 21.2 87.7 49.3 77.9 366.8 219.3 67.3
Bannu 78.0 28.3 175.6 10.9 16.5 -33.9 0.6 30.1 -98.0 89.5 74.9 19.5
DIKhan 20.2 24.1 -16.2 3.5 17.0 -79.4 9.0 22.2 -59.5 32.7 63.3 -48.3
Table 2: Station Wise Minimum Temperature (°C) for each month & Season as a whole (April to June, 2009) April, 2009 May, 2009 June, 2009 Season (Apr – Jun ) Stations Actual Normal Dep Actual Normal Dep Actual Normal Dep Actual Normal Dep
Chitral 7.1 8.4 -1.3 12.5 12.2 0.3 14.4 17.2 -2.8 11.3 12.6 -1.3
Drosh 10.3 10.7 -0.4 15.7 15.4 0.3 16.6 20.8 -4.2 14.2 15.6 -1.4
Dir 6.9 7.6 -0.7 10.8 11.5 -0.7 13.0 15.5 -2.5 10.2 11.5 -1.3
Balakot 11.7 12.8 -1.1 17.5 17.4 0.1 19.4 20.8 -1.4 16.2 17.0 -0.8
Kakul 9.5 10.8 -1.3 13.6 14.7 -1.1 15.9 18.6 -2.7 13.0 14.7 -1.7
Cherat 12.2 13.6 -1.4 19.0 17.9 1.1 20.0 21.5 -1.5 17.1 17.7 -0.6
Peshawar 16.2 16.5 -0.3 20.6 21.5 -0.9 22.4 25.4 -3.0 19.7 21.1 -1.4
Kohat 16.9 16.3 0.6 22.6 21.1 1.5 24.4 24.4 0.0 21.3 20.6 0.7
Parachinar 6.2 8.5 -2.3 16.4 12.9 3.5 19.7 16.9 2.8 14.1 12.8 1.3
Bannu 16.4 17.0 -0.6 23.2 21.9 1.3 24.4 26.9 -2.5 21.3 21.9 -0.6
DIKhan 18.4 18.5 -0.1 24.2 23.4 0.8 25.7 26.3 -0.6 22.8 22.7 0.0
29 The Rainfall Activity and Temperatures Distribution over NWFP …. Vol. 6
Table 3: Station Wise Maximum Temperature (°C) for each month & Season as a whole (April to June, 2009) April, 2009 May, 2009 June, 2009 Season Stations Actual Normal Dep Actual Normal Dep Actual Normal Dep Actual Normal Dep
Chitral 19.7 22.5 -2.8 28.9 28.2 0.7 30.4 34.3 -3.9 26.3 28.3 -2.0
Drosh 19.8 23.6 -3.8 29.0 29.4 -0.4 31.2 35.8 -4.6 26.7 29.6 -2.9
Dir 20.9 22.5 -1.6 29.4 27.8 1.6 30.6 32.4 -1.8 27.0 27.6 -0.6
Balakot 24.8 25.5 -0.7 31.8 31.0 0.8 33.4 35.1 -1.7 30.0 30.5 -0.5
Kakul 23.2 23.4 -0.2 30.3 28.3 2.0 32.5 31.9 0.6 28.7 27.9 0.8
Cherat 21.0 22.4 -1.4 30.3 28.3 2.0 33.2 32.3 0.9 28.2 27.7 0.5
Peshawar 29.1 30.6 -1.5 37.2 36.6 0.6 39.1 40.2 -1.1 35.1 35.8 -0.7
Kohat 30.3 28.4 1.9 37.3 34.1 3.2 39.8 37.6 2.2 35.8 33.4 2.4
Parachinar 17.1 21.3 -4.2 27.4 26.6 0.8 31.1 30.7 0.4 25.2 26.2 -1.0
Bannu 30.4 32.9 -2.5 38.8 38.5 0.3 41.2 40.7 0.5 36.8 37.4 -0.6
DIKhan 32.6 33.5 -0.9 39.6 39.0 0.6 40.7 41.1 -0.4 37.6 37.9 -0.2
Table 4: Station Wise Mean Temperature (°C) for Each Month (April to June 2009) April, 2009 May, 2009 June, 2009 Season Stations Mean Normal Dep Mean Normal Dep Mean Normal Dep Mean Normal Dep
Chitral 13.4 15.5 -2.1 20.7 20.2 0.5 22.4 25.8 -3.4 18.8 20.5 -1.7
Drosh 15.1 17.2 -2.1 22.4 22.4 0.0 23.9 28.3 -4.4 20.4 22.6 -2.2
Dir 13.9 15.1 -1.2 20.1 19.7 0.5 21.8 24.0 -2.2 18.6 19.6 -1.0
Balakot 18.3 19.2 -0.9 24.7 24.2 0.4 26.4 28.0 -1.6 23.1 23.8 -0.7
Kakul 16.4 17.1 -0.8 22.0 21.5 0.4 24.2 25.3 -1.1 20.8 21.3 -0.5
Cherat 16.6 18.0 -1.4 24.7 23.1 1.6 26.6 26.9 -0.3 22.6 22.7 -0.1
Peshawar 22.7 23.6 -0.9 28.9 29.1 -0.2 30.8 32.8 -2.1 27.4 28.5 -1.1
Kohat 23.6 22.4 1.3 30.0 27.6 2.4 32.1 31.0 1.1 28.6 27.0 1.6
Parachinar 11.7 14.9 -3.3 21.9 19.8 2.1 25.4 23.8 1.6 19.7 19.5 0.1
Bannu 23.4 25.0 -1.6 31.0 30.2 0.8 32.8 33.8 -1.0 29.1 29.7 -0.6
DIKhan 25.5 26.0 -0.5 31.9 31.2 0.7 33.2 33.7 -0.5 30.2 30.3 -0.1
30 Pakistan Journal of Meteorology Vol. 6, Issue 12
Investigation of Seismic Hazard in NW-Himalayas, Pakistan using Gumbel’s First Asymptotic Distribution of Extreme Values Naseer Ahmed1, Dr. Zulfiqar Ahmed2; Dr. Gulraiz Akhtar2 Abstract NW Himalayan fold and thrust belt is one of the most seismically active belt of Pakistan. The study area falls with in latitude of 320-350 30/ E and longitude 710-750 15/ N covering an area of 1, 40, 000 sq2 km. It is nearly 250km wide and 560 km long irregularly shaped mountaneous belt forming very active transpersional regime which includes many active faults like MKT, MMT, MCT, and MBT etc. The on going collision between Eurassian and Indian plate resulted compresional forces in the N-W Himalayan fold and thrust belt. The documented historical and instrumental record shows that many devastating earthquakes have been stucked in this area. Among them the most prominent earthquakes are Pattan, 1974; batagram 2004 and most recently the deadly Muzafarabad earthquake of 7.6Mw are well known. The study is made to determine the probabilities for the return periods of earthquakes in this area in the future years using Annual Extreme Values Method of Gumbel (1958) by using the earthquakes equal or greater than M ≥ 4 that occurred for the time interval 1904-2008.Also, The methodology used to estimate the magnitudes of future earthquakes is by statistical method developed by Richter and Gutenberg (1942) in which all the earthquakes of the past of any size may be taken into calculations. According to this method proposed by Gumbel, the distribution of the annual maximum earthquake magnitudes is given which yielded that the probability of an earthquake occurrence of magnitude = 7.5 in 100 years is 63 percent and its return period is 100 years
Introduction The NW Himalayan Fold-and-Thrust Belt is one of the active Fold-and-Thrust Belt in the world. The Himalayan mountain ranges have been formed due to the continental collision between the Indo-Pak and Eurasian plates. The study area has recently been activated on 1st and 20th November 2002 (Bunji Earthquakes), and 14th February 2004 (Batgram Earthquake) with two devastating earthquakes of magnitudes 5.5 Mw. At the same time the most of the country consists of non-engineered structures, which are a constant threat to lives and property. And most recently the Muzafarabad Earthquake of magnitude 7.6 is warning for future construction in his area. Therefore effective engineering solutions are required to meet the challenges in this area regarding earthquakes. This study will be very helpful for future construction in the area because through this we can estimate the probable return periods of the earthquakes and there magnitudes.
Seismicity of the Area The NW Himalayan fold and thrust belt is one of the active fold–and–thrust belt along the northwestern margin of the Indo–Pakistan Plate (Jadoon, 1992). The Panjal-Khairabad fault divides it into hinterland zone toward the north and the foreland zone into the south. The hinterland zone is also referred to as the Hazara Crystalline Zone (Bender and Raza, 1995) and Himalayan Crystalline Zone (Cornell, 1968) whereas the foreland zone lies between the Panjal-Khairabad Fault and the Salt Range Thrust along with its westward extension. (Kazmi and Abbas, 2001). The seismicity pattern in terms of magnitude of earthquakes shows concentration in N-W Himalayan zone in figure 2. The documented historical and instrumental record shows that many devastated earthquakes have been stuck in this area among them the most prominent earthquakes are Pattan, 1974, batagram 2004 and most recently a deadly muzafarabad earthquake of 7.6Mw are well known.
1 Pakistan Nuclear Regulatory Authority. 2 Qauid-e-Azam University, Islamabad.
31 Invertigation of Seismic Hazard in NW-Himalayas, ……. Vol. 6
Figure 1: Seismic Zonation Map of the Area
Table 1: g values for different zones of Pakistan. SEISMIC ZONE PEAK HORIZONTAL GROUND ACCELERATION 1 0.05 to 0.08g 2A 0.08 to 0.16g 2B 0.16 to 0.24g 3 0.24 to 0.32g 4 > 0.32g
32 Issue 12 Naseer Ahmed, Dr. Zulfiqar Ahmed, Dr. Gulraiz Akhtar
Figure 2: Seismicity map of the NW Himalayas, Pakistan Methodology The methodology used in this study is a statistical approach. The main purpose is to estimate the future return periods of earthquake and magnitudes. The earthquake catalog used for this study is made by PMD, Nespak, GSP and IRIS. The data used is of interval 1904 to 2008. And years without documented earthquakes are considered of magnitude 4. The methodology used in this study is developed by Gumbel and Gutenberg and Richter. Application of Gumbel’s Annual Extreme values method for NW Himalayas City The methodology used to estimate the magnitudes of future earthquakes is by statistical method developed by Richter and Gutenberg (1942) in which all the earthquakes of the past of any size may be taken into calculations. It is very hard to find all values. That’s why Gumbel’s Annual Extreme Values Method is interested with only largest past earthquakes may be very useful and effective (Nurlu, 1999). The extreme value methodology is given by
(1) G (M) = e −α e − βM ………………………… M: Magnitude of earthquake α,β: Regression coefficient G (M): Probability of not exceeding the earthquakes having magnitudes more than M in one year. In 1956 Gutenberg-Richter have proposed the following relationship which relates the earthquake magnitude to the total number of earthquakes in one year. (Gutenberg-Richter, 1956)
33 Invertigation of Seismic Hazard in NW-Himalayas, ……. Vol. 6
LogN = a – b (M) …………………………. (2) a, b: Regressions coefficients N: The number of earthquakes in one year whose magnitude is M or greater There are as following the correlations between Gumbel and Richter formulations (Tezcan, 1996).
α = 10a, a = Logα ………………..…… (3) β = b / Loge, b = βLoge…………………...(4) N = α e –βM = -LnG……………………….. . (5) The regression constants mentioned in Gumbel’s equation are found by selecting maximum earthquake of the year from 1800 to 2008 from different catalogs given by USGS, ISC, IMD,PMD etc in table 1.We assumed Mmax = 4 for years with no earthquakes. And the number of this earthquake (j) and relative frequency (f = j / (n+1)) have been determined (n: seismic history period being investigated). Then G (M), N and LogN values calculated using (1) and (5) equations as in Table 3. Table 2: Annual Maximum Earthquake magnitudes for Time Interval 1904-2008. year Mmax year Mmax year Mmax 1904 5.9 1973 5 1992 5 1914 6.3 1974 5.9 1993 5 1915 5.6 1975 5.3 1994 4.5 1919 5.9 1976 5 1995 5.2 1924 5.2 1977 5.5 1996 5.6 1927 5.4 1978 5.4 1997 5 1928 5.4 1979 5.3 1998 5.3 1953 5.5 1980 5 1999 5.4 1962 5.1 1981 5.1 2000 5.5 1963 5.4 1982 5.4 2001 5.3 1964 5 1983 5.1 2002 6.8 1965 4.8 1984 5.2 2003 6.4 1966 5.2 1985 5.2 2004 6.1 1967 4.3 1986 5.3 2005 7.6 1968 4.1 1987 5.1 2006 6.7 1969 4.4 1988 5.1 2007 6.3 1970 5.1 1989 4.6 2008 6.5 1971 5.2 1990 4.7 1972 5.4 1991 4.4
34 Issue 12 Naseer Ahmed, Dr. Zulfiqar Ahmed, Dr. Gulraiz Akhtar
Figure 3; Variation of Earthquakes of Maximum Magnitude during time Interval (1904 - 2008)
Table 3: Calculations for Gumbel's Annual Maximum Distribution M j f G(M) N=-LnG LOGN 4 49 0.460 0.460 0.776529 -0.10984 4.1 1 0.010 0.470 0.756087 -0.12143 4.3 1 0.010 0.479 0.736005 -0.13312 4.4 2 0.019 0.498 0.697012 -0.15676 4.5 1 0.010 0.508 0.678071 -0.16872 4.6 1 0.010 0.517 0.659482 -0.1808 4.7 1 0.010 0.527 0.641233 -0.19298 4.8 1 0.010 0.536 0.62331 -0.2053 5 7 0.067 0.603 0.506115 -0.29575 5.1 6 0.057 0.660 0.415552 -0.38138 5.2 6 0.057 0.717 0.332513 -0.47819 5.3 5 0.048 0.765 0.268222 -0.57151 5.4 7 0.067 0.831 0.184639 -0.73368 5.5 3 0.029 0.860 0.150851 -0.82145 5.6 2 0.019 0.879 0.128943 -0.8896 5.9 3 0.029 0.908 0.096957 -1.01342 6.1 1 0.010 0.917 0.086518 -1.06289 6.3 2 0.019 0.936 0.065962 -1.18071
35 Invertigation of Seismic Hazard in NW-Himalayas, ……. Vol. 6
M j f G(M) N=-LnG LOGN 6.4 1 0.010 0.946 0.05584 -1.25305 6.5 1 0.010 0.955 0.04582 -1.33895 6.7 1 0.010 0.965 0.035899 -1.44492 6.8 1 0.010 0.974 0.026075 -1.58377 7.6 1 0.010 0.984 0.016347 -1.78656
Regression constants a, b are found by using least squares method in fig below.
Figure 4: Relation between Magnitude and logN
Magnitude – Frequency Relation Magnitude – frequency relation have been found for this area as following:
LogN = 2.268 – 0.5471M Gumbel’s regressions coefficients (α, β) have been calculated using the magnitude-frequency relation as follows:
α = 10a = 10 2.268 = 185.35 β = b / loge = 1.26 Calculation of Annual Risk (Probability) for different Return Periods The earthquake number greater than a certain magnitude M for one year N(M), return period (Tr), risk for one year (R1) and for D year have been calculated using the following formulas (Tezcan,1996): N (M) = α exp -βM
36 Issue 12 Naseer Ahmed, Dr. Zulfiqar Ahmed, Dr. Gulraiz Akhtar
Tr = 1 / N (M) R1 (M) = 1 – e –N (M) RD (M) = 1 – e –DN (M)
The probabilities of earthquake occurrence for the seismic source are calculated for periods of T = 1, 50, 100 years and magnitudes of M = 4.5, 5, 5.5, 6.0, 6.5,7, 7.5 and they are presented in Table 4. Table 4: Probabilities of Earthquake for Different Periods. M N(M) R1 50N R50 100N R100 Tr 4.5 0.63 0.462 31.5 1 63 1 1.58 5 0.34 0.283 17 1 34 1 2.94 5.5 0.18 0.165 9 0.99 18 1 5.55 6 0.09 0.082 4.5 0.98 9 0.99 11.11 6.5 0.05 0.047 2.5 0.91 5 0.99 20 7 0.02 0.019 1 0.63 2 0.86 50 7.5 0.01 0.009 0.5 0.39 1 0.63 100
Figure 5: Magnitude Distribution with respect to Return Period.
The above calculations clearly shows that the probability of an earthquake occurrence of equal or greater than magnitude = 7.5 in 100 years within this area is calculated about 63 percent and its return period is 100 years. Now for given annual risk, we can calculate the maximum magnitude and the risk (Rd) for given structure life (Td) using the following formulas and vulnerability analysis in this area can be done in light of above calculated risk. (Tezcan, 1996).
M = Ln [-α / Ln (1-R1)] / β
Rd = 1 – (1-R1) Td
37 Invertigation of Seismic Hazard in NW-Himalayas, ……. Vol. 6
38 Issue 12 Naseer Ahmed, Dr. Zulfiqar Ahmed, Dr. Gulraiz Akhtar
Need for appropriate Seismicity Risk Studies for this Area Simplified regional seismic hazard maps as shown in fig 1 are adequate for designing a majority structures and for zoning and planning purpose. But after the muzafarabad earthquake there is need to update all the data like isoseismal maps, seismic hazard zonation maps and probabilistic hazard analysis studies etc, according to latest information and observations. However in earthquake prone country, much more specific seismic site evaluation should be carried out for different structures like dams, bridges, freeways, high rise buildings, nuclear power plants and reactors etc (Bolt, 2006). The estimates in this study can be very helpful in analyzing further this area regarding earthquakes.
Conclusions Literature review shown that this area one most seismically active region of Pakistan. The historical earthquake data shows that any major earthquake can stuck the area. The Gumbel’s extreme value method is considered to be very helpful to estimate the future earthquake return periods because it uses the maximum magnitude of each year and also it is very helpful with respect to construction because it estimates the return periods if earthquakes of maximum magnitudes. In this study the return period calculated for 7.5 magnitude earthquake is 100 years and its probability of occurrence is about 63%. And the Gutenberg-Richter methodology is considered to be very important for magnitude –frequency analysis, regression analysis and to calculate the magnitude of earthquakes.
References • Gumbel, E.J., 1958, Statistics of Extremes, Columbia University Press, N.Y., U.S.A. • Gutenberg, B., Richter, C.F., 1956, Earthquake Magnitude, Intensity, Energy and Acceleration, Bull. Seism. Soc. of America, Vol. 32, No.3, July. • Tezcan, S.S., 1996, Probability Analysis of Earthquake Magnitudes, Turkish Earthquake Foundation, 26p. • Cornell, A.C. 1968. Engineering seismic risk analysis. Bull. Seism. Soc. Am. 58:1583-1606. • Jadoon, I.A.K. 1992. Thin-skinned tectonics on continent/ocean transitional crust, Sulaiman Range, Pakistan. Ph.D. thesis, Geology Department, Oregon State University, USA. • Bender, F.K. and H.A. Raza, 1995, Geology of Pakistan. Printed by Tutt Drukerei GmbH, Salzweg-Passau, Germany. P.11-61. • Kazmi, A.H. and Abbas, S.G., 2001. Metallogeny and mineral depodits of Pakiatan. Graphic publishers, Karachi. Pp.47-58. • Özmen, B., Nurlu, M., Güler, H., Kocaefe, S., 1999, Seismic Risk Analysis for the city of Ankara, Second Balkan Geophysical Congress and Exhibition, 5-9 July 1999, The Marmara Hotel Conference Hall, İstanbul, Turkey • Bolt,B.A.Earthquakes.New York: freeman and company,2006
39 Pakistan Journal of Meteorology Vol. 6, Issue 12
Case Study: Heavy Rainfall Event over Lai Nullah Catchment Area Muhammad Afzal1, Qamar-ul-Zaman1 Abstract This research is an effort to understand the heavy rainfall phenomenon which gripped the upper area of Pakistan from 4–9 July 2008. This week lasting event created close to flood situation in Lai Nullah basin of Islamabad. The focus is mainly kept at flooding occurred in the Nullah on 5th July. This study was also an effort to effectively forecast the amount of precipitation expected as a result of such event so that flood like situation can be timely forecasted. The NCEP reanalysis (2.5° × 2.5°) data sets were utilized for this purpose. Different meteorological fields were used to get a picture of atmosphere. It also helped in comparison of both the observed and reanalysis data sets for one particular event. NCEP reanalysis data set though of coarse resolution presented good picture of event in terms of interaction between two main weather systems. The analysis revealed that the south-easterly incursion from the Arabian Sea was activated due to the westerly trough approaching the HKH mountain ranges. The results showed that Vertical wind Velocity (omega) and constant pressure surfaces are good predictors for this particular study. Keywords: Lai Nullah, NCEP reanalysis, Arabian Sea, South-easterly.
Introduction Pakistan being situated between 23° 35' – 37° 05' North latitude and 60° 50' -77° 50’ East longitude falls in the Extratropics. It defines the western limits of Easterlies in South Asia. Precipitation is received in both summer and winter seasons. Western Disturbances (WD) approaches the upper parts of the country throughout the year; while Easterlies are dominant only in summer. Both the systems sometimes overlap to extract a heavy downpour for the study area (Lai Nullah Basin).The lofty Himalayas, Karakoram and Hindukush (HKH) mountains play their role to provide orographic lifting to the weather systems. The Micro and meso-scale low pressure vortices are formed generally in the western side of 850 E as a result of extended southerly troughs. They have a short life span and sometime produce heavy downpours [5]. Islamabad and Rawalpindi are situated along the Margallah hills. In this paper severe rainfall spell 4-9 July 2008 is analyzed. Our main focus is the flooding which occurred on 5th July due to 104mm rainfall received in only 100 minutes; 162 mm rainfall was recorded only in 5 hours at PMD Headquarters Islamabad. Densely populated low lying areas along Lai Nullah faced flood like situation causing massive destruction of property and life. Three people died in the flash flood. It was the heaviest short period rainfall in last six years, reminding the cloud burst of 23rd July 2001. Temporal coincidence of July 2001 and July 2008 shows that both the events occurred between 00-09 UTC.
Main tributaries of Lai Nullah start from the foot of Margallah Hills. Water from Islamabad piles up into a single stream and flows through the densely populated areas of Rawalpindi. The Basin area is approximately 240 Km², out of which 38.6 % residential. Length of Nullah is about 30 Km [7]. Accelerated urbanization and population growth minimize the water oozing lands and enhance aerosol concentration in the area. The banks of the Lai Nullah could not be identified (due to encroachment) and flow at its middle attains 30 km/h speed in general [11]. Rasul et al (2004) revealed the facts about the deflected monsoon currents that played an enormous role for heavy downpour in the Lai Nullah Basin in 2001 [11]. The purpose of our study is to describe the atmospheric condition instigated by southern moisture flux which became responsible for severe flooding
1 Pakistan Meteorological Department
39 Case Stuy: Heavy Rainfall Event over Lai Nullah Catchment Area Vol. 6 in the study area on 5th July, 2008. The precise forecast can save loss of valued lives and economical damage of infrastructure in the civic population.
Figure 1: Lai Nullah Basin Data & Software ∗ Observed rainfall is taken from PMD is plotted in excel worksheets. ∗ NCEP (National Center for Environmental Prediction) Data of spatial resolution of (2.5ox2.5o) reanalyzed to study the particular phenomenon. The temporal resolution of data is Six hourly. ∗ GrADS (Grid Analysis & Display System) help is taken in to account & NCEP gridded data is shown in the form of images. Horizontal and vertical picture of atmosphere is made by using different meteorological parameters. ∗ Diagrams displaying amounts of precipitation are the plotted by using Surfer soft ware.
Results and Discussion The data has been studied to investigate the event. Following are the favourable weather parameters which caused the severe thunderstorm activity. (a) Temperature Low level heating in tropics plays key role for mesoscale and microscale convective phenomena. The surface heating raised the temperature of Lai Nullah basin up to 42oC on 4th July, 12z (Fig.2a). Temperature remained up to 34oC during the whole preceding time of event even after radiation cooling over night (Figure.2b& 2c). Seasonal low over Balochistan accentuated and displaced towards west. At 200hpa Tibetan plateau warmer than surroundings caused more moisture transportation from lower troposphere [1].Lower level heating and upper level support ultimately became clear evidence for uplifting mechanism.
40 Issue 12 Muhammad Afzal , Qamar-ul-Zaman
a b c
d e
Figure 2: Temperature, Surface & Upperair (b) Pressure & Geopotential Height Synoptic chart for 00UTC analyzed at National Weather Forecasting Center gave a good picture of low pressure over the sub-humid mountainous zone. The low pressure cell was also present over southern Punjab along with the main Seasonal low over Balochistan. These lows caused constant moisture feeding in the lower troposphere. Southerly and south easterly currents remained on the go (Fig.3d). It is evident by reviewing daily observed data at PMD that no much heating occurred in 2008, the heat low over Balochistan could not deepenn but Western Disturbances remained active through out the year. The same occurred on 5th July event when at mid troposphere level (500hpa) an upper air westerly trough penetrated northern parts of Pakistan. Westerly trough gave cooling effect to the warm-moist air beneath and behaved as a triggering agent to extract the moisture from monsoonal currents. The Westerly trough became prominent on 00UTC, 5th July. Figure.3 (a-d) shows the constant advancement and penetration of the westerly trough over the foot of Marghalla hills. The orientation of westerly made an adequate cold air advection from higher latitudes to the upper parts of country. After the heavy rainfall (at 06UTC) high, pushing the westerly trough to the higher latitudes, dominated and gripped the entire Pakistan, Kashmir and north-western part of India. The contour 5880 (gpm) remained a constant cold air supplier for the lower atmospheric monsoon winds throughout the week. The similar short period patterns remained rainfall contributor up to 9th July, 2008. Yet the rigorous condition was the first episode of the spell, which is 5th July morning.
41 Case Stuy: Heavy Rainfall Event over Lai Nullah Catchment Area Vol. 6
a b c
e d
Figure 3: Surface Chart of 00UTC 5th July and Upper air CP Charts
(c) Winds and Moisture flux The accumulation of moisture occurred along the foot hills of Himalayas due to constant currents approaching from Arabian Sea. Generally wind takes 12 to 18 hours from Arabian Sea to reach the study area.The streamline pattern for 850hPa shows the continuous moisture supply from the Arabian Sea. The stream line flow from south to north remained steady from 3rd July afternoon to the morning of 5th July (Fig.4.a-c). Thus sufficient moisture accumulated over the northern areas of Pakistan before the occurrence of flashy rainfall. Figure 4(d-f) is clearly showing the invasion of westerly trough at 500hpa level. It remained dominant over the study area from evening of the preceding day (4th July) and persisted up to the morning of the day of episode. The bubble high appearing in figure 4-f is the result of light downdraft producing 2mm of precipitation before occurrence of the main event. The wind flow at 200hpa level became favorable about 18-24 hours before the major downdraft. Figure4-g shows that divergence started from morning of 4th July and gradually shifted towards east. Due to this strong divergence the surface convection became vigorous and vertical transport of moisture drift enhanced from the lower troposphere [13].
42 Issue 12 Muhammad Afzal , Qamar-ul-Zaman
a b c
d e f
g h i
Figure 4: Stream line field on 4-5 July,2008 at (a-c) 850hpa, (d-f) at 500hpa& (g-i) at 200hpa The moisture reaching the north made the area humid, The figure 5(a-b) shows that the northern Pakistan and north-western India along the great Himalayas are rich in moisture. Hot and dry air advection over the central Punjab from the west which caused the low relative humidity over there. But in the morning of 5th July (Fig.5-c) the whole of the above said areas enriched in humidity. A critical look shows that the increased humidity along the Himalayas is well defined in terms of streamlines over 850hpa (Fig.4 a-c). This moisture made the air lighter and the orographic lifting supported the vertical wind flow.
a b c
a b
Figure 5: Relative Humidity (%) at Surface
43 Case Stuy: Heavy Rainfall Event over Lai Nullah Catchment Area Vol. 6
(d)Vertical Profile and Instability The winds are plotted in the form of vertical streamlines by fixing one graticule for a particular time. Fig 6(a-b) depict the vertical distribution of winds for 12UTC, 4th July. The moist air uplifting was up to 700hpa pressure level. The upper air divergence provided cold air aloft at 300hpa. So the cold air ceiling over the warm moist tropical air made the atmosphere unstable. The accumulation of moisture below provided the victuals for the system to develop gradually. Fig.6-c displays the isotachs along with the vertical streamline field. By fixing 33.4N, the maximum value of isotach is over 73E, at 850hPa over Lai Nullah catchment area (Fig6-c). The uplift of air remains active the whole night before even at 00UTC the convection remained without any restraint.
a b
c d
Figure 6: Vertical Stream-lines Flow
Vertical uplift near the surface became positive during night which is an evidence of strong uplift. Fig.7 (a-c) shows that over the study area the near surface winds were uplifting with greater thrust as the time for activity came nearer. Fig.7-d provides the information that the vertical velocity of moisture laden air is positive over the Lai Nullah Basin. Strong heating was the main reason for omega (hPa/Sec-1) to rise up to mid troposphere. In Fig.7(e-f) Vertical profile for relative humidity is shown. Again very clear that as the time passed by the moist column of air floated in lower troposphere over the twin cities, Rawalpindi-Islamabad. The upper air very humid westerly can also be seen in the figure at 500hPa.
44 Issue 12 Muhammad Afzal , Qamar-ul-Zaman
a b c
d e f
Figure 7: (a-d).Vertical Wind Velocities Omega (hPa/s) and ,(e-f) RH(%) (e). Cloud Formation and Rainfall Mechanism The vertical shooting of warm air made the cumuli-type clouds which resulted in to thunder storm (TS) activity. TS & heavy showers are the main features when a cold westerly joins the warm, moist monsoon air underneath. Generally Warm moist air is below the cold westerly so the absolute instable atmosphere produces to give severe output. Main clouds which caused the activity on 5th July, 2008 were Cumulonimbus(Cb), Stratocumulus(Sc) and Altostratus(As). Mesoscale convective Cb two to three cells contributed to give 67mm and 104mm rainfall in successive turns. The Altostratus sheets enhanced the downpour. The flip-flop pattern of precipitation was due to As and Sc clouds. Two main spells were the outcomes from Cb mentioned in the table.1. Table 1: Clouds, rainfall amount and wind Cloud Genera & Amount Wind Speed Sr. No. Time UTC Rain fall (octas) (m/s) 1 1845-1935 Cb2, Sc3, As3 2.0 mm Calm 2 0120-0205 Cb3, Sc2,As3 67.0 mm SW-08 3 ……..-0500 Cb3, Sc2,As4 104.0 calm Sourc(NWFC, PMD)
The fig.8-a shows the rainfall amounts during the week when the westerly on and off penetrated the upper parts of the country gave rainfall. Whereas fig.8-b shows the real time rainfall calculated at Flood Forecast and easrly Warning System (FFWS), PMD, Islamabad. The rainfall received after 0300UTC on 5th July was reported on the next day
45 Case Stuy: Heavy Rainfall Event over Lai Nullah Catchment Area Vol. 6
Rainfall in Islamabad (July-2008) Half hourly_Rainfall 4-5 July, 2008 a b 140 80
120 67 ) 60 m
) 100 m (m l m 80 41 ( 40 ll fal 35
a f n
60 i in a a R
R 20 40 15 20 4 0 2 00 1 0 0 0 0 0
0 C 00 00 00 00 00 00 456789 0 0 0 0 0: 1: 2: 3: 4: 5: UT 20: 21: 22: 23: Date Figure 8: Rainfall (a). During week (b) Flooding event
Table 2: Rainfall in Islamabad 4-9 July, 2008 4-9 Normal Date/Station 4 5 6 7 8 9 July July
AIRPORT TR 26 82 60 13 24 205 305.3
DHAMIAL 0 0 38 86 26 20 170 …….
SAID PUR 0 30 20 44 40 5 139 ……
SHAMSABAD 0 39 2 41 35 8 125 …….
PMD Headquarters 0 67 128 33 50 15 273 343.2
Sourc(NWFC, PMD) Table 2 gives the rainfall amounts in (mm) of different stations installed in Lai Nullah catchment area. Raifall recorded in only five days was 205mm. It is greater than 60% of the monthly normal rainfall i.e. 343.2mm for PMD Headqurters. The normal rainfall for the month of July is shown in Fig.9 below.
Conclusion In this paper sudden heavy showers of 128 mm in 24 hours over the Lai Nullah basin has been studied. This is the second heaviest downpour during the past hundred years. The relative weather elements have been analyzed to explore the consequences of this heavy rainfall event. Based on the analysis following conclusions have been drawn. • The intensive heating over entire country and Arabian peninsula specially the presence of 510C isotherm over the junction of Iran, Pakistan and Afghanistan. This warm area moved eastward and penetrated to the northeast up to the study area uptill 00UTC of 5th July. • The synoptic pressure pattern 00UTC of 5th July shows consecutive low pressure areas expanding from southwestern to the northern parts of the country. These lows supported each other to drift the moisture to the higher latitudes.
46 Issue 12 Muhammad Afzal , Qamar-ul-Zaman
Figure 9: July Normal (1971-2000) rainfall(mm)
• The development of cyclonic vortex at 850hpa over Sindh-Balochistan region (06UTC4th July) and its movement to northwest centered over northwestern Balochistan providing sufficient racking for moisture flux from Arabian sea. • Gradual displacement of subtropical high at 500hPa level let the westerly trough to penetrate over the Lai Nullah Basin. • Strong divergence at 200hPa level exactly over the study area (06UTC,4th July) enhanced the lower tropospheric convection. The presence of strong divergence 18 to 24 hours prior to the even can be used as a good tool for precise forcasting of such heavy rainfall over the twin cties. • RH has good equivalence to the streamline analysis that moisture coming from Arabian Sea accumulates along the foothills of Himalayas with great vertical extent. • Vertical picture of preceding hours of main episode shows surface convection with sufficient vertical uplift and upper air cold advection. Mesoscale systems are not prominently elaborated by NCEP reanalysis data due to coarse resolution. So for further studies, it is recommended that real time data sets availability may be assured particularly the radiosonde or rawinsonde data. Forecasters should have vigilant look in to the mechanism and precursors of such systems when superposition of westerly and monsoon currents takes place.
47 Case Stuy: Heavy Rainfall Event over Lai Nullah Catchment Area Vol. 6
Acknowledgments The authors are grateful to Mr. Khurrum Waqas and Mr. Aleemul Hasan for their cooperation and valuable suggestions to finalize this study.
References Anjum M. Akram, 2004: Tibetan Plateau and mechanism for Indo-Pak Monsoons’ Evolution, Pakistan Journal of Meteorology, vol.1 Issue:1. Biswas, N.C., U.S. De and D.R. Sikka, 1998: The role of Himalayan massif – Tibetan Plateau and the mid-tropospheric tropical ridge over north India during the advance phase of the outhwest monsoon, Mausam, 49, 285-298. Chaudhry, Q.Z., G. Rasul., 2007: Development of a mesoscale convective system over the foothills of Himalaya into a severe storm. Mountains witnesses of Global Changes, ELSEVIER, Ch.33, pp 301-311. Climatic Normal of Pakistan (1971-2000), Climatic Data Processing Center (CDPC) Karachi, Published 2005, Pakistan Meteorological Department. Ding Yihui, Fu Xiuqin and Zhang Baoyan, 1984: Study of the structure of a monsoon depression over the Bay of Bengal during summer MONEX. Advance Atmospheric Sciences,Vol.1(1), pp 62-75. Das, Someshwar, S.V. Singh, E.N. Rajagopal and R. Gall, 2003: Mesoscale modeling for mountain weather forecasting over the Himalayas. Bulletin of the American Meteorological Society, Vol. 84, no. 9, pp 1237-1244. Kamal Ahmed, 2004. Pakistan: Lai Nullah basin flood problem Islamabad – Rawalpindi cities WMO/GWP Associated Programme on Flood Management. Khan, J.A. 1993. The Climate of Pakistan. Rehbar Publishers, Karachi. Krishnamurti, T. N., 1971: Observational study of the tropical upper tropospheric motion field during the northern hemispheric summer. J. Appl. Meteor., 10, 1066–1096. Krishnamurti, T. N., and T. Murakami, 1981: On the onset vortex of the summer Monsoon. Mon. Wea. Rev.,109, 344–363.c Rasul, G., Q. Z. Chaudhary, S.X. Zhao and Q.C Zeng, 2004: A Diagnostic study of record heavy rain in twin cities Islamabad-Rawalpindi, Advances in Atmospheric Sciences, 21, pp. 976-988. Rasul, G., Q. Z. Chaudhry, Z. Sixiong, Z. Qingcum, QI. Linlin, and Z. Gaoying, 2005. A Diagnostic Study of Heavy rainfall in Karachi Due to Merging of a Mesoscale Low and a Diffused Tropical depression during South Asian Summer Monsoon. Advances in Atmospheric Sciences, Vol. 22, No. 3, 2005, 375-391. Rao, Y. P., V. Srinivasan, and S. Raman, 1970: Effect of middle latitude westerly systems on Indian Monsoon Syp. Trop. Met. Hawaii. No. IV 1-4. Shamshad, K.M., Published 1988. The Meteorology of Pakistan, First Edition, Royal Book Company Publishers, Karachi.
48 Pakistan Journal of Meteorology Vol. 6, Issue 12
An Outbreak of Tornado on March 28, 2001: A Case Study Nadeem Faisal1, Akhlaq Jameel1 Abstract Pakistan has the history of occurrence of severe weather phenomena in different parts of country - mainly in northern and central parts of country. A tornado occurred at Chak Misran, Bhalwal in Sargodha district on the evening of March 28, 2001 at about 1530 PST. This tornado reaches F2 intensity (120 miles/hour) and caused considerable loss of lives, injuries and destruction across the path of the Tornado. A well marked low pressure area was lying over Northern Areas of the Punjab, as evident on surface level charts of March 28, 2008 from 0000 UTC to 0900 UTC, appreciable pressure gradient existed further north-westwards of he low pressure system. The system was extended upto mid-tropospheric level (500 hpa). There was appreciable incursion of moisture from North Arabian Sea and a cold air was penetrating into North Western parts of the country. Thunderstorms activity has been reported by a number of meteorological observing stations of North Punjab. In this study effort has been made to understand the synoptic feature of this particular event. Suggestions have also been presented to better forecast the tornadoes and its preparedness.
Introduction Tornadoes, local storms of short duration, may be ranked as one of the most destructive natural phenomena. Also called twisters, tornadoes are violent windstorms taking the form of rotating column of air, or vortex extending downward from a cumulonimbus cloud. Tornadoes are usually in the form of visible condensation funnel, whose narrow end touches the ground and is often encircled by a cloud of debris. They are approximately 250 feet (75 m) across and travel a few miles (several kilometers) before dissipating. Most tornadoes have wind speed between 40 mph (64 km h-1) and 110 mph (177 km h-1). Some attain wind speed of more than 300 mph (480 km h-1), stretch more than a mile (1.6 km) across, and stay on the ground for dozens of miles (more than 100 km). In a study of Nebraska tornadoes over a 22 year period, found that 95% of the tornadoes formed with the surface cold front, 1 % with the surface warm front, 3 % indicated similar places of inception, and 1 % gave no clues as to the places of origin. (Joseph G. Galway). Weightman (1933) found that tornadoes occurred under a great variety of meteorological conditions: for example, with warm-front thunderstorms, hurricanes, ill-defined discontinuities, the centre of lows, and the passage of the surface cold fronts. (Joseph G. Galway), Tornadoes usually form in spring and summer season, but they can occur any time of the year. Tornadoes are most likely to occur between 3 and 9 p.m. but have been known to occur at all hours of the day or night. The average forward speed is 30 mph but may vary from nearly stationary to 70 mph. Tornadoes are not very common in Pakistan. The phenomena which usually occur in plain areas of the NWFP and Punjab are the same like tornado but of very low intensity and they usually cannot be categorized as tornado. People of Pakistan generally do not know the name of tornado. They call it “Bhanwar” “Chakkari” and “Wahwaraila” in their local languages. The intensity of tornado is measured in Fujita Scale. Fujita scale classifies tornadoes into six categories of wind speed F0 to F5 based upon the damages. F0 (light damage) with wind speed 40 – 72 mph to F5 (incredible damage) with wind speed 261 – 318 mph. This intensity is based on the damages caused by tornado on human built structure and vegetation. The category of the tornado is determined by the meteorologists and engineers after making a ground/aerial survey of the destruction and damages of the affected area. The scale was introduced in 1971 by Tetsuya "Ted" Fujita of the University of Chicago who developed the scale together with Allen Pearson. Enhanced Fujita Scale has been introduced in 2007 in USA (Wikipedia).
1 Pakistan Meteorological Department. 49
An Outbreak of Tornado on March 28, 2001. Vol. 6
Synoptic Situation On March 28, 2001, at about 1530 PST, a tornado of F2 intensity was produced in village Chak Misran, Bhalwal in Sargodha district. A well marked low pressure area was formed over northern areas of the Punjab on March 28, 2001 as evident in surface charts of 0000 to 0900 UTC. Appreciable pressure gradient existed further north-westwards of the low pressure system. The system was extended upto mid- tropospheric level (500 hpa). There was appreciable incursion of moisture from North Arabian Sea and cold air was penetrating into northwestern parts of the country. Thunderstorms activity has been reported by a number of meteorological observing stations of North Punjab. Surface chart of 0000 UTC At surface chart of 0000 UTC, a well marked low pressure area of 1000 hpa was seen over central areas of the Punjab. Strong pressure gradient was observed in northwest and northeast of the low pressure system. Cold air ridge penetrated into northwestern parts of the country. A southward flow of wind was evident to the west of the system, penetrating up to the coastal areas which indicate a cold air incursion to the system.
Figure 2: Surface Chart 28th March, 2001 Figure 1: Surface Chart 28th March, 2001
Surface chart of 0600 UTC At surface chart of 0600 UTC, the system persisted over the area. The pressure gradient persisted towards west of the system. Dry and cold continental air penetrated southwards from the west of the system and picked up moisture from Arabian Sea. Surface chart of 0900 UTC The well marked low over central parts of Punjab was clearly evident on the surface chart of 0900 UTC. A number of stations in northern areas of the country showed rain and thunderstorm activity. Strong pressure gradient was seen northwestwards of the system. Cold air ridge was penetrating into northwestern parts of the country. A southerly trend of the isobars from the west of the system was clearly seen on the chart, dipping into the Arabian Sea and picking up moisture to the system from there. At 850 hpa level chart of 0000 UTC of March 28, 2001, the well marked low on the surface level was clearly evident on central areas of the Punjab and neighborhood. Dry continental air was penetrating
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Issue 12 Nadeem Faisal, Akhlaq Jameel
from the North to coastal areas of Balochistan and Sindh. Moisture incursion to this low pressure system was clearly evident from 850 hap level chart.
10 40N 16 00
10 14 00
10 12 35N 00 1 00 80 0 1 00 60 0 30N 10 04 00
25N
20N
15N 101800 101600 101600 101400 101200 101000 100800 100600 100600 10N
30E 35E 40E 45E 50E 55E 60E 65E 70E 75E 80E 85E 90E Figure 3: Surface Chart 28th March, 2001(0900 UTC)
Figure 4: 850 hpa level chart March 28, 2001 (0000) UTC) Figure 5: 850 hpa level chart March 28, 2001 (0000) UTC) At 700 hpa level chart of 0000 UTC of March 28, 2001, the well marked low pressure at surface level was clearly evident at this level too. The low at this level was slightly shifted northwestwards over northeastern parts of Balochistan and adjoining Afghanistan. Cold air from north northwest was sweeping downwards towards coastal areas of the country and picking up moisture from Arabian Sea as evident from Fig -05. At 500 hpa level of 0000 UTC of March 28, 2001, the surface level well marked low pressure was extended up to mid-tropospheric level. The low was shifted further northwestwards as compared to 700 hpa level chart. Contours from north northwest were sweeping downwards up to the coastal areas of the country as shown in Fig-06.
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An Outbreak of Tornado on March 28, 2001. Vol. 6
Figure 6: 500 hpa level chart March 28, 2001 (0000 UTC) Figure 7: Satellite imagery (0938 UTC) Right from surface to 18000 ft, charts depicted some what intense circulation over central Punjab with centre near Sargodha area that acted as a vortex of southwest relatively warm-moist currents and cold continental air from north-northwest. (Khan, A. H.). Consequently a squall line of thunderstorms developed. A number of stations in North Punjab and Upper NWFP including Lahore, Sargodha, Mianwali, Rawalpindi and Peshawar reported thundershowers in their respective synop messages of 0900 & 1200 UTC (Table-1). The vortex formation was further accentuated as the day progressed and moist atmosphere absorbed maximum energy. Vortex formation further intensified during afternoon as evident from the satellite imagery of 0938Z (Khan, A. H). A very thick cloud mass is visible between longitude 71° E to 74 ° E and latitude 32° N to 35° N over Sargodha and surrounding area, which indicate thunder storm activity over Sargodha and neighboring area (Fig-7).
Impact of the Tornado Very heavy equipments and buildings were damaged by the Tornado. Steel electric poles were uprooted and some of them were found far away from their original position. This reflects that poles were carried away aloft quite high. Wind whirl pulled up heavy equipment like tractor trolleys and wheat thrasher showing the spinning motion of the air and thrown far away from their original locations. Loss of 10 lives with human and animal dead bodies and their dismembered parts were dispersed quite far off. Also injuries to more than 100 persons were reported due to flying debris. It is a clear indicator that the destruction was outcome of a tornado. (Khan, A.H.)
Forecasting of a Tornado Even today’s advanced radar technology and other sophisticated equipments, it is very difficult to forecast tornadoes, as they can form suddenly and unexpectedly. Being a small and short – lived phenomenon tornadoes are among the most difficult weather features to forecast precisely. A forecaster can make advance weather forecasts of weather conditions favorable for severe storms over a general area, but these predictions cannot give the exact time a severe storm will be at a definite point. Specific warnings can be made only after a storm is in progress and a report has been received on the type and location of the storm. The forecaster can then determine the direction of the path of the storm to a very accurate degree for the next few hours and warn people who may be affected. (Joseph G. Galway).
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However Doppler radars can detect air movement towards or away from the radar. Early detection of increasing rotation aloft within a thunderstorm can allow life-saving warnings to be issued before the tornado forms. A tornado watch is issued when meteorological parameters favor tornado development. A severe thunderstorms watch is issued when meteorological parameters favor large hail and damaging thunderstorm wind gusts. The average watch is valid for approximately six hours and is a parallelogram that covers an area of approximately 59800 km2 (23000 n m2) (Richard W. Anthony and Preston W. Leftwich, Jr.) Besides having all that modern equipment and radar technology available, there are some environmental clues for a forecaster, which are as follows: • Dark, often greenish sky • Wall cloud • Large hail • Loud roar; similar to a freight train Some tornadoes appear as a visible funnel extending only partially to the ground. Look for signs of debris below the visible funnel. Some tornadoes are clearly visible while others are obscured by rain or nearby low-hanging clouds. There are some meteorological points which may be extremely helpful in forecasting of a tornado. ∗ There should be a cyclonic wind shear at surface level. ∗ At forecast time, pressure fall should be more than 6 hpa over that particular area. ∗ Narrow dew point ridge extends into area with dew point 13° C or higher. ∗ Sign of any lifting mechanism approaching e.g. Squall line, frontal system, thunderstorm area or precipitation area. ∗ Widespread heavy rain is unfavorable for tornado formation. ∗ Location of threat area within favorable region (SE quadrant) of cyclone – especially for family types of tornadoes. ∗ Large irregular 3 hourly pressure changes. ∗ Low level (850 hpa) moist southerly current of 30 knots or more into the area. ∗ Upper level (about 14000 ft.) westerly current of dry and cooler air into area. ∗ 700 hpa temperature advection pattern showing warm advection east of trough and cold advection to west of trough. ∗ Strong 500 hpa temperature gradient to west of area. ∗ Cold air pocket at 500 hpa to west of area. ∗ Upper level (500 or 300 hpa) trough to west of threat area deepening or accelerating toward area ∗ An approaching 300 hpa jet maxima. (Joseph G. Galway) Time of the day, season and geographical location of threat area should also be taken into consideration by the forecaster.
Preparedness Although tornadoes are not very common in Pakistan and people generally don’t know what precautionary measures have to be adopted in case of an outbreak of tornado. Awareness of the capability of destruction of the tornado needs to be developed in general public through electronic and print media.
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An Outbreak of Tornado on March 28, 2001. Vol. 6
There are some precautionary measures taken into consideration in case of occurrence of any threatening weather. Before the Storm • Develop a severe weather plan for home, work, school and when outdoors. • Know the area where you live, and keep a highway map nearby to follow storm movement from weather bulletins. • Listen to radio and television for latest information. • If planning a trip outdoors, listen to the latest forecasts and take necessary action if threatening weather is possible. If Warning is Issued or If Threatening Weather Approaches • Occasionally, tornadoes develop so rapidly that advance warning is not possible but however the amount of time between the issuance of a Tornado warning and the touchdown of a Tornado ranges from 12 minutes to 55 minutes, providing critical time for emergency message to sound from radio, television and tornado sirens(if available). • In a home or building, move to a pre-designated shelter, such as a basement. • If an underground shelter is not available, move to an interior room or hallway on the lowest floor and get under a sturdy piece of furniture. • Stay away from windows and doors. • Opening windows allows damaging winds to enter the structure. Close the windows and immediately go to a safe place. • Get out of automobiles. • Do not try to outrun a tornado in your car; instead, leave it immediately. • Remain alert for signs of an approaching tornado. Flying debris from tornadoes causes most deaths and injuries. With these measures taken we can mitigate the would-be losses and damages.
Conclusion The synoptic conditions on the surface charts of 0000 UTC to 0900 UTC and upper air charts of 850 hpa, 700 hpa and 500 hpa level, satellite imagery of 0900 UTC were thoroughly investigated and found that synoptic situation as well as season and time of the formation of a tornado fulfill the criteria as mentioned under the caption “forecasting of a tornado”. Tornadoes are not very common in Pakistan, hence the level of its preparedness is insufficient and the March 28 Tornado resulted in substantial casualties. Most people were and are still not aware of what to do in the event of Tornado. With the recent introduction and operational use of various Numerical Weather Prediction models in PMD, the capacity of forecasting & warning system has been enhanced, especially for major events. The preparedness program still need improvement, the collaboration between public and private sector institution can provide excellent warning and information to the
Acknowledgment Authors are grateful to Khan Azmat Hayat, for providing access to data and information of Bhalwal Tornado. Comments on manuscript offered by Sardar Sarfaraz & Muhammad Hanif contributed to valuable improvement. We also wish to thank Ferdinand Valk for supplying the HRPT images.
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References Charles A. Doswell and Harold E. Brooks, 2002: Lessons Learned from the Damage Produces by the Tornadoes of 3 May 1999, Weather and Forecasting, volume 17, 611-618 Joseph G. Galway, 1992: Early Severe Thunderstorm Forecasting and Research by the United States Weather Bureau, Kansas City, Missouri, 7, 568, 569, 577 Richard W. Anthony and Preston W. Leftwich, Jr., 1992: Trends in Severe Local Storm Watch Verification at the National Severe Storms Forecast Centre, National Weather Service, National Severe Storms Forecast Centre, Kansas City, Missouri, 7, 614 Weightman, R.H., 1933: Report on progress in studies regarding the occurrence of tornadoes. Bull, Ame. Meteor. Soc., 14, 99-101 Khan. A. H, Tornado hits Chak Misran-A village of valley Soan-Skacer- Pakistan, http://www.pakmet.com.pk/journal/chakmisranreport.htm Wikipedia, http://en.wikipedia.org/wiki/Tornado NCEP, http://nomad1.ncep.noaa.gov/
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Table 1: Significant weather on March 28, 2001
Temperature (°C) Clouds (Oktas) e Wind Weather nc i Pa) ty % s h i l ( r a e t L
er o t ath e ion ve i (mm) re MS t w weath u Max. t s en and amount and amount Hour/ Station Dry Bulb Direct Wet Bulb Dew point Speed (Kts) 0300 hours GMT Pas Total amount rogress Pres Pres Relative Humid P Type Type Lahore AP 0900Z 999.9 29.0 22.5 --- 19.1 55 SCCU4 --- 4 Trace E 12 96 5 1200Z 999.3 27.0 21.5 30.0 18.5 60 SCCU3 AC1 4 Trace E 15 611 5 Sargodha 0900Z 999.2 29.0 22.0 --- 18.9 52 SC2 AC5 7 0.0 NE 5 1 4 1200Z 998.8 24.0 17.0 --- 11.7 46 CBTRSC3 AC5 7 0.0 N 10 9 4 Mianwali 0900Z 1002.9 20.0 17.0 --- 14.9 73 CB1SC4 ACAS7 7 7.0 NE 12 9 95 1200Z 1000.3 20.0 16.0 23.5 13.0 64 SCCU3 AC5 7 14.0 N 16 9 2 Peshawar 0900Z 1005.5 16.5 14.5 --- 13.5 80 CBTSCCU AC4 7 20.0 NE 6 9 4 1200Z 1005.0 15.5 14.5 30.5 13.2 89 CB1SCCU4 ACAS7 7 21 SE 10 96 99 Islamabad 0900Z 1001.2 24.2 16.7 --- 11.1 --- CBTSCCU AC5 7 0.0 NW 16 ------1200Z 1001.9 17.8 13.3 33.8 9.4 59 CB1SC4 ACAS7 8 3.0 W 40 17 99
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Pakistan Journal of Meteorology Vol. 6, Issue 11
An Empirical Microclimate Analysis of the Coastal Urban City and Comparison of Vicinal Urban and Rural Areas with It Naeem Sadiq1 and Ijaz Ahmad1 Abstract This Paper is an innovation of microclimate analysis of Karachi (coastal-urban) accompanying comparison of Hyderabad (urban) and Badin (rural) with it. Temperature along several interrelated tenement meteorological parameters like precipitation, thunderstorm and rainy days, wind, clouds, relative humidity, and sunshine are studied, analyzed and depicted. The study is asserted by characterizing statistical parameters, correlations, different calculations, scatter & dot plots with maintaining HIE, HII, range and trend graphs. Manora station recorded highest values of minimum temperatures, relative humidity and wind speed among all stations. Masroor town experiences the highest amount of precipitation, thunderstorm and rainy days. Hyderabad is leading in sunshine hours possessing heat island effect (HIE) and hence because of heat island intensity (HII) found not only significantly warmer than Badin but more than Karachi also. Rising Trends of ranges between urban and rural area indicates the increasing anthropogenic activity in urban area. Further details of each parameter for every station are incorporated in Paper. Key words: Microclimate, Heat Island Effect, Heat Island Intensity, Urban, Coastal-Urban, Rural.
Introduction Karachi is located in south of Pakistan and directly north of the Arabian Sea, covering an area of 3,527 square kilometers, which is largely comprised of flat or rolling plains with hills on the western and northern boundaries of the urban sprawl (Khan, 1993). Two rivers namely Malir and Liyari Rivers pass through the city from northeast to the center and from north to south respectively. Many other smaller rivers pass through the city as well. The Karachi Harbour is sheltered bay to the southwest of the City, and is protected from storms by Kemari and Manora Islands, as well as by Oyster Rocks (Shahid, 1998). Together, these natural barriers block the greater part of the harbour entrance in the west. Karachi is delimited by the Arabian sea towards the south by a chain of warm-water beaches. There are some important micro skin textures of Karachi, which are also helpful to understand the general pattern of weather here. As regards for Physical Geography, the hilly area in north and northwest, vault of Drigh road, Lower valley of Hab, Lyari and Malir and the coastal area are the five prominent physiographic divisions (pittahwala et.al., 1953). Precipitation, wind speed, temperature and even humidity are different in these parts. Topography of low hill areas like Bathe Island, Ghizri and Clifton has a little changed climatic conditions contrast than the interior part of the city. While Korangi, Manora, Sandspit and Hawks Bay are the chic parts of the City. The heights of the hills of Mongho Pir are not enough to catch the saturated clouds in monsoon season. This range is also fail to prevent cold and hot spells in winter and summer respectively. Generally, the almost sterile low hills in the City do not help the climate in any way instead it is the source of dust annoying. This Waterfront City near Arabian Sea also lies between deserts of Baluchistan (in the west) and Ther- Rajputana (in the east). Sea plays significant role in temperature control of Karachi. Little balminess in winter and soothing sea breeze spells in daytime and land breeze during nights in summer are consecrations of this sea, which also counterbalance the low and high temperatures. As the reflective power of water is higher than the land, accordingly sea takes time to be heated and cooled. This factor helps to make the climate of Karachi placid and tolerable for most of the year. The river valleys in the neighborhood of Karachi are entirely dry and their beds are sandy, so that the temperatures are raised and radiation is increased even in these river valleys. At the same time, the desert also participates substantially. But for the sea and the underground supply of fresh water, in the river alluvium, Karachi would have been a desert. Always in Sind there is period of 40 days in May-June, called “Chaliho”,
1 Pakistan Meteorological Department 57
An Empirical Microclimate Analysis of the Coastal Urban City……. Vol. 6 which is considered to be a terrible time for Karachi as well (Shamshad, 1988). Only cooling systems make it comfortable to some extent. The climate of Karachi is affected by several complex factors including the above mentioned factors and since the city is now expanding more rapidly in all the ways including population, pollution and infrastructure by developing and new colonies, satellite towns, suburbs, etc., it seems necessary to inquire the micro-climates of Karachi with their causes and effects in different parts of the city. As this Metropolitan coastal city is big with respect to area structure and population, different parts of it show different climatic peculiarities, needed to study further separately (Sadiq, 2007). This inspires to analyze further divisional climatic study of Karachi by dividing the city into microclimate zones. Further we analysed the available nearest rural area Badin and urban city Hyderabad for comparison.
Data and Methodology To accomplish microanalysis of the climate of Karachi, weather logs of several different places within the city limits would have been very useful. In the absence of such an ideal network, we have tried to make use of the meteorological data of the four available observatories of Karachi (with observatories of Hyderabad and Badin), mentioned in the following section. The study utilizes the mean monthly and mean annual data of ten meteorological variables viz Maximum (Tx) Minimum (Tn) and Mean (T) temperatures, Precipitation (Ppt), number of rainy days (Pptd), thunderstorm days (Td), relative humidity (Rh), wind speed (Ws), Cloud amount (C), and sunshine hours (SSH). We examined Karachi as an urban-coastal city, comprises the observatories of Karachi airport (KHI), Faisal town (FST), Masroor Town (MST), and Manora Observatory (MNS). The nearest available urban area Hyderabad (HYD) further which is near to rural area of Badin (BDN) is utilized for comparison. To incorporate this comparative analysis we employ the maximum available data of different parameters for different stations (cf. Table 1). Initially the characterization of mentioned meteorological parameters undertaken with the help of basic statistical parameters including Mean, Median, Standard Deviation, Maximum & Minimum values and the range between these values. Then after going over the compatibility and suitability through correlations, data is found appropriate to use for the undertaking of comparison.
Findings Calculations of characterizing statistical parameters reveals that for KHI the period from 1931 to 2008 shows almost doubled standard deviation value (6.33) of minimum (Tn) to the maximum temperature (Tx) (3.20), while for HYD and BDN, Tn shows somewhat higher values than Tx (cf. Table 2). Sunshine hours and amount of clouds at BDN are greater than HYD but less than KHI. Seasonal rain and relative humidity show maximum standard deviation because of their highly variable nature. The correlation matrix indicates the interdependence to the variables (cf. Table 3), which suggests that meteorological variables are strongly correlated to each other and compatible for comparison. Ascertaining of the analysis with related calculations and diagrams are discussed in subsequent sections Temperature Maximum, minimum and average mean monthly and mean annual temperatures are drawn in figure 1 to 3 and the resulting important values regarding mean annual temperature(s) are summarizes in table 4.
Mean Monthly and Mean Annual Maximum Temperature (Tx) In monthly analysis HYD and BDN appears with first and second highest values from March to October respectively, while among Karachi observatories MST touches the peak values for the
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same months (Fig. 1a). MNS is lying with least values and KHI shows almost the same attitude like FST throughout the year. HYD stands with highest values among all because of its heat island effect and second berth is for BDN (Fig. 1b). Among the stations of Karachi, FST has the maximum values of Tx. The widest range is covered by BDN and HYD respectively with values above than 3oC. KHI and FST acquire equal range with different values (cf. Table 4). The graph also shows that the highest value of Tx of MNS is less than initial values of KHI & FST. Most Recurring values (MRV) of Tx for all stations is also summarized in Table 4.
If we compare the Karachi observatories with the HYD (Urban), the maximum value of the Tx for Karachi stations lie almost at the minimum value of HYD. BDN has the more scattered value of maximum temperature. The Tx range of BDN is less than HYD but higher than observing stations of Karachi.
Mean Monthly and Mean Annual Minimum Temperature (Tn) From May to September almost all stations shows singularity. The maximum difference appears in December and January (peak winter months). As Badin is a rural area with no signs of HEI so it reaches to the minimum values of Tn (Fig. 2a). MNS because of the nearness to the sea shows highest values. MST, KHI and FST are showing very slight difference, whereas HYD has greater values than BDN.
Tn for MNS has the greater values of temperature. It is because of the creek island characteristics of MNS due to which its temperature does not decrease so much as the land area. MST and FST are next to it, while FST is mostly consisting of scattered values (Fig. 2b). KHI acquire the widest range of Tn. If we compare HYD with BDN, the most common repetitive values of Tn are found higher than BDN and lower than MNS (cf. Table 4), while values of FST and MST are almost lying in the range of HYD. KHI appears with most disperse values whose initial and final values are more expanded than HYD.
The minimum values of Tn appear at rural area of BDN because of no heat island effect. The highest value of Tn for BDN is the lowest value for MNS. The temperature of HYD is higher than BDN due to HIE, which will discuss in later sections. Mean Monthly and Mean Annual Temperature (T) During February and November all the stations shows analogues while in May and June most distant values (Fig. 3a). The scheme of the temperature positions for all observatories is somewhat similar as for Tx (with different temperature values). Comparison of Karachi observatories reveals that MNS has the least value of T and as it is an island, so the annual variation in temperature is less as compared to the other stations (Fig. 3b). The least value of temperature for MNS is the highest value for FST. As regards MST, the values of temperature are more scattered. The most scattered values of KHI are notable. Comparing all observatories, HYD has the highest temperature and BDN is the next one and then KHI. As HYD is an urban area and also away from the coast, so its temperature is higher due to Heat Island Effect. The minimum value of temperature for HYD starts from 27.5ºC, which is greater than maximum value of MNS (26.7ºC). The maximum value of temperature for HYD can reach up to 28.9ºC, which is the highest value. If we compare BDN with Karachi stations, then it is clear that BDN has also the highest range of temperature than Karachi stations. The reason is that BDN is the land area, away from the coastal area. The value of temperature of BDN is less than HYD because HYD is an urban area.
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Heat Island effect (HIE) Heat Island Effect indicates a metropolitan area which is significantly warmer than its surrounding rural areas. Urban Cities consume enormous amounts of energy in heating, cooling and transportation (WMO, 1997). The released energy (heat) together with that generated by each city dweller and his or her activity, heat is reflected by the physical fabric of the city itself, bouncing from one hard surface to another and often trapped under a layer of pollution that arches over the city and prevents its from escaping (Oliver & Hidore, 2003). Added to this, building material and roads surfaces store heat; cities for typically several degree warmer than surrounding rural land (Khan & Arsalan, 2007). The same effect for the Hyderabad for the first day of January 2008 is shown in figure 4a. In between 1200 and 1500 GMT (at about 0700 and 1000 PST) the graph lines crosses each other showing that HYD now releasing its absorbing heat less rapidly than BDN till 0100GMT. Stronger heat retention and release, including fuel consumption, construction, gives significant increase in temperature of Hyderabad (Urban) creating this effect. Three observatories of Karachi (data of MNS not available) are also shown in the same graph. That after 1200 GMT the difference between lines of same pattern appear in which KHI releases its heat more rapidly and MST more slowly because of nearness to the sea. So the observation shows that HYD shows the prominent Heat Island Effect at night for HYD. The situation of 1st May 2008 is shown in figure 4b. As per expectation the diurnal variations of BDN and HYD are more spectacular than the observatories of Karachi, which appears with less diurnal variation due to coastal effect. At 0000 GMT both urban and rural area shows temperature lesser coastal city observatories but HYD at about 0400 GMT and BDN at about 0600 GMT starts absorbing heat more rapidly. HYD leads BDN till night. Heat Island Intensity (HII) The degree of Heat Island Effect is expressed as Heat Island Intensity (Park, 1987), which is calculated for KHI, HYD and BDN in terms of urban, rural and coastal-urban areas for T, Tx and Tn. The difference between HIE factor for ∆Tu-c and ∆Tu-r appears most striking for Tx, relatively less prominent for T and negative for Tn (cf. Table 5). Depictions of the calculated Intensity are shown in Fig 5. For Mean Monthly Temperature (T)
It is evident that Heat Island Intensity for T and Tx is more in between urban and coastal area (Fig. 5b) than the urban and rural area (Fig. 5a), while intensity of urban and rural areas put more effect on Tn (Fig. 5c) than Tx and Tn. Regarding Intensities for T, urban and rural areas has maximum values of 1.4, 1.6 and 1.3 during main monsoonal months of July, August and September respectively (Fig. 5a). The contrast of temperature is maximum in the monsoon season due to HIE of HYD. Similarly, minimum values comes out for the months of December, January and February (0.4, 0.3 and 0.3 respectively) shows that this effect is minimum in the winter season. Seasonally the HII for summer is four fold as compared to the winter season.
For Mean Monthly Maximum Temperature (Tx) ∆Tu-c shows that the temperature of the KHI is less than the BDN (Fig. 5b). The greater temperature contrast (3Co) is due to the greater heat capacity of the Arabian Sea lying in the vicinity of KHI. The graph further shows that the coastal effect diminishes the Island Effect of KHI and the highest value of heat island intensity is observed in the pre-monsoon months. The notable feature is the minimum negative Heat Island Intensity for the main winter months of December & January viz. -0.6, 0.3 and -0.5 respectively. While the maximum values appear for the months of April, May & June viz. 2.5, 3.1 and 2.6 respectively express that urban land
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become hotter more rapidly than the coastal area. This shows that as the summer season approaches, the temperature of urban area increases rapidly and that of the coastal area increases slowly as the specific heat of water is greater.
The most pronounced effect of HII regarding Tx found for ∆Tu-c, which is even greater than ∆Tu-r, while ∆Tu-r acquires the highest value throughout the summer season. However the intensity for urban and coastal area is minimum during the winter as temperature of the urban area decreases rapidly and coastal-urban city shows less variation due coastal effect.
For Mean Monthly Minimum Temperature (Tn)
For Tn, the ∆Tu-r is more pronounced in the winter season as compared to the summer season. It is because of HIE in winter, when Tn of urban area is higher than rural area (Fig. 5c). It shows that the temperature of rural area is marked decreasing in winter whereas in summer months due to decreasing and increasing Tn values of HYD and BDN respectively, the resulting Heat Island Intensity is also decreasing. Three notable crests appears for ∆Tu-c, showing peaks from March to April, July to August and for October to November, which indicate that during this period the temperature of urban city is exceeding from coastal urban city. It is also important that in acme of summer and winter intensity becomes analogous for urban and coastal-urban areas. Further the mean annual temperature(s) ranges for ∆Tu-r and ∆Tu-c are also considered for Karachi, Hyderabad and Badin in the subsequent sections. Mean Annual Temperature (T) Range The notable abrupt increase in the range during late seventies and eighties (figure 6a); shows the beginning and development of industrialization in the urban area from 1975 to 1988 due to which both HIE and hence HII increases. However the linear trend model equation shows slight increase of 0.043 per decade. Figure 6(b) shows that the trend line with relatively sharp decreasing rate of 0.127 per decade. This means that the sea surface temperature is rising due to which the temperature of coastal-urban area is also increasing with time. This increment might be due to rapid growing population, sloppy structure and complex composition of Karachi city, as well as the thermal properties of coastal construction materials. Additional factors for rising temperature of Karachi includes massive transportation and pollution supportive industrialization.
Mean Annual Maximum Temperature (Tx) Range
The Tx range for urban and rural area (Fig. 7a) is found analogous to Figure 6a but with steeper slope of 0.104 increment per decade, indicates that the maximum temperature of the urban area of Hyderabad is increasing. Linear equation reveals the most steep (of decreasing tendency) trend for Tx range regarding urban and coastal areas (Fig. 7b). This means that the Tx of the coastal- urban area is increasing due to urbanization as well as due to coast effect (specific heat of water body). The temperature of the coastal-urban city sharply increasing after 1988 to 2000 is notable.
Mean Annual Minimum Temperature (Tn) Range
The Tn of the coastal area is increasing as compared to the urban area. It is important that almost from 1960 to 1990, the urban area (HYD) is warmer than the coastal-urban area (Fig. 8a). However in the last decade the temperature of KHI is increasing drastically as compared to the HYD. However the combined effect comes out as an almost straight resulting trend line. Considering ∆Tu-c for Tn range (Fig. 8b), decreasing tendency of -0.065 per decade appears. From 1960 to early nineties increase in temperature of urban area and from late nineties to onwards sharp increase for coastal area is notable.
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Rainfall (Ppt) & Rainy days (Rd) Except FST and BDN which shows normally distributed attribute (and hence greatest amount of rainfall appears in July and August) all others station’s show positively skewed distribution of rainfall because of which they all show highest rainfall only in the month of July (Fig. 9a). In the peak month of monsoon ascending order with respect to amount of rainfall found viz. HYD, FST, MNS, BDN, KHI and MST. From Mid of November to March bunch of BDN, HYD and KHI distinguished from the bunch consisting of MST, FST and MNS. Similarity between the KHI and BDN’s deportment in the month of October and from April to July is a highlighted feature. Mean monthly graph also shows co-effect (i.e. coastal & urban) of Karachi, leads to the increased buoyancy, convection and moisture resulting in increased Ppt at Karachi. However, the increased amount of ppt of Badin than Hyderabad in monsoon season shows that the monsoon season is much more prevalent in Badin. The annual amount of precipitation of MST and KHI is greater than FST and MNS. The value of precipitation for MST lies with in the range from 2.5 to 957 mm in 1967 (CDPC, 2005). The maximum values lie within the range from 20 to 140 mm/yr (Fig. 9b). The rainfall for KHI is more scattered and has the values within the range from 0 to 713 mm/yr. The Ppt for Karachi stations is of similar pattern throughout the year. In early winter the amount of rainfall is greater over KHI and FST. However in March and from mid of June to mid-July, MST surmount than all other stations. In the month of April and May, the Ppt of HYD and BDN is greater from all the stations of Karachi. In the month August, FST is at the top in the graph and sharply decreases in the months of September to October as compared to the other stations. In the pre- monsoon season, BDN has the highest rainfall, which indicates that, the monsoon season lag behind the stations of Karachi and HYD. BDN has the precipitation with in the range from 0 to 913mm in 1994 (CDPC, 2005) and has accumulated values with in the range from 160mm to 360 mm/yr. Among Karachi observatory of Masroor town acquire the maximum number of rainy days (Fig. 10a), as it lies in the west of the city near the Arabian sea where drizzle and light rain is common due to frequent appearance of stratus clouds. Annual rainy days of MST are greater than any station of Karachi. For MST the annual rainy days varies in the range of 1 to 45 days (Fig.10b). The greater amounts of rainy days over MST are due to the drizzling in the months of August and September because of the stratus clouds. The maximum values of rainy days occur in the range from 9 to 30 days. MNS has the maximum rainy days with in the range from 1 to 14 days. KHI has the maximum amount of rainy days with in the range from 3 to 12 days. The minimum rainy days occur in May and October when land breeze invade in the southern part of the country while maximum rainy days usually occur in July & August. Pptd of BDN are less than HYD in winter season while in summer BDN acquire somewhat greater values than HYD. The possible reason is that in winter the temperature of BDN is less than HYD (due to Heat Island Effect) and so there is no convection of air available for the development of clouds. If HYD is compared with Karachi stations, it has the lower amount of rainy days. HYD has the maximum rainy days within the range from 1 to 8 days. BDN is the station, which has the least amount of rainy days with in the range from 0 to 23 days. The maximum values of rainy days lie in the range from 3 to 9 days for BDN. The notable feature is that all the stations have minimum rainy days in the months of May and October. Thunderstorm Days (Td) These days are maximum at MST through out the year except in the months March, April and May when HYD has the maximum Td (Fig.11a). The annual Td is maximum over the MST with minimum value of 0 and maximum value of 30 days. The maximum value of thunderstorm occurs within the range from 3 to 7 days.
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Among the stations of Karachi, MNS has the least thunderstorm activity in all the months. All the stations appear with least Td during the months of May & October because of less convection and due to transitional period between winter & summer. The least values of Td over MNS are due to the possible reason that the station is located very near to the Arabian Sea and there is no enough heating for the development of convectional clouds, as a result and only stratus clouds are frequently developed. In winter (November, December, January & February) HYD possess less Thunder activity and in the months of March, April, May and June has stronger activity. BDN has the least Thunderstorm activity in the winter season as its temperature drops and no convectional clouds are developed. Considering mean annual thunderstorm days among the stations of Karachi, MNS has the least value within the range from 0 to 5 days (Fig. 11b). Comparison of HYD with Karachi stations brings out that HYD has Td less than MST & KHI and more than MNS. Td of HYD also comes out as more than BDN. It might be because of the fact that area where the temperature is higher has the high probability of development of convectional clouds (cumulonimbus and towering cumulonimbus), which are main source of thunderstorm. HYD has the maximum values of Td within the range from 2 to 6 days and has maximum values as high as 20 days. The least amount of Thunder storm over MNS is due to the fact that the temperature of MNS cannot increase as much as necessary for the convectional clouds. Wind speed (Ws) All Karachi stations are windier than Hyderabad (Urban) and Badin (rural) except KHI for June to September when BDN’s wind is equal or greater than KHI (Fig.12b). As the area of MST is comparatively more open, Ws here is much greater than FST & KHI throughout the year; also greater than MNS but both become equal in December & January. From October to March Ws of FST becomes less than MNS, which is otherwise dominating in other months. Similarly from June to September HYD’s wind is dominating to KHI’s, which is otherwise lesser in other months. In general HYD’s wind is greater than BDN throughout the year except February when both become equal to each other. Considering mean annual values BDN’s wind ranges from 2.5 to about 10.1 knots while MST’s range start form 9.6 kt to about almost 17.1kt. A notable feature of HYD wind is the absence of 9.6 to 12.6 kt wind. While its first range is from 4 to 10 kts and other is 12.5 to 14.8 kts (Fig. 12b). HYD acquire most scattered values of Ws, lies in the range from 3.8 to 14.8 kts. The highest values of wind speed lies in the range from 3.5 to 6.0 kts. BDN has the lowest wind speed than all other the stations, ranges from 4.5 to 9.0 kts. Regarding coastal city, MST experiences highest values of wind speed (9.6 to 17.1 kts) as it is more open and less friction area as compared to the other stations. Its highest values of Ws lie within the range of 11.5 to 14.5 kts. The lowest speed at KHI is due its location, as now it is comparably more inside the city so friction area for wind is more. Relative Humidity (Rh) This important factor is undertaken because in-coupled with temperature it may affect changes in cooling rates and therefore heat island development (Jauregui, 1996). The lowest values for Karachi stations exist for the winter months while for BDN and HYD least values found in March and April (Fig. 13a). HYD observed less humid than BDN as Badin lying in south of Hyderabad. Mean monthly pattern also shows highest value for summer months as the moisture holding capacity of sea breeze increases due to increasing temperature while peak value appear for the month of August (peak monsoon in this region) for all stations. As per expectation MNS (as it is creek island in southwestern side of the city) and KHI (lies in north eastern part of the city) are found most and least humid Karachi stations respectively. Mean annual dot picture also establish higher amount of relative humidity for the stations lying nearest to coast. For MNS the relative humidity lies in the range from
63 An Empirical Microclimate Analysis of the Coastal Urban City……. Vol. 6
60.5% to 78.2%. Most of the values are accumulated in the range from 63% to 70% (Fig. 13b). MST is second to MNS whose values lie in the range from 50% to 63.7%. Karachi stations have the decreasing trend of humidity as the distance from the coast increases. As KHI has maximum distance among the Karachi stations so it has the lower relative humidity among these stations. Likewise if Karachi stations are compared with HYD, the HYD has least relative humidity due to higher temperature, distance and elevation from the coastal area. For HYD the relative humidity lies in the range from 28% to 42%. Most of the values are accumulated in the range from 30% to 40%. Comparing HYD with BDN, the relative humidity of BDN is greater than HYD because BDN is a rural area, has comparably lower temperature and nearest to the Run of Kutch area of India. Clouds (C) In summer MNS is most cloudy than any other station of Karachi as total amount of clouds are more in the summer & monsoon season at all Karachi stations. The overall cloudiness over Karachi stations (KHI, MST, FST, MNS) is more (Fig. 14a) than HYD & BDN in all seasons because of sudden and frequent cloud (mostly stratus) appearances here. As the HYD & BDN is comparably far from the coastal area, so the total amount of clouds over these stations is comparably less. However comparison of HYD and BDN shows, that the cloud amount over HYD is greater in winter season and lesser in summer than BDN. In monsoon season, as the BDN station is near to the coastal area than HYD, so there are many clouds over BDN than HYD and hence more thunder storm, rainy days and precipitation over BDN. Mean annual graph shows that all the stations of Karachi are covered with high amount of clouds almost in the range from 2.0 to 4.0 oktas (Fig. 14b). The clouds are more scattered at MNS. Over KHI the clouds are mostly accumulated within the range from 2.4 to 2.8 oktas. The least amount of clouds is found over BDN with scattered values. Over HYD, the clouds are greater than BDN and are mostly accumulated within the range from 1.4 to 2.2 oktas. Sun shine hours (SSH) Hyderabad acquire maximum sunshine leading to Badin while Karachi get the least value with a common (but with different amounts) attitude that the minimum hours are observed in peak monsoon period (July and August) and maximum increasing tendency found in seasonal shifting from winter to summer, where as in monsoon period there is sharp decrease observed (Fig. 15a). Karachi appears with least values because of its coastal attitude as clouds are very common here which block the isolation and this behavior is at peak in July and August (monsoon season) when the absorption, scattering and reflection of solar radiation is maximum. The graph also shows that the sunshine hours for Hyderabad (urban) is maximum in the summer and minimum in winter than KHI & BDN. Mean monthly sunshine hours (for annual and monsoon) and its percentage is summarizes in table 5. As regards the values of total annual sunshine hours of KHI, it lie within the range of 2477 hrs/yr to 3168 hrs/yr while maximum hours lies with in the range from 2840 to 2980 hrs/yr (Fig.15b). Sunshine hours for HYD is greater than KHI and BDN may be because of relatively far distance of HYD from the coastal area and hence fewer amounts of clouds reached there, hence increases the amount of sunshine hours. The values of total annual sunshine hours for HYD lie with in the range from 230 hrs/yr to 3501 hrs/yr. The maximum value of sunshine lies with in the range from 2960 to 3380 hrs/yr.
Conclusion Undertaken analysis comprises of the microclimate of coastal-urban city (Karachi) and further comparison of urban (Hyderabad) and rural (Badin) areas with it. The anthropogenic activities make some substantial changes to the climatic parameters of urban areas especially in respect of temperature, so this parameter studied in more detail with other selected meteorological variables.
64 Issue 12 Naeem Sadiq, Ijaz Ahmad
The pattern of average and maximum mean monthly temperature is found with similar pattern (with different values). Hyderabad (highest) and Badin (2nd highest) acquire the values greater than Karachi stations while MNS acquire the least values among all. Very slight values of MST are observed greater than FST in-coupled with KHI. As far as minimum temperature is concern, MNS is at the top and then MST, FST, HYD and BDN for winter months respectively. Urban area (HYD) shows marked Heat Island Effect when comparing with rural area (BDN) and Karachi. HII is greater for HYD and KHI regarding mean and maximum temperatures while marked less for minimum temperature, which indicates the rapid climatic changes regarding Karachi climate due to Pollution, population, industrialization, transportation etc. Intensity also shows the marked decrease in temperature of BDN during winter as there is no existence of HIE for it. The maximum effect of HII observed on mean annual maximum temperature ranges, which shows the most steeper (upward and downward) trends of urban & rural and urban & coastal. Among all stations MST get highest and HYD get the least amount of precipitation in monsoon while FST gain the least amount among Karachi stations. For considered areas, very less amount of rainfall due to western disturbance (in winter) appear as compared to monsoon. For all stations least rainfall occur in the months of April, May, October and November. During monsoon MST and MNS came out with most and least values for rainy days within Karachi stations. Looking at all stations, MST and HYD last with maximal and minimal rainfall days respectively. Least rainy days appear for the months of May, October and November, while in winter minimum rainy days occur for BDN. Considering all stations during monsoon the highest and least thunderstorm days occur at MST and MNS respectively. Second highest values acquire by KHI and then BDN. Throughout the year MNS remain most windy whereas HYD posses least values of wind indicating that urbanization (due to modified structure) reduces the wind speed and HYD observed with peak and bottom values of winds. MST is at second position while FST is analogous to KHI with somewhat higher values and then BDN came out with a marked difference. The pattern of relative humidity demonstrates that the examined stations are inversely related to the distance between sea and observatory location being highest for MNS and lowest for HYD. Concerning cloud amount all observatories of Karachi shows a close bunch with (more cloudy than BDN and HYD) very little difference in between but marked difference with BDN and HYD. Values are closer in October and far in monsoon. A notable feature of HYD’s cloud amount is found that from October to April its values become very closer to Karachi stations. Percentage of HYD sunshine is 16 and 23 percent greater than BDN and KHI while annually it is only 4 and 8 percent higher. The pressure of continuous growing population and pollution is emerging as a serious long-term threat by increasing in diseases and death rates in the urban area, which needs to be further investigated.
Acknowledgment The authors are gratefully obliged the Director General Meteorological Services, Dr. Qamar-uz-Zaman Chaudhry for his research encouragement to us and providing the computer facility in this respect. We also acknowledge the facilitation of Pakistan Metrological Department for providing the meteorological data for this study.
65 An Empirical Microclimate Analysis of the Coastal Urban City……. Vol. 6
References Climatological Data Processing Centre, (2005), Climatic Normals of Pakistan (1971-2000), Pakistan Meteorological Department, Karachi. Jauregui E, (1996), Urban Climatology and its Applications with Special Regard to Tropical Areas, WMO No. 652, Geneva, Switzerland. Khan J A (1993), The Climate of Pakistan, Rehbar Publishers, Karachi. Khan J A and Arsalan M H (2007), General Climatology, Department of Geography, University of Karachi. Oliver J E and Hidore J J (2003), Climatology: An Atmospheric Science, Pearson Education, Singapore. Park H, (1987), “Variations in the urban heat island intensity affected by geographical environments”, Environ. Res. Center Papers, 11, Tsukuba Univ., 1-79 Pithawalla and Shamshad K M (1953), The Climate of Karachi, Pakistan Herald Press, Karachi. Sadiq N, (2007): “Classification of weather at Karachi region”, M.Phil. Thesis, University of Karachi. Shahid M I (1998), Everyday Science, Carvan Book house, Lahore. Shamshad K M (1988), The Meteorology of Pakistan, Royal Book Company, Karachi. WMO (1997), Weather and Water in Cities, WMO No. 853, Geneva, Switzerland.
66 Issue 12 Naeem Sadiq, Ijaz Ahmad
Table 1: Available meteorological data of different parameters verses observatories Station Lat/Lon Elev T, Tx,Tn SSH Rh Pptd Ppt Td C Ws 24º 70′N 78 Yrs 62 Yrs 78 Yrs 48 Yrs 78 Yrs 48 Yrs 78 Yrs 64 Yrs KHI 21m & 67º13′E 1931-08 1947-08 1931-08 1961-08 1931-08 1961-08 1931-08 1945-08 24º 90′N 52 Yrs 52 Yrs 48 Yrs 52 Yrs 48 Yrs 52 Yrs 52 Yrs MST 11m ----- & 66º93′E 1957-08 1957-08 1961-08 1957-08 1961-08 1957-08 1957-08 24º 88′N 38 Yrs 38 Yrs 38 Yrs 38 Yrs 38 Yrs 38 Yrs 38 Yrs FST 6m ------& 67º12′E 1971-08 1971-08 1971-08 1971-08 1971-08 1971-08 1971-08 24º 48′N 57 Yrs 57 Yrs 26 Yrs 57 Yrs 26 Yrs 55 Yrs 46 Yrs MNS 2m ------& 66º59′E 1931-87 1931-87 1962-87 1931-87 1962-87 1933-87 1942-87 25º 38′N 78 Yrs 40 Yrs 76 Yrs 48 Yrs 78 Yrs 48 Yrs 76 Yrs 67 Yrs HYD 40m & 68º42′E 1931-08 1969-08 1933-08 1961-08 1931-08 1962-87 1933-08 1942-08 78 24º 63′N 35 Yrs 72 Yrs 48 Yrs 78 Yrs 48 Yrs 72 Yrs 67 Yrs BDN 10m Yrs1931- & 68º90′E 1974-08 1937-08 1961-08 1931-08 1961-08 1937-08 1942-08 08
Table 2: Characterizing statistical parameters Variable Station Mean Median St.Dev Maximum Minimum Range KHI 26.18 28.20 4.60 31.50 18.10 13.40 T HYD 27.61 30.20 5.80 34.10 17.60 16.50 BDN 26.81 29.25 5.45 33.00 17.30 15.70 KHI 31.80 32.30 3.20 35.30 25.60 9.70 Tx HYD 34.42 36.35 5.57 41.70 24.50 17.20 BDN 33.73 34.40 4.52 40.10 25.70 14.40 KHI 20.54 21.75 6.33 28.10 10.60 17.50 Tn HYD 20.79 22.30 6.26 28.00 10.70 17.30 BDN 19.92 21.70 6.72 27.50 9.00 18.50 KHI 51.43 46.90 13.90 71.50 36.00 35.50 Rh HYD 34.85 30.20 11.20 54.90 22.50 32.40 BDN 39.44 34.15 13.07 62.30 25.00 37.30 KHI 8.33 8.75 2.30 11.30 4.90 6.40 Ws HYD 7.63 6.80 2.97 11.70 4.10 7.60 BDN 6.192 6.00 2.29 9.30 3.00 6.30 KHI 240.0 264.5 54.0 290.3 133.4 156.9 SSH HYD 265.13 266.75 27.27 305.70 209.10 96.60 BDN 252.5 269.7 49.1 299.6 148.8 150.8 KHI 2.58 1.90 1.65 5.70 0.80 4.90 C HYD 1.72 1.65 0.82 3.40 0.60 2.80 BDN 1.60 1.15 1.41 4.50 0.40 4.10 KHI 0.50 0.30 0.58 2.00 0.10 1.90 Td HYD 0.55 0.45 0.56 1.70 0.00 1.70 BDN 0.48 0.15 0.64 1.80 0.00 1.80 KHI 0.93 0.65 1.03 3.30 0.00 3.30 Pptd HYD 0.76 0.50 0.71 2.30 0.20 2.10 BDN 0.67 0.30 0.80 2.40 0.10 2.30 KHI 17.93 8.15 26.59 89.40 0.70 88.70 Ppt HYD 14.54 4.25 21.27 59.50 1.50 58.00 BDN 19.03 4.30 30.06 87.20 1.50 85.70
67 An Empirical Microclimate Analysis of the Coastal Urban City……. Vol. 6
Table 3: Correlations Variable Station T Tx Tn Rh Ws S.Sd C Td Pptd KHI 0.92 Tx HYD 0.98 BDN 0.94 KHI 0.98 0.83 Tn HYD 0.98 0.92 BDN 0.97 0.85 KHI 0.83 0.56 0.92 RH HYD 0.41 0.22 0.57 BDN 0.46 0.15 0.64 KHI 0.88 0.70 0.92 0.87 Ws HYD 0.85 0.76 0.90 0.68 BDN 0.78 0.67 0.81 0.51 KHI -0.37 -0.04 -0.52 -0.76 -0.48 S.Sd HYD -0.00 0.17 -0.15 -0.75 -0.35 BDN -0.29 -0.01 -0.46 -0.83 -0.53 KHI 0.44 0.09 0.58 0.81 0.60 -0.97 C HYD 0.13 -0.00 0.24 0.61 0.45 -0.84 BDN 0.31 0.03 0.48 0.84 0.51 -0.93 KHI 0.35 0.05 0.48 0.69 0.49 -0.94 0.93 T.d HYD 0.65 0.52 0.74 0.77 0.83 -0.65 0.78 BDN 0.48 0.20 0.64 0.91 0.62 -0.94 0.96 KHI 0.27 -0.03 0.41 0.64 0.42 -0.96 0.94 0.98 Pptd HYD 0.39 0.23 0.52 0.84 0.62 -0.84 0.89 0.93 BDN 0.41 0.13 0.58 0.90 0.54 -0.89 0.97 0.97 KHI 0.34 0.05 0.46 0.67 0.45 -0.91 0.89 0.98 0.96 Ppt HYD 0.42 0.25 0.55 0.86 0.64 -0.82 0.85 0.93 0.98 BDN 0.40 0.12 0.56 0.88 0.52 -0.89 0.96 0.96 0.98
Table 4: Analytical values for mean annual temperature(s) Variable Station Minimum Maximum From Range MRV Value Value KHI 30.9 33.1 30.9 to 33.1 2.2 31.0 to 32.4 HYD 32.8 35.9 32.8 to 35.9 3.1 33.4 to 35.2 BDN 32.3 35.6 32.3 to 35.6 3.3 32.4 to 34.8 Tx MST 30.2 32.4 30.2 to 32.4 2.2 30.6 to 32.0 FST 31.9 32.7 31.9 to 32.7 2.1 31.2 to 32.8 MNS 28.4 31.0 28.4 to 31.0 1.6 29.0 to 30.8 KHI 19.4 22.5 19.4 to 22.5 3.1 19.6 to 20.8 HYD 19.7 22.1 19.7 to 22.1 2.4 20.0 to 22.3 BDN 18.8 21.0 18.8 to 21.0 2.3 19.2 to 20.2 Tn MST 20.1 22.1 20.1 to 22.1 2.0 20.9 to 21.7 FST 20.0 22.7 20.0 to 22.7 2.7 20.6 to 21.0 MNS 21.4 23.3 21.4 to 23.3 1.9 21.9 to 22.6 KHI 25.3 27.5 25.3 to 27.5 2.2 25.6 to 25.9 HYD 27.6 8.9 27.6 to 28.9 2.3 27.1 to 28.0 BDN 25.8 27.7 25.8 to 27.7 1.9 26.5 to 27.5 T MST 25.4 27.2 25.4 to 27.2 1.8 25.8 to 26.2 FST 25.8 27.7 25.8 to 27.7 1.9 26.0 to 26.5 MNS 25.1 26.7 25.1 to 26.7 1.6 25.6 to 27.1
68 Issue 12 Naeem Sadiq, Ijaz Ahmad
Table 5: Mean, Maximum and Minimum Temperatures and their respective differences for Karachi, Hyderabad and Badin. o o o o o o S.No Station T ( C) ∆T (C ) Tx ( C) ∆T(C ) Tn ( C) ∆T(C )
1 HYD 27.61 ∆Tu-c = 1.41 34.41 ∆Tu-c = 2.6 20.8 ∆Tu-c = 0.3
2 KHI 26.2 ∆Tu-r = 0.81 31.8 ∆Tu-r = 0.7 20.5 ∆Tu-r = 0.9 3 BDN 26.8
Table 6: Mean monthly sunshine hours and percentage Station Hours Percentage HYD (Urban) 265.1 74 Annual BDN (Rural ) 252.5 70 KHI (Coastal Urban) 240 66 HYD (Urban) 217.5 60 July & BDN (Rural ) 160.8 44 August KHI (Coastal Urban) 135.3 37
45.0
MNS
) 40.0
C º ( e MST ur t 35.0 a FST er np e
T 30.0 HYD
BDN 25.0 1 2 3 4 5 6 7 8 9 10 11 12 KHI Months 28.8 30.0 31.2 32.4 33.6 34.8 36.0 KHI BDN HYD MS T FST MNS Temperature (Celcius)
Figure 1: Mean Monthly and Mean Annually Maximum Temperatures for Urban, Rural and Coastal Urban
30 .0
25.0
) MNS C º 20.0 e ( MST ur 15.0 FST erat p m e 10.0 HYD
T
5.0 BDN 1 2 3 4 5 6 7 8 9 10 11 12 Months KHY KHI BDN HYD MST FST MNS 19.2 19.8 20.4 21.0 21.6 22.2 22.8 23.4 Temperature (Celcius)
Figure 2; Mean Monthly and Mean Annually Minimum Temperatures for Urban, Rural and Coastal Urban
69 An Empirical Microclimate Analysis of the Coastal Urban City……. Vol. 6
35
) 30 C º MNS e (
ur MST at 25
per FST m e
T 20 HYD
15 BDN 1 2 3 4 5 6 7 8 9 10 11 12 Months KHI 25.2 25.8 26.4 27.0 27.6 28.2 28.8 KHI BDN HYD MST FST MNS Temperature (Celcius)
Figure 3: Mean Monthly and Mean Annually Temperatures for Urban, Rural and Coastal Urban
(a) (b) 25 45
20 40
35 15 )
T (ºC) ºC (30 T
10 25
20 5 0 3 6 9 12 15 18 21 0 3 6 9 12151821 Time (UTC) Time (UTC) KHI MST FST HY D BDN KHI MST FST HYD BDN
Figure 4: Diurnal Variations of Temperature for 1st January and 1st May 2008
(a) (b) 3.5 7.0 3.0 6.0 2.5 5.0 2.0 4.0 ) s ) 3.0 u 1.5 s i iu lc 1.0 c 2.0 e l C 0.5 e 1.0 ( (C T 0.0 T 0.0 ? ? -0.5 -1.0 -1.0 -2.0 123456789101112 123456789101112 Months Months
?Tu-r ?Tu-c ?Tu-r ?Tu-c
70 Issue 12 Naeem Sadiq, Ijaz Ahmad
(c) 2.0
1.5 ) s u i 1.0 c l e 0.5 T(C
∆ 0.0
-0.5 123456789101112 Months ∆T(u-r) ∆T(u-c)
Figure 5: a. Mean Monthly Temperature Heat Island Intensity b. Mean Monthly Maximum Temperature Heat Island Intensity c. Mean Monthly Minimum Temperature Heat Island Intensity
(a) y = 0.0043x - 7.6797 (b) y = -0.0127x + 26.407 2.0 2.5
) 1.5 2.0 ) º º 1.0 1.5 (C -r (C 0.5 -c
Tu 1.0 Tu ∆
0.0 ∆ 0.5 -0.5 0.0 1930 1940 1950 1960 1970 1980 1990 2000 2010 Year 1930 1940 1950 1960 1970 1980 1990 2000 2010 Year Range Linear (Range) Range Linear (Range)
Figure 6: a. Mean Annual Temp. Range between HYD & BDN b. Mean Annual Temp. Range between HYD & KHI
(a) y = 0.0104x - 19.843 (b) y = -0.0212x + 44.363 3.0 4.0 )
º 3.0 2.0 ) º C ( c -r(C 1.0 - 2.0 Tu Tu ∆
∆ 0.0 1.0 -1.0 0.0 1930 1940 1950 1960 1970 1980 1990 2000 2010 1930 1940 1950 1960 1970 1980 1990 2000 2010 Year Year Range Linear (Range) Range Linear (Range)
Figure 7: a. Mean Annual Max. Temp. Range between HYD & BDN b. Mean Annual Max Temp. Range between HYD & KHI
71 An Empirical Microclimate Analysis of the Coastal Urban City……. Vol. 6
(a) y = 0.0002x + 0.4959 (b) y = -0.0065x + 13.021 2.5 2.0 2.0 1.0 ) ) º 1.5 º (C
-r (C 1.0
-c 0.0 u u T T
∆ 0.5 ∆ 0.0 -1.0 -0.5 -2.0 1930 1940 1950 1960 1970 1980 1990 2000 2010 Year 1930 1940 1950 1960 1970 1980 1990 2000 2010 Year Range Linear (Range) Linear (Range) Range Linear (Range)
Figure 8: a. Mean Annual Min. Temp. Range between HYD & KHI b. Mean Annual Min Temp. Range between HYD & BDN
100.0
80.0 m)
m (
n 60.0 MNS io t a t i 40.0 MST ip c e
r FST P 20.0 HYD 0.0 BDN 1 2 3 4 5 6 7 8 9 10 11 12 Months KHI KHI BDN HYD MST FST MNS 0 140 280 420 560 700 840 980 Precipitation (mm)
Figure 9: Mean Monthly and Mean Annually Precipitation for Urban, Rural and Coastal Urban
6.00 5.00 MNS
4.00 s MST y a
d FST y 3.00 n i
Ra 2.00 HYD
1.00 BDN 0.00 1 2 3 4 5 6 7 8 9 10 11 12 KHI Months 0 6 12 18 24 30 36 42 KHI BDN HYD MST FST MNS Rainy days
Figure 10: Mean Monthly and Mean Annually rainy days for urban, rural and coastal urban
72 Issue 12 Naeem Sadiq, Ijaz Ahmad
3.00
2.50
)
s MNS day 2.00 (
m
or MST t 1.50 s FST 1.00 under
h T HYD 0.50 BDN 0.00 1 2 3 4 5 6 7 8 9 10 11 12 KHI Months 0 4 8 12 16 20 24 28 KHI BDN HYD MST FST MNS Number of thunder storm (days)
Figure 11: Mean Monthly and Mean Annually Thunder storm of Karachi stations (KHI, MST, FST, MNS), Hyderabad and Badin
18.0 16.0
) 14.0 MNS
S T 12.0 K ( MST 10.0
peed FST
s 8.0 d n i
w 6.0 HYD 4.0 2.0 BDN 1 2 3 4 5 6 7 8 9 10 11 12 Months KHI KHI BDN HY D MS T FST MNS 2.5 5.0 7.5 10.0 12.5 15.0 17.5 Wind Speed (Knots)
Figure 12: Mean Monthly and Mean Annually wind speeds for urban, rural and coastal urban
80.0
) 70.0 % MNS ( y
t i 60.0 d i MST m 50.0 Hu
e FST v i
t 40.0 a l
Re 30.0 HYD 20.0 BDN 1 2 3 4 5 6 7 8 9 10 11 12 Months KHI KHI BDN HYD MST FST MNS 28 35 42 49 56 63 70 77 Relatuve Humidity (%)
Figure 13: Mean Monthly and Mean Annually relative humidity for urban, rural and coastal urban
73 An Empirical Microclimate Analysis of the Coastal Urban City……. Vol. 6
6.0
5.0
MNS
) 4.0 as t k
O ( 3.0 MST
ouds FST l
C 2.0 HYD 1.0
BDN 0.0 1 2 3 4 5 6 7 8 9 10 11 12 Months KHI KHI BDN HYD MST FST MNS 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Clouds (Oktas) Figure 14: Mean Monthly and Mean Annually Total Clouds of Karachi stations (KHI, MST, FST and MNS), Hyderabad and Badin
320.0 300.0
) s 280.0 hr 260.0 on( i t
a 240.0 r u 220.0 KHI
ne D 200.0 hi 180.0 HYD unS
S 160.0 140.0 BDN 2340 2520 2700 2880 3060 3240 3420 3600 120.0 Sunshine Hours 1 2 3 4 5 6 7 8 9 10 11 12 Months KHI HYD BDN
Figure 15: Mean Monthly and Mean annually sunshine for urban, rural and coastal urban
74 Pakistan Journal of Meteorology Vol. 6, Issue 12
Recent Water Requirement of Cotton Crop in Pakistan Ghazala Naheed1, Ghulam Rasul1 Abstract Cotton is one of the foremost crops which are grown in the kharif season and it plays a very important role in Pakistan’s economy, around two third of the country’s export earnings are from the cotton. Like all crops, a cotton plant's water requirement vary by the age as well as the environment it grows in. The dryer and hotter the environment, the more water the plant requires. Cotton is very drought tolerant and uses about the same amount of water as other crops like millet and sorghum etc. Cotton is mainly grown in Punjab and Sindh as it likes hot and dry climate . FAO-modified Penmen Monteith method was used to estimate the water requirement of cotton crop by using the climatic data (1971-2000) of mean daily temperature, relative humidity, relative sunshine hours and wind speed of sixteen meteorological stations of Pakistan. Precipitation data was used to calculate the supplementary water requirement to compare the deficit .After analysis it was observed that only the upper parts of Punjab cotton may be sown under rainfed conditions but it is very rare in this area. In central Punjab, cotton may not be sown under rainfed conditions without supplementary irrigation in May. In southern Punjab and Sindh regions, cotton requires supplementary irrigation throughout its life cycle. Therefore, proper irrigation is required to fulfill the water needs of the cotton crop throughout its life cycle for successful growth. It has also been seen that water requirement has also increased in recent decades relative to the change occurred in climate .Water demand of the atmosphere which is closely related to the thermal regime has followed the warming trend of climate. Following the similar methods which were incorporated in the past, the recent requirement of water for Cotton will not only provide the quantitative assessment of irrigation water needs but also serve to take adaptation measures for coping the challenges of climate change ensuring sustainable crop production. Keywords: Water Requirement, Evapotranspiration, Rainfed, Irrigation, Climate change & Cotton
Introduction Pakistan is a country with diversified topography and extremely variable climate. The climate of the territory varies from very humid in some parts of North to extremely arid region in South. Pakistan has rich and vast agriculture resource base, covering various ecological and climatic zones and has great potential for producing many types of food and fiber. Unfortunately, ¾th of the total area lies under arid zone and only a small patch under humid climate [1]. In arid zone, food crop production is uneconomical under rainfed conditions, where as frequent and heavy rains in a humid zone exposes the rainfed agriculture to a great risk. In between these two extremes, the climates may be designated more or less crop failure risk-free zones. Due to this complexity of the weather, water requirement through out the country is not distributed uniformly. As the southern part and extreme north regions of the country remain dry due to which the seasonal crops could not be sown under rainfed conditions [2]. Cotton commonly known as “white gold” is an important cash crop for Pakistan and normally grow in agriculture plains of Punjab and Sindh. It contributes 8.2 percent of the value-added share in national agricultural and about 3.2% to GDP; around two third of the country’s export earnings come from the cotton made-up and textile which adds over $2.5 billion to the national economy; while hundreds of ginning factories and textile mills in the country heavily depends upon cotton [3]. Primarily millions of farmers are dependent on this crop, in addition to millions of people employed along the entire cotton value chain, from weaving to textile and garment exports. The area under the cultivation of cotton crop has been increased significantly in the last 30 years - around 7.85 million acres in 2005-06 as compared to 7.2 million acre in 2002-03[4]. Besides being the world’s fourth-largest cotton producer and the third largest exporter of raw cotton and a leading exporter of yarn in the world our yield per acre ranks 13th in the world. As a result Pakistan annually imports around 1.5-2.00 million bales of cotton to meet growing demand from local textile mills; therefore it has become vital for Pakistan to increase its yield potential.
1 Pakistan Meteorological Department 75
Recent Water Requirement of Cotton Crop in Pakistan Vol. 6
Like all crops, a cotton plant's water requirement mainly dependent on the environment. The dryer and hotter the environment is, the more water plant requires. Cotton has been wrongly cited as a water intensive crop, but it is very drought tolerant and uses about the same amount of water as other major crops like millet, sorghum etc [5] . Cotton's global water foot print is about 2.6% of the world's water use, lower than other commodities (e.g., Soybeans 4%, Maize 9%, Wheat 12%, Rice 21%)[6]. Cotton is mainly grown in Punjab and Sindh .About 80% of Cotton grown area is in Punjab and the Cotton belt in Sindh comprising of the left bank of Indus accounts for only 19% ,whereas 0.72% area lies in rest of the country[7]. Cotton growing areas of Pakistan, percentage of crop cultivated areas in province and crop calendar is shown in figure 1(a-c).
Percent of Total Productior a. by Province (2007-08)
b.
Sindh 19%
Punjab 80%
Major Cotton growing areas
Cotton Calendar for most of Pakistan c. Plant Harvest
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 1: a. Cotton growing areas of Pakistan. b. Province wise area under Cotton Crop. c. Crop Calendar followed in Pakistan
Climate change due to global warming has greatly affected the agricultural activities not only in Pakistan but also our the globe. An increase in temperature observed in the country, especially during the last few decades it is very sharp. Future trend is also showing the rapid increase in temperature Fig-2. This increase in temperature will directly influence the evapotranspiration rate and water requirement of crops, in the country. Previous study on water requirement of cotton crop was conducted by Rasul and Farooqi, 1993 in which it was concluded that cotton crop generally grows better on low elevated plains. The increase in temperature is being observed in the southern parts of the country which lies in low latitude. Good rainfall received in southern parts but due to its less quantity there is always shortage of water [9]. By Keeping in view above and importance of cotton in the country along with rapid increase in temperature during the last decades it was necessary to calculate the recent water requirement. This study
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Issue 12 Ghazala Naheed, Ghulam Rasul will help the farmers and agriculture scientists to cope with the current and future challenges of water management issues for cotton.
20.5 Trend (CRU 1901-50) = 0.13C per decade Trend (CRU 1951-2000) = 0.04C per decade Trend (CRU 1901-2000) = 0.057C per decade 20 Trend (JRA) = 0.4C per decade
19.5
C) o
( 19 s e ur at
er p 18.5
Tem
18
17.5
17 01 06 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 01 06
19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 20 20 Years Figure 2: Average Temperature of Pakistan during 1901 - 2008. Methodology Materials and Methods To measure the water requirement of cotton crop, Climatic Normals (1971-2000) used for precipitation, mean temperature, relative humidity, relative sunshine duration and wind speed of fifteen meteorological stations of Pakistan located in cotton zone (Table 1) and their geographical location is shown in fig-3. Monthly reference crop Evapotranspiration (ETo) was calculated by using FAO modified Penmen-Monteith method.
Table 1: List of Stations of cotton Growing Areas in Pakistan Station Name Latitude Longitude Altitude Station Name Latitude Longitude Altitude (m) (m) Bahawalnagar 29.95 73.25 161 Hyderabad 25.38 68.42 40 Bahawalpur 29.40 71.78 116 Jacobabad 28.25 68.47 55 Faisalabad 31.43 73.10 183 Larkana 27.53 68.20 174 Lahore 31.50 74.40 215 Moen-jo-Daro 27.37 68.10 52.1 Khanpur 28.65 70.68 87 Nawabshah 26.25 68.37 37 Multan 30.20 71.43 122 Padidan 26.85 68.13 46 Sargodha 32.05 72.67 187 Rohri 27.70 68.90 66 Badin 24.63 68.90 10
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Recent Water Requirement of Cotton Crop in Pakistan Vol. 6
Figure 3: Map of the Study Area
FAO Penman-Monteith Equation As Pakistan has diversified type of climate and over all performance of FAO Penman-Monteith has shown better results than other evapotranspiration calculation method. This method shows the minor deviations from the actual evapotranspiration data through out the year in climate of Pakistan [11]. The FAO Penman-Monteith equation is a close, simple representation of the physical and physiological factors governing the evapotranspiration process. The mathematical expression for the sake of calculation simplified as follow:
Where: ETO, reference evapotranspiration (mm per day) Ra, net radiation at the crop surface (MJ/m² per day) G, soil heat flux density (MH/m² per day) T, mean daily air temperature at 2m height (°c) U2, wind speed at 2m height (m/s) Es, saturation vapor pressure (kPa) Ea, Actual vapor pressure (kPa) es – ea, saturation vapor pressure deficit (kPa) ∆, Slope of vapor pressure curve ( kPa per °C) γ, Psychometric constant (kPa per °C)
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Issue 12 Ghazala Naheed, Ghulam Rasul
The equation uses standard meteorological records of solar radiation (sunshine), air temperature, humidity and wind speed. To ensure the integrity of computations, the measurements of weather parameters should be made at 2m (or converted to that height) above an extensive surface of green grass, shading the ground and not short of water. It is important to note that all the parameters recorded at the same place, standard hours and under the same environment [12]. Calculation of Crop Water Requirement
After determining ETo, the ETcrop or Crop Water Requirement (CWR) can be calculated using the appropriate crop-coefficient (Kc)
ETcrop = Kc. ETo or CWR = Kc. ETo Crop coefficient (Kc) is actually the ratio of maximum crop Evapotranspiration to reference crop Evapotranspiration[13]. For Cotton, this ratio becomes greater than 1 during the reproductive cycle (heading to grain formation) otherwise it remains less than 1 bearing minimum values during the early age of the crop and at maturity. The crop water requirement was calculated for the period from emergence to maturity. The sowing of cotton starts from the month of May and picking of cotton starts with the end of the summer season and continue till the peak of winter. A schematic variation of the crop coefficient related to different crop development stages under normal conditions is given in figure 4. Crop Cofficient Cu rve For Cotton Crop 1.3 1.2 1.1 1 0.9 0.8 )
C 0.7 (K 0.6 0.5 0.4 0.3 0.2 0.1 May June July August September October November December Mont hs Kc Values
Figure 4: March of Crop Coefficient (Kc) for normal duration of Cotton growing season (Emergence of Maturity)(Rasul. 1995)
Results and Discussions Water Requirement is mainly dependant on climatic factors such as air temperature, solar radiation, relative humidity, wind velocity etc. and agronomic factors like stage of the crop development as well[14]. It is possible for a crop to utilize soil moisture when rainfall (P) is more than the water requirement (WR) or P is less than half of WR. But when P will be less than one fourth of WR or one-eight of the WR, the crop experiences water stress and both the crop growth and final yield are affected [14]. Satisfaction index (%) is the indicator which shows the available moisture to sustain growth and development of Wheat crop under different moisture regimes. Humid (H) regions reflect the areas having
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Recent Water Requirement of Cotton Crop in Pakistan Vol. 6 amount of rainfall greater than crop water requirements means rainfall fulfills 100% of the crop water requirement and sufficient amount of water is available for crops. The areas showing moist (M) situation represent the optimum growth i.e. rainfall is meeting 50% of the crop water requirement responding towards the fair growth means optimum demand level of water on which crops may sustain itself. Under moderately dry (MD) and dry (D), representing the crop retardations and severe moisture stress level where supplementary along with proper irrigation must be arranged for the proper maintenance of crop growth respectively[15]. May is one of the hottest months in the region. Temperature approches maximum values and low precipitation occurs in the month. According to long term average, Central Punjab remained 10 mm to 25 mm and rest of the agricultural plains of the country less than 10 mm. The evaporative demand of the atmosphere during May normally shoot up as compared to April. May is normally the sowing month of cotton crop in the country. Water requirement in Sindh, central and lower Punjab remain dry to moderately dry.(Fig -5). June is generally the hottest and driest month of the year in Pakistan except some pre-monsoon showers. Due to intense heating and relatively clear skies, the evaporative demand of atmosphere increases sharply. Due to westerly trough and pre-monsoon system,more than 50mm of rainfall observed in northern parts of the country, 25 to 30mm of rain fall in central Punjab and about 10mm rainfall over lower Sindh and southern Punjab is observed. Upper Sindh normally remains partially dry. Water requirement in upper parts of punjab remain good, but in some of central Punjab and central Sindh, moderately dry conditions prevails while rest of the cotton growing areas, dry conditions prevails due to which proper irrigations is required for the cultivation of cotton.(Fig -6)
Figure 5: Monthly water requirement of Cotton during May Figure 6: Monthly water requirement of Cotton during June
July is normally the starting month of monsoon season in Pakistan. Northern parts of Punjab, which lies in the monsoon track receive more than 200 mm precipitation, Central Punjab about 100 mm and southern Punjab around 80 mm. Sindh gets adequate amount of precipitation in Agro- meteorological point of view i.e. less than 25 mm during this month. Evaporative demand of the atmosphere is likely almost equivalent to the level of June, which is close to normal in temperature and wetter than the normal. Due to high amount of rainfall, central and upper parts of Punjab remains moist to humid while some parts of lower Sindh and southern Punjab remains moderately dry. Most parts of Sindh remain dry and needs proper irrigation(Fig-7).
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August is the peak month of monsoon rain with heavy precipitation in most parts of the country. These rains are of immense importance for the farmers regarding the crop requirements. In the absence of proper land management, the heavy rains may erode the upper soil layers and fertility of the soil is sometimes badly affected. If soil conservation and soil moisture conservation measures are employed, the farmers of the area may have benefit through the available moisture for sowing and early growth of Rabi crops. Higher rainfalls are normally observed i.e. exceeding 300 mm in Northern Punjab, 90 mm to 165 mm over Central Punjab, whereas over Southern Punjab 40-100 mm of rainfall during the month. Lower Sindh receives precipitation less than 50 mm while Upper Sindh receives upto 40 mm. The evaporative demand of the atmosphere decreases as compared to July due to less solar radiation intensity and increasing level of humidity. Water requirement remains satisfactory in upper Punjab while in central Punjab and lower regions of Sindh needs supplementary irrigation and rest of the Sindh and Southern Punjab requires proper irrigation. (Fig-8)
Figure 7: Monthly water requirement of Cotton during July Figure 8: Monthly water requirement of Cotton during August
September is normally the end of monsoon season in Pakistan. The amount of rainfall decreasing throughout the country as compare to August.In Sindh and Southern Punjab, rainfall ranges from few millimeters to 30 mm. Over Northern and North Eastern Punjab, precipitation ranges between 80 mm to 110 mm. Despite some drop in air temperature and smaller day length, the evaporative demand of the atmosphere generally increases as compared to August. In upper Punjab, moderately dry conditions prevail while rest of the regions remained dry (Fig-9). October is generally the transition month between the summer and winter weather systems and also the driest month of the year. The tropical maritime air mass is replaced by modified sub-polar continental air mass over this region. Summer monsoon airmass practically recedes resulting in a sharp decrease in precipitation over the area as compared to monsoon season. Quantitatively, Northern Punjab receives 30 mm to 100 mm of rainfall while rest of the country remain dry in Agrometeorological perspect i.e. average rainfall is not likely to exceed 10 mm. Despite the shorter days, cooler atmosphere and less intense solar radiation, evaporative demand of the atmosphere decreases. The water requirements exceeds in upper Punjab as dry conditions surrounds more areas, while rest of the country remains dry as in September. (Fig-10)
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Figure 9: Monthly water requirement of Cotton during Figure 10: Monthly water requirement of Cotton during September October
November like October is considered the driest month and transition period in the country. Due to shorter days, lower solar intensities and light winds are as compared to October, the evaporative demand of atmosphere is expected to fall but whole cotton growing regions needs proper irrigation (Fig-11). During the month of December, winter weather systems commonly known as “Western Disturbances” become active over the country. Under the influence of western rain bearing systems, northern Punjab receives precipitation between the ranges of 25mm to 45mm while in rest of the country it may range from few millimeters to 15 mm. Due to low temperature the evaporative demand remained low. During this month, only in the upper parts of Punjab water requirement is satisfactory to favorable conditions are observed (Fig-12).
Figure 11: Monthly water requirement of Cotton during Figure 12: Monthly water requirement of Cotton during November December
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By comparing the water requirement of cotton crop calculated for the period 1961-90 and 1971-00, it is observed that it has quantitatively increased due to increasing temperature over the last few years. The overall increase in the water requirement of cotton in Punjab and Sindh province is as shown in table-2
Table 2: Quantitative Increase of Water Requirement during the Period (1971-2000) than the Period (1961-1990) S.No. Months Punjab Sindh (mm/month) (mm/month) 1 May 7.7 7.4 2 June 12.3 3.0 3 July 26.1 24.2 4 August 3.4 24.9 5 September 39.6 36.2 6 October 35.4 32.3 7 November 34.2 34.9 8 December 13.9 20.1
Conclusion ∗ From the above analysis it is observed ∗ Cotton cannot be grown under rainfed condition unless supplementary / proper irrigation is required to fulfill the needs of the water required by cotton. ∗ Because of high evapotranspiration at lower latitudes, water requirement is high at lower latitudes. ∗ In Sindh, water requirement is high as compare to the Punjab. ∗ Water requirement of cotton is highest during August and September. ∗ Maximum increase in water requirement has observed during September to November.
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∗
Reference: Chaudhry, Q.Z. and Rasul, G.; 2004: “Agroclimatic Classification of Pakistan”, Science Vision (Vol.9, No.1-2&3-4), (July-Dec, 2003 &Jan-Jun, 2004), 59-66. Ghazala, N. and A.Mahmood, 2009. “Water requirement for Wheat Crop in Pakistan” Pakistan Journal of Meteorology, Vol.6.No.11, July 2009, pp 89-96. Ijaz, A.R, 2009:”First Bio-technology Cotton Grown in Pakistan”. Bahawalpur, report available on, http://www.pakissan.com/.../first.bt.cotton.grown.in.pakistan.shtml. GoP, 2008- Economic Survey of Pakistan (2007-08), Ministry of Finance, Government of Pakistan, Pakistan. Zwart, S.J. and G.M. Bastiaanssen. 2004. “Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize”. Agricultural Water Management 69(2):115-133. Hoekstra, A. Y. and A. K. Chapagain. 2007. “Water footprints of nations: Water use by people as a function of their consumption pattern”. Water Resour Manage 21:35–48. Agricultural Statistics of Pakistan.2007-08: Statistics Division, Federal Bureau of Statistics, Ministry of Economic Affairs, Pakistan Rasul, G. and A. B. Farooqi, 1995: “Water Requirement of Cotton Crop in Pakistan”. J. of Engg. & App. Sci. Vol. 4 No.2. pp. 154-165. Adnan, S. and Azmat H.K, 2009:”Effective Rainfall for Irrigated Agriculture Plains of Pakistan” Pakistan Journal of Meteorology, Vol.6.No.11, July 2009, pp 61-72. Climatical Normal (1971-2000) of Pakistan, 2005, Pakistan Meteorological Department. Karachi. Rasul, G., 2009: “Performance Evaluation of Different Methods for Estimation of Evapotranspiration in Pakistan’s Climate”. Pakistan Journals of Meteorology, Vol.5, No.10, Jan-June, 2009, pp.25-36. Allen, R.G., Pereira, L.S., Dirk Raes, Martin Smith, 1998. ”Crop evapotranspiration. FAO Irrigation and Drainage Paper 56”, Rome, p. 300. Doorenbos, J. 1992: “Guide Line for Predicting Crop Water Requirements” FAO, 24.Rome. Raman, C. R. V., B. S. Murthi, 1971: “Water availability periods for crop planning” (Rep. No.173), India Meteorological Department, Poona. Rasul, G., 1993: “Water requirement of wheat crop in Pakistan”. J. of Engg. & App. Sci. Vol. 12 No.2.Jul-Dec,1993.
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Pakistan Journal of Meteorology Vol. 6, Issue 12
Rise in Summer Heat Index over Pakistan Maida Zahid1, Ghulam Rasul1 Abstract: Heat index is a serious threat to health in Southern Punjab, almost all parts of Sindh, South eastern Balochistan extending upto coast and plains of North Eastern Balochistan of Pakistan particularly during summer. The temperature is rising due to anthropogenic activities resulting in global warming. Summer season has prolonged while winters have become short in Pakistan. Summers have become hotter and thus affecting the lives of the people engaged in outdoor activities during scorching sun hours. The rise in heat index has been calculated from 1961 to 2007 during summer (May- September) in different regions of the country. The aim of this paper is to identify the spots most vulnerable to heat strokes, heat waves and sun burns due to high heat index. The results show rising trend of heat index in almost all the regions of Pakistan. The highest increase in heat index value has been observed in areas of Azad Jammu & Kashmir. The second highest increase is observed in Punjab and Sindh regions where thermal regime is already a challenge to human tolerance. Even Northern Areas, Balochistan and NWFP have also shown rising trend of heat index. The summer season analysis for the entire Pakistan has also done combining all the regions of the country to calculate the total change in heat index over the time scale of 47 years. . The average increase in apparent air temperature in Pakistan is 3°C. (276.15K). Pakistan experiences a clear shift in heat index pattern from its southern half towards northern half. Key words: Heat index, Ambient dry-bulb temperature, Relative humidity, Heat stroke and Heat cramps.
Introduction Pakistan lies in South Asia between latitudes 24 o N to 37 o N and longitudes 60 o E to 75 o E. The country covers such an area of land that the climate in one place is quite different from that in the other. The climate is generally arid, characterized by hot summers and cool or cold winters and wide variations between extremes of temperature at given locations (Chaudhry & Rasul, 2004). The sustained high temperature and high humidity for a certain period have long been recognized as a significant weather hazard. The high values of the heat index can pose a health risk to anyone engaged in outdoor activity over a short period of time; a greater general danger to public health exists when the heat index remains high for an extended period of time. Heat index is a measure of the stress placed on humans by elevated levels of atmospheric temperature & moisture. As the atmospheric moisture content increases the ability of human body to release heat through evaporation is inhibited thereby causing discomfort and stress. The regions most prone to this effect include humid regions of tropics and summer hemisphere extra tropics including southeastern United States, India, Southeast Asia and Northern Australia (Delworth et al., 1999) Heat affects every individual’s performance, attitude and overall health. Projections from the climate models suggest that global surface air temperature will increase substantially in future due to radiative effects of enhanced atmospheric concentrations of gases (Delworth et al., 1999). The rise in heat index results in higher heat related mortality rate during summer. The combination of heat and high humidity may cause discomfort, heat stroke or even death to humans and animals. These heat related incidences have been studied extensively by various authors (Keatinge et al., 2000, Guest et al., 1999, Kumar, 1998, Pan & Li, 1995, Donaldson et al., 2003, Cristo et al., 2003 & Piver et al., 1999). The heat related-illness and casualties are likely to increase with predicted incidence of global warming and increasing duration of heat waves. The thermoregulatory control of human skin blood flow is vital to maintain the body heat storage. Heat load exceeds heat dissipation capacity which alters the cutaneous vascular responses along with other body physiological variables (Aggarwal et al., 2007). The relationship between the heat index and heat disorders has been summarized in Table 1. Tropical people
1 Pakistan Meteorological Department
85 Rise in Summer Heat Index over Pakistan Vol. 6 accept as comfortable considerably higher levels of air temperature and humidity than are accepted by temperate zone groups shown in Table 1. Out door workers also tolerate higher temperatures than sedentary indoor workers. Adults at rest appropriately clothed for the environment and not exposed to solar radiation or to relative humidity above 50% have the following model comfortable dry-bulb temperatures: cool temperate zone 17°C (290.15K), temperate zone 23°C (296.15K), subtropics 25°C (298.15K), tropics 27°C (300.15K). It appears that tropical people accept some sweating as a component of comfort and thus heat index range from 27-32°C is tolerable for the people of our region (Gadiwala & Sadiq, 2008).
Heat Index Health Effects
27°C – 32°C Fatigue possible with prolonged exposure and/or physical activity. (300.15 - 305.15 K)
32 – 41 °C Heat cramps and heat exhaustion possible with prolonged exposure (305.15 - 314.15 K) and/or physical activity.
41 – 54 °C Heat cramps or heat exhaustion likely and heatstroke possible with (314.15 - 327.15K) prolonged exposure and/or physical activity.
> 54 °C Heatstroke highly likely with continued exposure. >327.15 K or higher
In this study trends have been drawn among different regions of Pakistan using average heat index anomalies. The intent of this study is to calculate change in heat index in all the regions of Pakistan during summer (May-September) for a period 1961-2007. The summer season analysis has also been done for the entire Pakistan (all the regions of the country) to calculate the total change in heat index over the time scale of 47 years. Its results illustrate that enhanced level of atmospheric temperature and humidity in summer is causing significant rise in heat index. A shift in heat index patterns has also been calculated.
Methodology The Heat Index (Steadman, 1979, 1984) is usually simplified as a relationship between ambient temperature and relative humidity versus skin (or apparent) temperature. There is a base relative humidity at which an apparent temperature ‘‘feels’’ like the same air temperature. Increasing (or decreasing) humidity and temperature result in increasing (or decreasing) apparent temperature. In order to arrive at an equation which uses more conventional independent variables, a multiple regression analysis was performed on the data from Steadman's table. The resulting equation could be considered a Heat Index equation (Rothfusz, 1990) HI = -42.379 + 2.04901523T + 10.14333127R – 0.22475541TR – 6.83783 × 10-3T2 -5.481717×10-2R2 + 1.22874×10-3 T2R + 8.5282×10-4 TR2 – 1.99×10-6T2R2 (Eq. 1) Where, T = ambient dry bulb temperature R = relative humidity
86 Issue 12 Maida Zahid, Ghulam Rasul
Such a formula (Equation 1) is applicable only when air temperature and humidity are higher than 26ºC and 39%, respectively. Because this equation is obtained by multiple regression analysis, the heat index value (HI) has an error of ±1.3°F (1.5ºC or 274.65K). The values of heat index were then further converted in Celsius scale. The real time data of mean monthly maximum temperature and relative humidity for a period 1961-2007 was obtained from Pakistan Meteorological Department, Islamabad in order to calculate heat index. The average (MJJAS) heat index anomalies were obtained by subtracting the actual values of heat index with the 1971-2000 mean appropriate values during the period 1961-2007. In order to illustrate the normal situation of heat index during summer (May-September) in Pakistan, the mean monthly maximum temperature and relative humidity of Climate Normals from 1971-2000 were used to calculate the normal values of heat index (Fig .1). Surfer version 8.04 was used to map the normal scenario of heat index during summer (May-September) in Pakistan.
Results & Discussion The normal scenario of heat index in Pakistan during summer i.e. (May-September) is presented in Figure 1. Most of the country is under the effect of high heat index. The areas of Southern Punjab, South eastern Balochistan extending upto coast, low elevation and plains of North Eastern Balochistan lies within the danger zones from May till September. Almost all the areas of Sindh particularly Nawabshah, Larkana, Jaccobabad, Hyderabad, Karachi, Chhor and Padidan are under extreme danger zone where health problems such as heat strokes and sunburns are most likely to humans and animals exposed for an extended forenoon to afternoon hours. The rest of the regions lie in relatively the comfort zone, which is not beyond the human tolerance of the genetic features of inhabitants of this area. Punjab (Bahawalpur, Faisalabad, Islamabad, Jehlum, Khanpur, Lahore, Multan, Murree, Sialkot, and Sargodha) comprises upper Indus plain, where land remains under extensive agriculture for most of the year. In summer high temperatures have been recorded in this region of Pakistan. The trend drawn with the help of average Effects of Heat Index (C) 27 – 32 Tolerable (MJJAS) heat index anomalies from 32 – 41 Caution/Extreme Caution 1971-2000 has shown tremendous 41 – 54 Danger rise in apparent temperature in > 54 Extreme Danger Punjab during the study period. The results are shown in Figure 2 (a). The total increase calculated is Figure 1: Normal Scenario of Heat Index during Summer (May to Sept) in 3.5ºC (277K) which is statistically Pakistan significant at 95% confidence level. This rise in apparent temperature will not only add to the discomfort of humans but also greatly affect flora and fauna of the region. The highest peak is observed in 2000 when relative humidity and maximum temperature both are increasing side by side in Figure 2 (b).
87 Rise in Summer Heat Index over Pakistan Vol. 6
7 6 (2a) 5 4 3
omaly (C) n 2 1 0 -1
Heat Index A -2 y = 0.0753x - 2.1192 -3 R2 = 0.3762 -4 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
1.5 Year 6 1 (2b) 4 R e aly (C) l 0.5 a t i 2 v e
0
H e Anom u -0.5 0 m i d i t y
peratur -1 -2 A m n e -1.5 o -4 m a l
-2 y
( -6 %
-2.5 )
Maximum T -3 -8
1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
Relative Humidity Anomaly - - - - - Maximum Temperature Anomaly
Figure 2: a. Heat Index Anomalies in Punjab (1961 - 2007) b. Maximum Temperature and Relative Humidity Profile of Punjab (1961 - 2007)
Pakistan experiences serious heat index in its south eastern parts (Chhor, Badin, Hyderabad, Jaccobabad, Nawabshah, Padidan, Karachi and Rohri) which falls under desert like hyper-arid climates. Chances of heat stroke/sunstroke are much prominent in the peak summer days (Gadiwala & Sadiq, 2008). Figure 3 (a) shows sharp increase in the rising trend of heat index in this region. The total increase calculated was 3ºC (276.15K) which is also a significant rise in apparent temperature during the 47 years period i.e. from 1961-2007. The rise in heat index started from 1980. The highest peak of heat index is observed in 1988.The mean maximum temperature at most of the stations exceeded 41ºC and high values of relative humidity are the reason for this peak. Even if the temperature is moderate still high values of humidity during summer makes life uncomfortable in this region Figure 3 b. In most parts of Balochistan (Panjgur, Quetta, Pasni, Sibbi, Dalbandin, Jiwani, Nokundi, and Zhob) winters are extremely cold and summers are unbearably hot. Mercury drops below 0ºC (273.15K) at certain points due to invasion of cold northerly winds in other areas dry and bare mountains absorb heat during day time and retain high temperature for long. The apparent temperature is showing an increasing trend in this region as well Figure 4a. The total rise in heat index calculated for summer season is 1.34ºC (274.5K). One of the hottest spot in the world i.e. Sibbi is also part of this region. The highest peak is observed in the year 1988 because of high maximum temperature and relative humidity during summer
88 Issue 12 Maida Zahid, Ghulam Rasul whose cumulative effect cause sharp rise in heat index as shown in Figure 4b. In this region Pasni, Sibbi and Jiwani, inspite of monsoon rain effects, record higher temperatures. Nokkundi and Dalbandin have no monsoon rain effect and so the temperature continues to rise up in this area of Balochistan. With increasing trend of temperature and decreasing trend of humidity heat index will decrease, because rate of evaporation increases with low humidity and high temperature. The internal body temperature remains maintained and humans do not feel heat stressed.
5 4 (3a)
3 2 1
x Anomaly (C) 0 -1
Heat Inde -2 y = 0.0628x - 1.639 -3 R2 = 0.3008 -4
1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
6 1.5 Year
(3b) R
4 e aly (C) 1 l a t i v 2 e
0.5 H e Anom u 0 m i d
0 i
t y peratur -2 A m n
e -0.5 o -4 m a
l y
-1 ( -6 % )
Maximum T -8 -1.5 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
Relative Humidity Anomaly - - - - - Maximum Temperature Anomaly
Figure 3: a. Heat Index Anomalies in Punjab (1961-2007) b. Maximum Temperature & Relative Humidity Profile of Sindh (1961-2007)
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4 (4a) 3 2
omaly (C) 1 n
0
-1
Heat Index A -2 y = 0.0287x - 0.7499 R2 = 0.1353 -3 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
1.5 5
(4b) Year 4 R e l a
aly (C) 1 3 t i
v e
2 H
u m e Anom 0.5
1 i d i t y
0 A peratur
0 n m o e -1 m
a l -2 y -0.5 ( %
-3 )
Maximum T -1 -4 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
Relative Humidity Anomaly - - - - - Maximum Temperature Anomaly
Figure 4: a. Heat Index Anomalies in Balochistan (1961-2007) b. Maximum Temperature & Relative Humidity Profile of Balochistan (1961-2007)
The atmosphere and lands are very dry in Northern Areas (Bunji, Chilas, Gilgit, Skardu, and Astore) of Pakistan. This region claims some of the world’s highest mountain peaks like K2, Nanga Parbat and Raka Poshi. There is a big difference in temperature between exposure in the sunshine and in the shade. Nights are very cold and days are comparatively warm. Figure 5a shows that there is a gradual rise in heat index values. The total increase calculated in this region is 2 ºC (275.15K) over the time scale of 47 years. The highest peak is observed in 1990 due to high values of humidity and ambient air temperature shown in Figure 5b. Summer rainfall or cloudiness is most appropriate reason for rising trend of humidity and decreasing trend of maximum temperatures in Northern areas.
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4 (5a) 3
2
omaly (C) 1 n
0
-1
Heat Index A -2 y = 0.0414x - 1.1122 R2 = 0.2381 -3 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
3 10 (5b) 8 2 6 R e aly (C) l
a
4 t i v 1 e
2 H e Anom u
0 m i 0 d i t y peratur -2
A m n e -4 -1 o m
-6 a l y
-8 ( -2 % )
Maximum T -10 -3 -12 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
Relative Humidity Anomaly - - - - - Maximum Temperature Anomaly
Figure 5: a. Heat Index Anomalies in Northern Areas (1961-2007) b. Maximum Temperature & Relative Humidity Profile of Northern Areas (1961-2007) Northern half of North-West Frontier Province (NWFP) (Balakot, Cherat, Chitral, Drosh, Parachinar, and Peshawar) receives substantial amount of rain in summer and winter therefore a good vegetation cover the soil during the year. Southern half is, however, dry and experiences temperatures and humid conditions like central Punjab. Semi arid to arid climates of the province get heated touching the extreme daytime temperature above 40ºC (313.15K). NWFP is also showing an increasing trend in heat index Figure 6a. The total increase in heat index is almost 1ºC (274.15K) in this region which is statistically a significant change. The trend of relative humidity in NWFP have shown sharp increase during the study but at the same time maximum temperature is decreasing therefore there is a very slight increase in heat index in this region Figure 6 b.
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2.5 2 (6a) 1.5 1 0.5 omaly (C) n 0 -0.5 -1 -1.5 Heat Index A -2 y = 0.0216x - 0.5947 2 -2.5 R = 0.1123 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
1.2 Year 6 1 (6b)
4 R e aly (C) 0.8 l
a t i 2 v
0.6 e
H e Anom
0.4 u 0 m i d
0.2 i t y peratur
-2 A m 0 n e o -0.2 m
-4 a l y
-0.4 ( -6 % )
Maximum T -0.6 -0.8 -8 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
Relative Humidity Anomaly - - - - - Maximum Temperature Anomaly
Figure 6: a. Heat Index Anomalies in NWFP (1961-2007) b. Maximum Temperature & Relative Humidity Profile of NWFP (1961-2007)
Climatic conditions of Azad Jammu & Kashmir (Muzaffarabad, Kotli and Garhi Dupatta) have great similarities to that of upper NWFP. The area enjoys both summer and winter precipitation and falls within sub humid climatic zones. There is quite a major boost in heat index values during summer season in Azad Kashmir during the study period. Figure 7a is showing gradual increase in the trend of heat index and the total change calculated in this region is 6 ºC (279.15K). The maximum change calculated in this region is due to an increasing trend in temperature and humidity both as shown in Figure 7b. Kashmir is the land of pastures so the rate of evapotranspiration is also maximum here making the atmosphere more humid.
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5
4 (7a) 3 2 1 omaly (C)
n 0 -1 -2
-3
Heat Index A -4 -5 y = 0.1205x - 3.0027 R2 = 0.5754 -6 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
2 6 (7b)
4 R
1.5 e l
a aly (C) t
2 i v
1 e
0 H u m e Anom 0.5 i -2 d i t y
A
peratur -4
0 n m o e m -6 a l
-0.5 y
(
-8 % )
-1 -10 Maximum T -1.5 -12 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
Relative Humidity Anomaly - - - - - Maximum Temperature Anomaly Figure 7: a. Index Anomalies in Azad Jammu & Kashmir (1961-2007) b. Maximum Temperature & Relative Humidity Profile of Azad Jammu & Kashmir (1961-2007)
In Pakistan normally heat index and its possible effects starts from May and extends upto September. Seasonal analysis of heat index for the entire Pakistan depicts that there is significant increase in apparent temperature for the last 47 years i.e. 1961-2007. The total increase in heat index calculated during the season is 3°C (276.15K) Figure 8a. The maximum temperature and relative humidity profile for the whole Pakistan is presented in Figure 8b. There is a significant rising trend of both the factors which is the ultimate cause of sharp increase in heat index values in Pakistan. The total change in humidity calculated during summer from 1961-2007 for entire Pakistan is 6.2% and total change in maximum temperature is 0.25°C (273.4K).
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3 (8a)
2
1 omaly (C) n 0
-1
Heat Index A -2 y = 0.0609x - 1.599 R2 = 0.4385 -3 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
1 Year 4
0.8 (8b) 2 R e aly (C) 0.6 l
a t i v 0.4 0 e
H e Anom u 0.2 m i d i
-2 t y peratur
0 A m n e o -0.2 -4 m a l y
( -0.4 % )
Maximum T -6 -0.6
-0.8 -8 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006
Relative Humidity Anomaly - - - - - Maximum Temperature Anomaly Figure 8: a. Heat Index Anomalies in Pakistan (1961-2007) b. Maximum Temperature & Relative Humidity Profile of Pakistan (1961-2007)
Shift in Heat Index Pattern Pakistan has experienced an obvious shift in heat index pattern from its Southern half towards Northern half. This shift has been studied by comparing the heat index values of 1961-1990 with 1971-2000. Fig (9a) shows that in 1961-90 Nawabshah, Padidan, Rohri, Moenjedaro, Hyderabad, Chhor and Jaccobabad and Sibbi lies within extreme danger level of heat index whereas D.I Khan, Peshawar, Kotli, Karachi, Badin, Jiwani, Nokundi, Pasni, Ormara, Panjgur, Barkhan and all parts of Punjab lies within the dangerous level of heat index. The slight effect of heat index have been observed in Muzaffarabad, Garhi Dupatta, Saidu Sharif, Dir, Zhob, Quetta, Dalbandin, Khuzdar and Chilas and rest of the areas including Northern areas, Kalat and Parachinar lies within the comfort zone. Fig (9b) clearly shows that the areas within extreme danger level of heat index have been extended upto some parts of southern Punjab.
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Muzaffarabad, Garhi Dupatta and Saidu Sharif which were under the slight effects of heat index are now lies within the dangerous zone of heat index. Some parts of Northern Areas & Northern NWFP (Gupis, Giligit, Bunji, Chitral and Drosh) which were previously a part of comfort zone now comes under the slight effect of heat index.
a. b.
Figure 9: Pakistan Normal Heat Index (C) May – Sep a. (1961-1990) b. (1971 – 2000) Conclusion On the basis of discussed results it is quite obvious that the trend of heat index (apparent temperature) has shown significant increase during the summer season from 1961-2007 in different regions of Pakistan. Almost all the regions have shown rising trend. Change in heat index is associated with two factors. The first one is daytime temperature and the other is relative humidity. Climate change is not only contributing to rise in temperature but also shifting rainfall patterns. In summer season we receive monsoon rain which is the ultimate cause of enhanced level of humidity in almost all regions of the country. The highest change in heat index is calculated in Azad Jammu & Kashmir areas (6ºC/279.15K). Punjab (3.5ºC/277K) and Sindh (3ºC/276.15K), have also revealed remarkable increase in heat index where thermal regime is already a challenge to human tolerance. Northern Areas (2ºC/275.15K), Balochistan (1.34ºC/274.5K) and NWFP (1ºC/274.15K) have also shown increase in heat index. These areas are not only having high temperatures during summer but the trend of humidity is also showing sharp increase with the exception of Balochistan, where humidity have shown decreasing trend in comparison to all other areas included in the study. So it is concluded that the increase in humidity is responsible for the considerable fraction of total increases in heat index. Thus evaluating the impact of heat index on human health and comfort it must be stressed that changes in surface air temperature is not the only reason but instead changes in humidity also plays a significant role. The summer season analysis for the whole Pakistan has also done which showed that the total increase in heat index for a period i.e. 1961-2007 is 3ºC (276.15K). An obvious shift in heat index pattern has been observed from Southern half towards Northern half of Pakistan.
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References Aggarwal, Y., B. M. Karan., B. N. Das., and R. K. Sinha., 2007: Prediction of Heat-Illness symptoms with the prediction of human vascular response in hot environment using rescuing condition. J Med Syst (2008) 32:167-176. Chaudhary, Q. Z., and G. Rasul., 2004: Agro-Climatic Classification of Pakistan. Science Vision, Vol.9 No. 1-2 (Jul-Dec 2003) & No. 3-4 (Jan-Jun 2004), 59-66. Climate Normals (1971-2000) of Pakistan., 2005: Pakistan Meteorological Department Karachi. Cristo, R. Di., A. Mazzarella and R. Viola., 2006: An analysis of heat index over Naples (Southern Italy) in the context of European heat wave 2003. Nat hazard (2007) 40:373-379. Delworth, T. L., J. D. Mahlman., and T. R. Knutson., 1999: Changes in heat index associated with CO2-indused global warming. Kluwer Academic publishers Netherland, 369-386. Donaldson, G. C., W. R. Keatinge., and S. Nayha., 2003: Changes in summer temperature and heat related mortality since 1971 in North Carolina, South Finland, and Southeast England. Environ Res 91:1- 7. Gadiwala, M. S., and N. Sadiq., 2008: The apparent temperature analysis of Pakistan using bio- meteorological indices. Pakistan Journal of Meteorology, Vol 4 Issue 8: 15 -26. Guest, C. S., K. Wilson., A. Woodward., K. Hennessy., L. S. Kalkstein., C. Skinner., and A. J. McMichael., 1999: Climate and mortality in Australia: retrospective study, 1979-1990 and predicted impacts in five major cities in 2030. Clim Res 13:1 – 15. Keatinge, W. R., G. D. Donaldson, E. Cordioli., M. Martinelli., A. E. Kunst., J. P. Mackenbach., S. Nayha., and I. Vuori., 2000: Heat related mortality in warm and cold regions of Europe: observational study. Br Med J 321: 670 – 673. Kumar, S., 1998: Indian heat wave and rains results in massive death toll. Lancet 351:1869. Pan, W. H., and Li, L. A., 1995: Temperature extremes and mortality from coronary heart disease and cerebral infarction in elderly Chinese. Lancet, 345,353-356. Piver, W. T., M. Ando., F. Ye., and C. J. Portier., 1999: Temperature and air pollution as risk factors for heat stroke in Tokyo. July and August 1980-1995, Environmental Health Perspectives Vol 107 No 11:911 – 916. Rothfusz, L. P., 1990: The heat index equation. Technical attachment, Scientific Services Division NWS Southern Region Headquarters, Fort Worth, TX, SR 90-23. Steadman, R. G., 1979: The assessment of sultriness. Part I: A temperature humidity index based on human physiology and clothing science. Journal of Applied Meteorology, 18, 861- 873. Steadman, R. G., 1984: A universal scale of apparent temperature. Journal of Climate and Applied Meteorology, 23, 1674 - 1687.
96 Pakistan Journal of Meteorology Vol. 6, Issue 12
Estimation of Water Discharge from Gilgit Basin using Remote Sensing, GIS and Runoff Modeling Furrukh Bashir1, Dr. Ghulam Rasul1 Abstract Northern Pakistan is one of the biggest repositories of snow and glaciated ice, outside polar region, throughout the world. Seasonal snow, bestowed by western depression on lofty mountains of HKH range in winter, generates runoff on melting in summers and, plays a significant role in shaping economy of Pakistan by providing hydrological resources for power generation and agriculture. Considering such vital significance of frozen water resources it is imperative to engineer a model to forecast potential availability of water from these frozen reserves. This strive is aimed to estimate runoff from Gilgit Basin in summer of 2003, by synergizing snow cover derived through remote sensing using Normalized Difference Snow Index (NDSI), digital elevation model and meteorological parameters in the snowmelt runoff model (SRM), eventually computed runoff is compared with measure runoff. Keywords: Snowmelt Runoff Model, MODIS, NDSI, Gilgit Basin, Seasonal Melting, Snow Cover Monitoring.
Introduction: Economy of Pakistan is largely dependent on hydrological resources for agriculture and power generation. A large portion of hydrological resources available to Pakistan is delivered by the melting of frozen reserves in Northern Pakistan in summers. These frozen reserves are comprised of seasonal snow cover and perennial glaciated ice. Scientific community is working hard to device a system that may forecast the potential availability of water from frozen hydrological reserves well-before melting time. Such forecast will be highly valuable for the planning in realm of hydroelectric power generation, flood control and hydro-resources management. In literature, two main approaches are available to calculate snow melt and subsequent runoff; they are energy balance approach and temperature degree-day method. The energy exchange at snowpack surface can be explained by four major components as, the shortwave radiation exchange, long wave radiation exchange, convective heat transfer and advective heat transfer. These radiations exchanges and heat transfer can be estimated if information on various parameters as cloud covers, albedo, wind, cloud temperature and dew point temperature are known(Anderson, 1976). Information on these parameters is normally not available; therefore, degree-day approach is used for operational forecasting (Quick and Pipes, 1988). Degree-day approach has been successfully used to develop Snowmelt-Runoff Model (SRM). It is designed to forecast and simulate daily stream flow of mountainous basins (Rango and Martinec, 1995; Martinec, et al., 1994) Northern Pakistan is one of the largest repositories of snow and glaciated ice outside Polar Regions. Runoff from mountainous glaciers and snowmelt is contributing significantly in overall stream runoff (Singh et al., 1997). Therefore, assessment of glacial and snow extent is important for estimation of stream runoff. This endeavor is an attempt to calibrate Snowmelt Runoff Model (SRM) developed by Martinec (1975) for Gilgit basin. For this purpose melting season of year 2003 is chosen. Meteorological parameters like precipitation and temperature recorded by observatory of Pakistan Meteorological Department situated at Gilgit at 1460 m.a.s.l are used. Measured runoff gauged by Pakistan Water and Power Development Authority (WAPDA) at Alam Bridge Gilgit for 2003 is utilized.
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Estimation of Water Discharge from Gilgit Basin using Remote Sensing …… Vol. 6
Study Area: A major part of the snow and glaciated ice of the Pakistan’s HKH is concentrated in the watershed of the Indus basin. This watershed can be divided into ten distinct river basins, namely Indus, Jhelum, Shingo, Shyok, Shigar, Astor, Swat, Chitral, Gilgit and Hunza river. Out of these ten river basins Gilgit basin is selected as the study area. The river basin in the north is bordered with Afghanistan and China. The Gilgit River network comprises of the Ghizar, Yasin, Ishkuman and Hunza River and joins the Indus River near Jaglot. The upper reaches of the basin are mostly glaciated and covered with permanent snow.
Figure 1: Gilgit Basin
Characteristics of Gilgit Basin: The Gilgit basin boundary (Figure 1) is defined by the location of the streamgauge installed by WAPDA at Alam Bridge Gilgit, and the watershed divide is identified on a topographic map that is derived from Digital Elevation Model (DEM) made by Shuttle Radar Topography Mission (SRTM). After examining the elevation range between the streamgauge and the highest point in the basin (total basin relief), elevation zones are delineated in intervals of about 1000 m or 3280 ft as mentioned in Table 1.
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Figure 2: Topographic Map Gilgit Basin
Table 1: Division of Gilgit Basin According to Elevation Range and Area of Respective Zone S.No Zone Name Elevation Range masl Mean Hypsometric Height Area (KM2) msal 1 A 1247-2500 2052 896 2 B 2500-3500 3091 2322 3 C 3500-4500 4083 6912 4 D 4500-5500 4782 3863 5 E 5500-6404 5660 311 Total 14304
(Figure2) shows contours delineation of zones in Gilgit Basin. Zone A ranges from 1274-2500 masl with mean hypsometric height at 2052 masl and area equals to 896 km2. Zone B of Gilgit Basin ranges from 2500-3500 masl with mean hypsometric height as 3091masl and covers area of 2322km2. Zone C covers spatial area of 6912km2 i.e, largest zone with elevation range from 3500-4500 masl and mean hypsometric height as 4083 masl. Elevation ranges of Zone D is 4500-5500 masl with mean at 4782 masl and covers nearly 3863 km2, second only to zone A. Last is zone E with least spatial coverage i.e, 311 km2 only, and it ranges from 5500 to 6404 masl with mean at 5660 masl as expressed in Table 1. Snow cover is mapped on all elevation heights with the help of SRTM DEM as shown in Figure 2 and 3.
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Figure 3: Topog raphic Map Gilgit Basin Table 2: Division of Gilgit Basin According to Elevation Range and Area of Respective Zone S.No Zone Name Elevation Range masl Mean Hypsometric Height Area (KM2) msal 1 A 1247-2500 2052 896 2 B 2500-3500 3091 2322 3 C 3500-4500 4083 6912 4 D 4500-5500 4782 3863 5 E 5500-6404 5660 311 Total 14304
(Figure2) shows contours delineation of zones in Gilgit Basin. Zone A ranges from 1274-2500 masl with mean hypsometric height at 2052 masl and area equals to 896 km2. Zone B of Gilgit Basin ranges from 2500-3500 masl with mean hypsometric height as 3091masl and covers area of 2322km2. Zone C covers spatial area of 6912km2 i.e, largest zone with elevation range from 3500-4500 masl and mean hypsometric height as 4083 masl. Elevation ranges of Zone D is 4500-5500 masl with mean at 4782 masl and covers nearly 3863 km2, second only to zone A. Last is zone E with least spatial coverage i.e, 311 km2 only, and it ranges from 5500 to 6404 masl with mean at 5660 masl as expressed in Table 1. Snow cover is mapped on all elevation heights with the help of SRTM DEM as shown in Figure 2 and 3.
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Figure 4: Zone Division SRTM DEM Data According to methodology that is discussed in detail below, the simulation to correlate the water produced from snowmelt and rainfalls with actual gauged discharge of the river, the following data will be used: 1) Basic data a. Snow Cover in square kilometer, derived through NDSI technique from MODIS data b. Temperature (oC) c. Precipitation (mm) 2) Derived data a. Depletion Curves of snow coverage with time on all elevation ranges b. Runoff Coefficient c. Degree Day Factor d. Lapse Rates e. Recession Coefficient
Methodology Structure of the SRM: Each day, the water produced from snowmelt and rainfall is computed, superimposed on the calculated recession flow and transformed into daily discharge from the basin, according to Equation (1):
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Estimation of Water Discharge from Gilgit Basin using Remote Sensing …… Vol. 6
A.10000 Q =[C .a ()T +∆T S +C P ] · (1-K ) + Q k n+1 sn n n n n RN n 86400 n+1 n n+1 Equation 1 Where:
Q = Average Daily Discharge [m3s-1]
c = Runoff Coefficient expressing the losses as a ratio (runoff/precipitation), with Cs referring to snowmelt and CR to rain a = Degree-Day Factor [cm oC-1d-1] indicating the snowmelt depth resulting from 1 degree- day T = Number of Degree-Days [oC d] ∆T = the adjustment by temperature lapse rate when extrapolating the temperature from the station to the average hypsometric elevation of the basin or zone [oC d] S = Ratio of the snow covered area to the total area
P = Precipitation contributing to runoff [cm]. A pre-selected threshold temperature, TCRIT, determines whether this contribution is rainfall and immediate. If precipitation is determined by TCRIT to be new snow, it is kept on storage over the hitherto snow free area until melting conditions occur. A = area of the basin or zone [km2] k = recession coefficient indicating the decline of discharge in a period without snowmelt or rainfall: Q k = m+1 where (m, m + 1 are the sequence of days during a true recession flow period). Qm n = sequence of days during the discharge computation period. Equation (1) is written for a time lag between the daily temperature cycle and the resulting discharge cycle of 18 hours. In this case, the number of degree-days measured on the nth day corresponds to the discharge on the n + 1 day. Various lag times can be introduced by a subroutine.
10000 = Conversion from cm·km2d-1 to m3 s-1 86400
T, S and P are variables to be measured or determined each day, CR, CS, lapse rate to determine ∆T, TCRIT, k and the lag time are parameters which are characteristic for a given basin or, more generally, for a given climate. If the elevation range of the basin exceeds 500 m, it is recommended that the basin be subdivided into elevation zones of about 500 m each. But here Gilgit Basin is divided among 5 zones (A, B, C, D, and E) each of them is nearly 1000m wide in elevation. Methodology of division is discussed below.
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SRM METHODOLOGY
MODIS DERIVED SNOW DIGITAL ELEVATION COVER MODEL
SNOW COVER MAPPING
METEOROLOGICAL DATA PARAMETERS CRITICAL DAILY TEMPERATURE TEMPERATURE RECESSION COEFFICIENT
DAILY SNOW COVER DEPLETION
SNOWMELT RUNOFF MODEL
SNOWMELT RUNOFF SIMULATED FORECAST
Figure 4: SRM METHODOLOGY
Satellite Snow Cover Mapping of Gilgit Basin: To estimate the snow cover through satellite based sensor it is vital to understand the electromagnetic properties of snow. Fresh dry snow appears white to the receptors of human eye. So it is asserted as highly reflective, with little variation over the visible wavelength range (0.4-0.64 µm). Rees (2001) fully developed this argument and implied that the reflection coefficient of a snow pack should be smaller if the grain size is larger, since the number of air-ice interface and hence scattering opportunities will be reduced. Besides this, the usually increasing absorption at longer wavelength implies a commensurate reduction in reflectance at these wavelengths. Although, the density of snow does not affects reflectance directly, however, the process that causes the increase in density over time also leads to an increase in snow grain size and consequently a decrease in reflectance is observed. Furthermore, with the passage of time snow pack ages and can acquire a covering of dust or soot which may also decrease the reflectance of electromagnetic radiations (Hall and Martinec 1985).
Reflectance of the snow gets little effect by presence of the liquid water in a snow pack. The amount of water rarely exceeds 10% by volume and there is in any case competent dielectric contrast between water and ice to ensure that the multiple-scattering phenomenon continues to take place. The absorption of electromagnetic radiation in water is similar to that in ice in the visible and near-
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Estimation of Water Discharge from Gilgit Basin using Remote Sensing …… Vol. 6 infrared regions. On the second end, the presence of liquid water does have an indirect effect on the optical properties, since it promotes clustering of the ice crystals leading to a larger effective grain size and hence lower reflectance. Model stimulations presented by (Green et al, 2001) take into account both grain size and liquid water contents as influence on the reflectance of snow. The most significant effect of increasing water content appears to be a small shift of the absorption feature at 1030nm to shorter wavelength. Reflection from a snow pack is anisotropic, with enhance specular scattering due to reflection from ice crystals (Middleton and Mungall 1952; Hall et al 1993; Knap and Reijmer 1998; Jin and Simpson 1999). However, most of the studies have neglected this effect (Konig, Winther, and Isaksson 2001). According to Zhen and Li, (1998) Areal and spatial distribution of snow and ice cover in alpine regions vary significantly over time, due to seasonal and inter-annual variations in climate. Therefore, there is need for monitoring the Gilgit basin consisting snow-covered lofty mountains with some reliable sensor, so Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra spacecraft of Earth Observing System (EOS) of National Aeronautics and Space Administration (NASA) is selected to map Snow cover. The capabilities of various satellites for snow cover monitoring and mapping are summarized in Table 2. Normalized Differential Snow Index (NDSI) through MODIS: MODIS holds special potential to address Normalized Difference Snow Index (NDSI). NDSI is prototype of Normalized Differences indices. It is originated from well known NDVI (Normalized Difference Vegetation Index). This Index artfully exploits the interaction of snow with electromagnetic radiation. Snow responses visible portion of electromagnetic spectrum with high reflectivity and in mid-infrared it reciprocates with absorption, furthermore, in same region of electromagnetic radiation (EMR) clouds are more reflective than snow (Dozier 1989). This analogy helps to discriminate snow from clouds at 1.65µm based fraction of EMR. Using this technique cirrus clouds remain troublesome to differentiate from snow (Hall et al, 1995). Mathematical expression of Normalized Difference Snow Index (NDSI) is as follows: (MODIS _ BAND4 − MODIS _ BAND6) NDSI = (MODIS _ BAND4 + MODIS _ BAND6)
Equation 2 Resultant pixel values exceeding 0.4 in (Equation 2) are considered as snow but it is found that this threshold limit varies with season (Vogel 2002). An effort was made by Klein, Hall, and Riggs in 1998 and they successfully designed algorithm to map snow by using MODIS instrument. In this endeavor they defined that at pre-launch version of NDSI for MODIS, the pixels with NDSI value greater than or equal to 0.4 were considered as snow. But later on many problems were revealed in detection; two more classification criteria were introduced (Klein et al, 1998). “The first is an absolute reflectance in MODIS band 2 of greater than 11%. This criterion is necessary to separate snow from water, which also may have high NDSI values. The second criterion is a reflectance in MODIS band 4 of greater or equal to 10%. This prevents dark targets from being classified as snow despite high NDSI values.” All criteria are followed to map snow of Gilgit for year 2003, as expressed in (Figure 5).
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Table 3: Characteristics of Satellites for Snow cover mapping
Figure 5: Snow Cover Derived through NDSI operation on Data of MODIS Terra
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Estimation of Water Discharge from Gilgit Basin using Remote Sensing …… Vol. 6
MODIS data is procured from NASA for year 2003 (Table 3) at regular intervals to Map snow in for Gilgit Basin. Table 4; Image Date and Percentage of Snow cover in all Zones. Image Date Zone A Zone B Zone C Zone D Zone E 2003005 05-Jan-2003 28.60335 40.55319 46.07716 49.31613 40.32258 2003021 21-Jan-03 76.26642 69.20758 68.50405 63.73285 16.72026 2003033 2-Feb-03 34.17722 34.49612 35.74942 46.54414 9.250804 2003037 6-Feb-03 100.6904 92.72179 90.07523 88.37691 19.93569 2003053 22-Feb-03 46.72037 75.1938 81.04745 93.78721 18.32797 2003065 6-Mar-03 64.44189 58.9578 56.46701 57.72716 8.038585 2003069 10-Mar-03 101.3809 98.32041 98.77025 98.93865 21.22186 2003085 26-Mar-03 57.5374 68.26012 73.81366 85.19286 19.93569 2003097 7-Apr-03 60.87457 63.65202 64.33738 75.56303 17.36334 2003101 11-Apr-03 50.51784 62.36003 69.7338 79.67901 18.00643 2003113 23-Apr-03 59.60875 55.34022 54.78877 47.06187 13.50482 2003129 9-May-03 62.14039 52.62705 53.99306 43.9037 8.038585 2003133 13-May-03 52.24396 47.20069 48.91493 62.56795 18.32797 2003149 28-May-03 91.25432 81.00775 72.25116 75.30417 16.07717 2003161 10-Jun-03 55.81128 50.68906 54.25347 42.9459 8.681672 2003165 14-Jun-03 51.78366 52.28252 45.34144 53.79239 15.11254 2003177 26-Jun-03 38.08976 37.7261 57.55208 73.59565 -- 2003261 18-Oct-03 1.243094 7.92975 7.206724 8.866837 13.46154 2003277 4-Nov-03 13.80898 28.98363 30.96065 32.56536 9.646302
Depletion Curves: Depletion curves (Figure 6) of the snow coverage is interpolated from periodical snow cover mapped trough MODIS for Gilgit Basin so that the daily values may be retrieved to be incorporated as input variable to SRM. Snow Cover of the days in-between the images is interpolated by using linear interpolation method given as follows:
S 2 − S1 S n = S1 + × (t n − t1 ) Equation 3 t 2 − t1 th Where Sn is Snow Cover at n day. S1 is Snow Cover derived from first imagery. S2 is Snow Cover derived from second imagery. tn is number of nth day. T1 is Julian date of first image
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Zone E
Zone B Zone D
Zone A Zone C
Figure 6: Snow Covered Area of all Zones of Gilgit Basin
Variables to be Incorporated in SRM: Temperature (T): Daily average temperature of year 2003 is utilized. The data is recorded by Pakistan Meteorological Department observatory situated at Gilgit Airport at Elevation of nearly 1460 masl. Snowmelt Runoff Model WinSRM Version 1.10 is programmed to accept either the daily mean temperature or two temperature values on each day: TMax, TMin. The temperatures are extrapolated by the program from the base station elevation to the hypsometric mean elevations of the respective elevation zones (Martinec, J., Rango, A. & Roberts, R. (1992). The model is programmed to accept either temperature data (Figure 7) from a single station or from several stations. For Gilgit observatory, the altitude of the station is entered and temperature data are extrapolated to the hypsometric mean elevations of all zones using the lapse rate. Precipitation (P): It is really difficult to evaluate the magnitude of rainfall of whole area in terrain like Gilgit Basin where Meteorological Observatory is situated at elevation of 1460 masl only. The Orography plays its role and magnitude of precipitation varies with respect to elevation and topography of valley. The Snowmelt Runoff Model WinSRM Version 1.10 accepts either a single, basin-wide precipitation input or different precipitation inputs zone by zone. If the program is switched to former option and only one station happens to be available, for example Gilgit Observatory in the zone A, precipitation data entered for zone A must be copied to all other zones. Otherwise no precipitation from these zones is taken into account by the program. In basins like Gilgit Basin with a great elevation range, the precipitation input may be underestimated if only low altitude precipitation stations are available.
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Figure 7: Average Temperature Data of Year 2003,Recorded at Gilgit Observatory
A critical temperature is used to decide whether a precipitation event will be treated as rain (T ≥ TCRIT
) or as new snow ( T < TCRIT). When the precipitation event is determined to be snow, its delayed effect on runoff is treated differently depending on whether it falls over the snow-covered or snow- free portion of the basin. The new snow that falls over the previously snow-covered area is assumed to become part of the seasonal snowpack and its effect is included in the normal depletion curve of the snow coverage. The new snow falling over the snow-free area is considered as precipitation to be added to snowmelt, with this effect delayed until the next day warm enough to produce melting. (Figure 8) is graphical depiction of incorporated precipitation of Gilgit Basin recorded at 1460 masl elevation. Runoff coefficient (c): This coefficient is referred to the losses (Martinec, J., Rango, A. & Roberts, R. (1992) , i.e., difference between the available water volume (Liquid and solid precipitation) and the outflow from the basin. It corresponds to the ratio of the measured precipitation to the measured runoff. Runoff coefficient values are obtained by comparing historical precipitation and runoff ratios for a long time. However, these ratios are difficultly obtained in view of the precipitation gauge catch deficit which particularly affects snowfall and of inadequate precipitation data from rigorous mountain regions. At the start of the snowmelt season, losses are usually very small as they are confined to evaporation from the snow surface, especially at high elevations. In the letter stage, with the exposure of soil and growth of vegetation, more losses are expected due to evapotranspiration and interception. Towards the end of the snowmelt season, direct channel flow from the remaining snowfields and glaciers may prevail in some basins which lead to a decrease of losses and to an increase of the runoff coefficient. In addition, c is usually different for snowmelt and for rainfall.
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Figure 8: Precipitation March- September 2003
Of the SRM parameters, the runoff coefficient appears to be the primary candidate for adjustment if a runoff simulation is not at once successful. (Martinec, J., Rango, A. & Roberts, R. (1992) Degree-day factor (a): The degree-day factor a [cmoC-1d-1] converts the number of degree-days T [oC·d] into the daily snowmelt depth M [cm]:
M = a·T Equation 4
Another approach to evaluate Degree Day is comparing Degree-Day values with the daily decrease of the snow water equivalent which is measured by radioactive snow gauge, snow pillow or a snow lysimeter. Such measurements (Martinec, 1960) have shown a considerable variability of degree-day ratios from day to day. This is understandable because the degree-day method does not take specifically into account other components of the energy balance, notably the solar radiation, wind speed and the latent heat of condensation. For terrain like Gilgit basin where topography experience various types of meteorological conditions according to elevation, aspect and landforms, one value of Degree Day Factor can not be considered justified for all sort of conditions in a zone. This is limitation of SRM model. Furthermore, the degree-day factor is not a constant. It changes according to the changing snow properties during the snowmelt season.
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During PMD expedition to Hinarchi Glacier, Bagrot Valley, Gilgit, in 2008 Degree Day Factor was measured for different type of glaciated ice and snow, which includes debris covered glacier, firn, bare ice cliffs and snow bestowed by summer snowfalls. These values range from 0.001 up to 0.6 cmoC-1d-1. In this strive SRM is simulated at different, nearly real time degree-day factor values to yield best possible results.
Temperature Lapse Rate (γ): A true picture of lapse rate is made by using Automatic Weather Station (AWS) data placed at Askole and Urdukas at Baltoro Glacier. Details of lapse rate derived through monthly averages are discussed in Table 4. Table 5: Average Lapse Rate for Gilgit Basin Month Average Lapse Rate oC/ 100 m January 0.656 February 0.645 March 0.738 April 0.767 May 0.814 June 0.768 July 0.711 August 0.733 September 0.651 October 0.902 November 0.864 December 0.774
Recession Coefficient (k): As already discussed recession coefficient indicating the decline of discharge in a period without snowmelt or rainfall: Q k = m+1 Equation 5 Qm Where (m, m + 1 are the sequence of days during a true recession flow period). Recession coefficient is derived through measured runoff. Its value varied with the season. For instance its value is above 1 for season attributed with increase of snowmelt runoff, where as in months of August and September it remarkably decreased.
Results: Seasonal Snow Cover for Gilgit basin for year 2003 is estimated using MODIS data and NDSI technique. Gaps between the snow cover data is filled through linear interpolation method. Meteorological data based on average temperature and precipitation of the melt season of year 2003 is incorporated. Runoff measured by WAPDA at Alam Bridge for Gilgit Basin for year 2003 is used for comparison and calibrations. Parameters like runoff coefficient, degree day factor, temperature lapse rate and recession
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Issue 12 Furrukh Bashir, Ghulam Rasul coefficient, either derived from field and evaluated from raw date or from historical record is provided for SRM Version 1.10. SRM holds very promising results for the Gilgit Basin. Minor adjustments in Recession Coefficients and Degree Day Factor brought very articulate results. Volume difference between Measured Runoff and Computed Runoff varied from 1.62% to 29.73%, which is acceptable. Graphical comparison of measured and computed runoff is presented in (Figure 8). SRM with MODIS derived Snow Cover and articulate DEM is very effective to estimate seasonal snowmelt runoff. Once parameterized it is also efficient to forecast un-gauged basins. (Table 5) defines the comparison, between Measured and Computed Runoff Volumes and, between Average Measured and Computed Runoff along with volume different (%). (Figure 9) is graphical depiction of Runoff (measured vs computed) for September 2003.
Measured Runoff Computed Runoff
Measured Runoff Volume 1665.706 Average Measured Runoff 621.903
Computed Runoff Volume 2161.023 Avg Computed Q 806.834
Volume Difference (%) -29.7362 Coefficient of Determination -.9983
Figure 9: Runoff (Measured vs. Computed) for August 2003. Table 6: Comparison of Measured and Computed Runoff Volumes and Averages Runoff Measured Average Computed Average Volume Runoff Measured Runoff Computed Month Difference Volume Runoff Volume Runoff (%) (106m3) (m3/s) (106m3) (m3/s) March 2003 133.4 49.806 138.755 51.805 -4.01 April 2003 181.44 70 162.73 62.78 10.31 May 2003 616.46 230.161 639.37 238.71 -3.71 June 2003 2666.82 1028.86 2710.09 1045.56 -1.62 July 2003 3163.27 1181.03 3253.06 1214.5 -2.83 August 2003 1665 621 2161 806 -29.73 September 2003 1024.79 395.36 1048.681 404.504 -2.321
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Conclusions, Limitations and Recommendations It is really difficult to evaluate the magnitude of rainfall of whole area in terrain like Gilgit Basin where Meteorological Observatory is situated at elevation of 1460 masl only. The Orography plays its role and magnitude of precipitation varies with respect to elevation and topography of valley and in basins like Gilgit Basin with a great elevation range, the precipitation input may be underestimated if only low altitude precipitation stations are available. Furthermore, the correlation between measured and computed runoff offered exaggerated values because of insufficient data of precipitation for basin. Similarly in case of Runoff Coefficient, as it corresponds to the ratio of the measured precipitation to the measured runoff, and Runoff coefficient values are obtained by comparing historical precipitation and runoff ratios for a long time. These ratios are difficultly obtained in view of the precipitation gauge catch deficit, which particularly affects snowfall and of inadequate precipitation data from rigorous mountain regions. Among the SRM parameters, the runoff coefficient appears to be the primary candidate for adjustment if a runoff simulation is not at once successful. Another issue is Degree Day Factor, for terrain like Gilgit basin where topography experience various types of meteorological conditions according to elevation, aspect and landforms, one value of Degree Day Factor cannot be considered justified for all sort of conditions in a zone. This is limitation of SRM model. Furthermore, the degree-day factor is not a constant. It changes according to the changing snow properties during the snowmelt season. In purview of above mentioned limitations, it is suggested that a number of AWS must be installed at various locales and different altitudes to draw an effective picture of temperature and precipitation. As in present study only one station observations are used, that led many parameters to be estimated hypothetically. Similarly, it is need to measure snow depth at multiple points in study area with some articulated method to design an in-depth understanding of behaviour of Degree Day Factor. In the last, it is needed to imitate this study for same and other basin for numbers of years to sketch a good picture of inputs and resultant outputs, so this approach can be used for forecasting runoff during melting season in Northern Pakistan.
References: Anderson, E.A (1976). National river forecast system-snow accumulation and ablation model, NOAA Tech. Memo. NWS Hydro-17, US Dept of Commerce, Silver Spring, USA. Green, R. O., J. Dozier, D. Roberts, and T. Painter. 2002. Spectral snow-reflectance models for grain size and liquid water fraction in melting snow for the solar reflected spectrum. Annals of Glaciology 32:71-73. Hall, D.K., and J. Martinec. 1985. Remote Sensing of Ice and Snow. London: Chapman and Hall. Jin, Z., and J.J Simpson. 1999. Bidirectional anisotropic reflection of snow and sea in AVHRR channel 1 and 2 spectral regions—Part1: theoretical analysis. IEEE Transaction on geosciences and Remote Sensing 379(1): 543-554 Klein, A. G., Hall, D. K., & Riggs, G. A. (1998). Global snow cover monitoring using MODIS. In Proceedings of the 27th International Symposium on Remote Sensing of the Environment, 8-12 June 1998, Tromso, Norway. ISRSE.
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Knap, W.H., and C.H. Rejimer. 1998. Anisotropy of the reflected eradiation field over malting glacier ice: Measurements in Landsat TM Bands2 and 4. Remote Sensing of Environemnt65:93-104. Konig, M., J-G. Winther, and E. Isaksson. 2001. Measuring snow and glacier ice properties from satellite. Reviews of Geophysics 39 (1):1-27. Martinec, J. (1975) Snowmelt-Runoff Model for stream flow forecasts. Nordic Hydrol. 6(3), 145-154. Martinec, J., Rango, A. & Roberts, R. (1992), The Snowmelt-Runoff Model (SRM) User’s Manual, Edition 2005, USDA Jornada Experimental Range, New Mexico State University, Las Cruces, NM, U.S.A. Martinec, J., Rango, A. and Roberts R. (1994). The Snowmelt-Runoff Model (SRM) User's Manual (Ed.: M.F. Baumgartner). Geographica Bemesia, Department of Geography, University of Bern, Bern, Switzerland, 29p.) (Rango, A. and Martinec, J. (1995). Revisiting the degree-day method for snowmelt computation. Middleton, W.E., and A.G. Mungall. 1952. The luminous directional reflectance of snow. Journal of the Optical Society of America 42:572-579.) (Hall, D.K., J.L. Foster, J.R. Irons, and P.W. Dabney. 1993. Airborne bidirectional radiances of snow-cover surface in Montana, USA. Annals of Glaciology 17:35- 40. Quick, M.C. and Pipes, A. (1988). High mountain snowmelt and application to runoff forecasting, Proc. of Workshop on Snow Hydrology, held at Manali from Nov. 23-26, 1988, pp IV1-32. Rees, W Gareth., Published 2006., REMOTE SENSING OF SNOW AND ICE. Taylor & Francis Group. Singh P., Jain, S. K. and Naresh Kumar (1997). Estimation of snow and glacier-melt contributing to the Chenab river, Western Himalaya, Mountain Research and Development, 17(1):49- 56. Zhen, X., and Li, J., 1998, Remote Sensing monitoring of glaciers, snow and lake ice in Qinghai-Xizang (Tibetan) Plateau and their Influence on Environments, edited by M. Tang, G. Cheng and Z. Lin (Guangzhou, China: Guandong Sciences and Technology Press) pp.279-308. Vogal, S.W. 2002. Usage of high-resolution Landsat 7 and 8 for single-band snow-cover classification. Anneal of Glaciology, vol.34, pp.53-57.
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Information for Authors Pakistan Journal of Meteorology (PJM) is an international journal on the dynamics, physics and chemistry of the atmosphere and with papers across the full range of the atmospheric sciences, published bi-annually by Pakistan Meteorological Department. The journal includes Articles, Notes, and Correspondence. Contributions from all over the world are welcome. Submission: A submission to PJM must be the original work of the author(s) and must not be published in any language elsewhere or under consideration for another publication in a similar form. Manuscript in English should be submitted to Dr. Ghulam Rasul, Editor PJM, Pakistan Meteorological Department, P.O. Box 1214, Islamabad. Please include your email address, phone and fax numbers on the bottom of the first page of the manuscript. Two complete copies of the manuscript should be submitted by mail along with its electronic version in MS Word format on a CD. Manuscript Preparation: Each manuscript should have been carefully reviewed for correctness and clarity of expression before submission. Complete manuscript (including tables, references, and figure captions) must be double spaced, printed on one side of the page only, with wide margins, and all pages must be numbered consecutively. Components of a manuscript: Each manuscript should include the following components, presented in the order shown below: • Title: name and affiliation of each author and email address of the corresponding author. These items should be written on the title page. • Abstract: a brief, concise abstract is required at the beginning of each manuscript. Authors should summarize their conclusions and methodology in the abstract. First person construction should not be used in the abstract, and references should be omitted since they are not available for abstracting services. • Key words: 4-6 keywords should be provided. • Text: The text should be divided into sections, each with a separate heading and numbered consecutively (e.g. 1, 1.1, 1.1.1,). • Acknowledgements. • Appendix: Lengthy, mathematical analyses whose details are subordinate to the main theme of the paper should normally appear in an appendix. Each appendix should have a title. • References: References should be arranged alphabetically without numbering. The text citation should consist of the author’s name and year of publication [e.g., “according to Robert (1990),” or “as shown by an earlier study (Robert, 1990)”].When there are two or more papers by the same author in the same year, the distinguishing suffix (a, b, etc.) should be added. • Figure caption: Each figure must be provided with a self-explanatory caption. Authors should include captions below the figures used for the reviewer copies. • Illustrations and tables: Each figure and table should be numbered consecutively and cited specifically in the text by number. All tables should have a title and legend. Figures: Authors should send high quality hard copy originals of all figures. Authors should strive to submit their figure at the size they will appear (but do not submit photocopy reductions in place of larger, higher quality originals). Mathematical formulae, symbols, units: Different typefaces should be set for different kinds of variables. Scalar variables are set as italic, and vectors, matrices and tensors are set as bold italic (e.g., V). Units should be SI with the exception of a few approved non-SI units of wide meteorological or oceanographic usage. Units should be set in Roman font using exponents rather than the solidus (/) and with a space between each unit in a compound set (e.g., m s-1). References: References must be complete and properly formatted. Technical reports and conference proceedings should be referenced only when no other source of the material is available. Examples: Boville, B.A., and J.W. Hurrels, 1998: A comparison of the atmospheric circulations simulated by the CCM3 and GSM1. J. Climate, 11, 1327 – 1341. Krishnamurti, T.N., and T. Murakami, 1981: on the onset vortex of the summer Monsoon. Mon. Wea. Rev., 109, 344-363. Rao, Y. P., V. Srinivasan, and S. Raman, 1970: Effect of middle latitude westerly systems on Indian Monsoon Syp. Trop. Met. Hawaii. No. IV 1-4. ISSN - 1810 - 3979
List of Contents:
1. Review of Advance in Research on Asian Summer Monsoon 1 Ghulam Rasul, Q.Z Chaudhry 2. The Rainfall Activity and Temperatures Distribution Over NWFP during the Pre- 11 Monsoon Season (April to June) 2009 Syed Mushtaq Ali Shah, Alam Zeb, Shahzad Mahmood 3. Investigation of Seismic Hazard in NW-Himalayas, Pakistan using Gumbel’s First 31 Asymptotic Distribution of Extreme Values Naseer Ahmed, Dr. Zulfiqar Ahmed; Dr. Gulraiz Akhtar 4. Case Study; Heavy Rainfall Event over Lai Nullah Catchment Area 39 Muhammad Afzal, Qamar-ul-Zaman 5. An Outbreak of Tornado on March 28, 2001, A Case Study 49 Nadeem Faisal, Akhlaq Jameel 6. An Empirical Microclimate Analysis of the Coastal Urban City and Comparison 57 of Vicinal Urban and Rural Areas with It Naeem Sadiq and Ijaz Ahmad 7. Recent Water Requirement of Cotton Crop in Pakistan 75 Ghazala Naheed, Ghulam Rasul 8. Rise in Summer Heat Index over Pakistan 85 Maida Zahid, Ghulam Rasul 9. Estimation of Water Discharge from Gilgit Basin using Remote Sensing, GIS and 97 Runoff Modeling Furrukh Bashir, Dr. Ghulam Rasul
PAKISTAN JOURNAL OF METEOROLOGY Published by Pakistan Meteorological Department P. O. Box No. 1214, Sector H-8/2, Islamabad Pakistan
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