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Using data archived in the Coordinated Enhanced Observing Period (CEOP) project, this study presents an initial evaluation of the prediction skill of five General Circulation Models (GCMs) and three Land Surface Models (LSMs). Comparisons between observations and the GCMs show that all the models are able to produce an afternoon peak in precipitation, but other major features are not well produced, including the total amount of precipitation, onset time of the afternoon peak, the early-evening low (around 1800 LST), and the partition between convective and stratiform rainfall. The ratios of evaporation to precipitation differ among the GCMs. Evaporation in some of the GCMs is even greater than precipitation, perhaps due to the model spin-up effect. In terms of the surface radiation budget, the GCMs generally over-predict downward shortwave radiation and under-predict downward longwave radiation; further investigations of the causes of these trends require cloudiness observations. In terms of the surface energy budget, the GCMs generally over-predict nighttime downward sensible heat fluxes Corresponding author: Kun Yang, Dr., River Lab, Department of Civil Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan. E-mail: yangk@hydra.t.u-tokyo.ac.jp ( 2007, Meteorological Society of Japan and under-predict diurnal ranges of surface-air temperature difference, as heat transfer resistances are under-predicted. Finally, three offline LSMs driven by identical forcing are evaluated, and we note that the reproduction of surface temperature is not a sufficient condition for a LSM to reproduce surface energy partition.

This paper introduces the process of development and practical use implementation of an advanced river management system for supporting integrated water resources management practices in Asian river basins under the framework of GEOSS Asia water cycle initiative (AWCI). The system is based on integration of data from earth observation satellites and in-situ networks with other types of data, including numerical weather prediction model outputs, climate model outputs, geographical information, and socio-economic data. The system builds on the water and energy budget distributed hydrological model (WEB-DHM) that was adapted for specific conditions of studied basins, in particular snow and glacier phenomena and equipped with other functions such as dam operation optimization scheme and a set of tools for climate change impact assessment to be able to generate relevant information for policy and decision makers. In situ data were archived for 18 selected basins at the Data Integration and Analysis System (DIAS) of Japan and demonstration projects were carried out showing potential of the new system. It included climate change impact assessment on hydrological regimes, which is presently a critical step for sound management decisions. Results of such three case studies in Pakistan, Philippines, and Vietnam are provided here. integrated water resources management tools, climate change impact assessment, Asian river basins, Asian Water Cycle Initiative Citation: Koike T, Koudelova P, Jaranilla-Sanchez P A, et al. 2015. River management system development in Asia based on Data Integration and Analysis System (DIAS) under GEOSS. Science China: Earth Sciences, 58: 76 -95,The global Earth observation system of systems (GEOSS) Asian water cycle initiative (AWCI) was established in 2007 as a response to the recognized needs for accurate, timely, and long-term water cycle information to implement integrated water resources management (IWRM) practices and with regards to the commonality in the water-related issues and socio-economic needs in the Asia-Pacific region. Implementing IWRM at the river basin level, while respecting the physical, social and political context, is an essential element to managing water resources in a more sustainable way, leading to long-term social, economic and environmental benefits (GWP, 2009). It requires a wide range of disparate data from multiple disciplines and various sources and appropriate tools for processing these data and integrating and translating them into relevant information for water resources practitioners and policy decision makers. A system for supporting IWRM practices thus must be able to simulate and predict a wide range of flows from droughts to floods and to be applicable for long-term, cli-

Big Data has great potential to be applied to research in the field of geosciences. Motivated by the opportunity provided by the Data Integration and Analysis System (DIAS) of Japan, we organized an intensive two-week course that aims to educate participants on Big Data and its exploitation to solve water management problems. When developing and implementing the Program, we identified two main challenges: (1) assuring that the training has a lasting effect and (2) developing an interdisciplinary curriculum suitable for participants of diverse professional backgrounds. To address these challenges, we introduced several distinctive features. The Program was based on experiential learning -the participants were required to solve real problems and worked in international and multidisciplinary teams. The lectures were strictly relevant to the case-study problems. Significant time was devoted to hands-on exercises, and participants received immediate feedback on individual assignments to ensure skills development. Our evaluation of the two occasions of the Program in 2015 and 2016 indicates significant positive outcomes. The successful completion of the individual assignments confirmed that the participants gained key skills related to the usage of DIAS and other tools. The final solutions to the case-study problems showed that the participants were able to integrate and apply the obtained knowledge, indicating that the Program's format and curriculum were effective. We found that participants used DIAS in subsequent studies and work, thus suggesting that the Program had long-lasting effects. Our experience indicates that despite time constraints, short courses can effectively encourage researchers and practitioners to explore opportunities provided by Big Data.