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2007 ), the Manasi River in north-west China (Yuan et al.,
2007 ) and the Maule River in Chile (1590
and then compute runoff through the use of a rating curve.
Alsdorf et al.( 2007 ) reviews progress in this area of
research.
Figure 5.21 presents the results from an application of
remote sensing to obtain distributed estimates of annual
runoff across Sri Lanka, taken from Bastiaanssen and
Chandrapala ( 2003 ). In this case, the SEBAL technique
of Bastiaanssen et al.( 1998 ) was used to first estimate
annual actual evaporation, and then, through combining
these estimates with measured and interpolated rainfall, a
rainfall surplus (gross rainfall minus actual evaporation)
was obtained, which was then partitioned into several large
basins. Figure 5.21b shows comparisons of monthly runoff
for two selected river basins, and Figure 5.21c presents
comparisons between measured and estimated (based on
remote sensing) annual runoff volumes for the majority of
river basins across the country, indicating that there is
considerable potential for this method to estimate annual
runoff over large river basins.
-
2000) (Urrutia
et al., 2011 ).
All the above techniques are based on correlating annual
runoff to a proxy time series record. Saito et al.( 2008 ) and
Gray and McCabe ( 2010 ) extended this approach by
incorporating a simple water balance model with annual
time series of tree ring data modified to represent annual
precipitation as input. Gray and McCabe ( 2010 ) incorpor-
ated temperature as well. The procedure requires further
research but the results are encouraging.
Beyond tree ring analysis Xu et al.( 2012 ) recently
showed that vegetation cover may be useful as a proxy
for annual runoff. Their study used elasticity analysis to
quantify the effects of climate variability on hydrological
partitioning (including total, surface and subsurface runoff)
and vegetation cover (including total, woody and non-
woody vegetation cover). They concluded that annual
runoff, evaporation and runoff coefficient increase with
vegetation cover for catchments in which woody vegeta-
tion is dominant and annual precipitation is relatively high.
These results suggest that vegetation cover may be used as
a runoff proxy, but further research is needed.
5.5 Comparative assessment
The aim of the comparative assessment of annual runoff
predictions in ungauged basins is to learn from the similar-
ities and differences between catchments in different
places, and to interpret the differences in performance in
terms of
Remote sensing
Despite tremendous promise, remote sensing data do not
yet offer reliable means to estimate time series of annual
runoff at ungauged sites. Research to estimate annual run-
off via remote sensing follows two lines of enquiry. In the
first approach, remote sensing is used to estimate the
components of the water balance independently. For
example, evaporation can be estimated using thermal
methods that relate evaporation to the temperature differ-
ence between soil and vegetation canopies; or through
residual energy balance techniques in which thermal obser-
vations of air and surface temperature are used to estimate
net radiation and soil heat flux, and a variety of competing
schemes then employed to parameterise sensible heat
fluxes. These techniques are employed by the land surface
schemes SEBAL, SEBS and ALEXI/DIS-ALEXI (Couralt
et al., 2005 ; Bastiaanssen et al., 1998 ; Anderson and
Kustas, 2008 ). Precipitation can be estimated using satel-
lite radar data (e.g., TRMM), microwave products are
available to estimate shallow soil moisture and, at large
scales, the GRACE mission can constrain estimates of
storage change. Runoff is then computed as the residual
of the water balance. Gao et al.( 2010 ), however, found
that closure of the water balance is not currently possible
using remote sensing in large catchments. Combining
modelling and observations through data assimilation,
however, offers an opportunity to reduce modelling errors.
In the second approach, remote sensing is used to estimate
hydraulic aspects of surface water, such as river top width,
landscape controls.
Understanding these controls sheds light on the nature of
catchments as complex systems and provides guidance on
what methods to choose in a particular environment. The
assessment is performed at two levels (see Section 2.4.3).
The Level 1 assessment is a meta-analysis of studies
reported in the literature. The Level 2 assessment involves
a more focused and detailed analysis of individual basins
from selected studies of Level 1 in terms of how the
performance depends on climate and catchment character-
istics as well as on the method chosen. In Level 1 and
Level 2 assessments, the performance was evaluated by
leave-one-out cross-validation (or just goodness of fitted
regressions where the cross-validation results were not
available). In the leave-one-out cross-validation each
catchment was treated as ungauged and the runoff predic-
tions were then compared to the observed runoff. The
performances obtained by the comparative assessment are
estimates of the total uncertainty of runoff predictions in
these ungauged basins.
the underlying climate
-
5.5.1 Level 1 assessment
Table A5.1 lists the 34 studies evaluating mean annual
runoff and Table A5.2 lists the 9 studies evaluating inter-
annual runoff variability used in the Level 1 assessment.
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