Integrating an agent-based model of malaria mosquitoes with a geographic information system

Agent-based models (ABMs) are used to model infectious diseases and disease-transmitting vectors. Malaria is a deadly infectious disease in humans, transmitted by Anopheles mosquito vectors. Although geographic information system (GIS) has been used before with ABMs, no ABM-based malaria study showed the usage of custom-built spatial outputs integrated within a modeling framework. In this paper, we show how to effectively integrate a malaria ABM with GIS-based, spatially derived parameters. For a specific study area, we process GIS data layers, create hypothetical scenarios, produce maps, and analyze biological insights. Results indicate that availability of resources and relative distances between them are crucial determinants for malaria transmission. The maps also reveal potential hotspots for the measured variables. We argue that such integrated approaches, which combine knowledge from entomological, epidemiological, simulation-based, and geo-spatial domains, are required for the identification of relationships between spatial variables, and may have important implications for malaria vector control. © 2013 DIME UNIVERSITAT DI GENOVA

Investigating summer thermal stratification in Lake Ontario

Summer thermal stratification in Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). Summer temperature differences establish strong vertical density gradients (thermocline) between the epilimnion and hypolimnion. Capturing the stratification and thermocline formation has been a challenge in modeling Great Lakes. Deviating from EFDC's original Mellor-Yamada (1982) vertical mixing scheme, we have implemented an unidimensional vertical model that uses different eddy diffusivity formulations above and below the thermocline (Vincon-Leite, 1991; Vincon-Leite et al., 2014). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers; and lake bathymetry is interpolated on a 2-km grid. The model has 20 vertical layers following sigma vertical coordinates. Sensitivity of the model to vertical layers' spacing is thoroughly investigated. The model has been calibrated for appropriate solar radiation coefficients and horizontal mixing coefficients. Overall the new implemented diffusivity algorithm shows some successes in capturing the thermal stratification with RMSE values between 2-3°C. Calibration of vertical mixing coefficients is under investigation to capture the improved thermal stratification

Numerical modeling of thermal bar and stratification pattern in Lake Ontario using the EFDC model

Thermal bar is an important phenomenon in large, temperate lakes like Lake Ontario. Spring thermal bar formation reduces horizontal mixing, which in turn, inhibits the exchange of nutrients. Evolution of the spring thermal bar through Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers, and lake bathymetry. The simulation is performed from April to July, 2011; on a 2-km grid. The numerical model has been calibrated by specifying: appropriate initial temperature and solar radiation attenuation coefficients. The existing evaporation algorithm in EFDC is updated to modified mass transfer approach to ensure correct simulation of evaporation rate and latent heatflux. Reasonable values for mixing coefficients are specified based on sensitivity analyses. The model simulates overall surface temperature profiles well (RMSEs between 1-2°C). The vertical temperature profiles during the lake mixed phase are captured well (RMSEs < 0.5°C), indicating that the model sufficiently replicates the thermal bar evolution process. An update of vertical mixing coefficients is under investigation to improve the summer thermal stratification pattern. Keywords: Hydrodynamics, Thermal BAR, Lake Ontario, GIS

Empirical comparison of correlation measures and pruning levels in complex networks representing the global climate system

Climate change is an issue of growing economic, social, and political concern. Continued rise in the average temperatures of the Earth could lead to drastic climate change or an increased frequency of extreme events, which would negatively affect agriculture, population, and global health. One way of studying the dynamics of the Earth's changing climate is by attempting to identify regions that exhibit similar climatic behavior in terms of long-term variability. Climate networks have emerged as a strong analytics framework for both descriptive analysis and predictive modeling of the emergent phenomena. Previously, the networks were constructed using only one measure of similarity, namely the (linear) Pearson cross correlation, and were then clustered using a community detection algorithm. However, nonlinear dependencies are known to exist in climate, which begs the question whether more complex correlation measures are able to capture any such relationships. In this paper, we present a systematic study of different univariate measures of similarity and compare how each affects both the network structure as well as the predictive power of the clusters. © 2011 IEEE

Contaminant biotransport by Pacific salmon to Lake Michigan tributaries

The Great Lakes are ideal systems for evaluating the synergistic components of environmental change, such as exotic species introductions and legacy pollutants. Introduced Pacific Salmon (Oncorhynchus spp.) represent an intersection of these drivers because they are non-native species of economic importance that bioaccumulate contaminants during the open water phase of their life cycle. Furthermore, Pacific salmon can deliver a significant pulse of contaminated tissue to tributaries during spawning and subsequent death. Thus, salmon represent a key pathway by which contaminants accumulated in Lake Michigan are transported inland to tributaries that otherwise lack point source pollution. Our research has revealed that salmon exhibit basin-specific persistent organic pollutant (POP) and mercury (Hg) concentrations reflecting pollutant inputs from both current and historic sources. Overall, Lake Michigan salmon were more contaminated with POPs and Hg than conspecifics from Lakes Huron or Superior. Consequently, Lake Michigan salmon pose a higher risk and magnitude of contaminant biotransport and transfer. Resident stream fish (e.g., brook trout) sampled from salmon spawning reaches had higher pollutant concentrations than fish sampled from upstream reaches lacking salmon, but the extent of fish contamination varied among lake basins and streams. In general, Lake Michigan tributaries were the most impacted, suggesting a direct relationship between the extent of salmon-derived contaminant inputs and resident fish contaminant levels. Within and among lake basins, contaminant biotransport by salmon is context dependent and likely reflects a suite of ecological characteristics such as species identity and trophic position, dynamics of the salmon run, watershed land-use, and instream geomorphology such as sediment size. We suggest that future management of salmon-mediated contaminant biotransport to stream communities in the Great Lakes basin should consider biological, chemical, and physical factors that constitute the environmental context

Unsupervised classification of saturated areas using a time series of remotely sensed images

The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or siting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can be difficult and time consuming. A technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs. This study describes a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution Landsat 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to characterize the areas susceptible to saturation. We found that within a single land cover, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of the vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas (accuracies from 49 to 79). This methodology shows that remote sensing can be used to capture temporal variations in vegetation phenology as well as spatial/temporal variation in surface water content, and appears promising for delineating saturated areas in the landscape

Contaminant biotransport by Pacific Salmon in Lake Michigan: analysis of salmon and stream-resident fish in Great Lakes tributaries

Pacific salmon (Oncorhynchus spp.) can deliver a significant pulse of biomass, including its bioaccumulated contaminants, to tributaries during spawning runs. Thus, salmon transport contaminants accumulated in the Great Lakes (e.g., persistent organic pollutants [POPs], total mercury [THg]) to tributaries that otherwise lack point source pollution. We used a combination of observational surveys, experimental manipulations, and modeling, to (1) assess the extent of salmon-mediated biotransport across the upper Great Lakes; (2) determine pathways by which stream fish become contaminated by salmon; and (3) forecast areas at significant risk from salmon biotransport. Resident stream fish (e.g., brook trout Salvelinus fontinalis) in salmon spawning reaches had higher POP concentrations than fish in upstream reaches lacking salmon, but the extent of contamination varied among lake basins and streams. In contrast, THg concentrations in the same fish did not differ between reaches with and without salmon spawners but exhibited considerable among-site variability. In general, resident fish in Lake Michigan tributaries were the most contaminated by POPs, suggesting a direct relationship between salmon-derived contaminant inputs and resident fish contaminant levels. Experimental exposure to salmon carcasses and eggs for 50 days increased brook trout POP concentrations by 50 times. Eggs are elevated in POPs but depleted in THg compared to whole salmon, suggesting that resident fish contaminant levels reflect direct consumption of eggs rather than indirect food web pathways. Our model suggests that salmon-mediated bioaccumulation is primarily influenced by the size and duration of salmon runs, and secondarily by factors including individual consumption rates, temperature regime, and background pollutant levels. Overall, our research provides increased understanding on the physical, chemical, and biological controls of salmon contaminant biotransport in the Great Lakes region. This research will help inform management decisions in this region with respect to legacy pollution, dam removal, stream connectivity, fish stocking, and non-native species in stream ecosystems

Calibration of Landsat thermal data and application to water resource studies

The newest in the Landsat series of satellites was launched April 15, 1999. The imagery collected by Landsat is used for a myriad of applications, from coral reef studies to land management. In order to take advantage of Landsat 7 data, the Enhanced Thematic Mapper+ (ETM+) instrument must be calibrated. This study focuses on the immediate postlaunch calibration verification of the Landsat 7 thermal band (Band 6), specifically so that it can be useful in water resource studies. Two year's worth of thermal calibration results using a combination of underfiight data and ground truth show the ETM+ to be extremely stable, though the prelaunch calibration produces an offset of 0.261 W/m2 sr μm. This paper focuses on the details of the calibration process, including problems faced with ground truth instrumentation. While the technical emphasis in this paper is the calibration of Landsat thermal data, it is presented in the context of the water resource studies for which calibrated thermal data are required. At certain times in the year, water quality in large lakes, particularly the spatial structure of water quality, is driven by temperature of lake waters. During the spring warming, a phenomena called the thermal bar drives the current and sedimentation of large water bodies. A long-term goal of this study is to use thermally driven hydrodynamic models of lake processes to better understand and monitor water quality in large lakes. This paper presents the hydrodynamic model and the relationship between temperature and water quality in the Great Lakes as one example of why high-resolution, well-calibrated data are critical to earth observing. © 2001 Elsevier Science Inc. All rights reserved

Simulating the thermal behavior in Lake Ontario using EFDC

The thermal behavior of Lake Ontario (spring warming, thermal bar formation, and summer stratification) is simulated using the three-dimensional thermo-hydrodynamic model, Environmental Fluid Dynamics Code (EFDC). The model is forced with hourly meteorological data from weather stations around the lake and flow data from Niagara and St. Lawrence Rivers. The simulation is performed from April to July 2011 on a curvilinear grid, with cells approximately 2 2 km2 and bathymetry interpolated onto the grid. We implement model improvements by (a) updating the evaporation algorithm to ensure accurate simulation of evaporation rates and latent heat fluxes and (b) specifying appropriate solar radiation attenuation coefficients to ensure sufficient absorption of incoming solar radiation by the water column. The study also calibrated horizontal and vertical mixing coefficients. Results show that the model accurately simulated the overall surface temperature profiles with RMSEs between 1 and 2 °C and the vertical temperature profiles during the lake mixed phase with RMSEs &lt;0.5 °C. Overall, the modified EFDC model successfully replicated thermal bar evolution. © 2016 International Association for Great Lakes Research