It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R² of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.

The study goal was to determine spatiotemporal 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 Chl-a varied in response to changes in total nitrogen (TN), total phosphorus (TP), volume (V), and daily precipitation (P). Therefore, an empirical model for Chl-a prediction based on the product of TN, TP, and normalized functions of V and P was proposed, but their individual exponents as well as a general multiplicative factor were adjusted by linear regression for each reservoir. The fitted relationships were capable of representing algal temporal dynamics and blooms, with an average coefficient of determination of R² = 0.70. The results revealed that nutrients yielded better predictability of Chl-a than hydroclimatic variables. 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 deficiency due to increased turbidity. In 49% of the models, precipitation intensified 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 to higher nutrient concentration. In the other cases, Chl-a decreased with lower water levels due to wind-induced destratification of the water column, which potentially decreased the internal nutrient release from bottom sediment. Finally, applying 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.

Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

Chlorophyll a (Chla) concentration is an important indicator to characterize algal biomass, reflecting water quality and the level of eutrophication of aquatic environments. This study analyzed 4315 total nitrogen (TN) and Chla samples in 155 reservoirs in Ceará, Brazil. Relationships were obtained between TN and Chla through simple linear regression and classified according to the statistical performance of the coefficient of determination (R²). The dynamics of these correlations were examined by analyzing the variance between classification groups concerning watershed, reservoir size, coefficient of variability, and trophic state. The concentration of pollutants was influenced by seasonality, which increased significantly from the wet to the dry period in most basins. The coefficients of determination between Chla and TN for the watersheds followed the trends of classic curves in the literature, with satisfactory models (R² > 0.3) in most reservoirs (53.3%). The variance analysis attested that the models’ performance was affected by the basins, the volumetric variation, and the change in the trophic state levels. There was no significant variance of R² between reservoirs of different sizes.

Reservatórios em todo o mundo são susceptíveis à degradação da qualidade da água em razão do aumento da produção primária, e a necessidade de entender os fatores que a influenciam é questão fundamental para o controle da eutrofização. O objetivo deste estudo foi analisar a correlação entre fósforo total (PT) e clorofila a (Cla) em 155 reservatórios localizados no estado do Ceará, Brasil. Esta análise foi realizada por meio do levantamento de dados históricos (2008–2021) monitorados e divulgados pela companhia de gestão de recursos hídricos do estado. A relação entre PT e Cla foi analisada por meio de ajustes simples, utilizando como métrica de avaliação o desempenho do coeficiente de determinação (R²). Posteriormente, investigou-se como a dinâmica desses ajustes pode ser influenciada com relação à bacia hidrográfica, ao porte do reservatório, ao estado trófico (quanto à concentração de Cla e PT) e ao coeficiente de variabilidade volumétrica. De forma geral, os ajustes resultaram em modelos insatisfatórios (R² < 0,4) para a maioria dos reservatórios (n = 115). Esses modelos, apesar de insatisfatórios, seguem as tendências de modelos literários clássicos, mostrando que na maioria das bacias a disponibilidade de PT na coluna d’água favorece o aumento da concentração de Cla. A análise dos fatores que influenciam o padrão e a dispersão dos ajustes entre Cla e PT demonstrou que a performancedos R² está relacionada às diferentes bacias hidrográficas, à variabilidade volumétrica e às concentrações de Cla. A variância entre os R² de reservatórios de diferentes portes e estados tróficos conforme a concentração de PT não foi significativa.

A caracterização do consumo de água determina quais atividades mais consomem água, as que menos consomem, e quais os fatores que influenciam no consumo, como a cultura, fenômenos naturais como a seca, e o clima. O presente trabalho objetivou realizar a caracterização do consumo de água da população de Pau dos Ferros através de métodos distintos com base na renda per capita, valores de micromedição, coeficiente de retorno de esgoto, sugestões literárias e calculo através de médias obtidas por entrevista com os habitantes, definindo quais os usos da água são os mais predominantes nas residências. A média encontrada para o consumo per capita da cidade de acordo com a entrevista foi de 417,4 l/hab.dia. Entre as atividades que mais contribuem para o consumo per capita foram identificadas o uso do chuveiro e pia da cozinha, precedidas pelo volume usado nas bacias sanitárias. Para todos os métodos obteve-se um consumo crescente conforme os anos.