The majority of landsliding episodes in the area north of Lisbon are associated with rainfall events of short (less than 5 days) medium (5–20 days) or long duration (more than 20 days). The precipitation regime in Portugal is highly irregular, with large differences between wet and dry years. We have assessed the impact of the North Atlantic Oscillation (NAO) on both the winter precipitation and the timing and magnitude of associated landslide events. Results show that the large inter-annual variability of winter precipitation is largely modulated by the NAO mode. The precipitation composite corresponding to high NAO index presents a considerable lower median value (47 mm/month) than the corresponding low NAO index class (134 mm/month). The entire precipitation distribution associated with the low NAO index composite encompasses a wider range of values than the corresponding high NAO index composite. This non-linear behavior is reflected in the probability of occurrence of a very wet month (precipitation above the 90% percentile) that is just 1% for the positive NAO class and 23% for low NAO index months. Results for the low NAO class are crucial because these months are more likely associated with long-lasting rainfall episodes responsible for large landslide events. This is confirmed by the application of a 3-month moving average to both NAO index and precipitation time series. This procedure allowed the identification of many months with landslide activity as being characterized by negative average values of the NAO index and high values of average precipitation (above 100 mm/month). Finally, using daily data we have computed the return periods associated with the entire set of landslide episodes and, based on these results, obtained a strong linear relationship between critical cumulative rainfall and the corresponding critical rainfall event duration.

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE.