DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology

Abstract. This paper presents DECIPHeR (Dynamic fluxEs and ConnectIvity for Predictions of HydRology), a new model framework that simulates and predicts hydrologic flows from spatial scales of small headwater catchments to entire continents. DECIPHeR can be adapted to specific hydrologic settings and to different levels of data availability. It is a flexible model framework which includes the capability to (1) change its representation of spatial variability and hydrologic connectivity by implementing hydrological response units in any configuration and (2) test different hypotheses of catchment behaviour by altering the model equations and parameters in different parts of the landscape. It has an automated build function that allows rapid set-up across large model domains and is open-source to help researchers and/or practitioners use the model. DECIPHeR is applied across Great Britain to demonstrate the model framework. It is evaluated against daily flow time series from 1366 gauges for four evaluation metrics to provide a benchmark of model performance. Results show that the model performs well across a range of catchment characteristics but particularly in wetter catchments in the west and north of Great Britain. Future model developments will focus on adding modules to DECIPHeR to improve the representation of groundwater dynamics and human influences.

[1]  P. J. Smith,et al.  A novel framework for discharge uncertainty quantification applied to 500 UK gauging stations , 2015, Water resources research.

[2]  Martyn P. Clark,et al.  Diagnostic evaluation of multiple hypotheses of hydrological behaviour in a limits‐of‐acceptability framework for 24 UK catchments , 2014 .

[3]  Sabine Attinger,et al.  Toward seamless hydrologic predictions across spatial scales , 2017 .

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

[5]  Robert J. Moore,et al.  Development of a high resolution grid-based river flow model for use with regional climate model output , 2007 .

[6]  Guillaume Thirel,et al.  The suite of lumped GR hydrological models in an R package , 2017, Environ. Model. Softw..

[7]  Keith Beven,et al.  Dynamic Topmodel : a new implementation in R and its sensitivity to time and space 1 steps 2 , 2017 .

[8]  Keith Beven,et al.  Effects of spatial variability and scale with implications to hydrologic modeling , 1988 .

[9]  Berit Arheimer,et al.  Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case , 2015 .

[10]  D. Toll,et al.  Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data , 2010 .

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

[12]  Patrick M. Reed,et al.  When are multiobjective calibration trade‐offs in hydrologic models meaningful? , 2012 .

[13]  Patrick M. Reed,et al.  A top-down framework for watershed model evaluation and selection under uncertainty , 2009, Environ. Model. Softw..

[14]  Nigel W. Arnell,et al.  Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis , 2011 .

[15]  Hoshin Vijai Gupta,et al.  Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins , 2007 .

[16]  Luis Samaniego,et al.  Toward computationally efficient large‐scale hydrologic predictions with a multiscale regionalization scheme , 2013 .

[17]  Florian Pappenberger,et al.  Impacts of uncertain river flow data on rainfall‐runoff model calibration and discharge predictions , 2010 .

[18]  Murugesu Sivapalan,et al.  Scale issues in hydrological modelling , 1995 .

[19]  Hyungjun Kim,et al.  Recent progresses in incorporating human land–water management into global land surface models toward their integration into Earth system models , 2016 .

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

[21]  Murray C. Peel,et al.  Simulating runoff under changing climatic conditions: Revisiting an apparent deficiency of conceptual rainfall‐runoff models , 2016 .

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

[23]  S. Kanae,et al.  Incorporation of groundwater pumping in a global Land Surface Model with the representation of human impacts , 2015 .

[24]  S. Cole,et al.  CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological and other applications , 2015 .

[25]  Joseph Hamman,et al.  The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility , 2018, Geoscientific Model Development.

[26]  Keith Beven,et al.  A modelling framework for evaluation of the hydrological impacts of nature‐based approaches to flood risk management, with application to in‐channel interventions across a 29‐km2 scale catchment in the United Kingdom , 2017 .

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

[28]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[29]  S. Kanae,et al.  An integrated model for the assessment of global water resources – Part 1: Model description and input meteorological forcing , 2008 .

[30]  George Kuczera,et al.  Bayesian analysis of input uncertainty in hydrological modeling: 2. Application , 2006 .

[31]  M. Bierkens,et al.  Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources , 2013 .

[32]  K. Beven,et al.  The usability of 250 m resolution data from the UK Meteorological Office Unified Model as input data for a hydrological model , 2008 .

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

[34]  S. Kanae,et al.  A physically based description of floodplain inundation dynamics in a global river routing model , 2011 .

[35]  Murugesu Sivapalan,et al.  Scale issues in hydrological modelling: A review , 1995 .

[36]  Martyn P. Clark,et al.  Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance , 2014 .

[37]  S. L. Markstrom,et al.  The modular modeling system (MMS) — The physical process modeling component of a database-centered decision support system for water and power management , 1996 .

[38]  Hoshin Vijai Gupta,et al.  A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model , 2008 .

[39]  R. Woods,et al.  Climate and landscape controls on water balance model complexity over changing timescales , 2002 .

[40]  Keith Beven,et al.  Calibration of hydrological models using flow-duration curves , 2010 .

[41]  Jim Freer,et al.  Ensemble evaluation of hydrological model hypotheses , 2010 .

[42]  George Kuczera,et al.  There are no hydrological monsters, just models and observations with large uncertainties! , 2010 .

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

[44]  H. McMillan,et al.  Validation of a national hydrological model , 2016 .

[45]  G. Miguez-Macho,et al.  The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands , 2012 .

[46]  E. Sudicky,et al.  Hyper‐resolution global hydrological modelling: what is next? , 2015 .

[47]  R. Ibbitt,et al.  Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model , 2007 .

[48]  Sabine Attinger,et al.  Accelerating advances in continental domain hydrologic modeling , 2015 .

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

[50]  T. Oki,et al.  Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts , 2018, Environmental Research Letters.

[51]  Christopher Hutton,et al.  Most computational hydrology is not reproducible, so is it really science? , 2016, Water Resources Research.

[52]  Dmitri Kavetski,et al.  Elements of a flexible approach for conceptual hydrological modeling: 2. Application and experimental insights , 2011 .

[53]  Göran Lindström,et al.  Development and test of the distributed HBV-96 hydrological model , 1997 .

[54]  Jim Freer,et al.  Towards a limits of acceptability approach to the calibration of hydrological models : Extending observation error , 2009 .

[55]  H. Wheater,et al.  On inclusion of water resource management in Earth system models – Part 1: Problem definition and representation of water demand , 2014 .

[56]  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 .

[57]  Naota Hanasaki,et al.  Human–water interface in hydrological modelling : current status and future directions , 2017 .

[58]  Vijay P. Singh,et al.  The NWS River Forecast System - catchment modeling. , 1995 .

[59]  Luis Samaniego,et al.  Towards seamless large‐domain parameter estimation for hydrologic models , 2017 .

[60]  K. Beven,et al.  The in(a/tan/β) index:how to calculate it and how to use it within the topmodel framework , 1995 .

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

[62]  V. Bell,et al.  National-scale analysis of simulated hydrological droughts (1891–2015) , 2017 .

[63]  Dominik E. Reusser,et al.  Why can't we do better than Topmodel? , 2008 .

[64]  Dmitri Kavetski,et al.  Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models , 2011 .

[65]  C. Luce Runoff Prediction in Ungauged Basins: Synthesis Across Processes, Places and Scales , 2014 .

[66]  M. Ek,et al.  Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water , 2011 .

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

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

[69]  Eric F. Wood,et al.  HydroBlocks: a field‐scale resolving land surface model for application over continental extents , 2016 .

[70]  B. Arheimer,et al.  Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales , 2010 .

[71]  Keith Beven,et al.  Modelling the chloride signal at Plynlimon, Wales, using a modified dynamic TOPMODEL incorporating conservative chemical mixing (with uncertainty) , 2007 .

[72]  Daryl B. Simons,et al.  Nonlinear kinematic wave approximation for water routing , 1975 .

[73]  J. Freer,et al.  Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality , 2012 .

[74]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

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

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

[77]  Fang Zhao,et al.  Human impact parameterizations in global hydrological models improve estimates of monthly discharges and hydrological extremes: a multi-model validation study , 2018 .

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

[79]  P. Soille Optimal removal of spurious pits in grid digital elevation models , 2004 .

[80]  François Anctil,et al.  Impact of spatial aggregation of inputs and parameters on the efficiency of rainfall‐runoff models: A theoretical study using chimera watersheds , 2004 .

[81]  C. Perrin,et al.  Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments , 2001 .

[82]  Thorsten Wagener,et al.  Uncertainty in hydrological signatures for gauged and ungauged catchments , 2016 .

[83]  Jens Christian Refsgaard,et al.  Methodology for construction, calibration and validation of a national hydrological model for Denmark , 2003 .

[84]  E. Blyth,et al.  Climate hydrology and ecology research support system potential evapotranspiration dataset for Great Britain (1961-2017) [CHESS-PE] , 2020 .

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

[86]  Jan Seibert,et al.  Upper and lower benchmarks in hydrological modelling , 2018 .

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