An educational interactive numerical model of the Chesapeake Bay

Scientists use sophisticated numerical models to study ocean circulation and other physical systems, but the complex nature of such simulation software generally make them inaccessible to non-expert users. In principle, however, numerical models represent an ideal teaching tool, allowing users to model the response of a complex system to changing conditions. We have designed an interactive simulation program that allows a casual user to control the forcing conditions applied to a numerical ocean circulation model using a graphical user interface, and to observe the results in real-time. This program is implemented using the Regional Ocean Modeling System (ROMS) applied to the Chesapeake Bay. Portions of ROMS were modified to facilitate user interaction, and the user interface and visualization capabilities represent new software development. The result is an interactive simulation of the Chesapeake Bay environment that allows a user to control wind speed and direction along with the rate of flow from the rivers that feed the bay. The simulation provides a variety of visualizations of the response of the system, including water height, velocity, and salinity across horizontal and vertical planes.     

Regional and seasonal variability of chlorophyll-a in Chesapeake Bay as observed by SeaWiFS and MODIS-Aqua

Concentrations of the phytoplankton pigment chlorophyll-a (C_a) provide indicators of nutrient over-enrichment that has negatively affected Chesapeake Bay, U.S.A. C_a time-series from the National Aeronautics and Space Administration (NASA) Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer aboard the Aqua spacecraft (MODIS-Aqua) provide observations on temporal and spatial scales that far exceed current field and aircraft sampling strategies. These sensors provide consistent, frequent, and high density data to potentially complement ongoing Bay monitoring activities. We used the in situ Water Quality Monitoring Data set of the Chesapeake Bay Program to evaluate decade-long time-series of SeaWiFS and MODIS-Aqua C_a retrievals in the Bay. The accuracy of the retrievals generally degraded with increasing latitude as the optical complexity increases northward. C_a derived using empirical (“band ratio”) algorithms overestimated in situ measurements by 10-50 and 40-100% for SeaWiFS and MODIS-Aqua, respectively, but with limited variability. C_a derived using spectral-matching algorithms showed less bias for both sensors, but with significant variability and sensitivity to radiometric errors. Regionally-tuned empirical algorithms performed best throughout the Bay, offering a combination of reasonable accuracy and high spatial coverage. The radiometric spectral resolution used as input to the algorithms strongly influenced the quality of C_a retrievals from both sensors. These results establish a baseline quantification of algorithm and sensor performance in a variable and stressed ecosystem against which novel approaches might be compared.     

Spectral optimization for constituent retrieval in Case 2 waters I: Implementation and performance

We describe in detail the implementation of the spectral optimization algorithm (SOA) for Case 2 waters for processing of ocean color data. This algorithm uses aerosol models and a bio-optical reflectance model to provide the top-of atmosphere (TOA) reflectance. The parameters of both models are then determined by fitting the modeled TOA reflectance to that observed from space, using non-linear optimization. The algorithm will be incorporated into the SeaDAS software package as an optional processing switch of the Multi-Sensor Level-1 to Level-2 code. To provide potential users with an understanding of the accuracy and limitations of the algorithm, we generated a synthetic data set and tested the performance of the SOA with both correct and incorrect bio-optical model parameters. Application of the SOA to actual SeaWiFS data in the Lower Chesapeake Bay (for which surface measurements were available) showed that 20% errors in the bio-optical model parameters still enabled retrieval of chlorophyll a and the total absorption coefficient of dissolved plus particulate detrital material at 443 nm with an error of less than 30% and 20%, respectively. In a companion paper we present a validation study of the application of the algorithm in the Chesapeake Bay.     

Spectral optimization for constituent retrieval in Case 2 waters II: Validation study in the Chesapeake Bay

Coastal waters (Case 2) are generally more optically complex than oceanic waters and contain much higher quantities of colored detrital matter (CDM, a combination of dissolved organic matter and detrital particulates) as well as suspended sediment. Exclusion of CDM in the retrieval can lead to an overestimation of chlorophyll a concentration (C). We present a validation of a Case 2 version of the coupled spectral optimization algorithm (SOA) for simultaneous atmospheric correction and water parameter retrieval using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) satellite ocean color data. Modeling of water constituents uses the Garver, Siegel and Maritorena (GSM) semi-analytic bio-optical model locally tuned for Chesapeake Bay. This includes a parameterization for CDM through its absorption spectrum.SOA-retrieved C and CDM are compared with in situ measurements in Chesapeake Bay. Results are also compared with output from two alternate models 1) the standard algorithm (Std) and 2) the standard atmospheric correction combined with the locally tuned GSM model (StdGSM). The comparisons indicate that the SOA is a viable alternative to both given models in Chesapeake Bay. In contrast, StdGSM appears to require improvement before it can be considered for operational use in these waters. Perhaps the most important result is the high-quality of CDM retrievals with the SOA. They suggest that there is value added using the SOA method in Chesapeake waters, as the Std method does not retrieve CDM. In a companion paper we describe in detail the model implementation, and its accuracy and limitations when applied to the Chesapeake Bay.     

Linking the growth of filamentous algae to the 3D-ecohydrodynamic model of the Gulf of Finland

One of the most visible symptoms of eutrophication in the Gulf of Finland is blooms of unattached filamentous algae. This decomposing algal biomass causes serious nuisance problems for recreational uses of the coastal zone, particularly when cast ashore. The nutrient availability for these macroscopic filamentous algae is regulated by a superior competitor, the phytoplankton. Nutrients are left for the weaker competitor only when the conditions are not suitable for the growth of phytoplankton. This happens during vertical mixing of the water column. The lack of stratification prevents the formation of phytoplankton blooms, but does not limit the growth of filamentous algae when still growing attached to the bottom. A simple growth model was developed to describe the growth and biomass of filamentous algae. In the absence of suitable nutrient measurements, the model was linked to a 3D-ecohydrodynamic model which generated the nutrient input data. The model was calibrated with three-year monitoring data of filamentous algal biomass at one location. Validation was carried out with one-year monitoring data from an adjacent location. The model was able to describe the number of biomass peaks and their timing with good accuracy. After further development, the filamentous algal model will be used as one of the management tools for the evaluation of the sustainable nutrient load to the Gulf of Finland.     

Virtual Chesapeake Bay: interacting with a coupledphysical/biological model

The Chesapeake Bay Virtual Environment (CBVE) is a multidisciplinary, collaborative project that fuses 3D visualizations of numerically generated output, observations and other data products into a large-scale, interactive virtual world that supports investigation of coupled physical/biological and environmental processes. Although still under development, CBVE provides an application framework for integrating circulation and biological models with the computer visualization paradigm of the virtual world. In this article, we first briefly describe the physical environment and the observed effects of winds, tides and river runoff on the Chesapeake Bay system. Then we describe the CBVE components and conclude with our efforts directed at understanding how environmental variability may affect the recruitment and retention of the larval phase of certain local marine species     

An evaluation of algorithms for the remote sensing of cyanobacterial biomass

Most remote sensing algorithms for phytoplankton in inland waters aim at the retrieval of the pigment chlorophyll a (Chl a), as this pigment is a useful proxy for phytoplankton biomass. More recently, algorithms have been developed to quantify the pigment phycocyanin (PC), which is characteristic of cyanobacteria, a phytoplankton group of relative importance to inland water management due to their negative impact on water quality in response to eutrophication.We evaluated the accuracy of three published algorithms for the remote sensing of PC in inland waters, using an extensive database of field radiometric and pigment data obtained in the Netherlands and Spain in the period 2001–2005. The three algorithms (a baseline, single band ratio, and a nested band ratio approach) all target the PC absorption effect observed in reflectance spectra in the 620 nm region. We evaluated the sensitivity of the algorithms to errors in reflectance measurements and investigated their performance in cyanobacteria-dominated water bodies as well as in the presence of other phytoplankton pigments.All algorithms performed best in moderate to high PC concentrations (50–200 mg m^? 3) and showed the most linear response to increasing PC in cyanobacteria-dominated waters. The highest errors showed at PC < 50 mg m^? 3. In eutrophic waters, the presence of other pigments explained a tendency to overestimate the PC concentration. In oligotrophic waters, negative PC predictions were observed. At very high concentrations (PC > 200 mg m^? 3), PC underestimations by the baseline and single band ratio algorithms were attributed to a non-linear relationship between PC and absorption in the 620 nm region. The nested band ratio gave the overall best fit between predicted and measured PC. For the Spanish dataset, a stable ratio of PC over cyanobacterial Chl a was observed, suggesting that PC is indeed a good proxy for cyanobacterial biomass. The single reflectance ratio was the only algorithm insensitive to changes in the amplitude of reflectance spectra, which were observed as a result of different measurement methodologies.     

Remote sensing of tidal chlorophyll-a variations in estuaries

Simultaneous acquisition of surface chlorophyll-a concentrations for 39 samples from boats and Daedalus 1260 Multispectral Scanner data from a U-2 aircraft was conducted in the northern reaches of San Francisco Bay on 28 August 1980. These data were used to develop regression models for predicting surface chlorophyll-a concentrations over the study area for ebb-tide (8.40 a.m. P.D.T. (Pacific Daylight Time)) and flood-tide (3.10 p.m. P.D.T.) conditions. After selection of a single ‘best fitting’ model for both morning and afternoon data sets, the chlorophyll-a concentration was predicted for ebb and flood tide for the entire study area at approximately 40m × 40m resolution. The predicted spatial display of chlorophyll-a revealed a localized area of high phytoplankton biomass that has been inferred from field surveys and appears to be a common summer phenomenon.

Knowledge of the distribution of phytoplankton and the location of this zone of high biomass is valuable in establishing management policies for this ecologically important estuary. Furthermore, the techniques used here may provide an alternative cost-effective method for assessing water-quality conditions and they may prove useful for studying spatial variations (patchiness) and seasonal variations in phytoplankton biomass in other estuaries and coastal waters.     

Model integration and the role of data

Model integration is becoming increasingly important as our impacts on the environment become more severe and the systems we analyze become more complex. There are numerous attempts to make different models work in concert. However model integration usually treats models as software components only, ignoring the evolving nature of models and their constant modification and re-calibration to better represent reality. As a result, the changes that used to impact only contained models of subsystems, now propagate throughout the integrated system, across multiple model components. This makes it harder to keep the overall complexity under control and, in a way, defeats the purpose of modularity, where efficiency is supposed to be gained from independent development of modules. We argue that data that are available for module calibration can serve as an intermediate linkage tool, sitting between modules and providing a module-independent baseline, which is then adjusted when scenarios are to be run. In this case, it is not the model output that is directed into the next model. Rather, model output is presented as a variation around the baseline trajectory, and it is this variation that is then fed into the next module down the chain. The Chesapeake Bay Program suite of models is used to illustrate these problems and the possible remedy.     

基于CBERS-2卫星数据的艾比湖浮游植物生物量的反演研究

以艾比湖湖区为研究对象,利用CBERS\|2卫星影像的光谱信息分析了遥感影像提取的各因子与浮游植物实测生物量之间的相关关系,建立了相关性显著的遥感因子与浮游植物生物量的线性和非线性回归模型。通过对比分析和残差分析得到最优模型,对艾比湖进行了浮游植物生物量的遥感反演,分析湖区浮游植物生物量的分布特征并估算湖体浮游植物生物总量。艾比湖浮游植物生物量的最优估测模型是二元线性回归模型:Y=3.819-0.027(G-B)-0.04(G-R),其拟合度为0.832,平均残差系数为6.9%,艾比湖湖体浮游植物的总生物量为9.95×10⁵ kg。利用遥感方法研究艾比湖浮游植物的生物量对于艾比湖水域生物估产及其生物量消长规律,以及艾比湖生态系统具有重要意义。研究分析艾比湖生物量的空间分布特征,为艾比湖水体大范围\,快速\,长期的动态监测和获取浮游生物信息和水质参数提供了有力依据。     

MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes

Chlorophyll-a (Chla) concentrations and ‘water-leaving’ reflectance were assessed along transects in Keweenaw Bay (Lake Superior) and in Green Bay (Lake Michigan) (two of the Laurentian Great Lakes, USA), featuring oligotrophic (0.4–0.8 mg Chla m^? 3) and eutrophic to hyper-eutrophic waters (11–131 mg Chla m^? 3), respectively. A red-to-NIR band Chla retrieval algorithm proved to be applicable to Green Bay, but gave mostly negative values for Keweenaw Bay. An alternative algorithm could be based on Chla fluorescence, which in Keweenaw Bay was indicated by enhanced reflectance near 680 nm. Bands 7, 8 and 9 of the Medium Resolution Imaging Spectrometer (MERIS) have been specifically designed to detect phytoplankton fluorescence in coastal waters. A quite strong linear relationship was found between Chla concentration and fluorescence line height (FLH) computed with these MERIS bands. The same relationship held for observations on oligotrophic waters elsewhere, but not for Green Bay, where the FLH diminished to become negative as Chla increased. The remote sensing application of the algorithms could be tested because a MERIS scene was acquired coinciding with the day of the field observations in Keweenaw Bay and one day after those in Green Bay. For Green Bay the pixel values from the red-to-NIR band algorithm compared well to the steep Chla gradient in situ. This result is very positive from the perspective of satellite use in monitoring eutrophic inland and coastal waters in many parts of the world. Implementation of the FLH relationship in the scene of Keweenaw Bay produced highly variable pixel values. The FLH in oligotrophic inland waters like Lake Superior appears to be very close to or below the MERIS detection limit. An empirical algorithm incorporating three MERIS bands in the blue-to-green spectral region might be used as an alternative, but its applicability to other regions and seasons remains to be verified. Moreover, none of the algorithms will be suitable for mesotrophic water bodies. The results indicate that Chla mapping in oligotrophic and mesotrophic areas of the Great Lakes remains problematic for the current generation of satellite sensors.     

Testing the CORMIX model using thermal plume data from four Maryland power plants

Historical thermal plume studies from four Maryland power plants (Calvert Cliffs, Chalk Point, Dickerson, and Wagner) were used to test the realism of the CORnell MIXing Zone Expert System (cormix). Test data were from a wide range of challenging discharge environments, including a large freshwater river (Potomac), a narrow tidal estuary (Patuxent), a wide tidal estuary (Chesapeake Bay), and a wind-driven tidal estuary (Baltimore Harbor). Historical case studies were simulated, and results were compared qualitatively and quantitatively with historical measurements. Qualitative results show that the model performed optimally for simple discharges into large basins such as Chesapeake Bay. For complex discharges and complex ambient environments, the model often mixed plumes too rapidly, resulting in smaller modeled plumes that were cooler than the measured plumes. The mixing model also could not account for the re-entrainment of effluent from previous tidal cycles. Sensitivity results show that sensitivity is often dependent on model run time and discontinuities in the cormix flow classification scheme. Users of the cormix model need to be aware of these limitations in applying the model to complex situations. cormix results should be used with caution in evaluating the effects of a discharge and only in conjunction with information from the field.     

New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers

The aim of this paper is to propose a new hybrid data mining model based on combination of various feature selection and ensemble learning classification algorithms, in order to support decision making process. The model is built through several stages. In the first stage, initial dataset is preprocessed and apart of applying different preprocessing techniques, we paid a great attention to the feature selection. Five different feature selection algorithms were applied and their results, based on ROC and accuracy measures of logistic regression algorithm, were combined based on different voting types. We also proposed a new voting method, called if_any, that outperformed all other voting methods, as well as a single feature selection algorithm's results. In the next stage, a four different classification algorithms, including generalized linear model, support vector machine, naive Bayes and decision tree, were performed based on dataset obtained in the feature selection process. These classifiers were combined in eight different ensemble models using soft voting method. Using the real dataset, the experimental results show that hybrid model that is based on features selected by if_any voting method and ensemble GLM + DT model performs the highest performance and outperforms all other ensemble and single classifier models.     

Evaluation of shortwave infrared atmospheric correction for ocean color remote sensing of Chesapeake Bay

The NASA Moderate Resolution Imaging Spectroradiometer onboard the Aqua platform (MODIS-Aqua) provides a viable data stream for operational water quality monitoring of Chesapeake Bay. Marine geophysical products from MODIS-Aqua depend on the efficacy of the atmospheric correction process, which can be problematic in coastal environments. The operational atmospheric correction algorithm for MODIS-Aqua requires an assumption of negligible near-infrared water-leaving radiance, nL_w(NIR). This assumption progressively degrades with increasing turbidity and, as such, methods exist to account for non-negligible nL_w(NIR) within the atmospheric correction process or to use alternate radiometric bands where the assumption is satisfied, such as those positioned within shortwave infrared (SWIR) region of the spectrum. We evaluated a decade-long time-series of nL_w(λ) from MODIS-Aqua in Chesapeake Bay derived using NIR and SWIR bands for atmospheric correction. Low signal-to-noise ratios (SNR) for the SWIR bands of MODIS-Aqua added noise errors to the derived radiances, which produced broad, flat frequency distributions of nL_w(λ) relative to those produced using the NIR bands. The SWIR approach produced an increased number of negative nL_w(λ) and decreased sample size relative to the NIR approach. Revised vicarious calibration and regional tuning of the scheme to switch between the NIR and SWIR approaches may improve retrievals in Chesapeake Bay, however, poor SNR values for the MODIS-Aqua SWIR bands remain the primary deficiency of the SWIR-based atmospheric correction approach.     

Design and implementation of a general software library for using NSGA-II with SWAT for multi-objective model calibration

Calibrating watershed-scale hydrologic models remains a critical but challenging step in the modeling process. The Soil and Water Assessment Tool (SWAT) is one example of a widely used watershed-scale hydrologic model that requires calibration. The calibration algorithms currently available to SWAT modelers through freely available and open source software, however, are limited and do not include many multi-objective genetic algorithms (MOGAs). The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been shown to be an effective and efficient MOGA calibration algorithm for a wide variety of applications including for SWAT model calibration. Therefore, the objective of this study was to create an open source software library for multi-objective calibration of SWAT models using NSGA-II. The design and implementation of the library are presented, followed by a demonstration of the library through a test case for the Upper Neuse Watershed in North Carolina, USA using six objective functions in the model calibration.     

Playing with state-based models for designing better algorithms

State-based models provide a very convenient framework for analyzing, verifying, validating and designing sequential as well as concurrent or distributed algorithms. Each state-based model is considered as an abstraction, which is more or less close to the target algorithmic entity. The problem is then to organize the relationship between an initial abstract state-based model expressing requirements and a final concrete state-based model expressing a structured algorithmic state-based model. A simulation (or refinement) relation between two state-based models allows to structure these models from an abstract view to a concrete view. Moreover, state-based models can be extended by assertion languages for expressing correctness properties as pre/post specification, safety properties or even temporal properties. In this work, we review state-based models and play scores for verifying and designing concurrent or distributed algorithms. We choose the Event-B modeling language for expressing state-based models and we show how we can play Event-B scores using Rodin and methodological elements to guarantee that the resulting algorithm is correct with respect to initial requirements. First, we show how annotation-based verification can be handled in the Event-B modeling language and we propose an extension to handle the verification of concurrent programs. In a second step, we show how important is the concept of refinement and how it can be used to found a methodology for designing concurrent programs using the coordination paradigm.     

Satellite data assimilation and estimation of a 3D coastal sediment transport model using error-subspace emulators

This paper describes sequential assimilation of data into a three-dimensional coastal ocean model using fast and cheap statistical surrogates of the model (emulators). The model simulates resuspension and deposition of fine sediments in a macro-tidal environment of the Fitzroy Estuary and Keppel Bay, North-East Australian coast. The assimilation algorithm was applied first to synthetic observations produced by a twin model run, and then with real data obtained from satellite observation. The latter are derived from remote sensing algorithms customised to the study region. The main objective of simulations was to test the data assimilation scheme using synthetic observations and identify potential issues and challenges when assimilating real data sets. The assimilation algorithm proved capable of substantially reducing a prior uncertainty of the model for both the scenario with the synthetic observations and the scenario with the satellite data. Significant remaining error in western Keppel Bay after assimilating satellite data is diagnostic of an underlying error in the system conceptualisation – in other words, it indicates that the primary source of error is not in the parameter values specified, but in the model structure, in the interpretation of satellite data or in the other input data. The results of our study show the utility of the developed technique for the data assimilation into the three-dimensional sediment transport model of the Fitzroy estuary and Keppel Bay. More research is required to understand the capacity of this technique to generalise to other models and regions.     

Software reliability prediction model based on support vector regression with improved estimation of distribution algorithms

Software reliability prediction plays a very important role in the analysis of software quality and balance of software cost. The data during software lifecycle is used to analyze and predict software reliability. However, predicting the variability of software reliability with time is very difficult. Recently, support vector regression (SVR) has been widely applied to solve nonlinear predicting problems in many fields and has obtained good performance in many situations; however it is still difficult to optimize SVR's parameters. Previously, some optimization algorithms have been used to find better parameters of SVR, but these existing algorithms usually are not fully satisfactory. In this paper, we first improve estimation of distribution algorithms (EDA) in order to maintain the diversity of the population, and then a hybrid improved estimation of distribution algorithms (IEDA) and SVR model, called IEDA-SVR model, is proposed. IEDA is used to optimize parameters of SVR, and IEDA-SVR model is used to predict software reliability. We compare IEDA-SVR model with other software reliability models using real software failure datasets. The experimental results show that the IEDA-SVR model has better prediction performance than the other models.     

Development and application of a marine ecosystem dynamic model

A nutrient-phytoplankton-zooplankton-detritus (NPZD) type of marine ecosystem model was developed in this study, and was further coupled to a three-dimensional primitive-equation ocean circulation model with a river discharge model and a solar radiation model to reproduce the dynamics of the low nutrition level in the Bohai Sea (BS). The simulation results were validated by observations and it was shown that the seasonal variation in the phytoplankton biomass could be characterized by the double-peak structure, corresponding to the spring and summer blooms, respectively. It was also found that both nitrogen and phosphate declined to the lowest level after the onset of the summer bloom, since the large amounts of nutrients were exhausted by phytoplankton for photosynthesis, and the concentrations of nutrients could resume in winter after a series of the biogeochemical-physical processes. By calculating the nitrogen/phosphorus (N/P) ratio, it is easy to see that the phytoplankton dynamics is nitrogen-limited as a whole in BS, though the phosphorus limitation may occur in the Yellow River (YR) Estuary where the input of riverine nitrogen is much more than that of phosphate.     

A bridging model for multi-core computing

Writing software for one parallel system is a feasible though arduous task. Reusing the substantial intellectual effort so expended for programming a second system has proved much more challenging. In sequential computing algorithms textbooks and portable software are resources that enable software systems to be written that are efficiently portable across changing hardware platforms. These resources are currently lacking in the area of multi-core architectures, where a programmer seeking high performance has no comparable opportunity to build on the intellectual efforts of others. In order to address this problem we propose a bridging model aimed at capturing the most basic resource parameters of multi-core architectures. We suggest that the considerable intellectual effort needed for designing efficient algorithms for such architectures may be most fruitfully expended in designing portable algorithms, once and for all, for such a bridging model. Portable algorithms would contain efficient designs for all reasonable combinations of the basic resource parameters and input sizes, and would form the basis for implementation or compilation for particular machines. Our Multi-BSP model is a multi-level model that has explicit parameters for processor numbers, memory/cache sizes, communication costs, and synchronization costs. The lowest level corresponds to shared memory or the PRAM, acknowledging the relevance of that model for whatever limitations on memory and processor numbers it may be efficacious to emulate it. We propose parameter-aware portable algorithms that run efficiently on all relevant architectures with any number of levels and any combination of parameters. For these algorithms we define a parameter-free notion of optimality. We show that for several fundamental problems, including standard matrix multiplication, the Fast Fourier Transform, and comparison sorting, there exist optimal portable algorithms in that sense, for all combinations of machine parameters. Thus some algorithmic generality and elegance can be found in this many parameter setting.