Estimation of river flow using CubeSats remote sensing

Landscape-Scale Modeling to Forecast Fluvial-Aeolian Sediment Connectivity in River Valleys

Sedimentary landforms on Earth and other planetary bodies are built through scour, transport, and deposition of sediment. Sediment connectivity refers to the hypothesis that pathways of sediment transport do not occur in isolation, but rather are mechanistically linked. In dryland river systems, one such example of sediment connectivity is the transport of fluvially deposited sediment by wind. However, predictive tools that can forecast fluvial-aeolian sediment connectivity at meaningful scales are rare. Here we develop a suite of models for quantifying the availability of river-sourced sediment for aeolian transport as a function of river flow, wind regime, and land cover across 168 km of the Colorado River in Grand Canyon, USA. We compare and validate these models using topographic changes observed over 10 years in a coupled river sandbar-aeolian dunefield setting. The models provide a path forward for directly linking fluvial hydrology with the management and understanding of aeolian landscapes.

Path Planning for Self-Collision Avoidance of Space Modular Self-Reconfigurable Satellites

Space modular self-reconfigurable satellite (SMSRS) is a new type of satellite. The research on self-collision avoidance of SMSRS is important for its on-orbit safety but is not completely solved. This paper offers a new method for joint path planning for self-collision avoidance of SMSRS. Firstly, we establish the collision detection model for SMSRS based on forward kinematics and the spherical nonholonomic envelope to detect the collision occurring in SMSRS. Then, to achieve offline path planning in joint space, we proposed the self-collision avoidance strategy, which splices multiple C-spaces based on the pre-defined joint path into a binary map, and then transforms the binary map into a map with the dangerous potential field, and planning algorithms based on a map with the dangerous potential field is proposed to find the optimal collision-free path. The new method is applied to two cases and both find collision-free joint paths for SMSRS successfully, which demonstrates the feasibility of the method. In addition, this study bridges the gap in the study of self-collision avoidance of super-redundant self-reconfigurable satellites.

A systematic approach to understand hydrogeochemical dynamics in large river systems: Development and application to the River Ganges (Ganga) in India

Large river systems, such as the River Ganges (Ganga), provide crucial water resources for the environment and society, yet often face significant challenges associated with cumulative impacts arising from upstream environmental and anthropogenic influences. Understanding the complex dynamics of such systems remains a major challenge, especially given accelerating environmental stressors including climate change and urbanization, and due to limitations in data and process understanding across scales. An integrated approach is required which robustly enables the hydrogeochemical dynamics and underpinning processes impacting water quality in large river systems to be explored. Here we develop a systematic approach for improving the understanding of hydrogeochemical dynamics and processes in large river systems, and apply this to a longitudinal survey (> 2500 km) of the River Ganges (Ganga) and key tributaries in the Indo-Gangetic basin. This framework enables us to succinctly interpret downstream water quality trends in response to the underpinning processes controlling major element hydrogeochemistry across the basin, based on conceptual water source signatures and dynamics. Informed by a 2019 post-monsoonal survey of 81 river bank-side sampling locations, the spatial distribution of a suite of selected physico-chemical and inorganic parameters, combined with segmented linear regression, reveals minor and major downstream hydrogeochemical transitions. We use this information to identify five major hydrogeochemical zones, characterized, in part, by the inputs of key tributaries, urban and agricultural areas, and estuarine inputs near the Bay of Bengal. Dominant trends are further explored by investigating geochemical relationships (e.g. Na:Cl, Ca:Na, Mg:Na, Sr:Ca and NO₃:Cl), and how water source signatures and dynamics are modified by key processes, to assess the relative importance of controls such as dilution, evaporation, water-rock interactions (including carbonate and silicate weathering) and anthropogenic inputs. Mixing/dilution between sources and water-rock interactions explain most regional trends in major ion chemistry, although localized controls plausibly linked to anthropogenic activities are also evident in some locations. Temporal and spatial representativeness of river bank-side sampling are considered by supplementary sampling across the river at selected locations and via comparison to historical records. Limitations of such large-scale longitudinal sampling programs are discussed, as well as approaches to address some of these inherent challenges. This approach brings new, systematic insight into the basin-wide controls on the dominant geochemistry of the River Ganga, and provides a framework for characterising dominant hydrogeochemical zones, processes and controls, with utility to be transferable to other large river systems.

Multi-Temporal Surface Water Classification for Four Major Rivers from the Peruvian Amazon

We describe a new minimum extent, persistent surface water classification for reaches of four major rivers in the Peruvian Amazon (i.e., Amazon, Napo, Pastaza, Ucayali). These data were generated by the Peruvian Amazon Rural Livelihoods and Poverty (PARLAP) Project which aims to better understand the nexus between livelihoods (e.g., fishing, agriculture, forest use, trade), poverty, and conservation in the Peruvian Amazon over a 35,000 km river network. Previous surface water datasets do not adequately capture the temporal changes in the course of the rivers, nor discriminate between primary main channel and non-main channel (e.g., oxbow lakes) water. We generated the surface water classifications in Google Earth Engine from Landsat TM 5, 7 ETM+, and 8 OLI satellite imagery for time periods from circa 1989, 2000, and 2015 using a hierarchical logical binary classification predominantly based on a modified Normalized Difference Water Index (mNDWI) and shortwave infrared surface reflectance. We included surface reflectance in the blue band and brightness temperature to minimize misclassification. High accuracies were achieved for all time periods (>90%).