Xihua Yang

This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.

Abstract Climate change threatens global wheat production and food security, including the wheat industry in Australia. Many studies have examined the impacts of changes in local climate on wheat yield per hectare, but there has been no assessment of changes in land area available for production due to changing climate. It is also unclear how total wheat production would change under future climate when autonomous adaptation options are adopted. We applied species distribution models to investigate future changes in areas climatically suitable for growing wheat in Australia. A crop model was used to assess wheat yield per hectare in these areas. Our results show that there is an overall tendency for a decrease in the areas suitable for growing wheat and a decline in the yield of the northeast Australian wheat belt. This results in reduced national wheat production although future climate change may benefit South Australia and Victoria. These projected outcomes infer that similar wheat‐growing regions of the globe might also experience decreases in wheat production. Some cropping adaptation measures increase wheat yield per hectare and provide significant mitigation of the negative effects of climate change on national wheat production by 2041–2060. However, any positive effects will be insufficient to prevent a likely decline in production under a high CO 2 emission scenario by 2081–2100 due to increasing losses in suitable wheat‐growing areas. Therefore, additional adaptation strategies along with investment in wheat production are needed to maintain Australian agricultural production and enhance global food security. This scenario analysis provides a foundation towards understanding changes in Australia's wheat cropping systems, which will assist in developing adaptation strategies to mitigate climate change impacts on global wheat production.

This paper presents a pilot study on riparian vegetation delineation and mapping using remote sensing and geographic information systems (GIS) in the Hunter Region, Australia. The aim of the study was to develop appropriate and repeatable assessment and mapping techniques to quantify the extent of riparian vegetation within the region. Ortho‐rectified digital aerial photographs, SPOT‐4 multispectral (XS) and Landsat‐7 ETM+ images were tested to delineate the riparian vegetation and to develop a quantifiable and repeatable method of mapping the extent of that vegetation. Image processing techniques such as parallelepiped classification, tasselled cap transformation, and vegetation index clustering were used in an attempt to delineate riparian vegetation from remotely sensed images. Specific GIS analysis techniques were used for riparian zone buffering and segmentation, vegetation cover estimation, and mapping. Specific GIS scripts were developed for those processes so that they were automatic, fast, and repeatable. Various vegetation indices (VI) were evaluated and compared for their ability to discriminate riparian vegetation and its extent. The riparian vegetation was assessed and mapped at designed segmental interval (e.g. 1 km) with polygon and lineal representations. The classification accuracy was assessed against field observation and air photo interpretation (API). The overall accuracy of the photo‐based classification was about 81%, SPOT‐4 63%, and Landsat‐7 ETM+ 53%. Statistic analysis shows that there is little agreement between photo‐based classification and that from satellite imagery.

Soil loss due to water erosion, in particular hillslope erosion, can be estimated using predictive models such as the Revised Universal Soil Loss Equation (RUSLE). One of the important and dynamic elements in the RUSLE model is the cover and management factor (C-factor), which represents effects of vegetation canopy and ground cover in reducing soil loss. This study explores the potential for using fractional vegetation cover, rather than traditional green vegetation indices (e.g. NDVI), to estimate C-factor and consequently hillslope erosion hazard across New South Wales (NSW), Australia. Values of the C-factor were estimated from the emerging time-series fractional cover products derived from Moderate Resolution Imaging Spectroradiometer (MODIS). Time-series C-factor and hillslope erosion maps were produced for NSW on monthly and annual bases for a 13-year period from 2000 to 2012 using automated scripts in a geographic information system. The estimated C-factor time-series values were compared with previous study and field measurements in NSW revealing good consistency in both spatial and temporal contexts. Using these time-series maps, the relationship was analysed between ground cover and hillslope erosion and their temporal variation across NSW. Outcomes from this time-series study are being used to assess hillslope erosion hazard, sediment and water quality (particularly after severe bushfires) across NSW at local, catchment and regional scales.