ALBERT

Description: Thyroid cancer (TC) incidence in China has increased rapidly in recent years. Hangzhou is one of the areas with the highest TC incidence in China. However, the composite space–time variation and risk factors of TC are rarely investigated. We acquired 7147 TC cases from 2008 to 2012 in Hangzhou. Descriptive statistics were employed to compare the incidence disparities in different sub-populations. Geographical information systems were used to create spatial distribution maps. Hotspot analysis was applied to detect high/low incidence clusters, and the GeogDetector model was implemented to investigate the relationship between TC incidence and environmental factors. TC incidence in Hangzhou increased dramatically from 2008 to 2012: a noticeable 244.9 % increase, from 10.04 to 34.63 per 100,000 individuals, with a female to male ratio of 3.0, an urban to rural ratio of 3.2 and iodine sufficient to iodine deficient ratio of 3.5. Significantly high TC cluster was detected in the northeast area of Hangzhou. Elevation was found to be the most powerful determinant of TC distribution, followed by soil parent materials and slope. TC incidence decreased as elevation and slope increased. Concerning soil parent materials, deposited materials were generally linked to higher TC incidence than were eluvium ones. The spatial/temporal pattern of TC incidence is affected by geomorphology and soil property variations. Excessive iodine exposure may be a TC risk factor. Health research and management should pay sufficient attention to the improved understanding and prediction of the composite space–time distribution of the quickly increasing TC incidence described in this study.

Description: Accurate and precise estimation of return levels is often a key goal of any extreme value analysis. For example, in the UK the British Standards Institution (BSI) incorporate estimates of ‘once-in-50-year wind gust speeds’—or 50- year return levels —into their design codes for new structures; similarly, the Dutch Delta Commission use estimates of the 10,000- year return level for sea-surge to aid the construction of flood defence systems. In this paper, we briefly highlight the shortcomings of standard methods for estimating return levels, including the commonly-adopted block maxima and peaks over thresholds approach, before presenting an estimation framework which we show can substantially increase the precision of return level estimates. Our work allows explicit quantification of seasonal effects, as well as exploiting recent developments in the estimation of the extremal index for handling extremal clustering. From frequentist ideas, we turn to the Bayesian paradigm as a natural approach for building complex hierarchical or spatial models for extremes. Through simulations we show that the return level posterior mean does not have an exceedance probability in line with the intended encounter risk; we also argue that the Bayesian posterior predictive value gives the most satisfactory representation of a return level for use in practice, accounting for uncertainty in parameter estimation and future observations. Thus, where feasible, we propose a Bayesian estimation strategy for optimal return level inference.

Description: In this study, a stochastic programming with imprecise probabilities (SP-IP) model is proposed for planning water resources systems. The SP-IP model is capable of addressing multiple uncertainties in the forms of intervals with random boundaries and imprecise probability distributions. The stochastic optimization model can be transformed into a deterministic equivalence in a straightforward manner. A case study of regional water resources allocation is used to demonstrate the applicability of the proposed model. Results indicate that the total net benefits would be decreased with increased probabilities of occurrence, reflecting a potential trade-off between economic benefits and associated risks. The SP-IP model is also capable of providing a variety of decision alternatives under different scenarios of water policies, which is useful for water managers to formulate an appropriate water management policy in an uncertain environment.

Description: In order to evaluate the exposure risk of lead and cadmium in seafood for coastal residents in the coastline of the South China, representative seafood such as sea fish, crustaceans and molluscs were collected and used as research samples in many sampling points. By determining lead and cadmium content in sample using graphite furnace atomic absorption spectrometry, we carried out a safety evaluation of lead and cadmium contamination of seafood by single factor pollution index method. By calculating lead, cadmium intake of the coastal residents eating seafood, their exposure risk of lead, cadmium was assessed. It was found that, firstly, the content of lead and cadmium in mollusks was both higher than sea fish and crustaceans in the same waters. Secondly, the lead and cadmium pollution in seafood near the mouth of Pearl River was the most serious. Thirdly, lead and cadmium intakes of the coastal population eating seafood were at a basic level of security, but there were certain risks on the males less than 17 years old eating molluscs in Shenzhen Bay.

Description: The surface water quality monitoring is an important concern of public organizations due to its relevance to the public health. Statistical methods are taken as consistent and essential tools in the monitoring procedures in order to prevent and identify environmental problems. This work presents the study case of the hydrological basin of the river Vouga, in Portugal. The main goal is discriminate the water monitoring sites using the monthly dissolved oxygen concentration dataset between January 2002 and May 2013. This is achieved through the extraction of trend and seasonal components in a linear mixed-effect state space model. The parameters estimation is performed with both maximum likelihood method and distribution-free estimators in a two-step procedure. The application of the Kalman smoother algorithm allows to obtain predictions of the structural components as trend and seasonality. The water monitoring sites are discriminated through the structural components by a hierarchical agglomerative clustering procedure. This procedure identified different homogenous groups relatively to the trend and seasonality components and some characteristics of the hydrological basin are presented in order to support the results.

Description: Due to rapid growth of population and development of economy, water resources allocation problems have aroused wide concern. Therefore, optimization of water resources systems is complex and uncertain, which is a severe challenge faced by water managers. In this paper, a factorial multi-stage stochastic programming with chance constraints approach is developed to deal with the issues of water-resources allocation under uncertainty and risk as well as their interactions. It can deal with uncertainties described as both interval numbers and probability distributions, and can also support the risk assessment within a multistage context. The solutions associated with different risk levels of constraint violation can be obtained, which can help characterize the relationship between the economic objective and the system risk. The inherent interactions between factors at different levels and their effects on total net benefits can be revealed through the analysis of multi-parameter interactions.

Description: The joint occurrence of extreme hydroclimatic events, such as simultaneous precipitation deficit and high temperature, results in the so-called compound events, and has a serious impact on risk assessment and mitigation strategies. Multivariate frequency analysis (MFA) allows a probabilistic quantitative assessment of this risk under uncertainty. Analyzing precipitation and temperature records in the contiguous United States (CONUS), and focusing on the assessment of the degree of rarity of the 2014 California drought, we highlight some critical aspects of MFA that are often overlooked and should be carefully taken into account for a correct interpretation of the results. In particular, we show that an informative exploratory data analysis (EDA) devised to check the basic hypotheses of MFA, a suitable assessment of the sampling uncertainty, and a better understanding of probabilistic concepts can help to avoid misinterpretation of univariate and multivariate return periods, and incoherent conclusions concerning the risk of compound extreme hydroclimatic events. Empirical results show that the dependence between precipitation deficit and temperature across the CONUS can be positive, negative or not significant and does not exhibit significant changes in the last three decades. Focusing on the 2014 California drought as a compound event and based on the data used, the probability of occurrence strongly depends on the selected variables and how they are combined, and is affected by large uncertainty, thus preventing definite conclusions about the actual degree of rarity of this event.

Description: This paper is purposed to detect the spatial laws of gastric cancer in the rapid urbanization area and analyze relationships between gastric cancer and urbanization. Gastric cancer incidence data in Xiamen between 2006 and 2009 was collected from Xiamen CDC (Centre of Disease Control and Prevention). Urbanization age of Xiamen Island was calculated from remote images and terrain maps. Analysis results showed that 995 gastric cancer cases, accounting for 61.64 % was in Xiamen island, compared with 619 cases, accounting for 38.36 % outside. The average gastric cancer incidence was 32.98/1000,000 in Xiamen Island, compared to 16.13/1000,000 outside. The top ten sub-districts of gastric cancer all located in Xiamen Island, and urban areas have as more than twice gastric cancer incidence as rural area, with 28.62/100,000. The most likely spatial cluster of gastric cancer was Xiamen Island. Correlation analysis results indicated the urbanization age and spatial cluster degree had a significantly positive correlation relationship. Rapid urbanization in Xiamen changed the environment, namely acquired factors including land use change, air–water–soil pollution, less activity, electromagnetic radiation and contaminated sea foods. These are risk factors of gastric cancer. This research indicated that Xiamen Island was prevalent of gastric cancer with a positive correlation with urbanization age.

Description: Acquiring the structures of porous media is very important when predicting flow properties in porous media. However, direct measurements of 3D microstructures of porous media with the resolution of microns or even nanometers are difficult to achieve due to the expensive cost of high-precision equipment. Therefore, as a typical stochastic simulation method, multiple-point statistics (MPS) was used to perform reconstruction based on real 3D volume data of porous media scanned by micro-CT. Because the ensemble of patterns extracted from a training image (TI) cannot be embedded into a linear space, the traditional MPS methods using linear dimensionality reduction, including filter-based simulation and distance-based pattern simulation, are not suitable to deal with the nonlinear situation. A new MPS method using isometric mapping, which is a method of nonlinear dimensionality reduction, to achieve nonlinear dimensionality reduction is proposed to decrease redundant data of TIs so that the subsequent simulation can be faster and more accurate for the reconstruction of porous media. Entropy theory is introduced to select a proper size of data template to balance the CPU cost and reconstructed quality. The comparisons between the reconstructed images and the target image show that the structural characteristics of reconstructed porous media using our method are similar to those of real volume data. This method also has shown advantages in reconstruction quality over typical methods using linear dimensionality reduction.

Description: Long-term shoreline evolution due to longshore sediment transport is one of the key processes that need to be addressed in coastal engineering design and management. To adequately represent the inherent stochastic nature of the evolution processes, a probability density evolution model based on a Liouville-type equation is proposed for predicting the shoreline changes. In this model, the standard one-line beach evolution model that is widely used in coastal engineering design is reformulated in terms of the probability density function of shoreline responses. A computational algorithm involving a total variation diminishing scheme is employed to solve the resulting equation. To check the accuracy and robustness of the model, the predictions of the model are evaluated by comparing them with those from Monte Carlo simulations for two idealised shoreline configurations involving a single long jetty perpendicular to a straight shoreline and a rectangular beach nourishment case. The pertinent features of the predicted probabilistic shoreline responses are identified and discussed. The influence of the density distributions of the input parameters on the computed results is investigated.

Description: Salinization threatens the viability of water resources and is common in many important inland freshwater lakes worldwide, especially in arid and semi-arid areas. Bosten Lake is a typical inland freshwater lake that has evolved into a subsaline lake and is located in the arid region of Northwest China. The water resources of Bosten Lake are important for supplying regional drinking water and agricultural irrigation and for economic development. In this study, changes in salinity with time and space were analyzed in Bosten Lake. Overall, the salinity increased from 0.39 g/L in 1958 to 1.87 g/L in 1987, reaching its highest value in 1987. After 1987, the salinity decreased to 1.17 g/L in 2003 and increased to 1.45 g/L in 2010. Increased salinity adversely affects aquatic lake systems, regional eco-environments and water resource use, and has become a serious environmental problem in Bosten Lake. Thus, the causes of increasing salinity are discussed in this paper. Overall, the influences of climate variations and human activities resulted in the salinization of the lake. Understanding the salinization processes in Bosten Lake can be useful for implementing actions that improve water quality and water resource use in the lake.

Description: 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.

Description: Six global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 under three Respectively Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) were used to assess the impact of climate change on streamflow for the Huangnizhuang catchment (HNZ) in China. Change factor method was used for bias correction between GCM outputs and observations and the SWAT model was used to simulate the hydrological processes. The results indicated that the SWAT model performed well in the study catchment with a monthly Nash–Sutcliffe efficiency (NS) of 0.93 and 0.91 and daily NS of 0.63 and 0.68 for calibration and validation periods respectively. Their corresponding relative errors were −2.2 and 8.9, and −2.6 and 8.5 % respectively. The ensemble of multi-GCMs projected an increase of precipitation in the middle and end of twenty-first century over the HNZ, ranging from −2.4 to 9 %. However, streamflow is likely to decline in the future, ranging −6.9 to 0.8 %, mainly due to an increase of evapotranspiration in a warming world, as air temperature shows steadily increases for all the GCMs and RCPs. Average monthly streamflow from six GCMs are likely to increase in August and September but decline from October to June. The associated uncertainties of the reported results were also discussed. It includes, but is not limit to, different GCMs, emissions scenarios, downscaling techniques as well as hydrological simulations. The results of this study can inform planning of long-term basin water management strategies taking into account global change scenarios.

Description: Soil salinization of the reclaimed tidelands is problematic. Therefore, there is a need to characterize the spatial variability of soil salinity associated with soil moisture and other soil properties across the reclaimed tidelands. One approach is the use of easily-acquired ancillary data as surrogates for the arduous conventional soil sampling. In a reclaimed coastal tideland in the south of Hangzhou Gulf, backscattering coefficient (σ 0 ) from remotely sensed ALOS/PALSAR radar imagery (HH polarization mode) and apparent soil electrical conductivity (ECa) from a proximally sensed EM38 were used to indicate the spatial distribution of soil moisture and salinity, respectively. After that, response surface methodology (RSM) was employed to determine an optimal set of 12 soil samples using spatially referenced σ 0 and ECa data. Spatial distributions of three soil chemical properties [i.e. soil organic matter (SOM), available nitrogen (AN), and available potassium (AK)] were predicted using inverse distance weighted method based on the 12 samples and were then compared with the predictions generated using 42 samples obtained from a conventional grid sampling scheme. It was concluded that combination of radar imagery and EM induction data can delineate the spatial variability of two key soil properties (i.e. moisture and salinity) across the study area. Besides, RSM-based sampling using radar imagery and EM induction data was highly effective in characterizing the spatial variability of SOM, AN and AK, compared with the conventional grid sampling. This new approach may be used to assist site specific management in precision agriculture.

Description: A new wavelet-based estimation methodology, in the context of spatial functional regression, is proposed to discriminate between small-scale and large scale variability of spatially correlated functional data, defined by depth-dependent curves. Specifically, the discrete wavelet transform of the data is computed in space and depth to reduce dimensionality. Moment-based regression estimation is applied for the approximation of the scaling coefficients of the functional response. While its wavelet coefficients are estimated in a Bayesian regression framework. Both regression approaches are implemented from the empirical versions of the scaling and wavelet auto-covariance and cross-covariance operators, characterizing the correlation structure of the spatial functional response. Weather stations in ocean islands display high spatial concentration. The proposed estimation methodology overcomes the difficulties arising in the estimation of ocean temperature field at different depths, from long records of ocean temperature measurements in these stations. Data are collected from The World-Wide Ocean Optics Database. The performance of the presented approach is tested in terms of 10-fold cross-validation, and residual spatial and depth correlation analysis. Additionally, an application to soil sciences, for prediction of electrical conductivity profiles is also considered to compare this approach with previous related ones, in the statistical analysis of spatially correlated curves in depth.

Description: Water quality management is a significant item in the sustainable development of wetland system, since the environmental influences from the economic development are becoming more and more obvious. In this study, an inexact left-hand-side chance-constrained fuzzy multi-objective programming (ILCFMOP) approach was proposed and applied to water quality management in a wetland system to analyze the tradeoffs among multiple objectives of total net benefit, water quality, water resource utilization and water treatment cost. The ILCFMOP integrates interval programming, left-hand-side chance-constrained programming, and fuzzy multi-objective programming within an optimization framework. It can both handle multiple objectives and quantify multiple uncertainties, including fuzziness (aspiration level of objectives), randomness (pollutant release limitation), and interval parameters (e.g. water resources, and wastewater treatment costs). A representative water pollution control case study in a wetland system is employed for demonstration. The optimal schemes were analyzed under scenarios at different probabilities ( p i , denotes the admissible probability of violating the constraint i ). The optimal solutions indicated that, most of the objectives would decrease with increasing probability levels from scenarios 1 to 3, since a higher constraint satisfaction probability would lead to stricter decision scopes. This study is the first application of the ILCFMOP model to water quality management in a wetland system, which indicates that it is applicable to other environmental problems under uncertainties.

Description: This study investigates long-term trends in annual and seasonal precipitation at 16 stations in the upper Blue Nile River basin. The non-parametric Mann–Kendall test modified by effective sample size is used to detect linear trends in the precipitation time series. The trends magnitudes and starting time of significant trends are determined using the Sen’s slope approach and the sequential Mann–Kendall test, respectively. Albeit annual precipitation shows a tendency to decrease in more than 80 % of the stations, statistically significant trends are found at only two stations. The significant decreasing trends of −40.3 and −168.1 mm/year per decade in annual precipitation at Debre Brihan and Gore stations started in the late 1970s and early 1980s respectively, which is consistent with the devastating droughts and famine during that period in the region. Owing to the great contribution of the main rainy season’s (June to September: JJAS) precipitation to annual precipitation in the upper Blue Nile basin, the variation pattern of the JJAS precipitation is very similar to that of annual precipitation. Insignificant decreasing/increasing trends in the short rainy season’s (March to May: MAM) precipitation are clearly predominant in the basin, where only one significant decreasing trend is detected in the time series.

Description: We created a fault model with a Tohoku-type earthquake fault zone having a random slip distribution and performed stochastic tsunami hazard analysis using a logic tree. When the stochastic tsunami hazard analysis results and the Tohoku earthquake observation results were compared, the observation results of a GPS wave gauge off the southern Iwate coast indicated a return period equivalent to approximately 1,709 years (0.50 fractile), and the observation results of a GPS wave gauge off the shore of Fukushima Prefecture indicated a return period of 600 years (0.50 fractile). Analysis of the influence of the number of slip distribution patterns on the results of the stochastic tsunami hazard analysis showed that the number of slip distribution patterns considered greatly influenced the results of the hazard analysis for a relatively large wave height. When the 90 % confidence interval and coefficient of variation of tsunami wave height were defined as an index for projecting the uncertainty of tsunami wave height, the 90 % confidence interval was typically high in locations where the wave height of each fractile point was high. At a location offshore of the Boso Peninsula of Chiba Prefecture where the coefficient of variation reached the maximum, it was confirmed that variations in maximum wave height due to differences in slip distribution of the fault zone contributed to the coefficient of variation being large.

Description: We present a detailed downscaling simulation methodology for generating precipitation events, conditioned on external climate covariates, on a network of meteorological stations. These events can be input to hydrological models. To simulate an event on a future day t , the method uses the K-nearest neighbor algorithm to identify close neighbors from the historical record, and resamples a past event with resampling probabilities determined from external covariates. This preserves the spatial dependence of precipitation on the network, and other important distributional features of precipitation. Large numbers of arbitrary tuning parameters and model assumptions are reduced through the use of a multivariate Gaussian model relating climate covariates to historical precipitation. The approach is demonstrated by simulating daily precipitation, maximum temperature, and minimum temperature on a network of 93 locations in North Carolina, all conditioned on climate model output. The downscaling is based on a regional climate model (RCM) embedded within a global NCEP reanalysis model. The method is demonstrated using precipitation in North Carolina with the Canadian climate RCM as an NCEP driven RCM.

Description: The importance of uncertainty inherent in measured calibration/validation data is frequently stated in literature, but it is not often considered in calibrating and evaluating hydrologic and water quality models. This is due to the limited amount of data available to support relevant research and the limited scientific guidance on the impact of measurement uncertainty. In this study, the impact of considering measurement uncertainty during model auto-calibration was investigated in a case study example using previously published uncertainty estimates for streamflow, sediment, and NH 4 -N. The results indicated that inclusion of measurement uncertainty during the auto-calibration process does impact model calibration results and predictive uncertainty. The level of impact on model predictions followed the same pattern as measurement uncertainty: streamflow 〈 sediment 〈 NH 4 -N; however, the direction of that impact (increasing or decreasing) was not consistent. In addition, inclusion rate and spread results did not indicate a clear relationship between predictive uncertainty and the magnitude of measurement uncertainty. The purpose of this study was not to show that inclusion of measurement uncertainty produces better calibration results or parameter estimation. Rather, this study demonstrated that uncertainty in measured calibration/validation data can play a crucial role in parameter estimation during auto-calibration and that this important source of predictive uncertainty should be not be ignored as it is in typical model applications. Future modeling applications related to watershed management or scenario analysis should consider the potential impact of uncertainty in measured calibration/validation data, as model predictions influence decision-making, policy formulation, and regulatory action.

Description: The joint partition function approach to multifractal analysis (Meneveau et al., Phys Rev A 41:894–913, 1990 ) has been widely employed in order to characterize scale relationships between two variables coexisting along a single geometric support. The main contribution of this study was conducting a multifractal analysis for three variables coexisting in the same geometric support, in order to describe the influence across temporal scales of a meteorological and chemistry variable (temperature and NO 2 ) on tropospheric ozone concentrations. Hourly time series were recorded in the city of Seville (Spain) for summer 2011. Joint multifractal analysis was conducted by considering both the strange attractor formalism and the method of moments. Results confirmed the scale dependence among the studied variables and demonstrated the capability of joint multifractal analysis to completely characterize the scaling behaviour among three variables. Temporal variability in temperature is strongly reflected on ozone concentrations across analyzed temporal scales, but the joint multifractal spectrum for nitrogen dioxide and ozone suggest a lower degree of correlation. A loss of multifractality is found when both high temperatures and nitrogen dioxide concentrations occur. By contrast, greater variability is found in the opposite scenario.

Description: The forecasting of evaporative loss ( E ) is vital for water resource management and understanding of hydrological process for farming practices, ecosystem management and hydrologic engineering. This study has developed three machine learning algorithms, namely the relevance vector machine (RVM), extreme learning machine (ELM) and multivariate adaptive regression spline (MARS) for the prediction of E using five predictor variables, incident solar radiation ( S ), maximum temperature ( T max ), minimum temperature ( T min ), atmospheric vapor pressure ( VP ) and precipitation ( P ). The RVM model is based on the Bayesian formulation of a linear model with appropriate prior that results in sparse representations. The ELM model is computationally efficient algorithm based on Single Layer Feedforward Neural Network with hidden neurons that randomly choose input weights and the MARS model is built on flexible regression algorithm that generally divides solution space into intervals of predictor variables and fits splines (basis functions) to each interval. By utilizing random sampling process, the predictor data were partitioned into the training phase (70 % of data) and testing phase (remainder 30 %). The equations for the prediction of monthly E were formulated. The RVM model was devised using the radial basis function, while the ELM model comprised of 5 inputs and 10 hidden neurons and used the radial basis activation function, and the MARS model utilized 15 basis functions. The decomposition of variance among the predictor dataset of the MARS model yielded the largest magnitude of the Generalized Cross Validation statistic (≈0.03) when the T max was used as an input, followed by the relatively lower value (≈0.028, 0.019) for inputs defined by the S and VP . This confirmed that the prediction of E utilized the largest contributions of the predictive features from the T max , verified emphatically by sensitivity analysis test. The model performance statistics yielded correlation coefficients of 0.979 (RVM), 0.977 (ELM) and 0.974 (MARS), Root-Mean-Square-Errors of 9.306, 9.714 and 10.457 and Mean-Absolute-Error of 0.034, 0.035 and 0.038. Despite the small differences in the overall prediction skill, the RVM model appeared to be more accurate in prediction of E . It is therefore advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss.

Description: We propose a spectral turning-bands approach for the simulation of second-order stationary vector Gaussian random fields. The approach improves existing spectral methods through coupling with importance sampling techniques. A notable insight is that one can simulate any vector random field whose direct and cross-covariance functions are continuous and absolutely integrable, provided that one knows the analytical expression of their spectral densities, without the need for these spectral densities to have a bounded support. The simulation algorithm is computationally faster than circulant-embedding techniques, lends itself to parallel computing and has a low memory storage requirement. Numerical examples with varied spatial correlation structures are presented to demonstrate the accuracy and versatility of the proposal.

Description: Management of energy use and reduction of greenhouse gas emissions (GHG) in agricultural system is the important topic. For this purpose, many methods have been proposed in different researches for solution of these items in recent years. Obviously, the selection of appropriate method was a new concern for researchers. Accordingly, the energy inputs and GHG emissions of orange production in north of Iran were modeled and optimized by artificial neural networks (ANN) and multi-objective genetic algorithm (MOGA) in this study and the results obtained were compared with the results of data envelopment analysis (DEA) approach. Results showed that, on average, an amount of 25,582.50 MJ ha −1 was consumed in orange orchards in the region and the nitrogen fertilizer was accounted for 36.84 % of the total input energy. The outcomes of this study demonstrated that on average 803 kg carbon dioxide (kgCO 2eq. ) is emitted per ha and diesel fuel is responsible for 35.7 % of all emissions. The results of ANN signified that they were capable of modeling crop output and total GHG emissions where the model with a 13-4-2 topology had the highest accuracy in both training and testing steps. The optimization of energy consumption using MOGA revealed that the total energy consumption and GHG emissions of orange production can be reduced to the values of 13,519 MJ ha −1 and 261 kgCO 2eq.  ha −1 , respectively. A comparison between MOGA and DEA clearly showed the better performance of MOGA due to simultaneous application of different objectives and the global optimum solutions produced by the last generation.

Description: To study the impact of the uncertainties existed in nature and management on the ecological planning and the achievement of the planning objective under uncertainty, the General restoration planning of Shiyanghe river basin , which is for Shiyanghe river basin located in an arid inland area in China, is selected as the research object. The general thought of the essay is to get the probability of the objective achievement of ecological planning by analysing the floating region and possibility distribution of the uncertainties which is of the critical factors in achieving the planning objective. Firstly, the uncertainties of the critical factors are identified and analysed separately. Secondly, the two uncertainties are combined to study its impacts on the achievement of planning objective in each scenario. Lastly, the probability and the possible deviation of ecological objective achievement are analysed in each scenario. The methods used in the study include the scenario analysis, BPF based on BP neural network, PPCC analysis and interval analysis. The results show that the achievement of ecological objective is greatly influenced by the uncertainties, the objective achievement could be get only in the scenario of water resources utilization and management meet the planning requirement and high flow year in planning year, the results also show that the greater volume of local water resources and the higher level of local water resources utilization and management lead to the higher probability of achieving ecological objective and the lower possible deviation.

Description: Integrated noises are used in this study for tackling uncertainties in the dynamic energy budget model (DEB) to study bacterial degradation kinetics in water environment. According to the Fourier transform algorithm, the R 2 coefficients in the regression equation are greater than 0.9 and more than 80 % of the data are close to true ones, indicating that the transform algorithm is satisfactory in identifying intensity (B) and correlation time (τ) of noises existing in the DEB model. The major findings include: (1) bacterial cells are not suitable for survival under certain integrated-noises scenarios (intensity B is more than 10 −2 or constant time τ is greater than 100); (2) the conversion value is closer to the true value when the estimate sample is greater; (3) a well-identified running number (around 400 times in this study) is helpful in improving the estimation accuracy; (4) the larger the true values of B and τ, the lower the estimation accuracy. In addition, more efforts are still desired for noises characterization, for example, the reorganization of integrated noises and correlation among noises.

Description: It is expected that climate warming will be experienced through increases in the magnitude and frequency of extreme events, including droughts. This paper presents an analysis of observed changes and future projections for meteorological drought for four different time scales (1 month, and 3, 6 and 12 months) in the Beijiang River basin, South China, on the basis of the standardized precipitation evapotranspiration index (SPEI). Observed changes in meteorological drought were analysed at 24 meteorological stations from 1969 to 2011. Future meteorological drought was projected based on the representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, as projected by the regional climate model RegCM4.0. The statistical significance of the meteorological drought trends was checked with the Mann–Kendall method. The results show that drought has become more intense and more frequent in most parts of the study region during the past 43 years, mainly owing to a decrease in precipitation. Furthermore, long-term dryness is expected to be more pronounced than short-term dryness. Validation of the model simulation indicates that RegCM4.0 provides a good simulation of the characteristic values of SPEIs. During the twenty first century, significant drying trends are projected for most parts of the study region, especially in the southern part of the basin. Furthermore, the drying trends for RCP8.5 (or for long time scales) are more pronounced than for RCP4.5 (or for short time scales). Compared to the baseline period 1971–2000, the frequency of drought for RCP4.5 (RCP8.5) tends to increase (decrease) in 2021–2050 and decrease (increase) in 2051–2080. The results of this paper will be helpful for efficient water resources management in the Beijiang River basin under climate warming.

Description: A novel grid-free geostatistical simulation method (GFS) allows representing coregionalized variables as an analytical function of the coordinates of the simulation locations. Simulation on unstructured grids, regridding and refinement of available realizations of natural phenomena including, but not limited to, environmental systems are possible with GFS in a consistent manner. The unconditional realizations are generated by utilizing the linear model of coregionalization and Fourier series-based decomposition of the covariance function. The conditioning to data is performed by kriging. The data can be measured at scattered point-scale locations or sampled at a block scale. Secondary data are usually used in conjunction with primary data for the improved modeling. Satellite imaging is an example of exhaustively sampled secondary data. Improvements and recommendations are made to the implementation of GFS to properly assimilate secondary exhaustive data sets in a grid-free manner. Intrinsic cokriging (ICK) is utilized to reduce computational time and preserve the overall quality of the simulation. To further reduce the computational cost of ICK, a block matrix inversion is implemented in the calculation of the kriging weights. A projection approach to ICK is proposed to avoid artifacts in the realizations around the edges of the exhaustive data region when the data do not cover the entire modeling domain. The point-scale block value representation of the block-scale data is developed as an alternative to block cokriging to integrate block-scale data into realizations within the GFS framework. Several case studies support the proposed enhancements.

Description: Community-scale simulations were performed to investigate the risk to groundwater and indoor air receptors downgradient of a contaminated site following the remediation of a long-term source. Six suites of Monte Carlo simulations were performed using a numerical model that accounted for groundwater flow, reactive solute transport, soil gas flow, and vapour intrusion in buildings. The model was applied to a three-dimensional, community-scale (250 m × 1000 m × 14 m) domain containing heterogeneous, spatially correlated distributions of the hydraulic conductivity, fraction of organic carbon, and biodegradation rate constant, which were varied between realizations. Analysis considered results from both individual realizations as well as the suite of Monte Carlo simulations expressed through several novel, integrated parameters, such as the probability of exceeding a regulatory standard in either groundwater or indoor air. Results showed that exceedance probabilities varied considerably with the consideration of biodegradation in the saturated zone, and were less sensitive to changes in the variance of hydraulic conductivity or the incorporation of heterogeneous distributions of organic carbon at this spatial scale. A sharp gradient in exceedance probability existed at the lateral edges of the plumes due to variability in lateral dispersion, which defined a narrow region of exceedance uncertainty. Differences in exceedance probability between realizations (i.e., due to heterogeneity uncertainty) were similar to differences attributed to changes in the variance of hydraulic conductivity or fraction of organic carbon. Simulated clean-up times, defined by reaching an acceptable exceedance probability, were found to be on the order of decades to centuries in these community-scale domains. Results also showed that the choice of the acceptable exceedance probability level (e.g., 1 vs. 5 %) would likely affect clean up times on the order of decades. Moreover, in the scenarios examined here, the risk of exceeding indoor air standards was greater than that of exceeding groundwater standards at all times and places. Overall, simulations of coupled transport processes combined with novel spatial and temporal quantification metrics for Monte Carlo analyses, provide practical tools for assessing risk in wider communities when considering site remediation.

Description: In the global carbon cycle, terrestrial biomass plays an important role in both as a sink and source. To evaluate biomass variation due to various natural hazards, it is necessary to detect the location, extent and duration of vegetation disturbance at a large spatial scale with an efficient method. This study contributes to develop such a method, and only the moderate resolution imaging spectroradiometer (MODIS) MOD13Q1 enhanced vegetation index (EVI) products are used to generate a continuous vegetation damage index (CVDI) for detecting severe vegetation disturbance in large areas. To verify the performance of this new index, this study takes the identification of the vegetation damage due to the Wenchuan earthquake in China occurred on 12 May 2008 as a case study. This study calculates the CVDI for the earthquake stricken areas, and delineates the regions with considerable EVI abnormal variation. The study result reveals that those delineated regions with severe vegetation damage are normally consistent with the areas with the landslides caused by the earthquake. Moreover, according to the changes of other vegetation-related MODIS datasets since 12 May 2008, this study discloses that the EVI value in most of the areas, where the vegetation was damaged due to the earthquake, has not reached to the normal value in 2012, which is 4 years after the earthquake. Finally, to validate the vegetation damage areas determined by CVDI method, the high resolution images and field survey information are used. This study confirms that CVDI method can effectively delineate large-scale terrestrial biomass disturbance due to the earthquake and can accurately identify the vegetation recovery process.

Description: 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.

Description: With a booming development characterized by new urbanization in current China, urban water consumption attracts growing concerns. An efficient and probabilistic prediction of urban water consumption plays a vital role for urban planning toward sustainable development, especially in megacities limited by water resources. However, the data insufficiency issue commonly exists nowadays and seriously restricts further development of urban water simulation. In this article, we proposed a consolidated framework for probabilistic prediction of water consumption under an incompletely informational circumstance to deal with the challenge. The model was constructed based on a state-of-the-art Bayesian neural networks (BNNs) technique. Three dominated influencing factors were identified and included into the BNN model. Future impact factors were generated by using a variety of methods including a quadratic polynomial model, a regression and auto-regressive moving average combination model and a Grey Verhulst model. Thereafter, water consumption projection (2013–2020) and uncertainty estimates was done. Results showed that the model matched well with observations. Through reducing the dependence on large amount of information and constructing a probabilistic means incorporating uncertainty estimation, the new approach can work better than conventional means in support of water resources planning and management under an incompletely informational circumstance.

Description: To assess whether changes in the frequency of heavy rainfall events are occurring over time, annual maximum records from 21 rainfall gauges in Ontario are examined using frequency analysis methods. Relative RMSE and related boxplots are used to characterize assessment for selecting distributions; the Gumbel distribution is verified as one of the most suitable distributions to provide accurate quantile estimates. Records were divided into two time periods, and tested using the Mann-Kendall test and lag-1 autocorrelations to ensure that data in each period are identically distributed. The confidence intervals of design rainfalls for each return period (2, 5, 10, and 25-year) are derived by using resampling method, and compared at 90 % confidence levels. The changes in heavy rainfall intensities are tested at gauges across the Province of Ontario. Several significant decreases in heavy rainfall intensities are identified in central and southern Ontario. Increases in heavy rainfall intensities are identified in gauges at Sioux Lookout and Belleville. The sensitivity analysis of changes identified with respect to the year of splitting indicates changes are occurring during the 1980s and 1990s.

Description: Drought/wetness conditions are fundamental not only for agricultural production but also ecology, human health, and economic activity. Dryness/wetness is a function of precipitation, temperature, vegetation and potential evapotranspiration. Regions with low moisture are often characterized by aridity which, in turn, reflects the degree of meteorological drought. Observed climatic data from eleven meteorological stations in and around Shiyang River basin, China, were used to calculate the aridity index (AI) which was defined as the ratio of potential evapotranspiration (ET 0 ) to precipitation (P). ET 0 was calculated using the Penman–Monteith method. The ordinary kriging method was used to interpolate the spatial variability of ET 0 , P and AI. The Mann–Kendall test with a pre-whitening method was employed using the Yue and Wang autocorrelation correction to detect temporal trends. The Theil–Sen estimator was used to estimate the slopes of trend lines. Results showed a higher AI in the north basin and a lower AI in the Qilian Mountain region. Annual ET 0 and P had increasing trends with a slope of 0.672 and 0.459 mm per year, respectively, but trends were not statistically significant for most stations. While annual AI had a slight decreasing trend with a slope of −0.01 per year, the trend was not statistically significant for all stations. The decreasing trends in winter AI (at a rate of −0.313/a) was more significant than that in other seasons. The study indicates that the Shiyang River basin is getting slightly wetter, especially in winter.

Description: Hydrological simulations to delineate the impacts of climate variability and human activities are subjected to uncertainties related to both parameter and structure of the hydrological models. To analyze the impact of these uncertainties on the model performance and to yield more reliable simulation results, a global calibration and multimodel combination method that integrates the Shuffled Complex Evolution Metropolis (SCEM) and Bayesian Model Averaging of four monthly water balance models was proposed. The method was applied to the Weihe River Basin, the largest tributary of the Yellow River, to determine the contribution of climate variability and human activities to runoff changes. The change point, which was used to determine the baseline period (1956–1990) and human-impacted period (1991–2009), was derived using both cumulative curve and Pettitt’s test. Results show that the combination method from SCEM provides more skillful deterministic predictions than the best calibrated individual model, resulting in the smallest uncertainty interval of runoff changes attributed to climate variability and human activities. This combination methodology provides a practical and flexible tool for attribution of runoff changes to climate variability and human activities by hydrological models.

Description: The analysis and design of offshore structures necessitates the consideration of environmental loads. Realistic modeling of the environmental loads is particularly important to ensure reliable performance of these structures. In this paper, structural reliability analysis of offshore structures subjected to a time varying environment is investigated. In this work, an extreme value statistical model for the wave height is adopted as a basis for the performance assessment of a jacket structure. Due to the changing environment, the model parameters are modeled to be time varying. To deal with this issue, two segmentation algorithms are proposed and applied to the observed data in order to derive piecewise stationary processes for a statistical analysis. The investigation includes the extreme value modeling of the wave height in the characterization of the sea load. The implementation of the segmentation algorithms in the original data eventually leads to approximations of the safety quality of the existing structure within different time interval. The computed result is developed to reflect the time varying effects in the failure probability of structures. The results are compared with the traditional extreme values approach in view of the accuracy and information content. The investigation is also extended to a case where the design of the structure ignores the time varying property.

Description: Hydrological processes are complex non-linear phenomena. Canonical correlation analysis (CCA) is frequently used in regional frequency analysis (RFA) to delineate hydrological neighborhoods. Although non-linear CCA (NL-CCA) is widely used in several fields, it has not been used in hydrology, particularly in RFA. This paper presents an overview of techniques used to reproduce non-linear relationships between two sets of variables. The approaches considered in this work are based on NL-CCA using neural networks (CCA-NN), coupled to a log-linear regression model for flood quantile estimation. In order to demonstrate the usefulness of these approaches in RFA, a comparative study between the latter and linear CCA is performed using three different databases from North America. Results show that CCA-NN is more robust and can better reproduce the non-linear relationship structures between physiographical and hydrological variables. This reflects the high flexibility of this approach. Results indicate that for all three databases, it is more advantageous to proceed with the non-linear CCA approach.

Description: Spatial data are commonly minimal and may have been collected in the process of confirming the profitability of a mining venture or investigating a contaminated site. In such situations, it is common to have measurements preferentially taken in the most critical areas (sweet spots, allegedly contaminated areas), thus conditionally biasing the sample. While preferential sampling makes good practical sense, its direct use leads to distorted sample moments and percentiles. Spatial clusters are a problem that has been identified in the past and solved with approaches ranging from ad hoc solutions to highly elaborate mathematical formulations, covering mostly the effect of clustering on the cumulative frequency distribution. The method proposed here is a form of resample, free of special assumptions, does not use weights to ponder the measurements, does not find solutions by successive approximation and provides variability in the results. The new method is illustrated with a synthetic dataset with an exponential semivariogram and purposely generated to follow a lognormal distribution. The lognormal distribution is both difficult to work with and typical of many attributes of practical interest. Testing of the new solution shows that sample subsets derived from resampled datasets can closely approximate the true probability distribution and the semivariogram, clearly outperforming the original preferentially sampled data.

Description: Extreme precipitation event is rare and mostly occurs on a relatively small local scale, which presents marked uncertainties when analyzing its characteristics. Using daily precipitation data covering 1959–2009 from 62 stations over the Pearl River Basin, the percentile method (PM) and the absolute critical value method (ACVM) are applied to define extreme precipitation thresholds (EPT), and their different impacts on the spatial–temporal distribution of extreme precipitation event were analyzed in this study. The findings of this study show: (1) Using the K -means clustering algorithm in terms of precipitation indices and the topography, longitude and latitude of each station, the whole basin is divided into eight precipitation zones. (2) The extreme indices, including extreme precipitation frequency, extreme precipitation proportion and proportion of extremely n-day precipitation, calculated by PM are markedly higher than those calculated by ACVM during five decades, which is particularly obvious in the low precipitation area such as the west-northern of the basin since more daily precipitation events are treated as extreme precipitation in this region if EPT is defined by PM. (3) The spatial distributions of extreme frequencies respectively calculated by these two methods are quite different across the basin. The spatial distribution of extreme frequencies calculated by ACVM shows a high-value center in the southeast coastal areas and a low-value center in the northwest mountain areas. However, the extreme frequencies calculated by PM distribute evenly over the basin, which is obviously inconsistent with the empirical results, an area with heavy precipitation usually has a high extreme precipitation frequency, and vice versa.

Description: Sewer inlet structures are vital components of urban drainage systems and their operational conditions can largely affect the overall performance of the system. However, their hydraulic behaviour and the way in which it is affected by clogging is often overlooked in urban drainage models, thus leading to misrepresentation of system performance and, in particular, of flooding occurrence. In the present paper, a novel methodology is proposed to stochastically model stormwater urban drainage systems, taking the impact of sewer inlet operational conditions (e.g. clogging due to debris accumulation) on urban pluvial flooding into account. The proposed methodology comprises three main steps: (i) identification of sewer inlets most prone to clogging based upon a spatial analysis of their proximity to trees and evaluation of sewer inlet locations; (ii) Monte Carlo simulation of the capacity of inlets prone to clogging and subsequent simulation of flooding for each sewer inlet capacity scenario, and (iii) delineation of stochastic flood hazard maps. The proposed methodology was demonstrated using as case study design storms as well as two real storm events observed in the city of Coimbra (Portugal), which reportedly led to flooding in different areas of the catchment. The results show that sewer inlet capacity can indeed have a large impact on the occurrence of urban pluvial flooding and that it is essential to account for variations in sewer inlet capacity in urban drainage models. Overall, the stochastic methodology proposed in this study constitutes a useful tool for dealing with uncertainties in sewer inlet operational conditions and, as compared to more traditional deterministic approaches, it allows a more comprehensive assessment of urban pluvial flood hazard, which in turn enables better-informed flood risk assessment and management decisions.

Description: Linking atmospheric and hydrological models is challenging because of a mismatch of spatial and temporal resolutions in which the models operate: dynamic hydrological models need input at relatively fine temporal (daily) scale, but the outputs from general circulation models are usually not realistic at the same scale, even though fine scale outputs are available. Temporal dimension downscaling methods called disaggregation are designed to produce finer temporal-scale data from reliable larger temporal-scale data. Here, we investigate a hybrid stochastic weather-generation method to simulate a high-frequency (daily) precipitation sequence based on lower frequency (monthly) amounts. To deal with many small precipitation amounts and capture large amounts, we divide the precipitation amounts on rainy days (with non-zero precipitation amounts) into two states (named moist and wet states, respectively) by a pre-defined threshold and propose a multi-state Markov chain model for the occurrences of different states (also including non-rain days called dry state). The truncated Gamma and censored extended Burr XII distributions are then employed to model the precipitation amounts in the moist and wet states, respectively. This approach avoids the need to deal with discontinuity in the distribution, and ensures that the states (dry, moist and wet) and corresponding amounts in rainy days are well matched. The method also considers seasonality by constructing individual models for different months, and monthly variation by incorporating the low-frequency amounts as a model predictor. The proposed method is compared with existing models using typical catchment data in Australia with different climate conditions (non-seasonal rainfall, summer rainfall and winter rainfall patterns) and demonstrates better performances under several evaluation criteria which are important in hydrological studies.

Description: The French Mediterranean area is subject to intense rainfall events which might cause flash floods, the main natural hazard in the area. Flood-risk rainfall is defined as rainfall with a high spatial average and encompasses rainfall which might lead to flash floods. We aim to compare eight multivariate density models for multi-site flood-risk rainfall. In particular, an accurate characterization of the spatial variability of flood-risk rainfall is crucial to help understand flash flood processes. Daily data from eight rain gauge stations at the Gardon at Anduze, a small Mediterranean catchment, are used in this work. Each multivariate density model is made of a combination of a marginal model and a dependence structure. Two marginal models are considered: the Gamma distribution (parametric) and the Log-Normal mixture (non-parametric). Four dependence structures are included in the comparison: Gaussian, Student t, Skew Normal and Skew t in increasing order of complexity. They possess a representative set of theoretical properties (symmetry/asymmetry and asymptotic dependence/independence). The multivariate models are compared in terms of three types of criteria: (1) separate evaluation of the goodness-of-fit of the margins and of the dependence structures, (2) model selection with a leave-one-out evaluation of the Anderson-Darling and Cramer-Von Mises statistics and (3) comparison in terms of two hydrologically interpretable quantities (return periods of the spatial average and conditional probabilities of exceedances). The key outcome of the comparison is that the Skew Normal with the Log-Normal mixture margins outperform significantly the other models. The asymmetry introduced by the Skew Normal is an added-value with respect to the Gaussian. Therefore, the Gaussian dependence structure, although widely used in the literature, is not recommended for the data in this study. In contrast, the asymptotically dependent models did not provide a significant improvement over the asymptotically independent ones.

Description: In general, there are few studies that analyse the impact of low temperatures on mortality, and even fewer that extend this analysis to specific causes of mortality. This study had a twofold aim: Firstly, to analyse the trend in natural-, circulatory- and respiratory-cause mortality associated with cold waves in Castile-La Mancha (Spain) across a period of analysis of 34 years, which would confer an important degree of temporal representativeness on the results obtained; and secondly, to ascertain whether this impact had decreased over the years. Time series analysis using multivariate ARIMA models with data on daily natural-, circulatory- and respiratory-cause mortality in Castile-La Mancha. The independent variables were minimum daily temperature, mean daily pressure and mean daily relative humidity. We controlled for seasonalities and trend of the series, as well as influenza epidemics, cold-wave duration and chronological number in any given year. Data were stratified in three ten-year stages, i.e., 1975–1985, 1986–1996 and 1997–2008. The mortality trigger temperature was set at a minimum daily temperature of −2 °C, corresponding to the 4th ‰ of the minimum temperature series for the winter months considered. The impact on daily natural-cause mortality for each degree that the minimum daily temperature was below −2 °C was: 10.4 % (95 % CI 9.6–11.2) in the first decade; 11.9 % (95 % CI 11.0–12.8) in the second decade; and fell to 1.6 % (95 % CI 0.9–2.3) in the third. This same pattern was observed for circulatory- and respiratory-cause mortality, with the effect of cold being greater for respiratory causes. Socio-economic factors -both of adaptation and demographic- could account for this sharp decrease in mortality associated with low temperatures. These results question climate models which predict the effects of cold over long-term time horizons, while maintaining the risk attributable to low temperatures constant. Studies similar to ours should be undertaken in other regions to confirm whether it is solely local characteristics that explain this pattern or, on the contrary, whether the pattern is generalised.

Description: This study develops three microbial growth models in the sewage biodegradation process driven by white noise, colored noise and hybrid noises, respectively. The proposed models are more universal in reflecting the impact of uncertainty on microbial systems, compared with the previous efforts. An improved Box–Mueller algorithm is used to solve the model. The modeling results show that the different noise types have remarkable effects on microbial growth kinetics. To better understanding the insights of various noises affecting the system, the growth process of microbial in the sewage biodegradation process is discussed under different conditions with varied noise properties (i.e. intensity and correlation time). The results indicate that the effect of noise on the microbial growth kinetics decreases with the reduction of the noise intensity and the correlation time. Therefore, a known noise can be relieved by changing the noise intensity or the correlation time. As the model driven by noises is capable of addressing the system’s uncertainty, it is useful in supporting stochastic simulation, risk analysis, and process design of a sewage biological treatment system. Future works may focus on the development of more effective statistical-inference methods for the noises based on observed data.

Description: Projections of changes in climate are important in assessing the potential impacts of climate change on natural and social systems. However, current knowledge on assembling different GCMs to estimate future climate change over the Pear River basin is still limited so far. This study examined the capability of BMA and arithmetic mean (AM) method in assembling precipitation and temperature from CMIP5 under RCP2.6, RCP4.5 and RCP8.5 scenarios over the Pearl River basin. Results show that the BMA outperforms the traditional AM method. Precipitation tends to increase over the basin under RCP2.6 and RCP4.5 scenarios, whereas decrease under RCP8.5. The most remarkable increase of precipitation is found in the northern region under RCP2.6 scenario. The linear trend of the monthly mean near-surface air temperature increases with the growing CO 2 concentration. The warming trends in four seasons are distinct. The warming rate is prominent in summer and spring than that in other season, meanwhile it is larger in western region than in other parts of the basin. The findings can provide beneficial reference to water resources and agriculture management strategies, as well as the adaptation and mitigation strategies for floods and droughts under the context of global climate change.

Description: In this paper we introduce a novel functional clustering method, the Bagging Voronoi K-Medoid Aligment (BVKMA) algorithm, which simultaneously clusters and aligns spatially dependent curves. It is a nonparametric statistical method that does not rely on distributional or dependency structure assumptions. The method is motivated by and applied to varved (annually laminated) sediment data from lake Kassjön in northern Sweden, aiming to infer on past environmental and climate changes. The resulting clusters and their time dynamics show great potential for seasonal climate interpretation, in particular for winter climate changes.

Description: We propose a stochastic methodology for risk assessment of a large earthquake when a long time has elapsed from the last large seismic event. We state an approximate probability distribution for the occurrence time of the next large earthquake, by knowing that the last large seismic event occurred a long time ago. We prove that, under reasonable conditions, such a distribution is exponential with a rate depending on the asymptotic slope of the cumulative intensity function corresponding to a nonhomogeneous Poisson process. As it is not possible to obtain an empirical cumulative distribution function of the waiting time for the next large earthquake, an estimator of its cumulative distribution function based on existing data is derived. We conduct a simulation study for detecting scenario in which the proposed methodology would perform well. Finally, a real-world data analysis is carried out to illustrate its potential applications, including a homogeneity test for the times between earthquakes.

Description: In this paper, we address the problem of getting order statistics for georeferenced functional data by means of depth functions. To reach this aim, we introduce the concept of spatial dispersion function for functional data in a specific location of the geographic space. Then we generalize the notion of modified half-region depth to spatial dispersion functions. Through the use of spatial dispersion functions we show how the data ordering criterion depends not only on the functional but also on the spatial component. The proposal is applied to two wide simulation studies and to real data coming from sensors.

Description: The acceleration of the industrialization process in China has increased the demand for electricity and triggered a power-plant building boom, especially along China’s eastern coast, where the economy gets off early and enjoys a fast development. The thermal plumes, residual chlorine, nuclides and other pollutions produced by the thermal and nuclear power plants have exerted an impact on the coastal eco-environment. To monitor the thermal pollution from the power plants at Yueqing Bay on the eastern coast, in this research, the distribution of sea surface temperature (SST) surrounding the power plants is obtained by using the SST retrieval methods developed for Landsat Enhanced Thematic Mapper Plus (ETM+), HJ-1B infrared sensor (IRS) and Terra moderate resolution imaging spectroradiometer (MODIS) data. The comparison of the SST retrieval results before and after the operation of power plants indicates that the total area of sea waters that is impacted by the thermal discharge from the two power plants at Yueqing Bay is approximately 17.95 km 2 , with the highest SST rise of 4.5 °C appearing over the waters around the outlet of the Huaneng Yuhuan power plant on the eastern shore, whereas the highest SST rise around the Zheneng Yueqing power plant on the western shore reaches 3.8 °C. The intensity and scope of influence of the thermal discharge mainly depend on the installed capacity of power plants, coastal terrain, and tide. Although the area where the SST rise is more than 3 °C is not large, thermal discharge still has an impact on bay ecosystems due to the relatively closed nature of the bay environment. Due to the influence of rising water temperatures on the reproduction and individual evolution of fish, shrimp, crabs, shellfish and other aquatic creatures, in the long term, the thermal pollution from coastal power plants will affect the volume of natural fishery and biological resources throughout the waters. The quantitative retrieval results also suggest that relative to MODIS data, Landsat ETM+ and HJ-1B IRS data with a high spatial resolution are more applicable to the estimation of small-scale SST, and IRS data with a high temporal resolution are more helpful in the study of spatio-temporal variability of thermal plumes from coastal power plants.

Description: Variation in disease risk underlying observed disease counts is increasingly a focus for Bayesian spatial modelling, including applications in spatial data mining. Bayesian analysis of spatial data, whether for disease or other types of event, often employs a conditionally autoregressive prior, which can express spatial dependence commonly present in underlying risks or rates. Such conditionally autoregressive priors typically assume a normal density and uniform local smoothing for underlying risks. However, normality assumptions may be affected or distorted by heteroscedasticity or spatial outliers. It is also desirable that spatial disease models represent variation that is not attributable to spatial dependence. A spatial prior representing spatial heteroscedasticity within a model accommodating both spatial and non-spatial variation is therefore proposed. Illustrative applications are to human TB incidence. A simulation example is based on mainland US states, while a real data application considers TB incidence in 326 English local authorities.

Description: The conditional merging (CM) spatial interpolation technique was applied to obtain the composite soil moisture products using the AMSR2 and in situ soil moisture for the 51 days of the summer through the late fall season of the year 2012 in Korean Peninsula. The ‘leave one out cross-validation’ analysis was conducted to assess the performance of the composite soil moisture products in estimating the soil moisture in ungagged locations. The control variable for comparison was the soil moisture products obtained by spatially interpolating the in situ soil moisture data measured at eight gage locations using the Ordinary Kriging (KR) technique. The results show that the composite soil moisture products are more accurate than the in situ only soil moisture products in estimating the soil moisture for the following cases: (1) when the spatial correlation of in situ soil moisture data is low. Such case includes when there is little rainfall and where the altitude is high (mountainous area) and (2) where the gage density is low or the area located further away from the in situ gages. For both cases, the KR method cannot use enough information due to the low spatial correlation of the in situ measurement for interpolation, while the CM method can take advantage of the satellite soil moisture measurement not affected by the spatial correlation of the in situ data.

Description: The present study assessed the health risks associated with road dusts in major traffic hotspots and unpaved road sites in Abeokuta metropolis, Ogun State, southwestern Nigeria. Dust samples were collected from forty-seven sites (unpaved and paved roads) between February and March 2015. Three soil samples were also collected from the farmland of the Federal University of Agriculture, Abeokuta, as control. A total of 50 road dust and control soil samples were collected and subjected to laboratory assays using standard procedures. The physical and chemical parameters analyzed were pH, electrical conductivity and metal content (Cu, Zn, Fe, Mn, Pb, Ni, Cd, Cr, V, Ba, Na and K). The health risk indices of non-carcinogenic effects [hazard quotient and hazard index (HI)] and cancer risk of toxic metals in soil/dust samples were assessed. Data were evaluated for descriptive and inferential statistics using the Statistical Package for Social Sciences (SPSS) for Windows package. Results indicated higher significant (p 〈 0.05) values of Zn at the roadsides (paved = 94.1 ± 52.1 mg kg −1 , unpaved = 101.5 ± 69 mg kg −1 ) than control (27.6 ± 16.5 mg kg −1 ). Pb concentrations of road dusts (paved = 31.8 ± 33.6 mg kg −1 , unpaved = 50.8 ± 48.9 mg kg −1 ) were also statistically higher (p 〈 0.05) than those of control samples (6.33 ± 3.36 mg kg −1 ). However, Mn was measured at significantly (p 〈 0.05) higher concentration in control soil than road dust samples. The varimax rotated Principal Component Analysis revealed four major emission sources of metals in both paved and unpaved dust samples. The health risk assessment of metals showed HI values less than 1.0 in adults and greater than 1.0 in children. The health assessment results showed children to be at higher risk of metal exposure in road dust than the adults. The order of CR values for metals in road dusts and control soil follows Cr 〉 Cd 〉 Ni 〉 Pb for adults and children. The CR values of Cr, Cd and Ni in road dusts were higher than the acceptable safe limit of 1.0 × 10 −4 indicating probable carcinogenic adverse effects.

Description: Earthquakes are one of the most destructive natural disasters and the spatial distribution of their epicentres generally shows diverse interaction structures at different spatial scales. In this paper, we use a multi-scale point pattern model to describe the main seismicity in the Hellenic area over the last 10 years. We analyze the interaction between events and the relationship with geological information of the study area, using hybrid models as proposed by Baddeley et al. ( 2013 ). In our analysis, we find two competing suitable hybrid models, one with a full parametric structure and the other one based on nonparametric kernel estimators for the spatial inhomogeneity.

Description: The aim of this study is to estimate likely changes in flood indices under a future climate and to assess the uncertainty in these estimates for selected catchments in Poland. Precipitation and temperature time series from climate simulations from the EURO-CORDEX initiative for the periods 1971–2000, 2021–2050 and 2071–2100 following the RCP4.5 and RCP8.5 emission scenarios have been used to produce hydrological simulations based on the HBV hydrological model. As the climate model outputs for Poland are highly biased, post processing in the form of bias correction was first performed so that the climate time series could be applied in hydrological simulations at a catchment-scale. The results indicate that bias correction significantly improves flow simulations and estimated flood indices based on comparisons with simulations from observed climate data for the control period. The estimated changes in the mean annual flood and in flood quantiles under a future climate indicate a large spread in the estimates both within and between the catchments. An ANOVA analysis was used to assess the relative contributions of the 2 emission scenarios, the 7 climate models and the 4 bias correction methods to the total spread in the projected changes in extreme river flow indices for each catchment. The analysis indicates that the differences between climate models generally make the largest contribution to the spread in the ensemble of the three factors considered. The results for bias corrected data show small differences between the four bias correction methods considered, and, in contrast with the results for uncorrected simulations, project increases in flood indices for most catchments under a future climate.

Description: In a case of radioactive release in the environment, modeling the radionuclide atmospheric dispersion is particularly useful for emergency response procedures and risk assessment. For this, the CEA has developed a numerical simulator, called Ceres-Mithra, to predict spatial maps of radionuclide concentrations at different instants. This computer code depends on many uncertain scalar and temporal parameters, describing the radionuclide, release or weather characteristics. The purpose is to detect the input parameters the uncertainties of which highly affect the predicted concentrations and to quantify their influences. To this end, we present various measures for the sensitivity analysis of a spatial model. Some of them lead to as many analyses as spatial locations (site sensitivity indices) while others consider a single one, with respect to the whole spatial domain (block sensitivity indices). For both categories, variance-based and dependence measures are considered, based on recent literature. All of these sensitivity measures are applied to the C-M computer code and compared to each other, showing the complementarity of block and site sensitivity analyses. Finally, a sensitivity analysis summarizing the input uncertainty contribution over the entirety of the spatio-temporal domain is proposed.

Description: Due to natural heterogeneity in runoff processes, the analysis of response of stream channels to the variation of lateral inflow is therefore viewed in terms of stochastic spatiotemporal processes. Based on the representation theorem, a closed-form expression is derived to describe the spectral response characteristic of stream subject to spatiotemporal fluctuations in lateral inflow. It provides a basis for evaluating the induced discharge variability in stream channels. It is found that the evolutionary power spectrum of the stream flow discharge process and therefore the variance is increased with the distance from the upstream boundary and the characteristic length scale of the lateral inflow process. Flow discharge prediction in the downstream region has a high degree of uncertainty by solving the deterministic partial differential equation.

Description: While dendroclimatic studies have extended the knowledge of drought variations in Tien Shan, these have been almost exclusively based on tree-ring data from Tien Shan in China. We present a drought reconstruction for Almaty based on a tree-ring width chronology developed from sites of the Schrenk spruce in Tien Shan, Kazakhstan. The drought reconstruction, spanning AD 1785–2014, was developed by calibrating tree-ring series with the mean August to January standardized precipitation evapotranspiration index (SPEI). The drought reconstruction was verified with independent data and accounts for 41.9 % of the actual SPEI variance during the common period. The drought reconstruction compares well with some tree-ring-based drought/precipitation reconstructions from Western Tien Shan and reveals the large-scale drought signals of Western Tien Shan. The wavelet analysis indicates the existence of some decadal (60 and 11 years) and interannual (2.0–4.0 years) periodicities, which may potentially be the fingerprints of large-scale land–atmosphere–ocean circulations. This study provides the first long-term drought reconstruction and drought assessment for Almaty and will aid in future plans to address climate change of Kazakhstan.

Description: A system approach is used to investigate the potential risk of groundwater contamination from a failure associated with hydraulic fracturing. The focus is on the role of permeability anisotropy, initial saturation of the medium, leakage depth and leakage rate in controlling the contamination risk at environmentally sensitive locations. We numerically simulate the fluid flow and chemical transport in the geological formations, and use the Monte Carlo algorithm to quantify uncertainty. Geological and operational parameters are selected as random variables. We develop a risk framework to assess three environmental performance metrics: the solute concentration, the arrival times from source to receptor, and the ingestion hazard of the contaminated aquifer. We define risk as the probability of exceeding a certain threshold level for each metric. The effect of parametric uncertainty in risk is also analyzed. The results show that risk strongly depends on water saturation and the anisotropy of the permeability distribution. Furthermore, the measured risk value is more sensitive to leakage depth and leakage rate when compared to the hydrogeological properties. Findings of this study may be applied to situations with more stringent well integrity requirements to ensure that hydraulic fracturing is practiced in an environmentally safe and sound manner, with minimal risk to water contamination.

Description: The creeping characteristics of drought make it possible to mitigate drought’s effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, we proposed a new probabilistic scheme to forecast droughts that used a discrete-time finite state-space hidden Markov model (HMM) aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The standardized precipitation index (SPI) with a 3-month time scale was employed to represent the drought status over the selected stations in South Korea. The new scheme used a reversible jump Markov chain Monte Carlo algorithm for inference on the model parameters and performed an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to perform a probabilistic forecast of SPI at the 3-month time scale that considered uncertainties. The point forecasts which were derived as the HMM-RCP forecast mean values, as measured by forecasting skill scores, were much more accurate than those from conventional models and a climatology reference model at various lead times. We also used probabilistic forecast verification and found that the HMM-RCP provided a probabilistic forecast with satisfactory evaluation for different drought categories, even at long lead times. In a drought event analysis, the HMM-RCP accurately predicted about 71.19 % of drought events during the validation period and forecasted the mean duration with an error of less than 1.8 months and a mean severity error of 〈0.57. The results showed that the HMM-RCP had good potential in probabilistic drought forecasting.

Description: This paper develops a minimum relative entropy theory with frequency as a random variable, called MREF henceforth, for streamflow forecasting. The MREF theory consists of three main components: (1) determination of spectral density (2) determination of parameters by cepstrum analysis, and (3) extension of autocorrelation function. MREF is robust at determining the main periodicity, and provides higher resolution spectral density. The theory is evaluated using monthly streamflow observed at 20 stations in the Mississippi River basin, where forecasted monthly streamflows show the coefficient of determination ( r 2 ) of 0.876, which is slightly higher in the Upper Mississippi ( r 2  = 0.932) than in the Lower Mississippi ( r 2  = 0.806). Comparison of different priors shows that the prior with the background spectral density with a peak at 1/12 frequency provides satisfactory accuracy, and can be used to forecast monthly streamflow with limited information. Four different entropy theories are compared, and it is found that the minimum relative entropy theory has an advantage over maximum entropy (ME) for both spectral estimation and streamflow forecasting, if additional information as a prior is given. Besides, MREF is found to be more convenient to estimate parameters with cepstrum analysis than minimum relative entropy with spectral power as random variable (MRES), and less information is needed to assume the prior. In general, the reliability of monthly streamflow forecasting from the highest to the lowest is for MREF, MRES, configuration entropy (CE), Burg entropy (BE), and then autoregressive method (AR), respectively.

Description: This paper introduces a new geostatistical model for counting data under a space-time approach using nonhomogeneous Poisson processes, where the random intensity process has an additive formulation with two components: a Gaussian spatial component and a component accounting for the temporal effect. Inferences of interest for the proposed model are obtained under the Bayesian paradigm. To illustrate the usefulness of the proposed model, we first develop a simulation study to test the efficacy of the Markov Chain Monte Carlo (MCMC) method to generate samples for the joint posterior distribution of the model’s parameters. This study shows that the convergence of the MCMC algorithm used to simulate samples for the joint posterior distribution of interest is easily obtained for different scenarios. As a second illustration, the proposed model is applied to a real data set related to ozone air pollution collected in 22 monitoring stations in Mexico City in the 2010 year. The proposed geostatistical model has good performance in the data analysis, in terms of fit to the data and in the identification of the regions with the highest pollution levels, that is, the southwest, the central and the northwest regions of Mexico City.

Description: Assessing the response of flood risk caused by climate change and social development is very important in terms of determining high risk areas in different periods as well as making disaster mitigating plans. We establish a flood risk assessment model based on geographic information system and natural disaster risk assessment theory. In order to compare the index value in different periods and spaces, we utilize the spatial and temporal standardization method to standardized index. To avoid one-sidedness caused by using one weight calibration method only, we employ the least square method to synthesize weights determine by the Analytic Hierarchy Process (AHP) method and the Entropy weight method. We adopt the observed data of the Huaihe River basin from 1960 to 2010 to assess the changing of flood risk between period I (1960–1980) and period II (1980–2010). After pre-processing the atmosphere–ocean coupled global circulation models (AOGCM) data, including bias correction and downscaling, we use the corrected data to predict the flood risk during future period III (2010–2040). The results show that high risk areas and moderate to high risk areas during period I take up 17.68 and 33.88 % of the total area of the Huaihe River basin, respectively. During period II, the high risk areas show an increasing percent change of 1.93 % and a decreasing trend in moderate to high risk areas of 3.8 %. Compared with period II, the high risk areas and the moderate to high risk areas during period III show an increasing trend of 8.02 and 0.77 %, which is the result of the combined effects of climate change and social development. The results presented here can provide useful information for decision-makers.

Description: We employ a stochastic dominance (SD) approach to analyze the components that contribute to environmental degradation over time. The variables include countries’ greenhouse gas (GHG) emissions and water pollution. Our approach is based on pair-wise SD tests. First, we study the dynamic progress of each separate variable over time, from 1990 to 2005, within 5-year horizons. Then, pair-wise SD tests are used to study the major industry contributors to the overall GHG emissions and water pollution at any given time, to uncover the industry which contributes the most to total emissions and water pollution. While CO 2 emissions increased in the first-order SD sense over 15 years, water pollution increased in a second-order SD sense. Electricity and heat production were the major contributors to the CO 2 emissions, while the food industry gradually became the major water polluting industry over time.

Description: The main purpose of this study was to determine the most dominant periodic components that affect the annual and seasonal precipitation trends in each homogenous rainfall region in the Langat River Basin, Malaysia for the period 1982–2011. Performing this research could be essential because in the previous studies on detection of trend in Malaysia, the details of variations of different time scales and the periodic responsible for the observed trends were not investigated. Using discrete wavelet transform (DWT) coupled with Mann–Kendall at the regional scale for the first time particularly in the context of Malaysia is the contribution of this study. In order to form the homogenous rainfall regions, first the total annual and seasonal precipitation in each year was spatialized into 5 km × 5 km grids using the inverse distance weighting method. The obtained precipitation series for the grids were then grouped applying the Ward’s clustering method based on the similarity of precipitation time series. After allocating a cluster number to each grid, the boundary of the regions was formed in ArcGIS software. Following which, in each homogenous region the areal precipitation series were computed by the Thiessen polygon method. The Mann–Kendall (MK) test was used to detect trend and the DWT coupled with the MK test and the sequential MK analysis were then utilized in order to find out the time scale which affected the observed trend in each homogenous region. On annual scale, it was found that D 1 (plus approximation) component in regions Annual Cluster1 (AC1) and AC2 was the periodic mode responsible for trends. On seasonal scale, in regions Northeast monsoon Cluster 1 (NC1), NC3, SC1 and Southwest monsoon Cluster 2 (SC2), D 1 (with approximation), in regions NC4, Inter monsoon 1 Cluster 1 (I1C1), I1C2, Inter monsoon 2 Cluster 1 I2C1 and I2C2, Detail 2 (D 2 ) (plus approximation) and in region NC2, Detail 3 (D 3 ) (with approximation added) component were the most influential periodicity for trends.

Description: We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods.

Description: The Tarim River basin, China, is a typical arid inland river basin in China. Management of water resources and agricultural development rely heavily on the understanding of droughts. In this study, an integrated drought index is proposed, based on the Standardized Precipitation Evapotranspiration Index and the Standardized Runoff Index, and then the changing properties of drought regimes have been analyzed using a Markov Chain model. Results indicate that: (1) the Kaidu and Aksu Rivers are dominated by prompt transition between hydrological and meteorological droughts. Long-lasting droughts heavily impact agricultural development in the Kaidu and Yarkand River basins; (2) the Kaidu and Aksu River basins are influenced mainly by hydro-meteorological droughts and hydrological drought is dominant in the Yarkand River basin; (3) different drought conditions are the results of different sources of river runoff. Increasing precipitation alleviate droughts in the Tarim River basin. However, a drying tendency can still be found in the Kaidu River basin; (4) higher probability is detected for the transition from both meteorological and hydrological wet to both meteorological and hydrological dry, implying that the Tarim River basin is sensitive to both meteorological and hydrological droughts. Results of this study are of practical value for the regional management of water resources, planning of agricultural irrigation, and measures for mitigation of hydrological and meteorological droughts.

Description: Spatial-depth functional regression is applied for the estimation of ocean temperature, with projection onto the eigenvectors of the empirical covariance operator of the functional response (i.e., onto the Empirical Orthogonal Functions in space and depth). Moment-based estimation is performed to approximate the regression operators in the subspace generated by the empirical eigenvectors associated with nonnull eigenvalues. In addition, Bayesian estimation is performed to approximate the regression operators in the subspace generated by the empirical eigenvectors associated with almost null eigenvalues. The cross-validation results obtained, together with the spatial-depth residual correlation analysis carried out on a real data set for the South Atlantic area, to the east of Argentina and the Falkland Islands, represent an improvement on those provided by the wavelet-based approach recently proposed in Fernández-Pascual (Stoch Environ Res Risk Assess 30:523–557, 2016 ).

Description: This article discusses the method of higher-order L-moment (LH-moment) estimation for the Wakeby distribution (WAD), and describes and formulates details of parameter estimation using LH-moments for WAD. Monte Carlo simulation is performed, to illustrate the performance of the LH-moment method via heavy-tail quantiles (over all quantiles) using WAD. The LH-moment method proves as useful and effective as the L-moment approach in handling data that follow WAD, and it is then applied to annual maximum flood and wave height data.

Description: In this paper, we examined the peak flow distribution on a realization of networks obtained with stochastic network models. Three network models including the uniform model, the Scheidegger model, and the Gibbsian model were utilized to generate networks. The network efficiency in terms of drainage time is highest on the Scheidegger model, whereas it is lowest on the uniform model. The Gibbsian model covers both depending on the parameter value of β . The magnitude of the peak flow at the outlet itself is higher on the Scheidegger model compared to the uniform model. However, the results indicate that the maximum peak flows can be observed not just at the outlet but also other parts of the mainstream. The results show that the peak flow distribution on each stochastic model has a common multifractal spectrum. The minimum value of α, which is obtained in the limit of a sufficiently large q , is equal to the fractal dimension of a single river. The multifractal properties clearly show the difference among three stochastic network models and how they are related. Moreover, the results imply that the multifractal properties can be utilized to estimate the value of β for a given drainage network.

Description: Water reservoirs exercise a considerable influence on hydrological processes and their influence can be treated as one of the influences of human activities on the hydrological cycle at the regional and even global scale. Long daily streamflow series from two gauging stations, Cuntan and Yichang, are analyzed to quantify the effect of the Gezhouba- and the Three Gorges Dams on the Yangtze River flow variations. The Cuntan- and Yichang stations are located up- and downstreams of these two dams, respectively. The quantification entails the employment of conventional multifractal analysis (MFA) and MF-detrended fluctuation analysis (MF-DFA). The streamflow series are divided into six segments based on the time when the Gezhouba- and Three Gorges Dams were constructed. Thus, the effect of these two dams can be compared through MF properties of streamflow before and after the construction of water reservoirs. The effect of the Gezhouba Dam on streamflow downstream may not be reflected by conventional MFA but can be seen from the results of MF-DFA. It should be due to the fact that MF-DFA is on the basis of fluctuations around the dominant trend, reflecting more local information; while the box-counting algorithms investigate the streamflow from the whole view. Particularly, for the inter-station comparison of results obtained by MF-DFA-based analysis, the strongest impact on the streamflow downstream is indicated by the most significant difference in generalized fractal dimension spectrum appearing during the construction of Gezhouba Dam. In addition, after the construction of Gezhouba Dam, the minimal MF dimension at Yichang station start to be less than that at Cuntan station, suggesting that the streamflow becomes less fluctuated, which should be attributed to the filter effect of water reservoir. This study presents a feasible way to evaluate, wholly and locally, the impact of water reservoirs on streamflow in other river basins in the world.

Description: Reference evapotranspiration ( ET 0 ) is a key parameter in hydrological and meteorological studies. In this study, the FAO Penman–Monteith equation was used to estimate ET 0 , and the change in ET 0 was investigated in China from 1960 to 2011. The results show that a change point around the year 1993 was detected for the annual ET 0 series by the Cramer’s test. For the national average, annual ET 0 decreased significantly ( P  〈 0.001) by −14.35 mm/decade from 1960 to 1992, while ET 0 increased significantly ( P  〈 0.05) by 22.40 mm/decade from 1993 to 2011. A differential equation method was used to attribute the change in ET 0 to climate variables. The attribution results indicate that ET 0 was most sensitive to change in vapor pressure, followed by solar radiation, air temperature and wind speed. However, the effective impact of change in climate variable on ET 0 was the product of the sensitivity and the change rate of climate variable. During 1960–1992, the decrease in solar radiation was the main reason of the decrease in ET 0 in humid region, while decrease in wind speed was the dominant factor of decreases in ET 0 in arid region and semi-arid/semi-humid region of China. Decrease in solar radiation and/or wind speed offset the effect of increasing air temperature on ET 0 , and together led to the decrease in ET 0 from 1960 to 1992. Since 1993, the rapidly increasing air temperature was the dominant factor to the change in ET 0 in all the three regions of China, which led to the increase in ET 0 . Furthermore, the future change in ET 0 was calculated under IPCC SRES A1B and B1 scenarios with projections from three GCMs. The results showed that increasing air temperature would dominate the change in ET 0 and ET 0 would increase by 2.13–10.77, 4.42–16.21 and 8.67–21.27 % during 2020s, 2050s and 2080s compared with the average annual ET 0 during 1960–1990, respectively. The increases in ET 0 would lead to the increase in agriculture water consumption in the 21st century and may aggravate the water shortage in China.

Description: This paper presents a general spatio-temporal model for assessing the air quality impact of environmental policies which are introduced as abrupt changes. The estimation method is based on the EM algorithm and the model allows to estimate the impact on air quality over a region and the reduction of human exposure following the considered environmental policy. Moreover, impact testing is proposed as a likelihood ratio test and the number of observations after intervention is computed in order to achieve a certain power for a minimal reduction. An extensive case study is related to the introduction of the congestion charge in Milan city. The consequent estimated reduction of airborne particulate matters and total nitrogen oxides motivates the methods introduced while its derivation illustrates both implementation and inferential issues.

Description: The stochastic continuum (SC) representation is one common approach for simulating the effects of fracture heterogeneity in groundwater flow and transport models. These SC reservoir models are generally developed using geostatistical methods (e.g., kriging or sequential simulation) that rely on the model semivariogram to describe the spatial variability of each continuum. Although a number of strategies for sampling spatial distributions have been published in the literature, little attention has been paid to the optimization of sampling in resource- or access-limited environments. Here we present a strategy for estimating the minimum sample spacing needed to define the spatial distribution of fractures on a vertical outcrop of basalt, located in the Box Canyon, east Snake River Plain, Idaho. We used fracture maps of similar basalts from the published literature to test experimentally the effects of different sample spacings on the resulting semivariogram model. Our final field sampling strategy was based on the lowest sample density that reproduced the semivariogram of the exhaustively sampled fracture map. Application of the derived sampling strategy to an outcrop in our field area gave excellent results, and illustrates the utility of this type of sample optimization. The method will work for developing a sampling plan for any intensive property, provided prior information for a similar domain is available; for example, fracture maps or ortho-rectified photographs from analogous rock types could be used to plan for sampling of a fractured rock outcrop.

Description: Support vector machine (SVM) classification models were constructed using a radial basis functions (RBF). These models were used for classification according to dissolved oxygen, permanganate index, ammoniac nitrogen, total nitrogen, or total phosphorus. Cross-validation and grid-search were applied to find satisfactory parameters for RBF for the improved models. Then the improved models were used to assess water quality utilizing a real-world data set (surface water quality monitoring data). The data set was comprised of more than 2,000 water samples representing 172 different sites monitored for one hydrological year. The results showed that the method presented in this paper had excellent performance, and the SVM classification models performed relatively better than the Linear Discriminant Analysis and Quadratic Discriminant Analysis models for classification.

Description: This paper explores our ability to map the extent of large floods in near real time using coarse resolution C-band radar remote sensing. The European Space Agency’s advanced synthetic aperture radar aboard the Envisat satellite, operating in global monitoring mode (GM), is considered for Australia due to its high temporal frequency, comprehensive coverage and ease of acquisition. Challenges are identified which relate both to the use of radar generally, and also in particular to GM data, in the demarcation of water and land. In Australia, the need for a better understanding of the expected backscatter response from inundated areas in tropical savanna, which covers one third of its landmass, is targeted. The backscatter responses to two large flood events in the tropical savanna of northern Australia are investigated, showing markedly different results. One flood allows the accurate classification of inundated extents, while the other is almost completely indistinguishable from the surrounding wet vegetation. Data from water height loggers established in the neighbouring Mitchell floodplain over a dry/wet season period provide an insight into the interaction of these particular vegetation conditions under flood. Results concur with the work of others, that backscatter response is a complex combination of effects depending on relative water height, vegetation spatial density, biomass, and verticality, or enmeshment, of super-surface grasses. Opportunities are also identified that relate to future space missions, the synoptic use with optical data, and better knowledge of the processes that govern the applicability of radar data for mapping large flood events.

Description: The Evaluation of flood risk is a difficult task due to its numerous and complex impact factors. This article built a classification and regression tree (CART) model for the flood risk assessment with the available data of Hunan Province. This model is able to extract the major impact factors from many complex variables, determine the factors’ thresholds, and evaluate the levels of flood risk objectively. To construct the model, 18 explanatory variables were selected as the influential factors, including meteorological conditions, surface conditions and social vulnerability. Economic loss density from flood was chosen as the response variable for the quantitative and comprehensive evaluation of flood risk. The final model showed that meteorological conditions have the most significant influence on flood risk. Additionally, the relationship between meteorological factors and flood risk is rather complex. The variability of rainstorm days during the seasonal alternate period from the end of spring (May) to the early summer (June) is the source of the highest flood risk. In addition, the regional embankment density and population density as social vulnerability indicators and the relief degree of land surface as a surface condition indicator were also included in the flood risk assessment for Hunan. A region with dense dams appeared at a relatively higher risk. Densely inhabited areas with greater topographical relief also demonstrated a higher flood risk in the study area. The conditions obtained from the final tree for different levels of risk demonstrate the objectivity of selecting impact factors and a reduction of complexity for the risk evaluation process. Furthermore, the evaluation of high-level risk using the proposed method requires fewer conditions, which allows for a rapid risk assessment of serious floods. The CART method shows a decreased root mean squared error compared with that of a multiple linear regression model. In addition, the cross-validation error was improved for the high-risk levels that represent the most important classes in risk management. The verification with the available historical records showed that the output of the model is reliable. In summary, the CART method is feasible for extracting the main impact factors and their associated thresholds for the comprehensive assessment of regional flood risk.

Description: Reservoir operation is one of the challenging problems for water resources planners and managers. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. This paper discusses an overview of simulation and optimization modeling methods utilized in resolving critical issues with regard to reservoir systems. In designing a highly efficient as well as effective dam and reservoir operational system, reservoir simulation constitutes one of the most important steps to be considered. Reservoirs with well-functional and reliable optimization models require very accurate simulations. However, the nonlinearity of natural physical processes causes a major problem in determining the simulation of the reservoir’s parameters (elevation, surface-area, storage). Optimization techniques have shown high efficiency when used with simulation modeling and the combination of the two methods had given the best results in the reservoir management. The principal concern of this review study is to critically evaluate and analyze simulation, optimization and combined simulation–optimization modeling approach and present an overview of their utility in previous studies. Inferences and suggestions which may assist in improving quality of this overview in the future are provided. These will also enable future researchers, system analysts and managers to achieve more precise optimal operational system.

Description: The Yangtze River Delta region is the region with highest urbanization speed in China. In this study, 6 typical urbanization areas in Yangtze River Delta were selected as the objectives of study. Flood risk assessment index system was established based on the flood disaster formation mechanism, and analytic hierarchy process was utilized to define the weight of indices. The flood hazard, the exposure of disaster bearing body, the vulnerability of disaster bearing body and the comprehensive flood risk corresponding to three typical years in different urbanization stages, 1991, 2001 and 2006 were assessed. The results show that the flood hazard and the exposure of disaster bearing body in the 6 areas are all with an increasing trend in the process of urbanization, among which, the increasing trend of the exposure of disaster bearing body is especially obvious. Though the vulnerabilities of disaster bearing body in the 6 areas are all with decreasing trend owe to the enhancement of flood control and disaster mitigation capability, the comprehensive flood risks in the 6 areas increased as a whole, which would pose a serious threat to urban sustainable development. Finally, effective countermeasures for flood risk management of urbanization areas in Yangtze River Delta were put forward based on the assessment results.

Description: Daily discharges of two springs lying in a karstic environment were simulated for a period of 2.5 years with the use of a multi-layer perceptron back-propagation neural network. Two models were developed for the springs, one relying on the original data and another where the missing discharge values were supplemented by assuming linear relationships during base flow conditions. For both springs the mean square error of the two models did not differ significantly, with an improvement exhibited at the extremes, during the network’s training phase, by the model that utilized the extended data set, the results of which are reported here. The time lag between precipitation and spring discharge differed significantly for the two springs indicating that in karstic environments hydraulic behavior is dominated, even within a few hundred meters, by local conditions. Optimum training results were attained with a Levenberg–Marquardt algorithm resulting in a network architecture consisting of two input layer neurons, four hidden layer neurons, and one output layer neuron, the spring’s discharge. The neural network’s predictions captured the behavior for both springs and followed very closely the discontinuities in the discharge time series. Under-/over-estimation of observed discharges for the two springs remained below 3 %, with the exception of a few local maxima where the predicted discharges diverged more strongly from observed values. Inclusion of temperature data did not add to the improvement of predictions. Finally, optimum predictions were attained when past discharge data were added to the input record and discharge differentials rather than direct discharges were calculated resulting in elimination of any local maximum discrepancy between observed and predicted discharge values.

Description: The Kuye River is the primary tributary located in the sediment concentrated regions in the Middle Yellow River in China. Significant decrease in streamflow has been observed in the Kuye River. The non-parametric Mann–Kendall test was applied to detect the change in annual streamflow for the period of 1960 to 2006. Mean annual streamflow in the Kuye River was 84.9 mm from 1960 to 1979 (period I), while it decreased to 58.2 mm from 1980 to 1998 (period II) and 20.5 mm from 1999 to 2006 (period III), respectively. The climate elasticity method and the hydrological modeling method were individually employed to assess the impact of climate variability and human activities on the decrease in streamflow. The results showed that climate variability was responsible for 29.6 and 27.1 % of the streamflow decrease from the climate elasticity method and the hydrological modeling method, respectively; while human activities accounted for 70.4 and 72.9 % of the streamflow decrease in period II. In period III, climate variability contributed 40.9 and 39.3 % of the streamflow decrease from the climate elasticity method and the hydrological modeling method, respectively; while human activities accounted for 59.1 and 60.7 % of the streamflow decrease. Therefore, human activities were the main reason of the streamflow decrease. Soil conservation measures (planting trees, improving pastures, building terraces and sediment-trapping dams) and coal mining led to the streamflow reduction in the Kuye River.

Description: Understanding the frequency and causes of extreme events is crucial for environmental, social and economic protection and planning. In Australia this was never more apparent than January 2011 when widespread flooding across Queensland, New South Wales (NSW), and Victoria resulted in the loss of human lives and devastating impacts to infrastructure and local economies. However, understanding the interplay between the geomorphology of catchments and their hydrology remains poorly developed in floodplain planning guidelines. This paper seeks to explain spatial patterns of flood inundation in terms of downstream variations in channel morphometry; and to discuss the significance of these findings within the context of improving flood risk avoidance strategies and environmental outcomes for urban streams. A prominent characteristic of streams draining catchments in the Lockyer Valley south east Queensland and the Illawarra region of NSW, for example, are well developed macrochannels that have formed in mid-catchment zones. Detailed hydraulic modeling using HEC-RAS, HEC-GeoRAS and ArcGIS indicates that these macrochannels are scaled to accommodate high magnitude floods by operating as ‘bankfull’ channels during such events. In south east Queensland, locations where macrochannels debouch onto unconfined low gradient floodplains appear especially vulnerable to catastrophic flooding because of the efficient delivery and minimal attenuation of flood peaks generated in headwater catchments. Macrochannels and associated landforms can be clearly distinguished and mapped on fine-scale digital elevation models, offering the opportunity to integrate analyses of fluvial landforms and channel processes into hydraulic modeling studies, and ultimately, flood-risk avoidance strategies. Such an approach has the potential to improve on traditional flood risk avoidance methods that are focused primarily on design-flood heights by enabling the interpretation of hydraulic modeling outputs in the context of fluvial landforms that exert a significant control on flood behaviour.

Description: Regional ecological degradation induced by hydroelectric project construction (HPC) is of great concern in the field of landscape ecology research. Using GIS-based spatial analysis, we predicted and assessed the impacts of HPC on the ecological integrity of the Nuozhadu Nature Reserve (NNR). The results show that, after Nuozhadu HPC, the naturality of the NNR will be modified due to changes in the landscape composition such that larger areas covered by vegetation will be occupied by construction land and flooded by water areas. Meanwhile, landscape diversity will increase due to the additional landscape types of construction land and submerged areas, while landscape stability will decrease because of the splitting and contagion of the landscape after Nuozhadu HPC. The human disturbance index shows that the HPC will contribute to increasing the disturbance of the ecosystem. From buffer analyses, we conclude that the impacts of HPC will mainly occur in buffer zones over the distance of 800 m from the Lancang River in the NNR, and tend to be moderate in the 800–5,000 m buffer zone. Therefore, within the 800 m buffer zone, taking the naturality, diversity and stability of the ecosystem as well as anthropogenic interference as evaluation indicators, we calculated the ecological integrity index; the results indicate that the ecological integrity of the NNR will decrease by 7.6 % after project construction.

Description: The rate of neural tube defects (NTDs) in Shanxi Province is the highest world widely. Both human and environmental factors can induce NTDs, but various studies ignored contextual effects. This research examines whether there are significant soil type contextual effects on the rate of NTDs. A spatial two-level regression model is used to quantify the magnitude of contextual effects. Spatial autocorrelated errors structure is used to control autocorrelation of residuals. The results suggest that the spatial multilevel model fit the data better than non-spatial multilevel models. Our findings indicate that there are significant soil type contextual effects on the rate of NTDs, even after taking into account of fertilizer and net income. More attentions should be focused on how characteristics of each soil type may affect the rates of NTDs in further studies, which is a relevant issue for understanding etiology of NTDs.

Description: The model performance is usually influenced by the quality of the data used in model construction. If the model performance is less affected by data quality, the resulting estimates will be more reliable. In this paper, the variation in model performance due to different data quality is explored in a field-scale application. Hence, two models, the proposed support vector machine (SVM) based model and the Stephen and Stewart (SS) model, are employed for daily estimation of evaporation at an experiment station. Five scenarios corresponding to different data qualities are designed to evaluate the effect of data quality on model performance. Additionally, the most effective meteorological variables influencing evaporation are obtained by a systematic input determination process. These most effective meteorological variables are used as inputs to the SVM-based model. The results show that the model performance decreases as the data quality decreases (i.e. the percentage of missing data increases). However, the estimation accuracy of SVM-based models is still better than that of the SS model. Moreover, the variation of model performance of the SVM-based model is smaller than that of the SS model. That is, the negative impact of different data quality is effectively decreased by using the SVM-based model instead of the SS model.

Description: Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

Description: This study modifies a real-time correction method for water stage forecasts (named the RTEC_TS&KF model) using the time series method developed by Wu et al. (Stoch Environ Res Risk Assess 26:519-531, 2012 ) (named the RTEC_TS model), by incorporating the Kalman filter (KF) model. The RTEC_TS&KF model adjusts the corrected water stage forecasts resulting from the RTEC_TS model by taking into account the uncertainties in the model structure/inputs as well as the measurement bias. In detail, the water stage forecasts are corrected by separately adding the forecasted errors by the times series model and KF method into the stage forecasts. As compared to the results from the RTEC_TS model using the forecasted and observed water stages for Typhoons Morakot (2009), Saola (2012) and Soulik (2013), the RTEC_TS&KF model not only effectively lessens the uncertainties in regard to the water stage forecasts, but also consistently presents high correction performance of water level forecasts for various rainstorm events. This reveals that the RTEC_TS&KF model is superior to the RTEC_TS model in the correction of water stage forecasts. In the future, the RTEC_TS&KF model will be applied in the real-time corrections of other hydrological variates, such as the outflow of a reservoir, in the case of observation being provided on time.

Description: Drought is an environmental disaster which is frequently and world-widely occurred in recent years. Precisely assessment and prediction of drought is important for water resources planning and management. Sampling uncertainty commonly exists in frequency analysis-based hydrological drought assessment due to the limited length of observed data series. Based on the daily streamflow data of the Yichang hydrological station from 1882 to 2009, the streamflow drought index (SDI) series with 12-month time scale was calculated and the hydrological drought of the upper Yangtze River was assessed. By employing the bootstrap method, the impact of sample size on the sampling uncertainty of the SDI was analyzed. The longer record is used to derive the SDI, the narrower the shifting ranges of the parameters of the streamflow volume probability distribution functions and corresponding interval estimators of SDI are. The upper Yangtze River basin has experienced successive alternation of wet and dry years, and the spring seems to be the driest season within a year. The current difficulty in fighting against increasing droughts in upper Yangtze River basin is upgrading. Considering the possible misjudgment of drought degree results from the sampling uncertainty, attention should be paid to the preparation of drought relief strategies in order to reduce the potential losses.

Description: Flood is one of the most commonly occurred natural hazards worldwide. Severe flood occurrences in Kelantan, Malaysia cause damage to both life and property every year. Due to the huge losses in this area, development of appropriate flood modeling is required for the government. Remote sensing and geographic information system techniques can support overall flood management as they can produce rapid data collection and analysis for hydrological studies. The existing models for flood mapping have some weak points that may improve through more sophisticated and ensemble methods. The current research aimed to propose a novel ensemble method by integrating support vector machine (SVM) and frequency ratio (FR) to produce spatial modeling in flood susceptibility assessment. In the literature, mostly statistical and machine learning methods are used individually; however, their integration can enhance the final output. The FR model can perform bivariate statistical analysis and evaluate the correlation between the flooding and classes of each conditioning factors. The weights achieved by FR can be assigned to each conditioning factor and the resulted factors can be used in SVM analysis. In order to examine the efficiency of the proposed ensemble method and to show the proficiency of SVM, another machine learning algorithm such as decision tree (DT) was applied and the results were compared. To perform the methods, the upper catchment of the Kelantan basin in Malaysia was chosen. First, a flood inventory map with a total of 155 flood locations were extracted from various sources over the study area. The flood inventory map was randomly divided into two dataset; 70 % (115 flood locations) for the purpose of training and the remaining 30 % (40 flood locations) was used for validation. The spatial database included digital elevation model, curvature, geology, river, stream power index, rainfall, land use/cover, soil type, topographic wetness index and slope. For model validation, area under curve method was used and both success and prediction rate curves were calculated. The validation results for ensemble method showed 88.71 and 85.21 % for success rate and prediction rate respectively. The DT model showed 87.00 and 82.00 % for the success rate and prediction rate respectively. It is evident that the accuracies were increased using the ensemble method. The acquired results proved the efficiency of the proposed ensemble method as rapid, accurate and reasonable in flood susceptibility assessment.

Description: Presence-only data are referred to situations in which a censoring mechanism acts on a binary response which can be partially observed only with respect to one outcome, usually denoting the presence of an attribute of interest. A typical example is the recording of species presence in ecological surveys. In this work a Bayesian approach to the analysis of presence-only data based on a two levels scheme is presented. A probability law and a case-control design are combined to handle the double source of uncertainty: one due to censoring and the other one due to sampling. In the paper, through the use of a stratified sampling design with non-overlapping strata, a new formulation of the logistic model for presence-only data is proposed. In particular, the logistic regression with linear predictor is considered. Estimation is carried out with a new Markov Chain Monte Carlo algorithm with data augmentation, which does not require the a priori knowledge of the population prevalence. The performance of the new algorithm is validated by means of extensive simulation experiments using three scenarios and comparison with optimal benchmarks. An application to data existing in literature is reported in order to discuss the model behaviour in real world situations together with the results of an original study on termites occurrences data.

Description: Understanding the relationships between hydrological regime and climate change is important for water resources management. In this study, the streamflow response to climate change was investigated in the Lake Dianchi watershed, which is one of the most important eutrophic lakes in China. Daily time-series of temperature and precipitation in the future periods (2020, 2050 and 2080s) were projected from HadCM3 model. Statistical downscaling model (SDSM) and the previously calibrated and validated Soil and water assessment tool (SWAT) model were used to quantify the impacts of climate change on streamflow in this watershed. The results showed that SDSM can well capture the statistical relationships between the large scale climate variables and the observed weather at regional scale. The downscaled results showed that annual average maximum and minimum temperature would rise by 4.28 (3.25) and 4.71 °C (3.33 °C) in the 2080s under A2 (B2) scenario. Annual average precipitation would decrease within the range between 20.34 and 74.12 mm under both scenarios in the future. Based on SWAT model simulation, annual average streamflow would decrease in the future by the declination of −7.12 to −21.83 % and −6.34 to −17.09 % under A2 (B2) scenarios in the outlet of this watershed. The frequency of drought and extreme rainfall events would increase in the future, which is not beneficial to protect Lake Dianchi. This study could lead to a better understanding of the streamflow response under climate change and also raised concerns about the sustainability of future water resources in Lake Dianchi watershed.

Description: The simultaneous occurrence of extreme events, such as simultaneous storms and floods at different locations, has a serious impact on risk assessment and mitigation strategies. The joint occurrence of extreme events can be measured by the so-called upper tail dependence (UTD) coefficient λ U . In this study, we reconsider the properties of the most popular λ U estimators and show that their strong bias and uncertainty make most of the empirical results reported in the hydrological literature questionable. In order to overcome the limits of λ U analysis, we test several alternative tools such as a pool of formal statistical tests devised for recognizing upper tail independence and graphical diagnostics based on binary correlation and binary entropy. The reliability of all the methods is preliminarily checked by Monte Carlo experiments. Statistical tests and graphical diagnostics are therefore applied to three different rainfall data sets that allow us to explore the properties of the spatial dependence structure of rainfall extremes over a wide range of spatio-temporal scales ranging from 30 min and 1 km to 30 days and \(\approx \) 3000 km. Results highlight that (1) classical estimators provide non zero tail dependence even for cases where it should be zero; (2) formal tests and binary correlation highlight that the pairwise spatial dependence structure can be weaker than Gaussian, thus excluding UTD calculated in a pairwise manner; (3) the binary entropy computed on triples of locations shows that the pairwise UTD is not enough to explain the spatial dependence structure of extreme rainfall, whose complexity becomes evident only after resorting to higher order correlation measures. The results concerning the bias and uncertainty of λ U estimators are fully general and suggest avoiding their use especially for the short time series usually available in hydrology.

Description: Anthropogenic activities have altered the climate and led to changes in the water cycle. Understanding the climate change and hydrological responses is critical to derive adaptive strategies for sustainable water resources management. In this study, we diagnosed the trends of primary climate elements and hydrological components during the past half century (1960–2009) for the humid Xiangjiang River Basin in central-south China at multiple temporal and spatial scales. The air temperature trend demonstrated an overall warming climate but with a quicker pace in recent years; however, the wind speed reduced significantly in the early period, and this downtrend had largely disappeared after the mid-1990s. Under such a shifting climate, the hydrological responses were not monotonic during the past 50 years: the evapotranspiration behaved in a decreasing trend in the early 35 years (1960–1994), followed by an uptrend in the later period (1995–2009). The stepwise analysis of soil water content and baseflow demonstrated a wetting trend followed by a drying one but with a steeper slope, indicating an accelerated drying trend which may cause a concern in stream water availability especially in the dry season. Spatial trend analysis showed that some areas experienced a downtrend (drying) in the dry season, but most areas had an uptrend (wetting) in the wet season for the whole study period. Overall, the analyses of temporal and spatial changes are useful for decision makers to deal with the continuing changes in climate and hydrology. This study also highlighted the necessity of climate change studies at multiple temporal and spatial scales.

Description: Climate change in North China would result in significant changes in temperature, precipitation and their spatial/temporal distributions. Consequently, these induced changes will have profound effects on the hydrological cycle and water resources in both agricultural and natural ecosystems. Panjiakou reservoir in the middle Luanhe River basin—a tributary of the Haihe River basin—is one of the important sources of water for industrial and agricultural development in Beijing, Tianjin and Hebei province, China. Any significant change in the magnitude and/or timing of runoff from the reservoir induced by changes in climatic variables would have significant implication for the economic prosperity in North China. This paper investigates the impacts of climate change on hydrological processes in the Luanhe River basin as follows. Firstly, spatial and temporal patterns of precipitation, temperature and runoff at both annual and seasonal scales from 1957 to 2000 in the Luanhe River basin are analyzed using Mann–Kendall trend analysis, linear regression methods and inverse distance weighted interpolation. For the impact study, four Global Climate Models (GCMs) (named CSIRO, HadCM3, CNRM and GFDL) were used to produce precipitation and temperature data under A2 scenario by mean of a widely used quantile–quantile transformation. Projected meteorological variables were used to force a two-parameter hydrologic model to simulate the hydrological response to climate change in the future (2021–2050). Moreover, a sensitivity analysis is conducted to assess how precipitation and temperature affect the runoff. Results suggested that most part of the Luanhe River basin was dominated by significant increasing trends of temperature and no significant trends of precipitation in annual and seasonal scale during the past decades. Annual, spring and autumn runoffs present significant decreasing trends in the Panjiakou reservoir basin. Meanwhile, runoff is more strongly related to precipitation than to temperature. All GCMs projected precipitation and temperature series after bias correction indicated increasing temperature and increasing precipitation trends for the period 2021–2050 except that CNRM showed a slight decreasing trend in precipitation. Great enhancements can be found in projected runoff except CNRM by driving the two-parameter water balance model. The study provides valuable information on the assessment of the impact of the climate change on water resources in the Luanhe River basin as well for allocating and designing water resources projects.

Description: The paper combines simple general methodologies to obtain new classes of matrix-valued covariance functions that have two important properties: (i) the domains of the compact support of the several components of the matrix-valued functions can vary between components; and (ii) the overall differentiability at the origin can also vary. These models exploit a class of functions called here the Wendland–Gneiting class; their use is illustrated via both a simulation study and an application to a North American bivariate dataset of precipitation and temperature. Because for this dataset, as for others, the empirical covariances exhibit a hole effect, the turning bands operator is extended to matrix-valued covariance functions so as to obtain matrix-valued covariance models with negative covariances.

Description: This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback–Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times.

Description: This paper discusses some aspects of flood frequency analysis using the peaks-over-threshold model with Poisson arrivals and generalized Pareto (GP) distributed peak magnitudes under nonstationarity, using climate covariates. The discussion topics were motivated by a case study on the influence of El Niño–Southern Oscillation on the flood regime in the Itajaí river basin, in Southern Brazil. The Niño3.4 (DJF) index is used as a covariate in nonstationary estimates of the Poisson and GP distributions scale parameters. Prior to the positing of parametric dependence functions, a preliminary data-driven analysis was carried out using nonparametric regression models to estimate the dependence of the parameters on the covariate. Model fits were evaluated using asymptotic likelihood ratio tests, AIC, and Q–Q plots. Results show statistically significant and complex dependence relationships with the covariate on both nonstationary parameters. The nonstationary flood hazard measure design life level (DLL) was used to compare the relative performances of stationary and nonstationary models in quantifying flood hazard over the period of records. Uncertainty analyses were carried out in every step of the application using the delta method.

Description: During exploration and pre-feasibility studies of a typical petroleum project many analyses are required to support decision making. Among them is reservoir lithofacies modeling, preferably using uncertainty assessment, which can be carried out with geostatistical simulation. The resulting multiple equally probable facies models can be used, for instance, in flow simulations. This allows assessing uncertainties in reservoir flow behavior during its production lifetime, which is useful for injector and producer well planning. Flow, among other factors, is controlled by elements that act as flow corridors and barriers. Clean sand channels and shale layers are examples of such reservoir elements that have specific geometries. Besides simulating the necessary facies, it is also important to simulate their shapes. Object-based and process-based simulations excel in geometry reproduction, while variogram-based simulations perform very well at data conditioning. Multiple-point geostatistics (MPS) combines both characteristics, consequently it was employed in this study to produce models of a real-world reservoir that are both data adherent and geologically realistic. This work aims at illustrating how subsurface information typically available in petroleum projects can be used with MPS to generate realistic reservoir models. A workflow using the SNESIM algorithm is demonstrated incorporating various sources of information. Results show that complex structures (e.g. channel networks) emerged from a simple model (e.g. single branch) and the reservoir facies models produced with MPS were judged suitable for geometry-sensitive applications such as flow simulations.

Description: A deterministic geometric approach, the fractal–multifractal (FM) method, already found useful in modeling storm events, is adapted here in order to encode, for the first time, highly intermittent daily rainfall records gathered over a water year and containing many days of zero rain. Through application to data sets gathered at Laikakota in Bolivia and Tinkham in Washington, USA, it is demonstrated that the modified FM approach can represent erratic rainfall records faithfully, while using only a few FM parameters. It is shown that the modified FM approach, by capturing the rain accumulated over the season, ends up preserving other statistical attributes as well as the overall “texture” of the records, leading to FM sets that are indistinguishable from observed sets and certainly within the limits of accuracy of measured rainfall. This fact is further corroborated comparing 20 consecutive years at Laikakota and a modified FM representation, via common statistical qualifiers, such as histogram, entropy function, and inter-arrival times.

Description: Mass transport is known to depend on heterogeneity in geological formations. This entails geological bodies with complex geometries. The major interest of multiple-point simulation is its ability to reproduce such geological features through the use of a training image. The idea behind the training image is to describe a geological concept with the expected geological architecture. Its structural content is then used to infer multiple-point statistics. This yields a database with a variety of possible patterns or events. In this paper, we present a hybrid algorithm combining geostatistical multiplepoint and texture synthesis techniques for simulating geological reservoir models constrained to hard data. The proposed algorithm is a two steps process, involving first analysis with the building of an organized database from the training image content, and second synthesis with the simulation of a realization. Various tests are performed to investigate the potential of the algorithm in terms of computation time and ability to properly reproduce the shapes and connectivity features of the objects represented in the training image. We also propose a few improvements to make the algorithm more efficient. Last, six examples are presented based upon different kinds of training images depicting large-scale channelized and fractured media as well as fine-scale porous media.

Description: To alert the public to the possibility of tornado ( T ), hail ( H ), or convective wind ( C ), the National Weather Service (NWS) issues watches ( V ) and warnings ( W ). There are severe thunderstorm watches ( SV ), tornado watches ( TV ), and particularly dangerous situation watches ( PV ); and there are severe thunderstorm warnings ( SW ), and tornado warnings ( TW ). Two stochastic models are formulated that quantify uncertainty in severe weather alarms for the purpose of making decisions: a one-stage model for deciders who respond to warnings, and a two-stage model for deciders who respond to watches and warnings. The models identify all possible sequences of watches, warnings, and events, and characterize the associated uncertainties in terms of transition probabilities. The modeling approach is demonstrated on data from the NWS Norman, Oklahoma, warning area, years 2000–2007. The major findings are these. (i) Irrespective of its official designation, every warning type { SW , TW } predicts with a significant probability every event type { T , H , C }. (ii) An ordered intersection of SW and TW , defined as reinforced warning ( RW ), provides additional predictive information and outperforms SW and TW . (iii) A watch rarely leads directly to an event, and most frequently is false. But a watch that precedes a warning does matter. The watch type \(\{SV\) , TV , \(PV\}\) is a predictor of the warning type \(\{SW\) , RW , \(TW\}\) and of the warning performance: It sharpens the false alarm rate of the warning and the predictive probability of an event, and it increases the average lead time of the warning.