Effects of rainfall and land use on nutrient responses in rivers in the Brazilian semiarid region

Freire, Letícia Lacerda; Costa, Antônio Celso Spı́nola; Neto, Iran Eduardo Lima

doi:10.1007/s10661-023-11281-y

Cited by 8 publications

(3 citation statements)

References 91 publications

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“…These reservoirs are notably susceptible to eutrophication due to a combination of hydroclimatic characteristics that favor photosynthesis and biodegradation [28,29], such as interannual variability of precipitation and stored volume [30], high temperatures and evaporation rates [31], and prolonged hydraulic retention time [32]. Moreover, this susceptibility is further aggravated by continued anthropogenic pressure on water bodies due to internal enrichment from aquaculture practices [33,34], livestock and agriculture practices [35], inadequate coverage of sanitation systems [36], and a dense reservoir network [37,38].…”

Section: Study Site Locationmentioning

confidence: 99%

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Oliveira Santos,

Guimarães,

Neto

et al. 2024

Remote Sensing

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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.

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Section: Study Site Locationmentioning

confidence: 99%

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Oliveira Santos,

Guimarães,

Neto

et al. 2024

Remote Sensing

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“…These reservoirs are notably susceptible to eutrophication due to hydroclimatic characteristics that combined favor photosynthesis and biodegradation [15,16], such as interannual variability of precipitation and stored volume [17], high temperatures and evaporation rates [18], and prolonged hydraulic retention time [19]. Moreover, this susceptibility is further aggravated by continued anthropogenic pressure on water bodies due to internal enrichment from aquaculture practice [3,20], inadequate coverage of sanitation systems [21], and a dense reservoir network [22,23].…”

Section: Introductionmentioning

confidence: 99%

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Oliveira Santos,

Guimarães,

Lima Neto

et al. 2024

Preprint

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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.

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“…Reservoirs are important structures for the development of society, notably in tropical semiarid regions such as the Northeast of Brazil (NEB), where a High-density Reservoirs Network (HdRN) has been developed to minimize the impacts of recurrent droughts on water availability (Campos, 2015;Rabelo et al, 2021;Freire et al, 2023). However, such hydrologic infrastructures transform a lotic aquatic environment into a lentic environment which, added to the tendency of accumulation of nutrients and sediments, can favor the phenomenon of eutrophication (Moura et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Adaptation and application of the fuzzy synthetic evaluation (FSE) method for characterizing the trophic state of tropical semiarid reservoirs

Raulino,

Lima Neto

2023

Environ Monit Assess

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Eutrophication is a recurrent problem in water bodies, especially in tropical semiarid reservoirs. The Trophic State Index (TSI) is an important tool for the environmental management of aquatic systems. However, determining the TSI involves uncertainties that can affect decision making. This study aimed to adapt and apply the Fuzzy Synthetic Evaluation (FSE) to characterize the TSI considering the uncertainties of the reference eutrophication classi cation system. The Castanhão reservoir, the largest in the State of Ceará, Brazil, was taken as a case study. The results showed that: i) the uncertainty of the trophic classi cation system can be characterized by the triangular and trapezoidal membership functions; ii) the result matrix associates the global trophic level with a degree of certainty, providing greater con dence to the decision maker; iii) the eutrophication index (EI) is not an adequate tool for hierarchizing the trophic degree; and iv) the membership level of the global trophic state generated by the FSE method is a suitable alternative to the EI.

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Effects of rainfall and land use on nutrient responses in rivers in the Brazilian semiarid region

Cited by 8 publications

References 91 publications

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning

Adaptation and application of the fuzzy synthetic evaluation (FSE) method for characterizing the trophic state of tropical semiarid reservoirs

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