Gridded Data

Many bioclimatic modelling efforts are based on the use of gridded climatic datasets that inadequately account
for topoclimatic effects. We evaluate a mesoscale atmospheric model, TAPM, as an alternative means of generating spatially
explicit topoclimatic data applicable to small scale (200-m resolution) bioclimatic research. Temporal and spatial assessments
of TAPM were carried out across New Zealand’s mountains. First, goodness-of-fit analyses of TAPM-simulated meteorology
against three weather station observations for January and July, 2001–2007, were carried out. Second, TAPM-simulated
January/July mean daily temperature extremes and wind speeds were compared against interpolated climate grid data
for approximately 37 000 grid cells across New Zealand. Third, deviations of TAPM-simulated data from the gridded
climate dataset baseline were modelled against GIS-derived landscape position in order to quantify the nature and extent
of topoclimatic effects. Temporal assessments indicated that TAPM provided reasonably accurate simulations for hourly
temperatures and wind speeds, moderately good estimates for solar radiation and poor estimates for humidity. On the whole,
the greatest simulation errors occurred for daily extreme values. Relative to the climate grid baseline, TAPM-simulated
temperatures and wind speeds were also relatively unbiased. However, TAPM-simulated values varied widely around climate
grid values, and these deviations were significantly related to landscape position. The relative congruence of simulation biases
across time and space suggests that uncertainties are systematic and inherent to the model architecture rather than due to
location-related variability. While results suggest caution in using TAPM to simulate daily extremes, biases may be less
problematic for studies where relative differences in topoclimate across locations are of interest. Topoclimatic effects appear
pervasive across New Zealand’s mountain systems and are predictable as a function of topography. On the basis of our results,
further investigations into the use of mesoscale atmospheric models to generate topoclimate data in support of research in
mountainous areas are merited.

Central Chile is facing dramatic projections of climate change, with a consensus for declining precipitation, negatively affecting hydropower generation and irrigated agriculture. Rising from sea level to 6000 mwithin a distance of 200 km, precipitation characterization is difficult because of a lack of long-term observations, especially at higher elevations. For understanding current mean and extreme conditions and recent hydro- climatological change, as well as to provide a baseline for downscaling climate model projections, a tempo- rally and spatially complete dataset of daily meteorology is essential. The authors use a gridded global daily meteorological dataset at 0.258 resolution for the period 1948–2008, adjusted by monthly precipitation ob- servations interpolated to the same grid using a cokriging method with elevation as a covariate. For validation, daily statistics of the adjusted gridded precipitation are compared to station observations. For further vali- dation, a hydrology model is driven with the gridded 0.258 meteorology and streamflow statistics are com- pared with observed flow. The high elevation precipitation is validated by comparing the simulated snow extent to Moderate Resolution Imaging Spectroradiometer (MODIS) images. Results show that the daily meteorology with the adjusted precipitation can accurately capture the statistical properties of extreme events as well as the sequence of wet and dry events, with hydrological model results displaying reasonable agree- ment with observed streamflow and snow extent. This demonstrates the successful use of a global gridded data product in a relatively data-sparse region to capture hydroclimatological characteristics and extremes.

Design of hydropower project components is very much dependent on the correct assessment of hydrology of the project. Hydropower project hydrology is essentially the estimation of water availability and design floods. A reliable assessment of project hydrology is generally handicapped by non-availability of sufficient historical data in the remote mountainous regions where hydropower projects are mostly located. Small hydropower development, which is the most prospective in the days to come, is more constrained because data scarcity is more acute in the tributary catchments. Even in some areas where historical data is available, non-stationarity of the data observed in the last few decades coupled with climate change prospects looming over in the future have rendered the job more critical. Some recent developments in technology are however offering the possibility of increasing the much needed confidence level of the hydrological projections. An important development is in the repre...