Precipitation data and their uncertainty as input for rainfallinduced shallow landslide models

Physical models used toforecast the temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws.Owing to the existing measuring technology and our knowledge of the physical laws controlling landslide initiation,model uncertainties are due to an inability to accurately quantify the model input parameters and rainfall forcing data.An uncertainty analysis of slope instability prediction provides a rationale for refining the geotechnical models.The Transient Rainfall Infiltration and Grid-based Regional Slope Stability-Probabilistic(TRIGRS-P)model adopts a probabilistic approach to compute the changes in the Factor of Safety(FS)due to rainfall infiltration.Slope Infiltration Distributed Equilibrium(SLIDE)is a simplified physical model for landslide prediction.The new code(SLIDE-P)is also modified by adopting the same probabilistic approach to allow values of the SLIDE model input parameters to be sampled randomly.This study examines the relative importance of rainfall variability and the uncertainty in the other variables that determine slope stability.The precipitation data from weather stations,China Meteorological Administration Land Assimilation System 2.0(CLDAS2.0),China Meteorological Forcing Data set precipitation(CMFD),and China geological hazard bulletin are used to drive TRIGRS,SLIDE,TRIGRS-P and SLIDE-P models.The TRIGRS-P and SLIDE-P models are used to generate the input samples and to calculate the values of FS.The outputs of several model runs with varied input parameters and rainfall forcings are analyzed statistically.A comparison suggests that there are significant differences in the simulations of the TRIGRS-P and SLIDE-P models.Although different precipitation data sets are used,the simulation results of TRIGRS-P are more concentrated.This study can inform the potential use of numerical models toforecast the spatial and temporal occurrence of regional rainfall-induced shallow landslides.

Rainfall erosivity estimation over the Tibetan plateau based on high spatial-temporal resolution rainfall records

The Tibetan Plateau(TP)in China has been experiencing severe water erosion because of climate warming.The rapid development of weather station network provides an opportunity to improve our understanding of rainfall erosivity in the TP.In this study,1-min precipitation data obtained from 1226 weather stations during 2018–2019 were used to estimate rainfall erosivity,and subsequently the spatial-temporal patterns of rainfall erosivity in the TP were identified.The mean annual erosive rainfall was 295 mm,which accounted for 53%of the annual rainfall.An average of 14 erosive events occurred yearly per weather station,with the erosive events in the wet season being more likely to extend beyond midnight.In these cases,the precipitation amounts of the erosive events were found to be higher than those of the daily precipitations,which may result in implicit bias as the daily precipitation data were used for estimating the rainfall erosivity.The mean annual rainfall erosivity in the TP was 528 MJ mm·ha^(-1)·h^(-1),with a broader range of 0–3402 MJ mm·ha^(-1)·h^(-1),indicating a significant spatial variability.Regions with the highest mean annual rainfall erosivity were located in the forest zones,followed by steppe and desert zones.Finally,the precipitation phase records obtained from 140 weather stations showed that snowfall events slightly impacted the accuracy of rainfall erosivity calculation,but attention should be paid to the erosion process of snowmelt in the inner part of the TP.These results can be used as the reference data for soil erosion prediction in normal precipitation years.