How far are we from the use of satellite rainfall products in landslide forecasting?

The Assessment and Comparison of TMPA and IMERG Products Over the Major Basins of Mainland China

“…Precipitation is a crucial component of global water and energy cycles, and the reliability of FFS is strongly dependent on the quality of the rainfall inputs (Brunetti et al, 2018). Generally, rain gauges and groundbased weather radars are major sources of precipitation data, and they are considered as the most reliable sources (Tapiador et al, 2012).…”

Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding

“…Firstly, landslide inventories are inherently biased towards high-impact landslide events and regions that are most accessible, while their accuracy is constrained by the scientific validity of the reporting sources, especially in data-scarce low-capacity environments [1,[28][29][30][31]. Secondly, rainfall data comprise uncertainties related to the spatial representativeness of rain gauges or biases in satellite-derived estimates [32,33]. Thirdly, the definition of rainfall parameters, with intensity and duration forming the most frequently used parameter couple [3,5], varies strongly across studies [3].…”

“…A weakness of such methods is, however, that they are generally tailored to a specific area and available data sets, which often prevents straightforward transferability to other regions and data sets [35]. Nevertheless, transferability is not only essential for evaluating and comparing landslide hazard over different regions of the world [10,36], but also valuable in the context of the increasing availability of ever higher-resolution data relevant for threshold analysis, such as rainfall estimates from global-scale satellite data [32].The most influential statistical threshold techniques include the probabilistic approach through Bayesian inference [10,37], the use of receiver operating characteristics (ROC) analysis with different optimization metrics [38,39], and the frequentist approach developed by [40]. The Bayesian and ROC approaches compare conditions that resulted or not in landsliding, the former fundamentally relying on prior and marginal probabilities [37] and the latter attempting to balance the true and false positive rates derived from a confusion matrix [39].…”

“…Here, the bootstrap procedure involves many threshold calibrations (e.g., 5000 [9]), each of which based on n randomly sampled data (with replacement) from a data set of size n. The final threshold parameters and associated uncertainties are calculated as the mean and their standard deviations, respectively, of their many estimates. This approach has proved to be transferable over different regions where abundant information on landsliding and rainfall was available [9,41,42].Recently, this frequentist approach with bootstrapping [32,41,43] has been modified by [35] through coupling a dynamic rainfall variable (AR) with a static indicator of the spatially varying predisposing ground conditions (landslide susceptibility, S) (further referred to as the AR-S approach). The first step in AR-S threshold estimation is similar to the frequentist method developed by [40] and [9], calculating the residuals of the least-square fit on the log-transformed data.…”

“…A weakness of such methods is, however, that they are generally tailored to a specific area and available data sets, which often prevents straightforward transferability to other regions and data sets [35]. Nevertheless, transferability is not only essential for evaluating and comparing landslide hazard over different regions of the world [10,36], but also valuable in the context of the increasing availability of ever higher-resolution data relevant for threshold analysis, such as rainfall estimates from global-scale satellite data [32].…”