Quantifying differences between reservoir inflows and dam site floods using frequency and risk analysis methods   总被引：1，自引：1，他引：0  

Yixuan?Zhong  Shenglian?GuoEmail author  Zhangjun?Liu  Yun?Wang  Jiabo?Yin 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):419-433

Reservoirs are the most important constructions for water resources management and flood control. Great concern has been paid to the effects of reservoir on downstream area and the differences between inflows and dam site floods due to the changes of upstream flow generation and concentration conditions after reservoir’s impoundment. These differences result in inconsistency between inflow quantiles and the reservoir design criteria derived by dam site flood series, which can be a potential risk and must be quantificationally evaluated. In this study, flood frequency analysis (FFA) and flood control risk analysis (FCRA) methods are used with the long reservoir inflow series derived from a multiple inputs and single output model and a copula-based inflow estimation model. The results of FFA and FCRA are compared and the influences on reservoir flood management are also discussed. The Three Gorges Reservoir (TGR) in China is selected as a case study. Results show that the differences between the TGR inflow and dam site floods are significant which result in changes on its flood control risk rates. The mean values of TGR’s annual maximum inflow peak discharge and 3 days flood volume have increased 5.58 and 3.85% than the dam site ones, while declined by 1.82 and 1.72% for the annual maximum 7 and 15 days flood volumes. The flood control risk rates of middle and small flood events are increased while extreme flood events are declined. It is shown that the TGR can satisfy the flood control task under current hydrologic regime and the results can offer references for better management of the TGR.  相似文献   

A stochastic multivariate framework for modeling movement of discrete sediment particles in open channel flows   总被引：1，自引：1，他引：0  

Jungsun?Oh  Christina?W.?TsaiEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):385-399

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Simulating rainfall time-series: how to account for statistical variability at multiple scales?     

Fabio?OrianiEmail authorView author&#;s OrcID profile  Raj?Mehrotra  Grégoire?Mariethoz  Julien?Straubhaar  Ashish?Sharma  Philippe?Renard 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):321-340

Daily rainfall is a complex signal exhibiting alternation of dry and wet states, seasonal fluctuations and an irregular behavior at multiple scales that cannot be preserved by stationary stochastic simulation models. In this paper, we try to investigate some of the strategies devoted to preserve these features by comparing two recent algorithms for stochastic rainfall simulation: the first one is the modified Markov model, belonging to the family of Markov-chain based techniques, which introduces non-stationarity in the chain parameters to preserve the long-term behavior of rainfall. The second technique is direct sampling, based on multiple-point statistics, which aims at simulating a complex statistical structure by reproducing the same data patterns found in a training data set. The two techniques are compared by first simulating a synthetic daily rainfall time-series showing a highly irregular alternation of two regimes and then a real rainfall data set. This comparison allows analyzing the efficiency of different elements characterizing the two techniques, such as the application of a variable time dependence, the adaptive kernel smoothing or the use of low-frequency rainfall covariates. The results suggest, under different data availability scenarios, which of these elements are more appropriate to represent the rainfall amount probability distribution at different scales, the annual seasonality, the dry-wet temporal pattern, and the persistence of the rainfall events.  相似文献   

Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments     

Amir?NafiEmail author  Eric?Crastes  Rehan?Sadiq  Denis?Gilbert  Olivier?Piller 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):527-544

Performing a comprehensive risk analysis is primordial to ensure a reliable and sustainable water supply. Though the general framework of risk analysis is well established, specific adaptation seems needed for systems such as water distribution networks (WDN). Understanding of vulnerabilities of WDN against deliberate contamination and consumers’ sensitivity against contaminated water use is very vital to inform decision-maker. This paper presents an innovative step-by-step methodology for developing comprehensive indicators to perform sensitivity, vulnerability and criticality analyses in case of absence of early warning system (EWS). The assessment and the aggregation of these indicators with specific fuzzy operators allow identifying the most critical points in a WDN. Intentional intrusion of contaminants at these points can potentially harm both the consumers as well as water infrastructure. The implementation of the developed methodology has been demonstrated through a case study of a French WDN unequipped with sensors.  相似文献   

Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall–runoff modeling     

Vahid?NouraniEmail authorView author&#;s OrcID profile  Afshin?Partoviyan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):545-562

Successful modeling of stochastic hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. On the other hand in training phase of any Artificial Intelligence based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. Accordingly in the present article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily and multi-step-ahead rainfall–runoff process of the Milledgeville station of the Oconee River and the Pole Saheb station of the Jighatu River watersheds, respectively located in USA and Iran. The proposed hybrid data pre-processing approach in the present study is used for the first time in modeling of time series and especially in modeling of hydrological processes. Furthermore, the impacts of denoising (smoothing) and noise injection (jittering) have been simultaneously investigated neither in hydrology nor in any other engineering fields. To evaluate the modeling performance, the outcomes were compared with the results of multi linear regression and Auto Regressive Integrated Moving Average models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed data pre-processing approach which serves both denoising and jittering techniques could improve performance of the ANN and ANFIS based single-step-ahead rainfall–runoff modeling of the Milledgeville station up to 14 and 12% and of the Pole Saheb station up to 22 and 16% in the verification phase. Also the results of multi-step-ahead modeling using the proposed data pre-processing approach showed improvement of modeling for both watersheds.  相似文献   

BARENTS16: a 1-D velocity model for the western Barents Sea     

Myrto?PirliEmail author  Johannes?Schweitzer 《Journal of Seismology》2018,22(1):69-81

A minimum 1-D seismic velocity model for routine seismic event location purposes was determined for the area of the western Barents Sea, using a modified version of the VELEST code. The resulting model, BARENTS16, and corresponding station corrections were produced using data from stations at regional distances, the vast majority located in the periphery of the recorded seismic activity, due to the unfavorable land–sea distribution. Recorded seismicity is approached through the listings of a joint bulletin, resulting from the merging of several international and regional bulletins for the region, as well as additional parametric data from temporary deployments. We discuss the challenges posed by this extreme network-seismicity geometry in terms of velocity estimation resolution and result stability. Although the conditions do not facilitate the estimation of meaningful station corrections at the farthermost stations, and even well-resolved corrections do not have a convincing contribution, we show that the process can still converge to a stable velocity average for the crust and upper mantle, in good agreement with a priori information about the regional structure and geology, which reduces adequately errors in event location estimates.  相似文献   

Allocating river water in a cooperative way: a case study of the Dongjiang River Basin,South China     

Yanhu?He  Jie?Yang  Xiaohong?ChenEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(11):3083-3097

Water resources provide the foundation for human development and environmental sustainability. Water shortage occurs more or less in some regions, which usually causes sluggish economic activities, degraded ecology, and even conflicts and disputes over water use sectors. Game theory can better reflect the behaviors of involved stakeholders and has been increasingly employed in water resources management. This paper presents a framework for the allocation of river basin water in a cooperative way. The proposed framework applies the TOPSIS model combined with the entropy weight to determine stakeholders’ initial water share, reallocating water and net benefit by using four solution concepts for crisp and fuzzy games. Finally, the Fallback bargaining model was employed to achieve unanimous agreement over the four solution concepts. The framework was demonstrated with an application to the Dongjiang River Basin, South China. The results showed that, overall, the whole basin gained more total benefits when the players participated in fuzzy coalitions rather than in crisp coalitions, and \(\left\{ {NHS_{Fuzzy} \,and\, SV_{Crisp} } \right\}\) could better distribute the total benefit of the whole basin to each player. This study tested the effectiveness of this framework for the water allocation decision-making in the context of water management in river basins. The results provide technical support for water right trade among the stakeholders at basin scale and have the potential to relieve water use conflicts of the entire basin.  相似文献   

Estimation of carbon stock for greenhouse gas emissions from hydropower reservoirs     

Amit?KumarEmail authorView author&#;s OrcID profile  M.?P.?Sharma  Tao?Yang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(11):3183-3193

The identification and accurate quantification of sources or sinks of greenhouse gas (GHG) have become a key challenge for scientists and policymakers working on climate change. The creation of a hydropower reservoir, while damming a river for power generation, converts the terrestrial ecosystems into aquatic and subsequently aerobic and anaerobic decomposition of flooded terrestrial soil organic matter resulting in the emission of significant quantity of GHG to the atmosphere. Tropical/subtropical hydropower reservoirs are more significant sources of GHG compared to boreal or temperate one. This paper aims to estimate the emission factor (gCO₂eq./kWh) and net GHG emission from Koteshwar hydropower reservoir in Uttarakhand, India. Further, estimated GHG are compared with those from global reservoirs located in the same eco-region so that its impact could be timely minimized/mitigated. Results have shown that emission factor and net GHG emission of Koteshwar reservoir are, respectively, estimated as 13.87 gCO₂eq./kWh and 167.70 Gg C year^?1 which are less than other global reservoirs located in the same eco-region. This information could be helpful for the hydropower industries to construct reservoirs in tropical eco-regions.  相似文献   

A direct scaling analysis for the sea level rise     

Giuseppe?Roberto?Tomasicchio  Letizia?Lusito  Felice?D’AlessandroEmail author  Ferdinando?Frega  Antonio?Francone  Samuele?De?Bartolo 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(12):3397-3408

The estimation of long-term sea level variability is of primary importance for a climate change assessment. Despite the value of the subject, no scientific consensus has yet been reached on the existing acceleration in observed values. The existence of this acceleration is crucial for coastal protection planning purposes. The absence of the acceleration would enhance the debate on the general validity of current future projections. Methodologically, the evaluation of the acceleration is a controversial and still open discussion, reported in a number of review articles, which illustrate the state-of-art in the field of sea level research. In the present paper, the well-proven direct scaling analysis approach is proposed in order to describe the long-term sea level variability at 12 worldwide-selected tide gauge stations. For each of the stations, it has been shown that the long-term sea level variability exhibits a trimodal scaling behaviour, which can be modelled by a power law with three different pairs of shape and scale parameters. Compared to alternative methods in literature, which take into account multiple correlated factors, this simple method allows to reduce the uncertainties on the sea level rise parameters estimation.  相似文献   

A catastrophe progression approach based index sensitivity analysis model for the multivariate flooding process     

Li-Na?Wang  Xiao-Hong?ChenEmail author  Yong-Xin?Xu  Ming-Zhi?Huang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(1):141-153

Flood mitigation should deal with those most sensitive flooding elements to very efficiently release risks and reduce losses. Present the most concerns of flood control are peak level or peak discharge which, however, may not always be the most sensitive flooding element. Actually, along with human activities and climate change, floods bring threats to bear on human beings appear in not only peak level and peak discharge, but also other elements like maximum 24-h volume and maximum 72-h volume. In this paper, by collecting six key flooding intensity indices (elements), a catastrophe progression approach based sensitivity analysis algorithm model is developed to identify the indices that mostly control over the flood intensity. The indices sensitivity is determined through a selected case study in the Wujiang River, South China, based on half a century of flow record. The model results indicate that there is no evident relationship of interplay among the index sensitivities, but the variability of the index sensitivity is closely related to the index variability and the index sensitivity increases with the decrease of index value. It is found that peak discharge is not the most influential flooding factor as is generally thought in this case. The sensitivity value of the maximum 24-h volume is the greatest influential factor among all the other indices, indicating that this index plays a leading role in the flood threat of the Wujiang River, South China. It is inferred that, for the purpose of flood warning and mitigation, the peak flood discharge is not always the most sensitive and dominant index as opposed to the others, depending on the sensitivity.  相似文献