HESS Opinions: The complementary merits of top-down and bottom-up modelling philosophies in hydrology

In hydrology, the two somewhat competing modelling philosophies of bottom-up and top-down approaches are the basis of most process-based models. Differing mostly (1) in their respective degree of detail in resolving the modelling domain and (2) in their respective degree of explicitly treating conservation laws, these two philosophies suffer from similar limitations. Nevertheless, a better understanding of their respective basis (i.e. micro-scale vs. macro-scale) as well as their respective short comings bears the potential of identifying the complementary value of the two philosophies for improving our models. In this manuscript we analyse several frequently communicated beliefs and assumptions to identify, discuss and emphasize the functional similarity of the two modelling philosophies. We argue that deficiencies in model applications largely do not depend on the modelling philosophy but rather on the way a model is implemented. Based on the premises that top-down models can be implemented at any desired degree of detail and that any type of model remains to some degree conceptual we argue that a convergence of the two modelling strategies may hold some value for progressing the development of hydrological models.

[1]  Martyn P. Clark,et al.  Spatial variability of hydrological processes and model structure diagnostics in a 50 km2 catchment , 2014 .

[2]  Hubert H. G. Savenije,et al.  Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration , 2013 .

[3]  Thomas C Winter,et al.  Delineation and Evaluation of Hydrologic-Landscape Regions in the United States Using Geographic Information System Tools and Multivariate Statistical Analyses , 2004, Environmental management.

[4]  Kevin Bishop,et al.  Simulating interactions between saturated and unsaturated storage in a conceptual runoff model , 2003 .

[5]  Marco Borga,et al.  The influence of soil moisture on threshold runoff generation processes in an alpine headwater catchment , 2010 .

[6]  Erwin Zehe,et al.  Comparison of conceptual model performance using different representations of spatial variability , 2008 .

[7]  Soroosh Sorooshian,et al.  Toward improved streamflow forecasts: value of semidistributed modeling , 2001 .

[8]  A. S. Gragne,et al.  Analyzing catchment behavior through catchment modeling in the Gilgel Abay, Upper Blue Nile River Basin, Ethiopia , 2010 .

[9]  Kevin J. McGuire,et al.  Topographic controls on shallow groundwater dynamics: implications of hydrologic connectivity between hillslopes and riparian zones in a till mantled catchment , 2010 .

[10]  H. McMillan,et al.  Influence of soil and climate on root zone storage capacity , 2015 .

[11]  Erwin Zehe,et al.  HESS Opinions: From response units to functional units: a thermodynamic reinterpretation of the HRU concept to link spatial organization and functioning of intermediate scale catchments , 2014 .

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

[13]  P. Matgen,et al.  Understanding catchment behavior through stepwise model concept improvement , 2008 .

[14]  François Anctil,et al.  Locating the sources of low‐pass behavior within rainfall‐runoff models , 2004 .

[15]  Keith Beven,et al.  Constraining Dynamic TOPMODEL responses using fuzzy rule based performance measures of uncertain water table information. EAE03-A-08753 , 2003 .

[16]  S. Uhlenbrook,et al.  Hydrological process representation at the meso-scale: the potential of a distributed, conceptual catchment model , 2004 .

[17]  Thomas C. Winter,et al.  THE CONCEPT OF HYDROLOGIC LANDSCAPES 1 , 2001 .

[18]  Hubert H. G. Savenije,et al.  Climate controls how ecosystems size the root zone storage capacity at catchment scale , 2014 .

[19]  Erwin Zehe,et al.  Patterns of predictability in hydrological threshold systems , 2007 .

[20]  Jeffrey J. McDonnell,et al.  Scale effects on headwater catchment runoff timing, flow sources, and groundwater‐streamflow relations , 2004 .

[21]  J. Freer,et al.  The evolution of root-zone moisture capacities after deforestation: a step towards hydrological predictions under change? , 2016 .

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

[23]  Hoshin Vijai Gupta,et al.  On the development of regionalization relationships for lumped watershed models: The impact of ignoring sub-basin scale variability , 2009 .

[24]  Axel Kleidon,et al.  A method of determining rooting depth from a terrestrial biosphere model and its impacts on the global water and carbon cycle , 1998 .

[25]  Hubert H. G. Savenije,et al.  HESS Opinions "Topography driven conceptual modelling (FLEX-Topo)" , 2010 .

[26]  S. Sorooshian,et al.  Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system , 2004, Journal of Hydrology.

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

[28]  Hubert H. G. Savenije,et al.  The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models , 2016 .

[29]  Felix Naef,et al.  Identifying runoff processes on the plot and catchment scale , 2006 .

[30]  H. McMillan.  Effect of spatial variability and seasonality in soil moisture on drainage thresholds and fluxes in a conceptual hydrological model , 2012 .

[31]  H. Winsemius,et al.  The effect of forcing and landscape distribution on performance and consistency of model structures , 2015 .

[32]  W. Bastiaanssen,et al.  Constraining model parameters on remotely sensed evaporation: Justification for distribution in ungauged basins? , 2008 .

[33]  Wolfgang-Albert Flügel,et al.  Delineating hydrological response units by geographical information system analyses for regional hydrological modelling using PRMS/MMS in the drainage basin of the River Bröl, Germany , 1995 .

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

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