The Criterion of information sufficiency with automation of hydrological measurements

Proposed systematic, multi-criteria drainage design evaluation framework.

Machine learning for hydrologic sciences: An introductory overview - Xu - 2021 - WIREs Water - Wiley Online Library

Model analysis of forest thinning impacts on the water resources during hydrological drought periods - ScienceDirect

Detection of hidden model errors by combining single and multi-criteria calibration - ScienceDirect

Introductory overview: Error metrics for hydrologic modelling – A review of common practices and an open source library to facilitate use and adoption - ScienceDirect

Energies, Free Full-Text

Hydroinformatics Projects Hydroinformatics Lab at UIOWA - UIHILab

HESS - All models are wrong, but are they useful? Assessing reliability across multiple sites to build trust in urban drainage modelling

Automated surface energy balance algorithm for land (ASEBAL) based on automating endmember pixel selection for evapotranspiration calculation in MODIS orbital images - ScienceDirect