人工衛星データを用いた陸面データ同化手法による土壌水分推定手法の広域適用可能性に関する研究

学位の種別: 論文博士審査委員会委員 : （主査）東京大学教授 小池 俊雄, 東京大学教授 田島 芳満, 東京大学教授 西村 拓, 東京大学准教授 平林 由希子, 東京大学准教授 沖 一雄, 東京大学准教授 竹内 渉University of Tokyo(東京大学

Data Integration and Analysis System (DIAS) as a Platform for Data and Model Integration: Cases in the Field of Water Resources Management and Disaster Risk Reduction

The development of data and model integration platforms has furthered scientific inquiry and helped to solve pressing social and environmental problems. While several e-infrastructure platforms have been developed, the concept of data and model integration remains obscure, and these platforms have produced few firm results. This article investigates data and model integration on the Data Integration and Analysis System (DIAS) platform, using three case projects from water-related fields. We provide concrete examples of data and model integration by analyzing the data transfer and analysis process, and demonstrate what platform functions are needed to promote the advantages of data and model integration. In addition, we introduce the Digital Object Identifier (DOI), a valuable tool for promoting data and model integration and open science. Our investigation reveals that DIAS advances data and model integration in five main ways: it is a “sophisticated and robust integration platform”; has “rich APIs, including a metadata management system, for high-quality data archive and utilization”; functions as a “core hydrological model”; and promotes a “collaborative R&D community” and “open science and data repositories”. This article will appeal especially to researchers interested in new methods of analysis, and information technology experts responsible for developing e-infrastructure systems to support environmental and scientific research

Data Integration and Analysis System (DIAS) as a platform for data and model integration: Cases in the field of water resources management and disaster risk reduction

The development of data and model integration platforms has furthered scientific inquiry and helped to solve pressing social and environmental problems. While several e-infrastructure platforms have been developed, the concept of data and model integration remains obscure, and these platforms have produced few firm results. This article investigates data and model integration on the Data Integration and Analysis System (DIAS) platform, using three case projects from water-related fields. We provide concrete examples of data and model integration by analyzing the data transfer and analysis process, and demonstrate what platform functions are needed to promote the advantages of data and model integration. In addition, we introduce the Digital Object Identifier (DOI), a valuable tool for promoting data and model integration and open science. Our investigation reveals that DIAS advances data and model integration in five main ways: it is a "sophisticated and robust integration platform"; has "rich APIs, including a metadata management system, for high-quality data archive and utilization"; functions as a "core hydrological model"; and promotes a "collaborative R&D community" and "open science and data repositories". This article will appeal especially to researchers interested in new methods of analysis, and information technology experts responsible for developing e-infrastructure systems to support environmental and scientific research

An Integrated Approach for the Climate Change Impact Assessment on the Water Resources in the Sangu River Basin, Bangladesh, under Coupled-Model Inter-Comparison Project Phase 5

The Sangu River basin significantly contributes to national economy significantly; however, exposures to water-related hazards are frequent. As it is expected that water-related disasters will increase manifold in the future due to global warming, the Government of Bangladesh has formulated the Bangladesh Delta Plan 2100 (BDP-2100) to enhanced climate resilience. Accordingly, this study assessed the hydro-meteorological characteristics of the Sangu River basin under the changing climate. This study scientifically selected five General Circulation Models (GCMs) to include the model climate sensitivity and statistically bias-corrected their outputs. The Water and Energy Budget-based Rainfall-Runoff-Inundation (WEB-RRI) model was used to simulate the hydrological responses of the basin. The analysis of five GCMs under the Representative Concentration Pathway (RCP8.5) revealed that all selected GCMs estimate a 2–13% increase in annual rainfall and a 3–12% increase in annual discharge in the near-future (2025–2050), whereas four GCMs project an 11–52% increase in annual rainfall and a 7–59% increase in annual discharge in the far-future (2075–2100). The projected more frequent and intense increased extreme rainfall and flood occurrences in the future indicate an increase in flood disaster risk, whereas increased meteorological and hydrological drought in the future reflects a scarcity of water during dry periods. The number of projected affected people shows an increasing trend due to the increased inundation in the future. However, an increasing trend of transpiration indicates agricultural productivity will increase in the future. Policymakers can utilize this evidence-based information to implement BDP-2100 and to reduce the disaster risks in the basin

Assessment of Climate Change Impacts for Balancing Transboundary Water Resources Development in the Blue Nile Basin

An assessment of climate impacts in the hydrologic system of the Blue Nile basin is useful for enhancing water management planning and basin-wide policymaking. Climate change adaptation activities predominantly require an understanding of the range of impacts on the water resource. In this study, we assessed climate change impacts on the Blue Nile River using 30-year in situ climate data (1981–2010) and five bias-corrected General Circulation Models (GCMs) for future (2026–2045) climate projections of RCP8.5. Both historical and GCM precipitation projections show inter-annual and spatial variability, with the most significant increases in the rainy season and a significant decrease in the dry season. The results suggest the probability of an increase in total precipitation. The intensity and frequency of future extreme rainfall events will also increase. Moreover, the hydrological model simulation results show a likely increase in total river flow, peak discharges, flood inundation, and evapotranspiration that will lead to a higher risk of floods and droughts in the future. These results suggest that the operation of water storage systems (e.g., the Grand Ethiopian Renaissance Dam) should be optimized for Disaster Risk Reduction (DRR) and irrigation management in addition to their intended purposes in the Nile basin

Assessing the impact of climate change on flood inundation and agriculture in the Himalayan Mountainous Region of Bhutan

Study region: The Punatshangchu River Basin and the Wangchu River Basin in Bhutan. Study focus: Mountain regions are more sensitive to changes in environment and climate. Changes in intensity and magnitude of floods due to climate change will have devastating impacts, including on agriculture, a main source of people’s livelihoods and a primary economic driver of many countries. We analyzed impacts of climate change on flood inundation and flood damage to agriculture in the Himalayan Mountains of Bhutan. Projected rainfall from general circulation models was used to simulate flood characteristics from the rainfall-runoff-inundation model, to calculate the agriculture damage loss to rice crops. New hydrological insights for the region: The results show an increase of approximately 60% in magnitude in annual rainfall towards the end of the century, with extreme rainfall events greater than 32% for the river basins. The increase in changes in rainfall will result in an increase in inundation of almost 60% and 90% for the Punatshangchu River Basin and the Wangchu River Basin, respectively. The inundation depth and volume could increase up to 60%. With the increase in inundation characteristics, the damage to agricultural areas is expected to increase by 2 to 6 times, and the damage loss value increase from 2 to 38 times in the river basins