Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Frederik Kratzert"'
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 6, Pp n/a-n/a (2024)
Abstract Sap flow offers key insights about transpiration dynamics and forest‐climate interactions. Accurately simulating sap flow remains challenging due to measurement uncertainties and interactions between global and local environmental controls
Externí odkaz:
https://doaj.org/article/bd7648fe6bab4c8f89ca4eeb9b886cdf
Autor:
Frederik Kratzert, Grey Nearing, Nans Addor, Tyler Erickson, Martin Gauch, Oren Gilon, Lukas Gudmundsson, Avinatan Hassidim, Daniel Klotz, Sella Nevo, Guy Shalev, Yossi Matias
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-11 (2023)
Abstract High-quality datasets are essential to support hydrological science and modeling. Several CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets exist for specific countries or regions, however these datasets lack st
Externí odkaz:
https://doaj.org/article/5b1436c4feef4818b712c766b37627f2
Autor:
Grey S. Nearing, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, Sella Nevo
Publikováno v:
Hydrology and Earth System Sciences. 26:5493-5513
Ingesting near-real-time observation data is a critical component of many operational hydrological forecasting systems. In this paper, we compare two strategies for ingesting near-real-time streamflow observations into long short-term memory (LSTM) r
Autor:
Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter, Grey Nearing
Publikováno v:
HYDROLOGY AND EARTH SYSTEM SCIENCES
Deep learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales. Uncertainty estimations are critical for actionable hydrological prediction, and while standardiz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c157769ee73460cc0ff81e5c17b4bee3
https://zenodo.org/record/8138405
https://zenodo.org/record/8138405
Autor:
Martin Gauch, Frederik Kratzert, Oren Gilon, Hoshin Gupta, Juliane Mai, Grey Nearing, Bryan Tolson, Sepp Hochreiter, Daniel Klotz
Publikováno v:
Water Resources Research.
Building accurate rainfall-runoff models is an integral part of hydrological science and practice. The variety of modeling goals and applications have led to a large suite of evaluation metrics for these models. Yet, hydrologists still put considerab
The persistence of errors: How evaluating models over data partitions relates to a global evaluation
Skillful today, inept tomorrow. Today's hydrological models have pronounced and complex error dynamics (e.g., small, highly correlated errors for low flows and large, random errors for high flows). Modellers generally accept that simple, variance bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::279587e473eb0655206150fca00cc887
https://doi.org/10.5194/egusphere-egu23-15221
https://doi.org/10.5194/egusphere-egu23-15221
Autor:
Grey Nearing, Martin Gauch, Daniel Klotz, Frederik Kratzert, Asher Metzger, Guy Shalev, Shlomo Shenzis, Tadele Tekalign, Dana Weitzner, Oren Gilon
Deep learning has become the de facto standard for streamflow simulation. While there are examples of deep learning based streamflow forecast models (e.g., 1-5), the majority of the development and research has been done with hindcast models. The pri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bead22a5df87f8c7001a9e0cc2e569e3
https://doi.org/10.5194/egusphere-egu23-16974
https://doi.org/10.5194/egusphere-egu23-16974
Autor:
Juliane Mai, Hongren Shen, Bryan Tolson, Étienne Gaborit, Richard Arsenault, James Craig, Vincent Fortin, Lauren Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan Shretha, Andre Guy Temgoua, Vincent Vionnet, Jonathan Waddell
Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6447bc19091ffeea73aebce8073754eb
https://doi.org/10.5194/egusphere-egu23-968
https://doi.org/10.5194/egusphere-egu23-968
Autor:
Frederik Kratzert, Martin Gauch, Daniel Klotz, Asher Metzger, Grey Nearing, Guy Shalev, Shlomo Shenzis, Tadele Tekalign, Dana Weitzner, Oren Gilon
The goal of Google’s Flood Forecasting Initiative is to provide timely and actionable flood warnings to everyone, globally. Until recently, Google provided operational flood warnings only for specific partner countries, namely India, Bangladesh, Sr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92464b7198e9e481fda2646b808254b9
https://doi.org/10.5194/egusphere-egu23-5326
https://doi.org/10.5194/egusphere-egu23-5326