Antecedent Flow Research Articles

Bedload transport and the stream power approach

Bedload transport is highly variable, and we address the issue of what value(s) of the transport rate a formula is expected to predict by incorporating two metrics of variance in an elementary scale correlation between the transport rate and stream power. The first captures uncertainty in the empirical relation we develop to describe the efficiency of the bedload transport process, and the second acknowledges that different patterns of transport are experienced under similar hydraulic conditions. Our simple, generalized expression explicitly applies to rivers in which there is always a high availability of mobile sediment on the bed, and we demonstrate that it provides a realistic portrayal of the observed range of transport rates in rivers with a sporadically mobile gravel armour and a characteristic bedload size that is approximately the same as that of the substrate. Accepting that the transport rate is influenced by the nature of the material available at the bed surface which is, in turn, moderated by antecedent flows, we suggest that if effort in the field is required to apply our methodology it should be expended on characterizing the sizes available on the bed between floods, rather than observing active transport during individual events.

Observation of mixed grain size sediment transport rate and streamwise nearbed flow velocity in a gravel-worked bed

This experiment observed the prediction of sediment movement for channel bed composed of mixed grain size and its relationship with nearbed turbulence. Changes in transport rate at different time elapsed was obtained by observing bedload transport rate and associated them with streamwise nearbed flow velocity. Regardless of the shape and duration, observations indicate that the total amounts of transported bedload during the antecedent flow hydrographs are the best indicators of bed stability as observed in the stability hydrographs. Experiments also suggest that high turbulence flow caused by dispersion in the average streamwise nearbed velocity roughly disrupted the bed, allowing dislodgement of coarser grain and preventing finer materials from being fully sheltered. As the result the production of transported material was very significantly increased. Systematic change is presumably absent in the variation of average instantaneous shear stress following an increasing and decreasing discharge as evidenced by average streamwise nearbed flow velocity during different pattern of antecedent flow hydrographs. The variations that produced slightly different fashion in transport rate led to the inference that the major difference in the transport rate response during stability tests were created by unsteady flow hydrographs with rapid increase and rapid decrease.

Can hydraulic measures of river conditions improve our ability to predict ecological responses to changing flows? Flow velocity and spawning of an iconic native Australian fish

Ecological responses to changing riverine flows are often evaluated by describing the relationship between river discharge and response. However, aquatic organisms experience the hydraulics (i.e. velocity, shear stress, depth) of a river, not its discharge. Hydraulic characterizations of riverine habitats may improve our ability to predict ecological responses. We used two-dimensional hydraulic models to translate river discharge into flow velocity. We used discharge and reach-averaged velocity, along with water temperature and 8 years of field observations of fish spawning, to develop predictive models of the spawning of golden perch (Macquaria ambigua) in the Goulburn River, south-east Australia. Probability of spawning was positively related to both discharge and reach-averaged velocity. Water temperature was critical for enabling the flow response, and antecedent flows prior to spawning had a weak positive effect. Against expectations, there was little difference in predictive uncertainty for the effect of flows when reach-averaged velocity was used as the main predictor rather than discharge. The lower Goulburn River has a relatively simple channel and so discharge and velocity are monotonically related over most flows. We expect that in a more geomorphically complex environment, improvement in predictive ability would be substantial. This research only explores one example of a hydraulic parameter being used as a predictor of ecological response; many others are possible. The extra effort and expense involved in hydraulic characterization of river flows is only justified if our understanding of flow-ecology relationships is substantially improved. Further research to understand which environmental responses might be best understood through different hydraulic parameters, and how to better characterize hydraulic characteristics relevant to riverine biota, would help inform decisions regarding investment in hydraulic models. Regardless, hydraulics offers a more process-based assessment of ecological responses to changing flows, has the potential to facilitate mechanistic understanding rather than just associations, and provides the opportunity to translate hydraulic metrics that drive ecological responses across river systems of differing sizes. However, while considering ecological responses in terms of river hydraulics is more physically realistic, our results suggest that average hydraulic conditions may not result in an improved ability to predict the effects of changing flows.

Fish movements in response to environmental flow releases in intermittent rivers

Abstract Variation in river flow is a strong behavioural determinant for the movement of many freshwater fish species and often is linked to key aspects of their life cycle. The alteration of natural flow regimes to meet human water demands can result in changes to this variability, and cause declines in water‐dependent biota. Environmental flows are used as a remediation tool in some regulated rivers with the intention of restoring aspects of the natural flow regime to benefit riverine species, although empirical data are required to inform the efficacy of these interventions. Using acoustic telemetry, we quantified the movement responses of two large‐bodied native fish species (freshwater catfish, Tandanus tandanus and Murray cod, Maccullochella peelii) to variations in river flow over 4 years in two intermittent regulated rivers in the northern Murray–Darling Basin, Australia. Both rivers received periodic environmental flow releases and also there were several large natural flow events during the study period. Both species displayed a range of intra‐specific movement behaviours. Analysis of individuals' movements revealed five distinct functional groups, which were represented in both species. We found that periods of environmental flow delivery played an important role in the movement behaviour of both species. Murray cod were more likely to move during periods of environmental flow releases, whereas freshwater catfish were less likely to move on an environmental release following higher antecedent flows. No large‐scale philopatric movements were observed, yet Murray cod were more likely to move during the breeding period, indicative of nest site selection. We also found that the likelihood of movement in both species was higher in the smaller of the two rivers for a given magnitude of flow. Our results suggest that environmental flows may benefit certain fish species by facilitating, rather than cueing breeding behaviours, allowing individuals improved access to, and provision of, higher‐quality nesting habitats. These findings will aid water managers in creating economical and targeted environmental flow releases, timing larger flow pulses for the species with flow‐cued reproductive strategies, and providing a continuity of smaller baseflows for species whose reproduction is not dependent on flow cues but may be enhanced by greater connectivity.

Prairie stream metabolism recovery varies based on antecedent hydrology across a stream network after a bank‐full flood

ABSTRACTIntermittent streams are characterized by significant periods of low to no flow, yet are also frequently subjected to flashy, high floods. Floods alter ecosystem function and result in variable successional patterns across the stream network. Yet, the timing of restored function after floods in intermittent stream networks is relatively unexplored. We measured recovery of stream ecosystem function using rates of gross primary production (GPP), ecosystem respiration (ER), net ecosystem production (NEP), and the primary production to respiration ratio (P/R) across eight locations in the Kings Creek drainage basin with differing preflood conditions (previously dry [intermittent] or flowing [perennial]) over a 30‐d period following a 2‐yr return interval flood. We found that all metabolic rates (GPP, ER, NEP, P/R) varied primarily by time (days since flood) and antecedent flow, but not spatial network position (i.e., drainage area). Intermittent sites exhibited high rates of ER (0.17–3.33 g dissolved oxygen [DO] m−2 d−1) following rewetting compared to perennial sites (0.03–1.17 g DO m−2 d−1), while GPP, NEP, and P/R were slower to recover and varied less between sites of differing preflood conditions. Metabolic rates were not strongly influenced by other environmental conditions. A large proportion of variation was explained by the random effect of location. Our results suggest that metabolism is temporally asynchronous and highly heterogenous across intermittent watersheds and that antecedent hydrology (drying prior to rewetting) stimulates heterotrophic activity, likely dependent on terrestrially derived organic matter and nutrient subsidies.

Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach

• New advanced machine learning (ML) models applied for suspended sediment load prediction. • Results compared with traditional ML models as a benchmark. • Manually and feature selection methods applied to explore the best input scenario. • Advanced hybrid Elastic Network model outperform of all developed models. • The outcomes have practical implications in watershed management applications. The distribution and transportation of suspended sediment load (Q ssl ) in rivers have a significant effect on the design of hydraulic structures, river morphology, water quality, and aquatic ecosystems. As direct measurement of Q ssl can be costly and time-consuming, reliable estimates are vital for watershed management. In the present study, four standalone models including an Elastic Network (EN), Alternating Model Tree (AM Tree), Reduced Error Pruning Tree (REP Tree) and the Dual Perturb and Combine Tree (DPC Tree), including four hybridized models that combine a standalone model with the Rotation Forest (RF), were developed and evaluated for Q ssl prediction at Talar Watershed, in the northern Iran. Multiple scenarios comprised of antecedent flow discharge (Q w ), rainfall (R) and Q ssl , with all constructed manually and automatically using a Wrapper Feature Selection (WFS) to predict the Q ssl at Shirgah hydrometric station from January 1, 2004 to September 22, 2019. The optimal model fitted results were evaluated using multiple graphical and quantitative metrics with the results revealing that the flow discharge is perhaps the best predictor of Q ssl . Based on the Nash-Sutcliffe Efficiency (NSE) metric, the RF-EN (NSE = 0.85), EN (NSE = 0.83), AM Tree (NSE = 0.79), RF-AM Tree (NSE = 0.81) and the RF-REP Tree (NSE = 0.79) models seemed to perform very well, with the REP Tree (NSE = 0.65) and RF-DPC (NSE = 0.71) performed well, while the DPC Tree model (NSE = 0.35) were notably unsatisfactory. The hybridized models, however, captured extreme values more accurately compared with the standalone models. Finally, the model outputs were compared to the well-known optimized ANFIS models with a metaheuristic approach (imperialist competitive algorithm (ICA) and BAT algorithms), and all these results revealed that most the of newly developed models outperformed the ANFIS-ICA and ANFIS-BAT algorithms. The new modelling approaches developed and testing using advanced hybrid Rotation Forest based Elastic Networks in this study have important practical implications for suspended sediment load modeling and applications.

Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River.

A data-driven approach to characterizing the risk of cyanobacteria-based harmful algal blooms (cyanoHABs) was undertaken for the Ohio River. Twenty-five years of river discharge data were used to develop Bayesian regression models that are currently applicable to 20 sites spread-out along the entire 1579 km of the river’s length. Two site-level prediction models were developed based on the antecedent flow conditions of the two blooms that occurred on the river in 2015 and 2019: one predicts if the current year will have a bloom (the occurrence model), and another predicts bloom persistence (the persistence model). Predictors for both models were based on time-lagged average flow exceedances and a site’s characteristic residence time under low flow conditions. Model results are presented in terms of probabilities of occurrence or persistence with uncertainty. Although the occurrence of the 2019 bloom was well predicted with the modeling approach, the limited number of events constrained formal model validation. However, as a measure of performance, leave-one-out cross validation returned low misclassification rates, suggesting that future years with flow time series like the previous bloom years will be correctly predicted and characterized for persistence potential. The prediction probabilities are served in real time as a component of a risk characterization tool/web application. In addition to presenting the model’s results, the tool was designed with visualization options for studying water quality trends among eight river sites currently collecting data that could be associated with or indicative of bloom conditions. The tool is made accessible to river water quality professionals to support risk communication to stakeholders, as well as serving as a real-time water data monitoring utility.

Application of a novel artificial neural network model in flood forecasting.

In this paper, a novel ANN flood forecasting model is proposed. The ANN model is combined with traditional hydrological concepts and methods, taking the initial Antecedent Precipitation Index (API), rainfall, upstream inflow and initial flow at the forecast river section as input of model, and flood flow forecast of the next time steps as output of the model. The distributed rainfall is realized as the input of the model. The simulation is processed by dividing the watershed into several rainfall-runoff processing units. Two hidden layers are used in the ANN, and the topology of ANN is optimized by connecting the hidden layer neurons only with the input which has physical conceptual causes. The topological structure of the proposed ANN model and its information transmission process are more consistent with the physical conception of rainfall-runoff, and the weight parameters of the model are reduced. The arithmetic moving-average algorithm is added to the output of the model to simulate the pondage action of the watershed. Satisfactory results have been achieved in the Mozitan and Xianghongdian reservoirs in the upper reaches of Pi river in Huaihe Basin, and the Fengman reservoir in the upper reach of Second Songhua river in Songhua basin in China.

Evaluating the performance of taxonomic and trait-based biomonitoring approaches for fine sediment in the UK

Fine sediment is a leading cause for the decline of aquatic biodiversity globally. There is an urgent need for targeted monitoring to identify where management methods are required in order to reduce the delivery of fine sediment to aquatic environments. Existing sediment-specific biomonitoring indices and indices for general ecological health (taxonomic and trait-based) developed for use in the UK were tested in a representative set of lowland rivers in England that consisted of a gradient of fine sediment pressures (deposited and suspended, organic and inorganic). Index performance was modelled against environmental variables collected during sampling and hydrological and antecedent flow variables calculated from daily flow data. Sediment-specific indices were indicative of surface sediment deposits, whereas indices for general ecological health were more closely associated with the organic content of fine sediment. The performance of biotic indices along fine sediment gradients was predominantly dependent on hydrological variability. Functional diversity indices were poorly related to different measures of fine sediment, and further development of traits-based indices and trait databases are recommended. In summary, the results suggest that sediment-specific biomonitoring tools are suitable for evaluating fine sediment stress in UK rivers when index scores are viewed within the context of local hydrology.

Effect of Increasing Antecedent Flows on Equilibrium Bed-Load Transport Rates in a Laboratory Channel with a Sand and Gravel Bed Channel

Experiments were conducted in a laboratory flume in which gravel and total bed-load rates were measured continuously using independent methods that allowed evaluation of the effect of four antecedent flow conditions on the transport of bed load under standard flow. It was found that both mean total bed-load rate and gravel transport rates are related to the magnitude of antecedent flow. The changes in mean bed-load rates were attributed to changes in the stability of the coarsest fractions of the bed material, caused by the previous high-flow event. This work indicates that long-term mean bed-load transport rates for a sand and gravel bed are not just a function of grain size and flow rate but also vary with the magnitude of antecedent flow.

The Drying Regimes of Non‐Perennial Rivers and Streams

AbstractThe flow regime paradigm is central to the aquatic sciences, where flow drives critical functions in lotic systems. Non‐perennial streams comprise the majority of global river length, thus we extended this paradigm to stream drying. Using 894 USGS gages, we isolated 25,207 drying events from 1979 to 2018, represented by a streamflow peak followed by no flow. We calculated hydrologic signatures for each drying event and using multivariate statistics, grouped events into drying regimes characterized by: (a) fast drying, (b) long no‐flow duration, (c) prolonged drying following low antecedent flows, (d) drying without a distinctive hydrologic signature. 77% of gages had more than one drying regime at different times within the study period. Random forests revealed land cover/use are more important to how a river dries than climate or physiographic characteristics. Clustering stream drying behavior may allow practitioners to more systematically adapt water resource management practices to specific drying regimes or rivers.

Abiotic predictors of fine sediment accumulation in lowland rivers

The delivery of excessive fine sediment (particles < 2 mm in diameter) to rivers can cause serious deleterious effects to aquatic ecosystems and is widely acknowledged to be one of the leading contributors to the degradation of rivers globally. Despite advances in using biological methods as a proxy, physical measures remain an important method through which fine sediment can be quantified. The aim of this study was to provide further insights into the environmental variables controlling sediment accumulation in lowland gravel bed rivers. We sampled 21 sites, during spring and autumn, selected to cover a gradient of excess fine sediment. Fine sediment was sampled using a range of methods including visual assessments, the disturbance method, and suspended sediment concentrations. A range of abiotic predictors was measured during sampling, and hydrological and antecedent flow indices were derived from local flow gauging station data. The results show reach scale visual estimates of fine sediment to be significantly and highly correlated with fully quantitative estimates of total surface sediment. Multivariate regression analysis showed that flow variables (regime, antecedent, and local flow characteristics) were strong predictors of deposited sediment metrics but poor predictors of suspended sediment. Organic content was shown to be relatively independent of total sediment quantity and is likely driven by other factors which influence the supply and breakdown of organic matter.

Using a hierarchical model framework to investigate the relationships between fish spawning and abiotic factors for environmental flow management

The construction of dams worldwide has substantially altered natural flow and thermal regimes, leading to profound influences on fish spawning. Environmental flow (EFlow) assessments provide information on the links between river flow and river health and are a useful technique for specifically restoring species' life-history requirements. A full understanding of the abiotic–biotic relationships coupled with information on hydrological events and ecological processes is a vital step for acquiring explicit water management options. However, the incorporation of concurrent and antecedent flow, thermal parameters, and other water quality information, particularly their complex interaction effects, into the prediction of the relationships between fish spawning and abiotic factors is poorly studied. The present study proposes a hierarchical modeling framework that accounts for the different concurrent and antecedent habitat factors and the interaction effects between factors to predict the spawning intensities of three important endemic fish species (which spawn drifting eggs) collected from the upper Yangtze River, China. Our results show that 1) the abiotic factors significantly affecting fish spawning vary with species, and these factors are intricately linked to species-specific ecology; 2) the water transparency during spawning and the accumulated temperature before spawning are important predictors of the spawning intensity of some fish species, while the effective duration of rising flow discharge before spawning plays a consistently important role in affecting the spawning intensity of all three fish species; and 3) there are explicit interaction effects, mainly between the hydrological and thermal variables, on fish spawning intensity. The present study suggests that investigation of the relationships between fish spawning and abiotic factors should not only analyze individual species but should also fully consider the accumulated temperature and effective duration of flow discharge rising before spawning, as well as the interaction effects between habitat factors.

Regulation of freshwater use to restore ecosystems resilience

Concern about the impacts of water regulation upon the aquatic environment has led to increasingly stringent regulatory constraints on the quantity and timing of freshwater withdrawals. For the time being these regulatory constraints tend to be articulated in terms of limits upon withdrawals, partly because of limited knowledge of the condition and resilience of the aquatic ecosystems. A more sophisticated approach to regulation would be more directly related to indicators of ecological condition. Moreover, it would consider ecosystem response to climatic events not present in the historical record. In this paper we use a combination of empirical evidence of ecosystem condition with simulation to propose and test reductions to regulatory limits on river water withdrawals and downstream minimum flow requirements. The study uses multi-level linear regression to relate the Lotic-invertebrate Index for Flow Evaluation (LIFE) to antecedent flow statistics observed in the Lee catchment, England. The selected flow statistics included extreme low (Q90) and high (Q10) flows in the summer season (April-Sept), and the median flows observed in the winter season (Oct-Mar). The derived model is used to forecast the response of the macroinvertebrate index to future flow scenarios and demand forecasts, incorporating the uncertainties in ecosystem response. Simulation is used to evaluate the sensitivity of the indices to different regulatory limits. Results indicate that macroinvertebrate health will worsen under 21st Century climate conditions, and that the existing regulation policy must be modified to maintain historically observed LIFE scores into the future. The framework demonstrates how regulations could move from precautionary limits on withdrawals to an approach based on observations, forecasting and simulation, allowing planners to refine the trade-offs between river health and reliable water supply in the face of uncertainty.

Investigating meteorological effect on river flow recession rate in an arid environment

ABSTRACT This study explores previously unreported linkage between recession rates of rainfall hyetograph and river flow hydrograph in an arid environment in Sultanate of Oman. Ephemeral streams are hydraulically disconnected from groundwater aquifers and depend on rainfall to produce water that flows only for hours or a few days at most. It therefore prompted to hypothesize that the recession rate of the rainfall event controls the corresponding recession of river flow. To test this assumption, 1-h river flow rates and 20-min rainfall rates in Al-Khoud catchment area for the period 1997–2013 were analysed. The river flow recession rate and antecedent river flow were found to be inversely proportional, while their relation improved with increasing time span of cumulating the antecedent river flow. The results further show that the simulation of river flow recession rate can be improved by incorporating the combined effects of rainfall recession rate and antecedent moisture content.

High-frequency analysis of dissolved organic carbon storm responses in headwater streams of contrasting forest harvest history

Dissolved organic carbon (DOC) in streams is critically important for aquatic ecosystems and human water use, and is affected by changes in land use and climate. The effects of land use change (e.g. forest harvest) on DOC are not fully understood. Past studies of the effects of forestry on DOC have found mixed results, and covariate effects complicate mechanistic understanding and may underlie regional differences. DOC is typically highest during storm events, and detailed time-resolved measurements of DOC over longer periods are just becoming feasible with advances in in situ sensors. In this paper, we investigated high frequency measurements of DOC concentration in headwater streams of contrasting forest harvest history in the UBC Malcolm Knapp Research Forest near Maple Ridge, British Columbia. We found base flow DOC concentration to be lower in the clear-cut stream (Clear cut: 2.26 ± 0.43 mg/L; Forested: 4.30 ± 0.83 mg/L). This may be due to reductions in carbon inputs like leaf litter, although the difference in drainage area, slope, and the presence of a bog lake in the forested catchment may also account for some of the observed difference. We found the DOC response to storms to be larger and faster at the clear-cut site (mean increase DOC: Clear-cut: 2.42 mg/L, Forested: 1.99 mg/L; mean rate of change DOC: Clear-cut: 0.16 mg/L/h, Forested: 0.11 mg/L/h). Elevated storm responses may be due to changes in flow paths related to forest harvesting. We found low antecedent flow and greater storm intensity to significantly predict elevated DOC storm response. This is important in the context of future climate scenarios, which predict lower summer flows and more intense storms in this region. Overall, this work contributes to our understanding of the intersection between effects of land use change and climate on water quality, with a focus on drinking water treatment implications.

Reservoir trophic state confounds flow-ecology relationships in regulated streams

Cultural eutrophication and river regulation have been identified as two of the most pressing threats to global freshwater biodiversity. However, we know little about their combined ecological effects, raising questions over biomonitoring practices that typically consider such stressors in isolation. To address this inconsistency, we examined a decade-long dataset of macroinvertebrate samples and environmental data collected downstream of three reservoirs spanning a broad gradient of trophic states, from mesotrophic to hypereutrophic. We analysed the responses of routine macroinvertebrate biomonitoring indices and community composition to antecedent flow, temperature and water quality, including parameters associated with eutrophication (total phosphorous, nitrate, nitrite, ammonia, chlorophyll a). Multi-model comparisons of linear regressor combinations, variation partitioning and distance-based redundancy analyses all revealed shifts in the relative significance of flow and water quality predictors across the trophic state gradient. At the mesotrophic site biomonitoring indices and community composition were most sensitive to seasonal flow variability– particularly high-flow conditions– whereas in the hypereutrophic system stronger associations with nutrient concentrations emerged, notably nitrite and nitrate. Patterns at the eutrophic site were broadly intermediate between these, with significant biotic responses to antecedent flows mediated by water quality. Based on these results we suggest that nutrient regimes should be regarded as an integral component of environmental flows science. We therefore call on practitioners to look beyond the stressor-specific indices widely used to assess ecological status in rivers to consider the interactive effects of flow and water quality.

Forecast of streamflows to the Arctic Ocean by a Bayesian neural network model with snowcover and climate inputs

Abstract Increasing water flowing into the Arctic Ocean affects oceanic freshwater balance, which may lead to the thermohaline circulation collapse and unpredictable climatic conditions if freshwater inputs continue to increase. Despite the crucial role of ocean inflow in the climate system, less is known about its predictability, variability, and connectivity to cryospheric and climatic patterns on different time scales. In this study, multi-scale variation modes were decomposed from observed daily and monthly snowcover and river flows to improve the predictability of Arctic Ocean inflows from the Mackenzie River Basin in Canada. Two multi-linear regression and Bayesian neural network models were used with different combinations of remotely sensed snowcover, in-situ inflow observations, and climatic teleconnection patterns as predictors. The results showed that daily and monthly ocean inflows are associated positively with decadal snowcover fluctuations and negatively with interannual snowcover fluctuations. Interannual snowcover and antecedent flow oscillations have a more important role in describing the variability of ocean inflows than seasonal snowmelt and large-scale climatic teleconnection. Both models forecasted inflows seven months in advance with a Nash–Sutcliffe efficiency score of ≈0.8. The proposed methodology can be used to assess the variability of the freshwater input to northern oceans, affecting thermohaline and atmospheric circulations.

Coupled flow accumulation and atmospheric blocking govern flood duration

We present a physically based Bayesian network model for inference and prediction of flood duration that allows for a deeper understanding of the nexus of antecedent flow regime, atmospheric blocking, and moisture transport/release mechanisms. Distinct scaling factors at the land surface and regional atmospheric levels are unraveled using this Bayesian network model. Land surface scaling explains the variability in flood duration as a function of cumulative exceedance index, a new measure that represents the evolution of the flood in the basin. Dynamic atmospheric scaling explains the cumulative exceedance index using the interaction between atmospheric blocking system and the synergistic model of wind divergence and atmospheric water vapor. Our findings underline that the synergy between a large persistent low-pressure blocking system and a higher rate of divergent wind often triggers a long-duration flood, even in the presence of moderate moisture supply in the atmosphere. This condition in turn causes an extremely long-duration flood if the basin-wide cumulative flow prior to the flood event was already high. Thus, this new land-atmospheric interaction framework integrates regional flood duration scaling and dynamic atmospheric scaling to enable the coupling of ‘horizontal’ (for example, streamflow accumulation inside the basin) and ‘vertical’ flow of information (for example, interrelated land and ocean-atmosphere interactions), providing an improved understanding of the critical forcing of regional hydroclimatic systems. This Bayesian model approach is applied to the Missouri River Basin, which has the largest system of reservoirs in the United States. Our predictive model can aid in decision support systems for the protection of national infrastructure against long-duration flood events.

Flow‐mediated predator–prey dynamics influence fish populations in a tropical river

Abstract Predator–prey interactions are an inherently local‐scale phenomenon, but the intensity of these interactions can be mediated by abiotic conditions that can exert a multi‐scaled influence through space and time. Understanding how multi‐scale abiotic factors may influence local‐scale biotic processes has proven challenging; however, the hierarchical nature of riverine flow regimes makes these environments an ideal setting to test how predator–prey relationships may vary with multi‐scaled flow variation. We developed a series of Bayesian hierarchical models to explore how predator–prey relationships between barramundi Lates calcarifer and their prey may be influenced by multi‐scaled flow variables in the Daly River, northern Australia. We found that spatio‐temporal variation in barramundi abundance was strongly related to both antecedent flow and the abundance of prey fishes (predictive r2 = 0.57), and that barramundi abundance is more likely to be influenced by bottom‐up, rather than top‐down predator–prey dynamics. We also found that the strength and direction of these relationships varied across the catchment and between seasons. We found stronger, positive relationships between barramundi abundance and prey abundance in the most downstream sites with higher mean annual flows, compared to upstream sites. These results indicate that the abundance of predatory fishes can be related to both recent abiotic (flow) conditions and the abundance of prey (biotic conditions), and provides strong support for the importance of bottom‐up trophic dynamics. Management of iconic predators such as barramundi should therefore consider both flow management and other key factors such as habitat maintenance to support their prey.