000			06208nas|a2200469 a 4500
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008			120924s2011 th uu|m rtt 0| a1eng d
035			_a.b12109356
099		9	_aAIT Diss. no.WM-10-01
100	1		_aKhattak, Muhammad Shahzad Khan
245	1	3	_aImpacts of climate change on the water resources and irrigation water demand in the Upper Indus River Basin, Pakistan
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2011
300			_a201 p. : _bill.
490	1		_aDissertation ; _vno. WM-10-01
500			_aA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Water Engineering and Management, School of Engineering and Technology
502			_aThesis (Ph.D.) - Asian Institute of Technology, 2010
520			_aSome of the most affected areas by climate change are hydrology, water resources and agriculture. Changes in climate alter a wide range of characteristics of a river basin: the snow accumulation, melting and hence, seasonal flows and crop and inigation requirements. An understanding of the likely trajectories of climate in a basin is thus a prerequisite for management and adaptation strategies for water sector. The overall objective of this study is to assess the impacts of climate change on the hydrology, water resources and irrigation water demand in the Upper Indus River Basin (UIRB). The specific objectives include assessment of past trends in hydro-climatic variables, selection of suitable GCMs, and impacts of future climate on streamflow and irrigation water demand in the study basin. Trend analysis of hydro-climatic variables is perfo1med in the portion of the UIRB lying in Pakistan. Due to high variability of climate, the study area is divided into three regions, the upper, middle and lower. A combination of non-parametric statistical approaches has been used to assess the trends in the historical hydro-meteorological data. The techniques include, Mann-Kendall and Sen's slope methods in combination with Trend Free PreWhitening approach to remove the effect of serial conelation, and then binomial and quantile-quantile tests are applied to the Mann-Kendall statistics to evaluate whether the trend is significant or not. For maximum coverage of the study area, and to ensure a common period for all data types, an analysis period of 39 years (l 967-2005) is chosen. A year is divided into four seasons of three months each, namely winter (Dec-Feb), spring (Mar-May), summer (Jun-Aug) and autumn (Sep-Nov). The meteorological variables, minimum temperature, maximum temperature, and precipitation and the hydrological variable, streamflow are considered. A total of 20 meteorological and eight hydrometric stations are used for the trend analysis. The decadal trend of hydro-climatic data is also performed. For several of the variables, many more trends are identified than can be expected to occur by chance. Winter TMX has revealed a significant increasing trends (p < 0.10) with a trend slopes of 1.79, 1.66 and l.2°C per 39 years for the upper, middle and lower regions, respectively. All the three regions show increasing trends for the spring season with p-value > 0.10. For the summer and autumn season, mixed trends are observed. Summer TMN has shown a statistically significant (p < 0.10) decreasing trends of 2.08, 1.05 and 2.11 °C for the three regions, respectively. No conclusive trends are observed in precipitation except increasing trend in autumn season. The summer season flow has decreased in six out of eight stations. The concurrent trend results show that winter and spring temperature and autumn precipitation have increased runoff in the study area during those seasons. The two decades 1967-1976 and 1997-2005 are the warmest and driest in the analysis period. The perfo1mance of various GCMs is evaluated based on statistical parameters of observed and simulated time series of temperature and precipitation at gauging stations and grid nodes. A considerable variability in different GCMs simulations for the observed climate was observed. The HadCM3, SRNIES, ECHAM4, and NCAR models could simulate the spatial variability of mean temperature well. All the models substantially underestimated the magnitude of temperature on a monthly basis. The estimated precipitation showed much higher variations compared to mean temperature for all GCMs and at all stations. None of the models could represent the observed magnitude and spatial variability of precipitation in the study area. However, on the basis of similar statistics of the observed
650		0	_aClimatic changes _zPakistan
650		0	_aIrrigation water _zPakistan
650		0	_aWater-supply _zPakistan
700	1		_aBabel, Mukand Singh, _eChairperson
700	1		_aClemente, Roberto S., _eExamination Committee
700	1		_aPerret, Sylvain Roger, _eExamination Committee
700	1		_aHonda, Kiyoshi, _eExamination Committee
700	1		_aTachikawa, Yasuto, _eExamination committee
710	2		_aN.W.F.P. University of Engineering &Technology, Peshawar, Pakistan - AIT Fellowship, _eScholarship donor
810	2		_aAsian Institute of Technology. _tDissertation ; _vno. WM-10-01
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B11364
907			_a.b12109356 _bmnait _cz
902			_a260523
998			_b0 _c120924 _dm _ea _fz _g0
962			_a000:001:PDF:b1210935:002327:0:0:0:0:0:0 _tAbstract--AIT Diss. no.WM-10-01 _vn
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