Hydrol. Earth Syst. Sci., 14, 1125–1138, 2010
www.hydrol-earth-syst-sci.net/14/1125/2010/
doi:10.5194/hess-14-1125-2010
© Author(s) 2010. CC Attribution 3.0 License.
Hydrology and
Earth System
Sciences
The utility of daily large-scale climate data in the assessment of
climate change impacts on daily streamflow in California
E. P. Maurer1, H. G. Hidalgo2, T. Das3, M. D. Dettinger4,3, and D. R. Cayan3,4
1Civil Engineering Dept., Santa Clara University, Santa Clara, CA, USA
2Escuela de Fı´sica, Universidad de Costa Rica, San Jose´, Costa Rica
3Scripps Institution of Oceanography, La Jolla, CA, USA
4US Geological Survey, Water Resources Division, La Jolla, CA, USA
Received: 3 February 2010 – Published in Hydrol. Earth Syst. Sci. Discuss.: 12 February 2010
Revised: 18 June 2010 – Accepted: 21 June 2010 – Published: 30 June 2010
Abstract. Three statistical downscaling methods were ap-
plied to NCEP/NCAR reanalysis (used as a surrogate for
the best possible general circulation model), and the down-
scaled meteorology was used to drive a hydrologic model
over California. The historic record was divided into an
“observed” period of 1950–1976 to provide the basis for
downscaling, and a “projected” period of 1977–1999 for as-
sessing skill. The downscaling methods included a bias-
correction/spatial downscaling method (BCSD), which relies
solely on monthly large scale meteorology and resamples the
historical record to obtain daily sequences, a constructed ana-
logues approach (CA), which uses daily large-scale anoma-
lies, and a hybrid method (BCCA) using a quantile-mapping
bias correction on the large-scale data prior to the CA ap-
proach. At 11 sites we compared three simulated daily flow
statistics: streamflow timing, 3-day peak flow, and 7-day low
flow. While all downscaling methods produced reasonable
streamflow statistics at most locations, the BCCA method
consistently outperformed the other methods, capturing the
daily large-scale skill and translating it to simulated stream-
flows that more skillfully reproduced observationally-driven
streamflows.
1 Introduction
As climate change science matures and is better able to es-
timate the regional magnitudes of potential climate change,
estimates of local and regional impacts to the resources at
risk are of increasing interest (IPCC, 2007a). As for much
of the globe, western United States (US) water resources, the
Correspondence to: E. P. Maurer
(emaurer@engr.scu.edu)
focus of this study, are particularly at risk, which has inspired
a plethora of recent studies aimed at estimating potential im-
pacts to hydrology and water resources systems (Barnett et
al., 2008; Cayan et al., 2008; Maurer, 2007; Vicuna et al.,
2007).
One common issue facing all regional assessments of cli-
mate change impacts is that the scale of general circulation
model (GCM) outputs are at too spatially coarse a scale for
direct use in impact models. Regional studies, such as those
examining hydrologic impacts of climate change, thus rely
on spatial downscaling to translate the large-scale climatic
shifts projected by GCMs to scales more representative of
local areas of interest (Christensen et al., 2007).
The recent availability of large databases of raw GCM out-
puts in a consistent format (Meehl et al., 2007) has facilitated
the use of multiple GCMs and greenhouse gas emissions sce-
narios in impact studies. The greatest value from studies
of multiple GCM runs is that model-to-model, scenario-to-
scenario, and even chaotic realization-to-realization uncer-
tainties in the physical response of the climate system to
changing greenhouse gas concentrations, the primary sources
of uncertainty in climate impacts analysis (Fowler and Ek-
stro¨m, 2009), can be quantified to some degree (Hawkins
and Sutton, 2009). Furthermore, the skill of a multimodel
ensemble consistently outperforms any individual model for
detection and attribution studies (Brekke et al., 2008; Gleck-
ler et al., 2008; Pierce et al., 2009). To consider many future
projections of climate in a regional impacts study requires
a downscaling procedure that is computationally very effi-
cient. This generally limits these studies to using statistical
downscaling techniques, where some large-scale signal is re-
lated statistically to local climate, as opposed to regional cli-
mate simulations, where a dynamical model of regional cli-
mate is used to simulate local climate responses to the global
Published by Copernicus Publications on behalf of the European Geosciences Union.
1126 E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California
projected changes. Past studies generally have found that the
differences between GCMs is much greater than the uncer-
tainty in downscaling techniques (Fowler et al., 2007).
While projected changes in long-term mean water supply
may have dire consequences for society (IPCC, 2007b; Oki
and Kanae, 2006; Shen et al., 2008), changes in the fre-
quency of extreme events are also of critical concern (Katz
et al., 2002), especially for regional hydroclimate (Leung et
al., 2004). Most climate projections suggest increases in the
frequency of temperature and precipitation extremes, both at
the monthly level (Benestad, 2006) and at the daily (and sub-
daily) level (Kharin et al., 2007). When downscaling from
GCM-scale climate simulations to regional scales to study
hydrologic impacts, the most desirable downscaling meth-
ods have the ability to translate the local changes in climatic
extreme events simulated by GCMs to the local scale needed
by hydrological models . While dynamic downscaling, using
a regional climate model (RCM) driven at the boundary by a
GCM, has been used in the western US to produce physically
realistic projections of changes in hydrologic extremes (Kim
et al., 2002; Snyder and Sloan, 2005), these types of mod-
els are still too computationally intensive to be applied to a
large ensemble of GCM output to characterize uncertainties
associated with inter-GCM variability and different emission
scenarios. For this reason, more computationally efficient
statistical downscaling approaches will continue to serve as
the methodological workhorse for downscaling ensembles of
long climate simulations.
Statistical methods, building statistical relationships be-
tween GCM-scale climate features and fine scale climate and
applying those to future projections, have been more widely
applied than dynamical model downscaling in studies of hy-
drologic impacts of climate change over the western United
States (Christensen and Lettenmaier, 2007; Maurer et al.,
2007; Payne et al., 2004; Wood et al., 2004). In most ap-
plications the focus has been on monthly, seasonal or annual
hydrologic changes and generally only monthly GCM out-
put was used. Some efforts have used daily GCM output to
study extremes in this region (e.g., Dettinger et al., 2004),
though this approach has generally been to downscale GCM
output directly to specific weather stations. To character-
ize both projected seasonal and extreme changes for larger
watersheds or over continental areas, a downscaling method
should have the ability to generate gridded fields of down-
scaled daily climate, to capture the spatial structure of cli-
mate features. To achieve this using daily GCM output was a
motivation for the development of the constructed analogues
(CA) approach (Hidalgo et al., 2008).
In a prior effort (Maurer and Hidalgo, 2008) the CA ap-
proach was contrasted with the bias-correction/spatial dis-
aggregation (BCSD) statistical downscaling approach, with
each applied over the western US for downscaling large-
scale observationally-derived reanalysis data as a surrogate
GCM. The methods take different approaches to downscal-
ing daily extreme precipitation and temperature. CA down-
scales each day’s output from the GCM simulation, captur-
ing projected changes in daily weather events that sum to-
gether to reflect long-term climate changes, while BCSD
works with GCM monthly output, then randomly selects a
month from the historical record and rescales its daily pre-
cipitation and temperature to match the projected monthly
values. Each has the ability to downscale to a gridded field
over a wide region, maintaining spatial correlations of driv-
ing hydroclimatic conditions that drive hydrologic impacts.
Wood et al. (2004) found the BCSD method performed well
when compared to several statistical and dynamic downscal-
ing methods in the context of assessing hydrologic impacts.
The ability of the CA method to exhibit considerable skill
of daily precipitation and temperature statistics has also been
demonstrated (Hidalgo et al., 2008). Both methods have been
widely used in regional studies in the United States and glob-
ally (Barnett et al., 2008; Cayan et al., 2009; Das et al., 2009;
Girvetz et al., 2009; Hayhoe et al., 2007; Maurer et al., 2009).
Both methods are capable, to some degree, of capturing
projected changes in extremes. They have been shown to
produce similar downscaling skill for many measures of tem-
perature and precipitation extremes (Maurer and Hidalgo,
2008). In that study, both CA and BCSD exhibited lim-
ited skill, attributed to substantial large scale precipitation
biases, for both wet and dry daily precipitation extremes and
the difference between the methods was not significant. Sta-
tistically significant differences were apparent, however, for
some measures, notably that CA demonstrated better skill
for downscaling cold-season low temperature extremes and
warm season high temperature extremes. This illustrated the
ability of CA to successfully translate large-scale daily skill
to a fine scale, where by contrast, the BCSD method, using
the assumption that climatological intra-monthly variability
does not change, showed lower skill. There were several im-
portant questions raised by this prior study, which are the
focus of this paper:
1. Is there any difference in the hydrology simulated by
climate downscaled with these methods?
2. For extreme streamflow measures, do the downscaling
approaches produce different results?
3. Are there opportunities to combine the best attributes of
the methods to improve downscaling performance?
Ultimately, the goal of this study is to address question 3, by
identifying, testing, and developing an improved statistical
downscaling method capable of skillfully downscaling ex-
treme hydroclimate, while being applied at regional to conti-
nental scales. To do this we refine the prior analysis in Mau-
rer and Hidalgo (2008) to evaluate how differences in the
downscaling approaches propagate through the hydrologic
system, and to determine whether improvements in down-
scaling methods, especially in the context of simulating hy-
drologic extremes, may be possible.
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E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California 1127
2 Methods and data
The approach for this study follows that of Maurer and Hi-
dalgo (2008), in which National Center of Environmental
Prediction and the National Center of Atmospheric Research
(NCEP/NCAR) reanalysis (Kalnay et al., 1996) was used as
a surrogate for a General Circulation Model (GCM) output,
as others have done (e.g., Widmann et al., 2003). The bene-
fit of using reanalysis rather than GCM output is that biases
will be expected to be lower, since atmospheric observations
are assimilated in the reanalysis framework. In addition, the
data assimilation process produces year-to-year and day-to-
day correspondence to observed climate and weather that an
unconstrained GCM would not, making it more defensible to
compare downscaling performance against observations. We
downscale the reanalysis daily and monthly precipitation and
temperature using different techniques, and use the down-
scaled data to drive a hydrologic model. The hydrologic
model skill is evaluated by comparing these simulations to
the hydrologic model output produced by driving the hydro-
logic model directly with the gridded observed precipitation
and temperature of Maurer et al. (2002). Since one of the
downscaling methods, BCSD, uses random daily sequences
that are not connected to observed daily meteorology, statis-
tics typically used for hydrologic model performance, such
as root mean square error or correlation, which assume ob-
served and simulated sequences to correspond to the same
events, are not used. Rather, we opt for statistical tests
that assess whether the flows produced with downscaled data
have the same statistical characteristics as those driven by ob-
served meteorology. Results are compared primarily using a
2-sample Kolmogorov-Smirnov (KS) test (Wilks, 2006) at a
0.05 significance level, with other tests applied selectively as
discussed in Sect. 3.2 below.
2.1 Reanalysis as a surrogate GCM
NCEP/NCAR reanalysis (Kalnay et al., 1996) data include
daily and monthly precipitation and temperature on a T62
Gaussian grid (approximately 1.9◦ square), a resolution com-
parable to recent GCMs. Reanalysis is often held up as an
example of the best possible historical GCM output (Reich-
ler and Kim, 2008), which makes it appropriate for use in
this study, as the focus is on how downscaling approaches
distinguish themselves in the presence of large-scale skill.
As noted by Maurer and Hidalgo (2008), because reanal-
ysis temperature is strongly connected to observations, the
comparisons of temperature skill will reflect differences al-
most exclusively in the downscaling techniques. However,
because precipitation observations are not assimilated into
reanalysis estimates, the intercomparison will reflect differ-
ences between the downscaling methods, plus influences of
the reanalysis precipitation biases and errors. The precipita-
tion and temperature daily variability in reanalysis has been
shown to be realistic in many locations in the western US
(Widmann and Bretherton, 2000), and so the existence of
skill in daily statistics of large-scale climate model output (in
this case, reanalysis) will be a major factor potentially distin-
guishing the downscaling methods compared in this study.
Following Maurer and Hidalgo (2008), we divide the sec-
ond half of the 20th century into two periods, with 1950–
1976 representing “observations” used as the sample cat-
alog from which model estimates are derived, and 1977–
1999 “projections” for which the model estimates are de-
rived and verified upon. The later period exhibits small but
statistically significant differences in both temperature and
precipitation compared to the early period, with 1977–1999
being generally wetter and warmer over the study domain.
While there are documented climatic drivers that could ex-
plain this difference, such as a 1976/77 shift in the Pacific
Decadal Oscillation phase (Mantua and Hare, 2002), there
are also changes in the sources of observations assimilated
in the NCEP/NCAR reanalysis beginning in 1979 (Kistler et
al., 2001). These differences provide the opportunity to as-
sess the performance of the downscaling techniques under a
climate that, while not dramatically different, is statistically
significantly different.
A more robust assessment of the comparative skill of the
downscaling methods studied here could be designed by ran-
domly assembling different sets of years for training the
methods and for validation, and employing a cross-validation
or bootstrapping method to assess skill (e.g., Feddersen,
2003; Feddersen et al., 1999; Li et al., 2010). For this study,
the two periods were intentionally selected to be observed se-
quences of years that differ significantly in both temperature
and precipitation, with the intention of creating a validation
condition that could highlight differences between the meth-
ods and to explore the potential for improving the methods.
2.2 Downscaling techniques
The two primary downscaling techniques used in this study
are the constructed analogues (Hidalgo et al., 2008; van
den Dool, 1994) and bias correction and spatial downscaling
(BCSD, Wood et al., 2004). These are described and con-
trasted in detail by Maurer and Hidalgo (2008). In general,
BCSD corrects for large scale biases using coarse-resolution
model output (from reanalysis or a GCM) and observations,
and then interpolates the bias-corrected anomalies onto a
fine-scale surface of observations. The CA technique be-
gins with a library of observed daily coarse-resolution and
corresponding high resolution climate anomaly patterns of
the variable to be downscaled, with each day’s library com-
piled from observations within ±45 days of the day to be
downscaled (Hidalgo et al., 2008). To downscale each
day, a subset of the 30 patterns (predictors) with the clos-
est similarity to the simulated anomalies are found from
the coarse-resolution library. A linear combination of the
coarse-resolution version of the predictors is used to produce
a coarse-resolution analogue, and the downscaled anomaly
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1128 E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California
is produced by applying the same linear combination to the
30 corresponding fine-scale library patterns. The most im-
portant distinction between the two methods is that by using
daily reanalysis (or GCM) output CA retains the daily se-
quencing of weather events from the coarse resolution, while
in BCSD only monthly reanalysis averages are used, with
daily patterns reconstructed by randomly resampling a his-
toric month and scaling its daily precipitation and tempera-
ture values to match the monthly projected values. Where
a climate model exhibits skill in simulating daily variabil-
ity, CA would in theory be capable of capturing that skill,
while BCSD would reflect historical intra-month variability.
Thus, for daily statistics, the two methods will be expected
to distinguish themselves only inasmuch as the large-scale
climate model exhibits skill at the daily time scale. Another
distinction between BCSD and CA has been observed in ar-
eas near coasts and other areas with sharp climate gradients at
a scale much finer than the large-scale climate model output
being downscaled. While BCSD reproduces climatological
patterns of precipitation and temperature, projected changes
tend to be smooth spatially. CA by contrast captures changes
in day-to-day variability, which can evolve differently than
the large-scale forcing, and thus CA can produce sharper spa-
tial gradients of precipitation and temperature changes than
BCSD.
A second distinction between CA and BCSD that bears on
the analysis that follows is that CA builds relationships be-
tween large-scale climate anomalies and fine-scale anomalies
based on observations, and then applies those relationships
to large-scale reanalysis (or GCM) anomalies. BCSD first
bias corrects the large scale monthly reanalysis data, using
a quantile-mapping approach (Panofsky and Brier, 1968), so
that for each month there is a statistical match (for the ob-
served period) for all statistical moments to those of large-
scale observations, and the bias-corrected monthly data are
then spatially downscaled. The implication of this is that
while CA accounts for potential biases in the mean by using
anomalies, higher order biases in reanalysis spatial or tem-
poral variability feed directly into the CA downscaled results
in ways that BCSD explicitly corrects and avoids.
2.3 Hydrologic modeling
To assess the ability to downscale to the watershed scale,
daily downscaled meteorology is used to drive the variable
infiltration capacity (VIC) hydrologic model (Cherkauer et
al., 2003; Liang et al., 1994). VIC is a spatially distributed
hydrologic model that solves the energy and water budgets
at the land surface. It has been widely applied in forecast-
ing and climate change analyses on spatial scales ranging
from watershed to continental areas (Abdulla et al., 1996;
Maurer, 2007; Maurer and Lettenmaier, 2003; Nijssen et
al., 1997; Wood et al., 2002). In this study, we apply the
VIC model at the same resolution (1/8 degree, approximately
12 km) and with the same parameterization as was used in
 
 
 1 
Figure 1. Location map of the 11 streamflow gauges listed in Table 1.2 
3 
4 
Fig. 1. Location map of the 11 streamflow gauges listed in Table 1.
several prior studies of the area (Barnett et al., 2008; Cayan
et al., 2008). Prior studies have assessed the VIC model per-
formance (with the same parameterization as in this study),
comparing observed flows with those simulated by VIC be-
ing driven by the same gridded observed meteorology as used
in this study. Maurer et al. (2007) used four of the sites
used in the current study, and found observed flows were
well simulated, with biases below 10%. The VIC model out-
put is processed through a stream routing network following
Lohmann et al. (1996), which is used to generate simulated
flow at the stream gauge locations listed in Table 1 and shown
on Fig. 1. These stream gauges are chosen to represent wa-
tersheds having much of their elevations above 1200 m, and
thus being dominated by snowmelt (with the exception of the
Consumnes, which has a smaller fraction of high elevation
area).
3 Results and discussion
Large scale skill in reanalysis temperature data is well es-
tablished, since observations of temperature are assimilated.
This skill has been demonstrated for monthly data as well
as for daily statistics. While precipitation is less well sim-
ulated in reanalysis, being model output rather than assimi-
lated data, some skill is evident. We summarize below the
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E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California 1129
Table 1. Streamflow gauges included in this study.
No. Gauge Name Average Elevation, m
1 SHAST Sacramento River at Shasta Dam 1480
2 SAC B Sacramento River at Bend Bridge 1310
3 OROVI Feather River at Oroville 1560
4 NF AM North Fork American River at North Fork Dam 1530
5 FOL I American River at Folsom Dam 1340
6 CONSU Cosumnes River at Michigan Bar 640
7 PRD C Mokelumne River at Pardee 1580
8 DPR I Tuolumne River at New Don Pedro 1760
9 LK MC Merced River at Lake McClure 1810
10 MILLE San Joaquin River at Millerton Lake 2120
11 KINGS Kings River – Pine Flat Dam 2200
ability to recover fine scale precipitation and temperature
statistics from the large-scale reanalysis, assess how the dif-
ferences in downscaling skill affect hydrology, and develop a
method for combining positive attributes of the two methods
to improve downscaling skill.
3.1 Downscaling meteorology for assessing hydrologic
impacts
Monthly and daily skill for downscaling precipitation and
temperature using the two downscaling methods were ana-
lyzed in a prior study (Maurer and Hidalgo, 2008), which
forms the basis for the current study. Monthly downscaling
skills for CA and BCSD were found in that study to be com-
parable, as were their skill levels for daily extreme precipi-
tation amounts (which was generally low for both methods,
reflecting the lack of skill in precipitation simulation at the
large native reanalysis scale). However, CA demonstrated
better skill at some locations in downscaling some of the
daily statistics, such as sequences of wet and dry days, and
high and low temperature extremes, where the large-scale re-
analysis data contain greater skill.
While correlations were higher for CA than for BCSD for
some variables, correlation analysis is unable to pick up sys-
tematic biases in the large-scale data. For example, while
Maurer and Hidalgo (2008) show comparable correlations
with observations for both CA and BCSD downscaled re-
analysis for monthly, daily, and extreme wet and dry precipi-
tation amounts, Fig. 2 reveals that both methods produce bias
in the downscaled precipitation intensity (the average rainfall
rate on rainy days, defined as days with non-zero precipita-
tion). Focusing on regions with high observed precipitation
intensity, especially January in the Pacific Northwest (PNW)
and the Sierra Nevada in California, two features emerge.
Most notably, CA shows a large negative bias in precipita-
tion intensity in California, and a positive bias in the PNW.
This bias in downscaled CA precipitation intensity in re-
gions with relatively low precipitation is similar to the well-
documented “drizzle” bias typical in GCMs (Iorio et al.,
2004; Mearns et al., 1995), where weak precipitation events
are overly common. Figure 3 illustrates that while reanaly-
sis produces average precipitation intensities (for a grid point
over central California) that appear reasonable, the frequency
of occurrence of events at the lowest intensities is oversim-
ulated. Approximately 40% of the daily January observa-
tions (from Maurer et al. (2002) aggregated to the Reanalysis
grid resolution) show zero precipitation (Fig. 3, center panel,
where the ”OBS” line intersects the ordinate at a value of
0.4), while all days in the reanalysis have some precipitation
(same panel, the dashed line never intersects the ordinate).
At higher precipitation intensities there is a similar bias, with
observed data indicating approximately 1% of daily values
above 9 mm, while Reanalysis shows 4% of daily precipita-
tion above this level and 1% of daily precipitation above 16
mm.
By working with anomalies, CA effectively removes the
biases in Reanalysis mean precipitation and mean tempera-
tures. However, it is evident from the biases in precipitation
intensity that, especially in light of our interest in hydrologic
extremes, that accounting for mean biases at the large scale
is inadequate. We introduce here a third downscaling ap-
proach, by combining the initial large-scale bias correction
step of BCSD prior to applying the CA method. We refer to
this approach as BCCA.
The bias correction employed in BCCA is conceptually
identical to that in BCSD, using the same quantile mapping
approach. However, rather than applying this to monthly
precipitation and temperature, the quantile mapping is used
for all daily (precipitation, maximum and minimum temper-
ature) values within each month. For example, all daily pre-
cipitation observations (aggregated to the reanalysis spatial
scale) for all Januarys in the 27-year “observed” period are
lumped into one pool to create a distribution of daily precip-
itation observations for January of n= 27×31= 837 days.
The pool of n daily observed precipitation values is then
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1130 E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California
 
 30 
 1 
Figure 2. Precipitation intensity in mm/d for four selected months for gridded observations 2 
(OBS, left panels), and the difference between downscaled CA and OBS (second column), 3 
between BCSD and OBS (third column), and BCCA and OBS (right panels). 4 
Fig. 2. Precipitation intensity in mm/d for four selected months for gridded observations (OBS, left panels), and the difference between
downscaled CA and OBS (second column), between BCSD and OBS (third column), and BCCA and OBS (right panels).
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E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California 1131
 
 31 
 1 
Figure 3. Cumulative distribution function (CDF) for daily precipitation for January at 2 
Reanalysis grid point located at 37.1422°N, 110.625°W. Reanalysis is compared to gridded 3 
observations aggregated to reanalysis scale. Left panel shows entire CDF, while center panel 4 
shows only daily precipitation values below 1 mm, and right panel shows precipitation over 9 5 
mm/d. 6 
7 
Fig. 3. Cumulative distribution function (CDF) for daily precip-
itation for January at Reanalysis grid point located at 37.1422◦ N,
110.625◦ W. Reanalysis is compared to gridded observations aggre-
gated to reanalysis scale. Left panel shows entire CDF, while center
panel shows only daily pr cipitation v lues below 1 mm, and right
panel shows precipitation over 9 mm/d.
sorted, and each day is ranked, as in BCSD, with a quantile
of rank/(n+1), and assembled into a cumulative distribution
as in Fig. 3. Daily January precipitation values for the large-
scale model output (reanalysis) is similarly arranged into a
cumulative distribution. Similar pairs of distributions are
prepared for all 12 months. The bias step is completed by
using these relationships for each day in the reanalysis time
series, where the precipitation value is converted to a quan-
tile using the cumulative distribution for reanalysis, and that
quantile is then drawn from the observed cumulative distri-
bution to obtain a new, bias-corrected precipitation value for
that day. For example, if reanalysis simulates a very small
precipitation amount that is exceeded 90% of the time (for
that month), and observations show 30% of the days with
no precipitation, the small amount of reanalysis precipitation
will be re-mapped to a value of zero. In this way, the bias-
corrected daily data will match the observations for the num-
ber of rainy days and the average rainfall intensity (for the
observed period). Finally, in BCCA since all biases are ex-
plicitly corrected, the constructed analogues are then devel-
oped on absolute values rather than anomalies, which con-
trasts with the use of anomalies in the original CA. Since the
biases in reanalysis temperatures are much smaller, in a rel-
ative sense, than precipitation biases, the discussion below
focuses on bias correction of precipitation.
While the bias correction included with BCCA forces
the cumulative distribution function to match observations
for the historical (observed) period, some biases due to the
downscaling methods remain. Figure 2 shows the compari-
son of BCCA to observations, after downscaling to the 1/8
degree spatial resolution. The high bias in precipitation in-
tensity in the PNW was successfully reduced by the bias cor-
rection process in BCCA, showing an improvement over CA.
This indicates that large scale bias may have been the pri-
mary factor for bias in downscaled precipitation intensity in
this area. The bias toward underestimation of precipitation
intensity with CA over California is not removed by the bias
correction, suggesting that some bias is introduced in the CA
method in this region, which was also noted by Hidalgo et
 
 32 
 1 
Figure 4. CDFs for the same grid cell as in Figure 3, but based on monthly average 2 
precipitation rate data for January for the “observed” period 1950-1976. 3 
Fig. 4. CDFs for the same grid cell as in Fig. 3, but based on
monthly average precipitation rate data for January for the “ob-
served” period 1950–1976.
al. (2008). Although the underestimation by CA and BCCA
in California is largest during the rainy season, January and
March in Fig. 2, the bias, while appearing large, is small rel-
ative to the mean observed intensity in these months (in the
leftmost column of Fig. 2).
While the daily bias correction ensures that the cumula-
tive distribution of daily precipitation (or maximum and min-
imum temperature) values will exactly match the observed
distribution for all the daily values for any month, it does not
explicitly force the monthly distributions to match. In other
words, by assembling all January daily values for 1950–1976
for a reanalysis grid cell into a single cumulative distribu-
tion function (as in Fig. 3), the bias correction only guaran-
tees that the entire set of daily values (for this example, 837
days) will match the statistics for the set of 837 days for the
observations. There is no guarantee that the distribution of
monthly precipitation values (for example, 27 January av-
erage precipitation values) is also improved. However, as
illustrated in Fig. 4, the monthly values are also largely cor-
rected for their biases at all quantiles when the daily values
are bias corrected. This indicates that the modeled precipita-
tion variability in reanalysis at the daily scale within a month
is consistent with observations, inasmuch as the monthly bias
is largely addressed by the daily bias correction.
3.2 Impact of downscaling approaches on daily
hydrology
Prior to analyzing daily metrics, we assessed the ability
of each downscaling method to reproduce annual flow vol-
umes for the projected 1977–1999 period at each gauge site
listed in Table 1. Three separate statistical tests were per-
formed: The Mann-Whitney U test (for central tendency,
the non-parametric alternative to mean) (Wilks, 2006); the
Siegel-Tukey test (for scale, the non-parametric alternative
to variance) (McCuen, 2003), and the KS test (noted above
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1132 E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California
Table 2. Statistical test results for BCSD, CA, and BCCA for
annual flow volume simulations. A gauge name in bold face in-
dicates that the downscaled streamflow using the indicated tech-
nique differs from the observations. Two tests are used: the Mann-
Whitney U (also referred to as the Wilcoxon-Mann-Whitney), and
the Kolmogorov-Smirnov 2-sample test (both tests performed at
p= 0.05).
Mann-Whitney Test Kolmogorov-Smirnov Test
BCSD CA BCCA BCSD CA BCCA
SHAST SHAST SHAST SHAST SHAST SHAST
SAC B SAC B SAC B SAC B SAC B SAC B
OROVI OROVI OROVI OROVI OROVI OROVI
NF AM NF AM NF AM NF AM NF AM NF AM
FOL I FOL I FOL I FOL I FOL I FOL I
CONSU CONSU CONSU CONSU CONSU CONSU
PRD C PRD C PRD C PRD C PRD C PRD C
DPR I DPR I DPR I DPR I DPR I DPR I
LK MC LK MC LK MC LK MC LK MC LK MC
MILLE MILLE MILLE MILLE MILLE MILLE
KINGS KINGS KINGS KINGS KINGS KINGS
in Sect. 2, for distribution characteristics, including central
tendency, scale, and shape). Results are summarized in Ta-
ble 2 for the Mann-Whitney U test and the KS test; The
Siegel-Tukey test detected no sites with varying scale char-
acteristics at the p= 0.05 level, and thus is not shown. For
the annual flow volume simulations, the Mann-Whitney and
KS test results in Table 2 show similar results at each site,
suggesting that differences in the central tendency (as de-
tected by the Mann-Whitney test) are the primary differ-
ence in the simulation of downscaled hydrology, as opposed
to changes in inter-annual variability (based on the Siegel-
Tukey test finding no significant differences between ob-
served and simulated scale/variability at any site). Based
on the KS test results, for BCSD, three sites had distribu-
tions of annual flow volumes that differed from the annual
flow volumes produced by the hydrologic model simulation
driven by observations. Similarly, CA differed at four of the
stream gauge sites. BCCA, by contrast, produced a distribu-
tion of annual flow volumes that were indistinguishable from
the observation-driven hydrologic model run, showing sub-
stantially improved downscaling skill even for annual mea-
sures of performance.
Three daily-scale streamflow metrics are evaluated in this
study: center timing (CT), 3-day peak flow, and 7-day low
flow. Center timing is defined as in Stewart et al. (2005) as
the day on which half of the annual (water year, 1 October–
30 September) flow volume has passed a particular point on
a stream. For each water year in the verification (or pro-
jection) period of 1977–1999 the metrics are calculated and
then the results are assembled into distributions for each met-
ric. These distributions are compared among downscaling
 
 33 
 1 
Figure 5. Center timing for each streamflow site using each downscaling method, and the 2 
observationally-derived streamflows. Day is day of the water year, so 1 corresponds to 3 
October 1. 4 
5 
Fig. 5. Center timing for each streamflow site using each down-
scaling method, and the observationally-derived streamflows. Day
is day of the water year, so 1 corresponds to 1 October.
methods and with the simulation using gridded 1/8 degree
observations to drive the VIC model. As was found with
the annual flow volumes (Table 2), the results of the Mann-
Whitney test very closely resemble the results of the KS test,
indicating that differences in the distribution of these daily
hydrologic metrics are due principally to differences in cen-
tral tendency rather than interannual variability, and thus KS
test results are relied upon as the primary measure of down-
scaling skill.
Figure 5 shows the performance of the three downscal-
ing techniques along with the observations-based stream-
flow simulation for the CT statistic. Since CT in snowmelt-
dominated basins tends to be driven more by temperature
than precipitation, the distribution of CTs simulated by CA
are able to capture the skill in daily temperature present in the
reanalysis (since temperature observations are assimilated in
the reanalysis product, bias is relatively low). CA appears
to perform better than BCSD at several locations, for exam-
ple OROVI, NF AM, and LK MC. What this demonstrates is
that there is skill in simulating CT at many sites with BCSD,
which assumes the distribution of daily values within any
month are statistically the same for the observed (or train-
ing) period of 1950–1976 as for the later projected period
of 1977–1999. The CA method, by contrast, recognizes
changes in the occurrence of large-scale climate patterns at
the daily scale, and produces downscaled daily values that re-
flect them, allowing the specific variations within months in
each given year to change in the projected period, which re-
sults in improved skill at some locations. BCCA does not ap-
pear to differ greatly from CA at most locations, suggesting
that the relatively low bias in reanalysis temperature causes
the bias correction step to have a relatively small effect on
this temperature-driven statistic. It should be noted that the
small (0.2 ◦C), but significant domain average temperature
difference between the 1950–1976 and 1977–1999 periods is
dwarfed by the large projections for later in the 21st century
Hydrol. Earth Syst. Sci., 14, 1125–1138, 2010 www.hydrol-earth-syst-sci.net/14/1125/2010/
E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California 1133
 
 34 
 1 
Figure 6. Center timing for the NF_AM site for 1977-1999 using each downscaling method, 2 
compared to the CT values for the observationally-derived streamflows. Units are day of the 3 
water year, as in Figure 5.  4 
Fig. 6. Center timing for the NF AM site for 1977–1999 us-
ing each downscaling method, compared to the CT values for the
observationally-derived streamflows. Units are day of the water
year, as in Fig. 5.
for this region (Cayan et al., 2008) of up to 4.5 ◦C. Thus, as
the climate diverges from the historical record to a greater de-
gree, it would be expected that the difference in skill between
BCSD and the analogue-based methods (CA and BCCA)
could become more stark.
Figure 6 shows the CT values for each water year for
the “projected” period of 1977–1999 at the NF AM site
for BCSD, CA, and BCCA relative to the observations-
driven simulated CT values. This supports the observation
in the prior paragraph, where the temperature-driven daily
CT statistic benefits from the large scale daily skill used by
the CA and BCCA downscaling methods, while the BCSD
method shows considerably less correlation, and suppressed
interannual variability, relative to the observations-driven CT
values. This suggests that skill in projections of how daily
temperature sequences may evolve under changed climatic
conditions can be captured by ingesting daily large-scale data
into a downscaling technique.
The first three columns of Table 3 summarize the KS test
performed to determine whether the 22 simulated CT val-
ues using the three downscaling methods can be assumed to
be drawn from the same distribution with 95% confidence.
This verifies that CA outperforms BCSD, providing a sta-
tistically significant improvement at two locations (NF AM
and LK MC). BCCA is generally as good or better than CA,
producing CT values with a distribution statistically indistin-
guishable from the CT values from the observationally driven
hydrologic simulation at all sites.
Figure 7 shows the results for the 3-day peak flow for dis-
tribution of values for each of the 22 water years from 1978–
1999 at each site. The statistical test results for peak flows
are in columns 4–6 in Table 3. In contrast to the CT mea-
sure, 3-day peak flow is much more highly driven by pre-
cipitation, which is less well represented in reanalysis, and
thus would be expected to benefit from the bias correction
 
 35 
 1 
Figure 7. 3-day peak flow for each streamflow site. Note the vertical axes are different for 2 
each of the panels. 3 
4 
Fig. 7. 3-day peak flow for each streamflow site. Note the vertical
axes are different for each of the panels.
to a greater degree than temperature driven phenomena. Fig-
ure 7 shows that CA has a tendency to somewhat underpre-
dict peak flow at most locations. BCCA produces visibly bet-
ter simulated peak flow values than CA at many sites (e.g.,
NF AM, FOL I, DPR I, MILLE, KINGS), with BCCA dis-
tributions showing a closer match to observations than CA.
Surprisingly, Table 3 shows peak flows derived using down-
scaled meteorology from all three techniques are statistically
indistinguishable from those driven by observations at all
sites at 95% confidence, so while BCCA appears to be an im-
provement over CA, the difference is small relative to natural
variability. This shows that, for precipitation-driven impacts,
the bias-correction step used in BCSD (and BCCA) effec-
tively accommodates the precipitation bias in the large-scale
raw forcing data. Also, the use of anomalies in CA, which
accounts for biases in the mean at the large scale, appears
to work adequately, if not as well as possible, for supporting
hydrologic skill of this peak flow statistic.
Figure 8 illustrates the performance of BCCA and BCSD
at one site relative to the hydrologic simulation using gridded
observations, showing one wet year and one dry year. For
the wet year both peak flows and low flows are captured rela-
tively well, compared to the observations-driven simulation,
for both BCSD and BCCA. BCCA shows the temporal corre-
spondence to the simulation driven by observations, demon-
strating that, even though the large scale reanalysis precipita-
tion is numerical model output rather than assimilated obser-
vations and has well-known biases, the bias correction pro-
cedure employed here recovers the daily signal present in the
observations. BCSD, by design, has no correspondence to
the sequencing in the daily observations-driven simulation.
However, even with its random generation of daily sequences
within any month, BCSD does produce numbers and mag-
nitudes of peak flows that resemble the observations-driven
peak flows.
www.hydrol-earth-syst-sci.net/14/1125/2010/ Hydrol. Earth Syst. Sci., 14, 1125–1138, 2010
1134 E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California
Table 3. Statistical test results for BCSD, CA, and BCCA for daily flow measures. A gauge name in bold face indicates that the distribution
of 22 values for downscaled streamflow differs from the observed distribution, based on a Kolmogorov-Smirnov 2-sample test (at p= 0.05).
Hence, non-bold face indicates the downscaling method produces values statistically indistinguishable from observations.
Center Timing 3- Day Peak 7-Day Low Flow
BCSD CA BCCA BCSD CA BCCA BCSD CA BCCA
SHAST SHAST SHAST SHAST SHAST SHAST SHAST SHAST SHAST
SAC B SAC B SAC B SAC B SAC B SAC B SAC B SAC B SAC B
OROVI OROVI OROVI OROVI OROVI OROVI OROVI OROVI OROVI
NF AM NF AM NF AM NF AM NF AM NF AM NF AM NF AM NF AM
FOL I FOL I FOL I FOL I FOL I FOL I FOL I FOL I FOL I
CONSU CONSU CONSU CONSU CONSU CONSU CONSU CONSU CONSU
PRD C PRD C PRD C PRD C PRD C PRD C PRD C PRD C PRD C
DPR I DPR I DPR I DPR I DPR I DPR I DPR I DPR I DPR I
LK MC LK MC LK MC LK MC LK MC LK MC LK MC LK MC LK MC
MILLE MILLE MILLE MILLE MILLE MILLE MILLE MILLE MILLE
KINGS KINGS KINGS KINGS KINGS KINGS KINGS KINGS KINGS
 
 36 
 1 
 
 
Figure 8. Simulated streamflow for the NF_AM site (listed in Table 1) using driving 2 
meteorology from BCCA and BCSD downscaling methods, and from the hydrologic model 3 
simulation driven by gridded observations. A wet water year (top panel) and dry water year 4 
(bottom panel) are shown. 5 
Fig. 8. Simulated streamflow for the NF AM site (listed in Ta-
ble 1) using driving meteorology from BCCA and BCSD down-
scaling metho s, and from the hydrologic m del simulation driv n
by gridded observations. A wet water year (top panel) and dry water
year (bottom panel) are shown.
The flows during the dry year in Fig. 8 show similar pat-
terns to the wet year. However, one example of the shortcom-
ing of selecting random daily sequences in BCSD is seen in
October–November where BCSD shows too many smaller
peak flows, whereas BCCA concentrates the flow on one
larger peak event, better matching the observations-driven
peak flow. The difficulty in matching the very low flows dur-
ing May–June, at the end of the snow melt season, in the
dry year by both downscaling procedures reinforces obser-
vations by others that small variations in precipitation can
result in larger differences in late season low flows (Vidal
and Wade, 2008). The large scale reanalysis signal of precip-
itation and temperature has been shown to be the most impor-
tant determinant of uncertainty in simulations of low flows,
with downscaling technique secondary (Wilby and Harris,
2006). Specifically related to the current study, Maurer and
Hidalgo (2008) found generally lower skill in reproducing
observed precipitation statistics with either the BCSD or the
CA downscaling technique (applied to the same reanalysis
data used in the current study), reflecting the limited daily
skill in the large-scale reanalysis precipitation fields. While
the bias correction included in each downscaling method can
accommodate systematic biases in the large-scale predictor,
it cannot produce skill where little exists in the large scale
signal. This demonstrates that since the different bias correc-
tion and downscaling procedures employed in this study will
inevitably still contain some biases at the fine scale, their ef-
fect on simulated flows may be especially evident during low
flow periods. However, it should be noted that Fig. 8 depicts
only one year; Fig. 5 shows that in general the seasonal cycle
of accumulation and runoff at the NF AM site, as expressed
by the streamflow timing, is well represented by the down-
scaled hydrology, both in mean and interannual variability,
especially by BCCA.
Simulating 7-day low flows with downscaled meteorology
is more problematic, as shown in Fig. 9 and columns 7–9
of Table 3. Several sites exhibit a distribution of low flows
that are statistically different for both BCSD and CA down-
scaling approaches from low flows simulated using gridded
observed meteorology. As with peak flows, CA appears to
Hydrol. Earth Syst. Sci., 14, 1125–1138, 2010 www.hydrol-earth-syst-sci.net/14/1125/2010/
E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California 1135
 
 37 
 1 
Figure 9. 7-day low flow simulated for each site. Note the y-axes have different scales for 2 
each panel. 3 
Fig. 9. 7-day low flow simulated for each site. Note the y-axes have
different scales for each panel.
have a tendency to produce low flows that are lower than ob-
served at many sites. While BCSD produces reasonable val-
ues at some sites, low flows are overpredicted in some loca-
tions, especially apparent at NF AM and FOL I. BCCA, by
contrast, appears to produce low flows values that are closer
to those produced by the observationally-driven simulation.
Table 3 bears these observations out, showing that at two
sites BCSD produces low flows different from observations,
and at four sites CA produces different values from observa-
tions, with high statistical confidence. For the low flow dis-
tribution, BCCA is again statistically indistinguishable from
observationally-driven hydrology at all sites. It is evident
that the choice of downscaling method may influence results
more for low flows than for other measures of streamflow.
A factor contributing to this may be the relatively greater re-
analysis skill (lower biases compared to reanalysis precip-
itation) for daily temperature, allowing the bias correction
to have a greater effect. Since low flows would be affected
by evapotranspiration more so than peak flows, a better rep-
resentation of daily temperatures, more closely resembling
observations, would improve skill for the BCCA method.
As a postscript, the improvement seen in applying the
bias correction to large-scale daily forcing data begged the
question of whether a post-downscaling bias correction, ap-
plied using the same quantile mapping approach at the 1/8
degree spatial scale, could provide additional improvement
in simulated hydrology. We conducted this experiment us-
ing both the BCCA and the BCSD downscaled meteorology,
performing quantile mapping bias correction of daily precip-
itation, and maximum and minimum temperatures, again us-
ing 1950–1976 as the “observed” period and 1977–1999 as
“projections.” We found no consistent improvement in the
simulated hydrologic measures used in this paper. This sug-
gested that, since the systematic, large-scale biases had al-
ready been removed in both BCSD and BCCA, the remain-
ing fine scale biases during 1950–1976 were not generally
the same as 1977–1999, and the assumptions embedded in
the quantile mapping at fine scales were not substantiated in
this study.
4 Summary and conclusions
We statistically downscaled NCEP/NCAR reanalysis precip-
itation and temperature over the western US using three dif-
ferent methods and drove a hydrologic model with the re-
sulting sets of downscaled meteorology. The historic record
was divided into an “observed” period of 1950–1976 and
“projections” from 1977–1999. Streamflow was estimated at
11 sites across California, and these were analyzed to deter-
mine the ability to estimate three streamflow statistics impor-
tant to hydrology: seasonal timing, peak flow, and low flow.
One method, BCSD, uses monthly large-scale output, and
rescales a historic month to estimate daily variability within
each month. A second method, CA, uses daily large-scale
output to downscale daily precipitation and temperature to a
1/8 degree grid. A new hybrid, the third method, BCCA,
combined the bias correction step of BCSD and the daily
downscaling of CA.
We found that daily large scale skill can be effectively
downscaled from the large scale to the regional scale to
simulate these streamflow statistics. Reanalysis assimilates
daily temperature observations, and thus has some large-
scale skill for temperature, though reanalysis precipitation is
solely model output and is prone to substantial biases. The
timing of the annual hydrograph was captured by all down-
scaling methods at most locations, though the hybrid BCCA
method was the only one to perform well at all sites. For
downscaling meteorology to generate extreme peak flows (3-
day annual peaks), all methods performed well at all sites.
The annual flow volume was reproduced with better skill by
the hybrid BCCA method than either the BCSD or CA meth-
ods, showing that the improvement with the BCCA method
is also evident at temporal scales longer than daily.
Low flows were more difficult to capture with the down-
scaled data. While most of the streamflow sites included
in our study had low flows simulated with downscaled data
that were statistically indistinguishable from those derived
when driving the hydrologic model with observations, BCSD
and CA had shortcomings. As with the seasonal flow tim-
ing statistic the BCCA method outperformed both the BCSD
and the CA methods, statistically matching observationally-
driven low flows at all sites.
In summary, to downscale large-scale climate data to gen-
erate estimates of extreme hydrologic events, downscaling
daily large-scale output can provide measurable improve-
ments in regional hydrologic skill, exceeding that of simply
assuming that variability within a month will be similar to
historical variability. However, without a bias correction step
to correct large-scale biases (which can only be expected to
be worse in free-running GCMs than in the data-assimilation
www.hydrol-earth-syst-sci.net/14/1125/2010/ Hydrol. Earth Syst. Sci., 14, 1125–1138, 2010
1136 E. P. Maurer et al.: Assessment of climate change impacts on daily streamflow in California
constrained reanalysis model outputs), the skillful signal in
the daily data was less likely to be exhibited in the down-
scaled data and the resulting hydrology. The bias correction
step, applied to daily large-scale meteorology prior to down-
scaling, produced some significant improvements in skill in
simulating hydrologic extremes. The biases exhibited at the
large scale are in both mean and variability, thus working
with anomalies (as in the CA method) is not adequate to
compensate for large scale biases, but the quantile mapping
approach used in BCCA appears more promising.
Acknowledgements. Support for this work was provided by the
State of California through the California Energy Commission
Public Interest Energy Research (PIER) Program. During the
development of the majority of this work, HH was working as
a scientist at Scripps Institution of Oceanography, and was also
funded by the United States Department of Energy. The CALFED
postdoctoral fellowship provided partial salary support for TD. The
authors are grateful to two anonymous reviewers for their careful
reading and insightful comments, which significantly improved the
manuscript.
Edited by: F. Pappenberger
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