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| Titel |
Propagation of biases in humidity in the estimation of global irrigation water |
| VerfasserIn |
Y. Masaki, N. Hanasaki, K. Takahashi, Y. Hijioka |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2190-4979
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| Digitales Dokument |
URL |
| Erschienen |
In: Earth System Dynamics ; 6, no. 2 ; Nr. 6, no. 2 (2015-07-20), S.461-484 |
| Datensatznummer |
250115471
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| Publikation (Nr.) |
 copernicus.org/esd-6-461-2015.pdf |
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| Zusammenfassung |
| Future projections on irrigation water under a changing climate are
highly dependent on meteorological data derived from general
circulation models (GCMs). Since climate projections include biases,
bias correction is widely used to adjust meteorological elements, such
as the atmospheric temperature and precipitation, but less attention
has been paid to biases in humidity. Hence, in many cases,
uncorrected humidity data
have been directly used to analyze the impact of future
climate change. In this study, we examined how the biases remaining
in the humidity data of five GCMs propagate into the estimation of
irrigation water demand and consumption from rivers using the global
hydrological model (GHM) H08. First, to determine the effects of
humidity bias across GCMs, we ran H08 with GCM-based
meteorological forcing data sets
distributed by the Inter-Sectoral Impact Model Intercomparison Project
(ISI-MIP).
A state-of-the-art bias correction method was applied to the data sets
without correcting biases in humidity.
Differences in the monthly relative humidity
amounted to 11.7 to 20.4 % RH (percentage
relative humidity)
across the GCMs and propagated into differences in
the estimated irrigation water demand, resulting in a range
between 1152.6 and 1435.5 km3 yr−1 for 1971–2000.
Differences in humidity
also propagated into future projections. Second, sensitivity analysis
with hypothetical humidity biases of ±5 % RH added homogeneously
worldwide revealed the large negative sensitivity of irrigation
water abstraction in India and East China,
which are heavily irrigated. Third, we performed another set of
simulations with bias-corrected humidity data to examine whether bias
correction of the humidity can reduce uncertainties in irrigation
water across the GCMs. The results showed that bias correction, even
with a primitive methodology that only adjusts the monthly
climatological relative humidity, helped reduce uncertainties across
the GCMs: by using bias-corrected humidity data,
the uncertainty ranges of irrigation water demand across the five GCMs
were successfully reduced from 282.9 to 167.0 km3 yr−1
for the present period, and from 381.1 to 214.8 km3 yr−1
for the future period (RCP8.5, 2070–2099).
Although different GHMs have different sensitivities to
atmospheric humidity because different types
of potential evapotranspiration formulae are implemented in them,
bias correction of the
humidity should be applied to forcing data, particularly for
the evaluation of evapotranspiration and irrigation water. |
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