| The metabolic regimes of flowing waters ES Bernhardt, JB Heffernan, NB Grimm, EH Stanley, JW Harvey, M Arroita, ... Limnology and Oceanography 63 (S1), S99-S118, 2018 | 335 | 2018 |
| Process‐guided deep learning predictions of lake water temperature JS Read, X Jia, J Willard, AP Appling, JA Zwart, SK Oliver, A Karpatne, ... Water Resources Research 55 (11), 9173-9190, 2019 | 292 | 2019 |
| Overcoming equifinality: Leveraging long time series for stream metabolism estimation AP Appling, RO Hall Jr, CB Yackulic, M Arroita Journal of Geophysical Research: Biogeosciences 123 (2), 624-645, 2018 | 178 | 2018 |
| Physics-guided architecture (pga) of neural networks for quantifying uncertainty in lake temperature modeling A Daw, RQ Thomas, CC Carey, JS Read, AP Appling, A Karpatne Proceedings of the 2020 siam international conference on data mining, 532-540, 2020 | 148 | 2020 |
| AquaSat: A data set to enable remote sensing of water quality for inland waters MRV Ross, SN Topp, AP Appling, X Yang, C Kuhn, D Butman, M Simard, ... Water Resources Research 55 (11), 10012-10025, 2019 | 123 | 2019 |
| Light and flow regimes regulate the metabolism of rivers ES Bernhardt, P Savoy, MJ Vlah, AP Appling, LE Koenig, RO Hall Jr, ... Proceedings of the National Academy of Sciences 119 (8), e2121976119, 2022 | 104 | 2022 |
| Reducing bias and quantifying uncertainty in watershed flux estimates: The R package loadflex AP Appling, MC Leon, WH McDowell Ecosphere 6 (12), 1-25, 2015 | 97 | 2015 |
| Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data F Rahmani, K Lawson, W Ouyang, A Appling, S Oliver, C Shen Environmental Research Letters 16 (2), 024025, 2021 | 95 | 2021 |
| Stoichiometric flexibility in response to fertilization along gradients of environmental and organismal nutrient richness SA Sistla, AP Appling, AM Lewandowska, BN Taylor, AA Wolf Oikos 124 (7), 949-959, 2015 | 89 | 2015 |
| Differentiable modelling to unify machine learning and physical models for geosciences C Shen, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, ... Nature Reviews Earth & Environment 4 (8), 552-567, 2023 | 83 | 2023 |
| The metabolic regimes of 356 rivers in the United States AP Appling, JS Read, LA Winslow, M Arroita, ES Bernhardt, NA Griffiths, ... Scientific data 5 (1), 1-14, 2018 | 79 | 2018 |
| Physics-guided recurrent graph model for predicting flow and temperature in river networks X Jia, J Zwart, J Sadler, A Appling, S Oliver, S Markstrom, J Willard, S Xu, ... Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 71 | 2021 |
| Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes P Savoy, AP Appling, JB Heffernan, EG Stets, JS Read, JW Harvey, ... Limnology and Oceanography 64 (5), 1835-1851, 2019 | 71 | 2019 |
| Predicting water temperature dynamics of unmonitored lakes with meta‐transfer learning JD Willard, JS Read, AP Appling, SK Oliver, X Jia, V Kumar Water Resources Research 57 (7), e2021WR029579, 2021 | 65 | 2021 |
| Floodplain biogeochemical mosaics: a multidimensional view of alluvial soils AP Appling, ES Bernhardt, JA Stanford Journal of Geophysical Research: Biogeosciences 119 (8), 1538-1553, 2014 | 58 | 2014 |
| Enhancement of primary production during drought in a temperate watershed is greater in larger rivers than headwater streams JD Hosen, KS Aho, AP Appling, EC Creech, JH Fair, RO Hall Jr, ... Limnology and Oceanography 64 (4), 1458-1472, 2019 | 52 | 2019 |
| Deep learning approaches for improving prediction of daily stream temperature in data‐scarce, unmonitored, and dammed basins F Rahmani, C Shen, S Oliver, K Lawson, A Appling Hydrological Processes 35 (11), e14400, 2021 | 45 | 2021 |
| streamMetabolizer: Models for estimating aquatic photosynthesis and respiration AP Appling, RO Hall, M Arroita, CB Yackulic R package version 0.10 9, 2018 | 42* | 2018 |
| Nutrient limitation and physiology mediate the fine-scale (de) coupling of biogeochemical cycles AP Appling, JB Heffernan The American Naturalist 184 (3), 384-406, 2014 | 42 | 2014 |
| Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C Varadharajan, AP Appling, B Arora, DS Christianson, VC Hendrix, ... Hydrological Processes 36 (4), e14565, 2022 | 35 | 2022 |
