Improving the theoretical underpinnings of process-based hydrologic models

In this Commentary, we argue that it is possible to improve the physical realism of hydrologic models by making better use of existing hydrologic theory. We address the following questions: (1) what are some key elements of current hydrologic theory; (2) how can those elements best be incorporated where they may be missing in current models; and (3) how can we evaluate competing hydrologic theories across scales and locations? We propose that hydrologic science would benefit from a model-based community synthesis effort to reframe, integrate, and evaluate different explanations of hydrologic behavior, and provide a controlled avenue to find where understanding falls short.

[1]  S. Schymanski,et al.  An optimality-based model of the coupled soil moisture and root dynamics , 2008 .

[2]  R. Dickinson,et al.  The Representation of Snow in Land Surface Schemes: Results from PILPS 2(d) , 2001 .

[3]  Hubert H. G. Savenije,et al.  A framework to assess the realism of model structures using hydrological signatures , 2012 .

[4]  Keith Beven,et al.  TOPMODEL : a critique. , 1997 .

[5]  Julien Lerat,et al.  Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments , 2012 .

[6]  Jeffrey J. McDonnell,et al.  On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration , 2002 .

[7]  John W. Pomeroy,et al.  Storage dynamics simulations in prairie wetland hydrology models: evaluation and parameterization , 2013 .

[8]  D. Pury,et al.  Simple scaling of photosynthesis from leaves to canopies without the errors of big‐leaf models , 1997 .

[9]  T. C. Chamberlin The Method of Multiple Working Hypotheses , 1931, The Journal of Geology.

[10]  E. Wood,et al.  Little change in global drought over the past 60 years , 2012, Nature.

[11]  Regine Hock,et al.  Temperature index melt modelling in mountain areas , 2003 .

[12]  J. Kirchner Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology , 2006 .

[13]  Enrico Bertuzzo,et al.  Hydrologic controls on basin‐scale distribution of benthic invertebrates , 2014 .

[14]  Keith Beven,et al.  Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system , 2002 .

[15]  Jeffrey J. McDonnell,et al.  Threshold relations in subsurface stormflow: 2. The fill and spill hypothesis , 2006 .

[16]  Steven T. Corneliussen Steven T. Corneliussen Should scientists think harder about explaining the concept “theory”? , 2015 .

[17]  K. Beven Rainfall-Runoff Modelling: The Primer , 2012 .

[18]  Kenneth W. Harrison,et al.  Land surface Verification Toolkit (LVT) – a generalized framework for land surface model evaluation , 2012 .

[19]  R. J. Granger,et al.  Storage dynamics and streamflow in a catchment with a variable contributing area , 2009 .

[20]  S. Schymanski,et al.  An optimality‐based model of the dynamic feedbacks between natural vegetation and the water balance , 2009 .

[21]  Eric F. Wood,et al.  A land-surface hydrology parameterization with subgrid variability for general circulation models , 1992 .

[22]  Kevin W. Manning,et al.  The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements , 2011 .

[23]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 2. Model implementation and case studies , 2015 .

[24]  Hoshin Vijai Gupta,et al.  Debates—the future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science , 2014 .

[25]  Yuqiong Liu,et al.  Reconciling theory with observations: elements of a diagnostic approach to model evaluation , 2008 .

[26]  R. Koster,et al.  Modeling the land surface boundary in climate models as a composite of independent vegetation stands , 1992 .

[27]  Erwin Zehe,et al.  HESS Opinions: Functional units: a novel framework to explore the link between spatial organization and hydrological functioning of intermediate scale catchments , 2014 .

[28]  B. McGlynn,et al.  Hierarchical controls on runoff generation: Topographically driven hydrologic connectivity, geology, and vegetation , 2011 .

[29]  Dmitri Kavetski,et al.  Reply to comment by K. Beven et al. on “Pursuing the method of multiple working hypotheses for hydrological modeling” , 2012 .

[30]  J. Jacobs Ecohydrology: Darwinian Expression of Vegetation Form and Function , 2003 .

[31]  D. Lawrence,et al.  Improving the representation of hydrologic processes in Earth System Models , 2015 .

[32]  Keith Beven,et al.  A dynamic TOPMODEL , 2001 .

[33]  Günter Blöschl,et al.  Hydrologic synthesis: Across processes, places, and scales , 2006 .

[34]  Pablo A. Mendoza,et al.  Implications of the Methodological Choices for Hydrologic Portrayals of Climate Change over the Contiguous United States: Statistically Downscaled Forcing Data and Hydrologic Models , 2016 .

[35]  H. Kyburg,et al.  How the laws of physics lie , 1984 .

[36]  Dmitri Kavetski,et al.  Catchment properties, function, and conceptual model representation: is there a correspondence? , 2014 .

[37]  John Wainwright,et al.  Sediment connectivity: a framework for understanding sediment transfer at multiple scales , 2015 .

[38]  W. Gray,et al.  A unifying framework for watershed thermodynamics: constitutive relationships , 1999 .

[39]  Richard P. Hooper,et al.  Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology , 2007 .

[40]  Erwin Zehe,et al.  A thermodynamic approach to link self-organization, preferential flow and rainfall-runoff behaviour , 2013 .

[41]  Eric F. Wood,et al.  Predicting the Discharge of Global Rivers , 2001, Journal of Climate.

[42]  Gary James Jason,et al.  The Logic of Scientific Discovery , 1988 .

[43]  J. Freer,et al.  Consistency between hydrological models and field observations: linking processes at the hillslope scale to hydrological responses at the watershed scale , 2009 .

[44]  Jan Seibert,et al.  Reliability of Model Predictions Outside Calibration Conditions , 2003 .

[45]  Peter Lehmann,et al.  Hydrology and Earth System Sciences Rainfall Threshold for Hillslope Outflow: an Emergent Property of Flow Pathway Connectivity , 2022 .

[46]  James C. I. Dooge,et al.  Looking for hydrologic laws , 1986 .

[47]  Richard P. Hooper,et al.  Testing and validating environmental models , 1996 .

[48]  Keith Beven,et al.  Towards integrated environmental models of everywhere: uncertainty, data and modelling as a learning process , 2007 .

[49]  Dmitri Kavetski,et al.  Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development , 2011 .

[50]  W. Collins,et al.  The Community Earth System Model: A Framework for Collaborative Research , 2013 .

[51]  Jeffrey J. McDonnell,et al.  Gauging the Ungauged Basin: Relative Value of Soft and Hard Data , 2015 .

[52]  Murugesu Sivapalan,et al.  Pattern, Process and Function: Elements of a Unified Theory of Hydrology at the Catchment Scale , 2006 .

[53]  K. Beven,et al.  Toward a generalization of the TOPMODEL concepts:Topographic indices of hydrological similarity , 1996 .

[54]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[55]  Keith Beven,et al.  The role of bedrock topography on subsurface storm flow , 2002 .

[56]  Jeffrey J. McDonnell,et al.  Virtual experiments: a new approach for improving process conceptualization in hillslope hydrology , 2004 .

[57]  M. Sivapalan,et al.  A unifying framework for watershed thermodynamics: balance equations for mass, momentum, energy and entropy, and the second law of thermodynamics , 1998 .

[58]  Fubao Sun,et al.  A general framework for understanding the response of the water cycle to global warming over land and ocean , 2013 .

[59]  T. C. Chamberlin The Method of Multiple Working Hypotheses: With this method the dangers of parental affection for a favorite theory can be circumvented. , 1965, Science.

[60]  Felipe J. Colón-González,et al.  Multimodel assessment of water scarcity under climate change , 2013, Proceedings of the National Academy of Sciences.

[61]  Peter A. Troch,et al.  What makes Darwinian hydrology "Darwinian"? Asking a different kind of question about landscapes , 2014 .

[62]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 1. Modeling concept , 2015 .

[63]  Hubert H. G. Savenije,et al.  On the calibration of hydrological models in ungauged basins: A framework for integrating hard and soft hydrological information , 2009 .

[64]  T. Dawson,et al.  Contrasting drought-response strategies in California redwoods. , 2015, Tree physiology.

[65]  Larry Mahrt,et al.  Grid-Averaged Surface Fluxes , 1987 .

[66]  M. Huss,et al.  Strong Alpine glacier melt in the 1940s due to enhanced solar radiation , 2009 .

[67]  Sabine Attinger,et al.  Multiscale and Multivariate Evaluation of Water Fluxes and States over European River Basins , 2016 .

[68]  Balaji Rajagopalan,et al.  Are we unnecessarily constraining the agility of complex process‐based models? , 2015 .

[69]  Sabine Attinger,et al.  Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations , 2013 .

[70]  A. Rinaldo,et al.  SEHR-ECHO v1.0: a Spatially Explicit Hydrologic Response model for ecohydrologic applications , 2014 .

[71]  K. Mitchell,et al.  A parameterization of snowpack and frozen ground intended for NCEP weather and climate models , 1999 .

[72]  Enrico Bertuzzo,et al.  Transport at basin scales: 1. Theoretical framework , 2005 .

[73]  Murugesu Sivapalan,et al.  Power law catchment‐scale recessions arising from heterogeneous linear small‐scale dynamics , 2009 .

[74]  Michael N. Gooseff,et al.  Hydrologic connectivity between landscapes and streams: Transferring reach‐ and plot‐scale understanding to the catchment scale , 2009 .

[75]  John S. Selker,et al.  Environmental temperature sensing using Raman spectra DTS fiber‐optic methods , 2009 .

[76]  Murugesu Sivapalan,et al.  A test of the optimality approach to modelling canopy properties and CO2 uptake by natural vegetation. , 2007, Plant, cell & environment.

[77]  Keith W. Oleson,et al.  Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models , 2002 .

[78]  Dmitri Kavetski,et al.  Pursuing the method of multiple working hypotheses for hydrological modeling , 2011 .

[79]  P. Milly,et al.  On the Hydrologic Adjustment of Climate-Model Projections: The Potential Pitfall of Potential Evapotranspiration , 2011 .

[80]  Ming Ye,et al.  Towards a comprehensive assessment of model structural adequacy , 2012 .

[81]  S. Attinger,et al.  Multiscale parameter regionalization of a grid‐based hydrologic model at the mesoscale , 2010 .

[82]  Atsumu Ohmura,et al.  Physical Basis for the Temperature-Based Melt-Index Method , 2001 .

[83]  Hoshin Vijai Gupta,et al.  Large-sample hydrology: a need to balance depth with breadth , 2013 .

[84]  Erwin Zehe,et al.  Advancing catchment hydrology to deal with predictions under change , 2013 .

[85]  Douglas B. Clark,et al.  Representing the effects of subgrid variability of soil moisture on runoff generation in a land surface model , 2008 .

[86]  W. J. Shuttleworth,et al.  COSMOS: the COsmic-ray Soil Moisture Observing System , 2012 .

[87]  K. Oleson,et al.  Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum , 2014 .

[88]  Yves Lejeune,et al.  A comparison of 1701 snow models using observations from an alpine site , 2013 .

[89]  Zong-Liang Yang,et al.  Validation of the energy budget of an alpine snowpack simulated by several snow models (Snow MIP project) , 2004, Annals of Glaciology.

[90]  Murugesu Sivapalan,et al.  Using an optimality model to understand medium and long-term responses of vegetation water use to elevated atmospheric CO2 concentrations , 2015, AoB PLANTS.

[91]  Keith Beven,et al.  Uniqueness of place and process representations in hydrological modelling , 2000 .

[92]  Murugesu Sivapalan,et al.  Effects of hydraulic conductivity variability on hillslope‐scale shallow subsurface flow response and storage‐discharge relations , 2009 .

[93]  Keith Beven,et al.  Searching for the Holy Grail of scientific hydrology: Q t =( S, R, Δt ) A as closure , 2006 .

[94]  R. Moore,et al.  Observations and modeling of hillslope throughflow temperatures in a coastal forested catchment , 2015 .

[95]  Jatin Narula,et al.  Maximum entropy production allows a simple representation of heterogeneity in semiarid ecosystems , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[96]  D. Marks,et al.  Radiative transfer modeling of a coniferous canopy characterized by airborne remote sensing , 2008 .

[97]  Jan Vanderborght,et al.  Proof of concept of regional scale hydrologic simulations at hydrologic resolution utilizing massively parallel computer resources , 2010 .

[98]  Andrea Rinaldo,et al.  Optimal Channel Networks - a Framework for the Study of River Basin Morphology , 1993 .

[99]  Keith Loague,et al.  Physics‐based hydrologic‐response simulation: Seeing through the fog of equifinality , 2006 .

[100]  Jeffrey J. McDonnell,et al.  Are all runoff processes the same? , 2013 .

[101]  J. McDonnell,et al.  Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures , 2004 .

[102]  Kevin W. Manning,et al.  The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins , 2011 .

[103]  S. Sorooshian,et al.  The distributed model intercomparison project - Phase 2: Experiment design and summary results of the western basin experiments , 2013 .

[104]  Martyn P. Clark,et al.  Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models , 2008 .

[105]  Hoshin Vijai Gupta,et al.  The quantity and quality of information in hydrologic models , 2015 .

[106]  Doerthe Tetzlaff,et al.  Do time‐variable tracers aid the evaluation of hydrological model structure? A multimodel approach , 2012 .

[107]  William J. Sutherland,et al.  The best solution , 2005, Nature.

[108]  M. Wigmosta,et al.  A distributed hydrology-vegetation model for complex terrain , 1994 .

[109]  Philipp Kraft,et al.  CMF: A Hydrological Programming Language Extension For Integrated Catchment Models , 2011, Environ. Model. Softw..

[110]  A. Jakeman,et al.  How much complexity is warranted in a rainfall‐runoff model? , 1993 .

[111]  N. McDowell,et al.  The interdependence of mechanisms underlying climate-driven vegetation mortality. , 2011, Trends in ecology & evolution.

[112]  Peter Troch,et al.  Dealing with Landscape Heterogeneity in Watershed Hydrology: A Review of Recent Progress toward New Hydrological Theory , 2009 .

[113]  Jaap Schellekens,et al.  Modelling of hydrological responses: the representative elementary watershed approach as an alternative blueprint for watershed modelling , 2003 .

[114]  Alessandro Marani,et al.  Energy dissipation, runoff production, and the three-dimensional structure of river basins , 1992 .

[115]  Eric F. Wood,et al.  One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model , 1996 .

[116]  Ciaran J. Harman,et al.  HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling , 2011, Hydrology and Earth System Sciences.

[117]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[118]  J. Famiglietti,et al.  Multiscale modeling of spatially variable water and energy balance processes , 1994 .

[119]  Dmitri Kavetski,et al.  Towards more systematic perceptual model development: a case study using 3 Luxembourgish catchments , 2015 .

[120]  R. Moore,et al.  A distribution function approach to rainfall runoff modeling , 1981 .

[121]  Reed M. Maxwell,et al.  Quantifying the effects of three-dimensional subsurface heterogeneity on Hortonian runoff processes using a coupled numerical, stochastic approach , 2008 .

[122]  Doerthe Tetzlaff,et al.  Developing a consistent process‐based conceptualization of catchment functioning using measurements of internal state variables , 2014 .

[123]  R. H. Hawkins,et al.  STEADY‐STATE ANALYSIS OF INFILTRATION AND OVERLAND FLOW FOR SPATIALLY‐VARIED HILLSLOPES , 1987 .

[124]  Keith Beven,et al.  Do we need a Community Hydrological Model? , 2015 .

[125]  Keith Beven,et al.  A manifesto for the equifinality thesis , 2006 .

[126]  D. Lawrence,et al.  Parameterization improvements and functional and structural advances in Version 4 of the Community Land Model , 2011 .

[127]  Peter A. Troch,et al.  The future of hydrology: An evolving science for a changing world , 2010 .

[128]  M. Sivapalan,et al.  Threshold behaviour in hydrological systems as (human) geo-ecosystems: Manifestations, controls, implications , 2009 .

[129]  Praveen Kumar,et al.  A catchment‐based approach to modeling land surface processes in a general circulation model: 1. Model structure , 2000 .

[130]  R. Freeze,et al.  Blueprint for a physically-based, digitally-simulated hydrologic response model , 1969 .

[131]  William E. Dietrich,et al.  Hydraulic Food-Chain ModelsAn approach to the study of food-web dynamics in large rivers , 1995 .

[132]  James P. McNamara,et al.  Simulated soil water storage effects on streamflow generation in a mountainous snowmelt environment, Idaho, USA , 2009 .

[133]  Erwin Zehe,et al.  Dynamical process upscaling for deriving catchment scale state variables and constitutive relations for meso-scale process models , 2006 .

[134]  Maarten G. Kleinhans,et al.  Terra Incognita: Explanation and Reduction in Earth Science , 2005 .

[135]  Keith Beven,et al.  Modelling hydrologic responses in a small forested catchment (Panola Mountain, Georgia, USA): a comparison of the original and a new dynamic TOPMODEL , 2003 .

[136]  David G. Tarboton,et al.  Sub-grid parameterization of snow distribution for an energy and mass balance snow cover model , 1999 .

[137]  Kelly K. Caylor,et al.  Ecohydrological optimization of pattern and processes in water‐limited ecosystems: A trade‐off‐based hypothesis , 2006 .

[138]  Clifford I. Voss,et al.  Climate change impacts on the temperature and magnitude of groundwater discharge from shallow, unconfined aquifers , 2014 .

[139]  Göran Lindström,et al.  Virtual laboratories: new opportunities for collaborative water science , 2014, Hydrology and Earth System Sciences.

[140]  Dara Entekhabi,et al.  Embedding landscape processes into triangulated terrain models , 2005, Int. J. Geogr. Inf. Sci..

[141]  Charles H. Luce,et al.  Macroscale hydrologic modeling of ecologically relevant flow metrics , 2010 .

[142]  Keith Beven,et al.  Comment on “Pursuing the method of multiple working hypotheses for hydrological modeling” by P. Clark et al. , 2012 .

[143]  Christina L. Tague,et al.  RHESSys: Regional Hydro-Ecologic Simulation System—An Object- Oriented Approach to Spatially Distributed Modeling of Carbon, Water, and Nutrient Cycling , 2004 .