New gridded dataset of rainfall erosivity (1950–2020) on the Tibetan Plateau

Abstract

Abstract. The risk of water erosion on the Tibetan Plateau (TP), a typical fragile ecological area, is increasing with climate change. A rainfall erosivity map is useful for understanding the spatiotemporal pattern of rainfall erosivity and identifying hot spots of soil erosion. This study generates an annual gridded rainfall erosivity dataset on a 0.25∘ grid for the TP in 1950–2020. The 1 min precipitation observations at 1787 weather stations for 7 years and 0.25∘ hourly European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) precipitation data for 71 years are employed in this study. Our results indicate that the ERA5-based estimates have a marked tendency to underestimate annual rainfall erosivity when compared to the station-based estimates, because of the systematic biases of ERA5 precipitation data including the large underestimation of the maximum contiguous 30 min peak intensity and relatively slight overestimation of event erosive precipitation amounts. The multiplier factor map over the TP, which was generated by the inverse distance-weighted method based on the relative changes between the available station-based annual rainfall erosivity grid values and the corresponding ERA5-based values, was employed to correct the ERA5-based annual rainfall erosivity and then reconstruct the annual rainfall erosivity dataset. The multiyear average correction coefficient over the TP between the station-based annual rainfall erosivity values and the newly released data is 0.67. In addition, the probability density and various quantile values of the new data are generally consistent with the station-based values. The data offer a view of large-scale spatiotemporal variability in the rainfall erosivity and address the growing need for information to predict rainfall-induced hazards over the TP. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.271833; Chen, 2021).

Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020)

“…Various studies show a high correlation between the extreme rainfall (>99th) obtained from SPPs with rain gauge adjustment and observed stations in South America [66,86]; accordingly, their spatial distribution shows a good correlation with the RE estimate [38,51,87]. Therefore, there is evidence of using hourly rainfall from IMERGF to estimate RE-IMERGF, with respective correction-based RE-AWS.…”

“…Several studies identified a high correlation in the spatiotemporal variability between precipitation and RE [23,51,63]; therefore, the correction of RE-IMERGF was performed by rescaling precipitation with observed data, for each independent pixel, using an annual factor average by season. By extending the approach of Chen et al [51], the correction process was as follows: (i) obtainment of the calibration factor by grouping the monthly series at the seasonal level (summer, autumn, winter, and spring) in the same period, 2015-2020; (ii) employ a linear regression between the point-gridded values from RE-AWS and RE-IMERGF in the four seasonal periods to obtain the slope of each linear model defined as the seasonal multiplicative factor (FME); (iii) spatial interpolation of the FME point values by using the inverse distance weighted interpolation (IDW) method, at the same native spatial resolution of IMERGF (0.1 • ); (iv) spatial aggregation applied to reduce the spatial resolution from 0.1 • to 0.25 • , with the aim of avoiding spatial inconsistencies as a consequence of the high variability of the multiply factor; (v) and finally, PISCO_reed was obtained as a result of multiplying RE-IMERGF by the FME maps. The validation was realised at the pixel level with the observed data from RE-AWS during the common period, 2015-2020.…”

“…More recently, a variety of research has examined the use of observed data and a satellite precipitation product (SPP) for RE estimation, with their respective limitations due to the source, data derivation model, and spatial scales [2,44,45]. Based on the above, the spatial estimation of RE can be grouped into three approaches: (i) observed-based RE: local estimates of weather stations and subsequent geospatial interpolation [44,46,47]; (ii) satellite-based RE: the use of satellite-based precipitation products (SPPs) [48][49][50]; and (iii) merged-based RE: a mix of both observed and simulated data sources, based on the correction of the RE obtained by the SPPs with respect to data from observed stations, at the national [8,18,[51][52][53], regional [11,54], and global scales [40,52]. In this research, the merged-based RE method is used through seasonal satellite correction factors based on automatic weather stations (AWS) on a national scale.…”

Temporal and Spatial Variation in Rainfall Erosivity in the Rolling Hilly Region of Northeast China

“…The spatial and temporal distribution of RE in China varies. Average RE on the Tibetan Plateau decreases from the southeast to the northwest, and the average RE has significantly increased [30,31], which is associated with the recovery of vegetation in recent years and reduction in sand transport on the Loess Plateau [32]. The degree of rainfall erosion in South China varies from season to season, and the erosivity of annual and seasonal rainfall has increased [33].…”

Rainfall Erosivity Mapping for Tibetan Plateau Using High-Resolution Temporal and Spatial Precipitation Datasets for the Third Pole

“…Yue et al [18] used hourly precipitation from 2381 stations in China and applied universal kriging interpolation to map 1 km average annual RE in China's regions from 1991 to 2020. Chen et al [27,28] combined weather station data with ERA5 data to calculate RE on the TP from 1950 to 2020, their results showing that the accuracy was influenced by the density of the stations. However, due to the varied topography of the TP with elevations ranging from 80 to 8653 m and limits imposed by geographic and transportation conditions, weather stations are concentrated in eastern areas with more human activities and lower elevations, which could result in the uncertain results for western and high-altitude areas.…”