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In this article, the mean daily streamflow at 139 streamflow-gaging stations (sites) in the southern and southeastern United States are analyzed for spatial and temporal patterns. One hundred and thirty-nine individual time-series of mean daily streamflow were reduced to five aggregated time series of Z scores for clusters of sites with similar temporal variability. These aggregated time-series correlated significantly with a time-series of several climate indices for the period 1950&ndash, 2015. The mean daily streamflow data were subset into six time periods&mdash, starting in 1950, 1960, 1970, 1980, 1990, and 2000, and each ending in 2015, to determine how streamflow trends at individual sites acted over time. During the period 1950&ndash, 2015, mean monthly and seasonal streamflow decreased at many sites based on results from traditional Mann&ndash, Kendall trend analyses, as well as results from a new analysis (Quantile-Kendall) that summarizes trends across the full range of streamflows. A trend departure index used to compare results from non-reference with reference sites identified that streamflow trends at 88% of the study sites have been influenced by non-climatic factors (such as land- and water-management practices) and that the majority of these sites were located in Texas, Louisiana, and Georgia. Analysis of the results found that for sites throughout the study area that were influenced primarily by climate rather than human activities, the step increase in streamflow in 1970 documented in previous studies was offset by subsequent monotonic decreases in streamflow between 1970 and 2015.

Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash–Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash–Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV – Hydrologiska Byråns Vattenavdelning – model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.

In the past, hydrologic modeling of surface water resources has mainly focused on simulating the hydrologic cycle at local to regional catchment modeling domains. There now exists a level of maturity among the catchment, global water security, and land surface modeling communities such that these communities are converging toward continental domain hydrologic models. This commentary, written from a catchment hydrology community perspective, provides a review of progress in each community toward this achievement, identifies common challenges the communities face, and details immediate and specific areas in which these communities can mutually benefit one another from the convergence of their research perspectives. Those include: (1) creating new incentives and infrastructure to report and share model inputs, outputs, and parameters in data services and open access, machine-independent formats for model replication or reanalysis; (2) ensuring that hydrologic models have: sufficient complexity to represent the dominant physical processes and adequate representation of anthropogenic impacts on the terrestrial water cycle, a process-based approach to model parameter estimation, and appropriate parameterizations to represent large-scale fluxes and scaling behavior; (3) maintaining a balance between model complexity and data availability as well as uncertainties; and (4) quantifying and communicating significant advancements toward these modeling goals.

Water-resource managers are challenged to balance growing water demand with protecting aquatic ecosystems and biodiversity. Management decisions can benefit from improved understanding of water-withdrawal impacts on hydrologic regimes and ecological assemblages. This study used ecological limit functions for fish groups within the Tennessee and Cumberland River basins to predict species richness responses under simulated constant-rate (CR) and percent-of-flow (POF) withdrawals and for different minimum flow level protections. Streamflow characteristics (SFC) and richness were generally less sensitive to POF withdrawals than CR withdrawals among sites, fish groups, and ecoregions. Species richness generally declined with increasing withdrawals, but responses were variable depending on site-specific departures of SFCs from reference conditions, drainage area, fish group, ecoregion, and minimum flow level. Under POF withdrawals, 10% and 20% daily flow reductions often resulted in loss of &lt, 1 species and/or &le, 5% richness among fish groups. Median ecological withdrawal thresholds ranged from 3.5&ndash, 31% for POF withdrawals and from 0.01&ndash, 0.92 m3/s for CR withdrawals across fish groups and ecoregions. Application of minimum flow level cutoffs often resulted in damping effects on SFC and richness responses, indicating that protection of low streamflows may mitigate hydrologic alteration and fish species richness loss related to water withdrawals. Site-specific and regionally summarized responses of flow regimes and fish assemblages under alternative withdrawal strategies in this study may be useful in informing water-management decisions regarding streamflow allocation and maintaining ecological flows.

This paper presents a conceptual framework to operationalize flow–ecology relationships into decision-support systems of practical use to water-resource managers, who are commonly tasked with balancing multiple competing socioeconomic and environmental priorities. We illustrate this framework with a case study, whereby fish community responses to various water-management scenarios were predicted in a partially regulated river system at a local watershed scale. This case study simulates management scenarios based on interactive effects of dam operation protocols, withdrawals for municipal water supply, effluent discharges from wastewater treatment, and inter-basin water transfers. Modeled streamflow was integrated with flow–ecology relationships relating hydrologic departure from reference conditions to fish species richness, stratified by trophic, reproductive, and habitat characteristics. Adding a hypothetical new water-withdrawal site was predicted to increase the frequency of low-flow conditions with adverse effects for several fish groups. Imposition of new reservoir release requirements was predicted to enhance flow and fish species richness immediately downstream of the reservoir, but these effects were dissipated further downstream. The framework presented here can be used to translate flow–ecology relationships into evidence-based management by developing decision-support systems for conservation of riverine biodiversity while optimizing water availability for human use.

Ecological limit functions relating streamflow and aquatic ecosystems remain elusive despite decades of research. We investigated functional relationships between species richness and changes in streamflow characteristics at 662 fish sampling sites in the Tennessee River basin. Our approach included the following: (1) a brief summary of relevant literature on functional relations between fish and streamflow, (2) the development of ecological limit functions that describe the strongest discernible relationships between fish species richness and streamflow characteristics, (3) the evaluation of proposed definitions of hydrologic reference conditions, and (4) an investigation of the internal structures of wedge-shaped distributions underlying ecological limit functions. Twenty-one ecological limit functions were developed across three ecoregions that relate the species richness of 11 fish groups and departures from hydrologic reference conditions using multivariate and quantile regression methods. Each negatively sloped function is described using up to four streamflow characteristics expressed in terms of cumulative departure from hydrologic reference conditions. Negative slopes indicate increased departure results in decreased species richness. Sites with the highest measured fish species richness generally had near-reference hydrologic conditions for a given ecoregion. Hydrology did not generally differ between sites with the highest and lowest fish species richness, indicating that other environmental factors likely limit species richness at sites with reference hydrology. Use of ecological limit functions to make decisions regarding proposed hydrologic regime changes, although commonly presented as a management tool, is not as straightforward or informative as often assumed. We contend that statistical evaluation of the internal wedge structure below limit functions may provide a probabilistic understanding of how aquatic ecology is influenced by altered hydrology and may serve as the basis for evaluating the potential effect of proposed hydrologic changes. Published 2013. This article is a U.S. Government work and is in the public domain in the USA. Ecohydrology published by John Wiley & Sons, Ltd.

Nineteen ecologically relevant streamflow characteristics were estimated using published rainfall–runoff and regional regression models for six sites with observed daily streamflow records in Kentucky. The regional regression model produced median estimates closer to the observed median for all but two characteristics. The variability of predictions from both models was generally less than the observed variability. The variability of the predictions from the rainfall–runoff model was greater than that from the regional regression model for all but three characteristics. Eight characteristics predicted by the rainfall–runoff model display positive or negative bias across all six sites; biases are not as pronounced for the regional regression model. Results suggest that a rainfall–runoff model calibrated on a single characteristic is less likely to perform well as a predictor of a range of other characteristics (flow regime) when compared with a regional regression model calibrated individually on multiple characteristics used to represent the flow regime. Poor model performance may misrepresent hydrologic conditions, potentially distorting the perceived risk of ecological degradation. Without prior selection of streamflow characteristics, targeted calibration, and error quantification, the widespread application of general hydrologic models to ecological flow studies is problematic. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

Predictive equations were developed for 19 ecologically relevant streamflow characteristics within five major groups of flow variables (magnitude, ratio, frequency, variability, and date) for use in the Tennessee and Cumberland River basins using stepbackward regression. Basin characteristics explain 50% or more of the variation for 12 of the 19 equations. Independent variables identified through stepbackward regression were statistically significant in 78 of 304 cases (α > 0.0001) and represent four major groups: climate, physical landscape features, regional indicators, and land use. Of these groups, the regional and climate variables were the most influential for determining hydrologic response. Daily temperature range, geologic factor, and rock depth were major factors explaining the variability in 17, 15, and 13 equations, respectively. The equations and independent datasets were used to explore the broad relation between basin properties and streamflow and the implication of streamflow to the study of ecological flow requirements. Key results include a high degree of hydrologic variability among least disturbed Blue Ridge streams, similar hydrologic behaviour for watersheds with widely varying degrees of forest cover, and distinct hydrologic profiles for streams in different geographic regions. Published in 2011. This article is a US Government work and is in the public domain in the USA.

Quantification of streamflow characteristics in ungauged catchments remains a challenge. Hydrological modeling is often used to derive flow time series and to calculate streamflow characteristics for subsequent applications that may differ from those envisioned by the modelers. While the estimation of model parameters for ungauged catchments is a challenging research task in itself, it is important to evaluate whether simulated time series preserve critical aspects of the streamflow hydrograph. To address this question, seven calibration objective functions were evaluated for their ability to preserve ecologically relevant streamflow characteristics of the average annual hydrograph using a runoff model, HBV-light, at 27 catchments in the southeastern United States. Calibration trials were repeated 100 times to reduce parameter uncertainty effects on the results, and 12 ecological flow characteristics were computed for comparison. Our results showed that the most suitable calibration strategy varied according to streamflow characteristic. Combined objective functions generally gave the best results, though a clear underprediction bias was observed. The occurrence of low prediction errors for certain combinations of objective function and flow characteristic suggests that (1) incorporating multiple ecological flow characteristics into a single objective function would increase model accuracy, potentially benefitting decision-making processes, and (2) there may be a need to have different objective functions available to address specific applications of the predicted time series.