| 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 | 493* | 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 | 404 | 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 | 320 | 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 | 239 | 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 | 215 | 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 | 174 | 2015 |
| 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 | 153 | 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 | 148 | 2009 |
| 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 | 140 | 2021 |
| 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), 5351-5359, 2014 | 120 | 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 | 116 | 2012 |
| The quantity and quality of information in hydrologic models GS Nearing, HV Gupta Water Resources Research 51 (1), 524-538, 2015 | 114 | 2015 |
| 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 | 105 | 2022 |
| 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 | 104 | 2017 |
| 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 | 101 | 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 | 90 | 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 | 89 | 2021 |
| 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 | 78 | 2022 |
| 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 | 71 | 2014 |
| 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 | 70 | 2021 |
