Geology topics

Amy M. Russell

Publications and source records attributed to Amy M. Russell.

5 recordsLinked to original sources

USGS2023

The consequences of neglecting reservoir storage in national-scale hydrologic models: An appraisal of key streamflow statistics

A better understanding of modeled streamflow errors related to basin reservoir storage is needed for large regions, which normally have many ungaged basins with reservoirs. We quantified the difference between modeled and observed streamflows for one process-based and three statistical-transfer hydrologic models, none of which explicitly accounted for reservoir storage. Streamflow statistics representing low to high flows, seasonality, annual variability, and daily autocorrelation were examined at 1082 study basins across the conterminous USA. All models increasingly overpredict (or decreasingly underpredict) observed annual maximum flows with increasing storage. Correlations between absolute values of errors for low-flow statistics and storage are often larger in magnitude than those for signed errors—additional storage is associated with increases in model errors in both directions even when its overall effect in one direction is weak. The rate of increase in absolute values of model errors was nonlinear for most statistics. For low flows, model errors had a change point to larger errors at 48 days of reservoir storage (relative to long-term mean daily flow); mean and high flows had change points at 147 to 176 days. We present predicted-to-observed errors for nine streamflow statistics over a large range of reservoir storage to help modelers and users of modeled streamflow understand the amount of storage for which explicit reservoir modeling is needed.

Journal of the American Water Resources Associatio↗

USGS2020

Comparing trends in modeled and observed streamflows at minimally altered basins in the United States

We compared modeled and observed streamflow trends from 1984–2016 using five statistical transfer models and one deterministic, distributed-parameter, process-based model, for 26 flow metrics at 502 basins in the United States that are minimally influenced by development. We also looked at a measure of overall model fit and average bias. A higher percentage of basins, for all models, had relatively low trend differences between modeled and observed mean/ median flows than for very high or low flows such as the annual 1-day high and 7-day low flows. Mean-flow metrics also had the largest percentage of basins with relatively good overall model fit and low bias. The five statistical transfer models performed better at more basins than the process-based model. The overall model fit for all models, for mean and/or high flows, was correlated with one or more measures of basin precipitation or aridity. Our study and previous studies generally observed good model performance for high flows up to 90th or 95th percentile flows. However, we found model performance was substantially worse for more extreme flows, including 99th percentile and annual 1-day high flows; this shows the importance of including more extreme high flows in analyses of model performance.

Water↗

USGS2018

Assessing the impact of site-specific BMPs using a spatially explicit, field-scale SWAT model with edge-of-field and tile hydrology and water-quality data in the Eagle Creek watershed, Ohio

The Eagle Creek watershed, a small subbasin (125 km 2 ) within the Maumee River Basin, Ohio, was selected as a part of the Great Lakes Restoration Initiative (GLRI) “Priority Watersheds” program to evaluate the effectiveness of agricultural Best Management Practices (BMPs) funded through GLRI at the field and watershed scales. The location and quantity of BMPs were obtained from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) database. A Soil and Water Assessment Tool (SWAT) model was built and calibrated for this predominantly agricultural Eagle Creek watershed, incorporating NCP BMPs and monitoring data at the watershed outlet, an edge-of-field (EOF), and tile monitoring sites. Input air temperature modifications were required to induce simulated tile flow to match monitoring data. Calibration heavily incorporated tile monitoring data to correctly proportion surface and subsurface flow, but calibration statistics were unsatisfactory at the EOF and tile monitoring sites. At the watershed outlet, satisfactory to very good calibration statistics were achieved over a 2-year calibration period, and satisfactory statistics were found in the 2-year validation period. SWAT fixes parameters controlling nutrients primarily at the watershed level; a refinement of these parameters at a smaller-scale could improve field-level calibration. Field-scale modeling results indicate that filter strips (FS) are the most effective single BMPs at reducing dissolved reactive phosphorus, and FS typically decreased sediment and nutrient yields when added to any other BMP or BMP combination. Cover crops were the most effective single, in-field practice by reducing nutrient loads over winter months. Watershed-scale results indicate BMPs can reduce sediment and nutrients, but reductions due to NCP BMPs in the Eagle Creek watershed for all water-quality constituents were less than 10%. Hypothetical scenarios simulated with increased BMP acreages indicate larger investments of the appropriate BMP or BMP combination can decrease watershed level loads.

Ohio↗

USGS2018

Refinement of a regression-based method for prediction of flow-duration curves of daily streamflow in the conterminous United States

Regional regression is a common tool used to estimate daily flow-duration curves (FDCs) at ungaged locations. In this report, several refinements to a particular implementation of the regional regression method for estimating FDCs are evaluated by consideration of different methodological options through a leave-one-out cross-validation procedure in the 19 major river basins of the conterminous United States. Regression analyses in this report are based on streamflow data from water years 1981–2013 (October 1, 1980 to September 30, 2013) from 1,378 mostly undisturbed watersheds. Linear regression using selected basin characteristics at 27 quantiles with nonexceedance probabilities ranging from 0.02 to 99.98 percent was applied. The regression computations were primarily by weighted least squares, with left-censored Gaussian regression solved by maximum likelihood in the presence of zero-valued quantiles. The regional regression method as applied to the FDC estimation problem includes several methodological options that require determination of the better of two or more choices. The options considered in this report include (1) the setting of the maximum number of basin characteristics considered in the regression models for each region, (2) the method of placing the quantiles into groups (“flow regimes”) having the same basin characteristics used as independent variables, (3) the maximum number of candidate models retained from regressions at the single-quantile level that are retained for testing of the best model at the flow-regime scale, and (4) whether drainage area should be forced into the models. In all, 5 binary options were considered for most regions, resulting in 32 methodological combinations. Leave-one-out cross-validation predictions of FDC quantiles at each streamgage used in the study were used to evaluate compared options. Various performance measures were computed based on the predicted quantiles; these were combined by region and the methods were ranked for each measure. Based on examination of the ranked methods compared across the measures, the following treatments produced the more accurate results: (1) using fewer basin characteristics (of the two options considered), (2) utilizing a variance of the unit FDC-based method of determining the flow regimes rather than fixed regimes, (3) retaining more models from the quantile-level regressions regime-wide consideration, and (4) forcing drainage area into the regression models. Results of analyses also indicate that performance varies more by region than by methodological option, with FDCs in arid regions and those with a large value of a measure of intraregional FDC heterogeneity being harder to predict, particularly with respect to the low-flow quantiles.

Scientific Investigations Report↗

USGS2018

Calibration of a field-scale Soil and Water Assessment Tool (SWAT) model with field placement of best management practices in Alger Creek, Michigan

Subwatersheds within the Great Lakes “Priority Watersheds” were targeted by the Great Lakes Restoration Initiative (GLRI) to determine the effectiveness of the various best management practices (BMPs) from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) Database. A Soil and Water Assessment Tool (SWAT) model is created for Alger Creek, a 50 km 2 tributary watershed to the Saginaw River in Michigan. Monthly calibration yielded very good Nash–Sutcliffe efficiency (NSE) ratings for flow, sediment, total phosphorus (TP), dissolved reactive phosphorus (DRP), and total nitrogen (TN) (0.90, 0.79, 0.87, 0.88, and 0.77, respectively), and satisfactory NSE rating for nitrate (0.51). Two-year validation results in at least satisfactory NSE ratings for flow, sediment, TP, DRP, and TN (0.83, 0.54, 0.73, 0.53, and 0.60, respectively), and unsatisfactory NSE rating for nitrate (0.28). The model estimates the effect of BMPs at the field and watershed scales. At the field-scale, the most effective single practice at reducing sediment, TP, and DRP is no-tillage followed by cover crops (CC); CC are the most effective single practice at reducing nitrate. The most effective BMP combinations include filter strips, which can have a sizable effect on reducing sediment and phosphorus loads. At the watershed scale, model results indicate current NCP BMPs result in minimal sediment and nutrient reductions (<10%).

Michigan↗