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Sobradinho reservoir has been suffering a severe water loss caused by multi-year drought in the Northeast Brazil. This reservoir contributes to the socio-economic development of the semi-arid region, and the monitoring of water shortage is crucial for people living in this climate-vulnerable region. In this study, we evaluate the surface water change and turbidity variability of Sobradinho reservoir during recent drought years (2013–2017). A time-series dataset was created using 109 Landsat-8 OLI images for mapping the water extent in the reservoir. A non-linear regression between measured turbidity and surface reflectance (red band) was developed and applied for turbidity retrievals. Additionally, we performed a long-term precipitation analysis (17-year) to assess the rainfall deficit over the catchment area. Our results show that the annual precipitation regimes are below the long-term average during 2012–2017 period, except 2013. We also found that negative anomalies occur during 26 out of 36 months between 2014 and 2016, mostly in the rainy season. Since the rainfall regimes and river discharges are the major drivers for water recharge, these drought years have a critical impact on the reservoir level. According to our results, the water surface receded about 2073 km2 (out of total 3303 km2) during September 2017; this represents a reduction of 62.8% in the total water extent. The surface water change is spatially distinct across the reservoir. For instance, the upper section of the reservoir was almost totally dried during September 2017, and the water coverage was ~8% (91.25 km2 out of 1128 km2). Although other sections had a relatively low water change (reduction of ~40%), the losses are significant in terms of area (~1035.5 km2). The receding of water extent affects the people living near to the reservoir, and local communities are more distant from water (up to 13 km). We also observed that the turbidity is seasonally dependent, and water clarity presents a strong variability between rainy and dry seasons. In general, the turbidity levels vary from clear water (0–20 NTU) during the dry season to turbid condition (>50 NTU) during the rainy season. A lack of access to clean and safe drinking water in some periods might be harmful to humans, livestock and domestic animals. Finally, this research contributes to the assessment of drought-related impacts in the Sobradinho, the largest reservoir in the Northeast Brazil. The water shortage is a recurring concern in the semi-arid region, and the remote sensing techniques provide spatially explicit information to enhance the livelihood resilience during drought years.

Made available in DSpace on 2022-04-29T08:30:36Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-08-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Optical Water Type (OWT) is a useful parameter for assessing water quality changes related to different turbidity levels, trophic state and colored dissolved organic matter (CDOM) while also helpful for tuning chlorophyll-a algorithms. For this reason, interest in the satellite remote sensing of OWTs has recently increased in recent years. This study develops a machine learning method for monitoring Brazilian OWTs using the Sentinel-2 MSI, which can detect OWTs already assessed by field measurements and recognize new OWTs. The already assessed OWTs used for calibrating the machine learning algorithm are clear, moderate turbid, eutrophic turbid, eutrophic clear, hypereutrophic, CDOM richest, turbid, and very turbid waters. The classification method consists of two Support Vector Machines for classifying the known OWTs, while a novelty detection method based on sigmoid functions is used for assessing new OWTs. Results show the classification based on Sentinel-2 MSI bands simulated using field radiometric data is accurate (accuracy = 0.94). However, when radiometric errors are simulated, the accuracy significantly decreases to 0.75, 0.56, 0.45, and 0.37 as the mean absolute percent error increases to 10%, 20%, 30%, and 40%, respectively. Considering the errors retrieved when comparing the field and satellite measurements, the expected accuracy of Sentinel-2 MSI images is 0.78. In the satellite images, the novelty detection distinguishes new OWTs originated from the mixture among the known OWTs and a new OWT that was not part of the training database (clear blue waters). Two examples of time series in the Funil reservoir and the Curuai lake are used to show the applicability of monitoring OWTs. In the Funil reservoir, OWTs could indicate eutrophication and turbid changes caused by river inflow and sediment sinking. In the Curuai lake, OWTs could indicate areas susceptible to algae bloom and turbidity increases related to river inflow and particle resuspension. In the future, the proposed algorithm could be used for large-scale assessment of water quality degradation and supports rapid mitigation and recovery responses. For improving the classification accuracy, adjacency correction and more robust glint removal methods should be developed. Remote Sensing Division National Institute for Space Research CDTec Federal University of Pelotas Image Processing Division National Institute for Space Research Center of Marine Studies Federal University of Paraná Department of Cartography São Paulo State University Department of Cartography São Paulo State University FAPESP: 2008/56252–0 FAPESP: 2012/19821–1 FAPESP: 2013/09045–7 FAPESP: 2014/23903–9

Water siltation caused by artisanal gold mining has impacted the Tapajos River Basin in Brazil for the past 40 years, however spatial-temporal information about changes in water quality and consequences to the aquatic environment is lacking. To address this, the Landsat satellite family sensors were used to retrieve total suspended solids (TSS) of the water of the Tapajos River from 1973 to 2013. An image processing approach that includes atmospheric correction, based on the 6S model, and glint removing, based on shortwave infrared correction, was applied and validated with in situ radiometric data. An optimization of the atmospheric correction having dark dense forest spectra as reference was applied and allowed a robust correction of MSS, TM and OLI signal to surface reflectance values. Sediment concentration was estimated based on a non-linear empirical regression between measured TSS and satellite surface reflectance at red band. The multi-temporal analysis of TSS showed that the sediment load in the Tapajos aquatic system is in synchrony with mining activities, and a constant seasonal variation of water siltation is observed throughout the time frame of this study. At the end of the rainy season, mining activities intensify and, coupled with low water flow, TSS increases. During the high water level, TSS concentrations were consistently lower because of high water dilution and low mining activity. In a decadal analysis, a peak of sediment concentration coincides with a peak of gold production in all sites analyzed during early 1990s. More recently, due to the currently gold rush, an increase in suspended solids has been observed mainly in the Novo and Tocantins rivers where industrial mining has been installed.