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River flows exert dominant controls on in-stream biota. Quantifying linkages between hydrology and biology is important for assessing the effects of flow alteration on ecological functions. Hydrologic models are often used to quantify these flow-ecology relationships and guide management actions. Traditional model calibration techniques typically focus on a best overall fit criterion that may not be suitable for environmental flow applications where certain elements of the flow regime exert a dominant influence on biotic composition. We present an approach for hydrologic model calibration that improves the accuracy of calculated flow metrics known to be significant drivers of ecosystem response. First, we developed regional flow-ecology relationships based on streamflow gage and benthic macroinvertebrate data from southern California to determine which streamflow metrics best explain variability in taxonomic and trait-based biotic indices. Next, we developed and calibrated a series of hydrologic models to minimize error in these important flow metrics. For our study sites, flow flashiness and low flow frequency (indicative of drying) were found to best explain biotic condition. Hydrologic models calibrated specifically to minimize errors in these flow metrics predicted macroinvertebrate indices better than models calibrated to maximize fit to the overall flow regime. This ecological-calibration approach requires some a priori knowledge of flow-ecology relationships, but it produces results that can improve assessment of the impacts of changing flow regimes on biota and guide the development of strategies to mitigate ecological degradation.

Excessive river erosion and sedimentation threatens critical infrastructure, degrades aquatic habitat, and impairs water quality. Tools for predicting the magnitude of erosion, sedimentation, and channel evolution processes are needed for effective mitigation and management. We present a new numerical model that simulates coupled river bed and bank erosion at the watershed scale. The model uses modified versions of Bagnold’s sediment transport equation to simulate bed erosion and aggradation, as well as a simplified Bank Stability and Toe Erosion Model (BSTEM) to simulate bank erosion processes. The model is mechanistic and intermediate complexity, accounting for the dominant channel evolution processes while limiting data requirements. We apply the model to a generic test case of channel network response following a disturbance and the results match physical understanding of channel evolution. The model was also tested on two field data sets: below Parker Dam on the lower Colorado River and the North Fork Toutle River (NFTR) which responded dramatically to the 1980 eruption of Mount St. Helens. It accurately predicts observed channel incision and bed material coarsening on the Colorado River, as well as observations for the upstream 18 km of the NFTR watershed. The model does not include algorithms for extensive lateral migration and avulsions and therefore did not perform well in the lower NFTR where the channel migrated across a wide valley bottom. REM is parsimonious and useful for simulating network scale channel change in single thread systems responding to disturbance.

Summary The decision to use either daily-averaged or sub-daily streamflow records has the potential to impact the calculation of sediment transport metrics and stream channel design. Using bedload and suspended load sediment transport measurements collected at 138 sites across the United States, we calculated the effective discharge, sediment yield, and half-load discharge using sediment rating curves over long time periods (median record length = 24 years) with both daily-averaged and sub-daily streamflow records. A comparison of sediment transport metrics calculated with both daily-average and sub-daily stream flow data at each site showed that daily-averaged flow data do not adequately represent the magnitude of high stream flows at hydrologically flashy sites. Daily-average stream flow data cause an underestimation of sediment transport and sediment yield (including the half-load discharge) at flashy sites. The degree of underestimation was correlated with the level of flashiness and the exponent of the sediment rating curve. No consistent relationship between the use of either daily-average or sub-daily streamflow data and the resultant effective discharge was found. When used in channel design, computed sediment transport metrics may have errors due to flow data resolution, which can propagate into design slope calculations which, if implemented, could lead to unwanted aggradation or degradation in the design channel. This analysis illustrates the importance of using sub-daily flow data in the calculation of sediment yield in urbanizing or otherwise flashy watersheds. Furthermore, this analysis provides practical charts for estimating and correcting these types of underestimation errors commonly incurred in sediment yield calculations.

Summary Semiarid channels exhibit an extreme sensitivity to upstream urban development, particularly in unconfined valleys with unprotected grades. For example, one of our study streams in southern California has increased its cross-sectional area by nearly 14-fold relative to its pre-developed channel form in a watershed that has been only lightly developed (10.4% imperviousness). Multivariate regression models of cross-sectional channel enlargement at 61 sites were highly dependent on the ratio of post- to pre-urban sediment-transport capacity over cumulative duration simulations of 25 yrs (Lr), which explained nearly 60% of the variance. The proximity of a channel hard point such as bedrock or artificial grade control was also significant, indicating that channel enlargement increased moving upstream from grade control. The enlargement models point to the importance of balancing the post-developed sediment transport to the pre-developed setting over an entire range of flows rather than a single flow in order to reduce the risk of adverse channel responses to hydromodification. The need for controlling a wide range of flows was underscored by logistic-regression analyses that indicated a high risk of instability in systems with Lr > 1, especially for fine-grained systems (i.e., d50

summary In 15 mountain stream reaches containing instream wood, we characterized velocity and flow resistance at bankfull through low flows. These data were: (1) used to assess the accuracy of previously published velocity prediction techniques for high-gradient channels; and (2) were combined with field data from other studies to develop general methodologies for predicting velocity and flow resistance in alluvial and mixed alluvial-bedrock channels both with and without step-forming instream wood. Velocity and flow resistance were poorly predicted by variables characterizing grain size and relative grain submergence. Conversely, methods based on detrended standard deviation of bed elevations (rz) and relative bedform submergence (h/rz) explained up to 84% of the variance in the measured flow resistance coefficients and 97% of the variance in dimensionless velocity. With an average velocity of 0.44 m/s for the collected measurements, velocity was predicted with RMS (root mean square) error as low as 0.071 m/s (16% of average) when discharge and bedform geometry is known and 0.10 m/s (23%) when only bedform geometry is known. Additionally, an empirical relationship indicates V=u � ¼ h=r z, supporting previouslypublished laboratory findings using a field-based dataset in complex high-gradient channels. Interactions between instream wood and clasts result in substantially enhanced step heights and flow resistance. This compound effect defies description by grain size and relative grain submergence. However, rz and h/rz quantify variability due to both clasts in combination with wood and clasts alone, providing relatively accurate predictions for the tested dataset and indicating substantial predictive capabilities in channels where bedforms are the primary source of flow resistance. Published by Elsevier B.V.

s u m m a r y Forty-three US Geological Survey gauges with records greater than �15 yrs located in watersheds less than �250 km 2 were used to model the effects of suburbanization on streams in semi-arid southern California. The watersheds spanned a gradient of urban development, ranging 0–23% total impervious area in 2001. With little flow control at the subdivision scale, most impervious area in the region is relatively well-connected to surface-drainage networks and hydrologically effective. Consequently, total impervious area was an effective hydrologic surrogate for urbanization, emerging from an expansive array of geospatially-derived hydrologic variables as a statistically-significant ( p < 0.05) predictor of instantaneous peak-flow rates at the 1.5- and 2-yr recurrence intervals and the durations of all geomorphically-important flows. To represent the effects of urbanization on flow durations, we developed duration density functions by using power functions (typical R 2

Mountain snowpack is the main source of water in the semi-arid Colorado River Basin (CRB), and while the demands for water are increasing, competing and often conflicting, the supply is limited and has become increasingly variable over the 20th Century. Greater variability is believed to contribute to lower accuracy in water supply forecasts, plus greater variability violates the assumption of stationarity, a fundamental assumption of many methods used in water resources engineering planning, design and management. Thus, it is essential to understand the underpinnings of hydroclimatic variability in order to accurately predict effects of climate changes and effectively meet future water supply challenges. A new methodology was applied to characterized time series of temperature, precipitation, and streamflow (i.e., historic and reconstructed undepleted flows) according to the three climate regimes that occurred in CRB during the 20th Century. Results for two tributaries in the Upper CRB show that hydroclimatic variability is more deterministic than previously thought because it entails complementary temperature and precipitation patterns associated with wetter or drier conditions on climate regime and annual scales. Complementary temperature and precipitation patterns characterize climate regime type (e.g., cool/wet and warm/dry), and the patterns entail increasing or decreasing temperatures and changes in magnitude and timing of precipitation according to the climate regime type. Accompanying each climate regime on annual scales are complementary temperature (T) and precipitation (P) patterns that are associated with upcoming precipitation and annual basin yield (i.e., total annual flow volume at a streamflow gauge). Annual complementary T and P patterns establish by fall, are detectable as early as September, persist to early spring, are related to the relative magnitude of upcoming precipitation and annual basin yield, are unique to climate regime type, and are specific to each river basin. Thus, while most of the water supply in the Upper CRB originates from winter snowpack, statistically significant indictors of relative magnitude of upcoming precipitation and runoff are evident in the fall, well before appreciable snow accumulation. Results of this study suggest strategies that may integrated into existing forecast methods to potentially improve forecast accuracy and advance lead time by as much as six months (i.e., from April 1 to October 1 of the previous year). These techniques also have applications in downscaling climate models and in river restoration and management.

Streamflow prediction in ungauged basins provides essential information for water resources planning and management and ecohydrological studies yet remains a fundamental challenge to the hydrological sciences. A methodology is presented for stratifying streamflow regimes of gauged locations, classifying the regimes of ungauged streams, and developing models for predicting a suite of ecologically pertinent streamflow metrics for these streams. Eighty-four streamflow metrics characterizing various flow regime attributes were computed along with physical and climatic drainage basin characteristics for 150 streams with little or no streamflow modification in Colorado, Washington, and Oregon. The diverse hydroclimatology of the study area necessitates flow regime stratification and geographically independent clusters were identified and used to develop separate predictive models for each flow regime type. Multiple regression models for flow magnitude, timing, and rate of change metrics were quite accurate with many adjusted R2 values exceeding 0.80, while models describing streamflow variability did not perform as well. Separate stratification schemes for high, low, and average flows did not considerably improve models for metrics describing those particular aspects of the regime over a scheme based on the entire flow regime. Models for streams identified as ‘snowmelt’ type were improved if sites in Colorado and the Pacific Northwest were separated to better stratify the processes driving streamflow in these regions thus revealing limitations of geographically independent streamflow clusters. This study demonstrates that a broad suite of ecologically relevant streamflow characteristics can be accurately modeled across large heterogeneous regions using this framework. Applications of the resulting models include stratifying biomonitoring sites and quantifying linkages between specific aspects of flow regimes and aquatic community structure. In particular, the results bode well for modeling ecological processes related to high-flow magnitude, timing, and rate of change such as the recruitment of fish and riparian vegetation across large regions.