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: 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: 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: The effects of elevated CO 2 on vegetation dynamics and the hydrological cycle have been widely studied at the site level. However, quantitative assessments of these effects on a regional scale remain a challenge. We conducted numerical simulations to predict the possible responses of vegetation and the hydrological cycle in the Sino-Mongolia arid and semi-arid region (SMASR) to doubled CO 2 and its associated climate change using the Community Earth System Model in tandem with a dynamic global vegetation model. The results showed that the doubled CO 2 had a positive effect on the leaf area index of the SMASR, but its associated climate change exerted a negative effect in most parts of the SMASR. Although climate change had a weak negative effect on ground runoff at the regional scale, a 4.74 mm increase was predicted under the combined effect of doubled CO 2 and climate change, largely due to the positive effect of doubled CO 2 . Spatially, the evident increase in ground runoff, which primarily occurred in the southeastern part of the SMASR, resulted from decreased ground evaporation and canopy transpiration under the doubled CO 2 condition. A negative effect was predicted in the central west as a result of increased temperature and a changed precipitation under doubled CO 2 . These findings implied that the condition of water resources would be improved slightly under a doubled CO 2 condition, whereas there would be a larger spatial heterogeneity in relation to different sensitivities of vegetation and hydrological variables to doubled CO 2 and associated climate change.

Description: This paper investigates a harvest-season level unbalanced panel data (PD) of farmers crop delivery for monitoring the gathering activity and for aiding to support reception and storage decisions making of an agricultural cooperative. To achieve these purposes, the fitting and the prediction of the daily farmers crop delivery quantities were realised based-on the total expected quantity of the whole harvest season, the daily volume of precipitation and the amount of sunshine. In order to capture and extrapolate data patterns, both the PD regression and the multivariate adaptive regression approaches were implemented and tested for a real life agricultural cooperative case study. The obtained results exhibit an accurate predictive modelling of the farmers crop delivery behaviour for harvest seasons ahead.

Description: Over the past few decades, energy and water fluxes have been directly measured by a global flux network, which was established by regional and continental network sites based on an eddy covariance (EC) method. Although, the EC method possesses many advantages, its typical data coverage could not exceed 65 % due to various environmental factors including micrometeorological conditions and systematic malfunctions. In this study, four different methodologies were used to fill the gap in latent heat flux (LE) data. These methods were Food and Agriculture Organization Penman–Monteith (FAO_PM) equation, mean diurnal variation (MDV), Kalman filter, and dynamic linear regression (DLR). We used these methods to evaluate two flux towers at different land cover types located at Seolmacheon (SMC) and Cheongmicheon (CMC) in Korea. The LE estimated by four different approaches was a fairly close match to the observed LE, with the root mean square error ranging from 4.81 to 61.88 W m −2 at SMC and from 0.89 to 60.27 W m −2 at CMC. At both sites, the LE estimated by DLR showed the best result with the value of the coefficient of correlation (R), equal to 0.99. Cost-effectiveness analysis for evaluating four different gap-filling methods also confirmed that DLR showed the best cost effectiveness ratio (C/R). The Kalman filter showed the second highest C/R rank except in the winter season at SMC followed by MDV and FAO_PM. Energy closures with estimated LE led to further improved compare to the energy closure of the observed LE. The results showed that the estimated LE at CMC was a better fit with the observed LE than the estimated LE at SMC due to the more complicated topography and land cover at the SMC site. This caused more complex interactions between the surface and the atmosphere. The estimated LE with all approaches used in this study showed improvement in energy closure at both sites. The results of this study suggest that each method can be used as a gap-filling model for LE. However, it is important to consider the strengths and weaknesses of each method, the purpose of research, characteristics of the study site, study period and data availability.

Description: Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily streamflow simulation in the Upper Huai River Basin. Study results have demonstrated that the SWAT model with hourly rainfall inputs performed better than the model with daily rainfall inputs in daily streamflow simulation, primarily due to its better capability of simulating peak flows during the flood season. The sub-daily SWAT model estimated that 58 % of streamflow was contributed by baseflow compared to 34 % estimated by the daily model. Using the future daily and 3-h precipitation projections under the RCP (Representative Concentration Pathways) 4.5 scenario as inputs, the sub-daily SWAT model predicted a larger amount of monthly maximum daily flow during the wet years than the daily model. The differences between the daily and sub-daily SWAT model simulation results indicated that temporal rainfall resolution could have much impact on the simulation of hydrological process, streamflow, and consequently pollutant transport by SWAT models. There is an imperative need for more studies to examine the effects of temporal rainfall resolution on the simulation of hydrological and water pollutant transport processes by SWAT in river basins of different environmental conditions.

Description: The purpose of this paper is to categorize and analyze various risk factors in Irans gas refineries for insurance purposes. Using the failure modes and effects analysis method as a subset of probability risk assessment technique and gas refineries data for the period March 2011 till March 2012, risk priorities numbers are calculated from the perspectives of both the insured party (gas industries) and the insurer (insurance companies). Our empirical results indicate that various property damage risk factors embodied in gas refineries including fire, explosion, error and omission, and machinery breakdown are insurable risks. Risks of pressurized vessels defects are in safe category and can be tolerated by the industry owner. The policy implication of this paper for Iranian policy makers in the energy sector is that, gas refineries are insurable in the market with reasonable risk premium. Insuring gas refineries will definitely reduce capital losses which can otherwise be enormous for the economy in general and for oil and gas industries in particular.

Description: In some diseases it is well-known that a unimodal mortality pattern exists. A clear example in developed countries is breast cancer, where mortality increased sharply until the nineties and then decreased. This clear unimodal pattern is not necessarily applicable to all regions within a country. In this paper, we develop statistical tools to check if the unimodality pattern persists within regions using order restricted inference. Break points as well as confidence intervals are also provided. In addition, a new test for checking monotonicity against unimodality is derived allowing to discriminate between a simple increasing pattern and an up-then-down response pattern. A comparison with the widely used joinpoint regression technique under unimodality is provided. We show that the joinpoint technique could fail when the underlying function is not piecewise linear. Results will be illustrated using age-specific breast cancer mortality data from Spain in the period 1975–2005.

Description: Optimizing a pumping system in the wastewater treatment process by improving its operational schedules is presented. The energy consumption and outflow rate of the pumping system are modeled by a data-driven approach. A mixed-integer nonlinear programming (MINLP) model containing data-driven components and pump operational constraints is developed to minimize the energy consumption of the pumping system while maintaining the required pumping workload. A greedy electromagnetism-like (GEM) algorithm is designed to solve the MINLP model for optimized operational schedules and pump speeds. Three computational cases are studied to demonstrate the effectiveness of the proposed data-driven modeling and GEM algorithm. The computational results show that significant energy saving can be obtained.

Description: Long flood series are required to accurately estimate flood quantiles associated with high return periods, in order to design and assess the risk in hydraulic structures such as dams. However, observed flood series are commonly short. Flood series can be extended through hydro-meteorological modelling, yet the computational effort can be very demanding in case of a distributed model with a short time step is considered to obtain an accurate flood hydrograph characterisation. Statistical models can also be used, where the copula approach is spreading for performing multivariate flood frequency analyses. Nevertheless, the selection of the copula to characterise the dependence structure of short data series involves a large uncertainty. In the present study, a methodology to extend flood series by combining both approaches is introduced. First, the minimum number of flood hydrographs required to be simulated by a spatially distributed hydro-meteorological model is identified in terms of the uncertainty of quantile estimates obtained by both copula and marginal distributions. Second, a large synthetic sample is generated by a bivariate copula-based model, reducing the computation time required by the hydro-meteorological model. The hydro-meteorological modelling chain consists of the RainSim stochastic rainfall generator and the Real-time Interactive Basin Simulator (RIBS) rainfall-runoff model. The proposed procedure is applied to a case study in Spain. As a result, a large synthetic sample of peak-volume pairs is stochastically generated, keeping the statistical properties of the simulated series generated by the hydro-meteorological model. This method reduces the computation time consumed. The extended sample, consisting of the joint simulated and synthetic sample, can be used for improving flood risk assessment studies.

Description: Construction of dams and the resulting water impoundments are one of the most common engineering procedures implemented on river systems globally; yet simulating reservoir operation at the regional and global scales remains a challenge in human–earth system interactions studies. Developing a general reservoir operating scheme suitable for use in large-scale hydrological models can improve our understanding of the broad impacts of dams operation. Here we present a novel use of artificial neural networks to map the general input/output relationships in actual operating rules of real world dams. We developed a new general reservoir operation scheme (GROS) which may be added to daily hydrologic routing models for simulating the releases from dams, in regional and global-scale studies. We show the advantage of our model in distinguishing between dams with various storage capacities by demonstrating how it modifies the reservoir operation in respond to changes in capacity of dams. Embedding GROS in a water balance model, we analyze the hydrological impact of dam size as well as their distribution pattern within a drainage basin and conclude that for large-scale studies it is generally acceptable to aggregate the capacity of smaller dams and instead model a hypothetical larger dam with the same total storage capacity; however we suggest limiting the aggregation area to HUC 8 sub-basins (approximately equal to the area of a 60 km or a 30 arc minute grid cell) to avoid exaggerated results.

Description: This study presents the application of different chemometric approaches on the dataset obtained during the monitoring of offset printing wastewater quality in Pozarevac, Serbia. Collecting of wastewaters was performed during a working week, five working days, in five offset printing facilities. Twenty five physico-chemical parameters were analyzed in wastewaters using the standard analytical and instrumental methods. The obtained dataset were subjected to cluster analysis and principal component analysis. Cluster analysis showed four groups of similarity between the printing facilities reflecting the different physico-chemical characteristics and pollution levels of studied wastewaters. Principal component analysis identified two principal components responsible for the data structure explaining 86 % of total variance of offset printing wastewaters. The obtained principal components indicate the parameters that are the most responsible for variation of offset printing wastewaters. This study clearly demonstrates the usefulness of chemometric methods in analysis of printing wastewater quality, identification of the main sources and understanding of spatial variations in wastewater quality. Also, it could be useful for the selection of an appropriate wastewater treatment plant.

Description: Problem of soil acidity regularization is modeled as stochastic adaptive control problem with a linear difference equation of the dynamics of a field pH level. Stochastic component in the equation represents an individual time variability of soil acidity of an elementary section. We use Bayesian approach to determine a posteriori probability density function of the unknown parameters of the stochastic transition process. The Kullback–Leibler information divergence is used as a measure of difference between true distribution and its estimation. Algorithm for the construction of an adaptive stabilizing control in such a linear control system is proposed in the paper. Numerical realization of the algorithm is represented for a problem of a field soil acidity control.

Description: Information entropy is introduced to describe the interactions between diverse agents in urban ecosystems. Basing on maximum information entropy method, a holistic structural parameter and its dynamic equation are derived to reflect urban ecosystem health (UEH). In this way, a new UEH assessment model has been proposed. We then apply the model to assess the UEH of Beijing, Dalian, Shanghai, Wuhan, Xiamen and Guangzhou in China. It is shown that the holistic structural parameter, the radar chart, and the associated correlations from the model can reveal the health features of different cities. According to the calculated ranges of the holistic structural parameter, a new UEH assessment grade standard is suggested and applied to the UEH assessment of some typical cities in China. It is demonstrated that the new model and the new assessment grade standard are precise and readily operational, which can be widely used in other urban ecosystems.

Description: The change in the mean temperature in Finland is investigated with a dynamic linear model in order to define the sign and the magnitude of the trend in the temperature time series within the last 166 years. The data consists of gridded monthly mean temperatures. The grid has a 10 km spatial resolution, and it was created by interpolating a homogenized temperature series measured at Finnish weather stations. Seasonal variation in the temperature and the autocorrelation structure of the time series were taken account in the model. Finnish temperature time series exhibits a statistically significant trend, which is consistent with human-induced global warming. The mean temperature has risen very likely over 2 °C in the years 1847–2013, which amounts to 0.14 °C/decade. The warming after the late 1960s has been more rapid than ever before. The increase in the temperature has been highest in November, December and January. Also spring months (March, April, May) have warmed more than the annual average, but the change in summer months has been less evident. The detected warming exceeds the global trend clearly, which matches the postulation that the warming is stronger at higher latitudes.

Description: The need for irrigation water in arid and semi-arid regions is mostly supplied by groundwater. Furthermore, the agricultural development in these areas is not generally based on a comprehensive plan, which can cause aquifers depletion. On the other hand, to properly manage an aquifer and to have an optimal crop plan, the stochastic nature of the different parameters of a groundwater system such as groundwater recharge and water demands should be taken into consideration. In this paper, we develop an explicit stochastic optimization model for Firouzabad aquifer in Iran. This formulation is based on the first and second moment analysis for groundwater head which has been initially proposed for surface water resources management by Fletcher and Ponnambalam. We extend the model to create a new random withdrawal policy for conjunctive use setting in which the randomness in available precipitation is taken into account. The interesting point is that the model provides the respective probabilities of shortage and surplus without imposing the extra decision variables into the optimization model. A genetic-based algorithm is used to solve the stochastic nonlinear and non-convex formulation. The outcome results indicate that the current crop pattern should be changed, that is, the allocated areas of some crops have to be meaningfully reduced. Finally, to validate our model efficiency, we demonstrate that how much close the statistical characteristics obtained from the optimization model are to those estimated from the Monte Carlo simulation. Furthermore, the optimal benefits obtained using the proposed optimization model are as suitable as the benefits achieved using the corresponding Monte Carlo-based optimization model.

Description: With climate change and the rapid increase in water demand, droughts, whose intensity, duration and frequency have shown an increasing trend in China over the past decades, are increasingly becoming a critical constraint to China’s sustainable socio-economic development, especially in Northern China, even more so. Therefore, it is essential to develop an appropriate drought assessment approach in China. To propose a suitable drought index for drought assessment, the Luanhe river basin in the northern China was selected as a case study site. Based on the Principal Component Analysis of precipitation, evapotranspiration, soil moisture and runoff, the three latter variables of which were obtained by using the Variable Infiltration Capacity land surface macro-scale hydrology model, a new multivariate drought index (MDI) was formulated, and its thresholds were determined by use of cumulative distribution function. To test the applicability of the newly developed index, the MDI, the standardized precipitation index (SPI) and the palmer drought severity index (PDSI) time series on a monthly scale were computed and compared during 1962–1963, 1968 and 1972 drought events. The results show that the MDI exhibited certain advantages over the PDSI and the SPI, i.e. better assessing drought severity and better reflecting drought evolution. The MDI formulated by this paper could provide a scientific basis for drought mitigation and management, and references for drought assessment elsewhere in China.

Description: We analyse the concentration of five trace elements (As, Cu, Ni, Pb and Zn) in the topsoil of the Kozani-Ptolemais basin where four coal-fired power plants run to provide almost 47.8 % of electricity requirements in Greece. We assume that if the power plants have altered the spatial (co)variation of the analysed elements through their toxic by-products, their effect would be observable only on a small spatial scale, since deposition of airborne pollutants is more evident if it is near the emission source. We used Factorial Cokriging to estimate the small-scale correlations among soil elements and to compare them to large spatial-scale correlations. Soil samples were collected from 92 sites. Given the low concentrations in soil heavy metals and As, we observed no serious soil contamination risk. We estimated correlations among the analysed elements on two spatial scales. On the larger scale, Ni and As exhibited higher correlation and received higher weights for the first regionalized factor, contrary to Cu, Pb and Zn which weighted more for the second regionalized factor. On the small spatial scale As associated with neither Ni nor other heavy metals. We conclude that soil arsenic has been altered by enrichment caused by some power plants through fly ash deposition and/or disposal. However, enrichment of soil elements was detectable only on the smaller spatial scale because anthropogenic inputs in soil through airborne emissions and subsequent deposition are evident only in the vicinity of the emission source.

Description: Streamflows are influenced by various hydroclimatic variables in complex ways. Accurate prediction of monthly streamflows requires a clear understanding of the dependence patterns among these influencing variables and streamflows. A graphical modeling technique, employing conditional independence, is adopted in this study to quantify the interrelationships between streamflows and a suite of available hydroclimatic variables, and to identify a reduced set of relevant variables for parsimonious model development. The nodes in the undirected graph represent relevant variables, and the strengths of the connections among the variables are learnt from the data. The graphical modeling approach is compared to the state-of-the-art method for predictor selection based on partial mutual information. For a synthetic benchmark dataset and a watershed in southern Indiana, USA, the graphical modeling approach shows more discriminating results while being computationally efficient. Along with artificial neural networks and time series models, results of the graphical model are used for formulating a variational relevance vector machine to predict monthly streamflows and perform probabilistic classification of hydrologic droughts in the watershed being studied. The parsimonious models developed for prediction at different lead times performed as well as the non-parsimonious models during both the calibration and testing periods. Drought forecasting for the study watershed at 1-month lead time was performed using the two selected predictors—soil moisture and precipitation anomalies alone, and the model performance was evaluated. The graphical model shows promise as a tool for predictor selection, and for aiding parsimonious model development applications in statistical hydrology.

Description: The quantile of a probability distribution, known as return period or hydrological design value of a hydrological variable is the value corresponding to fixed non-exceedence probability and is very important notion in hydrology. In hydraulic engineering design and water resources management, confidence interval (CI) estimation for a population quantile is of primary interest and among other applications, is used to assess the pollution level of a contaminant in water, air etc. The accuracy on such estimation directly influences the engineering investments and safety. The two parameter Weibull, Pareto, Lognormal, Inverse Gaussian, Gamma are some commonly used probability models in such applications. In spite of its practical importance, the problem of CI estimation of a quantile of these widely applicable distributions has been less attended in the literature. In this paper, a new method is proposed to obtain a CI for a quantile of any distribution for which [or the probability distribution of any one-to-one function of the underlying random variable (RV)] generalized pivotal quantities (GPQs) exist for its parameters. The proposed method is elucidated by constructing CIs for quantiles of Weibull, Pareto, Lognormal, Extreme value distribution of type-I for minimum, Exponential and Normal distributions for complete as well as type II singly right censored samples. The empirical performance evaluation of the proposed method evinced that the proposed method has exact well concentrated coverage probabilities near the nominal level even for small uncensored samples as small as 5 and for censored samples as long as the proportion of censored observations is up to 0.70. The existing methods for Weibull distribution have poor or dispersed coverage probabilities with respect to the nominal level for complete samples. Applications of the proposed method in ground water monitoring and in the assessment of air pollution are illustrated for practitioners.

Description: Environmental change not only undergoes in mean environmental conditions but also in their degree of stochasticity. Changes in waterborne metal variability are often associated with altered disturbance regimes and temporal patterns of source availability. Here copper (Cu) was used as an example because Cu sulfate (CuSO 4 ) has been extensively used as a chemical tool to exterminate phytoplankton for controlling skin lesions and gill disease of fish in aquatic ecosystems. This study showed that increased variability of waterborne Cu concentrations strongly promotes a key process of biokinetics, bioaccumulation. In experimental tilapia populations, the mean growth cost coefficient in pulsed Cu exposures was 7 % lower than the control group. On the other hand, the double-pulse, constant low, and single-pulse scenarios had similar effect on biomass change (2.2–2.4 %). The greatest biomass change (~10 %) occurred where Cu concentrations were gradually increasing over time or at a constant high rate. Most importantly, this study demonstrated that chronic exposure of tilapia to a low Cu concentration rate that approximated a single large pulse of field-realistic levels damaged bioenergetic mechanisms and increased energy acquisition. This study also showed that interactions across multiple pulsed or fluctuating Cu exposures were involved in accumulation changes that could also be achieved by controlling pulse timing and duration. It can be concluded that increased metal variability can promote biokinetic and bioenergetic responses in fish; and that changes in environmental variability may interact with other global change processes and thereby substantially accelerate change in aquatic ecosystems.

Description: Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

Description: Three common stochastic tools, the climacogram i.e. variance of the time averaged process over averaging time scale, the autocovariance function and the power spectrum are compared to each other to assess each one’s advantages and disadvantages in stochastic modelling and statistical inference. Although in theory, all three are equivalent to each other (transformations one another expressing second order stochastic properties), in practical application their ability to characterize a geophysical process and their utility as statistical estimators may vary. In the analysis both Markovian and non Markovian stochastic processes, which have exponential and power-type autocovariances, respectively, are used. It is shown that, due to high bias in autocovariance estimation, as well as effects of process discretization and finite sample size, the power spectrum is also prone to bias and discretization errors as well as high uncertainty, which may misrepresent the process behaviour (e.g. Hurst phenomenon) if not taken into account. Moreover, it is shown that the classical climacogram estimator has small error as well as an expected value always positive, well-behaved and close to its mode (most probable value), all of which are important advantages in stochastic model building. In contrast, the power spectrum and the autocovariance do not have some of these properties. Therefore, when building a stochastic model, it seems beneficial to start from the climacogram, rather than the power spectrum or the autocovariance. The results are illustrated by a real world application based on the analysis of a long time series of high-frequency turbulent flow measurements.

Description: In the recent centuries, one of the most important ongoing challenges is energy consumption and its environmental impacts. As far as agriculture is concerned, it has a key role in the world economics and a great amount of energy from different sources is used in this sector. Since researchers have reported a high degree of inefficiency in developing countries, it is necessary for the modern management of cropping systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization (MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results.

Description: Understanding the heterogeneity arising from the complex architecture of sedimentary sequences in alluvial fans is challenging. This paper develops a statistical inverse framework in a multi-zone transition probability approach for characterizing the heterogeneity in alluvial fans. An analytical solution of the transition probability matrix is used to define the statistical relationships among different hydrofacies and their mean lengths, integral scales, and volumetric proportions. A statistical inversion is conducted to identify the multi-zone transition probability models and estimate the optimal statistical parameters using the modified Gauss–Newton–Levenberg–Marquardt method. The Jacobian matrix is computed by the sensitivity equation method, which results in an accurate inverse solution with quantification of parameter uncertainty. We use the Chaobai River alluvial fan in the Beijing Plain, China, as an example for elucidating the methodology of alluvial fan characterization. The alluvial fan is divided into three sediment zones. In each zone, the explicit mathematical formulations of the transition probability models are constructed with optimized different integral scales and volumetric proportions. The hydrofacies distributions in the three zones are simulated sequentially by the multi-zone transition probability-based indicator simulations. The result of this study provides the heterogeneous structure of the alluvial fan for further study of flow and transport simulations.

Description: Investigation of the precipitation phenomenon as one of the most important meteorological factors directly affecting access to water resources is of paramount importance. In this study, the precipitation concentration index (PCI) was calculated using annual precipitation data from 34 synoptic stations of Iran over a 50-year period (1961–2010). The trend of precipitation and the PCI index were analyzed using the Mann–Kendall test after removing the effect of autocorrelation coefficients in annual and seasonal time scales. The results of zoning the studied index at annual time scale revealed that precipitation concentration follows a similar trend within two 25-year subscales. Furthermore, the PCI index in central and southern regions of the country, including the stations of Kerman, Bandarabbas, Yazd, Zahedan, Shahrekord, Birjand, Bushehr, Ahwaz, and Esfahan indicates a strong irregularity and high concentration in atmospheric precipitations. In annual time scale, none of the studied stations, had shown regular concentration (PCI 〈 10). Analyzing the trend of PCI index during the period of 1961–2010 witnessed an insignificant increasing (decreasing) trend in 16 (15) stations for winter season, respectively, while it faced a significant negative trend in Dezful, Saghez, and Hamedan stations. Similarly, in spring, Kerman and Ramsar stations exhibited a significant increasing trend in the PCI index, implying significant development of precipitation concentration irregularities in these two stations. In summer, Gorgan station showed a strong and significant irregularity for the PCI index and in autumn, Tabriz and Zahedan (Babolsar) stations experienced a significant increasing (decreasing) trend in the PCI index. At the annual time scale, 50 % of stations experienced an increasing trend in the PCI index. Investigating the changes in the precipitation trend also revealed that in annual time scale, about 58 % of the stations had a decreasing trend. In winter, which is the rainiest season in Iran, about 64 % of stations experienced a decreasing trend in precipitation that caused an increasing trend in PCI index. Comparing the spatial distribution of PCI index within two 25 years sub-periods indicated that the PCI index of the second sub-period increased in the spring time scale that means irregularity of precipitation distribution has been increased. But in the other seasons any significant variations were not observed. Also in the annual time scale the PCI index increased in the second sub-period because of the increasing trend of precipitation.

Description: Two-station pairing approaches are routinely used to infill missing information in incomplete rainfall databases. We evaluated the performance of three simple methodologies to reconstruct incomplete time series in presence of variable nonlinear correlation between data pairs. Nonlinearity stems from the statistics describing the marginal peak-over-threshold (POT) values of rainfall events. A Monte Carlo analysis was developed to quantitatively assess expected errors from the use of chronological pairing (CP) with linear and nonlinear regression and frequency pairing (FP). CP is based on a priori selection of regression functions, while FP is based on matching the probability of non-exceedance of an event from one time series with the probability of non-exceedance of a similar event from another time series. We adopted a generalized Pareto (GP) model to describe POT events, and a t-copula algorithm to generate reference nonlinearly correlated pairs of random temporal distributions distributed according with the GP model. The results suggest that the optimal methodology strongly depends on GP statistics. In general, CP seems to provide the lowest errors when GP statistics were similar and correlation became linear; we found that a power-2 function performs well for the selected statistics when the number of missing points is limited. FP outperforms the other methods when POT statistics are different and variables are markedly nonlinearly correlated. Ensemble-based results seem to be supported by the analysis of observed precipitation at two real-world gauge stations.

Description: Flooding hazard evaluation is the basis of flooding risk assessment which has significances to natural environment, human life and social economy. This study develops a spatial framework integrating naïve Bayes (NB) and geographic information system (GIS) to assess flooding hazard at regional scale. The methodology was demonstrated in the Bowen Basin in Australia as a case study. The inputs into the framework are five indices: elevation, slope, soil water retention, drainage proximity and density. They were derived from spatial data processed in ArcGIS. NB as a simplified and efficient type of Bayesian methods was used, with the assistance of remotely sensed flood inundation extent in the sampling process, to infer flooding probability on a cell-by-cell basis over the study area. A likelihood-based flooding hazard map was output from the GIS-based framework. The results reveal elevation and slope have more significant impacts on evaluation than other input indices. Area of high likelihood of flooding hazard is mainly located in the west and the southwest where there is a high water channel density, and along the water channels in the east of the study area. High likelihood of flooding hazard covers 45 % of the total area, medium likelihood accounts for about 12 %, low and very low likelihood represents 19 and 24 %, respectively. The results provide baseline information to identify and assess flooding hazard when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in the study offer an integrated approach in evaluation of flooding hazard with spatial distributions and indicative uncertainties. It can also be applied to other hazard assessments.

Description: Different epidemiological studies have shown that high temperatures are directly related to mortality, furthermore many studies on the effects of climate change on future mortality are being conducted. The objective of this study is to estimate the effect of extreme hot temperatures on daily mortality in Zaragoza (Spain) from 2014 to 2021, utilising various climate-change scenarios. The relationship between temperature and mortality is defined by the concepts of heat wave, threshold temperature and the relative risk of daily deaths according to extreme temperatures in 1987–2006 period. The effect on future mortality is projected by estimating deaths attributable to extreme temperatures in 2014–2021. This estimation was calculated utilising exposure–response functions for three scenarios (A1B, A2 and B1) from the ECHAM5 general circulation model after applying a statistical downscaling technique. Because this study considers the effect of rising temperatures from a health perspective, minimising uncertainty was added to the numerical values obtained from the projected future relation between temperature and mortality. The results shows that expected mortality in Zaragoza will increase by 0.4 % for the period 2014–2021, an excess that can be directly attributed to extreme temperatures. This effect is expected to increase in the 2040s and 2050s until the end of the twenty first century because of a predicted increase in temperatures over this period, with special emphasis on the need to continue studying this line of inquiry and local studies as which arises. Finally, this study will luckily be used to create prevention plans for minimising the effect on health of the high temperatures.

Description: In this paper we employ a novel method to find the optimal design for problems where the likelihood is not available analytically, but simulation from the likelihood is feasible. To approximate the expected utility we make use of approximate Bayesian computation methods. We detail the approach for a model on spatial extremes, where the goal is to find the optimal design for efficiently estimating the parameters determining the dependence structure. The method is applied to determine the optimal design of weather stations for modeling maximum annual summer temperatures.

Description: In applications of the weight of evidence (WofE) method, the informational redundancy in similar evidential patterns causes a significant increase in the posterior probability. Consequently, to estimate the posterior probability, combinations that pass the established conditional independence (CI) tests are considered rather than the combination of the ‘best’ information layers. This study introduces two methodological approaches to extend the WofE using a correction factor that eliminates the informational redundancy that is contained in different evidential layers. The proposed approaches allow the use of associated data in the same model without having to address issues with the constraints of the CI. The basic WofE approach that is used to estimate the weights is not changed, and only the interactions of the parameter layers and the transformation of the weights into probability values are considered. The method is applied to a real dataset that is used in a landslide susceptibility analysis on Lombok Island, Indonesia.

Description: In many fields of study, and certainly in hydrogeology, uncertainty propagation is a recurring subject. Usually, parametrized probability density functions (PDFs) are used to represent data uncertainty, which limits their use to particular distributions. Often, this problem is solved by Monte Carlo simulation, with the disadvantage that one needs a large number of calculations to achieve reliable results. In this paper, a method is proposed based on a piecewise linear approximation of PDFs. The uncertainty propagation with these discretized PDFs is distribution independent. The method is applied to the upscaling of transmissivity data, and carried out in two steps: the vertical upscaling of conductivity values from borehole data to aquifer scale, and the spatial interpolation of the transmissivities. The results of this first step are complete PDFs of the transmissivities at borehole locations reflecting the uncertainties of the conductivities and the layer thicknesses. The second step results in a spatially distributed transmissivity field with a complete PDF at every grid cell. We argue that the proposed method is applicable to a wide range of uncertainty propagation problems.

Description: Influent river carrying nutrient pollutants from watershed loads makes a great contribution to the eutrophication in river-fed lake. It is scientific standard to make policies on river pollution control based on loading capacity of the river of interest. To control eutrophication in Taihu Lake has been the focal point of “The Twelfth Five-Year Guideline” proposed by Chinese government. The Taigeyunhe, Caoqiaohe and Yincungang Rivers which were the most polluted influent rivers in Taihu Lake Basin were scheduled for nutrient total maximum daily load (TMDL). A variety of mechanistic and empirical models are applied worldwide for TMDL development. However, model selection depends on management objectives, site-specific characteristics and availability of data resource. In this study, based on watershed characteristics and limited data, a nutrient TMDL is developed using flow and temporally variable daily load expressions. The simple and effective approaches specify allowable daily maximum loads for controlling on instantaneous high load and allowable daily median loads for achieving long-term TMDL allocation. For the entire river system, loading capacities are much lower during low flows. The maximum percent load reductions for biochemical oxygen demand, ammonia nitrogen, total nitrogen in spring and total phosphorus in winter can be obtained when pollution source inputs seasonally vary. This study provides local authority with two different alternatives in decision-making for pollution control on influent rivers and then to reduce external loads to the lake.

Description: One mechanism giving rise to the hypergeometric distribution is the number of matches in a random reordering of a sequence of forecasts of binary events. This provides a simple means of invalidating a time series of binary forecasts if in fact the forecasting method has no skill. The hypergeometric distribution has a long history of application in this context but appears not to have a high profile in the environmental sciences. Attention is drawn to the utility of the distribution as a simple nonparametric test of the null hypothesis of no skill when forecasting binary environmental outcomes.

Description: This paper proposes methods to detect outliers in functional data sets and the task of identifying atypical curves is carried out using the recently proposed kernelized functional spatial depth (KFSD). KFSD is a local depth that can be used to order the curves of a sample from the most to the least central, and since outliers are usually among the least central curves, we present a probabilistic result which allows to select a threshold value for KFSD such that curves with depth values lower than the threshold are detected as outliers. Based on this result, we propose three new outlier detection procedures. The results of a simulation study show that our proposals generally outperform a battery of competitors. We apply our procedures to a real data set consisting in daily curves of emission levels of nitrogen oxides (NO \(_{x}\) ) since it is of interest to identify abnormal NO \(_{x}\) levels to take necessary environmental political actions.

Description: Coastal aquifers are at threat of salinization in most parts of the world. This work investigated the seasonal hydrochemical evolution of coastal groundwater resources in Urmia plain, NW Iran. Two recently proposed methods have been used to comparison, recognize and understand the temporal and spatial evolution of saltwater intrusion in a coastal alluvial aquifer. The study takes into account that saltwater intrusion is a dynamic process, and that seasonal variations in the balance of the aquifer cause changes in groundwater chemistry. Pattern diagrams, which constitute the outcome of several hydrochemical processes, have traditionally been used to characterize vulnerability to sea/saltwater intrusion. However, the formats of such diagrams do not facilitate the geospatial analysis of groundwater quality, thus limiting the ability of spatio-temporal mapping and monitoring. This deficiency calls for methodologies which can translate information from some diagrams such Piper diagram into a format that can be mapped spatially. Distribution of groundwater chemistry types in Urmia plain based on modified Piper diagram using GQI Piper(mix) and GQI Piper(dom) indices that Mixed Ca–Mg–Cl and Ca-HCO 3 are the dominant water types in the wet and dry seasons, respectively. In this study, a groundwater quality index specific to seawater intrusion (GQI SWI ) was used to check its efficiency for the groundwater samples affected by Urmia hypersaline Lake, Iran. Analysis of the main processes, by means of the Hydrochemical Facies Evolution Diagram (HFE-Diagram), provides essential knowledge about the main hydrochemical processes. Subsequently, analysis of the spatial distribution of hydrochemical facies using heatmaps helps to identify the general state of the aquifer with respect to saltwater intrusion during different sampling periods. The HFE-D results appear to be very successful for differentiating variations through time in the salinization processes caused by saltwater intrusion into the aquifer, distinguishing the phase of saltwater intrusion from the phase of recovery, and their respective evolutions. Both GQI and HFE-D methods show that hydrochemical variations can be read in terms of the pattern of saltwater intrusion and groundwater quality status. But generally, in this case (i.e. saltwater and not seawater intrusion) the HFE-D method was presented better efficiency than GQI method (including GQI Piper and GQI SWI ).

Description: Predictions of a warmer climate over the Great Lakes region due to global change generally agree on the magnitude of temperature changes, but precipitation projections exhibit dependence on which General Circulation Models and emission scenarios are chosen. To minimize model- and scenario-specific biases, we combined information provided by the 3rd phase of the Coupled Model Intercomparison Project database. Specifically, the results of 12 GCMs for three emission scenarios B1, A1B, and A2 were analyzed for mid- (2046–2065) and end-century (2081–2100) intervals, for six locations of a hydroclimatic transect of Michigan. As a result of Bayesian Weighted Averaging, total annual precipitation averaged over all locations and the three emission scenarios increases by 7 % (mid-)–10 % (end-century), as compared to the control period (1961–1990). The projected changes across seasons are non-uniform and precipitation decreases by 3 % (mid-)–5 % (end-) for the months of August and September are likely. Further, average temperature is very likely to increase by 2.02–2.85 °C by the mid-century and 2.58–4.73 °C by the end-century. Three types of non-additive uncertainty sources due to climate models, anthropogenic forcings, and climate internal variability are addressed. When compared to the emission uncertainty, the relative magnitudes of the uncertainty types for climate model ensemble and internal variability are 149 and 225 % for mean monthly precipitation, and they are respectively 127 and 123 % for mean monthly temperature. A decreasing trend of the frost days and an increasing trend of the growing season length are identified. Also, a significant increase in the magnitude and frequency of heavy rainfall events is projected, with relatively more pronounced changes for heavy hourly rainfall as compared to daily events. Quantifying the inherent natural uncertainty and projecting hourly-based extremes, the study results deliver useful information for water resource stakeholders interested in impacts of climate change on hydro-morphological processes.

Description: This study presents an integrated optimal groundwater remediation design approach. It incorporates numerical simulation, health risk assessment, uncertainty analysis, and nonlinear optimization within a general framework. It is capable of dealing with not only health risk itself (generally caused by uncertainty), but also parameter uncertainty (e.g., slope factor and reference dose) in health risk assessment. This approach is applied to a contaminated site in western Canada for creating a set of optimal remediation strategies. Carcinogenic and noncarcinogenic risks associated with the strategies are further evaluated under four confidence levels (68.26, 90, 95 and 99.72 %). Results from the case study indicate that (i) the wells have varied contributions to groundwater remediation under different remediation periods and environmental standards; (ii) total pumping rate is mainly controlled by health risk constraints and a stringent health risk standard leads to a high total pumping rate; (iii) remediation period has a significant impact on health risk mitigation, but the marginal impact does not always increase; (iv) the impact of confidence level of slope factor on health risk is obvious, i.e., the larger the confidence level, the higher the health risk.

Description: The paper includes the identification of the main factors responsible for the temporal variations of indoor pollutants during three daily intervals in a photocopying shop. The measurements of concentration levels of total volatile organic compounds, ozone, carbon monoxide, carbon dioxide, nitrogen dioxide, ammonia, perchloroethylene and non-methane hydrocarbons were performed. The individual concentrations of target pollutants were subjected to principal component analysis (PCA) using a software XLSTAT 2014.1.10. Pearson correlation model indicated the relatively weak correlation between the investigated pollutants in a photocopying environment. PCA extracted three principal components (PCs) from the indoor air pollution data set. Obtained PCs explained 56.72 % of the total variance. The summarized biplots showed which pollutants are responsible for photocopying indoor pollution per sampling day/sampling point/time interval/number of measurement. The results pointed out that the main PCs were related to the usage of toners, electrostatic discharge, heating of photocopiers as well as general intensifying of photocopying processes.

Description: This article describes the use of non-stationary covariance functions with compact support to estimate and simulate a random function. Based on the kernel convolution theory, the functions are derived by convolving hyperspheres in \(\mathbb{R}^n\) followed by a Radon transform. The order of the Radon transform controls the differentiability of the covariance functions. By varying spatially the hyperspheres radius one defines non-stationary isotropic versions of the spherical, the cubic and the penta-spherical models. Closed-form expressions for the non-stationary covariances are derived for the isotropic spherical, cubic, and penta-spherical models. Simulation of the different non-stationary models is easily obtained by weighted average of independent standard Gaussian variates in both the isotropic and the anisotropic case. The non-stationary spherical covariance model is applied to estimate the overburden thickness over an area composed of two different geological domains. The results are compared to the estimation with a single stationary model and the estimation with two stationary models, one for each geological domain. It is shown that the non-stationary model enables a reduction of the mean square error and a more realistic transition between the two geological domains.

Description: As lake ecosystem assessment is the foundation to achieve lake monitoring, environmental management and ecological restoration, a new concept of lake ecosystem health and driving force-pressure-state-impact-response-management framework was proposed to find out the causal relationship of the system and health distance model was taken to represent the health level of ecosystem. An assessment indicator system comprised of water quality, ecological and socio-economic criteria was established. The evaluation models were applied for the assessment of the ecosystem health level of a typical lake, Nansi Lake, China. Depends on the values of health distance, the heath level was described as 5°: very healthy, healthy, general healthy, sub-healthy and diseased. Using field investigation data and statistic data within the theory and applied models, the results of comprehensive assessment show that: (1) the health distances of water quality indicators, ecological indicators, socio-economic indicators and comprehensive health distance were 0.3989, 0.2495, 0.4983 and 0.4362, respectively. The health level was in general healthy condition. Ecological indicators were in healthy condition, which indicate that the stability was high. The distance of water quality had shown a tendency to approach general healthy level. As the health distance of socio-economic indicators have shown a bad impact form human beings, more effective measures need to be developed. (2) The results of a case study demonstrated that the methods in this paper provide a similar result corresponding with the actual lake health condition. Therefore, this paper shows that the proposed method is efficient and worths generalization.

Description: Watershed-scale water quality (WWQ) models are now widely used to support management decision-making. However, significant uncertainty in the model outputs remains a largely unaddressed issue. In recent years, Markov Chain Monte Carlo (MCMC), a category of formal Bayesian approaches for uncertainty analysis (UA), has become popular in the field of hydrological modeling, but its applications to WWQ modeling have been rare. This study systematically evaluated the applicability of MCMC in assessing the uncertainty of WWQ modeling, using Differential Evolution Adaptive Metropolis (DREAM (ZS) ) and SWAT as the representative MCMC algorithm and WWQ model, respectively. The nitrate pollution in Newport Bay watershed was the case study for numerical experiments. It has been concluded that the efficiency and effectiveness of a MCMC algorithm would depend on some critical designs of the UA, including: (i) how many and which model parameters to be considered as random in the MCMC analysis; (ii) where to fix the non-random model parameters; and (iii) which criteria to stop the Markov Chain. The study results also indicate that the MCMC UA has to be management-oriented, that is, management objectives should be factored into the designs of the UA, rather than be considered after the UA.

Description: Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze \( {\text{NO}}_{ 3}^{ - } \) concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the \( {\text{NO}}_{3}^{ - } \) data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using \( {\text{NO}}_{3}^{ - } \) concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and \( {\text{NO}}_{3}^{ - } \) concentration values. After model training, the AI models are verified by the second \( {\text{NO}}_{3}^{ - } \) concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.

Description: Stochastic numerical simulations were performed to investigate the evolution of plumes from pulse sources that can result from accidental leaks. The stochastic advection–dispersion equation was solved for hydraulic conductivities typical to the heterogeneous sandy and gravel aquifers encountered in the United Arab Emirates. Dispersivities were similar to those found in field studies at sandy aquifers, such as those conducted at Borden and Cape Cod, and at the Vejen, Denmark tracer tests. Our work showed that the detection probability, P d , of a monitoring network was affected strongly by the medium’s dispersivity with a large number of wells (larger than 12) required, even in relatively simple geological environments, in order to detect contaminants with confidence. Monitoring systems following minimum regulatory requirements in terms of the number of wells were able to detect contamination at best in only one out of five cases. The frequency of sampling did not appear to be critical when the dispersivity was low and bi-annual sampling appeared to be satisfactory. In highly dispersive media monthly sampling was needed in order to increase detection. Increase of a medium’s dispersivity in relative homogeneous aquifers reduced the performance of large well-systems to less than 50 %. Strongly heterogeneous and dispersive subsurface environments led all monitoring systems to fail in detection, irrespectively of frequency of sampling. Finally, large contaminant quantities did not improve the detection capabilities of low density well-systems with detection enhancements restricted to high density ones.

Description: The generalized likelihood uncertainty estimation (GLUE) technique is an innovative uncertainty method that is often employed with environmental simulation models. Over the past years, hydrological literature has seen a large increase in the number of papers dealing with uncertainty. There are now a lot of citations to their original paper which illustrates GLUE tremendous impact. GLUE’s popularity can be attributed to its simplicity and its applicability to nonlinear systems, including those for which a unique calibration is not apparent. The GLUE was introduced for use in uncertainty analysis of watershed models has now been extended well beyond rainfall-runoff watershed models. Given the widespread adoption of GLUE analyses for a broad range or problems, it is appropriate that the validity of the approach be examined with care. In this article, we present an overview of the application of GLUE for assessing uncertainty distribution in hydrological models particularly surface and subsurface hydrology and briefly describe algorithms for sampling of the prior parameter in hydrologic simulation models.

Description: This study aims to identify the parameters that are most important in controlling the Noah land surface model (LSM), the analysis of parameter interactions, and the evaluation of the performance of parameter optimization using the parameter estimation software PEST. We found it necessary to analyze parameter sensitivity in order to properly simulate hydrological variables such as latent heat flux in the Huaihe River Basin, China. The parameters under study in the Noah LSM link thermodynamic and hydrological parts into a complete model. To our knowledge, this parameter interaction in the Noah LSM has never been studied before. There are, however, several studies concerning the influence of vegetation types and climate conditions on parameter sensitivity of the Noah LSM. Three sensitivity analysis methods, the including local sensitivity analysis method SENSAN, regional sensitivity analysis, and Sobol’s method, were tested. Five experimental sites in the Huaihe River Basin were chosen to perform the simulations. The results show that the Noah LSM parameter sensitivities were impacted by the choice of the analysis method. The local method SENSAN often produced significant differences in results compared to the two global methods. The parameter interactions investigated made a significant contribution towards elucidating how one process influences another in the Noah LSM. The results show that parameters were not transferable solely based on vegetation types but also rely on climate conditions. According to the sensitivity analysis results, four sensitive parameters were chosen to be optimized using the PEST method. PEST is a widely used method for estimating parameters in models. Root-mean-square error was used to evaluate the effect of the optimization. Generally in all sites, the optimized parameters values perform better than the original parameter values.

Description: In this study, an inexact joint probabilistic programming (IJPP) approach is developed for risk assessment and uncertainty reflection in water resources management systems. IJPP can dominate random parameters in the model’s left- and right-hand sides of constraints and interval parameters in the objective function. It can also help examine the risk of violating joint probabilistic constraints, which allows an increased robustness in controlling system risk in the optimization process. Moreover, it can facilitate analyses of various policy scenarios that are associated with different levels of economic consequences when the promised targets are violated within a multistage context. The IJPP method is then applied to a case study of planning water resources allocation within a multi-reservoir and multi-period context. Solutions of system benefit, economic penalty, water shortage, and water-allocation pattern vary with different risks of violating water-demand targets from multiple competitive users. Results also demonstrate that different users possess different water-guarantee ratios and different water-allocation priorities. The results can be used for helping water resources managers to identify desired system designs against water shortage and for risk control, and to determine which of these designs can most efficiently accomplish optimizing the system objective under uncertainty.

Description: An overall and comparative ecological risk assessment of heavy metals (including Cd, Cr, Cu, Pb, Zn, Hg and As) in surface sediments from China’s eight major aquatic bodies was conducted to better understand their potential risks on a national scale. By applying the joint approach of Hakanson risk index ( RI ) and Monte Carlo simulation, ecological risk in this work is expressed as probability distribution of RI values instead of single point calculations to reflect the uncertainties in risk assessment process. The results show that the highest ecological risks posed by heavy metals existed in Xiangjiang River and Dianchi Lake. Although only a slim margin of high risk (651.88/600 = 1.08 and 700.61/600 = 1.17) was identified based on average RI values, the probabilities of high risk level derived from Monte Carlo simulation reached as high as 56.7 and 52.9 % in these two aquatic bodies, respectively. And the probability of low risk level was less than 1.6 %. Furthermore, the risk was mainly contributed by Hg and Cd, discharged through local intensive mining and industrial activities. The findings indicate that rigid control and effective management measures to prevent heavy metal pollution are urgently needed in China, especially for the high-risk aquatic bodies. This study shows that the joint approach can be used to identify the high risk water bodies and the major metal pollutants. It may avoid overestimating or underestimating the ecological risk and provide more decision-making support for risk alleviation in the polluted aquatic bodies.

Description: Trend analysis is a frequently used tool in hydrology and climatology for the identification of long-term changes. However, studies are usually only oriented on local trends. This paper rather focuses on the spatial application of trend analysis in groundwater data. For this purpose, a modification of the Mann-Kendall test was developed, based on the trend-free pre-whitening approach. This method was successfully tested on 157 series of yields from headwater springs collected in Czechia during the 1971–2007 period. The analysis was done separately for year, each season and each month. Field significant trends in spring yields were identified in hydrogeological regions. The results showed that the field significant trends are outnumbered when cross-correlation is not taken into account. In the case of annual series, 4 of 18 hydrogeological regions investigated showed a significant decreasing trend after corrections for cross-correlation, compared to 12 regions with field significant trend when not considering cross-correlation.

Description: In ecotoxicology species reproduction tests and multiple testing of reproduction data are wide spread. While normal approximation of the data is a minor problem often the requirement of variance homogeneity is not fulfilled. Variance homogeneity is necessary to assure the proper application of statistical procedures like pairwise t tests, Dunnett t test, and Williams t test. A Poisson model can solve this issue preserving meaningful results and rendering statistical analysis more reliable. Moreover, sequential application of pairwise statistical “control vs. treatment” tests is a drawback concerning \(\alpha \) -inflation. The closure principle (CP) for hypothesis testing is used to generate a step-wise approach for detection of the No/Lowest Observed Effect Concentration using the computational approach test (CAT). The advantages and disadvantages of the combined CPCAT approach compared to the widely used t tests are pointed out and results of real data and fictitious data analysis are compared revealing the superiority of the Poisson model and CPCAT.

Description: Each type of drought has different characteristics in different regions. It is important to distinguish different types of droughts and their correlations. Based on gauged precipitation, temperature, simulated soil moisture, and runoff data during the period 1951–2012, the relationships among meteorological, agricultural, and hydrological droughts were analyzed at different time scales in Southwest China. The standardized precipitation evapotranspiration index (SPEI), soil moisture anomaly percentage index (SMAPI), and standardized runoff index (SRI) were used to describe meteorological, agricultural, and hydrological droughts, respectively. The results show that there was a good correlation among the three indices. SMAPI had the best correlation with the 3 month SPEI and SRI values. It indicates that agricultural drought was characterized by a 3-month scale. The three drought indices displayed the similar special features such as drought scope, drought level, and drought center during the extreme drought of 2009–2010. However, the scope and level of SPEI were bigger than those of SMAPI and SRI. The propagation characteristics of the three types of droughts were significantly different. The temporal drought process in typical grids reflect that the meteorological drought occurred ahead of agricultural and hydrological droughts by about 1 and 3 months, respectively. Agricultural drought showed a stable drought process and reasonable time periods for the drought beginning and end. These results showed the quantitative relationships among three types of drought and thus provided an important supporting evidence for regional drought monitoring and strategic decisions.

Description: Radar systems have been widely employed to measure precipitation and predict flood risks. However, radar as a rainfall measuring device and the produced rainfall estimate contain uncertainties and errors resulting from sources such as mis-calibration, beam blockage, anomalous propagation, and ground clutter. Previously, these radar errors have been individually studied. However, in practical applications, separating and estimating these errors are not possible. In the current study, to analyze the effects of radar rainfall errors, especially for their effect on the peak discharge, through a synthetic runoff simulation, a spatial error model based on univariate Gaussian random numbers was employed. Furthermore, a Monte Carlo simulation, one of the most widely used techniques for intensive simulation toward obtaining practical results, was performed. The results indicated that the variability of the peak discharge increases as the assumed true rainfall increases. In addition, the higher standard deviation of the tested radar rainfall error leads to a higher peak discharge bias. To investigate the cause of this bias, an additional simulation was performed. This simulation revealed that the regression line for the peak discharge corresponding to rainfall amount increases quadratically. The results show that the higher bias is a result of the higher deviation of peak discharges in the cells, with a greater than mean rainfall, even with the same number of cells for lower and higher rainfall amounts.