Going beyond data-centric - digital models for water cycle management and water facilities

Initiated from the cyber-physics field, Digital Twin (DT) technologies have huge potential in today’s
water resource management and water facilities [1]. With today’s data-centric digital solution at water sector, a basic
version of DT is integrated in the natural and urban water cycle [2]. However, a full realization of a DT requires
additional advances and smartness to the running systems. Barriers such as data quality and safety, as well as the
technology gaps between different fields, are delaying the advances of water DT development. Among these barriers,
one posing problem is the lack of knowledge-centric solutions in existing digital solutions. Knowledge-centric
solutions, such as mechanistic models and statistic models which conveying abundant field knowledge, are essential
part of water DT development. Since many prior knowledges are hard to encapsulate in the data repositories or to use
directly in the running digital platform. Therefore, knowledge-centric models should be involved from initial operation
of the DT, and the life cycle management of DT. Combined with current data-centric digital water platform, digital
models in water DT will assemble the domain knowledge acquired over the last century, and to generate new values
from data-centric solutions. This step will further improve the full realization of water DT, which will further reduce
operational cost, mitigate the climate change impact on water resources, and to optimize water efficiency for whole
water cycle.