Yang, X, Zhang, X, Lv, D, Yin, S, Zhang, M, Zhu, Q, Yu, Q & Liu, B 2020, 'Remote sensing estimation of the soil erosion cover-management factor for China's Loess Plateau', Land Degradation and Development.View/Download from: Publisher's site
© 2020 John Wiley & Sons, Ltd. 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.
Zhang, M, Wang, B, Cleverly, J, Liu, DL, Feng, P, Zhang, H, Huete, A, Yang, X & Yu, Q 2020, 'Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau', REMOTE SENSING, vol. 12, no. 11.View/Download from: Publisher's site
Zhang, M, Wang, B, Liu, DL, Liu, J, Zhang, H, Feng, P, Kong, D, Cleverly, J, Yang, X & Yu, Q 2020, 'Incorporating dynamic factors for improving a GIS-based solar radiation model', Transactions in GIS.View/Download from: Publisher's site
© 2020 John Wiley & Sons Ltd Solar radiation has been a major input to agricultural, hydrological, and ecological modeling. However, solar radiation is usually influenced by three groups of dynamic factors: sun–earth position, terrain, and atmospheric effects. Therefore, an integrated approach to accurately consider the impacts of those dynamic factors on solar radiation is essential to estimate solar radiation over rugged terrain. In this study, a spatial and temporal gap-filling algorithm was proposed to obtain a seamless daily MODIS albedo dataset. A 1 km-resolution digital elevation model was used to model the impact of local topography and shading by surrounding terrain on solar radiation. A sunshine-based model was adopted to simulate radiation under the influence of clouds. A GIS-based solar radiation model that incorporates albedo, shading by surrounding terrain, and variations in cloudiness was used to address the spatial variability of these factors in mountainous terrain. Compared with other independent solar radiation products, our model generated a more reliable solar radiation product over rugged terrain, with an R2 of 0.88 and an RMSE of 2.55 MJ m−2 day−1. The improved solar radiation products and open source app can be used further in practice or scientific research.
Zhu, Q, Yang, X, Ji, F, Liu, DL & Yu, Q 2020, 'Extreme rainfall, rainfall erosivity, and hillslope erosion in Australian Alpine region and their future changes', International Journal of Climatology, vol. 40, no. 2, pp. 1213-1227.View/Download from: Publisher's site
© 2019 Royal Meteorological Society The Australian Alpine region is highly vulnerable to extreme climate events such as heavy rainfall and snow falls, these events subsequently impact rainfall erosivity and hillslope erosion in the region. In this study, the relationship between extreme rainfall indices (ERIs) and rainfall erosivity was examined across the Alpine region in New South Wales (NSW) and Australian Capital Territory (ACT) and the surrounding areas including Murray and Murrumbidgee and South East and Tablelands (SET). Rainfall erosivity, hillslope erosion, and their changes were estimated in the future periods using the revised universal soil loss equation and the NSW/ACT Regional Climate Modeling (NARCliM) projections. Results from the study demonstrate a good relationship between ERIs (especially Rx5Day) and rainfall erosivity. The rainfall erosivity and hillslope erosion are projected to increase about 2 and 8% for the near future (2020–2039), further increase to 8 and 18% for the far future (2060–2079) in the Alpine region assuming the groundcover is maintained at the current condition. The change in rainfall erosivity and erosion risk is highly uneven in space and in season with the highest erosion risk in summer with an increase about 33% in the next 50 years. The highest erosion risk area is predicted within SET (maximum rate 19.95 Mg ha−1 year−1), but on average, the ACT has the highest erosion rate, which is above 1.36 Mg ha−1 year−1 in all periods. The snowmelt in spring in the Alpine region is estimated to increase the rainfall erosivity by 13% in the baseline period, up to 24% in the near future, but far less (about 1%) in the far future due to predicted temperature rise and less snow available in the Alpine region in the next 50 years.
Liu, DL, Wang, B, Evans, J, Ji, F, Waters, C, Macadam, I, Yang, X & Beyer, K 2019, 'Propagation of climate model biases to biophysical modelling can complicate assessments of climate change impact in agricultural systems', International Journal of Climatology, vol. 39, no. 1, pp. 424-444.View/Download from: Publisher's site
© 2018 Royal Meteorological Society Regional climate model (RCM) simulations are being increasingly used for climate change impact assessments, but their application is challenging due to considerable biases inherited from global climate model (GCM) simulations and generated from dynamical downscaling processes. This study assesses the biases in NARCliM (NSW and ACT regional climate modelling) simulations and quantifies the consequence of the climate biases in the downstream assessment of climate change impact on wheat crop system, using the Agricultural Production System sIMulator (APSIM). Results showed that post-processing bias-corrected temperature and rainfall data from NARCliM had small annual mean biases but large biases in the crop growing season (CGS). During the CGS, the mean bias error of rainfall was generally positive for rainfall probability and negative for intensity, which subsequently resulted in APSIM simulating negative biases for runoff and deep drainage and positive bias in soil evaporation. Bias in soil water balance and water availability resulted in less plant transpiration and less N uptake, ultimately, leading to large negative biases in crop yields. A simple bias correction of the simulated crop yield driven by RCMs could result in a largely consistent distribution with those generated with APSIM simulations forced by observed climate. Our results showed that RCM simulation biases could confound with the climate change signal and produced an unreliable estimate of the effects of the changes in climate and farm management variables on crop yields. The results suggested that RCM simulations with the current bias correction on the RCM-simulated outputs applied on an annual basis were inadequate for climate change assessments which involve biophysical models. Our study highlights the need for improved RCM simulations by eliminating the systemic biases associated with rainfall characteristics, although suitable post-processing bias correctio...
Shan, L, Yang, X & Zhu, Q 2019, 'Effects of DEM resolutions on LS and hillslope erosion estimation in a burnt landscape', SOIL RESEARCH, vol. 57, no. 7, pp. 797-804.View/Download from: Publisher's site
She, L, Xue, Y, Yang, X, Leys, J, Guang, J, Che, Y, Fan, C, Xie, Y & Li, Y 2019, 'Joint Retrieval of Aerosol Optical Depth and Surface Reflectance over Land Using Geostationary Satellite Data', IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 3, pp. 1489-1501.View/Download from: Publisher's site
© 1980-2012 IEEE. The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: R = 0.88 , root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of ± 0.2 AOD AERONET ±0.05. A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land.
Tulau, MJ, McInnes-Clarke, SK, Yang, X, McAlpine, RA, Karunaratne, SB, Zhu, Q & Morand, DT 2019, 'The Warrumbungle Post-Fire Recovery Project—raising the profile of soils', Soil Use and Management, vol. 35, no. 1, pp. 63-74.View/Download from: Publisher's site
© 2018 British Society of Soil Science 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.
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: 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.
She, L, Xue, Y, Yang, X, Guang, J, Li, Y, Che, Y, Fan, C & Xie, Y 2018, 'Dust Detection and Intensity Estimation Using Himawari-8/AHI Observation', REMOTE SENSING, vol. 10, no. 4.View/Download from: Publisher's site
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.
Wang, B, Liu, DL, O'Leary, GJ, Asseng, S, Macadam, I, Lines-Kelly, R, Yang, X, Clark, A, Crean, J, Sides, T, Xing, H, Mi, C & Yu, Q 2018, 'Australian wheat production expected to decrease by the late 21st century.', Global change biology, vol. 24.View/Download from: Publisher's site
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 CO2 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.
Wang, B, Liu, DL, Asseng, S, Macadam, I, Yang, X & Yu, Q 2017, 'Spatiotemporal changes in wheat phenology, yield and water use efficiency under the CMIP5 multimodel ensemble projections in eastern Australia', Climate Research, vol. 72, no. 2, pp. 83-99.View/Download from: Publisher's site
© Inter-Research 2017. The New South Wales (NSW) wheat belt is one of the most important regions for winter crops in Australia; however, its agricultural system is significantly affected by water stress and ongoing climate change. Statistically downscaled scenarios from 13 selected global climate models with RCP4.5 and RCP8.5 scenarios were combined with crop simulation models to simulate wheat productivity and water use. We projected that multi-model median yields could increase by 0.2% for RCP4.5 and 9.0% for RCP8.5 by 2061-2100. Although RCP4.5 showed a small decrease in median yield in the dry southwestern parts of the wheat belt, the higher CO 2 concentration in RCP8.5 compensated some of the negative effects, resulting in 12.6% yield increase. Our results show that drier areas would benefit more from elevated CO 2 than would the wetter areas. Without the increase in CO 2 concentration, wheat yields decrease rapidly under RCP4.5 by 2061-2100 and much more so under RCP8.5 compared to the present. A decline in growing season length and a decrease in rainfall resulted in reduced crop water consumption. As a consequence, simulated evapotranspiration decreased by 10.2% for RCP4.5 and 16.9% for RCP8.5 across the NSW wheat belt. Increasing yields combined with decreasing evapotranspiration resulted in a simulated increase in water use efficiency by 9.9% for RCP4.5 and 29.7% for RCP8.5. Wheat production in water-limited, low-yielding environments appears to be less negatively impacted or in some cases even positively affected under future climate and CO 2 changes, compared to other growing environments in the world.
Di, A, Xue, Y, Yang, X, Leys, J, Guang, J, Mei, L, Wang, J, She, L, Hu, Y, He, X, Che, Y & Fan, C 2016, 'Dust Aerosol Optical Depth Retrieval and Dust Storm Detection for Xinjiang Region Using Indian National Satellite Observations', REMOTE SENSING, vol. 8, no. 9.View/Download from: Publisher's site