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SCVWD Runoff and System Operation Analysis
[Authors]
Introduction
Human-caused climate disruption is already affecting hydrologic systems and will produce increasing
impacts through the 21st century (Melillo et al., 2014). In California these impacts include increasing
intensity and duration of droughts (Diffenbaugh et al., 2015; Mann & Gleick, 2015) and increasing floods
due to changing precipitation patterns and snowmelt dynamics (Das et al., 2013; Pierce et al., 2013).
For managed water systems, these changes can cause complex interactions (Anderson et al., 2008;
Hanak & Lund, 2012), which can require modifications to operating decisions or produce changes in the
reliability of water deliveries or the level of flood protection.
To assess the risks posed by climate change to a managed water system, a typical approach uses
projected meteorology (precipitation, temperature) to drive a hydrology model, which produces new
inflows to the managed system (e.g., Brekke et al., 2004; Hamlet & Lettenmaier, 1999; Vicuna et al.,
2010). While hydrology models can perform quite well by many metrics, even small biases can be
enough to confound the use of the generated flows by a water resources system model. For this reason,
alternative approaches are often employed that make use of existing data sets of inflows, perturbing
them to reflect system inflows in a changed climate scenario (Snover et al., 2003; Vicuna et al., 2007).
Bias correcting land surface runoff or routed streamflows has been successfully used in different
hydrologic forecasting environments, as well as in climate change studies (Vano et al., 2010; Ye et al.,
2014; Yuan & Wood, 2012).
In this study we examine potential impacts of changed monthly inflow sequences under a climate
change scenario for the Santa Clara Valley Water District (SCVWD). We produce sets of perturbed
inflows for the SCVWD reservoir model, where the modified flows are representative of approximately
2040, a planning horizon applicable to local water resources planning activities. We compare two
methods of perturbing the inflows: a simple delta method, where each monthly inflow is perturbed by a
fixed factor, and a more sophisticated hybrid delta approach that uses a continuous function of
perturbation factors based on flow quantiles. We then assess the impacts of changes in inflows on
operating decisions of the system and the reliability of water supply with changed conditions.
Methods
The focus region of this study is shown in Figure 1, which illustrates the catchments contributing flow to
the 9 major reservoirs operated by the SCVWD. Because of the relatively coarse scale of the climate and
hydrology projections, these two regions are represented as two points as indicated: West (W) and East
(E). Climate projections for the region were extracted from the online archive described in detail
elsewhere (Maurer et al., 2014; Reclamation, 2013). These projections are based on precipitation and
temperature output from global climate models (or general circulation models, GCMs) simulations
conducted as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5, Taylor et al., 2012),
which formed the basis for the climate projections used in the Fifth Assessment of the
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Intergovernmental Panel on Climate Change (IPCC, 2013). Since the climate model output is at a native
spatial scale of 100 km or more, it must be downscaled to provide regionally-applicable information. The
precipitation and temperature data obtained for this study was downscaled to 1/8° latitude/longitude
resolution (~ 12 km) using the BCSD method, originally developed by Wood et al. (2004; 2002). The
BCSD method has been widely applied across the western U.S. (e.g., Barnett et al., 2008; Das et al.,
2013; Ficklin et al., 2012) and around the globe (Girvetz et al., 2009) in studies of the hydrological
impact of climate change.
As part of a past effort (Reclamation, 2011), the downscaled precipitation and temperature projections
for the period 1950-2099 were used to drive the Variable Infiltration Capacity (VIC, Liang et al., 1994)
hydrology model, producing runoff estimates across the conterminous U.S. For this study, we used
precipitation, temperature, and runoff produced by the 10 GCMs listed in Table 1. For the data based on
each GCM run, we used a historical simulation and the future projection associated with the emission
pathway RCP8.5 (for which the total added radiative forcing at year 2100 is 8.5 W/m2), which represents
increasing greenhouse gas emissions essentially as a business-as-usual scenario and also most closely
traces observed emissions thus far in the 21st century (Fuss et al., 2014).
[describe the delta method here]
A second method used to perturb the design SCVWD inflow sequences was the Hybrid Delta method
(Tohver et al., 2014), as applied directly to streamflow for bias correction (Snover et al., 2003). This
method as used in this study applies a common quantile mapping procedure (Gudmundsson et al., 2012;
Panofsky & Brier, 1968) to monthly streamflows. For each representative point (labeled E and W in
Figure 1) sequences of runoff were obtained from the archive described above, and these were adjusted
as described below.
Each runoff sequence was bias-corrected using quantile mapping, using the overlapping 1950-1999
period as the base climatology. This procedure involves separating the data (for 1950-1999) by month,
and for each month assembling all inflow values for both the historic sequence (the SCVWD data) and
the VIC-produced simulated runoff into cumulative distribution functions (CDFs), producing 12 CDFs for
the VIC simulated flows and 12 for observed inflows. Then for each month in the VIC simulation (using
the entire 1950-2099 period), the quantile is determined using the CDF for VIC simulations, and a new
value is drawn for the same quantile from the observation-based CDF. This produces a new 1950-2099
runoff sequence that statistically matches the observations for the 1950-1999 period (though,
importantly, sequencing is not preserved) but evolves into the future as simulated by each GCM. These
are referred to here as ‘bias-corrected’ inflows
Finally, an inverse quantile mapping is applied to ‘perturb’ the historic SCVWD record of reservoir
inflows. This uses a similar method to that above, but develops new sets of CDFs, both based on the
bias-corrected inflows. The first is for 1950-1999, representing recent historical values, and the second
based on 2025-2054, representing a changed climatology applicable to the 2040 planning horizon. The
historical SCVWD inflows (for 1922-2015) are then adjusted with a quantile mapping from the historic to
the future CDFs. This results in a sequence of inflows that has the same temporal pattern as the
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historical sequence, including extended dry and wet periods, but including projected changes as
indicated by the downscaled GCM precipitation and temperature.
[describe SWAT modeling]
[describe methods of analyzing output, statistical tests, …]
Results and Discussion
Conclusions
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References
Anderson, J., Chung, F., Anderson, M., Brekke, L. D., Easton, D., Ejeta, M., Peterson, R., & Snyder, R.
(2008). Progress on incorporating climate change into management of California’s water
resources. Climatic Change, 87 (Suppl 1), S91–S108, DOI 110.1007/s10584-10007-19353-10581.
Barnett, T. P., Pierce, D. W., Hidalgo, H. G., Bonfils, C., Santer, B. D., Das, T., Bala, G., Wood, A. W.,
Nozawa, T., Mirin, A. A., Cayan, D. R., & Dettinger, M. D. (2008). Human-Induced Changes in the
Hydrology of the Western United States. Science, 319(5866), 1080-1083, doi:
1010.1126/science.1152538.
Brekke, L. D., Miller, N. L., Bashford, K. E., Quinn, N. W. T., & Dracup, J. A. (2004). Climate change
impacts uncertainty for water resources in the San Joaquin River Basin, California. Journal of the
American Water Resources Association, 40(1), 149-164.
Das, T., Maurer, E. P., Pierce, D. W., Dettinger, M. D., & Cayan, D. R. (2013). Increases in flood
magnitudes in California under warming climates. Journal of Hydrology, 501(0), 101-110.
Diffenbaugh, N. S., Swain, D. L., & Touma, D. (2015). Anthropogenic warming has increased drought risk
in California. Proceedings of the National Academy of Sciences, 112(13), 3931-3936.
Ficklin, D. L., Stewart, I. T., & Maurer, E. P. (2012). Projections of 21st Century Sierra Nevada Local
Hydrologic Flow Components Using an Ensemble of General Circulation Models1. JAWRA Journal
of the American Water Resources Association, 48(6), 1104-1125.
Fuss, S., Canadell, J. G., Peters, G. P., Tavoni, M., Andrew, R. M., Ciais, P., Jackson, R. B., Jones, C. D.,
Kraxner, F., Nakicenovic, N., Le Quere, C., Raupach, M. R., Sharifi, A., Smith, P., & Yamagata, Y.
(2014). Betting on negative emissions. Nature Clim. Change, 4(10), 850-853.
Girvetz, E. H., Zganjar, C., Raber, G. T., Maurer, E. P., Kareiva, P., & Lawler, J. J. (2009). Applied ClimateChange Analysis: The Climate Wizard Tool. PLoS ONE, 4(12), e8320.
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012). Technical Note:
Downscaling RCM precipitation to the station scale using statistical transformations - a
comparison of methods. Hydrol. Earth Syst. Sci., 16(9), 3383-3390.
Hamlet, A. F., & Lettenmaier, D. P. (1999). Effects of climate change on hydrology and water resources in
the Columbia River Basin. 35(6), 1597-1624.
Hanak, E., & Lund, J. R. (2012). Adapting California’s water management to climate change. Climatic
Change, 111(1), 17-44.
IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United
Kingdom and New York, NY, USA: Cambridge University Press.
Liang, X., Lettenmaier, D. P., Wood, E., & 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.
Mann, M. E., & Gleick, P. H. (2015). Climate change and California drought in the 21st century.
Proceedings of the National Academy of Sciences, 112(13), 3858-3859.
Maurer, E. P., Brekke, L., Pruitt, T., Thrasher, B., Long, J., Duffy, P., Dettinger, M., Cayan, D., & Arnold, J.
(2014). An enhanced archive facilitating climate impacts and adaptation analysis. Bulletin of the
American Meteorological Society.
Melillo, J. M., Richmond, T. T., & Yohe, G. (2014). Climate change impacts in the United States. Third
National Climate Assessment.
Panofsky, H. A., & Brier, G. W. (1968). Some Applications of Statistics to Meteorology. University Park,
PA, USA: The Pennsylvania State University.
Pierce, D. W., Cayan, D. R., Das, T., Maurer, E. P., Miller, N. L., Bao, Y., Kanamitsu, M., Yoshimura, K.,
Snyder, M. A., Sloan, L. C., Franco, G., & Tyree, M. (2013). The key role of heavy precipitation
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events in climate model disagreements of future annual precipitation changes in California.
Journal of Climate, 26(16), 5879-5896.
Reclamation (2011). West-Wide Climate Risk Assessments: Bias-Corrected and Spatially Downscaled
Surface Water Projections, Technical Memorandum No. 86-68210–2011-01. Denver, Colorado:
U.S. Dept. of Interior – Bureau of Reclamation, Technical Service Center, p. 138.
Reclamation (2013). Downscaled CMIP3 and CMIP5 Climate Projections: Release of Downscaled CMIP5
Climate Projections, Comparison with Preceding Information, and Summary of User Needs
Denver, Colorado, USA: U.S. Department of the Interior, Bureau of Reclamation, Technical
Service Center, pp. 116, available at:
http://gdodcp.ucllnl.org/downscaled_cmip_projections/techmemo/downscaled_climate.pdf.
Snover, A. K., Hamlet, A. F., & Lettenmaier, D. P. (2003). Climate-Change Scenarios for Water Planning
Studies: Pilot Applications in the Pacific Northwest. Bulletin of the American Meteorological
Society, 84(11), 1513-1518.
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012). An Overview of CMIP5 and the experiment design.
Bulletin of the American Meteorological Society, 93, 485-498, doi: 410.1175/BAMS-D-111100094.00091.
Tohver, I. M., Hamlet, A. F., & Lee, S.-Y. (2014). Impacts of 21st-Century Climate Change on Hydrologic
Extremes in the Pacific Northwest Region of North America. JAWRA Journal of the American
Water Resources Association, 50(6), 1461-1476.
Vano, J. A., Scott, M. J., Voisin, N., Stöckle, C. O., Hamlet, A. F., Mickelson, K. E. B., Elsner, M. M., &
Lettenmaier, D. P. (2010). Climate change impacts on water management and irrigated
agriculture in the Yakima River Basin, Washington, USA. Climatic Change, 102(1), 287-317.
Vicuna, S., Dracup, J. A., Lund, J. R., Dale, L. L., & Maurer, E. P. (2010). Basin-scale water system
operations with uncertain future climate conditions: Methodology and case studies. Water
Resources Research, 46, W04505, doi:04510.01029/02009WR007838.
Vicuna, S., Maurer, E. P., Joyce, B., Dracup, J. A., & Purkey, D. (2007). The sensitivity of California water
resources to climate change scenarios. Wiley Online Library.
Wood, A. W., Leung, L. R., Sridhar, V., & Lettenmaier, D. (2004). Hydrologic implications of dynamical
and statistical approaches to downscaling climate model outputs. Climatic Change, 62(1-3), 189216.
Wood, A. W., Maurer, E. P., Kumar, A., & Lettenmaier, D. P. (2002). Long-range experimental hydrologic
forecasting for the eastern United States. Journal of Geophysical Research-Atmospheres,
107(D20), 4429, doi:4410.1029/2001JD000659.
Ye, A., Duan, Q., Yuan, X., Wood, E. F., & Schaake, J. (2014). Hydrologic post-processing of MOPEX
streamflow simulations. Journal of Hydrology, 508, 147-156.
Yuan, X., & Wood, E. F. (2012). Downscaling precipitation or bias-correcting streamflow? Some
implications for coupled general circulation model (CGCM)-based ensemble seasonal hydrologic
forecast. Water Resources Research, 48(12), W12519.
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Table 1 - GCMs included in this study.
GCM No.
1
2
3
Model Name
gfdl-esm2g
canesm2
cnrm-cm5
4
csiro-mk3-6-0
5
6
inmcm4
miroc5
7
mpi-esm-lr
8
9
10
mri-cgcm3
ccsm4
noresm1-m
Modeling Group
NOAA Geophysical Fluid Dynamics Laboratory
Canadian Centre for Climate Modelling and Analysis
Centre National de Recherches Météorologiques / Centre Européen
de Recherche et Formation Avancée en Calcul Scientifique
Commonwealth Scientific and Industrial Research Organization in
collaboration with Queensland Climate Change Centre of Excellence
Institute for Numerical Mathematics
Atmosphere and Ocean Research Institute (The University of Tokyo),
National Institute for Environmental Studies, and Japan Agency for
Marine-Earth Science and Technology
Max-Planck-Institut für Meteorologie (Max Planck Institute for
Meteorology)
Meteorological Research Institute
National Center for Atmospheric Research
Norwegian Climate Centre
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Figure 1 - Region of Study: cross-hatching indicates areas draining into SCVWD reservoirs; points labeled W and E are the
two representative locations used for each side of the valley.
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