Deep learning, hydrological processes and the uniqueness of place
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[1] Jeffrey J. McDonnell,et al. Connectivity at the hillslope scale: identifying interactions between storm size, bedrock permeability, slope angle and soil depth. , 2009 .
[2] James C. I. Dooge,et al. Looking for hydrologic laws , 1986 .
[3] Sepp Hochreiter,et al. NeuralHydrology - Interpreting LSTMs in Hydrology , 2019, Explainable AI.
[4] Vazken Andréassian,et al. Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments , 2008 .
[5] Keith Beven,et al. Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system , 2002 .
[6] Keith Beven,et al. On landscape space to model space mapping , 2001 .
[7] Keith Beven,et al. Use of spatially distributed water table observations to constrain uncertainty in a rainfall–runoff model , 1998 .
[8] Keith Beven,et al. A dynamic TOPMODEL , 2001 .
[9] Ciaran J. Harman,et al. Age‐Ranked Storage‐Discharge Relations: A Unified Description of Spatially Lumped Flow and Water Age in Hydrologic Systems , 2019, Water Resources Research.
[10] Chaopeng Shen,et al. Full‐flow‐regime storage‐streamflow correlation patterns provide insights into hydrologic functioning over the continental US , 2017 .
[11] C. Birkel,et al. Storage dynamics in hydropedological units control hillslope connectivity, runoff generation, and the evolution of catchment transit time distributions , 2014, Water resources research.
[12] Keith Beven,et al. Hysteresis and scale in catchment storage, flow and transport , 2015 .
[13] B. McGlynn,et al. Hierarchical controls on runoff generation: Topographically driven hydrologic connectivity, geology, and vegetation , 2011 .
[14] Keith Beven,et al. Uniqueness of place and process representations in hydrological modelling , 2000 .
[15] E. Kuichling,et al. The Relation Between the Rainfall and the Discharge of Sewers in Populous Districts , 1889 .
[16] B. McGlynn,et al. Spatiotemporal processes that contribute to hydrologic exchange between hillslopes, valley bottoms, and streams , 2016 .
[17] K. Beven,et al. Developing observational methods to drive future hydrological science: Can we make a start as a community? , 2019, Hydrological Processes.
[18] Jan Seibert,et al. Multi‐criterial validation of TOPMODEL in a mountainous catchment , 1999 .
[19] J. Kirchner. Catchments as simple dynamical systems: Catchment characterization, rainfall‐runoff modeling, and doing hydrology backward , 2009 .
[20] Alison L. Kay,et al. Are seemingly physically similar catchments truly hydrologically similar? , 2010 .
[21] Karsten Schulz,et al. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks , 2018, Hydrology and Earth System Sciences.
[22] J. McDonnell,et al. Debates—The future of hydrological sciences: A (common) path forward? A call to action aimed at understanding velocities, celerities and residence time distributions of the headwater hydrograph , 2014 .
[23] A. Rinaldo,et al. Using SAS functions and high‐resolution isotope data to unravel travel time distributions in headwater catchments , 2017 .
[24] A history of the concept of time of concentration , 2020 .
[25] K. Beven. Towards a new paradigm in hydrology , 2007 .
[26] Keith Beven,et al. Towards integrated environmental models of everywhere: uncertainty, data and modelling as a learning process , 2007 .
[27] Keith Beven,et al. Testing the distributed water table predictions of TOPMODEL (allowing for uncertainty in model calibration): The death of TOPMODEL? , 2002 .
[28] Keith Beven,et al. On constraining TOPMODEL hydrograph simulations using partial saturated area information , 2002 .
[29] Keith Beven,et al. Searching for the Holy Grail of scientific hydrology: Q t =( S, R, Δt ) A as closure , 2006 .
[30] K. Beven,et al. Nonparametric direct mapping of rainfall‐runoff relationships: An alternative approach to data analysis and modeling? , 2004 .
[31] Gordon S. Blair,et al. Models of everywhere revisited: A technological perspective , 2019, Environ. Model. Softw..
[32] Frederik Kratzert,et al. What Role Does Hydrological Science Play in the Age of Machine Learning , 2020 .
[33] Landscape-scale water balance monitoring with an iGrav superconducting gravimeter in a field enclosure , 2017 .
[34] Chaopeng Shen,et al. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists , 2017, Water Resources Research.
[35] Heng Li,et al. Deep Learning of Subsurface Flow via Theory-guided Neural Network , 2019, ArXiv.
[36] Keith Beven,et al. Towards a methodology for testing models as hypotheses in the inexact sciences , 2019, Proceedings of the Royal Society A.
[37] Peter C. Young,et al. Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems , 2013 .
[38] K. Beven. Towards a coherent philosophy for modelling the environment , 2002, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[39] Keith Beven,et al. On hypothesis testing in hydrology: Why falsification of models is still a really good idea , 2018 .
[40] Sepp Hochreiter,et al. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets , 2019 .
[41] J. McDonnell,et al. Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures , 2004 .
[42] S. Hochreiter,et al. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning , 2019, Water Resources Research.