A geostatistical approach to down-scale climate forecasts

Posted 17 February 2013

A storm from space (credit: NASA).

Researchers have found a new technique for generating small-scale climate models from large-scale climate models.

While general models of global circulation are the tool of choice for forecasting the effects of climate change, their spatial resolutions are too broad for the needs of regional planners.

To provide locally-relevant information, modellers typically employ one of two techniques: producing a new forecast using a regional dynamic model, or statistically down-scaling the projections of the larger model.

Jha et al. have proposed a geostatistical technique to translate climate modelling results onto a smaller spatial scale.

This approach geostatistical approach used climate model output from a regional dynamical model for the years 1985 to 2005 to create an image of the Murray-Darling Basin, a 1 million square kilometre watershed in south eastern Australia.

From the model data, the authors identified spatial patterns for three variables: the latent heat flux, soil moisture, and the temperature of the uppermost land surface layer.

The regional model used could produce projections at two different resolutions, with either 50 or 10 square kilometre grid cells.

For the years 1985 to 2005, the authors used model calculations at both resolutions to train their model and learn the relationship between large- and small-scale patterns. For 2006, the authors used their geostatistical technique to statistically downscale the 50 kilometre-resolution model output onto a 10 kilometre grid. They used the 10 kilometre-resolution model output to check their approach.

The authors found that for latent heat flux, surface temperature, and soil moisture, their approach did a good job down-scaling the 50-kilometre model output.

For soil moisture, high resolution artefacts such as rivers and lakes unavoidably impacted the downscaled response.

The authors suggest that although they used a high-resolution regional model to determine spatial patterns, the same could be done using remote sensing techniques.

The technique also has the potential in feature sharpening in remote sensing applications through image fusion, filling gaps in spatial data, and aggregating/disaggregating hydrological and groundwater variables for catchment studies.

Links

Latest news

CWI team member gives keynote address at Water Institute for Sustainability Forum in Thailand

CWI team member gives keynote address at Water Institute for Sustainability Forum in Thailand

9 February 2017

Connected Waters Initiative research fellow Dr Landon Halloran was a keynote speaker at the Water Institute for Sustainability Forum in Bangkok, Thailand in January 2017.

Read more…

CWI team member appointed to mining industry advisory panel

CWI team member appointed to mining industry advisory panel

3 November 2016

Dr Wendy Timms was recently appointed to the Independent Expert Scientific Committee on Coal Seam Gas and Large Coal Mining Development (IESC).

Read more…

CWI at IAH 2016

CWI at IAH 2016

6 October 2016

CWI team members presented their research at the recent 43rd annual congress Hydrogeology Congress in Montpellier, France.

Read more…

Integrated Groundwater Management: Concepts, Approaches and Challenges

Integrated Groundwater Management: Concepts, Approaches and Challenges

5 September 2016

Contributions from the CWI team feature in a new open access book that is among the first to cover hydrogeology, sustainable development, water policy, governance, and management.

Read more…

Rethinking Water Law and Governance: Successes, Challenges and Future Directions

Rethinking Water Law and Governance: Successes, Challenges and Future Directions

2 August 2016

Outcomes of the recent meeting of water law specialists hosted by the UNSW Faculty of Law and the Connected Waters Research Initiative Research Centre (CWI) have been brought together in a special issue of Environmental Planning and Law Journal (EPLJ).

Read more…