Flood Risk Management Demonstration Project Under the Asian Water Cycle Initiative for the Global Earth Observation System of Systems (FRM/AWCI/GEOSS)

FINAL REPORT for APN PROJECT ARCP2009-01CMY-Fukami

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The following collaborators worked on this project: Kazuhiko FUKAMI

International Centre for Water Hazard and Risk Management Under the auspices of UNESCO(UNESCO-ICHARM), Public Works Research Institute, Japan, [email protected] Srikantha HERATH United Nations University, [email protected]

Flood Risk Management Demonstration Project under the Asian Water Cycle Initiative for the Global Earth Observation System of Systems (FRM/AWCI/GEOSS)

Project Reference Number: ARCP2009-01CMY-Fukami Final Report submitted to APN

©Asia-Pacific Network for Global Change Research

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OVERVIEW OF PROJECT WORK AND OUTCOMES

Non-technical summary

This project is aiming to build up a scientific basis for sound decision-making and developing policy options for most suitable flood risk management in the Asia Pacific Region, through the full utilization of new opportunities on global, regional and in-situ dataset, knowledge and/or resources under the framework of Asian Water Cycle Initiative (AWCI) contributing to GEOSS (Global Earth Observation System of Systems). To attain the goal above, the following three objectives were specified: 1. To convert observations and data, both through space borne platforms and data integration initiatives, to usable information for flood reduction 2. To improve quantitative forecasts for coupled precipitation - flood-forecasting systems 3. To facilitate flood risk assessment through the provision of scenarios and data for exposure estimation The research result is constructed by an aggregation of each voluntary research activity in each member country based on mutual intensive information/human exchanges and cooperative research activities. Several key technologies such as WEB-DHM, DRESS & FLOWSS of UT, IFAS of ICHARM, RegHCM-PM of NAHRIM, and so forth, and practical approaches were developed and validated. All of those technologies and practices will be the basis for sustainable flood risk management in the Asia Pacific Region in the future.

Objectives

This project was aiming at enhancing and utilizing regional cooperation to achieve the following three targets with the use of the resources and knowledge available at various specialized institutions in the Asia Pacific Region under the framework of Asian Water Cycle Initiative (AWCI) contributing to GEOSS (Global Earth Observation System of Systems). 1. To convert observations and data, both through space borne platforms and data integration initiatives, to usable information for flood reduction 2. To improve quantitative forecasts for coupled precipitation - flood-forecasting systems 3. To facilitate flood risk assessment through the provision of scenarios and data for exposure estimation

Amount received and number years supported

The Grant awarded to this project was: US$ 42,000 for Year1, 2008-2009: US$ 42,000 for Year 2, 2009/2010:

Activity undertaken

The researches of this project were conducted through utilizing the framework and activities of FINAL REPORT FINAL

- GEOSS-AWCI. Namely, the activities are composed by the following steps and cycles: 1) To exchange information/knowledge on studies in member countries relevant to the above goal

and objectives through the GEOSS-AWCI regular meetings. FUKAMI - 2) To develop (and/or adapt existing, with improvements) methodologies, tools and basic datasets

related to the objectives, in each member country, as a demonstration project, referring to the 01CMY

- above information/knowledge. Demonstration projects include the following two typical patterns:

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a) To enhance in-situ field hydrological observations for each study basin, to apply the above methodology/tool to the basin, to validate and verify the applicability of the methodology/tool and to clarify the future subjects to make it from research phase to operational phase b) To investigate historical water-related (in particular, flood) disaster events, to collect hydrological/meteorological/socioeconomic data for the extracted flood events, to apply the above methodology/tool to the past flood events as case studies, to validate and verify the applicability of the methodology/tool and to clarify the future subjects to make it from research phase to operational phase 3) To exchange research results/findings/experiences through the above demonstration projects and discuss the furthermore interactive cooperation among member countries through the above meetings. The GEOSS-AWCI’s regular meetings have been held as International Coordination Group (ICG) Meetings five times during the project period. In addition to that, another specifically-focused in- depth workshop titled 1st International Workshop on Application and Validation of Global Flood Alert System (GFAS)

Results

As a result of 2-year cooperative research activities under the framework of Flood WG of GEOSS- AWCI, there have emerged many promising technologies and practices for the future sustainable flood risk management. Most typical new technologies developed and/or validated through those activities are WEB-DHM, DRESS & FLOWSS of UT, IFAS of ICHARM, RegHCM-PM of NAHRIM, and so forth. Through repetitive meetings, discussions and cooperative activities, advanced technologies and many other innovative practices have been also shared among all the members of Flood WG of GEOSS-AWCI, which will be expected to lead to updating and enhancing a variety of science- & data- based foundations toward sound decision-making and developing policy options for effective flood disaster risk reduction in Asia.

Relevance to APN’s Science Agenda and objectives

This project contributes to the Asian Water Cycle Initiative (AWCI) contributing to the Global Earth Observation System of Systems (GEOSS), which will assess regional vulnerability of natural and human systems from floods under changing environmental conditions, and contributes to the development of policy options for appropriate local and regional responses. These objectives are directly in line with the mission of APN and thus are relevant to the APN science and policy agenda. The project will directly contribute to enhancing regional cooperation, strengthen interaction between scientists and policy makers and will improve scientific and technical capabilities of Asian region nations, fitting very well to the APN activity framework.

Self evaluation

Several promising technologies have been developed and validated in this APN project such as WEB- DHM, DRESS, FLOWSS, IFAS, RegHCM-PM, and more that can be basic ones for efficient & effective

integrated flood risk management in the Asian Pacific Region. Besides, a variety of practices and REPORT FINAL - experiences of other case studies in many Asian countries using new emerging technologies,

methodologies and data have been accumulated in each country, which will be the basis for the FUKAMI establishment of methodologies and systems for integrated flood risk management as well. There - still remains relatively a long way from the present research phase to the future operational phase, it

should be highly evaluated that each of three objectives are substantially met through such several 01CMY key technologies and practices. Finally, the importance of human connections and confidence- -

building with each other among members of Flood WG of GEOSS-AWCI strengthened through

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cooperative activities should be emphasized. This will surely be useful for future further development of our mutual international cooperation and coordination toward real implementation of new-generation flood risk management system using GEOSS data.

Potential for further work

The same as the above (self evaluation) should be mentioned here. That is, the developed promising technologies and practices will be the basis for the establishment of methodologies and systems for integrated flood risk management. As mentioned above as well, there still remains relatively a long way from the present research phase to the future operational phase, which should be the future subject.

Publications

Kwak, Y.J., Hasegawa A., Inomata H., Magome, J., Fukami K., and Takeuchi K. (2011) A new assessment methodology for flood risk: a case study in the Indus River basin, IAHS Publ.347-07, Risk in Water Resources Management (Proceedings of Symposium H03 held during IUGG2011 in Melbourne, Australia, July 2011).

Ozawa, G., Y. Shiraishi, H. Inomata and K. Fukami (2011) APPLICABILITY OF GSMAP CORRECTION METHOD TO TYPHOON “MORAKOT” IN TAIWAN, Annual Journal of Hydraulic Engineering, JSCE, Vol.55, accepted.

Saavedra, O. and Koike, T. (in press, 2010) Towards Global River Discharge using a Distributed Hydrological Model. Investigación & Métodos, Vol. Journal of the Bolivian Private University, accepted in December 2009.

Saavedra, O., Koike, T., Yang, D., Nyunt, C., T., Khanh, D. V. & Chau, L.C. (2009) Flood simulation using different sources of rainfall in the Huong River, Vietnam. Hydrological Sciences Journal, 54(5), pp. 909-917.

Saavedra, O., Koike, T., Yang, K. & Yang, D. (2010) Optimal Dam Operation during Flood Season using a Distributed Hydrological Model and a Heuristic Algorithm, Journal of Hydrological Engineering, ASCE, manuscript accepted in October 2009 (in press).

Saavedra, O., Toshio Koike, Lei Wang, L. (2010) Multi-Reservoir Operation Using a Distributed Biosphere Hydrological Model, Annual Journal of Hydraulic Engineering, JSCE, vol.54, pp.103-108.

Sayama, T., G. Ozawa, T. Kawakami, S. Nabesaka and K. Fukami (2011) Rainfall-Runoff-Inundation

Analysis of Pakistan Flood 2010 at the Kabul River Basin, Hydrological Sciences Journal (submitted).

Shiraishi et al. (2009) A proposal of correction method using the movement of rainfall area on satellite-based rainfall information by analysis in the Yoshino River Basin, Annual Journal of Hydraulic

Engineering, JSCE, vol. 53, pp. 385-390 (in Japanese). FINAL REPORT FINAL - Shrestha, M., Lei Wang, Toshio Koike (2010) Investigating the applicability of WEB-DHM to the

FUKAMI Himalayan river basin of Nepal, Annual Journal of Hydraulic Engineering, JSCE, vol.54, 55-60. -

Sugiura et al.(2009) Development of Integrated Flood Analysis System (IFAS) and its applications, 01CMY - Proceedings of the 8th International Conference on Hydroinfomatics, January 12-16, 2009,

Concepción, Chile.

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Sugiura, T., T. Kawakami, G. Ozawa, J. Magome and K. Fukami (2010) Experimental application of flood forecasting system (IFAS) using satellite-based rainfall, 9th International Conference on Hydroinformatics 2010.

Wang, L., T. Koike, K. Yang, and P. Yeh (2009) Assessment of a distributed biosphere hydrological model against streamflows and MODIS land surface temperature in the upper Tone River Basin, Journal of Hydrology, 377, 21-34.

Wang, L., T. Koike, D. Yang, and K. Yang (2009), Improving the hydrology of the Simple Biosphere Model 2 and its evaluation within the framework of a distributed hydrological model, Hydrological Sciences Journal, 54(6), 989-1006.

Wang, L., C. T. Nyunt, T. Koike, O. Saavedra, L. C. Nguyen, T. V. Sap (2010) Development of an integrated modeling system for improved multi-objective reservoir operation, Frontiers of Architecture and Civil Engineering in China, 4(1), 47-55.

Wang, L., T. Koike, K. Yang, R. Jin, and H. Li (2010) Frozen soil parameterization in a distributed biosphere hydrological model, Hydrology and Earth System Sciences, 14, 557-571.

Wang, L., Z. Wang, T. Koike, H. Yin, D. Yang, S. He (2010) The assessment of surface water resources for the semi-arid Yongding River Basin from 1956 to 2000 and the impact of land use change. Hydrological Processes, 24, 1123-1132.

References

There are many references for this report. They are listed in the “Reference” of the following “TECHNICAL REPORT”.

Acknowledgments

I would like to express my sincerest thanks to all the members and collaborators of Flood WG of the GEOSS-AWCI for their kind provision of all the presentation materials t as references to this report. I would also like to express my warmest thanks to Dr. Toshio KOIKE, Dr. Petra Koudelova, Dr. Akiko GODA & Katsunori TAMAGAWA of UT, Dr. Srikantha Herath of UNU, Dr. Chu ISHIDA & Dr. Yoko INOMATA of JAXA for their outstanding cooperation and coordination to organize the regular

GEOSS-AWCI ICG meetings.

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TECHNICAL REPORT

Preface Asian Pacific countries have been suffering from flood disasters every year, which have been big barriers not only to reduce natural disaster casualties/damages but also to promote social welfares and economy in those countries. This project was proposed to contribute to flood disaster reduction through enhancing sustainable flood risk management with GEOSS data. Under the cooperative framework of GEOSS-AWCI, information exchanges and cooperative studies were promoted, and lots of new developments and local studies were conducted in each AWCI member country. The report summarizes the outline of those research activities. Please refer to references for more details of each study.

Table of Contents 1.0 INTRODUCTION ...... 6 2.0 METHODOLOGY ...... 7 3.0 RESULTS & DISCUSSION ...... 10 3.1 DEMONSTRATION RESEARCHES AND PRACTICES IN EACH COUNTRY...... 10 3.1.1 BANGLADESH...... 10 3.1.2 BHUTAN ...... 12 3.1.3 CAMBODIA...... 12 3.1.4 CHINA ...... 14 3.1.5 INDIA ...... 14 3.1.6 INDONESIA ...... 15 3.1.7 JAPAN ...... 18 3.1.8 KOREA ...... 25 3.1.9 LAO PDR ...... 25 3.1.10 MALAYSIA ...... 26 3.1.11 MONGOLIA ...... 29 3.1.12 MYANMAR ...... 29 3.1.13 NEPAL ...... 32 3.1.14 PAKISTAN ...... 33 3.1.15 PHILIPPINES ...... 36 3.1.16 SRI LANKA ...... 37 3.1.17 THAILAND ...... 37 3.1.18 UZBEKISTAN ...... 39

3.1.19 VIETNAM ...... 40

3.2 DISCUSSIONS ...... 41 4.0 CONCLUSIONS ...... 43 5.0 FUTURE DIRECTIONS ...... 43

REFERENCES ...... 44

FINAL REPORT FINAL - APPENDIX ...... 50

CONFERENCES/WORKSHOPS ...... 50 FUKAMI - GLOSSARY OF TERMS ...... 50

LIST OF MAJOR COLLABORATORS FOR FLOOD WG ACTIVIITIES OF GEOSS-AWCI ...... 51 01CMY - LIST OF DEMONSTRATION RIVER BASIN OF EACH AWCI MEMBER COUNTRY ...... 53

MAJOR PDF SLIDES OF PRESENTATIONS AT GEOSS-AWCI-ICG MEETINGS AND WORKSHOP ...... 53

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1.0 Introduction

Asian countries have been still suffering from flood disasters repeatedly every year. Moreover, the number and influence of flood disasters are increasing recently (Fig.1 & 2). In order to mitigate flood disasters as much as quickly and efficiently, since it is almost impossible to prevent from flood hazards completely with sufficient structural measures due to its high cost and long time to finish, we should establish integrated flood management including flood Fig.1 Trend of water-related disasters by type (1980-2006) forecasting systems and a best balance of structural (infrastructural) measures and non-structural ones for reducing vulnerability against flood risk and, on the contrary, for increasing coping capacity against it . Flood forecasting is a real-time reaction to mitigate flood disasters, while the consideration toward a best balance between structural and non-structural measures, such as hazard mapping, vulnerability assessing and its reduction, etc., are required for planning process at post- and/or pre-phase of flood disasters. Anyway, we need to plan suitably those measures considering the identification & prediction of the changing trend of floods & disasters induced by them. Thus, we need reliable historical and real-time databases of meteorology and hydrology Fig.2 Percentage of to plan and establish those measures, based on the flood risk and casualties by water- vulnerability assessment, considering global change in terms of climate, related disasters urbanization, etc. However, such data are often lacking for many of ） developing countries in Asia. The goal of this proposed project was, (1980-2006 therefore, to build up a scientific basis for sound decision-making and developing policy options for most suitable flood risk management for each country and region in Asia, through the full utilization of new opportunities on global, regional and in-situ dataset under the scheme of AWCI (contributing to GEOSS), which had been established in 2005. The goal of the proposed project was just linked with the mission of the APN’s Second Strategic Plan (2005-2010).

To attain the goal, considering the two approaches stated in the above section, we need to provide

methodologies, tools and basic datasets to derive such required information to convert both global- scale and local observations and data to really useful information and knowledge for flood risk management. On the basis of those fundamental technology and data, we need to improve real- time flood forecasting system for short-term crisis management and to assess flood risk and

vulnerability and then to make flood scenarios for long-term integrated flood risk management. FINAL REPORT FINAL Namely, we set up our three objectives as follows: - 1. To convert observations and data, both through space borne platforms and data integration

initiatives, to usable information for flood reduction FUKAMI 2. To improve quantitative forecasts for coupled precipitation - flood-forecasting systems -

3. To facilitate flood risk assessment through the provision of scenarios and data for exposure 01CMY estimation -

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If we fulfill all the #1, #2 and #3 objectives, then we will be able to establish fundamental databases and capacity to effectively and suitably plan and realize the real-time based and long-term based measures to mitigate flood disasters promptly and efficiently. From technological point of view, it was expected to be possible for us to set those objectives, based on recent scientific achievements on climatology, meteorology and hydrology in the Asian monsoon regions such as those of GAME (GEWEX Asian Monsoon Experiments, WCRP) and on satellite-based observation of rainfall, physical & socioeconomic quantities of earth surfaces and numerical weather reanalysis, downscaling & prediction. It is, however, indispensable for us to consider the disparity in existing resources and capabilities among different countries/regions as well as their varied needs and environments. Therefore, we established the Flood Working Group in the framework of AWCI in 2006, to construct an international/inter-organizational cooperative field to promote research on a “demonstration project” for each different study basin to build up the first successful implementation about any of the above objectives and to make it a good showcase to strengthen appropriate interactions among scientists and policy-makers and to provide scientific input (corresponding to Policy Agenda of APN Strategic Plan) for the real implementation of the systems all over the flood-prone areas in Asia. Considering the disparity of capabilities of countries in Asia as above, the demonstration project will be coupled with capacity development offered by advanced global-change research network activities and organizations in Asia such as the University of Tokyo (UT), Japan Aerospace Exploration Agency (JAXA), the United Nations University (UNU), the International Centre for Water Hazard and Risk Management under the auspices of UNESCO (ICHARM), Kasetsart University of Thailand and so forth. Those organizations were expected to facilitate the development of research infrastructure and the transfer of know-how and technology, even in poorly-gauged river basins, for converting the coupling of global-scale and local-scale observational data into any information and/or knowledge on flood risk management (objective #1), for promoting the implementation of flood forecasting system (objective #2), flood risk assessment and flood scenario generation (objective #3) in this proposed project. These activities will lead to improving the scientific and technical capabilities of nations in the regions of Asia in terms of hydrological forecasting and planning, and finally lead to (a) identify, refine and make available best practices, tools and methodologies for flood risk reduction in the Asia Pacific Region and to (b) develop training programs and modules on the use of global and regional data sets and observations.

Through the two-year project by the APN fund, the GEOSS-AWCI and its Flood-WG members have tried to understand realities and problems in flood risk management in each country, to consolidate those state-of-the-art technologies to couple global and local data, to apply them to a demonstration project in each country, and to share any interim results, findings and lessons among GEOSS-AWCI Flood Workings. Now we have accumulated and shared many practices in Asia for flood risk management, and some of them can be regarded as the best practices. However, most of such “top runners” are limited to well-financed cases by external funds. We should continue to make every effort to consolidate and customize our accumulated state-of-the-art know-how and

technology in meteorological and hydrological science and engineering for real implementation of

integrated flood risk management in Asia. The details of our achievements are described below.

2.0 Methodology

FINAL REPORT FINAL - As mentioned in 1.0 Introduction, the activities of this project have been conducted under the framework of Flood WG in the GEOSS-AWCI (Implementation Plan for Global Earth Observation

FUKAMI System of Systems Asian Water Cycle Initiative (GEOSS/AWCI)). -

The AWCI was established in 2005 to develop an information system of systems for promoting the 01CMY

- implementation of integrated water resources management (IWRM) through data integration and

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sharing and improvement of understanding and prediction of the water cycle variation as a basis for sound decision making of national water policies and management strategies. The objectives for AWCI are defined as follows: • to develop Integrated Water Resources Management (IWRM) approaches; • to share timely, quality, long-term information on water quantity and quality, and their variation as a basis for sound national and regional decision making; • to construct a comprehensive, coordinated and sustained observational system of systems, such as prediction systems and decision support capabilities, under the GEOSS; • to develop capacity building for making maximum use of globally integrated data and information for local purposes as well as for observation and collecting data. The AWCI is a new type of an integrated scientific challenge in cooperation with meteorological and hydrological bureaus and space agencies. Its uniqueness is described as follows: • Effective combination of the architecture and data and the capacity building; • Advanced data infrastructure availability including a river basin meta-data registration system, a data quality control interface, and data-integration and downscaling methods; • A clearly described data sharing policy agreed among the participating countries; • Strong linkage among science communities, space agencies, and decision makers; • Well coordination between the research communities and operational sectors with clear strategy for transferring scientific achievements to operational use; • Effective cooperation with international projects and cooperative frameworks. The above objectives and uniqueness of GEOSS-AWCI are very relevant to this project in terms of integrated flood risk management. This is the reason why this project has been conducted under the framework of GEOSS-AWCI.

Before starting this APN project, the Flood WG of GEOSS-AWCI had been established and two International Coordination Group meetings had been held in advance. Through those prior activities to the APN ARCP project, the GEOSS-AWCI implementation plan including the selection of “demonstration basin” was summarized in November 2008. Then in order to implement the AWCI implementation plan and this project proposal, Flood WG technical meetings have been conducted several times during the project period as one of the sessions of general AWCI International Coordination Group (ICG) meetings. The general AWCI-ICG meetings have been co-sponsored not only by this APN-ARCP project but also by two other APN-CAPaBLE projects (led by Dr. Ailikun of China and by Dr. Chu ISHIDA of JAXA). The list of the meetings during this APN project is as follows:

1. “The 4th Conference of the Asia Pacific Association of Hydrology and Water Resources (APHW) & the 3rd International Coordination Group meeting of the GEOSS-AWCI”, Beijing, China, November 3- 6, 2008. Total Participants: 43 ICHARM support: 9

2. “The 3rd GEOSS Asia Pacific Symposium & the 4th International Coordination Group Meeting of the GWOSS-AWCI”, Kyoto, Japan, February 4-7, 2009. Total Participants: 66 ICHARM support: 5

3. “The 5th meeting of the GEOSS Asia Water Cycle Initiative (AWCI) International Coordination REPORT FINAL - Group (ICG)”, Tokyo, Japan, December 15-17, 2009.

Total Participants: 154 ICHARM support: 6 FUKAMI

- 4. “The 4th GEOSS Asia Pacific Symposium and the 6th meeting of the GEOSS Asia Water Cycle

Initiative (AWCI) International Coordination Group (ICG)”, Bali, Indonesia, March 10-13, 2010. 01CMY Total Participants: 37 ICHARM support: 5 (including Cancel 1) -

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5. “The 7th Meeting of the GEOSS-AWCI International Coordination Group (ICG)”, Tokyo, Japan, October 5-6, 2010. Total Participants: 79 ICHARM support:5

6. “The 1st International Workshop on Application and Validation of Global Flood Alert System (GFAS)”, Tsukuba, Japan, August 3-7, 2009 Total Participants: 6 (cancel 1) ICHARM support: 7 (including cancel 1)

The number of total participants includes other guests sponsored not only by the APN grants but also by other funds and general domestic participants. “ICHARM support” means the number of participants funded by this APN ARCP fund.

The last 6th workshop is not merely a general information-exchange & discussion meeting but a tightly-focused in-depth workshop on a specific technology: GFAS (Global Flood Alert System). GFAS is an initiative proposed by IFNet (International Flood Network) and is subdivided into the two: i) GFAS-Rainfall & ii) GFAS-Streamflow. GFAS-Rainfall is the original GFAS and a heavy rainfall alert (identification) on a global scale using satellite-based rainfall data (NASA-3B42RT and JAXA-GSMaP). GFAS-Streamflow is a conceptually-derived from the GFAS-Rainfall, i.e. an initiative to produce useful warning and forecasting information even for the basin where very limited (hydrologic & geophysical) in-situ data with securing the worldwide possibility to apply flood analysis and forecasting system in a wide variety of climatic and hydrologic conditions in the world. As a toolkit or platform for the GFAS-Streamflow, IFAS (Integrated Flood Analysis System) was developed. At the 6th workshop, the background, concept, theory, validations and applications of GFAS & IFAS were introduced, discussed and practiced. The detailed fruitful results of the workshop are also introduced and discussed in the next chapter (ex. Indonesia and Japan).

The common approaches in promoting this project utilizing the opportunities to hold the above meetings have been as follows:

1) To exchange information/knowledge on studies in member countries relevant to the above goal and objectives through the above meetings. 2) To develop (and/or adapt existing, with improvements) methodologies, tools and basic datasets related to the objectives, in each member country, as a demonstration project, referring to the above information/knowledge. Demonstration projects include the following two typical patterns: a) To enhance in-situ field hydrological observations for each study basin, to apply the above methodology/tool to the basin, to validate and verify the applicability of the methodology/tool and to clarify the future subjects to make it from research phase to operational phase b) To investigate historical water-related (in particular, flood) disaster events, to collect hydrological/meteorological/socioeconomic data for the extracted flood events, to apply the

above methodology/tool to the past flood events as case studies, to validate and verify the

applicability of the methodology/tool and to clarify the future subjects to make it from research phase to operational phase 3) To exchange research results/findings/experiences through the above demonstration projects and

discuss the furthermore interactive cooperation among member countries through the above FINAL REPORT FINAL - meetings.

The results of those cooperative research activities are described in the next chapter.

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3.0 Results & Discussion

Taking the approaches explained in the previous chapter, this research project has been enhanced and progressed. The research result of this project is, therefore, an aggregation of each voluntary research activity in each member country in principle. Based on this understanding, the each- country’s research result based on such mutual intensive information/human exchanges and cooperative activities is described, country by country, first of all. Then the overall findings and significance of the whole project are discussed later.

3.1 Demonstration researches and practices in each country

3.1.1 Bangladesh

Bangladesh is recognized as the most disaster prone area in the world. Since the half of the land of Bangladesh is less than 5m in altitude and the 68% of the land is vulnerable to flood, Bangladesh has suffered from many catastrophic cyclones floods historically since the great Bakerganj Cyclone in 1876.

Cyclone -SIDR,12-16 Nov,07

Fig.3 The track and affecting area of Cyclone Cider, 12-16 November, 2007 (Quadir, 2009)

Islam(2009, 2010) discussed the development of cyclone-induced disaster management in Bangladesh. The most famous cyclones are the Bhola Cyclone in 1970 that caused more than 300,000 fatalities, and the Bangladesh Cyclone in 1991 that caused 138,000 fatalities. Therefore,

Bangladesh has tried to establish cyclone forecasting and warning. Bangladesh Meteorological FINAL REPORT FINAL Deparment (BMD) is mandated for tropical cyclone forecasting and warning, based on the - information from 4 ground-based meteorological radars and satellite (MTSAT) images, and the

Cyclone Preparedness Program (CPP) under the Bangladesh Red Crescent Society (BDRCS) FUKAMI - established to forward cyclone warning bulletins to 42,675 coastal volunteers for saving coastal

vulnerable people. High-floor shelters were constructed to save people living in vulnerable areas. 01CMY As a result of those efforts, Bangladesh was successful to reduce casualties so much from the order -

of one hundred thousand to the order of several thousand including missed people, for the Cyclone

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Cidr case in 2007 (Fig.3). Certainly this disaster was very big, but we should not forget the truth that the casualties were SO MUCH reduced compared with the past big high-tide disasters. It may have been relatively better for Bangladesh that the Cyclone Sidr landed around the time of low tide. But the strength of the cyclone in 1970 was 966hPa and 57m/s, therefore the cyclone itself was stronger in 2007. Consequently such a big (two-order!) decrease of casualties can be more properly explained by the prevailing of early warning system and high-floor shelters (around 2000). Of course, many lessons are still remaining but the point Bangladesh succeeded in such big disaster mitigation should be emphasized. Typical lessons indicated are as follows: 1) Early warning system coupling the Radar information of the Meteorological Department and local volunteers announcing flooding alerts (CPP: Cyclone Preparedness Program) played a big role to inform the central alert information to local municipal offices, and to reduce the disaster for most residential people, except for fishermen due to improper maintenance of communication system. 2) In many cases, the timing to start evacuation is still late. Beside, there were some cases residential people in floodplains did not escape because they were afraid their properties such as cattle were stolen. 3) The improvement of accuracy of high-tide and flood forecasting should be made for each local point to raise the reliability and credibility for local residential people. Firstly more water level stations are required. . 4) High-floor shelters, which have been often used as schools during ordinary times, were also very useful for reducing the disaster, but the number of them is apparently insufficient and a part of them has become old. 5) The embankment surrounding polders seemed useful to mitigate flooding flow as well, but the height of embankment are not necessarily enough against high-tide flooding flow. Proper design is required. Besides, trees over the embankment seem never good to protect embankments, because the shaking of trees by strong winds weakens the embankments. 6) More resilient house against flooding should be studied.

Regarding the point 1), if regular CPP drills were conducted involving residential and fisherman people, then such improper situation could be detected in advance. The point 3) is true of developed counties, but the case above has much room to improve the system with existing methodology. The point 4) may be escapable if proper knowledge about the design of embankments were introduced. In this context, capacity building is indispensable and can play a big role for reducing disasters.

Other than tidal floods, three types of flood occur in

Fig.4 Flood types in Bangladesh Bangladesh: riverine flood, heavy rainfall flood and flash (Islam, 2010) flood (Fig.4). Quadir (2009) discussed such flood disasters in Bangladesh. Recently, there were five major floods in

1987, 1988, 1996, 2004 and 2007. He showed a frequency analysis on flood-affected area. Islam FINAL REPORT FINAL - (2010) showed us a detailed analysis on 2007 flood. He discussed the importance of the combination of structural & non-structural measures, and introduced a study for updating Dhaka

Integrated Flood Control Embankment coupled with the Eastern Bypass Road Project. By means of FUKAMI - numerical flood inundation modeling, he indicated the effect of the project on the reduction of flood

inundation around the Dhaka area quantitatively and necessary structures for that. He also pointed 01CMY

- out the importance and needs for long-term flood forecasting for riverine floods. This is a very

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typical example of studies for integrated flood risk management. Such a comprehensive study would be further promoted, including the demonstration river basin: Megna River.

Besides, every report indicated the possible adverse effect of climate change on flood disasters. Islam (2010) discussed climage-change impacts on not only flood but also salinity intrusion (drinking water) and drought (agriculture). These studies let to the new generation of the climate change adaptation WG in the GEOSS-AWCI.

Finally, the importance of sharing meteorological and hydrological data for integrated flood and water management in the three river basins: Ganges, Brahmaputra and Meghna (Fig.5). Most of floods and water resources for Bangladesh come from the upstream area of those basins in foreign countries. Referring to the objective of the GEOSS-AWCI activities, such data sharing should be promoted and enhanced from the perspective not only of disaster reduction and recovery but also of the social & economic development of the region of South Asia.

Fig.5 Three major river basins with big influence to Bangladesh (Quadir, 2009)

3.1.2 Bhutan

The Major issue for water resources management in Bhutan is flood management caused by GLOF (Glacier Lake Outburst Flood), heavy rainfall-induced flash flood, and/or landslide burst flood. To cope with this issue, Chhophel(2007) indicated capacity building needs and the concept of the

demonstration project at the Punatsangchhu River (13,263 km2). Its hydrological data during 1998 and 2009 are already shared on the DIAS. Chhophel (2010) reported the existence of many projects related to flood/water-resources management and climate change, and their priority ranking. Substantial progress report on the demonstration project is expected.

FINAL REPORT FINAL 3.1.3 Cambodia -

Cambodia is located in the upstream area of the Mekong River Delta. Therefore, Cambodia has FUKAMI suffered from seasonal flood from the Mekong River every year. If the flood is too big, then the -

flood causes flood disasters, as seen in 2000 flood. However, if the seasonal flood is too little, then 01CMY this causes drought, since the flooding water is water resource for agriculture, fishery, domestic -

water, navigation, etc., especially in the area surrounding the Tonle Sap Lake. There sometimes

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happens flash floods in the tributaries of the Mekong River, induced by heavy monsoon rainfall and/or typhoons, which have often caused flood disasters. For example, Saravuth (2010) reported the damage of flood disasters caused by the Typhoon Ketsana in 2009 came up with the direct damage cost to water management and irrigation: USD 2.779 million, the indirect losses, which involve from farmer water user and selling fish in the reservoir: USD 0.013 million, and the total damage and losses thus reached USD 2.792 million (only for water sector). Therefore, flood forecasting is very important not only for disaster mitigation but also for planning agricultural activities such as seeding, harvesting, and all the other water-related life in Cambodia.

Saravuth et al.(2009) made a report on the historical evolution of meteorological and hydrological observational network in Cambodia. Due to the civil war in 1970’s, there are almost no data during the period. After the middle of 1990s’, systematic data collection has been enhanced. He reported 102 stations are in operation as of December 2006. The Mekong River Commission (MRC) is now enhancing real-time water-level data network using the GSM telephone (4 stations in Cambodia). The Department of Hydrology and River Works (DHRW) of the Ministry of Water Resources and Meteorology (MOWRAM) is now getting near real-time hydrologic data from 10 stations through telephone and sending flood forecasting information to mass media and 40 agencies/villages. Land Data Assimilation Soil Moisture Water Resources System Management in-situ obs. AWS Yield Discharge in-situ obs. Land Surface Model (SiB2)

validation soil moisture

ε, Tsfc PALSAR Radiative Transfer Model * high spatial resol. Crop Model

optimization (SCE) Distributed Hydro. Model AMSR-E TB * high tmprl resol. Rainfall

in-situ Atmosphere obs.

Regional Climate Model (ARPS) TRMM

downscaling In-situ obs.

Initial GCM combined (radiosonde) cond. reanalysis, prediction product

Fig.6 Proposed water resources management system coupled with GEOSS data

(Monichoth et al., 2010) FINAL REPORT FINAL

- Monichoth et al.(2010) introduced a study to make a prototype for state-of-the-art water-resource

management using global and in-situ observational data coupled with meteorological and FUKAMI - hydrological models(Fig.6). The target area is just the demonstration basin: Sangker River basin (2961 km2). They installed an automatic weather station to measure not only meteorological but

01CMY also soil-moisture data. By coupling land-surface model with remotely-sensed soil moisture data - using microwave radiometric data (AMSR) and/or synthetic aperture radar (SAR) data, they

produced a dataset for areal soil-moisture for the target area. They are going to manage agricultural

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] 13

activities such as irrigation, harvest, etc. on the basis of this system and to enlarge this system to a comprehensive flood/water-resource forecasting and management system. This study is one of the most typical examples of converting the combination of global and in-situ data to usable information for flood reduction and water resource management. The further development and verification are expected.

3.1.4 China

The demonstration projects and activities of China have been focused to drought and climate- change impact study issues under the GEOSS-AWCI.

3.1.5 India

Kumar (2008, 2009) overviewed the outline of flood disasters and flood disaster mitigation practices in India. The average annual rainfall of India is 1160 mm but it fluctuates very widely from place to place. The highest rainfall in India of about 11,690 mm was recorded at Mousinram near Cherrapunji in Meghalaya in the northeast of India. The average flood loss is more than 50 billion US dollars and 40,000 people were killed per year in the last decade of the 20th Century.

Therefore, India pursued for a variety of the combination of structural & non-structural measures. He focused on flood hazard/risk zone mapping based on frequency analysis of Fig.7 Example of flood inundation and depth mapping for 1000 year streamflow data, return period flood for the study area (Kumar, 2005) numerical flooding simulation (Fig.7), and the verification of flooding patterns with satellite data. He also introduced typical flood forecasting technologies in India, including artificial neural network technique, and a plan to construct a decision support system for integrated water resources management.

Kaur (2010) reported a flash flood and mudflow on August 6, 2010, caused by extremely heavy storm (“cloud burst”) at the Leh city in the Kashmir District, located in the upstream area of the Indus River. The Leh city was inundated with 1.2m-deep water and many buildings were destroyed (Fig.8). About 150 people were killed. The adjacent period had highly larger rainfall amount than

the normal year. It is apparent this storm was one of the consecutive events to generate great 2010 FINAL REPORT FINAL flood in the Indus River. She mentioned a trial to apply WRF model to downscale macro-scale - weather conditions for meteorological analysis on this event. This is a typical example to apply a

global dataset to a local flood risk assessment. FUKAMI -

India has been taking a variety of actions to mitigate flood disasters. It would be very beneficial to 01CMY

all the members of GEOSS-AWCI to share those experiences and lessons more intensively, since India -

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has very unique experiences to manage rivers and water resources with a wide range of climatic, hydrologic, geologic and spatial-scale conditions.

Fig.8 Damaged houses by flash floods in Leh the City, India, August 6, 2010 (Kaur, 2010)

3.1.6 Indonesia

Indonesia has been leading GEOSS-AWCI Flood WG activities. First of all, Indonesia arranged the first training seminar and workshop under the framework of AWCI, as Dr. Loebis (2008) reported. This was the Seminar and Workshop on Use Satellite Based Information in Flood Risk Management, 21 – 24 July 2008. The Seminar was held at the Ministry of Public Works of Indonesia in Jakarta. Prof. Koike (UT), Dr. Herath (UNU), Mr. Fukami (ICHARM) and Dr. Saavedra (UT) made lectures on integrated water cycle prediction with remotely-sensed data, downscaling technology for rainfall prediction, integrated flood management practices in Japan, and distributed hydrologic modeling, respectively. Then the Training Workshop was held at the Research Center for Water Resources in Bandung. While Dr. Herath and Dr. Saavedra gave training exercises for their topics, respectively, Mr. Fukami gave training exercises on the Integrated Flood Analysis System (IFAS) as a basis for flood runoff modeling and forecasting. The selection of those topics of the seminar and workshop was just according to the mutual discussions and conclusions on the priority area of common requirements among AWCI member countries at the 3rd GEOSS Asian Water Cycle Symposium, Beppu, Japan, 2-4 December 2007. This seminar and workshop was a good chance for researchers and engineers in Indonesia to keep in touch with state-of-the-art meteorological and hydrological

observational & modeling technologies, using global observational and modeling data, which have FINAL REPORT FINAL

- become available under the era of GEOSS.

FUKAMI

01CMY

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] 15

Fig.9 Basin boundary and major river channels delineated by IFAS (Memberamo River) (Loebis, 2009)

As described later, ICHARM organized the 1st International Workshop on Application and Validation of Global Flood Alert System (GFAS) at Tsukuba, Japan during 3-7 August, 2009. Representing Indonesia, Dr. Loebis attended this workshop. He learned how to apply and operate IFAS (Integrated Flood Analysis System, a tool for implementing “GFAS-Streamflow” concept) for flood runoff analysis under poorly-gauged conditions. After that, he reported their application of IFAS at the two river basins: Memberamo River Basin in the New Guinea Island (about 79,000 km2, a demonstration basin, Fig.9) and Citarum River Basin in the West Java (about 4,500 km2). In this study, the Research Center for Water Resources (RCWR) at Bandung played a big role. This has become a good example that the two core regional research institutes, namely, ICHARM and RCWR have jointly coordinated research and development activities in this APN-project members. Besides, those experiences became one of the backgrounds to start a new project to implement the IFAS-based operational

flood forecasting and warning system for the Bengawan Solo River Basin in the East Java (about 16,000 km2) as a Regional Technical Assistance Project sponsored by ADB during 2010-11 (fig.10). This ADB-RETA project is still now ongoing and being executed by ICHARM in cooperation with the Bengawan Solo River Basin Agency of the Ministry of Public Works of Indonesia. Since this project is

not a research for the future but a real implementation project for now in terms of operational real- FINAL REPORT FINAL time flood risk management, there are quite a few challenges to solve. Despite of that, ICHARM and - the Indonesian Agency have been making every effort to cope with them. This project will be one of

the most typical showcases to pragmatize the concept of GEOSS-AWCI and this APN project.

FUKAMI

01CMY

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500km

Basin area Flood in Dec. 2007 Flood in Dec. 2007 16,100km2

Daily ground rainfall observation points Smooth evacuation Daily discharge aaa observation point

Bengawan Solo River Basin (A:16,100km2, L:600km)

1st IFAS training, Mar. 2010

Fig.10 Concept of IFAS-based operational flood forecasting system of the Bengawan Solo River

FINAL REPORT FINAL -

Fig.11 Example of flood disaster risk potential map based on historical experiences(Loebis, 2010) FUKAMI -

Mulyantari (2010) described a fundamental framework for early warning system for floods. She 01CMY

- indicated that flood forecasting system should be conducted on the basis of rainfall-runoff model with real-time rainfall data, including satellite-based one, for the upstream of a river, and on the

basis of hydraulic model, including empirical water-level correlation model, and showed an example

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] 17

of water-level correlation models for the Bengawan Solo River. This idea will be followed by the above ADB-RETA project.

Kusuma (2010) reported an interesting diagnostic study on flood risk in the Citarum River. He described the importance of the river for the most productive area of the Java Island such as Jakarta, and he also described the natural and social history of the basin of the Citarum River and its inherent flood risk as a result. He pointed out the flood risk assessment tools and mutual cooperation among stakeholders. In this sense, a trial of flood potential risk map based on past flood disaster experiences is very interesting, which Loebis (2010) reported. The map is created for every district of Indonesia, for every month, in advance(Fig.11). This is not a real-time forecast, but will be very useful to raise awareness of residents living in the flood-prone areas against such flood disaster risks.

3.1.7 Japan

Japan is regarded as one of the countries with most severe natural water-related hazards in the world, such as floods, landslides, debris/mudflows, storm surges, etc. induced by the combination of extreme weather events such as typhoons, “Baiu”-fronts and convective storms, with its geophysical conditions such as tectonic and fragile geology with mountainous and alluvial conditions. Since 70% of the national land of Japan is mountainous, about a half of the population and three-fourths of the total property exist in alluvial plains, which account for only 10% of the area. Areas below sea level in the three major bays of Japan (Tokyo, Ise and Osaka Bays) occupy an area of 577 km2, accommodating 4.04 million habitants. The combination of such high water-related hazards and highly-developed land use conditions results in high risk of water-related disaster risk if we don’t take any actions. Therefore, water-related disaster prevention and mitigation has been always a “far-sighted policy”, i.e. a top priority in national land administration in Japan since its long history, especially since 1890’s. However, long wars during latter part of 1930’s and the former part of 40’s depleted the resilience against flood risk, and extensive flood disasters consecutively occurred such as the extensive flood disaster in the Kanto Plain caused by the Typhoon Cathleen in 1947. Before 1965, annual flood disaster casualties had been almost more than 1,000 every year. After 1965, i.e. after the enactment of the Basic Act for Disaster Countermeasures (1961), the diffusion of flood control infrastructures such as embankments, floodways, retarding basins, etc. and the spread of weather & flood warning information through radios and televisions, annual flood disaster casualties decreased to several hundreds. In particular, after 1985, the number of casualties was almost less than one hundred every year. This is regarded as a most prominent success story in the world to reduce flood disasters through strengthening coping capacities against flood risks in spite of very high flood hazards. Such historical evolutions of policies and experiences were introduced and shared among GEOSS-AWCI members by Okazumi (2009) and Ootsuki (2009, 2010).

However, as Okazumi (2009) and Ootsuki (2010) mentioned as well, Japan is now facing two types of

new flood risk problems: one is flash flood typically on small to middle scale of river in urbanized or mountains, and the other is a emerging risk induced by climate change.

For example, we have to cope with very rapid water-level rise such as 134cm-rise in 10 minutes!

which really happened at the Toga River, Hyogo Prefecture killing 5 people (including 3 children) in FINAL REPORT FINAL August, 2008. Similar flash flood disasters often occurred in 2008 & 2009, then the implementation - of flood warning system for people in rivers and riversides has progressed. It was also realized that

some of the specifications of conventional C-band radars covering all over Japan were not always FUKAMI enough to cope with such flash floods, especially in urban small-scale rivers, since the C-band radar’s -

spatial resolution was 1km, the temporal resolution was 5 minutes and the data delivery time was 01CMY almost 5 minutes after the real observation so as to make on-line calibration with ground-based -

telemeter raingauge stations. Therefore, the River Bureau of the Japanese Ministry of Land,

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Infrastructure, Transport and Tourism (MLIT) decided to implement X-band multi-parametric (MP) radars in high-flood-risk areas. This X-band MP radar will improve the spatial resolution (250m), the temporal resolution (1 minute) and the data delivery time (1-2 minutes after the real observation), since the MP radar does not necessarily require any on-line calibration system with ground-based rainfall data. The test-phase operation of X-band MP radars has started since July of 2010. New researches to improve short-term rainfall forecast using such a new-generation MP radar data have been also promoted by a new research grant scheme of the MLIT. This is really a new challenge in the field of hydrological forecasting.

Another challenge is how to cope with the increase of flood hazard and risk due to climate change. Japan has been continuously implementing flood-control infrastructures, including the construction of continuous levees and flood-storage structures, such as dams. These efforts steadily improved the flood safety level against medium-scale flood hazard. However, the construction of planned flood control structures has been slow due to the limitation of budget and still remains at a low completion level, only reaching about 60% of the government’s current goal, which is set to implement flood control measures against rainfall with a return period of 30-40 years for large rivers and 5-10 years for small and medium rivers. According to a projection of the impact of climate change on hydrology in Japan as of 2009, annual maximum daily rainfall was evaluated to increase around 10% (20% in the northern part of Japan).

Those discussions highlight the two problems. One problem is how to make such a future projection reliable enough to change the authorized long-term flood control plan. Regarding this question, Ostuki (2010) proposed a concept of flexible approach considering not only long-term (~100 years) but also medium-term (25-30 years) projection. That is, the medium-term climate projection should be more reliable than the long-term projection since such near-future projections computed by most world-wide GCMs are very close with each other. Thus, first of all, taking some reasonable actions based on the medium-term projection, and some years later, evaluating the actions with monitoring data & updated future log-term & medium-term projection and taking the next actions based on such evaluations, monitoring & new projections are proposed (Fig. 12). Flood Risk Range of projections relative to 2010 The range (degree of uncertainty) of future projections become increasingly narrow over time because of projection accuracy improvement, etc. As the range of projections becomes narrower, it is important to review adaptation measures as appropriate. Range of Range of 2060 2085 projection projections

Range of 2035 projections

Range of 2010 projections

FINAL REPORT FINAL -

The increase in flood risk (●) can

be moderated if adaptation FUKAMI - measures are taken.

2010 2035 2060 2085 2110

01CMY - Present

Fig.12 Concept to deal with uncertainty in long-term climate projections (Otsuki, 2010)

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] 19

Another highlighted problem should be the recognition that the probable increase of extreme hydrological conditions will make it more difficult to fulfill the final goal (i.e. safety level) of flood control projects using only engineering measures. Therefore, the Panel on Infrastructure Development (2008) pointed out that we should aim at more disaster-resistant society through combining mitigation and adaptation in the future, since structural and engineering measures cannot fully meet the required safety level. Our next-step priority target should be to achieve “no loss of lives (zero casualty)" because ‘’zero damage’’ from disasters is almost impossible under the era of climate change. Therefore, flexible adaptation measures and holistic approach to couple structural and non-structural measures should be taken at basin level to deal with all possible floods of different scales, based on flood disaster risk assessment. Those new approaches were summarized and discussed in the “Japan Practical Guidelines on Strategic Climate Change Adaptation Planning - Flood Disasters –“ (MLIT, 2010).

Referring to the importance of non-structural measures against floods, the University of Tokyo has been developing a decision support system for real-time optimal operation of multiple dam reservoirs, including flood forecasting system coupled with numerical weather forecast data (GPV), as Oliver and Koike (2010) reported and summarized. The system consists of 1) Dam Release Support System (DRESS) for dam release decision support to reduce flood peaks and store volume for water-use and 2) Flood Warning Support System (FLOWSS) for dissemination of flood warning to perform evacuation timely. DRESS is a combination of ensemble of precipitation forecasts and WEB- DHM (Water and Energy Budget-based Distributed Hydrological Model, Fig.13). The model was

Subgrid Parameterization

Wang, Koike et al., 2009 Fig.13 Concept of WEB-DHM of UT (Saavedra et al., 2010)

originally developed for the Upper Tone River (a demonstration basin) and was verified. Fig.14 REPORT FINAL - shows an example of results of optimal operation of multiple dam reservoirs based on the DRESS,

reducing flood peak level and saving water storage in dams from normal operations. FLOWSS is an FUKAMI integrated flood forecasting system using not only ground-based raingauge and numerical - precipitation forecast data but also satellite-based rainfall data. The FLOWSS was applied to the

Huong River and the Red River in Vietnam, as mentioned later. This is one of the most advanced 01CMY - hydrological forecasting examples, fully utilizing global numerical & satellite data for integrated flood

management on a local scale and therefore a typical achievement of GEOSS-AWCI flood-WG

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activities and this APN project, i.e., 1) to convert observations and data, both through space borne platforms and data integration initiatives, to usable information for flood reduction, and 2) to improve quantitative forecasts for coupled precipitation - flood-forecasting systems. 15% Flood GAIN 12.3 4000 1200 660 With SUG_REL reduction 1000 650

With OBS_REL /s] Fujiwara

3000 3 800 640 /s]

3 Qobs 600 630

400 620 waterlevel [m] 2000 discharge[m 200 610

0 600 discharge[m 1000 1 4 7 10 13 16 19 22 25 28 31 34 37 40 Sim release Obs release Sim water level Obs water level High water level Low water level 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40

500 570 1800 570 400 550

GAIN 17.4 /s] 550 3 GAIN 6.2

/s] 300 3 1200 530

Sonohara 530 200 Aimata waterlevel [m] discharge[m 510

600 100 waterlevel [m] discharge[m 510

0 490 0 490 1 4 7 10 13 16 19 22 25 28 31 34 37 40 1 4 7 10 13 16 19 22 25 28 31 34 37 40 Sim release Obs release Sim water level Sim release Obs release Sim water level Obs water level High water level Low water level Obs water level High water level Low water level TOTAL GAIN 35.9 Fig. 14 Effect of DRESS on effective flood-peak reduction and water-storage saving through optimal dam reservoirs operation, Upper Tone River, Japan (9-11 July 2002). (Saavedra et al., 2010)

In parallel to the above, International Centre for Water Hazard and Risk Management under the auspices of UNESCO (ICHARM) has been making every effort how to transfer high technology to use global databases for local management to developing countries and how to lead to real implantation of the state-of-the-art technology for integrated flood management under the condition of limited in-situ observational network and resources (budget and capacity) in those countries. In this context, ICHARM has been developing a fundamental common toolkit for flood runoff analysis and forecasting system using not only ground-based but also satellite-based rainfall data in order for the countries lack of surface rainfall data to establish flood forecast system rapidly and effectively, as Fukami et al.(2008) introduced. This system was named as Integrated Flood Analysis System (IFAS, Fig.15). The design concepts of IFAS were as follows: 1. To prepare interfaces to get satellite-based rainfall data in addition to ground-based rainfall data, to secure the worldwide availability of input data for flood forecasting/analysis system. 2. To adopt two types of distributed-parameter hydrologic models, PWRI Distributed Hydrologic Model (PDHM) Ver.2 and Block-wise TOP (BTOP) model, the parameters of which can be estimated as the first approximation based on globally-available GIS databases to secure the worldwide availability of hydrologic models for flood forecasting/analysis.

FINAL REPORT FINAL 3. To implement GIS analysis modules in the system to set up the parameters for the flood - forecasting/analysis model, therefore no need to depend on external GIS softwares.

4. To prepare a series of easy-to-understand graphical user interfaces for data input, modeling, FUKAMI - runoff-analysis, and displaying the outputs. 5. To distribute the executable program, free of charge, from the ICHARM/PWRI website.

01CMY The IFAS Ver.1 was developed under a joint research with the Infrastructure Development Institute -

(IDI) and nine major civil-engineering consultancy companies during FY2005-2007.

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] 21 Concept of IFAS (Integrated Flood Analysis System) -Toward Prompt Implementation of Flood Forecasting / Warning Systems with the Sense of Ownership of local users in Developing Countries -

Global observation of rainfall Satellite-based by earth observation satellites Topographic data Other GIS data for runoff mode near real-time (Land use, soil, etc.) rainfall data

Ex.) IFNet-GFAS, NASA- 3B42RT, JAXA-GSMaP Data download through Internet, free of charge

Flood IFAS(A basis for flood forecasting/w arning system ) disaster Real- time input: Satellite & ground rainfall prevention & G IS data input for setting param eters mitigation G IS analysis to build runoff model Runoff analysis and flood simulation Current situation User- friendly interfaces for output Despite of the needs for flood forecasting/warning,

Project:AB C D EFG D ate Time:2007/7/9 450 G rid No:482 上流域平均雨量 No rainfall, GIS data, nor analytical tools 400 5

350 10 河道流量 (G482)  Required much money & time for implementation 300 15 ○○実績河道流

250 20

流量雨量

200 25

150 30 After the application of IFAS: 100 35 50 40

Prompt & efficient implementation

2007/7/9 0:00 2007/7/9 2:00 2007/7/9 4:00 2007/7/9 6:00 2007/7/9 8:00 2007/7/9

2007/7/9 12:00 2007/7/9 14:00 2007/7/9 16:00 2007/7/9 18:00 2007/7/9 19:00 2007/7/9 20:00 2007/7/9 No need to develop original core system 10:00 2007/7/9 Step-by-step improvement of accuracy with hydrological observational network Fig.15 Concept of Integrated Flood Analysis System (IFAS)

Consequently, IFAS can incorporate not only ground-based rainfall data (csv data) but also satellite- based near-real-time rainfall products such as NASA-3B42RT, NOAA-CMORPH, JAXA-GSMaP_NRT, etc. as shown in Table 1.

Table 1 Major satellite-based global rainfall data products available thought the internet Product name 3B42RT CMORPH GSMaP_NRT Developer and provider NASA/GSFC NOAA/CPC JAXA/EORC Coverage N60°- S60° Resolution 0.25° 0.25° 0.1° Resolution time 3 hours 3 hours 1 hour Time lag 10 hours 15 hours 4 hours Coordinate system WGS

Historical data Dec 1997- Dec 2002- Dec. 2007～

TRMM/TMI

TRMM/TMI Aqua/AMSR -E Aqua/AMSR -E Aqua/AMSR-E AMSU -B ADEOS -Ⅱ/ Sensors AMSU-B DMSP/SSM/I AMSR DMSP/SSM/I TRMM/TMI SSM/I

IR IR IR FINAL REPORT FINAL

AMSU-B -

From the table, GSMaP_NRT seems more promising for the purpose of flood forecasting due to its

high temporal & spatial resolution and its quick delivery. According to some validation studies, FUKAMI however, it was clarified that GSMaP_NRT tended to underestimate rainfall intensity, in particular, -

for heavy cases. Then Shiraishi et al.(2009) found out an empirical relationship between spatial 01CMY correlation factor (presumably corresponding to the movement speed of precipitation cells) and the -

degree of underestimation. Based on this empirical relationship, they developed a self-correction

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Moving fast Underestimation Moving slowly  Better coincidence Present Present ＋＋１hour later １hour later ＋＋ 2 hour later

2 hour later ＝

＝ 3h additional 3h additional rainfall rainfall

Small spatial variance of cumulative rainfall Large spatial variance of cumulative rainfall on a certain scale on a certain scale 150 Self- USA Relative Raw A hypothesis on the reason why this corrected Japan-Yoshino error [%] data data self-correction is empirically effective. Japan-Tone Yoshino R. 84.0 20.2 Japan-Sendai ） 100 Sendai R. 47.1 -4.8 High cloud speed Self-corrected Low cloud speed

GSMaP mm/3h 1 3 2 2 2 1 1 0 （ Low wind speed Improvement High wind speed 50 0 1 2 0 0 of 0 0 0

GSMaP_MVK+(mm/3h) coincidence GSMaP GSMaP 0 1 1 1 1 0 1 1 Low orographic rainfall Heavy orographic rainfall Raw GSMaP in lower layers of atmosphere in lower layers of atmosphere Almost good estimation 0 0 1 2 4 3 5 5 Underestimation with high-frequency microwave with high-frequency microwave 0 radiometer aboard satellite radiometer aboard satellite 0 0 501 6 13 13100 1 1 8 10 150 Groundground-gauged-based rainfallprecipitation data (GGP) (mm/3h (mm/3h) ） 2 6 12 18 18 2 1 20 18 Fig.16 Concept, validity and background of self-correction algorithm for GSMaP_NRT 4 7 13 26 32 without3 5 49 any32 ground-based data 4 7 29 52 49 3 5 35 32 method for GSMaP_NRT without any ground-based rainfall data (Fig.16). This method is expected to 6 22 39 67 66 5 6 44 39 be very practical and useful for poorly-gauged river basins to use satellite-based rainfall data for flood forecasting36 purpose52 62 since80 it82 is difficult7 4 63 to49 implement fully-designed network of telemeterred

raingauges. Accordin80 68 g to61 another86 84 study6 1 for58 the50 case of Typhoon Morakot in Taiwan in 2009, this method seemed effective to get the first-order approximated areal rainfall distribution in poorly- gauged river basins50 5(0 Fig.1768 ). 8 6 However,69 5 8 we50 have36 also found out some cases which self-corrected

44 50 86 92 61 5 5 36 21 satellite-based rainfall data cannot

0 0 2 0 0 0 0 4 3 0 0 72 0 0 2 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 2 3 2 0 0 0 1 0 0 0 0 0 1 1 1 1 3 28 0 88 0 50 1 0 0 0 0 0 2 1 1 1 30 0 2 00 0 4 1 53 1 0 0 0 0 1 0 3 0 1 2 4 0 0 0 54 4 0 0 133 1 9 0 73 1 0 0 1 0 0 0 1 2 0 0 0 0 16 1 0 0 10 2 1 4 5 0 3 0 12 0 0 1 21 2 2 0 0 4 2 1 2 0 22 1 2 2 0 0 0 7 0 0 1 18 1 0 0 0 0 1 0 2 0 2 7 4 2 6 2 reproduce flood hydrographs 6 0 0 0 1 0 0 0 0 7 5 7 11 16 10 1 2 0 23 5 0 25 2 0 6 3 0 9 4 0 27 2 0 32 68 808 3 1 0 76 40 3 2 10 26 2 1 23 21 13 3 0 0 3 0 1 0 0 1 4 0 2 1 1 1 2 0 0 0 0 0 0 0 21 17 23 32 0 0 15 16 37 36 0 0 4 4 2 0 0 0 9 5 5 2 2 0 7 5 2 0 1 2 4 16 27 51 7 0 0 4 0 7 6 21 5 0 1 21 0 1 62 0 119 0 66 69 0 0 1 12 2 7 14 59 23 0 0 0 2 133 0 0 0 1 15 95 0 0 2 0 0 5 1 1 1 9 70 51 66 ground-based 28 24 32 35 98 53 0 4 9 82 3 10 2 23 15 4 97 21 25 20 27 10 29 44 31 23 8 70 70 19 46 10 1 5 8 6 7 4 32 0 4 1 43 1 1 59 38 9 85 properly (Fig.18). It was clarified 22 61 116 2 17 18 9 11 35 49 95 37 16 25 7 71 0 1 8 12 0 0 14 17 1 74 19 36 72 4 19 18 24 5 4 47 39 19 5 65 104 81 19 1 1 21 16 7 5 29 40 7 99 89 2 14 18 7 6 16 38 8 6 60 64 2 2 0 1 2 18 1 2 4 27 19 25 40 51 9 37 18 16 42 23 66 96 47 18 18 34 25 87 38 1 21 2 2 4 31 5 4 13 78 3 1 2 14 8 10 13 43 3hr rainfall 20 26 10 18 25 30 31 38 27 55 52 74 59 45 110 4 2 6 2 3 1 3 17 19 12 16 28 9 35 38 22 1 44 2 55 2 17 35 8 35 1 39 29 18 2 5 3 23 21 20 2 21 21 17 57 62 30 65 87 51 2 19 12 7 23 25 26 8 15 44 47 5 5 5 22 1 28 0 1 22 19 6 1 3 38 39 6 3 3 34 51 3 2 14 16 28 28 20 90 43 32 93 2 28 1 25 45 3 12 41 2 11 0 17 5 1 21 2 23 37 37 32 42 75 10 93 53 73 99 85 8 41 22 35 87 47 23 73 54 45 48 64 121 45 88 21 46 19 28 89 71 2 67 212 130 5 45 103 117 4 that such bad results could happen if 22 58 45 0 41 69 57 0 34 76 55 1 2 0 6 2 5 2 30 55 53 113 44 129 13 71 80 18 161 16 112 22 0 35 68 15 5 8 7 3 1 10 5 7 10 5 24 30 28 54 12 58 64 5 66 9 102 7 43 73 1 65 147 4 22 81 3 30 68 65 101 36 134 139 30 93 100 140 1 56 7 97 1 76 3 39 73 5 87 140 7 111 99 63 33 10 118 72 21 58 54 11 45 42 105 71 76 63 99 200 129 27 39 102 18 110 121 171 52 97 87 84 37 19 35 93 82 86 21 57 93 116 8 79 128 181 215 16 53 62 101 134 12 32 26 100 103 73 216 60 44 108 33 86 43 36 117 93 216 53 127 34 46 37 104 121 65 47 78 66 22 9 53 17 76 24 30 42 40 57 9 51 72 79 101 14 58 60 49 65 4 37 46 141 35 74 213 39 38 121 the frequency of real observation of 59 64 49 91 36 42 75 65 ƒŒƒ“ƒW Œ´“_Z(3h) 11 87 73 99 5 52 72 144 0 35 41 54 96 65 45 43 9 36 6 51 0 28 74 118 18 24 38 11 23 30 7 97 87 26 27 29 16 29 30 11 60 30 21 28 28 19 93 28 29 69 79 110 24 25 37 20 18 30 81 90 16 32 14 46 162 72 74 82 22 19 95 28 20 - 999 ` 0 81 19 10

51 49 19 17 14 22 45 105 29 55 72 15 23 36 rainfall field from the space was not 0 ` 10 12 11 3 69 98 Observed Rainfall [mm49 /57 3h] 11 15

138 44 2 10 ` 20 152 (2009/ 08/ 08 21:00 ` 23:0060 (UTC)) 3 20 ` 40 enough during the rainfall increasing 0 0 0 0 0 0 0 0 1 040 0 `0 0 60 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 10 00 00 00 00 00 00 00 00 00 00 00 00 017 09 020 027 026 025 030 045 049 148 139 931 830 832 315 9 07 6 01 9 00 130 0 180 0 160 0 150 0 200 0 280 7 340 380 380 420 440 410 230 2832 2135 2037 2938 1581 1388 8190 2123 0113 059 259 055 054 053 049 037 031 1432 1730 2511 268 260 27 27 18 18 24 22 19 25 32 42 39 40 48 60 80 77 83 88 94 76 73 57 59 68 83 91 98 91 87 69 53 40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 00 00 10 00 00 00 00 10 00 00 00 00 00 00 05 012 012 028 033 032 038 049 058 849 939 1739 1541 1427 121216 9128 4 05 6 0 0 130 0 9 0 0 9 0 0 190 2 330 7 380 400 440 480 470 430 390 271 1932 1034 1151 1355 8106 897 372 088 084 179 674 259 059 060 059 546 1243 2936 2727 2622 2510 27 25 24 23 23 25 25 31 43 49 52 51 52 61 70 80 88 92 87 56 56 50 63 71 90 90 94 103 84 68 53 45

0 0 0 0 0 0 0 1 1 060 0 `1 0 80 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 00 00 00 00 O00bse0r0ved00 Ra00infa0l0l [m10m/030h] 00 00 00 00 08 06 027 036 035 039 054 069 257 447 144 1343 1839 91833 71417 41213 0120 4 0 0 O6 0b0se7 r0v0 e14d0 0R4a30in4 f4a0l0l [4m30m/4630h]480 470 380 290 142 2126 1530 2038 2347 1666 1084 773 079 059 059 959 755 663 561 2564 3259 4260 4547 3837 3539O3b81s2 e3r9ved29 Ra3i5nfa3l8l [m32m/323h] 27 44 46 54 57 59 59 57 59 81 97 74 58 47 47 47 53 70 88 91 99 106 82 68 54 44 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 103 103 102 (21002 0910/2080/0 08 0003:0000 ～00 0500:00 00(UT10C))00 00 00 00 03 026 022 034 036 054 158 083 055 848 1047 144 51236 3127 8 817 9 311(620 6096 /0 6086 /0 60814 00864:00000 4～50 04980:0051 0(U4T70C)3)90 120 120 111 1527 1830 1753 2155 2578 2049 542 041 368 1588 2693 3155 3151 3352 31857 455:9 4262 425304251(2407405 940/420684/908 521:0520 ～409 2345:00 3(5UT3C7 )) 41 47 56 61 61 67 63 74 89 88 58 45 44 49 58 55 70 85 93 97 97 74 62 52 45 0 0 0 0 0 0 0 0 0 080 0 `0 0 10 00 01 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 01 103 09 08 08 03 00 00 00 00 00 00 00 00 00 00 00 01 05 029 034 035 852 1547 091 078 161 260 1157 131151 10348 14243 19032 14011 7 0 9 6 010 230 6 440 0 430 463 4712 327 187 140 120 9 0 810 2027 1938 2219 2168 2061 1439 046 154 2346 3142 3241 3265 3661 3960 3759 3756 3851 4064 4252 4341 46 46 46 46 47 46 43 40 45 58 62 73 80 80 79 87 91 64 49 51 51 58 58 70 80 93 99 96 80 63 55 49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 08 100 104 104 102 102 102 00 00 00 00 00 00 00 00 00 00 1:023 000306 000309 036 734 1305 666 184 084 069 164 12255 12255 14055 20049 23036 16031 12018 31016 44018430 440 420 2417 116 110 111 151 1510 116:3200102309 0103405 2056 1918 1029 015 1443 1442 3049 3645 3555 3751 3865 3868 3861 4453 4153 4150 4147 40 40 36 40 44 49 39 40 38 54 64 72 1:92500900 00804 87 90 72 69 71 51 47 53 71 77 79 91 92 73 period.62 55 44 To minimize this influence, 0 0 0 0 0 0 0 0 0 0>0 11 00 01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 02 03 102 09 08 08 03 00 00 00 00 00 00 00 00 00 00 00 032 034 031 1319 1401 1439 2753 1745 556 062 4 061 7 061 11360 21360 27060 22040 18031 39028 41021430 430 395 2118 1313 116 103 155 217 2323 1733 129 1680 1062 057 097 023 1010 8 9 3211 3647 3462 4066 4085 3969 3157 3253 2647 2257 24 27 26 34 39 43 43 43 42 50 57 67 81 99 84 81 91 83 78 62 49 50 54 62 77 82 92 92 79 62 52 38 0 0 0 0 0 0 0 0 0 0 1 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 01 0 00 20 22 02 04 03 00 00 00 00 00 00 00 00 00 00 00 00 016 020 037 046 437 1369 179 2070 1791 279 42074 5 159 11357 26062 31058 21054 17051 33037 43037400 420 340 165 918 6 5 3 5 610 2015 1617 198 1510 128 967 063 059 073 111 5 8 1810 2617 3018 3119 2578 2342 1560 1256 1054 1062 10 13 14 27 34 36 43 43 45 47 55 61 69 77 77 86 92 86 56 57 49 46 50 59 79 93 99 95 76 56 47 27

0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 06 102 0 01 20 50 50 20 00 00 00 00 00 00 00 00 00 00 00 00 00 00 01 031 039 051 739 2533 1947 3251 3259 112467 9 962 9 062 19344 33354 31070 25063 38050 46048410 310 160 125 820 212 0 4 515 1020 1316 617 013 0 3 057 087 0137 054 147 3 6 620 1422 2221 2420 1524 745 659 956 552 761 1 4 14 9 18 1 25 33 41 50 53 61 66 71 81 89 90 86 60 52 47 39 44 59 78 88 102 76 71 49 44 21

0 0 0 0 0 0 0 0 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04 09 08 101 102 20 50 50 20 00 00 00 00 00 00 00 03 012 07 00 00 00 00 00 016 035 028 243 1436 2343 3254 3833 15333 83042 91254 15555 19558 22454 30051 37055 51055430 290 9 0 8 0 820 5 7 8 2 917 1026 529 017 216 1150 848 033 044 032 071 060 318 451 1042 1740 1525 629 248 456 264 346 2 0 0 0 0 0 11 19 33 42 54 66 70 80 77 82 95 74 66 49 45 46 46 59 76 79 69 61 58 46 42 27

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03 102 106 205 105 00 00 07 14 00 00 00 00 00 00 00 017 07 00 03 01 05 00 00 042 036 053 366 1541 2839 343 153234 83347 82855 13266 18561 18054 25049 47057 49054430 440 210 102 912 7 6 9 4 1219 1128 531 1127 1127 314 251 046 047 059 036 032 024 230 1453 2042 829 337 339 152 159 045 0 0 0 0 0 0 0 12 21 30 40 48 64 89 76 84 93 87 64 51 49 49 52 63 74 70 65 62 54 47 40 24

0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02 06 102 206 300 207 100 06 10 17 16 00 00 00 00 02 012 09 014 018 010 06 04 03 013 020 034 012 516 179 2613 10348 93611 103452 132166 12079 21067 30041 43054 45054430 460 340 190 146 130 180 1718 1222 631 633 1536 1044 028 032 034 055 067 059 032 464 1158 1259 821 324 243 247 084 057 0 0 0 0 0 0 7 8 18 26 31 42 54 92 79 95 89 90 70 58 51 44 54 75 68 74 72 70 54 51 23 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 01 09 204 207 301 208 109 02 05 00 00 00 00 00 00 04 00 017 020 013 08 011 020 036 043 063 03 09 28 118 10259 113213 153514 121116 17811024360 32055 40055 42052430 470 366 143 122 150 9 0 9 3 512 0 7 010 014 017 038 041 055 031 040 040 044 444 1058 1174 828 521 026 038 066 075 0 0 1 8 0 0 0 8 28 29 38 37 47 85 83 88 94 92 67 54 54 57 62 72 73 80 82 68 45 we22 9 8 would strongly desire and 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 02 05 05 103 300 300 206 09 00 00 00 00 00 00 00 00 02 012 08 017 017 023 023 046 449 050 079 05 17 418 5 420 9 514 121819 14815 201426 221342 24748 38963 412484213 4717 385 244 150 7 2 7 0 103 183 1521 1420 1127 231 049 054 048 048 053 077 036 657 1163 1072 856 517 122 032 060 086 0 0 2 14 12 14 0 5 25 26 41 36 42 73 82 92 70 54 50 47 47 66 61 77 90 90 79 52 28 16 9 6

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 02 05 04 08 203 05 00 00 00 00 00 00 00 00 00 04 05 04 031 036 041 034 430 432 031 057 064 021 0 050 6 041 6 044 121426 161523 162232 251537 381151 421056449 4615 3310 176 182 122 170 220 170 1 0 2 2 2 8 0 7 032 043 042 037 030 228 328 436 767 848 867 455 049 062 034 070 0 0 18 18 19 17 4 3 5 26 40 39 42 58 77 82 65 45 44 44 49 58 73 85 96 117 72 28 24 15 13 8

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 00 0 0 00 00 00 06 03 100 08 00 00 00 00 00 00 00 00 00 00 00 03 018 031 036 331 936 032 036 038 023 7 030 3 052 6 039 31225 62623 92926 182426 352150 401891399 3920 4316 2811 208 182 170 150 100 0 0 0 9 021 026 0 1 0 5 015 020 021 921 1933 1643 1546 1763 1468 056 054 068 078 063 0 0 15 18 22 21 7 6 6 20 41 42 43 62 65 77 65 48 46 48 54 65 81 95 101 98 57 35 27 23 12 10

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 04 0 0 00 00 00 00 104 108 106 107 105 04 00 00 06 03 03 00 00 00 03 024 028 029 046 749 054 055 051 046 15030 4 040 3 045 3 027 52019 22814 82922 192046 3619793624 4227 4428 4122 347 228 170 100 0 0 0 0 012 624 024 0 2 0 4 021 025 1133 2049 2348 2649 2561 1366 778 0106 0113 0102 067 070 0 0 19 20 1 11 14 6 0 1 36 44 44 56 68 64 54 47 49 56 71 85 94 97 92 76 62 49 34 22 14 22

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 04 0 0 00 00 00 00 05 107 202 202 201 016 020 017 05 04 00 00 00 00 01 03 07 28 040 257 035 030 021 019 31046 24045 7 063 2 035 2 521 02013 72522 152530 2625734018 4319 4325 4618 287 146 2 0 2 0 0 0 0 0 031 041 045 0 7 0 7 022 135 2540 3056 2890 2683 2487 1372 884 0109 075 088 083 065 0 0 18 9 0 2 7 3 0 0 23 38 43 53 59 58 49 57 50 57 76 81 93 90 76 65 48 49 46 26 13 10 self0 0 0 0 0 -0 corrected0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03 0 0 00 00 00 00 00 07 107 204 207 020 018 020 019 05 06 162 160 110 148 516 424 29 54 012 016 013 011 010 34018 33040 17044 7 056 0 057 0 756 01447 62220 221454426 4125 3925 4518 4212 2622 415 0 5 0 0 0 9 024 036 041 0 8 215 227 1031 2948 3265 2979 1978 1879 1180 391 096 077 065 076 044 0 14 20 18 2 0 4 9 0 0 23 29 40 44 47 51 47 50 52 68 78 88 96 87 82 62 53 46 42 anticipate32 10 8 the real implementation 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 20 21 16 10 6 4 3 0 0 00 0 0 00 00 00 00 00 00 00 101 104 024 013 015 015 010 04 170 310 350 319 2149 1244 1100 816 336 036 045 054 059 28061 35056 15055 9 064 9 079 6 075 5 061 6 315 21039430 4128 4331 4226 3816 2820 1326 031 021 0 9 025 034 239 420 1126 1236 941 1847 2873 2470 1189 9102 998 099 0103 389 079 082 061 17 31 22 15 5 0 3 6 0 0 22 32 29 45 38 22 17 36 58 64 79 94 114 116 110 78 51 49 43 23 13 6

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1 1 34 39 31 18 11 8 6 03 00 0 0 00 00 00 00 00 00 00 00 03 03 015 013 06 06 00 00 100 220 409 3394 1470 1309 1105 88 219 036 041 061 29056 36052 38048 29067 14070 0 4114 4 062 6 030 12241470 4620 4428 4421 2919 2223 629 231 017 020 228 329 1636 1222 1336 1255 1052 1363 1773 1878 1085 0110 0107 0117 0124 086 088 282 660 37 37 38 23 8 6 8 6 7 5 18 22 31 46 34 19 17 45 58 66 86 101 108 115 102 86 55 45 29 5 11 8

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 14 24 38 34 30 14 11 8 07 00 0 0 00 00 00 00 02 07 06 00 00 04 015 025 014 06 011 240 330 332 3163 3268 2395 2205 1164 1106 1322 040 046 066 28063 29054 42047 38149 20352 171861 9 875 6 548 14947376 4823 4714 3028 1624 1031 436 037 042 037 929 1125 1736 1834 1846 1662 1556 1468 1772 1378 694 0120 095 0108 10135 876 867 979 3457 39 36 38 27 24 13 8 7 2 1 0 1 15 19 24 28 25 65 74 78 86 86 86 74 79 65 55 45 24 11 7 8 satellite0 0 0 0 0 0 0 0 0 0 0 -0 based0 0 0 0 0 10 22 35 30 3 4 32 34 34 32 27 14 14 07 04 0 0 00 00 00 109 104 08 09 101 103 08 012 022 1241 2233 1285 346 378 3152 3105 3213 2372 2248 3127 1280 734 051 058 071 36079 36083 38782 43283 342153 372160 251272 18776 1413402416 3323 284 2017 1118 1433 637 1038 1246 3044 3229 1821 3729 3442 3254 2963 2657 2774 1885 491 690 0108 073 4121 16103 861 955 2163 4058 36 41 39 40 43 35 20 11 8 8 5 8 7 9 18 17 25 54 63 65 79 74 65 71 74 61 48 40 34 10 11 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 15 34 32 38 40 37 37 35 33 35 35 17 10 08 03 0 0 00 00 01 108 204 108 101 105 109 011 013 1118 2299 2288 3316 3367 4401 3260 2199 3228 3238 338 3215 525 037 038 054 064 42078 37079 3511002413181 443062 503061 461961 221076 1516562724 4113 380 213 387 4032 3939 3140 4747 4441 4224 4018 3829 3742 3357 3465 3064 2681 2397 12105 0104 084 058 781 1884 1153 1041 2148 4245 40 41 39 46 47 44 14 25 15 9 6 4 9 10 19 24 33 71 66 60 61 56 52 48 54 48 34 33 30 21 9 8

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 32 31 30 33 38 22 32 28 28 30 32 20 1 0 00 0 0 09 105 08 300 206 200 108 200 202 026 019 124 3218 3322 3338 3450 3492 045 040 036 030 040 234 042 051 049 455 565 43075 39089 388102423499 443862 603955 602254 391558 2213473315 406 370 430 391 3734 3841 4843 4747 4526 4215 4015 4333 4241 4056 4364 3573 2490 17119 4135 0111 1680 1154 549 862 855 1153 1742 4739 48 44 48 48 50 44 14 15 17 30 11 4 11 13 22 25 34 53 61 62 64 61 63 59 55 39 28 28 14 8 4 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 35 34 35 35 34 8 8 0 0 0 0 1 0 03 08 0 0 100 308 309 303 306 201 105 108 300 027 023 616 3177 3327 3442 142 048 054 043 040 042 035 037 050 054 245 948 460 41074 40277 433183 433875 463960 494650 513146 412843 161432204 310 300 430 450 4726 5238 5031 4825 4918 4713 4813 5925 5442 3262 1969 390 0115 0130 7126 0107 1782 2456 2849 862 1056 854 5045 4722 53 52 62 54 52 44 11 11 12 9 11 12 14 20 28 28 34 55 60 55 58 62 62 58 28 18 25 22 7 of0 0 0 GPM (Global Precipitation 0 0 0 0 0 0 0 0 0 0 0 0 0 1 7 3 16 37 41 40 37 24 6 6 0 0 0 0 0 0 03 109 101 09 304 404 403 308 301 300 202 105 022 024 320 2103 4205 3484 448 053 067 051 041 027 029 045 052 049 043 062 079 34078 43081 402076 354264 274269 374760 464450 373741 231826215 136 270 573 563 5719 5624 6726 6415 5913 5112 5618 5633 4849 2765 1478 898 0127 3129 0114 0104 1581 3858 2745 541 824 1441 5630 579 51 49 56 45 38 16 10 19 27 21 17 17 14 21 34 44 46 59 62 54 56 49 31 13 5 5 5 5 2 0 0 0

0 0 3hr0 0 0 0 0 0rainfall0 0 0 0 0 0 8 3 7 25 36 29 1 0 1 7 0 0 0 0 0 02 108 108 08 104 109 403 408 408 406 503 303 300 018 020 235 728 2254 3206 652 033 029 111 821 123 028 045 047 045 450 074 011131010139983 402274 294466 224364 244858 354944 313929 4820133913 2513 303 554 519 4818 4826 4816 4919 5613 6015 5526 5642 4453 2666 3086 1396 4116 18121 0107 592 1178 2467 2854 831 1622 3316 577 543 50 48 46 28 13 13 14 26 22 15 27 25 27 35 46 58 61 70 57 57 56 39 28 17 7 5 4 5 4 3 2 1

0 0 0 0 0 0 0 0 0 0 0 0 4 18 10 5 11 24 34 29 0 3 0 6 7 0 0 0 6 02 105 100 104 05 208 400 409 403 602 404 303 204 026 025 33 1261 2259 3314 3245 123 019 014 524 1249 041 053 062 854 1462 377 011225810329179 342874 294256 204431 225835 175025 273811 39297 2621 2919 397 429 3213 377 3510 3815 3610 5018 5527 5239 4348 4358 2767 3377 3592 13110 0113 2101 788 2381 1972 851 1134 1124 3618 388 364 29 29 23 18 12 17 21 27 25 34 37 30 22 38 55 67 70 69 57 55 59 38 17 9 6 4 4 7 5 4 3 1

0 0 0 0 0 0 0 0 0 0 0 0 0 27 10 4 7 29 34 37 17 15 8 10 2 0 0 12 7 101 103 108 204 301 300 405 502 403 503 503 309 307 040 035 036 47 1298 2487 3446 3373 1145 1185 1392 843 042 162 1656 2781 1674 4109 596 22563 213084 243778 294455 224937 104423 124223 264213 35308 3831 4529 4912 458 3915 334 322 299 3617 3623 4233 3049 2753 2561 2368 2975 3687 1103 0101 297 1083 2075 1159 1248 3035 4125 4219 5210 446 39 23 20 26 18 15 32 26 23 27 38 46 44 58 65 67 74 74 59 49 49 34 11 8 6 6 6 7 5 4 2 0

0 0 0 0 0 0 0 0 0 0 0 0 26 23 23 2 11 25 34 36 26 28 21 1 0 1 10 18 20 09 105 209 303 501 401 502 504 502 505 500 405 501 051 046 059 453 1475 2389 3447 4410 1163 1168 036 053 148 162 364 3637 1475 3105 1675 04067 14476 195365 185244 215039 164226 104624 84414 16398 3639 4425 5010 516 460 510 340 624 4016 3022 2744 2257 1961 1268 1070 3377 3785 093 091 185 876 1366 1253 2138 3629 2524 5518 4711 466 55 37 27 24 13 29 38 29 29 45 36 66 66 67 67 67 67 72 56 51 48 12 8 5 7 7 8 5 4 4 2 0

0 0 0 0 0 0 0 0 0 0 0 0 21 23 25 2 3 14 28 35 11 20 6 17 22 6 8 16 19 100 104 301 502 606 600 504 409 506 502 505 408 406 057 061 041 034 531 2201 3232 3480 1341 827 749 2416 2408 1693 3853 2941 584 4109 177 04262 04953 14960 135133 235031 123925 154128 184217 1143112732 3913 438 450 480 520 570 544 5416 5933 2551 1150 1672 1279 779 1980 1885 083 080 073 266 1262 2750 2737 5129 5525 4919 5313 498 51 51 46 29 26 31 42 37 43 52 60 68 66 67 60 58 62 63 53 47 36 10 6 4 7 9 9 4 3 3 2 0

0 0 0 0 0 0 0 0 0 1 4 21 18 24 25 9 9 6 35 36 14 17 15 25 29 26 22 16 6 102 109 404 603 607 509 609 705 506 506 505 407 309 022 036 033 217 612 138 289 3246 842 1328 1562 3433 3601 383 2970 187 585 1586 3297 05059 04075 03462 125328 25128 93624 134224 85020 1330142216 2711 350 440 460 510 522 527 5025 5136 5445 2460 1972 1286 692 2286 3487 2879 173 1163 659 2556 4647 3136 6631 5626 5422 5619 5615 61 62 50 34 29 40 57 67 67 72 63 62 63 63 63 58 59 58 50 41 32 10 2 2 4 8 9 5 3 Measurement)1 0 0 mission, which has 0 0 0 0 (JAXA0 0 0 0 3 3 4 14 20 25 22-26 13 19 20 35 15 22 26 24 37 25 24 31 29 401 503 605 601 606 700 709 803 704 703 501 408 108 015 04 415 926 1215 1187 2133 2225 448 1356 2377 3602 2790 2866 1899 186 2694 41310 573 05049 04273 06343 18829 58324 96424 76819 84321 1219191412 196 210 390 360 490 517 5116 5129 5044 5350 4561 2669 2396 1498 2789 4088 4280 1570 3157 2856 4754 4343 5735 5831 5727 7221 6017 5616 71 55 48 46 47 57 79 59 75 67 46 51 64 60 63 59 47 41 32 26 25 9 7 5 6 10 4 3 2 1 0 0 0 0 0 0 15 0 0 0 3 5 6 14 21 24 22 37 15 21 22 30 6 14 30 25 35 30 18 35 40 400 503 609 509 1693 800 1026 1004 900 603 306 209 102 08 03 69 1334 1353 1296 2129 2315 549 1343 2467 3720 3744 2924 1963 1811 11100 11808 281 04544 05271 16643 46427 48221 79027 64322 83023 71221114 120 120 220 203 236 385 5119 4815 5349 5853 5460 5388 47109 3698 4298 2790 2977 5066 5854 4350 4944 5437 5632 6831 9227 9321 9416 7915 67 63 73 49 60 105 77 64 65 46 49 56 60 60 54 37 35 19 19 17 10 7 4 6 12 13 11 2 1 0 0 0

0 0 0 0 0 0 0 0 3 7 13 23 25 29 29 7 34 28 22 29 19 38 32 36 31 32 18 20 50 507 505 603 609 609 809 904 708 606 503 503 207 09 05 03 08 217 521 622 1376 2209 1368 1309 2752 2910 2935 2857 1740 566 580 1086 2380 03369 04070 03545 14724 35230 54825 63124 72026 7 720102 8 0 150 163 145 1411 1916 3417 4530 5350 5761 5673 4390 54115 50102 36101 3086 4079 5768 8156 7047 5841 7536 7131 7429 10122 7115 7213 614 64 73 79 93 102 90 82 69 68 53 62 60 50 34 37 30 15 13 12 11 7 4 2 6 14 17 13 4 0 0 0 0

0 0 0 0 0 0 6 3 10 7 20 25 28 30 30 29 30 30 20 14 13 25 26 33 21 14 23 43 58 609 704 600 702 705 1030 1025 907 805 700 408 304 208 011 010 014 030 024 021 213 429 051 452 2712 2938 2618 965 1224 692 1595 3193 4179 04671 0458 03834 07620 38631 36931 57130 75821 718211010 126 1710 1911 2023 1840 1943 3447 4448 5058 5260 5371 5296 5998 5101 4795 4483 4078 6860 13255 11147 7437 1030 12526 10726 1324 8024 6316 574 68 63 101 91 59 74 76 74 79 62 61 55 35 12 10 14 11 7 8 14 1 2 4 4 16 19 13 0 0 0 0 0 GSMaP_NRT0 0 0 0 0 6 6 8 14 16 20 27 29 30 30 26 27 30 12 17 9 12 16 32 29 31 50 69 72 701 508 504 907 900 1012 1021 904 809 608 409 309 206 020 032 041 055 046 042 021 026 056 259 676 1988 2533 3803 31402 31308 31401 4486 5367 0560 07457 07835 07320 28335 37641 45139 55327 650241222 1421 1923 2029 1843 1846 2152 2654 2858 4150 3462 3950 41103 3597 37101 6885 7277 7568 8255 11848 12635 11430 13129 21027 13328 15133 13331 6722 9211 87 86 45 37 28 38 66 67 66 59 52 46 13 8 9 6 6 12 13 24 3 4 6 6 15 17 12 0 0 0 0 0 0 0 0 0 3 6 5 9 14 15 23 26 28 32 22 21 21 9 9 13 10 8 7 13 34 25 50 75 89 704 702 609 906 609 1019 1012 904 804 601 409 407 404 051 040 055 058 057 053 056 056 059 091 256 960 252 31111 4935 41804 484 5166 4878 05564 05960 06846 16228 26042 25849 45552 44735 53729 828 944 1446 1948 1846 2051 2558 2460 1864 3469 3460 2673 2584 2999 2390 5881 6573 7162 6556 7745 7637 7433 9130 10924 13926 14024 8627 6927 8822 47 32 16 30 50 57 65 56 54 52 41 34 8 6 4 5 6 23 27 30 17 11 13 11 16 12 11 0 0 0 0 0 FINAL REPORT FINAL been planned by JAXA, NASA and 0 0 2 3 3 5 5 9 16 10 24 26 28 30 22 24 25 6 7 6 9 10 7 9 11 22 43 70 61 704 900 1019 1042 1018 1038 1016 609 609 808 507 609 606 076 056 069 059 062 069 058 049 065 045 046 264 688 11212 21407 2977 4675 483 5176 06266 09047 010375 010343 110430 26655 34655 34147 43642 556 977 1373 1465 1362 1368 1468 1573 2287 22106 19100 34103 23101 24100 1885 5071 5462 5753 6847 5941 5740 6534 6028 6827 8025 6724 7124 5623 7217 27 22 16 29 62 55 57 52 47 48 38 29 4 3 2 4 17 25 21 6 27 15 13 14 15 12 4 0 0 0 0 0

- 0 1 1 3 2 4 10 9 16 22 13 25 29 36 25 25 25 8 6 10 8 4 5 9 21 37 61 67 72 1031 604 1032 1039 1030 1018 605 504 704 805 804 1000 908 1004 085 081 063 079 098 078 061 087 059 048 050 598 11031 11713 4677 5874 7177 7367 08358 010412 012328 111319 212498 26558 24452 25942 38644 4102 7129 6121 881 1078 987 1394 1497 19104 21117 22109 22115 2590 2683 2865 4765 4752 4950 5744 4940 4837 4130 5627 5628 6725 5522 5218 368 3418 57 16 38 46 51 58 51 49 35 38 20 14 3 1 0 0 12 11 15 2 20 29 16 13 16 13 3 0 0 0 0 0

1 1 1 2 4 2 8 4 9 22 13 25 28 35 25 25 25 8 7 11 21 21 23 27 26 50 62 84 50 104 500 1067 1062 1086 1024 809 704 1028 1022 1012 1009 1024 1005 1005 1005 1014 1000 1025 1008 247 859 1709 1601 1511 11217 21007 2815 2763 5784 6974 860 09254 011471 016482 217470 210449 25063 24338 26348 312464 3164 4112 599 688 6112 5124 8146 1114 17101 1596 20100 2284 2087 2262 2663 2250 2349 2747 3543 4442 4240 4633 4733 4825 4627 5415 458 396 425 33 36 36 41 43 43 39 21 23 23 5 3 0 0 0 0 6 7 12 11 20 29 22 16 13 13 0 0 0 0 0 0

_Corrected1 1 1 1 2 3 4 8 3 13 12 20 24 24 26 26 7 7 )6 9 19 21 25 26 31 49 58 61 73 106 908 1058 1078 1019 1030 1020 1029 1053 1042 1006 906 907 1033 1058 1035 1015 1016 1004 047 233 750 1719 1530 1469 21302 2938 2971 3743 383 5271 671 010469 013368 214367 212338 210561 28063 28857 215427 313379 3112 4108 4104 5119 5131 6160 6141 1146 12119 13114 2116 17112 1695 1470 1660 2248 2453 2147 3442 2442 3334 3630 2131 3017 2717 298 283 360 320 33 27 24 37 36 40 27 11 8 6 3 0 0 0 0 0 5 11 21 33 36 32 22 19 18 0 0 0 0 0 0 0

1 1 1 1 2 3 3 3 3 6 17 19 22 21 23 21 22 7 10 7 10 20 28 24 22 28 49 63 87 903 609 603 904 901 1002 1032 2015 1088 1025 1021 1013 1077 2008 2072 1039 1020 1084 1018 064 029 028 284 868 1741 21102 2845 2752 268 4074 4765 7465 010561 014354 212401 212505 211570 215605 320760 316658 312586 3121 4140 4166 5179 5167 6204 6160 7138 7126 10115 10103 10101 1586 1579 1961 1261 1852 1548 1939 2540 2035 4230 931 1228 5 7 103 241 300 3010 18 19 18 30 36 17 10 7 10 0 0 0 0 0 0 0 3 10 18 38 35 32 20 19 4 5 0 0 0 0 0 0

1 2 1 2 2 1 2 1 1 2 11 7 13 14 13 11 9 3 5 4 11 15 11 14 19 21 25 31 34 206 402 207 306 300 300 904 2034 1072 1028 1002 409 2027 2012 2072 1049 1036 1019 1010 071 032 026 1007 158 563 11218 1901 1625 1643 1763 3164 5954 27945 08238 26262 25363 26063 311619 320781 316677 411688 4120 4136 5184 6203 5170 6176 6148 7125 7117 11110 10103 984 1372 1071 1467 1558 2053 2040 2742 2637 3333 3131 1828 328 412 8 1 1122 3822 2922 28 14 23 14 28 8 8 3 4 0 0 0 0 0 0 0 11 20 19 37 36 27 22 24 0 0 0 0 0 0 0 0 2 2 2 3 2 1 1 1 1 3 6 6 9 16 14 5 6 2 3 3 6 7 11 15 20 24 28 34 30 402 405 607 300 300 204 904 801 1028 1046 208 312 1014 1157 1791 894 1419 1209 1010 037 022 018 086 188 262 560 1508 1424 1467 2050 2967 7065 28253 28038 26653 23958 36154 27278 216646 321517 416594 4146 5181 5184 6186 6180 6132 6129 6103 7111 990 1089 984 973 1071 1266 1257 2360 2143 3448 5542 4736 4235 3230 622 318 713 1421 3331 2229 8 9 2 7 7 4 1 1 0 0 0 0 0 0 0 0 21 21 31 39 36 30 23 23 0 0 0 0 0 other0 0 0 space agencies in the world 3 3 3 3 3 2 5 2 3 7 11 9 11 18 11 9 6 5 6 5 9 14 18 22 25 26 35 42 50 709 805 506 400 300 302 619 1014 507 217 313 322 1446 1434 1723 1735 1548 276 187 143 047 116 1220 476 590 666 1807 178 2419 2639 3955 7561 26048 37255 34753 34071 34493 39182 313626 423537 519564 5203 5182 6167 6158 6133 7130 6118 7105 8101 892 981 976 674 1179 1573 2662 3057 3949 4246 4345 4342 3936 3132 3822 3315 817 1223 1324 927 9 2 0 6 1 4 1 1 0 0 0 0 0 0 0 0 27 24 35 36 36 30 27 7 0 0 0 0 0 0 0 0

3 3 3 3 3 13 16 24 34 30 36 32 30 30 25 17 10 13 15 19 22 27 28 30 30 39 48 59 65 606 609 509 505 301 361 271 1435 1571 692 1408 1676 1033 1325 1405 1533 1709 892 848 949 1205 915 1702 1926 1944 11527 2808 2844 3703 3958 4641 5268 35571 55160 44450 53344 57480 510908 422782 418526 51653 5163 9151 12149 6122 7106 7105 798 1192 1789 2383 2070 2069 2067 1969 2576 2968 3857 3851 3946 3746 3243 3038 2930 2921 355 1 5 021 025 025 0 2 0 0 0 0 1 0 8 0 0 0 0 13 7 25 26 26 35 36 32 16 12 0 0 0 0 0 0 0 0 0

3 3 3 3 9 11 21 29 34 35 37 40 41 38 33 23 23 27 31 25 33 37 49 39 45 51 64 60 66 700 702 609 403 227 346 1293 2742 3712 3924 3247 3466 3360 3321 2999 2256 1680 1868 1272 2138 2201 2155 2195 3154 3619 31202 31502 4816 3693 4962 5362 5975 36064 54957 54358 63456 63040 913727 1020780 1018575 101452210151 12150 12114 1104 985 1485 1982 2087 2385 2379 2675 2471 2466 2766 3364 3566 3554 3246 3042 2639 2638 2736 2432 1926 9 7 9 0 6 0 018 012 1 0 0 0 0 1 1 12 20 14 0 0 1 13 9 29 25 22 32 38 22 12 12 0 0 0 0 0 0 0 0 0

2 2 3 3 3 14 22 28 35 38 41 33 42 40 36 31 37 32 36 43 48 51 52 58 51 60 63 62 69 704 704 700 107 1032 11103 1753 2743 2792 3537 3305 3222 2480 3520 3226 3513 2555 21316 2855 3519 4102 4230 4145 4995 570 41800 482 4784 4683 5863 5756 6671 86768 75458 74068 103765 103155 123164 618749 1017518 616534 6138 6126 6109 893 1583 1979 2293 2266 2278 2469 2266 2153 2261 2158 3162 3260 2961 2249 1738 2535 1334 1930 631 1426 1117 111 9 0 5 0 6 5 7 3 6 0 0 0 12 24 27 29 21 1 17 15 13 25 23 22 32 36 11 0 2 0 0 0 0 0 0 0 0 0

3 3 3 2 3 4 15 21 34 45 53 59 50 53 50 51 51 42 40 48 51 50 53 58 59 60 63 67 68 609 700 608 601 507 1028 11030 1599 2568 3638 3580 4312 4305 3231 4311 3115 3203 3254 3873 4617 5602 5573 5793 6623 5694 5989 4687 5651 5752 6863 759 7149 97263 89072 86369 132862 112959 117383 67268 615566 615595 6139 6105 694 1682 2071 2282 1967 1754 2055 1848 1249 1147 850 1458 1358 1759 857 749 744 532 631 624 626 721 612 7 4 100 6 0 8 0 4 5 6 0 0 5 12 23 28 31 6 9 20 7 15 22 22 16 29 32 2 0 0 0 0 0 0 0 0 0 0 0 9 7 7 7 6 6 5 7 20 32 20 21 30 59 67 58 69 64 38 45 47 49 55 56 58 56 59 62 60 506 506 507 408 503 508 704 640 1529 1558 2596 4202 3976 5729 5655 5263 4683 4710 3794 4555 5446 6416 6627 6727 6762 6985 10573 12774 12616 7060 7451 766 87868 107565 129758 123172 94768 89776 716634 613503 514487 8135 8117 1584 1681 1876 1755 1448 1127 834 533 438 343 233 344 555 556 550 348 541 433 532 424 313 613 5 7 5 4 4 0 5 0 7 0 3 0 0 0 0 9 18 27 29 30 26 13 6 7 21 27 21 21 17 16 0 0 0 0 0 0 0 0 0 and0 0 0 will make it possible to observe 18 11 11 11 8 19 9 16 6 8 9 10 4 11 20 31 31 37 108 95 84 114 98 64 82 67 54 48 48 701 805 704 403 401 403 607 508 602 436 599 1534 146 1856 2798 4624 6810 5453 5423 4470 5637 6713 6857 6659 668 6899 7601 951 10578 6844 7643 754 87743 87147 97060 75992 77286 613766 515636 514535 513419 5116 5106 1118 9100 880 561 437 527 630 459 230 349 230 131 347 449 644 343 437 336 422 419 3 8 3 6 2 3 3 0 3 0 3 0 1 0 0 0 0 0 4 9 18 24 28 4 20 5 8 17 23 25 19 20 8 0 0 0 0 0 0 0 0 0 0 0 0 0

24 17 25 27 26 27 23 21 19 4 2 1 4 12 14 12 20 23 33 45 48 50 46 52 52 33 32 31 36 303 401 303 406 306 C309orre723cte60d6 -G605SM60a2P T407ota64l7 Ra432infa85l1l in95 7Ta1i4w37 an156 3[m2m467] 2467 3476 4488 5672 6530 6283 6353 6466 6464 6378 6481 6939 6532 6644 76944 7644 6C54o63rr6e569c2t5e9d287-G613S725M41a465P9 41T151o3t4a11l40 4R4a10i8nf4a9l5l in41 0T0 a3i8w7 an39 9[m4m75] 444 428 254 384 565 855 126 325 030 034 335 329 431 323 312 313 2 8 2 0 0 0 C0o0rre0c0te0d0-G0S0Ma0 P T0 ota0l Ra2infa9ll in1 5Tai2w8 an2 6[mm15] 7 10 9 13 21 22 22 13 9 3 0 0 0 0 0 0 0 0 0 0 0 0 0 (2009/08/08 03:00 ～ 08/08 05:00 (UTC)) (2009/08/08 06:00 ～ 08/08 08:00 (UTC)) (2009/08/08 21:00 ～ 08/08 23:00 (UTC)) 29 27 29 30 32 36 37 37 36 9 12 14 14 17 16 14 13 13 16 37 50 25 23 33 25 25 25 29 29 305 208 304 505 538 584 884 1548 44 502 436 428 433 361 962 945 1406 1442 2431 3500 3782 4388 530 5252 6201 5399 5399 5491 553 5833 5744 5947 35144 65348 44649 43873 64078 98576 310518 310489 28836 482 379 388 277 266 078 356 031 468 762 662 254 042 024 026 030 220 420 317 210 2 8 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 9 24 32 31 12 9 11 15 13 12 18 17 8 7 3 0 0 0 0 0 0 0 0 0 0 0 0 0

34 32 36 33 37 36 37 37 39 38 42 36 25 19 20 36 33 47 46 47 54 60 52 65 59 36 55 44 56 509 406 502 800 1637 1931 1894 2717 2531 2504 1410 1473 1347 1542 1334 1633 1402 1411 1570 2537 2586 3594 3498 4203 424 4381 4373 4565 452 4243 4039 4454 24253 34051 43757 23470 33474 03367 07962 08245 07435 068 067 165 061 056 068 073 067 079 056 048 042 041 024 121 228 222 212 013 0 7 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 18 34 31 11 12 17 18 15 16 11 9 5 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1:2000000 1:2000000 1:2000000

29 29 22 22 22 19 26 33 34 43 51 49 22 25 33 49 29 43 41 38 49 52 67 71 70 72 80 95 98 807 902 506 901 1928 11042 1960 1690 2607 2522 2345 2343 2233 1199 1415 1371 1521 845 1413 1279 2307 2354 2288 2167 2174 3303 3419 335 3429 3638 339 3546 03155 02969 03469 03077 02968 03966 03354 05947 06434 061 051 047 058 045 063 074 067 062 053 049 052 036 024 022 230 320 314 012 0 6 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 3 6 8 13 24 30 31 14 17 18 17 14 10 8 8 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 FUKAMI

26 24 19 20 19 17 17 16 17 32 34 33 45 43 37 36 44 41 44 47 47 58 64 66 67 71 86 103 98 707 609 507 708 907 408 506 692 1654 1519 1273 1177 1153 1157 816 951 1435 1538 834 1502 1339 1365 1270 2207 1184 1392 2457 2455 2368 2540 2541 3040 02639 02969 02482 03877 03067 04155 05154 04341 04034 041 047 037 034 031 059 053 062 057 049 046 049 023 423 415 312 513 510 210 0 1 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 7 0 11 8 13 23 31 30 27 18 17 17 16 13 7 5 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 26 27 30 30 18 18 17 22 28 44 45 45 14 18 26 19 31 42 20 11 32 29 28 59 46 53 69 77 106 803 805 705 705 801 705 507 703 500 400 208 203 108 014 015 525 927 1745 1615 1410 1340 1485 1360 1370 1256 1259 2426 346 2426 2749 2942 3239 04149 0460 03974 03670 03860 03754 03950 03341 03725 033 033 026 023 028 029 057 053 049 048 052 050 118 310 711 1011 1413 7 8 2 7 2 0 0 0 0 0 0 0 1 0 4 0 4 0 8 0 4 0 160 15 15 19 27 24 20 20 11 10 10 14 13 8 4 2 0 0 0 0 0 0 0 0 0 1 1 0 0 0 any0 0 0 area in the world once in three 28 30 33 40 33 33 35 33 42 46 16 10 5 14 15 11 8 8 3 11 15 16 21 22 41 54 60 69 77 602 603 601 600 603 705 502 603 507 500 302 208 209 021 027 030 266 1745 1831 1550 1494 2247 2394 3315 2368 245 257 3625 350 4049 3768 3755 03475 03463 03367 04263 04062 03051 03344 02932 02326 026 025 028 019 017 013 056 051 051 045 147 147 112 713 9 7 145 206 204 7 7 3 0 0 0 0 0 0 0 2 0 5 0 7 0 150 200 260 26 24 18 13 17 12 8 10 9 6 6 6 3 0 0 0 0 0 0 0 0 3 3 2 1 1 0 0 0 0 0 0

32 32 33 38 38 38 44 44 41 32 29 19 19 24 17 10 8 7 8 9 18 15 18 29 43 58 63 64 74 602 602 701 701 700 700 207 500 609 701 401 400 302 037 043 054 069 382 1836 1546 1452 2300 329 3326 338 3502 2786 3861 3659 3560 4168 4159 03573 04164 03766 04558 03055 03048 03039 03032 02413 029 022 020 010 012 010 031 044 045 043 444 337 3 8 105 194 2810 226 172 8 4 140 2 0 1 0 0 0 0 0 0 0 7 0 220 300 310 31 25 19 9 3 5 4 3 4 2 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 34 33 38 41 45 49 45 38 44 44 46 52 28 18 14 18 10 11 12 10 15 17 26 30 55 59 53 55 3:00504 500 505 502 309 403 305 -501 5:00UTC305 401 309 503 605 065 050 068 079 079 092 889 1440 1386 2328 3424 2492 2690 2751 272 6:002763 3166 3578 3659 04157 04268 03975 0-4358 0308:00UTC953 03537 03138 03019 02312 020 011 012 0 7 0 7 010 026 045 243 239 631 824 286 288 266 329 330 2021:002 154 9 0 3 0 0 0 2 0 1 0 2 0 150 290 -290 23:00UTC310 27 17 17 3 2 2 2 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 29 30 37 39 41 46 49 39 39 40 45 40 42 36 38 35 40 28 16 14 19 18 18 24 38 40 37 33 33 302 404 508 302 208 208 205 309 404 407 309 503 608 087 071 072 083 086 078 482 853 1423 143 1564 2653 2627 1782 2714 174 2479 2578 2359 04462 04083 04181 04768 04950 03135 02624 02019 0177 014 412 910 0 7 0 6 0 8 023 239 340 340 1327 1017 310 370 351 337 330 230 190 9 0 7 0 3 0 4 0 2 0 110 230 270 260 250 21 15 11 1 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

26 31 34 39 41 40 45 40 37 37 41 27 27 23 31 31 35 32 18 20 17 22 22 28 30 43 39 33 41 402 405 309 303 207 304 300 402 400 702 509 707 702 084 076 076 092 087 075 058 447 654 1508 977 1721 1751 1762 167 1634 1679 2178 2975 04059 04583 04778 04361 03939 03532 02919 03210 0184 014 413 1312 138 136 1 8 124 238 639 435 1713 285 370 380 350 333 350 270 260 110 110 6 0 6 0 8 0 180 210 210 180 150 9 7 1 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 22 35 35 39 40 40 34 37 37 38 8 27 22 31 30 32 32 32 28 19 21 20 28 32 35 44 44 36 307 301 305 305 303 406 507 505 604 700 600 804 808 078 076 082 083 079 077 063 050 165 464 677 777 1741 1722 1532 1864 2079 2083 3175 03159 03282 04875 04248 03644 03542 02918 0237 3192 817 1115 1314 2311 1 7 313 329 734 1137 1032 2810 293 340 360 367 3911 330 270 220 100 110 7 0 130 160 180 140 110 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 hours.0 0 0 If this project is realized, the

Fig. 17 Comparison of self-corrected JAXA-GSMaP_NRT temporal resolution and reliability of 01CMY - rainfall data with ground-based rainfall data satellite-based rainfall data would be (Taiwan, Typhoon Morakot, 8 August 2009) expected to be much improved. In

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] 23

reality, it would not be likely the case to implement an operational flood forecasting and warning system just only based on satellite-based rainfall data even with the self-correction method. In spite of that, this satellite-based rainfall information would be useful to understand the effectiveness and the limitation of global data for each local river basin. This kind of analysis is to be conducted at the first phase of flood runoff analysis and to be a milestone to upgrade the flood runoff analysis and/or forecasting system, step by step, to improve the reliability and accuracy with the implementation of in-situ hydrological observation network, which will surely lead to the enhancement of local ownership of the system.

Fig.18 Successful & unsuccessful cases of self-correction algorithm for GSMaP_NRT and its relationship with the frequency of direct microwave radiometric observations from the space

In order to spread this IFAS system widely and to apply it effectively for flood management issues to contribute to the reduction of flood disaster in the world, ICHARM has organized quite a few workshops/sessions to train local practitioners/engineers of developing countries since 2008. Most typical one is the 1st International Workshop on Application and Validation of Global Flood Alert System (GFAS), jointly organized/sponsored by ICHARM, IFNet (IDI), APN, held at ICHARM, Tsukuba, Japan, during 3-7 August 2009 (Fig. 19). The objective of the workshop was to study how to undertake hydrological forecasting in poorly-gauged river basins through initiating GFAS/IFAS validation and to discuss issues regarding the accuracy and applicability of the system in each area. This workshop was planned according to the agreement on the necessity of it at the Flood WG in the 4th International Coordination Group Meeting of the GEOSS-AWCI, Kyoto, Japan, February 4-7, 2009, although this workshop had been originally planned to be held at AIT (Asian Institute of Technology) in Thailand. The participants were recruited through the network of GEOSS-AWCI Flood WG, and consequently, six people were invited from Bangladesh, India, Indonesia, Lao PDR, Nepal, and

Viet Nam. Some of them were members of GEOSS- AWCI Flood WG. Many of the participants took their in-situ hydrological data with them and tried to validate hydrological modeling with IFAS with the Fig.19 Participants of the 1st international

in-situ data. This workshop was very successful to REPORT FINAL Workshop on Application and Validation share IFAS technology and its applications & -

of GFAS, at ICHARM, Tsukuba, Japan, problems among APN countries and led to FUKAMI August 3-7, 2009. enhancing case studies by their own capacities in - some countries, such as Indonesia, as mentioned

above, and Vietnam, as mentioned later. Moreover, 01CMY a project to implement IFAS-based operational flood forecasting/warning system in the Bengawan -

Solo River of Indonesia has been sponsored by ADB, as mentioned above as well.

ARCP2009

Such development, applications, disseminations and implementations of IFAS are also typical achievements of GEOSS-AWCI flood-WG activities and this APN project, i.e., 1) to convert observations and data, both through space borne platforms and data integration initiatives, to usable information for flood reduction, and 2) to improve quantitative forecasts for coupled precipitation - flood-forecasting systems.

3.1.8 Korea

Korea is also focusing on the impact analysis of climate change on hydrology and water resources. Lee et al.(2008) demonstrated a comparative study on soil moisture modeling with hydrological models based on global data at the demonstration basin in Korea. Bae (2009) discussed the importance of local data for hydrologic modeling even for such hydrologic models based on global data. Bae (2010) proposed a next-step climate change impact assessment and adaptation study for water resources. He discussed that there were sources of uncertainty of climate change impact assessment in every process such as GCM projection, downscaling and hydrologic modeling, and that the importance of consideration of uncertainty of hydrologic modeling which had not been incorporated very well so far. He proposed a multi-model ensemble approach (Fig.20). Those reports will be the basis for the activities of climate change WG of AWCI in the future.

Fig.20 Scatter plots of GCM outputs(precipitation) and runoff analysis with different models to show the necessity of multi-model ensemble approach

FINAL REPORT FINAL - 3.1.9 Lao PDR

FUKAMI Lao PDR has also suffered from flood disasters caused by floods from the Mekong River and its - tributaries (flash floods & landslides). Amphaychih (2010) mentioned there were 28 notable floods

from 1966 to 2007. In particular, there were 6 large floods (1966, 1971, 1978, 1995, 1996, 2000 and 01CMY - 2002). He also mentioned flash flood in the mountainous area was becoming common. A typical

example occurred in the Luangnamtha Province (Fig.21) in 2006 as mentioned below.

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] 25

Amphaychih et al.(2008) also described the characteristics of flash flood disasters. Flash floods in Lao PDR have occurred in the northern part of Lao PDR and around the border between Luangnamtha Lao PDR and Vietnam. Especially, the Luangnamtha Province often experiences flash floods. Significant flash floods occurred in the Namtha River in 2002, 2005, 2006, 2007 and 2008 by 2008, almost annually. Regarding the case of 2006 flood (August 6-7), heavy daily rainfall with 52.8 – 112.6 mm occurred at many stations during 6-7 August 2006, which caused destructive high-flow flash floods. Flood inundation occurred at the Hongleuai water-level station around noon of August 7 and flood overflowed the Namtha Dam around 1- 3pm of August 7 (Fig. 22). As a result, 132 villages were inundated with 1.2m depth. He summarized the lessons learned, as follows: Fig.21 Location of the Luangnamtha Province - The unplanned human settlements which were in Lao PDR developed into mountainous area caused significant damages. - Since the resources are limited, public mobilization in flood management and mitigation is very important. - Short and medium term flood forecasting, warning and dissemination are very crucial for flash flood preparedness activities. In order to cope with those

issues, Lao PDR established Fig.22 Overflow from theNamtha Dam(7 August 2006) the provincial disaster management committee with the objective to implement flood management and mitigation measures. Integrated flood management based on the quantitative understanding of flood hazards and risks utilizing global GEOSS and local data are expected to go well.

3.1.10 Malaysia

Malaysia is located in the tropical monsoon region, and suffering from a variety of floods (Kuala Lumpur, 1926). According to Shaaban (2009), flood prone area is around 26,720km2, about 9% of

the total land area of Malaysia, affecting 4.9 million people. The average annual flood damage is FINAL REPORT FINAL around 1 billion RM (263 million USD). He indicated the causes of floods were as follows: - 1) Natural phenomena

- Heavy rainfall caused by the northeast monsoon from November to February FUKAMI - High tide -

2) Human activities 01CMY

- Landuse change, rapid development and urbanization -

- inadequate drainage facilities and obstacles in rivers

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Therefore, climage change impact assessment on floods is one of the major concerns for hydrologists in Malaysia.

CGCM 1 MESOSCALE MODEL (MM5)

Global Scale Topography MM5 Atmospheric Boundary & Conditions & Model Landcover Ocean Outer (USGS) Domain Data Initial Soil (FAO) CGCM, NCEP Fields

Boundary Conditions

MM5 Initial Model Fields 2nd Domain Model Nesting Boundary Conditions

MM5 Initial Model Fields Inner Domain

REGIONAL HCM-PM Topography, Watershed Scale IRSHAMLand Landcover Hydro-climate HydrologicalModel & Output Domain Soil Model (NAHRIM)

Fig.23 Flowchart of climate change impact assessment on a river watershed scale with RegHCM-PM

Malaysia is one of the leading countries in the climate change impact assessment studies. The National Hydraulic Research Institute of Malaysia (NAHRIM) has an outstanding downscaling technology for hydrometeorological projection of future climate on a regional scale, i.e. the Regional Hydroclimate Model of Peninsular Malaysia (RegHCM-PM) (Shaaban et al., 2008, Fig.23). The atmospheric component of RegHCM-PM is the MM5 (5th Generation Mesoscale Model) developed by NCAR (US National Center for Atmospheric Research), a non-hydrostaticmodel which can be downscaled even to 0.5km spatial resolution, which makes it very desirable for downscaling climate study results to the scale or river watersheds. The land hydrology component of RegHCM-PM is taken from the Integrated Regional Scale Hydrologic-Atmospheric Model (IRSHAM), a fully

physically-based regional land surface hydrology model, which was jointly developed by the Hydrologic Research Laboratory, University of California at Davis, USA and by the Public Works Research Institute of Japan (PWRI). By using the RegHCM-PM, NAHRIM downscaled global climate change simulation data (Canadian GCM1 = CGCM) at very coarse resolution (-410km) to fine spatial

resolution (-9km) via medium resolution (-27km) for the future period of 2025-2050 (2025-2034 & FINAL REPORT FINAL - 2041-2050), including the past climate during 1984 and 1993. Those downscaled data have been archived as the Future Hydroclimate Data Retrieval System, which enables very flexible climate-

FUKAMI change impact assessment studies in Malaysia (Amin, 2010). For example, Amin (2010) assessed - the possible future changes of monthly rainfall anomaly, annual maximum 1-3 day rainfall, probable

maximum precipitation (PMP using statistical Hershfield method) and flood hydrographs at the 01CMY - Bekok Dam for examining the impact on its spillway based on real flood events of December 2006

and January 2007 (Fig.24). His conclusion is as follows, regarding flood issues:

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] 27

- It can be seen that in general future flooding is going to be worse but there are slight variations. But if we consider more exceptional flood events such as the PMF (Probable Maximum Flood), the impact is more significant as compared to conventional design floods of 10, 50 and 100- year return period. Reducing uncertainties of those projections will be the next-step subject for the climate change impact assessment studies. Climate change adaptation studies are also expected to be enhanced and shared among flood WG of GEOSS-AWCI.

1-day probable maximum rainfall (110-

460mm)

FINAL REPORT FINAL

FUKAMI - Fig.24 Maximum 1-day rainfall estimation over the peninsula of Malaysia

in the future (2025-2034 & 2040-2050) 01CMY - (Amin, 2010)

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3.1.11 Mongolia

Mongolia is an arid and semi-arid country with annual average precipitation of 224mm and only 9.9% of it forms surface runoff, partially recharging into groundwater aquifers (Oyunbaatar, 2009). However, Mongolia has also severe storm and flood. Through the GEOSS-AWCI meetings, those flood issues and possible future countermeasures have been discussed. Fig.25 Flash floods in the Ulaanbaatar City, 18 July 2005) Oyunbaatar (2009) divided (Oyunbaatar, 2009) floods in Mongolia into three categories: snowmelt flood (spring flood), rainfall flood and flash flood. Davaa (2010) showed the climate change impact assessment in Mongolia. According to him, the annual mean temperature of Mongolia increased by 2.14 degree during the last 70 years whereas the number of occurrence of natural disasters related with convective storm increased two times in 70 years. According to the future projection with the Hadley Center GCM (HadCM3), annual rainfall will be decreased by 2-4% in 2011-2030 but increased by 7-11% in 2080-2099. However, temperature and evaporation will also increase in the future, and as a result of that, river flow will be decreased in the future. Consequently, rain flood may decrease, but flash flood may still increase in the future. Historically the maximum daily rainfall at Ulaanbaatar is 103.5mm in July 1966. On 15 August 1982, 44mm rainfall was recorded in 20 minutes at Ulaanbaatar. This intensive rainfall caused severe flash flood and several tens of casualties (Fig.25). Considering such a recent situation, climate change may increase such flash flood risk, especially around the Ulaanbaatar City, since many hillslopes around the city have been being urbanized rapidly. It is now recognized that the Ulaanbaatar City should prepare more capacity for draining flash flood for hillslopes and early flood forecasting/warning system utilizing a radar raingauge installed in the vicinity of the Ulaanbaatar City.

3.1.12 Myanmar Table 2 Flood records in Myanmar since 1966

DangerLevel Max. WL above DL Stations Flood Duration Year (cm) (cm) (m) 2 Myanmar has the area of 678,000km and its Ayeyarwady Myitkyina 1200 1411 4 days 12 Hrs 2.11 1979 annual rainfall ranges from 600mm in the Bhamo 1150 1338 8 days 2 Hrs 2.38 2004 Katha 1040 1154 7 days 6 Hrs 1.14 1979 central area to 4,000mm in the coastal areas. Mandalay 1260 1382 16 Days 1.22 2004

FINAL REPORT FINAL Sagaing 1150 1274 17 Days 6 Hrs 1.24 2004 - Myanmar has suffered from many flood Nyaung Oo 2120 2263 16 Days 12 Hrs 1.43 2004 disasters every year, including riverine floods Chauk 1450 1532 12 Days 12 Hrs 0.82 1974 Minbu 1700 1982 17 Days 12 Hrs 2.82 1974 Aunglan 2550 2737 15 Days 1.87 1974

FUKAMI along large-scale continental rivers such as - Pyay 2900 3025 13 Days 1.25 1974 Ayeyarwaddy River, Thanlwin River, etc. and Hinthada 1342 1461 13 Days 6 Hrs 1.19 1966 Chindwin flash/storm floods along smaller-scale rivers. Hkamti 1360 1771 18 Days 6 Hrs 4.11 1991

01CMY Homalin 2900 3107 18 Days 6 Hrs 2.07 1968 - Ms. Htay Htay Than (2009) reported past Mawlaik 1230 1608 15 Days 12 Hrs 3.78 1976 maxmimum flood records for each river system Kalewa 1550 1920 10 Day 12 Hrs 3.70 2002

Monywa 1000 1099 9 Days 6 Hrs 0.99 2002

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as shown in Table 2.

It is apparent that big floods beyond the danger water level for more than 1 to 4m have happened. Therefore, Myanmar has made the following implementation plan for the demonstration Fig.26 Photos at the IFAS Training Seminar, 22-24 June 2010, Nae river basin, Shwegyin River Pyi Taw, Myammar (1,700km2). 1) To install two telemetering stations and one receiving station in the Shwegyin river basin, in order to get early warning system 2) To develop forecasting technique for flash flood Requirement:- Advance technology for satellite rainfall estimation Advance technology transfer for flash Fig.27 Location of Hkamti flood forecasting hydrological stations in the 3) To display the hydrological behavior of Chindwin River watersheds after estimation the geomorphologic parameters from digital elevation model 4) To develop the design flood and design rainfall for different return periods and probable maximum precipitation for duration of one to three days 5) To produce the unit hydrograph, which is of great use in the development of flood hydrograph for extreme rainfall magnitudes for use in design of hydraulic structure and development of flood forecasting and warning systems based on rainfall 6) To develop accurate flood risk maps based on flows the hydrologic model using all available

Rainfall(ground) Rainfall (GSMaP-corrected) Rainfall (GSMaP) data including GIS data sources Simulated (ground) Simulated (GSMaP-corrected) Simulated (GSMaP) Observed According to the implementation plan, river 60000 0 morphologic parameters, observed 1-3day

50000 50 maximum rainfall, probable 1-3day maximum rainfall with non-exceedance probability with 40000 100 1/10, 1/20, 1/50, 1/100, 1/200, and 1/500 ,

30000 150 and probable maximum water level with the Rainfall Rainfall (mm/hour)

Discharge Discharge (m3/s) 20000 200 same sets of non-exceedance probabilities FINAL REPORT FINAL were estimated (Htay Htay Than, 2009). - 10000 250 Besides, an IFAS Training Seminar was held 0 300 during June 22-24, 2010 at the Deparment of

1-May 1-Jun 2-Jul 2-Aug 2-Sep 3-Oct FUKAMI Meteorology and Hydrology (DMH), Ministry - Fig.28 Comparison of IFAS simulation for 2008 of Transport in the Nae Pyi Taw City, Myanmar,

using ground-based and satellite-based rainfall 01CMY to develop capacities for satellite rainfall - data with observed river flow (Htay Htay Than, estimation and flood forecasting (Fig.26). This

2009)

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Rainfall(ground) Rainfall (GSMaP-corrected) Rainfall (GSMaP) IFAS Seminar was sponsored by JAXA under Simulated (ground) Simulated (GSMaP-corrected) Simulated (GSMaP) Observed the framework of Flood Monitoring WG for 60000 0 the Sentinel Asia Project of the APRSAF (Asia- 50000 50 Pacific Regional Space Agency Forum). Mr.

40000 100

/s) Mizumoto (JAXA), Mr. Fukami and Dr. 3

30000 150 Miyamoto (ICHARM) and Mr. Matsuki (IDI)

Rainfall Rainfall (mm/hour) attended the seminar as lecturers. From the Discharge Discharge (m 20000 200 DMH side, 15 engineers participated in this 10000 250 seminar. Mr. Mizumoto explained JAXA’s 0 300 activities in earth observations from satellites 1-May 1-Jun 2-Jul 2-Aug 2-Sep 3-Oct Flooded and Mr. Matsuki of IDI (Infrastructure 2004 Development Institute) explained the IFNet Rainfall(ground) Rainfall (GSMaP-corrected) Rainfall (GSMaP) Simulated (ground) Simulated (GSMaP-corrected) Simulated (GSMaP) (International Flood Network) activities such Observed 60000 0 as heavy-rainfall alert e-mails through GFAS

50000 50 (Global Flood Alert System). Then Mr. Fukami and Dr. Miyamoto were engaged in IFAS

40000 100 /s) 3 trainings. At first, an exercise was conducted

30000 150 with a set of demonstration data from a Rainfall Rainfall (mm/hour) Discharge Discharge (m 20000 200 Japanese river. Then another exercise was

10000 250 conducted with Myanmar’s original data from the Hkamti Station of the Chindwin River 0 300 2 1-May 1-Jun 2-Jul 2-Aug 2-Sep 3-Oct (upstream area: 27,420km , Fig. 27). The 2003 preliminary result of this exercise was shown in Figs. 28 and 29. In this basin, there is only Fig.29 Comparison of IFAS simulation for 2003 one ground-based rainfall station at the & 2004 using ground-based and satellite-based Htkami Station. As a result of this, the runoff rainfall data with observed river flow (Htay simulation with ground-based rainfall data was Htay Than, 2009) never good. The runoff simulation with raw satellite-based rainfall data (JAXA-GSMaP) was too little compared with observation. Finally, the runoff simulation with the ICHARM’s self- corrected GSMaP_NRT attained the best result. Although this best result among three-type simulation cannot necessarily be referred as enough good result compared with examples of best simulations in fully-gauged river basins, especially in terms of low-flows, the Figure 26 showed the satellite-based IFAS simulation clearly identified two biggest flood events in the beginning of July and middle of August, 2008. Besides, Fig. 29 showed the potential to identify flood inundation in the upstream of the Hkamti Station through the comparison of the satellite-based IFAS simulation with real observation. These results show the high potential and practicability of satellite-based rainfall data (especially, JAXA-GSMaP_NRT corrected by the ICHARM’s algorithm) and IFAS (PDHM) as a flood runoff analysis system with world-wide availability, minimum cost and very easy operation. Of

course, we have to emphasize the importance of in-situ local data to verify and further improve the

flood analysis system lastly. The number of participants of this IFAS Seminar from Myanmar was 15, each one pair of participants from major river work offices in each region of Myanmar. They went back to their own office, pair by pair, with a PC in which the IFAS had been installed during the

Seminar. Therefore, this IFAS system is expected to be prevailed nationwide including the FINAL REPORT FINAL - demonstration basin.

Myanmar has also paid much attention to the impact of climate change on floods and water FUKAMI - resources. Tin Yi (2010.03) reported the detailed analysis on past trends in monsoon onset,

monsoon withdrawal, number of monsoon depressions, monsoon rainfalls, etc. Tin Ang Tun (2010) 01CMY - reported most recent flood situations.

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] 31

We hope those analytical efforts and experiences will raise capacities for flood risk management not only for the demonstration basin but also all over the Myanmar territory and will lead to reduce flood damages in Myanmar.

3.1.13 Nepal

Nepal has a variety of geographical conditions from the Terai Plain to the Himalayan high mountains, and from very dry to wet areas, and very steep hydraulic conditions (Sharma, 2009, Fig.30).

Fig.30 Five regions of Nepal (Sharma, 2009)

Nepal has heavy monsoonal rain during June to September and relatively weak geological formation. Therefore, water-related disasters are associated with landslides and debris flows in high and mid mountain areas, sediment deposit and bank erosion in lower mountains and upper plain areas, and bank erosion and flooding in lower plain areas. Nepal has also 3,252 glaciers covering 5,324 km2. 20 glacier lakes are said to be potentially dangerous and the risk of Glacier Lake Outburt Flood (GLOF) would be increased under global warming (Sharma, 2009.12). Sharma (2009.02) reported that Nepal had a few hundred casualties due to floods and landslides and that the loss of properties was increasing year by year (more than 288 million NRs in 2006). On the other hand, water is a very important natural resource of Nepal as water resources for agricultural irrigation and 43,000 Mw of hydropower. Therefore, Nepal is going to promote integrated water resources management and flood management for flood disaster mitigation, navitation improvement and economic development.

Sharma (2009) discussed the issues of GLOF. Historically, there are 16 GLOFs so far, and its risk is increasing due to climate change, he indicated. Typical mitigation measures are 1) reducing volume of water, 2) protecting infrastructure, 3) monitoring system, and 4) preventive measures against

avalanches. He introduced such an example around the Imja Lake, using satellite images. This is an REPORT FINAL - example of the utilization of GEOSS for local flood management. He indicated the importance of

collaborative & systematic investigation. FUKAMI

- Shresta (2010) showed an intensive study at the Bagmati River Basin, a demonstration area of Flood

WG of GEOSS-AWCI in Nepal. The Bagmati River has the about 3,700 km2 of watershed area up to 01CMY - the India-Nepal boarder. It originates from the Shivapuri Hill (2731 m amsl) and flows down south

into the plain (75 m amsl). Rainfall is influenced by mainly south-west monsoon during June and

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September. Orographic effect is pronounced and governs the rainfall pattern. Average annual rainfall in the southern part of the basin is 1500mm whereas in the northern part is around 2000 mm. There are 26 raingauge stations in the basin, especially in the Kathmandu Valley area and therefore the basin has relatively a good network of rainfall observation in Nepal. Two streamflow gauging stations were focused and the annual/monthly average flow, expected flow for some specific probabilities, and probable flood corresponding to 2-, 10- and 200-year return period. This is another example of flood risk assessment based on in-situ database. For the basin, Sharma (2008) discussed further implementation plan as follows: - Establish a network of real-time data transmission using CDMA commercially available; - Integrate this network to the central processing system; - Develop tools to automate processing - Integrate techniques of downscaling global met info to the rainfall runoff model - Constantly validate the flood warning to the observed data - Extend this system Appropriate for IWRM Information exchange, capacity building and funding efforts through GEOSS-AWCI would be expected to make it possible to implement the above plan on the basis of their own resources in near future.

3.1.14 Pakistan

Sheikh(2009) described the Pakistan’s wide variety of geographical, climatic and hydrological conditions. The northern mountains comprise parts of the Himalayan and Karakoram ranges with a small part of the Hindukush range. Pakistan has more glaciers than any other land outside north and south Poles and covers some 13,680 km2 as glaciated area. Karakoram is the abode of sizable glaciers and as high as 37% of Karakoram area is under its glaciers against Himalayas’ 17% and Alps’ 22%. Areas below approximately 31°N constitute deserts. Thar desert spans the border between India and Pakistan. Hamun-e-Mashkhel, some 87 km long and 35km wide is the largest desert found in Balochistan. The Dasht and Kharan deserts of Balochistan lie towards the western border of Pakistan with Iran. Regarding the climate, the northern half of Pakistan above 31°N is semi-arid to humid with a sub-humid belt running along the southern slopes of the sub-mountainous regions. The southern half of Pakistan below 31°N is mostly arid to hyper arid. The weighted precipitation in the southern half (~175mm) is one-third of the precipitation in the northern half (~600mm) and is almost one half of the Pakistan’s weighted precipitation (around 330 mm). Pakistan receives around 50% of the rainfall during the monsoon (JJAS) season while about 30% is received in the winter (DJFM) season.

Sheikh(2009) also discussed the past trend of climate and water, and probable impacts of climate change on hydrology. The area averaged mean annual temperature over Pakistan increased by 0.57C (in agreement with the global trend) over the period 1901-2000. The 48-year period 1960-

2007 recorded a slope of mean annual temperature as 0.24C per decade as compared to 0.06 per decade during 1901-2000 reflecting much increased rate of warming in recent years. This increase of air temperature has caused the retreat of many glaciers in the upstream of Indus as Rasul (2009) mentioned. The areally-averaged mean annual precipitation over Pakistan increased by 25% during

the previous century. Monsoon precipitation increased elsewhere except in coastal regions (where FINAL REPORT FINAL - there was a significant drop) and the Western Balochistan Plateau during 1951-2000. During the above period, the winter rains decreased in most of the districts, in particular by 13-20% in Sindh

FUKAMI and western Balochistan. El Nino years generally tend to be in drought conditions whereas La Nina - years behave the other way round and bring excess rains.

01CMY - Besides, Rasul(2009) discussed several extreme events by that time such as 1) five-year prolonged

severe drought (1998-2003), 2) heavy downpour record in the Capital in July 2001 (620mm in 10

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] 33

hours), 3) the heaviest rainfall in an arid zone, Karachi City in July 2003 (120mm in 14 hours), 4) snowmelt flood in the upstream of Indus in June 2005, 5) tropical cyclones, Gonu & Yemyin in 2007 and 6) two GLOF events in 2008.

Therefore, it may be very probable that Pakistan would be threatened under the climate change era by severer and more frequent extreme events such as floods due to heavy downpour, snowmelt and/or GLOF, droughts due to the retreat of glaciers and the decrease of rainfall in winter.

The great Indus Flood in 2010, affected more than 20 million people, may be a typical example of future extreme events under the increasing Fig.31 Location of Gilgit & Hanza River Basins climate variability. (Ahmad, 2010) Considering the importance as a source area of floods including snowmelt & flash floods and water resources from glaciers, two of the upper Indus River Basin, the Gilgit & the Hunza River Basins (Fig. 31) were selected as

demonstration basins in Pakistan, and the Pakistan Meteorological Department implemented an

FINAL REPORT FINAL

FUKAMI -

Fig.32 Temporal variation of snowcover in the Hunza River Basin over 10 years from 2000 to 2009, 01CMY where the daily data were estimated by the linear interpolation of 8-day MODIS images, with a slight -

increasing trend line during the period. (Ahmid, 2010)

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intensive meteorological & hydrological observational network for the basins. Ahmad (2010) reported a study on snowcover and snowmelt modeling in the Hunza Basin using a snowmelt runoff model, SRM, using snowcover monitoring data with MODIS (Fig.32). The simulation results were quite successful for 2003-2004 season(Fig.33). This is another typical example of the output of GEOSS-AWCI concept and is expected to lead to better understanding and management of flood and water resources in the upper Indus River Basin.

Fig.33 Comparison of SRM simulations with observed hydrograph during the hydrological year of 2003-04 in the Hunza River Basin (Ahmid, 2010)

Fukami (2010) reported a study to apply satellite-based rainfall data to the flood runoff & flash- flood-inundation simulation in the Kabul River, where the biggest number of casualties was recorded 50,000 0 in the Indus Flood 2010. Satellite rainfall(3B42RT) The flood 40,000 〃 (original GSMaP) 2 runoff 〃 (corrected GSMaP) simulation 〃 (corrected GSMaP by Ground) using IFAS

/sec) Calculated discharge(3B42RT) 3 30,000 〃 (original GSMaP) 4 with the 〃 (corrected GSMaP) ICHARM’s 〃 (corrected GSMaP by Ground) self-corrected 20,000 Measured discharge 6 GSMaP

Rainfall (mm/hour) seemed Discharge (m 10,000 8 apparently

quite FINAL REPORT FINAL - matched with

0 10 the observed FUKAMI - 7/27 7/28 7/29 7/30 7/31 8/1 8/2 8/3 8/4 streamflow Date (2010,GMT) data at the

Nowshera 01CMY - Fig.34 Comparison of IFAS simulations using various rainfall-data sources station. But, with observed riverflow (Fukami et al, 2010) if six ground-

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] 35

based daily rainfall data were incorporated to correct the original GSMaP data directly, which were expected to be more reliable than the self- corrected GSMaP since the data were based on the real ground rainfall data, the result of the simulation became overestimated (Fig.34). This may be caused by the decrease of flood peak due to flood inundation in the upstream of the gauging station or by some observational error, and so forth. In any case, those simulations showed the high potential of satellite-based rainfall as one of the source of flood-runoff simulation even in poorly-gauged river basins. He also showed a comparison between a flood inundation simulation using a rainfall-runoff- inundation coupled model (RRI model, Sayama et al.,2010) with GSMaP and the flood inundation Fig.35 Comparison of flood inundation areas area monitored by MODIS. The simulation of RRI estimated from ICHARM’s RRI model with model reproduced severe flash flooding in minor satellite-based rainfall (above) and monitored valleys but the MODIS images could not monitor from MODIS (below) (Fukami et al., 2010) such flash flood occurrences, the existence of which was confirmed by house-damage data

(Fig.35). This example shows us another potential of global-GIS-based hydrologic simulation with satellite-based rainfall data to reveal the reality of flood disasters under very limited real-time hydrologic-data conditions.

3.1.15 Philippines

The Philippines is a country composed of about 7,100 islands, located in the boundary of the Eurasian Plate and the Filipino Plate. Therefore, the Philippines is a typical example of a mobile belt area (or a tectonic zone) with very vulnerable geology and many volcanos & earthquakes. Besides, the Philippines is very close to the North-West Pacific Ocean where a lot of typhoons are generated every year and is just under the Intertropical Convergence Zone with the northeast & southwest monsoons. Therefore, the Philippines, like Japan, has lots of sources of natural hazards, including floods, landslides, etc.

IMPACTS OF ENSO ON PHILIPPINE RAINFALL Hilario (2009) reported a study on the Legend: relationship between Severe drought impacts hydrological extreme Drought impacts with major losses events and the ENSO (El Moderate drought impacts Niño -Southern Near normal to above normal Oscillation). El Niño has condition caused droughts in

Way above FINAL REPORT FINAL normal condition many areas and, on the - Potential for flood damage other hand, La Niña Severe flood

damage has often caused FUKAMI extreme rainfall events - RED colored years are EL NINO years, BLUE colored years are LA NINA (Fig.36). For example,

years and BLACK colored years are NON_ENSO years 01CMY

in February 2006, a - Fig.36 Interannual variation of hydrological impacts huge-scale landslide from 1977 to 1988 in the Philipines (Hilario, 2009)

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occurred at the town of Guinsaugon after a heavy rainfall event and the town was almost buried by the landslide with about 1,000 people. She mentioned the damage of typhoons such as “Milenyo (September 2006)” and “Frank (Fengshen, June 2008)”. Janillo (2009) reported the damage of Typhoon “Ketsana (Ondoy, October 2009), which recorded the highest 24-hr rainfall amount (455mm) in the Metro Manila. The number of casualties was around 300.

Janillo (2009) mentioned also the PAGASA (Philippine Atmospheric Geophysical Astronomical Services Administration) activities to cope with those flood hazards. The PAGASA implemented meteorological stations including 35 automatic weather stations and 10 Doppler Radars (in plan). She indicated the major challenges as follows: a) Extreme Event (Flood and drought) management system b) Rainfall downscaling and forecast (short and long term) c) Flood Simulation d) Climate Change Scenario building and Impact Assessment Hilario (2010) reported a national project for the climate change impact assessment and adaptation in the Philippines during 2009-2011. This is a joint project among may sectors of the government and the United Nations. The total cost of the project is 8 million US dollars. The project includes five demonstration projects in each region of the country. The downscaling of GCM outputs are also included. Through those projects and the incorporation of GEOSS-AWCI knowledge and information, more effective flood disaster management system is expected to be developed and be used in operation.

3.1.16 Sri Lanka

According to Weerakoon (2009), flood disaster is the most typical natural disaster since the percentage of flood disasters among natural disasters is more than 80%. For example, 488 thousand people were affected and 48 people Fig.37 Flood inundation risk area corresponding to 50-year return were killed by floods. Therefore, flood disaster period around the Rathapura City in the Kalu Ganga Basin prone area has been identified and mapped.

At the demonstration basin of Sri Lanka, the Kalu Ganga River basin (catchment area: 2,690km , annual rainfall: more than 4,000mm), a flood inundation & damage simulation around the Rathnapura City in the basin was conducted using HEC-RAS and HEC-GeoRAS, including flood risk assessment corresponding to 5-year and 50-year floods (Fig.37). To conduct similar studies in other

flood-prone areas, the importance and difficulty to secure accurate DEM were indicated and FINAL REPORT FINAL - discussed through the report of the study at the AWCI-ICG meeting.

FUKAMI 3.1.17 Thailand - The demonstration basin of the GEOSS-AWCI is the Mae Wang River Basin in the northern Thailand.

Therefore, flood disasters in the northern part of Thailand have been mainly discussed at the AWCI-

01CMY - ICG meetings. Flash floods and riverine floods occurs mostly in the wet season from May to October

when continuous heavy rain influenced by southwest monsoon from the Indian Ocean, tropical

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] 37

storm from the South China Sea, low pressure trough or frontal encounter of different pressure air masses. Thada (2009) reported that typical flood disasters in the Mae Wang River Basin are in September 2006, September 2007, & November 2008. He Fig.38 Comparison of maximum 1-day rainfall record before 200 and after mentioned the 2000 (Thada, 2009) increasing trend of maximum 1-day rainfall in the northern Thailand was identified through the comparison between the period before 2000 and that after 2000 (Fig.38). The flooding area has also increased. Under such a condition, the Royal Irrigation Department (RID) of Thailand implemented 13 rainfall stations & 5 water level stations in the Mae Discharge and water level measurement by hydrological stations Wang River Basin, in cooperation with the Institute of Industrial Science of the University of Tokyo. The major Station P.82 Ban Sob Win flood prone area is located around the downstream of the

P. Mae Wang River, and a water level station, 18km- P. upstream from the water level station near the flood Hydrology and Water management Center Station P.84 Ban Phan Ton prone area, is for Upper Northern Region Fig.39 Two correlated hydrological stations of the Mae Wang River located at the confluence point of

for flood forecasting (Thada, 2009) the most part of the

mountainous areas (Fig.39). Therefore, the correlation between the two water level stations enabled to make flood

forecasting with 4-5 hour lead time. The RID paid also attention to the way how to raise public FINAL REPORT FINAL awareness and preparedness against floods there. Water-level measuring staff gauges were - colored with green, yellow & red corresponding to the range of safety level (Fig.40), and a flood

information board with the display of real-time digital numbers (Fig.41) was installed to let the FUKAMI public know the updated information about the upcoming flood. This is a typical successful -

showcase not only in the implementation of flood warning system but also in flood risk management 01CMY including the raise of public awaren ess & preparedness in flood prone areas of the Asian monsoon -

region in the GEOSS-AWCI activities. At the next stage, RID is going to study how to utilize raingauge

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network and distributed- parameter Flood hydrologic model for the demonstration Critical basin and to apply range the developed technology to other flood-prone Normal areas of Thailand. This study will make another good showcase of GEOSS-AWCI Fig.40 Colored staff gauge to easily identity flood risk (Thadu, 2009) activities in the implementation of flood warning system with GEOSS information.

3.1.18 Uzbekistan

Major water-related problems for Uzbekistan seem drought and the impact of climate change on water resources apparently, but mudflows and floods caused by rain storm, snowmelt, GLOF, etc. are also the issues of interest and attention. Dergacheva (2010) reported that five ministries and province/city administrations have been cooperating, since 2007, to Fig.41 Flood information board for public monitor hydrometeorological situations to define dangerous hydrometeorological phenomena, to assess the future potential threat of hazards, to make short- term (up to 5 days) or long-term forecast on such hazards and to archive those data. The monitoring system contains 78 meteorological stations, 145 hydrological stations, remote sensing (from NOAA),

cameras, geophysical information system based on ACCESS, ArcView and MAPOBJECT (Fig.42). This system is one of the most advanced hydrological monitoring and archiving system. She summarized the next steps to improve the monitoring system as follows:

1) To ensure the infrastructure for the collection, transmission and processing of hydrometeoro- FINAL REPORT FINAL - logical data (air temperature, precipitation, snow accumulation, water levels, etc.) directly from the object and the representative mountain glacier basin;

FUKAMI 2) To develop criteria for operational assessment of the stability of the object and the risk of an - emergency situation according to the comprehensive monitoring of the state of the object;

3) To carry out the practical implementation of theoretical concepts for the formation and 01CMY - movement of the flood formed after outburst of glacial lake, speed of movement of flood wave

and areas of possible flooding;

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] 39

4) To develop the concept of the practical implementation of an early warning system and preventive measures on the basis of the integrated monitoring. It is expected that this monitoring system will provide us not only with water-related disaster forecast but also with monitoring & modeling the probable impact of climate change on hydrology and water resources. Pictures and video material Morphometric characteristics of lake

Bathygraphic curve

The geographical position of the lake

Hypsography of the basin

Fig.42 Uzbekistan’s hydrometeorological monitoring system (Dergacheva, 2010)

3.1.19 Vietnam

Vietnam is also a country exposed to typical flood disasters caused by typhoons and heavy rainfall storm affected by Asian monsoons. Tinh(2009) reported the biggest floods on the Red River in August and November of 2009. The former flood caused 120 casualties and the latter was the biggest flood in the 60-year record which caused the damage of about 3 billion US dollars. According to Tinh(2010), in recent years, there were more typhoons with higher intensity affecting Vietnam.

Typhoon track has a tendency of moving southward and the typhoon season tends to end later. There were more typhoons with abnormal movement. Since drought is expected to be severer under climate change, drought would be the first priority to cope with in Vietnam, but flood is also another issue of big concern.

REPORT FINAL - Tinh(2009) mentioned that Vietnam planned to spend over 1.95 trillion VND for the National Climate

Change Adaptation Programme until 2015. Around the half of the budget will be mobilized from FUKAMI foreign sources. The State budget will cover 30 percent of the remaining half of the fund and an - additional 20 percent will be drawn from local budgets and private economic sectors.

01CMY Under the circumstances above, Vietnam and Japan started a joint demonstration project on the -

sustainable water management in the Huong River Basin (2,830km2) of Vietnam, a GEOSS-AWCI

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demonstration basin, under the framework of GEOSS-AWCI and APRSAF-SAFE (Space Application for Environment). Saavedra(2010) reported that the UT’s DRESS and FLOWSS based on WEB-DHM (see the Japan’s paragraph 3.1.7) was applied for real-time flood forecasting of the Huong River. If the rainfall prediction data of the GPV (Grid Point Value) calculated by a global numerical prediction model was used as the rainfall input to the WEB-DHM, then the forecasted river discharge was underestimated. On the other hand, both the flood forecast results with the 6-hr lead-time downscaled rainfall prediction and with the satellite-based (TRMM) rainfall data were almost coincident with the observed river discharge in terms of flood peak’s timing and level (Fig.43). The same modeling system was also applied to the Red River Basin (160,000km2) successfully, including the flood-control function through three dam reservoirs. These results show the high potential of downscaled forecasting and satellite-based rainfall data. This is also a typical example of integrated flood management with GEOSS data.

7000 5.0 m Flood warning level III

6000 /s] 3 5000 4000 3000 2.8 m Flood warning level II 2000 Discharge [m 1000 0 23 24 25 26 27 28 29 30 Nov, 2004 Gauge Obs TRMM HRM-6hr GPV

Fig.43 Comparison of flood runoff simulations of Flowss using various rainfall forecasting & monitoring data with observed riverflow in the Huong River (Saavedra et al., 2010)

3.2 Discussions

As described and summarized above, each member country of Flood WG of GEOSS-AWCI has been

making every effort to build up a scientific basis for sound decision-making and developing policy options for most suitable flood risk management. To attain the goal above, we set up the following three objectives: 1. To convert observations and data, both through space borne platforms and data integration

FINAL REPORT FINAL initiatives, to usable information for flood reduction - 2. To improve quantitative forecasts for coupled precipitation - flood-forecasting systems

3. To facilitate flood risk assessment through the provision of scenarios and data for exposure FUKAMI

- estimation Namely, each representative of the International Coordination Group (ICG) for GEOSS-AWCI has had

01CMY regular meetings, on average twice a year, reported new developments, new research findings, - trials-and-errors of each country and conducted information exchanges to share them, based on the

APN fund. Sometimes, some workshops or training seminars, to understand new technologies/

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] 41

approaches more and to activate joint cooperative activities more, were held. The 1st International Workshop on Application and Validation of Global Flood Alert System (GFAS)”, Tsukuba, Japan, during 3-7 August 2009 is a typical example of such workshops.

As a result of those mutual cooperative activities, a variety of research activities including demonstration projects with demonstration basins’ data and/or historical flood events’ data have been conducted to attain the goal and objectives above.

For example, the University of Tokyo has developed 1) Dam Release Support System (DRESS), a combination of ensemble of precipitation forecasts and WEB-DHM (Water and Energy Budget-based Distributed Hydrological Model), for dam release decision support to reduce flood peaks and store volume for water-use and 2) Flood Warning Support System (FLOWSS) for dissemination of flood warning to perform evacuation timely. Those models were applied to the Tone River, the demonstration river basin of Japan, the Huong River, the demonstration basin of Vietnam, and more, and were validated. This research and development is the most typical and advanced example toward the objectives 1) & 2) above, directly, in the sense that the system is one of the most state- of-the-art science-oriented inventions in the world to cope with uncertainties of hydrological forecasts through ensemble simulation with downscaled numerical weather prediction data updated by satellite-based rainfall data.

The above approach of the UT has definitively the very high potential to solve many problems in flood forecasting and risk analysis, but it may require highly-skilled human resources, state-of-the- art computer environments and relatively high cost & long time to implement. Therefore, UT is now going to implement the WEB-DHM systems on DIAS, which is now being developed as a platform to share GEOSS-AWCI databases.

The UNESCO-ICHARM’s approach will give us another choice. That is, ICHARM has developed IFAS (Integrated Flood Analysis System) as a toolkit to enhance flood forecasting and analysis system in poorly-gauged river basins through combining not only ground-based but also satellite-based rainfall data and already-existing hydrologic modeling technologies such as simplified distributed-parameter hydrologic models, the parameters of which can be estimated from global GIS data. The executable system of IFAS has been distributed to the world through the ICHARM’s website, free of charge. The workshops and training seminars of IFAS were held in Japan, Indonesia, Myanmar under the framework of GEOSS-AWCI. Then IFAS was applied and validated in the participating countries including those three countries. This is also another typical example of the activities toward the objectives 1) & 2) directly, in the sense that IFAS would enable us to promptly & efficiently build up a flood runoff analysis and/or flood forecasting system, without developing a basic core system for them from the beginning, and to improve the accuracy of forecast, step by step, in accordance with the availability of resources to implement in-situ ground-based hydrological observational network.

The developments of integrated water resource management system with satellite & AWS data in Cambodia, WRF model applications to analyze extremely heavy storm in the Kashimir district of India, snowmelt runoff forecasting system with satellite-based snowcover monitoring with MODIS in

Pakistan are also one of such examples toward the objectives 1) & 2). The development of FINAL REPORT FINAL hydrometeorological monitoring system in Uzbekistan is an example toward the objective 1). The - development of flood forecasting system at the Mae Wang River of Thailand is another example

toward the objective 1) with a practical approach to raise public awareness & preparedness against FUKAMI flood disasters. -

01CMY

On the other hand, a variety of comprehensive and intensive studies toward the objective 3) have -

been also being tried and conducted, such as the detailed analysis of flood forecasting procedures

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and effects at the Cyclone Cidr and of climate change impacts in Bangladesh, flood hazard mapping with hydraulic models in India, flood risk mapping based on past experiences in Indonesia, climate change impact study with multi-model ensemble approach in Korea, climate change impact study with an advanced fully-coupled downscaling mesoscale model (RegHCM-PM) in Malaysia, diagnostic studies on historical & probable floods (& climate change impacts) in Bhutan, Lao PDR, Mongolia, Myanmar, Nepal, Pakistan, the Philippines, & Sri Lanka. In particular, the studies in Korea and Malaysia are regarded as the most typical state-of-the-art science-based climate change impact studies on hydrology and its extreme events.

As discussed above, there have emerged many promising technologies and practices for the future sustainable flood risk management in these APN-funded Flood-WG activities of GEOSS-AWCI since the middle of FY2008.

4.0 Conclusions

The Flood WG of GEOSS-AWCI has been promoting mutual cooperative research activities, through regular meetings & human/information exchanges, to build up a scientific basis for sound decision- making and developing policy options for most suitable flood risk management. To attain this goal, we set up the following three objectives: 1. To convert observations and data, both through space borne platforms and data integration initiatives, to usable information for flood reduction 2. To improve quantitative forecasts for coupled precipitation - flood-forecasting systems 3. To facilitate flood risk assessment through the provision of scenarios and data for exposure estimation As a result of 2-year cooperative research activities among Flood WG of GEOSS-AWCI, there have emerged many promising technologies and practices for the future sustainable flood risk management. Most typical new technologies developed and/or validated through those activities are WEB-DHM, DRESS & FLOWSS of UT, IFAS of ICHARM, RegHCM-PM of NAHRIM, and so forth. Through repetitive meetings, discussions and cooperative activities, advanced technologies and many other innovative practices have been shared among all the members of Flood WG of GEOSS- AWCI, which will be expected to lead to updating and enhancing a variety of science- & data-based foundations toward sound decision-making and developing policy options for effective flood disaster risk reduction in Asia.

5.0 Future Directions

The referred technologies above such as WEB-DHM, DRESS & FLOWSS of UT, IFAS of ICHARM, RegHCM-PM of NAHRIM and other emerging innovative practices will surely be some typical candidates of the best choices to effectively couple global / in-situ observational and numerical data

with state-of-the-art hydrological & meteorological analyzing & forecasting methodologies and to enhance and promote sustainable flood risk management. However, most of them are still at the stage of case-study or validation phase and the dissemination and effective applications of them throughout Asia has never been enough yet. Therefore, the Flood WG activities of GEOSS-AWCI,

including not only human/information exchanges but also capacity building, should still continue so FINAL REPORT FINAL - that such progressive approaches are prevailed among administrators and engineers in charge of

flood risk management and flood disaster reduction.

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01CMY

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References (relevant to Flood-WG activities of GEOSS-AWCI, only)

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India

Kaur, S .(2008) Demonstration Project – Seonath Basin, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Kumar, R. (2008) Flood hazard modeling and flood risk zoning for a river basin, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008.

rd REPORT FINAL Kumar, R. (2009) Recent signs of water related disasters in India, Presentation at the 3 GEOSS-AP - Symposium, Kyoto, Japan, February 2009.

Kaur, S. (2010) Cloudburst over LEH and flash floods, Presentation at the 7th AWCI-ICG Meeting, FUKAMI Tokyo, Japan, October 5-6, 2010. -

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Indonesia Syamstudin, F. , M.D.Yamanaka and R. Sulistyowati (2008) Development of climate-induced flood prediction system in Jakarta, Indonesia, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Loebis, J. (2008) The Seminar and Workshop on Use Satellite Based Information in Flood Risk Management, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Loebis, J. (2008) Application of distributed hydrological model for Mamberamo river basin for flood assessment and management, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Loebis, J. (2009) Recent sign of water-related disasters, Presentation at the 3rd GEOSS-AP Symposium, Kyoto, Japan, February 2009. Loebis, J. (2009) Country report :Mamberamo Demonstration Project, Application of IFAS model for flood management, Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009. Mulyantari, F. (2010) Hydrometeorological related to disaster, Presentation at the 4th GEOSS-AP Symposium, Bali, Indonesia, Mar. 10-13, 2010. Kusuma, M. (2010) Several challenges of flood control development in Citarum River toward MDG 2020, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Loebis, J. (2010) Country report, flood management related to climate change, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Japan Panel on Infrastructure Development (2008) Climate Change Adaptation Strategies to Cope with Water-related Disasters due to Global Warming (Policy Report), Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan (2010) Practical Guidelines on Strategic Climate Change Adaptation Planning - Flood Disasters –“ Ishida, C. and T. Koike (2008) GEOSS current status, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Wang, Y. , K. Takase, and S. Herath (2008) Assessment of soil moisture dynamics and storm-induced landslides hazard in forest landscapes, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Fukami, K. , T. Sugiura and J. Magome (2008) Development of integrated flood analysis system(IFAS) for poorly-gauged rivers, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Saavedra V. O. , T. Koike, Y. Kun and Y. Gawen (2008) Application of distributed hydrological model to AWCI demonstration river basins, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Sugiura, T. et al. (2009) Development of Integrated Flood Analysis System (IFAS) and its applications, Proceedings of the 8th International Conference on Hydroinfomatics, January 12-16, 2009,

Concepción, Chile.

Okazumi , T. (2009) Recent signs and countermeasures on water related disaster in Japan, Presentation at the 3rd GEOSS-AP Symposium, Kyoto, Japan, February 2009. Hirabayashi, Y. (2009) Development of global glacier model for water resources assessments,

Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009. FINAL REPORT FINAL - Takayabu, Y. (2009) Multi-model projection of typhoon genesis frequency using CMIP3 data archives, Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009.

Okazumi, T. (2009) Country report-Japan-, Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, FUKAMI - December 15-18, 2009.

Koike, T. (2009) Heavy Precipitation Events: Frequency increases over most areas, Presentation at 01CMY

- the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009.

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Shiraishi, Y. et al. (2009) A proposal of correction method using the movement of rainfall area on satellite-based rainfall information by analysis in the Yoshino River Basin, Annual Journal of Hydraulic Engineering, JSCE, vol. 53, pp. 385-390 (in Japanese). Fukami, K. , S. Herath (2010) GEOSS-Asian water Cycle Initiative(AWCI) Flood WG – Activity Report -, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Fukami, K. (2010) Capacity building activities at UNESCO-ICHARM , Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Inomata, Y. (2010) JAXA’s capacity building activities for Asia-Pacific region , Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. JAXA (2010) Cooperation with Asian Development Bank , Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Koike, T. (2010) Summary report including updates of the demonstration projects, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Herath, S. (2010) Capacity Development implementation , Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Hirabayashi, Y. (2010) Global Glacier Modeling, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Otsuki, E. (2010) Practical guideline on strategic climate change adaptation planning – flood disasters -, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Ishiwatari, M. (2010) Climate change adaptation in disaster risk management, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Takahashi, K. (2010) Re-analysis for climate change assessment & adaptation, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Imaoka, K. (2010) Satellite observations – JAXA EO data for water cycle observation -, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Wang, L. and T. Koike (2010) Hydrological modeling and applications for improved integrated water resources management, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Saavedra, O. and T. Koike (2010) Decision support systems using optimization algorithms at AWCI demo basins, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Fukami, K. , T. Sayama, S. Nabesaka, T. Kawakami and G. Ozawa (2010) Preliminary analysis on flash floods in the northwestern Pakistan, 2010, using IFAS with satellite‐based rainfall and global GIS data, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Koike, T., L. Wang, K. Yoshimura and H.Yamamoto (2010) Multi-model applications to the assessment of the climate change impacts on floods, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Korea Bae, D. , B. Lee and D. Lettanmaier (2008) A comparative study of model-driven soil moistures on the Chungju demonstration basin, Presentation at the 3rd AWCI-ICG meeting, Beijing, China,

November 3-6, 2008.

Lee, B. (2008) Brief progress report on Korean demonstration basin, Presentation at the 3rd AWCI- ICG meeting, Beijing, China, November 3-6, 2008. Bae, D. (2009) The Importance of Local Hydrologic Data for Global Climate Change Study,

Presentation at the 3rd GEOSS-AP Symposium, Kyoto, Japan, February 2009. FINAL REPORT FINAL Bae, D. (2010) Preparation for Implementation Plan for Climate Change Assessment & Adaptation - - Hydrological Modeling, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6,

2010. FUKAMI -

Lao PDR 01CMY

Amphaychith, C. ,S. Viana and P. Souvanmy (2008) Flash flood in the Luangnamtha area Lao PDR, -

Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008.

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Malaysia Jamalluddi, A. and J. Juhaimi (2008) Climate change for Malaysian Scenario, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Jamalluddi, A. (2009) Recent signs of water related disasters in Malaysia, Presentation at the 3rd GEOSS-AP Symposium, Kyoto, Japan, February 2009. Amin, M.Z. (2010) Climate change impacts on the hydrological and hydraulic performance of Bekok in Johor, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Mongolia Oyunbaatar, D. (2009) Floods in Mongolia, Presentation at the 3rd GEOSS-AP Symposium, Kyoto, Japan, February 2009. Davaa, G. (2009) Overview of current glacier studies in Mongolia, Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009. Davaa, G. (2010) Climate change impact assessment and adaptation aspects in Mongolia, Presentation at the 4th GEOSS-AP Symposium, Bali, Indonesia, Mar. 10-13, 2010. Myanmar Yi, T. (2008) Country report, Presentation at the 3rd International Coordination Group meeting of the GEOSS-AWCI”, Beijing, China, November 3-6, 2008. Than, H. (2009) Flood Study for Myanmar Rivers, Presentation at the 3rd GEOSS-AP Symposium, Kyoto, Japan, February 2009. Than, H. (2009) Flood study for Shwegyin river basin (demonstration project area) , Presentation at the 5th meeting of the GEOSS Asia Water Cycle Initiative (AWCI) International Coordination Group (ICG)”, Tokyo, Japan, December 15-17, 2009. Yi, T. (2010) Climate and flood in Myanmar, Presentation at the 4th GEOSS Asia Pacific Symposium, Bali, Indonesia, March 10-13, 2010. Tun, T. (2010) Water cycle initiative and climate change impact in Myanmar, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Nepal Sharma, S. (2008) Floods and Landslide, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Sharma, S. (2008) Moving science community to the flood affected community: AWCI INITIATIVE, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Sharma, S. (2008) AWCI progress, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Sharma, S. (2009) Recent signs of water related disasters in Nepal, Presentation at the 3rd GEOSS- AP Symposium, Kyoto, Japan, February 2009. Nepal country paper (2009) Impact of climate change on Himalayan range, Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009. Fujita, K. (2009) Glacial lake outburst floods(GLOF) in the Himalayas, Presentation at the 5th AWCI- ICG Meeting, Tokyo, Japan, December 15-18, 2009.

Sharma, S. (2010) Hydrology and water quality scenario of Bagmati River, Presentation at the 7th

AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Pakistan Rasul, G. (2009) Recent water-related disasters in Pakistan, Presentation at the 3rd GEOSS-AP

Symposium, Kyoto, Japan, February 2009. FINAL REPORT FINAL - Rasul, G. (2009) Snow, glacier and GLOF & report on demonstration river basin activities, Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009.

Sheikh, M.M. (2009) Climate change and drought in Pakistan, Presentation at the 5th AWCI-ICG FUKAMI - Meeting, Tokyo, Japan, December 15-18, 2009.

Rasul, G. (2010) Snow, glacier and GLOF, Presentation at the 4th GEOSS-AP Symposium, Bali, 01CMY

- Indonesia, Mar. 10-13, 2010.

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Ahmad, B. (2010) Report on demonstration river basin activities, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Shimada, M. (2010) Flood detection using ScanSAR imagery - A case study in Pakistan, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Philippines Hilario, F. (2008) Improvement of observation system, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Hilario, F. (2009) Recent signs of water-related disasters in the Philippines, Presentation at the 3rd GEOSS-AP Symposium, Kyoto, Japan, February 2009. Juanillo, E. (2009) Issues and challenges related to tropical cyclone concurrences in the Philippines, Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009. Hilario, F. (2010) Climate change impact assessment and adaptation in the Philippines, Presentation at the 4th GEOSS-AP Symposium, Bali, Indonesia, Mar. 10-13, 2010. Juanillo, E. (2010) The recent El Nino impacts to the Philippine water resources: Focus on Angat dam, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Sri Lanka Weerakoon,S. K Nandalal and U. Rathnayake (2008) Flood Modelling in the Mahaweli River Reach from Kotmale to Polgolla, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Weerakoon, S. (2008) Kaluganga – Sri lanka, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Weerakoon, S. (2009) Short country reports on recent signs of water-related disasters, Presentation at the 3rd GEOSS-AP Symposium, Kyoto, Japan, February 2009. Weerakoon, S. (2009) Country report, Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009. Weerakoon, S. (2010) Adaptation on climate change-hydrometeorological related disasters, Presentation at the 4th GEOSS-AP Symposium, Bali, Indonesia, Mar. 10-13, 2010. Thailand Sae-Chew, W. (2008) A study of disastrous flash flood in Khlong U-Tapao River Basin utilizing a combined physical and mathematical simulation model, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Sukhapunnaphan, T. (2008) Appropriate flood warning system in Northern Thailand, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Sukhapunnaphan, T. (2008) Recent signs of water-related disasters, Presentation at the 3rd GEOSS- AP Symposium, Kyoto, Japan, February 2009. Sukhapunnaphan, T. (2009) Flood warning system for Mae Wang basin, Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009. Vathananukij, H. (2010) Satellite analysis integration on drought risk in the Kingdom of Thailand, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010. Sukhapunnaphan, T. (2010) Climate change impact assessment and adaptation, Presentation at the 4th GEOSS-AP Symposium, Bali, Indonesia, Mar. 10-13, 2010. Uzbekistan Myagkov, S. (2009) Recent sign of water-related disasters in Uzbekistan, Presentation at the 3rd

GEOSS-AP Symposium, Kyoto, Japan, February 2009. FINAL REPORT FINAL Vilctovovna, D. (2010) Information system - Dangerous hydrometeorological phenomena of - Uzbekistan -, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5-6, 2010.

Vietnam FUKAMI Mai, D. , N. Viet (2008) Flood and landslide in central part of F Vietnam, Presentation at the 3rd -

AWCI-ICG meeting, Beijing, China, November 3-6, 2008. 01CMY

Tinh, D. (2008) An application of numerical weather forecast and satellite rainfall prediction to flood -

forecasting, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008.

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Tinh, D. (2008) An application of numerical weather forecast and satellite rainfall prediction to flood forecasting, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Mai, D. (2008) Flood and landslides in the central region of Vietnam, Presentation at the 3rd AWCI- ICG meeting, Beijing, China, November 3-6, 2008. Khanh, D. (2008) The progress on the demonstration projects activities in Vietnam, Presentation at the 3rd AWCI-ICG meeting, Beijing, China, November 3-6, 2008. Tinh, D. (2009) Recent signs of water-related disasters in Vietnam, Presentation at the 3rd GEOSS- AP Symposium, Kyoto, Japan, February 2009. Tinh, D. (2009) Country report, Presentation at the 5th AWCI-ICG Meeting, Tokyo, Japan, December 15-18, 2009. Tinh, D. (2010) Short Country report, Presentation at the 4th GEOSS-AP Symposium, Bali, Indonesia, Mar. 10-13, 2010. Tinh, D. (2010) Country report, Presentation at the 7th AWCI-ICG Meeting, Tokyo, Japan, October 5- 6, 2010.

FINAL REPORT FINAL

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Appendix

Conferences/Workshops

2. “The 3rd GEOSS Asia Pacific Symposium & the 4th International Coordination Group Meeting of the GWOSS-AWCI”, Kyoto, Japan, February 4-7, 2009.

3. “The 5th meeting of the GEOSS Asia Water Cycle Initiative (AWCI) International Coordination Group (ICG)”, Tokyo, Japan, December 15-17, 2009.

4. “The 4th GEOSS Asia Pacific Symposium and the 6th meeting of the GEOSS Asia Water Cycle Initiative (AWCI) International Coordination Group (ICG)”, Bali, Indonesia, March 10-13, 2010.

5. “The 7th Meeting of the GEOSS-AWCI International Coordination Group (ICG)”, Tokyo, Japan, October 5-6, 2010.

6. “The 1st International Workshop on Application and Validation of Global Flood Alert System (GFAS)”, Tsukuba, Japan, August 3-7, 2009

Agenda/Programme and participants list are attached as PDF files below, separately.

Glossary of Terms

ADB Asian Development Bank AMSR Advanced Microwave Scanning Radiometer APN Asia-Pacific Network for Global Change Research AWCI Asian Water Cycle Initiative BDRCS Bangladesh Red Crescent Society BMD Bangladesh Meteorological Department BTOP Block-Wise TOPMODEL CPC Climate Prediction Center of NOAA CPP Cyclone Preparedness Program DIAS Data Integration & Analysis System DHRW Department of Hydrology and River Works of Cambodia DRESS Dam Release Support System

ENSO El Niño -Southern Oscillation FLOWSS Flood Warning Support System FRM Flood Risk Management GAME GEWEX Asian Monsoon Experiment

GCM General Circulation Model FINAL REPORT FINAL GEOSS Global Earth Observation System of Systems - GEWEX Global Energy and Water Cycle Experiment

GFAS Global Flood Alert System FUKAMI GIS Geographical Information System -

GLOF Glacier Lake Outburst Flood 01CMY GPV Grid Point Value -

GSMaP_NRT Global Satellite Mapping of Precipitation – Near Real Time

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ICHARM International Centre for Water Hazard and Risk Management under the auspices of UNESCO ICG International Coordination Group IDI Infrastructure Development Institute, Japan IFAS Integrated Flood Analysis System IFNet International Flood Network IRSHAM Integrated Regional Scale Hydrologic-Atmospheric Model JAXA Japan Aerospace Exploration Agency MLIT Ministry of Land, Infrastructure, Transport and Tourism of Japan MM5 5th Generation Mesoscale Model MODIS Moderate Resolution Imaging Spectroradiometer NAHRIM National Hydraulic Research Institute of Malaysia NASA National Aeronautics and Space Administration of USA NCAR US National Center for Atmospheric Research NOAA National Oceanic and Atmospheric Administration of USA PAGASA Philippine Atmospheric, Geophysical and Astronomical Services Administration PDHM PWRI Distributed Hydrologic Model PWRI Public Works Research Institute of Japan RCWR Research Center for Water Resources of Indonesia RegHCM-PM Regional Hydroclimate Model of Peninsular Malaysia RETA Regional Technical Assistance (of ADB) RID Royal Irrigation Department of Thailand RRI Rainfall-Runoff-Inundation (Model of ICHARM) SAR Synthetic Aperture Radar UNESCO United Nations Educational, Scientific and Cultural Organization UNU United Nations University UT University of Tokyo WCRP World Climate Research Programme WEB-DHM Water and Energy Budget-based Distributed Hydrological Model

List of major collaborators for Flood WG activiities of GEOSS-AWCI

Country Representative & major contributors to AWCI Bangladesh Colonel Mohammad Ministry of Defense Ashfakul Islam Samarendra Karmakar Bangladesh Meteorologist Department

Abdul Quadir Ministry of Defense Bhutan Karma Chhophel Hydromet Service Division

Cambodia So Im Monichoth Ministry of Water Resource and Meteorology

FINAL REPORT FINAL - Long Saravuth Department Hydrology and River Works (DHWR)

China Dawen Yang Tsinghua University

FUKAMI -

India Nilkanth Y. Apte India Meteorological Department 01CMY

- Surinder Kaur India Meteorological Department

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Rakesh Kumar National Institute of Hydrology Indonesia Muhammad Syahril Institut Teknologi Bandung Badri Kusuma Joesron Loebis Research Institute for Water Resource, Ministry of Public Works Fransisca Mulyantari Research Institute for Water Resource, Ministry of Public Works Fadli Syamsudin Agency for the Assesment and Application of Technology (BPPT) Japan Yoko Inomata Japan Aerospace Exploration Agency (JAXA) Mikio Ishiwatari Japan International Cooperation Agency (JICA) Toshio Koike University of Tokyo Toshio Okazumi Ministry of Land, Infrastructure, Transport and Tourism (MLIT) Eiji Otsuki Ministry of Land, Infrastructure, Transport and Tourism (MLIT) Oliver V. Saavedra Tokyo Insitute of Technology (formerly, University of Tokyo) Lei Wang University of Tokyo Korea Deg-Hyo Bae Sejong University Lao PDR Chanthachith Lao National Mekong Committee Amphaychith Malaysia Mohd ZakI Bin Mat National Hydraulic Research Institute of Malaysia (NAHRIM) Amin Ahmad Jamalluddin bin National Hydraulic Research Institute of Malaysia (NAHRIM) Shaaban Mongolia Gombo Davaa Institute of Meteorology and Hydrology (IMH) Dambaravjaa Institute of Meteorology and Hydrology (IMH) Oyunbaatar Myanmar Htay Htay Than Department of Meteorology and Hydrology (DMH) Tin Aung Tun Department of Meteorology and Hydrology (DMH) Tin Yi Department of Meteorology and Hydrology (DMH) Nepal Shiv Kumar Sharma Department of Water Induved Disaster Prevention Pakistan Bashir Ahmad Water Resources Research Institute, National Agricultural Research Center Ghulam Rasul Pakistan Meteorological Department (PMD) Muhammad Munir Global Change Impact Studies Centre (GCISC)

Sheikh Philippines Flaviana Hilario Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) Edna Lee Juanillo Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA)

Sri Lanka Saman.B.Weerakoon University of Peradeniya

FINAL REPORT FINAL - Thailand Thada Sukhapunaphan Ministry of Agriculture and Cooperatives

FUKAMI Uzbekistan Irina Viktorovna NIGMI, Uzhydromet - Dergacheva

Sergey Myagkov Hydrometeorological Reserch Institute

01CMY -

Vietnam Duong Van Khanh National Hydro-meteorological Forecasting Center

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Dang Ngoc Tinh National Center for Hydrometeorological Forecasting WG Co-Chairs of AWCI Flood Kazuhiko Fukami International Centre for Water Hazard and Risk Management (UNESCO-ICHARM) Drought Ailikun Institute of Atmospheric Physics, Chinese Academy of Science Water Bilqis Amin Hoque Environment and Population Research Centre (EPRC) quality Invited Experts for AWCI Remote Chu Ishida Japan Aerospace Exploration Agency (JAXA) sensing Capacity Srikantha Herath United Nations University (UNU) Building Data Hansa Vathananukij Kasetsart University system AWCI Akiko Goda University of Tokyo Secretariat Petra Koudelova University of Tokyo Katsunori Tamagawa University of Tokyo

List of demonstration river basin of each AWCI member country

Country Demonstration river basin Bangladesh Meghna River Bhutan Punatsangchhu River Cambodia Sangker River India Seonath River Indonesia Mamberamo River Japan Upper Tone River Korea Upper Chungju-dam Lao PDR Sebangfai River Malaysia Langat River Mongolia Selbe River Myanmar Shwegyin River Nepal Bagmati River

Pakistan Swat River

Philippines Pampanga River Sri Lanka Kalu Ganga River

Thailand Mae Wang River FINAL REPORT FINAL - Uzbekistan Chirchik-Okhangaran River

Vietnam Huong River FUKAMI -

Major PDF slides of presentations at GEOSS-AWCI-ICG meetings and workshop 01CMY

Attached as PDF files below, separately.

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