Modelling rainfall erosivity for dynamic hillslope erosion estimation in events of wildland fires, snowmelt, and extreme rainfall

University of Technology Sydney. Faculty of Science.Rainfall erosivity and soil erosion have being significantly affected by more frequent extreme weather events and ongoing climate change. Projected warmer and drier climate in Australia will change the erosion rates through more intensive storm events, more severe and frequent wildfire and less snowmelt. To estimate the near real-time rainfall erosivity and erosion change, it is essential to link the extreme weather events and hillslope erosion model in response to provide effective ecosystem and environment management. In this study, I selected two case study areas in southeast Australia to assess the effect of extreme weather events on hillslope erosion modelling (e.g. Warrumbungle National Park (WNP) and NSW and ACT Alpine region). Radar rainfall data (1km, 10-min), calibrated by rain gauges rainfall were applied to estimate the near real-time rainfall erosivity on a daily basis. There was a positive correlation between radar-based and gauged rainfall. The highest rainfall erosivity was estimated as 826.76 MJ mm ha-1 hr-1 for a single storm event. The modelled average annual rate of hillslope erosion appears to be declining due to the vegetation recovery after the wildfire. Six extreme rainfall indices (ERIs) were selected to assess the extreme rainfall impact on rainfall erosivity over 60 years. In comparison with the result from Australia Bureau of Meteorology, it is possible to estimate the approximately erosivity value from ERIs especially to where without radar or gauged rainfall data. Snow and temperature projections for the 60 years derived from NARCliM were applied to adjust the snowmelt runoff and rainfall erosivity model during the melting season. Weekly measurements of snow depth and snow water equivalent at three filed sites in the Snowy Mountains were obtained to assess the snowmelt-adjusted rainfall erosivity model. Snowmelt in spring is estimated to increase the rainfall erosivity by 12.95% for baseline. However, the snow impact is projected to be 24.84% for the near future and then less (1.63%) for the fat future due to the projected higher temperature and less snow depth into NARCliM. The erosion amount and change is comprehensively derived from various factors, includes rainfall erosivity, groundcover, slope length and steepness and soil erodibility. These factors always combine and interact to influence and accelerate the mechanism of the erosion process under more frequent and more extreme weather events. The current outcomes would effectively enhance the capability of government, and provide adaptation and mitigation strategies in responding to a changing climate

Story Telling In Space and Time An Android Application for Ghost Tour in Edinburgh using Smart Phones

The increasing usage of geographical information sharing on smart phones has prompted the development of an Android project for storytelling. Compared with traditional software, mobile applications have played a larger role in information sharing with LBS (Location Based Services), especially in terms of tourism guides. This paper uses the Ghost Tour in Edinburgh as a city story sample. It will be narrated that utilizes GIS, express time order and path via the Geofencing API belonging to Google. Location data and story content are stored into the database, waiting to be triggered at a proper time, with the technical support by Google Genfence API. This application also guides the path of the storytelling by voice prompts. At the same time, users are able to take control of the storytelling via voice commands, and the voice recognition system has been provided by Google. Google Maps has been loaded as the background of the app to provide added guidance. Programming like Java and platform like Eclipse will be used to this topic. Results have been evaluated, supported by a questionnaire and the grading analysis to discover problems and resolve them. The analysis results illustrate that the smart phone triggered the storytelling fluently by tracking the location of tourists

Dynamic Modelling of Water and Wind Erosion in Australia over the Past Two Decades

Soil erosion caused by water and wind is a complicated natural process that has been accelerated by human activity. It results in increasing areas of land degradation, which further threaten the productive potential of landscapes. Consistent and continuous erosion monitoring will help identify the location, magnitude, and trends of soil erosion. This information can then be used to evaluate the impact of land management practices and inform programs that aim to improve soil conditions. In this study, we applied the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) to simulate water and wind erosion dynamics. With the emerging earth observation big data, we estimated the monthly and annual water erosion (with a resolution of 90 m) and wind erosion (at 1 km) from 2001 to 2020. We evaluated the performance of three gridded precipitation products (SILO, GPM, and TRMM) for monthly rainfall erosivity estimation using ground-based rainfall. For model validation, water erosion products were compared with existing products and wind erosion results were verified with observations. The datasets we developed are particularly useful for identifying finer-scale erosion dynamics, where more sustainable land management practices should be encouraged

Dynamic Modelling of Water and Wind Erosion in Australia over the Past Two Decades

Soil erosion caused by water and wind is a complicated natural process that has been accelerated by human activity. It results in increasing areas of land degradation, which further threaten the productive potential of landscapes. Consistent and continuous erosion monitoring will help identify the location, magnitude, and trends of soil erosion. This information can then be used to evaluate the impact of land management practices and inform programs that aim to improve soil conditions. In this study, we applied the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) to simulate water and wind erosion dynamics. With the emerging earth observation big data, we estimated the monthly and annual water erosion (with a resolution of 90 m) and wind erosion (at 1 km) from 2001 to 2020. We evaluated the performance of three gridded precipitation products (SILO, GPM, and TRMM) for monthly rainfall erosivity estimation using ground-based rainfall. For model validation, water erosion products were compared with existing products and wind erosion results were verified with observations. The datasets we developed are particularly useful for identifying finer-scale erosion dynamics, where more sustainable land management practices should be encouraged