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. As an application example, observed water quality data for several constituents at different monitoring points within the Cedar Creek watershed in north-east Indiana (USA) were utilized. Considering uncertainty in the data for the period 2002–2007, the R–R–V analysis revealed that the Cedar Creek watershed tends to be in compliance with respect to selected pesticides, ammonia and total phosphorus. However, the watershed was found to be prone to violations of sediment standards. Ignoring uncertainty in the water quality time-series led to misleading results especially in the case of sediments. Results indicate that the methods presented in this study may be used for assessing the effects of different stressors over a watershed. The method shows promise as a management tool for assessing watershed health.

► Risk analysis with widely used indicators to investigate watershed stream health.
► Two methods used to recreate daily water quality data based on sparse observations.
► One method ignored model uncertainty; the other explicitly incorporated it.
► Ignoring model uncertainty led to misleading risk assessments.
► Study provided a promising methodology for assessing watershed health.