Development,implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System

The NOAA Great Lakes Operational Forecast System (GLOFS) uses near-real-time atmospheric observations and numerical weather prediction forecast guidance to produce three-dimensional forecasts of water temperature and currents, and two-dimensional forecasts of water levels of the Great Lakes. This system, originally called the Great Lakes forecasting system (GLFS), was developed at The Ohio State University and NOAA’s Great Lakes Environmental Research Laboratory (GLERL) in 1989. In 1996, a workstation version of the GLFS was ported to GLERL to generate semi-operational nowcasts and forecasts daily. In 2004, GLFS went through rigorous skill assessment and was transitioned to the National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) in Silver Spring, MD. GLOFS has been making operational nowcasts and forecasts at CO-OPS since September 30, 2005. Hindcast, nowcast, and forecast evaluations using the NOS-developed skill assessment software tool indicated both surface water levels and temperature predictions passed the NOS specified criteria at a majority of the validation locations with relatively low root mean square error (4–8 cm for water levels and 0.5 to 1°C for surface water temperatures). The difficulty of accurately simulating seiches generated by storms (in particular in shallow lakes like Lake Erie) remains a major source of error in water level prediction and should be addressed in future improvements of the forecast system.     

Wave measurement and modeling in Chesapeake Bay

Three recently measured wind and wave data sets in the northern part of Chesapeake Bay (CB) are presented. Two of the three data sets were collected in late 1995. The third one was collected in July of 1998. The analyzed wind and wave data show that waves were dominated by locally generated, fetch limited young wind seas. Significant wave heights were highly correlated to the local driving wind speeds and the response time of the waves to the winds was about 1 h. We also tested two very different numerical wave models, Simulation of WAves Nearshore (SWAN) and Great Lakes Environmental Research Laboratory (GLERL), to hind-cast the wave conditions against the data sets. Time series model–data comparisons made using SWAN and GLERL showed that both models behaved well in response to a suddenly changing wind. In general, both SWAN and GLERL over-predicted significant wave height; SWAN over-predicted more than GLERL did. SWAN had a larger scatter index and a smaller correlation coefficient for wave height than GLERL had. In addition, both models slightly under-predicted the peak period with a fairly large scatter and low correlation coefficient. SWAN predicted mean wave direction better than GLERL did. Directional wave spectral comparisons between SWAN predictions and the data support these statistical comparisons. The GLERL model was much more computationally efficient for wind wave forecasts in CB. SWAN and GLERL predicted different wave height field distributions for the same winds in deeper water areas of the Bay where data were not available, however. These differences are as yet unresolved.     

Observed and Forecasted Intraseasonal Activity of Southwest Monsoon Rainfall over India During 2010, 2011 and 2012

The monsoon seasons of 2010 and 2011, with almost identical seasonal total rainfall over India from June to September, are associated with slightly different patterns of intraseasonal rainfall fluctuations. Similarly, the year 2012, with relatively less rainfall compared to 2010 and 2011, also witnessed different intraseasonal rainfall fluctuations, leading to drought-like situations over some parts of the country. The present article discusses the forecasting aspect of monsoon activity over India during these 3 years on an extended range time scale (up to 3 weeks) by using the multimodel ensemble (MME), based on operational coupled model outputs from the ECMWF monthly forecasting system and the NCEP’s Climate Forecast System (CFS). The average correlation coefficient (CC) of weekly observed all-India rainfall (AIR) and the corresponding MME forecast AIR is found to be significant, above the 98 % level up to 2 weeks (up to 18 days) with a slight positive CC for the week 3 (days 19–25) forecast. However, like the variation of observed intraseasonal rainfall fluctuations during 2010, 2011 and 2012 monsoon seasons, the MME forecast skills of weekly AIR are also found to be different from one another, with the 2012 monsoon season indicating significant CC (above 99 % level) up to week 2 (12–18 days), and also a comparatively higher CC (0.45) during the week 3 forecast (days 19–25). The average CC between observed and forecasted weekly AIR rainfall over four homogeneous regions of India is found to be the lowest over the southern peninsula of India (SPI), and northeast India (NEI) is found to be significant only for the week 1 (days 5–11) forecast. However, the CC is found to be significant over northwest India (NWI) and central India (CEI), at least above the 90 % level up to 18 days, with NWI having slightly better skill compared to the CEI. For the individual monsoon seasons of 2010, 2011 and 2012, there is some variation in CC and other skill scores over the four homogeneous regions. Thus the slight variations in the characteristics of intraseasonal monsoon rainfall over India is associated with variations in predictive skill of the coupled models and the MME-based predictions of intraseasonal monsoon fluctuations for 2–3 weeks, providing encouraging results. The MME forecast in 2010 is also able to provide useful guidance, well in advance, about an active September associated with a delayed withdrawal of the monsoon and also the heavy rainfall over north Pakistan.     

Predicting dissolved oxygen in the Chesapeake Bay: applications and implications

Eutrophic depletion of dissolved oxygen (DO) and its consequences for ecosystem dynamics have been a central theme of research, assessment and management policies for several decades in the Chesapeake Bay. Ongoing forecast efforts predict the extent of the summer hypoxic/anoxic area due to nutrient loads from the watershed. However, these models neither predict DO levels nor address the intricate interactions among various ecological processes. The prediction of spatially explicit DO levels in the Chesapeake Bay can eventually lead to a reliable depiction of the comprehensive ecological structure and functioning, and can also allow the quantification of the role of nutrient reduction strategies in water quality management. In this paper, we describe a three dimensional empirical model to predict DO levels in the Chesapeake Bay as a function of water temperature, salinity and dissolved nutrient concentrations (TDN and TDP). The residual analysis shows that predicted DO values compare well with observations. Nash–Sutcliffe efficiency (NSE) and root mean square error-observations standard deviation ratio (RSR) are used to evaluate the performance of the empirical model; the scores demonstrate the usability of model predictions (NSE, surface layer = 0.82–0.86; middle layer = 0.65–0.82; bottom layer = 0.70–0.82; RSR surface layer = 0.37–0.44; middle layer = 0.43–0.58 and bottom layer = 0.43–0.54). The predicted DO values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models, can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the Chesapeake Bay.     

Post‐processing of medium‐range probabilistic hydrological forecasting: impact of forcing,initial conditions and model errors

The impact of errors in the forcing, errors in the model structure and parameters, and errors in the initial conditions is investigated in a simple hydrological ensemble prediction system. The hydrological model is based on an input nonlinearity connected with a linear transfer function and forced by precipitation forecasts from the European Centre for Medium‐Range Weather Forecast (ECMWF) Ensemble Prediction System (EPS). The post‐processing of the precipitation and/or the streamflow using information from the reforecasts performed by ECMWF is tested. For this purpose, hydrological reforecasts are obtained by forcing the hydrological model with the precipitation from the reforecast data. In the present case study, it is found that the post‐processing of the hydrological ensembles with a statistical model fitted on the hydrological reforecasts improves the verification scores better than the use of post‐processed precipitation ensembles. In the case of large biases in the precipitation, combining the post‐processing of both precipitation and streamflow allows for further improvements. During the winter, errors in the initial conditions have a larger impact on the scores than errors in the model structure as designed in the experiments. Errors in the parameter values are largely corrected with the post‐processing. Copyright © 2014 John Wiley & Sons, Ltd.     

Resonance and sea level variability in Chesapeake Bay

A numerical model is used to determine the resonant period and quality factor Q of Chesapeake Bay and explore physical mechanisms controlling the resonance response in semi-enclosed seas. At the resonant period of 2 days, the mouth-to-head amplitude gain is 1.42 and Q is 0.9, indicating that Chesapeake Bay is a highly dissipative system. The modest amplitude gain results from strong frictional dissipation in shallow water. It is found that the spatial distribution of energy dissipation varies with forcing frequency. While energy at tidal frequencies is dissipated around topographic hotspots distributed throughout the Bay, energy dissipation at subtidal frequencies is mainly concentrated in the shallow-water lower Bay. An analytic calculation shows that the bottom friction parameter is much larger in Chesapeake Bay than in other coastal systems with strong resonance response. The model-predicted amplitude gains and phase changes agree well with the observations at semidiurnal and diurnal tidal frequencies. However, the predicted amplitude gain in the resonant frequency band (34–54 h period) falls below that inferred from band-passed sea level observations. This discrepancy can be attributed to the local wind forcing which amplifies the sea level response in the upper Bay. The model is also used to show that rising sea levels associated with global warming will shift the resonance period of Chesapeake Bay closer to the diurnal tides and thus exacerbate flooding problems by causing an increase in tidal ranges.     

Hindcast of breaking waves and its impact at an island sheltered coast,Karwar

Variability in the characteristics of depth-induced wave breakers along a non-uniform coastal topography and its impact on the morpho-sedimentary processes is examined at the island sheltered wave-dominated micro-tidal coast, Karwar, west coast of India. Waves are simulated using the coupled wind wave model, SWAN nested in WAVEWATCH III, forced by the reanalysis winds from different sources (NCEP/NCAR, ECMWF, and NCEP/CFSR). Impact of the wave breakers is evaluated through mean longshore current and sediment transport for various wave energy conditions across different coastal morphology. Study revealed that the NCEP/CFSR wind is comparatively reasonable in simulation of nearshore waves using the SWAN model nested by 2D wave spectra generated from WAVEWATCH III. The Galvin formula for estimating mean longshore current using the crest wave period and the Kamphuis approximation for longshore sediment transport is observed realistically at the sheltered coastal environment while the coast interacts with spilling and plunging breakers.     

Climate change impact on Caspian Sea wave conditions in the Noshahr Port

Excessive usage of fossil fuels and high emission of greenhouse gases have increased the earth’s temperature and consequently have led to changes in wind and wave regimes. The main effects of climate change on oceans are warming of the ocean water, melting of ice, acidification of ocean water, and change in the ocean currents. The main effects of climate change on coastal regions are change in the coast hydrodynamics, sea level rise, change in wave height, coastal erosion, coastal structure damage, food shortage, and storms. Due to the importance of waves in the coastal zone and its effect on erosion and sedimentation, it is necessary to study wave changes. In this study, the effect of climate change on wave specifications was evaluated in the southern coast of the Caspian Sea in Noshahr Port. To simulate wave parameters, the third generation spectral Simulating WAves Nearshore (SWAN) model was used. Wave modeling was carried out using the SWAN numerical model for two 30-yearly periods, including the control period (1984 to 2014) and the future period (2051 to 2080). For wave modeling in the control period, the European Center for Average Weather Forecast wind field was used, and for the future period, a downscaled wind field from Coordinated Regional Downscaling Experiment projection, which was sponsored by World Climate Research Programme, based on the most recent emission scenarios RCP2.6, RCP4.5, and RCP8.5, was used. The model results were calibrated and verified with buoy-recorded data. The effect of the climate change on the wave parameters was evaluated by studying the differences between the patterns in three scenarios and the control period. Results showed that the 30-year maximum significant wave height will increase because of climate change, and the wave direction will not change. In addition, the intensity of storms will increase in the future.

     

Assessment of Impact of Storm on Point Source Pollutant Transport in Estuary by Dissolved Tracer Modeling

A continuously discharged dissolved conservative tracer was simulated with the Chesapeake Bay Estuary Model Package to study pollutant transport in the estuary in response to point source loads and the impact of the November, 1985 storm. A visualization technology is applied to show 3-dimensional concentration variations in a continuous daily time sequence. The differential responses of daily net transport during storms versus inter-storm periods can be observed from an MPEG movie. It may take 2–3 months for a tracer to travel from the fall-line to the mouth of a river during relatively dry seasons, only 2 weeks in some medium storms, and less than 5 days in a big storm. Plots of daily concentrations from eleven selected locations in the estuary provide quantitative information on the response of tracer concentration to flows. The magnitude and time of tracer peaks related with different weather events in these locations reflect the combined effects of flows from various directions to these locations. The lower tributaries (which are closer to the Bay mouth) are affected more than the upper tributaries by a source discharged at a mid-tributary. A storm can transport materials more effectively to the Bay and affect adjacent tributaries more severely.     

Storm-driven sediment transport in Massachusetts Bay

Massachusetts Bay is a semi-enclosed embayment in the western Gulf of Maine about 50 km wide and 100 km long. Bottom sediment resuspension is controlled predominately by storm-induced surface waves and transport by the tidal- and wind-driven circulation. Because the Bay is open to the northeast, winds from the northeast (‘Northeasters’) generate the largest surface waves and are thus the most effective in resuspending sediments. The three-dimensional oceanographic circulation model Regional Ocean Modeling System (ROMS) is used to explore the resuspension, transport, and deposition of sediment caused by Northeasters. The model transports multiple sediment classes and tracks the evolution of a multilevel sediment bed. The surficial sediment characteristics of the bed are coupled to one of several bottom-boundary layer modules that calculate enhanced bottom roughness due to wave–current interaction. The wave field is calculated from the model Simulating WAves Nearshore (SWAN). Two idealized simulations were carried out to explore the effects of Northeasters on the transport and fate of sediments. In one simulation, an initially spatially uniform bed of mixed sediments exposed to a series of Northeasters evolved to a pattern similar to the existing surficial sediment distribution. A second set of simulations explored sediment-transport pathways caused by storms with winds from the northeast quadrant by simulating release of sediment at selected locations. Storms with winds from the north cause transport southward along the western shore of Massachusetts Bay, while storms with winds from the east and southeast drive northerly nearshore flow. The simulations show that Northeasters can effectively transport sediments from Boston Harbor and the area offshore of the harbor to the southeast into Cape Cod Bay and offshore into Stellwagen Basin. This transport pattern is consistent with Boston Harbor as the source of silver found in the surficial sediments of Cape Cod Bay and Stellwagen Basin.     

Ecosystem under pressure: ballast water discharge into Galveston Bay, Texas (USA) from 2005 to 2010

Ballast water exchange processes facilitate the dispersal and unnatural geographic expansion of phytoplankton, including harmful algal bloom species. From 2005 to 2010, over 45,000 vessels (≈ 8000 annually) travelled across Galveston Bay (Texas, USA) to the deep-water ports of Houston (10th largest in the world), Texas City and Galveston. These vessels (primarily tankers and bulkers) discharged ≈ 1.2 × 10(8) metrictons of ballast water; equivalent to ≈ 3.4% of the total volume of the Bay. Over half of the ballast water discharged had a coastwise origin, 96% being from US waters. Galveston Bay has fewer non-indigenous species but receives a higher volume of ballast water discharge, relative to the highly invaded Chesapeake and San Francisco Bays. Given the magnitude of shipping traffic, the role of Galveston Bay, both as a recipient and donor region of non-indigenous phytoplankton species is discussed here in terms of the invasibility risk to this system by way of ballast water.     

Regularities of hydrological processes in the Chesapeake Bay (USA): Case study of a classical estuary

The main regularities of hydrological and hydrological-environmental processes occurring within the complex estuary, the Chesapeake Bay and the mouths of its tributaries, are discussed. The peculiarities of the estuary morphological structure, including the structures of tidal and net currents, salinity and water turbidity fields and their variability, the environmental conditions, and their human-induced changes. Using the Chesapeake Bay as an example, it became possible to reveal the basic features of classical estuaries subject to a considerable impact of river runoff and featuring mixing of river and sea water and moderate stratification of the water mass. It is shown that the regularities of hydrological processes in the Chesapeake Bay are typical of many mouth water bodies of estuarine type (inlets, drowned river valleys, lagoons, and tidal estuaries proper).     

Long-term trends in the macrobenthos and water quality of the lower Chesapeake Bay (1985–1991)

Long-term trends in macrobenthic communities of the lower Chesapeake Bay, USA, were examined using data collected quarterly (March, June, September and December) from 1985 to 1991 at 16 stations along a salinity gradient from tidal freshwater regions of the major tributaries (James, York and Rappahannock rivers) to the polyhaline region of the main-stem of Chesapeake Bay. A non-parametric trend analysis procedure was applied to five parameters characterizing macrobenthic community structure: community biomass, species richness, abundance of individuals, proportion of biomass composed of opportunistic species (opportunistic biomass composition) and proportion of biomass composed of equilibrium species (equilibrium biomass composition). For the parameters tested 36 trends were detected. For community biomass, five trends were significant; all had positive slopes and occurred in the James and York rivers. For species richness, six trends were significant; all had positive slopes with three trends in the James River, two trends in the York River and one trend in the main-stem of Chesapeake Bay. For abundance of individuals, 17 trends were detected; all abundance trends were seasonally dependent, had positive slopes and occurred at 12 of the 16 stations. For opportunistic biomass composition, four trends were significant; all had positive slopes with one trend in the lower Rappahannock River and three trends in the main-stem of Chesapeake Bay. For equilibrium biomass composition four trends were significant; two trends had positive slopes (one in the James River and one in the York River) and two trends had negative slopes (one in the Rappahannock River and one in the main-stem of Chesapeake Bay). Trends in the James and York rivers were considered to indicate improving conditions for the benthos, while trends in the lower Rappahannock River and the main-stem of the Chesapeake Bay were considered to indicate deteriorating conditions. Deteriorating conditions for the benthos were associated with regions exposed to summer, low dissolved oxygen events. The trends in the indicators of benthic biological community health were inferentially related to trends observed in water quality conditions in the tributaries and main-stem of Chesapeake Bay. All major water quality and biotic trends appeared to correspond in an ecologically meaningful manner.     

Improvement of the Noah land surface model for warm season processes: evaluation of water and energy flux simulation

The Noah model is a land surface model of the National Centers for Environmental Prediction. It has been widely used in regional coupled weather and climate models (i.e. Weather Research and Forecasting Model, Eta Mesoscale Model) and global coupled weather and climate models (i.e. National Centers for Environmental Prediction Global Forecast System, Climate Forecast System). Therefore, its continued improvement and development are keys to enhancing our weather and climate forecast ability and water and energy flux simulation accuracy. North American Land Data Assimilation System phase 1 (NLDAS‐1) experiments indicated that the Noah model exhibited substantial bias in latent heat flux, total runoff and land skin temperature during the warm season, and such bias can significantly affect coupled weather and climate models. This paper presents a study to improve the Noah model by adding model parameterization processes such as including seasonal factor on leaf area index and root distribution and selecting optimal model parameters. We compared simulated latent heat flux, mean annual runoff and land skin temperature from the Noah control and test versions with measured latent heat flux, land surface skin temperature, mean annual runoff and satellite‐retrieved land surface skin temperature. The results show that the test version significantly reduces biases in latent heat, total runoff and land skin temperature simulation. The test version has been used for the NLDAS phase 2 (NLDAS‐2) to produce 30‐year water flux, energy flux and state variable products to support the US drought monitor of National Integrated Drought Information System. Copyright © 2012 John Wiley & Sons, Ltd.     

4D tropospheric tomography using GPS slant wet delays

Tomographic techniques are successfully applied to obtain 4D images of the tropospheric refractivity in a local dense network of global positioning system (GPS) receivers. We show here how GPS data are processed to obtain the tropospheric slant wet delays and discuss the validity of the processing. These slant wet delays are the observables in the tomographic processing. We then discuss the inverse problem in 4D tropospheric tomography making extensive use of simulations to test the system and define the resolution and the impact of noise. Finally, we use data from the Kilauea network in Hawaii for February 1, 1997, and a local 4 × 4 × 40 voxel grid on a region of 400 km² and 15 km in height to produce the corresponding 4D wet refractivity fields, which are then validated using forecast analysis from the European Center for Medium Range Weather Forecast (ECMWF). We conclude that tomographic techniques can be used to monitor the troposphere in time and space.     

亚洲地区ECMWF/NCEP资料计算 ZTD的精度分析

对流层延迟是卫星导航定位的主要误差源,气象观测的数值预报资料可用来计算对流层延迟改正量.本文通过分布于亚洲地区的49个GPS台站一年的实测ZTD资料,对利用欧洲中尺度天气预报中心(ECMWF)分析资料、美国国家环境预报中心(NCEP)再分析资料和NCEP预报资料,计算对流层天顶延迟(ZTD)改正的有效性和可能达到的精度进行了评估,分析了ECMWF和NCEP在亚洲地区的适用程度和其分辨率对计算ZTD精度的影响.研究结果表明:(1)相对于 GPS实测ZTD,用ECMWF资料计算ZTD的bias和rms分别为-1.0 cm 和2.7 cm,优于NCEP再分析资料,可用于高精度ZTD研究和应用;NCEP预报数据计算ZTD的bias和rms分别为2.4 cm 和 6.8 cm,足以满足广大GNSS实时导航定位用户对流层延迟改正的需要.(2)bias和rms呈现明显的季节性变化,总体上夏季大,冬季小;在空间分布上随着纬度的变化不明显,但随高度的增加rms总体上有递减趋势.另外还发现,亚洲东部地区夏季日平均bias和rms和南部热带地区冬季的日平均bias和rms变化相对较大.(3)ECMWF2.5°和0.5°的资料进行了对比分析,发现0.5°分辨率资料的rms比2.5°减小1~5 mm.这些结果,为在亚洲地区的空间大地测量、导航定位和INSAR等工作中,应用ECMWF/NCEP的资料进行对流层大气延迟改正的有效性和可能达到的精度提供了重要参考.     

Potential microbial bioinvasions via ships' ballast water, sediment, and biofilm

A prominent vector of aquatic invasive species to coastal regions is the discharge of water, sediments, and biofilm from ships' ballast-water tanks. During eight years of studying ships arriving to the lower Chesapeake Bay, we developed an understanding of the mechanisms by which invasive microorganisms might arrive to the region via ships. Within a given ship, habitats included ballast water, unpumpable water and sediment (collectively known as residuals), and biofilms formed on internal surfaces of ballast-water tanks. We sampled 69 vessels arriving from foreign and domestic ports, largely from Western Europe, the Mediterranean region, and the US East and Gulf coasts. All habitats contained bacteria and viruses. By extrapolating the measured concentration of a microbial metric to the estimated volume of ballast water, biofilm, or residual sediment and water within an average vessel, we calculated the potential total number of microorganisms contained by each habitat, thus creating a hierarchy of risk of delivery. The estimated concentration of microorganisms was greatest in ballast water>sediment and water residuals>biofilms. From these results, it is clear microorganisms may be transported within ships in a variety of ways. Using temperature tolerance as a measure of survivability and the temperature difference between ballast-water samples and the water into which the ballast water was discharged, we estimated 56% of microorganisms could survive in the lower Bay. Extrapolated delivery and survival of microorganisms to the Port of Hampton Roads in lower Chesapeake Bay shows on the order of 10(20) microorganisms (6.8 x 10(19) viruses and 3.9 x 10(18) bacteria cells) are discharged annually to the region.     

美国Chesapeake湾撞击坑研究进展及其启示

位于美国弗吉尼亚东海岸直径85 km的Chesapeake湾撞击坑,是十几年前发现的由一颗陨星撞击形成的一个复杂撞击坑. 该坑的研究经历四个阶段：地下水调查、撞击坑的发现、美国多学科多部门的综合研究和即将进行的国际钻探取心项目. 钻井岩心中的角砾成份和微体化石,提示撞击坑的存在,并确定撞击发生在35 Ma前,即始新始晚期. 地震反射剖面资料帮助寻找到撞击坑的具体位置,确定撞击坑的结构和形态特征. Chesapeake湾撞击坑埋藏在新生界沉积层之下,是全球已知最大的、保存最好的撞击坑之一. Chesapeake湾撞击坑主要形态像一顶倒置的宽边大草帽,包括外缘、环状洼地、峰环（内缘）、内盆和中央峰. 撞击坑的形成破坏了原来的含水层,撞击坑当时即被富含咸水的抛射角砾岩和海啸角砾岩充填,再被后来的沉积层覆盖. Chesapeake湾撞击坑导致地面沉降、河流变向、海岸含水层的中断、内陆咸水楔的出现、地震,决定Chesapeake湾本身的位置,至今仍然影响当地居民的生活. 了解Chesapeake湾撞击坑对我国撞击坑研究具有借鉴作用.     

Ecological condition of US Mid-Atlantic estuaries, 1997-1998

The Mid-Atlantic Integrated Assessment (MAIA-Estuaries) evaluated ecological conditions in US Mid-Atlantic estuaries during the summers of 1997 and 1998. Over 800 probability-based stations were monitored in four main estuarine systems--Chesapeake Bay, the Delaware Estuary, Maryland and Virginian coastal bays, and the Albemarle-Pamlico Estuarine System. Twelve smaller estuaries within the four main systems were also assessed to establish variance at the local scale. A subset of the MAIA-Estuaries data is used here to estimate the extent of eutrophication, sediment contamination, and benthic degradation in mid-Atlantic estuaries. An Environmental Report Card and Index of Environmental Integrity summarize conditions in individual estuaries, the four estuarine systems, and the entire MAIA region. Roughly 20-50% of the region showed signs of eutrophication (high nutrients, excessive production of organic matter, poor water clarity, or depleted dissolved oxygen), 30% had contaminated sediments, and 37% had degraded benthic communities. Compared with the Environmental Monitoring and Assessment Program (EMAP)-Virginian Province study in 1990-1993, larger fractions of Chesapeake Bay (17%) and Delaware River (32%) had increased metals or organics in sediments.     

Sea level acceleration and variability in the Chesapeake Bay: past trends,future projections,and spatial variations within the Bay

Ocean Dynamics - Fast sea level rise (SLR) is causing a growing risk of flooding to coastal communities around the Chesapeake Bay (hereafter, CB or “the Bay”), but there are also...