Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-27T12:59:29.388Z Has data issue: false hasContentIssue false

19 - Contribution of Hydrological Model Calibration Uncertainty to Future Hydrological Projections over Various Temporal Scales

A Case Study in the Boulder Creek Watershed

from Part III - Sustainable Water Management under Future Uncertainty

Published online by Cambridge University Press:  17 March 2022

By
Qinghuan Zhang ,
Qiuhong Tang ,
Guoyong Leng  and
Seyed-Mohammad Hosseini-Moghari
Edited by
Qiuhong Tang  and
Guoyong Leng
Qiuhong Tang
Affiliation:
Chinese Academy of Sciences, Beijing
Guoyong Leng
Affiliation:
Oxford University Centre for the Environment
Get access

Summary

In this study, we applied a multi-objective calibration approach to select a group of best performing parameter sets for the Variable Infiltration Capacity (VIC) model in the Boulder Creek Watershed, USA. We specifically applied 16 non-dominated parameter sets to simulate hydrologic variables, including streamflow (Q), evapotranspiration (ET) and soil moisture (SM) in two future phases (Phase 1: 2040–2069; Phase 2: 2070–2099). Relative to the historical period, Q and ET increased, and SM decreased. The magnitude of change was greater in Phase 2 than in Phase 1 for both ET (+19.7 per cent) and SM (-5.4 per cent). We found that the model calibration resultant parameter uncertainty could lead to a reversal of the change sign of annual Q during Phase 2. The uncertainty resulting from model calibration was up to 4.3 per cent and 19.6 per cent at the annual and monthly scales, respectively. Seasonally, uncertainty reached the highest levels during the spring snowmelt runoff period between February and May for Q and SM, and during the summer months for ET. These results suggest that the use of a single parameter set may yield substantial bias for hydrological projections, and more efforts should be devoted to constraining the model calibration uncertainty to enable effective water resources decision-making.

Keywords

Hydrologic modellingBoulder Creek WatershedUncertainty analysisClimate dataModel parameters
Type
Chapter
Information
Climate Risk and Sustainable Water Management , pp. 420 - 444
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abatzoglou, J. T., & Brown, T. J. (2012). A comparison of statistically downscaling methods suited for wildfire applications. International Journal of Climatology 32(5): 772–780.CrossRefGoogle Scholar
Addor, N., Rössler, O., Köplin, N., et al. (2014). Robust changes and sources of uncertainty in the projected hydrological regimes of Swiss catchments. Water Resources Research 50(10): 7541–7562.Google Scholar
Bales, R. C., Molotch, N. P., Painter, T. H., et al. (2006). Mountain hydrology of the western United States. Water Resources Research 42(8): W08432.Google Scholar
Barnett, T. P., Adam, J. C., & Lettenmaier, D. P. (2005). Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438: 303–309.CrossRefGoogle ScholarPubMed
Bastola, S., Murphy, C., & Sweeney, J. (2011). The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments. Advances in Water Resources 34(5): 562–576.Google Scholar
Chen, J. M., Chen, X., Ju, W., & Geng, X. (2005). Distributed hydrological model for mapping evapotranspiration using remote sensing inputs. Journal of Hydrology 305(1–4): 15–39.Google Scholar
Demaria, E. M., Nijssen, B., & Wagener, T. (2007). Monte Carlo sensitivity analysis of land surface parameters using the Variable Infiltration Capacity model. Journal of Geophysical Research 112(D11): D11113.Google Scholar
Dietz, J., Hölscher, D., Leuschner, C., & Hendrayanto, H. (2006). Rainfall partitioning in relation to forest structure in differently managed montane forest stands in Central Sulawesi, Indonesia. Forest Ecology and Management 237(1–3): 170–178.Google Scholar
Elsner, M. M., Gangopadhyay, S., Pruitt, T., et al. (2014). How does the choice of distributed meteorological data affect hydrologic model calibration and streamflow simulations? Journal of Hydrometeorology 15(4): 1384–1403.CrossRefGoogle Scholar
Ficklin, D. L., Stewart, I. T., & Maurer, E. P. (2013). Climate change impacts on streamflow and subbasin-scale hydrology in the upper Colorado River basin. PLoS One 8(8): e71297.CrossRefGoogle ScholarPubMed
Gao, H., Tang, Q., Shi, X., et al. (2010). Water budget record from Variable Infiltration Capacity (VIC) model. In Algorithm Theoretical Basis Document for Terrestrial Water Cycle Data Records (pp. 120–173). Available from: www.research.lancs.ac.uk/portal/en/publications/water-budget-record-from-variable-infiltration-capacity-vic-model(1e8618dd-212f-4c3b-bee3-65862ea5a4b9)/export.html (Last accessed 3 September 2021).Google Scholar
Guo, D., Johnson, F., & Marshall, L. (2018). Assessing the potential robustness of conceptual rainfall-runoff models under a changing climate. Water Resources Research 54(7): 5030–5049.Google Scholar
Gupta, H. V., Wagener, T., & Liu, Y. (2008). Reconciling theory with observations: Elements of a diagnostic approach to model evaluation. Hydrological Processes 22(18): 3802–3813.Google Scholar
Gutmann, E., Pruitt, T., Clark, M. P., et al. (2014). An intercomparison of statistical downscaling methods used for water resource assessments in the United States. Water Resources Research 50(9): 7167–7186.Google Scholar
Hadka, D., & Reed, P. (2013). Borg: An auto-adaptive many-objective evolutionary computing framework. Evolutionary Computation 21(2): 231–259.Google Scholar
Hadka, D., & Reed, P. (2015). Large-scale parallelization of the Borg multiobjective evolutionary algorithm to enhance the management of complex environmental systems. Environmental Modelling & Software 69(C): 353–369.Google Scholar
Hamlet, A. F., & Lettenmaier, D. P. (2007). Effects of 20th century warming and climate variability on flood risk in the western U.S. Water Resources Research 43(6): W06427.Google Scholar
Hansen, M. C., Defries, R. S., Townshend, J. R. G., & Sohlberg, R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing 21(6–7): 1331–1364.CrossRefGoogle Scholar
Harding, B. L., Wood, A. W., & Prairie, J. R. (2012). The implications of climate change scenario selection for future streamflow projection in the Upper Colorado River Basin. Hydrology and Earth System Sciences 16(11): 3989–4007.Google Scholar
Hattermann, F. F., Vetter, T., Breuer, L., et al. (2018). Sources of uncertainty in hydrological climate impact assessment: A cross-scale study. Environmental Research Letters 13(1): 015006.Google Scholar
Her, Y., Yoo, S. H., Cho, J., et al. (2019). Uncertainty in hydrological analysis of climate change: Multi-parameter vs. multi-GCM ensemble predictions. Scientific Reports 9: 4974.Google Scholar
Hewitson, B. C., & Crane, R. G. (2006). Consensus between GCM climate change projections with empirical downscaling: Precipitation downscaling over South Africa. International Journal of Climatology 26(10): 1315–1337.Google Scholar
Huang, M. (2005). Surface and Groundwater Interactions and their Impacts on Water and Energy Budgets at the Land Surface. Berkeley, CA: University of California Press.Google Scholar
Joseph, J., Ghosh, S., Pathak, , A., & Sahai, A. K. (2018). Hydrologic impacts of climate change: Comparisons between hydrological parameter uncertainty and climate model uncertainty. Journal of Hydrology 566: 1–22.Google Scholar
Kay, A. L., Davies, H. N., Bell, V. A., & Jones, R. G. (2009). Comparison of uncertainty sources for climate change impacts: Flood frequency in England. Climatic Change 92(1–2): 41–63.Google Scholar
Köplin, N., Schädler, B., Viviroli, D., & Weingartner, R. (2012). Relating climate change signals and physiographic catchment properties to clustered hydrological response types. Hydrology and Earth System Sciences 16: 2267–2283.Google Scholar
Liang, X., Lettenmaier, D. P., Wood, E. F., & Burges, S. J. (1994). A simple hydrologically based model of land surface water and energy fluxes for general circulation models. Journal of Geophysical Research 99(D7): 14415–14428.Google Scholar
Liu, X., Tang, Q., Cui, H., et al. (2017). Multimodel uncertainty changes in simulated river flows induced by human impact parameterizations. Environmental Research Letters 12(2): 025009.Google Scholar
Liu, X., Tang, Q., Voisin, N., & Cui, H. (2016). Projected impacts of climate change on hydropower potential in China. Hydrology and Earth System Sciences 20: 3343–3359.Google Scholar
Liu, Y., Hejazi, M., Li, H., Zhang, X., & Leng, G. (2018). A hydrological emulator for global applications – HE v1.0.0. Geoscientific Model Development 11(3): 1077–1092.Google Scholar
Lohmann, D., Nolte-Holube, R., & Raschke, E. (1996). A large-scale horizontal routing model to be coupled to land surface parametrization schemes. Tellus A: Dynamic Meteorology and Oceanography, 48(5): 708–721.Google Scholar
Maurer, E. P., Wood, A. W., Adam, J. C., & Lettenmaier, D. P. (2002). A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. Journal of Climate 15(22): 3237–3251.Google Scholar
Meinshausen, M., Smith, S. J., Calvin, K., et al. (2011). The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change 109(1–2): 213–241.Google Scholar
Mendoza, P. A., Clark, M. P., Mizukami, N., et al. (2015). Effects of hydrologic model choice and calibration on the portrayal of climate change impacts. Journal of Hydrometeorology 16(2): 762–780.Google Scholar
Mendoza, P. A., Clark, M. P., Mizukami, N., et al. (2016). How do hydrologic modeling decisions affect the portrayal of climate change impacts? Hydrological Processes 30(7): 1071–1095.Google Scholar
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., et al. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. ASABE 50(3): 885–900.Google Scholar
Murphy, S. F., Barber, L. B., Verplanck, P. L., & Kinner, D. A. (2003). Environmental setting and hydrology of the Boulder Creek Watershed, Colorado. In Murphy, S. F., Verplanck, P. L., & Barber, L. B. (eds.), Comprehensive Water Quality of the Boulder Creek Watershed, Colorado, during High-Flow and Low-Flow Conditions. 2000. Water Resources Investigation Report 03-4045. Denver, CO: US Geological Survey.Google Scholar
Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K., & Rasmussen, R. (2017). Slower snowmelt in a warmer world. Nature Climate Change 7: 214–219.Google Scholar
Poff, N. L., Allan, J. D., Bain, M. B., et al. (1997). The natural flow regime – A paradigm for river conservation and restoration. BioScience 47(11): 769–784.Google Scholar
Pradhanang, S. M., Mukundan, R., Schneiderman, E. M., et al. (2013). Streamflow responses to climate change: Analysis of hydrologic indicators in a New York City water supply watershed. Journal of American Water Resources Association 49(6): 1308–1326.Google Scholar
Raje, D., & Krishnan, R. (2012). Bayesian parameter uncertainty modeling in a macroscale hydrologic model and its impact on Indian river basin hydrology under climate change. Water Resources Research 48(8): W08522.Google Scholar
Rauscher, S. A., Pal, J. S., Diffenbaugh, N. S., & Benedetti, M. M. (2008). Future changes in snowmelt-driven runoff timing over the western US. Geophysical Research Letters 35(16): L16703.CrossRefGoogle Scholar
Reclamation (2013). Downscaled CMIP3 and CMIP5 Climate Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with Preceding Information, and Summary of User Needs. U.S. Department of the Interior, Bureau of Reclamation, 104 pp. Available from http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/techmemo/downscaled_climate.pdf (Last accessed 1 March 2018).Google Scholar
Ren, H., Hou, Z., Huang, M., et al. (2016). Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins. Journal of Hydrology 536: 92–108.Google Scholar
Riahi, K., Rao, S., Krey, V., et al. (2011). RCP8.5-A scenario of comparatively high greenhouse gas emissions. Climatic Change 109(1): 33–57.Google Scholar
Schewe, J., Heinke, J., Gerten, D., et al. (2014). Multimodel assessment of water scarcity under climate change. Proceedings of the National Academy of Sciences (USA) 111(9): 3245–3250.CrossRefGoogle ScholarPubMed
Seiller, G., Roy, R., & Anctil, F. (2017). Influence of three common calibration metrics on the diagnosis of climate change impacts on water resources. Journal of Hydrology 547: 280–295.CrossRefGoogle Scholar
Shi, X., Wood, A. W., & Lettenmaier, D. P. (2008). How essential is hydrologic model calibration to seasonal streamflow forecasting? Journal of Hydrometeorology 9(6): 1350–1363.Google Scholar
Soil Survey Staff (2015). Natural Resources Conservation Service. United States Department of Agriculture, Web Soil Survey. Available from http://websoilsurvey.nrcs.usda.gov/ (Last accessed 1 March 2018).Google Scholar
Stone, M. C., Hotchkiss, R. H., & Mearns, L. O. (2003). Water yield responses to high and low spatial resolution climate change scenarios in the Missouri River Basin. Geophysical Research Letters 30(4): 1186.Google Scholar
Thomson, A. M., Calvin, K. V., Smith, S. J., et al. (2011). RCP4.5: A pathway for stabilization of radiative forcing by 2100. Climatic Change 109(1–2): 77–94.Google Scholar
USBR (2013). Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with Preceding Information, and Summary of User Needs (47 pp.). Denver, CO: U.S. Department of the Interior, Bureau of Reclamation, Technical Services Center.Google Scholar
Vuuren, D. P. V., Stehfest, E., Elzen, M. G. J. D., et al. (2011). RCP2.6: Exploring the possibility to keep global mean temperature increase below 2℃. Climatic Change 109(1–2): 95–116.Google Scholar
Wagener, T., Sivapalan, M., Troch, P. A., et al. (2010). The future of hydrology: An evolving science for a changing world. Water Resources Research 46(5): W05301.Google Scholar
Watts, A., Grant, G., & Safeeq, M. (2016). Flows of the Future – How Will Climate Change Affect Streamflows in the Pacific Northwest? Science Findings, 187. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.Google Scholar
Wilby, R. L., Troni, J., Biot, Y., et al. (2009). A review of climate risk information for adaptation and development planning. International Journal of Climatology 29(9): 1193–1215.CrossRefGoogle Scholar
Yin, Y., Tang, Q., Liu, X., & Zhang, X. (2017). Water scarcity under various socio-economic pathways and its potential effects on food production in the Yellow River Basin. Hydrology and Earth System Sciences 21: 791–804.CrossRefGoogle Scholar
Yuan, F., Zhao, C., Jiang, Y., et al. (2017). Evaluation on uncertainty sources in projecting hydrological changes over the Xijiang River basin in South China. Journal of Hydrology 554: 434–450.Google Scholar
Zhang, Q., Knowles, J. F., Barnes, R. T., et al. (2018). Surface and subsurface water contributions to streamflow from a mesoscale watershed in complex mountain terrain. Hydrological Processes 32(7): 954–967.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×