Context of the case study
Bac Hung Hai irrigation system lies in the center of the Red River Delta.
It plays an important role in the economic development of the region by: (1) providing irrigation to 110.000 ha of rice, crop and tree lands; (2) supplying water for livestock, poultry, and aquaculture; (3) supporting water sources for more than 3 million people and industrial zones; (4) controlling inundations inside the dyked area.
It is now, like many irrigation systems in developing countries, under a number of threats, including water depletion upstream, pollution of sources or an increased diversity of demands.
Objectives
To build an integrated and hybrid framework consisting of innovative approaches (IoT for data collection/assessment, agent-based modeling, participatory simulations) for supporting transdisciplinary approaches to addressing these threats.
Four activities will be conducted with a strong participation of stakeholders and Bac Hung Hai management company:
- Activity 1: Data collection and assessment based on IoT low cost sensors and social, spatial, and legal surveys around the irrigation system in order to identify the actors, their issues and interactions.
- Activity 2: Participatory Modeling by selected actors and scientists, in order to design the agent-based model that will be simulated to support the exploration of alternative management scenarios. The idea is to place simulations at the center of interactions between actors, researchers and decision-makers.
- Activity 3: Participatory scenarios design, in order to draw a shared picture of the possible scenarios of evolution of the system and its surrounding context and to design indicators to assess its state and its evolution. Some will be identified by stakeholders, some will result from the expected evolution of the environmental, social and economic contexts.
- Activity 4: Participatory simulations and serious games with tangible supports (3D maps, interactive tables, virtual reality environments) in order to improve the way stakeholders visualize indicators and data, interact with simulations and between them, learn and understand the system and mechanisms that govern it.
Implementation
Expected methodological outcomes: dynamic combinations of participatory modeling, simulation and workshops; linkages between crowdsourcing activities and data assimilation in agent-based models.