Scott D Hamshaw

U.S. Geological Survey · Integrated Modeling & Prediction Division

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL...

Process-based, large-scale (e.g., conterminous United States [CONUS]) hydrologic models have struggled to achieve reliable streamflow drought performance in arid regions and for low-flow periods. Deep learning has recently seen broad implementation in streamflow prediction and forecasting research projects throughout the world with performance ofte...

The concepts of resistance, recovery, and resilience are in diverse fields from behavioral psychology to planetary ecology. These “three Rs” describe some of the most important properties allowing complex systems to survive in dynamic environments. However, in many fields—including ecology—our ability to predict resistance, recovery and resilience...

This article provides a commentary about the state of integrated, coordinated, open, and networked (ICON) principles in Earth and Planetary Surface Processes and discussion on the opportunities and challenges of adopting them. This commentary focuses on the challenges with current inclusive, equitable, and accessible science and highlights how rese...

Imputation of missing sensor-collected data is often an important step prior to machine learning and statistical data analysis. One particular data imputation challenge is filling large data gaps when the only related data comes from the same sensor station. In this paper, we propose a framework to improve the popular multivariate imputation by cha...

Hydrological storm events are a primary driver for transporting water quality constituents such as suspended sediments and nutrients. Analyzing the concentration (C) of these water quality constituents in response to river discharge (Q), particularly when monitored at high temporal resolution during a hydrological event, helps to characterize the d...

Climate change, including warmer winter temperatures, a shortened snowfall season, and more rain-on-snow events, threatens nordic skiing as a sport. In response, over-summer snow storage, attempted primarily using woodchips as a cover material, has been successfully employed as a climate change adaptation strategy by high-elevation and/or high-lati...

Hydrological storm events are a primary driver for transporting water quality constituents such as turbidity, suspended sediments and nutrients. Analyzing the concentration (C) of these water quality constituents in response to increased streamflow discharge (Q), particularly when monitored at high temporal resolution during a hydrological event, h...

The response of in-stream sediment concentration and discharge during rainfall-runoff events provides information about dominant watershed processes as it represents the amalgamation of the connectivity, erodibility, and the spatial location of sediment sources. A common way to collapse the sediment and streamflow response into a readily interpreta...

Excessive streambank erosion is a significant source of fine sediments and associated nutrients in many river systems as well as poses risk to infrastructure. Geomorphic change detection using high-resolution topographic data is a useful method for monitoring the extent of bank erosion along river corridors. Recent advances in an unmanned aircraft...

Climate change, including warmer winter temperatures, a shortened snowfall season, and more rain-on-snow events, threatens nordic skiing as a sport. In response, over-summer snow storage, attempted primarily using wood chip insulation, has been successfully employed as a climate change adaptation strategy by high elevation and/or high latitude ski...

Excessive erosion and bank failure within river corridors pose a significant risk to infrastructure and natural systems and reliable characterization of bank erosion is a critical component when designing mitigation strategies and conducting post-restoration monitoring. Advances in unmanned aircraft system (UAS) and digital photogrammetry technique...

Movement of sediment, and associated phosphorus, from stream banks to freshwater lakes is predicted to increase with greater frequency of extreme precipitation events. This higher phosphorus load may accelerate harmful algal blooms in affected water bodies, such as Lake Champlain in Vermont, New York, and Québec. In the Mad River, a subwatershed in...

Studying the hysteretic relationships embedded in high-frequency suspended-sediment concentration and river discharge data over 600⁺ storm events provides insight into the drivers and sources of riverine sediment during storm events. However, the literature to date remains limited to a simple visual classification system (linear, clockwise, counter...

Streambank movement is an integral part of geomorphic changes along river corridors and affects a range of physical, ecological, and socio-economic systems including aquatic habitat, water quality, and infrastructure. Various methods have been used to quantify streambank erosion, including bank pins, ground surveys, lidar, and analytical models, ho...

Streambank erosion is an important component of geomorphic responses to channel changes that affect a wide range of physical, ecological, and socio-economic issues in the fluvial environment including water quality and on- and near-stream infrastructure. A number of direct (e.g., longitudinal surveys, erosion pins, remote sensing) and indirect (e.g...

Tropical Storm Irene and spring flooding in 2011 exposed the vulnerabilities of mobile home parks in Vermont when 154 mobile homes in parks were destroyed. The question of mobile home parks’ relationship to floodplains was a pressing concern to state officials as displaced households sought to resettle. Little analysis had been done about the exten...