Analysis of atmospheric moisture transport to the Upper Paraná River basin

Abstract

The understanding of the atmospheric phase of the hydrological cycle is an important step forward in improving our abilities to predict droughts and floods, as well as the impacts of climate change on water resources. Here we focus on the study of the atmospheric moisture transport to the Upper Paraná River basin, Brazil, through the hybrid single‐particle Lagrangian integrated trajectory (HYSPLIT) dispersion model, which is used to generate air masses trajectories based on the 1970–2010 National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data. In terms of moisture source regions, we found that the tropical South Atlantic is the main contributor to the Upper Paraná River basin, exporting about 175 mm/month of water vapour during the rainy season. The North Atlantic contribution is relevant in February (about 38 mm/month). Other regions over the Atlantic Ocean (North and subtropical South) are also identified as sources, and moisture evaporated from these places has a significant impact on the streamflow variability at the Itaipu streamflow gauge, located at the basin outlet. The continental sources play a more relevant role during the dry season (May–October). The Amazon region is more relevant as a moisture source during the dry season, exporting an average of 45 mm/month. A similar pattern is observed in other continental sources, such as Chaco/Pantanal/Cerrado, which together export about 99 mm/month to the Paraná River basin during the dry season. An extreme event analysis reveals that changes in moisture advection from the tropical South Atlantic are related to floods and droughts in the region. Particularly, we observe that flood events are preceded by positive anomalies of moisture advection up to 2 months in advance, while persistent, negative anomalies of moisture transport lead seasonal droughts by up to 8 months beforehand.

A Novel Atmospheric River Identification Algorithm

“…The identification of associated atmospheric moisture transports with these extremes follows two main approaches. One is by tracking the movement of a large number of moist air parcels by different Lagrangian trajectory models, such as tropical moisture exports (Knippertz & Wernli, 2010;Najibi et al, 2017), FLEXPART (Stohl et al, 2005), and Hybrid Single-Particle Lagrangian Integrated Trajectory model (Santos et al, 2018;Stein et al, 2015;Zandonadi Moura & Lima, 2018). The other is by identifying atmospheric rivers (ARs) using different algorithms (Shields et al, 2018).…”

Influence of teleconnection patterns on global moisture transport during peak precipitation month

“…(2019): the North Pacific (NPAC), South Pacific (SPAC), Gulf of Mexico and Caribbean Sea (MEXCAR), North Atlantic (NATL), South Atlantic (SATL), Zanzibar Current and Arabian Sea (ZANAR), Agulhas Current (AGU), Indian Ocean (IND), Coral Sea (CORALS), Mediterranean Sea (MED), Red Sea (REDS), South America (SAM), Sahel region (SAHEL), and Southern Africa (SAFR). The methodology used here concerning the moisture computation was first established by Stohl and James (2004, 2005) and has been widely used (Zandonadi Moura and Lima, 2018; Drumond et al ., 2019; Algarra et al ., 2020). For the global experiment, to detect moisture sinks, particles located over each moisture source were selected for each day from 1980 to 2018, and the moisture variation experienced by each particle was computed, for up to 15 days forward in time, following the equation , where e and p represent evaporation and precipitation processed in time ( t = 6 hr) by each particle of mass m. Following this procedure, for each of the particles that left a specific source, on a specific day in the period 1979–2018, we obtained the ( e − p ) values for 60 time steps (15 days every 6 hr).…”