An official website of the United States government
Abstract Environmental flow releases are an effective tool to meet multiple management objectives, including maintaining river conveyance, restoring naturally functioning riparian plant communities, and controlling invasive species. In this context, predicting plant mortality during floods remains a key area of uncertainty for both river managers and ecologists, particularly with respect to how flood hydraulics and sediment dynamics interact with the plants’ own traits to influence their vulnerability to scour and burial.To understand these processes better, we conducted flume experiments to quantify different plant species’ vulnerability to flooding across a range of plant sizes, patch densities, and sediment condition (equilibrium transport versus sediment deficit), using sand‐bed rivers in the U.S. southwest as our reference system. We ran 10 experimental floods in a 0.6 m wide flume using live seedlings of cottonwood and tamarisk, which have contrasting morphologies.Sediment supply, plant morphology, and patch composition all had significant impacts on plant vulnerability during floods. Floods under sediment deficit conditions, which typically occur downstream of dams, resulted in bed degradation and a 35% greater risk of plant loss compared to equilibrium sediment conditions. Plants in sparse patches dislodged five times more frequently than in dense patches. Tamarisk plants and patches had greater frontal area, larger basal diameter, longer roots, and lower crown position compared to cottonwood across all seedling heights. These traits were associated with a 75% reduction in tamarisk seedlings’ vulnerability to scour compared to cottonwood.Synthesis and applications. Tamarisk's greater survivability helps to explain its vigorous establishment and persistence on regulated rivers where flood magnitudes have been reduced. Furthermore, its documented influence on hydraulics, sediment deposition, and scour patterns in flumes is amplified at larger scales in strongly altered river channels where it has broadly invaded. Efforts to remove riparian vegetation using flow releases to maintain open floodways and/or control the spread of non‐native species will need to consider the target plants’ size, density, and species‐specific traits, in addition to the balance of sediment transport capacity and supply in the river system.
more »
« less
Towards Understanding Underwater Weather Events in Rivers Using Autonomous Surface Vehicles
Climate change has increased the frequency and severity of extreme weather events such as hurricanes and winter storms. The complex interplay of floods with tides, runoff, and sediment creates additional hazards – including erosion and the undermining of urban infrastructure – consequently impacting the health of our rivers and ecosystems. Observations of these underwater phenomena are rare, because satellites and sensors mounted on aerial vehicles cannot penetrate the murky waters. Autonomous Surface Vehicles (ASVs) provides a means to track and map these complex and dynamic underwater phenomena. This work highlights preliminary results of high-resolution data gathering with ASVs, equipped with a suite of sensors capable of measuring physical and chemical parameters of the river. Measurements were acquired along the lower Schuylkill River in the Philadelphia area at high-tide and low-tide conditions. The data will be leveraged to improve our understanding of changes in bathymetry due to floods; the dynamics of mixing and stagnation zones and their impact on water quality; and the dynamics of suspension and resuspension of fine sediment. The data will also provide insight into the development of adaptive sampling strategies for ASVs that can maximize the information gain for future field experiments.
more »
« less
- Award ID(s):
- 1939132
- PAR ID:
- 10398273
- Date Published:
- Journal Name:
- OCEANS 2022, Hampton Roads
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data.more » « less
-
Abstract Bedrock rivers are the pacesetters of landscape evolution in uplifting fluvial landscapes. Water discharge variability and sediment transport are important factors influencing bedrock river processes. However, little work has focused on the sensitivity of hillslope sediment supply to precipitation events and its implications on river evolution in tectonically active landscapes. We model the temporal variability of water discharge and the sensitivity of sediment supply to precipitation events as rivers evolve to equilibrium over 106model years. We explore how coupling sediment supply sensitivity with discharge variability influences rates and timing of river incision across climate regimes. We find that sediment supply sensitivity strongly impacts which water discharge events are the most important in driving river incision and modulates channel morphology. High sediment supply sensitivity focuses sediment delivery into the largest river discharge events, decreasing rates of bedrock incision during floods by orders of magnitude as rivers are inundated with new sediment that buries bedrock. The results show that the use of river incision models in which incision rates increase monotonically with increasing river discharge may not accurately capture bedrock river dynamics in all landscapes, particularly in steep landslide prone landscapes. From our modeling results, we hypothesize the presence of an upper discharge threshold for river incision at which storms transition from being incisional to depositional. Our work illustrates that sediment supply sensitivity must be accounted for to predict river evolution in dynamic landscapes. Our results have important implications for interpreting and predicting climatic and tectonic controls on landscape morphology and evolution.more » « less
-
Scientists continue to study the red tide and fish-kill events happening in Florida. Machine learning applications using remote sensing data on coastal waters to monitor water quality parameters and detect harmful algal blooms are also being studied. Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) are often deployed on data collection and disaster response missions. To enhance study and mitigation efforts, robots must be able to use available data to navigate these underwater environments. In this study, we compute a satellite-derived underwater environment (SDUE) model by implementing a supervised machine learning model where remote sensing reflectance (Rrs) indices are labeled with in-situ data they correlate with. The models predict bathymetry and water quality parameters given a recent remote sensing image. In our experiment, we use Sentine1-2 (S2) images and in-situ data of the Biscayne Bay to create an SDUE that can be used as a Chlorophyll-a map. The SDUE is then used in an Extended Kalman Filter (EKF) application that solves an underwater vehicle localization and navigation problem.more » « less
-
null (Ed.)Since 2 June 2020, unusual heavy and continuous rainfall from the Asian summer monsoon rainy season caused widespread catastrophic floods in many Asian countries, including primarily the two most populated countries, China and India. To detect and monitor the floods and estimate the potentially affected population, data from sensors aboard the operational polar-orbiting satellites Suomi National Polar-Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA)-20 were used. The Visible Infrared Imaging Radiometer Suite (VIIRS) with a spatial resolution of 375 m available twice per day aboard these two satellites can observe floodwaters over large spatial regions. The flood maps derived from the VIIRS imagery provide a big picture over the entire flooding regions, and demonstrate that, in July, in China, floods mainly occurred across the Yangtze River, Hui River and their tributaries. The VIIRS 5-day composite flood maps, along with a population density dataset, were combined to estimate the population potentially exposed (PPE) to flooding. We report here on the procedure to combine such data using the Zonal Statistic Function from the ArcGIS Spatial Analyst toolbox. Based on the flood extend for July 2020 along with the population density dataset, the Jiangxi and Anhui provinces were the most affected regions with more than 10 million people in Jingdezhen and Shangrao in Jiangxi province, and Fuyang and Luan in Anhui province, and it is estimated that about 55 million people in China might have been affected by the floodwaters. In addition to China, several other countries, including India, Bangladesh, and Myanmar, were also severely impacted. In India, the worst inundated states include Utter Pradesh, Bihar, Assam, and West Bengal, and it is estimated that about 40 million people might have been affected by severe floods, mainly in the northern states of Bihar, Assam, and West Bengal. The most affected country was Bangladesh, where one third of the country was underwater, and the estimated population potentially exposed to floods is about 30 million in Bangladesh.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/OCEANS47191.2022.9977236
