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[HTML][HTML] Flood forecasting with machine learning models in an operational framework
Google's operational flood forecasting system was developed to provide accurate real-time
flood warnings to agencies and the public with a focus on riverine floods in large, gauged …
flood warnings to agencies and the public with a focus on riverine floods in large, gauged …
The CAMELS data set: catchment attributes and meteorology for large-sample studies
We present a new data set of attributes for 671 catchments in the contiguous United States
(CONUS) minimally impacted by human activities. This complements the daily time series of …
(CONUS) minimally impacted by human activities. This complements the daily time series of …
Epistemic uncertainties and natural hazard risk assessment–Part 1: A review of different natural hazard areas
This paper discusses how epistemic uncertainties are currently considered in the most
widely occurring natural hazard areas, including floods, landslides and debris flows, dam …
widely occurring natural hazard areas, including floods, landslides and debris flows, dam …
HESS Opinions: The complementary merits of competing modelling philosophies in hydrology
In hydrology, two somewhat competing philosophies form the basis of most process-based
models. At one endpoint of this continuum are detailed, high-resolution descriptions of small …
models. At one endpoint of this continuum are detailed, high-resolution descriptions of small …
On return period and probability of failure in hydrology
The return period measures the rareness of extreme events such as floods and droughts that
might cause huge damages to the society and the environment; hence, it lies at the heart of …
might cause huge damages to the society and the environment; hence, it lies at the heart of …
A comparison of methods for streamflow uncertainty estimation
Streamflow time series are commonly derived from stage‐discharge rating curves, but the
uncertainty of the rating curve and resulting streamflow series are poorly understood. While …
uncertainty of the rating curve and resulting streamflow series are poorly understood. While …
A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations
Benchmarking the quality of river discharge data and understanding its information content
for hydrological analyses is an important task for hydrologic science. There is a wide variety …
for hydrological analyses is an important task for hydrologic science. There is a wide variety …
How to make advances in hydrological modelling
K Beven - Hydrology Research, 2019 - iwaponline.com
After some background about what I have learned from a career in hydrological modelling, I
present some opinions about how we might make progress in improving hydrological …
present some opinions about how we might make progress in improving hydrological …
Uncertainty in hydrological signatures
Information about rainfall–runoff processes is essential for hydrological analyses, modelling
and water-management applications. A hydrological, or diagnostic, signature quantifies …
and water-management applications. A hydrological, or diagnostic, signature quantifies …
CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland
We present CAMELS-CH (Catchment Attributes and MEteorology for large-sample Studies-
Switzerland), a large-sample hydro-meteorological data set for hydrological Switzerland in …
Switzerland), a large-sample hydro-meteorological data set for hydrological Switzerland in …