On the performance of satellite precipitation products in riverine flood modeling: A review
V Maggioni, C Massari - Journal of hydrology, 2018 - Elsevier
This work is meant to summarize lessons learned on using satellite precipitation products for
riverine flood modeling and to propose future directions in this field of research. Firstly, the …
riverine flood modeling and to propose future directions in this field of research. Firstly, the …
Why do we have so many different hydrological models? A review based on the case of Switzerland
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 …
well known to suffer from an overwhelming diversity of models, particularly to simulate …
[HTML][HTML] Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
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 …
hydrological sciences. The problem currently is that traditional hydrological models degrade …
Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling
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 …
precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P …
MSWEP: 3-hourly 0.25 global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data
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 …
nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble …
Differentiable, learnable, regionalized process‐based models with multiphysical outputs can approach state‐of‐the‐art hydrologic prediction accuracy
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 …
water resources management as well as downstream applications such as ecosystem and …
Global‐scale regionalization of hydrologic model parameters
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) …
called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) …
Legacy, rather than adequacy, drives the selection of hydrological models
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 …
hydrological model selection is a crucial step, experience suggests that hydrologists tend to …
Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency
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 …
performance. The Kling-Gupta efficiency (R KG), which has been introduced as an …
[HTML][HTML] The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
As a genre of physics-informed machine learning, differentiable process-based hydrologic
models (abbreviated as δ or delta models) with regionalized deep-network-based …
models (abbreviated as δ or delta models) with regionalized deep-network-based …