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.

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.

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, theEI₃₀value is the product of storm energy,Ein 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 requireEI₃₀values 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‐basedEI₃₀after 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 km²and 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,E_c = 0.66). This study reveals great variation inEI₃₀values ranging from near zero to 826.76 MJ·mm·ha⁻¹·hr⁻¹for a single storm event. We conclude that weather radar rainfall data can be used to derive timelyEI₃₀and 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.

Establishing scientifically grounded soil erosion control targets and feasible vegetation restoration strategies is crucial for high quality development of the Chinese Loess Plateau which is severely affected by soil erosion. Currently, the commonly used tolerable soil loss (TSL) is inadequate for addressing the spatial heterogeneity in topography and temporal variability in climatic conditions attributed to its static characteristics. This study developed a dynamic assessment framework using the Revised Universal Soil Loss Equation (RUSLE) model to construct a monthly soil erosion rate raster dataset at a 500 m resolution for 2001–2024. Model validation was conducted through comparison with sediment yield data from 25 tributary hydrological stations. By analyzing the soil erosion rate and the prevailing climatic and land management factors, we established monthly erosion control targets (ECT) and the corresponding vegetation cover targets (VCT). Results showed that (1) the average erosion rate declined from 15.5 to 10.17 t ha ⁻¹ year ⁻¹ , with a mean of 10.53 t ha ⁻¹ year ⁻¹ , and peaks occurred in summer, especially July; (2) the estimated ECT values ranged from 13.4 to 8.9 t ha ⁻¹ year ⁻¹ , with a mean of 7.2 t ha ⁻¹ year ⁻¹ , peaking at 1.6 t ha ⁻¹ month ⁻¹ in July; (3) the estimated VCT ranged from 43.4% to 66.6%, averaging 59.8%. Overall, 51.6% of the Loess Plateau has already met the VCT requirements necessary to sustain the ECT. These findings provide scientific guidance for soil and water conservation and vegetation restoration tailored to local conditions, with potential applicability to other erosion‐prone regions.