| 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 | 319 | 2021 |
| 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 | 89 | 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 |
| Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks GS Nearing, D Klotz, AK Sampson, F Kratzert, M Gauch, JM Frame, ... Hydrology and earth system sciences discussions 2021, 1-25, 2021 | 29 | 2021 |
| On strictly enforced mass conservation constraints for modelling the rainfall‐runoff process JM Frame, F Kratzert, HV Gupta, P Ullrich, GS Nearing Hydrological Processes 37 (3), e14847, 2023 | 21 | 2023 |
| Deep learning rainfall-runoff predictions of extreme events J Frame, F Kratzert, D Klotz, M Gauch, G Shelev, O Gilon, LM Qualls, ... Copernicus GmbH, 2021 | 20 | 2021 |
| Post-processing a Conceptual Rainfall-runoff Model with an LSTM GS Nearing, AK Sampson, F Kratzert, J Frame EarthArXiv, 2020 | 16 | 2020 |
| Post processing the US national water model with a long short-term memory network J Frame, G Nearing, F Kratzert, M Rahman J. Am. Water Resour. As., https://doi. org/10.31223/osf. io/4xhac, 2020 | 13 | 2020 |
| Combining parametric land surface models with machine learning C Pelissier, J Frame, G Nearing IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium …, 2020 | 11 | 2020 |
| A., Rahman, M., Salas, FR, and Nearing, GS: Post-Processing the National Water Model with Long Short-Term Memory Networks for Streamflow Predictions and Model Diagnostics JM Frame, F Kratzert, II Raney J. Am. Water Resour. As 57, 885-905, 0 | 11 | |
| Physically informed machine learning for hydrological modeling under climate nonstationarity GS Nearing, CS Pelissier, F Kratzert, D Klotz, HV Gupta, JM Frame, ... | 10 | 2019 |
| Hierarchical fractional advection-dispersion equation (FADE) to quantify anomalous transport in river corridor over a broad spectrum of scales: Theory and applications Y Zhang, D Zhou, W Wei, JM Frame, H Sun, AY Sun, X Chen Mathematics 9 (7), 790, 2021 | 6 | 2021 |
| Hydrology research articles are becoming more topically diverse M Rahman, JM Frame, J Lin, GS Nearing Journal of Hydrology 614, 128551, 2022 | 4 | 2022 |
| The Next Generation Water Resources Modeling Framework: Open Source, Standards Based, Community Accessible, Model Interoperability for Large Scale Water Prediction F Ogden, B Avant, R Bartel, D Blodgett, E Clark, E Coon, B Cosgrove, ... AGU Fall Meeting Abstracts 2021, H43D-01, 2021 | 4 | 2021 |
| Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H Gholizadeh, Y Zhang, J Frame, X Gu, CT Green Science of the Total Environment 901, 165884, 2023 | 3 | 2023 |
| A distributed domain model coupling open channel flow and groundwater flow to quantify the impact of lateral hydrologic exchange on hydrograph S Wei, Y Zheng, X Liang, P Xu, Y Tian, JM Frame, Y Zhang Journal of Hydrology 611, 128010, 2022 | 3 | 2022 |
| Next Generation National Water Model: Strategy and Preliminary Performance of Initial Model Formulations L Cunha, K Jennings, A Wood, N Mizukami, F Ogden, X Feng, Y Liu, ... AGU Fall Meeting Abstracts 2021, H54G-04, 2021 | 2 | 2021 |
| Machine learning is central to the future of hydrological modeling G Nearing, F Kratzert, C Pelissier, D Klotz, J Frame, H Gupta EGU2020, 2020 | 2 | 2020 |
| Hidden stories: Topic modeling in hydrology literature M Rahman, G Nearing, J Frame Earth and Space Science Open Archive ESSOAr, 2020 | 2 | 2020 |
| On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results G Abramowitz, A Ukkola, S Hobeichi, J Cranko Page, M Lipson, ... EGUsphere 2024, 1-47, 2024 | 1 | 2024 |
