Evaluation of the impacts of climate change on streamflow through hydrological simulation and under downscaling scenarios: case study in a watershed in southeastern Brazil.

Among the problems related to water security, the effects of climate change on water availability stand out. Researchers have used hydrological models integrated with climate models in order to predict the streamflow behaviour in different hydrographic basins. This work aimed to analyse future climate scenarios for the Ribeirão do Lobo River Basin, located in the state of São Paulo, Brazil.

A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning.

Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing.