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Frequency analysis is a widely used method in hydrology; however, the conventional approaches to frequency analysis do not consider the spatial dependence between stations. This study proposes a novel approach for multisite frequency analysis using the copula method. This approach uses the framework of pair copula construction to extended bivariate copula functions for multivariate analysis, allowing for non-homogeneous marginal distribution and spatial dependence. Our approach was applied to analyze hourly rainfall in Taiwan's Lanyang watershed from 1996 to 2011, constructing the multisite rainfall distribution to estimate the frequency of extreme rainfall events. The results showed the nonlinear relationships and distinct frequency distributions of rainfall across the study area due to changes in elevations. Based on the copula-based multisite rainfall distribution, the study simulated extreme rainfall events at multiple sites with predetermined frequencies, preserving the spatial characteristics of rainfall more accurately than previous methods. This approach avoids potential underestimation and overestimation of hourly rainfall intensity at specified return periods resulting from assumptions of homogeneity and spatial independence. This study also evaluated possible multisite rainfall combinations at a given frequency using Monte-Carlo simulation. These simulations characterized spatial uncertainties in design multisite rainfall, providing critical information for water resources management and hydrological design. [ABSTRACT FROM AUTHOR]

Spatial heterogeneity in groundwater system introduces significant challenges in groundwater modeling and parameter calibration. In order to mitigate the modeling uncertainty, data assiilation methods have been applied in the parameter estimation by assessing the uncertainties from both groundwater model and observations. In practice, the observations from groundwater system can be limited, and furthermore, boundary conditions and hydrogeological parameters, such as hydraulic conductivity, can be uncertain and biased. In order to handle the uncertain observations, this study applied the Bayesian maximum entropy (BME) for a data assimilation approach that integrates groundwater model, MODFLOW, and a variety of observations with uncertainties. In BME, no distributional assumption is imposed in the uncertain observations. We conducted numerical simulation with datasets of hard data of heads and hydrogeological parameters, uncertain head data on boundary, and uncertain hydrogeological parameters, i.e., hydraulic conductivity and storage coefficient. Three numerical scenarios with differerent combinations of datasets were conducted. Results show that the proposed data assimilation approach can gradually improve the modeling performance in the sense of lower mean squared errors over time. Moreover, the inclusion of uncertain observations can further improve the efficiency and accuracy in parameter estimation and hydraulic head prediction. [ABSTRACT FROM AUTHOR]

An effective air quality monitoring network (AQMN) is able to promptly reflect the characteristics of ambient air pollutants for air pollution control and management nearby potential hazardous emissions. This research proposed an innovative spatiotemporal-spectrum manifold analysis to investigate the multi-pollutant emission variation and sampling. The analysis established a coordinate system with spatial relation, temporal correlation, and the frequency spectrum of the multi-pollutant emission data. The pairwise manifold distances were estimated accordingly. We formulated a manifold-based multi-objective optimization model, which optimizes the population coverage and monitoring efficiency of the AQMN nearby the emission. We applied the manifold analysis and the optimization model to a petrochemical complex in Taiwan. The spatial and temporal variation of the complex's multi-pollutant emission was analyzed. Then, the optimization model solved for the best configuration of AQMN that satisfied the design objectives. We examined the tradeoff between the multiple objectives. Improvement of the existing monitoring stations was suggested based on the optimization results. Adding one or two monitoring stations at the densely populated area was recommended to provide better coverage for residents. Our results also settled for an optimal addition location to increase monitoring efficiency. [ABSTRACT FROM AUTHOR]

This research proposes an innovative Kriging-approximation simulated annealing (KASA) optimization algorithm to increase optimization efficiency and reduce the computation time for the parameter calibration of simulation model. The newly developed KASA optimization algorithm utilizes the simulated annealing algorithm to search the global optimum; meanwhile, Kriging approximation, a statistical estimation method, is incorporated with simulated annealing to interpolate unknown objective values in solution spaces. Furthermore, this research establishes a network-based porous media flow simulation (NET-PFS) model and then KASA is applied in the calibration of the NET-PFS representative pore network. NET-PFS is a pore network based model constructing a representative pore network to approximate soil characteristics and pore geometry with limited access to pore-scale imaging processes. NET-PFS is applied to estimate the permeability of a sand-packing porous media. NET-PFS establishes a framework for simplifying the pore network but remaining the same hydraulic conductivity and the flow status of pore networks with limited information about the pore structure. In the case study, a quartz sand-packing porous media is scanned by X-ray micro computed tomography. The NET-PFS model is applied to estimate the hydraulic conductivity and flow velocity distribution from the original pore network. The results demonstrate the proposed KASA algorithm effectively calibrated the NET-PFS model; in addition, a representative pore network and the determined flow status in the pore network is presented by NET-PFS. [ABSTRACT FROM AUTHOR]

Energy and water are scarce resources and understanding the complicated energy-water nexus is an important issue for effective resource management. The purpose of this research was to analyze the competitive and cooperative relationships involving energy and water production and use. Specifically, tradeoff and integrated management of hydropower generation and water supplies are analyzed for energy-water systems. A Nash-Cournot model was established to analyze strategic behaviors among participants in energy-water systems. In the model, tradeoff analysis and integrated management of hydropower and water supplies were simulated for a reservoir system. In addition, hydropower and thermal power generation in competitive energy markets was examined. A case study of Dajia River reservoirs in the Tai-Chung and Chang-Hwa energy-water systems is presented. Dajia River is the second longest river in central Taiwan; the reservoirs system of Dajia River generates hydropower with installed capacity of 1150 MW. Strategic competitive and cooperative behaviors regarding energy-water linkage were quantified in the results. The results show that integrated management of hydropower and water supplies can increase renewable energy production, lower electricity equilibrium price, and decrease carbon dioxide emission. [ABSTRACT FROM AUTHOR]

Dengue fever is identified as one of the most widespread vector-borne diseases in the world. In the last decade, high incidence of dengue fever has been observed in southern Taiwan on an annual basis. There is an urgent need to develop a dengue fever early warning model for the area. Previous studies showed that dengue fever transmission has complex space-time dynamics highly depending upon the activities of human and vectors and their interactions, as well as hydrological processes. This study developed a 1-week-ahead dengue fever warning system in southern Taiwan considering weekly-based nonlinear temporal lagged associations between dengue cases and hydrological factors across space and time by investigating the disease database during 1998-2011. The proposed model is based on an integration of distributed lag nonlinear model and spatiotemporal dependence structure under an epistemic framework of Bayesian maximum entropy method. This study identified that minimum temperature and maximum 24-h rainfall are the most significant to dengue fever incidences. Their associations to dengue fever risks are presented with respect to both hydrological measurement levels and temporal lags. A nested spatiotemporal covariance of transmissions was used to account for the disease diffusion across space and time. Results show that the proposed approach can provide early warnings of the dengue fever occurrences in both initial and peak epidemic stages in 2012, and obtain the spatiotemporal distribution of dengue fever epidemics. These features can be useful for local governmental agencies to seek more effective strategies for dengue fever prevention and control. [ABSTRACT FROM AUTHOR]

In recent years, there has been a fast growing interest in the space-time data processing capacity of Geographic Information Systems (GIS). In this paper we present a new GIS-based tool for advanced geostatistical analysis of space-time data; it combines stochastic analysis, prediction, and GIS visualization technology. The proposed toolbox is based on the Bayesian Maximum Entropy theory that formulates its approach under a mature knowledge synthesis framework. We exhibit the toolbox features and use it for particulate matter spatiotemporal mapping in Taipei, in a proof-of-concept study where the serious preferential sampling issue is present. The proposed toolbox enables tight coupling of advanced spatiotemporal analysis functions with a GIS environment, i.e. QGIS. As a result, our contribution leads to a more seamless interaction between spatiotemporal analysis tools and GIS built-in functions; and utterly enhances the functionality of GIS software as a comprehensive knowledge processing and dissemination platform. [ABSTRACT FROM AUTHOR]

Tropical cyclones are one of the most serious natural disasters in northwestern Pacific Ocean. In general, an average of three to four typhoons invades the vicinity of Taiwan annually, which brings heavy rainfalls and strong winds resulting in disasters including flooding, mudflows, and landslides, leading to severe damage to economies and casualties. Studies show that different tracks of typhoon can cause distinct spatio-temporal patterns of rainfall events at different regions of Taiwan. As a result, understanding the trajectories of tropical cyclones and their relationship to climatic variables at global scale is crucial for hydrological modeling and disaster migration in Taiwan, especially under the conditions of climate change. This study applied a probabilistic curve clustering technique, which is based on a regression mixture model, to classify the best tracks of typhoons across the area within 6° around Taiwan during the period of 1951-2009. For the purposes of modeling and forecasting the typhoon trajectories, the track cluster is performed separately in different seasons due to their distinct driving forces to typhoon movements. A generalized linear model (GLM) is used to characterize the relationship between the identified typhoon tracks and the dominant climate features derived from NCEP reanalysis data. Results showed the six major typhoon tracks in the vicinity of Taiwan for different seasons respectively. The result of GLM cross validation showed that the frequency of typhoon tracks passing cross Taiwan in summer can significantly depend upon with two empirical orthogonal functions (EOFs) of sea level pressure, and the third EOF of sea surface temperature. [ABSTRACT FROM AUTHOR]

Understanding local precipitation patterns is essential to water resource management and flood mitigation. Precipitation patterns can vary in space and time depending on factors from different spatial scales such as local topographical changes and macroscopic atmospheric circulation. This study applied the two-stage classification method to distinguish the space-time patterns of local precipitations in the two identified distinct synoptic conditions, i.e. summer and autumn, from 24 gauges during 1996-2008 in Yilan County, Taiwan. The proposed method classifies the synoptic and local conditions for the space-time rainfall patterns by using K-means coupled with empirical orthogonal function analysis, and hierarchical ascending clustering method respectively. The proposed two-stage classification method considers not only the magnitude and the space-time distribution of rainfall events, but also the associated synoptic conditions. The results identified three primary patterns of extreme and two patterns of normal events in both seasons. Regarding the extreme events from typhoons, wind directions and the frontal accompanied effect are major contributors to the magnitude and spatial distribution of rainfall events in the summer and autumn, respectively. Spatiotemporal covariance structures are used to characterize the variability of normal events, showing the increasing frequency of wide spatial and temporal ranges from the summer to autumn. In summary, the proposed classification analysis provides patterns associated with distinct underlying physical mechanisms and space-time characteristics. The general characteristics of rainfall patterns can provide insights for the hydrological modeling of local catchments under different climatic scenarios. [ABSTRACT FROM AUTHOR]

The complex process of ozone formation, its precursor compounds (volatile organic compounds (VOCs) and nitrogen oxides (NO)) emissions, accompanying with meteorological conditions, makes troposphere ozone difficult to control. This study applies dynamic factor analysis (DFA) to investigate the time series of ambient ozone concentrations and their associations with meteorological variables. The analyses were applied on the hourly data collected at the four monitoring stations in Kaohsiung (Taiwan) during the 72-h periods with three events in high and low ozone episodes in 2009. According to the optimal DFA model, NO negatively control ozone variations in all events. Relative humidity (RH) only negatively influences the ozone fluctuations in low ozone episode. The sea-land wind speed (i.e. west direction) and air temperature positively affect ozone fluctuations in high ozone episode. CO significantly influences ozone fluctuations in main city area for high ozone episode and in all stations for low ozone episode. However, VOC did not significantly correlate with ozone fluctuations for both ozone episodes. The results show that ozone conditions of both episodes were in NO-saturated regimes, where increased NO would result in lower ozone. Temperature, RH, and sea-land wind speed can be treated as metrological variables, which significantly vary the concentrations of surface-level ozone. This study shows DFA can provide a quantitative insight into the temporal variations of CO, NO, and meteorological conditions effects on ozone variations that will be a reference to air quality management in the study area. [ABSTRACT FROM AUTHOR]

Using the Bayesian maximum entropy (BME) method of spatiotemporal statistics, the present study examines the geographical risk pattern of influenza mortality in the state of California during the time period 1997–2001. BME risk analysis is of considerable value, since influenza is the largest contributing factor to wintertime mortality increases in the US. By incorporating age-adjusted mortality data collected at the county level, informative influenza mortality maps were generated and composite space-time influenza dependences were assessed quantitatively. On the basis this analysis, essential risk patterns and correlations were detected across the state during wintertime. It was found that significantly high risks initially occurred during December in the west-central part of the state; in the following two weeks the risk distribution extended in the south and east-central parts of the state; in late February significant influenza mortalities were detected mainly in the west-central part of the state. These findings, combined with the results of earlier works, can lead to useful conclusions regarding influenza risk assessment in a space-time context and, also, point toward promising future research directions. [ABSTRACT FROM AUTHOR]

This paper describes the spatiotemporal epistematics knowledge synthesis and graphical user interface (SEKS–GUI) framework and its application in medical geography problems. Based on sound theoretical reasoning, the interactive software library of SEKS–GUI explores heterogeneous (spatially non-homogeneous and temporally non-stationary) health attribute distributions (disease incidence, mortality, human exposure, epidemic propagation etc.); expresses the health system’s dependence structure using (ordinary and generalized) spatiotemporal covariance models; synthesizes core knowledge bases, empirical evidence and multi-sourced system uncertainty; and generates a meaningful picture of the real-world system using space–time dependent probability functions and associated maps of health attributes. The implementation stages of the SEKS–GUI library are described in considerable detail using appropriate screens. The wide applicability of SEKS–GUI is demonstrated by reviewing a selection of real-world case studies. [ABSTRACT FROM AUTHOR]

Several reports published in the current issue of the journal are presented including on environmental concentration of cholera and water-borne disease incidences.