Reservoir Modeling in Brief
Reservoir modeling within Willamette Envision duplicates the rules of the US Army Corps of Engineers’ 2012 ResSim model for the Willamette River Basin (WRB). Each reservoir has operational zones, which are based on pool elevation on a particular date (Fig. 1) and rules sets associated with each zone. These rule sets are prioritized within each zone and across reservoirs, such that the model calculates release quantities and locations (e.g., Powerhouse, Regulating Outlet, Spillway) at each time step to meet the highest priority rule. Example rules are those that establish minimum or maximum flows or water elevations, such as the minimum flow targets established by the 2009 Biological Opinion (NMFS, 2008) for April to October, which vary based on the type of water year. These rules are often evaluated at important downstream locations, called control points, which are used to establish releases at reservoirs upstream. Many reservoirs can be influenced by a single control point (e.g., Salem), and many control points can be influenced by a single reservoir. For more information about reservoir modeling in Willamette Envision, refer to the supplemental materials from Jaeger et al. (2016).
Figure 1. Reservoir operating zones (Lookout Point). The conservation curve (green) is the primary rule curve. The zone above the rule curve is the Flood Control Zone, while below the curve is the Conservation Zone. If the pool elevation is above the elevation demarcating the primary flood control zone (horizontal dark blue line), the zone is labeled Top of Dam Zone. If the pool elevation falls below the red line, the zone is labeled the Buffer Zone. An alternative flood control zone exists in this case, when the pool elevation is above the rule curve but below the secondary flood control line (in light blue).
Evaluation of Predicted Reservoir Inflows
We compared simulated reservoir inflows to those observed historically to evaluate the reliability of the hydrologic model in replicating the key hydrologic processes of the catchment. The three measures and their interpretation are summarized below.
Root mean square error (RMSE) is an absolute measure of model fit, meaning that the units are directly interpretable, such that higher values represent higher errors. RMSE is calculated as the square root of the variance of the residual. There is no absolute value for a reliable RMSE, as it must be interpreted with respect to the range of predicted values and practical application.
Normalized RMSE (NRMSE) is a nondimensional form of the RMSE, normalized to the mean of the observed data, which is useful for comparing between basins and across seasons with different ranges of flows. Expressed as a percentage, the NRMSE is interpreted as the error relative to the mean.
Nash Sutcliff Efficiency (NSE) measures the signal-to-noise ratio of hydrologic models by comparing the magnitude of model residuals to the variance in the observed data. A value of 1 represents perfect fit between observed and simulated inflows, a value of 0 represents a model that is equally well represented by the mean, and a value less than 0 indicates a model of questionable value. The reliability of NSE is compromised when extreme values are present in the dataset.
Analysis of Reservoir Operational Performance
The 13 federal reservoirs in the WRB are operated with a primary objective of flood regulation, while power production, recreation, fish and wildlife conservation, irrigation, and water quality regulation provide ancillary benefits. In this analysis, we focused on the potential effect of climate change on two high priority operational objectives: flood regulation and fish and wildlife conservation (low flow targets). We evaluated the ability of reservoirs to meet these operating objectives based on the concept of operational reliability, where reliability is defined (e.g., Hashimoto, 1982; Ray et al., 2010) as the likelihood of achieving a flow target or level of flood protection.