HR: 1340h
AN: H43E-0408    [Abstracts]
TI: Determining Critical Water Quality Conditions For Inorganic Nitrogen in Dry Semi-urbanized Watersheds
AU: * Herr, J
EM: joel@systechengineering.com
AF: Systech Engineering, Inc, 3180 Crow Canyon Pl, San Ramon, CA 94583 United States
AU: Keller, A A
EM: keller@bren.ucsb.edu
AF: UCSB, Bren School of Environ Sci & Mgmt, Santa Barbara, CA 93106 United States
AU: Zheng, Y
AF: UCSB, Bren School of Environ Sci & Mgmt, Santa Barbara, CA 93106 United States
AU: Robinson, T H
AF: UCSB, Bren School of Environ Sci & Mgmt, Santa Barbara, CA 93106 United States
AB: Traditional approaches to establishing critical water quality conditions, based on statistical analysis of low flow conditions and expressed as a recurrence interval for low-flow conditions (e.g. 7Q10), may be inappropriate for drier watersheds. The use of 7Q10 as a standard design flow assumes year-round flow, but in these watersheds 7Q10 is zero or very small. In addition, the increasing use of multiple year dynamic water quality models at daily time steps, can supercede the use of steady-state approaches. Many of these watersheds are also under increasing urbanization pressure, which accentuates the flashiness of runoff and the episodic nature of critical water quality conditions. To illustrate, we consider the conditions in the Santa Clara River, California. A statistical analysis indicates that higher inorganic nitrogen concentrations correlate strongly with low flow. However, peaks in concentrations can occur during the first storms, particularly where non-point source contribution is significant. Critical conditions can thus occur at different flow regimes depending on the relative magnitude of flow and pollutant contributions from various sources. The use of steady-state models for these dry semi-urbanized watersheds based on 7Q10 flows is thus unlikely to accurately simulate the potential for exceeding water quality objectives. Dynamic simulation of water quality is necessary, and as the recent intense storm event sampling data indicates, the models should be formulated to consider even smaller time steps. This places increasing demand on computational resources and datasets to accurately calibrate the models at this temporal resolution.
DE: 1803 Anthropogenic effects
DE: 1857 Reservoirs (surface)
DE: 1860 Runoff and streamflow
DE: 1869 Stochastic processes
DE: 1871 Surface water quality
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting