The water in Earth’s rivers propagates as waves through space and time across hydrographic networks. A detailed understanding of river dynamics globally is essential for achieving the accurate knowledge of surface water storage and fluxes to support water resources management and water-related disaster forecasting and mitigation. Global in situ information on river flows are crucial to support such an investigation but remain difficult to obtain at adequate spatiotemporal scales, if they even exist. Many expectations are placed on remote sensing techniques as key contributors. Despite a rapid expansion of satellite capabilities, however, it remains unclear what temporal revisit, spatial coverage, footprint size, spatial resolution, observation accuracy, latency time, and variables of interest from satellites are best suited to capture the space-time propagation of water in rivers. Additionally, the ability of numerical models to compensate for data sparsity through model-data fusion remains elusive. We review recent efforts to identify the type of remote sensing observations that could enhance understanding and representation of river dynamics. Key priorities include: (a) resolving narrow water bodies (finer than 50-100 m), (b) further analysis of signal accuracy versus hydrologic variability and relevant technologies (optical/SAR imagery, altimetry, microwave radiometry), (c) achieving 1-3 days observation intervals, (d) leveraging data assimilation and multi-satellite approaches using existing constellations, and (e) new variable measurement for accurate water flux and discharge estimates. We recommend a hydrology-focused, multi-mission observing system comprising: (1) a cutting-edge single or dual-satellite mission for advanced surface water measurements, and (2) a constellation of cost-effective satellites targeting dynamic processes.

Increasing droughts and related water losses challenge lake systems. We analyzed water and sediment samples from five lakes in five subsequent dry and rainy seasons to study lakes’ vulnerability at times of significant environmental change, and to revisit the “old question” of whether different tropical lake water types can be observed in more complex datasets to better understand their limnological status, differences between lake types, and to broaden their baseline database for subsequent research. High temperatures combined with reduced oxygen levels (<<80% saturation) impose challenging boundary conditions for all biotas. Clearwater, black-, and whitewater can be differentiated by their signatures in dissolved ions, dissolved organic carbon, and selected major, minor and trace elements in water and sediment. Lake waters resembled remote rainwater. Lake sediment composition was compared with that of ‘terra firme’ soils in surrounding catchments for 47 chemical elements including carbon and nitrogen, macro- and micronutrients. With few exceptions (Ti; REE), sediment element concentrations showed mostly depletion when compared to the upper continental crust, reflecting regional soil chemistry and ongoing depletion of surrounding soils due to deforestation and changing landcover. Sediments act as intermediate sinks for eroded soil materials and show increasing buffer capacity from clearwater < blackwater < whitewater lakes. Under conditions of climate change, especially in areas close to the equator, the already pronounced oxygen depletion will pose further challenges for aquatic life. Lake, pisciculture, and catchment management should be adapted accordingly.

For nearly three decades, satellite radar altimetry has provided measurements of the water surface elevation (WSE) of rivers. These observations can be used to calculate the water surface slope (WSS), which is an essential parameter for estimating flow velocity and river discharge. In this study, we calculate a high-resolution WSS of 11 Polish rivers based on multi-mission altimetry observations from 11 satellites in the period from 1994 to 2022. The proposed approach is based on a weighted such gauge stations adjustment with an additional Laplace condition and an a priori gradient condition. The processing is divided into river sections not interrupted by dams and reservoirs. After proper determination of the WSE for each river kilometer (bin), the WSS between adjacent bins is calculated. To assess the accuracy of the estimated WSS, it is compared with slopes between gauge stations, which are referenced to a common vertical datum. Such gauge stations are available for 8 investigated rivers. The root mean squared error (RMSE) ranges from 3 mm/km to 80 mm/km, with an average of 26 mm/km. However, the mean RMSE decreases to 10 mm/km when the 2 mountain rivers are excluded. The WSS accuracies are also compared with those of slope datasets based on digital elevation models, ICESat-2 altimetry, and lidar. For 6 rivers the estimated WSS showed the highest accuracy. The improvement was particularly significant for mountain rivers. The proposed approach allows an accurate, high-resolution WSS even for small and medium-sized rivers and can be applied to almost any river worldwide.