Abstract: In order to utilize results from global climate models,
dynamical and statistical downscaling techniques have been
developed. For dynamical downscaling, usually a limited area
numerical model is used, with associated high computational cost.
This research proposes dynamic equation for specific space-time
regional climate downscaling from the Educational Global Climate
Model (EdGCM) for Southeast Asia. The equation is for surface air
temperature. This equation provides downscaling values of surface
air temperature at any specific location and time without running a
regional climate model. In the proposed equations, surface air
temperature is approximated from ground temperature, sensible heat
flux and 2m wind speed. Results from the application of the equation
show that the errors from the proposed equations are less than the
errors for direct interpolation from EdGCM.
Abstract: Recent perceived climate variability raises concerns
with unprecedented hydrological phenomena and extremes.
Distribution and circulation of the waters of the Earth become
increasingly difficult to determine because of additional uncertainty
related to anthropogenic emissions. The world wide observed
changes in the large-scale hydrological cycle have been related to an
increase in the observed temperature over several decades. Although
the effect of change in climate on hydrology provides a general
picture of possible hydrological global change, new tools and
frameworks for modelling hydrological series with nonstationary
characteristics at finer scales, are required for assessing climate
change impacts. Of the downscaling techniques, dynamic
downscaling is usually based on the use of Regional Climate Models
(RCMs), which generate finer resolution output based on atmospheric
physics over a region using General Circulation Model (GCM) fields
as boundary conditions. However, RCMs are not expected to capture
the observed spatial precipitation extremes at a fine cell scale or at a
basin scale. Statistical downscaling derives a statistical or empirical
relationship between the variables simulated by the GCMs, called
predictors, and station-scale hydrologic variables, called predictands.
The main focus of the paper is on the need for using statistical
downscaling techniques for projection of local hydrometeorological
variables under climate change scenarios. The projections can be then
served as a means of input source to various hydrologic models to
obtain streamflow, evapotranspiration, soil moisture and other
hydrological variables of interest.
Abstract: Many water supply systems in Australia are currently
undergoing significant reconfiguration due to reductions in long term
average rainfall and resulting low inflows to water supply reservoirs
since the second half of the 20th century. When water supply systems
undergo change, it is necessary to develop new operating rules,
which should consider climate, because the climate change is likely
to further reduce inflows. In addition, water resource systems are
increasingly intended to be operated to meet complex and multiple
objectives representing social, economic, environmental and
sustainability criteria. This is further complicated by conflicting
preferences on these objectives from diverse stakeholders. This paper
describes a methodology to develop optimum operating rules for
complex multi-reservoir systems undergoing significant change,
considering all of the above issues. The methodology is demonstrated
using the Grampians water supply system in northwest Victoria,
Australia. Initial work conducted on the project is also presented in
this paper.