ORCID Profile
0000-0002-2371-4626
Current Organisation
NSW Department of Primary Industries
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Publisher: Wiley
Date: 05-03-2020
DOI: 10.1002/LDR.3577
Abstract: The cover‐management factor (C‐factor) is used in the revised universal soil loss equation to represent the effect of vegetation cover and its management practices on hillslope erosion. Remote sensing has been widely used to estimate vegetation cover and the C‐factor, but most previous studies only used the photosynthetic vegetation (PV) or green vegetation indices (VI, e.g., normalized difference VI) for estimating the C‐factor and the important non‐PV (NPV) component was often ignored. In this study, we developed a new technique to estimate monthly time‐series C‐factor using the fractional vegetation cover (FVC) including both PV and NPV, and weighted by monthly rainfall erosivity ratio. The monthly FVC was derived from the moderate resolution imaging spectroradiometer and LANDSAT data with field validation. We conducted the case‐study over China's Loess Plateau and analysed the spatiotemporal variations of FVC and the C‐factor and their impacts on erosion over the Plateau. Our study reveals a significant increase in total vegetation cover (TC) from 56 to 76.8%, with a mean of 71.2%, resulting in about 20% decrease in the C‐factor and erosion risk during the 17‐year period. Our method has an advantage in estimating the C‐factor from TC at a monthly scale providing a basis for continuously and consistently monitoring of vegetation cover, erosion risk and climate impacts.
Publisher: Elsevier BV
Date: 05-2020
Publisher: Wiley
Date: 03-2019
DOI: 10.1111/SUM.12467
Abstract: The impacts of a wildfire and subsequent rainfall event in 2013 in the Warrumbungle National Park in New South Wales, Australia were examined in a project designed to provide information on post‐fire recovery expectations and options to land managers. A coherent suite of sub‐projects was implemented, including soil mapping, and studies on soil organic carbon ( SOC ) and nitrogen (N), erosion rates, groundcover recovery and stream responses. It was found that the loss of SOC and N increased with fire severity, with the greatest losses from severely burnt sandstone ridges. Approximately 2.4 million t of SOC and ~74,000 t of N were lost from soil to a depth of 10 cm across the 56,290 ha affected. Soil loss from slopes during the subsequent rainfall event was modelled up to 25 t ha −1 , compared to a long‐term mean annual soil loss of 1.06 t ha −1 year −1 . Groundcover averages generally increased after the fire until spring 2015, by which time rates of soil loss returned to near pre‐fire levels. Streams were filled with sand to bank full levels after the fire and rainfall. Rainfall events in 2015–2016 shifted creek systems into a major erosive phase, with incision through the post‐fire sandy bedload deposits, an erosive phase likely related to loss of topsoils over much of the catchment. The effectiveness of the research was secured by a close engagement with park managers in issue identification and a communications programme. Management outcomes flowing from the research included installation of erosion control works, redesign of access and monitoring of key mass movement hazard areas.
Publisher: Wiley
Date: 28-07-2019
DOI: 10.1002/LDR.3347
Publisher: MDPI AG
Date: 29-10-2022
DOI: 10.3390/RS14215437
Abstract: 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.
Publisher: Wiley
Date: 12-11-2019
DOI: 10.1002/LDR.3146
Publisher: Elsevier BV
Date: 05-2018
Publisher: Wiley
Date: 02-09-2020
DOI: 10.1002/JOC.6266
Publisher: CSIRO Publishing
Date: 2019
DOI: 10.1071/SR19043
Abstract: The combined slope length and slope steepness factor (LS) is crucial in soil erosion models such as the revised universal soil loss equation (RUSLE), and is often calculated from digital elevation models (DEMs). With high-resolution DEMs becoming increasingly available in recent years, we face considerable challenges in selecting the optimal DEM for erosion modelling. In this paper, we present a case study on LS factor computation using various DEMs at resolutions ranging from 1 to 90 m over a burnt national park in New South Wales, Australia, aiming to assess the effects of DEM resolution on LS and hillslope erosion estimation. The LS was calculated based on RUSLE specifications and incorporated a variable cutoff slope angle that improves the detection of the beginning and the end of each slope length. Results show the trend of an increase in the estimated LS value as the DEM resolution became coarser. We consider 5–10-m DEMs to have optimal resolution because the LS values calculated at this range were closer to the LS values measured at the 12 soil plots over the study area. We also assessed different s ling methods for LS value extraction and statistical analysis. The s ling method based on contributing area was more representative compared with point-based and buffer s ling methods. Findings from this study will be useful for choosing the optimal DEM resolution and s ling method in hillslope erosion modelling.
Publisher: CSIRO Publishing
Date: 2018
DOI: 10.1071/WF18011
Abstract: 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 in idual 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.
Publisher: CSIRO Publishing
Date: 2018
DOI: 10.1071/SR17058
Abstract: 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 100 cm. 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.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Qinggaozi Zhu.