In 2024, Rio Grande do Sul (RS), Brazil, faced the most severe flood event in its recorded history, which compromised several ground-based hydrological gauges. The SWOT (Surface Water and Ocean Topography) satellite, capable of measuring water surface elevation (WSE) in continental waters, is a valuable tool for providing critical data. This study investigates whether node-level WSE data from the SWOT satellite can effectively function as virtual hydrological stations under such extreme conditions. The study was applied in all of RS state considering 100 in situ gauges and was subdivided into three sections: (i) an evaluation of the variation in SWOTʹs WSE data compared to the variation in in situ levels from telemetric gauges, considering subsequent cycles of passes between July 2023 and April 2025, yielding an MAE = 35 cm and an RMSE = 73 cm after outlier removal; (ii) an evaluation of the variation in SWOTʹs WSE data compared to the variation in telemetric level data, considering one window prior to and another during the extreme event, resulting an MAE = 26 cm and an RMSE = 34 cm; (iii) an analysis of SWOTʹs data availability during the extreme event, when in situ telemetric data were unavailable. The results demonstrate an agreement between the variation observed in SWOT data and that in telemetric gauges in RS, even during extreme events. Moreover, in the absence of in situ data, SWOT was still able to capture WSE data.

This study demonstrates the potential for enhancing monthly streamflow forecasting in Brazil through the incorporation of climatic indices. It extends the conventional periodic autoregressive model (PAR) for streamflow forecasts by integrating climate information, represented by three key climate indices reflecting sea surface temperatures in the Pacific and Atlantic Oceans, as well as zonal wind patterns in southeastern Brazil. Using the Kling-Gupta Efficiency (KGE) skill metric, our findings reveal that the inclusion of climate data consistently outperforms existing PAR models in numerous scenarios. Notably, during May, the proposed model enhances forecasts for 79% of the reservoirs (124 out of 157), while in January, it reduces forecast variance for up to 90% of the reservoirs (141 out of 157).

Synthetic streamflow scenario generation is particularly important in countries like Brazil, where hydroelectric power generation plays a key role and properly handling the uncertainty of future streamflow is crucial. This paper showcases a collaborative effort within the Brazilian electrical sector to enhance streamflow scenario models, focusing on horizons up to one year. Five institutions proposed diverse methodologies, and their effectiveness was evaluated using a comparative framework. The results reveal the strengths and areas for improvement in each model. GHCen emerged as the top performer, excelling in both short-term and moving average analyses, while the PARX model demonstrated superior performance in specific regions. The PAR(p)-A, which is the official methodology in Brazil, was the second-best model in the moving average analysis. This research offers valuable insights for countries facing similar hydrothermal scheduling and scenario generation challenges.

In Brasilia the local regulations limit the maximum flow rate per unit area discharged in rivers by drainage systems. The purpose of this work is to analyze the possibility of optimizing the use of an existing detention pond, known to be oversized, by directing to it the stormwaters of adjacent areas not attended by any urban drainage system. SWMM, a hydrologic and hydraulic model, was used to design a new urban drainage system and to analyze it with the detention pond at the outlet. As a result, the new system designed complies with all local regulations including maximum flood peak for the design critical rainfall. However, the original drainage system for the area needs improvements considering that there were flooded manholes for all rainfall events tested. The flooding is clearly seen with the 2D modelling. It is shown that the existing detention pond can attend the studied area by adapting part of its area. This study showed the importance of hydrological and hydraulic modelling in urban drainage to manage this system and to propose sustainable measures – such as ponds.

Monitoring inland waters presents various challenges, from technological limitations to environmental and management issues. The accuracy of data collection is often compromised by the difficulty of accessing remote areas and the temporal and spatial variability of water bodies. These obstacles complicate the implementation of effective strategies for sustainable water resource management and the response to extreme events such as floods and droughts. The SWOT (Surface Water and Ocean Topography) mission is a collaborative satellite project designed to measure and monitor Earth's surface water and ocean topography through time. The primary hydrologically relevant metrics derived from SWOT include: (i) water surface elevation (WSE), (ii) river width, (iii) water surface slope, (iv) discharge-related variables derived from hydraulic relationships and modeling approaches, and (v) lake and reservoir storage variations. Although early calibration and validation studies have demonstrated encouraging performance for key observables such as water surface elevation and river width, important questions remain regarding the robustness of these metrics under diverse geomorphological, climatic, and hydrological conditions. This article aims to provide a systematic literature review on the metrics obtained through SWOT data for inland water. The review critically evaluates current capabilities, methodological limitations, and practical challenges associated with SWOT-derived hydrological variables, with particular emphasis on product selection, filtering strategies, and integration with hydrological and hydraulic modeling. By consolidating recent advances and identifying persistent gaps, this review aims to support more consistent and operational use of SWOT observations for inland water monitoring and water resources management.