Publications

This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or analysis; generating and exploring...

Multi-modal time series data in real applications often contain data of different dimensionalities, e.g., high-dimensional modality such as image data series, and low-dimensional univariate time series. Multi-modal time series data with missing high-dimensional modal values are ubiquitous in real-world classification and regression applications. To accurately predict the target labels...

The U.S. Geological Survey (USGS) has been a National Atmospheric Deposition Program (NADP) partner agency since 1981. NADP is comprised of five atmospheric monitoring networks that verify Clean Air Act effectiveness and provide essential data to protect human health and preserve ecosystems for current and future generations. Stakeholders include land management agencies overseeing...

This paper proposes a new data-driven method for predicting water temperature in stream networks with reservoirs. The water flows released from reservoirs greatly affect the water temperature of downstream river segments. However, the information of released water flow is often not available for many reservoirs, which makes it difficult for data-driven models to capture the impact to...

Prioritisation of mine pollution sources for remediation is a key challenge facing environmental managers. This paper presents a numerical modelling methodology to evaluate potential improvements in stream water quality from remediation of important mine pollution sources. High spatial resolution synoptic sampling data from a Welsh watershed were used to calibrate the OTIS solute...

USACE partnered with the United States Department of Agriculture, Agricultural Research Service, United States Geological Survey, and Texas A&M University to evaluate the erodibility of the river banks and levees to inform probabilistic numerical simulations using the Bank Stability and Toe Erosion Model (BSTEM). This paper, the second of two parts, addresses processing the collected...

USACE partnered with the United States Department of Agriculture, Agricultural Research Service, United States Geological Survey, and Texas A&M University to evaluate the erodibility of the river banks and levees to inform probabilistic numerical simulations using the Bank Stability and Toe Erosion Model (BSTEM). This paper discusses the measurement of the intrinsic erosion and...

No abstract available.

Aquifer storage and recovery (ASR) expands the portfolio of public water supply and improves resiliency to drought and future water demand. This study investigated the feasibility of ASR in the Bedell Flat Hydrographic Area using land-based methods including in-channel managed aquifer recharge (MAR) and rapid infiltration basins (RIB). Bedell Flat, one of two flow-through groundwater...

Process wastewaters from food, beverage, and feedstock facilities, although regulated, are an under-investigated environmental contaminant source. Food process wastewaters (FPWWs) from 23 facilities in 17 U.S. states were sampled and documented for a plethora of chemical and microbial contaminants. Of the 576 analyzed organics, 184 (32%) were detected at least once, with concentrations...

Two existing flood management structures in U.S. Army Garrison Fort Gordon, Georgia, were evaluated for potential retrofitting to address water-quality impacts, pursuant of U.S. Army Garrison Fort Gordon’s storm water management program. Stormwater calculations were computed according to the Georgia Stormwater Management Manual, including drainage area delineations, design-storm runoff...

Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables...