Grey Nearing
Grey Nearing
Verified email at
Cited by
Cited by
Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
F Kratzert, D Klotz, G Shalev, G Klambauer, S Hochreiter, G Nearing
Hydrology and Earth System Sciences 23 (12), 5089-5110, 2019
Toward improved predictions in ungauged basins: Exploiting the power of machine learning
F Kratzert, D Klotz, M Herrnegger, AK Sampson, S Hochreiter, GS Nearing
Water Resources Research 55 (12), 11344-11354, 2019
What role does hydrological science play in the age of machine learning?
GS Nearing, F Kratzert, AK Sampson, CS Pelissier, D Klotz, JM Frame, ...
Water Resources Research 57 (3), e2020WR028091, 2021
The plumbing of land surface models: benchmarking model performance
MJ Best, G Abramowitz, HR Johnson, AJ Pitman, G Balsamo, A Boone, ...
Journal of Hydrometeorology 16 (3), 1425-1442, 2015
A ranking of hydrological signatures based on their predictability in space
N Addor, G Nearing, C Prieto, AJ Newman, N Le Vine, MP Clark
Water Resources Research 54 (11), 8792-8812, 2018
Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes
SV Kumar, CD Peters-Lidard, JA Santanello, RH Reichle, CS Draper, ...
Hydrology and Earth System Sciences 19 (11), 4463-4478, 2015
Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network
M Gauch, F Kratzert, D Klotz, G Nearing, J Lin, S Hochreiter
Hydrology and Earth System Sciences 25 (4), 2045-2062, 2021
A philosophical basis for hydrological uncertainty
GS Nearing, Y Tian, HV Gupta, MP Clark, KW Harrison, SV Weijs
Hydrological Sciences Journal 61 (9), 1666-1678, 2016
Partitioning evapotranspiration in semiarid grassland and shrubland ecosystems using time series of soil surface temperature
MS Moran, RL Scott, TO Keefer, WE Emmerich, M Hernandez, ...
agricultural and forest meteorology 149 (1), 59-72, 2009
Deep learning rainfall–runoff predictions of extreme events
JM Frame, F Kratzert, D Klotz, M Gauch, G Shalev, O Gilon, LM Qualls, ...
Hydrology and Earth System Sciences 26 (13), 3377-3392, 2022
Debates-the future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science.
HV Gupta, GS Nearing
Water Resources Research 50 (6), 2014
Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment
GS Nearing, WT Crow, KR Thorp, MS Moran, RH Reichle, HV Gupta
Water Resources Research 48 (5), 2012
The quantity and quality of information in hydrologic models
GS Nearing, HV Gupta
Water Resources Research 51 (1), 524-538, 2015
Benchmarking of a physically based hydrologic model
AJ Newman, N Mizukami, MP Clark, AW Wood, B Nijssen, G Nearing
Journal of Hydrometeorology 18 (8), 2215-2225, 2017
Flood forecasting with machine learning models in an operational framework
S Nevo, E Morin, A Gerzi Rosenthal, A Metzger, C Barshai, D Weitzner, ...
Hydrology and Earth System Sciences 26 (15), 4013-4032, 2022
Benchmarking NLDAS-2 soil moisture and evapotranspiration to separate uncertainty contributions
GS Nearing, DM Mocko, CD Peters-Lidard, SV Kumar, Y Xia
Journal of hydrometeorology 17 (3), 745-759, 2016
Uncertainty estimation with deep learning for rainfall–runoff modeling
D Klotz, F Kratzert, M Gauch, A Keefe Sampson, J Brandstetter, ...
Hydrology and Earth System Sciences 26 (6), 1673-1693, 2022
Post‐processing the national water model with long short‐term memory networks for streamflow predictions and model diagnostics
JM Frame, F Kratzert, A Raney, M Rahman, FR Salas, GS Nearing
JAWRA Journal of the American Water Resources Association 57 (6), 885-905, 2021
A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling
F Kratzert, D Klotz, S Hochreiter, GS Nearing
Hydrology and Earth System Sciences 25 (5), 2685-2703, 2021
Estimating information entropy for hydrological data: One‐dimensional case
W Gong, D Yang, HV Gupta, G Nearing
Water Resources Research 50 (6), 5003-5018, 2014
The system can't perform the operation now. Try again later.
Articles 1–20