FINAL REPORT for APN PROJECT
Project Reference Number: ARCP2011-02CMY“River Management System
Development in Asia Based on
Data Integration and Analysis
System (DIAS) under the GEOSS”

The following collaborators worked on this project and contributed to the report:
Toshio Koike, University of Tokyo, Japan, tkoike@hydra.t.u-tokyo.ac.jp
Mohammad Ashfakul Islam, Ministry of Defense, Bangladesh, ashfak2258@yahoo.com
Karma Chhophel, Ministry of Economic Affairs, Bhutan, hmsd@druknet.bt
So Im Monichoth, Department Hydrology and River Works, Cambodia, simchoth@yahoo.com
Surinder Kaur, India Meteorological Department, India, skaurg@yahoo.com
Muhammad Syahril Badri Kusuma, University of Tokyo, Japan, msbadrik@yahoo.com
Deg-Hyo Bae, Sejong University, Korea, dhbae@sejong.ac.kr
Singthong Pathoummady, Ministry of Natural Resource and Environment, Lao PDR, p.singthong@yahoo.com
Mohd Zaki Mat Amin, NAHRIM, Malaysia, zaki@nahrim.gov.my
Gombo Davaa, Institute of Meteorology, Hydrology and Environment, Mongolia, watersect@yahoo.com
Hrin Nei Thiam, Dept. Meteorology and Hydrology, Myanmar, dg.dmh@mptmail.net.mm
Shiv Kumar Sharma, Dept. Water Induced Disaster Prevention, Nepal, shiv1301@gmail.com
Bashir Ahmad, Water Resources Research Institute, Pakistan, dr.bashir70@gmail.com
Flaviana Hilario, PAGASA, Philippines, fhilarioph@yahoo.com
S.B. Weerakoon, University of Peradeniya, Sri Lanka, sbweera@pdn.ac.lk
Thada Sukhapunaphan, Hydrology and Water Management Center, Thailand, thada999@yahoo.com
Irina Dergacheva, NIGMI, Uzbekistan, dergacheva_iv@mail.ru
Dang Ngoc Tinh, National Centre for Hydro-Meteorological Forecasting, Vietnam, tinhdangngoc@fpt.vn

“River Management System Development in Asia Based on Data Integration and Analysis
System (DIAS) under the GEOSS”

Project Reference Number: ARCP2011-02CMY-Koike
Final Report submitted to APN

Report prepared by Petra Koudelova, University of Tokyo, petra@hydra.t.u-tokyo.ac.jp©Asia-Pacific Network for Global Change Research

1PAGE LEFT INTENTIONALLY BLANKOVERVIEW OF PROJECT WORK AND OUTCOMES
Non-technical summary
This project was launched under the GEOSS/Asian Water Cycle Initiative (AWCI) framework todevelop an advanced river management system in AWCI member countries by exploiting the Data
Integration and Analysis System (DIAS) data and data integration capabilities. The system is based on
integration of data from earth observation satellites and in-situ networks with other types of data,
including numerical weather prediction model outputs, climate model outputs, geographicalinformation, and socio-economic data. Each country had nominated one demonstration basin forwhich in situ data were archived at DIAS and where a demonstration project was launched togenerate information for making sound water resources management decisions while taking global
climate change into account. The project included two meetings of AWCI International Coordination
Group (ICG), which comprises representatives of water-related ministries and agencies of theparticipating countries, and one training course focused on techniques for climate change
assessment. The meetings were organized with support of the two collaborating projects ARCP201105CMY-Bae and CBA2011-02CMY-Kaihotsu and provided good opportunities to present thecapabilities of the targeted system to decision makers by showing the results of demonstration
projects, which were carried out in cooperation between each country team and the experts of the
AWCI collaborating organizations.

Objectives
The main objectives of the project were:
1. Implementation of a demonstration project in each AWCI basin that would include a distributedhydrological model by integrated in-situ and satellite data and model outputs, while considering
regional and local specifics (e.g. snow and glaciers).
2. Assessment of climate change impacts on water resources in the demonstration basins using thedeveloped basin models and development of optimization schemes for IWRM practices.
3. Presenting and communicating results of demonstration projects to policy and decision makers
in order to promote the use of developed system in operational applications.

Amount received and number years supported
The Grant awarded to this project was:
US$ 45,000 for Year 1: 2010/2011
US$ 45,000 for Year 2: 2011/2012

Activity undertaken
Meeting events. The project included three AWCI ICG Meetings: (i) the 7th AWCI ICG Meeting inTokyo, October 2010 that discussed the implementation strategy of the project and led to awhitepaper on the AWCI Climate Change Assessment and Adaptation (CCAA) study, (ii) the 8th AWCIICG Meeting in Seoul, October 2011 that reviewed the project progress and initiated discussion on
moving to the AWCI next phase focused on operational applications and further data integration;and (iii) the AWCI session at the 5th GEOSS AP Symposium in Tokyo, April 2012, which introduced
draft implementation plan for the AWCI next phase based on each country input. In March 2011, a
training course on downscaling techniques, hydrological modeling, and model output precipitation
data bias correction method was held to kick off AWCI CCAA study.
Data collection and integration. Collection of in-situ data from the AWCI demonstration basins andarchiving them in the DIAS database had continued and was completed. In addition to the basic
dataset as agreed at the initial stage of AWCI, a long-term precipitation and temperature data fromselected stations at the demonstration basins were collected for the CCAA study.

1Hydrological model development. In AWCI demonstration basins hydrological models weredeveloped using the Water and Energy Balance Distributed Hydrological Model (WEB-DHM; Wang etal., 2008). These models were used in the CCAA study and are basis for the demonstration projects.
Tools and techniques development. The AWCI countries represent a wide variety of geographical,
climatic and hydrological conditions and thus various tools and methods are required to implement
the targeted system. A hydrological model for cold regions was developed by incorporating a multilayer energy-balance based snowmelt scheme into WEB-DHM. Also a method was developed for
estimation snow depth spatial distribution in mountain areas using a radiative transfer model andmicrowave radiometer observations (AMSR-E satellite data). Moreover, an improved statisticalmethod for GCM precipitation output bias correction was developed that cover extreme rainfall,normal rainfall and frequency of dry days. Furthermore, a dam operation optimization scheme(Saavedra et al., 2010) was incorporated into WEB-DHM and applied at some basins.
Climate Change Impact Assessment Study. The said whitepaper on the GEOSS/AWCI Climate Change
Impact Assessment and Adaptation (CCAA) Study (see the Appendix) was developed in cooperation
with Prof. Deg-Hyo Bae of the Sejong University, Korea, leader of the Project ARCP2011-05CMY-Bae.
The necessary data was collected and WEB-DHM models for the basins developed prior to the March2011 training course. The techniques explained at the training course were then applied to assess
impact of climate change on water cycle in the AWCI basins.
Preparation for AWCI Phase 2 Plan. The Seoul meeting in October 2011 initiated preparation fordrafting the AWCI Phase 2 implementation plan. A template for country inputs had been generated
and contributions from all AWCI countries collected by March 2012. These proposals were reviewed
at the 5th GEOSS AP Symposium, the AWCI parallel session and classified into three categories: (i)Framework based category - for collaboration between country and agencies (ii) Project based
category (iii) Regional collaboration category or topic based collaboration category. Further
discussions took place at the 9th AWCI ICG Meeting in Tokyo, 29 – 30 September 2012.

Results

Hydrological models at AWCI demonstration basins have been developed using WEB-DHM or othermodels as selected by national teams. A hydrological model for snow and glacier region (WEB-DHMS) has been developed and used in cold region basins. An integrated modeling system for improved
multi-objective reservoir operation was also developed. Demonstration projects have been carriedout using these models and the results are discussed in the technical part of this report.
A whitepaper on AWCI CCAA study has been produced that has resulted from AWCI ICG discussions
at previous meetings. Techniques for bias correction of climate model precipitation projections havebeen developed, which were necessary for the proposed CCAA study. At the focused training course
in March 2011, the AWCI ICG members and leaders of CCAA study in each country acquainted withthese tools and methodologies for assessment of possible impacts of climate change on water
resources. Consequently, the CCAA study has been initiated in each AWCI basin using the mentioned
tools and techniques. Results from the accomplished studies have been presented at the AWCI2

ARCP2011-02CMY-Koike - FINAL REPORT

In-situ data from the basins have been collected as planned including both, the AWCI Phase 1
dataset covering multiple stations observations in each of the AWCI basins over the period 2003 –2004 or longer, and the long-term (1981 – 2000) precipitation and temperature dataset of selected
stations in each basin requested for the CCAA study. The data have been archived at DIAS and while
the basic Phase 1 dataset is fully open and accessible through the DIAS portal(http://www.editoria.u-tokyo.ac.jp/projects/dias/tools.php?locale=en), sharing policy of the longterm CCAA dataset is still under discussion.

meetings and international conferences and are discussed in the technical part of this report.
A plan for the next phase of AWCI has been outlined that builds up on further data integration
functions of so called “Water Cycle Integrator” (provided in Appendix below) and aims at extended
cooperation with operational sector. The plan is based on individual inputs of AWCI countries thatwere reviewed at the 5th GEOSS AP Symposium in Tokyo, April 2012.

Relevance to the APN Goals, Science Agenda and to Policy Processes
The Project focused on specific issues related to water resources management and impacts ofclimate change on water resources as a way of addressing capabilities for sustainable development.
The developed system is intended for an operational sector as a tool for transferring scientificknowledge to information required by policy and decision-making sectors. Its development was
implemented through collaboration between research community and the members of AWCI ICG,
which consists of representatives of water-related ministries and agencies of participating countries
and of international science communities and space agencies members. The demonstration project
implementation and results was thus supervised by policy- and decision-makers of participating
countries, which has been helping to mainstream the developed/applied methodologies into
operational IWRM applications in these countries.

Self evaluation
We believe the project has accomplished its objectives as given above. In-situ data from the basins
have been collected as planned and stored at DIAS. Hydrological models have been developed in the
AWCI demonstration basins including those with regional specifics, namely snow and glacierphenomena. The CCAA study and the demonstration projects using the developed models andcollected in-situ data have been initiated in all basins. The work has advanced to different stages inindividual basins, being completed for several of them. The remaining basins are expected tocomplete the work at the beginning of AWCI Phase 2. The policy and decision maker groups havebeen invited to the held meeting events and the developed system has been demonstrated, showing
great potential of data integration approach for operational IWRM applications. Positive feedback
has been received from AWCI countries and all the ICG representatives agreed to step in the AWCI
Phase 2, aiming at further data integration and implementation in the operational sector.

Potential for further work
The project has moved the AWCI activities on to the 2nd phase, which targets closer cooperation with
operational sector. The developed tools and methods and the results of pilot studies have been
demonstrated to representatives from governmental sector of AWCI countries and all countries
approved their participation in the next phase with intention to implement the introduced approachin operational applications. The AWCI meetings, held often in conjunction with other related
international events, have facilitated to further strengthen the cooperative network of AWCI
countries and collaborating organizations including ODA agencies, who may become strategic
partners for activities in individual countries. An example of such collaboration is the JapaneseInternational Cooperation Agency (JICA) project “The study of Water Security Master Plan for Metro
Manila and its Adjoining Areas” (Jaranilla-Sanchez, 2013).

Publications
Duran-Ballen S., Tsutsui H., Koike, T, 2012: Snow Depth Spatial Distribution using Microwave Remote
Sensing at the Puna Tsang River Basin in Bhutan, AGU Fall Meeting.
Jaranilla-Sanchez, P.A., T. Koike, C.T. Nyunt, M. Rasmy, I. Hasegawa, A. Matsumura, and D. Ogawada,
2013: Hydrological Impacts of a Changing Climate on Floods and Droughts in Philippine River
Basins, Annual J. of Hydraulic Engineering, JSCE, Vol.57.

3

Nyunt, C.T., T. Koike, P.A. Jaranilla-Sanchez, A. Yamamoto, T. Nemoto, and M. Kitsuregawa, 2013:
Bias Correction Method for Climate Change Impact Assessments in the Philippines, Annual J. of
Hydraulic Engineering, JSCE, Vol.57.
Shrestha, M., L. Wang, T. Koike, Y. Xue, and Y. Hirabayashi, 2012: Modeling the Spatial Distribution of
Snow Cover in the Dudhkoshi Region of the Nepal Himalayas, J. Hydrometeorology, Vol. 13, pp.
204 – 221.
Tsujimoto, K. and T. Koike, Requisite Conditions for Post-Monsoon Rainfall in Cambodia by Looking
through 2009 Rainfall Data, 2012: J. of Hydroscience and Hydraulic Engineering, Vol.30, No.1.
Wang, L., and T. Koike, 2010b: Development of an integrated hydrological modeling system for nearreal-time multi-objective reservoir operation in large river basins, 2010 AGU Fall Meeting, San
Francisco, December 2010.

References
Jaranilla-Sanchez, P. A., L. Wang, and T. Koike, 2011: Modeling the hydrologic responses of the
Pampangga River Basin, Philippines: A quantitative approach for identifying droughts, Water
Resour. Res., doi:10.1029/2010WR009702.
Saavedra, O., T. Koike, K, Yang, T. Graf, X. Li, L. Wang, and X. Han, 2010: Decision support for dam
release during flood using a distributed biosphere hydrological model driven by quantitative
precipitation forecasts, Water Resour. Res., 46, doi: 10.1029/2010WR009502
Wang, L., T. Koike, K. Yang, T. Jackson, R. Bindlish, and D. Yang, 2008: Development of a distributed
biosphere hydrological model and its evaluation with the Southern Great Plains Experiments(SGP97 and SGP99), Journal of Geophysical Research-Atmospheres.

Awards

Acknowledgments
In addition to the support from APN, which made this project possible, we would like to thank thefollowing organizations and institutions for their scientific and technical contribution as well as
financial and in-kind support to the activities undertaken by this project and for their continued
collaboration with AWCI:
Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan; University of Tokyo,
Japan; Japan Aerospace Exploration Agency (JAXA), Japan; United Nations University (UNU); theInternational Centre for Water Hazard and Risk Management (ICHARM), Japan; Office for
Coordination of Climate Change Observation (OCCCO), Japan; Japan Agency for Marine-Earth
Science and Technology (JAMSTEC), Japan; Japan Meteorological Agency (JMA), Japan; Japan
International Cooperation Agency (JICA), Japan; Sejong University, South Korea; Hiroshima
University, Japan and others.
4

ARCP2011-02CMY-Koike - FINAL REPORT

Results of research work undertaken as a part of this project by young scientists at the University ofTokyo has been awarded:- Best Paper Award - International Session of the Japan Society of Civil Engineers (JSCE) 55th Annual
Meeting of Hydraulic Engineering, 2011, to Mr. Maheswor Shrestha for the paper: “Simulation ofinterannual variability of snow cover at Valdai (Russia) using a distributed biosphere hydrologicalmodel with improved snow physics” published at the Annual J. of Hydraulic Engineering, JSCE,
2011.
- JSCE Young Scientists' Paper Award, 2012, to Dr. Kumiko Tsujimoto for the paper: ”Two diurnal
cycle systems with different spatial scales and their effects on the post-monsoon rainfall in theinland of the Indochina Peninsula” published at the Annual J. of Hydraulic Engineering, JSCE, 2012.
- SSMS (Society for Social Management Systems) Outstanding Paper Award, 2012, to Dr. Patricia
Ann Jaranilla-Sanchez, Prof. Toshio Koike, and Prof. Lei Wang for the paper: “Towards Managing
Droughts in a Changing Climate: A study of Southeast Asian Watersheds” published at the Journalof Society for Social Management Systems, 2011.

TECHNICAL REPORT

Preface
The ever growing vulnerability that is induced by global and local changes such as population
changes, climate changes and variability, socio-economic issues and environmental degradation, canresult in increasing both the frequency and severity of extreme events, including droughts and floods.
Today’s challenges require the more holistic approach of the integrated water resourcesmanagement (IWRM) process to help piece together the water management puzzle and toharmonize the plans of different water users. Implementing IWRM at the river basin level is an
essential element to managing water resources in a more sustainable way, leading to long-termsocial, economic and environmental benefits.

Table of Contents
1.0 Introduction………………………………………………………………………………………..............................6
2.0 Methodology………………………………………………………………………………………………………………….7
2.1 Project Coordination……………………………………………………………………………………………7
2.2 Models, Tools, and Techniques..……………………………………………………………………………..7
2.3 Data…………………………………………………………………………………………………………………..14
2.4 Demonstration Projects…………………………………………………………………………………….16
3.0 Results & Discussion……………………………………………………………………………………………………..18
3.1 AWCI Database at DIAS……………………………………………………………………………………..18
3.2 Demonstration Projects and CCAA study at Individual Basins……………………………19
3.3 Implementation Plan for the Next Phase of AWCI……………………………………………..44
4.0 Conclusions…………………………………………………………………………………………………………………..45
5.0 Future Directions………………………………………………………………………………………………………….47
References………………………………………………………………………………………………………………………….48
Appendix……………………………………………………………………..........................................................505

1.0 Introduction
The Global Earth Observation System of Systems Asian Water Cycle Initiative (GEOSS/AWCI) wasestablished in 2007 as a response to the recognized needs for accurate, timely, and long-term water
cycle information to implement Integrated Water Resources Management (IWRM) practices andwith regards to the commonality in the water-related issues and socio-economic needs in the AsiaPacific region. This well coordinated regional challenge of 19 countries in Asia was initiated and built
up as a part of the APN ARCP 2006/2007 project called International Integrated Water Data Accessand Transfer in Asia (IIWaDATA). With substantial support of further projects funded under the APN
programmes ARCP and CAPaBLE, it has further evolved into a strong collaborative initiative focusing
on convergence and harmonization of observation activities, interoperability arrangements forobserved data and collected information, effective and comprehensive data management, andcapacity building of the participating countries.
Implementing IWRM at the river basin level is an essential element to managing water resources in amore sustainable way, leading to long-term social, economic and environmental benefits. It requires
wide range of disparate data from multiple disciplines and various sources and appropriate tools forprocessing these data and integrating and translating them into relevant information for water
resources practitioners and policy decision makers. GEOSS/AWCI has been striving to develop an
adequate database of local observations that complies with GEOSS interoperability standards. Insitu data from 18 selected basins of participating AWCI countries have been collected, quality
controlled, equipped with appropriate metadata, and archived at the Data Integration and Analysis
System (DIAS, http://www.editoria.u-tokyo.ac.jp/projects/dias/?locale=en), which was launched in2006 as part of the Earth Observation and Ocean Exploration System, which is one of five National
Key Technologies defined by the 3rd Basic Program for Science and Technology of Japan. By 2009,
DIAS has begun its application phase and become ready to support IWRM practices in AWCI
countries.

Moreover, in recognition of non-stationary nature of water cycle variability and considering growing
evidence of climate change impacts on water resources in Asia, it is necessary to develop a clear
consensus on how to best utilize model projections of climate and hydrology in conducting
frequency analysis of future hydrological hazards. Hydrological regime shifts and changes in extreme
events, including floods and droughts, are now fundamental threats. It is therefore essential toproperly asses these changes as a basis for identifying effective responses and developing adequate
adaptation strategies.
Reflecting on these needs, the main objectives of this project were:
1. Implementation of a demonstration project in each AWCI basin that would include a
distributed hydrological model by integrated in-situ and satellite data and model outputs,
while considering regional and local specifics (e.g. snow and glaciers).
2. Assessment of climate change impacts on water resources in the demonstration basins using

6

ARCP2011-02CMY-Koike - FINAL REPORTWith GEOSS assuring an easy access to the global earth observations and datasets of local in-situ
data readily available through the DIAS data portal, GEOSS/AWCI has proposed this project, RiverManagement System Development in Asia Based on Data Integration and Analysis System (DIAS)
under the GEOSS, which aims to develop an advanced river management system in member
countries by exploiting the DIAS data and data integration capabilities. The system is based on
integration of data from earth observation satellites and in-situ networks with other types of data,
including numerical weather prediction model outputs, climate model outputs, geographicalinformation, and socio-economic data to generate the required information for making sound water
resources management decisions.

the developed basin models and development of optimization schemes for IWRM practices.
3. Presenting and communicating results of demonstration projects to policy and decision
makers in order to promote the use of developed system in operational applications
including design of measures for climate change adaptations.

2.0 Methodology
2.1 Project Coordination
The project implementation took advantage of the well coordinated GEOSS/AWCI framework of 19
countries including Bangladesh, Bhutan, Cambodia, China, India, Indonesia, Japan, Korea, Lao PDR,
Malaysia, Mongolia, Myanmar, Nepal, Pakistan, Philippines, Sri Lanka, Thailand, Uzbekistan and
Vietnam. The network has been maintained through a series of regular (once or twice a year)
International Coordination Group (ICG) meeting events and conference calls. ICG consists ofrepresentatives of water-related ministries and agencies of the participating countries (onerepresentative per country) and experts in specific focus areas including data integration,hydrological modeling, flood, drought, water quality, climate change, and capacity building to assurethat the system is applicable to a complex set of water resources management issues. In addition,
representatives of collaborating organizations providing expertise in these as well as other related
disciplines are always invited and contribute to the AWCI ICG meetings. These meetings review the
progress in the on-going activities, present the achieved results, and formulate proposals and plansfor next steps. Involvement of scientists and modeling experts together with operational andgovernmental sector representatives in these events enables mutual communication amongscientists and developers and policy- and decision-makers and thus facilitates to the process of
implementing the developed tools and methodologies in operational applications.
To achieve the overall goal – implementation of the developed system in operation – an approach ofdemonstration studies have been adopted. Each country had nominated one demonstration basin
for which data have been archived at the DIAS system and where the demonstration project (DP)was carried out by a country team in cooperation with other member countries and experts fromthe GEOSS/AWCI collaborating organizations and programmes. Implementation process was led andcoordinated by AWCI ICG. By showing the success stories created through the demonstration
projects to decision makers, the GEOSS/AWCI activities including this project have been shifting theemphasis from scientific challenges to operational applications to yield societal benefits andestablish confirmed water management infrastructure against the water related problems.
2.2 Models, Tools, and Techniques
The AWCI countries represent a wide variety of geographical, climatic and hydrological conditions
and thus various tools and methods have been required to implement the demonstration projects inindividual basins although the overall approach is common among the countries. The technicalaspect of the AWCI activities is greatly supported by the University of Tokyo, research team of Prof.
Toshio Koike and DIAS, whose members have been continuously working on development andvalidation of relevant models and tools including hydrological models, snow models, data processing
and archiving techniques, satellite observation algorithms, climate projection bias correction anddownscaling techniques, land data assimilation system and others.
WEB-DHM and WEB-DHM-S
A core part of the developed River Management System is a Water and Energy Budget HydrologicalModel (WEB-DHM) that was developed at the University of Tokyo and verified at various basins. It
was developed by fully coupling a land-surface model, the Simple Biosphere Scheme (SiB2; Sellers et7

Figure 1: Overall structure of the WEB-DHM. (a) Division from a basin to subbasins; (b) subdivision froma subbasin to flow intervals comprising several model grids; (c) discretization from a model grid to anumber of geometrically symmetrical hillslopes; (d) process descriptions of water transfer fromatmosphere to river. Here, the land surface submodel is used to describe the transfer of turbulent
fluxes (energy, water, and CO2) between the atmosphere and land surface for each model grid, where
Rsw and Rlw are downward solar radiation and longwave radiation, H is the sensible heat flux, and λ isthe latent heat of vaporization. WEB-DHM simulates both surface and subsurface runoff using gridhillslope discretizatino, and then simulates flow routing in the river network.

Seasonal snow cover and glacier phenomena are an important component of the environment in a
number of AWCI countries, in particular (but not limited to) those in the Himalayan region. From a8

ARCP2011-02CMY-Koike - FINAL REPORT

al. 1996) with a geomorphology-based hydrological model (GBHM; Yang et al., 2004). The WEB-DHM
model enables consistent descriptions of water, energy and CO2 fluxes at the basin scale. It
physically describes ET using a biophysical land surface scheme for simultaneously simulating heat,moisture, and CO2 fluxes in the soil-vegetation-atmosphere transfer (SVAT) processes (Wang andKoike 2009; Wang et al. 2009; Wang et al. 2010a). The basin and subbasins are delineated employing
the Pfafstetter scheme, and subbasins are divided into a number of flow intervals based on the timeof concentration. All external parameters (e.g., land use, soil type, hillslope properties, andvegetation parameters) and a meteorological forcing dataset including precipitation are attributed
to each model grid, in which water, energy, and CO2 fluxes are calculated (Fig. 1). A hillslope-driven
runoff scheme employing a kinematic wave flow routing method is adopted in calculating runoff.

hydrological point of view, the temporal and spatial variability of the snow distribution on a basin
scale plays a key role in determining the timing and magnitude of snowmelt runoff. Considering theeffect of snow on land and atmospheric processes, it is essential that hydrological models accurately
describe seasonal snow evolution. For applications in the AWCI basins such a model was developedby coupling the three-layer energy balance snow physics of the Simplified Simple Biosphere model,
version 3 (SSiB3; Sun and Xue 2001; Xue et al. 2003) and the prognostic albedo scheme of the
Biosphere-Atmosphere Transfer Scheme (BATS; Dickinson et al. 1993, Yang et al. 1997) into WEBDHM. The resulting WEB-DHM with improved snow physics (WEB-DHM-S; Shrestha et al. 2010,2012) adds more features to the WEB-DHM for simulating the spatial distribution of snow variables
such as the snow depth, snow water equivalent, snow density, liquid water and ice contents in eachsnow layer, snow albedo, snow surface temperature, and snowmelt runoff. For snow-covered model
grids, a three-layer energy-balance-based snow accumulation and melting algorithm is used when
the simulated snow depth is greater than 5 cm; otherwise, a one-bulk-layer snow algorithm is used
(Fig. 2). Each model grid maintains its own prognostic snow properties (temperature, density, andice/water content) and/or land surface temperature and soil moisture content. The model was
validated in the Dudhkoshi region of the Koshi basin, located in the northeast Nepal Himalayas(Shrestha et al. 2012).

Figure 2: The soil model coupled with a three-layer snow model in WEB-DHM-S

Snow Depth Distribution Estimation Method for Mountain Regions using Remote Sensing
A new approach was developed to estimate snow amount using a microwave radiative transfer
model (RTM) in mountain region, which takes into account local terrain slope and incidence angle ofa readiometer scanner (Duran-Ballen et al. 2012). AMSR-E satellite observations of brightness
temperature (Tb) at 18.7 GHz and 36.5 GHz frequencies were compared to calculated values of Tb in
9

Statistical Bias Correction Method and Downscaling for Climate Change Impact Assessment
Realistic representation of precipitation fields in future projections from climate models is crucial forclimate change impact assessment. However, General Circulation Models (GCMs) often fail tosimulate regional climate features required by basin-scale impact studies due to inadequate
parameterization of several processes associated with cloud formation and land surface interactions
with the atmosphere. To overcome this issue a comprehensive statistical bias correction method on
the catchment scale was developed for applications in the AWCI basins (Nyunt et al. 2013). The
method was validated in the Pampanga (AWCI demonstration basin), Angat, and Kaliwa basin in
Philippines. The method uses an ensemble of available GCM outputs (daily values) and begins withselection of suitable GCMs for a given region. Then the daily precipitation output is corrected inthree steps including extreme rainfall correction, correction of frequency of wet days (no-rain-day
threshold), and correction of frequencies between the no-rain-day threshold and extreme events.

10

ARCP2011-02CMY-Koike - FINAL REPORT

Lookup Tables generated by the RTM model. The snow algorithm used to derive the snow depth andtemperature spatial distribution was validated in a flat region using in-situ recorded snow depth data
(Tsutsui and Koike 2009a, 2009b). However, remote sensing instruments are sensitive to the effectsof the terrain slope, where the local incidence angle is different than the 55-degree incidence angle
in case of a flat surface. In the developed method, the terrain DEM is used to calculate the slope andaspect of each grid and the local incidence angle is computed with the geolocation of the satellite as
it passes over. To overcome the difference of spatial resolution between the AMSR-E data (25x25km) and the DEM (resolution 1x1 km to express appropriately the terrain), Tb for the both
frequencies is estimated with the local incidence angle for each terrain grid and then averaged forthe larger satellite footprint grid using a weighted average based on the occurrence of the same
local incidence angle. A
uniform snow depth andtemperatureisassumedwithin each satellite footprint.
The lookup tables
aregenerated by inputting thesnow depth and temperature
into the RTM model for arange of local incidence angles(25° - 80°), snow depths (1 –
200
cm),
and
snow
temperatures (223 – 273 K).
One 18.7 GHz Tb and one 36.5
GHz Tb are calculated for eachcombination of snow depth
and temperature and for eachterrain grid. Then, theobserved Tb is compared tothe calculated average Tb in
the lookup tables andcorresponding snow depth
and temperature of eachfootprint grid is estimated.
The algorithm was validated at
Figure 3: Framework overview of the snow depth distribution
the Puna Tsang river basin inestimation method for mountain regions
Bhutan (AWCI demonstration
basin).

The GCM rainfall is corrected using historical in-situ rainfall observation. Moreover, there is a big gapof grid resolution between GCMs and catchment-scale hydrology models. To overcome thismismatch, downscaling of GCMs output (i.e. transforming information from climate models atcoarse resolutions to a fine spatial resolution) is essential for regional and local impact studies. A
statistical downscaling method based on Global Satellite Mapping of Precipitation (GSMaP; Kubota
et al. 2007) spatial downscaling was employed for some AWCI demonstration projects.
GCM selection. GCM selection is based on historical simulation of daily rainfall and otheratmospheric parameters of 24 GCMs from the Coupled Model Inter-comparison Project 3 (CMIP3)
project over a targeted area. The scores are determined from spatial correlation coefficient (Scorr)
and root mean square error (RMSE), which are derived from comparison of model outputs withreference datasets. Reference datasets include Global Precipitation Climatology Project (GPCP)
products for precipitation and Japanese 25-year Reanalysis Project (JRA-25) for other atmosphericparameters.
Rainfall Bias Correction. The analysis of extremes has traditionally been tested using annual
maximum series (AMS), and adjusted to suitable distributions (Gupta and Duckstein, 1975). However,this causes a loss of further high rainfall events within one year that may be considered extremes
and that may exceed the maximum rainfall of other years. Therefore a new approach is employed tocorrect both intensity and frequency of extreme events. It is based on partial duration series (PDS),
which are constructed using values above a threshold regardless of their year of occurrence, andpermit inclusion of more than one event per year (Hershfield, 1973). The generalized Pareto
distribution (GPD), which is the limit distribution of excess over a threshold series, is used to model
PDS (Bobee and Rasmussen, 1995). A major issue of using PDS is the selection of threshold rainfall
value to fit GPD. In this study, the lowest AMS from observed rainfall is defined as the first trial
threshold of GPD series. The same number of extreme events is defined for GCM gridded series by
ranking. Then, GCM series are fit to the GPD and bias-corrected GCM precipitation is calculated:
x’GCM= F-1OBS (FGCM (xGCM))(1)

where x’GCM is bias-corrected extreme rainfall, F-1OBS is the inverse function of GPD probability ofobserved rainfall, FGCM is the GPD probability of GCM rainfall, and xGCM is the raw GCM output.
Moreover, the corrected extreme rainfalls are calibrated by tuning the different thresholds tominimize RMSE between the corrected GCM and the observed extreme. The future projection
rainfall is corrected by applying the same transfer function between GCM and observations during
the historical period:
x’GCM_fut= F-1OBS (FGCM_past (xGCM_fut))(2)

where x’GCM_fut is the future bias-corrected rainfall FGCM_past is the GPD probability of GCM rainfall forhistorical period, and xGCM_fut is the raw GCM future projection.
The next step is to correct the frequency of wet days because most of the GCMs generate unrealistic
low-intensity rainfall during a large number of wet days. This issue is resolved by using the ranking
order statistics of the entire time series. The total frequency of wet days in the observed dataset isattained and applied to the GCM output to find the threshold rank and rainfall value, below which
the GCM output is then considered be zero, i.e. no-rain day. For future projection, the same
threshold for no-rain-day correction is used.
Finally, rainfall intensities between the extreme and no-rain-day thresholds are classified as normal
rainfall in both observed data and GCMs. A two-parameter gamma distribution is used to correct11bias of normal rainfall. It is assumed that the cumulative distribution function (CDF) of normal daily
rainfall at a certain grid point follows the gamma distribution function. The daily GCM and observed
rainfall data are fitted to a two-parameter gamma distribution for 12 months. Then, the CDF of daily
GCM rainfall is mapped to the CDF of observed data for each month. The corrected normal rainfalls
are calculated by the fitted gamma CDF using Eq. 1. This procedure adjusts only the rainfall intensity
at monthly scale; it does not correct any errors in monthly frequency. The same transfer function for
future projection is used (Eq. 2).
Statistical Downscaling using GSMaP. The employed downscaling method is based on GSMaP spatial
downscaling. Firstly, GSMaP rainfall monthly correction is done between gauge station (in-situ
observation) based gridded rainfall and GSMaP rainfall for a relevant period and areal average
spatial weight for each grid of GSMaP is calculated for each month. Gauge based gridded rainfall isgenerated using Inverse Distance Weight (IDW) interpolation between the gauges and GSMaP gridcentre. Then, bias-corrected GCMs rainfall is downscaled by using spatial weight distribution. This
downscaling scheme is similar to a factor correction and does not correct the frequency of rainy daysof GSMaP. The corrected GSMaP based spatial weights rather than the corrected GSMaP value itself
are used for downscaling. The performance of downscaled map is then evaluated for target period inpast (whole historical GCM simulation) and in future (targeted projection). Finally, downscaled
rainfall is evaluated by annual average rainfall, inter annual variation, mean intensity, standard
deviation, bias and root mean squared error of each GCM.

For drought assessment in AWCI demonstration project a method was developed based on astandardized anomaly index (SA), a variation of the standardized precipitation index (SPI) (McKee etal. 1993), to quantify droughts. The SPI utilized an equation fitting a gamma distribution to the dataset, and then converted it to a normal distribution. The method is limited to using only a single
gamma distribution for the entire data set. Hence, variations in the mean monthly values resulting
from the differences in distribution pattern of the data set are neglected. This assumption may betrue for hydrological parameters such as rainfall, if it follows a gamma distribution. However, forother parameters with different land and atmospheric conditions (e.g. tropical conditions) monthly
distributions patterns may vary. Another limitation is that it utilizes a simple average of precipitationfor each period. This means that the SPI cannot accurately account for the effects of monthly
differences or seasonality, nor for the fact that substantial water resources generated by rainfall that
occurred many months ago have already been lost because of outflow and evaporation.
To differentiate the modifications made in the original method of calculating for SPI, standardized
anomaly index (SA) was proposed for temporal and spatial drought classifications (Jaranilla-Sanchez
et al. 2011). This index fits a distribution pattern to the monthly hydrological parameter values fromthe inputs and outputs of the WEB-DHM simulations. This is transformed to the normal distribution
and then standardized by taking the anomaly (calculated as the difference of the parameter value
from its climatic mean (long-term monthly mean)), divided by the standard deviation of thetransformed parameter. The SPI categories (McKee et al. 1993) were used to classify droughts fromSA outputs, ranging from extremely wet (SA > 2.0) to extremely dry (SA < -2.0). The effects of

12

ARCP2011-02CMY-Koike - FINAL REPORT

Drought Indices
Drought is a natural hazard causing severe damage in many regions globally including Asia. This haseven worsened with the changing climate and has significant consequences for food, forestry
production, and food security. GEOSS/AWCI has organized a working group on drought, which is
focusing on (1) sharing and improving drought monitoring capabilities in AWCI countries; (2)establishment of a drought monitoring and research network; and (3) development of early warning
systems of drought hazard in those countries (Kaihotsu 2012). For monitoring and studying drought,
various types of drought indices have been developed and used.

monthly and seasonal differences can be identified by SA, and the quantitative effects ofevapotranspiration are integrated into calculations of other parameters using the physicallyconsistent model WEB-DHM. Another advantage of the SA is the ease with which it can be combinedwith different parameters in spatially identifying the average effects contributing to drought at thebasin scale.
Dam Operation Optimization
Another essential part of the developed river management system is a dam release support system(DRESS) intended for reservoir operators that is based on real-time observations and weather
forecast data. The system has been developed at the University of Tokyo and validated at the Tone
river basin (AWCI demonstration basin in Japan) and is in detail described in Saavedra et al., 2010.
Main purpose of the system is optimal dam operation during heavy rainfall to reduce flood peaks
downstream and at the same time, to maintain maximum possible storage in reservoirs for designed
purposes (e.g. power generation, water supply, irrigation) during lower-flow periods.
The DRESS employs the WEB-DHM with an embedded dam operation module coupled to a heuristic
algorithm for supporting release instructions. This comprehensive model solves the energy budget
explicitly and can simulate the initial soil moisture properly, which might be critical for flood
forecasting (Wang et al. 2009a). In normal runs, the model is forced by real-time observed radar
products calibrated with a high-density rain gauge telemetry network. The radar calibration is basedon a dynamic window,
yielding a high-quality
product almost free of
WEB-DHM model runs
shadows at high spatial
using observed data
and temporal resolutions.
WheneverDuring heavy rainfall theNoa new QPF
Eventmodel is driven by an
is issued
detected?
operational
Yes
Location
nonhydrostatic
mesoscale
Evaluation of forecast
Extension
error at previous steps
quantitative
precipitation
Intensity
forecast (QPF) having four
Ensemble of
Perturbation
distinctive features: (1)Precipitation Forecast
amplitude defined
forecast error weighting
by the weights
WEB-DHM run using
using a relatively wide
QPF for each member
domain according to
No special
location, intensity, andNo
flood control
extent; (2) generation of
Qsim > Qthre
operationensemble flood forecasts
Yesusing the evaluation of
Ensemble of suggested
previous forecast errors;a priori dam release(3) use of the WEB-DHM
Obtain optimal dam
forced by output from arelease using mean
non-hydrostaticand stand. dev. info.
Evaluation of releasemesoscale model; and (4)schedule using
Release schedule suggested
use of a heuristic
observed precipitation
based on QPF
algorithm
formultireservoir operations with
Figure 4: Steps of the dam decision support system DRESS for floodamulti-objective
control under extreme events.
approach.

13

The DRESS is summarized in Figure 4. The WEB-DHM runs in normal mode forced by observed
precipitation data until a heavy precipitation event is detected. At that point, the forecast error isevaluated. Recent observed precipitation and QPFs are compared over the previous time step
window in terms of location, intensity, and extent. Next, a weighting method is used to calculate theweights in each zone. These weights define the amplitude of QPF perturbation to generate ensemble
precipitation members over the total lead time. Then the WEB-DHM model is run to obtain theensemble streamflow forecast. For special dam operations, a priori dam release is calculated foreach ensemble member. Once the suggested optimal dam release schedule is determined using the
QPF signal, the efficiency is evaluated using the observed precipitation.
2.3 Data
The developed system is based on integration of data coming from various sources including
observation-based data (local in-situ and global satellite-borne observations) and model outputs(GCMs, regional climate models (RCMs), mesoscale atmospheric models) providing reanalyses,
forecasts and climate historical simulations and future projections. Particular satellite and model
datasets were selected as necessary and available for individual demonstration projects. Usuallythese include digital elevation model (DEM) data, land use and soil maps, leaf area index (LAI),
meteorological variables if not available/usable from in-situ observations or in case of future
projections and historical simulation evaluations, and others. Large amount of these data is available
through the DIAS system or they have been gathered from their original sites.

When the data had been uploaded, the data provider could access his data at the AWCI databaseand quality control them using another on-line tool, the AWCI Data Online Visualization and
Modifying System (http://ceop-qc.tkl.iis.u-tokyo.ac.jp/QC/AWCI.html). This system enables the user
to view and correct his data using multiple visualization and analysis functions, all available on-line.
The last step was metadata registration, an important element assuring data interoperability. This
step encompasses two parts, metadata of observation data and metadata of documentation and
was also supported by a sophisticated and user-friendly on-line tool (http://dias-d.tkl.iis.utokyo.ac.jp/awci_metadata/top/). Step-by step user manuals have been created for all these tools
and are available for download at the mentioned websites. In addition, multiple presentations andtraining sessions on the use of these tools have been provided at the occasions of the AWCI
meetings. The scheme of the data upload, quality control and metadata registration process isshown in Figure 5 below.

14

ARCP2011-02CMY-Koike - FINAL REPORT

Local in-situ data for demonstration basins were provided by participating countries. As agreed at
the initial planning meeting of AWCI, the period of 2003/01/01 – 2004/12/31 was the basic period
for demonstration projects and the countries committed to provide in-situ data at least for thisperiod or longer based on the data availability and each country possibilities. Advanced on-line data
upload, data quality control and metadata registration tools have been developed at DIAS to support
data providers and facilitate the whole process of data submission and quality control, which
therefore could be accomplished by individual data providers (i.e. country representatives) bythemselves with kind assistance of the data management team at the University of Tokyo, DIAS.
After the number and locations of observation stations and the list of observed parameters at a
specific basin had been identified by the data provider, the data management team registered thisinformation at the data upload system and created a user account for the provider. Then, the data
provider could easily upload his data from his PC to the AWCI database on the DIAS server using auser-friendly, on-line tool (http://dias-d.tkl.iis.u-tokyo.ac.jp/AWCI/upload/).

Data Provider (Observer)
Observation
Meta Data
Data

Meta Data＋(part of )Meta Data

Meta Data

Data Upload

Observation Point Inf.,
Contact Person Inf.,……

Input
Meta Data

Meta Data

UploadObservation
Data

Meta Data

Quality
Control

Meta Data
Registration

Data Archiving

Basic
Information

Data Quality
Control Process

Meta Data

Post-QC
Observation Data

Search IF

Data Download

Document Generator Visualization System•Search with Metadata•Data Download•Document Generation from Meta Data
•Data Visualization
・・

Data Integration

Data Integration and Analysis System (DA-09-02a)

User

Figure 5: Web-based Data Archiving & Integration System at DIAS.

Figure 6: Example screens of the data upload (left) and data quality control (right) tools.

Being quality controlled, equipped with adequate metadata and relatively complex within each basin,
the AWCI in-situ data represent top-quality datasets for multiple research activities related to water
cycle. At the same time, these datasets were prerequisite for the development of the intended rivermanagement system. The data are open and accessible through the DIAS data portal:
DIAS Data Access: http://www.editoria.u-tokyo.ac.jp/projects/dias/tools.php?locale=en_US
DIAS Search and Discovery System: http://dias-dss.tkl.iis.u-tokyo.ac.jp/ddc/finder?lang=en
DIAS homepage: http://www.editoria.u-tokyo.ac.jp/projects/dias/?locale=en
The structure of the DIAS system is shown in Figure 7.

15

User Communication & Management

User
Authentication

Communication ToolManagement Tool

User
Data Provider
Data

Interoperability
Portal

Search &Discovery
SystemData Storage Core System

AuthorizedUser

Analysis
Systems
By User

Data
Cleansing
System(QA)

Data
Loading
SystemMeta data
Standard

Data
Download
SystemScience
Societal Benefit

Processed Data

Meta data
Registration

Meta Data

Document
Registration

Document

Data
Archive

Data Integration & Analysis System

Figure 7: Schematic illustration of the DIAS structure.

As mentioned in Section 2.1, this project adopted an approach of demonstration studies. Eachcountry had nominated one demonstration basin for which data have been archived at the DIAS
system and for which the demonstration project (DP) was carried out by a country team incooperation with other member countries and experts from the GEOSS/AWCI collaboratingorganizations and programmes. While the main objective and implementation design might differ
from basin to basin, the overall structure was similar. It included:(a) Completion of the in-situ database at DIAS (data upload, data quality control, data anddocument metadata registration)(b) Development of a distributed hydrological model, mostly WEB-DHM at each demonstration
basin, its calibration using the provided in-situ observations (meteorological and streamflow)
together with satellite observation and model data.
(c) Applications for preferred objectives (flood control, drought monitor, etc.)(d) Preliminary Climate Change Assessment and Adaptation (CCAA) study as designed for the
March 2011 training course with possible further extension of the study including further
data and more complex assessment.
(e) Showing DP results and preparation for operational applications and moving to the next
AWCI Phase supporting country project proposals under the GEOSS Water Cycle Integrator
framework.
The demonstration basins with the basic data information are listed in the Table 1 below. The
location of the basins is shown in Figure 8.
16

ARCP2011-02CMY-Koike - FINAL REPORT

2.4 Demonstration Projects

Table 1: List of AWCI Demonstration Basins and overview of collected data.

Country
Basin Name
Data Period# of Sta. (Rec. Int.) Observed Elements
Bangladesh Meghna
2003/01-2008/12
9 (Daily)
Ta, Pr, Discharge, WL
BhutanPunatsangchhu 1989/01-2008/12
16 (Daily)Pr, Discharge
Cambodia Sangker
2003/12-2010/01
5 (Hourly)
Pr, WL
India
Seonath2000/06-2004/12
30 (Daily)Pr
Indonesia Mamberamo
1958/01-2007/12 3 (Daily, Monthly) Ta, RH, Pr, sun, ET
Japan
Tone
2002/12-2004/12
16 (Hourly)
Ta, WS, WD, Pr, sun, Dis.
Upper
7 Korea
2003/01-2004/12
68 (Daily)
Ta, RH, WD, Pr, sun, Dis.
Chungju-dam
8 Lao PDR
Sebangfai
2003/01-2007/12
6 (Daily)Pr, WL, AWS
9 Malaysia Langat
2003/01-2004/12
24 (Daily)Pr, Dis, Ta
10 Mongolia Selbe
2004/01-2006/12
4 (Daily)
Ta, WL, Dis, Pr
11 Myanmar Shwegyin
2003/01-2004/12
1 (Daily)
WL, Dis, Pr
12 NepalBagmati
2003/01-2004/12
22 (Daily)
Ta, RH, Pr, WS
13 Pakistan
Gilgit2000/01-2008/12
17 (Daily)
Ta, RH, WD, WD, Pr, Dis
14 Philippines Pampanga
1961/10-2005/12 4 (Daily, Monthly) Pr, Dis, WL
15 Sri Lanka Kalu Ganga
2003/01-2004/12
12 (Daily)Pr, Dis16 Thailand
Mae Wang
2006/05-2008/12
14 (10 min)
Ta, Pr, WL
Ap, Ta, DueTa, RH, WS, Pr,
Chirchik17 Uzbekistan
2003/01-2004/12
18 (Daily)
SD, SkinT, Sun, Dis, snow,
Okhangaran
Aqueous_Tension, etc
8 (Hourly, every
18 Vietnam
Huong
2003/12-2008/12
WL, Prevent)
12
3
4
5
6

Figure 8: AWCI Demonstration Basins.

17In addition to the demonstration project objectives, a preliminary climate change assessment andadaptation study (CCAA) has been carried out. The detailed proposal of the study including themethodology is described in the white paper on the GEOSS/AWCI Climate Change Impact
Assessment Activity in Appendix A. The preliminary study was undertaken as the part of the AWCI
Training Course for the Climate Change Assessment and Adaptation Study held on March 11 – 12,
2011. For this purpose, additional data has been collected covering the baseline historical period
1980 – 2000. For this study, some countries nominated different basin from that selected for thedemonstration project. These additional datasets, which will become basis for the second phase of
AWCI, have not been opened for public sharing though discussion on such possibility has been
initiated. An overview of the status as of 27 September 2012 is given in Table 2.
Table 2: List of AWCI CCAA Basins and overview of collected historical baseline data – AWCI phase 2 database
as of 27 September 2012. To be completed/augmented during AWCI phase 2.

4

Identical withDP basin?
Bangladesh Meghnayes
BhutanPunatsangchhu
yes
Cambodia Sangker
yes
no
India
Upper Bhima

5
6

Indonesia
Japan

Citarum
Tone

7

Korea

Upper
Chungju-dam

1
2
3

Country

8 Lao PDR
9 Malaysia
10 Mongolia
11 Myanmar
12 Nepal
13 Pakistan

Basin Name

SebangfaiLangatTuul
Shwegyin
Narayani
Hunza

14 Philippines Pampanga
15 Sri Lanka
16 Thailand

Kalu Ganga
Mae Wang
Chirchik17 Uzbekistan
Okhangaran
18 Vietnam
Huong

no
yes
yes
yes
yes
no
yes
no
no
yes
yes
yes
yes

# Sta.

Observed Elements

Data Period

8
14
5
36
17
10
37
4

Precipitation
Precipitation
Precipitation
Precipitation
Discharge
Air Temperature
Precipitation
Precipitation

1980 - 2000
1985 - 2010
1981 - 2008
1970 – 2006
1973 – 2001
1985 - 2002
1991 - 20091901 - 2000

*CCAA study carried out using tools developed by
Sejong university, Korea. Refer to the APN ARCP
project of Prof. Deg-Hyo Bae: ARCP2011-05CMY

pending
19
8
3
1
2
3
6
8
1

Precipitation
Precipitation
Precipitation
Precipitation
Precipitation
Precipitation
AWS
Precipitation
Precipitation

1980 - 2000
1980 - 2000
1980 - 2000
1978 - 2007
1999 - 2008
1961 – 2000
1961 - 20111980 - 2010
1979 - 1992

11

Precipitation

1979 - 2005

9

Precipitation

1976 - 2009

yes

3.0 Results & Discussion
3.1 AWCI Database at DIAS
Collection of the in-situ data from the selected AWCI demonstration basins (Table 1) has been
completed as committed by each participating country. All the data has been quality checked andequipped with adequate metadata, which includes both metadata of observation anddocumentation metadata (Fig. 9). The data has been archived at the DIAS database and is freely
accessible through the DIAS data portal (http://dias-dss.tkl.iis.u-tokyo.ac.jp/ddc/finder?lang=en).
18

ARCP2011-02CMY-Koike - FINAL REPORT#Figure 9: Bar chart of the AWCI in-situ data submission, quality control and metadata registration status.

In addition to the basic dataset for the demonstration projects as listed in the Table 1 above, data
covering a baseline historical period of 1980 – 2000 for climate change assessment has been
provided by the AWCI countries in order to carry out the preliminary CCAA study (Table 2). These
additional data has not been agreed for public sharing yet but have been stored at the University ofTokyo database for the purpose of the AWCI CCAA study and the associated training course. The
database should be completed and augmented during AWCI Phase 2 and the intention is to achieve
agreement for public sharing.
3.2 Demonstration Projects and CCAA study at Individual Basins
The Demonstration Projects have been initiated in all the AWCI countries and the data collection,
quality control and metadata registration is one substantial achievement for each participating
country as well as the AWCI data management group at the University of Tokyo. Further
development of DPs has progressed very individually, which was mainly caused by a different statusof water resources management practices in individual countries. While some countries havealready had implemented certain advanced computing tools for IWRM support in operational use,other countries have just initiated such process.
Most of the demonstration projects had begun with development and calibration of WEB-DHM at
the demonstration basins and if different, also at the basins nominated for the CCAA study.
Fulfillment of this task is summarized in Table 3 below. In many cases, the CCAA study has become
part of the country DP and in some countries DP and/or CCAA study has advanced to thedevelopment of a complex project involving country authorities, experts from AWCI collaboratingorganizations as well as Official Development Assistance (ODA) agencies assuring adequate funding.
19

These countries include Philippines, Pakistan, Cambodia, Indonesia, and Vietnam. Further results aredescribed for each country separately in following sub-sections.
Table 3: AWCI basins where WEB-DHM was developed and calibrated.

Bangladesh
Meghna Basin Introduction. Surma-Meghna River System is one of the three major river systems of
Bangladesh. It is the longest river (669 km) system in the country. It also drains one of the world's
heaviest rainfall areas (e.g. about 10,000 mm at Cherapunji, Meghalaya, India). The Surma river
originates in the hills of Shillong and Meghalaya of India. The main source is the Barak river, which
has a considerable catchment in the ridge and valley terrain of Naga-Manipur hills bordering
Myanmar. Barak-Meghna has a length of 950 km of which 340 km lies within Bangladesh. Parts ofMeghna Basin remain in 3 countries, of which Bangladesh covers 31,859 km2 or 41.1%, India 44,934
km2 or 58.0%, and Myanmar 690 km2 or 0.9% area. The Meghna Basin area in the north-east zone of
Bangladesh (Fig.10) is mostly flash flood prone, which occur by spring reversal. Spring reversal ischaracterized by rainfall ~490 mm in the SW to ~1290 mm in the NE. The area has a tropicalmonsoon climate characterized by twice-yearly reversal of air movement. Meghna Basin consists ofmosaic of wetland habitats, including rivers, streams and irrigation canals and large areas ofseasonally flooded cultivated plains. The annual average flow of Meghna is 500 ~ 30,000 m3/s. But inwet season the flow rises above 160,000 m3/s in the northern portion. Annual Sediment Transport isaround 106 tons.
Main goal of the DP• Developing information system for improved modeling and disaster forecasting• To make a bridge between regional and global scale data and information for sound decision
making and resource allocation.

20

ARCP2011-02CMY-Koike - FINAL REPORTNo. Country
Basin Name
WEB-DHM + calibration
1
Bangladesh Meghnayes
2
BhutanPunatsangchhu
yes
3
Cambodia Sangker
yes4
SeonathpendingIndia
4aUpper Bhima (CCAA)
pending
5
Mamberamo
pendingIndonesia
5aCitarum (CCAA)
yes
6
Japan
Tone
yes
7
Korea
Upper Chungju-dam Different hydrological model *8
Lao PDR
Sebangfai
pending
9
Malaysia
Langatyes
10
Selbe
pending
Mongolia
10a
Tuul (CCAA)
yes
11 Myanmar Shwegyin
yes
12
Bagmatipending
Nepal
12a
Narayani (CCAA)
yes
13
Gilgitpending
Pakistan
13a
Hunza (CCAA)
yes
14 Philippines Pampangayes
15 Sri Lanka
Kalu Ganga
yes
16 Thailand
Mae Wangyes
17 Uzbekistan Chirchik-Okhangaran
pending
18 Vietnam
Huong
yes

NorthDhaka• Transfer output of joint researchachievements
toformulateadaptation strategy and enhancing
social capacity building techniqueagainst disaster.

Collaborators in Bangladesh
Ministryof
Defense(MOD),
NE
Bangladesh
Meteorological
Bangladesh
Department (BMD), Space Researchand Remote Sensing Organization(SPARRSO), Bangladesh University ofEngineering and Technology (BUET),
Meghna river SAARC Agricultural Centre (SAC),
Bangladesh Water Development
Board (BWDB), Disaster Management
Bureau (DMB) etc.

Figure 10: Meghna river in Bangladesh and the flash floodprone area in the north-east Bangladesh.

Figure 11: Current Flood warning system in Bangladesh

21

Project status. Flash flood forecastingstudies have been carried out in thenorth-east region of the Meghnabasin. These studies were conductedby the Bangladeshi authorities using
ECMWF weather forecast data andthe forecasting system currently in
use in Bangladesh (Fig. 11).

In addition, a study using WEB-DHM has been carried out in cooperation with the University ofTokyo. In this study, the global dataset including corrected TRMM precipitation data was employed
to reproduce flood peaks in the Meghna river basin. The results showed suitability of the WEB-DHM
model for flood forecasting (Saavedra and Koike, 2008). The WEB-DHM is thus a candidate model for
improving the current system, which needs to be updated and upgraded including local level
information for better local level flood forecasting with increased lead-time.
Climate change impact assessment research has been intensively carried out in Bangladesh showing
the probable increase of risk associated with floods (extent and frequency), more frequent andintensive cyclones, river bank erosion, drought, sea level rise and salinity intrusion. CCAA study
following the AWCI proposal has been initiated by a preliminary work at he March 2011 training
course and is expected to continue during the 2nd phase of AWCI.
BhutanBhutan is small country with a population of 695,822 with an area of 38, 394 km2 located on theeastern fringes of the Himalayas. The landscape is mountainous with elevations ranging from 100m
to 7000m and the terrain is very rugged. The landscape is dominated with high forest cover (70.46%)
along steep slopes with very little land available for agriculture (2.93%). Bhutan’s Climate isdominated by the monsoon which is very active from June to October. Monsoon contributes morethan 70% of the rainfall in Bhutan. But spatial distribution of rainfall is varied with the southernfoothills receiving more rain than the inner valleys.
Topography.

LegendRiver

DEM (m)Low : 331

km
0 5 10 20 30 40

Figure 12: Location of the Punatangchhu basin in Bhutan and its topography.

The data available in Bhutan is considered very short for any meaningful analysis. However ananalysis of observed data from 2000-2009 shows an increasing trend for both maximum andminimum temperatures but no apparent trends in precipitation pattern. The temperature records
from the meteorological stations located in the study basin indicate this trend. Despite theinadequacy of data, various studies have been carried out such as the recently study for Bhutan’s
Second National Communication UNFCCC. Some of the programmes identified under the National
Adaption Programme of Action (NAPA) to Climate Change have been implemented. The notableones being the artificial lowering of the Thorthormi lake, identified as one of the dangerous glaciallake. This lake is located at the headwaters of the Punatsangchhu. A GLOF Early Warning System has
22

ARCP2011-02CMY-Koike - FINAL REPORTHigh : 7011

also been put in place. On the legislation front, the Royal Government of Bhutan recently enacted
the Bhutan Water Act and the provisions of this Act will enable setting up mechanisms for properbasin management.
The Punatsangchhu Basin (Fig. 12) was chosen as the demonstration basin on account of being thelargest single river basin, as the basin severely threatened by glacial lake outburst floods and also
due to huge developments taking place, with particular reference to the mega-hydropower projects.
The tasks undertaken and completed under this project include (i) in-situ data provision to the DIAS
as shown in Tables 1 and 2 above, (2) development and calibration of WEB-DHM, and (3)development and validation of the method for estimation of snow depth spatial distribution using
microwave remote sensing as it is described in Section 2.2 above. Due to the lack of in-situ snow
observation in the Punatsangchhu basin, it was validated against the outputs of snow depth of the
WEB-DHM-S. WEB-DHM-S outputs of stream discharge and snow cover area were validated withmeasured flow discharge at 4 gauge stations and MODIS snow cover area, respectively. As indicatedby comparison in Figure 13, the RTM successfully estimates the snow depth trend and seasonal
behaviour of the snow cover areas. The best spatial correlation of 0.3 was for Feb. 26, 2006.
The developed method is a useful tool for snow depth estimation from satellite observations in thesnow-melt dependent basins. Together with the WEB-DHM-S model, these tools are essential forfurther research on climate change impacts on water cycle in the AWCI countries with cold regions.

Figure 13: Snow depth spatial distribution over the Punatsangchhu basin from October 2005 to May 2006.

Cambodia
The Sangker river basin is one of the tributaries of Tonle Sap Lake, located in Battambang Province atsouthwest of Cambodia (Fig. 14). The middle basin is covered with mixed agriculture and urban areaand suffers from flash flood. The downstream region is inundated for 6 months in a year by the floodfrom Tonle Sap Lake and floating rice is cultivated. However, there is no large-scale systematic
irrigation system and thus it is difficult to manage rain-fed paddy fields in tropical monsoon climatic
areas with distinct dry and rainy seasons (Tsujimoto and Koike, 2012).The productivity of the crops isthus highly dependent on the amount and variability of the rainfall and the stream flow. Since mostof the paddy fields are rain-fed and most of the farmers rely only on their experience (not sciencebased), they would not accommodate themselves to the changing rainfall and water resources
under the climate change and thus food security in this region would be threatened. The main23

objectives of DP included study of precipitation patterns and prediction technology of agricultural
water resources considering also the impacts of climate change. The Cambodian DP has evolved into
a complex project being implemented through cooperation among the Cambodian governmental
authorities, namely Ministry of Water Resources and Meteorology (MOWRAM), the University ofTokyo, and JAXA (Space Applications For Environment (SAFE) project).

Tonle Sap Lake
Sangker River Basin

The in-situ data from the basin has been provided as committed (see Tables 1 and 2). One of theissues in Cambodia is very limited number of observation stations. Through cooperation with the
University of Tokyo, new precipitation gauges have been installed and the local staff trained to beable to maintain the stations and collect good quality data. In addition, an Automated Weather
Station (AWS) has been installed. Using the precipitation gauge and AWS data, satellite TRMM dataand numerical simulations by atmospheric models, a comprehensive study on precipitation patterns
in western Cambodia has been carried out (Tsujimoto and Koike 2012) that clarified the mechanismof post-monsoon rainfall and suggested three requisite conditions for these rainfall events: (i)abundance of precipitable water, (ii) development of a land breeze from the southwest of the Tonle
Sap Lake, and (iii) large-scale northeasterly wind of moderate strength.
In addition, a study was carried out with coupled WEB-DHM and the rice growth model SIMRIWrainfed to grasp the required hydro-meteorological information for rain-fed agriculture. The resultsof the coupled model were validated by the LAI and soil moisture in the Sangker River Basin. The
sensitivity analysis showed that rainfall prediction at the beginning of the dry season is very
important (Tsujimoto et al. 2013). Moreover, the preliminary CCAA study undertaken following the
March 2011 training course methodology indicated possible increase of flood peaks. Suchunderstanding is important for developing the targeted methodology for prediction of rainfall andagricultural water resources, which aims to make maximum use of knowledge, technologies,
infrastructures, and real-time and archived satellite data which are indispensable for development,
validation and application of the developed prediction system (Fig. 15).
24

ARCP2011-02CMY-Koike - FINAL REPORT

Figure 14: Location of the Sangker river basin and Tonle Sap Lake in Cambodia

Figure 15: Schematic chart of the developed system in Cambodia

India
The Seonath River Basin, which was selected for DP, is the largest tributary of Mahanadi basin where
the Indian Meteorological Department (IMD) had been pursuing an effort to establish estimation ofquantitative precipitation forecast by using dynamical downscaling technique. Accordingly, theobjective of the demonstration project was also to provide Quantitative Precipitation Forecast (QPF)
and Probability of Precipitation (POP) of the basin by a dynamical downscaling technique using the

Seonath Basin

Figure 16: Location of the Seonath basin at upstream of the Mahanadi basin.

25

NWP model products and other data to the station/basin level. The accuracy of QPF should be highenough so that it can be used in flood forecasting model (at least 80%). The basin rises in the
Chandrapur district of Maharashtra at an elevation of about 532m and meets Mahanadi river after
traversing a distance of about 383 km (Fig. 16). In-situ precipitation data of the 30 gauging station
within the catchment have been provided to the DIAS database, however WEB-DHM has not beendeveloped at this basin yet. The project at the Mahanadi basin has been implemented by IMD,
independently on AWCI.
For the CCAA study, the Upper Bhima Basin was nominated, which has a geographical area of 14,712
km2 and lies in western India in the state of Maharashtra (Fig. 17). Length of the Bhima River up to
Ujjani Dam is 275 km. The average annual rainfall is 700 mm and it decreases from west to east withthree regions of varying rainfall: the extreme western region of heavy annual rainfall (2,300 mm),the foot hill region where annual rainfall is moderate (800 to 1,000 mm) and the central and easternregion of lowest annual rainfall (400 to 600 mm). About 25% of the area in the Basin is hilly andhighly dissected, 55% is plateau and 20% plain and valley filled. Forestry covers 10.1% of the basin
and agricultural use in the basin makes up 76.3 %. Of the crops grown in the basin, 64.8% are underirrigation. Soils in the basin range from reddish brown on sloping land (basalt 38%), coarse shallow
soils (12%), medium black soils (26%), and Deep black soil (24%).
There are 18 projects with gross storage greater than 17.4 million m3 in the basin. The original
purpose of these projects was to catch the runoff from the Western Ghats to be used for irrigation
during the non-monsoon period. However, since the development of these projects, municipal andindustrial demands have since accounted for much of this supply of water as has the need for flood
protection. Six hydropower projects have a total installed capacity of 318MW. The other reservoirs
service the command areas. The water resources are managed by the Surface Water and GroundFigure 17: Location of the Upper Bhima Basin in India

26

ARCP2011-02CMY-Koike - FINAL REPORT

Upper Bhima BasinWater Resources Departments of the Maharashtra state. Quantification of the impacts andvulnerabilities and assessment of adaption strategies with combination of climate projections andintegrated assessment models by utilizing comprehensive data of climate water cycle and resources
are required for integrated water resources development and management of water resources ofthe basin.
Under the CCAA study, the rainfall and temperature data of Upper Bhima basin has been entered in
the DIAS network of Tokyo University for AOGCM quantitative evaluation. The suitable GCM output
for the region would be accepted after gap filling and bias correction of historical simulation
precipitation output and future projection precipitation output of selected models using observed
precipitation data. These future scenarios would be linked with hydrological model for assessing theimpact of climate change on water resources. Studies are required to be taken up for developing themodified methodologies for the assessment of water resources, hydrological design practices, floodand drought management, operation policies for the existing as well as proposed water resources
projects and assessment of available water for irrigation including the land uses and cropping
patterns. This work is ongoing.
Indonesia
The main objective of the Indonesian DP was flood forecast and support for flood decision-makers
and to improve the predictive capability for key water cycle variables and feedbacks throughimproved parameterization to better represent hydro-meteorological processes at the Mamberamo
river basin. This is relatively large basin (78,992 km2) on the New Guinea island. The in-situ data wasprovided to the DIAS database as committed and WEB-DHM model development was considered.
However, the in-situ data in particular river discharge observation is rather sparse (some variables
only monthly) and was not adequate for developing the desired flood forecasting system.
Considering the issues above, the Citarum river basin was nominated for the CCAA study. It is one of
the strategic Basins in West Java and flows from the mountainous area in Bandung, through thethree cascade dams: Saguling, Cirata,
Java Island
and Jatiluhur, before reaching the Java
Sea. Upper Citarum River Basin is theJava Sea
main water supply contributor (49%
inflow of Jatiluhur) for drinking water of
Jakarta
Karawang
Jakarta (capital city) and the mostIndramayu developed industrial area of Indonesia(Bekasi, Kerawang and Cikarang).

Jatiluhur Dam
Cirata Dam
Saguling Dam

Bandung

Indian Ocean
Figure 18: Location of the Citarum River Basin on theJava Island, Indonesia.

27

The data for the Citarum basin was
submitted to the AWCI database and the
WEB-DHM was developed. Rainfall anddischarge variability analysis in Upper
Citarum River Basin (CRB) was done bycomparing discharge stations data in
Nanjung (near Saguling Dam inlet) with
rainfall area in upper CRB. The results
suggested the Upper Citarum River Basintends to have higher discharge in wet
season and lower discharge in dry
season in recent year compared to 10 to
15 years ago. Change in Upper Citarum
River Basin condition is not only affected

by anthropogenic factor (land cover change, urban area development), but also affected by changes
in climate conditions. Rainfall data also showed that average monthly rainfall in wet season tends toincrease, while average monthly rainfall in dry season tends to decrease (Kusuma 2012).
The Citarum River Basin project is another one, which has advanced to the stage of preparation of acomplex multidisciplinary project covering fields of Flood and Drought, Water Quality, Water Policy,
Water Availability (Agriculture, Drinking, Industry, and Domestic) and Implementation of Technology.
It involves multiple partners including Indonesian Government institution, local stakeholders andrelevant research and funding organizations:- Government Institution: BAPPENAS (State Ministry of National Development Planning),
BAPPEDA (Regional Body of Planing and Development) and BBWSC (Board of Authority for
Citarum River).
- State Own Company: PDAM (Regional Water Utility Company) Bandung and Reservoir
Operator/Authority of Jatiluhur, Cirata and Saguling- Related University/Research Institution: University of Tokyo, Institute of Technology Bangung(ITB), UNPAD and PUSAIR (Center for Research of Water)- International Funding Institutions: Asian Development Bank (ADB), Japan International
Cooperation Agency (JICA)- Non-Government Organization (NGO) represent the Citarum River Community
Planning of the project has been initiated through focused workshops in which participation of all
the involved parties is assured.
Japan
The Upper Tone River basin was selected for DP in Japan (Fig. 19). It is located in the northernheadwaters of the Tone river basin. The Tone River is a very important source of water supply,
irrigation and power generation for the Tokyo area. Therefore its management is crucial for theregion. 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. The main target to beaddressed through demonstration was development of a system able to (i) reduce flood peaks atdownstream and (ii) replenish water levels in reservoirs after a flood event by using quantitative
precipitation forecast, which would be implemented as a support tool for dam operators.

NIKKO
TOCHIGI

Fujiwara
IBARAKI

AimataSonohara

MAEBASHISAITAMA

CHIBA

TOKYO

Figure 19: Location of the Tone River Basin on the Honshu Island, Japan with the Upper Tone Basinhighlighted by a black oval (left) and the Upper Tone Basin with discharge gauging stations (red dots) anddams (black rectangles) on the right side.

28

ARCP2011-02CMY-Koike - FINAL REPORTGUNMAThe in-situ data from the basin have been provided to the DIAS database including also the longterm precipitation record for the CCAA study. The targeted system was developed as briefly
introduced in Section 2.2, full description is given in Saavedra et al. 2010. Currently, the University ofTokyo research team is implementing the system into real-time operational use. In addition, theUpper Tone basin was used for development and testing of the WEB-DHM model as described in
Section 2.2 and fully explained in Wang et al. 2010a. The preliminary CCAA study has confirmed
findings of JMA that it is very likely that heavy rainfall will be increasing in future.
Korea
The Republic of Korea nominated the Upper Chungju-dam Basin (Fig. 20) for the DP as well as CCAA
study. The main objectives were (i)utilization of an integrated
KOREA
hydrological and meteorologicalforecast system for the optimal
dam operation and flood risk
reduction and (ii) understanding of
the regional and global water cycle CHINA
JAPAN
mechanisms in case of climatechange. The in-situ data have been
submitted to the DIAS database ascommitted for both, the DP and
CCAA study. The work was carried
Chungju dam
out by the Sejong University teamand presented at the AWCI
meetings (e.g. Bae et al. 2008; Baeet al. 2011) and would besummarized in details in the final
report of the ARCP2011-05CMYFigure 20: Location of the Upper Chungju-dam Basin in Korea.
Bae
project,which
isacollaborative project under the
AWCI framework.
The methodology employed includes multi model ensemble modeling approach, which had beendeveloped to cope with uncertainties produced by individual models. In this approach, not only theensemble of GCM outputs was used but also multiple hydrological models were run using these
GCM outputs considering various emission scenarios. The results showed great uncertainty that
increased for more distant future, however, certain conclusions in terms of impact on water
resources in South Korea could be made, namely that spring runoff is projected to decrease andsummer and winter runoff amounts are projected to increase and water resources management andpractices in South Korea is likely to be distressed by climate change.
Lao PDRThe Sebangfai River Basin, selected for the DP as well as CCAA in Laos, is located in the Khammouane
Province. At the source, 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 northeast-southwest into the Mekong (Fig 21). The main focus of DP was capacity building
in flood forecasting and analysis as well as improving the data transmission system. The in-situ data
for the DP has been provided to the DIAS database as committed. Further activities on the Sebangfai
basin include routine observations, monitoring, forecasting and early warning towards adverse
weather and river floods. This service has been provided by DMH to people at risk communities andto the decision making body such as National Disaster Management Committee (NDMC) in order for29

Figure 21: Location of the Sebangfai River Basin in the Mekong River Basin in Laos.

Malaysia
The Langat River Basin, Malaysia DP and CCAA basin, is located in the mid western part of Peninsular
Malaysia and encompasses areas within the states of Selangor, Negeri Sembilan and PutrajayaFederal Territory (Fig. 22). It lies adjacent to the south of Klang Valley, the most highly developed
urban conurbation in Malaysia. The total land area of the basin is estimated to be about 2,350 km2.
Two major dams namely Langat and Semenyih Dam are located in the upper basin as it provides
domestic and industrial water supply for Kuala Lumpur, Putrajaya and areas joining to it. During thepast decades, the Langat Basin experienced intensive urbanization, which resulted not only in rapid
creation of large impervious areas producing significant problems, such as regular flooding due toinadequate drainage facilities and managements, but also the increase of water demand fordomestic and industrial purposes. The frequency of water stress (droughts) and excess of water(floods) are expected to magnify in the near future which is mainly due to climate change andvariability.
A scientific research by means of climate change modeling and projection that has been conductedin Peninsular Malaysia indicates a possible increase in inter-annual and intra-seasonal variability withincreased hydrologic extremes which is higher high flows and lower low flows at various northernwatersheds in the future. Therefore, the primary objective of Langat demonstration project was tocarry out climate change assessment and adaptation (CCAA) on the resources, environment andsocio-economics of the basin. It intends to quantify the potential climate change impacts on water
resources and also to determine appropriate adaptation options for minimizing the impacts. The
CCAA has been carried out into two components: water supply (droughts) and floods analysis. The
30

ARCP2011-02CMY-Koike - FINAL REPORT

commands of taking appropriate actions. The tools utilized for these tasks the Water Resources andEnvironment Research Institute (WERI) include the Decision Support Framework (DSF) tools
developed by the Mekong River Commission (MRC) Programmes. With regards to the further
implementation of AWCI activities, Lao PDR has encountered many challenges and gaps to be filled
up. To fulfill the roles and overcome those challenges, Lao PDR is expecting to be part and benefit
from the accelerated coordination and integration efforts, which urgently focus on capacity buildingof both personnel expertise and sciences and integration of various fields.

LANGAT DAMSEMENYIH DAM

Figure 22: Location of the Langat River Basin in Peninsular Malaysia.

CCAA for drought and watersupply included storage-yield
analysis and water supply andwater demand modeling up to
the segmented time horizonof 2050. As for floods,
particularly for river flood
protection, analysis has been
carried out among others are
to examine structural integrityof existing or near future
proposedfloodsrelatedinfrastructuresattheprotection level of 100-year
average recurrence interval(ARI). Subsequently, the costand options of adaptation forwater resources will be
determined, and it is definedby a combination of the costof providing flood protection
and water supply, which are
estimated based on three
scenarios: (1) socio-economic
baseline, (2) baseline andclimate change, and (3)climate change scenario only.

The in-situ data from the
Langat basin have been
submitted to the DIASEXPECTEDRESERVOIRORSUFFICIENT
FUTURELOW
3 SYSTEM
FLOODS
FUTUREIDF
STORAGEdatabase as committed. As to
FLOW
PROTECTIONCAPACITY
IMPACTSutilize the DIAS system, thebasin climate change factor of
1 GCM
rainfall was derived from 18
1 CLIMATE
DYNAMIC VARIABILITY
STATISTICAL
GCMs with respect to 20 years
DOWNSCALING
recorded data of 7 rain gauges
reducefor the segmented time
NONvulnerability
2
2 CLIMATICof system
horizonof
1981-2000
BIAS CORR.
FACTOR(historical) and 2046-2065
3(future). The results showed
CC ‘LOAD’FACTOR
increase of the 1-day design
rainstorm
at100-years
EARTH
ADAPTATION4OBSER
PRODUCTS,
average recurrence interval
VATION
4
DISAGGREGATEACTIONS &1-DAY RAINOPTIONS(ARI) by average factor (overFigure 23: Framework of the CCAA project of AWCI Phase 2 inthe ensemble of GCMs)
Malaysia.
ranging from 1.04 – 1.23 indifferent part of the Langat
basin. In addition, it has also been derived from National Hydraulic Research Institute of Malaysia(NAHRIM) Regional Hydroclimate Model (RegHCM) 9km grid for the period of 2025-2034 and2042-2050. The analysis of future water availability over the peninsular Malaysia showed that in
1

CLIMATIC (CHANGE &VARIABILITY) & NONCLIMATIC FORCINGSYSTEM[MEDIUM]

231

OUTPUT –RIVER FLOW

number of areas, less water is expected while in others it may increase. The CCAA study continues tobe carried out by NAHRIM and has been evolving into a more complex project participating in the
AWCI Phase 2. The concept of the project is in Figure 23.
In line of Integrated Water Resources Management (IWRM), Integrated River Basin Management(IRBM) and Integrated Floods Management (IFM) which would accommodate and support theconcept of ‘green growth with blue’ (GGB), the CCAA is definitely able to assist and connect theknowledge generated by the scientific community and the specific needs of stake holders. There is aneed to re-examine the conventional practices of planning methodology which requires of imposing
the CCAA into planning methodology in order to achieve sustainable development of water
resources and to reduce future hydro-climate hazards. In addition, the adaptation actions could be
strengthened by means of the economics of adaptation to climate change (EACC) through costbenefit analysis (CBA) as to justify and determine adaptation cost and options. In this context, theMalaysian Government through the Economic Planning Unit (EPU) of Prime Minister’s Department
embarked with the economics of climate change study for Malaysia (ECCM) which aims to assess the
economic costs and benefits of adaptation and mitigation measures in water resources, agriculture
and energy sectors.
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 (6300 km2). In past 20 years, settling area and population havebeen rapidly increasing, which resulted in substantial increase of number of house buildings, paved
areas, groundwater wells and livestock pasture. In addition forest cut and cultivation took place insome extent. The main goals of the DP included surface and ground water monitoring and modelingto assist better management in light of anthropogenic influences and climate change impact andTuul basin

Figure 24: Selbe River and Tuul River Basins in Mongolia.

flood control, assistance in development of information systems for promoting the implementationof IWRM in the Selbe and Tuul River Basins. While the Selbe Basin was the focus of DP, the Tuul
Basin above the city of Ulaanbaatar was nominated for the CCAA study.
32

ARCP2011-02CMY-Koike - FINAL REPORT

Selbe basin

The in-situ data have been submitted and quality checked as committed for both, the DP and CCAA.
The WEB-DHM has been developed for the Tuul basin, but it is yet to be prepared for the Selbe basin.
A preliminary CCAA study was carried out using HadCM3 model output for future periods 2046-2065
and 2080-2099. The results showed increase in annual mean temperature as well as annual mean
precipitation. In addition, the Mongol AMSR/AMSR-E/ALOS Validation Experiment (MAVEX) is goingon in Mongolia, which includes extensive and intensive in-situ measurement of soil moisture forvalidation of satellite soil moisture observation algorithms. A few stations of MAWEX are also
contributing to the AWCI activities, namely the objectives of the Drought working group as provided
in detail in the final report of the CAPaBLE project CBA2011-02CMY-Kaihotsu.
Myanmar
The demonstration basin of
Myanmar is the Shwegyin
Basin, a relatively small
basin of about 1697.9 km2.
There
is
only
one
meteorologicalandhydrologicalobservation
station in this basin. The
main goal of DP was topromotetheflood
forecasting system in thebasin and this basin has also
been nominated for the
CCAA study. Daily meanwater level, discharge anddaily rainfall during 2003 Figure 25: Shwegyin Basin in Myanmar.
and 2004 and also dailywater level (1991-2010) were provided to the DIAS database as committed. Accomplished activitiesof DP include:(a) The geomorphologic parameters were determined by using River Tools Software.
(b) The design flood and design rainfall for different return periods and probable maximum
precipitation for duration of one to three days were calculated.
(c) The unit hydrograph was calculated, which is useful in the development of flood hydrographfor extreme rainfall magnitudes for use in design of hydraulic structures and development offlood forecasting and warning systems based on rainfall.
(d) Capacity building was also done by purchasing the GIS software (River tools and TNTmips
Image Processing Software), holding the training to develop the GIS application and also
training workshop on the IFAS model during June 2010. The IFAS training was held as part of
the AWCI Flood Working Group activities, supported by ICHARM (see also the final report of
the ARCP2009-01CMY-Fukami).
As for the CCAA study, the Department of Meteorology and Hydrology (DMH) of Myanmar carriedout studies on the climate change impacts using the MAGICC 5.3 model focusing on temperatureand precipitation during 3 periods (2001 – 2020, 2021 – 2050, and 2051 – 2100) and also using the
ECHAM5 Model with Global Warming Experiment. The results, which were presented at the 8th
AWCI ICG meeting in Seoul, Korea, October 2011, suggested that during the 21st century:• Annual, April, and May temperature of Myanmar will increase.
• Rainfall for SW Monsoon period is also expected to increase.

33

Late onset will be at deltaic area, central Myanmar and northern Myanmar and early
withdrawal from whole country.
• Predicted length of rainy season showed that it will be shorter than normal (144 days).
• Monsoon intensity will be generally moderate along the Myanmar coast.
(See: http://monsoon.t.u-tokyo.ac.jp/AWCI/meetings/Seoul_Oct2011/pdf/3-06_Yi.pdf)
In addition, the WEB-DHM was developed for the Shwegyin basin in cooperation with the UT teamand applied for a preliminary CCAA assessment at the March 2011 training course.
•

The Myanmar DMH has been involved in the Initial National Communication (INC) project ofMyanmar under UNFCCC as well as in the National Adaptation Plan of Action (NAPA) project, whichis commencing in Myanmar for vulnerability assessment and measures for reduction of impact andstrategy for adaptation.
Nepal
Nepal has proposed two river basins namely the Bagmati River Basin for the DP and the Narayani
River Basin for the CCAA study (Fig. 26). Bagmati is a medium type of river of Nepal with drainage
area of 3700 km2. It originates from Shivapuri Hill (2731 m) and flows down to south throughKathmandu valley up to Indo – Nepal border. Nakhhu, Kulekhani, Kokhajor, Marin and Chandi rivers

BagmatiFigure 26: River Basins of Nepal and locations of the Bagmati and Narayani Basins.

are its major tributaries. Flood damages, landslides, bank erosion and water pollution are acuteproblems in the river basin. There are 21 meteorological stations and two hydrometric stations
within and around the basin. The main objectives of the DP were (i) to assist in preparing theformulation of improved River Management Plans, (ii) to set priority activities and projects toincrease water security in the river basin which are expected to include flood forecasting andmanagement, drought management, water allocation, and pollution control, and (iii) to develop an

34

ARCP2011-02CMY-Koike - FINAL REPORT

Narayanieffective rainfall – runoff simulation Model. Among major issues with regards to IWRM practices isthe lack of a basin wise single institution responsible for water resources decision making.
Accordingly, one of the priority tasks is to address this issue and set up a single institution within thebasin, which can take water related decision based on IWRM principles. Such efforts are now
ongoing in Nepal. As for the technical part, the in-situ data has been provided to the DIAS databaseand quality assured as committed. While the WEB-DHM-S model has been developed and validated
using in-situ observations in Nepal, its development and application in the Bagmati basin is yet to be
done.
The Narayani River Basin is a major river basin of Nepal. It has total drainage area of 34960 km2 outof which 29626 km2 lies in Nepal and 5334 km2 lies in Tibet, China. The basin contains 1025 glaciersand 338 glaciers lakes out of which 4 are potentially dangerous for GLOF. It has 2030 km2 area under
ice and snow cover. Budhi Gandaki, Trishuli, Marsayndi, Kali Gandaki and Seti are major tributaries of
the Narayani River. GLOF, landslides, flash flood and flood inundation are severe problems in theriver basin. There are 58 metrological and 26 hydrometric stations in and around the basin. The
precipitation data for the CCAA preliminary study has been submitted to the AWCI database ascommitted. The WEB-DHM has been developed for the purposes of the preliminary work at the
March 2011 training course. Nevertheless, the full study requires application of WEB-DHM-S modelsuitable for glacier and snow areas.
As mentioned above and in Section 2.2, WEB-DHM-S model was developed and validated using
observations from Nepal (Shrestha et al. 2012). Namely, it was the Dudhkoshi region of the eastern
Himalayas using the observed data of the Global Energy and Water Cycle Experiment (GEWEX)
Coordinated Enhanced Observing Period (CEOP) for the snow season 2002/2003. Point evaluations(snow depth and upward short- and longwave radiation) at Pyramid (a station of the CEOP
Himalayan reference site) confirm the vertical-process representations of WEB-DHM-S in this region.
The simulated spatial distribution of snow cover is evaluated with the Moderate Resolution Imaging
Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2), demonstrating themodel’s capability to accurately capture the spatiotemporal variations in snow cover across thestudy area. Through these evaluations, WEB-DHM-S has demonstrated its capacity to address basinscale snow processes in the Himalayan region.
Pakistan
The Gilgit Basin consisting of two main rivers, Gilgit and Hunza, located in the upper Indus River
Basin has been nominated for the DP and the Hunza basin also for the CCAA (Fig. 27). These basins
are located in the Karakoram mountain region with steep slopes and large extent covered withglaciers and snow. The upper part of the whole Indus basin represents about 20% of its extent but
contributes about 80% of all discharge of the Indus River. Majority of the flow is produced by snow
and glacier melting during the summer season. Accordingly, the main focus of the DP and CCAA wason snow and glacier processes, in particular better ability to simulate and predict discharge for flood
forecasting and warning, including glacier lake outburst floods (GLOF), and to assess possible
impacts of climate change on glacier and snow water reservoirs in the area.
The in-situ data of the both, Gilgit and Hunza river basins have been provided to the DIAS databaseas committed (Table 1 and 2 above). Considering the natural conditions in the area, the snow-coverand snowmelt modeling capability is essential for any water cycle studies in this region and thus ahydrological model capable of snow and glacier processes simulation was required to implement the
DP and CCAA studies and implement the intended river management system. With coming of WEBDHM-S in 2011 and its fully validated version in 2012, this has become possible.

35Gilgit

Hunza

Figure 27: The Gilgit and Hunza River Basins in the Upper Indus River region in Pakistan.

Through the demonstrations at the AWCI meetings and training courses and positive experienceswith the AWCI framework, the original DP has evolved into a comprehensive project on assessmentof possible climate change impacts on water resources in selected basins in Pakistan, including the
Hunza basin representing the mountain region and Soan River Basin, which originates in the foothillsof Muree, north-east of Islamabad and passing through the steep slopes, it enters the plains near
Islamabad and follows to the west until it reaches the Indus River. The plains of the Soan Basin arelargely utilized for agriculture, which depends on received precipitation and assessment of theclimate change impacts is highly desirable. The project has been implemented with support of the
JAXA’s SAFE program (Satellite Applications for Environment) and University of Tokyo providing
expertise in hydrological modeling and climate change assessment. The study in the Soan Riverfollowed the AWCI CCAA methodology introduced in Section 2 and including bias correction of GCM
output, downscaling it and using it to run WEB-DHM to assess future water budgets. The results
showed that it is virtually certain that floods will occur more often but at the same the basin will also
very likely suffer from droughts in future. The Hunza Basin study focused on simulation of thesnowcover and snow- and glacier-melt runoff at current time, because methodology to correct
snowfall projections has yet to be developed. The model results showed acceptable accuracies insimulating the snow cover area and discharge. The contributions of glacier melt, snow melt runoff
and rainfall to total discharge were estimated showing minor contribution from rainfall (~10%) anddominant contribution by snowmelt (~40%) and glaciermelt (~50%). These studies should be
published in peer reviewed journals in 2013 or 2014.
36

ARCP2011-02CMY-Koike - FINAL REPORTIn addition, at the beginning stage of the ARCP2011-02CMY-Koike project, Pakistan experienced
benefits of the AWCI collaborative framework in a practical case. Serious damage happened inPakistan in August 2010 due to floods and mudslides caused by heavy rains which occurred
continuously since July 29, 2010. The flood damage spread from north to south in Pakistan along theIndus River and its tributaries. The Japan Aerospace Exploration Agency (JAXA) has madeobservations using the Advanced Land Observing Satellite (ALOS, "Daichi") to monitor the state ofthe damage and provided them to the responsible Pakistani institutions to support the disastermanagement efforts. First analysis of those ALOS images is available at the AWCI website at:
http://monsoon.t.u-tokyo.ac.jp/AWCI/data.htm. In addition, the AWCI collaborating institute,International Centre for Water Hazard and Risk Management (ICHARM), carried out a prompt flood
analysis using the IFAS system promoted by the AWCI Flood working group (http://monsoon.t.utokyo.ac.jp/AWCI/meetings/Tokyo_Oct2010/pdf/4-8.pdf).

Philippines
The Pampanga River Basin was selected for DP, which is the forth largest basin in the Philippines (Fig.
28). The total length of the main river is about 260 km. The basin is drained through the PampangaRiver and via the Labangan Channel into the Manila Bay. The main river is supported by several
tributaries. It has a relatively low-gradient channel at the middle and lower sections. There are twodams within the river basin and the main objective of DP was optimal multi-purpose reservoir
operation as well as downscaling techniques for IWRM and CCAA. The same basin was chosen for
the CCAA study.

Figure 28: The Pampanga, Angat and Kaliwa River Basins in Philippines.

The in-situ data from the Pampanga basin has been provided to DIAS as committed for the DP and
CCAA study. The WEB-DHM was developed and applied in a study on quantification drought
following the diagram in Figure 29 (Jaranilla-Sanchez et al. 2011). Following up on this study and a
preliminary climate change assessment efforts at the March 2011 training course, the original DP has
been expanded and evolved into a comprehensive project contributing to the Water Security Master
Plan for Metro Manila and its Adjoining Areas. The project was proposed by the Metropolitan
Waterworks and Sewerage System (MWSS) of Philippines and supported by the University of Tokyo,
Japan International Cooperation Agency (JICA), and Nippon Koei Co., Ltd., who carried out a study on
Climate Change Assessment and Runoff Simulation. The project includes the Pampanga basin andalso the Angat Basin with the multipurpose Angat dam and the Kaliwa Basin (Fig. 28) as watersupplying basins for Metro Manila. There were two main objectives of the study:
 To assess the effects of climate change on the water cycle in Metro Manila and its adjoining
areas, including the Angat, Kaliwa and Pampanga river basins, as a basis of the water balance
analysis and associated project assessment.
 To propose optimized operations of the water resources management facilities.

37

P

INPUTSWEB-DHM MODELQ

MODEL OUTPUTS

calibrate

Qobs

θ
GWl

Spatial Analysis

Temporal Analysis

ENSO

Using modified drought Indices
Combined effects ofdifferent droughts

Standardized Anomaly Index
3-monthly

yearlymonthly
Within-year Drought Analysis

AGRI
1987

1983

1991

1998

Hotspots

Watershed Management
Agriculture and Water sector

It has been acknowledged that the water supply balance in Metro Manila should be analyzed in acomprehensive manner, and that a comprehensive development project for the surrounding
regional development was needed to prioritize the individual issues unique to each project withrespect to the water resources development. Under climate change with frequent heavy rainfall
events, larger drought-affected areas and severe typhoons as pointed in the IPCC Forth Assessment
Report (2007), it is important to evaluate the vulnerability of long-term water resources and themerits of employing multiple water-use facilities. An integrated water cycle analysis during both the
low-water and high-water periods is needed, and such analyses should be conducted quantitatively,
continuously, and comprehensively. Furthermore, optimized operation of multiple water-use
facilities should be examined as a part of climate change adaptation. To accomplish these targets,
this study included Water supply analysis and climate change impact assessment;Collection of climate prediction simulation results in Metro Manila and its adjoining
river basins
Evaluation of the effects of climate change in Angat River Basin, Kaliwa River Basin, andPampanga River Basin following the methodologies outlined in Section 2.2 above (GCM
output selection, bias correction, downscaling)
Hydrological model development and river runoff simulation
Simulation of stream flow under the effects of climate change in the future Examination of the optimized operation of water-use facilities
The study has been completed and the results partly published (Jaranilla-Sanchez et al. 2013)
including the climate change assessment component. It indicated very likely increase in flood

38

ARCP2011-02CMY-Koike - FINAL REPORT

Figure 29: Diagram on methodology for quantifying drought. P = precipitation; Q = discharge (simulated
and observed); θ = soil moisture (root zone and surface); GWl = groundwater level measured from thesoil surface; AGRI = agricultural production de-trended and normalized; ENSO = Niño 3.4 Index

intensities and frequencies in all the basins. On the other hand, the results also suggested thatsevere droughts will very likely to occur in the Pampanga river basin but about as likely as not in
Angat and Kaliwa basins with local conditions playing a very important role in how floods anddroughts affect them. However, careful consideration of uncertainty should be considered for water
resource management planning factoring in future changes in climate in these basins. The results ofthe dam operation optimization study using the DRESS scheme introduced in Section 2.2 showedthat appropriate in-advance dam release based on QPF with reasonable accuracy can achieve floodrisk reduction and effective water use both and should be published in a peer reviewed journal in
the near future.
Sri Lanka
The demonstration basin selected in Sri Lanka for AWCI is the Kalu Ganga basin, the fourth largest
river basin on the island covering the area of 2720 km2. It is located at the South-Western part of Sri
Lanka (Fig. 30) and contains regions of high rainfall receiving an annual average rainfall of 3000 mm.
The Kalu Ganga is about 130 km long and discharges into the Indian Ocean at the coastal city of
Kalutara. The Kalu Ganga floods, which are inflicting heavy damages to properties and lives in theDistricts of Ratnapura and Kalutara, is an annual phenomenon of national importance. Thoughseveral attempts have been made to reduce the flood risks by the river, the flood disasters continue
in the basin due to climatic, hydrologic, topographic and land use characteristics in the basin.
The broad objective is to minimize the damages caused to the lives, economy and the environment
due to floods in the basin. The project demonstrates how distributed hydrological models
incorporated with remotely sensed data could be useful in flood disaster reduction. Specifically, theproject focused on (i) effect of alternative structural methods and non structural methods on floodrisk reduction and (ii) identification of inundation levels of different floods and the possibility of
implementation of early warning systems based on real time flood forecasting.
The in-situ data of the Kalu Ganga basin has been submitted to DIAS and quality controlled ascommitted for both, the DP and CCAA study. The WEB-DHM model has been developed and used forthe preliminary CCAA efforts at the March 2011 training course. In addition, the following has beenaccomplished by the national team in Sri Lanka (see presentation at the 8th AWCI ICG Meeting inSeoul, Korea, October 2011: http://monsoon.t.u-tokyo.ac.jp/AWCI/meetings/Seoul_Oct2011/pdf/303_Weerakoon.pdf):• Flood Modelling of Kalu-Ganga river basin has been carried out by the application of HEC-HMS
and HEC-RAS models.
• Weather modeling by WRF and downscaling rainfall at local scale from regional climate models.
• Analysis of variation of extreme rainfall in future due to climate change and application
adaptation techniques for reducing flood damages. The extreme rainfalls over the Kalu-Ganga
river basin have increased and therefore, more severe floods could be expected to occur in thebasin in future.
• In addition, identification of future extreme rainfall and flood conditions for proposing suitable
adaptation measures to minimize the risks of damages and losses caused by the floods affectedby climate change was carried out in the lower Kelani Basin (located north of the Kalu Ganga
and containing the capital city of Colombo) using the following models:Statistical Downscaling Model (SDSM) for rainfall forecasting under A2 & B2 scenarios
Hydrologic Engineering Center – Hydrologic Modeling System (HEC–HMS) – for rainfall
runoff simulations
FLO–2D model for flood and inundation analysis at lower catchment
The possible inundation maps for future flooding were developed.

39

River basin boundary
JaffnaRainfall gauging
stationMean Annual Rainfall (mm)

5000
4000 – 5000
3000 – 4000
2000 – 3000
1500 – 2000
1000 – 1500
500 – 1000
Trincomalee

Kelani

Batticaloa

Colombo

Kaluganga
Galle

ThailandAffected by global climate change and related factors, the problems of flood hazards, landslide anddebris flow in most regions of Thailand have obviously increased in recent years and still have trend
to occur with more frequency, severity and area extension. These disasters cause economic and life
losses nationwide in each year. The main cause of the floods comes from the same source that is therainfall. During the wet season, humidity brought from the ocean onto the land by southwest
monsoon influences upon the region is the main source of rainfall meanwhile tropical storms anddepression troughs with high intensive rainfall are the triggers of the floods. Deforestation,
encroachment of the upstream area for settlement and cropping including the extended settlement
into the vulnerable part of the urban area resulted by the population growth are also the antecedentfactors that support flood problem. Moreover the infrastructure development such as road
construction in the mountain areas and plains, bridge piers, dam and weir could become theobstructions against runoff drainage during the storm events and reinforce the severity of flood.
With realization of the flood hazard which causes suffer and losses each year, the Royal Irrigation
Department (RID) with cooperation and supports by AWCI, including the relevant APN projects, set
up the telemetering network system in the Mae Wang Basin, the upstream sub-basin of Chao Phraya
Basin in Chiang Mai province, northern Thailand (Fig. 31) as a part of the AWCI DP in Thailand. The
network consists of 16 automatic rain gauge stations (4 super-telemetering sites and 12 normal rain
gauge sites) to survey and collect the meteo-hydrological data related with flooding such as rainfall,
runoff, water level, air temperature, soil humidity and ground water level etc. The data collected by
40

ARCP2011-02CMY-Koike - FINAL REPORT

Figure 30: Location of the Kalu Ganga and Kelani River Basins in Sri Lanka

Figure 31: Mae Wang Basin in the North Thailand with locations of observation stations including
telemetric super sites (yellow dots) observing a set of hydro-meteorological variables, telemetric rain
gauges (turquoise), and ordinary rain gauges (green).

automatic data-logger transferred directly to the center office by cell phone modem connection is
helpful for real-time flood monitoring and early warning. Besides, the available information fromother sources such as the weather map, weather forecasting, storm-track satellite images, daily
rainfall reports and regional hourly rainfall radar images are also employed for concurrent analysis
before disseminating the flood forecating and warning. Anyway, although flood warning
announcement or flood information may be prepared from advance sources or some multiple hightechnology instruments but in the stage of dissemination to the public through any type of media, it
should be simplified into the friendly interfaces for the people to access and understandconveniently. Flood information board installed at the landmark point of the community is one of
the simple channels for public relation that people can monitor the current river situation bythemselves, symbolic colour painted staff gauge at the riverside could be a clear and simple concretewater level indicator and the information broadcasted on radio and television or even on internet
websites should not to be too complicated to understand.
The model of flood warning system from Mae Wang Basin DP now applied to The Chao Phraya Basin,
the main basin of the central region, but there are still some parameters to be adjusted forappropriate application due to the differences of physical factors and area characteristics. The
warning system aims to prevent and mitigate the hazard of flood disaster from any losses. People
need to be informed with quick, accurate and reliable information and be able to estimate the scaleof flood for preparation and dealing with the situation in any stages of pre-flood, during flood or
post-flood without panic or careless. Furthermore the results from the warning system research anddevelopment may partly lead us to find the resolution for global warming and climate crisis incomprehensive dimensions and sustainability.

41

In addition to the development of the above system, Thailand has submitted the DP and CCAA data
for the Mae Wang Basin as committed. The WEB-DHM has been developed for the purposes of the
March 2011 training course. The CCAA study is now on-going.

The data for DP and CCAA study inthe nominated basin has been
provided as committed. The WEBFigure 32: The Chirchik-Akhangaran Basin in Uzbekistan
DHM has not been developed yet
but climate change impact assessment study in the basin was undertaken by Uzhydromet, theCenter of Hydrometeorological Service (NIGMI). Various scenarios of greenhouse gases emission
were considered by a set of GCM for two periods in future, namely 2015 – 2030 and 2020 – 2050.
The results showed possible seasonal changes in the precipitation regime with decrease of spring
precipitation and increase in other seasons. It was also found that large valley glaciers are rather
vulnerable to unfavorable meteorological conditions like air temperature rise and that the presentday weather conditions are extremely unfavorable for the existence of these glaciers. Moreover,reduction of glaciations in foreseeable future will have negative consequences on the volume andregime of flow, as well as the quality of fresh waters in the basin. If the contribution of glacier andsnow component in the flow of the Chirchik river is evaluated as 60-75% at present time, then its
value will be decreased by 15-30% when realizing different scenarios of climate change.
In addition, the mathematical model of small mountain river flow generation, which had beendeveloped by NIGMI, allowed evaluating the reaction of the flow of small water collections to thepossible climate changes in different height and climatic zones of Uzbekistan according to thechanges of meteorological parameters. The range of reactions of small rivers to the climatic changes

42

ARCP2011-02CMY-Koike - FINAL REPORT

Uzbekistan
The selected Chirchik-Akhangaran
basin is located in northeasternpart of Uzbekistan. There are tworivers – Chirchik (161 km) andAkhangaran (223 km) and the basin
has 67 lakes with different genesis
type. Snowmelt induced runoff
generates 60-75% of the total
streamflow, which is exploited forhydropower generation at 19hydropower plants within the basin.
Floods including mudslides andsediment transportation are themajor issue in the basin. The main
objective of the DP wasdetermination of an adequate
flood warning system in the basin.
In addition, possible changes of theflow regime due to climate change
impacts are also of great interest in
thisbasin,
which
contains
remarkable mountain area coveredwithglaciers
andseasonal
snowcover.

is rather wider. Differentiated analysis based on the sources of feeding and regularity of flow
generation in different altitude zones showed that greater changes should be expected in the zoneof mountains, which are close to glaciers. These changes will be accompanied with the increase of
glacial component of flow and with the decrease of glaciations. The equality of volumes of snow andice feeding is usually observed on the rivers, where the glaciers occupy at least one third of the basin
area. The flow of these basins will be increased at the expected warming according to the chosen
gradations of precipitation change. In case of realization of scenarios causing moderate reduction of
precipitation simultaneously with air temperature increase, the flow from the middle and low
altitude zones will be decreased.
Viet Nam
The Huong River Basin was selected for the DP and CCAA study in Viet Nam (Fig. 33). It belongs to
the Thua Thien Hue province in the coastal part of the Central Viet Nam and covers the area of 2830
km2, of which over 80% are mountains. The Huong River system contains three main rivers: Ta Trach,
Huu Trach and Bo River. The Huong River is rather short and steep; it runs from mountains to thelow plain area. Time of concentration is short and the basin has low storage capacity. Floods andinundation often occur very quickly and severely. The main goal of the DP was to improve accuracyof flood forecast and to get efficient flood warning system.

Figure 33: The Huong River Basin in the Central Vietnam.

The in-situ data for DP and CCAA has been submitted to the DIAS database as committed. The WEBDHM model has been developed in the basin and applied in several activities including the targeted
flood forecasting system using satellite data and numerical rainfall forecast. In a test case, the floodinundation at Hue City was predicted for flood alarming and preparedness using weather forecast bya High Resolution Model (HRM) with 6 hours lead-time (Saavedra and Koike 2008). Two experts havebeen trained in using distributed hydrological models including WEB-DHM in collaboration with the
University of Tokyo. In addition, inter-disciplinary collaborative research in the Huong River has been
43

initiated and advanced that uses WEBDHM and includes water quality
component and involves water quality
and health science groups under theframeworks of the GEOSS/AWCI and
The Global Center of Excellence (GCOE).
A preliminary CCAA study activities
were undertaken and the March 2011
training course and plans are forfurther assessment for the basin basedon trained models and intensive
research work followed by testingsome modeling in water quality
management and improvement flood
forecasting system in line with climatechange.
Figure 34: The Red River Basin in China and Viet Nam.

In addition, WEB-DHM coupled withthe reservoir routing module and dam operation optimization scheme (DRESS) was applied to the
Red River basin in the northern part of Viet Nam (Fig. 34) for optimization of the Hoa Bihn reservoir
operation as a contribution to the IWRM practices implementation process in Viet Nam. This was apilot study using the observed data for a historical flood event, which was carried out in cooperation
with the University of Tokyo (in detail described in Wang et al 2010c). Spurring from this pilot study
a complex project has been proposed under the framework of Asia-Pacific Regional Space Agency
Forum (APRSAF)/SAFE in collaboration with JAXA and the University of Tokyo, which aims to develop
a robust dam operation support system utilizing weather forecast in the Red River basin.
Furthermore, a proposal was placed for Regional Capacity Development Technical Assistance (RCDTA) for applying remote sensing technology in river basin management for Philippines,
Bangladesh, Viet Nam with support of ADB. The project preparation activities have advanced and adraft implementation plan is underway.

At the 8th AWCI ICG meeting in Seoul, Korea in October 2011, strategies for the 2nd phase of AWCI
were discussed. The concept of the GEOSS Water Cycle Integrator (WCI, see Appendix B) was
introduced that aims at establishment of “work benches” where partners can share data,
information and applications in an interoperability way, exchange knowledge and experiences,
deepen mutual understanding and work together effectively. Country reports at this meeting(http://monsoon.t.u-tokyo.ac.jp/AWCI/meetings/Seoul_Oct2011/presentations_files.htm) included
country view on their possible involvement in the next AWCI phase. All AWCI countries expressed
their interest to continue to participate in the AWCI activities and welcomed the WCI concept as the
direction for AWCI. Based on the outcomes of two breakout group sessions, a template for country
inputs to the AWCI 2nd phase implementation plan was drafted and its structure introduced during
the closing session. Subsequently, the template was refined and sent to the ICG country
representatives to elaborate their contribution (see Appendix C).
All countries submitted their inputs by the end of February 2012 and these drafts have been collated
into a reference document for the AWCI parallel session of the 5th GEOSS AP Symposium held inTokyo in April 2012. The country proposals for the 2nd phase were presented at the Symposium:

44

ARCP2011-02CMY-Koike - FINAL REPORT

3.3 Implementation Plan for the Next Phase of AWCI

http://monsoon.t.u-tokyo.ac.jp/AWCI/meetings/Tokyo_Apr2012/AWCIpresentations_files.htm andclassified into three categories:(i) Framework based category - for collaboration between country and agencies(ii) Project based category(iii) Regional collaboration category or topic based collaboration category
Further discussions took place at the 9th AWCI ICG Meeting in Tokyo, 29 – 30 September 2012(http://monsoon.t.u-tokyo.ac.jp/AWCI/meetings/Tokyo_Sep2012/index.htm), which focused onsynthesizing the provided inputs into an implementation plan and it has been decided that thecountry inputs will be organized in a set of Project Design Matrices (PDMs), one matrix per country
project proposal including clear explanation of:
1. Overall Goal
2. Project Purpose
3. Outputs
4. Activities and Key Leaders
From these matrices, each country project implementation plan will be elaborated in cooperation
with involved partners and key leaders. Some countries have already begun this stage as explainedin the country reports in Section 3.2 (e.g. Cambodia, Indonesia, Philippines, Pakistan, Viet Nam). The
development of the Project Design Matrices will be undertaken as a part of the follow-up project
funded under the APN ARCP programme, namely the GEOSS/Asian Water Cycle Initiative/Water
Cycle Integrator (ARCP2012-16NMY-Ochiai; led by Dr. O. Ochiai, Japan Aerospace Exploration Agency,
JAXA).

4.0 Conclusions
This project was launched under the GEOSS/Asian Water Cycle Initiative (AWCI) framework todevelop an advanced river management system in AWCI member countries by exploiting the Data
Integration and Analysis System (DIAS) data and data integration capabilities.
The main objectives of the project were:
1. Implementation of a demonstration project in each AWCI basin that would include a
distributed hydrological model by integrated in-situ and satellite data and model outputs,
while considering regional and local specifics (e.g. snow and glaciers).
2. Assessment of climate change impacts on water resources in the demonstration basins using
the developed basin models and development of optimization schemes for IWRM practices.
3. Presenting and communicating results of demonstration projects to policy and decision
makers in order to promote the use of developed system in operational applications.
The project has accomplished all these objectives, even though not for all the AWCI demonstration
basins as it is described in the country reports above.
A particular success was full completion of the AWCI in-situ data archive at DIAS that includes
observed data of the 18 AWCI demonstration basins over the certain period that have been
rigorously quality checked and equipped with metadata to assure interoperability (http://diasdss.tkl.iis.u-tokyo.ac.jp/ddc/finder?lang=en). In addition, the data have been provided in compliancewith GEOSS data sharing policy, i.e. available for fully open access. Several tools have beendeveloped that are necessary for implementation of the targeted system. These tools include theadvanced Water and Energy Budget Distributed Hydrological Model (WEB-DHM), which enablesconsistent descriptions of water, energy and CO2 fluxes at the basin scale, and its upgraded version
WEB-DHM-S incorporating a three-layer energy-balance-based snow accumulation and melting
algorithm as well as a glacier-melt submodel that makes the WEB-DHM-S feasible for applications inbasins with snowcover and glaciers. The WEB-DHM and WEB-DHM-S have been validated at AWCI
basins. In addition, a method for estimation of snowcover distribution in the mountainous areasusing satellite AMSR-E data has been developed and validated in Bhutan. Furthermore, a45

comprehensive method for bias correction and downscaling of climate model precipitation output(rainfall only, the method for snowfall is now under development) for assessment of climate change
on water resources has been developed and validated. For drought assessment in AWCI
demonstration project a method was developed based on a standardized anomaly index (SA), a
variation of the standardized precipitation index. Finally, a support system was developed for dam
operation optimization utilizing weather forecast data and thus suitable for real-time operational
applications. The system was successfully validated in the Tone river basin in Japan and applied in
the Pampanga River Basin (Philippines), and Huong River Basin and Red River Basin (Vietnam).
The WEB-DHM or WEB-DHM-S have been developed at 14 basins and in 7 of them applied toward
the goals of country demonstration project (namely Bangladesh, Bhutan, Cambodia, Japan, Pakistan,Philippines, and Viet Nam. In some of the basins, other hydrological models have been used, namely
in Korea, Malaysia, Myanmar, Sri Lanka, and Thailand. Moreover, in case of Cambodia, Indonesia,
Pakistan, Philippines, and Viet Nam, the demonstration project activities have resulted into initiationof a complex project involving strategic partners from country’s governmental sector, international
ODAs and academia and considering operational use of the developed system as one of the project
outcomes. All these projects include assessment of impacts of climate change and thendevelopment/expansion of the IWRM support tools with specific focus according to each country
needs, e.g. agriculture water availability prediction in Cambodia, multipurpose reservoir operation
for flood reduction in Vietnam, and assessment of hydrological regime under the climate change inbasins in Pakistan and Philippines. Research activities of the projects in Cambodia, Philippines andPakistan have been already initiated and first conclusions derived suggesting that it is very likely that
more frequent and more intense floods will be occurring in all the studied basins in future, while thefrequency of occurrence and intensity of droughts significantly depends on local geographical andlandcover conditions in each basin.

Furthermore, three meetings were held that involved AWCI ICG members and served the purpose ofreviewing activity progress, further implementation planning and also presentation ofdemonstration project results to policy- and decision-makers of participating countries. This is why
an effort is always made to organize the AWCI meeting in conjunction with a larger relevant event
like GEOSS Asia Pacific Symposium, for example. During the meetings, poster and/or exhibition
sessions were held to facilitate promotion of the AWCI and collaborating organizations’achievements and their applications for operational use. Namely, these meetings were:- The 8th AWCI ICG Meeting and the 1st Climate Change Assessment and Adaptation (CCAA)
Workshop, Seoul, Korea, October 2011 (http://monsoon.t.utokyo.ac.jp/AWCI/meetings/Seoul_Oct2011/index.htm)- The AWCI Parallel Session at the 5th GEOSS AP Symposium, Tokyo, April 2012(http://monsoon.t.utokyo.ac.jp/AWCI/meetings/Tokyo_Apr2012/AWCIpresentations_files.htm)- The 9th AWCI ICG Meeting, Tokyo, September 2012 (http://monsoon.t.utokyo.ac.jp/AWCI/meetings/Tokyo_Sep2012/index.htm)

46

ARCP2011-02CMY-Koike - FINAL REPORTThe project organized a training course for the Climate Change Assessment and Adaptation Study(http://monsoon.t.u-tokyo.ac.jp/AWCI/meetings/Tokyo_Mar2011/index.htm). The training course
kicked-off the AWCI CCAA study, which in many cases has been (is) carried out at the same basins as
country DP. The participants learnt how to use the global model precipitation output of futureclimate projections for assessment of climate change impacts on hydrological regime in the basin.
The participants worked with their own data during the course and carried out preliminary
assessment using the corrected GCM output and applying the WEB-DHM model to their basin.

Implementation planning for the 2nd phase of AWCI has been initiated and all participating countries
have submitted proposal for their involvement. These country inputs were presented at the AWCI
parallel session of the 5th GEOSS AP Symposium and will be elaborated into a set of Project Design
Matrices. The original country inputs have been collated and made accessible at:
http://monsoon.t.u-tokyo.ac.jp/AWCI/doc/AWCI_IP2_Country_drft.pdf.

5.0 Future Directions
At the 8th AWCI ICG meeting in Seoul, October 2011, it was concluded that AWCI had completed thefirst phase focused on research, system development and demonstration activities and was ready tostep in to the next phase, aiming at operational applications of the first phase achievements. It wasagreed that the strategy would be in compliance with the GEOSS Water Cycle Integrator (WCI)
concept introduced at the meeting (see Appendix B below). All the participating countries expressed
their interest to continue their active involvement in the AWCI framework and submitted a proposal
for their specific contribution according to the WCI concept.
The intension is to prepare and initiate in each participating country a complex project involving
governmental sector, local stakeholders, and strategic partners including ODAs, academia and otherresearch institutions. Several such projects have been already established and developed to various
stages (Cambodia, Indonesia, Pakistan, Philippines, and Viet Nam) as explained above. In a similarway, projects in other countries will be established while considering specific needs and current
stage of technical and institutional advancement of each country. The main focus will be on thesystem for IWRM support while considering climate change impacts. This includes completion of the
CCAA study objectives (see Appendix A) in those basins, where only preliminary work has been
undertaken.
As a part of this process, two AWCI projects funded under the Asia Pacific Network for Global
Change Research (APN) programmes had been proposed and accepted for funding: (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), which will built up
on the achievements of this project and that were kicked-off at the 9th AWCI ICG meeting in Tokyo,
September 2012.

47

48

ARCP2011-02CMY-Koike - FINAL REPORT

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Bae, D.-H., 2011: Overview of Climate Change Impact Assessment on Water Resources. Presentation
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Jaranilla-Sanchez, P. A., L. Wang, and T. Koike, 2011: Modeling the hydrologic responses of the
Pampangga River Basin, Philippines: A quantitative approach for identifying droughts, Water
Resour. Res., doi:10.1029/2010WR009702.
Jaranilla-Sanchez, P.A., T. Koike, C.T. Nyunt, M. Rasmy, I. Hasegawa, A. Matsumura, and D. Ogawada,
2013: Hydrological Impacts of a Changing Climate on Floods and Droughts in Philippine River
Basins, Annual J. of Hydraulic Engineering, JSCE, Vol.57.
Kaihotsu, I., 2012: Drought monitoring system development by integrating in-situ data, satellite dataand numerical model output. Final report of the APN CAPaBLE project: CBA2011-02CMYKaihotsu.
Kubota, T., S. Shige, H. Hashizume, K. Aonashi, N. Takahashi, S. Seto, M. Hirose, Y. N. Takayabu, K.
Nakagawa, K. Iwanami, T. Ushio, M. Kachi, and K. Okamoto, 2007: Global Precipitation Map using
Satelliteborne Microwave Radiometers by the GSMaP Project: Production and Validation, IEEE
Trans. Geosci. Remote Sens., Vol. 45, No. 7, pp.2259-2275.
Kusuma, M. S. B., 2012: Indonesia - Preparedness Effort toward Climate Change Adaptation.
Presentation at the AWCI Parallel Session of the 5th GEOSS AP Symposium, Tokyo, April 2012(http://monsoon.t.u-tokyo.ac.jp/AWCI/meetings/Tokyo_Apr2012/pdf/2-04.pdf).
McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration
to timescales, paper presented at the 8th Conference on Applied Climatology, Am. Meteorol.
Soc., Anaheim, Calif.
Nyunt, C.T., T. Koike, P.A. Jaranilla-Sanchez, A. Yamamoto, T. Nemoto, and M. Kitsuregawa, 2013:
Bias Correction Method for Climate Change Impact Assessments in the Philippines, Annual J. of
Hydraulic Engineering, JSCE, Vol.57.
Saavedra, O. and T. Koike, 2008: Applications of a Distributed Hydrological Model to the AWCI
Demonstration River Basins, The 4th Conference of the Asia Pacific Association of Hydrology andWater Resources, the AWCI special session, Beijing, China, 5 – 6 November 2008.
Saavedra, O., T. Koike, K, Yang, T. Graf, X. Li, L. Wang, and X. Han, 2010: Decision support for dam
release during flood using a distributed biosphere hydrological model driven by quantitative
precipitation forecasts, Water Resour. Res., 46, doi: 10.1029/2010WR009502

Sellers, P.J, L. Bounoua, G.J. Collatz, D.A. Randall, D.A. Dazlich, S.O. Los, J.A. Berry, I. Fung, C.J. Tucker,
C.B. Field, and T.G. Jensen, 1996: Comparison of radiative and physiological effects of doubled
atmospheric CO2 on climate. Science, 271, 1402-1406, doi:10.1126/science.271.5254.1402.
Shrestha, M., L. Wang, T. Koike, Y. Xue, and Y. Hirabayashi, 2010: Improving the snow physics of
WEB-DHM and its point evaluation at the SnowMIP sites. Hydrol. Earth Syst. Sci., 14, 2577–2594,
doi:10.5194/hess-14-2577-2010.
Shrestha, M., L. Wang, T. Koike, Y. Xue, and Y. Hirabayashi, 2012: Modeling the Spatial Distribution of
Snow Cover in the Dudhkoshi Region of the Nepal Himalayas. J. Hydrometeorology, Vol. 13, pp.
204 – 221.
Sun, S., and Y. Xue, 2001: Implementing a new snow scheme in Simplified Simple Biosphere Model(SSiB). Adv. Atmos. Sci., 18, 335–354.
Tsujimoto, K. and T. Koike, 2012: Requisite Conditions for Post-Monsoon Rainfall in Cambodia by
Looking through 2009 Rainfall Data, J. of Hydroscience and Hydraulic Engineering, Vol.30, No.1.
Tsujimoto, K., K. Homma, T. Koike, and T. Ohta, 2013: Hydrological Model and A Rice Growth Modelfor Grasping Necessary Information for Rain-fed Agriculture. Annual J. of Hydraulic Engineering,
JSCE, Vol.57 (in Japanese).
Tsutsui, H. and T. Koike, 2009a: Estimation and discussion of long-term snow depth based on SSM/I
satellite data. J. Hydroscience & Hydraul. Eng. Vol. 27, No.1, pp. 49-60.
Tsutsui, H and T. Koike, 2009b: Long-term variation of snow depth in the Northern hemispherebased on SSM/I data. J. Remote Sensing Soc. Japan. Vol. 29, No.1, pp. 318-326.
Wang, L., Koike, T., Yang, K., Jackson, T., Bindlish, R., Yang, D. (2008), Development of a distributed
biosphere hydrological model and its evaluation with the Southern Great Plains Experiments(SGP97 and SGP99), Journal of Geophysical Research-Atmospheres.
Wang, L., T. Koike, K. Yang, T.J. Jackson, R. Bindlish, and D. Yang, 2009a: Development of a
distributed biosphere hydrological model and its evaluation with the Southern Great Plains
Experiments (SGP97 and SGP99), J. Geophys. Res., Vol. 114.
Wang, L. and T. Koike, 2009b: Comparison of a Distributed Biosphere Hydrological Model with GBHM.
Ann. Jour. Hydraul. Eng.-JSCE, pp.103-108.
Wang, L., T. Koike, K. Yang, and P. J.-F. Yeh, 2010a: Assessment of a Distributed Biosphere
Hydrological Model against Streamflow and MODIS Land Surface Temperature in the Upper Tone
River Basin. J. of Hydrology, Vol.377, pp. 21-34.
Wang, L., and T. Koike, 2010b: Development of an integrated hydrological modeling system for nearreal-time multi-objective reservoir operation in large river basins, 2010 AGU Fall Meeting, San
Francisco.
Wang, L., C.T. Nyunt, T. Koike, O. Saavedra, L.C. Nguyen, and T.V. Sap, 2010c: Development of an
integrated modeling system for improved multi-objective reservoir operation. Front. Archit. Civ.
Eng. China, Vol.4(1), pp.47-55.
Xue, Y., S. Sun, D. Kahan, and Y. Jiao, 2003: The impact of parameterizations in snow physics andinterface processes on the simulation of snow cover and runoff at several cold region sites. J.
Geophys. Res., 108, 8859, doi:10.1029/2002JD003174.
Yang, D., T. Koike, and H. Tanizawa, 2004: Application of a distributed hydrological model andweather radar observation for flood management in the upper Tone River of Japan, Hydrol.
Process., 18, doi:10.1002/hyp.5752.

49

Appendix
Appendix A:
Whitepaper on the GEOSS/AWCI Climate Change Impact Assessment Activity

Flowchart of an implementation plan toward assessing impacts of climate change and preparing
adaptation strategy – resulting version from breakout group discussions.
2. Mission
The main goal of the proposed activity is to set up a methodology for assessment of climate change impacts on
the water resources and water-related hazards that will be applicable in the AWCI member countries and byusing this methodology to carry out such assessment studies on the demonstration basins in each country
focusing on the three identified areas including flood, drought, and snow and glaciers phenomena.
3. Objectives
1. To set up a methodology for quantifying uncertainty of climate projection focusing on forcing
variables for hydrological models.
2. To set up a methodology for correcting bias of the projected forcing variables.

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1. BackgroundImpacts of climate change on water resources and water-related hazards in the Asia-Pacific region havealready become evident, as both scientific observations and the experiences of the region’s inhabitantsconfirm. In confronting the risks and challenges posed by changing climate, it is essential to properly asses its
impacts as a basis for identifying effective responses and developing adequate adaptation strategies.
In recognition of these challenges, the GEOSS Asian Water Cycle Initiative (AWCI) has proposed activities
focusing on climate change impact assessment in three specific areas including flood, drought, and snow andthglacier phenomena. At the 6 AWCI International Coordination Group (ICG) Meeting in Bali, March 2010, an
approach toward assessing impacts of climate change using the Climate Model Intercomparison Project (CMIP)
data was proposed and accepted as illustrated in the chart below. This Whitepaper is an elaboration of that
proposal.

3.
4.
5.

Produce projections of water resources and water hazard related variables by employing a properhydrological model forced by the corrected projected forcings.
Assess the impacts of climate change on changes in water resources and water-related hazards.
Recommendations for adaptation strategy.

4. Methodology
Step I for Climate & Bio-Physical Drivers
The activities will take an advantage of the CMIP3 (and later CMIP5) output including past simulations andfuture projections that are stored on the Data Integration and Analysis System (DIAS) of Japan. Close
collaboration is envisioned with the AWCI project “Climate Change Impact Assessment on the Asia-PacificWater Resources under GEOSS/AWCI” that is led by Prof. Deg-Hyo Bae, was approved for funding by APN in
April 2010 (http://monsoon.t.u-tokyo.ac.jp/AWCI/projects.htm#change). Approaches toward accomplishmentof the listed objectives may differ according to the focus area, i.e. whether the phenomena under assessment
will be related to flood, drought, or snow and glaciers.
1. Flood
Key mission for flood:
Impact analysis on PMF, flood frequency, dam safety, optimal dam operation, etc.
As heavy rainfall is the key factor in floods, the flood-oriented activities will focus on change in heavy rainfall
events and associated changes in flood peaks. Using available long-term historical records (at least 20-years) ofrainfall in the AWCI demonstration basins and the CMIP3 (or later CMIP5) precipitation outputs for thecorresponding period, the uncertainty in CMIP precipitation outputs will be identified. Consequently, the bias
in the daily rainfall data will be corrected in the model projections. The corrected data together with otherforcing variables taken from the CMIP projections will be used to run suitable hydrological models in thedemonstration basins that will generate projected river flow. The projected heavy rainfall events and flood
peaks will be compared with the historical series and studied for possible trends and/or changes in intensityand frequency of occurrence.
Method:1. Data registration of demonstration basin to DIAS
2. Capacity building for bias correction & downscaling – e.g. UNU seminar on downscaling in May 2011
3. Application of multiple (hopefully!) hydrologic models such as WEB-DHM, IFAS-PDHM or BTOP, any
locally-used model at the demonstration basin, etc.
2. Drought
Key mission for drought:
1. To carry out a long term monitoring of soil moisture, precipitation, air temperature, and snow by insitu and satellite with studying the definition of drought for climate change assessment.
2. To present an early warning system including seasonally forecasting for adaptations.
In case of drought, set of drought indices will be investigated. Similarly as for flood, available long-term
historical records of rainfall and CMIP3 precipitation output will be analyzed, the uncertainty identified forheavy rain, moderate rain, and low rain events, and the bias corrected. The observed in-situ precipitation andJRA25 output will be used to force an adequate hydrological model (e.g. Web-DHM) in the demonstration
basins to derive the drought indices for the historical period. Consequently, the projected forcing variables by
CMIP3 including the bias-corrected precipitation will be used to force the hydrological model for the future
period and projection river flow and basin states will be generated. Drought indices for the future period will
be derived and compared with those for the historical period. Possible trends and/or changes in drought
occurrence, severity and frequency of occurrence will be studied.
In addition to the above study involving numerical model experiments, the focus will also be on strengthening
the monitoring system and data analysis of precipitation, soil moisture phonological elements,
evapotranspiration, air temperature, land use, discharge, and groundwater.
3. Snow and Glaciers
Regarding the snow and glaciers focus, the abovementioned approaches are currently not applicable due to (i)lack of sufficient historical in-situ observation of snow and glacier variables (insufficient in terms of length ofrecord, density of observation network, temporal resolution of the observation) and (ii) lack of adequatehydrological model coping with the snow and glacier processes. Since quantifying uncertainty and bias
correction is difficult without long-term observation data, it is proposed to use satellite data for snow cover51

and glacier change monitoring, namely Landsat data and the high resolution ASTER GDEM. A statistical value ofa snow covered area will be generated from the CMIP3 output for the historical period using a hydrologicalmodel designed for cold regions. This statistical value will be compared with such a value resulting fromLandsat data and in this way the CMIP3 output will be evaluated and bias corrected.
Step II for Socio-Economic Drivers
To make a sound decision in water policy responding to changing political and socio-economic needs anddemands under climate change, it is vital to develop a comprehensive risk assessment method that covers
political and socio-economic aspects as well as natural scientific aspects. The method should be able to reflect
the effects of climate prediction uncertainty in an appropriate way.
1. Risk Assessment
With the socio-economic background, it is important to develop a comprehensive assessment system that canquantify socio-economic impacts induced by climate change on comprehensive societal benefits, including
complacency about the risks to life and environmental safety. To design an adaptive measure, it is necessary toevaluate currently available technology and its future direction, to consider the socio-economic and culturalcharacteristics of each target river basin, and then to quantify how much the risk to society can be reduced by
combining various measures.
The first step will be to review existing studies in each participating country. Cooperation with experts onsocio-economic studies in each country will be sought.
2. Multilayered Risk Management
We must identify and implement approaches that improve water security over a wide range of potentialconditions, including current climate variability. Multilayered approaches, including both structural and nonstructural ones, should be promoted. Also early warning systems either for flood or drought or other waterrelated hazards are important elements of the scheme. Effective management as a whole can be achieved byshifting the capacities for specific purposes among existing reservoirs. Effective water demand management,
including proper water distribution for different objectives of water use and negotiation among different
stakeholders during severe drought, should be promoted. It is undoubtedly effective to control urban land use
in flood plains and local low-lying lands where serious damage is caused by flooding and localized torrential
rainfall respectively.

5. Implementation strategy
For the step I, we had better take an aggregation approach on observation capability, data sets, data
infrastructure, models, prediction capabilities and knowledge.
1. Sharing observation capability
2. Sharing long tem data, especially long-term daily rainfall data and hourly data even short periods.
3. Sharing data infrastructure and climate projection data sets – an easy access to the GCM products needs
to be provided.
4. Sharing hydrological models including down scaling methods.
5. Sharing regional characteristics of the climate change impacts on river flow regimes.
For the step II, the shared ideas, data, experiences and knowledge should be applied to each demonstration
river basin considering its locality.

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3. Governance
Adaptation measures involve a wide range of stakeholders. We must build the capacity of society todemonstrate resilience in the face of changing climate through strengthening the adaptation capacities of
stakeholders of society as a whole for operationalizing the multilayered risk management with climate change
adaptation measures. It is also important to recognize water quality as an inseparable determinant ofsustainable environment and people’s well being. It is important to establish a platform consisting ofstakeholder organizations, experts, and academics at the early stages of planning where making decisions,sharing information, providing advice, and clarifying each organization’s role are conducted.

Appendix B
GEOSS Water Cycle Integrator
A proposal to CEOS and GEO
Toshio Koike, Japan GEO WG Chair
Background
World Bank Vice President, I. Serageldin, recently observed “Many of the wars of the 20th century
were about oil, but wars of the 21st century will be over water." In reference to the growing global andregional water issues. In 2001, the International Conference on Freshwater in Bonn identified “managing risks
to cope with variability and climate change” as one of its primary actions in dealing with governance issues.
Recent climate-related water catastrophes, such as floods in Pakistan, Australia, Brazil and South Africa, serve
to remind us that the most significant and harmful impacts of climate change will be experienced throughalterations in the water cycle. Climate change adds another formidable challenge, especially in water which isessential in the natural climate system and the human society. Although the impacts are currently far fromcertain, they are unlikely to be favorable.
Concept Design
Water is key which makes a bridge between the climate processes in atmosphere, oceans, cryosphere,
terrestrial carbon cycle, ecosystems and sea level rise, and the socio benefit areas including agriculture andforestry, health, energy, human settlement and infrastructure and the economy.

Figure 1. Model Integration for Assessment, Richard H. Moss, et al., Nature,
2010, modified by Author.
The global water cycle, which includes the transport and distribution of large amounts of water associatedwith its constant phase changes among solid, liquid and gaseous states, is a critical component of the Earth’s
climate system. Due to the effects of the atmospheric and ocean circulations and the variations of water
stored as snow and soil moisture, local and regional water cycle variations are correlated across areas andseasons.
People have been developing water cycle management systems considering the water cycle variability
as a stationary process. But now, under the current conditions this concept has been shown to be misleading
resulting in a need for radical change in approach to develop a clear consensus on how best to utilize model
projections of climate and hydrology in conducting frequency analysis of future hydrological hazards.
Hydrological regime sifts and changes in extreme events, including floods and droughts, are now fundamental
threats to human beings all over the world.
Increased water cycle variability impacts primarily through water, biological processes and humandimensions with implications for land use and societal development. It is critically important to recognize the53fundamental linkages among water; land use, including deforestation; carbon cycle and ecosystem services;and food-, energy- and health- securities. By sharing coordinated, comprehensive and sustained water cycle
and related Earth observations and information for sound decision making, GEOSS could lead in developing
effective interdisciplinary collaborations for working together based on coordinated and integrated efforts andsubsequently to both mitigation and adaptation benefits. Building resilience to the climate change andvariability is essential for establishment toward the final goal, the sustainable development of Earth’s societies
and ecosystems.

Figure 3 Implementation Design.
Implementation Design
To accelerate the coordinated and integrated efforts, we need to develop “GEOSS Water
Cycle Integrator (WCI)”, which develops a holistic coordination capability of the following function incooperation with various partners:

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Figure 2 Concept Design-observation integration
science and model integration
data integration & analysis
cross-Socio Benefit Areas and Community of Practices
management system integration
sustained education framework
GEOSS/WCI will set up “work benches” where partners can share data, information and applications in
an interoperability way, exchange knowledge and experiences, deepen mutual understanding and work
together effectively. (A work bench is a virtual geographical or phenomenological space where experts andmanagers work together to use information to address a problem within that space). GEOSS/WCI enhances thecoordination of efforts to strengthen individual, institutional and infrastructure capacities, especially for
effective interdisciplinary coordination and integration.
CEOS’s Key Roles in GEOSS/WCI
In order to implement GEOSS/WCI CEOS would be expected to lead “satellite observation integration”
and “data integration” for GEOSS/WCI. These elements are essential components for the technical andFigure 4 Coordinated Observation and data integration.
architectural elements of WCI. To quantify the impacts and vulnerabilities and develop and assess adaptation
options, it is important to combine climate projections with integrated assessment models by utilizing
comprehensive data of the climate, water cycle and resources for each societal benefit area observed bysatellites. This effort would address the need for a Bridge between the current CEOS constellation projects andpromote the development of a new observational and analysis integration capability.
This effort will build on the mutual cooperation between CEOSS/WGISS and WCRP/GEWEX, which
successfully implemented the Coordinated Enhanced Observing Period (CEOP) was and its integration
capability for in-situ and satellite observation data and numerical model outputs. The CEOS Water Portal is
now developing a wider and deeper data integration capability under GCI framework. A GEOSS/WCI data
integration function will be developed by accelerating the effort and incorporating developments andexpertise of other systems including those in NASA, ESA and other CEOS members. Other efforts will beneeded to build the networks and to involve the experts and managers who will test and utilize this system.
GEO has established GEOSS Asian Water Cycle Initiative (AWCI) and GEOSS African Water Cycle
Coordination Initiative (AWCCI). In Latin America, the GEOSS water capacity building programs are now ongoing. Through regional, inter-disciplinary and inter-agency coordination, and integrated efforts, GEOSS/WCI
will lead to effective actions and public awareness toward water security and sustainable development.

55

Appendix C
Template for Country Input to the AWCI Phase 2 Implementation Plan(based on the outcomes of the Seoul meeting breakout sessions)
1. Issues and Needs1.


Issues related climate system - water cycle - water use
Regionally common issues - identify which of the common issues are relevant to your country:- changes in climate and consequences – quantitative assessment
 intensification of variability (heavy rainfall and dry spells), cyclones
 frequency of extremes: flood (localized + social)and drought seasonal climate pattern (precipitation, dry and wet, maxima, )- Identify available capability/resources in your country – specify clearly- Identify lack of capability – specify clearly, including more details, which capability out of the following
ones is missing: monitoring, modeling, inventory of water resources, understanding planning &management
Describe critical and specific issues in your country, include more details:- landslides / erosion- Sea level rise
- Temperature riseGLOF- Depletion of ground water- Hydropower- Trans-boundary and international coordination (MRC)- Shifting snow residency, melting period, snow-linebiodiversity

2. Issues related to Water Nexus: agriculture, energy, health – water quality, biodiversity, and ecosystem
A. Introduce issues related to Water Nexus in your country and identify two directions (see the example
below):
1. Water and Climate Change affect each Socio-Benefit Area (SBA)
2. Each SBA affects water and environment



SBA
Agriculture:Energy:Urban:



Ecosystem and Biodiversity:
Health

Infrastructure:

CC, Water, and Environment
water scarcity and surplus, crop failures  quality of surface
and ground water (fertilizer, pesticide)hydropower
water quality, ground water depletion, increase of municipal
water demand, inefficient municipal water management (lowtariff, unplanned conjunctive), decrease of flood plains,change in flow pattern, water diversion
water bone diseases (dry and wet spells: Malaria, Dengue,
flood: Diarrhea)
design and management

C. Respond to each of the following questions by considering water and climate change specifically for yourcountry:- How can we address seasonal variability at national level?
- How can we manage water resources in proper way between upstream and downstream and among
different sector uses: hydropower, irrigation, water supply?
- How can we give the right information to these different sectors? They are demanding for morecustomized climate information?
- How can we adapt the design criteria to changing characteristics and magnitude of water hazards, e.g.
for new drainage?
- How can we share the data to the different sectors beyond laboratories?

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B. Introduce on-going projects and programs related to Water Nexus in your country

3.

Needs for functions and/or tools of WCI to address the identified issues

Specify needs for your country:
Observations:- in-situ telemetric network (mountain areas)- remote sensing (satellite, radar) currently and in future
Data Access- satellite data access (operationally coupled with in-situ near real-time data)- global data access (Numerical Weather Prediction, Reanalysis, Climate Projection)ModelsManagement systems- Forecasting- Early Warning- Decision support
 National/local government (climate proofing, urban management, risk reduction measures,
adaptation strategies)
 community-based
Platform for sharing data and knowledge and exchanging ideas and experiencesCapacity building – describe in other section (Part 2: Implementation Proposal)
4.

Needs for collaboration framework at the national level: inter-agency, interdisciplinary

Please introduce existing activities and what kind of activities/framework is needed in your country withregards to each of the following points:






We need to show a holistic view of water and climate change and their impacts on water nexus to all
the stakeholders through sharing data and information, exchanging ideas and experiences, andworking together.
We need a well-organized interdisciplinary and inter-sectoral body at professional- and/or policy
making- levels by involving academia and civil societies.
We need to implement demonstrations and exchange good (failure) practices through regional
conferences/workshops.
We need criterion to maintain data quality, at least for rainfall, water level and hopefully riverdischarge and technical standards to design infrastructures in terms of water.

2. Implementation proposal
1.

Please describe Steps and Strategy following the three approaches:

Framework development approach – describe desirable framework in your country
Demonstration design  infrastructure integrityIntroducing legislationhigh level coordination bodyresearch promotionImprovementawarenessprivate sector involvementof

Strategic approachShowcase: intention, background, objectives, collaborations, achievements with accuracy and feasibility,
benefits to other sectors, interest  involvement one by one starting with existing inter-agency
collaborations)
Demonstrations  regional and general commonality

Expansion of the AWCI demonstration studies to a whole region  sharing experiences a holistic
understanding and technology.
Technical approach – propose a technical approach considering your target basin/country
MonitoringunderstandingClimate change assessment including downscaling, bias correctiondetailassessmentmodeldemonstration mainstreaming creation of regional knowledge

57

2.

Additional resources – suggestion of potential collaborators



Please identify local, national, regional, and worldwide (including UN) collaborators in the field of
research, operation, administration, financial and human resources supports. Please fill the matrix:
Collaborators----------Field

Local

National

Regional

Worldwide

Research

Operation

Administration

Financial res.

Human res.



Mainstreaming water and climate change within the national policy by getting supports from water
nexus. Please describe mainstreaming strategy suitable for your country.

3.

Specific request to GEOSS and to international community (data/tools accessibility)

Describe in a concrete way and specifically for your country needs:






Coordination between water cycle integration and capacity development strategy- Identify contents of capacity development needs in your country- Introduce existing and on-going activities and the needs and support related to these five items:
•
•

•
•
•

Synchronize capacity development with national implementation programme coordinated by theregional programme.
Training for not only researchers but also practitioners from top level to operator/technician’s level,with appropriate standards depending on the level (various kinds of training) including trainer’s
training to be followed by practice and identify it as a postgraduate program in collaboration withinternational educational framework (e.g. UNU, UN-CECAR).
Short term capacity development workshops on specific observation and modeling skills and medium
to long term supports to regional resource centers.
Coordinate with national and regional centers of excellence (ex. WMO centre in Hanoi on WR)
Organize capacity development workshops in each country for the agencies involved in the project atnational level on the WCI implementation. Identify agencies and participating organizations formaking such an opportunity.

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ARCP2011-02CMY-Koike - FINAL REPORT

4.

Inventory and summary directory – what kind is needed in your specific case
Data request function responding to new needs – what kind of function
Data access and information exchange
Models and Tools: analysis, prediction, early warning, risk assessment, decision support – what kind
for what purpose
Regional office and/or data center – what kind of function you expect for the office5.

Schedule

Apr. 2012 5th GEOSS AP Symposium: Preparation for Implementation Plan
Oct. 2012 4th AWCI Symposium: Approval of the 2nd stage implementation plan
2013-2015: Step 1 - demonstration project (feasible study) at each basin
2016-2018: Step 2 - project implementation at national and/or regional scale
Please make a rough design for step 1 and step 2.
Due date for the input: 27 JANUARY 201259

Conferences/Symposia/Workshops
1. The AWCI training course for the Climate Change Assessment and Adaptation Study
March 11 – 12, 2011
AgendaThe course is designated for the CCAA study leaders to get acquainted with several methods useful andnecessary for carrying out the proposed CCAA study in nominated river basins. The course will last for two
days.
First day: Friday 11 March 2011~09:20 Registration
09:20 – 09:30
1. Welcome and Introduction
09:30 – 12:00
2. Rainfall bias correction and downscaling methods by the University of Tokyo group
09:30 – 10:00
2.1 Method introduction – lecture (Prof. Koike)
10:00 – 10:30
2.2 Climate Model Output Evaluation and Selection
10:30 – 11:00
2.3 Climate Model Output Download and gap-filling
11:00 – 11:15 Break
11:15 – 12:00
2.4 Rainfall Bias Correction
12:00 – 12:15
Photo session (in front of the No. 1 Building)
12:15 – 13:30 Lunch
13:30 – (14:30) 2. Rainfall bias correction and downscaling methods by the University of Tokyo group –continue if necessary
If the session is completed earlier, the WEB-DHM part will begin earlier.
(14:30) – 18:00 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 teamThe session will include a break when appropriate
Second day: Saturday 12 March 2011
09:00 – 12:30 4. Flood modeling system IFAS (Integrated Flood Analysis System) applications by Dr. Fukami
and the ICHARM team (including break)
12:30 – 13:30 Lunch
13:30 – 17:00 5. Multi-model Hydrological modeling in use for Climate Change assessment by Prof. Bae –Sejong University (including break)

List of ParticipantsFirst Name

Last Name

Bangladesh

MafizurRahmanBangladesh

Bilqis

Hoque

BhutanKarma

Chhophel

CambodiaLong

Saravuth

Cambodia

So Im

Monichoth

India

Manohar

Arora

Indonesia

Muhammad

Syahril Badri
KusumaAffiliation
Bangladesh University ofEngineering and Technology(BUET)

Emailmafizur@gmail.com
bilqisdhaka@yahoo.com

Hydro-met Services, Departmentof Energy, Ministry of Economic
Affairs
Ministry of Water Resources andMeteorology
Ministry of Water Resources andMeteorology
National Institute of Hydrology,
India (NIH)
Research Division of Water
Resources Engineering of Facultyof Civil and Environmental
Engineering of ITB

60

hmsd@druknet.bt,
chhophel@gmail.com
lsaravuth@online.com.kh
simchoth@yahoo.com
manohararora19@gmail.com;
arora@nih.ernet.in;
msbadrik@yahoo.com

ARCP2011-02CMY-Koike - FINAL REPORTCountryJapanJapan
Japan

ToshioIchirow
Patricia Ann

Koike
Kaihotsu
Jaranilla Sanchez

University of Tokyo
Hiroshima University
University of TokyoJapan

Maheswor

Shrestha

University of Tokyo

Japan

Lei

Wang

University of TokyoJapan

KumikoTsujimoto

University of TokyoJapan

Katsunori

Tamagawa

University of TokyoJapan

Masahiro

Ryo

Japan

Nuryanto

Sasmito Slamet

Japan
Japan

Cho Thanda
Petra

Nyunt
Koudelova

Korea

Hee Sook

Ji

Korea

Moon Hwan

Lee

Korea

Kyung Hwan

Son

Korea

Deg-HyoBae

Malaysia

Mohd

Zaki Mat Amin

Mongolia

Gomboluudev Purevjav

Tokyo Institute of Technology
University of Tokyo (now back toIndonesia)
University of Tokyo
University of Tokyo
Sejong University, Water
Resources / GIS Labatory
Sejong University, Water
Resources / GIS Labatory
Sejong University, Water
Resources / GIS Labatory
Sejong University, Water
Resources / GIS Labatory(Research Centre for Water
Quality and Environment National
Hydraulic Research Institute of
Malaysia (NAHRIM))
Institute of Meteorology andHydrology,

Myanmar

Zin

Aung

Pakistan
Philippines
Nepal

GhulamEdna
Kedar Kumar

Rasul
Juanillo
Shrestha

Sri Lanka

G.R.A.S.

Gunathilake

ThailandThaworn

Onpraphai

ThailandThanapatay

Dusit

Thailand

Hansa

Vathananukij

VietnamTrang

Nguyen Thi Thu(Department of Meteorology andHydrology (DMH) of Myanmar)tkoike@hydra.t.u-tokyo.ac.jp
kaihotu@hiroshima-u.ac.jp
patricia@hydra.t.u-tokyo.ac.jp
maheswor@hydra.t.utokyo.ac.jp
wanglei@itpcas.ac.cn
tsujimoto@hydra.t.utokyo.ac.jp
tamagawa@hydra.t.utokyo.ac.jp
ryo.m.aa@m.titech.ac.jp

nyunt11@hydra.t.u-tokyo.ac.jp
petra@hydra.t.u-tokyo.ac.jp
sookkool@gmail.com
ycleemh@paran.com
skhdr@paran.com
dhbae@sejong.ac.kr

zaki@nahrim.gov.my
gomboluudev@imh.mn,
p_gombo@hotmail.com
dg.dmh@mptmail.net.mm;

Pakistan Meteorology Department rasulpmd@gmail.com
PAGASA/DOST
ejuanillo@yahoo.com
Department of Irrigation
Kedarshrestha38@gmail.com
Central Engineering Consultancy
Bureau;Dept.of Crop Science & Natural
thaworn@chiangmai.ac.th,
Resources, Fac.of Agriculture,
thaworn22@gmail.com
Chiang Mai University(Kasetsart University, Faculty of
fengdus@ku.ac.th
Engineering)(Kasetsart University, Faculty ofhansa.v@ku.ac.th
Engineering, RM-GIS Center)
National Center for
Hydrometeorological Forecasting Thutrang210980@yahoo.com(NCHF)

2. The 8th Meeting of the GEOSS Asian Water Cycle Initiative International Coordination Group(AWCI ICG) and the 1st AWCI Climate Change Assessment and Adaptation (CCAA) study Workshop
Seoul, South Korea, 6 – 8 October, 2011
AgendaThursday 6 October
09:00 – 09:30
09:30 – 10:00

Registration
1. Opening Session, Welcome Remarks, Photo61

09:30 – 09:40
Deghyo Bae (Sejong University)
09:40 – 09:50
Jae Heyon Park (Water Resources division, Ministry of Land, Transport and Maritime Affairs)
09:50 – 10:00
Photo session
10:00 – 12:00
2. AWCI Working Group Activity Review Session
10:00 – 10:20
General review of the AWCI status: Toshio Koike
10:20 – 10:40 BREAK
10:40 – 10:55
Flood WG report:
Kazuhiko Fukami and Srikantha Herath
10:55 – 11:10
Drought WG report:
Ichirow Kaihotsu and Ghulam Rasul
11:10 – 11:25
Water Quality WG report: Bilqis Hoque
11:25 – 11:40
Climate Change WG report: Deghyo Bae and Mafizur Rahman
11:40 – 12:00
Capacity Building report:
Srikantha Herath
12:00 – 13:00 Lunch
13:00 – 17:00
3. Country Activity Review and Possible Contributions to the AWCI Next Stage Session
In this session, country and expert reports will be presented. The country reports should cover two main items:(i) Report on current country activities related to AWCI, which includes demonstration projects, Climate Change
Assessment and Adaptation (CCAA) study and other activities, and (ii) Ideas and views of possible country
involvement in and contribution to the next stage of AWCI that is envisioned in line with the GEOSS Water Cycle
Integrator (WCI). Introduction of the WCI mission and concept as well as instructions for presentation
preparation have been provided to the presenters in advance. One report per country is expected, presented byeither the ICG country representative or CCAA leader.
The expert reports include presentations on research and technical achievements related to the AWCI activities
that are not included in the country reports. Presentation time 15 min, i.e. 13 min presentation and 2 mindiscussion; i.e. four contributions in an hour
13:00 – 15:30
13:00 – 13:15
13:15 – 13:30
13:30 – 13:45
13:45 – 14:00
14:00 – 14:15
14:15 – 14:30
14:30 – 14:45
14:45 – 15:00
15:00 – 15:15
15:15 – 15:30

First part (10 country reports)
Vietnam:Tinh Dang Ngoc
Thailand:Thada Sukhapunnaphan
Sri Lanka:
S.B. Weerakoon
Philippines:
Rosalina de Guzman
Nepal:
Shiv Kumar Sharma
Myanmar:Tin YiMongolia:
Gombo Davaa
Malaysia:
Mohd Zaki Mat Amin
Laos:
Singthong Pathommady
Korea:
Deg-Hyo Bae

16:00 – 18:00
16:00 – 16:15
16:15 – 16:30
16:30 – 16:45
16:45 – 17:00
17:00 – 17:15
17:15 – 17:30
17:30 – 17:45
17:45 – 18:00
18:00 Adjourn
18:30 Reception

Second part (4 country reports and 3 expert presentations)India:
Rakesh Kumar
Cambodia:Long Saravuth
Bhutan:Karma Chhophel
Bangladesh:
Ashfakul Islam
Water Quality Challenges in Bangladesh:
Bilqis Hoque
Community Based Management of Agricultural Resources at Watershed Level for Sustainable
Livelihoods:
Thaworn Onpraphai
Climate Change Assessment and Adaptation:
Cho Thanda Nyunt
Discussion

Friday 7 October
09:00 – 10:30
3. Country Activity Review and Possible Contributions to the AWCI Next Stage Session – continue:
Third part (6 expert presentations)
Presentation time 15 min, i.e. 13 min presentation and 2 min discussion; i.e. four contributions in an hour
09:00 – 09:15
Thailand 2011 Experiences on Flood Monitoring:
Hansa Vathananukij
09:15 – 09:30
Flood Predictability and Optimization of Water Resources Management:
Oliver Saavedra
09:30 – 09:45
Introduction of GIT4CC (Green Infrastructure Technology for Climate Change): Hyoungkwan Kim
09:45 – 10:00
Drought Monitoring and Prediction:
Patricia Sanchez
10:00 – 10:15
Modeling of Snow- and Glacier- melt Runoff Simulation:
Maheswor Shrestha
10:15 – 10:20
Discussion
10:20 – 10:50 BREAK62

ARCP2011-02CMY-Koike - FINAL REPORT

15:30 – 16:00 BREAK

10:50 – 12:30
4. Capability of Observation, Data Integration and Prediction Session
The presentations will focus on capabilities possibly contributing to the GEOSS WCI framework. Each will beallocated ~ 20 minutes (including questions).
10:50 – 11:10
JAXA:
Toru Fukuda
11:10 – 11:30
JMA:
Hirotaka Kamahori
11:30 – 11:50
KMA: Climate Change Projections using Representative Concentration Patheays and HadGEM2-AO
Climate Model:
Kyung-On Boo
11:50 – 12:10
ICHARM:
Kazuhiko Fukami
12:10 – 12:30
UNU-CECAR:
Srikantha Herath
12:30 – 13:30 Lunch
13:30 – 14:30
4. Capability of Observation, Data Integration and Prediction Session - continue
13:30 – 14:30
GEOSS Water Cycle Integrator toward Asian regional coordination: Toshio Koike
14:30 – 16:00
5. Breakout discussion session 1 – GEOSS WCI: needs and capabilities
14:30 – 14:45
Introduction and instructions for the breakout groups: Toshio Koike
Individual groups (3 – 4) are to be decided. Reference material includes the WCI document and the “Green
growth with blue” document. Downloadable from the meeting website at:
http://monsoon.t.u-tokyo.ac.jp/AWCI/meetings/Seoul_Oct2011/agenda.htm
14:45 – 16:00
Breakout group discussion
The groups will discuss several points including (i) possible scope of involvement in the WCI framework
identifying needs and capabilities, (ii) coordination and integration of activities across the working groups under
the WCI framework, (iii) possible contribution to Rio+20.
16:00 – 16:20 BREAK
16:20 – 18:30
6. Breakout discussion session 2 – GEOSS WCI: practical implementation ideas
The groups will discuss practical implementation of the WCI targets at the AWCI demonstration basins. Ideas forparticular projects should be formed that will be complied into an Implementation plan before the January 2012
GEOSS Asia-Pacific Symposium.
Individual groups (3 – 4) are to be decided.
18:30 Adjourn
Saturday 8 October
09:00 – 09:30
7. Breakout session summary
09:30 – 10:30 8. Implementation planning for a regional coordination project targeting Climate Change Adaptation
This is a discussion plenary session, synthesizing the outcomes of the meeting into suggestions/instructions for
an implementation plan of the AWCI next step.
10:30 – 11:00 BREAK
11:00 – 11:30
9. Regional Proposal to Rio+20
This is a discussion plenary session that will produce a proposal of AWCI contribution to Rio+20
11:30 – 12:00
10. Summary and Closing session
12:30 Adjourn

List of Participants

NameCountry

Affiliation

Email

1 Jae-Hyun Park

Korea

Ministry of Land, Transport andtruss1@mltm.go.kr
Maritime Affairs, Korea

2 Deg-Hyo Bae

Korea

Sejong University

3 Kyung-On Boo

Korea

National Institute of Meteorologicalkyungon@korea.kr
Research, Korea

4 Hyungkwan Kim

Korea

Yonsei University

hyoungkwan@yonsei.ac.kr

5 Toshio Koike

Japan

University of Tokyo

tkoike@hydra.t.u-tokyo.ac.jp

6 Srikantha HerathJapan

United Nations University (UNU)

herath@unu.edu

7 Yi Wang

Japan/China

United Nations University (UNU)

ywang@unu.edu

8 Takao Akutsu

Japan

JAXA

akutsu.takao@jaxa.jp

9 Ochiai Osamu

Japan

JAXA

ochiai.osamu@jaxa.jp

63dhbae@sejong.ac.kr

10 Toru Fukuda

Japan

JAXA

11 Hirotaka Kamahori

Japan

MRI-JMA, Climate Research Divisionhkamahor@mri-jma.go.jp

12 Tomoko Fujita

Japan

UT

fujita@hydra.t.u-tokyo.ac.jp

13 Kazuhiko Fukami

Japan

ICHARM

k-fukami@pwri.go.jp

14 Karma Chophel

Bhutan(Hydro-met Services)(Department
RiverWorks)(Department
RiverWorks)

hmsd@druknet.bt

Hydrology

andHydrology

and

15 Long Saravuth

Cambodia

16 So Im Monichoth

Cambodia

17 Lei Wang

China

ITP/CAS (former UT)

wang@hydra.t.u-tokyo.ac.jp

18 Rakesh Kumar

India(National Institute of Hydrology)

rk18p@yahoo.co.inrakesh@nih.ernet.in

19 Manohar Arora

India(National Institute of Hydrology)

manohararora19@gmail.com

20

Singthong
PathoummadyLaos

DDG of DMH, MoNRE

singthong_dmh@etllao.com;
p.singthong@yahoo.com

21

Chanseng
Phongpachith

Laos

Head of Water Resource Division;
Water and Environment Research chanseng@wrea.gov.laInstitute, MoNRE

Myanmar(Department of Meteorology and dg.dmh@mptmail.net.mm;
Hydrology (DMH) of Myanmar)
tinyee@gmail.com

23 Rosalina de Guzman Philippines

24 Hansa Vathananukij

Thailand25 Thaworn Onpraphai Thailand
Thada
Sukhapunaphan
Nguyen Thi Thu
27
Trang (Ms)
26

Thailand
Vietnam

28 Dang Ngoc Tinh

Vietnam

29 Oliver SaavedraJapansimchoth@yahoo.com

Philippine Atmospheric, GeophysicalandAstronomical
Services
rdeguzmanph@yahoo.com
Administration(PAGASA),Departmentof Science of Technology(DOST)(Kasetsart University, Faculty of hans_vat@yahoo.com,
Engineering)hansa.v@ku.ac.th
Dept.of Crop Science & Natural
thaworn@chiangmai.ac.th,
Resources, Fac.of Agriculture, Chiang
thaworn22@gmail.com
Mai Univ. Chiang Mai, Thailand(Ministryof
Agriculture
andthada999@yahoo.com
Cooperatives)
National
Centreforthutrang210980@yahoo.com
HydroMeteorological(Deputy
ChiefofNorthern
Hydrological forecasting Division
National
Centerfor tinhdangngoc@fpt.vn
Hydrometeorological
Forecasting(NCHF))
TITECHsaavedra.o.aa@m.titech.ac.jp

30 Maheswor Shrestha Japan/Nepal

University of Tokyo

maheswor@hydra.t.u-tokyo.ac.jp

31 Thanda Nyunt

Japan/Myanmar

University of Tokyonyunt11@gmail.com

32 Petra Koudelova

Japan/Czech

University of Tokyo

petra@hydra.t.u-tokyo.ac.jp

Patricia Jaranilla
Sanchez
Mohammad34
Ashfakul Islam
33

Japan/Philippines University of Tokyo

patricia@hydra.t.u-tokyo.ac.jp

Bangladesh(Ministry of Defense)

ashfak2258@yahoo.com

35 Md. Mafizur Rahman Bangladesh(BUET)

mafizur@gmail.com

36 G. Karma Chhopel(National Environment Commission) gkchhopel@hotmail.com

Bhutan37 Mohd Zaki Mat Amin Malaysia;(Research Centre for Water Quality
and Environment National Hydrauliczaki@nahrim.gov.my
Research Institute of Malaysia(NAHRIM))

64

ARCP2011-02CMY-Koike - FINAL REPORT

22 Tin Yi

lsaravuth@online.com.kh

38 Gombo Davaa

Mongolia

Gomboluudev
Purevjav

Mongolia

40 S. B. Weerakoon

Sri Lanka

39(Institute of Meteorology andwatersect@yahoo.com
Hydrology)
Head of Forecast Research Lab,
Institute of Meteorology and p_gombo@hotmail.com
Hydrology
University of Peradenia

sbweera@pdn.ac.lk

41 G.R.A.S.Gunathilake Sri Lanka

Central Engineering Consultancy gunda_1962@yahoo.com;
Bureau; Deputy General Manager
cecbdigana@yahoo.com

42 Bashir Ahmad

Pakistan(Water Resources Research Institute/
National
Agriculture
Research dr.bashir70@gmail.com
Center)43 Ghulam Rasul

Pakistan(PMD) (Drought WG Co-Chair)rasulpmd@gmail.com

44 Shiv Kumar Sharma

Nepal(Department of Water
Disaster Prevention)

shiv1301@gmail.com

Kedar Kumar
Shrestha

Nepal(Department of Irrigation)

Shrestha_Kedarkumar@yahoo.com,
Kedarshrestha38@gmail.com

46 Bilqis Hoque

Bangladesh(Water Quality)

bilqisdhaka@yahoo.com

47 Sang-Hun Lee

Korea

Sejong University

sanghunchang@hotmail.com

48 Seong-Sim Yoon

Korea

Sejong University

lome970303@paran.com

49 Kyung-Hwan Son

Korea

Sejong University

skhdr@paran.com

50 Moon-Hwan Lee

Korea

Sejong University

ycleemh@paran.com

51 Jeong-Hun Shin

Korea

Sejong University

hianyone@naver.com

52 Sung-Keun Moon

Korea

Sejong University

crt2000@nate.com

Jehangir Ashraf
Awan

Korea/Pakistan

Sejong University

jehangir_awan@hotmail.com

54 Nguyen Huu Toan

Korea/Vietnam

Sejong University

Huutoanmt@gmail.com

45

53

Induced3. The GEOSS Asian Water Cycle Initiative (AWCI) Parallel Session of the 5th GEOSS AP Symposium,
Tokyo, Japan, 2 – 4 April, 2012
Agenda (AWCI Session on 3 April)Floods and droughts are a recurring problem in Asia. Rapid population and economic growth is straining
existing water resources and the ecosystem. The expansion of urbanization and the climate change isconsidered to make considerable impacts on such a vulnerable region. The AWCI aims to develop an
Integrated Water Resources Management (IWRM) by exploiting the Global Earth Observation System of
Systems (GEOSS) to address the water-related issues described above.
The AWCI member country reports presented at the 8th AWCI meeting in Seoul in October 2011 showed agreat progress in the AWCI demonstration projects and good preparedness of individual countries to step into
the next phase that envisions further integration and inter-disciplinary collaboration through the GEOSS Water
Cycle Integrator (WCI) concept of “work benches”. Following this concept, the member countries haveinitiated preparation of a draft implementation plan for the 2nd stage of AWCI.
The objective of this breakout session is to introduce each draft implementation plan and to establish make an
integrated cooperative framework among the member countries, the Earth observation communities, science
communities and the related international activities.
Co-Chairs:
Richard Lawford (GEO Water)
Syahril Badri Kusuma (Institute of Technology Bandung (ITB))
Toshio Koike (The University of Tokyo)

65Participants (AWCI Session on 3 April)
Representative

Country

1 Mohammad Ashfakul Islam Bangladesh
2 Md. Mafizur Rahman
Bangladesh
3 Karma Chophel
Bhutan4 Rakesh Kumar
India
5 Surinder Kaur
India

Affiliation

Email Address(Ministry of Defense)(BUET)(Hydro-met Services)(National Institute of Hydrology)(India Meteorological Department)

ashfak2258@yahoo.com
mafizur@gmail.com
hmsd@druknet.bt
rk18p@yahoo.co.inskaurg@yahoo.com

66

ARCP2011-02CMY-Koike - FINAL REPORT

09:30-09:40 Opening GEOSS/AWCI Breakout Session
Opening Remarks
D. Cripe, GEO Secretariat
09:40-10:30 GEOSS/AWCI Activity Reports
10min. each
1. Working Group Reports by Chair(s): Flood WG K.
Fukami, ICHARM
 Droughts WG
G. Rasul, PMD, I. Kaihotsu, HU
 Water Quality WG
P. Koudelova, UT
 Climate Change WG
M. Rahman, BUET
2. Capacity Building Activities by Lead(s):S. Herath, UN Univ.
10:30-12:30 Brief introduction to Draft Implementation Plans: Part1
5min. presentation + discussion
1. Bangladesh:
M.A. Islam
2. Bhutan:
K. Chophel
3. India:
R. Kumar/S. Kaur
4. Indonesia:
S.B. Kusuma
5. Japan:
T. Koike
6. Laos:
S.Pathoummady
7. Malaysia
M.Z.M. Amin
8. Mongolia:
G. Davaa
9. Myanmar :S. Lin10. Nepal:
S.K. Sharma
11. Pakistan:
G. Rasul/B. Ahmad
12. Philippines:
F. Hilario
13. Sri Lanka:
G.R.A.S. Gunathilake
14. Thailand:
T. Sukhapunaphan
15. Uzbekistan:
I. Dergacheva
16. Vietnam:
D.N. Tinh
12:30-13:30 Lunch Break
13:30-15:15 Inputs from Agencies
15min. each including discussion
1. Asia-Pacific Network for Global Change Research (APN)
2. United Nations Educational, Scientific and Cultural Organization (UNESCO) T. Sonoda
3. Network of Asian River Basin Organizations (NARBO)
T. Kawasaki
4. Japan International Agency (JICA)
Y. Amano
5. Japan Aerospace Exploration Agency (JAXA)
R. Oki
6. Japan Meteorological Agency (JMA)
K. Oonogi
7. International Centre for Water Hazard and Risk Management(ICHARM) K. Fukami
15:15-15:30 Break
15:30-17:15 Discussion towards an Integrated Cooperative Framework
All
Common Targets and Fields:
Regional Coordination FrameworkLinkage to Global Coordination Framework
Building capacityPlanning Strategy
17:15-17:30 Closing GEOSS/AWCI Breakout Session
 Session Summary
 Concluding Remarks

Indonesia
Laos(Faculty of Civil and Environmental
Engineering of ITB)
DDG of DMH, MoNRE

msbadrik@yahoo.com
singthong_dmh@etllao.com

8 Mohd Zaki Mat Amin
9 Gombo Davaa

Malaysia
Mongolia(Research Centre for Water Quality andEnvironment National Hydraulic ResearchInstitute of Malaysia (NAHRIM))(Institute of Meteorology and Hydrology)

zaki@nahrim.gov.mywatersect@yahoo.com

10 Sein Lin

Myanmar(Department of Meteorology and Hydrology
dg.dmh@mptmail.net.mm(DMH) of Myanmar)

11 Shiv Kumar Sharma
12 Ghulam Rasul

Nepal
Pakistan(Department of Water Induced Disaster
Prevention)(PMD) (Drought WG Co-Chair)shiv1301@gmail.com
rasulpmd@gmail.com

13 Bashir Ahmad
14 Flaviana Hilario

Pakistan
Philippines(Water Resources Research Institute/
National Agriculture Research Center)(PAGASA/ DOST)

bashirad@hotmail.com
fhilarioph@yahoo.com

15 G.R.A.S.Gunathilake
16 Thada Sukhapunaphan
17 Irina Dergacheva
18 Nguyen Thi Thu Trang

Sri Lanka
ThailandUzbekistan
VietnamCentral Engineering Consultancy Bureau;
Deputy General Manager(Ministry of Agriculture and Cooperatives)
NIGMI
National Centre for HydroMeteorologicalgunda_1962@yahoo.com
Thada999@yahoo.com
dergacheva_iv@mail.ru
thutrang210980@yahoo.com

Vietnam(Deputy Chief of Northern Hydrologicalforecasting Division
National Center for Hydrometeorological
Forecasting (NCHF))

tinhdangngoc@fpt.vn

6 Syahril Badri Kusuma
7 Singthong Pathoummady

19 Dang Ngoc Tinh

Funding sources outside the APN
1. University of Tokyo (Source: Ministry of Education, Culture, Sports, Science, and Technology(MEXT), Japan)- In kind support:(i) Data archiving and integration functions – DIAS system(ii) Project management and coordination functions(iii) Expertise in hydrological modeling and Climate change
assessment- Monetary support:
Travel support of AWCI members to attend meeting events; roughly
60,000 USD
2. Japan Aerospace Exploration Agency (JAXA)- In-kind support:(i) Expertise in remote sensing data(ii) Provision of satellite data and appropriate tools for theirprocessing
3. Japan Meteorological Agency- In-kind support:
Atmospheric model data (forecast and reanalysis)
4. Hiroshima University- In-kind support:

Expertise in drought and satellite soil moisture data

5. Sejong University- In-kind support:

Expertise in hydrological modeling and climate change assessment

6. Each AWCI member country- In-kind support:
In-situ data from selected river basins
67

Glossary of Terms
ADB
Asian Development Bank
AIT
Asian Institute of Technology
AOGCM
Atmosphere-Ocean General Circulation ModelAPRSAF
Asia-Pacific Regional Space Agency Forum
AWCS
Asian Water Cycle Symposium
AWCI
Asian Water Cycle Initiative
BATS
Biosphere-Atmosphere Transfer Scheme
CB
Capacity building
CCAA
Climate Change Assessment and Adaptation
CDF
Cumulative Distribution Function
CEOP
Coordinated Energy and Water Cycle Observations Project
CMI
Crop Moisture Index
DHM
Distributed Hydrological Model
DIAS
Data Integration and Analysis System
DP
Demonstration Project
DRESS
Dam Release Support System
GCM
General Circulation Model
GCOE
Global Center of Excellence
GEO
Group on Earth Observations
GEOSS
Global Earth Observation System of Systems
GEWEX
Global Energy and Water Cycle Experiment
GGB
Green Growth with Blue
GPD
General Pareto Distribution
GSMaP
Global Satellite Mapping of Precipitation
ICG
International Coordination Group
IFM
Integrated Floods Management
IIWaDATAInternational Integrated Water Data Access and Transfer in Asia
IRBM
Integrated River Basin Management
IWRM
Integrated Water Resources Management
ICHARM
International Centre for Water Hazard and Risk Management
JAXA
Japan Aerospace Exploration Agency
JICA
Japan International Cooperation Agency
LAI
Leaf Area Index
MEXT
Ministry of Education, Culture, Sports, Science, and Technology
ODA
Official Development Assistance
OCDI
Observation Convergence and Data Integration
NDVI
Normalized Difference Vegetation Index
NGO
Non-Governmental Organization
NWP
Numerical Weather Prediction

68

ARCP2011-02CMY-Koike - FINAL REPORT

List of Young Scientists
In addition to the AWCI ICG members including representatives of governmental institutions(ministries and meteorological and hydrological services), this project involved a number of
researchers, doctoral and master students affiliated with AWCI collaborating institutions andorganizations. At the University of Tokyo, Prof. Koike’s laboratory, certain activities of this projectand generally of AWCI (mostly the tools and techniques development and pilot studies in somebasins) were undertaken as part of PhD. or Master Course research of several international students
coming from AWCI participating countries (e.g. Indonesia, Myanmar, Nepal, Pakistan, Philippines, Sri
Lanka). Among those are also Dr. M. Shrestha (Nepal), Dr. K. Tsujimoto (Japan), and Dr. P.A. JaranillaSanchez (Philippines), who received the awards listed in the non-technical summary.

PDS
PDSI
PMD
PP
QPF
R-CDTA
RS
SA
SAFE
SPI
SVAT
SWSI
TRMM
TVDI
UT
UNU
WCRP
WEB-DHMWEB-DHM-S
WMO
WSSD

Partial Duration Series
Palmer Drought Severity Index
Pakistan Meteorological Department
Probability of Precipitation
Quantitative Precipitation Forecast
Regional Capacity Development Technical Assistance
Remote SensingStandardized Anomaly Index
Space Application for Environment
Standardized Precipitation Index
Soil-Vegetation-Atmosphere Transfer
Surface Water Supply Index
Tropical Rainfall Measurement Mission
Temperature Vegetation Dryness Index
University of Tokyo
United Nations University
World Climate Research Project
Water and Energy Budget Distributed Hydrological Model
Water and Energy Budget Distributed Hydrological Model – Snow (with advancedsnow and glacier scheme)
World Meteorological Organization
World Summit on Sustainable Development

Access to the data, meeting presentations and reports:
Access to the AWCI in-situ data is available through the DIAS portal:
http://dias-dss.tkl.iis.u-tokyo.ac.jp/ddc/finder?lang=en
Access to the meeting presentations and reports is through the AWCI website at:
http://monsoon.t.u-tokyo.ac.jp/AWCI/- page “Meetings”- page “Publications, Documents”

69