River networks modify material transfer from land to ocean. Understanding the factors regulating this function for different gaseous, dissolved, and particulate constituents is critical to quantify the local and global effects of climate and land use change. We propose the River Network Saturation (RNS) concept as a generalization of how river network regulation of material fluxes declines with increasing flows due to imbalances between supply and demand at network scales. River networks have a tendency to become saturated (supply) demand) under higher flow conditions because supplies increase faster than sink processes. However, the flow thresholds under which saturation occurs depends on a variety of factors, including the inherent process rate for a given constituent and the abundance of lentic waters such as lakes, ponds, reservoirs, and fluvial wetlands within the river network. As supply increases, saturation at network scales is initially limited by previously unmet demand in downstream aquatic ecosystems. The RNS concept describes a general tendency of river network function that can be
The use of acoustic Doppler current profilers (ADCP) for discharge measurements and three‐dimensional flow mapping has increased rapidly in recent years and has been primarily driven by advances in acoustic technology and signal processing. Recent research has developed a variety of methods for processing data obtained from a range of ADCP deployments and this paper builds on this progress by describing new software for processing and visualizing ADCP data collected along transects in rivers or other bodies of water. The new utility, the Velocity Mapping Toolbox (VMT), allows rapid processing (vector rotation, projection, averaging and smoothing), visualization (planform and cross‐section vector and contouring), and analysis of a range of ADCP‐derived datasets. The paper documents the data processing routines in the toolbox and presents a set of diverse examples that demonstrate its capabilities. The toolbox is applicable to the analysis of ADCP data collected in a wide range of aquatic environments and is made available as open‐source code along with this publication. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
Hydrology in many agricultural landscapes around the world is changing in unprecedented ways due to the development of extensive surface and subsurface drainage systems that optimize productivity. This plumbing of the landscape alters water pathways, timings, and storage, creating new regimes of hydrologic response and driving a chain of environmental changes in sediment dynamics, nutrient cycling, and river ecology. In this work, we nonparametrically quantify the nature of hydrologic change in the Minnesota River Basin, an intensively managed agricultural landscape, and study how this change might modulate ecological transitions. During the growing season when climate effects are shown to be minimal, daily streamflow hydrographs exhibit sharper rising limbs and stronger dependence on the previous-day precipitation. We also find a changed storage-discharge relationship and show that the artificial landscape connectivity has most drastically affected the rainfall-runoff relationship at intermediate quantiles. Considering the whole year, we show that the combined climate and land use change effects reduce the inherent nonlinearity in the dynamics of daily streamflow, perhaps reflecting a more linearized engineered hydrologic system. Using a simplified dynamic interaction model that couples hydrology to river ecology, we demonstrate how the observed hydrologic change and/or the discharge-driven sediment generation dynamics may have modulated a regime shift in river ecology, namely extirpation of native mussel populations. We posit that such nonparametric analyses and reduced complexity modeling can provide more insight than highly parameterized models and can guide development of vulnerability assessments and integrated watershed management frameworks.
High‐resolution topography reveals that floodplains along meandering rivers in Indiana commonly contain intermittently flowing channel networks. We investigated how the presence of floodplain channels affects lateral surface‐water connectivity between a river and floodplain (specifically exchange flux and timescales of transport) as a function of flow stage in a low‐gradient river‐floodplain system. We constructed a two‐dimensional, surface‐water hydrodynamic model using Hydrologic Engineering Center's River Analysis System (HEC‐RAS) 2D along 32 km of floodplain (56 km along the river) of the East Fork White River near Seymour, Indiana, USA, using lidar elevation data and surveyed river bathymetry. The model was calibrated using land‐cover specific roughness to elevation‐discharge data from a U.S. Geological Survey gage and validated against high‐water marks, an aerial photo showing the spatial extent of floodplain inundation, and measured flow velocities. Using the model results, we analyzed the flow in the river, spatial patterns of inundation, flow pathways, river‐floodplain exchange, and water residence time on the floodplain. Our results highlight that bankfull flow is an oversimplified concept for explaining river‐floodplain connectivity because some stream banks are overtopped and major low‐lying floodplain channels are inundated roughly 19 days per year. As flow increased, inundation of floodplain channels at higher elevations dissected the floodplain, until the floodplain channels became fully inundated. Additionally, we found that river‐floodplain exchange was driven by bank height or channel orientation depending on flow conditions. We propose a conceptual model of river‐floodplain connectivity dynamics and developed metrics to analyze quantitatively complex river‐floodplain systems.
Long-term prediction of environmental response to natural and anthropogenic disturbances in a basin becomes highly uncertain using physically based distributed models, particularly when transport time scales range from tens to thousands of years, such as for sediment. Yet, such predictions are needed as changes in one part of a basin now might adversely affect other parts of the basin in years to come. In this paper, we propose a simplified network-based predictive framework of sedimentological response in a basin, which incorporates network topology, channel characteristics, and transport-process dynamics to perform a nonlinear process-based scaling of the river-network width function to a time-response function. We develop the process-scaling formulation for transport of mud, sand, and gravel, using simplifying assumptions including neglecting long-term storage, and apply the methodology to the Minnesota River Basin. We identify a robust bimodal distribution of the sedimentological response for sand of the basin which we attribute to specific source areas, and identify a resonant frequency of sediment supply where the disturbance of one area followed by the disturbance of another area after a certain period of time, may result in amplification of the effects of sediment inputs which would be otherwise difficult to predict. We perform a sensitivity analysis to test the robustness of the proposed formulation to model parameter uncertainty and use observations of suspended sediment at several stations in the basin to diagnose the model. The proposed framework has identified an important vulnerability of the Minnesota River Basin to spatial and temporal structuring of sediment delivery.
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