Nutrient Removal Potential of Headwater Wetlands in Coastal Plains of Alabama, USA.

Headwater streams drain over 70% of the land in the United States with headwater wetlands covering 6.59 million hectares. These ecosystems are important landscape features in the southeast United States, with underlying effects on ecosystem health, water yield, nutrient cycling, biodiversity, and water quality.

Sub-monthly time scale forecasting of harmful algal blooms intensity in Lake Erie using remote sensing and machine learning.

Harmful algal blooms of cyanobacteria (CyanoHAB) have emerged as a serious environmental concern in large and small water bodies including many inland lakes. The growth dynamics of CyanoHAB can be chaotic at very short timescales but predictable at coarser timescales. In Lake Erie, cyanobacteria blooms occur in the spring-summer months, which, at annual timescale, are controlled by the total spring phosphorus (TP) load into the lake.

A Machine Learning Approach to Predict Watershed Health Indices for Sediments and Nutrients at Ungauged Basins.

Effective water quality management and reliable environmental modeling depend on the availability, size, and quality of water quality (WQ) data. Observed stream water quality data are usuallEEy sparse in both time and space. Reconstruction of water quality time series using surrogate variables such as streamflow have been used to evaluate risk metrics such as reliability, resilience, vulnerability, and watershed health (WH) but only at gauged locations.

A two-layer numerical model of soil moisture dynamics: Model development.

Simulating water moisture flow in variably saturated soils with a relatively shallow water table is challenging due to the high nonlinear behavior of Richards' equation (RE). A two-layer approximation of RE was derived in this paper, which describes vertically-averaged soil moisture content and flow dynamics in the root zone and the unsaturated soil below. To this end, the partial differential equation (PDE) describing RE was converted into two-coupled ordinary differential equations (ODEs) describing dynamic vertically-averaged soil moisture variations in the two soil zones subject to a deep or shallow water table in addition to variable soil moisture flux and pressure conditions at the surface.

Uncertainty quantification in reconstruction of sparse water quality time series: Implications for watershed health and risk-based TMDL assessment.

Despite the plethora of methods available for uncertainty quantification, their use has been limited in the practice of water quality (WQ) modeling. In this paper, a decision support tool (DST) that yields a continuous time series of WQ loads from sparse data using streamflows as predictor variables is presented. The DST estimates uncertainty by analyzing residual errors using a relevance vector machine.

Bayesian Monte Carlo and maximum likelihood approach for uncertainty estimation and risk management: Application to lake oxygen recovery model.

Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood estimation (BMCML) to calibrate a lake oxygen recovery model. We first derive an analytical solution of the differential equation governing lake-averaged oxygen dynamics as a function of time-variable wind speed.

Aggregate Measures of Watershed Health from Reconstructed Water Quality Data with Uncertainty.

Risk-based measures such as reliability, resilience, and vulnerability (R-R-V) have the potential to serve as watershed health assessment tools. Recent research has demonstrated the applicability of such indices for water quality (WQ) constituents such as total suspended solids and nutrients on an individual basis. However, the calculations can become tedious when time-series data for several WQ constituents have to be evaluated individually.

Applying a statewide geospatial leaching tool for assessing soil vulnerability ratings for agrochemicals across the contiguous United States.

A large-scale leaching assessment tool not only illustrates soil (or groundwater) vulnerability in unmonitored areas, but also can identify areas of potential concern for agrochemical contamination. This study describes the methodology of how the statewide leaching tool in Hawaii modified recently for use with pesticides and volatile organic compounds can be extended to the national assessment of soil vulnerability ratings. For this study, the tool was updated by extending the soil and recharge maps to cover the lower 48 states in the United States (US).

Watershed reliability, resilience and vulnerability analysis under uncertainty using water quality data.

A method for assessment of watershed health is developed by employing measures of reliability, resilience and vulnerability (R-R-V) using stream water quality data. Observed water quality data are usually sparse, so that a water quality time-series is often reconstructed using surrogate variables (streamflow). A Bayesian algorithm based on relevance vector machine (RVM) was employed to quantify the error in the reconstructed series, and a probabilistic assessment of watershed status was conducted based on established thresholds for various constituents.

An auxiliary method to reduce potential adverse impacts of projected land developments: subwatershed prioritization.

An index based method is developed that ranks the subwatersheds of a watershed based on their relative impacts on watershed response to anticipated land developments, and then applied to an urbanizing watershed in Eastern Pennsylvania. Simulations with a semi-distributed hydrologic model show that computed low- and high-flow frequencies at the main outlet increase significantly with the projected landscape changes in the watershed. The developed index is utilized to prioritize areas in the urbanizing watershed based on their contributions to alterations in the magnitude of selected flow characteristics at two spatial resolutions.