Primary Convener:
Guoqiang Tang, Wuhan University

Convener:
Hongli Liu, University of Alberta
Martyn Clark, University of Calgary
Andy Wood, NSF National Center for Atmospheric Research

Chair:
Martyn Clark, University of Calgary
Cyril Thebault, University of Calgary

As observing infrastructure and computational capabilities improve, numerous large-domain datasets are continuously developed based on data from stations, remote sensing, and climate models. The development of new data products plays a vital role in supporting hydrologic modeling and in improving operational predictions for hydrologic extremes. However, existing datasets still face problems of inadequacy and/or inaccuracy, especially in remote areas. The uncertainty quantification of large-domain datasets is still in its infancy, and is particularly needed under a changing climate. We invite contributions including but are not limited to following areas: (1) Development of large-domain meteorological and hydrologic datasets; (2) Application of large-domain or large-sample datasets in hydrologic model preparation, calibration, assimilation, forecast, and prediction; (3) Application of artificial intelligence to gain insights from extensive datasets and models; (4) Data uncertainty quantification and the use of probabilistic/ensemble datasets in hydrologic modeling and prediction.

Index Terms
1805 Computational hydrology
1816 Estimation and forecasting
1847 Modeling
1873 Uncertainty assessment

Cross-Listed:
A - Atmospheric Sciences
EP - Earth and Planetary Surface Processes
GC - Global Environmental Change

Neighborhoods:
3. Earth Covering