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Toward improved seasonal forecasting of water resources and North American monsoon
precipitation in the Southwestern United States
Hsin-I Chang1*, Christopher L. Castro1, Sharon Megdal2, Peter Troch1, Rajarshi Mukherjee1 ,Elia Tapia
1Dept.
of Hydrology and Atmospheric Sciences and Water Resources Research Center, University of Arizona, Tucson, AZ
Corresponding Author E-mail: [email protected]
Introduction and Motivation
Current and future streamflow in river basins within the Southwest is an increasing
concern for water management. Population growth and residential construction have
increased water demand in the Colorado River watershed, where water supply is highly
sensitive to climate variability and change. Though there is evidence that climate change is
presently occurring from the recent observational record on global and continental scales.
How these changes bear on the hydroclimate at the river basin scale is still uncertain.
Observed streamflow declines in the Colorado Basin in recent years are likely due to a
synergistic combination of anthropogenic global warming and natural climate variability,
which are creating an overall warmer and more extreme climate.
More reliable
projections of basin streamflow, that takes into account these dual effects, are urgently
needed for long-term water resource planning.
The research objective is to characterize how the
combination of climate change and natural climate
variability is changing the hydroclimate in the
southwestern US. A multi-scale downscaling modeling
approach is designed to incorporate both regional climate
and hydrologic modeling components.
Fig. 1: Upper and Lower
Colorado River Basins
Methodological approach
Long-Term Regional Climate Projections
Regional climate model data is generated by the WRF
model as part of North American CORDEX, at 25 and 50
km grid spacing. The RCM forcing is from selected IPCC
CMIP5 models (MPI-ECHAM6, HadGEM2). Fig. 2 shows
the mean and extreme precipitation climatology
difference between 20th and early 21st century, similar
to our prior downscaled CMIP3 study (Chang et al.
2015)
We consider the climatological performance of select dynamically
downscaled CMIP5 GCMs by applying the CPVM methodology to
analyze trends in extreme daily temperature and precipitation. Here we
show the results with WRF-ECHAM6. The trends in extremes depart
from the average trends shown in Fig. 2, where most of CONUS will be
drier for the near future. Early summer precipitation extremes in
association with Pacific SST variability have a similar trend between CPC
observed data and dynamically downscaled WRF-ECHAM6 results. Under
a positive CPVM signal (positive ENSO), extreme precipitation increases
in Northwestern U.S., parts of Texas, and Midwest (Fig. 6), thereby
enhancing the positive precipitation anomalies that occur in association
with this phase of the mode.
The most urgent need for water resource stakeholders is to find out what
is the trend of climate extremes in the near future, crucial for risk
management and resource planning. The near future trend of extremes
(precipitation and temperature, Fig. 7) indicates when a negative ENSO
signal is present, the anti-phase relationship in precipitation between
Southwest and Northwest will be intensifying. Therefore, wet region
become wetter cooler and dry regions become drier and warmer.
Fig. 6 : Extreme June/July precipitation difference rate (%) under
positive CPVM signal. Left: CPC, right: WRF-ECHAM6 (50km).
Period: [1981-2010] vs [1950-1980].
Fig. 7: WRF-ECHAM6 June/July extreme precipitation (left) and
extreme temperature (right) difference (K) under negative
CPVM mode. Period: [2011-2040] vs [1950-2010].
Fig. 8 shows CPVM analysis results with WRF-ECHAM6 simulations for 25
and 50 km grid spacing. The magnitude of precipitation increase over
lower Great Plains is reduced in the 25km simulation. The precipitation
pattern over complex terrain is also better resolved.
Fig. 8: Positive CPVM analysis in WRF-ECHAM6 as in Fig. 6. Left: 50 Km
resolution, right: 25km resolution. Period: [2011-2040] vs [1950-2010].
Colorado River Sub-basin Streamflow Projections under the
influence of climate change
Fig. 2: WRF-ECHAM6 mean and extreme
(>90%) precipitation difference (%)
Climate Analysis following Pacific SST variability
A new analytical approach (Combined Pacific Variability
Mode (CPVM)) objectively evaluates North American
climate variability related to remote sea Pacific SST
variability. A positive phase of the CPVM typically leads
to a drier Southwest and wetter Northwest during
summer, and vice versa for the negative phase. (Fig. 3) Fig. 3: Historic precipitation pattern under
positive CPVM [positive ENSO] mode
Streamflow Hydrologic Modeling with New Bias-Correction methodology
Colorado streamflow projection is conducted using
the Variable Infiltration Capacity (VIC) model,
calibrated for both upper and lower Colorado River
Basins in 1/8°resolution. Fig. 4 compares the 60-yr
simulated streamflow from Maurer et al. 2002
observed data to naturalized streamflow at Lee’s
Ferry. Applying climate data to basin-scale
hydrologic modeling, bias correction is often
required. A new bias correction technique, Scaled
Distribution Mapping (SDM, Switanek et al. 2016) is
applied to the RCM temperature, wind and
precipitation data. This technique not only corrects
the seasonal biases present in the historical and
future climate data, but also preserves the relative
change or trend in the raw climate model.
Summer Precipitation and Temperature Variability with Respect to Pacific SST Variability:
Period comparison: [1950-2010] vs [2011-2040] using Dynamically Downscaled CMIP5
Figure 4: Naturalized Streamflow at Lee’s Ferry
vs. simulated streamflow for Upper Colorado
basin.
Statistically
downscaled
CMIP
models projects an increase in high
and moderate flows. The equivalent
dynamically downscaled projection
shows larger streamflow reduction
in flows. (Fig. 9). There is also a shift
in the hydrograph peak from June to
May (Fig. 10)
Overall basin-scale precipitation
shows an increasing winter and
decreasing summer trend (Fig. 11).
The
dynamically
downscaled
projection has much drier future, and
reflects the larger streamflow
reduction shown in Fig. 10. Increasing
future winter precipitation is due to
warmer average temperature. The
changes in the spatial pattern of
precipitation
with
dynamical
downscaling more realistically follows
the orographic features, as compared
to statistical downscaling (not
shown).
Conclusions
• Regional climate datasets from dynamically
downscaled selected CMIP5 models have a
reasonable representation of summer precipitation.
Fig. 9: Colorado River near future
(2011-2040) streamflow projections
from statistical and dynamical
downscaled CMIP products.
Streamflow
Regime
• Dominant precipitation and temperature extremes
reflect strong Pacific SST influences similar to
observations and our previous CMIP3 analysis (Chang
et al. 2015)
• Streamflow projections for Colorado River sub-basins
have various degrees of future reduction, with
dynamical downscaling yielding the largest reduction.
• Methods of bias correction and downscaling
techniques have am influence on the magnitude of
streamflow reduction projected.
Fig. 10: Upper Colorado River
basin streamflow regime curve.
• The streamflow regime curve shifts from summer to
spring in the later part of 21st century
• Both statistical and dynamical downscaled
precipitation shows increases in winter and decreases
in summer, which directly relates to the shifts in
streamflow regime curve.
Fig. 11: Winter (left) and summer
(right) basin-scale precipitation %
change comparing historic and far
future periods. Shown with respect
to elevation.
Acknowledgements
This work is supported by the Department of Interior, United States
of Geological Survey, Southwest Climate Science Center; Bureau of
Reclamation, Salt River Project and Central Arizona Project;
Transboundary Aquifer Assessment Program, Water Resources
Research Center