 |
| Titel |
Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data |
| VerfasserIn |
M. C. Peel, R. Srikanthan, T. A. McMahon, D. J. Karoly |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 4 ; Nr. 19, no. 4 (2015-04-08), S.1615-1639 |
| Datensatznummer |
250120674
|
| Publikation (Nr.) |
 copernicus.org/hess-19-1615-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| Two key sources of uncertainty in projections of future runoff for climate
change impact assessments are uncertainty between global climate models
(GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM
output that occurs when running a scenario multiple times but each run has
slightly different, but equally plausible, initial conditions. The limited
number of runs available for each GCM and scenario combination within the
Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5)
data sets, limits the assessment of within-GCM uncertainty. In this second of
two companion papers, the primary aim is to present a proof-of-concept
approximation of within-GCM uncertainty for monthly precipitation and
temperature projections and to assess the impact of within-GCM uncertainty
on modelled runoff for climate change impact assessments. A secondary aim is
to assess the impact of between-GCM uncertainty on modelled runoff. Here we
approximate within-GCM uncertainty by developing non-stationary stochastic
replicates of GCM monthly precipitation and temperature data. These
replicates are input to an off-line hydrologic model to assess the impact of
within-GCM uncertainty on projected annual runoff and reservoir yield. We
adopt stochastic replicates of available GCM runs to approximate within-GCM
uncertainty because large ensembles, hundreds of runs, for a given GCM and
scenario are unavailable, other than the Climateprediction.net data set for
the Hadley Centre GCM. To date within-GCM uncertainty has received little
attention in the hydrologic climate change impact literature and this
analysis provides an approximation of the uncertainty in projected runoff,
and reservoir yield, due to within- and between-GCM uncertainty of
precipitation and temperature projections. In the companion paper, McMahon
et al. (2015) sought to reduce between-GCM uncertainty by removing poorly
performing GCMs, resulting in a selection of five better performing GCMs
from CMIP3 for use in this paper. Here we present within- and between-GCM
uncertainty results in mean annual precipitation (MAP), mean annual temperature
(MAT),
mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of
runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide
catchments. Based on 100 stochastic replicates of each GCM run at each
catchment, within-GCM uncertainty was assessed in relative form as the
standard deviation expressed as a percentage of the mean of the 100
replicate values of each variable. The average relative within-GCM
uncertainties from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were MAP
4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The
Gould–Dincer Gamma (G-DG) procedure was applied to each annual runoff time series
for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average
uncertainties in reservoir yield due to within-GCM uncertainty from the 17
catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our
approximation of within-GCM uncertainty is expected to be an underestimate
due to not replicating the GCM trend. However, our results indicate that
within-GCM uncertainty is important when interpreting climate change impact
assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and
reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall
within twice their respective relative uncertainty (standard
deviation/mean). Within-GCM uncertainty has significant implications for
interpreting climate change impact assessments that report future changes
within our range of uncertainty for a given variable – these projected
changes may be due solely to within-GCM uncertainty. Since within-GCM
variability is amplified from precipitation to runoff and then to reservoir
yield, climate change impact assessments that do not take into account
within-GCM uncertainty risk providing water resources management decision
makers with a sense of certainty that is unjustified. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|