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Extreme rainfall has caused severe road damage and landslide disasters in mountainous areas. Rainfall forecasting derived from remote sensing data has been widely adopted for disaster prevention and early warning as a trend in recent years. By integrating high-resolution radar rain data, for example, the QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system provides a great opportunity to establish the extreme climate-based landslide susceptibility model, which would be helpful in the prevention of hillslope disasters under climate change. QPESUMS was adopted to obtain spatio-temporal rainfall patterns, and further, multi-temporal landslide inventories (2003–2018) would integrate with other explanatory factors and therefore, we can establish the logistic regression method for prediction of landslide susceptibility sites in the Laonong River watershed, which was devastated by Typhoon Morakot in 2009. Simulations of landslide susceptibility under the critical rainfall (300, 600, and 900 mm) were designed to verify the model's sensitivity. Due to the orographic effect, rainfall was concentrated at the low mountainous and middle elevation areas in the southern Laonong River watershed. Landslide change analysis indicates that the landslide ratio increased from 1.5% to 7.0% after Typhoon Morakot in 2009. Subsequently, the landslide ratio fluctuated between 3.5% and 4.5% after 2012, which indicates that the recovery of landslide areas is still in progress. The validation results showed that the calibrated model of 2005 is preferred in the general period, with an accuracy of 78%. For extreme rainfall typhoons, the calibrated model of 2009 would perform better (72%). This study presented that the integration of multi-temporal landslide inventories in a logistic regression model is capable of predicting rainfall-triggered landslide risk under climate change. [ABSTRACT FROM AUTHOR]

In this study, a dual-polarimetric radar observation operator is established and modified for the Taiwan area for the purpose of model verification. A severe squall line case during the Southwest Monsoon Experiment Intensive Observing Period 8 (SoWMEX IOP#8) on 14 June 2008, is selected and examined. Because the operator is adopted from the use of the midlatitude region, sensitivity tests are performed to obtain the optimal setting of the operator in the subtropical region. To accurately capture the dynamic structure of the squall lines, the ensemble-based data assimilation system, which assimilates both radial wind and reflectivity data, is used to obtain the optimal analysis field on the mesoscale for evaluating the performance of model simulation. The characteristics of two microphysics schemes are investigated, and the results obtained using the schemes are compared with the S-band dual-polarimetric radar observations. The horizontal and vertical cross-sections show that the analyses resemble the observations. Both schemes can replicate the polarimetric parameter signature such as Z D R and K D P columns. When comparing model simulation with polarimetric parameters through the drawing of contour frequency by altitude diagrams (CFADs), the results reveal that the single moment microphysics scheme performs better than the double moment scheme in this case. However, the reflectivity field in the stratiform area is more accurately captured when using the double moment scheme. Furthermore, validation with polarimetric variables ( Z H , Z D R and K D P ) histograms shows underestimation of the K D P field in both schemes. Overall, this study indicates the benefit of assimilating radial wind and reflectivity data for the analyses of severe precipitation systems and the necessity of assimilating polarimetric parameters for the accuracy of microphysical processes, especially complex microphysics schemes in subtropical region. [ABSTRACT FROM AUTHOR]