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水資源計画学研究室 ロゴ Water Resources Planning Lab., TUAT, Japan 


Balancing Water, Agriculture and Ecosystems!

Water, agriculture and ecosystems are the main research components of this laboratory. We aim to find a balance between the three components for better water resources and ecosystems management for improved agricultural production and enhanced ecosystem services. To this end, field surveys will be conducted and then the observation data are analysed and used for mathematical modelling. Our research can be characterized by two approaches, namely data-driven modelling and process-based modelling. In data-driven modelling, machine learning and computational intelligence are applied in the fields of ecohydraulics, ecoinformatics, hydroinformatics, etc. Process-based modelling is mainly for spatio-temporal modelling of fish behaviour and watershed hydrology, in which machine learning and other data-driven will be used supplementarily. Most of our research topics are based on joint works with domestic and international colleagues.

Data-driven modelling


Ecohydraulics is an integrated science, linking hydraulics with ecology. This interdisciplinary approach is highly applicable when understanding the responses of aquatic ecosystems to hydrodynamic conditions in a target site. In ecohydraulics, various modelling approaches are used: for instance, habitat suitability models, hydrodynamic models (1D, 2D, 3D), fish behaviour models (mainly for fish pass studies), etc. With emerging survey technologies such as biotelemetry and ADCP (acoustic Doppler current profiler), this research field is now advancing in both ecology and engineering. The Water Resources Planning Laboratory employs both equipment for better understanding and modelling aquatic ecology under various hydrodynamic conditions.

photo for ecohydraulics


Ecoinformatics is an emerging research field in which new techniques for data analysis and modelling are being developed. Recent development in machine learning allows for predictive modelling and knowledge extraction useful for a better understanding of target ecosystems or species, contributing to improved management strategies under a given condition. Specifically, we apply various machine learning methods as a data-driven modelling method for habitat suitability assessment, species distribution modelling and the assessment of species invasions.  



Hydroinformatics has devoted to time-series analysis in hydrology such as rainfall-runoff modelling and is one of the major fields where advanced machine learning has been employed. This approach, in general, is more accurate compared to complex process-based models in which large amount of model parameters need to be optimised. Hydroinformatics can thus be a useful tool for short-term forecasting of hydrologic events and water quality dynamics.


 Data-driven modelling of pre-harvest and post-harvest systems 

Improving agricultural productivity is an important step for counteracting the increasing food demands for rapidly growing world population. In addition, food safety has become an emerging concern in globalised food supply chains. The Water Resources Planning Laboratory applies data-driven approaches to better model pre-harvest and post-harvest systems and extract various information useful for improved management systems.



Process-based modelling

 Spatiotemporal modelling of fish behaviour 

With spatiotemporal data from advanced biotelemetry surveys, we aims to model fish behaviour under dynamic environments, for which mathematical models based on ecological knowledge will be developed. Model results shall be used for assessing habitat use and migration patterns of target fish species in target rivers and agricultural canals. Numerical simulations will then be made for better management strategies that can balance benefits for human and aquatic ecosystems.


 Hydrologic modelling 

In contrast to the hydroinformatics approach, process-based hydrologic models are useful for understanding the detailed hydrologic processes in a watershed. For instance, hydrologic cycle and water quality dynamics in a watershed are both affected by topographic features and landuse patterns in the watershed. Although there are a lot of model parameters to be optimised in a process-based hydrologic model, such parameters can illustrate hydrologic processes, based on which improved management strategies for landuse planning and agricultural practices can be proposed. We seek for better modelling approaches according to local conditions and data availability in our target systems.



Our laboratory has re-established on February 17, 2014 when Dr. Shinji Fukuda joined TUAT. Thanks to the achievement in the previous phase of this laboratory, we could start research activities right after the restart. We welcome highly motivated and active students. Please come and visit us anytime you want (but, I would highly appreciate if you could send me a message prior to your visit).  Also, please click here to check our activities!!!


  Position Name E-mail* Research
Fukuda Assistant Professor Shinji Fukuda TUAT,ORCID shinji-f Ecological modelling; Ecoinformatics; Ecohydraulics
ChanMatsu MS2 Yuki Matsuzawa - Longitudinal distribution and habitat conditions of Lefua echigonia in Yagawa River
Aihara MS1 Seiya Aihara - Analyzing the relationship between fish fauna and aquatic flora in Fuchu-yosui based on physical habitat conditions
s-img_9985-3 UG4 Hideyuki Okano - Fish migration in a small spring-fed stream
s-img_9985-2 UG4 Kurea Kawai - Biotelemetry survey of invasive fish species
s-Fish UG3/MS1 Join us!!!  - Balancing Water, Agriculture and Ecosystems!!! (fish ecology, hydraulics, hydrology, eco- & hydro-informatics, etc.)

*followed by @cc.tuat.ac.jp

  Position Name Research
Marukawa UG4 Eriko Marukawa Data-driven fish habitat assessment using fine-scale hydraulic data
Aokichi UG4 Kosei Aoki Quantitative evaluation of the effectiveness of habitat restoration based on the critical swimming speed of Lefua echigonia
s-dsc00055-3 AIMS Amira L Safira Community-based management of a reservoir
Saito UG4 Shiho Saito Random forests as a tool for modelling range-expanding mammal species distributions in Japan
Onoue UG4 Satoshi Onoue Assessing genetic disturbance by Pseudorasbora Parva and its functional responses
s-IMG_2422 Postdoctoral Researcher Mitsuru Ohira Biodiversity, freshwater fish, macroinvertebrates
s-2014_Year-end-party (2) SSSV (2015) Giulia Lembo Caterina Efficient Eucalyptus production under climate change (Application of Random Forests for water use estimation)

Publications and Academic Activities

Please find the links below for our achievement (partly in Japanese).


Lab. Activities

Please check here.



Water Resources Planning Laboratory
Division of Environmental and Agricultural Engineering,
Institute of Agriculture
Tokyo University of Agriculture and Technology


3-5-8 Saiwai-cho, Fuchu, Tokyo 183-8509, Japan
 (Fuchu Campus, Building 3, Room 311)


See TUAT Website for access information.