Showing papers in "Stochastic Environmental Research and Risk Assessment in 2009"

Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling

Abstract: In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchments.

Changing properties of precipitation concentration in the Pearl River basin, China

Abstract: In this paper, precipitation concentrations across the Pearl River basin and the associated spatial patterns are analyzed based on daily precipitation data of 42 rain gauging stations during the period 1960-2005. Regions characterized by the different changing properties of precipitation concentration index (CI) are identified. The southwest and northeast parts of the Pearl River basin are characterized by lower and decreasing precipitation CI; the northwest and south parts of the study river basin show higher and increasing precipitation CI. Higher but decreasing precipitations CI are found in the West and East River basin. Comparison of precipitation CI trends before and after 1990 shows that most parts of the Pearl River basin are characterized by increasing precipitation CI after 1990. Decreasing precipitation CI after 1990 (compared to precipitation CI changes before 1990) is observed only in a few stations located in the lower Gui River and the lower Yu River. Significant increasing precipitation CI after 1990 is detected in the West River, lower North River and upper Beipan River. These changes of precipitation CI in the Pearl River basin are likely to be associated with the consequences of the well-evidenced global warming. These findings can contribute to basin-scale water resource management and conservation of ecological environment in the Pearl River basin.

Drought characterization: a probabilistic approach

Abstract: Using the alternative renewable process and run theory, this study investigates the distribution of drought interval time, mean drought interarrival time, joint probability density function and transition probabilities of drought events in the Kansabati River basin in India. The standardized precipitation index series is employed in the investigation. The time interval of SPI is found to have a significant effect of the probabilistic characteristics of drought.

Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP)

Abstract: Analytic hierarchy process (AHP) is a utility theory based decision-making technique, which works on a premise that the decision-making of complex problems can be handled by structuring them into simple and comprehensible hierarchical structures. However, AHP involves human subjective evaluation, which introduces vagueness that necessitates the use of decision-making under uncertainty. The vagueness is commonly handled through fuzzy sets theory, by assigning degree of membership. But, the environmental decision-making problem becomes more involved if there is an uncertainty in assigning the membership function (or degree of belief) to fuzzy pairwise comparisons, which is referred to as ambiguity (non-specificity). In this paper, the concept of intuitionistic fuzzy set is applied to AHP, called IF-AHP to handle both vagueness and ambiguity related uncertainties in the environmental decision-making process. The proposed IF-AHP methodology is demonstrated with an illustrative example to select best drilling fluid (mud) for drilling operations under multiple environmental criteria.

Statistical approach to inverse distance interpolation

Abstract: Inverse distance interpolation is a robust and widely used estimation technique. Variants of kriging are often proposed as statistical techniques with superior mathematical properties such as minimum error variance; however, the robustness and simplicity of inverse distance interpolation motivate its continued use. This paper presents an approach to integrate statistical controls such as minimum error variance into inverse distance interpolation. The optimal exponent and number of data may be calculated globally or locally. Measures of uncertainty and local smoothness may be derived from inverse distance estimates.

Adaptive Neuro-Fuzzy Inference System for drought forecasting

Abstract: Drought causes huge losses in agriculture and has many negative influences on natural ecosystems. In this study, the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS) for drought forecasting and quantitative value of drought indices, the Standardized Precipitation Index (SPI), is investigated. For this aim, 10 rainfall gauging stations located in Central Anatolia, Turkey are selected as study area. Monthly mean rainfall and SPI values are used for constructing the ANFIS forecasting models. For all stations, data sets include a total of 516 data records measured between in 1964 and 2006 years and data sets are divided into two subsets, training and testing. Different ANFIS forecasting models for SPI at time scales 1–12 months were trained and tested. The results of ANFIS forecasting models and observed values are compared and performances of models were evaluated. Moreover, the best fit models have been also trained and tested by Feed Forward Neural Networks (FFNN). The results demonstrate that ANFIS can be successfully applied and provide high accuracy and reliability for drought forecasting.

Uncertainty assessment of a process-based integrated catchment model of phosphorus

Abstract: Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km2), a River Wye tributary, on the England–Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination (E or R 2) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn’t sufficient data to support the many parameters.

Assessment of heavy metal contamination of roadside soils in Southwest China

Abstract: Topsoil (0–20 cm) samples were collected from the edge of roads to the locations about 200 m off the roads along the four roads with different transportation periods in October 2005. Total concentrations of As, Cd, Cr, Cu, Ni, Pb and Zn were determined using the inductively coupled plasma atomic absorption spectrometry in order to assess and compare road transportation pollution. Results showed that with the exception of As, Cu and Pb, the average concentrations of heavy metals were generally, higher than the regional elemental background values. Most soil samples were moderately or highly polluted by Cd or Ni, but the contamination index (P i ) values for As, Pb and Zn were lower than other heavy metals in all sites. Among the four roads, heavy metal pollution was heavier for Dali Road due to longer transportation periods, while low or no contamination could be observed for the other roads. However, the integrated contamination index (P c ) values showed a generally low contamination or no contamination level for all soil samples in this region, followed by the order of Dali Road > Dabao Highway > Road 320 > Sixiao Highway. The same pollution source of these heavy metals was found using factor analysis.

Two-stage fuzzy chance-constrained programming: application to water resources management under dual uncertainties

Abstract: In this study, a two-stage fuzzy chance-constrained programming (TFCCP) approach is developed for water resources management under dual uncertainties. The concept of distribution with fuzzy probability (DFP) is presented as an extended form for expressing uncertainties. It is expressed as dual uncertainties with both stochastic and fuzzy characteristics. As an improvement upon the conventional inexact linear programming for handling uncertainties in the objective function and constraints, TFCCP has advantages in uncertainty reflection and policy analysis, especially when the input parameters are provided as fuzzy sets, probability distributions and DFPs. TFCCP integrates the two-stage stochastic programming (TSP) and fuzzy chance-constrained programming within a general optimization framework. TFCCP incorporates the pre-regulated water resources management policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised amounts are not delivered. TFCCP is applied to a water resources management system with three users. Solutions from TFCCP provide desired water allocation patterns, which maximize both the system’s benefits and feasibility. The results indicate that reasonable solutions were generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of stream flows, α-cut levels and fuzzy dominance indices.

Artificial neural network models for forecasting monthly precipitation in Jordan

Abstract: Forecasting precipitation in arid and semi-arid regions, in Jordan in the Middle East for example, has particular importance since precipitation is the unique source of water in such regions. In this study, 1-month ahead precipitation forecasts are made using artificial neural network (ANN) models. Feed forward back propagation (FFBP), radial basis function (RBF) and generalized regression type ANNs are used and compared with a simple multiple linear regression (MLR) model. The models are tested on monthly total precipitation recorded at three meteorological stations (Baqura, Amman and Safawi) from different climatological regions in Jordan. For the three stations, it is found that the best calibrated model is FFBP with respect to all performance criteria used in the study, including determination coefficient, mean square error, mean absolute error, the slope and the intercept in the best-fit linear line of the scatter diagram. In the validation stage, FFBP is again the best model in Baqura and Amman. However, in Safawi, the driest station, not only FFBP but also RBF and MLR perform equally well depending on the performance criterion under consideration.

Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward

Abstract: During the last two decades or so, studies on the applications of the concepts of nonlinear dynamics and chaos to hydrologic systems and processes have been on the rise. Earlier studies on this topic focused mainly on the investigation and prediction of chaos in rainfall and river flow, and further advances were made during the subsequent years through applications of the concepts to other problems (e.g. data disaggregation, missing data estimation, and reconstruction of system equations) and other processes (e.g. rainfall-runoff and sediment transport). The outcomes of these studies are certainly encouraging, especially considering the exploratory stage of the concepts in hydrologic sciences. This paper discusses some of the latest developments on the applications of these concepts to hydrologic systems and the challenges that lie ahead on the way to further progress. As for their applications, studies in the important areas of scaling, groundwater contamination, parameter estimation and optimization, and catchment classification are reviewed and the inroads made thus far are reported. In regards to the challenges that lie ahead, particular focus is given to improving our understanding of these largely less-understood concepts and also finding ways to integrate these concepts with the others. With the recognition that none of the existing one-sided ‘extreme-view’ modeling approaches is capable of solving the hydrologic problems that we are faced with, the need for finding a balanced ‘middle-ground’ approach that can integrate different methods is stressed. To this end, the viability of bringing together the stochastic concepts and the deterministic concepts as a starting point is also highlighted.

Potential ecological risk of cadmium, lead and arsenic in agricultural black soil in Jilin Province, China

Abstract: Potential ecological risk of cadmium (Cd), lead (Pb) and arsenic (As) in agricultural black soil in Jilin Province, China was analyzed by the methods of risk assessment based on dose–effect relationships and ecological risk index. Heavy Cd-contamination occurred mainly around the coal mine region. The accumulation area for Pb appeared mostly in the suburbs and roadsides, whereas the higher As content was mainly found in the farmland of suburb and coal mine vicinity. In acute toxicity test, Cd, Pb and As in the soil had adverse effects on both roots and shoots growth in soybean with the greatest toxicity of arsenic and the least toxicity of lead at the same concentration levels. Exposed to Cd, Pb and As, the EC50 (50% effective concentration) values for the growth of soybean root (shoot) were 212.59 (376.70), 528.53 (828.69) and 194.60 (299.03) mg/kg, respectively. Results of potential ecological risk index showed that soil contamination from Cd in some samples had very high potential ecological risk; Pb contamination for almost all sampling sites had moderate ecological risk; while soil contamination from As had low ecological risk. With the present accumulation rate, concentrations of Cd, Pb and As in agricultural black soil near coal mine would reach the threshold values in 68, 175 and 120 years, respectively.

Changes of temperature extremes for 1960–2004 in Far-West China

Abstract: The spatial and temporal patterns of the temperature extremes defined by 5th and 95th percentiles based on daily maximum/minimum temperature dataset were analyzed using Mann-Kendall test and linear regression method. The research results indicate that: (1) the seasonal minimum temperature is in stronger increasing trend than the seasonal maximum temperature; (2) in comparison with the changes of the maximum temperature, more stations display significantly increasing trends of minimum temperature in frequency and intensity; (3) comparatively, more stations have significantly decreasing trends in the intra-seasonal extreme temperature anomaly in summer and winter than in spring and autumn. The areal mean minimum temperature is in stronger increasing trend than areal mean maximum temperature; (4) the warming process in the Far-West (FW) China is characterized mainly by significantly increasing minimum temperature. The research will be helpful for local human mitigation to alterations in water resource and ecological environment in FW China due to changes of temperature extremes, as the ecologically fragile region of China.

Emergency management evaluation by a fuzzy multi-criteria group decision support system

Abstract: Emergency risk management (ERM) is a process which involves dealing with risks to the community arising from emergency events. Emergency management evaluation as one of the important parts of ERM aims assessing and improving social preparedness and organizational ability in identifying, analyzing, and treating emergency risks. This study first develops an emergency management evaluation model. It then proposes an extended fuzzy multi-criteria group evaluation method, which can deal with both subjective and objective criteria under multi-levels by a group of evaluators, for emergency management evaluation. A fuzzy multi-criteria group decision support system (FMCGDSS) is then developed to implement the proposed method for the case of emergency operating center/system evaluation.

Bayesian methods for estimating multi-segment discharge rating curves

Abstract: This study explores Bayesian methods for handling compound stage–discharge relationships, a problem which arises in many natural rivers. It is assumed: (1) the stage–discharge relationship in each rating curve segment is a power-law with a location parameter, or zero-plane displacement; (2) the segment transitions are abrupt and continuous; and (3) multiplicative measurement errors are of equal variance. The rating curve fitting procedure is then formulated as a piecewise regression problem where the number of segments and the associated changepoints are assumed unknown. Procedures are developed for describing both global and site-specific prior distributions for all rating curve parameters, including the changepoints. Estimation and uncertainty analysis is evaluated using Markov chain Monte Carlo simulation (MCMC) techniques. The first model explored accounts for parameter and model uncertainties in the interpolated area, i.e. within the range of available stage–discharge measurements. A second model is constructed in an attempt to include the uncertainty in extrapolation, which is necessary when the rating curve is used to estimate discharges beyond the highest or lowest measurement. This is done by assuming that the rate of changepoints both inside and outside the measured area follows a Poisson process. The theory is applied to actual data from Norwegian gauging stations. The MCMC solutions give results that appear sensible and useful for inferential purposes, though the latter model needs further efforts in order to obtain a more efficient simulation scheme.

A length-biased version of the Birnbaum-Saunders distribution with application in water quality

Abstract: In this article, we develop a new model based on the Birnbaum-Saunders distribution that results to be both useful and practical for environmental sciences. The density, distribution and hazard functions, moments and properties of this new model are presented. A graphical analysis of the density is also provided. Furthermore, we estimate parameters, propose asymptotic inference and discuss influence diagnostics by using likelihood methods for the new distribution. An illustrative example with real data related to water quality indicates the adequacy on the new distribution.

A unified approach to environmental systems modeling

Abstract: The paper considers the differences between hypothetico-deductive and inductive modeling: between modelers who put their primary trust in their scientific intuition about the nature of an environmental model and tend to produce quite complex computer simulation models; and those who prefer to rely on the analysis of observational data to identify the simplest form of model that can represent these data. The tension that sometimes arises because of the different philosophical outlooks of these two modeling groups can be harmful because it tends to fractionate the effort that goes into the investigation of important environmental problems, such as global warming. In an attempt to improve this situation, the paper will outline a new Data-Based Mechanistic (DBM) approach to modeling that tries to meld together the best aspects of these two modeling philosophies in order to develop a unified approach that combines the hypothetico-deductive virtues of good scientific intuition and simulation modeling with the pragmatism of inductive data-based modeling, where more objective inference from data is the primary driving force. In particular, it demonstrates the feasibility of a new method for complex simulation model emulation, in which the methodological tools of DBM modeling are used to develop a reduced dynamic order model that represents the ‘dominant modes’ of the complex simulation model. In this form, the ‘dynamic emulation’ model can be compared with the DBM model obtained directly from the analysis of real data and any tensions between the two modeling approaches may be relaxed to produce models that suit multiple modeling objectives.

Stochastic-fuzzy multi criteria decision making for robust water resources management

Abstract: All realistic Multi Criteria Decision Making (MCDM) problems in water resources management face various kinds of uncertainty. In this study the evaluations of the alternatives with respect to the criteria will be assumed to be stochastic. Fuzzy linguistic quantifiers will be used to obtain the uncertain optimism degree of the Decision Maker (DM). A new approach for stochastic-fuzzy modeling of MCDM problems will be then introduced by merging the stochastic and fuzzy approaches into the Ordered Weighted Averaging (OWA) operator. The results of the new approach, entitled SFOWA, give the expected value and the variance of the combined goodness measure for each alternative, which are essential for robust decision making. In order to combine these two characteristics, a composite goodness measure will be defined. By using this measure the model will give more sensitive decisions to the stakeholders whose optimism degrees are different than that of the decision maker. The methodology will be illustrated by using a water resources management problem in the Central Tisza River in Hungary. Finally, SFOWA will be compared to other methods known from the literature to show its suitability for MCDM problems under uncertainty.

Global warming: a review of this mostly settled issue

Abstract: Global warming and attendant climate change have been controversial for at least a decade. This is largely because of its societal implications since the science is largely straightforward. With the recent publication of the Fourth Assessment Report of the United Nations’ Intergovernmental Panel on Climate Change (Working Group 1) there has been renewed interest and controversy about how certain the scientific community is of its conclusions: that humans are influencing the climate and that global temperatures will continue to rise rapidly in this century. This review attempts to update what is known and in particular what advances have been made in the past 5 years or so. It does not attempt to be comprehensive. Rather it focuses on the most controversial issues, which are actually few in number. They are: Finally there is a very brief discussion of the societal policy response to the scientific message. Note that much of the introductory material in each section is essentially the same as that which appears in Keller 2003 (hereafter referred to as OR = original review) and its update (Keller 2007). Despite continued uncertainties, the review finds an affirmative answer to these questions. Of particular interest are advances that seem to explain why satellites do not see as much warming as surface instruments, how we are getting a good idea of recent paleo-climates, and why the twentieth century temperature record was so complex. It makes the point that in each area new information could come to light that would change our thinking on the quantitative magnitude and timing of anthropogenic warming, but it is unlikely to alter the basic conclusions.

Multistage scenario-based interval-stochastic programming for planning water resources allocation

Abstract: In this study, a multistage scenario-based interval-stochastic programming (MSISP) method is developed for water-resources allocation under uncertainty. MSISP improves upon the existing multistage optimization methods with advantages in uncertainty reflection, dynamics facilitation, and risk analysis. It can directly handle uncertainties presented as both interval numbers and probability distributions, and can support the assessment of the reliability of satisfying (or the risk of violating) system constraints within a multistage context. It can also reflect the dynamics of system uncertainties and decision processes under a representative set of scenarios. The developed MSISP method is then applied to a case of water resources management planning within a multi-reservoir system associated with joint probabilities. A range of violation levels for capacity and environment constraints are analyzed under uncertainty. Solutions associated different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help water managers to identify desired policies under various economic, environmental and system-reliability conditions. Besides, sensitivity analyses demonstrate that the violation of the environmental constraint has a significant effect on the system benefit.

Streamflow drought time series forecasting: a case study in a small watershed in North West Spain

Abstract: Drought is a climatic event that can cause significant damage both in natural environment and in human lives. Drought forecasting is an important issue in water resource planning. Due to the stochastic behaviour of droughts, a multiplicative seasonal autoregressive integrated moving average model was applied to forecast monthly streamflow in a small watershed in Galicia (NW Spain). A better streamflow forecast obtained when the Martone index was included in the model as explanatory variable. After forecasting 12 leading month streamflow, three drought thresholds: streamflow mean, monthly streamflow mean and standardized streamflow index were chosen. Both observed and forecasted streamflow showed no drought evidence in this basin.

Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida

Abstract: Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low- and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May–June–July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study.

Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater

Abstract: A three-layer Artificial Neural Network (ANN) model (9:12:1) for the prediction of Chemical Oxygen Demand Removal Efficiency (CODRE) of Upflow Anaerobic Sludge Blanket (UASB) reactors treating real cotton textile wastewater diluted with domestic wastewater was presented. To validate the proposed method, an experimental study was carried out in three lab-scale UASB reactors to investigate the treatment efficiency on total COD reduction. The reactors were operated for 80 days at mesophilic conditions (36–37.5°C) in a temperature-controlled water bath with two hydraulic retention times (HRT) of 4.5 and 9.0 days and with organic loading rates (OLR) between 0.072 and 0.602 kg COD/m3/day. Five different dilution ratios of 15, 30, 40, 45 and 60% with domestic wastewater were employed to represent seasonal fluctuations, respectively. The study was undertaken in a pH range of 6.20–8.06 and an alkalinity range of 1,350–1,855 mg/l CaCO3. The concentrations of volatile fatty acids (VFA) and total suspended solids (TSS) were observed between 420 and 720 mg/l CH3COOH and 68–338 mg/l, respectively. In the study, a wide range of influent COD concentrations (CODi) between 651 and 4,044 mg/l in feeding was carried out. CODRE of UASB reactors being output parameter of the conducted anaerobic treatment was estimated by nine input parameters such as HRT, pH, CODi concentration, operating temperature, alkalinity, VFA concentration, dilution ratio (DR), OLR, and TSS concentration. After backpropagation (BP) training combined with principal component analysis (PCA), the ANN model predicted CODRE values based on experimental data and all the predictions were proven to be satisfactory with a correlation coefficient of about 0.8245. In the ANN study, the Levenberg-Marquardt Algorithm (LMA) was found as the best of 11 BP algorithms. In addition to determination of the optimal ANN structure, a linear-nonlinear study was also employed to investigate the effects of input variables on CODRE values in this study. Both ANN outputs and linear-nonlinear study results were compared and advantages and further developments were evaluated.

Fuzzy robust process capability indices for risk assessment of air pollution

Abstract: Air pollution is one of the most important threats for the humanity. It can damage not only human health but also Earth’s ecosystem. Because of the harmful effects of air pollution, it should be controlled very carefully. To do the risk assessment of air pollution in Istanbul, the process capability indices (PCIs) which are very effective statistics to summarize the performance of process are used in this paper. Fuzzy PCIs are used to determine the levels of the air pollutants which are measured in different nine stations in Istanbul. Robust PCIs (RPCIs) are used when air pollutants have correlation. Fuzzy set theory has been applied for both PCIs and RPCIs to have more sensitive results. More flexible PCIs obtained by using fuzzy specification limits and fuzzy standard deviation are used to evaluate the air pollution’s level of Istanbul. Additionally some evaluation criteria have been constructed for fuzzy PCIs to interpret the air pollution.

GIS-based risk assessment of grassland fire disaster in western Jilin province, China

Abstract: Grassland fire disasters have occurred frequently and adversely affected livestock agriculture and social-economic development greatly in the grassland regions of Jilin province, China. Moreover, both the frequency of grassland fire and loss from them are considered to be increasing with the global warming and economic development. This study presents a methodology for risk analysis and assessment of grassland fire disaster, taking western Jilin province as a case study area based on geographic information system (GIS). The composite grassland fire disaster risk index (GFDRI) combined the hazard of grassland fire, the exposure of the region, the vulnerability and emergency response and recovery capability for grassland fire disaster of the region were developed to assess and compare risk of grassland fire disaster in different counties in western Jilin province, China using the natural disaster risk index method (NDRIM), analytic hierarchy process (AHP) and weighted comprehensive method (WCM). Then, the risk degree of grassland fire disaster was assessed and regionalized in the western Jilin province, China based on GFDRI by using GIS. It is shown that the most places of western Jilin province were in mediate risk. Zhenlai, Tongyu were in heavy risk. Taobei, Ningjiang, Fuyu were in light risk. The information obtained from interviewing the district official committees in relation to result compiled was statistically evaluated. The GFDRI was developed to be an easily understandable tool that can be used to assess and compare the relative risk of grassland fire disaster in different counties in t western Jilin province, China, and to compare the different relative contributions of various factors, e.g., frequency of grassland fire and quality of emergency evacuation plan. The GFDRI is specifically intend to support local and national government agencies of grassland fire disaster management as they (1) make resource allocation decisions; (2) make high-level planning decisions; and (3) raise public awareness of grassland fire disaster risk, its causes, and ways to manage it.

Comprehensive evaluation of regional flood frequency analysis by L- and LH-moments. I. A re-visit to regional homogeneity

Abstract: As part I of a sequence of two papers, previously developed L-moments by Hosking (J R Stat Soc Ser B Methodol 52(2):105–124, 1990), and the LH-moments by Wang (Water Resour Res 33(12):2841–2848, 1997) are re-visited. New relationships are developed for regional homogeneity analysis by the LH-moments, and further establishment of regional homogeneity is investigated. Previous works of Hosking (J R Stat Soc Ser B Methodol 52(2):105–124, 1990) and Wang (Water Resour Res 33(12):2841–2848, 1997) on L-moments and LH-moments for the generalized extreme value (GEV) distribution are extended to the generalized Pareto (GPA) and the generalized logistic (GLO) distributions. The Karkhe watershed, located in western Iran is used as a case study area. Regional homogeneity was investigated by first assuming the entire study area as one regional cluster. Then the entire study area was designated “homogeneous” by the L-moments (L); and was designated “heterogeneous” by all four levels of the LH-moments (L1 to L4). The k-means method was used to investigate the case of two regional clusters. All levels of the L- and LH-moments designated the upper watershed (region A), “homogeneous”, and the lower watershed (region B) “possibly-homogeneous”. The L3 level of the GPA and the L4 level of the GLO were selected for regions A and B, respectively. Wang (Water Resour Res 33(12):2841–2848, 1997) identified a reversing trend in improved performance of the GEV distribution at the LH-moments level of L3 (during the goodness-of-fit test). Similar results were also obtained in this research for the GEV distribution. However, for the case of the GPA distribution the reversing trend started at L4 for region A; and at L2 for region B. As for the case of the GLO, an improved performance was observed for all levels (moving from L to L4); for both regions.

Stochastic multi-site generation of daily weather data

Abstract: Spatial autocorrelation is a correlation between the values of a single variable, considering their geographical locations. This concept has successfully been used for multi-site generation of daily precipitation data (Khalili et al. in J Hydrometeorol 8(3):396–412, 2007). This paper presents an extension of this approach. It aims firstly to obtain an accurate reproduction of the spatial intermittence property in synthetic precipitation amounts, and then to extend the multi-site approach to the generation of daily maximum temperature, minimum temperature and solar radiation data. Monthly spatial exponential functions have been developed for each weather station according to the spatial dependence of the occurrence processes over the watershed, in order to fulfill the spatial intermittence condition in the synthetic time series of precipitation amounts. As was the case for the precipitation processes, the multi-site generation of daily maximum temperature, minimum temperature and solar radiation data is realized using spatially autocorrelated random numbers. These random numbers are incorporated into the weakly stationary generating process, as with the Richardson weather generator, and with no modifications made. Suitable spatial autocorrelations of random numbers allow the reproduction of the observed daily spatial autocorrelations and monthly interstation correlations. The Peribonca River Basin watershed is used to test the performance of the proposed approaches. Results indicate that the spatial exponential functions succeeded in reproducing an accurate spatial intermittence in the synthetic precipitation amounts. The multi-site generation approach was successfully applied for the weather data, which were adequately generated, while maintaining efficient daily spatial autocorrelations and monthly interstation correlations.

Copula-based mixed models for bivariate rainfall data: an empirical study in regression perspective

Abstract: A comprehensive parametric approach to study the probability distribution of rainfall data at scales of hydrologic interest (e.g. from few minutes up to daily) requires the use of mixed distributions with a discrete part accounting for the occurrence of rain and a continuous one for the rainfall amount. In particular, when a bivariate vector (X, Y) is considered (e.g. simultaneous observations from two rainfall stations or from two instruments such as radar and rain gauge), it is necessary to resort to a bivariate mixed model. A quite flexible mixed distribution can be defined by using a 2-copula and four marginals, obtaining a bivariate copula-based mixed model. Such a distribution is able to correctly describe the intermittent nature of rainfall and the dependence structure of the variables. Furthermore, without loss of generality and with gain of parsimony this model can be simplified by some transformations of the marginals. The main goals of this work are: (1) to empirically explore the behaviour of the parameters of marginal transformations as a function of time scale and inter-gauge distance, by analysing data from a network of rain gauges; (2) to compare the properties of the regression curves associated to the copula-based mixed model with those derived from the model simplified by transformations of the marginals. The results from the investigation of transformations’ parameters are in agreement with the expected theoretical dependence on inter-gauge distance, and show dependence on time scale. The analysis on the regression curves points out that: (1) a copula-based mixed model involves regression curves quite close to some non-parametric models; (2) the performance of the parametric regression decreases in the same cases in which non-parametric regression shows some instability; (3) the copula-based mixed model and its simplified version show similar behaviour in term of regression for mid-low values of rainfall.

Bayesian optimal design of an avalanche dam using a multivariate numerical avalanche model

Abstract: For snow avalanches, passive defense structures are generally designed by considering high return period events. However, defining a return period turns out to be tricky as soon as different variables are simultaneously considered. This problem can be overcome by maximizing the expected economic benefit of the defense structure, but purely stochastic approaches are not possible for paths with a complex geometry in the runout zone. Therefore, in this paper, we include a multivariate numerical avalanche propagation model within a Bayesian decisional framework. The influence of a vertical dam on an avalanche flow is quantified in terms of local energy dissipation with a simple semi-empirical relation. Costs corresponding to dam construction and the damage to a building situated in the runout zone are roughly evaluated for each dam height–hazard value pair, with damage intensity depending on avalanche velocity. Special attention is given to the poor local information to be taken into account for the decision. Using a case study from the French avalanche database, the Bayesian optimal dam height is shown to be more pessimistic than the classical optimal height because of the increasing effect of parameter uncertainty. It also appears that the lack of local information is especially critical for a building exposed to the most extreme events only. The residual hazard after dam construction is analyzed and the sensitivity to the different modelling assumptions is evaluated. Finally, possible further developments of the approach are discussed.

Spatio-temporal changes of hydrological processes and underlying driving forces in Guizhou region, Southwest China

Abstract: Understanding the changes in streamflow and associated driving forces is crucial for formulating a sustainable regional water resources management strategy in the environmentally fragile karst area of the southwest China. This study investigates the spatio-temporal changes in streamflow of the Guizhou region and their linkage with meteorological influences using the Mann–Kendall trend analysis, singular-spectrum analysis (SSA), Lepage test, and flow duration curves (FDCs). The results demonstrate that: (1) the streamflow in the flood-season (June–August) during 1956–2000 increased significantly (confidence level ≥95%) in most catchments, closely consistent with the distinct increasing trend of annual rainfall over wet-seasons. The timings of abrupt change for streamflow in most catchments are found to occur at 1986; (2) streamflow in the Guizhou region experiences significant seasonal changes prior/posterior to 1986, and in most catchments the coefficient of variation of monthly streamflow increases; (3) spatial changes in streamflow indicate that monthly streamflow in the north-west decreases but increases in other parts; (4) the spatial high- and low-flow map (Q 5 and Q 95) reveals an increase in the extremely large streamflow in the five eastern catchments but a decrease in the extremely low streamflow in the four eastern catchments and three western catchments during 1987–2000. An increase in streamflow, particularly extreme flows, during the flood season would increase the risk of extreme flood events, while a decrease in streamflow in the dry season is not beneficial to vegetation restoration in this ecologically fragile region.