Hydrology plays a central role in applied and fundamental environmental sciences, but it is well known to suffer from an overwhelming diversity of models, particularly to simulate …
Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the hydrological sciences. The problem currently is that traditional hydrological models degrade …
We undertook a comprehensive evaluation of 22 gridded (quasi-) global (sub-) daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P …
Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble …
Predictions of hydrologic variables across the entire water cycle have significant value for water resources management as well as downstream applications such as ecosystem and …
Current state‐of‐the‐art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) …
N Addor, LA Melsen - Water resources research, 2019 - Wiley Online Library
The findings of hydrological modeling studies depend on which model was used. Although hydrological model selection is a crucial step, experience suggests that hydrologists tend to …
S Pool, M Vis, J Seibert - Hydrological Sciences Journal, 2018 - Taylor & Francis
Goodness-of-fit measures are important for an objective evaluation of runoff model performance. The Kling-Gupta efficiency (R KG), which has been introduced as an …
As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based …