THE CLIMATE CHANGE IMPACT ON WATER RESOURCES OF UPPER INDUS BASIN-PAKISTAN
By
Muhammad Akhtar M.Sc (Applied Environmental Science)
Under the Supervision of
Prof. Dr. Nasir Ahmad M.Sc. (Pb), Ph.D. (U.K)
A thesis submitted in the fulfillment of requirements for the degree of Doctor of Philosophy
INSTITUTE OF GEOLOGY UNIVERSITY OF THE PUNJAB, LAHORE-PAKISTAN
2008
Dedicated to my parents
CERTIFICATE
It is hereby certified that this thesis is based on the results of modelling work carried out
by Muhammad Akhtar under my supervision. I have personally gone through all the
data/results/materials reported in the manuscript and certify their correctness/
authenticity. I further certify that the materials included in this thesis have not been used
in part or full in a manuscript already submitted or in the process of submission in
partial/complete fulfillment for the award of any other degree from any other institution.
Mr. Akhtar has fulfilled all conditions established by the University for the submission of
this dissertation and I endorse its evaluation for the award of PhD degree through the
official procedure of the University.
SUPERVISOR
0. cL- ,Nasir Ahmad, PhDProfessor
Institute of GeologyUniversity of the PunjabLahore, Pakistan
i
ABSTRACT
PRECIS (Providing REgional Climate for Impact Studies) model developed by the Hadley
Centre is applied to simulate high resolution climate change scenarios. For the present
climate, PRECIS is driven by the outputs of reanalyses ERA-40 data and HadAM3P
global climate model (GCM). For the simulation of future climate (SRES B2), the
PRECIS is nested with HadAM3P-B2 global forcing. In the present day simulations,
climatic means and interannual variability are examined and biases are identified
focusing on the most important parameters (precipitation and temperature) for
hydrological modelling. In this study, both the meteorological station observations and
results of the PRECIS RCM are used as input in the HBV hydrological model in order to
investigate the effect of PRECIS simulated precipitation and temperature on the HBV
predicted discharge in three river basins of UIB region. For this, three HBV model
experiments are designed: HBV-Met, HBV-ERA and HBV-PRECIS where HBV is driven
by meteorological station data and by the outputs from PRECIS nested with ERA-40 and
HadAM3P data respectively. The robustness and uncertainties ranges of these models are
tested. The future water resources are quantified using the two approaches of
transferring the climate change signals i.e. delta change approach and direct use of
PRECIS data. The future discharge is simulated for three stages of glacier coverage: 100
% glaciers, 50 % glaciers and 0 % glaciers.
The PRECIS is able to reproduce the spatial patterns of the observed CRU mean
temperature and precipitation. However, there are notable quantitative biases over some
regions especially over the Hindukush-Karakorum-Himalaya (HKH) region, mainly due
to the similar biases in the driving forcing. PRECIS simulations under future SRES B2
scenario indicate an increase in precipitation and temperature towards the end of 21st
century.
The calibration and validation results of the HBV model experiments show that the
performance of HBV-Met is better than the HBV-ERA and HBV-PRECIS. However, using
input data series from sources different from the data used in the model calibration shows
that HBV-ERA and HBV-PRECIS are more robust compared to HBV-Met. The Gilgit and
Astore river basins, for which discharges are depending on the preceding winter
precipitation, have higher uncertainties compared to the Hunza river basin for which the
discharge is driven by the energy inputs. The smaller uncertainties in the Hunza river
ii
basin as compared to Gilgit and Astore river basins may be because of the stable
behavior of the input temperature series compared to the precipitation series. The
robustness and uncertainty ranges of the HBV models suggest that regional climate
models may be used as input in hydrological models for climate scenarios studies.
In a changed climate, the discharge will generally increase in both HBV-PRECIS and
HBV-Met in the 100 % glacier coverage stage up to 65% and 44%, respectively. At the 50
% glacier coverage stage, the discharge is expected to reduce up to 24% as predicted by
HBV-PRECIS and up to 30% as predicted by HBV-Met model. For the 0 % glacier
coverage under climate change, a drastic decrease in water resources is forecasted by
HBV-Met is up to 96 % and by HBV-PRECIS is up to 93%. At 100 % glacier coverage,
the magnitude of flood peaks is likely to increase in the future which is an indication of
higher risk of flood problems under climate change. There are huge outliers in annual
maximum discharge simulated with HBV-Met. This shows that the prediction of
hydrological conditions through the delta change approach is not ideal in the UIB region.
HBV-PRECIS provides results on hydrological changes that are more consistent with
climate change. This shows that the climate change signals in HBV-PRECIS are
transmitted more realistically than in HBV-Met. Therefore, the direct use of RCM outputs
in a hydrological model may be an alternative in areas where the quality of observed
data is poor. The modeled changes in future discharge and changes in peak flows under
climate change are not conclusive because more research is needed to evaluate the
uncertainties in this approach. Moreover, this technique needs to be tested with other
RCMs and hydrological models preferably to river basins in other parts of the world as
well.
iii
ACKNOWLEDGEMENTS
I would like to extend my sincere thanks to my research supervisor Prof. Dr. Nasir
Ahmad (Director Institute of Geology, University of the Punjab) for his keen interest,
proficient guidance, valuable suggestions, and encouraging attitude during the course of
this research work.
I would like to thank the PRECIS team at Hadley Centre, Meteorological Office, U.K, on
providing training in PRECIS regional climate modelling system and extending
continuous help in solving day to day operational simulation problems. Special thanks are
due to David Hein who provided boundary data of different GCMs on behalf of Hadley
Centre. The river discharge data and meteorological data have been taken from Water and
Power Development Authority (WAPDA) and Pakistan Meteorological Department
(PMD), respectively. I am grateful to the scientists at Swedish Meteorological and
Hydrological Institute (SMHI) for their useful comments and valuable suggestions during
the study.
I am indebted to the PMD on granting study leave for doctoral study at the University.
Financial support extended by the Higher Education Commission under the indigenous
PhD Scholarship Scheme is most gratefully acknowledged. I am thankful to ICTP,
Trieste, Italy, for providing two months fellowship, which enhanced my modelling
capabilities. The valuable suggestions and technical skill provided by Dr. Jermy Paul
during my stay at ICTP helped improve my understanding towards modelling technique.
Suggestions and critical comments by Dr. Martijn Booij of the Twente University,
Netherlands and Dr. David Hein and Dr. Wilfran-Moufouma of Hadley Centre, U.K.
greatly improved quality of my research work.
Finally, I would like to express my heartiest gratitude to my parents, wife, sisters,
brothers and friends whose cooperation, prayers and well wishes strengthened my
confidence to endure the hardships faced during this study.
iv
LIST OF TABLES
Table 1.1 Dimensions of some large glaciers in the UIB region 6
Table 3.1 Description of PRECIS RCM experiment 28
Table 3.2 Biases in mean temperature (˚C) as simulated with the PRECIS-Had, PRECIS-ERA and HadAM3P relative to CRU reference data for different seasons and seven sub regions of figure 3.3 (Summer= April-September, Winter = October-March)
39
Table 3.3 Biases in mean precipitation (%) as simulated with the PRECIS-Had, PRECIS-ERA and HadAM3P relative to CRU reference data for different seasons and seven sub regions of figure 3.3 (Summer= April-September, Winter = October-March)
47
Table 3.4 Seasonal changes of mean temperature and precipitation under SRES B2 scenario from PRECIS in 2071-2100 over the seven sub regions relative to 1961-1990 (Summer = April-September; Winter=October-March)
54
Table 4.1 Characteristics of study area 58
Table 4.2 Temperature and precipitation during two monsoon events at selected stations
60
Table 4.3 Biases in mean temperature (˚C) as simulated with PRECIS RCMs relative to CRU reference data for different seasons and river basins (Winter =October-March; Summer =April-September)
63
Table 4.4 Biases in precipitation (%) as simulated with PRECIS RCMs relative to CRU reference data for different seasons and river basins (Winter = October- March; Summer = April-September)
63
Table 4.5 Values and range of important parameters found in different studies using HBV model
75
Table 4.6 Parameter values for HBV for three river basins with three different input data sets
75
Table 4.7 Performance of three HBV models during calibration and validation periods in different river basins
76
Table 4.8 Efficiency Y of three HBV models using data sources different from the calibration sources during the hydrological years 1985 and 1986 in different river basins. The values of absolute relative deviations (ARD) are given in parentheses. The italic values indicate efficiency Y during calibration
86
Table 5.1 Seasonal changes of mean temperature and precipitation under PRECIS simulated SRES B2 scenario for the period 2071-2100 over three river basins relative to the period 1961-1990 (Summer = April-September; Winter=October-March)
94
v
Table 5.2 Mean relative change in future discharge (2071-2100) in a changed SRES B2 climate relative to the present discharge (1961-1990) for three glaciations stages and for three river basins
102
Table 5.3 Characteristics of future annual maximum discharge simulated by two HBV models in a changed SRES B2 climate for the three glaciations stages and for three river basins. The values in parentheses are future annual maximum discharge with outliers
106
vi
LIST OF FIGURES
Figure 1.1 Indus river basin 4
Figure 1.2 Mean annual hydrograph at different locations at the Indus river including some headwater tributaries
5
Figure 3.1 Topography of selected domain (a) Topography of the global climate model (HadAM3P), (b) Topography of the regional climate model (PRECIS), (c) Topography of the GTOPO30 2MIN DEM and (d) Deviation of PRECIS RCM topography from GCM topography
25
Figure 3.2 PRECIS RCM domain for experiments at 50 x 50 km resolution 27
Figure 3.3 Sub regions used for more detailed analysis of the PRECIS RCM fields. Region 1 (Afghanistan), Region 2 (Southern Pakistan and Rajasthan), Region 3 (Hindu Kush-Karakorum- western Himalaya), Region 4 (Central Pakistan and Northwestern India), Region 5 (Tibetan Plateau), Region 6 (Central Himalaya) and Region 7 (Central India)
29
Figure 3.4 Mean seal level pressure (MSLP) for the period 1981-1990 for May-June (MJ) season in (a) NCEP reanalyses data, (b) ERA-40 Reanalyses data, (c) HadAM3P GCM and (d) PRECIS-Had and (e) PRECIS-ERA
31
Figure 3.5 Observed and simulated (baseline) patterns of annual temperature (˚C) for (a) CRU data, (b) HadAM3P, (c) PRECIS-Had and (d) PRECIS-ERA
34
Figure 3.6 Bias of annual temperature (˚C) for (a) PRECIS-Had and (b) PRECIS-ERA with respect to CRU data
36
Figure 3.7 Observed and simulated (PRECIS-Had and HadAM3P) annual cycle of temperature averaged over the seven sub regions of figure 3.3
37
Figure 3.8 Observed and simulated (PRECIS-ERA) annual cycle of temperature averaged over the seven sub regions of figure 3.3
38
Figure 3.9 Observed (CRU) and simulated (PRECIS-Had and HadAM3P) seasonal temperature standard deviation averaged over the seven subregions of figure 3.3
39
Figure 3.10 Observed and simulated (baseline) patterns of annual precipitation (mm/day) for (a) CRU data, (b) HadAM3P, (c) PRECIS-Had and (d) PRECIS-ERA
42
Figure 3.11 Bias of annual precipitation (mm/day) for (a) PRECIS-Had and (b) PRECIS-ERA with respect to CRU data
44
Figure 3.12 Observed and simulated (PRECIS-Had and HadAM3P) annual cycle of precipitation averaged over the seven sub regions of figure 3.3
45
vii
Figure 3.13 Observed and simulated (PRECIS-ERA) annual cycle of precipitation averaged over the seven sub regions of figure 3.3
46
Figure 3.14 Observed (CRU) and simulated (PRECIS-Had and HadAM3P) seasonal precipitation coefficient of variation (CV) averaged over the seven sub regions of figure 3.3
47
Figure 3.15 Observed and simulated wet day frequencies averaged over the seven sub regions shown in figure 3.3.
48
Figure 3.16 Changes of mean temperature under SRES B2 scenario relative to present day climate (a) Annual, (b) Summer and (c) Winter
50
Figure 3.17 Changes of mean precipitation under SRES B2 scenario relative to present day climate (a) Annual, (b) Summer and (c) Winter
51
Figure 3.18 Annual cycle of temperature averaged over the seven sub regions for present (1961-1990) climate and future (2071-2100) climate under SRES B2 scenario
52
Figure 3.19 Annual cycle of precipitation averaged over the seven sub regions for present (1961-1990) climate and future (2071-2100) climate under SRES B2 scenario
53
Figure 4.1 Location map of Hunza, Gilgit and Astore river basins 58
Figure 4.2 Discharge of Hunza river, Gilgit river and Astore river during the rainfall events of (a) October, 1987 and (b) August, 1997
61
Figure 4.3 Mean annual cycle of temperature [˚C] over (a) Hunza river basin, (b) Gilgit river basin and (c) Astore river basin as simulated with PRECIS RCMs and from CRU data
64
Figure 4.4 Mean annual cycle of precipitation [mm/day] over (a) Hunza river basin, (b) Gilgit river basin and (c) Astore river basin as simulated with PRECIS RCMs and from CRU data
65
Figure 4.5 A schematic diagram of the hydrological model HBV (modified after Lindström et al., 1997), numbers in brackets refer to described equations
71
Figure 4.6 Sensitivity of HBV model parameters for (a) Hunza river basin, (b) Gilgit river basin and (c) Astore river basin
77
Figure 4.7 Sensitivity analysis with FC, GMELT, TT and DTTM for HBV-Met (Hunza river basin), with Y as a function of FC, GMELT, TT and DTTM
78
Figure 4.8 Sensitivity analysis with FC, GMELT, TT and DTTM for HBV-ERA (Hunza river basin), with Y as a function of FC, GMELT, TT and DTTM
79
Figure 4.9 Sensitivity analysis with FC, GMELT, TT and DTTM for HBV-PRECIS (Hunza river basin), with Y as a function of FC, GMELT, TT and DTTM
80
viii
Figure 4.10 Observed and simulated discharge (m3/s) of (a) HBV-Met, (b) HBV-ERA and (c) HBV-PRECIS for Hunza river basins during calibration period
81
Figure 4.11 Double mass-curve analysis relating observed and simulated discharge (m3/s) of (a) HBV-Met, (b) HBV-ERA and (c) HBV-PRECIS for Hunza river basin during calibration period
82
Figure 4.12 Observed and simulated (HBV-Met, HBV-PRECIS and HBV-ERA) mean annual discharge (m3/s) cycle of (a) Hunza river basin, (b) Gilgit river basin and (c) Astore river basin
83
Figure 4.13 Observed, HBV-Met simulated and HBV-PRECIS simulated annual maximum discharge as a function of return period for three river basins in the present day climate
87
Figure 4.14 Observed discharge (green line) and uncertainties in discharge (red shade) simulated through a) HBV-Met, b) HBV-ERA and c) HBV-PRECIS for Hunza river basin during the 1986 hydrological year
88
Figure 4.15 Observed discharge (green line) and uncertainties in discharge (red shade) simulated through a) HBV-Met, b) HBV-ERA and c) HBV-PRECIS for Gilgit river basin during the 1986 hydrological year
89
Figure 4.16 Observed discharge (green line) and uncertainties in discharge (red shade) simulated through a) HBV-Met, b) HBV-ERA and c) HBV-PRECIS for Astore river basin during the 1986 hydrological year
90
Figure 5.1 Mean annual cycle of temperature [˚C] over river basins (a) Hunza, (b) Gilgit and (c) Astore as simulated with PRECIS for present (1961-90) and future (2071-2100) climate under SRES B2 scenario
95
Figure 5.2 Mean annual cycle of precipitation [mm/day] over river basins (a) Hunza, (b) Gilgit and (c) Astore as simulated with PRECIS for present (1961-90) and future (2071-2100) climate under SRES B2 scenario
96
Figure 5.3 Annual discharge cycle simulated by HBV-Met for the present climate and future SRES B2 climate for three stages of glaciation for three river basins
100
Figure 5.4 Annual discharge cycle simulated by HBV-PRECIS for the present climate and future SRES B2 climate for three stages of glaciation for three river basins
101
Figure 5.5 HBV-PRECIS simulated annual maximum discharge as a function of return period for current and changed SRES B2 climate for three glacier stages for three river basins
104
Figure 5.6 HBV-Met simulated annual maximum discharge as a function of return period for current and changed SRES B2 climate for three glacier stages for three river basins
105
ix
ABBREVIATIONS
ARD Absolute Relative Deviation
CRU Climate Research Unit
DTR Diurnal Temperature Range
ECMWF European Centre for Medium-Range Weather Forecasts
ENSO El Nino Southern Oscillation
GCM Global Climate Model
GHG Greenhouse Gas
HBV Hydrologiska Byråns Vattenbalansavdelning
HKH Hindukush-Karakorum-Himalaya
IPCC Intergovernmental Panel on Climate Change
NAO North Atlantic Oscillation
NCEP National Centers for Environmental Predictions
NS Nash-Sutcliffe
MSLP Mean Sea Level Pressure
PMD Pakistan Meteorological Department
PRECIS Providing REgional Climates for Impact Studies
RCM Regional Climate Model
SMHI Swedish Meteorological and Hydrological Institute
SIHP Snow and Ice Hydrology Project
SRES Special Report on Emission Scenarios
SST Sea Surface Temperature
UIB Upper Indus Basin
WGMS World Glacier Monitoring Service
WMO World Meteorological Organization
CONTENTS
ABSTRACT i
ACKNOWLEDGEMENTS iii
LIST OF TABLES iv
LIST OF FIGURES vi
ABBREVIATIONS ix
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Introduction to the Study Area 2
1.3 Hydro Meteorology of the UIB 7
1.4 Climate Change Impact Assessment of Water Resources 8
1.5 Objectives of the study 9
1.6 Thesis Layout 10
CHAPTER 2 LITERATURE REVIEW 12
2.1 Background 12
2.2 Climate Change Impact on Water Resources 13
2.3 Scenarios in Climate Change Studies 14
2.4 Global Climate Models (GCMs) 15
2.5 Downscaling of GCMs 16
2.5.1 Statistical downscaling 17
2.5.2 Dynamical downscaling 17
2.6 Water Resource Modelling under Climate Change 19
2.7 Uncertainty in Hydrological Impact Modelling 20
CHAPTER 3 ANALYSES OF PRECIS RCM CLIMATE CHANGE SCENARIOS
22
3.1 Background 22
3.2 Description of the PRECIS RCM 22
3.3 Representation of Topography in PRECIS 23
3.4 Experimental Design 24
3.4.1 Domain size and resolution 24
3.4.2 Boundary conditions 24
3.5 Present Day Climate Simulation Capacity of PRECIS 28
3.5.1 Mean sea level pressure patterns 29
3.5.2 PRECIS temperature simulations 30
3.5.3 PRECIS precipitation simulations 40
3.5.4 PRECIS estimated wet day frequency 48
3.5 Climate Change Responses under SRES B2 Scenario for Period 2071-2100
48
3.6 Summary 54
CHAPTER 4 PRECIS SIMULATIONS AS INPUT TO HYDROLOGICAL MODELLING
57
4.1 Background 57
4.2 Influence of Temperature and Precipitation on Discharge 59
4.3 Present Day Climate Data Analysis 59
4.3.1 Temperature 62
4.3.2 Precipitation 62
4.3.3 Bias correction in PRECIS simulations 66
4.4 River Basin Modelling 66
4.4.1 Description of HBV model 67
4.4.2 Model experiments 70
4.4.3 Calibration and validation of HBV models 72
4.4.4 Representation of flood peaks 84
4.4.5 Robustness of HBV models 84
4.5 Summary 91
CHAPTER 5 CLIMATE CHANGE IMPACT ON WATER RESOURCES
93
5.1 Background 93
5.2 Change of Temperature and Precipitation in the Selected River Basins
93
5.3 Climate Change Signals Transfer from PRECIS RCM to HBV 97
5.3.1 Delta change approach 97
5.3.2 Scaling approach 98
5.4 Assessment of Water Resources under Climate Change 98
5.4.1 Simulation of annual discharge cycle 98
5.4.2 Future discharge peaks 102
5.5 Summary 106
CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE WORK
108
6.1 Conclusions 108
6.2 Recommendations for Future Research Work 111
REFERENCES 112
ANNEXURE LIST OF PUBLICATIONS 132
Chapter 1 Introduction
1
CHAPTER 1
INTRODUCTION
1.1 Background
Pakistan is a developing country. Its estimated population is over 162.6 million and about 76
% of this population lives in the country side (PCO, 2008; P & D, 2001). Its economy is
agro-based and highly dependent on the large scale Indus irrigation system (SIHP, 1990).
Forty million irrigated acres consume 100 million acre feet of water annually, which is
approximately 70 % of the total annual river runoff (WAPDA, 1990). Furthermore, per capita
water availability has been reportedly decreased from 5600 m3 to 1000 m3 since 1947
(Kahlown et al., 2007).
Climate change impact on the water resources is likely to affect irrigation system of Pakistan
(Wescoat, 1991). It has potential to affect the installed power capacity of the country as well.
Changes in flow magnitude in Indus river are likely to raise tensions among the provinces,
especially within the downstream areas because of reduced water flows in the dry season and
higher flows and resulting flood problems during the wet season. Changes in climate may
also increase the occurrence of hydrological extremes such as droughts and floods. This
situation demands to investigate the climate change impact on the present and future water
resources. It will provide a precise and comprehensive data base in order to conceptualize the
better strategies for water resource planning and management in terms of formulation of
policies for investments in irrigation system, agriculture, hydropower production and flood
protection measures.
In this study, an attempt has been made to assess the climate change impact on the future
river discharge in Pakistan. To achieve this end climate change scenarios are developed using
Providing Regional Climate for Impact Studies Regional Climate Model (PRECIS RCM).
The outputs from PRECIS RCM are used as input into the HBV (Hydrologiska Byråns
Vattenbalansavdelning) hydrological model to estimate the river discharge in the present and
future climate.
Chapter 1 Introduction
2
1.2 Introduction to the Study Area
The Indus River has a few large and many small tributaries (figure 1.1). The total area of
Indus river basin is about 970, 000 km2. However, this study is confined only to Upper Indus
Basin (UIB) that lies between the source of Indus river and Tarbela reservoir covering a
basin area of about 175, 000 km2 (NESPAK, 1997). Major tributaries of UIB region include
Shyok, Gilgit, Hunza and Astore rivers. Since the UIB constitutes a major part of Hindukush-
Karakorum-Himalaya (HKH) region, the terms UIB and HKH will now onward be used
alternatively in this study.
The water flow in streams of the study area is characterized by extreme seasonal variability
(figure 1.2). Some 80 % of total yield occurs in only 6-10 weeks of an average year. Seasonal
snowmelt and melting of glacier ice are both main contributors of river discharge, which is
obviously increased during summer because of higher temperature. Almost 80-90 % area of
UIB is snow covered with occasional exception of 60 % in winter season. The UIB lacks
major lakes and large forests and about one quarter of the area is occupied by glaciers
(SIHP,1990). The UIB rivers lie in the tectonically active regime (Seeber and Gornitz, 1983)
and have a potent danger of floods caused by landslides and rockslides (Hewitt, 1998).
Glaciers may also block the streams forming natural dams, which may cause floods on
rupturing (Hewitt, 1989).
The UIB region is dominated by large glaciers (table 1.1) and there is a fivefold to tenfold
increase in precipitation from glacier termini (~ 2500 m) to accumulation zones above 4800
m. Maximum precipitation occurs between 5000 and 6000 m (Hewitt et al. 1993). Most of
the glaciers are nourished mainly by avalanche snow. Westerly circulations and cyclonic
storms contribute two third of high altitude snowfall (Hewitt, et al. 1989), while one third
derives from summer snowfall mainly due to monsoon circulation (Wake, 1989). A huge loss
of ice mass and glacier recessions are observed in almost all Karakorum glaciers in 20th
century until the mid 1990s. Since then there has been thickening and advances in many
glaciers but confined to the highest watersheds of the central Karakorum (Hewitt, 2005). In
spite of surge type behavior of some glaciers in the HKH region (Diolaiuti et al., 2003), some
others (e.g. the Baltoro glacier) are stable during the last 100 years (Mayer et al. 2006) but
glaciers located in valleys are declining. According to weather stations records, there is a
Chapter 1 Introduction
3
shift towards positive mass balance thereby a reduction in run-off in the most heavily
glacierized Hunza basin (Fowler and Archer, 2006; Archer and Fowler, 2004). However,
sudden changes in glaciers and their confinement to the highest watersheds suggest that
thermal and hydrological threshold of glaciers is crossed, which triggers down slope
redistribution of ice by normal as well as surging flow with or without mass balance changes
(Hewitt, 2007).
In UIB, climatic variables are strongly influenced by altitude and an increase in snow pack
thickness is observed with height. Topography predominantly governs the spatial distribution
of snowfall and relative accumulation of snow and ice and their patterns of release by
melting. Heavy snowfall and accumulation of snow and ice occur at an altitude of above
3000 m. At an altitude of 2500 m, little precipitation and high evaporation is witnessed,
which may lead to severe aridity of the valleys. This phenomenon is more vivid at height
below 3000 m in the westerly and at an elevation of 4500 m in the easterly parts of the
Karakorum (SIHP, 1990). The HKH region receives a total annual rainfall in the range of
200-500 mm. Since these amounts are measured by valley-based stations and are not
representative for elevated zones. High-altitude precipitation estimates derived from
accumulation pits runoff above 4000 m range from 1000 mm to more than 3000 mm. These
estimates depend on the site and time of investigation as well as on the method applied
(Winger et al., 2005).
Chapter 1 Introduction
4
Figure 1.1 Indus River Basin
Chapter 1 Introduction
5
0
2000
4000
6000
8000
JAN MAR MAY JUL SEP NOVTime Period (Month)
Dis
char
ge (m
3 /s)
YogoAlam BridgeBeshameDoyianGilgitKachuraPartabShigarDainyorKarmongTarbela
Figure 1.2 Mean annual hydrograph at different locations at the Indus river including some headwater tributaries
Chapter 1 Introduction
6
Table 1.1 Dimensions of some large glaciers in the UIB region (Hewitt, 2003)
Elevations (m) Glacier
Length
(Km)
Area
(Km 2) Max Min Total Relief
Siachen 75.0 1181.0
Baltoro 62.1 756.3
Biafo 67.9 626.8
Hispar 53.1 621.6 7,885 3,040 4,845
Rimo 45.1 510.2
Skamri 41.0 427.4
Panmah 43.9 1515.1
Te Rong 27.4 295.3
Batura 59.6 285 7,795 2,540 5,255
Khurdopin 41 280 7,760 3,200 4,595
Sarpo Laggo 32.0 230.5
Braldu 35.1 202.0
Virjerab 36.1 189 6,600 3,600 3,000
Kero Lungma 20.9 150.2
Yazghil 30.2 145 7,880 3,055 4,825
Barpu 33.7 136 7,740 3,050 4,390
Malangutti 23 105 7,880 2,850 5,030
Yashkuk Y 24 125 6,700 3,500 2,900
Bualtar 21.5 105 7,200 2,450 4,850
Pasu 20.5 115 7,280 2,720 4,560
Ghulkin 18 55 7,310 2,420 4,890
Hassanabad 17 60 7,286 2,750 4,536
Minapin 16 58 7,196 2,350 4,846
Chapter 1 Introduction
7
1.3 Hydro Meteorology of the UIB
The climatology of the UIB is influenced by western disturbances and most of the
precipitation falls in winter and spring (Archer and Fowler, 2004; Treydte et al., 2006).
Indian monsoon systems bring occasional rain in UIB (Wake, 1987). Precipitation is found to
be increased during the late nineteenth and the twentieth centuries to yield the wettest
conditions of the past 1000 years (Treydte et al., 2006). Several researchers (Karl et al. 1993;
Easterling et al., 1997; Jones et al., 1999) have reported that globally mean temperature is
increasing and diurnal temperature range (DTR) is decreasing. However, things are other
way round in the UIB, especially over the western Himalayan region (Yadav et al., 2004;
Fowler and Archer, 2006; Klein-Tank et al., 2006; Bhutiyani et al., 2007). Where an increase
in DTR (Akhtar et al., 2005a; Fowler and Archer, 2006; Bhutiyani et al., 2007) and a cooling
of mean temperature during pre-monsoon season is reported (Kumar and Hingane, 1988;
Akhtar and Hussain, 2001; Archer, 2003; Archer, 2004; Akhtar et al., 2005b). It could be
perhaps due to local forcing factors such as topography and land use etc. The significant
variations in summer temperatures indicate potentially important impacts on river runoff
(Archer, 2003; Fowler and Archer, 2006). In addition, temperature trend along with
precipitation would influence glacier mass balance. Hewitt (1998, 2005) reports the
widespread expansion of larger glaciers in the central Karakorum while most of the world’s
mountain glaciers are reportedly shrinking during the 20th century. And at a warming rate of
0.04 K per annum, without increase in precipitation, few glaciers would survive until 2100
(Haeberli et al., 1999; WGMS, 2002; Mastny, 2000; Shrestha and Shrestha, 2004; Paul et. al.,
2004). If the warming rate is 0.01 K per annum and with an increase in precipitation of 10%,
it is predicted that overall loss would be restricted to 10 to 20% of the 1990 volume of the
glacier (Oerlemans et. al., 1998).
Indian summer monsoon is one of the most important components of the coupled ocean-land-
atmosphere system. Significant links have been identified between Indian monsoon rainfall
and global climate, including El Nino Southern Oscillation (ENSO) (Syed et al., 2006) and
Mediterranean pressure indices (Raicich et al., 2003). Both North Atlantic Oscillation (NAO)
and ENSO affect climate of UIB where a positive precipitation anomaly is found during the
positive NAO phase and warm ENSO conditions. Positive precipitation anomaly could also
be due to western disturbances as they are intensified over the region when are encountered
Chapter 1 Introduction
8
with low-pressure trough (Syed et al., 2006). There also exists an inverse relationship
between Indian monsoon rainfall and Euroasian winter snow cover (Bamzai and Shukla,
1999).
In the UIB, summer runoff is highly correlated with winter precipitation in middle-elevation
basins e.g. Gilgit and Astore river basins (Fowler and Archer, 2005). In contrast, summer
runoff in high-elevation river basins such as the Hunza and Shyok, fed by glaciers and
permanent snow pack, is not correlated with winter precipitation but highly linked with
summer temperature (Archer, 2003). Fowler and Archer (2006) estimated a 20 % decrease in
runoff in the Shyok and Hunza river basins resulted from 1°C fall in observed summer
temperature. Linear regression analysis by Archer (2003) suggests that a 1°C rise in mean
summer temperature arising from climate change would result in an increase of 17% and 16
% in summer runoff for the rivers Shyok and Hunza, respectively.
1.4 Climate Change Impact Assessment of Water Resources
Much efforts have been focused on the evaluation of climate change modelling and assessing
the merits and demerits of different downscaling methods but little work has been done on
the application of these methods for the hydrological impact assessment. For instance, there
were only ten publications on this important issue in 2005 (Fowler et al., 2007a). The climate
change impact assessment of future water resources is undertaken using a Global Climate
Model (GCM) data as input in hydrological models (Watson et al., 1996). However, the
hydrological modelling requires a GCM data of fine spatial resolution. One way to obtain
this resolution is through statistical downscaling (Wilby et al., 1999). Numerous researchers
(Bergström et al., 2001; Pilling and Jones, 2002; Guo et al., 2002; Arnell, 2003; Booij, 2005)
have attempted statistical downscaling of different GCMs for determining hydrological
responses to climate change. An alternative approach is to use fine scaled GCM data through
dynamical downscaling (Hay et al., 2002; Hay and Clark, 2003). In this approach, a regional
climate model (RCM) uses GCM data as initial and lateral boundary conditions over a region
of interest. The fine resolution of a RCM (about 25-50 km) is more appropriate for resolving
the influence of small-scale features of topography and land use on climatological variables.
Moreover, the high resolution of the RCM is ideal to capture the variability of precipitation
as input to hydrological models (Gutowski et al., 2003). Recent applications of RCM data in
Chapter 1 Introduction
9
hydrological impact studies are presented by other workers (Kay et al., 2006a, b; Fowler et
al., 2007b; Graham et al., 2007a,b; Leander and Buishand, 2007; Akhtar et al., 2008a) as
well.
Different scenarios of the future meteorological conditions (temperature and precipitation)
are used as input to a hydrological model of a river basin in order to calculate the
corresponding discharges. However, the outputs from RCMs are subject to systematic biases.
Thus, direct use of outputs from RCM present day simulations into hydrological model
simulations typically leads to considerable deviations in river discharge from observations
(Fowler et al., 2007a). To transfer the signals of climate change from the results of RCMs
into hydrological model an interface is required. One way is to apply these changes through
simple transformation rules. This approach is referred as the delta change approach (Hay et
al., 2002). In the delta change approach, an expected mean temperature change is added to
the observed temperature record to obtain a future temperature time series and precipitation
is usually multiplied by a fraction. Another way to estimate the future water resources is by
using RCM outputs directly in the hydrological model (Fowler et al., 2007b; Graham et al.,
2007b; Leander and Buishand, 2007). Nevertheless, some bias corrections are incorporated in
the RCM outputs before their use in hydrological models. The direct use of RCM output is
advantageous because of holding the physical correlation between the downscaled
temperature and precipitation (Fowler et al., 2007a). One of applications of hydrological
models is to create runoff scenarios for different glaciation conditions. However,
hydrological models (both conceptual and physical) are still not able to deal with glacier
storage completely. Hence, holistic approaches to study and model glacier storage are of
major importance to fully integrate glaciers into the hydrological balance to be used for water
resources and river flow predictions at all time scales (Jansson et. al., 2003).
1.5 Objectives of the Study
The main objective of this study is to assess the climate change impact on the water resources
in UIB of Pakistan.
The following objectives ensue from this major goal:
1. To generate high resolution climate scenarios using PRECIS regional climate model.
Chapter 1 Introduction
10
2. To evaluate the ability of PRECIS to simulate present day climate (1961-90) and to
predict the change in temperature and precipitation for the time period 2071-2100
under SRES B2 scenario.
3. To calibrate/validate hydrological model using different input data sources and to
investigate the performance of hydrological model.
4. To determine how well historic daily distribution of annual and seasonal flows is
predicted using RCM data as input into hydrological model.
5. To assess the impact of climate change on the future discharges and future peak
flows.
1.6 Thesis Layout
This thesis is comprised of six chapters. Chapter 1 highlights the rational of the study, briefly
introduces study area, summarizes the climate change impact assessment of water resources
and sets out the objective of the study.
Chapter 2 reviews the pertinent literature, including topics like hydro climatic changes in
present and future climate, developments in the evaluation of global climate models, methods
to downscale GCM data and ways to use climate change scenarios in water resource impact
studies.
Chapter 3 details the generation of high-resolution climate data using PRECIS model. It
describes the PRECIS regional climate model and its ability to simulate present day climate.
Future climate under SRES B2 scenario is also presented.
Chapter 4 describes the application of hydrological models for climate change impact
studies. It gives the description of HBV hydrological model and details its calibration and
validation procedure. The robustness of hydrological model is analyzed and ranges of
uncertainties are examined. The behavior of flood peaks at different return periods in the
present day climate is also simulated.
Chapter 5 assesses the climate change impact on future water resources. This is accomplished
by applying the climate change scenario to the hydrological model. Future water resources
and flood peaks are estimated under SRES B2 scenario at different stages of deglaciation.
Chapter 1 Introduction
11
Finally, Chapter 6 presents the conclusions of the research and suggests recommendations for
future work.
Chapter 2 Literature Review
12
CHAPTER 2
LITERATURE REVIEW
2.1 Background
There is several fold increase in the atmospheric contents of greenhouse gases (GHGs) due to
rapid industrialization. For instance, CO2 content has enhanced from preindustrial value of
about 280 ppm to 379 ppm in 2005 whereas concentration of CH4 has elevated from 715 ppb
to 1774 ppb during the same period (Forster et al., 2007). The higher contents of GHGs bring
a change in the radiative balance of the earth resulting into climate change in terms of
increase in temperature, change in precipitation pattern and probably a rise in the frequencies
of extreme events (IPCC, 2001; Meehl et al., 2007; Tett, et al., 2007). The unprecedented
warming in the past two decades of twentieth century, especially during the period spanned
over 1995–2006, is believed to be due to the anthropogenic forcing of climate (Mann and
Jones, 2003; Thorne et al., 2003; Trenberth et al., 2007). Meteorological data of the previous
century also suggest a global mean temperature rise of 0.07°C per decade (Folland et al.,
2001; Jones and Moberg, 2003).
Globally observed annual precipitation has reportedly increased ~ 0.98 % per decade in the
twentieth century (New et al., 2001). The intensity of extreme events has also increased
worldwide in this century (Sillmann and Roeckner, 2007). The global mean sea level has
risen by 10 to 20 cm. There has been a 40 % decline in Artic Sea ice thickness in late
summer to early autumn in the past 50 years (Kunkel et al., 1999; IPCC, 2001). The
frequency of severe floods in large river basins has increased during the 20th century (Milly
et al., 2002). The average annual discharge of fresh water from six of the largest Eurasian
rivers has increased by 7 % from 1936 to 1999 (Peterson et al., 2002).
Hence, majority of the scientific community now believes that climate change is certain
(IPCC, 2007; Saier, 2007). This chapter reviews the pertinent literature, including topics like
hydro climatic changes in present and future climate, developments in the evaluation of
global climate models, methods to downscale GCM data and ways to use climate change
scenarios in water resource impact studies.
Chapter 2 Literature Review
13
2.2 Climate Change Impact on Water Resources
The average global surface temperature is projected to increase by 1.4-3 ˚C from 1990 to
2100 for low emission scenarios and 2.5-5.8 ˚C for higher emission scenarios of GHGs in the
atmosphere (IPCC, 2007). It is argued that warming escalates the moisture holding capacity
of the atmosphere, alters the hydrological cycle and changes the characteristics of
precipitation (Fowler and Hennessy, 1995; Trenberth, et al., 2003). Changes in the
precipitation may however have a greater impact on human well being and ecosystem
dynamics than the temperature (IPCC, 2001; Trenberth and Shea, 2005) because
precipitation controls the volume of runoff whereas changes in temperature mostly affect the
timing of runoff (Barnett et al., 2004). In a changed climate, runoff is expected to be
decreased in some parts of the world, including the Mediterranean regions, parts of Europe,
central and southern America, and southern Africa. In other parts of the world, particularly in
southern and eastern Asia, there is likelihood that runoff would increase under climate
change, but this increase in discharge may not be very beneficial because it tends to come
during the wet season and the extra water may not be available during the dry season (Arnell,
2004). Therefore, modifications of the climate can change hydrological regime, which may
affect hydropower production, irrigated agriculture and increase water related risks such as
flood and droughts (Willis and Bonvin, 1995; Loukas et al., 2002; Jasper et al., 2004).
Changes in climate may have an impact on water resource availability, an increase in the
frequency and intensity of floods, drought and low flows (Milly et al., 2002; Huntigton,
2006; IPCC, 2007). Consequently, a warmer and more dynamic climate may lead to the
intensification of the hydrological cycle (Fowler and Hennessy, 1995, Trenberth, 1998,
1999). For instance, in the Illecillewaet river basin of British Columbia, Canada, the
magnitude and temporal distribution of flood frequency will be decreased due to a decrease
of snow pack and earlier snowmelt (Loukas et. al., 2004). During low flow period, drier
summer will lead to a decrease in the average discharge of Meuse river basin at Borgharen
gauging station in the Netherlands (De Wit et al., 2007). In case of Rhine river basin, there
appears higher winter discharge because of intensified snowmelt and increased winter
precipitation and decreased summer discharge due to reduced winter snow storage and an
increase of evapotranspiration (Middelkoop et al., 2001). The increase in temperature
Chapter 2 Literature Review
14
accompanied by a reduced precipitation will lead to a decrease in the discharge of Mulde
river basin, Southern Elbe, Germany (Menzel and Burger, 2002). The effect of climate
change on snow water equivalent, snowmelt runoff, glacial melt runoff and total stream flow
is examined for Spiti river, which is high altitude river located in the western Himalayan
region. It is found that with the change in temperature (1-3 ˚C) the annual snowmelt runoff,
glacial melt runoff and total stream flow has increased. However, the most prominent effect
of increase in temperature has been noticed on glacier melt runoff (Singh and Kumar, 1997).
This shows that climate change impacts on hydrological systems strongly depend on the
characteristics of studied hydro-climatic region (Mohseni and Stefan, 2001; Singh and
Bengtsson, 2005). For instance, hydrological regime of mountain areas is strongly influenced
by the water accumulation (in the form of snow and ice) and by the melt processes
(Middelkoop et al., 2001). An absence of melt water affects surface hydrology in
extratropical region causing significantly drier upper layer soils and changes the annual cycle
of runoff (Vavrus, 2007).
2.3 Scenarios in Climate Change Studies
Scenarios of future climate change are required to assess the impact of climate change on
various sectors such as water resources, food production and ecosystem. A climate scenario
is a plausible, self-consistent outcome of the future climate that has been constructed for
explicit use in investigating the potential consequences of anthropogenic climate projections.
These climate projections depend on the future changes in emissions or concentrations of
GHGs and other pollutants (e.g. sulphur dioxide), which in turn are based on assumptions
related to future socioeconomic and technological developments and are therefore subject to
substantial uncertainty. A first set of emission scenarios known as the IS92 scenarios is
developed by the IPCC in 1992 (Leggett et al., 1992), updated by the Special Report on
Emission Scenarios (SRES) (IPCC, 2000). SRES include six scenario groups like A1B, A2,
B1, B2, A1T and A1F1 (Nakicenovic et al., 2000). However, SRES A2 and B2 scenario are
widely used in climate change studies. Theses scenarios cover a range of approximately 60 %
of the full span of emission scenarios (Woth, 2006).
The A2 scenario family describes a heterogeneous world characterized as slow economic
growth and rapid population growth rate as compared to A1 scenario. The underlying theme
Chapter 2 Literature Review
15
is self-reliance and preservation of local identities. Economic growth is regionally oriented,
and hence both income growth and technological change are regionally diverse. The B2
scenario family describe a world in which the emphasis is on local solutions to economic,
social and environmental sustainability. Population increases at a lower rate than A2 but at a
higher rate than A1 and B1. This scenario is oriented towards environmental protection and
social equity (Nakicenovic et al., 2000).
2.4 Global Climate Models (GCMs)
Although there are a number of approaches to construct global climate scenarios, GCMs are
believed to be the most sophisticated tools being used to simulate global scale climate (Perks
et al., 2000). Global climate models are based on fundamental laws of atmospheric physics
and manifest the earth’s atmosphere in three-dimensional mathematical representations.
GCMs describe physical relationships such as the processes governing cloud, precipitation
and radiation and are used to provide information on how the climate may evolve or change
under certain conditions. The equations that govern GCM operation describe changes in
momentum, temperature, moisture and subdivide the atmosphere vertically into discrete
layers (Raisanen et al., 2007). Examples of GCMs are CCSR/NIES developed in
collaboration between the University of Tokyo and National Institute of Environmental
Studies, Japan (Emori et al., 1999), CGCM1/2 developed by Canadian Centre for Climate
Modelling and Analysis (Flato et al., 2000), ECHAM4/OPYC3 is developed in co-operation
between the Max-Planck-Institute for Meteorology and Deutsches Klimarechenzentrum in
Hamburg, Germany (Legutke et al., 1999), HadCM2/3 developed at the Hadley Centre, UK
(Johns et al., 1997; Gordon et al., 2000) and ModelE of NASA, USA (Schmidt, 2006).
GCMs are used to conduct two types of experiments such as equilibrium and transient
experiments for the estimation of future climate (Loaiciga, 1996). Historically, most GCM
simulations have been made in equilibrium mode providing a scenario for a point in the
future where the climate system has reached a balance with a given increase in the
concentration of GHGs. The conditions considered in these experiments represent the
combined effects of all the GHGs that would be equivalent to the radiative forcing of a
double concentration of atmospheric CO2. However, in transient simulations of the climate
system, CO2 level is increased at a fixed rate. Transient experiments are generally performed
Chapter 2 Literature Review
16
by using high resolution fully coupled global ocean atmospheric GCMs (Joubert and
Tyson,1996). It is known that GHGs cause warming whereas sulphate aerosols may produce
cooling (Piltz, 1998). Therefore, there is a need to differentiate GCM simulations including
sulphate forcing and those excluding sulphate aerosols. GCMs incorporating both greenhouse
gas and sulphate aerosol forcing give a better representation of the observed pattern of
temperature change (Santer et al., 1994; 1995; Santer, 1996). For instance, Stott et al., (2000)
show that their model simulates the 20th century global mean surface air temperature
remarkably well and warming in the second half of the century is the result of anthropogenic
increase in GHGs concentration. Raisanen et al., (2007) reviewed the reliability of climate
models and found that simulated and observed climate data showed good agreement for
many basic variables. However, the ability of GCMs in simulating the frequency of extreme
events is poor (Kharin and Zwiers, 2000; Kysely, 2002).
2.5 Downscaling of GCMs
GCMs have coarse resolution and are unable to resolve fine scale features such as
topography, clouds and land use (Grotch and MacCracken, 1991; Downing et al., 2002).
Therefore, their suitability for climate change impact assessment on various natural and
managed systems at regional scale is questioned (von Storch et al., 1993; Ciret and Sellers,
1998; Hellström and Chen, 2003; Gaffin et al., 2004; Linderson et al., 2004). Hence, there is
a need to get situation specific information about climate to investigate the climate change
impacts (Li and Sailor, 2000; van Vuuren et al., 2007). As a result, considerable efforts have
been focused on the development of techniques like downscaling in order to bridge the gap
of GCMs prediction skills. The approaches employed to change GCM data to a finer scale
could be broadly classified into two categories, statistical downscaling and dynamical
downscaling (Tripathi et al., 2006). Statistical downscaling method establishes an empirical
relationship between GCM climate variable and local climate (Karl, et al., 1990), whereas in
dynamical downscaling Regional Climate Model (RCM) is embedded within a GCM (Giorgi,
1990; Giorgi & Mearns, 1991). Wood et al., (2004) state that the minimum standard of any
useful downscaling method for hydrological applications requires the observed conditions to
be reproducible.
Chapter 2 Literature Review
17
2.5.1 Statistical downscaling
In this technique, regional or local climate information is derived by first determining a
statistical model, which relates large-scale climate variables to regional and local variables.
Then the large-scale output of a GCM simulation is fed into this statistical model to estimate
the corresponding local and regional climate characteristics. Many statistical downscaling
techniques (Karl, et al., 1990; Burger, 1996; Linderson et al., 2004) have been developed to
translate large scale GCM output into a fine scale. The simplest scheme is perturbation
method or delta change approach (Prudhomme et al., 2002; Fowler et al., 2007a). In this
method, difference between the present and future GCM simulations is applied to baseline
observations by simply adding or scaling the mean climate change factor. Therefore, it can be
rapidly applied to several GCMs to produce a range of climate scenarios. The main
limitations of this method are that for precipitation the temporal sequence of wet days are
unchanged and change factor only scale the mean, maxima and minima of climatic variable.
Moreover, delta change method ignores change in variability and assumes spatial patterns of
climate to be constant (Diaz-Nieto and Wilby, 2005). More sophisticated statistical
downscaling methods can be divided into three main categories including weather generators,
regression models and weather classification schemes (Wilby et al., 2004). The primary
advantage of these techniques is that they are computationally inexpensive, and thus can be
easily applied to output from different GCM experiments. The main disadvantages of these
methods include a requirement of long and reliable observed historical data, dependence on
the selection of predictors and absence of climate system feedback (Wilby and Wigley, 1997;
Fowler et al., 2007a; Wetterhall et al., 2007).
2.5.2 Dynamical downscaling
This approach allows direct modelling of the dynamics of the physical system that
characterizes the climate of the region. Dynamical downscaling techniques can be grouped
into two classes such as high resolution and variable resolution atmospheric GCM (AGCM)
and Regional climate models (RCMs) (Jones et al., 2004). An AGCM is run for a specific
period of interest with boundary conditions of surface temperature and ice concentration. A
typical resolution of an AGCM is 100 km (May and Roeckner, 2001) whereas regional
experiments are performed at 50 km resolution (Deque et al., 1998). In AGCMs, atmospheric
Chapter 2 Literature Review
18
and land-surface conditions interpolated from the corresponding GCM fields are used to
initialize the simulations. The main advantage of AGCM technique is that the resulting
simulations are globally consistent. However, the major disadvantage of this method is that it
is computationally very demanding (Jones et al., 2004).
RCM technique uses GCM data as lateral and initial conditions for selected time periods
(Dickinson et al., 1989; Giorgi, 1990). Information about other factors like sea surface
temperature, sea ice, greenhouse gas and aerosol forcing, as well as initial soil conditions are
also gathered from GCM data. The spatial patterns of climate produced by the RCMs are
usually in better agreement with observations compared to those of the GCMs. RCMs are
able to realistically simulate regional climate features such as orographic precipitation (Frei
et al., 2003), extreme climate events (Fowler et al., 2005; Frei et al., 2006) and regional scale
climate anomalies (Leung et al., 2003). However, model skill depends strongly on biases
inherited from the driving GCM and the presence and strength of regional scale forcing such
as orography, land-sea contrast and vegetation cover. Studies over regions where topographic
effects on temperature and precipitation are more prominent often reports more skilful RCM
downscaling than in areas where regional forcings are weak (Wang et al., 2004).
The main advantages of RCM include its ability to simulate high resolution information on a
large physically consistent set of climate variables and its better representation of extreme
events (Huntingford et al., 2002; Frei et al., 2003; Christensen and Christensen, 2003; Leung
et al., 2004). However, RCMs are computationally intensive and limited number of scenario
ensembles is available which restrict the model integrations to 30 years for present climate
from 1961-1990 and for a changed climate from 2071-2100 (Fowler et al., 2007a). This
makes climate change impacts for other periods difficult to assess. In RCMs, different
sources of uncertainty vary according to spatial domain, region and season (Deque et al.,
2005). However, the major contributor of uncertainties includes the errors in the driving
GCMs, RCM formulation and emission scenarios (Noguer et al., 1998; Christensen and
Christensen, 2001). Therefore, for each application careful consideration needs to be given to
some aspects of model configuration, such as physics parameterizations, model domain size
and resolution, and the technique for the assimilation of large scale meteorological forcing
(Giorgi and Mearns, 1999).
Chapter 2 Literature Review
19
2.6 Water Resource Modelling under Climate Change
The most widely used approach to simulate the hydrological impacts of climate change is to
combine the output of GCMs with a deterministic hydrological model that contains
physically based or conceptual mathematical descriptions of hydrological phenomena. In
other words, the hydrological impacts of climate change on a watershed are investigated by
developing hydrological models of the watershed and simulating river flows that result from
total precipitation and temperature data derived from GCM outputs corresponding to specific
climate change scenario. A number of investigations have been conducted in this area during
the past few years. Loukas et al. (2002) investigated the potential impact of future climate
change on the causes of flood flows in different watersheds in British Columbia using the
UBC Watershed Model. Eckhardt and Ulbrich (2003) explored the impact of climate change
on groundwater recharge and stream flow in a central European catchment using a
conceptual echo-hydrological model (SWAT-G). Rosenberg et al. (2003) analysed the impact
of HadCM2 projections in 18 major water resource regions in USA using the SWAT
watershed model. Fowler et al., (2007b) examined the impacts of climate change on water
resources in north-west England by using the Mospa model, which is a complex water
resource planning model. Graham et al., (2007b) studied the hydrological impacts from
future climate change over Europe using two hydrological models including a conceptual
HBV model and physical based distributed WASIM model. Akhtar et al., (2008a) also used
the HBV model to investigate the impacts of climate change on the water resources of HKH
region under different glaciation scenarios.
A mismatch exists between climate and hydrologic modelling in terms of the spatial and
temporal scales, and between GCM accuracy and the hydrological importance of the
variables. In particular, the reproduction of observed spatial patterns of precipitation and
daily precipitation variability is not sufficient (Salathe, 2003; Burger and Chen, 2005).
Moreover, quality of GCM outputs prevents their direct use for hydrological impact studies
(Prudhomme et al., 2002). However, linking downscaled data to hydrological models may
improve the results (Wilby et al.,1999; Fowler et al., 2007a). The simplest methods is to use
hypothetical climate change scenarios by modifying time series of meteorological variables
by change factor in accordance with GCM scenario results (Arnell and Reynard, 1996;
Boorman and Sefton, 1997). However, this method does not allow for change in temporal
Chapter 2 Literature Review
20
variability and so recent studies have used more sophisticated methods including
dynamically downscaled output (Graham et al., 2007a,b), bias-corrected dynamically
downscaled output (Wood et al., 2004; Fowler and Kilsby, 2007; Fowler et al., 2007b) and
statistical downscaling approaches (Pilling and Jones, 2002; Jasper et al., 2004).
The performance of different downscaling methods for hydrological impact assessment has
been assessed. Some studies suggest that statistical downscaling methods perform better
compared to the dynamical methods while simulating the snowmelt runoff in river basins
located in Colorado and Nevada, U.S (Hay and Clark, 2003). In case of river Thames, UK,
where runoff is not snowmelt driven, statistical downscaling methods perform poorly as
mean dry spell length is underestimated (Diaz-Nieto and Wilby, 2005). Kleinn et al., (2005)
report that using the bias corrected RCM data the discharge regime of Rhine river basin is
well simulated. Comparisons of different statistical downscaling methods give significantly
different hydrological impacts for the same river basin (Dibike and Coulibaly, 2005). This
may be because of the fact that statistical downscaling does not preserve correlation between
different downscaled variables. Moreover, statistical downscaling of precipitation is to be
less successful due to intermittent nature of precipitation (Li and Sailor, 2000) whereas RCM
gives better representation of precipitation variability (Hellström and Chen, 2003).
Additionally, the direct use of RCM data or use of bias corrected RCM data in impact studies
preserves the physical correlation between precipitation and temperature (Wood et al., 2004;
Fowler and Kilsby, 2007; Fowler et al., 2007a; Graham et al., 2007a,b).
2.7 Uncertainty in Hydrological Impact Modelling
In climate change impact studies, the quantification of hydrological modelling uncertainties
is important to assess whether the system modification is induced by climate change or by
model errors. These uncertainties arise from climate modelling, hydrological modelling and
from methods used to link climate change and hydrological models (Fowler et al., 2007a).
The hydrological modelling uncertainties are caused by different sources, including errors in
the input data, errors in recorded observations of phenomena to be modelled, errors in the
model structure and uncertainty due to model parameters (Refsgaard and Storm, 1996; Xu
and Singh, 2004). The observational errors are linked to measurement methods and to the
type of input required. A model is a simple representation of a natural phenomenon and
Chapter 2 Literature Review
21
would not be able to produce outputs that match observations perfectly. This source of
modelling uncertainty is referred as the model structure uncertainty. The type of errors
induced by the parameters depends on how they are estimated. However, the best parameter
set is difficult to find and different parameter sets can yield equally good results for the
model calibration (Beven and Binley, 1992; Gupta et al., 1998). The climate modelling
uncertainties mainly contain systematic errors in GCM data and deficiencies in downscaling
approaches (Fowler et al., 2007a).
Booij, (2002) reports that the overall uncertainties in discharge due to input data errors are
more significant than uncertainties due to hydrological model errors and parameter
estimation errors. Wilby and Harris (2006) assessed the uncertainties in climate change
impacts on low flows in the river Thames, UK. They explored that the cumulative
distribution functions of low flows were most sensitive to uncertainties in downscaling of
different GCMs whereas uncertainties due to hydrological parameter estimation were found
to be less important. Hingray et al., (2007) concluded that the uncertainty resulting from
climate model is bigger than uncertainty introduced by the hydrological model. Krysonva et
al., (2007) found that the water discharge and the groundwater recharge in the Elbe basin will
be most likely decreased under expected climate change, but the uncertainty in hydrological
response to changing climate is generally higher than the uncertainty in climate input. Akhtar
et al., (2008b) studied the influence of different input forcing data on the discharge behavior
of river basins in HKH region. They found that the uncertainties were higher in the river
basins where discharge was dependant on precipitation compared to the river basins where
discharge was governed by temperature. In high mountainous areas, the available
meteorological and hydrological data are scarce and due to the extreme weather conditions
data are highly error prone. This problem represents a considerable source of uncertainty for
runoff and water balance simulation, especially in the presence of glaciers (Schafli, 2005).
For instance, in HKH region the future discharge estimated by using the meteorological
observations data is significantly different from the discharge predicted through RCM output,
mainly due to the errors in observed data (Akhtar et al., 2008a).
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
22
CHAPTER 3
ANALYSES OF PRECIS RCM CLIMATE CHANGE SCENARIOS
3.1 Background
Hydrological modelling predominantly depends on the atmospheric parameters such as
precipitation, temperature and evapotranspiration. Precipitation in the form of snow and rain
is the major source of runoff generation and temperature influences the snowmelt processes.
Hence, reliable information of these parameters is an important prerequisite of the
hydrological modelling. The necessary information can be derived from observational
records. However, difficult topography of the area some times makes it inaccessible for
routine meteorological and climatological observations leading to scarcity of atmospheric
data. Therefore, for large-scale hydrological applications, especially for climate change
impact studies, RCM simulations can bridge this gap. RCMs generated regional data can
make it possible to drive other regional specific models analyzing local scale impacts
(Wilson et al., 2005). This study uses PRECIS (Providing REgional Climate for Impact
Studies) RCM to generate fine scale climate change scenarios as PRECIS has been used to
simulate features of present day climate in our region, including India and China (Kumar et
al., 2006; Yinlong et al., 2006; Lijuan et al., 2007).
3.2 Description of the PRECIS RCM
PRECIS is a high resolution atmospheric and land surface model, which covers a limited area
locatable over any part of the globe. It accounts for entities like dynamical flow, atmospheric
sulphur cycle, cloud and precipitation, radiative processes, land surface and the deep soil
coupled with the demarcation of boundary conditions (Jones et al., 2004). PRECIS is based
on the atmospheric component of the HadCM3 climate model (Gordon et al., 2000). The
atmospheric dynamics module of PRECIS is a hydrostatic version of the full primitive
equations and uses horizontal and vertical coordinates. There are 19 vertical levels, the
lowest at 825 hPa and the highest at 0.5 hPa (Cullen, 1993) with terrain following σ-
coordinates. An Arakawa B grid is used for horizontal discretization (Arakawa and Lamb,
1977) and horizontal diffusion is applied to control the accumulation of noise and energy at
the grid scale. PRECIS model also provides an option for the interactive reaction with sulfate
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
23
aerosols, which are simulated by using Lagrangian chemistry model STOCHEM (Collins et
al., 1997). The land-surface scheme employed in the PRECIS model is MOSES
(Meteorological Office Surface Exchange Scheme), which has shown good skill in land-
surface simulation (Bowling et al., 2003; Nijssen et al., 2003). It has a vegetated canopy that
provides fluxes of heat and moisture to the atmosphere and rainfall runoff. The MOSES uses
four soil layers in the vertical with depths chosen to capture important soil temperature
cycles. The scheme describes two components of runoff including surface runoff and
subsurface runoff (Cox et al., 1999 and Essery et al., 2003). The seasonal and daily varying
cycles of incoming solar radiation are also included. The boundary layers can occupy up to
the bottom five model layers. Observed sea surface temperatures (SSTs) and sea ice are used
for the base line climate simulations. For the future climate, changes in SSTs and sea ice
under SRES scenarios relative to baseline is derived from HadCM3 simulations (Jones et al.,
2004).
3.3 Representation of Topography in PRECIS
The representation of topography is an important input to climate models as it has a strong
impact on the simulated climate fields, especially in terms of spatial rainfall distribution. In
case of a vast flat terrain (thousands of kilometers) far away from coasts, the coarse
resolution of a GCM may not matter. However, HKH region has complex orographic
features, which play a key role in determining local climate and redistribution of solar energy
by surface interception. Precipitation is also highly sensitive to local topographic
characteristics and observations show that it increases with height (Winger et al., 2005).
Figure 3.1 shows the topographic features depicted by HadAM3P, PRECIS, GTOPO30
2MIN Digital Elevation Model (DEM) and the difference of surface elevation in HadAM3P
and PRECIS. The representation of elevation over HKH region is 500-3000 m higher in
PRECIS compared to the HadAM3P. To perceive the realism in the topographic details,
PRECIS and HadAM3P elevations are compared with the best estimates of topography i.e.
GTOPO30 2MIN DEM data. The representation of topographic features in PRECIS is quite
similar to that of GTOPO30 2MIN DEM (figure 3.1 b and c). However, there exist some
differences in orography, especially over the southwestern Pakistan where PRECIS results
show higher elevations compared to the GTOPO30. Anyhow, resolution of topographic
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
24
feature appears to be much better and PRECIS generated data are fit for the climate change
studies in the HKH region.
3.4 Experimental Design
Three experiments were designed in this study. Two experiments were aimed to simulate
present day climate and one experiment was exclusively for future climate simulation. The
experiments were mainly governed by factors such as study area, objectives of the study and
the ultimate use of PRECIS results. Table 3.1 gives some of the salient features of PRECIS
experiment. The brief descriptions of experimental components such as boundary data,
domain size and resolution of experiment are given in the following sections.
3.4.1 Domain size and resolution
The domain size is bounded by latitude 12 to 41 ˚N and longitude 55 to 97 ˚E (figure 3.2).
The horizontal resolution is 0.44˚ x 0.44˚ in rotation coordinates (~ 50 km). At this
resolution, the domain covers 89 grids in the longitude and 88 grids in the latitude. This
domain is reasonably large and covers most of South Asian region including India, Pakistan,
Afghanistan and Tibetan Plateau. It allows full development of internal mesoscale circulation
(e.g. monsoon circulation) and includes relevant regional forcings.
3.4.2 Boundary conditions
For the present climate simulations, PRECIS is nested with two global data sets. The global
forcing data from the ERA-40 reanalyses and HadAM3P GCM are used. The boundary data
of HadAM3P is the update version of atmospheric component of Hadley Centre coupled
ocean-atmospheric GCM HadCM3 in coarse resolution 3.75˚ in longitude and 2.5˚ in
latitude. To provide high-resolution boundary to PRECIS, HadAM3P is rerun in the
resolution of 1.875˚ in longitude and 1.25˚ in latitude based on the simulation of HadCM3.
For the baseline climate, it covers the period 1960-1990 (Wilson et al., 2005). ERA-40 is a
re-analysis of meteorological observations produced by the European Centre for Medium-
Range Weather Forecast (ECMWF). It covers the period 1957-2002, has a spatial resolution
of 1.875˚ by 1.25˚ and is extensively described in Uppala et al. (2005).
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
25
Figure 3.1 Topography of selected domain (a) Topography of the global climate model
(HadAM3P), (b) Topography of the regional climate model (PRECIS), (c) Topography of the GTOPO30 2MIN DEM and (d) Deviation of PRECIS RCM topography from GCM topography
a)
b)
Continued …….
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
26
Figure 3.1 Continued …….
c)
d)
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
27
For each global forcing, i.e. ERA-40 and HadAM3P, experiment were carried out at a
horizontal resolution of 50 x 50 km, hereafter referred to as PRECIS-ERA and PRECIS-Had,
respectively. The time period of PRECIS-ERA simulation was 1975-2001 whereas the
simulation of PRECIS-Had covers the period from 1960 to 1990. The period of PRECIS-
ERA experiment was restricted to start from 1975 because first 12 years ERA-40 boundary
data was corrupted (David Hein, personal communication). For future climate experiment the
global forcing data termed as HadAM3P: SRES B2 was used, hereafter referred to as
PRECIS HadB2. All experiments were performed by interfacing the sulphur cycle with
PRECIS. The first year in each experiment was considered as a spin-up period and data for
that period was not used in any analysis. After post processing of each experiment, the data
were prepared for further analysis.
Figure 3.2 PRECIS RCM domain for experiments at 50 x 50 km resolution
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
28
Table 3.1 Description of PRECIS RCM experiment
Experiment Driving Fields Resolution (km)
Number of Grids
Time Period
PRECIS-Had HadAM3P-Baseline 50 89 x 88 1960-1990
PRECIS-ERA ERA-40 50 89 x 88 1975-2001
PRECIS-HadB2 HadAM3P:B2 50 89 x 88 2070-2100
3.5 Present Day Climate Simulation Capacity of PRECIS The fine resolution regional simulations generated by PRECIS have been analyzed in detail
in order to assess the ability of PRECIS to model the regional climatological features. Model
validation was undertaken by following procedure after Giorgi et al., (2004). To evaluate the
systematic errors, the biases in PRECIS simulation were examined. The PRECIS simulated
temperature and precipitation was compared to the Climate Research Unit (CRU) data sets,
which is a 0.5° latitude/longitude gridded dataset of monthly observations for the period
1901-2002 (Mitchell and Jones, 2005). To estimate the errors caused by boundary data, the
PRECIS simulations were compared with the HadAM3P data. To analyze the errors in
internal model physics of PRECIS, we compared the biases in PRECIS-Had and PRECIS-
ERA climatology. For detailed analysis, land points of PRECIS domain were divided into
seven sub regions as shown in figure 3.3. The basic measure of temperature interannual
variability is the temporal standard deviation (STDV) as given in equation 3.1
1)( 2
N
TTSTDV i i (3.1)
where N is the number of years, Ti is the initial temperature and T is the average
temperature.
For the precipitation, the coefficient of variation (CV) was determined by the equation 3.2,
where the standard deviation of precipitation ( PSTDV ) is normalized by the average
precipitation ( P ) because PSTDV is affected by the mean, so that the CV is more
independent measure of interannual variability.
PSTDVCV P (3.2)
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
29
The ability of PRECIS to simulate patterns of mean sea level pressure (MSLP) was
investigated by comparing PRECIS simulated MSLS with those of NCEP reanalyses, ERA-
40 reanalyses and HadAM3P simulations (Kalnay et al., 1996; Uppala et al., 2005; Wilson et
al., 2005).
Figure 3.3 Sub regions used for more detailed analysis of the PRECIS RCM fields. Region 1 (Afghanistan), Region 2 (Southern Pakistan and Rajasthan), Region 3 (Hindu Kush-Karakorum- western Himalaya), Region 4 (Central Pakistan and Northwestern India), Region 5 (Tibetan Plateau), Region 6 (Central Himalaya) and Region 7 (Central India)
3.5.1 Mean sea level pressure patterns
The mean sea level pressure (MSLP) during the onset phase (May and June) of the Indian
summer monsoon is mainly important in order to understand the biases in regional
simulations. Figure 3.4 displays the MSLP for the period 1981-1990 for May-June (MJ) as
depicted by NCEP, ERA-40, HadAM3P, PRECIS-Had and PRECIS-ERA. A comparison of
MSLP patterns in NCEP reanalyses and ERA-40 reanalyses shows some differences over the
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
30
region. The main difference appears in the representation of heat low region. The minimum
pressure represented by ERA-40 reanalyses (~ 999 hPa) prevails over the Pakistan and
northwest India (Heat low region) lobbing towards the east coast of India. Whereas minimum
pressure represented by NCEP reanalyses (~ 1000 hPa) exists over the southern Pakistan and
Rajasthan. A trough of low pressure prevails over Tibetan Plateau, which is quite evident and
looks extended westward in ERA-40 data compared to NCEP data. The MSLP simulated by
both HadAM3P and PRECIS-Had (~ 994 hPa), over the heat low region, is more intense
compared to NCEP reanalyses. Kripalani et al. (2007) found similar pressure patterns in the
HadCM3 and linked the intensification of pressure systems to the decline in winter and
spring snowfall. The intense pressure systems in HadAM3P and PRECIS-Had may increase
the moisture and heat transport in the region. The heat low area in both HadAM3P and
PRECIS-Had simulations as compared to NCEP reanalyses appears to be shifted 1-2˚
northward. PRECIS-ERA and PRECIS-Had simulated MSLP are close to MSLP in ERA-40
and HadAM3P respectively. This indicates somehow the large-scale consistency between
RCM and driving forcing data. Compared to NCEP reanalyses data, HadAM3P and PRECIS-
Had simulate a very intense trough over Tibetan Plateau. Although PRECIS seems to
improve these anomalies in MSLP over heat low region and over the Tibetan Plateau,
however, the improvements are not much significant.
3.5.2 PRECIS temperature simulations
Figure 3.5 compares the observed (CRU) and simulated annual temperature. Compared to the
CRU observations the PRECIS-Had and PRECIS-ERA simulated temperature is relatively
low in the Tibetan Plateau and in the HKH region. The relatively low temperature is
substantive in PRECIS-Had as compared to HadAM3P and can best be attributed to the
steeps slopes escarpment over the Western Ghats, Tibetan Plateau and HKH region.
Compared to CRU data both PRECIS-Had and PRECIS-ERA show a general cold bias over
Tibetan Plateau and in the HKH region (figure 3.6). The presence of these errors in both
experiments suggests the inherent limitation of the model. Some of these cold biases may be
related to the positive precipitation bias because excess precipitation may cause extensive
wet soils resulting into high latent heat fluxes and low sensible heat fluxes. As a result
surface cooling (Bonan, 1998).
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
31
Figure 3.4 Mean seal level pressure (MSLP) for the period 1981-1990 for May-June (MJ)
season in (a) NCEP reanalyses data, (b) ERA-40 Reanalyses data, (c) HadAM3P GCM, (d) PRECIS-Had and (e) PRECIS-ERA
a)
b)
c)
Continued …….
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
32
Figure 3.4 Continued …….
Quantitative estimates of the temperature biases in the PRECIS-Had and HadAM3P can be
obtained from figure 3.7, which presents CRU, PRECIS-Had and HadAM3P temperatures
averaged over the subregions of figure 3.3. The annual cycle of temperature simulated by
both models matches reasonably well with the observed variations over all regions. Annual
cycle of temperature simulated by PRECIS-ERA also follows the patterns of observed
variations over all regions (figure 3.8). Generally, the profile of the temperature cycle shows
warm bias during the pre-monsoon season (i.e. April-June), while for the other seasons it is
underestimated. In monsoon-dominated regions (Region 4, Region 6 and Region 7), an
abrupt fall in temperature is observed during monsoon period. Kumar et al., 2006, also
observed this characteristic of PRECIS simulation nested in HadCM3 in all Indian mean
d)
e)
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
33
temperature. Table 3.2 shows that the subregional biases in the HadAM3P and PRECIS-Had
have a similar seasonal distribution. On average, the magnitude of temperature biases in
PRECIS-ERA is somewhat less compared to biases in PRECIS-Had. However, the
magnitude of biases during summer is generally higher in PRECIS–ERA compared to biases
in PRECIS-Had during the summer season. In all seven regions, the magnitude of biases is
higher during winter compared to summer season. Generally, the biases appeared to be
highest in the regions of complex topographic features (Region 3 and Region 5) and are
found to be lowest in relatively plane areas (Region 2 and Region 7). The cold bias observed
over mountainous areas (Region 3 and Region 5) is a common feature of regional climate
simulations over different regions of the world (Giorgi et al. 2004; Solman et al., 2008). It is
reported that CRU data of elevated areas may be warm biased due to the predominance of
less elevated stations and thus the simulated temperature may be underestimated in these
areas (New et al, 2000; Giorgi et al., 2004). The similar patterns of biases in both PRECIS-
Had and HadAM3P simulations demonstrate that the regional model have inherent biases due
to driving forcing.
Figure 3.9 shows the standard deviation of CRU temperature data and PRECIS-Had and
HadAM3P temperature simulations averaged over seven regions of figure 3.3. In January-
February-March (JFM) the temperature standard deviation varies in the range of 0.59 over
the central India (Region7) to about 1.39 in Afghanistan (Region 1). In July-August-
September (JAS), standard deviation for CRU data is more homogeneous in all regions and
varies in a narrow range of 0.34 to 0.64. Compared to the CRU observations, both the
PRECIS-Had and HadAM3P tend to overestimate the interannual variability in all seasons
except for the JAS where standard deviation of CRU data and PRECIS-Had and HadAM3P
simulations are in close agreement.
Generally, the standard deviations in the PRECIS-Had and HadAM3P are close to each other
which reflects that PRECIS inherent a good proportion of its temperature interannual
variability from global forcing data. Giorgi (2002) analyzed the dependence of surface
climate interannual variability on spatial scales and found that the temperature and
precipitation interannual variability tends to increase at finer scale, most markedly in the
summer. This scale affect on the interannual temperature variability is not very significant in
our PRECIS simulations.
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
34
Figure 3.5 Observed and simulated (baseline) patterns of annual temperature (˚C) for (a)
CRU data, (b) HadAM3P, (c) PRECIS-Had and (d) PRECIS-ERA
a)
b)
Continued …….
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
35
Figure 3.5 Continued …….
c)
d)
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
36
Figure 3.6 Bias of annual temperature (˚C) for (a) PRECIS-Had and (b) PRECIS-ERA with
respect to CRU data
a)
b)
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
37
Figure 3.7 Observed and simulated (PRECIS-Had and HadAM3P) annual cycle of
temperature averaged over the seven sub regions of figure 3.3
-505
1015202530
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C)
CRU PRECIS-Had HadAM3P
Region 1
05
10152025303540
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C)
CRU PRECIS-Had HadAM3P
Region 2
-20-15-10
-505
101520
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C)
CRU PRECIS-Had HadAM3P
Region 3
0
5
10
15
20
25
30
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C)
CRU PRECIS-Had HadAM3P
Region 4
-25-20-15-10
-505
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C)
CRU PRECIS-Had HadAM3P
Region 5
0
5
10
15
20
25
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C)
CRU PRECIS-Had HadAM3P
Region 6
05
10152025303540
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C
)
CRU PRECIS-Had HadAM3P
Region 7
Chapter 3 Analyses ofP:RECIS RCM Climate Change Scenarios
I
Figure 3.8
t ~~r~ 20~ 15
: 10~ 5
0
.......................................------.
............................
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Region1Month
! 4--CRU - . .. - . .PRECIS-ERAI'
Q' 40
I
f"
~ 30: 20
11: 1
""'''''''''''''''''''''''''---'''''---'' ---.............
, .
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MonthRegion2
I 4--CRU . - .PRECIS-ERAI
20Q:;,. 10~
~ 0
~.101 .'.~-20
Jan Feb Mar Apr May Jun Jut Aug Sep Oct Nov Dec
Region3 Month
1 4-- CRU - . .. - . .PRECIS-ERAI
-,
40 ........--..........;;
~ 30
i 20"J 10
0
--..........--................--.--..--.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Region4Month
I 4--CRU. . .. . . .PRECIS-ER~I
10,......--....--....----..--.............
;> 5
~ 0,;; -5~ -10~-15.:-20
-25 1 :Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec I
Month
Region5
1 4-- CRU . . .. - . .PRECIS-ERAJ
~ .-.
;> ~:
I
."""'---"''''''
.
---''''''~'~':''
... ,"~ 15 '.. .~ 10 ""J 5
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Moolth
Region6
1 4-- CRU . .-:-. :-. . PR-ECIS.ER~J I
.J
~ ::j
:.~.."":':'.';':'.':':"':"."'''''''''''''':''''~ :~ 20 .' .'.. '~ '. :
J1: l' :Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Region7
Month
1 4-- CRU - . .. - . .~RECIS-ERAI
Observed and simulated (PRECIS-ERA) annual cycle of temperature averagedover the seven sub regions of figure 3.3
38
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
39
Figure 3.9 Observed (CRU) and simulated (PRECIS-Had and HadAM3P) seasonal temperature standard deviation averaged over the seven subregions of figure 3.3
Table 3.2 Biases in mean temperature (˚C) as simulated with the PRECIS-Had, PRECIS-ERA and HadAM3P relative to CRU reference data for different seasons and seven sub regions of figure 3.3 (Summer= April-September, Winter = October-March)
Temperature (˚C)
PRECIS-Had HadAM3P PRECIS-ERA Region Winter Summer Annual Winter Summer Annual Winter Summer Annual
Region 1 -2.18 1.57 -0.31 -2.53 1.21 -0.66 -0.68 0.71 0.02
Region 2 -1.85 -0.03 -0.94 -1.99 0.01 -0.99 0.74 1.25 1.00
Region 3 -6.75 -2.84 -4.80 -7.04 -2.90 -4.97 -5.04 -3.56 -4.30
Region 4 -3.95 -0.38 -2.16 -4.09 -0.29 -2.19 -1.02 0.46 -0.28
Region 5 -3.99 -1.72 -2.85 -3.51 -1.45 -2.48 -2.95 -2.38 -2.66
Region 6 -3.98 -1.31 -2.64 -3.85 -1.42 -2.63 -1.81 -0.31 -1.06
Region 7 -1.64 -1.41 -1.52 -1.47 -1.09 -1.28 -2.50 2.07 -0.21
Average -3.48 -0.87 -2.18 -3.49 -0.85 -2.17 -1.89 -0.25 -1.07
0.00
0.50
1.00
1.50
R1 R2 R3 R4 R5 R6 R7
Subregions
STD
V (˚
C)
CRU PRECIS HadAM3P
JAS
0.00
0.50
1.00
1.50
R1 R2 R3 R4 R5 R6 R7
Subregions
STD
V (˚
C)
CRU PRECIS HadAM3PJFM
0.00
0.50
1.00
1.50
R1 R2 R3 R4 R5 R6 R7
Subregions
STD
V (˚
C)
CRU PRECIS HadAM3P
AMJ
0.00
0.50
1.00
1.50
R1 R2 R3 R4 R5 R6 R7
Subregions
STD
V (˚
C)
CRU PRECIS HadAM3POND
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
40
3.5.3 PRECIS precipitation simulations
Observed (CRU data) and HadAM3P, PRECIS-Had and PRECIS-ERA simulated annual
precipitation patterns are shown in figure 3.10. The spatial distribution of precipitation in the
PRECIS simulation is very similar to the CRU data, which illustrates that the baseline
simulations adequately represent the present day conditions. However, some quantitative
biases in the spatial patterns also exist and the maximum biases are present over HKH region
(figure 3.11). The common biases in both PRECIS-Had and PRECIS-ERA experiments
indicate that these errors are due to deficiencies in the internal model physics. However,
some of the biases may be related to the inadequate representation of land surface in PRECIS
because seasonal variations in surface albedo, roughness and leaf area index could have a
significant effect on the climate (Hudson and Jones, 2002). The model currently uses
vegetation distribution and soil properties based on the climatology of Wilson and
Henderson-Sellers (1985) which does not account for these factors.
Figure 3.12 shows CRU observed, PRECIS-Had and HadAM3P simulated annual cycle of
precipitation whereas the annual cycle of precipitation simulated through PRECIS-ERA is
shown in figure 3.13. The annual cycle of precipitation as simulated through PRECIS-Had,
PRECIS-ERA and HadAM3P agrees well with observed variations in all regions. Over
Afghanistan (Region 1) and Hindu Kush-Karakorum-western Himalaya (Region 3)
precipitation reaches to maximum during the winter season, while precipitation over other
regions (Region 2, Region 4, Region 5, Region 6 and Region 7) is maximum during summer
season. Generally, it appears that PRECIS-Had and HadAM3P simulations overestimate
precipitation when compared to the observational data. Similar patterns are noted in
PRECIS-ERA simulations in all regions except for the Region 7 wherein PRECIS-ERA
underestimate precipitation. There are systematic differences in precipitation simulated
through PRECIS-Had and PRECIS-ERA as well. A systematic difference is also noted
between the precipitation simulated by HadAM3P and PRECIS-Had wherein the PRECIS-
Had tends to produce greater precipitation than HadAM3P. This is may be due to various
reasons. For example, Giorgi and Marinucci (1996) showed that the simulation of
precipitation may be sensitive to model resolution regardless of the topographic forcing. In
their experiments, precipitation tends to increase at finer resolution and topographic forcing
is found to be further strengthening this effect. Kumar et al. (2006) reported that during the
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
41
onset phase of the monsoon season, a significant bias is present in the Indian mean
precipitation estimated through PRECIS nested with HadCM3 boundary data. They inferred
that some of the biases in PRECIS simulation are inherited from boundary data. Notable
differences in PRECIS simulated precipitation over the Tibetan Plateau is also reported in a
study carried out in China (Yinlong et al., 2006). Kriplani et al., (2007) analyzed 22 GCMs to
investigate the South Asian summer monsoon variability, and found an intensification of
summer monsoon because of the intensification of low pressure over the Indo-Gigantic plain
and the land-ocean pressure gradient during the onset phase of the summer monsoon.
Therefore, the increase in precipitation in PRECIS-Had over the Tibetan Plateau and Hindu
Kush-Karakorum- Himalayan region could also be attributed to the intensification of the heat
low over these areas during the onset phase of the monsoon. Table 3.3 presents the
precipitation biases in PRECIS-Had, PRECIS-ERA and HadAM3P simulations compared to
the CRU data. The models generally overestimate the precipitation in all regions. However,
PRECIS-ERA is an exception, which underestimates precipitation in Region 1, Region 2 and
Region 7. On average, the precipitation biases in PRECIS-ERA simulations are somewhat
less compared to PRECIS-Had simulations. In some cases, HadAM3P gives slightly better
matching with CRU observations than PRECIS-Had. Many other researchers (Giorgi et al.
2004; Solman et al., 2008) strongly endorse this finding as well. They argued that the
apparent improvement in the GCM precipitation data was not due to the better representation
of the regional circulation features but owe to the compensations of model errors in the
GCM.
The coefficient of variation for precipitation is shown in figure 3.14. The observed
coefficient of variation ranging between 0.1-0.5 shows a relatively uniform distribution
throughout the region. The interannual variability of precipitation is generally higher during
the winter compared to the summer. This is due to the effect of dividing the standard
deviation by low precipitation amounts. Compared to the CRU observations, both the
PRECIS-Had and HadAM3P shows an underestimation of interannual variability in the high
mountain regions (Region 3 and Region 5) and overestimation in relatively flat regions
(Region 2 and Region 7). The PRECIS-Had and HadAM3P coefficients of variations are
generally in good agreement which shows that PRECIS obtain a good proportion of its
precipitation interannual variability from boundary data.
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
42
Figure 3.10 Observed and simulated (baseline) patterns of annual precipitation (mm/day)
for (a) CRU data, (b) HadAM3P, (c) PRECIS-Had and (d) PRECIS-ERA
a)
b)
Continued …….
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
43
Figure 3.10 Continued …….
c)
d)
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
44
Figure 3.11 Bias of annual precipitation (mm/day) for (a) PRECIS-Had and (b) PRECIS-
ERA with respect to CRU data
a)
b)
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
45
Figure 3.12 Observed and simulated (PRECIS-Had and HadAM3P) annual cycle of precipitation averaged over the seven sub regions of figure 3.3
0
0.5
1
1.5
2
2.5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-Had HadAM3PRegion 1
0
1
2
3
4
5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-Had HadAM3P
Region 2
0
1
2
3
4
5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-Had HadAM3P
Region 3
0
2
4
6
8
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-Had HadAM3P
Region 4
0
1
2
3
4
5
6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-Had HadAM3P
Region 5
0
2
4
6
8
10
12
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-Had HadAM3P
Region 6
02468
101214
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-Had HadAM3P
Region 7
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
46
Figure 3.13 Observed and simulated (PRECIS-ERA) annual cycle of precipitation averaged
over the seven sub regions of figure 3.3
0
0.5
1
1.5
2
2.5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-ERARegion 1
0
1
2
3
4
5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-ERA
Region 2
0
1
2
3
4
5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-ERA
Region 3
012345678
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-ERA
Region 4
0
1
2
3
4
5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-ERA
Region 5
0
2
4
6
8
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-ERA
Region 6
0
2
4
6
8
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
CRU PRECIS-ERA
Region 7
i ~~ 1..~...,.:...i...~..i ;...'..~...J i...~....0.00 L---i--l-
Rl R2 R3 R4 R5 R6 R7
SubregionsJFM
~PRECIS .II>.HadAM3P I
100 1" ,"":"'"'''''';'''''''''''':''''''''''';'''''''''''':''''''''''~'''''''''''
# 0 751-_n _~_nnn:n_n_n:nnnn:---nn"
I. ::: l ~--i--:---i---~---:---:-_.:-_I- ; ~---:0.00 -- --;
R1 R2 R3 R4 R5 R6 R7
Subregions
AMJFCRU .PRECIS .II>.HadAM3P I
'"''1 00
I :::[~'''''''''.:'''''''''':,,-'';'' ",'" : -'. - - ,
" O.<O'.rO'.:m m i '0.. 0 ,.~ '
I::.oili'i:~~" +-;;_10.00 '
R2 R3 R4 R5Rl
Subregions
JAS
10 CRU . PRECIS.II>. H;;dAM 3P J
--,
f :.: l~iiIoI~:.:m~...~ I
~ 0 ;11:' .[; 025. n_n_;nn: - -: On. n, , .
, , '
0 .00 '---u_+ m~ +.._u '-"._"", ,,;
Rl R2 R3 R4 R5 R6 R7
Subregions
ON
10CRU . PRECIS .II>.HadA~
Observed (CRU) and simulated (PRECIS-Had and HadAM3P) seasonalprecipitation coefficient of variation (CV) averaged over the seven sub regionsof figure 3,3 '
Figure 3,14
47
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
Table 3,3 Biases in mean precipitation (%) as simulated with the PRECIS-Had, PRECIS-ERA and HadAM3P relative to CRU reference data for different seasons and
seven sub regions of figure 3,3 (Summer= April-September, Winter = October-t March)
Precipitation (%)
Region PRECIS-Had HadAM3P 0 PRECIS-ERA
Winter Summer Annual Winter Summer Annual Winter Summer Annual
Region I -15 58 22 -5 57 26 -15 -1 -8
Region 2 36 46 41 24 32 28 -45 -17 -31
Region 3 92 101 97 76 55 65 126 109 117
Region 4 132 77 105 102 25 63 62 24 43
Region 5 21 102 61 36 83 60 33 77 55
Region 6 99 50 75 78 30' 54 27 7 17
Region 7 6 65 36 ,.., 33 15 -59 -41 -50-.)
Average 53 71 62 44 45 44 18 23 21
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
48
3.4.4 PRECIS estimated wet day frequency
Besides the evaluation of mean precipitation, we also analyze the ability of regional model in
simulating high frequency precipitation statistics in terms of wet day frequency, which is the
number of days per season with precipitation amounting greater than 0.1 mm. Figure 3.15
shows the frequency of wet days. It is evident that there is a close agreement in the simulated
PRECIS-Had and HadAM3P frequency of wet days with CRU observed frequencies in
Afghanistan (Region 1) and in central Himalaya (Region 4). The simulated wet days are
underestimated in southern Pakistan and Rajasthan (Region 2) and in central India (Region
7). An exceptionally high overestimation of frequency of wet days appears over Hindu Kush-
Karakorum- western Himalaya (Region 3) and Tibetan Plateau (Region 5).This
overestimation could better be explained due to the presence of substantially high pressure
over these regions.
0.00
0.20
0.40
0.60
0.80
1.00
R1 R2 R3 R4 R5 R6 R7
Region
Wet
day
freq
uenc
y
CRU PRECIS HadAM3P
Figure 3.15 Observed and simulated wet day frequencies averaged over the seven sub regions shown in figure 3.3
3.5 Climate Change Responses under SRES B2 Scenario for Period 2071-2100
PRECIS simulated annual, summer and winter spatial patterns of temperature change over
the period 2071-2100 for SRES B2 scenarios is illustrated in figure 3.16. Annual, summer
and winter spatial pattern of temperature change indicates an overall warming and maximum
warming is predicted over the western Pakistan. However, warming signals are weak over
the Himalayan mountain range. Infact, the cooling is likely in some small patches over the
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
49
Jammu and Kashmir region. Generally, winter season is more warmer compared to the
summer season. The mean annual cycle of temperature for present and future climate,
averaged over the seven sub regions, is presented in figure 3.17. A future increase in
temperature is evident over all sub regions. The future temperature follows the cyclic pattern
of present day climate. The predicted change in temperature is given in table 3.4. The mean
annual temperature change ranges between 2.9-3.3 ˚C. The maximum temperature change is
expected over Region 2 where predicted winter temperature is 3.4 ˚C. In the Region 3 (study
area), mean annual temperature change is 3.1 ˚C. On average, the annual mean temperature
rise is 3.1 ˚C and winter is 0.2 ˚C warmer than summer. In monsoon-dominated region 7, the
difference in summer and winter temperature change (0.6 ˚C) is relatively large.
Annual, summer and winter spatial patterns of precipitation change during 2071-2100 as
simulated by the PRECIS under SRES B2 scenario is given in figure 3.18. An annual mean
precipitation appears to be increased over most areas with a maximum over the Himalayan
mountain range, western and eastern Ghats. Whereas low precipitation change is evident
predominantly over border areas between Pakistan and India i.e. eastern parts of Sindh
Pakistan, Rajasthan India and southern parts of Punjab (both Indian and Pakistani parts of
Punjab). The spatial patterns of summer and winter precipitation change are similar.
However, the magnitude of summer precipitation change is higher compared to winter
precipitation change. The mean annual cycles of precipitation for present and future
precipitation are presented in figure 3.19. The future annual precipitation cycle follows the
pattern of present day precipitation. This shows that major shift in seasons is not expected in
future. In Regions 4 and 7, precipitation is expected to increase during summer season
whereas in Regions 1 and 3 the increase is predicted during winter season. There is an overall
increase in precipitation in SRES B2 scenario. Table 3.4 presents the annual and seasonal
changes in precipitation under SRES B2 scenarios for seven sub regions. Out of which only
Region 1 (i.e. Afghanistan) has shown a 7 % decrease in the mean annual future
precipitation. The rise in mean annual precipitation ranges from 1 to 21 %. In monsoon
dominated regions (i.e. Regions 4, 6 and 7), the precipitation during the summer season is
predicted to be increased up to 15%. In Region 3 (covering most of the UIB), the winter
precipitation with an expected rise of 3 % is important from the hydrological point of view.
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
50
Figure 3.16 Changes of mean temperature under SRES B2 scenario relative to present day
climate (a) Annual, (b) Summer and (c) Winter
a)
b)
c)
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
51
Figure 3.17 Changes of mean precipitation under SRES B2 scenario relative to present day
climate (a) Annual, (b) Summer and (c) Winter
a)
b)
c)
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
52
Figure 3.18 Annual cycle of temperature averaged over the seven sub regions for present
(1961-1990) climate and future (2071-2100) climate under SRES B2 scenario
-10
0
10
20
30
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C
)
Present Temperature Future Temperature
Region 1
-10
0
10
20
30
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C)
Present Temperature Future Temperature
Region 2
-30
-20
-10
0
10
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C
)
Present Temperature Future Temperature
Region 3
-10
0
10
20
30
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C
)
Present Temperature Future Temperature
Region 4
-30
-20
-10
0
10
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C
)Present Temperature Future Temperature
Region 5
-10
0
10
20
30
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C
)
Present Temperature Future Temperature
Region 6
0
10
20
30
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Tem
pera
ture
(˚C
)
Present Temperature Future Temperature
Region 7
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
53
Figure 3.19 Annual cycle of precipitation averaged over the seven sub regions for present
(1961-1990) climate and future (2071-2100) climate under SRES B2 scenario
0
1
2
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
Present Precipitation Future Precipitation
Region 1
0
1
2
3
4
5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
Present Precipitation Future Precipitation
Region 2
0
1
2
3
4
5
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
Present Precipitation Future Precipitation
Region 3
0
2
4
6
8
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
Present Precipitation Future Precipitation
Region 4
0
2
4
6
8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
Present Precipitation Future Precipitation
Region 5
0
2
4
6
8
10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
Present Precipitation Future Precipitation
Region 6
02468
10121416
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Pre
cipi
tatio
n (m
m/d
ay)
Present Precipitation Future Precipitation
Region 7
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
54
Table 3.4 Seasonal changes of mean temperature and precipitation under SRES B2 scenario
from PRECIS in 2071-2100 over the seven sub regions relative to 1961-1990 (Summer = April-September; Winter=October-March)
Temperature Change (˚C) Precipitation Change (%) Region Annual Winter Summer Annual Winter Summer
Region 1 3.2 3.3 3.2 -7 -8 -7
Region 2 3.3 3.2 3.4 1 -6 9
Region 3 3.1 3.1 3.2 6 8 3
Region 4 3.1 3.0 3.3 10 7 12
Region 5 2.9 2.9 2.8 21 10 32
Region 6 3.1 2.9 3.3 9 1 18
Region 7 2.9 2.6 3.2 15 15 16
Average 3.1 3.0 3.2 8 4 12
3.6 Summary
To simulate regional climate scenarios we have used PRECIS regional climate modelling
system, which is successfully set up in the South Asian region in some recent studies (Kumar
et al., 2006; Yinlong et al., 2006; Islam et al., 2008; Akhtar et al., 2008a,b). The scenarios
presented here are very useful for the impact assessment in various sectors. The PRECIS
output contains a large number of parameters. However, this study has focused mainly on the
temperature and precipitation. The analysis of the simulation mainly evaluates the capability
of PRECIS in representing spatial patterns of seasonal mean climate, its annual cycle,
interannual variability, wet day frequency and future change in temperature and precipitation
under SRES B2 scenario.
PRECIS improves the representation of mean climate compared to boundary data in many
aspects. The first feature to note is the representation of heat low area. Nevertheless, it
produces the intense pressure system and fails to produce the correct position of the heat low
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
55
compared to the NCEP reanalyses even then the representation of mean climate is better
compared to boundary forcing data. Kripalani et al. (2007) studied 22 GCMs to investigate
the South Asian summer monsoon variability and found an intensification of the heat low
over northwest India during the beginning of monsoon season. They linked intensification of
pressure systems with the decline in winter and spring snowfall. The intensity of heat low in
HadCM3 and ECHAM5 models investigated by them is comparable to HadAM3P and
PRECIS-Had simulations in our study. PRECIS simulated MSLP are close to MSLP of
driving forcing data (as expected) and this indicates somehow the large-scale consistency
between RCM and driving GCM.
The mean spatial patterns of temperature and precipitation agree reasonably well with CRU
observations though some model biases have been identified. For precipitation, biases
include an overestimation of precipitation especially over the mountainous regions.
Overestimation of precipitation is a common behavior in regional simulations over elevated
terrain of different parts of the world (Leung et al., 2003; Giorgi et al., 2004; Solman et al.,
2008; Islam et al., 2008). Therefore, higher orography in the PRECIS compared to GCM may
produce more precipitation (and more rainy days) over the mountain areas. This increase
could also be attributed to an intensification of the heat low. Kriplani et al., (2007) also found
an increase in summer monsoon because of the escalation of low pressure over the heat low
region.
Temperature shows in general a warm bias during the pre-monsoon months (i.e. April-June)
whereas for remaining months it is underestimated. Mean annual temperature cycle shows an
abrupt fall during monsoon period, which is a characteristic of all Indian mean temperature
(Kumar et al., 2006). The magnitude of temperature and precipitation biases in PRECIS-ERA
is somewhat less compared to PRECIS-Had. Overall, the magnitude of cold bias in
temperature is higher during winter compared to summer season bias whereas the magnitude
of precipitation bias is higher during summer compared to winter season bias. The seasonal
variations in temperature biases may be due to the variations in the latent heat flux in
different seasons (Uchiyama et al., 2006) which may not be well distinguished by the
PRECIS as well as by the driving forcing. The biases are higher generally in the region with
complex topographic features (e.g. Regions 3 and 5) compared to relatively plane areas (e.g.
Regions 2 and 7). This is a common feature of regional climate simulations found in different
Chapter 3 Analyses of PRECIS RCM Climate Change Scenarios
56
parts of the world as well (Giorgi et al. 2004; Solman et al., 2008). Some of the biases may
also be related to the errors in CRU data (New et al, 2000; Giorgi et al., 2004) .Generally, the
estimated biases are comparable with the biases found in other studies in South Asian
countries ( Kumar et al., 2006; Yinlong et al., 2006; Islam et al., 2008).
On average, the interannual variability of simulated temperature is slightly higher compared
to the interannual variability in observed temperature. The interannual variability of
precipitation is somewhat less compared to the interannual variability in observed
precipitation in high mountain regions (Regions 3 and 5) whereas it is overestimated in
relatively flat regions (Regions 2 and 7). The similar values of interannual variability of both
temperature and precipitation in both PRECIS-Had and HadAM3P depict that PRECIS
inheriting a good proportion of its interannual variability from HadAM3P. Giorgi (2002)
found that the temperature and precipitation interannual variability tends to increase at finer
scale. However, this scale effect is not found in our simulations which is may be because of
same model physics used in PRECIS and HadAM3P. Despite the systematic errors discussed
here, the results are encouraging since the dynamical downscaling techniques are the most
reliable tool to estimate future projections of climate change with enough spatial detail, as
needed for impact studies.
PRECIS RCM simulation under SRES B2 scenario shows marked increase in temperature
and precipitation by the end of 21st century relative to the present day climate. However, the
spatial patterns of mean temperature changes show that over the Himalayan region, the
warming signals are week. Considering the average of seven regions (roughly average of all
land points) the average annual increase in temperature is predicted to be 3.1 ˚C whereas
precipitation is expected to rise by 8 %.
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
57
CHAPTER 4
PRECIS SIMULATIONS AS INPUT TO HYDROLOGICAL MODELLING
4.1 Background
Conceptual water balance models are often believed to be useful in assessing the impact of
climate change on the regional hydrology (Arnell, 1999). These models have several
advantages over lumped models and/or physically based models. Main advantages include
flexibility and ease of use (Xu, 1999; Booij, 2002; Te Liunde et al., 2007). The most
important climatological inputs required for the calibration and validation of hydrological
models are temperature and precipitation that can be derived from observational records or
alternatively from regional climate models (RCMs). Meteorological data has a considerable
influence on the performance of hydrological model. For instance, inadequate representation
of spatial variability of precipitation in modelling can be partially responsible for modelling
errors. This may also lead to problems in parameter estimation of conceptual hydrological
models. It is reported that the uncertainties in discharge due to errors in meteorological inputs
are larger than uncertainties in hydrological model errors and parameter estimation errors
(Booij, 2002; Te Liunde et al., 2007). Therefore, a better understanding of the use of
meteorological data from various sources (observations and RCM simulations) in
hydrological models will enhance the confidence in predicted hydrological change.
Hence, the effect of precipitation and temperature, simulated with PRECIS RCM nested in
different global data sets, on the discharge simulated with the HBV model is examined.
Three river basins including Hunza, Gilgit and Astore are chosen to study the effect of
meteorological data on simulated discharge. Figure 4.1 gives the location and table 4.1 enlists
the salient features of these river basins of the study area (UIB region). The basin areas of
Hunza, Gilgit and Astore rivers are about 13925 km2, 12800 km2 and 3750 km2, respectively
and 34 %, 7 % and 16 % of which are glacial covered, respectively.
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
58
Figure 4.1 Location map of Hunza, Gilgit and Astore river basins
Table 4.1 Characteristics of study area
River Basins Parameters Hunza Gilgit Astore
Gauging Station Dainyor Gilgit Doyian
Latitude 35˚ 56‘ 35˚ 56‘ 35˚ 33‘
Longitude 74˚ 23‘ 74˚ 18‘ 74˚ 42‘
Elevation of gauging station (m) 1450 1430 1583
Drainage area (km2) 13925 12800 3750
Glacier covered area (km2) 4688 915 612
Mean elevation (m) 4472 3740 3921
% area above 5000 meter 35.8 2.9 2.8
No. of meteorological stations
Precipitation - 2 1
Temperature - 2 1
No. of PRECIS grid points at 50 km 6 5 2
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
59
4.2 Influence of Temperature and Precipitation on Discharge
Understanding of hydrological regime in UIB requires the joint consideration of affect of
temperature during extreme rainfall events and their impact on runoff. To depict the role of
temperature, two extreme rainfall spells are examined (table 4.2). The storm event of 11
October 1987 appeared to be one of the most sever episodes and spread over the entire area
of northern Pakistan. Precipitation occurred from 10 to 13 October but with heavy falls on 11
October resulting in a drop in mean daily temperature. In case of storm event of 28 August
1997, precipitation occurred from 27 to 28 August with maximum rainfall on 28 August
resulting in a decline in mean daily temperature. In both cases, daily maximum temperature
is more affected as compared to daily minimum temperature. Figure 4.2 shows the discharge
pattern of Hunza, Gilgit and Astore rivers during these two events. Monsoon rainfall record
suggests that the direct contribution of rainfall to river flow in the highly glaciated Hunza
river basin is small and the occurrence of rainfall is accompanied by a sharp fall in flood
hydrograph. The reduction in melt runoff in high altitude basin (Hunza river) is generally due
to reduced temperature and energy input. However, the hydrograph of Gilgit and Astore
rivers shows a small rise in the discharge during these rainfall events. The discharge behavior
of these river basins shows that both the energy inputs and intensity of rainfall events are
important considerations for hydrological modelling in the UIB.
4.3 Present Day Climate Data Analysis
In this section, temporal patterns of observed and simulated present day climate over selected
river basins are examined. CRU observed, PRECIS-Had and PRECIS-ERA simulated
temperature and precipitation data are extracted for each river basin. These data sets are
compared with each other to determine the ability of simulated fields in representing the
climate of river basins. The biases between PRECIS simulated temperature and precipitation
data and CRU reference data for different seasons are also presented.
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
60
Table 4.2 Temperature and precipitation during two monsoon events at selected stations
Temperature (°C) Precipitation (mm) Ev
ent Station Date Max. Min. Mean Hr.12 of
previous date to Hr. 0 of date
Hr.0 To Hr.3 of Date
Past 24 Hrs. Preceding Hr. 3 Of
Date
Hr.3 To Hr.12
of Date
9 22 9 15.5 0.0 0.0 0.0 -1.0 10 19 2.2 10.6 33.0 0.0 33.0 14.2 11 4.2 0.3 2.25 5.0 0.0 19.2 4.7 12 8.4 2.2 5.3 6.5 0.0 11.2 23.8
Astore
13 6.4 0 3.2 5.0 0.0 28.8 2.3
9 31 10.8 20.9 0.0 0.0 0.0 0.0 10 26.7 11.9 19.3 6.0 3.0 9.0 2.5 11 13.9 7.8 10.85 42.0 10.0 54.5 2.1 12 9.8 7.8 8.8 0.4 0.0 2.5 8.9
Gilgit
13 10.8 6.7 8.75 1.3 0.0 10.2 -1.0
9 27.8 11 19.4 0.0 0.0 0.0 0.0 10 24.9 9.6 17.25 8.0 8.0 16.0 226.0 11 13.3 4.5 8.9 295.0 5.0 526.0 41.0 12 9.3 5.4 7.35 114.0 0.0 155.0 23.0
A
Skardu
13 12.8 5.6 9.2 46.0 13.0 82.0 11.0
26 25.6 11.7 18.65 0.0 0.0 0.0 0.0 27 22.8 6.7 14.75 32.0 6.8 38.8 39.6 28 8.1 5.6 6.85 12.2 0.0 51.8 0.0 29 13.9 7.8 10.85 0.0 0.0 0.0 0.0
Astore
30 20.1 10.1 15.1 0.0 -1.0 0.0 3.0
26 27.5 17 22.25 0.0 0.0 1.2 0.0 27 29.8 14.2 22 20.0 13.6 33.6 34.6 28 14.8 9.8 12.3 14.1 0.0 48.7 0.0 29 23 11.8 17.4 0.0 0.0 0.0 0.0
Gilgit
30 28 14.5 21.25 0.0 0.0 0.0 0.0
26 30 14.2 22.1 0.0 0.0 0.0 1.0 27 23.3 11.3 17.3 15.0 2.0 18.0 19.0 28 12.2 9 10.6 14.7 0.0 33.7 0.0 29 18.3 10.3 14.3 0.0 0.0 0.0 0.0
B
Skardu
30 25.3 13 19.15 0.0 0.0 0.0 0.0
A: Event of October, 1987, B: Event of August 1997
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
61
Figure 4.2 Discharge of Hunza river, Gilgit river and Astore river during the rainfall events of (a) October, 1987 and (b) August, 1997
0
400
800
1200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Day
Disc
harg
e (m
3 /s)
Hunza Gilgit Astore
b) Discharge during August 20 to September 8, 1997
0
200
400
600
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20Day
Dis
char
ge (m
3 /s)
Hunza Gilgit Astore
a) Discharge during October 1-20, 1987
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
62
4.3.1 Temperature
Figure 4.3 compares the simulated and observed mean annual temperature cycle for three
river basins. PRECIS-Had, PRECIS-ERA and CRU data show a bell type distribution of
temperature cycle. Generally, in all river basins the characteristics of the mean annual cycle
of temperature in PRECIS RCM simulations are similar to CRU data with highest mean
temperature observed in July and the lowest in January. It is also noted that the highest mean
temperature is apparent in the Astore river basin while the lowest in the Hunza river basin.
This is mainly because of the fact that Hunza river basin is situated at a higher elevation
compared to Astore river basin. Some differences between the PRECIS simulations and CRU
observations are also evident. Differences are present in PRECIS-Had and PRECIS-ERA
simulated temperature as well. However, in some months these differences are minor. The
biases in PRECIS RCM with respect to CRU data are presented in table 4.3. In all river
basins, PRECIS RCM simulations underestimate mean temperature. The magnitude of the
cold bias depends on the driving boundary data and varies from season to season. The cold
bias during winter is somewhat higher in PRECIS-Had compared to PRECIS-ERA while it is
somewhat less in PRECIS-Had compared to PRECIS-ERA during summer. The cold bias in
PRECIS simulations may be because of the deficiencies of the GCM simulations (McGregor,
1997). It is also observed that cold biases in the winter (October to March) are relatively
higher than in the summer (April to September) biases. This is may be because of the fact
that PRECIS RCM simulations give excessive precipitation during OND and JFM seasons
(figure 4.5) resulting into extremely wet soils. Consequently, there is cooling because of high
latent heat flux and low sensible heat flux of wet soils (Bonan, 1998). It is also noted that the
magnitude of cold biases in case of higher altitude Hunza river basins is relatively higher
than in the lower altitude Astore river basin.
4.3.2 Precipitation
For three river basins, the mean annual cycles of precipitation from CRU observations and
PRECIS RCM simulations are shown in figure 4.4. CRU curve shows that the maximum
precipitation is reached in March whereas lowest in September in all river basins. The
PRECIS RCM experimental curves also show similar variability patterns for theses river
basins. Nevertheless, PRECIS RCM simulated precipitation is in general overestimated.
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
63
There is stronger variability in the mean annual cycle of precipitation when compared with
the mean annual cycle of temperature. PRECIS results also exhibit an overall wet bias, which
is somewhat higher in case of PRECIS-ERA simulation compared to PRECIS-Had
simulation (table 4.4). Moreover, these biases are higher during winter season as compared to
summer season. The wet biases can be best explained by the steep topography of the area,
which can lead to excessive accumulated orographic precipitation (Giorgi, et. al., 1994).
However, some of the biases may be because of the deficiencies in GCMs (McGregor, 1997).
Table 4.3 Biases in mean temperature (˚C) as simulated with PRECIS RCMs relative to CRU reference data for different seasons and river basins (Winter =October- March; Summer =April-September)
Temperature ( ˚C)
PRECIS-Had PRECIS-ERA River Basin
Winter Summer Winter Summer
Astore -5.4 0.4 -3.8 -2.4
Gilgit -9.3 -2.9 -7.2 -4.4
Hunza -9.2 -4.9 -7.1 -6.5
Table 4.4 Biases in precipitation (%) as simulated with PRECIS RCMs relative to CRU reference data for different seasons and river basins (Winter =October- March; Summer =April-September)
Precipitation (%)
PRECIS-Had PRECIS-ERA River Basin
Winter Summer Winter Summer
Astore 135 57 361 133
Gilgit 43 69 103 44
Hunza 167 217 297 210
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
Figure 4.3
"',""'::; -5;;;:;; -10E~ -15
i::,~
~ -5:;;CLE -10~,f-
E~,::;;;; 0:;;CL
E -5~.f-
15
]CIa iHunza RlVerBas;n
0/' - -::-'>(- ' . -:-'X:.~ "-
,,:,.x., '''''XIo,- -.x' ....
,- -"" ''>/,~ ~
;:../" A,,;,; ""
- ' ' ,x,- "x ' .-- '" ,; ~'; , , , X
/' ',-20
- "
.30
IAN FEB MAY SEP DECOCT NOVJlIN IUL AUGMAR APR
Mdnth
1- - - - PRECIS.fud- CRU. . . X . . . PRECIS-ERA I
15
b) Gilglt River Bas in10
/- -::: :x =-. :--.""",'" ,x' "-
'" " '
,-",~,., ~,'" "'..,' '"
,;.x' .').'-'.T" ~
,'/ ".,' -,x;,/ '.
X '" ~",'" <:,'.
,; ," "x
,"-
,
0
-15
-20
-25
IAN MAR IUN AUG NOV DECSEP OCTAPR MAY IULFEB
Month
1- - - - PRECIS-fud50~ CRU. . . X - - -PRECIS.ERAI
20
15c) As tore R;yerBasll1
10 ~ :--:_--::-.~,"',' " '"
/.,; ,,X' . . 'X" -. .," '"
'" ,x' ',' -,; . '~
,- " x, "-,;,~ ,-(. ' '"..:-~. ~'"
,'/ , "x..-x,- ,x" / ,
/ ./'"
-10
-15
-20
IAN FEB APR' MAY WL SEP DECOCT NOVMAR IUN AUG
Month
1- - - -PREeIS-fud50-eRU . "X' - 'PRECIS-ERAJ
Mean annual cycle of temperature [DC]over (a) Hunza river basin, (b) Gilgitriver basin and (c) Astore river basin as simulated with PRECIS RCMs andfrom CRU data
64
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
Figure 4.4
=.." ,"'" -E..§. 4.::0
'j 3:9-.e. ,.~ -
cL
1
0
~-"- ',-"
/' x. ,'x '.,
' '."'" '",
x '" ~/ ",
'" "'X. ------'" " ""'><'-"'-:'><""'X' - --
7rI
6 1!
,x..a)Hullza RLverB,HIIL
'-
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.....--...../
=.
~ 4.S
.Q 3'"
lcIl..
='"~ 8S
:i (,
.§- 4:::
Il..2
0
TAN FEB MAR, , ;
APR MAY TUN TUL AUG SEP OCT NOV DEC
6 r!
5 -;
Month
1- - - -PREClS-H,d-CRD"'X" 'PRECIS-ERA1
..................... .-..-..--..........-.......... -.._---..--..............
b) Gilglt RlverBas 111
x
.- -,, r -.. "
: '" " ,' '" '"
,'", 'x.",>y "" \
.' '" 'x,.' '" \,
x'", ~ "'.I,./"" \ x,'" "
0-1---
.x
TAN MAR
Month
FEB
12 ,""""""
10 .,,,'
"
x
r ;
APR MAY TUN TUL AUG SEP OCT NOV DEC
1- - - - PREClS-.Hod"- CRD. - .X . . . PRECIS-ERA 1
-.......................... --.......................
c)Astore RIVerBas.,.x
;xx
0/< . .
/"' ""' , x.,.. "'~-_./ ',"''''..x' 'X',.' /
I '- - - -, ". .' /'" , ~ 'x --'--
TAN FEB
-----r--i
MAR APR MAY TUN TUL AUG SEP OCT NOV DEC
Month
1- - - -PREcrS.H,dSO-CRD"'X" -PREClS.ERA1
Mean annual cycle of precipitation [mm/day] over (a) Hunza river basin, (b)Gilgit river basin and (c) Astore river basin as simulated with PRECIS RCMsand from CRU data
65
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
66
4.3.3 Bias correction in PRECIS simulations
Upper Indus Basin is a data sparse region. In some cases, such as Hunza river basin, the daily
meteorological data required for hydrological modelling is not available. Therefore, to
calibrate a hydrological model the necessary information can be drawn from RCMs.
However, biases in RCM simulations may mount to erroneous results of hydrological model
(Akhtar et al., 2008a). Therefore, bias correction in RCM data is deemed necessary to
produce realistic sequence of stream flow (Wood et al., 2004) and the biases in PRECIS
simulations are required to be corrected before applying the temperature and precipitation
data series as input into the hydrological model. An approach as used by Durman et al.
(2001) was applied for the bias correction. In this approach, a monthly factor based on the
ratio of present day simulated value to observed data on a grid box basis is applied to the
modelled climatic variables. Recently, Fowler et al. (2007b) also used this approach to study
the impact of climate change on the water resources in north-west England.
4.4 River Basin Modelling
River basin modelling can be undertaken using various types of hydrological models,
including empirical, conceptual and physically based models. Empirical models are based on
mathematical equations that do not take into account the underlying physical processes and
therefore are not useful for the simulation of various model components. Physically based
models incorporate physical laws based on the conservation of mass, momentum and energy.
The governing equations include lot of parameters and must be solved numerically. The high
amount of parameters may result in different parameter combinations giving equally good
output performances. Moreover, these models generally incorporate too many processes and
too complex formulations at a too detailed scale that is not needed in the context of climate
change. Therefore, the conceptual models seem to be an attractive alternative. These models
are usually able to capture the dominating hydrological processes at the appropriate scale
with accompanying formulations. Therefore, conceptual models can be considered as a nice
compromise between the need for simplicity on the one hand and the need for a firm physical
basis on the other hand. A disadvantage may be that it is generally impossible to derive the
model parameters directly from field measurements and therefore calibration techniques must
be used (Booij, 2002). In this study, HBV model of the Swedish Hydrological and
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
67
Meteorological Institute (SMHI) has been chosen to study the climate change impact on the
water resources. It has been applied in more than 40 countries and its applications cover
basins in different climatological and geographical regions, ranging in size from less than 1
to more than 40 000 km2 (SMHI, 2005).
4.4.1 Description of HBV model
For river discharge simulation, the hydrological model HBV of the SMHI (Bergström, 1995;
Lindström et al., 1997) is used. The model has been widely used in Europe and other parts of
the world in climate change impact studies (Liden and Harlin, 2000; Bergström et al., 2001;
Menzel and Bürger, 2002; Booij, 2005, Menzel et. al., 2006). Using inputs from RCMs this
model estimated the discharge fairly well for the Suir river in Ireland (Wang et. al. 2006). A
study by Te Linde et al., (2007) compared the performance of two rainfall-runoff models
(HBV and VIC) using different atmospheric forcing data sets and recommended HBV model
for climate change scenarios studies. HBV is a semi-distributed conceptual hydrological
model using sub-basins as the primary hydrological units. It takes into account area-elevation
distribution and basic land use categories (glaciers, forest, open areas and lakes). Sub-basins
are considered in geographically or climatologically heterogeneous basins. The model
consists of six routines, which are a precipitation routine representing rainfall and snow, a
soil moisture routine determining actual evapotranspiration and controlling runoff formation,
a quick runoff routine and a base flow routine which together transform excess water from
the soil moisture routine to local runoff, a transformation function and a routing routine (see
figure 4.5).
The precipitation accounting routine defines actual precipitation (P) as rainfall (RF) or
snowfall (SF) by applying of a threshold value (TT) shown in equation 4.1 and 4.2
respectively.
TTTifPrfcfpcorrRF .. (4.1)
TTTifPsfcfpcorrSF .. (4.2)
where (T) is actual temperature, rfcf is rainfall correction factor, sfcf is snowfall correction
factor and pcorr is precipitation correction factor. In this routine, snowmelt (Sm) is based on
a simple degree-day relation given in equation (4.3). The snow pack is assumed to retain melt
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
68
water as long as the amount does not exceed a certain fraction of the snow. When
temperature decreases below TT, melt water refreeze (Rmw) according to the equation (4.4).
TTTcfmaxSm (4.3)
).(max. TTTcfcfrRmw (4.4)
where cfmax is a melting factor and cfr a refreezing factor.
Glacier melting (Gm), which occurs only in glacier zones is taken into account by equation
(4.5).
TTTgmeltGm (4.5)
where gmelt is a glacier melting factor.
The soil moisture routine is the main part controlling runoff formation in which direct runoff,
indirect runoff and actual evapotranspiration are generated. Direct runoff occurs if the soil
moisture volume (SM) in the catchment, conceptualised through a soil moisture reservoir
representing the unsaturated soil, exceeds the maximum soil moisture storage denoted by
parameter FC. Otherwise, precipitation infiltrates in the soil moisture reservoir. This
infiltrating precipitation (IN) either replenishes the soil moisture content, seeps through the
soil layer (R) or evapotranspirates. The indirect runoff (R) through the soil layer is
determined by the amount of infiltrating water and the soil moisture content through a power
relationship with parameter BETA, which is shown in equation (4.6).
BETA
FCSMINR
(4.6)
This indicates that indirect runoff increases with increasing soil moisture content. In case of
zero infiltration, indirect runoff also becomes zero. Actual evapotranspiration (Ea) depends
on the measured potential evapotranspiration (Ep), the soil moisture content and parameter
LP which is a limit where above the evapotranspiration reaches its potential value. This is
shown in equations (4.7) and (4.8).
pa EFCLP
SME
FCLPSMif (4.7)
pa EE FCLPSMif (4.8)
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
69
At the quick runoff routine, three components are distinguished. Components are percolation
to the base flow reservoir, capillary transport to the soil moisture reservoir and quick runoff.
Percolation is denoted by parameter PERC which is a constant percolation rate. This occurs
when water is available in the quick runoff reservoir. Capillary transport is a function of the
maximum soil moisture storage, the soil moisture content and a maximum value for capillary
flow (CFLUX) as shown in equation (4.9). If the yield from the soil moisture routine is
higher than the percolation and the capillary flow, the water becomes available in the quick
runoff reservoir for quick flow which is shown by equation (4.10).
FCSMFCCFLUXC f
(4.9)
ALFAf UZKQ 1
0 (4.10)
where UZ is the storage in the quick runoff reservoir, ALFA a measure for the non-linearity
of the flow in the quick runoff reservoir and Kf a recession coefficient.
The slow flow of the catchment is generated in the base flow routine through equation (4.11).
LZKQ s 1 (4.11)
where LZ is the storage in the base flow reservoir and Ks a recession coefficient.
In the transformation routine, the discharge of each sub-catchment is routed through a
triangular distribution function and in the routing routine the discharges from the sub-
catchments are linked.
Several other parameters such as lapse rate, parameters for temperature, precipitation and
evapotranspiration, forest dependent parameters and snow, lake and glacier parameters can
be used. Furthermore, sub-basins can contain different elevation zones and for each elevation
zone different land use types are considered (the most important are field and forest). Finally,
simplifications such as long-term mean values for evapotranspiration corrected by the actual
temperature can be used instead of measured evapotranspiration.
In order to assess the performance of the model in simulating observed discharge behaviour
an objective function Y is used, which combines the Nash-Sutcliffe efficiency coefficient NS
(Nash and Sutcliffe, 1970) and the relative volume error RE and is defined as
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
70
RENSY
1
(4.12)
where
Ni
ioo
Ni
ios
QiQ
iQiQNS
1
21
2
-1 (4.13)
Ni
io
Ni
ios
iQ
iQiQRE
1
1100 (4.14)
where i is the time step, N is the total number of time steps, Qs represents simulated
discharge, Qo is observed discharge and oQ is the mean of Qo over the calibration/validation
period. For a favorable model performance, the efficiency NS should be as high as possible
and the RE value should be close to zero.
4.4.2 Model experiments
To study the climate change impacts on the water resources, the influence of meteorological
forcing data on the performance of hydrological model is tested as a first step. Three data sets
used in this test are meteorological stations observations, PRECIS-ERA and PRECIS-Had
bias corrected simulations. Depending on the source of input data (meteorological stations
data, PRECIS-ERA and PRECIS-Had), three HBV models were developed hereafter referred
as HBV-Met, HBV-ERA and HBV-PRECIS, respectively. These models were calibrated and
validated for selected three river basins. The robustness of HBV models was tested by
calibrating the model with one data source and applying the calibrated model to other two
different data sources.
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
71
Figure 4.5 A schematic diagram of the hydrological model HBV (modified after Lindström et al., 1997), numbers in brackets refer to described equations
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
72
4.4.3 Calibration and validation of HBV models
During the calibration of HBV-Met, HBV-ERA and HBV-PRECIS models parameters were
estimated with each model for each river basin. The parameters were estimated in two steps.
Firstly, the key parameters were determined. Secondly, a sensitivity analysis was conducted
on the basis of key parameters to obtain optimal parameter set for the HBV-Met, HBV-ERA
and HBV-PRECIS models. The parameters were selected on the basis of physical reasoning,
previous studies (table 4.5) and univariate sensitivity analyses. These parameters are well
documented in the literature (Killingtveit and Saelthun, 1995; Diermanse, 2001; Carr, 2003;
SMHI, 2005; Booij, 2002, 2005) and the values and ranges of some key parameters are
summarized in table 4.5.
For each river basin, a univariate sensitivity analysis was performed to assess the influence of
individual parameter on the output of the model. This was done by varying the value of one
parameter while keeping other parameters constant (default value). Figure 4.6 illustrates the
sensitivity of model parameters. For the three river basins, parameters like gmelt, FC, DTTM,
TT, PERC, and cfmax are found to be most sensitive. There appeared a strong
interdependence among these parameters. In the second step, a multivariate sensitivity
analysis was performed to estimate the most sensitive parameters of the HBV-Met, HBV-
ERA and HBV-PRECIS models for each river basin. Figures 4.7 through 4.9 show the
multivariate sensitivity analysis using these variables (FC, gmelt, TT and DTTM) in the
HBV-Met, HBV-ERA and HBV-PRECIS models for Hunza river basin, respectively. The
values of remaining key parameters (PERC and cfmax) were optimized by univariate
sensitivity analysis. The optimal values of parameters obtained from this sensitivity analysis
are given in table 4.6, while default values of remaining parameters (RFCF, SCFC, PCALT,
ATHORN, TCALT, ALFA, BETA, PCORR and PCALTL) as provided in a study by SMHI
(2005) were used. The value of Hq was derived from measured data following the procedure
given in the same study. Similar analyses were carried out for other two river basins (Gilgit
and Astore). However, figures were not shown in order to avoid redundancy.
Table 4.6 shows the calibrated HBV parameter values for three river basins with three
different input data sets. It shows that the parameter values of HBV-Met, HBV-ERA and
HBV-PRECIS generally fall within the limits described in other studies (Uhlenbrook et
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
73
al.,1999; Krysanova et al., 1999; SMHI, 2005; Booij, 2002, 2005). However, the parameter
values vary between the three data sources in different river basins. During calibration, it is
noted that for the investigated river basins threshold temperature is the most critical
parameter because PRECIS RCM simulations (figure 4.3 and 4.4) generally show that most
of the precipitation occurs under freezing conditions when the precipitation is in the form of
snow. On the other hand, most of the runoff is generated in summer when temperature is
above the freezing point.
Table 4.7 presents the efficiency (Y), NS, relative volume error and mean observed and
simulated discharge by the three HBV models during calibration and validation periods for
the three river basins. All three HBV models show that the average simulated and observed
discharge is close to each other during the calibration period and consequently the relative
volume error is very small. General testing of conceptual models has shown that NS values
higher than 0.8 are above average for runoff modelling in glaciated catchments (Rango,
1992). Therefore, NS values during calibration are satisfactory for all HBV models and the
highest values are achieved generally by HBV-Met (e.g. 0.67 < NS < 0.87). Figure 4.10
shows the observed and HBV-Met, HBV-ERA and HBV-PRECIS simulated discharge
during calibration period for Hunza river basin. The peak values are in general
underestimated and discharge during low flow period is better simulated by the HBV models.
The double-mass curves constructed from the observed and simulated discharge of Hunza
river basin shows a straight line (figure 4.11). This is an indication of similar behavior of
observed and HBV simulated discharge during calibration. The discharge behavior simulated
by the HBV models for other two river basins has shown similar response (figures are not
given for redundancy). During the calibration period, efficiency (Y) values and visual
inspection of hydrographs demonstrate that the performance of all HBV models is
satisfactory. During validation, the RE values show that in most of the cases all models
underestimate discharge in the three river basins. Overall, only one out of nine combinations
of river basins and HBV models shows a higher efficiency (Y) in the validation period
compared to the calibration mainly due to large relative volume error. The values of the
performance criteria show that during the validation period overall performance of HBV-Met
(e.g. 0.63 < Y < 0.90) is somewhat better compared to the overall performance of HBV
models driven by PRECIS outputs (e.g. 0.42 < Y < 0.81). The efficiency is highest for Hunza
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
74
river basin compared to the Gilgit and Astore river basins as already observed during
calibration. However, comparison of Y values between different river basins has to be done
carefully because this statistical measure is strongly influenced by runoff variability. This
may explain the relatively low values at Astore river basin, where runoff variability is highest
due to the small size of the river basin. The mean annual cycle of observed discharge and
simulated discharge is shown in figure 4.12. It appears that HBV-PRECIS simulated mean
annual discharge closely follows the pattern of observed discharge i.e. peak discharge in
Hunza river is reached in the month of August and for Gilgit and Astore rivers it is evident in
the month of July. However, peak discharge simulated by the HBV-ERA and HBV-Met
deviates from observed pattern and both model simulated peak discharge in both Gilgit and
Astore rivers in the month of August. It is noteworthy that all HBV models overestimate
discharge at the end of melt season (September-October) and underestimate discharge during
peak flow period (July-August). The extreme events inside the calibrated range are either
overestimated or underestimated and make it difficult to separate the effects of errors in the
input data and model structure (Weerts, 2003). It means that the inherent uncertainty is
enhanced when the models are used outside their calibrated range, which is common practice
in climate scenarios studies. Therefore, to select a model for climate change impact study the
robustness of HBV models is ought to be tested.
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
75
Table 4.5 Values and range of important parameters found in different studies using HBV model
KHQ FC LP TT DTTM GMELT CFMAX PERC
28-125 0.5-1 -2-2 2.5-7 0-0.1 Carr, (2003)
0.005-0.2 100-1500 <=1 -2-2 -2-2 4 2-4.5 0.01-6 SMHI, (2005)
0.09 1.0 0 -0.5 4 3.0 0.5 SMHId, (2005)
0.01-0.17 100-660 0.2-0.8 0.4-0.8 Booij,(2002, 2005)
0-580 0.8 0 0 4 0.6 Diermanse, (2001)
75-300 0.7-1 -1-2 -1-2 3-6 0.5-1 Killingtveit and Saelthun, (1995)
d = default value
Table 4.6 Parameter values for HBV for three river basins with three different input data sets
HBV-Met HBV-ERA HBV-PRECIS
River basins Parameter
Hunza Gilgit Astore Hunza Gilgit Astore Hunza Gilgit Astore
cfmax 3 3 4.5 3.2 3 3.5 3 3 3.5
DTTM 0 -2.5 -2.5 -1 -1.5 -1.5 -1.5 -2.5 -2.5
FC 1500 700 700 100 700 700 1100 700 700
gmelt 3.5 4 4.5 3.5 3.5 4 4 3.4 4.5
PERC 0.5 0.8 0.8 0.5 0.5 0.5 0.5 0.9 0.5
TT 0 -2 -2.5 -0.3 -1.5 0 0.4 -2 -1.5
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
76
Table 4.7 Performance of three HBV models during calibration and validation periods in different river basins
Calibration Validation Model Rive Basin
Period Qo Qs NS RE Y Period Qo Qs NS RE Y
Hunza 1981-90 306.5 305.0 0.874 -0.4 0.87 1991-96 280.5 276.6 0.910 -1.4 0.90
Gilgit 1981-90 266.8 265.7 0.825 -0.4 0.82 1991-96 292.6 257.6 0.770 -11.7 0.69
HB
V-M
et
Astore 1981-90 133.4 131.7 0.677 -1.2 0.67 1991-96 171.8 145.7 0.726 -15.2 0.63
Hunza 1981-90 306.5 306.7 0.891 0 0.89 1991-96 280.5 285.3 0.828 1.6 0.81
Gilgit 1981-90 266.8 267.6 0.750 0.2 0.75 1991-96 292.6 261.0 0.759 -10.6 0.69
HB
V-E
RA
Astore 1981-90 133.4 133.3 0.577 0 0.58 1991-96 171.8 132.7 0.515 -22.7 0.42
Hunza 1981-90 306.5 306.5 0.769 0 0.77 1975-80 394.4 294.1 0.696 -25.4 0.56
Gilgit 1981-90 266.8 267.7 0.740 0.3 0.74 1965-70 302.6 275.8 0.758 -8.8 0.70
HB
V-P
REC
IS
Astore 1981-90 133.4 130.2 0.620 -2.3 0.61 1975-80 121.8 127.9 0.622 5.0 0.59
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
77
Figure 4.6 Sensitivity of HBV model parameters for (a) Hunza river basin, (b) Gilgit river basin and (c) Astore river basin
FC
LP
rfcf
scfc
TT
DTTM
cfmax
BETA
PERC
ALFA
K4
gmelt
(a) Hunza river basin
FC
LP
rfcf
scfc
TT
DTTM
cfmax
BETA
PERC
ALFA
K4
gmelt
(b) Gilgit river basin
FC
LP
rfcf
scfc
TT
DTTM
cfmax
BETA
PERC
ALFA
K4
gmelt
(c) Astore river basin
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
78
Figure 4.7 Sensitivity analysis with FC, GMELT, TT and DTTM for HBV-Met (Hunza river basin), with Y as a function of FC,
GMELT, TT and DTTM
900 1200 1500-1
0
1
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
900 1200 1500-1
0
1
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4DTTM=-1
900 1200 1500-1
0
1
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4.5DTTM=-1
900 1200 1500-1
0
1
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=3.5DTTM=0
900 1200 1500-1
0
1
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4DTTM=0
900 1200 1500-1
0
1
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4.5DTTM=0
900 1200 1500-1
0
1
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=3.5DTTM=1
900 1200 1500-1
0
1
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4DTTM=1
900 1200 1500-1
0
1
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4.5DTTM=1
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
79
Figure 4.8 Sensitivity analysis with FC, GMELT, TT and DTTM for HBV-ERA (Hunza river basin), with Y as a function of FC,
GMELT, TT and DTTM
100 600 11000.2
-0.3
-0.8
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
100 600 11000.2
-0.3
-0.8
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4DTTM=-1
100 600 11000.2
-0.3
-0.8
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4.5DTTM=-1
100 600 11000.2
-0.3
-0.8
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=3.5DTTM=0
100 600 11000.2
-0.3
-0.8
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4DTTM=0
100 600 11000.2
-0.3
-0.8
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4.5DTTM=0
100 600 11000.2
-0.3
-0.8
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=3.5DTTM=1
100 600 11000.2
-0.3
-0.8
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4DTTM=1
100 600 11000.2
-0.3
-0.8
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4.5DTTM=1
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
80
Figure 4.9 Sensitivity analysis with FC, GMELT, TT and DTTM for HBV-PRECIS (Hunza river basin), with Y as a function of FC,
GMELT, TT and DTTM
1100 1300 1500-0.1
0.4
0.9
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
1100 1300 1500-0.1
0.4
0.9
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4DTTM=-1
1100 1300 1500-0.1
0.4
0.9
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4.5DTTM=-1
1100 1300 1500-0.1
0.4
0.9
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=3.5DTTM=-1.5
1100 1300 1500-0.1
0.4
0.9
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4DTTM=-1.5
1100 1300 1500-0.1
0.4
0.9
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4.5DTTM=-1.5
1100 1300 1500-0.1
0.4
0.9
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=3.5DTTM=-2
1100 1300 1500-0.1
0.4
0.9
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4DTTM=-2
1100 1300 1500-0.1
0.4
0.9
FC
TT
0.85-0.90.8-0.850.75-0.80.7-0.750.65-0.70.6-0.650.55-0.60.5-0.55
GMELT=4.5DTTM=-2
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
81
Figure 4.10 Observed and simulated discharge (m3/s) of (a) HBV-Met, (b) HBV-ERA and (c) HBV-PRECIS for Hunza river basin during calibration period
0
500
1000
1500
2000
1981 1982 1983 1984 1985 1986 1987 1988 1989
Dis
char
ge (m
3 /s)
Simulated discharge (Qs) Observed discharge (Qo)
a) HBV-Met
0
500
1000
1500
2000
1981 1982 1983 1984 1985 1986 1987 1988 1989
Dis
char
ge (m
3 /s)
Simulated discharge (Qs) Observed discharge (Qo)
b) HBV-ERA
0
500
1000
1500
2000
1981 1982 1983 1984 1985 1986 1987 1988 1989
Dis
char
ge (m
3 /s)
Simulated discharge (Qs) Observed discharge (Qo)
c) HBV-PRECIS
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
:s
11200~1000.,"
~
.~ 800"'"
]: 600.,"-,.~ 400v,~
{; 200~ a'-''".,:
OJ,~ 1200~7. 10002"...,
"* 800'6
~ 600
] 400'.."'"
~ 200:5Ei'iQ-0:
OJ,
~ 1200S2
E' 1000;7: 800'6
':i: 600~:5E 400'Vi
~ 200...,
:5Ei'i.:;.-0:
a) HBV-Met
a 200 400 600 800 1000 1200
Accum ulated 0 bserved discharge (1J3m3/s)
b) HBV-ERA
aa 400 1000 1200600 800200
Accumulated observed d~scharge (1J3m3/s)
c) HBV-PRECIS
aa 200 400 600 800 1000 1200
Accumulated 0 bserved discharge (1J3m3/s)
Figure 4.11 Double mass-curve analysis relating obser~ed and simulated discharge (m3/s) of(a) HBV-Met, (b) HBV-ERA and (c) HBV-PRECIS for Hunza river basinduring calibration period
82
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
83
Figure 4.12 Observed and simulated (HBV-Met, HBV-PRECIS and HBV-ERA) mean annual discharge (m3/s) cycle of (a) Hunza river basin, (b) Gilgit river basin and (c) Astore river basin
0
200
400
600
800
1000
1200
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Dis
char
ge (m
3 /s)
HBV-PRECIS Observed HBV-Met HBV-ERA
a) Hunza river basin
0
200
400
600
800
1000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Dis
char
ge (m
3 /s)
HBV-PRECIS Observed HBV-Met HBV-ERA
b) Gilgit river basin
0
100
200
300
400
500
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Dis
char
ge (m
3 /s)
HBV-PRECIS Observed HBV-Met HBV-ERA
c) Astore river basin
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
84
4.4.4 Representation of flood peaks
Extreme value analysis based on the Gumbel extreme value distribution is carried out to
estimate the ability of HBV models to simulate flood peaks for three river basins. For this,
the maximum discharge per hydrological year is determined from both measured and
simulated discharges of three river basins. In this analysis, observed discharge data from the
period 1981-1996, HBV-Met discharge data for the same period and HBV-PRECIS
discharge data from the period 1961-1990 are used. Figure 4.13 shows the extreme value
distribution of floods derived from observed discharge data and depicted by HBV-Met and
HBV-PRECIS models. Overall trend of present day simulated annual maximum discharge by
HBV models is an underestimation of flood peaks at all return levels. The highest differences
between observed and modeled extreme discharges are found in the Astore river basin, which
may owe to the small size of the river basin. However, it is difficult to compare the observed
extreme values with simulated extreme values because the extreme discharge return values
are influenced by the period of study. Moreover, observed and HBV-Met simulated extreme
values are based on relatively few extreme flood events, which make the extrapolation to
large return periods highly prone to errors.
4.4.5 Robustness of HBV models
The robustness of HBV models is tested by calibrating the model with one data source and
applying the calibrated model to other two data sources. The Absolute Relative Deviation
(ARD) in the efficiency (Y) is quantified by equation (4.15)
c
ca
YYY
ARD
100 (4.15)
where Yc is the efficiency of the model during calibration and Ya is the efficiency of the
model during the application of a different data source.
The efficiency (Y) and Absolute Relative Deviation (ARD) of the three HBV models using
input data from sources different from the data used in the model calibration are shown in
table 4.8. The values of the efficiencies during the period 1985-86 show that overall
performance of HBV-Met (0.20 < Y < 0.65) is somewhat less compared to the models using
PRECIS RCM data sources (0.31 < Y < 0.86). The ARD values indicate that the errors in
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
85
HBV-Met (e.g. 18 < ARD < 70) are higher compared to the errors in models using PRECIS
data sources (e.g. 6 < ARD < 46). This can be best explained due to the fact that for each
river basin temperature and precipitation data of only one meteorological station is used as
input to HBV-Met model, which might have lead to extreme behavior and HBV-Met
appeared to be less robust as compared to HBV-ERA and HBV-PRECIS. Moreover, the
values of ARD in case of HBV-PRECIS are somewhat less compared to HBV-ERA values.
This may be attributed to the small precipitation biases in PRECIS-Had compared to
PRECIS-ERA. The application of bias correction only corrects the monthly mean and does
not account for corrections in the variability. Any other sophisticated approach for bias
correction (Leander and Buishand, 2007) may give better results. These results however
indicate that the robustness of HBV models is affected by the input data.
The effect of three different input forcing data series on the simulated discharge of HBV is
analyzed by calculating the uncertainty range. The uncertainty range in a HBV model is the
difference between the maximum and minimum values of the three simulated discharge
series. Figures 4.14-4.16 show the uncertainty range in three HBV models by applying inputs
from three different data sources for three river basins during the 1986 hydrological year.
This is an average hydrological year and is used as an example. The uncertainties are
minimum in the biggest river basin (Hunza river basin) and maximum in the smallest river
basin (Astore river basin). Generally, the uncertainties are higher during the start (Aprial-
May) and end (August-September) of the melt season. During the peak flows, uncertainties
are somewhat less in HBV-PRECIS compared to the HBV-Met and HBV-ERA. These
results indicate that forcing data largely influence the performance values and HBV-PRECIS
performed better compared to HBV-Met and HBV-ERA in terms of robustness and
uncertainty. The uncertainties in the three HBV models show that three forcing data series
have a large influence on the simulated discharge. The uncertainty range varies among the
three river basins. The uncertainties are somewhat less in the Hunza river basin compared to
the Gilgit and Astore river basins. This may be due to the fact that the Hunza river basin is
heavily glaciated (34 %) and temperature play a major role in the summer discharge, whereas
the discharge of less glaciated Gilgit (7 %) and Astore (16 %) river basins depends on the
preceding winter precipitation (Archer, 2003). Since in the three different forcing data sets
the temperature series are stable compared to the precipitation series, the bias correction
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
86
technique applied has a larger impact on the precipitation series compared to the temperature
series. Consequently, there are less uncertainties in the simulated discharge in the Hunza
river basin compared to Gilgit and Astore river basins.
Table 4.8 Efficiency Y of three HBV models using data sources different from the
calibration sources during the hydrological years 1985 and 1986 in different river basins. The values of absolute relative deviations (ARD) are given in parentheses. The italic values indicate efficiency Y during calibration
Data Source Applied River Basin Model
Met Observations
PRECIS-ERA PRECIS-Had
HBV-Met 0.87 0.65(25) 0.53(39)
HBV-ERA 0.49(45) 0.89 0.73(18) Hunza
HBV-Had 0.56(27) 0.86(12) 0.77
HBV-Met 0.82 0.55(33) 0.62(24)
HBV-ERA 0.57(24) 0.75 0.63(16) Gilgit
HBV-Had 0.67(9) 0.64(14) 0.74
HBV-Met 0.67 0.20(70) 0.55(18)
HBV-ERA 0.31(46) 0.58 0.37(36) Astore
HBV-Had 0.57(6) 0.35(43) 0.61
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
87
Figure 4.13 Observed, HBV-Met simulated and HBV-PRECIS simulated annual maximum discharge as a function of return period for three river basins in the present day climate
0
500
1,000
1,500
2,000
2,500
3,000
-2 -1 0 1 2 3 4
Reduced Gumbel variate
Dis
char
ge (m
3 /s)
HBV-Met
HBV-PRECIS
Observed
a) Hunza River Basin
0
500
1,000
1,500
2,000
2,500
3,000
-2 -1 0 1 2 3 4
Reduced Gumbel variate
Dis
char
ge (m
3 /s)
HBV-Met
HBV-PRECIS
Observed
b) Gilgit River Basin
0
500
1000
1500
2000
-2 -1 0 1 2 3 4
Reduced Gumbel variate
Dis
char
ge (m
3 /s)
HBV-Met
HBV-PRECIS
Observed
c) Astore River Basin
1 2 5 10 20 50
Return period (years)
1 2 5 10 20 50
Return period (years)
1 2 5 10 20 50
Return period (years)
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
88
Figure 4.14 Observed discharge (green line) and uncertainties in discharge (red shade)
simulated through a) HBV-Met, b) HBV-ERA and c) HBV-PRECIS for Hunza river basin during the 1986 hydrological year
0
500
1000
1500
2000
1 31 61 91 121 151 181 211 241 271 301 331 361
Day
0
500
1000
1500
2000c) HBV-PRECIS
0
500
1000
1500
2000
1 31 61 91 121 151 181 211 241 271 301 331 361
Day
0
500
1000
1500
2000b) HBV-ERA
0
500
1000
1500
2000
1 31 61 91 121 151 181 211 241 271 301 331 361
Day
0
500
1000
1500
2000a) HBV-Met
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
89
Figure 4.15 Observed discharge (green line) and uncertainties in discharge (red shade)
simulated through a) HBV-Met, b) HBV-ERA and c) HBV-PRECIS for Gilgit river basin during the 1986 hydrological year
0
500
1000
1500
2000
1 31 61 91 121 151 181 211 241 271 301 331 361
Day
0
500
1000
1500
2000c) HBV-PRECIS
0
500
1000
1500
2000
1 31 61 91 121 151 181 211 241 271 301 331 361
Day
0
500
1000
1500
2000b) HBV-ERA
0
500
1000
1500
2000
1 31 61 91 121 151 181 211 241 271 301 331 361
Day
0
500
1000
1500
2000a) HBV-Met
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
90
Figure 4.16 Observed discharge (green line) and uncertainties in discharge (red shade) simulated through a) HBV-Met, b) HBV-ERA and c) HBV-PRECIS for Astore river basin during the 1986 hydrological year
0
500
1000
1 31 61 91 121 151 181 211 241 271 301 331 3610
500
1000a) HBV-Met
0
500
1000
1 31 61 91 121 151 181 211 241 271 301 331 361
Day
0
500
1000
b) HBV-ERA
0
500
1000
1 31 61 91 121 151 181 211 241 271 301 331 361
Day
0
500
1000
c) HBV-PRECIS
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
91
4.5 Summary
It appears that hydro meteorological variables pertaining to the Upper Indus Basin are
strongly influenced by altitude. Difficult topography makes most of the region inaccessible
for routine meteorological and climatological observations and data are scarce. Most of the
observed hydro meteorological parameters, required for hydrological modelling, are not
available. The observed meteorological records show a reduction of mean temperature during
heavy rainfall events, which leads to a sharp decrease in discharge of highly glaciated Hunza
river basin. However, the discharge of Gilgit and Astore rivers appears to be less sensitive to
this condition. This is mainly because of the fact that discharge of these river basins is highly
correlated with preceding winter precipitation (Fowler and Archer 2005). In contrast,
discharge in Hunza river basin is uncorrelated with winter precipitation but highly correlated
with summer mean temperature (Archer 2003).
The temporal patterns of the simulated mean annual cycle of temperature and precipitation
are similar to CRU data in all river basins. However, some quantitative differences between
PRECIS simulations and CRU data also exist. Generally, PRECIS simulations underestimate
temperature and overestimate precipitation with respect to CRU data, which is a common
phenomena observed in mountain areas (Giogori, 2002; Kumar et al., 2006; Solman et al.,
2008). The biases are highly influenced by the driving forcing data. Overall, in three river
basins the magnitude of temperature biases is somewhat higher in PRECIS-Had compared to
PRECIS-ERA simulation whereas the magnitude of precipitation biases is somewhat less in
PRECIS-Had compared to PRECIS-ERA simulation. Application of this data in hydrological
impact studies without bias correction may lead to wrong interpretation because during the
calibration, biases in RCM simulations might adjust parameters of hydrological model in
such a way that lead to false results (Akhtar et al., 2008a). Therefore, bias correction of RCM
data is deemed necessary to produce realistic sequence of stream flow (Wood et al., 2004).
Hence, the biases observed in PRECIS simulations are corrected before applying the
temperature and precipitation data series as input into the hydrological model by following
the approach of Durman et al., (2001).
The calibration and validation results of the HBV hydrological model driven by observed
data and PRECIS RCM present day simulations show that the HBV model can reproduce the
Chapter 4 PRECIS Simulations as Input to Hydrological Modelling
92
discharge reasonably well. In terms of performance criteria, HBV calibrated with observed
station data simulates discharge behaviour somewhat better than HBV calibrated with
PRECIS RCM data. During the validation period, overall performance of HBV-Met is also
somewhat better compared to the overall performance of HBV models driven by PRECIS
outputs. All three HBV models overestimate discharge at the end of the melt season and
underestimate discharge during the peak flow period. Using the input data series from
sources different from the data used in the model calibration shows that HBV models
calibrated with PRECIS output generally have higher efficiency (Y) and lower absolute
relative deviation (ARD) values compared to the corresponding values of HBV-Met. This
indicates that HBV-Had and HBV-ERA are more robust compared to HBV-Met model. The
patterns of uncertainties are similar in the three HBV models. The magnitude of uncertainties
is higher in the river basins where discharge is dependent on the preceding winter
precipitation (i.e. Gilgit and Astore river basins) compared to the river basin where discharge
is driven by energy inputs (i.e. Hunza river basin). This is may be because of the fact that the
bias correction technique applied here has a larger impact on the precipitation series
compared to the temperature series that resulted in smaller uncertainty in the simulated
discharge of the Hunza river basin. In terms of both robustness and uncertainty ranges, the
HBV models calibrated with PRECIS output performed better compared to HBV-Met.
Therefore, it is recommended that in data sparse regions like the HKH region data from
regional climate models may be used as input in hydrological models for climate scenarios
studies.
Chapter 5 Climate Change Impact on Water Resources
93
CHAPTER 5
CLIMATE CHANGE IMPACT ON WATER RESOURCES
5.1 Background
Climate change may affect, one way or other, the human being and ecosystem. It may
influence agricultural areas because of shifts in growing seasons and/or increase in water
demand (Doll, 2002). It may also has negative effect on the river flow due to intensification
of water cycle and higher frequency of flood events (Milly et al., 2002; Huntigton, 2006;
Kundzewicz et al., 2007). In developing countries, like Pakistan, climate change may
transmit an additional stress on socioeconomic system, which is already under tremendous
pressure due to several factors, including fast population growth rate, rapid urbanization and
fierce economic competition.
The tremendous importance of water for both society and nature emphasize the need of
evaluating the climate change impact on the future water resources of Pakistan. This is
accessed by applying the climate change scenario to the previously calibrated/validated
hydrological model. Future water resources and flood peaks are estimated under SRES B2
scenario at different stages of deglaciation. Different methods as direct and delta approach
are used to simulate discharge for the future climate.
5.2 Change of Temperature and Precipitation in the Selected River Basins
For three river basins, the mean annual cycles of temperature and precipitation for the present
and future climate simulated with PRECIS are presented in Figs. 5.1 and 5.2, respectively. A
general increase in temperature and precipitation during the period 2071-2100 is evident. The
northern river basins (i.e. Hunza and Gilgit river basins) experience more warming relative to
the southern river basin (i.e. Astore river basin). For the southern river basin, larger increases
in precipitation are expected as compared to the northern river basins. Table 5.1 presents the
seasonal changes in temperature and precipitation in the three river basins with climate
change. The annual mean temperature rise by the end of the century ranges from 0.83 to 3.05
˚C. The warming is more pronounced in the Hunza (1.80˚C) and Gilgit (3.09˚C) river basins
when compared with the Astore (0.83˚C) river basin. In the Astore river basin, the weak
Chapter 5 Climate Change Impact on Water Resources
94
temperature change signals may be because of the fact that in this basin future PRECIS RCM
simulations give excessive precipitation changes (figure 5.2), which tend to result in
excessively wet soils causing high latent heat and low sensible heat fluxes. Consequently,
surface cooling (Bonan, 1998).
PRECIS estimates a rise in annual mean precipitation (6 to 23%) by the end of the 21st
century. The increase in precipitation is observed in all seasons. The precipitation changes in
the Hunza (6%) and Gilgit (9%) river basins are somewhat similar, while precipitation
changes in the Astore (23%) river basin are comparatively large. The excessive increase in
precipitation in the Astore river basin may be because of an increase in monsoon activities
(southern Astore river basin experiences more monsoon influence compared to northern
Hunza and Gilgit river basins). Generally, the magnitude of predicted temperature and
precipitation change in SRES B2 scenario is somewhat less compared to SRES A2 scenario
as reported by Akhtar et al., (2008a). However, the spread of change is different in SRES B2
as compared to SRES A2 scenario. This may be due to the fact that the domain and
resolution of experiments used in this study are different from that of Akhtar et al., (2008a).
The increase in temperature and precipitation is overall consistent with the projected increase
in temperature and precipitation reflected in SRES B2 scenario of neighboring areas such as
southwest China and northwest India (Yinlong et al., 2006; Kumar et al., 2006).
Table 5.1 Seasonal changes of mean temperature and precipitation under PRECIS simulated SRES B2 scenario for the period 2071-2100 over three river basins relative to the period 1961-1990 (Summer = April-September; Winter=October-March)
Temperature Change (˚C) Precipitation Change (%) River Basins
Annual Winter Summer Annual Winter Summer
Hunza 1.80 2.01 1.59 6 7 3
Gilgit 3.09 3.05 3.14 9 12 4
Astore 0.83 0.92 0.75 23 28 17
Chapter 5 Climate Change Impact on Water Resources
95
Figure 5.1 Mean annual cycle of temperature [˚C] over river basins (a) Hunza, (b) Gilgit
and (c) Astore as simulated with PRECIS for present (1961-90) and future (2071-2100) climate under SRES B2 scenario
-30-25-20-15-10-505
10
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Mea
n te
mpe
ratu
re (˚
C)
Present Temperature Future Temperature
a) Hunza River Basin
-30
-20
-10
0
10
20
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Mea
n te
mpe
ratu
re (˚
C)
Present Temperature Future Temperature
b) Gilgit River Basin
-20-15-10-505
101520
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Mea
n te
mpe
ratu
re (˚
C)
Present Temperature Future Temperature
c) Astore River Basin
Chapter 5 Climate Change Impact on Water Resources
96
Figure 5.2 Mean annual cycle of precipitation [mm/day] over river basins (a) Hunza, (b)
Gilgit and (c) Astore as simulated with PRECIS for present (1961-90) and future (2071-2100) climate under SRES B2 scenario
0
2
4
6
8
10
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Pre
cipi
tatio
n (m
m/d
ay)
Present Precipitation Future Precipitation
a) Hunza River Basin
0
2
4
6
8
10
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Pre
cipi
tatio
n (m
m/d
ay)
Present Precipitation Future Precipitation
b) Gilgit River Basin
0
2
4
6
8
10
12
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Pre
cipi
tatio
n (m
m/d
ay)
Present Precipitation Future Precipitation
c) Astore River Basin
Chapter 5 Climate Change Impact on Water Resources
97
5.3 Climate Change Signals Transfer from PRECIS RCM to HBV
The interpretation of climate change in terms of hydrological change is not a straightforward
process as meteorological variables derived from climate models are often subject to
systematic errors. Therefore, a reliable interface mechanism is required to transfer results
from RCM to hydrological impact model. There are two commonly practiced approaches,
including delta change approach and scaling approach (Graham et al., 2007b; Lenderink et
al., 2007; Fowler et al., 2007a,b). In another approach, Akhtar et al., (2008a) calibrated a
hydrological model with RCM data without applying any bias correction. In this method,
there is no need to scale the future climate data series. This approach is not yet extensively
tested and there is a risk that potential biases in RCM simulations may lead to over
parameterization. Hence, in this study only delta change and scaling approaches are applied
to assess the climate change impact on future water resources.
5.3.1 Delta change approach
The delta change approach has already been used in many climate change impact studies
(Arnell, 1998; Gellens and Roulin, 1998; Middelkoop et al., 2001). In this approach, the
observed climate data series are adapted with estimated monthly climate changes from the
PRECIS RCM. The observational database used for the delta change approach covers the
period 1981-1996. This is the same period to which HBV -Met has been calibrated and
validated. The future daily temperature ( dailyfT , ) and daily precipitation ( dailyfP , ) time series
are constructed by using equations 5.1 and 5.2, respectively.
monthlypmonthlyfdailyodailyf TTTT ,,,, (5.1)
monthlyp
monthlyfdailyodailyf P
PPP
,
,,, (5.2)
Where dailyoT , is the observed daily temperature, dailyoP , is the observed daily precipitation,
monthlyfT , is the mean monthly PRECIS simulated future temperature, monthlypT , is the mean
monthly PRECIS simulated present temperature, monthlyfP , is the mean monthly PRECIS
Chapter 5 Climate Change Impact on Water Resources
98
simulated future precipitation and monthlypP , is the mean monthly PRECIS simulated present
precipitation.
5.3.2 Scaling approach
The delta change approach does not include changes in variability between RCM present and
future scenario simulations. Therefore, to use information derived from climate models
optimally while producing reasonable hydrological simulations is to use a scaling approach.
Scaling implies an adjustment of specific variables to reduce systematic biases. The scaling
factors derived from the present day simulation of a particular climate model are applied to
adjust future scenarios simulated from the same RCM, with the aim of altering RCM results
as little as possible (Graham et al., 2007b; Fowler et al., 2007b; Lenderink et al., 2007). The
future precipitation and temperature data series are corrected with the same bias correction
factor that is used to correct the present day temperature and precipitation data.
5.4 Assessment of Water Resources under Climate Change
We have estimated the future water resources from previously calibrated and validated HBV-
Met and HBV-PRECIS models using both the delta change and scaling approaches. The
effect of climate change on river discharge is simulated for the current glacier extent (100 %
glacier scenario) and for two stages of deglacierisation, i.e. after an areal reduction by 50%
(50 % glacier scenario) and after complete melting (0 % glacier scenario).
5.4.1 Simulation of annual discharge cycle
Figure 5.3 shows the mean annual discharge cycle simulated by HBV-Met for the present
and future climate for three stages of glacier coverage: 100 % glaciers, 50 % glaciers and 0 %
glaciers. The amplitude of the annual discharge cycle is increased in a changed climate under
the 100 % glacier scenario. Snow melting starts one month earlier and discharge rises
towards its peak in summer (August). However, this case has to be regarded as a hypothetical
one because future 100 % glacier extent is not realistic with climate change. If the glacierised
area is reduced by 50%, the discharge is decreased during peak flow season. However, in
Astore river basin snowmelt still begins one month earlier and discharge is increased during
Chapter 5 Climate Change Impact on Water Resources
99
the month of March. The monthly runoff for the 0 % glacier scenario is reduced drastically in
all three river basins.
The present and future HBV-PRECIS simulated discharge assuming a glacier coverage of
100 %, 50 % and 0 % are given in figure 5.4. The simulated seasonal discharge pattern
appears to be generally similar to the pattern derived through HBV-Met. The amplitude of
the seasonal discharge cycle is increased in a changed climate under the 100 % glacier
scenario as well. Snowmelt starts one month earlier. There is an increase in river discharge
throughout the year in all river basins. The highest peak is observed in August. If the
glacierised area is reduced by 50%, discharge is decreased in all rivers. The reduction in
discharge mainly predominates during the months of July and August. After complete
reduction of the glaciers, there is a considerable decrease in discharge.
Table 5.2 presents the mean relative changes in future discharge (2071-2100) in a changed
climate relative to the present discharge (1961-1990) for the three glaciations stages in three
river basins. There is a big discrepancy between the results of changes in discharge simulated
by HBV-Met and HBV-PRECIS. Under the 100 % glacier scenario, both models predict an
increase in water resources. Whereas under 50% glacier scenario, the discharge is predicted
to be decreased. The magnitude of predicted increase is higher in HBV-PRECIS compared to
HBV-Met estimates whereas the magnitude of decrease is higher in HBV-Met compared to
HBV-PRECIS. Without glaciers, HBV-Met and HBV-PRECIS predict a drastic decrease in
the discharge up to 96 % and 93 %, respectively. There is neither forest nor any major lake
present in the three river basins and glaciers and fields (area without forest) are considered as
the only two land use classes in the hydrological model framework. Therefore, the effect of
complete melting of glaciers on the hydrological cycle will depend on the degree of
glaciation in the river basins and response of the river basins to climate change. For instance,
looking at the patterns of climate change in the three river basins the highly glaciated Hunza
river is expected to react more severely compared to the least glaciated Gilgit river basin.
However, HBV-Met shows that more drastic changes are expected in the Gilgit river basin
compared to the Hunza river basin. This may be because of the inaccurate transfer of climate
change signals through the delta change approach.
Chapter 5 Climate Change Impact on Water Resources
100
Figure 5.3 Annual discharge cycle simulated by HBV-Met for the present climate and future SRES B2 climate for three stages of glaciation for three river basins
0
500
1000
1500
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Dis
char
ge (m
3 /s)
Future Discharge-100% glaciation Future Discharge-50% glaciation
Future Discharge-0% glaciation Present simulated discharge
a) Hunza river basin
0
500
1000
1500
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Dis
char
ge (m
3 /s)
Future Discharge-100% glaciation Future Discharge-50% glaciation
Future Discharge-0% glaciation Present simulated discharge
b) Gilgit river basin
0
100
200
300
400
500
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Dis
char
ge (m
3 /s)
Future Discharge-100% glaciation Future Discharge-50% glaciation
Future Discharge-0% glaciation Present simulated discharge
c) Astore river basin
Chapter 5 Climate Change Impact on Water Resources
101
Figure 5.4 Annual discharge cycle simulated by HBV-PRECIS for the present climate and
future SRES B2 climate for three stages of glaciation for three river basins
0
500
1000
1500
2000
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Dis
char
ge (m
3 /s)
Future Discharge-100% glaciation Future Discharge-50% glaciation
Future Discharge-0% glaciation Present simulated discharge
a) Hunza river basin
0
500
1000
1500
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Dis
char
ge (m
3 /s)
Future Discharge-100% glaciation Future Discharge-50% glaciation
Future Discharge-0% glaciation Present simulated discharge
b) Gilgit river basin
0
100
200
300
400
500
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
Dis
char
ge (m
3 /s)
Future Discharge-100% glaciation Future Discharge-50% glaciation
Future Discharge-0% glaciation Present simulated discharge
c) Astore river basin
Chapter 5 Climate Change Impact on Water Resources
102
Table 5.2 Mean relative change in future discharge (2071-2100) in a changed SRES B2 climate relative to the present discharge (1961-1990) for three glaciations stages and for three river basins
Mean change in discharge (%) Model River Basin
Under present day glaciations
Under 50 % reduction in glaciers
Without glaciers
Hunza 26 -27 -81
Gilgit 44 -30 -96
H
BV
-Met
Astore 17 -27 -72
Hunza 65 -13 -91 Gilgit 40 -20 -78
HB
V-P
REC
IS
Astore 46 -24 -93
5.4.2 Future discharge peaks
Extreme value analysis based on the Gumbel extreme value distribution is carried out to
estimate the impact of climate change on floods under SRES B2 scenario for three river
basins with two HBV models and for three glaciation stages. For this, the maximum
discharge per hydrological year is determined from both HBV-Met and HBV-PRECIS
simulated discharge series under SRES B2 scenario in three river basins.
The flood frequency results under climate change for three glacier stages estimated through
HBV-PRECIS and HBV-Met are presented in figure 5.5 and 5.6, respectively. In all river
basins, both HBV models show an increase in flood magnitude for all return periods in a
changed climate under the 100 % glacier scenario. These results are in agreement with the
study of Milly et al. (2002) who found an overall increase in flood peaks during the twentieth
century and this trend is expected to continue in the future. The magnitude of flood frequency
under climate change in the 50 % and 0 % glacier coverage stages is decreased in all three
river basins. The change in peak discharge in the HBV-PRECIS at 20-year return level in the
100 % glacier stage is 20 %, 12 % and 6 % in the Hunza, Gilgit and Astore river basins,
respectively. For the 50% and 0% glacier scenarios the flood peaks at 20 year return level
Chapter 5 Climate Change Impact on Water Resources
103
decreased in the Hunza (40 % and 91 %, respectively) Gilgit (31 % and 66 %, respectively)
and Astore (36 % and 81 %, respectively) river basins. The change in peak discharge in the
HBV-Met at 20-year return level in the 100 % glacier stage is 18 %, 32 % and 9 % in the
Hunza, Gilgit and Astore river basins, respectively. For the 50% and 0% glacier scenarios the
flood peaks at 20 year return level decreased in the Hunza ( 28 % and 70 %, respectively)
Gilgit ( 35 % and 89 %, respectively) and Astore ( 18 % and 31 %, respectively) river basins.
The characteristics of future annual maximum discharge values under climate change are
given in table 5.4. There are huge outliers in HBV-Met simulated future annual maximum
discharge values in the Hunza river basin. The outliers in HBV-Met are explained by the fact
that in each river basin only one meteorological station is used for temperature and
precipitation input into HBV-Met. Observed precipitation is considered as areally averaged
precipitation but actually point precipitation. Unfortunately, sufficient precipitation stations
were not available to assess the areally averaged basin scale precipitation in a right way.
Consequently, observed precipitation shows too much variability and extreme behavior.
Parameters are estimated under variable and extreme conditions. For example, in Hunza river
basin at Skardu meteorological station there are three heavy rainfall spells in the month of
October 1987 (average rainfall is 37.0 mm in October, 1987 while the climate normal for
October is 6.4 mm). When we use climate change scenarios derived from PRECIS (in
October there is an increase in precipitation of 57 %), HBV-Met gives extremely high peaks
in October 1987 (an increase in mean discharge of 289 % in October 1987). Therefore, the
quality of input data used in HBV-Met seems to be too poor to simulate extreme discharge
behavior.
The modeled changes in flood frequency under climate change are just estimations that are
based on simulations using input data from only one RCM run using one emission scenario
and single GCM for the boundary data. Other GCMs could result in quite different flood
frequency predictions. For instance, the flood frequency changes in this study under SRES
B2 scenario are different from the estimates given by Akhtar et al., (2008a) under SRES A2
scenario. It also appears that assessment method significantly affect the future predictions.
Despite all uncertainties, the behavior of peak discharges predicted by the two HBV models
supports the direct use of RCM output as input to hydrological models in this area.
Chapter 5 Climate Change Impact on Water Resources
104
Figure 5.5 HBV-PRECIS simulated annual maximum discharge as a function of return
period for current and changed SRES B2 climate for three glacier stages for three river basins
0
500
1000
1500
2000
2500
3000
3500
-2 -1 0 1 2 3 4
Reduced Gumbel variate
Dis
char
ge (m
3 /s)
Future-100% glaciation Future-50% glaciation
Future-0% glaciation Present simulateda) Hunza River Basin
1 2 5 10 20 50
0
500
1000
1500
2000
2500
-2 -1 0 1 2 3 4
Reduced Gumbel variate
Dis
char
ge (m
3 /s)
Future-100% glaciation Future-50% glaciat ion
Future-0% glaciation Present simulated
b) Gilgit River Basin
0
500
1000
1500
-2 -1 0 1 2 3 4
Reduced Gumbel variate
Dis
char
ge (m
3 /s)
Future-100% glaciat ion Future-50% glaciat ion
Future-0% glaciation Present simulated
c) Astore River Basin
1 2 5 10 20 50
Return period (years)
1 2 5 10 20 50
Return period (years)
Return period (years)
Chapter 5 Climate Change Impact on Water Resources
105
Figure 5.6 HBV-Met simulated annual maximum discharge as a function of return period
for current and changed SRES B2 climate for three glacier stages for three river basins
0
500
1000
1500
2000
2500
3000
3500
-2 -1 0 1 2 3 4
Reduced Gumbel variate
Dis
char
ge (m
3 /s)
Future-100% glaciat ion Future-50% glaciat ion
Future-0% glaciat ion Present simulateda) Hunza River Basin
1 2 5 10 20 50
0
500
1000
1500
2000
2500
-2 -1 0 1 2 3 4
Reduced Gumbel variate
Dis
char
ge (m
3 /s)
Future-100% glaciat ion Future-50% glaciat ion
Future-0% glaciat ion Present simulatedb) Gilgit River Basin
0
500
1000
1500
-2 -1 0 1 2 3 4
Reduced Gumbel variate
Dis
char
ge (m
3 /s)
Future-100% glaciat ion Future-50% glaciat ion
Future-0% glaciation Present simulatedc) Astore River Basin
1 2 5 10 20 50
Return period (years)
1 2 5 10 20 50
Return period (years)
Return period (years)
Chapter 5 Climate Change Impact on Water Resources
106
Table 5.3 Characteristics of future annual maximum discharge simulated by two HBV models in a changed SRES B2 climate for the three glaciations stages and for three river basins. The values in parentheses are future annual maximum discharge with outliers
Means (m3/s) Standard Deviation (m3/s)
Glaciation Scenario Model River Basin
100 % 50 % 0 % 100 % 50 % 0 %
Hunza 1773.9 890.5 83.0 108.4 57.5 33.7
Gilgit 1083.5 633.8 246.3 197.1 182.6 126.1
HB
V-P
REC
IS
Astore 521.6 278.6 43.0 20.3 30.4 28.5
Hunza 1573.6 (1868.7)
866.5 (1182.3)
252.4 (500.4)
256.6 (1169.5)
298.6 (1256.7)
346.5 (1016.8)
Gilgit 1282.7 605.1 34.6 144.3 83.1 40.3
HB
V-M
et
Astore 533.1 357.6 215.7 28.4 49.7 73.3
5.5 Summary There is a general increase in temperature and precipitation towards the end of the 21st
century in the three river basins. Climate change under SRES B2 scenario is showing
reduced magnitude of temperature and precipitation change relative to changes in SRES A2
scenario (Akhtar et al., 2008a). Among the three river basins, the amplitude of temperature
change is highest in Gilgit river basin whereas it is lowest in Astore rivers basin. The annual
mean temperature rise in the three rivers basins by the end of the century ranges between
0.83-3.09 ˚C. In these river basins, the precipitation change ranges from 6 to 23%.
In a changed climate, HBV does not calculate the new glacier area size automatically. To
bridge this deficiency, we have used three glacier coverage scenarios as applied by Hagg et
al. (2007) while modelling the hydrological response to climate change in glacierized Central
Asian catchments. However, future glacier extent may be predicted separately by using a
simple hypsographic modelling approach (Paul et al. 2007). The use of such a predicted
Chapter 5 Climate Change Impact on Water Resources
107
future glacier extent in HBV would give a more realistic hydrological change. To quantify
the future water resources, the delta change approach is used for HBV-Met and direct use of
PRECIS RCM data is done for HBV-PRECIS. There are differences in the results of both
approaches. In a changed climate, the discharge will generally increase in both HBV-
PRECIS and HBV-Met in the 100 % glacier coverage stage up to 65% and 44%,
respectively. At the 50 % glacier coverage stage, the discharge is expected to reduce up to 24
% and 30 % both in HBV-PRECIS and HBV-Met, respectively. For the 0 % glacier coverage
a drastic decrease in water resources is predicted by HBV-Met (up to 96 %) and HBV-
PRECIS (up to 93%).
There are huge outliers in annual maximum discharge simulated with HBV-Met. This shows
that the prediction of hydrological conditions through the delta change approach is not ideal
in the Hindukush-Karakorum-Himalaya region. HBV-PRECIS provides results on
hydrological changes that are more consistent with RCM changes. This shows that the
climate change signals in HBV-PRECIS are transmitted more realistically than in HBV-Met.
Therefore, the direct use of RCM outputs in a hydrological model may be an alternative in
areas where the quality of observed data is poor. The direct use of RCM outputs (HBV-
PRECIS model) has shown that the magnitude of annual maximum flood peaks is likely to
increase in the future. Hence, overall results are indicative of a higher risk of flood problems
under climate change. The modeled changes in future discharge and changes in flood
frequency under climate change are not conclusive because more research is needed to
evaluate the uncertainties in this approach. Moreover, this technique needs to be tested with
other RCMs and preferably to river basins in other parts of the world as well.
Chapter 6 Conclusions and Recommendations for Future Work
108
CHAPTER 6
CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE WORK
6.1 Conclusions
Following conclusions are drawn from this study.
The spatial patterns of mean temperature and precipitation simulated through PRECIS
RCM agree reasonably well with observed data (CRU data) despite of inherent biases
in modeled data. For example, model results show an overestimation of precipitation
and an underestimation of mean temperature over the high relief (mountainous)
regions.
PRECIS RCM simulations generally overestimate the interannual variability in
temperature for all seasons except for the July-August-September (JAS) season. The
standard deviations in the PRECIS-Had and HadAM3P simulated temperature are
close to each other, a sign that the nested model is inheriting a good proportion of its
temperature interannual variability from HadAM3P. The interannual variability of
precipitation is overall higher during the winter compared to the summer. This owes
to the fact of dividing the standard deviation with very low precipitation value.
Compared to the observational data (CRU data), PRECIS-Had and HadAM3P
simulations show an underestimation of interannual variability in the high
mountainous regions and overestimation in relatively flat regions. The PRECIS-Had
and HadAM3P coefficients of variations are generally in good agreement but
PRECIS-Had some time shows slightly overestimation. This fact leads to the
conclusion that PRECIS-Had data are highly sensitive to the quality of boundary data.
Future scenario, SRES B2, shows an increase in temperature and precipitation by the
end of 21st century relative to the present day climate. The spatial patterns of mean
temperature changes show that warming signals are weak over the mountainous
region. Taking into account only land points, the average annual predicted increase in
temperature and precipitation is 3.1 ˚C and 8 %, respectively. The winters are
envisaged to be warmer than summer. The annual mean temperature rise in the
Chapter 6 Conclusions and Recommendations for Future Work
109
selected rivers basins (Hunza, Gilgit and Astore river basins) is expected to be in
range of 0.83 to 3.09 ˚C whereas precipitation change might vary between 6 to 23%.
In Hunza, Gilgit and Astore river basins, PRECIS RCM simulations underestimate
temperature and overestimate precipitation with respect to CRU data. Overall, the
magnitude of temperature biases is somewhat higher in PRECIS-Had compared to
PRECIS-ERA simulation whereas the magnitude of precipitation biases is somewhat
less in PRECIS-Had compared to PRECIS-ERA simulation. The biases in PRECIS
RCM simulations may lead to some serious discrepancies in hydrological impact
studies. For example, biased data may influence parameters of hydrological model in
a way leading to erroneous results. Therefore, bias correction to PRECIS RCM data
in terms of temperature and precipitation is necessary before applying that data as
input in the hydrological model.
The calibration and validation results of the HBV hydrological model driven by
observed data and PRECIS RCM present day simulated data show that the HBV
model can reproduce the discharge reasonably well. In terms of performance criteria,
HBV model calibrated with observed data simulates discharge behaviour somewhat
better than HBV calibrated with PRECIS RCM data. During the validation period,
overall performance of HBV-Met is also fairly good compared to the overall
performance of HBV models driven by PRECIS outputs. However, all three HBV
models overestimate discharge at the end of the melt season and underestimate
discharge during the peak flow period.
Using the input data series from sources different from the data used in the model
calibration shows that HBV models calibrated with PRECIS output generally have
higher efficiency (Y) and lower absolute relative deviation (ARD) values compared to
the corresponding values of HBV-Met. This indicates that HBV-Had and HBV-ERA
are more robust compared to HBV-Met model.
The patterns of uncertainties are similar in the three HBV models. The magnitude of
uncertainties is higher in the river basins where discharge is dependent on the
preceding winter precipitation (Gilgit and Astore river basins) compared to the river
basin where discharge is driven by energy inputs (Hunza river basin). This owes to
Chapter 6 Conclusions and Recommendations for Future Work
110
the fact that the bias correction technique applied has a notable impact on the
precipitation data compared to the temperature data that resulted in smaller
uncertainty in the simulated discharge of the Hunza river basin.
In terms of both robustness and uncertainty ranges, the HBV models calibrated with
PRECIS output performed better compared to HBV model calibrated with observed
data. Therefore, it is recommended that in data sparse regions like the HKH region,
data from regional climate models may be used as input in hydrological models for
climate scenarios studies.
To quantify the future water resources, the delta change approach is used for HBV-
Met and direct use of PRECIS RCM data is done for HBV-PRECIS. There are
differences in the results of both approaches. In a changed climate, the discharge will
generally increase in both HBV-PRECIS and HBV-Met in the 100 % glacier
coverage stage up to 65% and 44%, respectively. At the 50 % glacier coverage stage
the discharge is expected to reduce up to 24 % and 30 % both in HBV-PRECIS and
HBV-Met, respectively. For the 0 % glacier coverage a drastic decrease in water
resources is predicted by HBV-Met (up to 96 %) and HBV-PRECIS (up to 93%). At
100 % glacier coverage the magnitude of flood peaks is likely to increase in the future
which is an indication of higher risk of flood problems under climate change.
There are huge outliers in annual maximum discharge simulated with HBV-Met. This
shows that the prediction of hydrological conditions through the delta change
approach is not ideal in the HKH region. HBV-PRECIS provides results on
hydrological changes that are more consistent with RCM changes. This shows that
the climate change signals in HBV-PRECIS are transmitted more realistically than in
HBV-Met. Therefore, the direct use of RCM outputs in a hydrological model may be
an alternative in areas where the quality of observed data is poor.
Chapter 6 Conclusions and Recommendations for Future Work
111
6.2 Recommendations for Future Research Work
Recommendations for future work are given as below:
The modeled changes in future discharge and changes in flood frequency under
climate change are not conclusive because more research is needed to evaluate the
uncertainties in the assessment methods. Moreover, the modelling technique is
required to be tested with other RCMs and preferably to river basins in other parts of
the world as well. Uncertainties in climate change projections may result from
different sources including future emissions, model parameterisation and natural
climate variability. Further work must concentrate to examine the uncertainties
associated with the whole climate system modelling and in hydrological impact
modelling.
In a changed climate, HBV does not calculate the new glacier area size automatically.
To bridge this deficiency, we have used three hypothetical glacier coverage scenarios.
However, future glacier extent may be predicted separately by using a simple
hypsographic modelling approach. The use of such a predicted future glacier extent in
HBV would give a more realistic hydrological change. Therefore, further work on the
future glacier coverage modelling is needed.
The use of RCM data in hydrological model is tested only for snow and glacial melt
river basins. Therefore, it is recommended that the modelling techniques applied here
should also be tested for some rain fed river basins. In this study, only one GCM and
one emission scenario have been applied, further work can be extended by using other
GCMs and other emission scenarios.
References
112
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ANNEXURE
PUBLISHED SCIENTIFIC PAPERS
Akhtar, M., Ahmad, N. and Booij, M.J.2008. Use of regional climate model simulations as input for hydrological models for the Hindukush-Karakorum-Himalaya region. Hydrology and Earth System Science Discussion. 5, 865-902.
Akhtar, M., Ahmad, N., and Booij, M.J., 2008. The impact of climate change on the water resources of Hindukush-Karakorum-Himalaya region under different glacier coverage scenarios. Journal of Hydrology. 355, 148-163.
Akhtar, M., Ahmad, N, Chaudhry, M. N, and Babur, K. 2005. Spatial and temporal variations in precipitation and temperature in the Upper Indus Basin, Pakistan, Proceedings of International Conference Environmentally Sustainable Development (ESDev-2005), 25-27 June, 2005, COMSATS, Institute of Information Technology, Abbotabad- Pakistan, 639-651.
Akhtar, M., Ahmad, N, Chaudhry, M. N, and Hussain, S.P. 2005. Climate change in the Upper Indus Basin of Pakistan: A Case Study, proceedings of National Workshop on “ Global change prospective in Pakistan–challenges, impacts, opportunities and prospects” 28-30 April, 2005, Pakistan Academy of Science (PAS), Islamabad-Pakistan, edited by Dr Amir Muhammad and Dr. Sajidin Hussain, 14-28.