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.     

Addressing data challenges in riverine nutrient load modeling of an intensively managed agro-industrial watershed

Data limitations often challenge the reliability of water quality models, especially in intensively managed watersheds. While numerous studies report successful hydrological model setup and calibration, few have addressed in detail the data challenges for multisite and multivariable model calibration to an intensively managed watershed. In this study, we address some of these challenges based on our reflective experience calibrating the Soil and Water Assessment Tool (SWAT) to the Upper Sangamon River Watershed in central Illinois based on daily flow, annual crop yield, and monthly sediment, nitrate, and total phosphorus loads. We highlight some challenges in SWAT calibration processes due to data errors and inconsistencies, and insufficient precipitation and water quality observations. Following, we demonstrate the merits of additional weather and water quality observations that could help reduce input uncertainties, and we provide suggestions for selecting appropriate observations for the model calibration. After dealing with the data issues, we show that the SWAT model could be calibrated with acceptable results for the case study watershed.     

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.     

Conceptual Framework of Connectivity for a National Agroecosystem Model Based on Transport Processes and Management Practices

There is a critical need for a national agroecosystem model for conservation policy and environmental planning, driven by issues including harmful algal blooms, water scarcity, flooding, and other weather‐related extremes. In this study, we illustrate the feasibility of a national agroecosystem model that will downscale processes to individual fields and first‐order channels. We propose to conceptually divide the conterminous United States (U.S.) into process domains as a framework for simulating processes and management at relevant scales. Specifically, we are proposing five domains: field (1–50 ha), transition (0.2–2.0 km²), headwater (1–15 km²), tributaries (15–150 km²), and main river (>150 km²). The proposed conceptual framework hydrologically connects fields across the U.S. using the National Hydrography Dataset (NHDPlus version 2). Parameterizing the Soil and Water Assessment Tool for the national agroecosystem model resulted in 4,880,000 agricultural fields, 2,250,000 non‐agricultural hydrologic response units, and 7,130,000 transition, 1,610,000 headwater, 591,000 tributary, and 432,400 main channels. Application of this framework was shown for Hydrologic Unit Code 07120002 in central Illinois and Indiana to demonstrate the feasibility of the approach using data that is readily available across the U.S. The new connectivity framework has the potential to dramatically improve national conservation and environmental assessments performed by U.S. Department of Agriculture and U.S. Environmental Protection Agency.     

IDW‐Plus: An ArcGIS Toolset for Calculating Spatially Explicit Watershed Attributes for Survey Sites

Watershed characteristics such as land‐use and land‐cover affect stream condition at multiple scales, but it is widely accepted that conditions in close proximity to the stream or survey site tend to have a stronger influence. Although spatially weighted watershed metrics have existed for years, nonspatial lumped landscape metrics (i.e., areal mean or percentage) are still widely used because relatively few technical skills are needed to implement them. The Inverse Distance Weighted Percent Land Use for Streams (IDW‐Plus) custom ArcGIS toolset provides the functionality to efficiently calculate six spatially explicit watershed metrics which account for the Euclidean or flow length distance to the stream or outlet, as well as the probability for overland runoff. These include four distance‐weighted metrics, those being inverse Euclidean distance to the stream or outlet, and the inverse flow length to the stream or outlet. Two tools are also included to generate hydrologically active (i.e., runoff potential), inverse flow length to the stream or outlet metrics. We demonstrate the tools using real data from Southeast Queensland, Australia. We also provide detailed instructions, so readers can recreate the examples themselves before applying the tools to their own data.     

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.     

Development of an Earthen Dam Break Database

Earthen embankment dams comprise 85% of all major operational dams in the United States. Assessment of peak flow rates for these earthen dams and the impacts on dam failure are of high interest to engineers and planners. Regression analysis is a frequently used risk assessment approach for earthen dams. In this paper, we present a decision support tool for assessing the applicability of nine regression equations commonly used by practitioners. Using data from 108 case studies, six parameters were observed to be significant factors predicting for peak flow as a metric for risk analysis. We present our work on an expanded earthen dam break database that relates the regression equations and underlying data. A web application, regression selection tool, is also presented to assess the appropriateness of a given model for a given test point. This graphical display allows users to visualize how their data point compares with the data used for the regression equation. These contributions improve estimates and better inform decision makers regarding operational and safety decisions.     

Representing the Connectivity of Upland Areas to Floodplains and Streams in SWAT+

In recent years, watershed modelers have put increasing emphasis on capturing the interaction of landscape hydrologic processes instead of focusing on streamflow at the watershed outlet alone. Understanding the hydrologic connectivity between landscape elements is important to explain the hydrologic response of a watershed to rainfall events. The Soil and Water Assessment Tool+ (SWAT+) is a new version of SWAT with improved runoff routing capabilities. Subbasins may be divided into landscape units (LSUs), e.g., upland areas and floodplains, and flow can be routed between these LSUs. We ran three scenarios representing different extents of connectivity between uplands, floodplains, and streams. In the first and second scenarios, the ratio of channelized flow from the upland to the stream and sheet flow from the upland to the floodplain was 70/30 and 30/70, respectively, for all upland/floodplain pairs. In the third scenario, the ratio was calculated for each upland/floodplain pair based on the upland/floodplain area ratio. Results indicate differences in streamflow were small, but the relative importance of flow components and upland areas and floodplains as sources of surface runoff changed. Also, the soil moisture in the floodplains was impacted. The third scenario was found to provide more realistic results than the other two. A realistic representation of connectivity in watershed models has important implications for the identification of pollution sources and sinks.