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Over the past several decades, concern has grown over the rising mortality of the Amazon river dolphin ('boto') from increased human–dolphin interactions. Among these interactions are tourist attractions involving up-close feeding encounters with the botos, confrontations with fishers, and an illegal fishing practice that uses dolphin flesh as fish bait. Drawing on original data sourced from in-depth semi-structured interviews and household surveys, existing studies on boto habitat preferences and seasonal movement, and remotely-sensed data, this paper discusses the spatial and temporal overlap between humans and dolphins in a region outside of Manaus, Amazonas in the central Brazilian Amazon. Results suggest that there is considerable spatial overlap between boto habitat and spaces used for fishing and tourism activities; additionally, overall potential for conflict is greatest during the high-water season. [ABSTRACT FROM AUTHOR]

The Floodwater Depth Estimation Tool (FwDET) calculates water depth from a remote sensing-based inundation extent layer and a Digital Elevation Model (DEM). FwDET's low data requirement and high computational efficiency allow rapid and large-scale calculation of floodwater depth. Local biases in FwDET predictions, often manifested as sharp transitions or stripes in the water depth raster, can be attributed to spatial or resolution mismatches between the inundation map and the DEM. To alleviate these artifacts, we are introducing a boundary cell smoothing and slope filtering procedure in version 2.1 of FwDET (FwDET2.1). We present an optimization analysis that quantifies the effect of differing parameterization on the resulting water depth map. We then present an extensive intercomparison analysis in which 16 DEMs are used as input for FwDET Google Earth Engine (FwDET-GEE) implementation. We compare FwDET2.1 to FwDET2.0 using a simulated flood and a large remote sensing derived flood map (Irrawaddy River in Myanmar). The results show that FwDET2.1 results are sensitive to the smoothing and filtering values for medium and coarse resolution DEMs, but much less sensitive when using a finer resolution DEM (e.g., 10 m NED). A combination of ten smoothing iterations and a slope threshold of 0.5% was found to be optimal for most DEMs. The accuracy of FwDET2.1 improved when using finer resolution DEMs except for the MERIT DEM (90 m), which was found to be superior to all the 30 m global DEMs used. [ABSTRACT FROM AUTHOR]