Variabilidade Intra-Anual E Interanual Da Precipitação Pluviométrica Em Região Semiárida

Campos, Débora A.; Braga, E. C.; Maciel, A. M.; Cavalcante, Regina Régia Rodrigues; Andrade, Eunice Maia de

doi:10.18068/ivsbrns.2019.130

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the wet season (P1), blooms are distributed evenly throughout the year. However, in P2 and P3, marked by lower accumulation volumes and high variability in precipitation events, most concentration peaks were identi ed between January and May (mainly in April and March), which generally comprises 80% of the state's annual precipitation (Campos, 2011), and in November, which has the highest mean monthly TN observed (2.19 mg.L − 1 ). Previous studies have reported that extreme precipitation events followed by periods of drought are the ideal scenario for blooms.…”

Section: Chlorophyll-a Modeling and Bloom Predictionmentioning

confidence: 99%

Chlorophyll-a prediction in tropical reservoirs as a function of hydroclimatic variability and water quality

Guimarães

Neto

2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

The study goal was to determine spatio-temporal variations in chlorophyll-a (Chl-a) concentration using models that combine hydroclimatic and nutrient variables in 150 tropical reservoirs in Brazil. The investigation of seasonal variability indicated that Chla varied in response to changes in total nitrogen (TN), total phosphorus (TP), volume (V), and daily precipitation (P). Simple linear regression models showed that nutrients yielded better predictability of Chl-a than hydroclimatic variables. Fitted relationships between Chl-a and the above-mentioned parameters resulted in equations capable of representing algal temporal dynamics and blooms, with an average coe cient of determination of R² = 0.70. Chl-a blooms presented seasonal and interannual variability, being more frequent in periods of high precipitation and low volume. The equations demonstrate different Chl-a responses to the parameters. In general, Chl-a was positively related to TN and/or TP. However, in some cases (22%), high nutrient concentrations reduced Chl-a, which was attributed to limited phytoplankton growth driven by light de ciency due to increased turbidity. In 49% of the models, precipitation intensi ed Chl-a levels, which was related to increases in the nutrient concentration from external sources in rural watersheds. Contrastingly, 51% of the reservoirs faced a decrease in Chl-a with precipitation, which can be explained by the opposite effect of dilution of nutrient concentration at the reservoir inlet in urban watersheds. In terms of volume, in 67% of the reservoirs, water level reduction promoted an increase in Chl-a as a response of higher nutrient concentration. In the other cases, Chl-a decreased with lower water levels due to wind-induced destrati cation of the water column, which potentially decreased the internal nutrient release from bottom sediment. Finally, an application of the model to the two largest studied reservoirs showed greater sensitivity of Chl-a to changes in water use classes regarding variations in TN, followed by TP, V, and P.

show abstract

Section: Chlorophyll-a Modeling and Bloom Predictionmentioning

confidence: 99%