Abstract Changes in land use and land cover (LULC) can result in significant changes in a hydrographic basin flow regime. Future projections about LULC and its interference with water availability help to identify extreme events in advance and help propose appropriate management measures. Thus, this study aimed to make the LULC projection for the year 2030 for the Alto Rio Grande (ARG) sub-basin, located in Southeastern Brazil. This region was chosen because of its intense water resources use and for having recently faced water scarcity as result of prolonged droughts and inadequate water resources management. To identify the LULC trend for the year 2030, the Land Change Modeler (LCM) was used, the map obtained was inserted in the Soil and Water Assessment Tool (SWAT) model previously calibrated and validated for the region’ environmental and climatic conditions. The ARG sub-basin was affected by heavy rains in 2011, which resulted in changes in the landscape due to landslides. This particularity of the region contributed to the projection of LULC for the year 2030 to present an increase in forest and pastures to the agricultural areas detriment. When evaluating the impacts of these changes in water availability, it was observed that the SWAT model presented, for the same rainfall conditions, a reduction in peak streamflows of up to 59% and a reduction in the average monthly flow of up to 63% in 2030 in relation to the LULC observed in 2017. Thus, this study provides an important contribution by identifying a considerable reduction in water availability. These results will help to formulate strategies for water resources management and the adoption of measures to promote water security in the region.

Resumen Los cambios en el uso y la cobertura del suelo pueden resultar en cambios significativos en el régimen del flujo de una cuenca hidrográfica. Los estudios predictivos sobre el uso del suelo y su interferencia con la disponibilidad del agua ayudan a identificar eventos extremos con anticipación a fin de proponer medidas de gestión adecuadas. De esta forma, este estudio tuvo como objetivo realizar la predicción del uso del suelo para el año 2030 para la subcuenca del Alto Río Grande (ARG), ubicada en el sureste de Brasil. Esta región fue elegida por el uso intenso de los recursos hídricos y por haber enfrentado, recientemente, escasez de agua como resultado de sequías prolongadas y una gestión inadecuada de los recursos hídricos. Para la predicción del uso del suelo en 2030 se utilizó el Land Change Modeler (LCM), el mapa obtenido se insertó en el modelo Soil and Water Assessment Tool (SWAT) previamente calibrado y validado para las condiciones ambientales y climáticas de la región. La subcuenca ARG fue afectada por fuertes lluvias en 2011 que resultaron en cambios en el paisaje debido a deslizamientos de tierra. Esta particularidad de la región contribuyó para que la predicción del uso del suelo para el año 2030 presente un aumento de bosques y pastos en detrimento de las áreas agrícolas. Al evaluar los impactos de estos cambios en la disponibilidad del agua, se observó que el modelo SWAT presentó, para las mismas condiciones de precipitación, una reducción en los caudales pico de hasta 59% y una reducción en el caudal promedio mensual de hasta 63% en 2030 en relación con el uso del suelo observado en 2017. Es decir, este estudio hace un aporte importante al identificar una reducción considerable en la disponibilidad del agua. Estos resultados ayudarán a formular estrategias para la gestión de los recursos hídricos y la adopción de medidas para promover la seguridad hídrica en la región.

Abstract: The use of acoustic systems for mapping submerged areas is the most accurate way. However, echosounders are expensive and, in addition, the equipment requires a great deal of experience on the part of the specialist. From another perspective, orbital and aerial images (acquired by RPA’s- Remotely Piloted Aircraft) can offer bathymetric maps of larger locations that are difficult to access at a low operating cost. Therefore, the present study’s main objective was to evaluate the utility of RGB images obtained with RPA’s in water reservoirs. Thus, Artificial Neural Networks were used for depth training and prediction. Subsequently, it compared to the bathymetric data from the same pond in question, raised from acoustic sensors, quantifying the vertical uncertainty through three estimators. Regarding the statistical analysis, the RMSE and Ф estimators showed better reliability. The 300-point sample showed the best quality in processing. The results showed that the methodology could improve the management of water resources. The method allows reduced execution time and lowers cost, especially for using only the green, red and blue channels, easily found in most cameras coupled to RPA’s.

Abstract: Due to waterway transport efficiency, mainly for commercial trades, the use of sea/river routes has grown recently. So, the importance of producing high quality nautical charts stands out. A nautical chart is the hydrographic survey final product and its accuracy depends on data quality collected, primarily of the vertical quality (depth). In this sense, despite the theoretical and practical difficulty in obtaining homologous points in hydrographic surveys, even when performing check lines, bathymetric data must always be delivered with a statistically proven confidence level. Thus, this study has two main objectives: i) to propose a method, called Point to Point (P2P), for obtaining “homologous points” for hydrographic surveys carried out with multibeam systems, without resorting to mathematical and/or statistical interpolations, called Point to Point (P2P); ii) to quantify the magnitude of the difference between the statistical evaluation using check lines (CL) and by overlapping successive sounding lines (SL), applying the P2P method. The results showed that P2P is easy to application, provide low computation effort, is robust and consistent. Besides that, was possible to applied successive regular lines to get a validation of the hydrographic survey.