Publications

@article{DOST2026106820,

title = {Freshwater modeling in Aotearoa New Zealand: Current practice and future directions},

author = {Katharina Dost and Kohji Muraoka and Anne-Gaelle Ausseil and Rubianca Benavidez and Brendon Blue and Nic Conland and Chris Daughney and Annette Semadeni-Davies and Linh Hoang and Anna Hooper and Theodore Alfred Kpodonu and Tapuwa Marapara and Richard McDowell and Trung Nguyen and Dang Anh Nguyet and Ned Norton and Deniz \"{O}zkundakci and Lisa Pearson and James Rolinson and Ra Smith and Tom Stephens and Reina Tamepo and Ken Taylor and Vincent Uitregt and Bethanna Jackson and Theo Sarris and Alexander Elliott and J\"{o}rg Wicker},

url = {https://www.sciencedirect.com/science/article/pii/S1364815225005043},

doi = {10.1016/j.envsoft.2025.106820},

issn = {1364-8152},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {Environmental Modelling \& Software},

volume = {197},

pages = {106820},

abstract = {Freshwater modeling is vital for addressing environmental and societal challenges. In two workshops preceding this article, we revealed issues in current modeling practices in New Zealand, with a focus on catchment-level water quality modelling. Predominant were low trust in models, lack of transparency, and models unfit for purpose. This article uses a root-cause analysis to explore these issues, identify causes, and propose solutions. We find that current best practices and research are a good foundation but insufficient to fulfill our freshwater research and management needs. We advocate for long-term national strategies with centralized funding, standardized documentation, data, models, evaluation techniques, and communication methods, along with a centralized open-access platform for collaboration. Our vision is to streamline modeling projects, enhance the accessibility and reliability of models, and foster more effective decision-making processes for the sustainable management of freshwater ecosystems.},

keywords = {best practice, Catchment modeling process, inland and coastal waters, machine learning, model trustworthiness, Modelling platform design, reliable machine learning, root-cause analysis, water quality},

pubstate = {published},

tppubtype = {article}

}