CHARACTERISTICS OF LONG‐TERM FRESHWATER TRANSPORT IN APALACHICOLA BAY1

ABSTRACT: Long‐term freshwater transport is an important factor affecting estuarine aquatic ecosystems. In this study, a primitive equation, prognostic, three‐dimensional, hydrodynamic model was applied to Apalachicola Bay, Florida, for the summer and fall seasons of 1993. In response to the river freshwater discharge, tide, and wind forces, the model simulations were used to characterize the long‐term freshwater transport processes in the bay. Analysis of spatial distributions of seasonal average salinity and currents shows that the long‐term freshwater transport was strongly affected by the forcing functions of wind and density gradient in the bay. Average freshwater input was approximately the same in the summer and fall seasons of 1993. However, in the summer season, more freshwater moved to the east direction due to the predominant wind from the west, while in the fall season more freshwater moved to the west in response to the wind primarily from the east. The water column was strongly stratified near the river mouth, and it gradually changed to well mixing near the ocean boundaries. Vertical stratification in the bay changed due to wind‐induced mixing and mass transport. Due to the density gradient effect, surface residual currents carrying fresher water were in the direction from the river toward the Gulf, while the bottom residual currents with saltier water entered the bay from the Gulf of Mexico.     

Twenty‐One‐Year Simulation of Chesapeake Bay Water Quality Using the CE‐QUAL‐ICM Eutrophication Model

The CE‐QUAL‐ICM (Corps of Engineers Integrated Compartment Water Quality Model) eutrophication model was applied in a 21‐year simulation of Chesapeake Bay water quality, 1985‐2005. The eutrophication model is part of a larger model package and is forced, in part, by models of atmospheric deposition, watershed flows and loads, and hydrodynamics. Results from the model are compared with observations in multiple formats including time series plots, cumulative distribution plots, and statistical summaries. The model indicates only one long‐term trend in computed water quality: light attenuation deteriorates circa 1993 through the end of the simulation. The most significant result is the influence of physical processes, notably stratification and associated effects (e.g., anoxic volume), on computed water quality. Within the application period, physical effects are more important determinants of year‐to‐year variability in computed water quality than external loads.     

EFFECTS OF SCALE ON LAND USE AND WATER QUALITY RELATIONSHIPS: A LONGITUDINAL BASIN‐WIDE PERSPECTIVE1

ABSTRACT: Human land use is a major source of change in catchments in developing areas. To better anticipate the long‐term effects of growth, land use planning requires estimates of how changes in land use will affect ecosystem processes and patterns across multiple scales of space and time. The complexity of biogeochemical and hydrologic interactions within a basin makes it difficult to scale up from process‐based studies of individual reaches to watershed scales over multiple decades. Empirical models relating land use/land cover (LULC) to water quality can be useful in long‐term planning, but require an understanding of the effects of scale on apparent land use‐water quality relationships. We empirically determined how apparent relationships between water quality and LULC data change at different scales, using LIJLC data from the Willapa Bay watershed (Washington) and water quality data collected along the Willapa and North Rivers. Spatial scales examined ranged from the local riparian scale to total upstream catchment. The strength of the correlations between LTJLC data and longitudinal water quality trends varied with scale. Different water quality parameters also varied in their response to changes in scale. Intermediate scales of land use data generally were better predictors than local riparian or total catchment scales. Additional data from the stream network did not increase the strength of relationships significantly. Because of the likelihood of scale‐induced artifacts, studies quantifying land use‐water quality relationships performed at single scales should be viewed with great caution.     

Mapping Three‐Dimensional Water‐Quality Data in the Chesapeake Bay Using Geostatistics1

Abstract:  Data interpretation and visualization software tools with geostatistical capabilities were adapted, customized, and tested to assist the Chesapeake Bay Program in improving its water‐quality modeling protocols. Tools were required to interpolate, map, and visualize three‐dimensional (3D) water‐quality data, with the capability to determine estimation errors. Components of the software, originally developed for ground‐water modeling, were customized for application in estuaries. Additional software components were developed for retrieval, and for pre‐ and post‐ processing of data. The Chesapeake Bay Program uses the 3D mapped data for input to the Bay water‐quality model that projects the future health of the Bay and its tidal tributary system. In determining water‐quality attainment criteria, 3D kriging estimation errors are needed as a statistical measure of uncertainty. Furthermore, given the high cost of installing and operating new monitoring stations, geostatistical techniques can assist the Chesapeake Bay Program in the identification of suitable data collection locations. Following the evaluation, selection, and development of the software components phase, 3D ordinary kriging techniques with directional semi‐variograms to account for anisotropy were successfully demonstrated for mapping 3D fixed station water‐quality data, such as dissolved oxygen and salinity. Additionally, an improved delineation tool was implemented to simulate the upper and lower pycnocline boundary surfaces allowing the segregation of the interpolated 3D data into three separate zones for a better characterization of the pycnocline layer.     

Computing Atmospheric Nutrient Loads to the Chesapeake Bay Watershed and Tidal Waters

Application of integrated Chesapeake Bay models of the airshed, watershed, and estuary support air and water nitrogen controls in the Chesapeake. The models include an airshed model of the Mid‐Atlantic region which tracks the estimated atmospheric deposition loads of nitrogen to the watershed, tidal Bay, and adjacent coastal ocean. The three integrated models allow tracking of the transport and fate of nitrogen air emissions, including deposition in the Chesapeake watershed, the subsequent uptake, transformation, and transport to Bay tidal waters, and their ultimate influence on Chesapeake water quality. This article describes the development of the airshed model, its application to scenarios supporting the Chesapeake Total Maximum Daily Load (TMDL), and key findings from the scenarios. Key findings are that the atmospheric deposition loads are among the largest input loads of nitrogen in the watershed, and that the indirect nitrogen deposition loads to the watershed, which are subsequently delivered to the Bay are larger than the direct loads of atmospheric nitrogen deposition to Chesapeake tidal waters. Atmospheric deposition loads of nitrogen deposited in coastal waters, which are exchanged with the Chesapeake, are also estimated. About half the atmospheric deposition loads of nitrogen originate from outside the Chesapeake watershed. For the first time in a TMDL, the loads of atmospheric nitrogen deposition are an explicit part of the TMDL load reductions.     

HYSTAR Sediment Model: Distributed Two‐Dimensional Simulation of Watershed Erosion and Sediment Transport Using Time‐Area Routing

An erosion and sediment transport component incorporated in the HYdrology Simulation using Time‐ARea method (HYSTAR) upland watershed model provides grid‐based prediction of erosion, transport and deposition of sediment in a dynamic, continuous, and fully distributed framework. The model represents the spatiotemporally varied flow in sediment transport simulation by coupling the time‐area routing method and sediment transport capacity approach within a grid‐based spatial data model. This avoids the common, and simplistic, approach of using the Universal Soil Loss Equation (USLE) to estimate erosion rates with a delivery ratio to relate gross soil erosion to sediment yield of a watershed, while enabling us to simulate two‐dimensional sediment transport processes without the complexity of numerical solution of the partial differential governing equations. In using the time‐area method for routing sediment, the model offers a novel alternative to watershed‐scale sediment transport simulation that provides detailed spatial representation. In predicting four‐year sediment hydrographs of a watershed in Virginia, the model provided good performance with R² of 0.82 and 0.78 and relative error of ?35% and 11% using the Yalin and Yang's sediment transport capacity equations, respectively. Prediction of spatiotemporal variation in sediment transport processes was evaluated using maps of sediment transport rates, concentrations, and erosion and deposition mass, which compare well with expected behavior of flow hydraulics and sediment transport processes.     

Investigating the Fate and Transport of Escherichia coli in the Charles River,Boston, Using High‐Resolution Observation and Modeling1

Abstract: The processes affecting the fate and transport of Escherichia coli in surface waters were investigated using high‐resolution observation and modeling. The concentration patterns in Boston’s Charles River were observed during four sampling events with a total of 757 samples, including two spatial surveys with two along‐river (1,500 m length) and three across‐river (600 m length) transects at approximately 25‐m intervals, and two temporal surveys at a fixed location (Community Boating) over seven days at hourly intervals. The data reveal significant spatial and temporal structure at scales not resolved by typical monitoring programs. A mechanistic, time‐variable, three‐dimensional coupled hydrodynamic and water quality model was developed using the ECOMSED and RCA modeling frameworks. The computational grid consists of 3,066 grid cells with average length dimension of 25 m. Forcing functions include upstream and downstream boundary conditions, Stony Brook, and Muddy River (major tributaries) combined sewer overflow (CSO) and non‐CSO discharge and wind. The model generally reproduces the observed spatial and temporal patterns. This includes the presence and absence of a plume in the study area under similar loading, but different hydrodynamic conditions caused by operation of the New Charles River Dam (downstream) and wind. The model also correctly predicts an episode of high concentrations at the time‐series station following seven days of no rainfall. The model has an overall root mean square error (RMSE) of 250 CFU/100 ml and an error rate (above or below the USEPA‐recommended single sample criteria value of 235 CFU/100 ml) of 9.4%. At the time series station, the model has an RMSE of 370 CFU/100 ml and an error rate of 15%.     

The Shallow‐Water Component of the Chesapeake Bay Environmental Model Package

The shallow‐water component of the Chesapeake Bay Environmental Model Package emphasizes the regions of the system inside the 2‐m depth contour. The model of these regions is unified with the system‐wide model but places emphasis on locally significant components and processes, notably submerged aquatic vegetation (SAV), sediment resuspension, and their interaction with light attenuation (Ke). The SAV model is found to be most suited for computing the equilibrium distribution of perennial species. Addition of plant structure and propagation are recommended to improve representation of observed trends in SAV area. Two approaches are taken to examining shallow‐water Ke. The first compares observed and computed differences between deep‐ and shallow‐water Ke. No consistent difference in observations is noted. In the preponderance of regions examined, computed shallow‐water Ke exceeds computed deep‐water Ke. The second approach directly compares Ke measured in shallow water with modeled results. Model values are primarily lower than observed, in contrast to results in deep water where model values exceed observed. The shortfall in computed Ke mirrors a similar shortfall in computed suspended solids. Improved model representation of Ke requires process‐based investigations into suspended solids dynamics as well as increased model resolution in shallow‐water regions.     

A Probabilistic,Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

This study contributes a bathtub‐style inundation prediction model with abstractions of coastal processes (i.e., storm surge and wave runup) for flood forecasting at medium‐range (weekly to monthly) timescales along the coastline of large lakes. Uncertainty from multiple data sources are propagated through the model to establish probabilistic bounds of inundation, providing a conservative measure of risk. The model is developed in a case study of the New York Lake Ontario shoreline, which has experienced two record‐setting floods over the course of three years (2017–2019). Predictions are developed at a parcel‐level and are validated using inundation accounts from an online survey and flyover imagery taken during the recent flood events. Model predictions are compared against a baseline, deterministic model that accounts for the same processes but does not propagate forward data uncertainties. Results suggest that a probabilistic approach helps capture observed instances of inundation that would otherwise be missed by a deterministic inundation model. However, downward biases are still present in probabilistic predictions, especially for parcels impacted by wave runup. The goal of the tool is to provide community planners and property owners with a conservative, parcel‐level assessment of flood risk to help inform short‐term emergency response and better prepare for future flood events.     

Total Maximum Daily Load Criteria Assessment Using Monitoring and Modeling Data

Applications of Total Maximum Daily Load (TMDL) criteria for complex estuarine systems like Chesapeake Bay have been limited by difficulties in estimating precisely how changes in input loads will impact ambient water quality. A method to deal with this limitation combines the strengths of the Chesapeake Bay's Water Quality Sediment Transport Model (WQSTM), which simulates load response, and the Chesapeake Bay Program's robust historical monitoring dataset. The method uses linear regression to apply simulated relative load responses to historical observations of water quality at a given location and time. Steps to optimize the application of regression analysis were to: (1) determine the best temporal and spatial scale for applying the WQSTM scenarios, (2) determine whether the WQSTM method remained valid with significant perturbation from calibration conditions, and (3) evaluate the need for log transformation of both dissolved oxygen (DO) and chlorophyll a (CHL) datasets. The final method used simple linear regression at the single month, single WQSTM grid cell scale to quantify changes in DO and CHL resulting from simulated load reduction scenarios. The resulting linear equations were applied to historical monitoring data to produce a set of “scenario‐modified” DO or CHL concentration estimates. The utility of the regression method was validated by its ability to estimate progressively increasing attainment in support of the 2010 Chesapeake Bay TMDL.     

Development and Application of the 2010 Chesapeake Bay Watershed Total Maximum Daily Load Model

The Phase 5.3 Watershed Model simulates the Chesapeake watershed land use, river flows, and the associated transport and fate of nutrient and sediment loads to the Chesapeake Bay. The Phase 5.3 Model is the most recent of a series of increasingly refined versions of a model that have been operational for more than two decades. The Phase 5.3 Model, in conjunction with models of the Chesapeake airshed and estuary, provides estimates of management actions needed to protect water quality, achieve Chesapeake water quality standards, and restore living resources. The Phase 5.3 Watershed Model tracks nutrient and sediment load estimates of the entire 166,000 km² watershed, including loads from all six watershed states. The creation of software systems, input datasets, and calibration methods were important aspects of the model development process. A community model approach was taken with model development and application, and the model was developed by a broad coalition of model practitioners including environmental engineers, scientists, and environmental managers. Among the users of the Phase 5.3 Model are the Chesapeake watershed states and local governments, consultants, river basin commissions, and universities. Development and application of the model are described, as well as key scenarios ranging from high nutrient and sediment load conditions if no management actions were taken in the watershed, to low load estimates of an all‐forested condition.     

AN INTEGRATED ONE‐DIMENSIONAL AND TWO‐DIMENSIONAL URBAN STORMWATER FLOOD SIMULATION MODEL1

ABSTRACT: Flash flooding is the rapid flooding of low lying areas caused by the stormwater of intense rainfall associated with thunderstorms. Flash flooding occurs in many urban areas with relatively flat terrain and can result in severe property damage as well as the loss of lives. In this paper, an integrated one‐dimensional (1‐D) and two‐dimensional (2‐D) hydraulic simulation model has been established to simulate stormwater flooding processes in urban areas. With rainfall input, the model simulates 2‐D overland flow and 1‐D flow in underground stormwater pipes and drainage channels. Drainage channels are treated as special flow paths and arranged along one or more sides of a 2‐D computational grid. By using irregular computation grids, the model simulates unsteady flooding and drying processes over urban areas with complex drainage systems. The model results can provide spatial flood risk information (e.g., water depth, inundation time and flow velocity during flooding). The model was applied to the City of Beaumont, Texas, and validated with the recorded rainfall and runoff data from Tropical Storm Allison with good agreement.     

Integration of SWAT and HSPF for Simulation of Sediment Sources in Legacy Sediment‐Impacted Agricultural Watersheds

A total maximum daily load for the Chesapeake Bay requires reduction in pollutant load from sources within the Bay watersheds. The Conestoga River watershed has been identified as a major source of sediment load to the Bay. Upland loads of sediment from agriculture are a concern; however, a large proportion of the sediment load in the Conestoga River has been linked to scour of legacy sediment associated with historic millpond sites. Clarifying this distinction and identifying specific segments associated with upland vs. channel sources has important implications for future management. In order to address this important question, we combined the strengths of two widely accepted watershed management models — Soil and Water Assessment Tool (SWAT) for upland agricultural processes, and Hydrologic Simulation Program FORTRAN (HSPF) for instream fate and transport — to create a novel linked modeling system to predict sediment loading from critical sources in the watershed including upland and channel sources, and to aid in targeted implementation of management practices. The model indicates approximately 66% of the total sediment load is derived from instream sources, in agreement with other studies in the region and can be used to support identification of these channel source segments vs. upland source segments, further improving targeted management. The innovated linked SWAT‐HSPF model implemented in this study is useful for other watersheds where both upland agriculture and instream processes are important sources of sediment load.     

Spatial and Temporal Patterns of Low Streamflow and Precipitation Changes in the Chesapeake Bay Watershed

Spatial and temporal patterns in low streamflows were investigated for 183 streamgages located in the Chesapeake Bay Watershed for the period 1939–2013. Metrics that represent different aspects of the frequency and magnitude of low streamflows were examined for trends: (1) the annual time series of seven‐day average minimum streamflow, (2) the scaled average deficit at or below the 2% mean daily streamflow value relative to a base period between 1939 and 1970, and (3) the annual number of days below the 2% threshold. Trends in these statistics showed spatial cohesion, with increasing low streamflow volume at streamgages located in the northern uplands of the Chesapeake Bay Watershed and decreasing low streamflow volume at streamgages in the southern part of the watershed. For a small subset of streamgages (12%), conflicting trend patterns were observed between the seven‐day average minimum streamflow and the below‐threshold time series and these appear to be related to upstream diversions or the influence of reservoir‐influenced streamflows in their contributing watersheds. Using multivariate classification techniques, mean annual precipitation and fraction of precipitation falling as snow appear to be broad controls of increasing and decreasing low‐flow trends. Further investigation of seasonal precipitation patterns shows summer rainfall patterns, driven by the Atlantic Multidecadal Oscillation, as the main driver of low streamflows in the Chesapeake Bay Watershed.     

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%.     

A Multi‐Scale Analysis of Single‐Family Residential Water Use in the Phoenix Metropolitan Area

Studies that evaluate determinants of residential water demand typically use data from a single spatial scale. Although household‐scale data are preferred, especially when econometric models are used, researchers may be limited to aggregate data. There is little, if any, empirical analysis to assess whether spatial scale may lead to ecological fallacy problems in residential water use research. Using linear mixed‐effects models, we compare the results for the relationship of single‐family water use with its determinants using data from the household and census tract scales in the city of Phoenix. Model results between the household and census tract scale are similar suggesting the ecological fallacy may not be significant. Common significant determinants on these two spatial scales include household size, household income, house age, pool size, irrigable lot size, precipitation, and temperature. We also use city/town scale data from the Phoenix metropolitan area to parameterize the linear mixed‐effects model. The difference in the parameter estimates of those common variables compared to the first two scales indicates there is spatial heterogeneity in the relationship between single‐family water use and its determinants among cities and towns. The negative relationship between single‐family house density and residential water use suggests that residential water consumption could be reduced through coordination of land use planning and water demand management.     

Multiscale Analysis of Hydrology in a Mountaintop Mine‐Impacted Watershed

In the Appalachian region of the eastern United States, mountaintop removal mining (MTM) is a dominant driver of land‐cover change, impacting 6.8% of the largely forested 4.86 million ha coal fields region. Recent catastrophic flooding and documented biological impairment downstream of MTM has drawn sharp criticism to this practice. Despite its extent, scale, and use since the 1970s, the impact of MTM on hydrology is poorly understood. Therefore, the goal of this study was a multiscale evaluation to establish the nature of hydrologic impacts associated with MTM. To quantify the extent of MTM, land‐cover change over the lifetime of this practice is estimated for a mesoscale watershed in southern West Virginia. To assess hydrologic impacts, we conducted long‐term trend analyses to evaluate for systematic changes in hydrology at the mesoscale, and conducted hydrometric and response time modeling to characterize storm‐scale responses of a MTM‐impacted headwater catchment. Results show a general trend in the conversion of forests to mines, and significant decreases in maximum streamflow and variability, and increases in base‐flow ratio attributed to valley fills and deep mine drainage. Decreases in variability are shown across spatial and temporal scales having important implications for water quantity and quality. However, considerable research is necessary to understand how MTM impacts hydrology. In an effort to inform future research, we identify existing knowledge gaps and limitations of our study.     

Forecasting Resilience in Arctic Societies: Creating Tools for Assessing Social–Hydrological Systems1

Altaweel, Mark R., Lilian N. Alessa, and Andrew D. Kliskey, 2009. Forecasting Resilience in Arctic Societies: Creating Tools for Assessing Social–Hydrological Systems. Journal of the American Water Resources Association (JAWRA) 45(6):1379‐1389. Abstract: Arctic communities are increasingly faced with social–ecological changes that act at variable speeds and spatial scales. Such changes will affect vital resources, particularly water supplies. Currently, there are few computational tools that integrate multiple social and environmental processes in order to aid communities’ adaptation to change through decision support systems. This paper proposes a modeling and simulation approach that can integrate such processes at different spatiotemporal scales in order to address issues affecting community water supplies. In this paper, a modeling and simulation tool is developed and applied to a case study on the Seward Peninsula. Initial results, using both field observations and computation, show projected patterns of water use, perceptions of water availability, and long‐term consumption trends. More broadly, the paper demonstrates the need for developing tools that address issues at the community level for better understanding human and hydrological interactions and policy decisions affecting water supplies.