Evaluating Flow Metric‐Based Stream Classification Systems to Support the Determination of Ecological Flows in North Carolina

Hydroecological classification systems are typically based on an assemblage of streamflow metrics and seek to divide streams into ecologically relevant classes. Assignment of streams to classes is suggested as an initial step in the process of establishing ecological flow standards. We used two distinct hydroecological river classification systems available within North Carolina to evaluate the ability of a hydrologic model to assign the same classes as those determined by observed streamflows and to assess the transferability of such systems to ungaged streams. Class assignments were examined by rate of overall matches, rate of class matches, spatial variability in matches, and time period used in class assignment. The findings of this study indicate assignments of stream class: (1) are inconsistent among different classification systems; (2) differ between observed and modeled data; and (3) are sensitive to the period of record within observed data. One clear source of inconsistency/sensitivity in class assignments lies with the use of threshold values for metrics that distinguish stream classes, such that even small changes in metric values can result in different class assignments. Because these two hydroecological classification systems are representative of other classification systems that rely on quantitative decision thresholds, it can be surmised that the use of such systems based on stream flow metrics is not a reliable approach for guiding ecological flow determinations.     

Empirical Estimation of Stream Discharge Using Channel Geometry in Low‐Gradient,Sand‐Bed Streams of the Southeastern Plains

Manning's equation is used widely to predict stream discharge (Q) from hydraulic variables when logistics constrain empirical measurements of in‐bank flow events. Uncertainty in Manning's roughness (n_M) is the major source of error in natural channels, and sand‐bed streams pose difficulties because flow resistance is affected by flow‐dependent bed configuration. Our study was designed to develop and validate models for estimating Q from channel geometry easily derived from cross‐sectional surveys and available GIS data. A database was compiled consisting of 484 Q measurements from 75 sand‐bed streams in Alabama, Georgia, South Carolina, North Carolina (Southeastern Plains), and Florida (Southern Coastal Plain), with six New Zealand streams included to develop statistical models to predict Q from hydraulic variables. Model error characteristics were estimated with leave‐one‐site‐out jackknifing. Independent data of 317 Q measurements from 55 Southeastern Plains streams indicated the model (Q = A_cR_H^0.6906S^0.1216; where A_c is the channel area, R_H is the hydraulic radius, and S is the bed slope) best predicted Q, based on Akaike's information criterion and root mean square error. Models also were developed from smaller Q range subsets to explore if subsets increased predictive ability, but error fit statistics suggested that these were not reasonable alternatives to the above equation. Thus, we recommend the above equation for predicting in‐bank Q of unbraided, sandy streams of the Southeastern Plains.