The city of Arequipa, the second most important city in Per & uacute;, faces numerous daunting challenges, including high-intensity but short-induration rainfalls that leads to floods and the swelling of the Chili River (mud and landslides). This situation aggravates the vulnerability of the population settled on the margins of the gorges and gullies, due to little or no territorial planning from public institutions. The local news evidence negligence every year, both in terms of human lives and infrastructure loss. The frequency of these events has increased with time and that is the reason for prompting the establishment of rainfall thresholds and the compilation of a 41-year record (1981-2021), with the aim of informing about the dangerousness of an adverse meteorological phenomenon, either predicted or in progress. For the hydrological model, the authors used the highest 24-hour precipitation data from the SENAMHI's stations (National Service of Meteorology and Hydrology of Peru) to generate the liquid hydrograph for different return periods with the Hydrologic model of HEC-HMS. Soil mechanics studies were also carried out to determine the rheological parameters of the non-Newtonian flow and then calibrate through historical events in a hydraulic model of HEC-RAS. Finally, cartographic maps in QGIS were prepared to evaluate the hazard zones flooding in the Del Pato, San L & aacute;zaro, Venezuela and Los Incas gullies.

Synthetic Aperture Radar Interferometry (InSAR), which can map subtle ground displacement over large areas, has been widely utilized to recognize active landslides. Nevertheless, due to various origins of subtle ground displacement, their presence on slopes may not always reflect the occurrence of active landslides. Therefore, interpretation of exact landslide-correlated deformation from InSAR results can be very challenging, especially in mountainous areas, where natural phenomenon like soil creep, anthropogenic activities and erroneous deformational signals accumulated during InSAR processing can easily lead to misinterpretation. In this paper, a two-phase interpretation method applicable to regional-scale active landslide recognition utilizing InSAR results is presented. The first phase utilizes statistical threshold and clustering analysis to detect unstable regions mapped by InSAR. The second phase introduces landslide susceptibility combined with empirical rainfall threshold, which are considered as causative factors for active landslides triggered by rainfall, to screen unstable regions indicative of active landslides. A case study validated by field survey indicates that the proposed interpretation method, when compared to a baseline model reported in the literature, can achieve better interpretation accuracy and miss rate.