Amanda L. Cox

Several reservoirs across the United States are filling with sediment, which jeopardizes their functionality and increases maintenance costs. USACE developed the Reservoir Sedimentation Inf... Practical ApplicationsMany communities depend on reservoirs for a variety of socioeconomic benefits, such as providing reliable water sources and flood mitigation. Rivers entering reservoirs are a ...

Several reservoirs across the US are filling with sediment, which jeopardizes their functionality and increases maintenance costs. The US Army Corps of Engineers (USACE) developed the Reservoir Sedimentation Information (RSI) system to assess reservoir aggradation and track dam operation suitability for water-resource management and dam safety. The RSI dataset contains historical elevation-capacity data for approximately 400 dams (excluding navigation structures) which correspond to less than 1% of dams across the US. Thus, there is a critical need to develop methods for estimating reservoir sedimentation for unmonitored sites. The goal of this project was to create a generalized method for estimating reservoir sedimentation rates using reservoir design information and watershed data. To meet this objective, geospatial tools were used to build a refined composite dataset to complement the RSI system’s data with precipitation and watershed characteristics. Nine deep learning models were then used on the benchmark dataset to determine its accuracy at predicting capacity loss for the RSI reservoirs: four supervised machine learning models, four deep neural network (DNN) models, and a multilinear power regression model. A DNN model, containing a progressively increasing node and layer construction, was deemed the most accurate, with R2 values from its calibration and validation datasets being 0.83 and 0.70, respectively. The best model was recalibrated over the entire dataset, which showed greater accuracy on the prediction of RSI reservoir’s capacity loss, with an R2 of 0.81. This predictive model could be used to evaluate the capacity loss of unmonitored reservoirs, forecast sedimentation rates under future climate conditions, and identify reservoirs with the highest risk of losing functionality.

River morphology data are critical for understanding and studying river processes and for managing rivers for multiple socio-economic uses. While such data have been acquired extensively over time, several issues hinder their use for river morphology studies such as data accessibility, variety of data formats, lack of data models for data storage, and lack of processing tools to assemble the data in products readily usable for research, management, and education. A multi-university research team has prototyped a web-based river morphology information system (RIMORPHIS) for hosting and creating new information and data processing tools to be shared with the broader earth science communities. The RIMORPHIS design principles include: (i) broad access via a publicly and freely available platform-independent system; (ii) flexibility in handling existing and future data types; (iii) user-friendly and interactive interfaces; and (iv) interoperability and scalability to ensure platform sustainability. Development of such an ambitious community resource is only possible by continuously engaging stakeholders from the inception of the project. This paper highlights the research team’s strategy and activities to connect and engage with river morphology data producers and potential users from academia, research, and practice. The paper also details outcomes of stakeholder engagement and illustrates how these interactions are positively shaping RIMORPHIS development.

Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R² > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.

Bendway weirs are submerged, in-stream rock structures that redirect impinging flow away from outer-banks toward the center of a channel. Riprap weirs are composed of stone and sized using the average channel velocity in conjunction with existing design guidance. A physical model study and field study were conducted to determine the magnitude of the convective velocity that accelerates around the tip or toe of the weir. The ratio of the maximum tip velocity to the average channel velocity (prior to weir installation) was determined to be approximately 1.70. Stone sizing criteria that significantly increase the riprap median size over traditional approaches were presented for consideration.