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.

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.

Composite measures of watershed health from a water quality perspective.

Water quality data at gaging stations are typically compared with established federal, state, or local water quality standards to determine if violations (concentrations of specific constituents falling outside acceptable limits) have occurred. Based on the frequency and severity of water quality violations, risk metrics such as reliability, resilience, and vulnerability (R-R-V) are computed for assessing water quality-based watershed health. In this study, a modified methodology for computing R-R-V measures is presented, and a new composite watershed health index is proposed.