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Water fluxes in highly impounded regions are heavily dependent on reservoir properties. However, for large and remote areas, this information is often unavailable. In this study, the geometry and volume of small surface reservoirs in the semi-arid region of Brazil were estimated using terrain and shape attributes extracted by remote sensing. Regression models and data classification were used to predict the volumes, at different water stages, of 312 reservoirs for which topographic information is available. The power function used to describe the reservoir shapes tends to overestimate the volumes; therefore, a modified shape equation was proposed. Among the methods tested, four were recommended based on performance and simplicity, for which the mean absolute percentage errors varied from 24 to 39%, in contrast to the 94% error achieved with the traditional method. Despite the challenge of precisely deriving the flooded areas of reservoirs, water management in highly reservoir-dense environments should benefit from volume prediction based on remote sensing.
There is a shortage of sediment-routing monitoring worldwide, despite its relevance to environmental processes. In drylands, where water resources are more vulnerable to the sediment dynamics, this flaw is even more harmful. In the semi-arid Caatinga biome in the North-east of Brazil, rivers are almost all intermittent and hydro-sedimentological monitoring is scarce. In the biome, water supply derives from thousands of surface reservoirs, whose water availability is liable to be reduced by siltation and sediment-related pollution. The goal of this research was to evaluate the potential of multi-temporal high-resolution satellite imagery (RapidEye) to assess the suspended sediment concentration (SSC) in the medium-sized intermittent Jaguaribe River, Brazil, during a 5-year period. We validated 15 one-, two- and three-band indices for SSC estimation based on RapidEye spectral bands deduced in the context of the present investigation and nine indices proposed in the literature for other optical sensors, by comparing them with in-situ concentration data. The in-situ SSC data ranged from 67 mg.L-1 to 230 mg.L-1. We concluded that RapidEye images can assess moderate SSC of intermittent rivers, even when their discharge is low. The RapidEye indices performed better than those from literature. The spectral band that best represented SSC was the near infrared, whose performance improved when associated with the green band. This conclusion agrees with literature findings for diverse sedimentological contexts. The three-band spectral indices performed worse than those with only one or two spectral bands, showing that the use of a third band did not enhance the model ability. Besides, we show that the hydrological characteristics of semi-arid intermittent rivers generate difficulties to monitor SSC using optical satellite remote sensing, such as time-concentrated sediment yield; and its association with recent rainfall events and, therefore, with cloudy sky.