3.4 Objective 4: implications for estimating the magnitude ofEwc across large catchments in complex terrain
The results from objectives 1 to 3 show that Ewc losses up to
approximately 40 mm d-1 have been observed at
temperate sites around the world and that meteorological conditions that
have the potential to give rise to such large losses can exist in
mountainous regions of the UK. However, these findings must be treated
with caution because concurrent meteorological observations are rarely
reported with CWB Ewc data, particularly during extreme events,
and Penman-Monteith estimates are extremely sensitive toestimated aerodynamic exchange and small changes in RH at
the higher windspeeds that often prevail during the large rainfall
events considered here. The analysis has also shown that both wind speed
and RH varies significantly with spatial location. Given that Ewc
estimates are required for hydrological simulation of large catchments,
a representation of this spatially variable control of Ewcmagnitude is required; it is not appropriate to sample a statistical
distribution of Ewc loss generated from the worldwide observations ofEwc data (for a given gross rainfall total) as the
autocorrelation of Ewc , controlled by autocorrelated
meteorological variables, through sequences of real events is
needed. With respect to this requirement, even a spatially sparse time
series of meteorological observations contains important information
describing temporal patterns of some of the primary controls onEwc and this information must be retained. Spatial interpolation
and extrapolation from these sparse meteorological observations will
inevitably be inherently uncertain but is an important prerequisite for
appropriate estimation of Ewc losses.
Although simple empirical models can be used to estimate Ewc where there
is a scarcity of adequate meteorological data and knowledge of
appropriate parameter values for more complex models (e.g. see Lu,
McNulty & Amatya, 1995), their use is limited as they may not
explicitly include important meteorological controls. Consequently, the
Penman-Monteith equation is still used to simulate evaporation from
wetted surfaces in the majority of Ewc models (Muzylo et
al ., 2009). Thus, Penman-Monteith equation remains a useful method to
determine the potential for Ewc loss but the magnitude of any
estimates made will be highly uncertain without meaningful calibration
of critical and sensitive parameters such as \(r_{a}\_s\). However, as
there are so few Ewc data associated with concurrent
meteorological observations, particularly large rainfall events, it is
rarely possible to calibrate the parameters of the Penman-Monteith
equation and any calibration would need to include the joint-calibration
of parameters of an (e.g. Rutter-type) effective canopy store
model (e.g. see Calder, 1977).
Our theoretical analyses show that it is possible to get a very wide
range of Ewc estimates depending upon, in particular, the way
that \(r_{a}\_s\) is estimated. These analyses used 3 scenarios of\(r_{a}\_s\) which were based upon a range of published values derived
both directly from micrometeorological observations and via model
calibration. Ratios of \(z0\_s\)/\(z0\_m\) have been reported to be:
of the order 0.1-0.2 (Klingaman, Levia, & Frost, 2007; Lankreijer,
Hendriks, & Klaassen, 1993); approximately 0.3-0.5 (Brutstaert, 1982,
p114; Stewart & Thom, 1973) and around 1 in some cases (Bosvelt, 1999;
Gash, Valente, & David, 1999; Moors, 2012). Significant uncertainties
exist when estimating \(r_{a}\_m\) and the relative magnitude of\(r_{a}\_s\) compared to \(r_{a}\_m\). When only momentum is
considered, representing the degree of exchange is not simple as it has
been shown to vary, and to be enhanced compared to theoretical
estimates, in complex terrain and over tall canopies (Cellier &
Brunet,1992; Holwerda et al ., 2012); \(r_{a}\_m\) also varies
with canopy roughness and canopy density (Brutstaert, 1982, Fig. 5.1;
Cellier & Brunet,1992; Holwerda et al ., 2012) as well as
atmospheric stability and wind speed (Bosvelt, 1999; Cellier &
Brunet,1992; Szeicz, & Endrödi, 1969). The ratio \(z0\_s\)/\(z0\_m\)also varies widely and with the same factors as \(r_{a}\_m\) and
current understanding of scalar exchange for tall canopies in complex
terrain remains rudimentary (Belcher, Harman & Finnigan, 2012). There
are, however, a relatively large number of published studies which
report \(r_{a}\_m\) and \(r_{a}\_s\) for various vegetation of
differing roughness which may help elucidate the relevant range of\(r_{a}\_s\) for use in Ewc estimation for a given application:
a review of these studies is, however, beyond the scope of this paper.
Given the need for interpolation and extrapolation from sparse
meteorological data to estimate meteorological controls on Ewcspatially, uncertainties will be very large such that a scenario-based
approach may be most appropriate. Any defined scenario will beconditional on the evidence base used in its development and any
additional modelling assumptions. The conditionality of each scenario
must be made explicit and each scenario can be associated with a
confidence-weighting which can be propagated to simulation results. This
will be the subject of future publications.