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This article focuses on the “second wave” of the COVID-19 pandemic in the Arctic and examines spatiotemporal patterns between July 2020 and January 2021. We analyse available COVID-19 data at the regional (subnational) level to elucidate patterns and typology of Arctic regions with respect to the COVID-19 pandemic. This article builds upon our previous research that examined the early phase of the COVID-19 pandemic between February and July 2020. The pandemic’s “second wave” observed in the Arctic between September 2020 and January 2021 was severe in terms of COVID-19 infections and fatalities, having particularly strong impacts in Alaska, Northern Russia and Northern Sweden. Based on the spatiotemporal patterns of the “second wave” dynamics, we identified 5 types of the pandemic across regions: Shockwaves (Iceland, Faroe Islands, Northern Norway, and Northern Finland), Protracted Waves (Northern Sweden), Tidal Waves (Northern Russia), Tsunami Waves (Alaska), and Isolated Splashes (Northern Canada and Greenland). Although data limitations and gaps persist, monitoring of COVID-19 is critical for developing a proper understanding of the pandemic in order to develop informed and effective responses to the current crisis and possible future pandemics in the Arctic. Data used in this paper are available at https://arctic.uni.edu/arctic-covid-19.

Vector-borne diseases inflict a heavy health burden. Numerous vector species have expanded their ranges while some vector-borne diseases have emerged in new geographic areas or reemerged in former endemic areas, potentially due to global climate change. The continued health burden and the expansion in vector and vector-borne disease ranges have led to an increased need for understanding and predicting in space and time the potential abundance and distribution of vectors and the transmission of vector-borne diseases. Advances in computing technologies, increased spatial data availability, and greater understanding of ecological drivers have made it commonplace to attempt to understand and estimate, in time and space, vector populations and vector-borne disease incidence. Although there have been great advances there are still limitations due to complex and varying ecologies and epidemiological contexts of vector-borne diseases as well as data limitations. In this paper, we will provide an overview of and examine advances and limitations in the use of geospatial technologies for understanding and prediction of the drivers of vector populations and vector-borne diseases.

The incidence of human West Nile virus (WNV) varies spatially and temporally and is influenced by a wide range of biotic and abiotic factors. There are numerous important vector species, with variable geographic ranges and ecologies, considered crucial to the transmission of WNV in the coterminous United States. To date there has been a lack of a systematic investigation in the United States, at a regional scale, of the wide variety of landscape, land use, and demographic influences on WNV incidence. In this study, we use published vector species distribution maps, as well as prominent landscape features, to define six distinct regions of the coterminous United States. We relate data on demographic, landscape, and land use conditions to the incidence of human WNV by region recorded at county level in the coterminous United States from 2002-2009. The observed relationships varied by region with the Great Plains, Northwest, and Southwest regions showing high WNV incidence associated with rural irrigated landscapes, indicating the importance of Culex tarsalis as the primary vector. In the Southeast, the percent of the population in poverty was positively associated with high WNV incidence, potentially indicating the quality of housing in relation to the vector Culex quinquefasciatus, a mosquito that often feeds indoors. The Northeast region human WNV incidence was positively associated with agricultural landscapes, potentially implying the importance of Culex restuans in a region generally thought of as being dominated by Culex pipiens transmission. There was strong spatial autocorrelation in most of the regions, but with a spatial autologistic term accounted for in binary logistic regression models, there were significant landscape, land use, and demographic covariates for each region.

Airborne light detection and ranging (LiDAR) topographic data provide highly accurate representations of the earth's surface. However, large data volumes pose computing issues when disseminating and processing the data. The main goals of this paper are to evaluate a vertex decimation algorithm used to reduce the size of the LiDAR data and to test parallel computation frameworks, particularly multicore CPU and GPU, in processing the data. In this paper we use a vertex decimation technique to reduce the number of vertices available in a triangulated irregular network (TIN) representation of LiDAR data. In order to validate and verify the algorithm, the authors have used last returns only (LRO) and all returns (AR) of points from four tiles of LiDAR data taken from flat and undulating terrains. The results for flat terrain data showed decimation rates of roughly 95% for last returns only and 55% for all returns without significant loss of accuracy in terrain representation. Accordingly, file sizes were reduced by about 96.5% and 60.5%. The processing speed greatly benefited from parallel programming using the multicore CPU framework. The GPU usage demonstrated an additional impediment caused by noncomputational overhead. Nonetheless, tremendous acceleration was demonstrated by the GPU environment in the computational part alone.

The objectives of this study were to develop and evaluate a GIS-based modeling system called ArcPRZM-3 for spatial modeling of pesticide leaching potential from soil surface towards groundwater. The ArcPRZM-3 was developed by coupling a commonly used FORTRAN-based Pesticide Root Zone Model version 3 (PRZM-3) with user-friendly input and output interfaces through links to GIS using customized programming. A Visual PRZM-3 interface simplifies the entry of model inputs and links to the databases of crops, soils, and pesticides. The ArcPRZM-3 produces user-friendly outputs from the PRZM-3 batch simulations in the form of tables, charts, and maps. The Visual PRZM-3 can be used to run a single simulation for site-specific studies or simultaneous multiple simulations for spatial distributed modeling. The ArcPRZM-3 was applied to simulate maximum dissolved bentazon concentration at 0.75m soil depth for a period of 2 years. These simulation results were used to develop a health risk map for Woodruff County, Arkansas, based on the U.S. Environmental Protection Agency's Lifetime Health Advisory Level (USEPA-LHA) of bentazon in drinking water. The ArcPRZM-3 was evaluated by comparing the bentazon detection data from monitoring wells from the same area with the predicted bentazon health risk map. The results showed that 100% of the wells where bentazon was detected were within the high risk category based on the ArcPRZM-3 predictions. However, uncertainty in the ArcPRZM-3 model and the timing of groundwater well monitoring could both complicate the interpretations of the ArcPRZM-3 simulation results.

Spatial decision support systems (SDSS) are decision support tools which have been used widely in addressing complicated issues involving a spatial component. The use of SDSS has increased greatly over the last few decades especially in fields such as planning, natural resources management, and environmental science. Traditionally, SDSS have been developed with Geographic Information Systems (GIS) technology as a major component and used in application areas in which the use of GIS technology has been common. GIS software is often expensive and requires significant expertise, which can lead to under-utilization of GIS-based SDSS. In this paper, we describe the development of a freely available SDSS extension developed for Microsoft Excel, a very commonly used spreadsheet application. The purpose of this SDSS is to expand potential SDSS use to a wider potential audience for research, management, and teaching purposes.

Ecological niche modeling (ENM) algorithms, Maximum Entropy Species Distribution Modeling (Maxent) and Genetic Algorithm for Rule-set Prediction (GARP), were used to develop models in Iowa for three species of mosquito - two significant, extant West Nile virus (WNV) vectors (Culex pipiens L and Culex tarsalis Coquillett (Diptera: Culicidae)), and the nuisance mosquito, Aedes vexans Meigen (Diptera: Culicidae), a potential WNV bridge vector. Occurrence data for the three mosquito species from a state-wide arbovirus surveillance program were used in combination with climatic and landscape layers. Maxent successfully created more appropriate niche models with greater accuracy than GARP. The three Maxent species' models were combined and the average values were statistically compared to human WNV incidence at the census block group level. The results showed that the Maxent-modeled species' niches averaged together were a useful indicator of WNV human incidence in the state of Iowa. This simple method for creating probability distribution maps proved useful for understanding WNV dynamics and could be applied to the study of other vector-borne diseases.

Nitrous oxide (N2O) is a powerful greenhouse gas. The UK government is committed to reducing all greenhouse gas emissions and is required to make an inventory of the sources and emissions of these gases. Here, we extend work from a pilot study at the catchment scale reported in an earlier paper. This paper reports on the upscaling measurements of emissions to derive annual emission rates for specific combinations of soil type, land management and fertiliser practices to the national scale. Digital soil, climate and land use maps were combined within Geographic Information Systems (GIS) software. Upscaling of field emissions measurements involves adjusting measured annual N2O emissions to fit combinations of crop growth cycles, soil wetness and the amount and timing of fertiliser applications. We have also taken account of the differences in emission rates from grazed pasture land due to differences in land management between land utilised for dairy production and land utilised for beef production. Calculated annual emission rates were then spatially scaled to derive national figures through the use of a GIS modelling framework, termed NitOx. The annual emission of N2O from Scotland was determined as approximately 6 000 000 kg N yr−1 (2.8 Mt carbon dioxide (CO2) equivalents) and compares favourably with other national scale estimates such as the IPCC (1997). The combination of animal grazing, high N inputs, climatic warmth and poorly drained soils means that the south west contributes significantly to the national total N2O emissions. Localised areas of high emission can also be identified, but identification could be improved by applying this modelling approach at a larger scale. It would be beneficial to target these areas with mitigation strategies.

Soil erosion is an important economic and environmental concern throughout the world. In order to assess soil erosion risk and conserve water and soil resources, soil erosion modeling at the watershed scale is urgently needed. This study integrated the Modified Universal Soil Loss Equation (MUSLE) in a Geographic Information System (GIS) framework in the form of a tool called ArcMUSLE, an extension of ArcGIS® software, to assist soil and water conservation agencies in soil erosion risk assessment and prioritization of critical areas for soil erosion control practices. With widely available spatial data, this tool can be applied to determine curve numbers, to estimate runoff, peak flow, and soil loss for a rainfall event within a watershed. An application example for a watershed in Black Hawk County, Iowa, USA, is presented. Copyright © 2008 John Wiley & Sons, Ltd.

Many countries expend considerable resources collecting and reporting data from national groundwater quality monitoring networks to assess diffuse nitrate contamination, but the reliability of these data depends on the effectiveness of the network. Without a representative monitoring network of reliable monitoring points, there is a risk that groundwater management policies could be developed and implemented based on poor evidence. To help increase confidence in nitrate data, a robust, practical and repeatable method was developed to assess the effectiveness of groundwater nitrate monitoring networks, and applied to a network in Scotland. The method combines a rapid site assessment of monitoring points and local pollution pressures in order to judge the reliability of monitoring points, with a wider characterization of the network to identify which land uses and hydrogeological environments are being monitored. The analysis indicates that, with minor exceptions, Scotland's groundwater nitrate monitoring network broadly represents the diversity of conditions expected in Scotland.

The Nitrogen Risk Assessment Model for Scotland (NIRAMS) has been developed as a screening tool for prediction of streamwater N concentrations draining from agricultural land in Scotland. The objective of the model is to be able to predict N concentrations for ungauged catchments, to fill gaps in monitoring data and provide guidance in relation to policy development. The model uses national land use, soils and meteorology data sets and has been developed within an ArcView GIS user interface. The model includes modules to calculate N inputs to the land, residual N remaining at the end of the growing season, weekly time-series of leached N and transport of N at the catchment scale. The N leaching and transport are controlled by hydrological modules, including a national water balance model and a catchment scale transport model. Preliminary testing of NIRAMS has been carried out on eight Scottish catchments, diverse in terms of geographic location as well as land use. The model is capable of predicting the correct mean level of stream N concentrations, as well as the basic characteristics of seasonal variation. As such the model can be of value for providing estimates of N concentrations in ungauged areas.

The Nitrogen Risk Assessment Model for Scotland (NIRAMS) has been developed for prediction of streamwater N concentrations draining from agricultural land in Scotland. The objective of the model is to predict N concentrations for ungauged catchments, to fill gaps in monitoring data and to provide guidance in relation to policy development. The model uses nationally available data sets of land use, soils, topography and meteorology and has been developed within a Geographic Information System (GIS). The model includes modules to calculate N inputs to the land, residual N remaining at the end of the growing season, weekly time-series of leached N and transport of N at the catchment scale. This paper presents the methodology for calculating N balances for different land uses and for predicting the time sequence of N leaching after the end of the growing season. Maps are presented of calculated residual N and N leaching for the whole of Scotland and the spatial variability in N leaching is discussed. The results demonstrate the high variability in N leaching across Scotland. The simulations suggest that, in the areas with greatest residual N, the losses of N are not directly proportional to the amount of residual N, because of their coincidence with lower rainfall. In the companion paper, the hydrological controls on N transport within NIRAMS are described, and results of the full model testing are presented. Keywords: nitrogen, diffuse pollution, agriculture, leaching, land use, model, national, catchment

The amount and concentration of N in catchment runoff is strongly controlled by a number of hydrological influences, such as leaching rates and the rate of transport of N from the land to surface water bodies. This paper describes how the principal hydrological controls at a catchment scale have been represented within the Nitrogen Risk Assessment Model for Scotland (NIRAMS); it demonstrates their influence through application of the model to eight Scottish catchments, contrasting in terms of their land use, climate and topography. Calculation of N leaching rates, described in the preceding paper (Dunn et al., 2004), is based on soil water content determined by application of a weekly water balance model. This model uses national scale datasets and has been developed and applied to the whole of Scotland using five years of historical meteorological data. A catchment scale transport model, constructed from a 50m digital elevation model, routes flows of N through the sub-surface and groundwater to the stream system. The results of the simulations carried out for eight different catchments demonstrate that the NIRAMS model is capable of predicting time-series of weekly stream flows and N concentrations, to an acceptable degree of accuracy. The model provides an appropriate framework for risk assessment applications requiring predictions in ungauged catchments and at a national scale. Analysis of the model behaviour shows that streamwater N concentrations are controlled both by the rate of supply of N from leaching as well as the rate of transport of N from the land to the water. Keywords: nitrogen, diffuse pollution, hydrology, model, transport, catchment

Spatial decision support systems (SDSS) are decision support tools which have been used widely in addressing complicated issues involving a spatial component. The use of SDSS has increased greatly over the last few decades especially in fields such as planning, natural resources management, and environmental science. Traditionally, SDSS have been developed with Geographic Information Systems (GIS) technology as a major component and used in application areas in which the use of GIS technology has been common. GIS software is often expensive and requires significant expertise, which can lead to under-utilization of GIS-based SDSS. In this paper, we describe the development of a freely available SDSS extension developed for Microsoft Excel, a very commonly used spreadsheet application. The purpose of this SDSS is to expand potential SDSS use to a wider potential audience for research, management, and teaching purposes.

Background West Nile virus (WNV) emerged as a threat to public and veterinary health in the Midwest United States in 2001 and continues to cause significant morbidity and mortality annually. To investigate biotic and abiotic factors associated with disease incidence, cases of reported human disease caused by West Nile virus (WNV) in the state of Iowa were aggregated by census block groups in Iowa for the years 2002–2006. Spatially explicit data on landscape, demographic, and climatic conditions were collated and analyzed by census block groups. Statistical tests of differences between means and distributions of landscape, demographic, and climatic variables for census block groups with and without WNV disease incidence were carried out. Entomological data from Iowa were considered at the state level to add context to the potential ecological events taking place. Results Numerous statistically significant differences were shown in the means and distributions of various landscape and demographic variables for census block groups with and without WNV disease incidence. Census block groups with WNV disease incidence had significantly lower population densities than those without. Landscape variables showing differences included stream density, road density, land cover compositions, presence of irrigation, and presence of animal feeding operations. Statistically significant differences in the annual means of precipitations, dew point, and minimum temperature for both the year of WNV disease incidence and the prior year, were detected in at least one year of the analysis for each parameter. However, the differences were not consistent between years. Conclusion The analysis of human WNV disease incidence by census block groups in Iowa demonstrated unique landscape, demographic, and climatic associations. Our results indicate that multiple ecological WNV transmission dynamics are most likely taking place in Iowa. In 2003 and 2006, drier conditions were associated with WNV disease incidence. In a significant novel finding, rural agricultural settings were shown to be strongly associated with human WNV disease incidence in Iowa.

Landscape and climatic factors regulate distributions of mosquitoes (Diptera: Culicidae) over time and space. The anthropogenic control of mosquito populations is often carried out at a local administrative scale, and it is applied based on the relevant agency's experiential knowledge rather than systematic analysis of spatial and temporal data. To address this shortcoming, a spatial and temporal analysis of landscape and climatic parameters in relation to mosquito populations in Black Hawk County, IA, USA, has been carried out. Adult mosquito sampling took place using CDC light traps from May to August 2003 in representative landscapes. Mosquitoes were identified to species level with Aedes trivittatus (Coquillet) and Aedes vexans (Meigen) dominating the collection totals. The best publicly available spatial data on landscape and demographic attributes were collated and included land cover, human census, soils, floodplain, elevation, wetlands, hydrography, roads, and vegetation indices derived from satellite imagery. Spatial processing was carried out to organize landscape attributes for statistical comparison with abundance data from the potentially important West Nile virus (family Flaviviridae, genus Flavivirus, WNV) vector species Ae. vexans and Ae. trivittatus. Landscape parameters shown to be significantly correlated with mosquito counts included soil hydrological properties, presence in floodplain, wetland areas, and deciduous and bottomland forest cover. Data on temperature and precipitation were used to investigate the climatic influence on the temporal occurrence of mosquito population abundances. Late spring rain provided ample moisture for mosquito development, but low temperatures delayed widespread emergence of Ae. trivittatus and Ae. vexans until June 2003. Landscape and climatic impacts on adult mosquito population distributions were demonstrated, and these results could form the basis for the development of a spatiotemporal modeling framework that would inform anthropogenic mosquito control anld vector-borne disease surveillance. A qualitative discussion concerning Culex pipiens (L.) and Culex restuans Theobald is included.

After several years of low West Nile virus (WNV) occurrence in the United States of America (USA), 2012 wit- nessed large outbreaks in several parts of the country. In order to understand the outbreak dynamics, spatial clustering and landscape, demographic and climatic associations with WNV occurrence were investigated at a regional level in the USA. Previous research has demonstrated that there are a handful of prominent WNV mosquito vectors with varying ecological requirements responsible for WNV transmission in the USA. Published range maps of these important vectors were georef- erenced and used to define eight functional ecological regions in the coterminous USA. The number of human WNV cases and human populations by county were attained in order to calculate a WNV rate for each county in 2012. Additionally, a binary value (high/low) was calculated for each county based on whether the county WNV rate was above or below the rate for the region it fell in. Global Moran’s I and Anselin Local Moran’s I statistics of spatial association were used per region to examine and visualize clustering of the WNV rate and the high/low rating. Spatial data on landscape, demographic and climatic variables were compiled and derived from a variety of sources and then investigated in relation to human WNV using both Spearman rho correlation coefficients and Poisson regression models. Findings demonstrated significant spatial clustering of WNV and substantial inter-regional differences in relationships between WNV occurrence and landscape, demographic and climatically related variables. The regional associations were consistent with the ecologies of the dominant vectors for those regions. The large outbreak in the Southeast region was preceded by higher than normal winter and spring precipitation followed by dry and hot conditions in the summer.

Background West Nile virus (WNV) is a vector-borne illness that can severely affect human health. After introduction on the East Coast in 1999, the virus quickly spread and became established across the continental United States. However, there have been significant variations in levels of human WNV incidence spatially and temporally. In order to quantify these variations, we used Kulldorff's spatial scan statistic and Anselin's Local Moran's I statistic to uncover spatial clustering of human WNV incidence at the county level in the continental United States from 2002–2008. These two methods were applied with varying analysis thresholds in order to evaluate sensitivity of clusters identified. Results The spatial scan and Local Moran's I statistics revealed several consistent, important clusters or hot-spots with significant year-to-year variation. In 2002, before the pathogen had spread throughout the country, there were significant regional clusters in the upper Midwest and in Louisiana and Mississippi. The largest and most consistent area of clustering throughout the study period was in the Northern Great Plains region including large portions of Nebraska, South Dakota, and North Dakota, and significant sections of Colorado, Wyoming, and Montana. In 2006, a very strong cluster centered in southwest Idaho was prominent. Both the spatial scan statistic and the Local Moran's I statistic were sensitive to the choice of input parameters. Conclusion Significant spatial clustering of human WNV incidence has been demonstrated in the continental United States from 2002–2008. The two techniques were not always consistent in the location and size of clusters identified. Although there was significant inter-annual variation, consistent areas of clustering, with the most persistent and evident being in the Northern Great Plains, were demonstrated. Given the wide variety of mosquito species responsible and the environmental conditions they require, further spatio-temporal clustering analyses on a regional level is warranted.