Articles | Volume 24, issue 12
https://doi.org/10.5194/hess-24-5953-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-5953-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2020
Research article |
| 16 Dec 2020
| 16 Dec 2020
Flexible vector-based spatial configurations in land models
Shervan Gharari
CORRESPONDING AUTHOR
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta, Canada
Martyn P. Clark
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta, Canada
Naoki Mizukami
National Center for Atmospheric Research, Boulder, Colorado, USA
Wouter J. M. Knoben
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta, Canada
Jefferson S. Wong
Global Institute for Water Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Alain Pietroniro
Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada
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Cited articles
Beven, K. J.: Rainfall-runoff modelling: the primer, John Wiley and Sons, 2011.
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area
model of basin hydrology/Un modèle à base physique de zone d'appel
variable de l'hydrologie du bassin versant, Hydrolog. Sci. J.,
24, 43–69, 1979.
Beven, K. J., Cloke, H., Pappenberger, F., Lamb, R., and Hunter, N.:
Hyperresolution information and hyperresolution ignorance in modelling the
hydrology of the land surface, Sci. China Earth Sci., 58,
25–35, https://doi.org/10.1007/s11430-014-5003-4, 2015.
Blöschl, G., Grayson, R. B., and Sivapalan, M.: On the
representative elementary area (REA) concept and its utility for distributed
rainfall-runoff modelling, Hydrol. Process., 9, 313–330, 1995.
Chaney, N. W., Van Huijgevoort, M. H. J., Shevliakova, E., Malyshev, S., Milly, P. C. D., Gauthier, P. P. G., and Sulman, B. N.: Harnessing big data to rethink land heterogeneity in Earth system models, Hydrol. Earth Syst. Sci., 22, 3311–3330, https://doi.org/10.5194/hess-22-3311-2018, 2018.
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for process‐based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, 2498–2514, https://doi.org/10.1002/2015WR017198, 2015.
Clark, M. P., Schaefli, B., Schymanski, S. J., Samaniego, L., Luce, C. H., Jackson, B. M., Freer, J. E., Arnold, J. R., Moore, R. D., Istanbulluoglu, E., and Ceola, S.: Improving the theoretical underpinnings of process‐based hydrologic models, Water Resour. Res., 52, 2350–2365, https://doi.org/10.1002/2015WR017910, 2016.
Clark, M. P., Bierkens, M. F. P., Samaniego, L., Woods, R. A., Uijlenhoet, R., Bennett, K. E., Pauwels, V. R. N., Cai, X., Wood, A. W., and Peters-Lidard, C. D.: The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism, Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, 2017.
Dang, T. D., Chowdhury, A. F. M. K., and Galelli, S.: On the representation of water reservoir storage and operations in large-scale hydrological models: implications on model parameterization and climate change impact assessments, Hydrol. Earth Syst. Sci., 24, 397–416, https://doi.org/10.5194/hess-24-397-2020, 2020.
Demaria, E. M., Nijssen, B., and Wagener, T.: Monte Carlo sensitivity analysis
of land surface parameters using the Variable Infiltration Capacity model,
J. Geophys. Res.-Atmos., 112, D11113, https://doi.org/10.1029/2006JD007534, 2007.
Fischer, G., Nachtergaele, F., Prieler, S., van Velthuizen, H. T., Verelst, L., and Wiberg, D.: Global Agro-ecological Zones Assessment for Agriculture (GAEZ
2008), IIASA, Laxenburg, Austria and FAO, Rome, Italy, 2008.
Flügel, W. A.: 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, Hydrol.
Process., 9, 423–436, 1995.
Gharari, S., Hrachowitz, M., Fenicia, F., and Savenije, H. H. G.: Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment, Hydrol. Earth Syst. Sci., 15, 3275–3291, https://doi.org/10.5194/hess-15-3275-2011, 2011.
Gharari, S., Hrachowitz, M., Fenicia, F., Gao, H., and Savenije, H. H. G.: Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration, Hydrol. Earth Syst. Sci., 18, 4839–4859, https://doi.org/10.5194/hess-18-4839-2014, 2014.
Gharari, S., Clark, M., Mizukami, N., Wong, J. S., Pietroniro, A., and
Wheater, H.: Improving the representation of subsurface water movement
in land models, J. Hydrometeorol., 2401–2418, https://doi.org/10.1175/JHM-D-19-0108.1, 2019.
Gupta, H. V., Wagener, T., and Liu, Y.: Reconciling theory with observations: elements of a diagnostic approach to model evaluation, Hydrol. Process., 22, 3802–3813, https://doi.org/10.1002/hyp.6989, 2008.
Haddeland, I., Matheussen, B. V., and Lettenmaier, D. P.: Influence of
spatial resolution on simulated streamflow in a macroscale hydrologic model,
Water Resour. Res., 38, 29-1–29-10, https://doi.org/10.1029/2001WR000854, 2002.
Haddeland, I., Lettenmaier, D. P., and Skaugen, T.: Effects of irrigation on
the water and energy balances of the Colorado and Mekong river basins,
J. Hydrol., 324, 210–223, 2006.
Hrachowitz, M. and Clark, M. P.: HESS Opinions: The complementary merits of competing modelling philosophies in hydrology, Hydrol. Earth Syst. Sci., 21, 3953–3973, https://doi.org/10.5194/hess-21-3953-2017, 2017.
Jarvis, P. G.: The interpretation of the variations in leaf water potential
and stomatal conductance found in canopies in the field, Philos.
T. Roy. Soc. B,
273, 593–610, 1976.
Kalogirou, S.: Solar Energy Engineering, edited by: Kalogirou, S. A.,
Academic Press, https://doi.org/10.1016/B978-0-12-374501-9.00013-3, 2009.
Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006.
Kirkby, M. J. and Weyman, D. R.: Measurements of contributing area in very
small drainage basins, Department of Geography,
University of Bristol, Bristol, UK, 1974.
Knoben, W. J. M., Freer, J. E., Peel, M. C., Fowler, K. J. A., and Woods,
R. A.: A brief analysis of conceptual model structure uncertainty using 36
models and 559 catchments, Water Resour. Res., 56, e2019WR025975,
https://doi.org/10.1029/2019WR025975, 2020.
Knudsen, J., Thomsen, A., and Refsgaard, J. C.: WATBALA Semi-Distributed,
Physically Based Hydrological Modelling System, Hydrol. Res., 17,
347–362, https://doi.org/10.2166/nh.1986.0026, 1986.
Koster, R. D. and Suarez, M. J.: Modeling the land surface boundary in
climate models as a composite of independent vegetation stands, J.
Geophys. Res.-Atmos., 97, 2697–2715, 1992.
Kouwen, N., Soulis, E. D., Pietroniro, A., Donald, J., and Harrington, R. A.:
Grouped response units for distributed hydrologic modeling, J. Water
Res. Pl., 119, 289–305, 1993.
Latifovic, R., Zhu, Z., Cihlar, J., Giri, C., and Olthof, I.: Land cover
mapping of North and Central America – Global Land Cover 2000, Remote
Sens. Environ., 89, 116–127, https://doi.org/10.1016/j.rse.2003.11.002, 2004.
Lehner, B., Verdin, K., and Jarvis, A.: HydroSHEDS technical documentation,
version 1.0, World Wildlife Fund, Washington DC, USA, 1–27, 2006.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res.-Atmos.,
99, 14415–14428, 1994.
Liang, X., Guo, J., and Leung, L. R.: Assessment of the effects of spatial
resolutions on daily water flux simulations, J. Hydrol., 298,
287–310, 2004.
Liu, H., Tolson, B. A., Craig, J. R., and Shafii, M.: A priori discretization
error metrics for distributed hydrologic modeling applications, J. Hydrol., 543,
873–891, 2016.
Manabe, S.: Climate and the ocean circulation: I. The atmospheric
circulation and the hydrology of the earth's surface, Mon. Weather Rev.,
97, 739–774, 1969.
Marsh, C. B., Pomeroy, J. W., and Spiteri, R. J.: Implications of mountain
shading on calculating energy for snowmelt using unstructured triangular
meshes, Hydrol. Process., 26, 1767–1778, 2012.
Matott, L. S.: OSTRICH: An optimization software tool: Documentation and
users guide, University at Buffalo, Buffalo, NY, USA, 2005.
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015.
Melsen, L., Teuling, A., Torfs, P., Zappa, M., Mizukami, N., Clark, M., and Uijlenhoet, R.: Representation of spatial and temporal variability in large-domain hydrological models: case study for a mesoscale pre-Alpine basin, Hydrol. Earth Syst. Sci., 20, 2207–2226, https://doi.org/10.5194/hess-20-2207-2016, 2016.
Merz, R., Tarasova, L., and Basso, S.: Parameter's controls of distributed
catchment models – How much information is in conventional catchment
descriptors?, Water Resour. Res., 56, e2019WR026008, https://doi.org/10.1029/2019WR026008, 2020.
Mizukami, N., Clark, M. P., Sampson, K., Nijssen, B., Mao, Y., McMillan, H., Viger, R. J., Markstrom, S. L., Hay, L. E., Woods, R., Arnold, J. R., and Brekke, L. D.: mizuRoute version 1: a river network routing tool for a continental domain water resources applications, Geosci. Model Dev., 9, 2223–2238, https://doi.org/10.5194/gmd-9-2223-2016, 2016.
Naef, F., Scherrer, S., and Weiler, M.: A process based assessment of the
potential to reduce flood runoff by land use change, J. Hydrol.,
267, 74–79, 2002.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Newman, A. J., Clark, M. P., Winstral, A., Marks, D., and Seyfried, M.: The use
of similarity concepts to represent subgrid variability in land surface
models: Case study in a snowmelt-dominated watershed, J.
Hydrometeorol., 15, 1717–1738, 2014.
Nijssen, B., O'Donnell, G. M., Hamlet, A. F., and Lettenmaier, D. P.: Hydrologic
sensitivity of global rivers to climate change, Climatic Change, 50,
143–175, 2001.
Niu, G. Y., Yang, Z. L., Dickinson, R. E., and Gulden, L. E.: A simple
TOPMODEL-based runoff parameterization (SIMTOP) for use in global climate
models, J. Geophys. Res.-Atmos., 110, D21106, https://doi.org/10.1029/2005JD006111, 2005.
Olivera, F., Valenzuela, M., Srinivasan, R., Choi, J., Cho, H., Koka, S., and
Agrawal, A.: ArcGIS-SWAT: A Geodata Model and GIS Interface for SWAT,
J. Am. Water Resour. As., 42,
295–309, https://doi.org/10.1111/j.1752-1688.2006.tb03839.x, 2006.
Oudin, L., Kay, A., Andréassian, V., and Perrin, C.: Are seemingly
physically similar catchments truly hydrologically similar?, Water Resour,
Res, 46, W11558, https://doi.org/10.1029/2009WR008887, 2010.
Park, S. J. and Van De Giesen, N.: Soil-landscape delineation to define
spatial sampling domains for hillslope hydrology, J. Hydrol.,
295, 28–46, 2004.
Pietroniro, A., Fortin, V., Kouwen, N., Neal, C., Turcotte, R., Davison, B., Verseghy, D., Soulis, E. D., Caldwell, R., Evora, N., and Pellerin, P.: Development of the MESH modelling system for hydrological ensemble forecasting of the Laurentian Great Lakes at the regional scale, Hydrol. Earth Syst. Sci., 11, 1279–1294, https://doi.org/10.5194/hess-11-1279-2007, 2007.
Pigeon, K. E. and Jiskoot, H.: Meteorological controls on snowpack formation
and dynamics in the southern Canadian Rocky Mountains, Arct. Antarct.
Alp. Res., 40, 716–730, 2008.
Pitman, A. J.: The evolution of, and revolution in, land surface schemes
designed for climate models, Int. J. Climatol., 23, 479–510, 2003.
Rasmussen, R. and Liu, C.: High Resolution WRF Simulations of the Current
and Future Climate of North America, Research Data Archive at the National
Center for Atmospheric Research, Computational and Information Systems
Laboratory, https://doi.org/10.5065/D6V40SXP, 2017.
Reggiani, P., Hassanizadeh, S. M., Sivapalan, M., and Gray, W. G.: A unifying
framework for watershed thermodynamics: constitutive relationships, Adv.
Water Resour., 23, 15–39, 1999.
Rennó, C. D., Nobre, A. D., Cuartas, L. A., Soares, J. V., Hodnett, M. G.,
Tomasella, J., and Waterloo, M. J.: HAND, a new terrain descriptor using
SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia, Remote
Sens. Environ., 112, 3469–3481, 2008.
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter
regionalization of a grid-based hydrologic model at the mesoscale, Water
Resour, Res., 46, W05523, https://doi.org/10.1029/2008WR007327, 2010.
Samaniego, L., Kumar, R., Thober, S., Rakovec, O., Zink, M., Wanders, N., Eisner, S., Müller Schmied, H., Sutanudjaja, E. H., Warrach-Sagi, K., and Attinger, S.: Toward seamless hydrologic predictions across spatial scales, Hydrol. Earth Syst. Sci., 21, 4323–4346, https://doi.org/10.5194/hess-21-4323-2017, 2017.
Sarbu, I. and Sebarchievici, C.: Thermal Energy Storage, in: Solar Heating and Cooling Systems, edited by: Sarbu, I. and Sebarchievici, C., Academic Press, 99–138, https://doi.org/10.1016/B978-0-12-811662-3.01001-X, 2017.
Savenije, H. H. G.: HESS Opinions “Topography driven conceptual modelling (FLEX-Topo)”, Hydrol. Earth Syst. Sci., 14, 2681–2692, https://doi.org/10.5194/hess-14-2681-2010, 2010.
Shrestha, P., Sulis, M., Simmer, C., and Kollet, S.: Impacts of grid resolution on surface energy fluxes simulated with an integrated surface-groundwater flow model, Hydrol. Earth Syst. Sci., 19, 4317–4326, https://doi.org/10.5194/hess-19-4317-2015, 2015.
Singh, R. S., Reager, J. T., Miller, N. L. and Famiglietti, J. S.: Toward
hyper-resolution land-surface modeling: The effects of fine-scale topography
and soil texture on CLM 4.0 simulations over the S outhwestern US, Water
Resour. Res., 51, 2648–2667, 2015.
Troy, T. J., Wood, E. F., and Sheffield, J.: An efficient calibration method
for continental-scale land surface modeling, Water Resour. Res.,
44, W09411, https://doi.org/10.1029/2007WR006513, 2008.
Uhlenbrook, S., Roser, S., and Tilch, N.: Hydrological process representation
at the meso-scale: the potential of a distributed, conceptual catchment
model, J. Hydrol., 291, 278–296, 2004.
Vionnet, V., Fortin, V., Gaborit, E., Roy, G., Abrahamowicz, M., Gasset, N., and Pomeroy, J. W.: Assessing the factors governing the ability to predict late-spring flooding in cold-region mountain basins, Hydrol. Earth Syst. Sci., 24, 2141–2165, https://doi.org/10.5194/hess-24-2141-2020, 2020.
Vivoni, E. R., Ivanov, V. Y., Bras, R. L., and Entekhabi, D.:
Generation of triangulated irregular networks based on hydrological
similarity, J. Hydrol. Eng., 9, 288–302, 2004.
Winter, T. C.: The concept of hydrologic landscapes, J.
Am. Water Resour. As., 37, 335–349, 2001.
Wood, E. F., Sivapalan, M., Beven, K., and Band, L.: Effects of spatial
variability and scale with implications to hydrologic modeling, J.
Hydrol., 102, 29–47, 1988.
Wood, E. F., Roundy, J. K., Troy, T. J., Van Beek, L. P. H., Bierkens, M. F.,
Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., and Gochis, D.:
Hyperresolution global land surface modeling: Meeting a grand challenge for
monitoring Earth's terrestrial water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., and Pavelsky,
T.: MERIT Hydro: A high-resolution global hydrography map based on latest
topography datasets, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019WR024873, 2019.
Yassin, F., Razavi, S., Elshamy, M., Davison, B., Sapriza-Azuri, G., and Wheater, H.: Representation and improved parameterization of reservoir operation in hydrological and land-surface models, Hydrol. Earth Syst. Sci., 23, 3735–3764, https://doi.org/10.5194/hess-23-3735-2019, 2019.
Yoon, J.-H., Shoemaker, C. A.: Improved real-coded GA for groundwater
bioremediation, J. Comput. Civil Eng., 15, 224–231, 2001.
Zhao, R.-J., Zhang, Y.-L., Fang, L.-R., Liu, X.-R., and Zhang, Q.-S.: The Xinanjiang Model, Hydrological Forecasting Proceedings Oxford Symposium, IASH, 129, 351–356, 1980.
Short summary
This work explores the trade-off between the accuracy of the representation of geospatial data, such as land cover, soil type, and elevation zones, in a land (surface) model and its performance in the context of modeling. We used a vector-based setup instead of the commonly used grid-based setup to identify this trade-off. We also assessed the often neglected parameter uncertainty and its impact on the land model simulations.
This work explores the trade-off between the accuracy of the representation of geospatial data,...
