eWaterCycle II

Overcoming the challenge of locality using a Community Multi-Model Environment

Overcoming the challenge of locality using a Community Multi-Model Environment

An accurate global hydrological model will enable us to predict droughts, floods, navigation hazards, and reservoir operations. Creation and dissemination of such information to the global community would have tremendous societal value. In addition, such a model will allow determination of anthropogenic and climate change impacts on the hydrological cycle. The eWaterCycle II project builds on results of the eWaterCycle project by taking on the scientific challenge of extreme spatial variability in hydrology.

From a hydrological point of view, every field, every street, every part of the world, is different. We understand quite well how water moves through plants and soils at small scales but the medium is never the same from one spot to the next. This is the curse of locality. It is difficult to capture such processes with a single global model. Instead, we introduce a community multi-model environment that allows rapid and easy combination of local hydrological models with global models, leading to a collaborative environment where anyone can easily contribute to the greater goal of a community built and shared global Hydrological model.

The eWaterCycle II project will build and maintain an e-Infrastructure that allows for quick and safe inclusion of sub-models and model concepts into global hydrological models, leading to a better understanding of the Hydrological cycle. The foreseen e-infrastructure will have a number of unique advantages, including an ability for knowledge gap discovery, machine-assisted model curation, and easily changeable model parts.

The eWaterCycle II project offers a set of community tools that can be of use to all scientists studying the hydrological cycle. Although used in this project in a hydrological setting, the underlying framework will be suitable outside of hydrology, wherever a collaborative environment is required. eScience aspects such as large scale data assimilation (DA) techniques, generic multi-model multi-scale environments, FAIR data as well as FAIR software, will all benefit from research done in this project. The largest non-technical challenge will be to build a user community. We will make use of the “challenge” model, a highly successful open science approach for gathering a community to work towards a common goal.

Technical Lead Analytics Dr. Willem van Hage

Willem´s main research topics in the past 10 years are semantics, augmented sense making, visual analytics, information integration, and text mining.

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eScience Research Engineer Dr. Niels Drost

Niels works on the water management project as well as general eScience infrastructure. Niels is also part-time guest researcher at the Leiden Observatory, where he applies distributed computing techniques to the AMUSE computational astronomy simulation framework.

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