| From skid trails to landscapes: Vegetation is the dominant factor influencing erosion after forest harvest in a low relief glaciated landscape ZP McEachran, RA Slesak, DL Karwan Forest Ecology and Management 430, 299-311, 2018 | 26 | 2018 |
| Direct and indirect effects of forest harvesting on sediment yield in forested watersheds of the United States ZP McEachran, DL Karwan, RA Slesak JAWRA Journal of the American Water Resources Association 57 (1), 1-31, 2021 | 23 | 2021 |
| Nonstationary flood-frequency analysis to assess effects of harvest and cover type conversion on peak flows at the Marcell Experimental Forest, Minnesota, USA ZP McEachran, DL Karwan, SD Sebestyen, RA Slesak, GHC Ng Journal of Hydrology 596, 126054, 2021 | 11 | 2021 |
| Knowledge-guided Machine Learning for Modeling Multi-scale Processes and Data Assimilation: An Application to Streamflow Forecasting for River Basins V Kumar, A Renganathan, R Ghosh, ZP McEachran, M Steinbach, ... AGU24, 2024 | | 2024 |
| Knowledge-Guided Machine Learning for Interpretable Operational Flood Forecasting Z McEachran, R Ghosh, A Renganathan, S Sharma, K Lindsay, ... Authorea Preprints, 2024 | | 2024 |
| Parsimonious streamflow forecasting system based on a dynamical systems approach ZP McEachran, J Kietzmann, M Johnston Journal of Hydrology 641, 131776, 2024 | | 2024 |
| Pop-Up Session: Hydrology at Our Doorstep AS Mayer, ZP McEachran WaterSciCon24, 2024 | | 2024 |
| Knowledge-Guided Machine Learning for Real-Time Flood Forecasting and Data Assimilation ZP McEachran, R Ghosh, A Renganathan, S Sharma, B Connelly, C Duffy, ... WaterSciCon24, 2024 | | 2024 |
| Building Knowledge to Support Equitable Climate Resilience in the Upper Mississippi River Basin Z McEachran, T Twine, MA Kenney, M Woloszyn, B Connelly, AJ Peters, ... 104th AMS Annual Meeting, 2024 | | 2024 |
| Integrating Forcings into Dynamical Systems Forecasting of Streamflow JP Kietzmann, ME Johnston, ZP McEachran AGU Fall Meeting Abstracts 2023 (1417), H11J-1417, 2023 | | 2023 |
| Knowledge-guided Machine Learning for Modeling Multi-scale Processes and Data Assimilation: Streamflow Forecasting in Hydrology V Kumar, R Ghosh, ZP McEachran, A Renganathan, S Sharma, C Duffy, ... AGU Fall Meeting Abstracts 2023, H42E-06, 2023 | | 2023 |
| Building Knowledge to Support Equitable Climate Resilience in the Upper Mississippi River Basin TE Twine, ZP McEachran, MA Kenney, B Connelley, A Peters, ... AGU Fall Meeting Abstracts 2023, H23B-06, 2023 | | 2023 |
| Effects of forest disturbance on water yield and peak flow in low‐relief glaciated catchments assessed with Bayesian parameter estimation ZP McEachran, GC Reese, DL Karwan, RA Slesak, J Vogeler Hydrological Processes 37 (8), e14956, 2023 | | 2023 |
| Effects of forest disturbance on water yield and peak flow in low-relief glaciated catchments assessed with remotely sensed drivers and Bayesian parameter estimation Z McEachran, G Reese, D Karwan, R Slesak, J Vogeler Authorea Preprints, 2023 | | 2023 |
| Assimilating a Deterministic Rainfall-Runoff Model and Historical Data into Bayesian Statistical Models to Improve Streamflow Forecasts in in the Face of Uncertainty ZP McEachran AGU Fall Meeting Abstracts 2022, H45I-1483, 2022 | | 2022 |
| Investigating the effects of forest disturbances on streamflow for mesoscale postglacial catchments using remotely sensed datasets and the case study approach ZP McEachran, R Slesak, DL Karwan, J Vogeler AGU Fall Meeting Abstracts 2020, H085-0006, 2020 | | 2020 |
| Effects of Forest Cover Change on Streamflow in Low Relief Glaciated Catchments ZP McEachran University of Minnesota, 2020 | | 2020 |
| Nonstationary Flood-Frequency Analysis for Streams at the Marcell Experimental Forest, Minnesota, USA ZP McEachran, DL Karwan, SD Sebestyen, GHC Ng, R Slesak AGU Fall Meeting Abstracts 2019, H43G-2072, 2019 | | 2019 |
