Access the full text.
Sign up today, get DeepDyve free for 14 days.
M.C. Blake, A.S. Jayko, R.J. McLaughlin (1985)
Tectonostratigraphic Terranes of the Circumpacific Region. Circumpacific Council for Energy and Mineral Resources
X. Sun, S. Roberts, B. Croke, A. Jakeman (2017)
A comparison of global sensitivity techniques and sampling method
H. Gupta, H. Kling, K. Yilmaz, G. Martinez (2009)
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modellingJournal of Hydrology, 377
M. Werner, J. Schellekens, P. Gijsbers, M. Dijk, O. Akker, K. Heynert (2013)
The Delft-FEWS flow forecasting systemEnviron. Model. Softw., 40
Chaopeng Shen, M. Phanikumar (2010)
A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface couplingAdvances in Water Resources, 33
B. Vincendon, V. Ducrocq, G. Saulnier, L. Bouilloud, K. Chancibault, F. Habets, J. Noilhan (2010)
Benefit of coupling the ISBA land surface model with a TOPMODEL hydrological model version dedicated to Mediterranean flash-floodsJournal of Hydrology, 394
Xiaosong Zhao, Yao Huang (2015)
A Comparison of Three Gap Filling Techniques for Eddy Covariance Net Carbon Fluxes in Short Vegetation EcosystemsAdvances in Meteorology, 2015
Y. Gan, Xin‐Zhong Liang, Q. Duan, Fei Chen, Jianduo Li, Yu Zhang (2018)
Assessment and Reduction of the Physical Parameterization Uncertainty for Noah‐MP Land Surface ModelWater Resources Research, 55
M. Stieglitz, D. Rind, J. Famiglietti, C. Rosenzweig (1997)
An Efficient Approach to Modeling the Topographic Control of Surface Hydrology for Regional and Global Climate ModelingJournal of Climate, 10
B. Decharme, H. Douville (2006)
Introduction of a sub-grid hydrology in the ISBA land surface modelClimate Dynamics, 26
M. Clark, A. Slater, D. Rupp, R. Woods, J. Vrugt, H. Gupta, Thorsten Wagener, L. Hay (2008)
Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological modelsWater Resources Research, 44
H. Bilal, S. Black (2006)
QAOOSE 2006 Proceedings: 10th ECOOP Workshop on Quantitative Approaches in Object‐Oriented Software Engineering, 3 July 2006 ‐ Nantes, France
A. Senatore, G. Mendicino, D. Gochis, Wei Yu, D. Yates, H. Kunstmann (2015)
Fully coupled atmosphere‐hydrology simulations for the central Mediterranean: Impact of enhanced hydrological parameterization for short and long time scalesJournal of Advances in Modeling Earth Systems, 7
Haider Bilal, S. Black (2006)
Computing ripple effect for object oriented software
L. Bouilloud, K. Chancibault, B. Vincendon, V. Ducrocq, F. Habets, G. Saulnier, S. Anquetin, E. Martin, J. Noilhan (2010)
Coupling the ISBA Land Surface Model and the TOPMODEL Hydrological Model for Mediterranean Flash-Flood Forecasting: Description, Calibration, and ValidationJournal of Hydrometeorology, 11
H. McMillan, I. Westerberg, F. Branger (2017)
Five guidelines for selecting hydrological signaturesHydrological Processes, 31
Robert, L., Walko, Larry, E., Band, J. Baron, Timothy, F. G., Kittel, R. Lammers, Tsengdar, J., Lee, D. Ojima, C. Taylor, C. Tague, Craig, Tremback (2000)
Coupled Atmosphere–Biophysics–Hydrology Models for Environmental ModelingJournal of Applied Meteorology, 39
J. Schaake, V. Koren, Q. Duan, K. Mitchell, Fei Chen (1996)
Simple water balance model for estimating runoff at different spatial and temporal scalesJournal of Geophysical Research, 101
N. Fenton, A. Melton (1990)
Deriving structurally based software measuresJ. Syst. Softw., 12
M. Clark, Bart Nijssen, J. Lundquist, D. Kavetski, D. Rupp, R. Woods, J. Freer, E. Gutmann, A. Wood, J. Arnold, L. Brekke, D. Gochis, R. Rasmussen, D. Tarboton, V. Mahat, G. Flerchinger, D. Marks (2015)
The structure for unifying multiple modeling alternatives (SUMMA), Version 1.0: Technical Description
G. Niu, Zong‐Liang Yang, R. Dickinson, Lindsey Gulden (2005)
A simple TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate modelsJournal of Geophysical Research, 110
(2015)
Until Further Notice, Please Cite the WRF-Hydro Modeling System as Follows: WRF-Hydro V5 Technical Description
K.J. Beven, R. Lamb, P.F. Quinn, R. Romanowicz, J.E. Freer (1995)
Computer Models of Watershed Hydrology
Jarallah AlGhamdi (2007)
Measuring software coupling, 6
M. Garrick, C. Cunnane, J. Nash (1978)
A criterion of efficiency for rainfall-runoff modelsJournal of Hydrology, 36
K. Beven (1984)
Infiltration into a class of vertically non-uniform soilsHydrological Sciences Journal-journal Des Sciences Hydrologiques, 29
H. McMillan, M. Clark, R. Woods, M. Duncan, Srinivasan, A. Western, D. Goodrich (2009)
Improving perceptual and conceptual hydrological models using data from small basinsIAHS-AISH publication, 336
T. Lahmers, C. Castro, P. Hazenberg (2020)
Effects of Lateral Flow on the Convective Environment in a Coupled Hydrometeorological Modeling System in a Semiarid EnvironmentJournal of Hydrometeorology
N. Chaney, Peter Metcalfe, E. Wood (2016)
HydroBlocks: a field‐scale resolving land surface model for application over continental extentsHydrological Processes, 30
A. Givati, D. Gochis, Thomas Rummler, H. Kunstmann (2016)
Comparing One-Way and Two-Way Coupled Hydrometeorological Forecasting Systems for Flood Forecasting in the Mediterranean RegionHydrology, 3
R. Moore (1985)
The probability-distributed principle and runoff production at point and basin scalesHydrological Sciences Journal-journal Des Sciences Hydrologiques, 30
(2015)
Rwrfhydro
K. Beven, M. Kirkby, J. Freer, R. Lamb (2020)
A history of TOPMODELHydrology and Earth System Sciences
R. Cronshey, R. Roberts, N. Miller (1985)
Urban Hydrology for Small Watersheds (TR-55 Rev.)Hydraulics and Hydrology in the Small Computer Age
K. Beven (1997)
TOPMODEL : a critique.Hydrological Processes, 11
G. Coxon, J. Freer, Rosanna Lane, Toby Dunne, W. Knoben, N. Howden, Niall Quinn, T. Wagener, R. Woods (2018)
DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRologyGeoscientific Model Development
L. Hay, M. Clark, M. Pagowski, G. Leavesley, W. Gutowski (2006)
One-Way Coupling of an Atmospheric and a Hydrologic Model in ColoradoJournal of Hydrometeorology, 7
F. Habets, G. Saulnier (2001)
Subgrid runoff parameterizationPhysics and Chemistry of The Earth Part B-hydrology Oceans and Atmosphere, 26
R. Mcmanamay, C. Derolph (2019)
A stream classification system for the conterminous United StatesScientific Data, 6
(1975)
Hydrograph Modelling Strategies.
R. McLaughlin, S. Ellen, M. Blake, A. Jayko, W. Irwin, K. Aalto, G. Carver, S. Clarke, J. Barnes, J. Cecil, K. Cyr (2000)
Geology of the Cape Mendocino, Eureka, Garberville, and Southwestern Part of the Hayfork 30 x 60 Minute Quadrangles and Adjacent Offshore Area, Northern CaliforniaMiscellaneous Field Studies Map
S. Gharari, M. Clark, N. Mizukami, Jefferson Wong, A. Pietroniro, H. Wheater (2019)
Improving the Representation of Subsurface Water Movement in Land ModelsJournal of Hydrometeorology, 20
S. Gharari, M. Clark, N. Mizukami, W. Knoben, Jefferson Wong, A. Pietroniro (2020)
Flexible vector-based spatial configurations in land modelsHydrology and Earth System Sciences
H. McMillan, T. Krueger, J. Freer (2012)
Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water qualityHydrological Processes, 26
Huidae Cho, Jeongha Park, Dongkyun Kim (2019)
Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODELWater
A. Sigdel, R. Jha, Dhruba Bhatta, R. Abou-Shanab, V. Sapireddy, Byong-hun Jeon (2011)
Applicability of TOPMODEL in the Catchments of Nepal: Bagmati River BasinGeosystem Engineering, 14
J. Seibert, J. Seibert, M. Vis, E. Lewis, H. Meerveld (2018)
Upper and lower benchmarks in hydrological modellingHydrological Processes, 32
D. Moriasi, J. Arnold, M. Liew, R. Bingner, R. Harmel, T. Veith (2007)
Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed SimulationsTransactions of the ASABE, 50
X. Jin, Chong-yu Xu, Qi Zhang, V. Singh (2010)
Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological modelJournal of Hydrology, 383
Doreen Meier (2016)
Structured Design Fundamentals Of A Discipline Of Computer Program And Systems Design
J. Refsgaard, J. Sluijs, James Brown, P. Keur (2004)
A framework for dealing with uncertainty due to model structure errorAdvances in Water Resources, 29
P. Mendoza, M. Clark, M. Barlage, B. Rajagopalan, L. Samaniego, G. Abramowitz, H. Gupta (2015)
Are we unnecessarily constraining the agility of complex process‐based models?Water Resources Research, 51
M. Blake, A. Jayko, R. McLaughlin (1985)
Tectonostratigraphic terranes of northern California, 1
Patrice Yapo, H. Gupta, S. Sorooshian (1996)
Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration dataJournal of Hydrology, 181
(2006)
We, therefore, propose that coupling method uncertainty should be considered as a major source of model structural uncertainty when developing model coupling options
H. Gupta, S. Sorooshian, Patrice Yapo (1998)
Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of informationWater Resources Research, 34
D. Rempe, W. Dietrich (2018)
Direct observations of rock moisture, a hidden component of the hydrologic cycleProceedings of the National Academy of Sciences, 115
J. Arnault, S. Wagner, Thomas Rummler, B. Fersch, J. Bliefernicht, Sabine Andresen, H. Kunstmann (2015)
Role of Runoff–Infiltration Partitioning and Resolved Overland Flow on Land–Atmosphere Feedbacks: A Case Study with the WRF-Hydro Coupled Modeling System for West AfricaJournal of Hydrometeorology, 17
A. Offutt, M. Harrold, Priyadarshan Kolte (1993)
A software metric system for module couplingJ. Syst. Softw., 20
H. McMillan (2020)
Linking hydrologic signatures to hydrologic processes: A reviewHydrological Processes, 34
O. Conrad, B. Bechtel, M. Bock, Helge Dietrich, E. Fischer, L. Gerlitz, J. Wehberg, V. Wichmann, J. Böhner (2015)
System for Automated Geoscientific Analyses (SAGA) v. 2.1.4Geoscientific Model Development Discussions, 8
M. Kirkby, K. Beven (1979)
A physically based, variable contributing area model of basin hydrology, 24
Meilir Page-Jones (1980)
The practical guide to structured systems design
Mostaquimur Rahman, R. Rosolem (2017)
Towards a simple representation of chalk hydrology in land surface modellingHydrology and Earth System Sciences, 21
M. Sadegh, J. Vrugt (2013)
Approximate Bayesian Computation in hydrologic modeling: equifinality of formal and informal approachesHydrology and Earth System Sciences Discussions, 10
N. Mölders (2016)
Concepts for coupling hydrological and meteorological models
(1974)
Measurements of Contributing Area in Very Small Drainage Basins.
Patrice Yapo, H. Gupta, S. Sorooshian (1998)
Multi-objective global optimization for hydrologic modelsJournal of Hydrology, 204
J. Stedinger, R. Vogel, Seung Lee, R. Batchelder (2008)
Appraisal of the generalized likelihood uncertainty estimation (GLUE) methodWater Resources Research, 44
J. Nossent, W. Bauwens (2012)
Application of a normalized Nash-Sutcliffe efficiency to improve the accuracy of the Sobol' sensitivity analysis of a hydrological model
T. Mathevet, C. Michel, V. Andréassian, C. Perrin (2006)
A bounded version of the Nash-Sutcliffe criterion for better model assessment on large sets of basinsIAHS-AISH publication
D. Tarboton, D. Watson, R. Wallace, K. Schreuders, Jeremy Neff (2001)
Terrain Analysis Using Digital Elevation Models
Andrea Saltelli, P. Annoni, I. Azzini, F. Campolongo, M. Ratto, S. Tarantola (2010)
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity indexComput. Phys. Commun., 181
Donald Johnson, N. Frazier, F. Ogden, Shengting Cui, D. Blodgett, J. Hughes, P. Norton, R. Cabell (2019)
Next Generation National Water Model Architecture: Organizing principles to support evolving capabilities.
K. Beven, I. Westerberg (2011)
On red herrings and real herrings: disinformation and information in hydrological inferenceHydrological Processes, 25
K. Beven, A. Binley (1992)
The future of distributed models: model calibration and uncertainty prediction.Hydrological Processes, 6
(2000)
GIS Hydrological Modeling System by Using Programming Interface of GRASS
D. Legates, G. McCabe (1999)
Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validationWater Resources Research, 35
G. Niu, Zong‐Liang Yang, R. Dickinson, Lindsey Gulden, H. Su (2007)
Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment dataJournal of Geophysical Research, 112
E. Moges, Y. Demissie, Hongyi Li (2020)
Uncertainty propagation in coupled hydrological models using winding stairs and null-space Monte Carlo methodsJournal of Hydrology, 589
M. Clark, Ying Fan, D. Lawrence, J. Adam, D. Bolster, D. Gochis, R. Hooper, Mukesh Kumar, L. Leung, D. Mackay, R. Maxwell, Chaopeng Shen, S. Swenson, X. Zeng (2015)
Improving the representation of hydrologic processes in Earth System ModelsWater Resources Research, 51
Ying Fan, M. Clark, D. Lawrence, S. Swenson, L. Band, S. Brantley, P. Brooks, W. Dietrich, A. Flores, G. Grant, J. Kirchner, D. Mackay, J. McDonnell, P. Milly, P. Sullivan, C. Tague, H. Ajami, N. Chaney, A. Hartmann, P. Hazenberg, J. McNamara, J. Pelletier, J. Perket, E. Rouholahnejad-Freund, T. Wagener, X. Zeng, E. Beighley, J. Buzan, Maoyi Huang, B. Livneh, B. Mohanty, Bart Nijssen, M. Safeeq, M. Safeeq, Chaopeng Shen, W. Verseveld, J. Volk, Dai Yamazaki (2019)
Hillslope Hydrology in Global Change Research and Earth System ModelingWater Resources Research, 55
P. Mantovan, E. Todini (2006)
Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodologyJournal of Hydrology, 330
S. Archfield, M. Clark, B. Arheimer, L. Hay, H. McMillan, J. Kiang, J. Seibert, K. Hakala, A. Bock, Thorsten Wagener, W. Farmer, V. Andréassian, S. Attinger, A. Viglione, R. Knight, S. Markstrom, T. Over (2015)
Accelerating advances in continental domain hydrologic modelingWater Resources Research, 51
J. Jeziorska, T. Niedzielski (2018)
Applicability of TOPMODEL in the mountainous catchments in the upper Nysa Kłodzka river basin (SW Poland)Acta Geophysica, 66
V. Andréassian, A. Hall, N. Chahinian, J. Schaake (2006)
Large Sample Basin Experiments for Hydrological Model Parameterization: Results of the Model Parameter Experiment — MOPEXAustralasian Journal of Water Resources, 11
Huiping Deng, Shufen Sun (2012)
Incorporation of TOPMODEL into land surface model SSiB and numerically testing the effects of the corporation at basin scaleScience China Earth Sciences, 55
Z. Nan, Lele Shu, Yanbo Zhao, Xin Li, Yong-jian Ding (2011)
Integrated modeling environment and a preliminary application on the Heihe River Basin, ChinaScience China Technological Sciences, 54
Buytaert W. (2011)
Topmodel: Implementation of the Hydrological Model TOPMODEL in RGlobal Change Biology, 13
Z. Xue, D. Gochis, Wei Yu, B. Keim, R. Rohli, Zhengchen Zang, K. Sampson, A. Dugger, D. Sathiaraj, Qian Ge (2018)
Modeling Hydroclimatic Change in Southwest Louisiana RiversWater, 10
P. Seaber, F. Kapinos, G. Knapp (1987)
Hydrologic unit maps
G. Myers (1975)
Reliable software through composite design
M. Morita, B. Yen (2000)
Numerical methods for conjunctive two-dimensional surface and three-dimensional sub-surface flowsInternational Journal for Numerical Methods in Fluids, 32
B. Hurk, M. Best, P. Dirmeyer, A. Pitman, J. Polcher, J. Santanello (2011)
Acceleration of Land Surface Model Development over a Decade of GlassBulletin of the American Meteorological Society, 92
K. Loague (2010)
Rainfall-Runoff Modelling
M. Butts, J. Payne, M. Kristensen, H. Madsen (2004)
An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulationJournal of Hydrology, 298
P. Nguyen, A. Thorstensen, S. Sorooshian, K. Hsu, A. Aghakouchak, B. Sanders, V. Koren, Z. Cui, Michael Smith (2016)
A high resolution coupled hydrologic-hydraulic model (HiResFlood-UCI) for flash flood modelingJournal of Hydrology, 541
J. Vrugt, C. Braak, H. Gupta, B. Robinson (2009)
Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?Stochastic Environmental Research and Risk Assessment, 23
Beven K.J. (1977)
Towards a Simple, Physically Based, Variable Contributing Area Model of Catchment HydrologyBulletin of the International Association of Scientific Hydrology, 24
W. Knoben, J. Freer, K. Fowler, M. Peel, R. Woods (2019)
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulationsGeoscientific Model Development
M. Jansen (1999)
Analysis of variance designs for model outputComputer Physics Communications, 117
J. Linton (2008)
Is the Hydrologic Cycle Sustainable? A Historical–Geographical Critique of a Modern ConceptAnnals of the Association of American Geographers, 98
J. Nash, J. Sutcliffe (1970)
River flow forecasting through conceptual models part I — A discussion of principles☆Journal of Hydrology, 10
Wei Zhao, Ainong Li (2015)
A Review on Land Surface Processes Modelling over Complex TerrainAdvances in Meteorology, 2015
I. Sobol (1967)
On the distribution of points in a cube and the approximate evaluation of integralsUssr Computational Mathematics and Mathematical Physics, 7
Beven K. (1984)
Infiltration into a Class of Vertically Non‐Uniform SoilsTaylor & Francis, 29
(2019)
CUAHSI/Nwm_subsetting: CUAHSI Subsetter.
The study had two objectives; (1) Substitute National Water Model’s (NWM) runoff calculation with a conceptual hydrologic model (TOPography‐based hydrological MODEL [TOPMODEL]) to simplify the model structure and resolve potential drawbacks of applying NWM in headwater catchments. (2) Investigate how varying the coupling interface (location of coupling, type of fluxes used, modification of sub‐models) affects model behavior of when one‐way coupling the NWM’s land surface model (LSM; Noah‐Multi Parameterization) with TOPMODEL using six different scenarios. The one‐way coupled model outperformed NWM and noncoupled TOPMODEL. The coupling option limiting reliance on LSM’s surface and subsurface water fluxes by constraining them within the TOPMODEL structure was the most successful. Performance declined when coupling configurations relied more on LSM calculated fluxes to override TOPMODEL internal processes. Varying the coupling interface brought unexpected changes in TOPMODEL’s parameter sensitivity and water budget even while the statistical score remained similar. The coupling interface represents a source of structural uncertainty that could be identified through conventional evaluation of performance, uncertainty, and sensitivity due to the simple structure of our one‐way coupling design. The study shows that the benefits of combining the strengths of land surface and conceptual hydrological models, while recognizing that structural uncertainty from coupling design needs to be acknowledged.
Journal of the American Water Resources Association – Wiley
Published: Feb 1, 2022
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.