Front. Water Frontiers in Water Front. Water 2624-9375 Frontiers Media S.A. 10.3389/frwa.2022.1121534 Water Editorial Editorial: Hydrological modeling, analyses, and predictions: Opportunities and challenges Siddique Ridwan 1 * Sharma Sanjib 2 McCreight James 3 1Electric Power Research Institue, Palo Alto, CA, United States 2College of Earth and Mineral Sciences, Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA, United States 3National Center for Atmospheric Research, Boulder, CO, United States

Edited and reviewed by: Harrie-Jan Hendricks Franssen, Julich Research Center (HZ), Germany

 *Correspondence: Ridwan Siddique ✉ ridwan.siddique@gmail.com

This article was submitted to Water and Hydrocomplexity, a section of the journal Frontiers in Water

 06 02 2023 2022 4 1121534 11 12 2022 23 12 2022 Copyright © 2023 Siddique, Sharma and McCreight. 2023 Siddique, Sharma and McCreight

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

 Editorial on the Research Topic Hydrological modeling, analyses, and predictions: Opportunities and challenges water and hydrocomplexity hydrologic modeling hydrologic forecasting machine learning uncertainty

Hydrological forecasts are critical to inform decision-making in various areas of water policy and management, including flood and drought preparedness, reservoir operation, and agricultural planning. The skill of hydrologic prediction depends critically on the quality of inputs in addition to the details of the hydrologic models themselves.

It is assumed that meteorological forcing error contributes significantly to the overall hydrological model prediction errors. One article in this collection quantifies how errors in precipitation translate to streamflow prediction errors, underscoring that improving and understanding hydrometerological fields remains a critical endeavor for hydrologists (Bárdossy et al.). Improving other environmental datasets as inputs to hydrologic models remains another prominent challenge. A second paper in the collection aims at generating synthetic river images for understanding and modeling river dynamics and geomorphology (Gautam et al.). Highlighting the importance of both meteorological and non-meteorlogical data for predicting hydrologic response, another paper in the collection demonstrates the importance of soil moisture anomalies, in addition to precipitation anomalies, for predicting groundwater table dynamics (Ma et al.).

Hydrologic models are constantly evolving with ideas coming from other domains. In particular, advances in deep neural networks have opened up new avenues of predictability in hydrology. A prominent neural network model used for streamflow prediction, the LSTM, is applied in a new hydrologic context (groundwater anomaly prediction) in this collection (Ma et al.). The creation of synthetic river images is also performed by neural networks in an application of Generative Adversarial Networks (Gautam et al.). Another article in the collection turns to so-called “small-world” networks to help quantify streamflow timeseries predictability and to classify its underlying dynamics as chaotic or stochastic (Ghimire et al.). These novel approaches to hydrologic process prediction and characterization advance the state of the science in important ways and help keep the field abreast of modeling developments in the broader scientific and engineering communities.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.