A WATERSHED‐LEVEL ECOLOGICAL RISK ASSESSMENT METHODOLOGY¹

Abstract

: We present an ecological risk assessment methodology at the watershed level for freshwater ecosystems. The major component is a pollutant transport and fate model (a modified EUTROMOD) with an integrated uncertainty analysis utilizing a two‐phase Monte Carlo procedure. The uncertainty analysis methodology distinguishes between knowledge uncertainty and stochastic variability. The model assesses the ecological risk of lentic (lake) ecosystems in response to the stress of excess phosphorus resulting in eutrophication. The methodology and model were tested on the Wister Lake watershed in Oklahoma with the lake and its trophic state as the endpoint for ecological risk assessment. A geographic information system was used to store, manage, and manipulate spatially referenced data for model input.

A dynamic hydrological Monte Carlo simulation model to inform decision-making at Lake Toolibin, Western Australia

“…Results of first-order Monte Carlo simulations for an initiating event equivalent to the 90th percentile of July flow and for an operational threshold at which water is diverted around (and released from) Toolibin Lake of (a) 1000 mg/L and (b) 5000 mg/L TDS. The plots show the probability of the simulation terminating (i.e., the ecological endpoint ceasing to be satisfied) at month m. Hession et al (1996) and Moschandreas and Karuchit (2002). respectively. That is, for the initiating conditions specified, there is an approximate ten-fold increase in the chance of six consecutive months or more of inundation greater than 1 m depth and less than the operating threshold for salt concentration.…”

“…Insights from results allow model users to identify steps that can be taken to reduce total uncertainty, to gauge the implications of system changes, and to avoid serious errors stemming from the confounding of variability and incertitude (Frey and Rhodes, 1996;Hession et al, 1996;Hoffman and Hammonds, 1994;Moschandreas and Karuchit, 2002;Wu and Tsang, 2004). In standard first-order Monte Carlo, stochastic parameters are estimated to reflect environmental variation and are fixed.…”

A coupled bubble plume–reservoir model for hypolimnetic oxygenation

“…There are three primary sources of uncertainty in hydrodynamic and transport modeling studies: (1) the structure of the model, (2) the use of parameters with unknown value (knowledge uncertainty), and (3) the intrinsic stochastic nature of hydrological and/or meteorological phenomena forcing the transport in aquatic systems (stochastic uncertainty) [ Hession et al , 1996; Melching , 1995; van Straten and Keesman , 1991]. A large source of uncertainty in the structure of the coupled model is the momentum or velocity assigned to plume entrainment and detrainment.…”

Water quality modeling under hydrologic variability and parameter uncertainty using erosion-scaled export coefficients

“…Since the export coefficient method does not consider hydrologic variability, significant uncertainty is expected in its calibrated coefficients and its future predictions of P exports. Hession and Storm (2000) and Hession et al (1996) demonstrated the importance of considering the effect of both hydrologic variability and uncertainty in model parameters on the uncertainty of model output. They used a two-stage Monte Carlo analysis to study the relative contribution of each uncertain source to the overall uncertainty in the prediction using a lake eutrophication model.…”