Zhu, Q, Yang, X, Yu, B, Tulau, M, McInnes-Clarke, S, Nolan, RH, Du, Z & Yu, Q 2019, 'Estimation of event-based rainfall erosivity from radar after wildfire', Land Degradation and Development, vol. 30, no. 1, pp. 33-48.View/Download from: UTS OPUS or Publisher's site
© 2018 John Wiley & Sons, Ltd. Rainfall erosivity impacts all stages of hillslope erosion processes and is an important factor (the 'R factor') in the Revised Universal Soil Loss Equation. It is estimated as the average annual value of the sum of all erosive events (EI30) over a period of many years. For each storm event, the EI30 value is the product of storm energy, E in MJ ha−1, and peak 30-min rainfall intensity (I30, mm hr−1). Previous studies often focused on estimation of the R factor for prediction of mean annual or long-term soil losses. However, many applications require EI30 values at much higher temporal resolution, such as postfire soil erosion monitoring, which requires a time step at storm events or on a daily basis. In this study, we explored the use of radar rainfall data to estimate the storm event-based EI30 after a severe wildfire in Warrumbungle National Park in eastern Australia. The radar-derived rainfall data were calibrated against 12 tipping bucket rain gauges across an area of 239 km2 and subsequently used to produce a time series of rainfall erosivity maps at daily intervals since the wildfire in January 2013. The radar-derived daily rainfall showed good agreement with the gauge measurements (R2 > 0.70, Ec = 0.66). This study reveals great variation in EI30 values ranging from near zero to 826.76 MJ·mm·ha−1·hr−1 for a single storm event. We conclude that weather radar rainfall data can be used to derive timely EI30 and erosion information for fire incident management and erosion control. The methodology developed in this study is generic and thus readily applicable to other areas where weather radar data are available.
Yang, X, Zhu, Q, Tulau, M, McInnes-Clarke, S, Sun, L & Zhang, X 2018, 'Near real-Time monitoring of post-fire erosion after storm events: A case study in Warrumbungle National Park, Australia', International Journal of Wildland Fire, vol. 27, no. 6, pp. 413-424.View/Download from: UTS OPUS or Publisher's site
© IAWF. Wildfires in national parks can lead to severe damage to property and infrastructure, and adverse impacts on the environment. This is especially pronounced if wildfires are followed by intense storms, such as the fire in Warrumbungle National Park in New South Wales, Australia, in early 2013. The aims of this study were to develop and validate a methodology to predict erosion risk at near real-Time after storm events, and to provide timely information for monitoring of the extent, magnitude and impact of hillslope erosion to assist park management. We integrated weather radar-based estimates of rainfall erosivity with the revised universal soil loss equation (RUSLE) and remote sensing to predict soil loss from individual storm events after the fire. Other RUSLE factors were estimated from high resolution digital elevation models (LS factor), satellite data (C factor) and recent digital soil maps (K factor). The accuracy was assessed against field measurements at twelve soil plots across the Park and regular field survey during the 5-year period after the fire (2013-17). Automated scripts in a geographical information system have been developed to process large quantity spatial data and produce time-series erosion risk maps which show spatial and temporal changes in hillslope erosion and groundcover across the Park at near real time.
Yang, X, Gray, J, Chapman, G, Zhu, Q, Tulau, M & McInnes-Clarke, S 2018, 'Digital mapping of soil erodibility for water erosion in New South Wales, Australia', Soil Research, vol. 56, no. 2, pp. 158-170.View/Download from: Publisher's site
© CSIRO 2018. Soil erodibility represents the soil's response to rainfall and run-off erosivity and is related to soil properties such as organic matter content, texture, structure, permeability and aggregate stability. Soil erodibility is an important factor in soil erosion modelling, such as the Revised Universal Soil Loss Equation (RUSLE), in which it is represented by the soil erodibility factor (K-factor). However, determination of soil erodibility at larger spatial scales is often problematic because of the lack of spatial data on soil properties and field measurements for model validation. Recently, a major national project has resulted in the release of digital soil maps (DSMs) for a wide range of key soil properties over the entire Australian continent at approximately 90-m spatial resolution. In the present study we used the DSMs and New South Wales (NSW) Soil and Land Information System to map and validate soil erodibility for soil depths up to 100cm. We assessed eight empirical methods or existing maps on erodibility estimation and produced a harmonised high-resolution soil erodibility map for the entire state of NSW with improvements based on studies in NSW. The modelled erodibility values were compared with those from field measurements at soil plots for NSW soils and revealed good agreement. The erodibility map shows similar patterns as that of the parent material lithology classes, but no obvious trend with any single soil property. Most of the modelled erodibility values range from 0.02 to 0.07 t ha h ha-1 MJ-1 mm-1 with a mean (± s.d.) of 0.035±0.007 t ha h ha-1 MJ-1 mm-1. The validated K-factor map was further used along with other RUSLE factors to assess soil loss across NSW for preventing and managing soil erosion.
Du, Z, Ge, L, Ng, AHM, Zhu, Q, Yang, X & Li, L 2018, 'Correlating the subsidence pattern and land use in Bandung, Indonesia with both Sentinel-1/2 and ALOS-2 satellite images', International Journal of Applied Earth Observation and Geoinformation, vol. 67, pp. 54-68.View/Download from: UTS OPUS or Publisher's site
© 2018 Elsevier B.V. Continuous research has been conducted in Bandung City, West Java province, Indonesia over the past two decades. Previous studies carried out in a regional-scale might be useful for estimating the correlation between land subsidence and groundwater extraction, but inadequate for local safety management as subsidence may vary over different areas with detailed characters. This study is focused primarily on subsidence phenomenon in local, patchy and village scales, respectively, with Sentinel-1 and ALOS-2 dataset acquired from September 2014 to July 2017. The Sentinel-1 derived horizontal movement map confirmed that the vertical displacement is dominant of the Line-of-Sight (LoS) subsidence. Moreover, both Sentinel-1 and ALOS-2 derived InSAR measurements were cross-validated with each other. In order to understand the subsidence in a more systematic way, six 10-cm subsidence zones have been selected known as Zone A–F. Further analyses conducted over multiple scales show that industrial usage of groundwater is not always the dominant factor that causes the land subsidence and indeed it does not always create large land subsidence either. Regions experiencing subsidence is due to a combined impact of a number of factors, e.g., residential, industrial or agricultural activities. The outcome of this work not only contributes to knowledge on efficient usage of the satellite-based monitoring networks, but also assists developing the best hazard mitigation plans. In the future work, as we cannot draw the conclusion which is the dominant factor within each sub-zone due to the lack of statistical data, e.g., the groundwater consumption rates per square kilometre for different land types, further datasets are still needed to examine the core factor.
Yang, X & Yu, B, 'Predicting Changes of Rainfall Erosivity and Hillslope Erosion across New South Wales, Australia', Journal of Earth Science & Climatic Change, vol. 07, no. 03.View/Download from: Publisher's site
Zhu, Q, Yang, X & Yu, Q 2016, 'Assess The Topographic Resolution Impact On Soil Loss', Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),, EE International Geoscience and Remote Sensing Symposium (IGARSS),, IEEE, Beijing, China.View/Download from: UTS OPUS or Publisher's site
Soil erosion is one of the thorniest issues which has brought severe environmental problems such as degradation of water quality and soil productivity. Topographic factor, as known as slope length and steepness factor (LS) plays a vital role in soil erosion model, it is multiplied with rainfall erosivity (R), ground cover (C), soil erodibility (K) and soil conservation practices factor (P) to modelling soil loss via Revised Universal Soil Loss Equation (RUSLE). In this research, different resolution of elevation model products were used to calculate the LS factor of soil loss, and also compare with each other to access and determine the most appropriate interval to predict soil loss rate in Warrumbungle National Park (WNP). LS factor was calculated via the Digital Elevation Model (DEM) at 30m from Shuttle Radar Topography Mission (SRTM) and High Resolution Light Detection and Ranging (LiDAR) at 1m, 5m and 10m. Twelve soil sites were selected to collect field-based data, which would compare with the soil loss modelling results. GIS technique was applied to model and geo-visualize the LS factor map. Meanwhile, daily soil loss ratio was predicted from LS factor along with rainfall erosivity, groundcover, soil structure and composition.