GROUNDWATER VULNERABILITY ASSESSMENT USING STATISTICAL METHODS

Groundwater vulnerability maps are considered as an essential component for sustainable environmental planning and management. Statistical methods are increasingly being used to produce scientifically defensible groundwater vulnerability maps which meaningfulness and reliability however must be carefully evaluated before being distributed. The Weigth of Evidence method (WofE) was used for assessing groundwater vulnerability to nitrate contamination of two different aquifers with the aim of testing its robustness as exploratory and predictive tool and addressing more general issues related to the use of statistical methods to produce groundwater vulnerability maps. The spatial variability of different maps showing similar performances in term of predictive power, the influence of using different thresholds in the analysis, and the limits of using statistical methods to assess groundwater vulnerability were the main aspects evaluated in this study. Results showed that: a) the WofE represents a powerful tool for selecting the appropriate explanatory variables and producing reliable maps; b) different maps, generated using different combinations of explanatory variables, always present some degree of spatial variability that can be analyzed through multiple validation techniques; c) the use of different thresholds produce similar or different results depending on if the spatial distribution of the vulnerability is observed at broad and small scale, respectively. Furthermore, a new research challenge was identified in trying to integrate the temporal component in the vulnerability analysis

Estimating hurricane hazards using a GIS system

Abstract. This paper develops a GIS-based integrated approach to the Multi-Hazard model method, with reference to hurricanes. This approach has three components: data integration, hazard assessment and score calculation to estimate elements at risk such as affected area and affected population. First, spatial data integration issues within a GIS environment, such as geographical scales and data models, are addressed. Particularly, the integration of physical parameters and population data is achieved linking remotely sensed data with a high resolution population distribution in GIS. In order to assess the number of affected people, involving heterogeneous data sources, the selection of spatial analysis units is basic. Second, specific multi-hazard tasks, such as hazard behaviour simulation and elements at risk assessment, are composed in order to understand complex hazard and provide support for decision making. Finally, the paper concludes that the integrated approach herein presented can be used to assist emergency management of hurricane consequences, in theory and in practice.</p

Modelling changing population distributions: an example of the Kenyan Coast, 1979-2009

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit. Here we make use of recently released multi-temporal high-resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields

Spatiotemporal patterns of population in mainland China, 1990 to 2010

According to UN forecasts, global population will increase to over 8 billion by 2025, with much of this anticipated population growth expected in urban areas. In China, the scale of urbanization has, and continues to be, unprecedented in terms of magnitude and rate of change. Since the late 1970s, the percentage of Chinese living in urban areas increased from ~18% to over 50%. To quantify these patterns spatially we use time-invariant or temporally-explicit data, including census data for 1990, 2000, and 2010 in an ensemble prediction model. Resulting multi-temporal, gridded population datasets are unique in terms of granularity and extent, providing fine-scale (~100 m) patterns of population distribution for mainland China. For consistency purposes, the Tibet Autonomous Region, Taiwan, and the islands in the South China Sea were excluded. The statistical model and considerations for temporally comparable maps are described, along with the resulting datasets. Final, mainland China population maps for 1990, 2000, and 2010 are freely available as products from the WorldPop Project website and the WorldPop Dataverse Repository

Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010–16: a subnational analysis of cross-sectional surveys

Background Understanding subnational variation in age-specific fertility rates (ASFRs) and total fertility rates (TFRs), and geographical clustering of high fertility and its determinants in low-income and middle-income countries, is increasingly needed for geographical targeting and prioritising of policy. We aimed to identify variation in fertility rates, to describe patterns of key selected fertility determinants in areas of high fertility. Methods We did a subnational analysis of ASFRs and TFRs from the most recent publicly available and nationally representative cross-sectional Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016 for 70 low-income, lower-middle-income, and upper-middle-income countries, across 932 administrative units. We assessed the degree of global spatial autocorrelation by using Moran's I statistic and did a spatial cluster analysis using the Getis-Ord Gi* local statistic to examine the geographical clustering of fertility and key selected fertility determinants. Descriptive analysis was used to investigate the distribution of ASFRs and of selected determinants in each cluster. Findings TFR varied from below replacement (2·1 children per women) in 36 of the 932 subnational regions (mainly located in India, Myanmar, Colombia, and Armenia), to rates of 8 and higher in 14 subnational regions, located in sub-Saharan Africa and Afghanistan. Areas with high-fertility clusters were mostly associated with areas of low prevalence of women with secondary or higher education, low use of contraception, and high unmet needs for family planning, although exceptions existed. Interpretation Substantial within-country variation in the distribution of fertility rates highlights the need for tailored programmes and strategies in high-fertility cluster areas to increase the use of contraception and access to secondary education, and to reduce unmet need for family planning. Funding Wellcome Trust, the UK Foreign, Commonwealth and Development Office, and the Bill & Melinda Gates Foundation

Examining the correlates and drivers of human population distributions across low-and middle-income countries

Geographical factors have influenced the distributions and densities of global human population distributions for centuries. Climatic regimes have made some regions more habitable than others, harsh topography has discouraged human settlement, and transport links have encouraged population growth. A better understanding of these types of relationships enables both improved mapping of population distributions today and modelling of future scenarios. However, few comprehensive studies of the relationships between population spatial distributions and the range of drivers and correlates that exist have been undertaken at all, much less at high spatial resolutions, and particularly across the low-and middle-income countries. Here, we quantify the relative importance of multiple types of drivers and covariates in explaining observed population densities across 32 low-and middle-income countries over four continents using machine-learning approaches. We find that, while relationships between population densities and geographical factors show some variation between regions, theyare generally remarkably consistent,pointing to universal drivers of human population distribution. Here,we find that a set of geographical features relating to the built environment, ecology and topography consistently explain the majority of variability in population distributions at fine spatial scales across the low-and middle-income regions of the world.</p

Aquifer nitrate vulnerability assessment using positive and negative weights of evidence methods, Milan, Italy

Statistical methods are extensively used by hydrogeologists for assessing groundwater vulnerability. Several of these methods require to express the response variable as binary and to select a threshold distinguishing between positive and negative indicators of contamination that are usually identified as occurrences and non-occurrences, respectively. In this study, both occurrences and non-occurrences were alternately used as training points (TPs) in the weights of evidence (WofE) for assessing groundwater vulnerability to nitrate contamination of a shallow, unconfined, porous aquifer. This was done to better understand the individual role and the combined effect of explanatory variables in both protecting and exposing groundwater from and to nitrate contamination in the study area. The idea behind this approach is that, for a given aquifer, each explanatory variable should have an unequivocal effect on the physical process of groundwater contamination. As part of this study, a procedure for multi-class generalization was developed. Results showed that an evidential theme, even if it appears to be a statistically significant predictor of occurrences, can show an equivocal spatial relationship with the positive and the negative indicators of contamination due to the presence of a sampling bias between the TPs and the evidential theme.It was demonstrated that, if sampling bias is not recognized and corrected, the use of such evidential theme in the analysis could lead to obtain unreliable groundwater vulnerability maps. In order to deal with this issue, a quantitative methodology to correct the effects of sampling bias was successfully tested. Indeed, once the spatial relationships between the different type of TPs and the considered evidential themes were corrected for the effects of sampling bias, the WofE method was found to be a reliable modeling technique for assessing groundwater vulnerability and proved to be capable of identifying areas characterized by different degrees of vulnerability

Groundwater vulnerability maps derived from a time-dependent method using satellite scatterometer data

Introducing the time variable in groundwater vulnerability assessment is an innovative approach to study the evolution of contamination by non-point sources and to forecast future trends. This requires a determination of the relationship between temporal changes in groundwater contamination and in land use. Such effort will enable breakthrough advances in mapping hazardous areas, and in assessing the efficacy of land-use planning for groundwater protection. Through a Bayesian spatial statistical approach, time-dependent vulnerability maps are derived by using hydrogeological variables together with three different time-dependent datasets: population density, high-resolution urban survey, and satellite QuikSCAT (QSCAT) data processed with the innovative dense sampling method (DSM). This approach is demonstrated extensively over the Po Plain in Lombardy region (northern Italy). Calibrated and validated maps show physically consistent relations between the hydrogeological variables and nitrate trends. The results indicate that changes of urban nitrate sources are strongly related to groundwater deterioration. Among the different datasets, QSCAT-DSM is proven to be the most efficient dataset to represent urban nitrate sources of contamination, with major advantages: a worldwide coverage, a continuous decadal data collection, and an adequate resolution without spatial gaps. This study presents a successful approach that, for the first time, allows the inclusion of the time dimension in groundwater vulnerability assessment by using innovative satellite remote sensing data for quantitative statistical analyses of groundwater quality changes