Toward computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme

Water Resources Research

Volumn 49, Issue 9, 2013, Pages 5700-5714

  Kumar, R ^a   Livneh, B ^b   Samaniego, L ^a  

^a HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

^b UNIVERSITY OF COLORADO   (United States)

Author keywords

downscaling; hydrologic model; MPR; PUB; upscaling

Indexed keywords

CALIBRATION; FORECASTING;

COMPUTATIONALLY EFFICIENT; DOWN-SCALING; GENERAL CIRCULATION MODEL; HYDROLOGIC MODELING; HYDROLOGIC SIMULATIONS; PARAMETER REGIONALIZATION; PREDICTIONS IN UNGAUGED BASINS; UPSCALING;

PARAMETER ESTIMATION;

CLIMATE EFFECT; GENERAL CIRCULATION MODEL; HYDROLOGICAL MODELING; LAND COVER; MASS TRANSFER; PARAMETERIZATION; SIMULATION;

CENTRAL EUROPE; NORTH AMERICA;

EID: 84883698414     PISSN: 00431397     EISSN: 19447973     Source Type: Journal    
DOI: 10.1002/wrcr.20431     Document Type: Article

References (63)

1. Abdulla, F., and, D. Lettenmaier, (1997), Development of regional parameter estimation equations for a macroscale hydrologic model, J. Hydrol., 197, 230-257. (Pubitemid 27335612)
2. Abdulla, F. A., D. P. Lettenmaier, E. F. Wood, and, J. A. Smith, (1996), Application of a macroscale hydrologic model to estimate the water balance of the Arkansas-Red River Basin, J. Geophys. Res., 101 (D3), 7449-7459.
3. Bergström, S., (1995), The HBV model. In:, Singh, V. P., (Ed.), Computer Models of Watershed Hydrology, Water Resour. Publ., Highlands Ranch, Colo., pp. 443-476.
4. Beven, K. J., and, H. L. Cloke, (2012), Comment on "Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water" by Eric F. Wood et al., Water Resour. Res., 48, W01801, doi: 10.1029/2011WR010982.
5. Boone, A., et al. (2004), The rhone-aggregation land surface scheme intercomparison project: An overview, J. Clim., 17 (1), 187-208. (Pubitemid 38272350)
6. Clark, M. P., A. G. Slater, D. E. Rupp, R. A. Woods, J. A. J. A. Vrugt, H. V. Gupta, T. Wagener, and, L. E. Hay, (2008), Framework for understanding structural errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, doi: 10.1029/2007WR006735.
7. Farnsworth, R. K., and, E. S. Thompson, (1982), Mean monthly, seasonal, and annual pan evaporation for the United States, NOAA Technical Report NWS 34, Washington, D. C., 82 p.
8. Fernandez, W., R. M. Vogel, and, A. Sankarasubramanian, (2000), Regional calibration of a watershed model, Hydrol. Sci. J., 45 (5), 689-707.
9. Gan, T. Y., and, S. J. Burges, (2006), Assessment of soil-based and calibrated parameters of the Sacramento model and parameter transferability, J. Hydrol., 320 (1-2), 117-131.
10. Götzinger, J., and, A. Bárdossy, (2007), Comparison of four regionalization methods for a distributed hydrological model, J. Hydrol., 333, 374-384. (Pubitemid 46053966)
11. Haddeland, I., B. V. Matheussen, and, D. P. Lettenmaier, (2002), Influence of spatial resolution on simulated streamflow in a macroscale hydrologic model, Water Resour. Res., 38 (7), 1124, doi: 10.1029/2001WR000854.
12. Hargreaves, G. H., and, Z. A. Samani, (1985), Reference crop evapotranspiration from temperature, Appl. Eng. Agric., 1, 96-99.
13. Hundecha, Y., and, A. Bárdossy, (2004), Modeling effect of land use changes on runoff generation of a river basin through parameter regionalization of a watershed model, J. Hydrol., 292, 281-295. (Pubitemid 38583612)
14. Hundecha, Y., T. B. M. J. Ouarda, and, A. Bárdossy, (2008), Regional estimation of parameters of a rainfall-runoff model at ungauged watersheds using the spatial structures of the parameters within a canonical physiographic-climatic space, Water Resour. Res., 44, W01427, doi: 10.1029/2006WR005439.
15. Jin, H., D. Jespersen, P. Mehrotra, R. Biswas, L. Huang, and, B. Chapman, (2011), High performance computing using MPI and OpenMP on multi-core parallel systems, Parallel Comput., 37 (9), 562-575.
16. Koren, V., R. Finnerty, J. Schaake, M. Smith, D. Seo, and, Q. Duan, (1999), Scale dependencies of hydrologic models to spatial variability of precipitation, J. Hydrol., 217, 285-302. (Pubitemid 29207819)
17. Koren, V. I., M. Smith, D. Wang, and, Z. Zhang, (2000), Use of soil property data in the derivation of conceptual rainfall-runoff model parameters, in 15th Conference on Hydrology, pp. 103-106, Am. Meteorol. Soc., Long Beach, Calif.
18. Kumar, R., L. Samaniego, and, S. Attinger, (2010), Effect of the spatial discretization and model regionalization on the prediction of seasonal runoff characteristics, J. Hydrol., 392, 54-69.
19. Kumar, R., L. Samaniego, and, S. Attinger, (2013), Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, 1-20, doi: 10.1029/2012WR012195.
20. Liang, X., J. Guo, and, L. R. Leung, (2004), Assessment of the effects of spatial resolutions on daily water flux simulations, J. Hydrol., 298, 287-310. (Pubitemid 39098777)
21. Livneh, B., E. A. Rosenberg, C. Lin, V. Mishra, K. M. Andreadis, E. P. Maurer, and D. P. Lettenmaier (2013), Extension and spatial refinement of a long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States, J. Clim., doi: 10.1175/JCLI-D-12-00508.1 (in press).
22. Livneh, B., and, D. P. Lettenmaier, (2012), Multi-criteria parameter estimation for the Unified Land Model, Hydrol. Earth Syst. Sci., 16 (8), 3029-3048.
23. Lohmann, D., et al. (1998), The project for intercomparison of land-surface parameterization schemes (PILPS) phase 2(c) Red-Arkansas River basin experiment: 3. Spatial and temporal analysis of water fluxes, Global Planet. Change, 19 (1-4), 161-179. (Pubitemid 28490041)
24. Lohmann, D., et al. (2004), Streamflow and water balance intercomparisons of four land surface models in the North American Land Data Assimilation System project, J. Geophys. Res., 109, D07S91, doi: 10.1029/2003JD003517.
25. Martinez, G., and, H. Gupta, (2010), Toward improved identification of hydrological models: A diagnostic evaluation of the "abcd" monthly water balance model for the conterminous United States, Water Resour. Res, 46, W08507, doi: 10.1029/2009WR008294.
26. Maurer, E., A. Wood, J. Adam, D. Lettenmaier, and, B. Nijssen, (2002), A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States, J. Clim., 15 (22), 3237-3251. (Pubitemid 36006341)
27. Merz, R., and, G. Blöschl, (2004), Regionalization of catchment model parameters, J. Hydrol., 287 (1-4), 95-123.
28. Moriasi, D., J. Arnold, M. Van Liew, R. Bingner, R. Harmel, and, T. Veith, (2007), Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Trans. ASABE, 50 (3), 885-900. (Pubitemid 47074809)
29. Nash, J. E., and, J. V. Sutcliffe, (1970), River flow forecasting through conceptual models, 1. A discussion of principles, J. Hydrol., 10, 282-290.
30. Nijssen, B., G. M. O'Donnell, D. P. Lettenmaier, D. Lohmann, and, E. F. Wood, (2001), Predicting the discharge of global rivers, J. Clim., 14 (15), 3307-3323. (Pubitemid 33053101)
31. Oudin, L., V. Andréassian, C. Perrin, C. Michel, and, N. Le Moine, (2008), Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments, Water Resour. Res., 44, W03413, doi: 10.1029/2007WR006240.
32. Parajka, J., R. Merz, and, G. Blöschl, (2005), A comparison of regionalization methods for catchment model parameters, Hydrol. Earth Syst. Sci., 9 (3), 157-171. (Pubitemid 41555703)
33. Parajka, J., G. Blöschl, and, R. Merz, (2007), Regional calibration of catchment models: Potential for ungauged catchments, Water Resour. Res., 43, W06406, doi: 10.1029/2006WR005271.
34. Parajka, J., A. Viglione, M. Rogger, J. L. Salinas, M. Sivapalan, and, G. Blöschl, (2013), Comparative assessment of predictions in ungauged basins-Part 1: Runoff-hydrograph studies, Hydrol. Earth Syst. Sci., 17 (5), 1783-1795.
35. Pokhrel, P., H. V. Gupta, and, T. Wagener, (2008), A spatial regularization approach to parameter estimation for a distributed watershed model, Water Resour. Res., 44, W12419, doi: 10.1029/2007WR006615.
36. Samaniego, L., A. Bárdossy, and, K. Schulz, (2008), Supervised classification of remotely sensed imagery using a modified k-NN technique, IEEE Trans. Geosci. Remote Sens., 46 (7), 2112-2125.
37. Samaniego, L., A. Bárdossy, and, R. Kumar, (2010a), Streamflow prediction in ungauged catchments using copula-based dissimilarity measures, Water Resour. Res., 46, W02506, doi: 10.1029/2008WR007695.
38. Samaniego, L., R. Kumar, and, S. Attinger, (2010b), Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523, doi: 10.1029/2008WR007327.
39. Samaniego, L., R. Kumar, and, M. Zink, (2013), Implications of parameter uncertainty on soil moisture drought analysis in Germany, J. Hydrometeorol., 14, 47-68.
40. Santhi, C., N. Kannan, J. G. Arnold, and, M. D. Luzio, (2008), Spatial calibration and temporal validation of flow for regional scale hydrologic modeling, J. Am. Water Resour. Assoc., 44(4), 829- 846.
41. Schaake, J., S. Cong, and, Q. Duan, (2006), The US MOPEX data set, in Large Sample Basin Experiments for Hydrological Model Parameterization: Results of the Model Parameter Experiment-MOPEX, edited by, V. Andreassian, et al., IAHS Publ., 307, 9-28. (Pubitemid 44935642)
42. Seibert, J., (1999), Regionalization of parameters for a conceptual rainfall-runoff model, Agric. For. Meteorol., 98-99, 279-293. (Pubitemid 30071119)
43. Singh, S. K., A. Bárdossy, J. Götzinger, and, K. P. Sudheer, (2012), Effect of spatial resolution on regionalization of hydrological model parameters, Hydrol. Processes, 26, 3499-3509.
44. Sivapalan, M., (2003), Prediction in ungauged basins: A grand challenge for theoretical hydrology, Hydrol. Processes, 17 (15), 3163-3170.
45. Smith, M., D. J. Seo, V. Koren, S. Reed, Z. Zhang, Q. Duan, F. Moreda, and, S. Cong, (2004), The distributed model intercomparison project (DMIP): Motivation and experiment design, J. Hydrol., 298 (1-4), 4-26. (Pubitemid 39098766)
46. Smith, M., et al. (2012), Results of the DMIP 2 Oklahoma experiments, J. Hydrol., 418-419, 17-48.
47. Stöckli, R., P. L. Vidale, A. Boone, and, C. Schär, (2007), Impact of scale and aggregation on the terrestrial water exchange: Integrating land surface models and Rhone catchment observations, J. Hydrometeorol., 8 (5), 1002-1015. (Pubitemid 350148660)
48. Troy, T. J., E. F. Wood, and, J. Sheffield, (2008), An efficient calibration method for continental-scale land surface modeling, Water Resour. Res., 44, W09411, doi: 10.1029/2007WR006513.
49. Vandewiele, G., and, A. Elias, (1995), Monthly water balance of ungauged catchments obtained by geographical regionalization, J. Hydrol., 170 (1-4), 277-291. (Pubitemid 26441735)
50. Viviroli, D., H. Mittelbach, J. Gurtz, and, R. Weingartner, (2009), Continuous simulation for flood estimation in ungauged mesoscale catchments of switzerland-Part ii: Parameter regionalisation and flood estimation results, J. Hydrol., 377 (1-2), 208-225.
51. Wagener, T., and, H. S. Wheater, (2006), Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty, J. Hydrol., 320 (1-2), 132-154.
52. Wagener, T., M. Sivapalan, P. Troch, and, R. Woods, (2007), Catchment classification and hydrologic similarity, Geogr. Compass, 1 (4), 901-931.
53. Wang, A., K. Li, and, D. Lettenmaier, (2008), Integration of the variable infiltration capacity model soil hydrology scheme into the community land model, J. Geophys. Res., 113, D09111, doi: 10.1029/2007JD009246.
54. Winsemius, H. C., B. Schaefli, A. Montanari, and, H. H. G. Savenije, (2009), On the calibration of hydrological models in ungauged basins: A framework for integrating hard and soft hydrological information, Water Resour. Res., 45, W12422, doi: 10.1029/2009WR007706.
55. Xia, Y., et al. (2012a), Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow, J. Geophys. Res., 117, D03110, doi: 10.1029/2011JD016051.
56. Xia, Y., et al. (2012b), Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res., 117, D03109, doi: 10.1029/2011JD016048.
57. Yadav, M., T. Wagener, and, H. Gupta, (2007), Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins, Adv. Water Res., 30 (8), 1756-1774. (Pubitemid 46712120)
58. Yilmaz, K., H. V. Gupta, and, T. Wagener, (2008), A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resour. Res., 44, W09417, doi: 10.1029/2007WR006716.
59. Zacharias, S., and, G. Wessolek, (2007), Excluding organic matter content from pedotransfer predictors of soil water retention, Soil. Sci. Soc. Am. J., 71 (1), 43-50. (Pubitemid 46203481)
60. Zhang, Y., and, F. H. S. Chiew, (2009), Relative merits of different methods for runoff predictions in ungauged catchments, Water Resour. Res., 45, W07412, doi: 10.1029/2008WR007504.
61. Zhang, Z., T. Wagener, P. Reed, and, R. Bhushan, (2008), Reducing uncertainty in predictions in ungauged basins by combining hydrologic indices regionalization and multiobjective optimization, Water Resour. Res, 44, W00B04, doi: 10.1029/2008WR006833.
62. Zhao, F., L. Zhang, F. H. Chiew, J. Vaze, and, L. Cheng, (2013), The effect of spatial rainfall variability on water balance modelling for south-eastern Australian catchments, J. Hydrol., 493, 16-29.
63. Zhu, J., and, B. Mohanty, (2002), Spatial averaging of van Genuchten hydraulic parameters for steady-state flow in heterogeneous soils: A numerical study, Vadose Zone J., 1 (2), 261-272.