FINAL REPORT for APN PROJECT
Project Reference Number: ARCP2011-05CMY-Bae“Climate Change Impact
Assessment on the Asia-PacificWater Resources under
AWCI/GEOSS”

The following collaborators worked on this project:
Deg-Hyo Bae, Sejong University, Korea, dhbae@sejong.ac.kr
Md. Mafizur Rahman, Bangladesh University, Bangladesh, mafizur@gmail.com
Toshio Koike, University of Tokyo, Japan, tkoike@hydra.t.u-tokyo.ac.jp
Bashir Ahmad, Pakistan Agricultural Research Council, Pakistan, bashirad@hotmail.com

“Climate Change Impact Assessment on the Asia-Pacific Water Resources under
AWCI/GEOSS”

Project Reference Number: ARCP2011-05CMY-Bae
Final Report submitted to APN© Asia-Pacific Network for Global Change Research

PAGE LEFT INTENTIONALLY BLANKOVERVIEW OF PROJECT WORK AND OUTCOMES
Non-technical summary
This project was started under the GEOSS/Asian Water Cycle Initiative (AWCI) framework to analyze
historical climate and hydrology trend with future climate change impact assessment. The analysis
was conducted over Asia including AWCI 18 basins. The temperature was observed to increase overmost regions in Asia during past 30 years but the precipitation and runoff was observed to decrease.
To analyze future climate change impact, three GCMs (CGCM2_3_2, CGCM3_T47, and CM4) wereselected according to the results of the probabilistic uncertainty analysis, correlation coefficient, and
RMSE. The temperature was expected to increase in all regions. The precipitation and runoff wasprojected to increase for all seasons over most regions in Asia except central Asia. The water balanceover selected 18 basins showed increasing trend toward future periods. This study enabled us tobetter understand the past and future trends of climate and hydrology over Asia. The results
analyzed in this study will be useful in establishing future climate adaptation measure over Asia.

Objectives
The main objectives of the project were:
1. Historical trend analysis of climate and hydrology over Asia including AWCI 18 countries using theMann-Kendall test.
2. Selection of suitable GCMs, scenario, and hydrology model to analyze the future climate andhydrology impact assessment.
3. Future climate and hydrology impact assessment over Asia including AWCI 18 countries.

Amount received and number years supported
The Grant awarded to this project was:
US$ 42,000 for Year 1: 2010/2011
US$ 42,000 for Year 2: 2011/2012

ARCP2009-XXX-XXX-FINAL REPORT

Activity undertaken
Meeting event: The project included two AWCI meetings: (i) the AWCI training course for the
Climate Change Assessment and Adaptation Study was held in Seoul, October 2011. Regarding themain objectives, aside from the usual review of the AWCI and its working group activities, theInternational Coordination Group (ICG) meeting focused on preparation for planning of the next stepof the Initiative. With its strong collaborative framework and data integration principles developed,
AWCI is now eligible as one of the pillars of the newly proposed GEOSS Water Cycle Integrator (WCI),
targeting enhanced coordination in the water cycle arena on the global level. (ii) the 9th AWCI ICG
meeting and the workshop in Tokyo, October 2012 on Climate Change Adaptation organized by
APWF was held. Main objective was to synthesize the provided country inputs into a complex
implementation plan that will be in line with the GEOSS WCI goals.

Results
The analysis of historical climate and hydrology trend with future climate change impact was
conducted over Asia region covering 18 designated basins of AWCI. The Mann-Kendall test wasemployed for historical trend analysis using observed gridded climate data from APHRODITE andhydrology data from VIC model at horizontal grid resolution of 0.5° x 0.5° for period of 1977 to 2006.
The trend analysis showed increase in temperature over Asia region except some parts of Southeast
Asia. The precipitation was observed to decrease about 100~200mm during past 30 years over Asian
region. Similarly, the decreasing trend was observed for runoff. The runoff was observed to decrease
approximately 70∼80mm over Asia region. The seasonal trend analysis showed increasing trend of
temperature for all the seasons. The seasonal precipitation showed decreasing trend over Asia withincrease in some parts. The seasonal runoff was also appeared to decrease like precipitation. The
spatial features of runoff were similar to that of precipitation. The hydrological components over
AWCI 18 basins were calculated using VIC model which revealed various distinct regional features at
each basin.
To analyze future climate change impact, the optimum GCMs were selected using probabilistic
uncertainty analysis, correlation coefficient, and RMSE methods. Selected GCMs include CGCM3_T47,
CGCM2_3_2, and CM4. The uncertainty of selected GCMs was also evaluated. The results showed
increased uncertainty of GCMs toward future periods. For future climate and hydrology impact, theensemble mean value of selected GCMs was used. The temperature was expected to increase in all
regions. The precipitation was projected to increase during all seasons in most regions except central
Asia. The future change in runoff was expected similar with that of precipitation. The latitudinalfeatures showed high increase in temperature change with increasing latitudes. The latitudinal
precipitation change was estimated high at high latitudes and low at low latitudes. The change rateof latitudinal runoff was high at mid latitude regions. The water balance for precipitation, runoff,
evapotranspiration and soil moisture was evaluated over 18 selected basins. The results showed
increasing tendency of water balance for the future periods.

Relevance to the APN Goals, Science Agenda and to Policy Processes
This project focused on historical trend analysis of climate and hydrology over Asia with futureclimate impact assessment. We were able to understand the historical trend of climate andhydrology over Asia region. Also, we found future climate change and hydrology impact. The results
analyzed in this study will be useful in establishing the water resources management and futureclimate adaptation measures over Asia.

We believe this project has accomplished its objectives as given above. At first, the historical climate
and hydrology trend was analyzed over Asia including AWCI 18 countries. The appropriate GCMs
were selected to analyze future climate change impact. The future climate change impact wasassessed over Asia using the multi GCMs and A2 scenario.

Potential for further work
The historical climate and hydrology trend with future climate change impact over Asia wasevaluated. The observed gridded data of 0.5° x 0.5° resolution, A2 scenario, and VIC mode was used.
For more detailed analysis of climate and hydrology features over Asia, high-resolution observation
data will be used. The multi GCM, hydrology model, and climate scenario will be used to evaluate
future climate change impact details. Furthermore, we used IPCC AR4 output in this study; we will
be able to obtain more useful results using Representative Concentration Pathway of IPCC AR5.

ARCP2011-05CMY –Bae -FINAL REPORT

Self evaluation

Publications
Son, K.H., Lee, M.H. and Bae, D.H. (2012) Runoff Analysis and Assessment Using Land Surface Modelon East Asia, Journal of Korean Water Resources Association, 45(2), pp. 165-178. (in Korean)Shin, S.H. and Bae, D.H. (2013) Future Projections of Koppen Climate Shifts in the Asia Regions Using
A2 Scenario, Journal of Korean Water Resources Association, 46(3), pp. 253-265. (in Korean)
Le, T. and Bae, D.H. (2013) Evaluating the Utility of IPCC AR4 GCMs for Hydrological Application inSouth Korea, Water Resources Management, 27(9), pp. 3227-3246.
Son, K.H. and Bae, D.H. (2012) Validity of Land Surface Model for Drought Analysis in the East Asia
Region, IAHR-APD 2012, Jeju-island, Korea, August 2012
Lee, M.H. and Bae, D.H. (2012) Uncertainty Assessment of Bias Correction methods for Climate
Change Impact Assessment on Water resources, IAHR-APD 2012, Jeju-island, Korea, August 2012.

References
Acknowledgments

ARCP2011-05CMY-Bae-FINAL REPORT

We would like to thank the APN which made this project possible. Also, we would like to thank fortheir continued collaboration AWCI.

3TECHNICAL REPORT
Preface
The historical climate and hydrology trend with future climate change impact over Asia including
AWCI 18 countries was analyzed. The temperature was observed to increase over most regions in
Asia during past 30 years but the precipitation and runoff was observed to decrease. To analyze
future climate change impact, three GCMs were selected. In future, the temperature was expected
to increase in all regions. The precipitation and runoff was projected to increase for all seasons overmost regions in Asia except central Asia.

Table of Contents
1.0 Introduction……………………………………………………………………………………………………………………5
2.0 Methodology………………………………………………………………………………………………………………….6
2.1 Study area……………………………………………………………………………………………………………………………..7
2.2 Data………………………………………………………………………………………………………………………………..……20
2.2.1 Observed meteorological data…………………………………………………………………………………….20
2.2.2 Geophysical data (DEM, Soil, Land use)……………………………………………………………………….20
2.3 Climate change scenario………………………………………………………………………………………………………22
2.3.1 Emission scenario………………………………………………………………………………………………………..22
2.3.2 Selection of GCMs……………………………………………………………………………………………………….23
2.3.3 Change factor method…………………………………………………………………………………………………29
2.4 Hydrologic model…………………………………………………………………………………………………………………30
2.4.1 VIC model……………………………………………………………………………………………………………………30
2.4.2 Parameter regionalization method……………………………………………………………………………..32
2.5 Data analysis for detecting trends……………………………………………………………………………………….34
3.1 Verification of Gridded Climate and Hydrological Datasets ………………………………………….……...38
3.2 Hydrological analysis…………………………………………………………………………………………………………….43
3.2.1 Classification of climate zone ………………………………………………………………………………………43
3.2.2 Parameter estimation and regionalization……………………………………………………………………44
3.3 Analysis of historical climate and hydrology…………………………………………………………………………46
3.3.1 Historical features……………………………………………………………………………………………………….46
3.3.2 Trend analysis……………………………………………………………………………………………………………..51
3.4 Analysis of future climate and hydrology……………………………………………………………………………..63
3.4.1 GCMs selection……………………………………………………………………………………………………………63
3.4.2 Future climate and hydrology analysis………………………………………………………………………..65

4.0 Conclusions…………………………………………………………………………………………………………………..77
5.0 Future Directions………………………………………………………………………….………………………………78
References…………………………..………………………………………………………………………………….………….78
Appendix…………………………..………………………………..……………………………………………………………..83
4

ARCP2011-05CMY –Bae -FINAL REPORT

3.0 Results & Discussion………………………………………………………………………………………….…………38

1.0 Introduction
The human-induced climate change is caused by the emissions of carbon dioxide and othergreenhouse gases (GHGs) that have accumulated in the atmosphere mainly over the past 100 years.
The scientific evidence that climate change is a serious and urgent issue is now compelling. It
warrants strong action to reduce greenhouse gas emissions around the world to reduce the risk ofvery damaging and potentially irreversible impacts on ecosystems, societies and economies. With
good policies the costs of action need not be prohibitive and would be much smaller than thedamage averted. Reversing the trend to higher global temperatures requires an urgent, world-wide
shift towards a low-carbon economy. Delay makes the problem much more difficult and action todeal with it much more costly. Managing that transition effectively and efficiently poses ethical andeconomic challenges. The current level or stock of greenhouse gases in the atmosphere is equivalent
to around 430 parts per million (ppm) CO2, compared with only 280ppm before the Industrial
Revolution. These concentrations have already caused the world to warm by more than half adegree Celsius and will lead to at least a further half degree warming over the next few decades,
because of the inertia in the climate system.

ARCP2011-05CMY-Bae-FINAL REPORT

To evaluate and resolve the impact of climate change, the General Circulation Model (GCM) is used.
A GCM is a mathematical model of the general circulation of a planetary atmosphere or ocean andbased on the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various
energy sources (radiation, latent heat). These equations are the basis for complex computer
programs commonly used for simulating the atmosphere or ocean of the Earth. Atmospheric andoceanic GCMs (AGCM and OGCM) are key components of global climate models along with sea ice
and land-surface components. GCMs and global climate models are widely applied for weather
forecasting, understanding the climate, and projecting climate change. Versions designed for decadeto century time scale climate applications were originally created by Syukuro Manabe and Kirk Bryan
at the Geophysical Fluid Dynamics Laboratory in Princeton, New Jersey.[1] These computationallyintensive numerical models are based on the integration of a variety of fluid dynamical, chemical,
and sometimes biological equations.
The Intergovernmental Panel on Climate Change (IPCC) used output of GCMs. According to 4th
assessment report of the Intergovernmental Panel on Climate Change (IPCC, 2007), thetemperatures during recent five decades have double-increased (approximately 0.13℃/decade),compared to temperature increasing (approximately 0.07℃/decade) trend of past hundred years(1906~2005). These 12 years (1998, 2005, 2003, 2002, 2004, 2006, 2001, 1997, 1995, 1999, 1990,
and 2000) of the record-breaking high temperatures after 1850’s have occurred after 1990, there are
ongoing events even now (IPCC, 2007). This climate change has been recently getting an attention
from human being since the changes on ecosystem and frequent extreme events have started
through global warming. Global warming has already produced significant impacts on many parts(IPCC, 2007).
In the Asia/Pacific region there is evidence of prominent increases in the intensity and/or frequency
of many extreme events such as heat waves, tropical cyclones, prolonged dry spells, intense rainfall,
tornadoes, snow avalanches, thunderstorms, and severe dust storms in the region. Furthermore, theregion is highly subject to natural hazards, such as the 2004 Indian Ocean tsunami, the 2005 Pakistan
5earthquake, and the 2006 landslides in the Philippines. Such impacts pose additional risks for alreadyvulnerable communities striving to combat poverty and achieve sustainable development. The
Asia/Pacific region accounted for 91% of the world’s total death and 49% of the world’s total
damage due to natural disasters in the last century. Therefore, climate change poses a serious andadditional threat to poor farmers and rural communities in the region who live in remote, marginalareas such as mountains, drylands and deserts; areas with limited natural resources, communicationand transportation networks and weak institutions. In particular, climate models indicate
temperature increases in the Asia/Pacific region on the order of 0.5-2.0°C by 2030 and 1.0-7.0°C by
2070. Temperatures are expected to increase more rapidly in the arid areas of northern Pakistan andIndia, and western China. Additionally, GCM indicate rising rainfall concentration throughout muchof the region, including greater rainfall during the summer monsoon. Furthermore, winter rainfall islikely to decline in South and Southeast Asia, suggesting increased aridity from the winter monsoon.
The region will be affected by an increase in global sea level of approximately 3-16cm by 2030 and 750cm by 2070 in conjunction with regional sea level variability. Other scientific studies have also
indicated the potential for more intense tropical cyclones and changes in important modes ofclimate variability such as the El Niño-Southern Oscillation.
Also, the Asia monsoon plays an important role on global water circulation and provides substantial
precipitations and water resources to the peoples living within the domain. It provides several
benefits such as power generation and transportation facilities, but also causes serious flood anddrought problems. Of course, there are various reasons for these water-related disasters, but thecurrent climate change makes them much more complicated and difficult to manage. Besides, at thismoment, management decisions in the water sector are often made with uncertain information
regarding the future state of climate and the available water resources. Thus, the prediction of
future climate trend could be the key point affecting further development of Asian region.
In this study, the historical climate and hydrology trend, and future climate change impact over Asia
including AWCI 18 countries were analyzed. For historical trend analysis, the gridded climatology andhydrology datasets (0.5o x 0.5o) were obtained from APHRODTIE and VIC model datasets respectively.
To analyze the future climate change and its impact on water resources the A2 climate scenario withbest three GCMs were used. This study demonstrated the past and future climate and hydrology
trends over Asia region with detailed analysis at 18 selected basins.

The methodology adopted to analyze the historical and future trend of climate and hydrology over
Asia including 18 AWCI basins is presented in Figure 2.1. Primarily, the past climatology (precipitation,
max/min/mean temperature, wind speed) data at horizontal grid resolution of 0.5 degree wascollected from APHRODITE and VIC model dataset, respectively. The VIC model was employed foranalysis of historical hydrology data. The Mann-Kendall test was used for trend analysis fromcollected data and hydrology model output. To analyze future trend, three best GCMs were selectedbased on methods of probabilistic uncertainty analysis, correlation coefficient, and RMSE. The futurescenarios (A2) of three selected GCMs were disaggregated to daily time scale using delta method.
Finally, the hydrology model was run using scenarios data to analyze future hydrology features.
6

ARCP2011-05CMY –Bae -FINAL REPORT

2.0 Methodology

Figure 2.1: Overall procedure of this study.

ARCP2011-05CMY-Bae-FINAL REPORT

2.1 Study area
In this study, Asia monsoon region including Asian Water Cycle Initiative (AWCI) 18 countries was
selected to analyze the historical and future trend of climate and hydrology (Figure 2.2). The study
area covered North, East, Southeast, Central, and South Asia which includes Bangladesh, Cambodia,
India, Indonesia, Japan, Laos, Malaysia, Mongolia, Nepal, Pakistan, Philippines, South Korea, Sri Lanka,
Thailand, Uzbekistan, Bhutan, Myanmar, and Vietnam. Moreover, the 18 demonstration basins one
from each country were selected for basin scale analysis. The demonstration basins were selectedbased on the following criteria.
∙ Importance of the basin from the viewpoint of the socio -economic benefit area andhydrological sciences∙ Minimum requirement of data availability:7a) Data type: Precipitation, maximum and minimum temperature, stream flow, wind speed.
b) Spatial density of observation stations: according to the WMO standards but local
specifics are to be considered.
c) Watershed characteristics information∙ Highly expected data:a) Upper air observation is highly recommended.
b) Near-real time data availability is highly recommended.
c) Ground water and water quality data availability are essential for the river basins
where those problems should be addressed.
22∙ Size of the watershed: 100 km - 1,000,000 km

Table 2.1 presents the selected demonstration basins of 18 countries. The basins were located fromtropical to high latitude regions. Most regions were in close proximity of coastal areas. Also, someareas were located in Southern Hemisphere. The climate regime of the most basins was humid andsemi-arid. The area of basins was different ranging from 303~61,021Km2. The detailed description ofindividual basins is provided in the following sections.
Table 2.1: General description of demonstration basins of AWCI 18 countries
No.

Country

Basin nameArea (Km2)

Climate regime

1
2

Bangladesh
BhutanMeghnaPunatsangchhu

61,021
13,263

Humid
Temperate

8

ARCP2011-05CMY –Bae -FINAL REPORT

Figure 2.2: Study area with AWCI 18 countries in this study

3
4
5
6
7
8
9
10

Cambodia
India
Indonesia
Japan
Korea
Lao PDR
Malaysia
Mongolia

Sangker
SeonathMamberamo
Tone
Upper Chungju-dam
SebangfaiLangatSelbe

2,961
30,76078,992
3,300
6,662
8,560
2,350
303

Very Humid
Humid
Humid
Humid
Temperate
Very Humid
Very Humid
Semi-arid

11
12
13
14
15
16

Myanmar
Nepal
Pakistan
Philippines
Sri Lanka
ThailandShwegyin
BagmatiGilgitPampanga
Kalu Ganga
Mae Wang

1,747
3,700
12,800
10,5402,720
600

Very Humid
Humid
Humid
Humid
Very Humid
Humid

17
18

Uzbekistan
Vietnam

Chirchik-Okhangaran
Huong

20,1602,830

Humid
Very Humid

ARCP2011-05CMY-Bae-FINAL REPORT

Bangladesh
The Meghna basin was selected from Bangladesh. Natural disasters like flood and drought occurs
almost every year in Bangladesh. In recent years (2004 and 2007), the occurrence of flood in
Meghna basin hampered the development of the country. Moreover, there is a shortage of
observational data that hampers proper monitoring of flood situation and utilization of forecasting
techniques. The inundation areas are widely extended throughout the country. Targets to beaddressed through demonstration are to set-up a hydrological and flood inundation model with useof satellite data and downscaling techniques.

Figure 2.3: Location of the Meghna basin in Bangladesh.
9

Bhutan
The Punatsangchhu river basin was selected from Bhuttan which is the second largest basin in
Bhutan and very important from an economic point of view. Punatsangchhu River is located in
Punakha-Wangdue which is one of the most fertile valley. In addition, the biggest hydropower plants
are also planned in this basin. On the other hand, the frequent glacier melt increases the risk ofGlacial Lake Outburst Floods (GLOF) and then decreasing flow in the rivers afterwards. Targets to beaddressed through demonstration are flood forecast, impacts on the hydropower generation, and asediment transport study.

Cambodia
Sangker river basin was selected from Cambodia which is one of the tributaries of Tonle Sap Lake,located in Battambang province at southwest of Cambodia. Two hydropower stations are being
planned at its upper basin. The middle basin is covered with mixed agriculture and urban area, andsuffers from flash flood. The downstream region is inundated for 6-months in a year by the floodfrom Tonle Sap Lake. Targets to be addressed through demonstration are impact of changes in the
demands of water intake and dams (basin simulation model), Impact of changes in climate, water
resources, land cover (hydrological model) and impact of changes in the canal level, sediment,
flooding, and river works (hydrodynamic model).

10

ARCP2011-05CMY –Bae -FINAL REPORT

Figure 2.4: Location of the Punatangchhu basin
in Bhutan.

Figure 2.5: Location of the Sangker river basin and Tonle Sap
Lake in Cambodia.

ARCP2011-05CMY-Bae-FINAL REPORT

India
Seonath basins was selected from India. Seonath is the largest tributary of Mahanadi basin. It rises in
the Chandrapur district of Maharashtra at an elevation of about 532m and meets Mahanadi Riverafter traversing a distance of about 383 km.

Figure 2.6: Location of the Seonath basin in India.

11

Indonesia
The Citarum river basin was selected from Indonesia. Indonesia is located in monsoon climate zone.
The flooding occurs in Indonesia during rainy season and nowadays also during drought season.
Targets to be addressed through demonstration are to take advantage of GIS database system andto support floods decision-makers.

Figure 2.7: Location of the Citarum river basin in Indonesia.

Figure 2.8: Location of the Tone river basin in Japan.
Korea
The Chungju-dam basin was selected from Korea. The basin is located at the middle-east of Korean
Peninsula. It is an upper part of South Han River and the outlet is at Chungju-dam, which is thelargest multi-purpose dam in South Han River basin. There have been significant damages from12

ARCP2011-05CMY –Bae -FINAL REPORTJapan
The Tone river basin was selected from Japan. The Tone River is a very important source of watersupply, irrigation and power generation for the Tokyo area. Therefore its management is crucial forthe region. According to Japan Meteorological Agency (JMA), the trend of frequency and intensity ofheavy rainfall in this region has been increasing on average from 1961-2001. Targets to be addressed
through demonstration: A system capable to reduce flood peaks at downstream and replenish waterlevels in reservoirs after a flood event by using quantitative precipitation forecast will be developed.
The error forecast will be also considered by the best means.

floods and flash floods in Korea. In addition to flood control works, some other non-structuralcountermeasures are needed to be considered. Targets to be addressed through demonstration: (i)The use of satellite data for flood and drought monitoring and prediction for enhancement of theexisting system (ii) Utilization of an integrated hydrological and meteorological forecast system forthe optimal dam operation and flood risk reduction.

ARCP2011-05CMY-Bae-FINAL REPORT

Figure 2.9: Location of the upper Chungju-dam
basin in Korea.
Laos
The Sebangfai river basin was selected from Laos which is located in Khammouane province. At thesource, the river flows from the Vietnam border in the southeast-northwest direction to Boualapha
District and changes direction to the west to Mahaxay District and then turns from the northeastsouthwest into the Mekong. With an annual increase of 2.6% since 1990, the total population in thebasin was estimated to reach 192,200 in 1998. Targets to be addressed through demonstration: (i)Capacity building of the staffs of Department of Meteorology and Hydrology in making a moreaccurate flood forecast and analysis (ii) Assessment of the current conditions of hydrometeorological stations (iii) Discussion on how to improve the current data transmission system andthe operation and maintenance of these stations.

13

Figure 2.10: Location of the Sebangfai river basin in the Mekong
river basin in Laos.

Figure 2.11: Location of the Langat river basin in Malaysia.

14

ARCP2011-05CMY –Bae -FINAL REPORTMalaysia
The Langat river basin was selected from Malaysia which is one of the four major basins in Selangor
State. Langat Dam is the major source of domestic water supply for Kuala Lumpur, Putrajaya andareas adjoining to it. Groundwater extraction for industrial use is the minor water source of thebasin. The Malaysian Government Administrative Centre Putrajaya is located at the centre of thebasin. Targets to be addressed through demonstration: (i) Impact of climate change and land usechange on hydrology, water resources and socio-economic activities (Hydro Climate Model) (ii)
Impact of changes in the demands of water intake and dam (Basin Simulation Model), and (iii)
Impact of changes in the sediment, river level, flooding, and river networks (Hydrodynamic Model).

ARCP2011-05CMY-Bae-FINAL REPORT

Mongolia
The selected Selbe river basin is located in center of Mongolia, in the north of Ulaanbaatar. It is theupper basin of the Tuul Stream basin (6,300 km2). From mid of 1990s, settling area and population
have been rapidly increasing. Following the human activities, individual house building, paved areas,
groundwater wells and livestock pasture are increasing. Also, forest cut and cultivation took place insome extent. Targets to be addressed through demonstration: (i) Environmental degradation i.e.
vegetation, soil degradation, deforestation and rapid urbanization impacting on surface and groundwater regime and interaction mechanism, ground water contamination, water scarcity and flood
control (ii) Surface and ground water monitoring and modelling to assist better management in lightof anthropogenic influences and climate change impact and flood control, and (iii) Assistance in thedevelopment of information systems for promoting the implementation of Integrated Water
Resources Management (IWRM) in the Selbe and Tuul Stream Basins.

Figure 2.12: Location of the Selbe river basin in
Mongolia.
Myanmar
The Shwegyin basin was selected from Myanmar. The Shwegyin town is situated on the mouth ofShwegyin river and composed of 8 wards and 26 villages. It is about 42 miles from north to southand 19 miles from east to west. The high mountains are formed in northern and eastern part, andplain areas are in the western and southern part of the basin. There are 5 rivers flowing through thetownship including Shwegyin river. Targets to be addressed through demonstration: (i) To install twotelemeter stations and a receiving station in Shwegyin basin (ii) To develop forecasting technique for15

flash flood and (iii) To develop accurate flood inundation maps using all available data including GIS
data sources.

Figure 2.13: Location of the Shwegyin Basin in Myanmar.

Figure 2.14: Location of the river basin in Nepal.
Pakistan
The Gilgit basin consisting of two main rivers, Gilgit and Hunza, located in the upper Indus river basin
was selected for the Hunza basin also for the Climate Change Assessment and Adaptation (CCAA).
These basins are located in the Karakoram mountain region with steep slopes and large extent
covered with glaciers and snow. The upper part of the whole Indus basin represents about 20% of its
16

ARCP2011-05CMY –Bae -FINAL REPORTNepal
The Bagmati river basin was selected from Nepal. The Bagmati River originates from the southernslopes of Shivapuri Lekh at Vagdhara, north of Kathmandu. It flows towards south west from its
origin and turns to west in Kathmandu city and emerges out of Kathmandu Valley at Chovar. The
observed maximum flood discharge is about 11,000 m3/s. Targets to be addressed throughdemonstration: (i) Reduce flood damages, bank erosion and inundation (ii) Reduce landslides anddebris flows by means of landslides hazard mapping and (iii) Water pollution is crucial since it is used
to meet the irrigation demand.

extent but contributes about 80% of all discharge of the Indus River. Majority of the flow is producedby snow and glacier melting during the summer season. Accordingly, the main focus of the CCAA wason snow and glacier processes, in particular better ability to simulate and predict discharge for flood
forecasting and warning, including GLOF and to assess possible impacts of climate change on glacierand snow water reservoirs in the area (APN, 2011).

Figure 2.15: Location of the Gilgit river basin in Pakistan.

ARCP2011-05CMY-Bae-FINAL REPORT

Philippines
The selected Pampanga river basin is the fourth largest basin in the Philippines. The total length ofthe main river is about 260 km. The basin is drained through the Pampanga River and via the
Labangan Channel into the Manila Bay. The main river is supported by several tributaries. It has arelatively low-gradient channel at the middle and lower sections. There are two dams within theriver basin. Targets to be addressed through demonstration: (i) Downscaling and (ii) optimal multipurpose reservoir operation.

Figure 2.16: Location of the Pampanga river
basin in Philippines.

17

Sri lanka
The Kaluganga River basin is located at the South-Western part of Sri Lanka. It is about 130 km longand discharges into the Indian Ocean. The flood disasters continue in the basin due to climatic,hydrologic, topographic and land use characteristics in the basin. There is a hydropower reservoir at
one of the upper reaches. Population density in Kalutara and Ratnapura districts are 677 and 314
person/km2. Targets to be addressed through demonstration: (i) Flood risk reduction (ii)
Identification of inundation levels and (iii) Implementation of early warning systems based on realtime flood forecasting.

Figure 2.17: Location of the Kaluganga river basin in Sri
Lanka.

Figure 2.18: Location of the Mae Wang basin in North Thailand.
18

ARCP2011-05CMY –Bae -FINAL REPORT

ThailandMae Wang basin is one of the sub-basins of the Mae Ping Basin in Northern Thailand. Most of thebasin is mountain area declining from west to east that consists of the mixed unit of steep slope
highland soil series. Targets to be addressed through demonstration: (i) To install telemetering
station for observation and collection of Hydro-meteorological data (ii) Developing a flood warningmodel and effective water management

Uzbekistan
The selected Chirchik-Okhangaran basin is located in northeastern part of Uzbekistan. Snowmelt
induced runoff of rivers of the Chirchik – Okhangaran basin comprised 60-90% of the total stream
flow. The Chirchik - Okhangaran river basins have 19 hydropower stations. There are two rivers –Chirchik (161 km) and Okhangaron (223 km). River basin has 67 lakes with different genesis type.
This basin has 2,385 thousand people. Targets to be addressed through demonstration: (i) Sedimenttransport (ii) impacts of hydrological processes and on hydropower generation and (iii) to monitor
the flow regimes in the rivers due to climate change.

ARCP2011-05CMY-Bae-FINAL REPORT

Figure 2.19: Location of the Chirchik-Akhangaran
basin in Uzbekistan.
Vietnam
The Huong river basin belongs to the Thua Thien Hue province in coastal area of the Central Vietnam.
The Huong river system contains 3 main rivers: Ta Trach, Huu Trach and Bo River. The Huong River isshort and steep which runs from mountain to the low plain area. Time of concentration is short andriver basin has low storage capacity. Floods and inundation often occur very quickly and severely.
Targets to be addressed through demonstration: To improve accuracy of the forecast, effective
natural disaster preparedness, prevention measures and reducing the losses.

19

Figure 2.20: Location of the Huong river basin basin in Veitnam.
2.2 Data

To analyze climate and hydrology trend over Asia, the precipitation and temperature data at 0.5o
horizontal grid resolution was obtained from APHRODITE (Asian Precipitation-Highly-Resolved
Observational Data Integration towards Evaluation of Water Resources). The APHRODITE project
develops state-of-the-art daily precipitation and temperature with high-resolution grids for Asia. The
data set is created primarily with data obtained from a rain-gauge-observation network (Yasutomi etal., 2011 and Yatagai et al., 2012). Also, to analyze hydrology trend over Asia, we used maximum
temperature, minimum temperature, and wind speed from the dataset of Variable Infiltration
Capacity (VIC) Macroscale Hydrologic Model. This dataset provides 0.5o resolution input data to run
VIC model. This Gridded data is generated using monthly precipitation from the University of
Delaware (UDel) Willmott and Matsuura (2007) gridded observation. Gauge precipitation adjusted
for gauge undercatch as described by Adam and Lettenmaier (2003) and for orographic effects as
described by Adam et al. (2006). Monthly gridded minimum and maximum temperatures wereobtained from the Climate Research Unit (CRU) of the University of Eastern Anglia (Mitchell et al.,2004). Wind speed data was obtained from NCEP-NCAR reanalysis (Kalnay et al. 1996). The daily
variability of NCEP/NCAR is used to create daily precipitation and temperatures data using monthly
CRU (for temperatures) and UDel (for Precipitation) data as a control. We used all datasets fromJanuary 1977 to December 2006.
2.2.2 Geophysical data (DEM, Soil, Land use)
The Digital Elevation Model (DEM) is basic elevation data to apply VIC model. The selection of
coordinates, resolution, grid points and extraction of elevation is conducted using the DEM analysis.
Also, spatial resolution of land use and soil map is determined from spatial resolution of the DEM. In
20

ARCP2011-05CMY –Bae -FINAL REPORT

2.2.1 Observed meteorological datathis study, 30 x 30 seconds DEM from USGS (http://eros.usgs.gov/) was obtained. The collected DEM
data was converted to 0.5 x 0.5 degree DEM to comply with other datasets of 0.5 degree used in thisstudy. Further, the elevation data at each grid point was extracted from DEM.
The soil map is also needed to build VIC model. To build soil data over Asia, 5 x 5 minutes resolution
data from the Food Agriculture Organization (FAO, 1998) was used. In the FAO system, approximately
50 attributes of soil are included. The FAO soil database has global coverage. Table 2.2 shows thecoordinate and kind of target area provided from FAO-soil. The target area in the FAO system isdivided into 15 regions.

Table 2.2: Coordinate and kind of target area provided from FAO-soil.
FileLand Area

Cols

Rows

AfSoilAfrica876

912

38°00'N

19°00'W

38°00'S

54°00'E

AusSoil

Australia

504

408

10°00'S112°00'E

44°00'S154°00'E

CAmSoilCentralAmerica

708

312

33°00'N 118°00'W

7°00'N

59°00'W

CntASoil

CentralAsia

504

252

56°00'N

46°00'E

35°00'N

88°00'E

EurSoil

Europe1128

468

73°00'N

25°00'W

34°00'N

69°00'E

FEsSoil

FarEast1044

588

54°00'N

60°00'E

5°00'N

147°00'E

NESoil516

336

40°00'N

32°00'E

12°00'N

75°00'E

2016

792

84°00'N 180°00'W 18°00'N

12°00'W

NZEastNear East
North America +Greenland
New Zealand

168

168

34°00'S

48°00'S

180°00'E

NZWestNew Zealand (part)

47

47

38°05'S 180°00'W 42°00'S

176°05'W

SAmSoilSouth America

696

834 130°00'N 92°00'W

56°30'S

34°00'W

SEAEast South East Asia (upto 180)

1056

624

29°00'N

23°00'S

180°00'E

SEAWest South East Asia (west side)

120

156

10°00'S 180°00'W 23°00'S

170°00'W

Sib180Siberia

1536

456

80°00'N

42°00'N

180°00'E

World

World

4320

1686 84°00'N 180°00'W 56°30'S

180°00'E

ARCP2011-05CMY-Bae-FINAL REPORTNewNAmNW-Corner

SE-Corner

166°00'E

92°00'E

52°00'E

The land use data was obtained from UMD (University of Maryland). The collected land use data wasconverted to 0.5 degree to comply with resolution of other datasets used in the study. Table 2.3
shows the global land use classification and area ratio. The land use data is divided into 14 elements.
Table 2.3: Global land use classification and area ratio.
No.

Land use classification

Area ratio (%)

1

Water

0.15

2

Evergreen Needleleaf Forest

4.18

3

Evergreen Broadleaf Forest

6.00

21

4

Deciduous Needleleaf Forest

0.67

5

Deciduous Broadleaf Forest

1.30

6

Mixed Cover

3.06

7

Woodland

10.24

8

Wooded Grassland

13.84

9

Closed Shrubland

2.48

10

Open Shrubland

17.31

11

Grassland

17.76

12

Cropland

13.25

13

Bare Ground

9.60

14

Urban and Built-Up

0.17

2.3 Climate change scenario
2.3.1 Emission scenario
The GCMs used in this study were driven by A2, A1B and B1 scenarios which have an important rolefor climate projection and water resource management in the future. Generally, IPCC DDC provides
the data from 24 GCMs and there are 16 GCMs which have all A2, A1B and B1 scenarios. However, in
this study we only tested 10 GCMs due to the availability of data.

The A1 storyline and scenario family describes a future world of very rapid economic growth, global
population that peaks in mid-century and declines thereafter, and the rapid introduction of new andmore efficient technologies. Major underlying themes are convergence among regions, capacity
building, and increased cultural and social interactions, with a substantial reduction in regional
differences in per capita income. The A1 scenario family develops into three groups that describe
alternative directions of technological change in the energy system. The three A1 groups are
distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T),
or a balance across all sources (A1B).
The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme isself-reliance and preservation of local identities. Fertility patterns across regions converge very slowly,
which results in continuously increasing global population. Economic development is primarily
regionally oriented and per capita economic growth and technological change is more fragmented
22

ARCP2011-05CMY –Bae -FINAL REPORT

By 2100, the world will have changed in ways that are difficult to imagine as difficult as it would havebeen at the end of the 19th century to imagine the changes of the 100 years since. Each storyline
assumes a distinctly different direction for future developments, such that the four storylines differ
in increasingly irreversible ways. Together they describe divergent futures that encompass asignificant portion of the underlying uncertainties in the main driving forces. They cover a wide
range of key “future” characteristics such as demographic change, economic development, andtechnological change. For this reason, their plausibility or feasibility should not be considered solely
on the basis of an extrapolation of current economic, technological, and social trends.

and slower than in other storylines.
The B1 storyline and scenario family describes a convergent world with the same global populationthat peaks in midcentury and declines thereafter, as in the A1 storyline, but with rapid changes in
economic structures toward a service and information economy, with reductions in material intensity,and the introduction of clean and resource-efficient technologies. The emphasis is on global
solutions to economic, social, and environmental sustainability, including improved equity, but
without additional climate initiatives.
The B2 storyline and scenario family describes a world in which the emphasis is on local solutions toeconomic, social, and environmental sustainability. It is a world with continuously increasing global
population at a rate lower than A2, intermediate levels of economic development, and less rapid andmore diverse technological change than in the B1 and A1 storylines. While the scenario is alsooriented toward environmental protection and social equity, it focuses on local and regional levels.

ARCP2011-05CMY-Bae-FINAL REPORT

Fig. 2.21 shows total global annual CO2 emissions from all sources (energy, industry, and land-use
change) from 1990 to 2100 (in gigatonnes of carbon (GtC/yr)) for the families and six scenario groups.
Each colored emission band shows the range of harmonized and non-harmonized scenarios withineach group.

Figure 2.21: Total global annual CO2 emission from all sources (energy, industry, and land-use
change) from 1990 to 2100 (in gigatonnes of carbon (GtC/yr)) for the families and six scenariogroups
2.3.2 Selection of GCMs
The objective of this section is to compare the effectiveness of each GCM for their future applicationin Asian region. The GCMs data was collected from the IPCC Data Distribution Center (DDC,
http://www.ipcc-data.org/index.html). The information of each GCM is shown in Table 2.4. The
23

simulated data for the study at hand consist of the surface nodal precipitation and temperature
estimates from 9 atmospheric global circulation models. The comparison of GCMs was based on
several statistical measures i.e. the probabilistic uncertainty analysis, correlation coefficient, and
RMSE. Firstly, we considered the effectiveness of each GCM using the score index of each test. We
then established the overall effectiveness of each GCM based on the results of all three tests. Finally,
we selected the best GCMs with high overall effectiveness score for future application in Asian region.
Table 2.4: List of model name, version, and simplified abbreviation for each model.
No.
1
2
3
4
5
6
7
8
9

Model (agency:
version)
MPIM: ECHAM5CSIRO: MK3.0
GFDL: CM2_1
CNRM: CM3
MRI: CGCM2_3_2
UKMO: HADCM3
IPSL: CM4
CCCMA:
CGCM3_T47
CONS: ECHO-G

Abb.

Country

Resolution(km x km)Number of
scenarios

Number ofensemble

ECHAM5
MK3.0
CM2_1
CM3
CGCM2_3_2
HADCM3
CM4

Germany
Australia
USA
FranceJapan
UK
France187.5 x187.5
187.5 x187.5
250 x 200
281.25 x 281.25
281.25 x 281.25
375 x 250
375 x 250

8
7
8
8
8
7
8

3
3
3
1
3
2
2

CGCM3_T47

Canada375 x 375

83ECHO-G

Germany/Korea

375 x 375

83Figure 2. Procedure for evaluating the utility of IPCC AR4 GCMs
24

ARCP2011-05CMY –Bae -FINAL REPORT

Fig. 2.22 illustrates the general methodology adopted in this study which has two main approaches.
Firstly, we used probabilistic screening methodology to examine the capability of GCMs indiscriminating the extreme events. After that, statistical tests of correlation coefficient and root
mean squared error were used to analyze the general capability of GCMs. We then quantified theeffectiveness of GCMs by using the score index. It should be noted that, in this study we provided
several approaches to examine the quality of GCMs and our method was generally more accurate
than previous studies. The statistical methods used for GCMs selection are explained in the following
sections.

Probabilistic screening methodology
The probabilistic screening methodology (Georgakakos, 2003) is based on the premise that times ofextremes in climate model nodal values (climate model scale) should correspond to times ofextremes of the target variable (watershed scale) for climate model simulations/predictions of acertain indicator variable (e.g., nodal precipitation) to be useful for water resources applications thatinvolve regional surface target variables (e.g., watershed mean areal precipitation). If there is no such‘‘signal’’ in the climate nodal values it is unlikely that they will be useful for applications that involvethe mentioned target variable (Bae et al., 2007). The theory accounts for potential shifts in climate
model output in space and time, and allows for ensemble simulation/predictions. It implicitly
involves downscaling climate model simulations to watershed scales. It may also be generalized forvector indicator and target variables given adequate record lengths (Georgakakos, 2003).

ARCP2011-05CMY-Bae-FINAL REPORT

Consider a watershed of interest and a region centered on it that contains several nodes of a climate
model. The end goal of this analysis is to determine if the climate simulations/predictions associatedwith nodes in the region have information that may be used for identifying and discriminatingwatershed hydroclimatic extreme events. Fig. 2.23 exemplifies the geometrical configuration for oneof the Korean watersheds and its analysis window. In Fig. 2.23, (xn, yn) are the longitude and latitude
coordinates of the nth node of the climate model under consideration, ex and ey are the inter-nodal
distances in the longitude and latitude directions, (x0, y0) are the coordinates of the centroids of thestudy watershed, and dx and dy are the longitude and latitude distances of the watershed centroid
from the boundaries of the window of analysis that contains all the climate model nodes used in theanalysis. The distances dx and dy are multiples of the inter-nodal distances ex and ey, respectively,
with factor λ specified by the user. The larger the value of λ, the larger the number of climate model
nodes used in the analysis.

Figure 2.23: Specific application basin and analysis window with climate
model nodes (Han river basin and CM2 climate model).
25

In this work, we used the formulation for the utility index, Ep, which is based on simple averaging of
nodal and ensemble simulations/predictions in the vicinity of the application watershed:

1 N KEp  PKS (D  Dn,k )N K n1 k 1

(1)

where K is the number of climate model ensembles, Nk is the number of climate-model nodes within
the window of analysis specified by λ, and PKS(D > Dn,k) is (for each node and ensemble) the
exceeding probability of the maximum difference in cumulative probabilities: (A) the cumulative
probability of the watershed target variable values that are concurrent with climate-model nodal
values that fall in a specified upper quantile of their distribution, and (B) the cumulative probabilityof the watershed target variable values that are concurrent with climate-model nodal values that fallin the corresponding lower quantile of their distribution. In particular, the nomenclature is such thatthe probability, PKS (D > Dn,k), refers to the event that an observed Dn,k is exceeded by an arbitraryvalue D (Bae et al., 2007).
The theory uses the formula of the Kolmogorov–Smirnov test to determine the utility index Ep. In
this study, we used the tercile for discriminating high and low extremes for the watershed target
variable.
In applications of the formulated utility index Ep to data, lower values of the index are moresignificant in general than higher values. The question arises as to what values of this index are
statistically significant and what values are likely to be obtained by chance in any given application.
For an individual watershed and using a tercile for high and low values, Monte Carlo simulation
indicates that the theoretical upper threshold of the index Ep for a significance level of 10% isapproximately equal to 1/3. In case, the index Ep of original run is smaller than the index Ep ofthreshold, it is defined as a significant result for given GCM and basin. The interested reader isreferred to Georgakakos (2003) for more information on the formulation and significance tests of theutility index (Bae et al., 2007).

The correlation coefficient and root mean square error are commonly used methods for model
evaluations. The correlation coefficient test has been known as a powerful and easy to use
goodness-of-fit test. Correlation coefficient is a measure of linear dependency between a pair ofrandom variables. For the problem discussed herein, the variables are GCM data and observed dataof both precipitation and temperature.
Let (x, y) denote a pair of climate model data and observed data (precipitation or temperature). The
correlation coefficient of x and y can be defined as:n ( x  x)( y  y)rxy  i 1

i

i

(2)

nS x S y26

ARCP2011-05CMY –Bae -FINAL REPORT

Statistical tests for examining the general capability of GCMs

where x and y are sample mean of x and y; Sx and Sy are sample standard deviation of x and y; n is
number of data. The window of analysis is similar as presented in the previous section ofprobabilistic uncertainty analysis with fixed parameter λ of 1.05.
The root mean square error (RMSE) shows the fitting between the GCM predicted data andobserved target data. The index ranges from zero to infinity, with zero corresponding to the ideal.
Let x denotes GCM data, y denotes observed data. The RMSE of GCM data compared with observed
data is then defined as:

1 n( xi  yi ) 2
n i 1

RMSE 

(3)

It is noted that, the analysis window used for RMSE test is similar to that of probabilistic uncertainty
test. However, the parameter λ is fixed with the value of 1.05. The final purpose of RMSE test is to
examine the error in simulation of a GCM in root mean squared sense.
Quantifying the overall effectiveness of GCMs by score index
It is noted that, to evaluate the utility of a GCM for general application using both simulation of
precipitation and temperature we need an overall score for each GCM. Therefore, in this section wesuggest a score index for each GCM, based on its results of both precipitation and temperature using
each statistical test.
ㆍ Probabilistic uncertainty test
The overall score index of probabilistic uncertainty test is defined as:
nb

nd

4

nb

nd

12

SP ,total   Si , j ,k  Si , j ,l

ARCP2011-05CMY-Bae-FINAL REPORTi 1 j 1 k 1

(4)i 1 j 1 l 1

where Si,j,k denotes component score of a GCM for given basin i (with nb is total number of basins)with type of data used j (with nd is number of type of data used) in the season k(k=1,2,3,4). Si,j,ldenotes the component score of a GCM for certain basin i with type of data used j in the monthl(l=1,2,…,12). Si,j,k and Si,j,l are defined as follows: For a given GCM, if there is a significant result at
basin i of season k or month l with type of data used j, then Si,j,k and Si,j,l are defined as 1, otherwise
these are defined as 0 (the significant result is defined in the section of Methodology).
ㆍ Statistical tests of correlation coefficient and root mean squared error
The overall score index of correlation coefficient test (or RMSE test) is defined as:

27

nb

nd

4

nb

nd

12

Sr ,total (SRMSE ,total )   Si , j ,k  Si , j ,li 1 j 1 k 1

(5)i 1 j 1 l 1

where Si,j,k denotes component score of a GCM for given basin i with type of data used j in theseason k (k=1,2,3,4) where nb is total number of basins and nd is number of type of data used. Si,j,ldenotes the component score of a GCM for certain basin i with type of data used j in the month l(l=1,2,…,12). For correlation coefficient test, Si,j,k or Si,j,l are defined as follows: for a given GCM m, if
the correlation coefficient (ri,j,k,m or ri,j,l,m) at basin i for season k (or month l) with type of data used j,
is greater than the average correlation coefficient (ri,j,k,average or ri,j,l,average) of all 10 GCMs at similarbasin, season (or month) , then Si,j,k (or Si,j,l) is defined as 1, otherwise as 0. Similarly, if the RMSE of agiven GCM m is smaller than the average RMSE of all GCMs then Si,j,k (or Si,j,l) is defined as 1otherwise as 0.
The variety of approaches allows us to examine and compare the effectiveness of all GCMs for fitting
between predicted data and observed data. From the component score index of each approach, we
may then build the overall utility of each GCM and select the best GCMs among them. The scoreindex system applied for correlation coefficient and RMSE test is very effective because it evaluates
the effectiveness of each GCM by directly comparing the value of correlation coefficient and root
mean squared error at given basin and season or month.
ㆍ Establishing the overall score
The overall score index of each GCM is computed based on their scores in the probabilistic
uncertainty test and statistical tests. Our goal is to build the overall score with equal contribution
from scores of these 3 tests. However, each test produces its own score with difference inmagnitude. In order to remove this difference, we firstly normalize the total score of each test. The
normalized scores of probabilistic uncertainty test, correlation coefficient test and RMSE test forgiven GCM(m) are given by:

St ,total ,m  St,total,min(6)

St,total,max  St ,total,min

where the index t denotes the type of tests i.e. probabilistic uncertainty test, correlation coefficient
test or RMSE test; St,normalized,m is normalized score of GCM m in the test t; St,total,m is the total score of
GCM m in the test t; St,total,min and St,total,max are the minimum and maximum score of all GCMs in thetest t, respectively.
We then define the overall normalized score of given GCM m by:

Soverall ,normalized ,m  S P,normalized ,m  0.5( Sr ,normalized ,m  S RMSE ,normalized ,m )(7)

where Soverall,normalized,m is the overall normalized score of GCM m; SP,normalized,m, Sr,normalized,m,
SRMSE,normalized,m are normalized scores of probabilistic uncertainty test, correlation coefficient test and28

ARCP2011-05CMY –Bae -FINAL REPORT

St ,normalized ,m 

RMSE test of GCM m, respectively. It is noted that, for each test of a given GCM, the normalized score
ranges from 0 to 1. Thus, the overall normalized score of a GCM ranges from 0 to 2.
2.3.3 Change factor method
To evaluate future climate change impact at regional scale, it is difficult to use GCM output directly
due to the coarser resolution of GCMs. The resolution of GCMs is generally more than 100 km (IPCC,
2007). The downscaling technique is required to utilize the GCM output for regional features. Somestudies used change factor method to analyze regional features (Hay et al., 2000; Reason et al.,2004). Figure 2.24 shows the change factor concept between observation and GCM output.

Figure 2.24: Concept of the change factor method between observation and GCM output.

The GCM grid-box output is used to calculate the change factor for delta method. The change inclimate for the delta-change experiments is calculated by adding the difference between the GCM
current and future monthly means for the grid-box closest to each basin, to the precipitation andtemperature observed time series. The changes in precipitation (in percent) and temperature (in °C)
at each month are used for the delta change scenario generation. The future scenario of
temperature using the change factor is given by:
m

m

i 1

i 1

ARCP2011-05CMY-Bae-FINAL REPORT

T fut ,i  Tobs ,i  ( Ts. fut ,i   Ts.ref ,i )(8)where Tfut is the future scenario of the temperature; Tobs,i denotes the observed temperature at grid
point i; Ts.fut,i and Ts.ref,i represent the future scenario and the reference scenario of temperature at
grid point i, respectively. Similarly, the future scenario of precipitation using the change factor
method is given by:

29

m

P

1
Pfut ,i  Pobs ,i  im

s . fut ,i

P
i 1

s . ref ,i(9)

2.4 Hydrologic model
2.4.1 VIC model

A number of modifications to VIC have been made to improve the model such that it can deal withcomplicated hydrological processes. Since the VIC model does not represent the geometry of the
sub-grid variations, a separate routing model has been developed to simulate the streamflow(Lohmann et al., 1996, 1998a, 1998b). To represent the cold land processes, the VIC model was
upgraded to include a two-layer energy balance snow model (Andreadis et al., 2009; Wigmosta et al.,
1994; Storck et al., 1998), frozen soil and permafrost algorithm (Cherkauer et al., 1999, 2003;
Cherkauer and Lettenmaier, 2003), and blowing snow algorithm (Bowling et al., 2004). To improve
the simulations of elevation-dependent components within a grid cell, elevation bands representing
topography were introduced (Nijssen et al., 2001b). With the evapotranspiration algorithm, canopy
responses to wind profile and surface radiation budget have been incorporated (Wigmosta et al.,1994), and the leaf area index (LAI) and the vegetation fraction were allowed to vary at each time
step (Liang et al., 1996). The effects of lake and wetlands on moisture storage and evaporation
30

ARCP2011-05CMY –Bae -FINAL REPORTIn this study, the VIC model was used to analyze water resources over Asia monsoon region. The VIC
model was developed for incorporation in GCMs, aiming to improve the representation of horizontal
resolution and subgrid heterogeneity in a simple way. Employing the infiltration and surface runoff
scheme in Xianjiang model (Zhao, 1980), VIC was first described as a single soil layer model by Woodet al. (1992) and implemented in the GFDL and Max-Planck-Institute (MPI) GCMs (Stamm et al.,1994). The single soil layer model requires three parameters: an infiltration parameter, anevaporation parameter, and a base flow recession coefficient. In 1994, Liang et al. (1994) generalized
the two-layer VIC model (VIC-2L) to include the multiple soil layers and spatially varying vegetationand evaporation within a grid cell. In VIC-2L, infiltration, drainage from the upper soil layer into thelower soil layer, surface and subsurface runoff are calculated for each vegetation cover tile (inaddition to the statistical parameterization of heterogeneity of infiltration and runoff generation
within a vegetation cover tile present in the original VIC model). Therefore, the sub-grid scale
heterogeneity is represented in soil moisture storage, evaporation, and runoff production. As asemi-distributed land surface model, VIC calculates the sensible and latent heat fluxes according tophysical formulations, but it uses conceptual schemes to represent the surface runoff and base flow.
In 1996, Liang et al. (1996) found that the VIC-2L tends to underestimate the evaporation due to thelow soil moisture in its upper soil layer, and the main cause of this error is the lack of a mechanism
for moving moisture from the lower to the upper soil layer. VIC-2L was then modified to allow
diffusion of moisture between soil layers, and to have an additional 10cm thin soil layer on top of
the previous upper soil layer. In this way the three-layer VIC model (VIC-3L) was generated, and the
VIC-3L framework has been used ever since. The model currently allows for more than three soil
layers if desired.

ARCP2011-05CMY-Bae-FINAL REPORT

which are particularly important for runoff at high latitude have been included (Bowling et al.,2003c; Bowling and Lettenmaier, 2009; Cherkauer et al., 2003). To simulate water management
impacts, a reservoir module has been implemented to the routing model and a sprinkle irrigation
scheme has been added to the soil moisture simulation (Haddeland et al., 2006a, 2006b, 2007).
Over view of VIC model process
The VIC model framework has been described in detail in literature (Liang et al. 1994; Liang et al.,
1996; Nijssen et al., 1997). The key characteristics of the grid-based VIC are the representation ofvegetation heterogeneity, multiple soil layers with variable infiltration, and non-linear base flow.
Figure 2.25 (A) shows the schematic of the VIC model with a mosaic representation of vegetation
coverage and three soil layers. The surface of each grid cell is described by N+1 land cover tiles,
where n = 1, 2, … , N represents N different tiles of vegetation, and n = N+1 represents bare soil. For
each vegetation tile, the vegetation characteristics, such as LAI, albedo, minimum stomatal
resistance, architectural resistance, roughness length, relative fraction of roots in each soil layer, anddisplacement length (in the case of LAI) are assigned. Evapotranspiration is calculated according to
the Penman-Monteith equation, in which the evapotranspiration is a function of net radiation andvapor pressure deficit. Total actual evapotranspiration is the sum of canopy evaporation andtranspiration from each vegetation tile and bare soil evaporation from the bare soil tile, weighted by
the coverage fraction for each surface cover class. Associated with each land cover type are a single
canopy layer, and multiple soil layers (three layers are used for description in this ATBD). The canopy
layer intercepts rainfall according to a Biosphere-atmosphere transfer scheme (BATS) parameterization (Dickinson et al., 1986) as a function of LAI. The top two soil layers are designed to represent
the dynamic response of soil to the infiltrated rainfall, with diffusion allowed from the middle layer
to the upper layer when the middle layer is wetter. The bottom soil layer receives moisture from the
middle layer through gravity drainage, which is regulated by a Brooks-Corey relationship (Brooks andCorey, 1988) for the unsaturated hydraulic conductivity. The bottom soil layer characterizes seasonalsoil moisture behavior and it only responses to short-term rainfall when the top soil layers are
saturated. The runoff from the bottom soil layer is according to the drainage described by the Arnomodel (Franchini and Pacciani, 1991). Moisture can also be transported upward from the roots
through evapotranspiration. Although vegetation subgrid-scale variability is a critical feature for the
VIC model, the soil characteristics (such as soil texture, hydraulic conductivity, etc.) are held constantfor each grid cell. In the model, soil moisture distribution, infiltration, drainage between soil layers,
surface runoff, and subsurface runoff are all calculated for each land cover tile at each time step.
Then for each grid cell, the total heat fluxes (latent heat, sensible heat, and ground heat), effective
surface temperature, and the total surface and subsurface runoff are obtained by summing over all
the land cover tiles weighted by fractional coverage.

31

Figure 2.25: Schematic of the VIC model with mosaic representation of vegetation coverage (A)and VIC network routing models (B).
Each grid cell in the VIC model is modeled independently without horizontal water flow. The grid-bas
ed VIC model simulates the time series of runoff only for each grid cell, which is non-uniformly distri
buted within the cell. Therefore, a stand-alone routing model (Lohmann., et al., 1996, 1998a) is empl
oyed to transport grid cell surface runoff and base flow to the outlet of that grid cell then into the riv
er system. In the routing model, water is never allowed to flow from the channel back into the grid cell. Once it reaches the channel, it is no longer part of the water budget. Figure 2.24 (B) shows the sc
hematic of the routing model. A linear transfer function model characterized by its internal impulse r
esponse function is used to calculate the within-cell routing. Then by assuming all runoff exits a cell in a single flow direction, a channel routing based on the linearized Saint-Venant equation is used to s
imulate the discharge at the basin outlet.

In this study, a regionalization method was used for parameter estimation of VIC model at ungauged
basins. The regionalization method assumes that the basins with similar topography and climate
exhibit similar hydrological response.
There are some studies about regionalization method. Bae et al. (2007) and Jung et al. (2007) applied
regionalization method for parameters estimation of PRMS model by using regression equation. Lee
et al. (2009) conducted runoff modeling at 109 basin of South Korea using seven parameters
calculated from multivariate statistics analysis. Also, Son et al. (2010) conducted runoff analysis at
overall South Korea using VIC parameters. However, these studies focused on regional basins andthere was no study of runoff analysis at large-scale area like as Asia monsoon region.

32

ARCP2011-05CMY –Bae -FINAL REPORT

2.4.2 Parameter regionalization methodOn the other hand, Nijssen et al. (2001a) conducted the global runoff analysis using parameters
separated by same temperature and precipitation regions. Xie et al., (2007) conducted runoff
analysis using same method of Nijssen et al. (2001a) and evaluated its applicability over China. In thisstudy, parameter regionalization method was used based on climate zone classification to localize
model parameters at Asia region. The Köppen climate classification method was used to obtainclimate zones over Asia. Table 2.5 shows the climate zone classification method of Köppen using
temperature and precipitation (Kottek et al. 2006). Köppen classification criteria for the climatic
types are based on annual and monthly sums of precipitation (cm) and annual and monthly
temperature means (degrees Celsius). Boundaries of the categories represent some vegetation
based threshold values. The fundamental scheme of the climate classification consists of five major
climate types A, B, C, D, E, (Köppen, 1931). In the following text the terms, used by Peel et al. (2007),
are employed.
Climate zone
Tropical rain climates A correspond to the climate in which the mean temperature of the coldest
month exceeds +18°C, and the annual precipitation amount is higher than the aridity thresholddefined for type B. A few subtypes are defined according to the annual cycles of precipitation:- Af Tropical rainforest climate - driest month has on average ≥ 6 cm rain
- Am Tropical monsoon climate - driest month has < 6 cm, rainfall in rainy season compensates thisenough to allow for forest growth:∙ Driest month precipitation (in cm)∙ Required annual sums of precipitation- Aw Savanna climate - driest month < 6 cm, annual rainfall insufficient compensate the shortageof precipitation

ARCP2011-05CMY-Bae-FINAL REPORT

Arid Climates B is defined in the following way (R denotes the average annual sum of precipitation in
cm, and T is the annual mean temperature in °C):
- R < 2T + 28 where summer is a dominating rain season- R < 2T + 14 a well pronounced dry season does not exist- R < 2T winters are the seasons with most rain
Within type B two subtypes are distinguished with respect to the value of the aridity threshold: BS(the steppe grassland climate) and BW (desert climate).
- BS Steppe climate (bush to grassland)∙ T + 14 ≤ R < 2T + 28 for summer rain areas∙ T + 7 ≤ R < 2T + 14 no marked dry season∙ T ≤ R < 2T for winter rain areas- BW Desert∙ R < T + 14 for summer rain areas∙ R < T + 7 no marked dry season∙ R < T for winter rain areas
Warm temperature climates C is defined according to a value of the mean temperature of thecoldest month which should be between –3°C and +18°C. The precipitation amounts must be higher
33

than the aridity threshold. Further subdivision is related to the annual cycles of both thetemperature and precipitation:- Cs wettest (winter) month of the year has ≥ 3 times the average rainfall of the driest (summer)
month, i.e. s means a summer dry season- Cf f is linked with the humid climate without a marked dry season- Cfa hot summers - mean temperature of the warmest month > +22°C- Cfb warm summers - mean temperature of the warmest month below +22°C, at least 4 months >+10°C- Cfc cool summers - only 1−4 months > +10°C, the coldest month temperature is higher than −38°C- Cw wettest (summer) month of the year has ≥ 10 times the average rainfall of the driest wintermonth, i.e. w denotes a winter dry season.
Cold climates D meets the following requirements: the mean temperature of the warmest monthmust be higher than 10°C, and the coldest month temperature should be below −3°C, precipitation
amounts exceed the aridity threshold. Subtypes are again related to the annual cycles:- Dw Same as under C- Df Same as under C- Dfa and Dwa - mean temperature of the warmest month > +22°C- Dfb and Dwb - at least 4 months > +10°C, the warmest one is of temperature below 22°C
- Dfc and Dwc - only 1−4 months > +10°C, the coldest month temperature is above −38°C.
Polar climates E is defined according to the mean temperature of the warmest month that must be
lower than 10°C. This type is subdivided into two subtypes: ET (tundra climate) and EF (polar desert frost climate):- ET mean temperature of the warmest month moves in the range 0 − 10°C
- EF mean temperature of the warmest month < 0°C.

Type

Description

CriterionA

Tropical climates

Tmin ≥ +18℃

Af

Rainforest

Pmin ≥ 60 ㎜

Am

MonsoonNot(Af) & Pmin ≥ 100-Pann/25

Aw

Savannah

Not(Af) & Pmin 〈 100-Pann/25

B

Arid climates

Pann 〈 10Pth

BS

Steppe climate

Pann 〉 5Pth

BW

Desert climate

Pann ≤ 5Pth

C

Warm temperate climates-3℃ 〈 Tmin 〈 +18℃

Cs

Warm temperate climate with dry summer

Cw

Warm temperate climate with dry winter34

Psmin 〈 Pwmin &Pwmax 〉 3Psmin & Psmin 〈 40 ㎜
Pwmin 〈 Psmin & Psmax 〉 10Pwmin

ARCP2011-05CMY –Bae -FINAL REPORT

Table 2.5 The climate zone classification method of Köppen using temperature and precipitation

Cf

Warm temperate climate without dry season

Neither Cs nor Cw

D

Cold climates

Tmin ≤ -3℃

Ds

Cold climate with dry summer

Dw

Cold climate with dry winterPwmin 〈 Psmin & Psmax 〉 10Pwmin

Df

Cold climate without dry season

Neither Ds nor Dw

E

Polar climates

Tmax 〈 +10℃

ET

Tundra climate

0℃ ≤ Tmax 〈 +10℃

EF

Frost climate

Tmax 〈 0℃

Psmin 〈 Pwmin &Pwmax 〉 3Psmin & Psmin 〈 40 ㎜

Tann : annual mean temperature(℃), Tmax : mean temperature of the warmest months (℃), Tmin : mean
temperature of the coldest months (℃), Tmon : monthly mean temperature(℃), Pann : annual
precipitation(㎜/year), Pmin : precipitation of most dry months (㎜/month), Psmin : precipitation of the most dry
months in summer (㎜/month), Psmax : precipitation of the most humid months in summer (㎜/month),
Pwmin : precipitation of the most dry months in winter (㎜/month), Pwmax : precipitation of the most humid
months in winter (㎜/month), Pth : ⅰ) 2{Tann} (when 70% of the annual precipitation falls in the coldest 6 months,㎜), ⅱ) 2{Tann}+28 (when 70% of the annual precipitation falls in the warmest 6 months, ㎜), ⅲ) 2{Tann}+14(when less than 70% of the annual precipitation falls in the warmest and coldest 6 months, ㎜)

ARCP2011-05CMY-Bae-FINAL REPORT

2.5 Data analysis for detecting trends
Trend analysis is an aspect of technical analysis that tries to predict the future movement of an event
based on past data. Trend analysis is based on the idea that what has happened in the past gives
traders an idea of what will happen in the future. For the analysis of past historical and futurescenario data of climate models, the Mann-Kendall and linear regression statistical tests were used inthis study to determine the significance of trends in precipitation and temperature of Asian countries.
The Mann–Kendall test is a non-parametric test for detecting trends in time series data. The test is
widely used for analyzing environmental data, including precipitation (Partal and Kahya, 2007),
streamflow (Liu and Zheng, 2004), and water quality data (Donohue et al., 2001). The Mann–Kendall
test is simple and robust, and it can cope with missing values and values below a detection limit. The
test was first created by Mann (1945) and Kendall (1975) and covariances between Mann–Kendall
statistics were developed by Dietz and Kileen (1981). The test was further extended in order toinclude seasonality (Hirsch et al., 1982), multiple monitoring sites (Lettenmaier, 1988) and covariates
representing natural fluctuations (Libiseller and Grimvall, 2002).
Calculation of the Mann-Kendall test
The Mann-Kendall test is a non-parametric test for identifying trends in time series data. The testcompares the relative magnitudes of sample data rather than the data values themselves (Gilbert,
1987). The advantage of this test is that the data need not to conform to any particular distribution.
Moreover, data reported as missing can be included by assigning them a common value that is35

smaller than the minimum measured value in the dataset. The procedure described in thesubsequent paragraphs assumes that there exists only one data value per time period. Whenmultiple data points exist for a single time period, the median value is used.
The data values are evaluated as an ordered time series. Each data value is compared to all
subsequent data values. The initial value of the Mann-Kendall statistic, S, is assumed to be 0 (e.g., notrend). If a data value from a later time period is higher than a data value from an earlier time period,
S is incremented by 1. On the other hand, if the data value from a later time period is lower than adata value sampled earlier, S is decremented by 1. The net result of all such increments anddecrements yields the final value of S.
Let x1, x2,… xn represent n data points where xj represents the data point at time j. Then the MannKendall statistic (S) is given by:n 1nS    sign( x j  xk )(10)

k 1 j  k 1

where:
sign (xj − xk) = 1 if xj − xk > 0= 0 if xj − xk = 0= -1 if xj − xk < 0
The higher positive value of S is an indicator of an increasing trend, and the higher negative value
indicates a decreasing trend. However, it is necessary to compute the probability associated with S
and the sample size n, to statistically quantify the significance of the trend. The procedure tocompute this probability is described in the following section.
Computation of probability associated with M-K statistic S- Calculate S as described in eq. 10.
- Calculate the variance (var) of S, as:var( S ) g1[n(n  1)(2n  5)   t p (t p  1)(2t p  5)]
18
p 1

(11)

where n is the number of data points, g is the number of tied groups (a tied group is a set of sample
data having the same value), and tp is the number of data points in the pth group. For example, in thesequence {2, 3, 4, 3, 4, 3}, we have n=6, g =2, t1 =2 for the tied value 4, and t2 =3 for the tied value 3.
- Compute the Mann–Kendall normalized test statistic Zc as:
36

ARCP2011-05CMY –Bae -FINAL REPORT

Kendall (1975) described a normal-approximation test that could be used for datasets with morethan 10 values, provided there are not many tied values within the dataset. The test procedure is asfollows:Zc 

S  1 if S>0var( S )(12)

Zc = 0 if S=0

Zc 

S  1 if S<0var( S )(13)- Compute the probability associated with normalized test statistic (Zc). The probability density
function for a normal distribution with a mean of 0 and a standard deviation of 1 (standard
normal distribution) is given by the following equations:2

1  x2f ( x) 
e
2
z

(14)

2

1 c  x2P
 e dx2 (15)

ARCP2011-05CMY-Bae-FINAL REPORT- Decide on a probability level of significance (95% typically).
- The trend is said to be decreasing if S is negative and the computed probability is greater than thelevel of significance. The trend is said to be increasing if the S is positive and the computed
probability is greater than the level of significance. If the computed probability is less than thelevel of significance, there is no trend.

37

3.0 Results & Discussion
3.1 Verification of Gridded Climate and Hydrological Datasets
In this study, the global observed runoff data was collected from Global Runoff Data Centre (GRDC)
to estimate parameters of hydrology model. Also, the gridded climate data was collected from
APHRODITE at 0.5o grid resolution. To verify the data quality of APHRODITE and GRDC, we comparedthese datasets with regional observed station data. The details of climate and hydrological data
verification are given in the following sections.
Verification of climate data

2000

Busan

ASOS
APHRODITE

1500
1000
500
0
85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

00

01

Monthly Precipitation

Year
2000

Seoul

ASOS
APHRODITE

1500
1000
500
0
85

86

87

88

89

90

91

92

93

Year
38

94

95

96

97

98

99

00

01

ARCP2011-05CMY –Bae -FINAL REPORTMonthly Precipitation

To verify the quality of gridded climate dataset (APHRODITE) the comparison test of both rainfall andtemperature was conducted for selected stations in South Korea. The grid point rainfall data wascompared with corresponding observed station data obtained from four Automated Surface
Observing System (ASOS) stations i.e. Busan, Seoul, Daejeon and Mokpo. Figure 3.1 shows thecomparison of APHRODITE grid points and their corresponding ASOS stations data. The comparison
shows that overall both datasets have same tendency for all selected stations. The APHRODITE
slightly underestimated rainfall especially for heavy precipitation events in Busan and Seoul. That isprobably because of smoothing effect in gridded dataset. The Correlation Coefficient (CC) andVolume Error (VE) computed between APHRODITE and ASOS stations is given in Table 3.1. The VE
was -27.20%, -25.68%, -12.07% and -19.55% for Busan, Seoul, Daejeon and Mokpo respectively. The
CC was 0.98 for Busan and Seoul, and 0.99 for Daejeon and Mokpo. It showed that the gridded
APHRODITE precipitation data was useful to analyze climate and hydrology trend and change impact
assessment over Asia region because the tendency between ASOS and APHRODITE was almost same.

Monthly Precipitation

2000

DaejeonASOS
APHRODITE

1500
1000
500
0
85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

00

01

Monthly Precipitation

Year
2000

Mokpo

ASOS
APHRODITE

1500
1000
500
0
85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

00

01

Year
Figure 3.1: APHRODITE Vs. ASOS monthly precipitation data for Busan, Seoul, Daejeon and Mokpo
from 1984 to 2001

ARCP2011-05CMY-Bae-FINAL REPORT

Table 3.1: CC and VE for selected four stations over South Korea.
Busan

Seoul

Daejeon

Mokpo

CC

0.98

0.98

0.99

0.99

VE(%)

-27.20-25.68-12.07-19.55

The gridded temperature data was also compared with station data for selected four ASOS stations.
Figure 3.2 shows the comparison of ASOS and APHRODITE monthly temperature data at Busan, Seoul,
Daejeon, and Mokpo. Both datasets followed same trend for all four stations. The averagetemperature (ASOS and APHRODITE) was 14.67oC and 13.92 oC for Busan, 12.44 oC and 11.78 oC for
Seoul, 12.65 oC and 12.16 oC for Daejeon, and 13.92 oC and 14.11 oC for Mokpo. The average monthly
temperature from ASOS data was higher than APHRODITE data for all the stations except Mokpo.
Table 3.2 shows the CC and VE for selected four stations. The CC was above 0.99 for all the stations.
The highest VE was -5.11% for Seoul station and lowest VE was 1.32% for Mokpo station. The average
VE for all four stations was -3.24%. These statistics show that the gridded APHRODITE temperature
data is reliable to use for analysis of climate and hydrology over Asia region.

39

Monthly Temperature

40
30

Busan

ASOS
APHRODITE

20
10
0
-10
85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

00

01

Monthly Temperature

Year
40
30

Seoul

ASOS
APHRODITE

20
10
0
-10
85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

00

01

Monthly Temperature

Year
40
30

DaejeonASOS
APHRODITE

20
10
0
-10
85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

00

01

40
30

Mokpo

ASOS
APHRODITE

20
10
0
-10
85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

00

01

Year

Figure 3.2: APHRODITE Vs. ASOS monthly temperature data for Busan, Seoul, Daejeon and Mokpo
from 1984 to 200140

ARCP2011-05CMY –Bae -FINAL REPORTMonthly TemperatureYearTable 3.2: Results of Correlation coefficient and volume error main four stations between station
data and gridded APHRODITE data over South Korea.
Busan

Seoul

Daejeon

Mokpo

CC

0.99

0.99

0.99

0.99

VE(%)

-5.11

-5.32

-3.83

1.32

Verification of hydrology data

ARCP2011-05CMY-Bae-FINAL REPORTIn this study, the global observed runoff data was collected from Global Runoff Data Centre (GRDC)
to estimate parameter of hydrology model. Figure 3.3 shows the location of runoff observatories andriver basins used in the study. The observatories were selected considering that the selected
observatory take effect from hydraulic structures as dam and have availability of observational data.
As a result twenty observatories were identified, among these seven were located in Japan, five in
China, two in each country Russia and Malaysia, and one in each country Mongolia, India andCambodia. For South Korea the Chungju dam was selected as target basin and runoff data was
obtained from Ministry of Land, Transport and Maritime Affairs (MLTM). Table 3.3 shows the selected
observatories, available data period and area of target basin for each country.

Figure 3.3: Location of selected station and river basins on East Asia
Table 3.3: Characteristics of the selected study basins and available data period
CountryNo.

Sation Name

Study Basins

Russia①

Birobidzhan

Bolshaya Bira
41

Characteristics
2

Area (km )Data Period (year)

5,910

1978 - 1986

②

Undurkham

Kerulen39,4001978 - 1984

③

Jilin

Songhua Jiang

44,1001978 - 1986④

Chiling

Liao He

120,764

1978 - 1985⑤

BengbuHuai He

121,330

1978 - 1989

⑥

Jian

Gan Jiang

56,2001978 - 1984

⑦

Boluo

Dong Jiang

25,325

1978 - 1987

North
Korea

⑧

Jong Chon

Sangwan

2,192

1982 - 1984

South
Korea

⑨

ChungJu Dam

Chung Ju

6,648

1986 - 2013⑩

OnobuchiSendai

1,348

1995 - 2000⑪

MiyasuYoshino1,996

1993 - 2000⑫

Hirakata

Yodo

7,281

1995 - 2005⑬

KashimaTenruy4,880

1993 - 2003⑭

FukawaTone

12,458

1995 - 2005⑮

Ishikari-Ohashi

Ishikari

12,697

1995 - 2002

⑯

Moiwa

Tokachi

8,277

1993 - 2001

India

⑰

Hardinge bridge

Ganges846,300

1985 - 1992

Thailand⑱

Stung Treng

Mekong635,000

1991 - 1994⑲

Buluh Kasap

Muar

3,130

1982 - 2000⑳

Kapit Wharf

Rajang34,0531982 - 1994

Mongolia

China

Japan

Malaysia

To analyze and check the quality of GRDC runoff data the Yeoju observatory in South Korea wasselected because the observed runoff data for this observatory was also available from MLTM Korea.
For comparison the daily data was used for the period of 1997 to 2007. Figure 3.4 shows thecomparison result between observation data from MLTM and GRDC data at Yeoju. As a result, runoff
movement was almost same. However, there was difference at movement of low-flow. These
differences are believed to be occurred due to transmission problem. The comparison showed thatthe reliability of GRDC runoff data is high to evaluate results of runoff analysis.

MLTM
GRDC

10000

1000

100

10
1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Time(daily)

Figure 3.4 : Comparison of local (MLTM) and Global (GRDC) runoff data at Yeoju station

42

2008

ARCP2011-05CMY –Bae -FINAL REPORTRunoff(m3/s)

1000003.2 Hydrological analysis
3.2.1 Classification of climate zone

ARCP2011-05CMY-Bae-FINAL REPORTThe climate classification over Asia was obtained using Köppen climate classification method toregionalize parameters of VIC model for ungauged area (Fig. 3.5). Looking into climate zones
according to latitude we can see that the tropical rainforest climate zone (Af) spreads over Indonesia,
Malaysia, and Philippines below 10°N. The tropical monsoon climate zone (Am) covers Greater Sunda
Islands in northern part of Indonesia, Cambodia and Vietnam between equatorial and 20°N. The
Tropical savannah climate zone is located over India, Thailand, Myanmar, Laos, Cambodia, and Sri
Lanka between 23°N and 40°N. The climate zone of warm temperature with dry summer (Cs) is
distributed over some parts of the Hindustan plains in India. The climate zone of warm temperature
with dry winter spreads over eastern part of Pakistan, Hindustan of India, southern and eastern partof China, and South Korea. The climate zone of warm temperature without dry season (Cf) appeared
over all area except the Southeastern part of China, and coastal area of South Korea. The Arid andCold climate zone is distributed over most region above 38°N. The arid clime zone covers region from
Iranian plateau to Gobi desert including Taklamakan desert. The desert climate zone (Bw) and Steppe
climate zone are located around Arid climate zone. The cold climate zone appeared overnortheastern part of China, North Korea, East of Russia, and Hokkaido Island of Japan. The Tundra
climate zone in Polar climate zone is distributed over Himalaya mountain range. For South Korea, theclimate zone of cold with dry winter (Dw) lies in northern part, the climate zone of warm
temperature without dry season lies in coastal area, and the climate zone of warm temperature withdry winter lies in inland area. These climate zones reflect current climate of South Korea well thatshows the reliability of classified climate zones.

Figure 3.5: Climate zones in Asia according to Köppen climate zone43

3.2.2 Parameter estimation and regionalization
To conduct runoff analysis, it is important to identify the characteristics of model parameters.
However estimation of model parameters is difficult task because model parameters are sensitive torunoff results. The parameters for VIC hydrological model were estimated from measurable basincharacteristics using geographical information system (GIS) database that includes DEM, river
network, soils, vegetation type, and land cover. Appropriate parameter estimation method is alsoneeded at ungauged basin. The regionalization method was used for parameter estimation of VIC
model at ungauged basins. The regionalization method assumes that the basins with similar
topography and climate exhibit similar hydrological response. Table 3.4 shows the list of VIC model
parameters used in regionalization. The b_infilt parameter is to determine infiltration fromprecipitation. The surface runoff increases with increasing value of b_infilt. The Dsmax, Ds, and Ws
determine movement and amount of baseflow. Specifically the Dsmax and Ds determine movement of
baseflow recession segment, and the Ws determine capacity of base flow. The d1, d2, and d3
represent depth of soil layer. The soil layer thickness determines the quantity of soil moisture,
evaporation and runoff.
Table 3.4: VIC model parameters
Descriptionb_infilt

Realistic range

Exponent of variable infiltration capacity curve

0.0-10.0

Ds

Fraction of maximum base flow

0.0-1.0

Dsmax

Maximum velocity of baseflow (m/day)

0.0-40.0

Ws

Fraction of maximum soil moisture content of thelower layer

0.0-1.0

Three soil layer thickness(m)0.05-2.0

d1, d2, d3

To verify the results of parameter estimation and regionalization methods in Asia region ten gauged
basins were used for calibration and remaining ten gauged basins were assumed to be ungauged andused for verification purpose. The model parameters were estimated for calibration basins and thatfor verification basins were regionalized based on similar basin characteristics. The monthly runoff
data was computed from daily GRDC runoff data for the analysis. The statistical measures i.e.
correlation coefficient (CC), model efficiency coefficient (ME), root mean square error (RMSE), andvolume error (VE) for calibration (Cal.) and verification (Ver.) basins are presented in Table 3.5. For
calibration basins the CC, ME, RMSE, and VE were 0.67∼0.96, 0.36∼0.91, 1.10∼63.29mm/month,and -25.72∼9.66%, respectively. For verification basins the same were 0.58∼0.96, 0.33∼0.92, 1.61
∼63.29mm/month, and less than 17.89%, respectively.
Table 3.5: Statistics results and estimated parameter at gauged (Cal) and ungauged (Ver) eachclimate zone.
-

Climate
Zone

No.

Statistics

Basin

CC
44

ME

RMSE

VE

ARCP2011-05CMY –Bae -FINAL REPORTParameters

⑳

Rajang

0.67

0.36

51.55

-4.84

⑱

Mekong

0.95

0.91

16.05

-2.25

②

Kerulen

0.79

0.59

1.10

-6.83

⑤

Huai He

0.81

0.63

17.19

-11.50⑦

Dong Jiang

0.87

0.66

27.29

-10.79⑨ChungJu Dam

0.96

0.88

31.05

-3.54

⑬

Tenryu

0.95

0.88

23.97

9.66

D

⑧

Sangwan

0.88

0.68

31.59

-25.72

E⑯

Tokachi

0.73

0.45

32.25

19.01

⑲

Buluh Kasap

0.58

0.33

63.29

0.46

⑰

Gangis

0.90

0.76

21.87

17.39

④

Liao He

0.77

0.58

1.61

0.59

⑥

Gan Jian

0.89

0.79

27.79

1.32

⑩

Sendai

0.96

0.92

41.49

-2.68

⑪

Yoshino

0.93

0.85

28.10

5.54

⑫

Yodo

0.86

0.71

30.76

10.60

⑭

Tone

0.88

0.75

21.58

14.83

D

③

Songhua Jiang

0.79

0.35

16.31

12.66

E

⑮

Ishikari

0.64

0.35

46.38

-8.52

A
B

Cal.

C

A
B

Ver.

C

Figure 3.6 shows the simulated and observed runoff for ungauged basins. The simulated runoff did
not reflect observed runoff well at Songhua Jiang, and Ishikari basins. For Gangis and Gan Jiang
basins, the simulated runoff captured the trend of observed runoff very well although the area ofbasin was more than 100,000km2. Similarly, the simulated runoff at Sendai and Tone was in good
agreement with observed runoff for six and eleven years respectively. Considering the data
resolution of 0.5o, the reliability of runoff result is high. Furthermore, application of built VIC model isuseful.
0

Runoff(mm)

ARCP2011-05CMY-Bae-FINAL REPORT

400

800
200

0

0

300

1200

400

OBS
CAL
PRCP

200

800
100

0

82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00

1200

86

Time(month)

87

88

89

Time(month)a) Buluh Kasapa) Gangis

45

90

91

92

Precipitation(mm)

400
OBS
CAL
PRCP

Precipitation(mm)

600

400

Runoff(mm)

800

0

400
10

0

600

79

80

81

82

83

84

85

0

1200

80

81

82

83

c) Liao Hed) Gan Jian

800

1200

400

1600

97

98

99

00

400
OBS
CAL
PRCP

400

800
200

0

2000

0

0

600

Runoff(mm)

OBS
CAL
PRCP

1200

01

94

95

96

97

98

Time(month)Time(month)e) Sendaif) Yoshino

800

0

OBS
CAL
PRCP

400

800
200

99

00

800

0

600

400
OBS
CAL
PRCP

400

800

200

Precipitation(mm)400

Precipitation(mm)

600

85

Precipitation(mm)

800

84

800

0

Precipitation(mm)Runoff(mm)800Time(month)400

Runoff(mm)

200

79

1200

400

OBS
CAL
PRCP

Time(month)

1600

96

400

86

Runoff(mm)Runoff(mm)200Precipitation(mm)

200
OBS
CAL
PRCP

Precipitation(mm)

30

600

Runoff(mm)

40

1200
0

1200

96

97

98

99

00

01

02

03

04

05

0

96

06

97

98

99

00

01

02

Time(month)Time(month)h) Yodoi) Tone

300

03

04

05

06

0

0

800
0

84

85

86

800

200

0

1000

83

400

1200

96

87

97

98

99

00

Time(month)Time(month)j) Songhua Jiangk) Ishikari

Figure 3.6: Simulated runoff at ungauged basin and observed runoff.
3.3 Analysis of historical climate and hydrology
3.3.1 Historical features
Asia region
46

01

02

03

ARCP2011-05CMY –Bae -FINAL REPORTRunoff(mm)

600
100

OBS
CAL
PRCP

400

Precipitation(mm)400

Precipitation(mm)

200
OBS
CAL
PRCP

200

Runoff(mm)600

To analyze the historical features of Asia region the 30-year (1977–2006) averages of climatologicalvariables i.e. temperature and precipitation, and hydrological variable i.e. runoff were computed asshown in Figure 3.7. The temperature decrease with increasing latitude is evident in the figure. As
per quantitative analysis the temperature range was 17∼32°C in Southeast Asia, 5∼32°C in SouthAsia, -3∼20°C in Central Asia , -6∼20°C in East Asia, and -6∼0°C in North Asia region. Especially forSouth Korea, average temperature was 12.9°C which was similar to average temperature of South
Korea. The analysis of precipitation showed low rainfall over continental regions as compared tocoastal regions. The average precipitation over tropical region was approximately 1,500∼3,200mm.
For South Asia, Southeast Asia, and East Asia regions which are influenced by sea winds the average
precipitation was 1,250∼3,000mm. The average precipitation over Central Asia and desert region of

ARCP2011-05CMY-Bae-FINAL REPORT

East Asia was 110∼250mm. In South Korea, the average precipitation was 1,566mm. The spatial
distribution of runoff was similar to precipitation. The western East Asia including Taklamakan desert,
Pakistan, south Afghanistan, and part of western Central Asia were the most low runoff regions. The
runoff in South Korea, Japan and Southern China was in range of 600~1600mm, and it was above
2000mm in tropical regions including Indonesia, Thailand and Southern Vietnam.

Figure 3.7: 30-year (1977-2006) averages of temperature (left of top), precipitation (right of top),
and runoff (bottom) over Asian region.
The seasonal analysis of historical 30-year averages for temperature, precipitation, and runoff was
conducted for Asia region (Fig. 3.8). The seasonal features of spring and autumn were almost samefor temperature, precipitation, and runoff. Temperature in spring and autumn decreased withincreasing latitude to northward. The most low temperature regions were Himalaya Mountain,
northern part of Mongolia and China. The summer temperature was high over all regions except
47

Himalaya Mountain. The most high temperature regions were north India, Pakistan, and south
Afghanistan. Regarding the autumn temperature, north part of East Asia and central Asia including
Himalaya Mountain were colder regions with below freezing temperature. The most hightemperature was observed over tropical regions including south India, Cambodia, Indonesia, andMalaysia.
The seasonal analysis of precipitation showed that the precipitation in spring and autumn was highnear tropical regions, and it was very low around Himalaya Mountain and western East Asia. In
summer the high precipitation region was between 10°N and 20°N including northern Southeast Asia,
India, and Nepal regions. In winter season, precipitation was low over all regions, as compared toother seasons. The most high precipitation region was southern Tropical region, and the lowest
precipitation was observed in Himalaya Mountain, China, and Mongolia regions.

48

ARCP2011-05CMY –Bae -FINAL REPORTThe spatial features of runoff were similar to features of precipitation. However, the intensity ofrunoff was small compared to the precipitation. In spring season the most high runoff regions were
almost same with spring precipitation, but the lowest runoff region existed over East Asia, Himalaya
Mountain, northwest India, and Pakistan. The summer runoff was increased in most regions
compared to spring season, especially the northwestern India region which showed significant
increase. The autumn runoff was almost same with spring except for western Central Asia that was
clearly increased. The winter runoff showed same distribution compared to the winter precipitation.
The winter runoff was very low at Central Asia and Northern Asia. The most high runoff was observed
near tropical regions.

Figure 3.8: Features of seasonal temperature (left), precipitation (middle), and runoff (right) over
Asian region. Figure from top to bottom mean spring to winter.

ARCP2011-05CMY-Bae-FINAL REPORTAWCI 18 basins
In this study, the hydrological components were calculated over Asia regions using the VIC model.
Figure 3.9 shows the monthly average of calculated hydrological components i.e. precipitation, runoff,
evapotranspiration, and soil moisture from 1977 to 2006 for AWCI 18 basins. Most of the basins hadhigh precipitation between June and September and low precipitation between December andFebruary. In Meghna basin, Bangladesh, the peaks of precipitation, runoff, evapotranspiration, andsoil moisture were observed between June and July. The peaks of hydrological components inPunatsanghhu basin, Bhutan, and Sanker basin, Cambodia were observed in July and September
respectively. In Seonath basin, India, the peak of precipitation was in August with almost no
precipitation between November and May. Other components in Seonath basin had similar features.
For Manberamo basin, Indonesia, the hydrological components did not show any distinct featurearound the year. In Tone basin, Japan, the peak of precipitation was in September, but runoff had one
more peak in April because of snow melt. In Upper Chungju basin of South Korea, the peaks of allhydrological components were in July. In case of Sebangfai basin, Laos, the peaks of all hydrologicalcomponents were in August. In Langat basin, Malaysia, there were two peaks in April and November
for all hydrological components. For Selbe basin, Mongolia, the peaks of all hydrological components
were in August except evapotranspiration which showed peak in July. In Shwegyin basin, Myanmar,
the features of hydrological components were similar to Selbe basin, Mongolia. The hydrologicalcomponents at Bagmati basin in Nepal were similar to Upper Chungju-basin in South Korea. In caseof Gilit basin, Pakistan, the peaks of precipitation and runoff were observed in April and that of soil
moisture in May. On the other hand, the evapotranspiration remained almost same during January toAugust and it decreased from September to December. All hydrological components in Pampanga ofPhilippines had peaks in July. In case of Kalu Ganga basin, Sri Lanka, all hydrological components had
distinct two peaks in May and October. In Mae Wang basin of Thailand, the precipitation had two
peaks in May and September. Other hydrological components were almost same in Mae Wang basin.
In case of Chirchik Okhangaran basin, Uzbekistan, the precipitation had two peaks in April and
December, but other hydrological components had only one peak in April. Lastly, in Huong basin,
Vietnam, the precipitation had one peak in October, and all other components showed similarfeatures. The analysis of calculated hydrological components for 18 basins showed that most of thebasins have special distinct features.

49b) Punatsangchhuc) Sankerd) Seonathe) Mamberamof) Toneg) ChungJu-Damh) Sebanfaii) Langatj) Selbe

50

ARCP2011-05CMY –Bae -FINAL REPORTa) Meghnak) Shwegyinl) Bagmatim) Gilgitn) Pampangao) Kalu Gangap) Mae Wangq) Chirchik Okhangaranr) Huong

Figure 3.9: Monthly average of calculated hydrological components from 1977 to 2006 at AWCI 18
countries.

ARCP2011-05CMY-Bae-FINAL REPORT

3.3.2 Trend analysis
Asia region
To analyze the historical trend over Asia region the Mann-Kendall test was employed on 30-years(1977-2006) averages of temperature, precipitation and runoff. The Mann-Kendall test is
nonparametric verification method to analyze trend of long term time series data. Other verification
methods are influenced by the sample size, dispersion, and distortion. However, the Mann-Kendall
test is not sensitive to the sample size and can analyze trend easily (Gibsons, 1990). The MannKendall test is being widely used for analysis of hydrological variables because it can be applied todata having missing values (Hirsch et al, 1984; Bae et al., 2008a; Jung et al., 2011).
51

Figures 3.10 to 3.12 show the Mann-Kendall test results of historical temperature, precipitation, andrunoff for period of 1977 to 2006 over Asia region. The left panels in figures indicate distribution ofannual temperature, precipitation, and runoff, and right panels in figures show statistical results ofMann-Kendall test. The distribution results in figures show the 95% and 90% mean significance level.
The arrows (↑) and (↓) denote the increase and decrease tendency respectively. During the past 30
years average temperature increased approximately 1∼1.5°C with R2=0.60 in the study area. Most
regions showed increasing trend except Thailand and Bangladesh. Especially, there were distinct
increasing trends (95% confidence level) over Indonesia, Malaysia, Tibetan Plateau, Mongolia, china,
North Korea, and Japan. Other regions also had increase tendency although confidence level was not
significant.

Figure 3.10: Mann-Kendall test results of annual historical temperature over Asian region. Graphmeans average of Asian region.

Figure 3.11: Mann-Kendall test results of annual historical precipitation over Asian region. Graphmeans average of Asian region.
52

ARCP2011-05CMY –Bae -FINAL REPORTThe Mann-Kendall test results for precipitation showed 100~200mm decrease in precipitation withslope coefficient -1.477 (R2=0.22) during 30 years over Asia region (Fig. 3.11) The decreasing trend of
precipitation appeared over Indonesia, Malaysia, inland of India, and east of North Asia. The
increasing trend was also observed (95% confidence level) over bordering regions between Southern
Asia and Central Asia, and southward of Tibetan Plateau. There was increasing tendency of
precipitation over Mongolia, west China, South Korea, Myanmar, Thailand, Vietnam, and Japan
although it was not significant statistically. On the other hand, the decreasing trend of precipitation
was observed over China, India, Afghanistan, and Uzbekistan. The change in precipitation was higher
than change in temperature over Asia region.

Figure 3.12 shows the Mann-Kendall test results of annual historical runoff over Asia. The runoff over
Asian region decreased approximately 70∼80mm during past 30 years (1977-2006). The distributionof runoff change was similar to precipitation. The runoff decrease over Central Asia and east part ofSoutheast Asia was statistically significant with 95% confidence level. The increasing trend of runoff
was observed over bordering regions between Southern Asia and Central Asia, and southward ofTibetan Plateau. Generally, the decreasing trend of runoff was sharp compared to precipitation.

Figure 3.12: Mann-Kendall test results of annual historical runoff over Asian region. Graph means
average of Asian region.

ARCP2011-05CMY-Bae-FINAL REPORTThe seasonal trend analysis (spring, summer, autumn, winter) using Mann-Kendall test for 30-years(1977-2006) averages of temperature, precipitation and runoff are depicted in Figure 3.13 to 3.15. In
spring season, the increasing temperature trend appeared over most regions of Asia except Thailand
and Myanmar (Fig. 3.13). Especially, distinct increasing trend was observed over Malaysia, China,
Japan and Mongolia. For precipitation, strong decreasing trend was shown over Indonesia, Malaysia,
and west and south part of China. The decreasing trend of precipitation was also observed overSouthern Asia and Central Asia although it was not statistically significant. In case of spring runoff,
the regional pattern was almost similar to precipitation, however the strong decreasing trend
appeared over China, India, and east part of Northern Asia. The increasing trend of runoff wasobserved over bordering regions between Central Asia and East Asia because of the snow melting
from Jengish Chokusu, K2, Nanga Parbat, Himalaya, and Everest.

53

Figure 3.13: Mann-Kendall test results of spring historical temperature (left), precipitation (middle),
and runoff (right) over Asian region. Graphs mean average of all Asian regions.
In summer season, distinct increasing trend of temperature was observed over China, Mongolia,
south part of Japan, Indonesia, and Malaysia (Fig. 3.14). The decreasing temperature trend existed
over some regions although it was not statistically significant. The trend analysis results for
precipitation showed increasing trend over Central Asia, northwest and southeast part of China,
North Korea, South Korea, and Japan. The decreasing precipitation trend was observed over
Mongolia, inland of China, and India, although it was not statistically significant. In summer the trendof runoff was similar to annual trend of runoff.

Figure 3.15 shows the Mann-Kendall test results of autumn for historical temperature, precipitation,and runoff over Asia region. Autumn temperature showed increasing trend over most regions of Asia
except west part of Northern Asia. The distinct increasing trend of temperature appeared overeastern part of Northern Asia, Tibetan Plateau of China, North Korea, some parts of Japan,
Afghanistan, Vietnam, Indonesia, and Malaysia. In autumn the increasing precipitation trend existed
over some regions although it was not statistically significant. The features of runoff in autumn
season were similar to precipitation.

54

ARCP2011-05CMY –Bae -FINAL REPORT

Figure 3.14: Mann-Kendall test results of summer historical temperature (left), precipitation(middle), and runoff (right) over Asian region. Graphs mean average of all Asian regions.

Figure 3.15: Mann-Kendall test results of autumn historical temperature (left), precipitation(middle), and runoff (right) over Asian region. Graphs mean average of all Asian regions.

ARCP2011-05CMY-Bae-FINAL REPORTThe Mann-Kendall test results of winter for historical temperature, precipitation, and runoff over Asia
regions are shown in Figure 3.16. The winter temperature increased over most regions except Indiaand east part of Kazakhstan. The increasing trend was strong over China, Tibetan Plateau, Myanmar,
Vietnam, Malaysia, and Indonesia. The trend analysis of precipitation in winter showed that theincreasing trend was high over northwest, northeast, and southeast parts of China, and south part ofVietnam compared to other regions. The average precipitation over Asia region did not show any
statistically significant increasing or decreasing trend. The increasing trend of winter runoff appeared
over south part of Mongolia, west part of china, and India, however the trend was not statisticallysignificant. Furthermore the increasing trend of runoff was weaker as compared to precipitation.

Figure 3.16: Mann-Kendall test results of winter historical temperature (left), precipitation(middle), and runoff (right) over Asian region. Graphs mean average of all Asian regions.
55

AWCI 18 basins
To evaluate the annual trend of temperature, precipitation, and runoff at basins scale the analysis
was conducted at AWCI 18 basins. Figure 3.17 shows the annual mean change of temperature,
precipitation, and runoff from 1977 to 2006 at each basin. In Meghna basin, Bangladesh, theincreasing trend was observed for temperature, precipitation, and runoff while R square was low.
The Punatsangchhu, Sangker, and Seonath basins located in Bhutan, Cambodia, and India,
respectively showed similar features. The temperature was observed to increase with slight decreasein precipitation and runoff, although it was not statistically significant. In Mamberamo basin,Indonesia, the temperature (R2=0.407) showed increasing trend, while precipitation (R2=0.456) andrunoff (R2=0.408) were observed to decrease. The value of R square in Mamberamo basin was highcompared to other basins. For Tone basin, Japan, and Chungju-Dam basin, Korea, the temperature,
precipitation, and runoff showed increasing trend, although R square was statistically low. In case of
Sebanfai basin, Laos, the temperature was observed to increase with slight increase in precipitationand decrease in runoff. The decrease in runoff was possibly because of increased temperature. In
Langat basin, Malaysia, temperature, precipitation, and runoff showed increasing trend. Especially,
the R square of temperature (0.62) and precipitation (0.32) was comparatively high. In Selbe basin,
Mongolia, the temperature showed increasing trend, but precipitation and runoff were observed todecrease. In case of Shwegyin and Bagmati basins in Myanmar and Nepal, respectively, all variables
had increasing tendency. Especially, Bagmati basin showed distinct increasing trend with R squarevalue of 0.69. In Gilgit basin, Pakistan, the increasing trend was observed for temperature (R2=0.31),
precipitation (R2=0.34), and runoff. In case of Pampanga, Kalu Ganga, and Mae Wang basins in
Philippines, Sri Lanka, and Thailand, respectively, there was no distinct change in temperature,
precipitation and runoff. In Chirchik-Okhangaran basin, Uzbekistan, the temperature was observed toincrease, but precipitation and runoff had no significance change. In Huong basin, Vietnam, allvariables showed increasing tendency although it was not statistically significant. Overall, thetemperature showed increasing trend in most of the regions. Although the precipitation wasobserved to increase in most regions, the decreasing tendency was also observed in some regions.
The trend of runoff was similar to that of precipitation. Furthermore, there was no significant impact
of temperature on runoff features.

24.5

24

23.5

23

3500

Annual Runoff(mm)

25

3000

Y = 6.007x + 2487.631
R2 = 0.020

3000

2500

2000

1500

76

78

80

82

84

86

88

90

92

94

Time(year)

96

98

00

02

04

06

Y = 5.791x + 1697.786
R2 = 0.022

2500

2000

1500

1000

500
76

78

80

82

84

86

88

90

92

94

Time(year)a) Meghna

56

96

98

00

02

04

06

76

78

80

82

84

86

88

90

92

94

Time(year)

96

98

00

02

04

06

ARCP2011-05CMY –Bae -FINAL REPORT

4000

Y = 0.009x + 24.304
R2 = 0.059

Annual Precipitation(mm)Annual Temperature(oC)

25.5

2800

13

12

11

10

1800

Y = -2.821x + 1701.465
R2 = 0.014

2400Annual Runoff(mm)Y = 0.018x + 10.880
R2 = 0.193

Annual Precipitation(mm)Annual Temperature(oC)

14

2000

1600

1200

800

9
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

1200

900

600

300

76

06

Y = -4.769x + 1033.201
R2 = 0.039

1500

78

80

82

84

86

88

Time(year)

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

88

Time(year)

90

92

94

96

98

00

02

04

06

96

98

00

02

04

06

96

98

00

02

04

06

96

98

00

02

04

06

96

98

00

02

04

06

Time(year)b) Punatsangchhu

2000

28.5

28

27.5

27

26.5

1000

Y = -1.196x + 1065.903
R2 = 0.003

1600

Annual Runoff(mm)Y = 0.014x + 27.421
R2 = 0.195

Annual Precipitation(mm)Annual Temperature(oC)

29

1200

800

400

0

76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

600

400

200

0

76

06

Y = -1.653x + 584.436
R2 = 0.014

800

78

80

82

84

86

88

Time(year)

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

88

Time(year)

90

92

94

Time(year)c) Sangker

1250

1950

26.5

26

25.5

25

24.5

Y = -1.518x + 1123.318
R2 = 0.005

1650

Annual Runoff(mm)Y = 0.004x + 25.754
R2 = 0.057

Annual Precipitation(mm)Annual Temperature(oC)

27

1350

1050

750

78

80

82

84

86

88

90

92

94

96

98

00

02

04

76

06

750

500

250

0

450

76

Y = -2.548x + 665.123
R2 = 0.026

1000

78

80

82

84

86

88

Time(year)

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

Time(year)

88

90

92

94

Time(year)d) Seonath

3500

24.5

24

23.5

23

2000

Y = -43.764x + 2432.635
R2 = 0.456

2800Annual Runoff(mm)Y = 0.018x + 23.513
R2 = 0.407

Annual Precipitation(mm)Annual Temperature(oC)

25

2100

1400

700

0

22.5
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

1200

800

400

0
76

06

Y = -27.529x + 1411.529
R2 = 0.408

1600

78

80

82

84

86

88

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

Time(year)

Time(year)

88

90

92

94

Time(year)

1920

11

Annual Precipitation(mm)Y = 0.029x + 9.228
R2 = 0.189

10

9

8

7

1400

Y = 6.155x + 1285.716
R2 = 0.082

1680

Annual Runoff(mm)12Annual Temperature(oC)

ARCP2011-05CMY-Bae-FINAL REPORTe) Mamberamo

1440

1200

960

720
76

78

80

82

84

86

88

90

92

94

Time(year)

96

98

00

02

04

06

Y = 5.175x + 793.178
R2 = 0.078

1200

1000

800

600

400
76

78

80

82

84

86

88

90

92

94

Time(year)f) Tone

57

96

98

00

02

04

06

76

78

80

82

84

86

88

90

92

94

Time(year)

2100

11

10

9

8

7

1500

Y = 11.628x + 1059.848
R2 = 0.199

1800

Annual Runoff(mm)Y = 0.007x + 8.977
R2 = 0.011

Annual Precipitation(mm)Annual Temperature(oC)

12

1500

1200

900

600
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

06

Y = 10.852x + 667.038
R2 = 0.222

1250

1000

750

500

250
76

78

80

82

84

86

88

Time(year)

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

88

Time(year)

90

92

94

96

98

00

02

04

06

96

98

00

02

04

06

96

98

00

02

04

06

96

98

00

02

04

06

Time(year)g) ChungJu-Dam

2800

26

25

24

23

1750

Y = 1.658x + 1682.691
R2 = 0.005

2400Annual Runoff(mm)Y = 0.022x + 23.534
R2 = 0.209

Annual Precipitation(mm)Annual Temperature(oC)

27

2000

1600

1200

800

22
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

1250

1000

750

500
76

06

Y = -0.988x + 1108.967
R2 = 0.002

1500

78

80

82

84

86

88

Time(year)

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

88

Time(year)

90

92

94

Time(year)h) Sebanfai

2800

29

28

27

26

1750

Y = 11.154x + 1666.851
R2 = 0.191

2400

Annual Runoff(mm)Y = 0.038x + 26.481
R2 = 0.623

Annual Precipitation(mm)Annual Temperature(oC)

30

2000

1600

1200

800

25
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

1250

1000

750

500

76

06

Y = 11.987x + 997.539
R2 = 0.324

1500

78

80

82

84

86

88

Time(year)

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

88

Time(year)

90

92

94

Time(year)i) Langat

600

-2

-3

-4

-5

-6

250

Y = -2.037x + 312.944
R2 = 0.064

480Annual Runoff(mm)Y = 0.056x - 4.569
R2 = 0.403

Annual Precipitation(mm)Annual Temperature(oC)

-1

360

240

120

0
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

06

Y = -1.436x + 136.148
R2 = 0.066

200

150

100

50

0

76

78

80

82

84

86

Time(year)

88

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

88

90

92

94

Time(year)

Time(year)

4000

28

27.5

27

26.5

3000

Y = 2.884x + 2581.656
R2 = 0.006

3500

Annual Runoff(mm)Y = 0.010x + 26.933
R2 = 0.096

Annual Precipitation(mm)Annual Temperature(oC)

28.5

3000

2500

2000

1500

26
76

78

80

82

84

86

88

90

92

94

Time(year)

96

98

00

02

04

06

Y = 3.674x + 1831.029
R2 = 0.012

2600

2200

1800

1400

1000

76

78

80

82

84

86

88

90

92

94

96

Time(year)k) Shwegyin

58

98

00

02

04

06

76

78

80

82

84

86

88

90

92

94

Time(year)

96

98

00

02

04

06

ARCP2011-05CMY –Bae -FINAL REPORTj) Selbe

2400

23

22

21

20

1500

Y = 4.902x + 1510.460
R2 = 0.044

2100

Annual Runoff(mm)Y = 0.044x + 20.909
R2 = 0.687

Annual Precipitation(mm)Annual Temperature(oC)

24

1800

1500

1200

19

900
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

06

Y = 4.477x + 830.943
R2 = 0.049

1250

1000

750

500

250
76

78

80

82

84

86

88

Time(year)

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

Time(year)

88

90

92

94

96

98

00

02

04

06

96

98

00

02

04

06

96

98

00

02

04

06

96

98

00

02

04

06

96

98

00

02

04

06

Time(year)l) Bagmati

500

1.5

0.5

-0.5

-1.5

-2.5

400

Y = 5.511x + 113.252
R2 = 0.339

400Annual Runoff(mm)Y = 0.055x - 0.637
R2 = 0.313

Annual Precipitation(mm)Annual Temperature(oC)

2.5

300

200

100

0
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

240

160

80

0
76

06

Y = 4.722x + 39.642
R2 = 0.350

320

78

80

82

84

86

88

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

Time(year)

Time(year)

88

90

92

94

Time(year)m) Gilgit

3360

26

25.5

25

24.5

24

2100

Y = 5.318x + 1826.106
R2 = 0.021

2800

Annual Runoff(mm)Y = 0.002x + 25.266
R2 = 0.004

Annual Precipitation(mm)Annual Temperature(oC)

26.5

2240

1680

1120

560
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

06

Y = 2.397x + 1131.715
R2 = 0.007

1750

1400

1050

700

350
76

78

80

82

84

86

Time(year)

88

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

Time(year)

88

90

92

94

Time(year)n) Pampanga

4000

27.5

27

26.5

26

3000

Y = -0.657x + 2894.143
R2 = 03500

Annual Runoff(mm)Y = 0.002x + 26.537
R2 = 0.005

Annual Precipitation(mm)Annual Temperature(oC)

28

3000

2500

2000

1500

25.5
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

06

Y = -0.479x + 2052.203
R2 = 0

2600

2200

1800

1400

1000

76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

Time(year)

Time(year)

88

90

92

94

Time(year)

1500

26

Annual Precipitation(mm)Y = 0.012x + 24.409
R2 = 0.070

25

24

23

22

1000

Y = 4.059x + 891.960
R2 = 0.045

1250Annual Runoff(mm)27Annual Temperature(oC)

ARCP2011-05CMY-Bae-FINAL REPORTo) Kalu Ganga

1000

750

500

250
76

78

80

82

84

86

88

90

92

94

Time(year)

96

98

00

02

04

06

Y = 3.718x + 444.324
R2 = 0.064

800

600

400

200

0
76

78

80

82

84

86

88

90

92

94

96

Time(year)p) Mae Wang

59

98

00

02

04

06

76

78

80

82

84

86

88

90

92

94

Time(year)

1000

10

9

8

7

6

700

Y = 1.544x + 453.225
R2 = 0.016

800Annual Runoff(mm)Y = 0.053x + 7.631
R2 = 0.380

Annual Precipitation(mm)Annual Temperature(oC)

11

600

400

200

0
76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

06

Y = 0.641x + 349.147
R2 = 0.005

560

420

280

140

0
76

78

80

82

84

86

Time(year)

88

90

92

94

96

98

00

02

04

06

76

78

80

82

84

86

Time(year)

88

90

92

94

96

98

00

02

04

06

Time(year)q) Chirchik-Okhangaran

3250

24.5

24

23.5

23

2400

Y = 17.234x + 1659.544
R2 = 0.175

2750

Annual Runoff(mm)Y = 0.021x + 23.642
R2 = 0.239

Annual Precipitation(mm)Annual Temperature(oC)

25

2250

1750

1250

750

22.5
76

78

80

82

84

86

88

90

92

94

Time(year)

96

98

00

02

04

06

Y = 17.020x + 1108.820
R2 = 0.195

2000

1600

1200

800

400

76

78

80

82

84

86

88

90

92

94

Time(year)

96

98

00

02

04

06

76

78

80

82

84

86

88

90

92

94

96

98

00

02

04

06

Time(year)r) Huong

Figure 3.17: Annual mean change of temperature, precipitation, and runoff at AWCI 18 basins.

The Mann-Kendall test results of precipitation revealed decreasing trend for annual precipitation atMamberamo basin, and increasing trend at Gilgit, Langat, and Huong basins with statisticalsignificance at 95% confidence level. All basins showed increasing trend for annual precipitation
except three basins. The seasonal precipitation increased at Gilgit basin and decreased at
Mamberamo basin for all four seasons. The precipitation at Shwegylin basin highly increased inspring and decreased at Sebangfai basin in summer and autumn season, The increase in precipitation
was observed at Langat and Huong basins in winter season.
The trend analysis of runoff showed decrease in the annual runoff at Mamberamo basin and increaseat both Langat and Gilgit basins. All basins had increasing trend for annual runoff except five basins.
The Mann-Kendall test results for seasonal runoff estimated decrease at Mamberamo basin andincrease at Gilgit basin for all four seasons. The runoff in spring season increased at Shwegylin basinand decreased at Seonath and Selbe basins. The runoff increased at Bagmati basin in summer, atHuong basin in winter and at Langat basin in both summer and winter season. Overall the decreasing
60

ARCP2011-05CMY –Bae -FINAL REPORTThe annual and seasonal trend was also evaluated at AWCI 18 basins. The results of Mann-Kendall
test for temperature, precipitation and runoff are shown in Tables 3.6∼3.8. The trend analysis ofannual temperature showed increasing trend for all the basins. Particularly, the temperature increaseat ten basins was statistically significant at 95% confidence level. The seasonal trend analysis oftemperature showed statistically significant increasing trend at four, five, six, and five basins forspring, summer, autumn, and winter respectively. In particular, the increasing trend at Mamberamobasin, Indonesia and Bagmati basin, Nepal was estimated high for all four seasons. The increasing
trend of temperature was estimated in spring and summer for Selbe basin, Mongolia, and in autumn
and winter for both Gilgit basin, Pakistan and Chirchik Okhangaran, Uzbekistan basin. The increasing
temperature trend was also estimated at some other basins, however it was not statisticallysignificant.

trend of runoff was high as compared to the precipitation decrease at selected basins.
Table 3.6: Trend analysis of temperature using the Mann-Kendall test at AWCI 18 countries. The
filled triangle indicate statistical significant at 95% confidence level.
No.

Country

Basin

1Bangladesh

2

Trend analysis of temperature
SpringSummerAutumnWinterYear

Meghna

▽

△

▽

▽

△

BhutanPunatsangchhu

△

▽

△

△

△

3

Cambodia

Sangker

▽

▲

△

△

▲

4

India

Seonath

△

△

△

△

△

5

Indonesia

Mamberamo

▲

▲

▲

▲

▲

6

Japan

Tone

△

△

△

△

▲

7

Korea

Chungju-dam

△

▽

△

△

△

8

Lao PDR

Sebangfai

△

△

▲

△

▲

9

Malaysia

Langat

▲

▲

▲

▲

▲

10

Mongolia

Selbe

▲

▲

△

△

▲

11

Myanmar

Shwegylin

▽

△

△

△

△

12

Nepal

Bagmati

▲

▲

▲

▲

▲

13

PakistanGilgit

△

△

▲

▲

▲

14

Philippines

Pampanga

▽

▽

△

△

△

15

Sri Lanka

Kalu Ganga

▽

△

△

△

△

16

ThailandMae Wang

▽

▽

▽

△

△

17

Uzbekistan

ChirchikOkhangaran

△

△

▲

▲

▲

18

Vietnam

Huong

△

△

△

△

▲

ARCP2011-05CMY-Bae-FINAL REPORT

Table 3.7: Trend analysis of precipitation using the Mann-Kendall test at AWCI 18 countries. The
filled triangle indicate statistical significant at 95% confidence level.
No.

Country

Basin

1Bangladesh

2

Trend analysis of precipitation
SpringSummerAutumnWinterYear

Meghna

▽

△

△

▽

△

BhutanPunatsangchhu

△

▽

▽

△

△

3

Cambodia

Sangker

▽

▽

▽

△

▽

4

India

Seonath

▽

△

△

△

▽

5

Indonesia

Mamberamo

▼

▼

▼

▼

▼

6

Japan

Tone

△

▽

△

△

△

61

7

Korea

Chungju-dam

△

△

△

△

△

8

Lao PDR

Sebangfai

△

▲

▼

▽

▽

9

Malaysia

Langat

▽

△

△

▲

▲

10

Mongolia

Selbe

▽

▽

▽

△

▽

11

Myanmar

Shwegylin

▲

▽

▽

△

△

12

Nepal

Bagmati

△

▲

▽

△

△

13

PakistanGilgit

▲

▲

▲

▲

▲

14

Philippines

Pampanga

△

△

▽

▲

△

15

Sri Lanka

Kalu Ganga

△

△

△

△

△

16

ThailandMae Wang

△

△

△

△

△

17

Uzbekistan

ChirchikOkhangaran

▽

△

△

△

△

18

Vietnam

Huong

△

△

▽

▲

▲

Table 3.8: Trend analysis of runoff using the Mann-Kendall test at AWCI 18 countries. The filled
triangle indicate statistical significant at 95% confidence level.
Country

Basin

1Bangladesh

2

Trend analysis of runoff
SpringSummerAutumnWinterYear

Meghna

▽

△

△

△

△

BhutanPunatsangchhu

△

▽

▽

△

▽

3

Cambodia

Sangker

▽

▽

▽

▽

▽

4

India

Seonath

▼

▽

△

▼

▽

5

Indonesia

Mamberamo

▼

▼

▼

▼

▼

6

Japan

Tone

△

▽

△

△

△

7

Korea

Chungju-dam

△

△

△

△

▲

8

Lao PDR

Sebangfai

△

△

▼

△

▽

9

Malaysia

Langat

△

▲

△

▲

▲

10

Mongolia

Selbe

▼

▽

▽

△

▽

11

Myanmar

Shwegylin

▲

▽

▽

△

△

12

Nepal

Bagmati

△

▲

▽

△

△

13

PakistanGilgit

▲

▲

▲

▲

▲

14

Philippines

Pampanga

△

△

▽

△

△

15

Sri Lanka

Kalu Ganga

△

△

△

△

△

16

ThailandMae Wang

△

△

△

▽

△

17

Uzbekistan

ChirchikOkhangaran

△

△

△

△

△

62

ARCP2011-05CMY –Bae -FINAL REPORT

No.

18

Vietnam

△

Huong

△

△

▲

▲

3.4 Analysis of future climate and hydrology
3.4.1 GCMs selection
The selection of appropriate GCM is a primary step for projection of future climate and hydrology. In
this study nine GCMs were compared for their future application in Asian region. The comparison
was made on several statistical measures i.e. the probabilistic uncertainty analysis, correlation
coefficient, and RMSE. The overall ranking of each GCM was computed based on the scores of all
three statistical tests. The target GCMs for comparison include MPIM: ECHAM5, CSIRO: MK3.0,
GFDL: CM2_1, CNRM: CM3, MRI: CGCM2_3_2, UKMO: HADCM3, IPSL: CM4, CCCMA: CGCM3_T47,
and CONS: ECHO-G. The results of statistical test are explained in the following sections.
Probabilistic uncertainty analysis
The results of probabilistic uncertainty test for selected nine GCMs are shown in Table 3.9. From
results it is evident that the best GCM was CGCM_T47 with highest total score of 131. It was followed
by CM4 and ECHO-G both with score of 109. The CGCM2_3_2 was ranked as 4th for probabilistic
uncertainty analysis test with score of 104. The other GCMs had the score ranging from 83 to 104.
Table 3.9: Score index of GCMs based on the results of probabilistic uncertainty test
No.
1
2
3

ARCP2011-05CMY-Bae-FINAL REPORT

4
5
6
7
8
9

Model
(agency: version)

Total score (SP,total)

Normalized score(SP,normalized)

Remarks

MPIM: ECHAM5CSIRO: MK3.0
GFDL: CM2_1
CNRM: CM3
MRI: CGCM2_3_2
UKMO: HADCM3
IPSL: CM4
CCCMA: CGCM3_T47
CONS: ECHO-G

94
83
89
103
104
93
109
131
109

0.23
0.00
0.13
0.42
0.44
0.21
0.54
1.00
0.54

5th
4th
2rd
1st
2rd

Correlation coefficient test
Table 3.10 shows the results of correlation coefficient test for nine GCMs. As can be seen from theresults, the best GCM was CGCM3_T47 with the score of 172 which was followed by CGCM2_3_2 andHADCM3 with the score of 157 and 156, respectively. It is noted that, CGCM3_T47 ranked best
among all GCMs for correlation coefficient test like the probabilistic uncertainty test. The GCMs
showing good result in this test can be assumed to provide the predicted data having high correlation
with the observed data.
63

Table 3.10: Score index of GCMs based on the results of correlation coefficient test
No.

Model (agency: version)

Total score(Sr,total)

Normalized score (Sr,normalized)

Remarks

1
2
3

MPIM: ECHAM5

152

0.59

5th

CSIRO: MK3.0
GFDL: CM2_1
CNRM: CM3
MRI: CGCM2_3_2
UKMO: HADCM3
IPSL: CM4
CCCMA: CGCM3_T47
CONS: ECHO-G

147
123
153
157
156
151
172
142

0.49
0.00
0.61
0.69
0.67
0.57
1.00
0.39

4th
2nd
3rd
1st
-

4
5
6
7
8
9

RMSE test
The results of RMSE test for nine GCMs are shown in Table 3.11. The results indicate that the
CGCM2_3_2 was best GCM with highest score of 245. Unlike probability uncertainty analysis andcorrelation coefficient test the CGCM3_T47 was ranked 4th for RMSE test. On the other hand
ECHAM5 and MK3.0 showed good performance for RMSE test with scores of 212 and 202
respectively. The RMSE test considers the quantity of each projected value. Thus, the GCM with good
performance in this test can be applied to most of the future events.

No.

Model (agency: version)

Total score(SRMSE,total)

Normalized score(SRMSE,normalized)

Remarks

1

MPIM: ECHAM5

212

0.58

2nd

2
3
4
5
6
7
8
9

CSIRO: MK3.0
GFDL: CM2_1
CNRM: CM3
MRI: CGCM2_3_2
UKMO: HADCM3
IPSL: CM4
CCCMA: CGCM3_T47
CONS: ECHO-G

202
188
167
245
166
189
198
198

0.46
0.28
0.01
1.00
0.00
0.29
0.41
0.41

3rd
1st
4th
5th

Quantifying the overall effectiveness of 9 GCMs
To obtain most appropriate GCMs we combined the scores of all three tests and computed overall
effectiveness score. Table 3.12 shows the result of 9 GCMs with their overall normalized scores. It isnoted that the range of overall normalized score for all GCMs was from 0 to 2. As can be seen fromthe table, CGCM3_T47 model showed the best results in all 3 tests with the score of 1.70, while the
GCM having lowest score was CM2_1 with score of 0.26. Based on overall effectiveness score the
GCMs CGCM3_T47, CGCM2_3_2 and CM4 with 1st, 2nd and 3rd ranking, respectively were selected for
future climate change and hydrology impact assessment over Asia.
64

ARCP2011-05CMY –Bae -FINAL REPORT

Table 3.11: Score index of GCMs based on the results of RMSE.

Table 3.12: Overall effectiveness of 9 GCMs.
No.

Model (agency: version)

Overall normalized score

Remarks

1
2
3
4
5
6
7
8
9

MPIM: ECHAM5CSIRO: MK3.0
GFDL: CM2_1
CNRM: CM3
MRI: CGCM2_3_2
UKMO: HADCM3
IPSL: CM4
CCCMA: CGCM3_T47
CONS: ECHO-G

0.82
0.47
0.26
0.73
1.28
0.55
0.97
1.70
0.94

2nd
3rd
1st
-

3.4.2 Future climate and hydrology analysis
GCMs uncertainty

ARCP2011-05CMY-Bae-FINAL REPORT

To evaluate uncertainty of selected GCMs for future scenarios the projection results of each GCM
were analyzed. Figure 3.18 shows the annual and seasonal change of temperature and precipitationin future periods (10 years mean) compared to the reference period (1977-2006). The simulations
projected annual temperature increase of about 3.6℃ in future. The CM4 showed high increase intemperature of 4.1℃ and CGCM3_T47 projected comparatively low increase of about 3.0℃. The
seasonal analysis showed temperature increase of 3.7℃, 3.6℃, 3.5℃ and 3.4℃ in winter, summer,
autumn and spring respectively by 2090s. To evaluate uncertainty of selected GCMs, the difference
between maximum and minimum projected values was used. The high uncertainty appeared insummer (1.4℃) and spring (1.3℃) season by 2090s. The uncertainty was comparatively low in winterseason with value of 0.8℃.
The GCMs simulations projected about 13.4% increase of annual mean precipitation by 2090s. On
seasonal scale, the precipitation was expected to increase by 13.0%, 12.6%, 18.8% and 14.3% in
spring, summer, autumn, and winter respectively. The projection results of summer precipitation
were similar to that of annual precipitation with very low uncertainty. On the other hand, there werelarge differences between spring and autumn projection results. The CGCM3_T47 showed decreasing
tendency of precipitation in spring season while other GCMs showed increasing trend. In autumn
season, CM4 projected quite high increase in precipitation as compared to other GCMs. The
uncertainty in spring and winter season was 15.0% and 14.5%, respectively.

65

3
2
1
0

20
10
0
-10
-20

A2_CGCM2_3_2
A2_CGCM3_T47
A2_CM4

Spring (Mar.-May)

1
0

Changes of Precipitation (%)

Period2

40
30

A2_CGCM2_3_2
A2_CGCM3_T47
A2_CM4

Spring (Mar.-May)

20
10
0
-10
-20
2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

PeriodPeriodA2_CGCM2_3_2
A2_CGCM3_T47
A2_CM4

Summer (Jun.-Aug.)

4
3
2
1
0

Changes of Precipitation (%)

2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

6

40
30

A2_CGCM2_3_2
A2_CGCM3_T47
A2_CM4

Summer (Jun.-Aug.)

20
10
0
-10
-20

2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

PeriodPeriod6
A2_CGCM2_3_2
A2_CGCM3_T47
A2_CM4

Autumn (Sep-Nov.)

4
3
2
1
0

40
30

A2_CGCM2_3_2
A2_CGCM3_T47
A2_CM4

Autumn (Sep-Nov.)

20
10
0
-10
-20

2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

PeriodPeriod6
5

Annual(Jan.-Dec.)

2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

35

A2_CGCM2_3_2
A2_CGCM3_T47
A2_CM4

Period4

5

30

2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

6
5

40

A2_CGCM2_3_2
A2_CGCM3_T47
A2_CM4Winter (Dec.-Feb.)

4
3
2
1
0

40
30

A2_CGCM2_3_2
A2_CGCM3_T47
A2_CM4Winter (Dec.-Feb.)

20
10
0
-10
-20

2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

2010s 2020s 2030s 2040s 2050s 2060s 2070s 2080s 2090s

PeriodPeriodFigure 3.18: Annual and seasonal temperature and precipitation in future periods (10 years mean)
66

ARCP2011-05CMY –Bae -FINAL REPORT

4

Changes of Precipitation (%)

Annual(Jan.-Dec.)

Changes of Precipitation (%)

5

A2_CGCM2_3_2
A2_CGCM3_T47
A2_CM4

Changes of Precipitation (%)

Changes of Temperature

Changes of Temperature
Changes of Temperature
Changes of Temperature
Changes of Temperature6

compared to reference period (1977-2006).
Annual average change
To investigate the features of future climate scenarios (0.5° x 0.5°) the spatial analysis of change inannual temperature, precipitation, and runoff relative to the reference period was conducted over
Asia region. Figure 3.19 shows the relative change of temperature, precipitation, and runoff at future
periods (2020s:2011-2040; 2050s:2041-70; 2080s:2071-2100). The projections showed consistentincrease of average temperature in future periods i.e. 2020s, 2050s and 2080s. In 2020s the averagetemperature over Asia was expected to rise by 0.71℃. The high increase in temperature(1.33~1.82℃) was projected over Russia, Mongolia, and Pakistan. In East Asia, the temperatures
were expected to increase by 0.64℃, 0.76℃, 0.53℃ and 0.91℃ in China, North Korea, South Korea
and Japan respectively by 2020s. In 2050s, average temperature over Asia will increase by 1.78℃,
with high increase of 2.5℃ in Mongolia. The average temperature over Asia was projected toincrease by 3.27℃ in 2080s. The expected increase in annual temperature may further intensify theregional bias in future.

ARCP2011-05CMY-Bae-FINAL REPORTThe projection results for annual precipitation showed 3.1% increase in 2020s compared to thereference period over Asia. However, the precipitation at west China, southeast China, Kazakhstan,
Tajikistan, Kyrgyzstan, east India, south India, Myanmar, Vietnam, and Thailand was expected todecrease about 4.0~15.0%. In South Korea, precipitation was projected to increase by 12.0% and17.0% in southern and central part, respectively. In 2050s, the decreasing rate was more prominent
over Thailand, Vietnam, and Southeast Asia. Over west China and northeast of India precipitation
change rate was expected to be -13.0%. On the other hand the precipitation was projected toincrease by 10.0~80.0% over west India and Pakistan in 2050s. In 2080s, the annual precipitation was
expected to increase by 11% over Asia except west China and Afghanistan.
Future scenarios projected increase in annual runoff over Asia region like precipitation. However,regional bias was expected to be intensified. In 2020s, the runoff over Asia region was expected toincrease by 5.9%. The decrease of 3.0~25.0% in annual runoff was projected over Russia, west China,
southeast China, south India, west India, Myanmar, Thailand, Cambodia, and Vietnam. In 2050s, therunoff rate over west China and northeast India was expected to be intensified with range 15.0%~61.0%. The simulations projected large increase in annual runoff over west India and Pakistanwith rate of 105.0~353.0%. In 2080s, runoff over most regions in Asia was appeared to increase.
However, the runoff at west China and Afghanistan was expected to decrease. Overall the change
rate of runoff was larger than the precipitation change.

67Figure 3.19: Relative change of temperature (top), precipitation (middle), and runoff (bottom) at
future at 2020s (left), 2050s (center), 2080s (right).

The projected change of seasonal temperature, precipitation, and runoff in 2080s relative to thereference period is portrayed in Figure 3.20. The seasonal analysis showed increase of 0.5℃
temperature in spring season over Asia region. The high increase in temperature (4.1~5.6℃) was
expected over Kazakhstan, north India, Pakistan, and Afghanistan. The spring precipitation and runoff
was projected to increase by 11.6% and 15.0%, respectively. The precipitation and runoff at west
China, central China, and India was expected to increase by 45.0~68.1% and 52.0~390.0%,
respectively. On the other hand, the precipitation and runoff at Afghanistan, Uzbekistan, Vietnam,
Thailand, and Japan was expected to decrease with rate of -5.0~-21.0% and -13.0~-64.0%,
respectively. In summer season the temperature was expected to increase by 3.6℃ over Asia, withhigh increase of 3.5~5.1℃ over west China, Pakistan, and Afghanistan. The average precipitation andrunoff over Asia during summer season was expected to increase by 9.2% and 14.1%, respectively.
The high increase in precipitation and runoff was projected over west China, central China, and India
with rate of 45.0~68.0% and 45.0~348.0%, respectively. On the other hand, the precipitation overAfghanistan and Uzbekistan was expected to decrease about -2.0~-46.0%. The decrease of runoff (5.0~-85.0%) was projected over west China, east Russia, Vietnam, and Thailand. The autumn
temperature was expected to increase about 3.5℃ over Asia. Particularly, the increase of 4.1~4.6℃
temperature was projected over east Russia, west China, and Pakistan. The average precipitation andrunoff during autumn season was appeared to increase about 10.0% and 8.6%, respectively. The highincrease in precipitation (45.0~68.0%) and runoff (15~191%) was projected over China, east
Mongolia, central Mongolia, Malaysia, India, and south Pakistan during autumn season. The
precipitation and runoff was appeared to decrease about -2.0~-15.0% and -3.0~-63.0% respectively
over Afghanistan, west China, east Russia, and Japan. The winter temperature was expected toincrease about 4.1℃ over Asia with high increase of 5.6℃ over southwest China and Tibetan Plateau.
The average precipitation and runoff over Asia was expected to increase about 14.0% and 11.2%,
respectively. Especially, the high increase (45.0~97.0%) of precipitation was estimated over north68

ARCP2011-05CMY –Bae -FINAL REPORT

Seasonal change

ARCP2011-05CMY-Bae-FINAL REPORT

China, Mongolia, east Kazakhstan, east Russia, and south Pakistan. The runoff was appeared toincrease by 60.0~368.0% over Pakistan, Uzbekistan, Turkmenistan, and Afghanistan. However, theincreasing rate of runoff during winter season over China, Mongolia, and Russia was low compared tothe precipitation. The decrease in runoff (-6.0~-65.0%) and precipitation (-4.0~-35.0%) was projectedduring winter season over China, South Korea, south Japan, Thailand, Myanmar, and Vietnam.

Figure 3.20: future change of spring (top), summer (second), autumn (third), and winter (bottom)
temperature (left), precipitation (center), and runoff (right) at 2080s.
Latitudinal Analysis
Generally, temperatures decrease with increasing latitudes. Several climate zones exist according tovarying latitudes. Therefore, it is important to analyze hydrological components at different latitudes.
Figure 3.21 shows the latitudinal temperature change at each season. The temperature wasprojected to increase consistently during future periods i.e. 2020s, 2050s and 2080s. The increasing69

rate was also observed to increase with increasing latitudes. The high change in temperature wasprojected at latitudes ranges 30°~35°N, 30°~50°N, 25°~55°N, and 25°~55°N for spring, summer,
autumn, and winter, respectively. Especially, in 2080s, the temperature increase of 4.35~4.91℃ was
expected in spring and winter season at 30°~35°N.
55

55
2020s
2050s
2080s

55
2020s
2050s
2080s

50

55
2020s
2050s
2080s

50

45

40

40

35

35

35

35

30

30

30

30

20

25
20

Latitude(o)4540Latitude(o)

45

40

25

25
20

25
20

15

15

15

15

10

10

10

10

5

5

5

5

0

0

0

0

-5

-5

-5

-5

-10

-10
0

1

2

3

4

5

6

-10
0

Degree Ceicius(oC)

1

2

3

4

5

6

2020s
2050s
2080s

50

45

Latitude(o)Latitude(o)

50

-10
0

Degree Ceicius(oC)

1

2

3

4

5

6

0

Degree Ceicius(oC)

1.2

2.4

3.6

4.8

6

7.2

Degree Ceicius(oC)Figure 3.21: Latitudinal temperature change in spring (left), summer (second), autumn (third),
and winter (right).
Figure 3.22 shows the latitudinal precipitation change rate for each season. In spring season, thedistinct increase and decrease in precipitation was projected for future periods at different latitudes.
The high increase rate of precipitation was expected at 40°~55°N, 25°~30°N, and 10°S~5°N. In
summer season, the change rate was low compared to spring. However, there was increasing
tendency at 25°~30°N and 10°S~5°N. In autumn season, the precipitation increase was low comparedto spring and summer. Also, there was no distinct latitudinal change rate. In winter season, thedifference of precipitation increase was appeared at high latitudes (40°~55°N) and low latitudes(0°~10°S). The winter precipitation during 2080s at high latitude region and low latitude region was
expected to increase by 37.0% and 29.0%, respectively. On the other hand, the precipitation at
5°~25°N was expected to decrease compared to the reference period. The difference of future three
periods was almost same.
55
2020s
2050s
2080s

55
2020s
2050s
2080s

50

55
2020s
2050s
2080s

50

50

45

45

40

40

40

40

35

35

35

35

30

30

30

30

25
20

25
20

Latitude(o)45Latitude(o)45Latitude(o)Latitude(o)

50

25
20

25
20

15

15

15

15

10

10

10

10

5

5

5

5

0

0

0

0

-5

-5

-5

-5

-10
-20

-10

0

10

20

Change(%)

30

40

-10
-15

0

15

30

45

60

75

-10
-20

-10

0

10

20

Change(%)

Change(%)

30

40

2020s
2050s
2080s

-10
-30

-15

0

15

30

45

60

Change(%)

Figure 3.22: Latitudinal precipitation change in spring (left), summer (second), autumn (third),
and winter (right).
70

ARCP2011-05CMY –Bae -FINAL REPORT

55

Figure 3.23 shows the latitudinal runoff change for future periods in spring, summer, autumn, andwinter seasons. During spring season, the high increase in runoff was projected at 45°~55°N,
25°~30°N, and 0°~10°S. Particularly, the runoff increase of about 43.0% was estimated at 25°~30°N
that was due to high increase in precipitation at same latitudes. In summer season, the features ofrunoff change were similar to summer precipitation. However, the increasing rate of runoff wasalmost two times compared to that of precipitation. The increasing rate of autumn runoff was lower
as compared to other seasons. In winter season, the runoff at high latitudes (40°~50°N) and low
latitudes (0°~10°S) was expected to highly increase. The runoff increase of about 34% and 41% wasprojected by 2080s at high and low latitudes respectively. On the other hand, the runoff at 5°~25°N
was expected to decrease up to -15%. The difference of change rate was not much at three future
periods.
55

55
2020s
2050s
2080s

55

55
2020s
2050s
2080s

50

2020s
2050s
2080s

50

45

40

40

35

35

35

35

30

30

30

30

20

25
20

Latitude(o)4540Latitude(o)

45

40

25

25
20

25
20

15

15

15

15

10

10

10

10

5

5

5

5

0

0

0

0

-5

-5

-5

-5

-10
-30

-15

0

15

30

45

60

Change(%)

-10
-30

-15

0

15

30

45

60

Change(%)

-10
-30

-15

0

15

30

Change(%)

45

2020s
2050s
2080s

50

45

Latitude(o)Latitude(o)

50

60

-10
-30

-15

0

15

30

45

60

Change(%)

Figure 3.23: Latitudinal runoff change in spring (left), summer (second), autumn (third), andwinter (right).

ARCP2011-05CMY-Bae-FINAL REPORT

Analysis of water balance
The evaluation of hydrological components was conducted over AWCI 18 basins. Figure 3.24 shows
the change of water balance for precipitation, runoff, evapotranspiration, and soil moisture at eachselected basin. All variables indicated high change rate in future compared to reference period.
Several distinct features were observed at each basin. In Meghna basin, Bangladesh, theprecipitation, runoff and soil moisture in 2020s were expected to decrease, but precipitation andrunoff appeared to increase by 28.0~50.0% during April and May, and soil moisture by 28.0% during
May in 2050s and 2080s. The Punatsangchhu basin, Bhutan, showed similar features to Meghnabasins with increase of May precipitation and runoff in 2050s and 2080s. In Sangker basin, Cambodia,
there was no significant increasing tendency. However, the evapotranspiration in March was
expected to decrease upto -68%. In Seonath basin, India, both precipitation and runoff was expected
to increase in May, June, and July. However, evapotranspiration in January, February, and Marchwas expected to decrease upto -50% at all future periods. In Mamberamo basin, Indonesia, thewater balance at all future periods was expected to increase, but there was no distinct seasonalchange. In Tone basin, Japan, there was no clear precipitation change except small increase inAugust. However, the runoff in April and May was expected to decrease upto -30.0%. These results
71a) Meghnab) Punatsangchhuc) Sangkerd) Seonath

72

ARCP2011-05CMY –Bae -FINAL REPORT

were influenced by impact of evapotranspiration according to increasing temperature. In Upper
Chungju-dam basin, South Korea, water balance at future periods was expected to increase.
Especially, evapotranspiration in warm season was appeared to increase due to increase of
temperature by global warming. In Sebangfai basin, Laos, the water balance was expected toincrease upto 35% in July, August, September, and October, however during other months thedecreasing tendency was expected at all future periods. Regarding the Langat basin in Malaysia, thewater balance had two peaks in April and November. On the other hand, the decreasing tendency
was projected in January, February, March, and December. In Selbe basin, Mongolia, theprecipitation and evapotranspiration was expected to increase in all months, however the increasein runoff was expected in between July and October. For Shwegyin basin, Myanmar, theprecipitation and runoff was expected to increase during August and September in 2050s and 2080s.
In Bagmati basin, Nepal, the precipitation and runoff was projected to increase only in 2080s. In
Gilgit basin Pakistan, the precipitation, runoff, and soil moisture had almost same features. However,
the evapotranspiration appeared to change dynamically with increasing tendency in April, May, andOctober, and decrease in other months. In case of Pampanga basin, Philippines, the small increase
was expected in water balance for all months except May and June. For Kalu Ganga basin, Sri Lanka,
the water balance had small increasing tendency at future periods around the year except for themonths of January to April. In Mae Wang basin, Thailand, the decreasing trend was projected for all
months except August, September, and October. In Chirchik-Okhangaran basin, Uzbekistan, thewater balance was expected to increase from May to September, and that was expected to increaseduring other months. The Huong basin, Vietnam, had almost similar features to Mae Wang basin.
Overall, the water balance was expected to increase over Asian region with increasing future periods.

ARCP2011-05CMY-Bae-FINAL REPORTe) Mamberamof) Toneg) Chungju-damh) Sebangfaii) Langatj) Selbek) Shwegylinl) Bagmati

73m) Gilgitn) Pampangao) Kalu Gangap) Mae Wangr) Chirchik Okhangarans) Huong

Table 3.13 shows the change rate of seasonal and annual precipitation at 2020s and 2080s over
AWCI 18 basins. In 2020s, spring was most expected to decrease until -7.1%, and winter also wasappeared to decrease until -4.1%. The annual precipitation was also expected to decrease about 1.2%. In 2080s, the precipitation in spring, summer, and autumn was expected to increase except
winter season (-5.6%). Especially, the precipitation in autumn was appeared to increase until 6.5%.
As a result, the precipitation over 18 basins has increasing tendency toward the future period.
Table 3.13: Change rate of seasonal and annual precipitation over AWCI 18 basins.
Demonstration
basins1

MeghnaSpring2020s 2080s

Summer2020s 2080s

Autumn2020s 2080s

Winter2020s 2080s

Annual
2020s 2080s

-6.2

-8.1

-1.9

-12.6

-6.4

24.5

2.1

74

-2.2

-23.9

6.6

ARCP2011-05CMY –Bae -FINAL REPORT

Figure 3.24: Change of water balance for precipitation, runoff, evapotranspiration and soil
moisture over AWCI 18 basins.

2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

Punatsangchhu
SankerSeonathMamberamo
Tone
ChungJu-Dam
Sebanfai
LangatSelbe
Shwegyin
BagmatiGilgitPampanga
Kalu Ganga
Mae Wang
ChirchikOkhangaran
Huong
Max.
Min
Ave.

-8.1
-15.6
-13.9
3.9
-1.1
7.0
-9.9
0.9
-1.3
-20.7
-17.6
3.9
-14.8
-2.5
-14.6

13.6
-6.9
30.2
20.8
-1.1
14.5
-9.2
4.1
10.0
-13.4
5.9
-15.1
-11.7
3.7
-5.1

-6.0
0.5
17.6
8.3
3.1
19.6
-3.1
-7.7
0.8
-1.4
1.6
-15.3
1.4
3.7
-2.3

6.2
1.2
19.2
22.3
16.4
25.7
10.4
1.8
16.1
0.4
12.0
-38.0
2.7
9.7
1.6

-8.3
-14.5
2.9
8.5
3.0
-0.2
-15.1
4.8
7.9
-9.9
-2.4
-14.3
4.3
4.5
-10.4

-4.9
4.5
0.4
14.1
-1.9
7.7
15.3
20.5
25.1
13.6
-2.5
-14.2
18.0
15.7
13.9

-16.4
-14.9
-26.3
7.5
-5.7
1.4
-2.7
-5.3
6.7
-3.5
-16.7
7.4
5.4
-12.6
2.5

-21.4
-25.3
-5.6
25.5
-8.3
-0.8
-21.8
-9.4
40.3
-32.7
-13.3
13.1
6.3
2.2
-26.7

-7.1
-8.7
11.8
6.8
1.0
11.6
-8.2
-0.9
1.9
-5.4
-1.9
-3.0
0.6
0.1
-7.6

4.8
0.4
14.9
21.1
4.1
17.7
7.2
5.7
17.6
1.6
7.9
-16.4
6.2
9.3
3.6

0.4

-7.6

-7.6

-33.3

1.7

-3.2

8.5

14.7

2.7

-1.5

-17.0
7.0
-20.7
-7.1

-16.2
30.2
-16.2
2.3

-2.5
19.6
-15.3
0.1

4.5
25.7
-38.0
4.5

-11.9
8.5
-15.1
-2.8

2.1
25.1
-14.2
6.8

3.4
8.5
-26.3
-4.1

-13.2
40.3
-32.7
-5.6

-8.3
11.8
-8.7
-1.2

-1.3
21.1
-16.4
6.1

The seasonal and annual change in runoff was also evaluated over AWCI 18 basins as shown in Table
3.14. The change rate of runoff was almost similar to that of precipitation. The average spring runoff
over 18 basins was expected to decrease upto -9.2%. Specifically, -22.4% runoff change wasprojected at Bagmati basin during spring season in 2020s. The annual mean runoff in 2020s wasappeared to decrease about -1.8%. However, the annual mean runoff in 2080s was expected toincrease upto 6.5% within increasing tendency in all four seasons.
Table 3.14: Change rate of seasonal and annual runoff over AWCI 18 basins.

ARCP2011-05CMY-Bae-FINAL REPORT

Demonstration
basins1
Meghna2 Punatsangchhu
3
Sanker4
Seonath5
Mamberamo
6Tone
7 ChungJu-Dam
8
Sebanfai
9Langat10
Selbe
11
Shwegyin
12
Bagmati
13
Gilgit14
Pampanga

Spring2020s 2080s

Summer2020s 2080s

Autumn2020s 2080s

Winter2020s 2080s

Annual
2020s 2080s

-11.4
-18.3
-18.1
-4.3
6.9
-6.1
6.6
-15.2
-2.4
0.0
-31.6
-22.4
5.6
-13.8

-11.7
-9.8
-7.6
30.3
10.2
2.5
25.0
-6.4
-10.1
-4.6
-4.9
1.8
-8.3
-1.1

-3.4
-11.2
-15.6
2.5
11.2
4.3
4.7
-16.7
4.8
5.0
-8.9
-3.0
-14.0
4.4

-8.2
-10.0
-10.0
-8.4
9.5
-2.9
8.1
-3.7
-1.1
0.9
-7.4
-6.9
-3.2
1.9

-9.7
-11.0
-12.8
18.8
9.2
0.5
15.4
-11.3
-1.2
-0.1
-7.1
-0.8
-3.5
0.6

35.0
31.1
-8.5
3.9
29.5
-18.4
12.5
-20.9
0.0
8.3
-14.2
22.8
-17.3
-12.3

0.7
4.3
-2.9
31.5
28.8
12.3
31.3
9.1
-0.4
19.5
-4.3
15.3
-30.2
-0.6

75

-5.4
-9.3
3.5
0.8
17.9
-0.9
9.8
17.9
24.9
17.9
15.5
-3.7
-27.4
17.5

-13.8
-12.5
-7.9
-3.9
32.1
17.2
12.4
-15.9
-5.4
3.1
-11.5
-2.5
8.6
6.3

6.1
3.8
-0.6
19.5
28.0
1.2
21.2
8.8
6.2
16.7
0.4
10.8
-19.9
6.5

15
16
17
18

Kalu Ganga
Mae Wang
ChirchikOkhangaran
Huong
Max.
Min
Ave.

-5.3
-18.6

1.6
-1.2

3.6
-12.1

10.8
-7.2

5.2
-11.3

17.9
17.1

-10.7
-3.4

4.0
-11.4

0.0
-11.7

10.3
4.3

1.9

-14.1

-5.3

-26.9

-1.4

5.0

20.5

64.3

3.1

-3.9

-18.8
6.9
-31.6
-9.2

-24.4
35.0
-24.4
0.7

-7.0
30.3
-12.1
-0.9

1.0
31.5
-30.2
5.1

-14.4
11.2
-16.7
-3.2

1.8
24.9
-27.4
6.7

0.9
20.5
-10.7
-1.9

-15.4
64.3
-15.9
2.7

-10.9
18.8
-12.8
-1.8

-2.7
28.0
-19.9
6.5

Table 3.15 shows the change rate of seasonal and annual evapotranspiration over AWCI 18 basins.
The features of evapotranspiration were slightly different compared to that of precipitation andrunoff because evapotranspiration was influenced by temperature increase. The projected change
rate of evapotranspiration was low as compared to that of runoff. In 2020s, the high decrease ofevapotranspiration (-3.4%) was estimated in spring season. Annual evapotranspiration averaged
over AWCI 18 basins was also expected to decrease about -0.2% in 2020s. However, theevapotranspiration in 2080s was expected to increase with average rate of 5.7%. The autumn
evapotranspiration was projected to increase upto 8.0% in 2080s

SpringDemonstration
basins
2020s 2080s
1
Meghna0.3
8.3
2 Punatsangchhu -1.6
3.0
3
Sanker
-10.5-5.7
4
Seonath-8.9
2.1
5
Mamberamo
2.4
11.5
6
Tone
1.1
7.0
7
ChungJu-Dam
2.5
8.6
8
Sebanfai
-5.1
-3.9
9
Langat
0.2
4.8
10
Selbe
1.3
12.4
11
Shwegyin
-9.3
-8.5
12
Bagmati
-13.0
0.3
13
Gilgit3.7
6.7
14
Pampanga
-9.3
-6.4
15
Kalu Ganga
-0.8
2.8
16
Mae Wang-6.6-3.3
Chirchik17
3.7
8.8
Okhangaran
18
Huong
-10.6
-9.1
Max.
3.7
12.4
Min
-13.0
-9.1
Ave.
-3.4
2.2

Summer2020s 2080s
1.3
8.4
-0.8
9.1
1.0
3.4
3.7
10.1
4.6
13.5
3.0
13.6
7.2
15.5
0.2
8.8
-2.7
4.1
3.5
19.2
2.0
7.7
-0.8
6.2
-7.5
-25.8
2.1
5.6
2.7
8.9
-0.9
2.2

Autumn2020s 2080s
1.9
8.1
-2.3
5.9
-6.3
5.5
2.5
5.6
4.8
10.3
1.3
8.5
1.1
9.2
-5.3
9.1
2.4
10.8
3.8
16.5
-3.1
7.5
-0.8
2.5
-7.1
-6.8
2.8
12.9
2.5
9.7
-3.9
9.5

Winter2020s 2080s
-7.3
-9.9
-10.8
-9.2
-5.8
-11.3
-8.4
2.9
3.9
14.1
-1.1
-0.2
-2.3
-5.2
-1.3
-11.2
-2.9
-2.2
5.8
19.9
1.1
-5.6
-11.1
-3.1
-0.1
-13.8
5.1
7.1
-5.4
3.9
1.8
-1.4

Annual
2020s 2080s
0.8
7.7
-1.7
6.2
-4.1
1.4
1.3
7.8
3.8
12.4
1.8
9.5
3.9
10.5
-2.5
4.4
-0.5
5.0
3.3
17.9
-1.2
4.5
-3.2
4.2
-2.4
-10.6
0.6
5.8
0.5
6.8
-3.0
2.8

-5.0

-27.8

0.3

12.7

11.4

67.7

1.6

4.3

0.1
7.2
-7.5
0.8

5.6
19.2
-27.8
4.9

-1.8
4.8
-7.1
-0.4

7.4
16.5
-6.8
8.0

3.8
11.4
-11.1
-1.3

-1.9
67.7
-13.8
2.3

-1.9
3.9
-4.1
-0.2

2.6
17.9
-10.6
5.7

The features of change in soil moisture over AWCI 18 basins are presented in Table 3.16. The
features of soil moisture were different compared to that of other hydrological components. The
76

ARCP2011-05CMY –Bae -FINAL REPORT

Table 3.15: Change rate of seasonal and annual evapotranspiration over AWCI 18 countries.

seasonal and annual soil moisture was expected to decrease for all future periods. The annual meansoil moisture averaged over 18 basins was projected to decreased upto -0.4% and -0.3% in 2020sand 2080s, respectively. The high decrease of upto -0.8% was projected during spring season in
2080s. The highest annual decrease in soil moisture was projected at Gilgit basin with change rate of
upto -1.3% and -3.0 in 2020s and 2080s, respectively.
Table 3.16: Change rate of seasonal and annual soil moisture over AWCI 18 countries.
Demonstration
basins1
Meghna2 Punatsangchhu
3
Sanker4
Seonath
5 Mamberamo
6Tone
7 ChungJu-Dam
8
Sebanfai
9Langat10
Selbe
11
Shwegyin
12
Bagmati
13
Gilgit14
Pampanga
15
Kalu Ganga
16
Mae Wang
Chirchik17
Okhangaran
18
Huong
Max.
Min
Ave.

Spring2020s 2080s

Summer2020s 2080s

Autumn2020s 2080s

Winter2020s 2080s

Annual
2020s 2080s

-0.8
-1.2
-1.8
-1.4
0.4
-0.4
0.1
-1.1
-0.3
0.0
-1.8
-0.7
-0.5
-0.2
-0.6
-1.4

-0.1
-2.6
-1.5
-0.1
1.6
-0.8
0.2
-2.1
-0.1
0.3
-1.6
-0.9
-1.8
-0.2
-0.4
-1.4

-0.7
-1.4
-1.0
0.7
0.5
0.2
1.3
-0.6
-0.6
-0.3
-1.0
-0.4
-1.4
-0.2
0.2
-1.5

-0.1
-1.6
-0.6
2.1
1.4
0.5
1.5
0.2
-0.1
0.1
-1.1
-0.1
-4.0
-0.3
0.7
-1.6

-0.2
-1.0
-1.1
0.2
0.6
0.3
0.2
-1.1
0.2
0.1
-1.0
-0.1
-1.9
0.3
0.4
-0.8

-0.3
-1.7
0.1
-0.1
0.9
-0.2
0.3
0.7
1.4
0.2
0.7
-0.2
-4.3
1.1
1.4
0.7

-0.3
-0.9
-0.5
-0.6
0.6
-0.1
0.3
-0.3
-0.3
0.0
-0.4
-0.3
-1.3
0.1
-0.8
-0.3

-0.6
-1.8
-0.5
-0.5
1.7
1.8
0.4
-0.8
-0.6
0.0
-0.8
-0.4
-1.8
0.3
0.0
-0.8

-0.5
-1.1
-1.1
-0.2
0.5
0.0
0.5
-0.8
-0.2
-0.1
-1.0
-0.4
-1.3
0.0
-0.2
-1.0

-0.3
-1.9
-0.6
0.4
1.4
0.3
0.6
-0.5
0.2
0.2
-0.7
-0.4
-3.0
0.2
0.5
-0.7

0.3

-1.0

0.1

-1.2

0.1

-1.0

0.5

0.3

0.3

-0.7

-1.1
0.4
-1.8
-0.7

-1.7
1.6
-2.6
-0.8

-0.6
1.3
-1.5
-0.4

-0.3
2.1
-4.0
-0.2

-1.2
0.6
-1.9
-0.3

0.1
1.4
-4.3
0.0

0.1
0.6
-1.3
-0.2

-0.9
1.8
-1.8
-0.3

-0.7
0.5
-1.3
-0.4

-0.7
1.4
-3.0
-0.3

ARCP2011-05CMY-Bae-FINAL REPORT

4.0 Conclusions
In this study, the analysis of historical climate and hydrology trend with future climate change impact
was conducted over Asia region covering 18 designated basins of AWCI. The Mann-Kendall test was
employed for historical trend analysis using observed gridded climate data from APHRODITE andhydrology data from VIC model at horizontal grid resolution of 0.5° x 0.5° for period of 1977 to 2006.
The trend analysis showed increase in temperature over Asia region except some parts of Southeast
Asia. The precipitation was observed to decrease about 100~200mm during past 30 years over Asian
region. Similarly, the decreasing trend was observed for runoff. The runoff was observed to decrease
approximately 70∼80mm over Asia region. The seasonal trend analysis showed increasing trend of
temperature for all the seasons. The seasonal precipitation showed decreasing trend over Asia withincrease in some parts. The seasonal runoff was also appeared to decrease like precipitation. The
spatial features of runoff were similar to that of precipitation. The hydrological components over77

AWCI 18 basins were calculated using VIC model which revealed various distinct regional features at
each basin.
To analyze future climate change impact, the optimum GCMs were selected using probabilistic
uncertainty analysis, correlation coefficient, and RMSE methods. Selected GCMs include CGCM3_T47,
CGCM2_3_2, and CM4. The uncertainty of selected GCMs was also evaluated. The results showed
increased uncertainty of GCMs toward future periods. For future climate and hydrology impact, theensemble mean value of selected GCMs was used. The temperature was expected to increase in all
regions. The precipitation was projected to increase during all seasons in most regions except central
Asia. The future change in runoff was expected similar with that of precipitation. The latitudinalfeatures showed high increase in temperature change with increasing latitudes. The latitudinal
precipitation change was estimated high at high latitudes and low at low latitudes. The change rateof latitudinal runoff was high at mid latitude regions. The water balance for precipitation, runoff,
evapotranspiration and soil moisture was evaluated over 18 selected basins. The results showed
increasing tendency of water balance for the future periods.
The detailed analysis of historical climate and hydrology trend with future climate change impact
established better understanding of changing climate and hydrology over Asia region The findings ofthis study will be useful to establish future climate adaptation measures over Asia.

5.0 Future Directions
In this study, the historical climate and hydrology trend with future climate change impact over Asia
was evaluated. The observed gridded data of 0.5° x 0.5° resolution, A2 scenario, and VIC model wereused. To conduct detailed analysis of climate and hydrology features, high-resolution observation
data will be used. The multi GCM, hydrology model, and climate scenario will be used to evaluate
future climate change impact details. Furthermore, in this study we used IPCC AR4 output. We will
be able to obtain more useful results using Representative Concentration Pathway of IPCC AR5.

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Zhao, R.J. (1980). The Xinanjiang model, Hydrological Forecasting Proceedings Oxford Symposium,
IASH 129, 351-356.

Appendix
Conferences/Symposia/Workshop
1. The AWCI training course for the Climate Change Assessment and Adaptation Study- Date: 11th - 12th March 2011
- Venue: University of Tokyo Hongo Campus, Tokyo, Japan- Meeting Objective
The Training Course was designated for the leaders of the AWCI Climate Change Assessment and
Adaptation (CCAA) Study to get familiar with necessary methods for correcting bias of Global Climate
Model (GCM) projection outputs, downscaling the GCM output, and use of hydrological models forassessment of possible climate change impact on water resources on a basin scale. The two-day
intensive course was organized by collaborative efforts of the University of Tokyo, Japan, Sejong
University, Korea, and ICHARM, Japan.
- Main Agenda & Photo

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1) Welcome and Introduction2) Rainfall bias correction and downscaling methods by the University of Tokyo group
3) Hydrological model WEB-DHM (Water and Energy Budget Distributed Hydrological Model) use
for the CCAA purposes by Dr. Lei Wang and the University of Tokyo team4) Flood modeling system IFAS (Integrated Flood Analysis System) applications by Dr. Fukami andthe ICHARM team5) Multi-model Hydrological modeling in use for Climate Change assessment by Prof. Bae – Sejong
University

2. The AWCI training course for the Climate Change Assessment and Adaptation Study83

- Date: 6th - 8th October 2011
- Venue: COEX, World Trade Center, Seoul, South Korea- Meeting Objective
Aside from the usual review of the AWCI and its working group activities, the ICG meeting focusedon preparation for planning of the next step of the Initiative. With its strong collaborative frameworkand data integration principles developed, AWCI is now eligible as one of the pillars of the newly
proposed GEOSS Water Cycle Integrator (WCI), targeting enhanced coordination in the water cycle
arena on the global level.
In addition to the ICG business, the meeting reviewed progress and results of the CCAA study andthe outcomes were considered for the planning of the later steps of AWCI. Accordingly, all the CCAA
leaders were invited to attend the meeting to share their view on pursuing the study.
- Main Agenda & Photo

3. The 9th AWCI International Coordination Group (ICG) Meeting and the Workshop on Climate
Change Adaptation organized by APWF
- Date: 29th September – 2nd October 201284

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1) Opening Session2) AWCI Working Group Activity Review Session3) Country Activity Review and Possible Contributions to the AWCI Next Stage Session4) Capability of Observation, Data Integration and Prediction Session5) Breakout discussion session 1 – GEOSS WCI: needs and capabilities
6) Breakout discussion session 2 – GEOSS WCI: practical implementation ideas
7) Breakout session summary8) Implementation planning for a regional coordination project targeting Climate Change
Adaptation
9) Summary and Closing session

- Venue: University of Tokyo Hongo Campus, Tokyo, Japan- Meeting Objective
To synthesize the provided country inputs into a complex implementation plan that will be in linewith the GEOSS Water Cycle Integrator (WCI) goals.
To kick-off two AWCI projects funded under the Asia Pacific Network for Global Change Research(APN) programmes: (i) Impact of Climate Change on Glacier Melting and Water Cycle Variability in
Asian River Basins (led by Dr. G. Rasul, Pakistan Meteorological Department, PMD) and (ii)
GEOSS/Asian Water Cycle Initiative/Water Cycle Integrator (led by Dr. O. Ochiai, Japan Aerospace
Exploration Agency, JAXA).
To contribute to the Asia Pacific Water Forum (APWF) Workshop on “Meta-Guidelines” for Climate
Change Adaptation held subsequently after the AWCI ICG meeting on 1 - 2 October.
- Main Agenda & Photo

ARCP2011-05CMY-Bae-FINAL REPORT

1) Opening Session2) AWCI Working Group Activity Review Session3) APN activities on Climate Change Adaptation
4) Country Reports on “Climate Change Adaptation and Water Nexus”
5) Inputs form International Cooperative Activities
6) Breakout discussion for Implementation Planning

List of Young Scientists
1. Le Vinh Thanh,- Thesis title: Detecting the future climate of Asian countries using information from IPCC AR4 GCMs
- Graduation date: Febuary 2012 (Mater degree)- APN project's Role: selection of GCMs for climate change impact assessment on water resources inAsia- e-mail: levinhthanh.lvt@gmail.com

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2. Shin Sang Hoon
- Thesis title : Future Projections of Climate Shifts in the Asia Regions Using A2 Scenario- Graduation date : August 2012 (Mater degree)- APN project's Role : Collection and analysis of the climate and hydrologic observation data
- e-mail: hjhssh@naver.com