SIMULATION OF FRESHWATER DISCHARGES FROM UNGAGED AREAS TO THE SEBASTIAN RIVER,FLORIDA1

ABSTRACT: Due to alterations in the natural drainage system over the past several decades and intensified agricultural practices, freshwater discharges to the Sebastian River, Florida, have increased substantially. As a result, salinity patterns in the Sebastian River and adjacent Indian River lagoon have been disrupted and the influx of nutrients has increased. Recently, the St. Johns River Water Management District has developed a 3‐D hydrodynamic and salinity model for the Sebastian River and adjacent Indian River to study the effects of freshwater inflows, and to set guidelines for management of future freshwater discharges. Freshwater inflows to the Sebastian River are part of the input data of the hydrodynamic model. Except for the downstream drainage areas, inflows are gaged, and the data were used for calibration of the hydrologic simulations. Collectively, the downstream ungaged areas constitute about 16 percent of the total drainage area. Because of the significant contribution to the total drainage area, reliable estimates of freshwater discharges from the ungaged areas to the Sebastian River are needed. This case study illustrates the development of a set of model parameters, reflecting the hydrologic and physiographic characteristics of the entire region. In this context region applies to the watersheds located in the coastal area along the Indian River from Titusville in the north to Vero Beach in the south. The parameter set was first tested on a number of gaged drainage basins in the region, and was then applied to the ungaged areas.     

Modeling Nutrient Export From Coastal California Watersheds

Nitrate and phosphate export coefficient models were developed for coastal watersheds along the Santa Barbara Channel in central California. One approach was based on measurements of nutrient fluxes in streams from specific land use classes and included a watershed response function that scaled export up or down depending on antecedent moisture conditions. The second approach for nutrient export coefficient modeling used anthropogenic nutrient loading for land use classes and atmospheric nutrient deposition to model export. In an application of the first approach to one watershed, the nitrate and phosphate models were within 20% of measured values for most storms. When applied to another year, both nitrate and phosphate models generally performed adequately with annual, storm‐flow, and base‐flow values within 20% of measured nutrient loadings. Less satisfactory results were found when applied to neighboring watersheds with difference percentages of land use and hydrologic conditions. Application of the second approach was less successful than the first approach.     

Estimation of Daily Streamflow of Southeastern Coastal Plain Watersheds by Combining Estimated Magnitude and Sequence

Commonly used methods to predict streamflow at ungauged watersheds implicitly predict streamflow magnitude and temporal sequence concurrently. An alternative approach that has not been fully explored is the conceptualization of streamflow as a composite of two separable components of magnitude and sequence, where each component is estimated separately and then combined. Magnitude is modeled using the flow duration curve (FDC), whereas sequence is modeled by transferring streamflow sequence of gauged watershed(s). This study tests the applicability of the approach on watersheds ranging in size from about 25‐7,226 km² in Southeastern Coastal Plain (U.S.) with substantial surface storage of wetlands. A 19‐point regionalized FDC is developed to estimate streamflow magnitude using the three most selected variables (drainage area, hydrologic soil index, and maximum 24‐h precipitation with a recurrence interval of 100 years) by a greedy‐heuristic search process. The results of validation on four watersheds (Trent River, North Carolina: 02092500; Satilla River, Georgia: 02226500; Black River, South Carolina: 02136000; and Coosawhatchie River, South Carolina: 02176500) yielded Nash‐Sutcliffe efficiency values of 0.86‐0.98 for the predicted magnitude and 0.09‐0.84 for the predicted daily streamflow over a simulation period of 1960‐2010. The prediction accuracy of the method on two headwater watersheds at Santee Experimental Forest in coastal South Carolina was weak, but comparable to simulations by MIKE‐SHE.     

DISCHARGE AND SUSPENDED SEDIMENT PATTERNS OF AN INTERMITTENT COLD DESERT STREAM1

Sage Creek in south‐central Wyoming is listed as impaired by the U.S. Environmental Protection Agency (USEPA) due to its sediment contribution to the North Platte River. Despite the magnitude of sediment impacts on streams, little research has been conducted to characterize patterns of sediment transport or to model suspended sediment concentration in many arid western U.S. streams. This study examined the relationship between stream discharge and suspended sediment concentration near the Sage Creek and North Platte River confluence from 1998 through 2003. The objectives were to determine patterns of stream discharge and suspended sediment concentration, produce a sediment prediction model, and compare sediment concentrations for the six‐year period. Stream discharge and suspended sediment transport responded rapidly to convective storms and spring runoff events. During the study period, events exceeding 0.23 m³/s accounted for 92 percent of the sediment load, which is believed to originate from erodible headwater uplands. Further analysis of these data indicates that time series modeling is superior to simple linear regression in predicting sediment concentration. Significant increases in suspended sediment concentration occurred in all years except 2003. This analysis suggests that a six‐year monitoring record was insufficient to factor out impacts from climate, geology, and historical sediment storage.     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

Featured Collection Introduction: Connectivity of Streams and Wetlands to Downstream Waters

Connectivity is a fundamental but highly dynamic property of watersheds. Variability in the types and degrees of aquatic ecosystem connectivity presents challenges for researchers and managers seeking to accurately quantify its effects on critical hydrologic, biogeochemical, and biological processes. However, protecting natural gradients of connectivity is key to protecting the range of ecosystem services that aquatic ecosystems provide. In this featured collection, we review the available evidence on connections and functions by which streams and wetlands affect the integrity of downstream waters such as large rivers, lakes, reservoirs, and estuaries. The reviews in this collection focus on the types of waters whose protections under the U.S. Clean Water Act have been called into question by U.S. Supreme Court cases. We synthesize 40+ years of research on longitudinal, lateral, and vertical fluxes of energy, material, and biota between aquatic ecosystems included within the Act's frame of reference. Many questions about the roles of streams and wetlands in sustaining downstream water integrity can be answered from currently available literature, and emerging research is rapidly closing data gaps with exciting new insights into aquatic connectivity and function at local, watershed, and regional scales. Synthesis of foundational and emerging research is needed to support science‐based efforts to provide safe, reliable sources of fresh water for present and future generations.     

Use of Hydrologic Landscape Classification to Diagnose Streamflow Predictability in Oregon

We implement a spatially lumped hydrologic model to predict daily streamflow at 88 catchments within the state of Oregon and analyze its performance using the Oregon Hydrologic Landscape (OHL) classification. OHL is used to identify the physio‐climatic conditions that favor high (or low) streamflow predictability. High prediction catchments (Nash‐Sutcliffe efficiency of (NS) > 0.75) are mainly classified as rain dominated with very wet climate, low aquifer permeability, and low to medium soil permeability. Most of them are located west of the Cascade Mountain Range. Conversely, most low prediction catchments (NS < 0.6) are classified as snow‐dominated with high aquifer permeability and medium to high soil permeability. They are mainly located in the volcano‐influenced High Cascades region. Using a subset of 36 catchments, we further test if class‐specific model parameters can be developed to predict at ungauged catchments. In most catchments, OHL class‐specific parameters provide predictions that are on par with individually calibrated parameters (NS decline < 10%). However, large NS declines are observed in OHL classes where predictability is not high enough. Results suggest higher uncertainty in rain‐to‐snow transition of precipitation phase and external gains/losses of deep groundwater are major factors for low prediction in Oregon. Moreover, regionalized estimation of model parameters is more useful in regions where conditions favor good streamflow predictability.     

Improvement in HSPF's Low‐Flow Predictions by Implementation of a Power Law Groundwater Storage‐Discharge Relationship

We have enhanced the ability of a widely used watershed model, Hydrologic Simulation Program — FORTRAN (HSPF), to predict low flows by reconfiguring the algorithm that simulates groundwater discharge. During dry weather periods, flow in most streams consists primarily of base flow, that is, groundwater discharged from underlying aquifers. In this study, HSPF's groundwater storage‐discharge relationship is changed from a linear to a more general nonlinear relationship which takes the form of a power law. The nonlinear algorithm is capable of simulating streamflow recession curves that have been found in some studies to better match observed dry weather hydrographs. The altered version of HSPF is implemented in the Chesapeake Bay Program's Phase 5 Model, an HSPF‐based model that simulates nutrient and sediment loads to the Chesapeake Bay, and is tested in the upper Potomac River basin, a 29,950 km² drainage area that is part of the Bay watershed. The nonlinear relationship improved median Nash‐Sutcliffe efficiencies for log daily flows at the model's 45 calibration points. Mean absolute percent error on low‐flow days dropped in five major Potomac River tributaries by up to 12 percentage points, and in the Potomac River itself by four percentage points, where low‐flow days were defined as days when observed flows were in the lowest 5th percentile range. Percent bias on low‐flow days improved by eight percentage points in the Potomac River, from ?11 to ?3%.     

Flow,Organic, and Inorganic Sediment Yields from a Channelized Watershed in the South Carolina Lower Coastal Plain

Many small streams in coastal watersheds in the southeastern United States are modified for agricultural, residential, and commercial development. In the South Carolina Lower Coastal Plain, low‐relief topography and a shallow water table make stream channelization ubiquitous. To quantify the impacts of urbanization and stream channelization, we measured flow and sediment from an urbanizing watershed and a small forested watershed. Flow and sediment export rates were used to infer specific yields from forested and nonforested regions of the urbanizing watershed. Study objectives were to: (1) quantify the range of runoff‐to‐rainfall ratios; (2) quantify the range of specific sediment yields; (3) characterize the quantity and quality of particulate matter exported; and (4) estimate sediment yield attributable to agriculture, development, and channelization activities in the urbanizing watershed. Our results showed that the urban watershed exported over five times more sediment per unit area compared with the forested watershed. Sediment concentration was related to flow flashiness in the urban watershed and to flow magnitude in the forested watershed. Sediments from the forested watershed were dominated by organic matter, whereas mineral matter dominated sediment from the urban stream. Our results indicated that a significant shift in sediment quality and quantity are likely to occur as forested watersheds are transformed by urbanization in coastal South Carolina.     

Biogeochemistry and cycling of zinc and copper in a dyked seasonally flooded savanna

There is very little information on the cycling of heavy metals in natural savannas. Venezuelan flooded savannas are characterised by acid soils with redox conditions which might induce Zn and Cu solubilisation. In those flooded savannas a network of small dykes has been constructed to control floods. The biomass accretion after dyking and the abundance of clay particles in the vertisols, dominant in the overflow plains, might be responsible for an increase in nutrient uptake and immobilisation. Due to the redox and pH conditions prevailing during flooding, some questions arise on the fate of the heavy metals. Are they significantly lost as soluble and particulate forms, which in turn, can induce a potential risk of microelement deficiency? Or, on the contrary, are heavy metal inputs in precipitation waters retained somewhere in the terrestrial pools of the watershed allowing for an adequate micronutrient economy? By using input-output budgets, which consider the total atmospheric input and total output in stream runoff (soluble and particulate) for zinc and copper we concluded that in Mantecal flooded savannas, a net accumulation of micronutrient in soils is actually occurring through organic and inorganic complexes, a process that is counterbalanced by the losses of particulates through erosion.