| Abstract: |
Observed data provide information on the attainability of water quality criteria in the time and space of the measurements. However, observations are usually in discrete time and locations, therefore, cannot provide enough spatial and temporal information, nor predict water quality conditions under management scenarios. On the other hand, computer models compensate the shortages of observations. However, all models have certain deviations from the actual. The model error in model calibration will propagate in model scenario predictions. When comparing a modeled value with a numerical water quality standard, the deviated model value may lead to a wrong conclusion on whether the water quality standard is attained or not. We developed data correction on model scenario based on observations and relative changes between model calibration and scenarios, yielding more likely observed values under the scenario conditions. This approach allows us to evaluate water quality attainability relaying on observations which are the only data to demonstrate if the water quality standards are achieved. This talk will discuss two approaches in dissolved oxygen data corrections and the application in the assessment of dissolved oxygen criteria attainment. |
