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Yu Zhong Yonghong Hao Xueli Huo Mingkun Zhang Qingyun Duan Yonghui Fan 《水文科学杂志》2013,58(11):2011-2023
ABSTRACTTo acquire better understanding of spring discharge under extreme climate change and extensive groundwater pumping, this study proposed an extreme value statistical decomposition model, in which the spring discharge was decomposed into three items: a long-term trend; periodic variation; and random fluctuation. The long-term trend was fitted by an exponential function, and the periodic variation was fitted by an exponential function whose index was the sum of two sine functions. A general extreme value (GEV) model was used to obtain the return level of extreme random fluctuation. Parameters of the non-linear long-term trend and periodic variation were estimated by the Levenberg-Marquardt algorithm, and the GEV model was estimated by the maximum likelihood method. The extreme value statistical decomposition model was applied to Niangziguan Springs, China to forecast spring discharge. We showed that the modelled spring discharge fitted the observed data very well. Niangziguan Springs discharge is likely to continue declining with fluctuation, and the risk of cessation by August 2046 is 1%. The extreme value decomposition model is a robust method for analysing the nonstationary karst spring discharge under conditions of extensive groundwater development/pumping, and extreme climate changes.
Editor D. Koutsoyiannis; Associate editor J. Ward 相似文献
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In China, 9·5% of the landmass is karst terrain and of that 47,000 km2 is located in semiarid regions. In these regions the karst aquifers feed many large karst springs within basins of thousands of square kilometres. Spring discharges reflect the fluctuation of ground water level and variability of ground water storage in the basins. However, karst aquifers are highly heterogeneous and monitoring data are sparse in these regions. Therefore, for sustainable utilization and conservation of karst ground water it is necessary to simulate the spring flows to acquire better understanding of karst hydrological processes. The purpose of this study is to develop a parsimonious model that accurately simulates spring discharges using an artificial neural network (ANN) model. The karst spring aquifer was treated as a non‐linear input/output system to simulate the response of karst spring flow to precipitation and applied the model to the Niangziguan Springs, located in the east of Shanxi Province, China and a representative of karst springs in a semiarid area. Moreover, the ANN model was compared with a previous time‐lag linear model and it was found that the ANN model performed better. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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Yonghong Hao Guoliang Liu Huamin Li Zhongtang Li Jiaojuan Zhao Tian‐Chyi J. Yeh 《水文研究》2012,26(20):3062-3069
Niangziguan Spring complex is the largest karst spring in North China. We investigate the karst hydrological processes by using Morlet wavelet transform analysis and cross wavelet analysis based on monthly precipitation from 1958 to 2010 and spring discharge from 1958 to 2009. From Morlet wavelet transform coefficients of precipitation and the spring discharge in Niangziguan Springs Basin, we find that the precipitation and discharge are characterized by the multi‐scale features in the time domain, and the energy distribution of the signal is highly irregular across scales. Although precipitation eventually becomes spring discharge by infiltrating and propagating through karst formations, the signals are attenuated. The results also show that the precipitation of Niangziguan Springs Basin has the main periodic components of 1‐, 5‐, 12‐, and 17‐year periods with alternating wet–drought cycle. Similarly, the spring discharge of Niangziguan Springs has the main components of 17‐year periods, but the 1‐, 5‐, and 12‐year periodicity of precipitation are not reflected in spring discharge, which is filtered by the aquifers. The results of cross wavelet analysis reveal that the precipitation and spring discharge share the common periodicity of 17 years. This means that those signals with high energy and long timescales can penetrate through the aquifer and be reflected in spring discharge, whereas other signals are filtered and modified. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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《水文科学杂志》2013,58(3):550-567
Abstract The multivariate extension of the logistic model with generalized extreme value (GEV) marginals is applied to provide a regional at-site flood estimate. The maximum likelihood estimators of the parameters were obtained numerically by using a multivariable constrained optimization algorithm. The asymptotic results were checked by distribution sampling techniques in order to establish whether or not those results can be utilized for small samples. A region in northern Mexico with 21 gauging stations was selected to apply the model. Results were compared with those obtained by the most popular univariate distributions, the bivariate approach of the logistic model and three regional methods: station-year, index flood and L-moments. These show that there is a reduction in the standard error of fit when estimating the parameters of the marginal distribution with the trivariate distribution instead of its univariate and bivariate counterpart, and differences between at-site and regional at-site design events can be significant as return period increases. 相似文献
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Yonghong Hao Jing Wu Qingxia Sun Yuen Zhu Yan Liu Zhongtang Li Tian‐Chyi J. Yeh 《水文研究》2013,27(18):2605-2613
Based on the groundwater development process, and regional economic and social developing history, we divided the spring hydrological process of the Liulin Springs Basin into two periods: pre‐1973 and post‐1974. In the first period (i.e. 1957–1973), the spring discharge was affected by climate variation alone, and in the second period (i.e. 1974–2009), the spring discharge charge was influenced by both climate variation and human activities. A piecewise analysis strategy was used to differentiate the contribution of anthropogenic activities from climate variation on karst spring discharge depletion in the second period. Then, the ARIMAX model was applied to spring flow time series of the first period to develop a model for the effects of climate variation only. Using this model, we estimated the spring discharge in the second period solely under the influence of climate variation. Based on the water budget, we subtracted observed spring discharge from the estimated spring discharge and acquired the contribution of human activities on spring discharge depletion for the second period. The results of the analysis indicated that the contribution of climate variation to the spring discharge depletion is?0.20 m3/s from 1970s to 2000s. The contribution of anthropogenic activities to the spring flow depletion was ?2.56 m3/s in 2000s, which was about 13 times more than that of climate variation. Our analysis further indicates that groundwater exploitation only accounts for 29% of the spring flow depletion due to the effects of human activities. The remaining 71% of the depletion is likely to be caused by other human activities, including dam building, dewatering during coal mining, and deforestation. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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A statistical study was made of the temporal trend in extreme rainfall in the region of Extremadura (Spain) during the period 1961–2009. A hierarchical spatio-temporal Bayesian model with a GEV parameterization of the extreme data was employed. The Bayesian model was implemented in a Markov chain Monte Carlo framework that allows the posterior distribution of the parameters that intervene in the model to be estimated. The results show a decrease of extreme rainfall in winter and spring and a slight increase in autumn. The uncertainty in the trend parameters obtained with the hierarchical approach is much smaller than the uncertainties obtained from the GEV model applied locally. Also found was a negative relationship between the NAO index and the extreme rainfall in Extremadura during winter. An increase was observed in the intensity of the NAO index in winter and spring, and a slight decrease in autumn. 相似文献
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Alex J. Cannon 《水文研究》2010,24(6):673-685
Parameters in a generalized extreme value (GEV) distribution are specified as a function of covariates using a conditional density network (CDN), which is a probabilistic extension of the multilayer perceptron neural network. If the covariate is time or is dependent on time, then the GEV‐CDN model can be used to perform nonlinear, nonstationary GEV analysis of hydrological or climatological time series. Owing to the flexibility of the neural network architecture, the model is capable of representing a wide range of nonstationary relationships. Model parameters are estimated by generalized maximum likelihood, an approach that is tailored to the estimation of GEV parameters from geophysical time series. Model complexity is identified using the Bayesian information criterion and the Akaike information criterion with small sample size correction. Monte Carlo simulations are used to validate GEV‐CDN performance on four simple synthetic problems. The model is then demonstrated on precipitation data from southern California, a series that exhibits nonstationarity due to interannual/interdecadal climatic variability. Copyright © 2009 Her Majesty the Queen in right of Canada. Published by John Wiley & Sons, Ltd. 相似文献
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Jing Wu Jian Yin Yonghong Hao Yan Liu Yonghui Fan Xueli Huo Youcun Liu Tian‐Chyi J. Yeh 《水文研究》2015,29(13):2855-2866
The traditional hydrological time series methods tend to focus on the mean of whichever variable is analysed but neglect its time‐varying variance (i.e. assuming the variance remains constant). The variances of hydrological time series vary with time under anthropogenic influence. There is evidence that extensive well drilling and groundwater pumping can intercept groundwater run‐off and consequently induce spring discharge volatility or variance varying with time (i.e. heteroskedasticity). To investigate the time‐varying variance or heteroskedasticity of spring discharge, this paper presents a seasonal autoregressive integrated moving average with general autoregressive conditional heteroskedasticity (SARIMA‐GARCH) model, whose the SARIMA model is used to estimate the mean of hydrological time series, and the GARCH model estimates its time‐varying variance. The SARIMA‐GARCH model was then applied to the Xin'an Springs Basin, China, where extensive groundwater development has occurred since 1978 (e.g. the average annual groundwater pumping rates were less than 0.20 m3/s in the 1970s, reached 1.20 m3/s at the end of the 1980s, surpassed 2.0 m3/s in the 1990s and exceeded 3.0 m3/s by 2007). To identify whether human activities or natural stressors caused the heteroskedasticity of Xin'an Springs discharge, we segmented the spring discharge sequence into two periods: a predevelopment stage (i.e. 1956–1977) and a developed stage (i.e. 1978–2012), and set up the SARIMA‐GARCH model for the two stages, respectively. By comparing the models, we detected the role of human activities in spring discharge volatility. The results showed that human activities caused the heteroskedasticity of the Xin'an Spring discharge. The predicted Xin'an Springs discharge by the SARIMA‐GARCH model showed that the mean monthly spring discharge is predicted to continue to decline to 0.93 m3/s in 2013, 0.67 m3/s in 2014 and 0.73 m3/s in 2015. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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This paper describes statistical procedures for developing earthquake damage fragility functions. Although fragility curves abound in earthquake engineering and risk assessment literature, the focus has generally been on the methods for obtaining the damage data (i.e., the analysis of structures), and little emphasis is placed on the process for fitting fragility curves to this data. This paper provides a synthesis of the most commonly used methods for fitting fragility curves and highlights some of their significant limitations. More novel methods are described for parametric fragility curve development (generalized linear models and cumulative link models) and non‐parametric curves (generalized additive model and Gaussian kernel smoothing). An extensive discussion of the advantages and disadvantages of each method is provided, as well as examples using both empirical and analytical data. The paper further proposes methods for treating the uncertainty in intensity measure, an issue common with empirical data. Finally, the paper describes approaches for choosing among various fragility models, based on an evaluation of prediction error for a user‐defined loss function. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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The paper deals with the probability estimates of temperature extremes (annual temperature maxima and heat waves) in the Czech Republic. Two statistical methods of probability estimations are compared; one based on the stochastic modelling of time series of the daily maximum temperature (TMAX) using the first-order autoregressive (AR(1)) model, the other consisting in fitting the extreme value distribution to the sample of annual temperature peaks.The AR(1) model is able to reproduce the main characteristics of heat waves, though the estimated probabilities should be treated as upper limits because of deficiencies in simulating the temperature variability inherent to the AR(1) model. Theoretical extreme value distributions do not yield good results when applied to maximum annual lengths of heat waves and periods of tropical days (TMAX 30°C), but it is the best method for estimating the probability and recurrence time of annual one-day temperature extremes. However, there are some difficulties in the application: the use of the two-parameter Gumbel distribution and the three-parameter generalized extreme value (GEV) distribution may lead to different results, particularly for long return periods. The resulting values also depend on the chosen procedure of parameter estimation. Based on our findings, the shape parameter testing for the GEV distribution and the L moments technique for parameter estimation may be recommended.The application of the appropriate statistical tools indicates that the heat wave and particularly the long period of consecutive tropical days in 1994 were probably a more rare event than the record-breaking temperatures in July 1983 exceeding 40°C. An improvement of the probability estimate of the 1994 heat wave may be expected from a more sophisticated model of the temperature series. 相似文献
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Assessing the probability of extreme precipitation events is consequential in civil planning. This requires an understanding of how return values change with return periods, which is essentially described by the generalized extreme value (GEV) shape parameter. Some works in the field suggest a constant shape parameter, while our analysis indicates a non-universal value. We re-analysed an older precipitation dataset (169 stations) extended by Norwegian data (71 stations). We showed that while each set seems to have a constant shape parameter, it differs between the two datasets, indicating regional differences. For a more comprehensive analysis of spatial effects, we examined a global dataset (1495 stations). We provided shape parameter maps for two models and found clear evidence that the shape parameter depends on elevation, while the effect of latitude remains uncertain. Our results confirm an explanation in terms of dominating precipitation systems based on a proxy derived from the Köppen-Geiger climate classification.
EDITOR D. KoutsoyiannisASSOCIATE EDITOR not assigned 相似文献
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This paper deals with the prediction of peak horizontal accelerations with emphasis on seismic risk and insurance concerns. Non‐linear mixed effects models are used to analyse well‐known earthquake data and the consequences of mis‐specifying assumptions on the error term are quantified. A robust fit of the usual model, using recently developed robust weighted maximum likelihood estimators, is presented. Outlying data are automatically identified and subsequently investigated. A more appropriate model accounting for the extreme value nature of the responses, is also developed and implemented. The implication on acceleration predictions is demonstrated. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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Complex void space structure and flow patterns in karstic aquifers render behaviour prediction of karstic springs difficult. Four support vector regression-based models are proposed to predict flow rates from two adjacent karstic springs in Greece (Mai Vryssi and Pera Vryssi). Having no accurate estimates of the groundwater flow pattern, we used four kernels: linear, polynomial, Gaussian radial basis function and exponential radial basis function (ERBF). The data used for training and testing included daily and mean monthly precipitation, and spring flow rates. The support vector machine (SVM) performance depends on hyper-parameters, which were optimized using a grid search approach. Model performance was evaluated using root mean square error and correlation coefficient. Polynomial kernel performed better for Mai Vryssi and the ERBF for Pera Vryssi. All models except one performed better for Pera Vryssi. Our models performed better than generalized regression neural network, radial basis function neural network and ARIMA models. 相似文献
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AbstractThis paper focuses on a regionalization attempt to partly solve data limitation problems in statistical analysis of high flows to derive discharge–duration–frequency (QDF) relationships. The analysis is based on 24 selected catchments in the Lake Victoria Basin (LVB) in East Africa. Characteristics of the theoretical QDF relationships were parameterized to capture their slopes of extreme value distributions (evd), tail behaviour and scaling measures. To enable QDF estimates to be obtained for ungauged catchments, interdependence relationships between the QDF parameters were identified, and regional regression models were developed to explain the regional difference in these parameters from physiographic characteristics. In validation of the regression models, from the lowest (5 years) to the highest (25 years) return periods considered, the percentage bias in the QDF estimates ranged from –2% for the 5-year return period to 27% for 25-year return period.
Editor D. Koutsoyiannis 相似文献
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山西地震带历史地震序列的统计分析 总被引：2，自引：0，他引：2
在工程地震工作中需要知道某地震带或某地区未来100年内的地震活动水平,以便适当选择历史地震的统计时段,从而得到和地震活动水平相适应的不同潜在震源区的各级地震的年平均发生率。 本文利用极值理论、最大熵原理、马尔可夫模型的方法、莫尔纳方法和伯努里模型方法计算了山西地震带未来100年内中强震的年平均发生率、平均重现期大于等于某级地震的个数和发震概率,计算结果表明,山西地震带未来百年内将发生≥6.0级地震3次,发震概率为0.94左右;将发生≥6.5级地震1—2次,发震概率为0.84左右;有可能发生≥7.0级地震,发震概率为0.53左右;发生≥7.5级地震的可能性不大,发震概率为0.32左右;发生≥8.0级地震的可能性很小,发震概率为0.15左右。 相似文献
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Dajun Qin Zhanfeng Zhao Yi Guo Wencai Liu Muhammed Haji Xiaohong Wang Baodong Xin Yu Li Yong Yang 《水文研究》2017,31(24):4297-4314
The deeply buried river‐connected Xishan karst aquifer (XKA) in western Beijing, China, has been suffering from diminishing recharge for several decades, which in turn leads to the disappearing of spring water outflows and continuously lowering of groundwater level in the area. Thus, it is important to correctly recognize the groundwater recharge and flow paths for the sustainable development of the XKA. To investigate these issues, the hydrochemical and isotopic compositions are analysed for both surface water and groundwater samples collected over an area of about 280 km2. Results show that (a) the river water is characterized by high Na contents; (b) the δ2H and δ18O values in the river water are distinctively higher than those of groundwater samples, after experiencing the long‐time evaporative enrichment in the upstream reservoir; (c) the Sr concentrations and 87Sr/86Sr ratios of groundwater clearly indicated the interaction between water and carbonate minerals but excluded the water–silicate interaction; and (d) the groundwater samples in the direct recharge area of the XKA have the lowest Na concentrations and the δ2H and δ18O values. Based on the large differences in the Na contents and 18O values of groundwater and surface water, a simple two‐component mixing model is developed for the study area and the fractions of the river water are estimated for groundwater samples. We find that the distribution pattern of the river water fractions in the XKA clearly shows a change of directions in the preferential flow path of the groundwater from its source zone to the discharge area. Overall, our results suggest that the recharged surface water can be a useful evidence for delineating the groundwater flow path in river‐connected karst aquifer. This study improves our understanding of the heterogeneity in karst groundwater systems. 相似文献
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Abstract Plant root systems can utilize soil water to depths of 10 m or more. Spatial pattern data of deep soil water content (SWC) at the regional scale are scarce due to the labour and time constraints of field measurements. We measured gravimetric deep SWC (DSWC) at depths of 200, 300, 400, 500, 600, 800 and 1000 cm at 382 sites across the Loess Plateau, China. The coefficient of variation was high for soil water content (SWC) in the horizontal direction (48%), but was relatively small for SWC in the vertical direction (9%). Semivariogram ranges for DSWC at different depths were between 198 and 609 km. Kriged distribution maps indicated that deep soil layers became moister along northwest to southeast transects. Multiple statistical analyses related DSWC to plant characteristics (e.g. plant age explained >21% of the variability), geographical location and altitude (8–13%), soil texture and infiltrability, evaporation zone and eco-hydrological processes (P < 0.05). Regional land management decisions can be based on our DSWC distribution data to determine land uses and plant species appropriate for the soil type and location that would maintain a stable soil water balance. Maintaining infiltrability is of great importance in this and other water-scarce regions of the world. Editor D. Koutsoyiannis; Associate editor J. Simunek Citation Wang, Y.Q., Shao, M.A., Liu, Z.P. and Warrington, D.N., 2012. Regional spatial pattern of deep soil water content and its influencing factors. Hydrological Sciences Journal, 57 (2), 265–281. 相似文献