Addendum: Arvor, D. et al. Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends. Remote Sens. 2017, 9, 889

Abstract

After publication of the paper [1] it was found that the Acknowledgments section did not mention the institutions that supported this research. This work was supported by (1) Rennes Métropole, who funded an "Allocation d'Installation Scientifique", and (2) the European Union, which funded the H2020-MSCA-RISE-2015 ODYSSEA project (Project Reference: 691053). The authors would like to apologize for any inconvenience caused. The change does not affect the scientific results. The manuscript will be updated and the original will remain online on the article webpage, with a reference to this addendum.

On the degradation of forest ecosystems by extreme events: Statistical Model Checking of a hybrid model

“…As a privileged case study, the Amazon forest is well-known to be particularly vulnerable to both climate and land cover changes [43], with the regions of highest vulnerability in ecosystem function overlapping areas of large-scale forest degradation and fragmentation at the southern and eastern edges of the basin [21,43]. Recent studies have confirmed that widespread droughts and floods have occurred in the Amazon with increased frequency and intensity in the past two decades, and with changes in precipitation patterns, including extreme precipitation [2,12,14,16,17,35,52]. A combination of the hybrid model and real world data on the forest state (intact versus degraded), land cover (forest or savanna), and extreme events, may thus provide important quantitative insights to the degree of fragility and support the urgency for preservation and protection of the Amazon forest, as a common good of humankind.…”

Surface water quality in Amazonian Floodplain Lakes, data set of the Lago Grande de Curuai Floodplain Lake, Pará‐Brazil

“…the variation in water level during a given period, namely January–April for rising, September–November for falling), varies significantly from one period to another. The water level rises and decreases faster during the 2001–2016 period compared to the 1983–2000 period (Arvor et al, 2018). Consequently, we recommend comparing the data considering water levels (available on the ANA website https://www.snirh.gov.br/hidroweb/serieshistoricas) and not only the hydrological periods.…”