Deep learning, hydrological processes and the uniqueness of place

[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.