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1.
ABSTRACT

To 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   

2.
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   

3.
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   

4.
We apply a complex hydro-meteorological modelling chain for investigating the impact of climate change on future hydrological extremes in Central Vietnam, a region characterized by limited data availability. The modelling chain consists of six General Circulation Models (GCMs), six Regional Climate Models (RCMs), six bias correction (BC) approaches, the fully distributed Water Flow and Balance Simulation Model (WaSiM), and extreme values analysis. Bias corrected and raw climate data are used as input for WaSiM. To derive hydrological extremes, the generalized extreme value distribution is fitted to the annual maxima/minima discharge. We identify limitations according to the fitting procedure and the BC methods, and suggest the usage of the delta change approach for hydrological decision support. Tendencies towards increased high- and decreased low flows are concluded. Our study stresses the challenges in using current GCMs/RCMs in combination with state-of-the-art BC methods and extreme value statistics for local impact studies.  相似文献   

5.
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   

6.
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7.
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   

8.
Stationarity is often assumed for frequency analysis of low flows in water resources management and planning. However, many studies have shown that flow characteristics, particularly the frequency spectrum of extreme hydrologic events, were modified by climate change and human activities. Thus, the conventional frequency analysis that fails to consider the nonstationary characteristics may lead to costly design. The analysis presented in this paper was based on the more than 100 years of daily flow data from the Yichang gauging station 44 km downstream of the Three Gorges Dam. The Mann–Kendall trend test under the scaling hypothesis showed that the annual low flows had a significant monotonic trend, whereas an abrupt change point was identified in 1936 by the Pettitt test. The climate‐informed low‐flow frequency analysis and the divided and combined method were employed to account for the impacts from related climate variables and nonstationarities in annual low flows. Without prior knowledge of the probability density function for the gauging station, six distribution functions including the generalized extreme values (GEV), Pearson Type III, Gumbel, Gamma, Lognormal and Weibull distributions have been tested to find the best fit, in which the local likelihood method is used to estimate the parameters. Analyses show that GEV had the best fit for the observed low flows. This study has also shown that the climate‐informed low‐flow frequency analysis is able to exploit the link between climate indices and low flows, which would account for the dynamic feature for reservoir management and provide more accurate and reliable designs for infrastructure and water supply. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

9.
Statistical analysis of extremes currently assumes that data arise from a stationary process, although such an hypothesis is not easily assessable and should therefore be considered as an uncertainty. The aim of this paper is to describe a Bayesian framework for this purpose, considering several probabilistic models (stationary, step-change and linear trend models) and four extreme values distributions (exponential, generalized Pareto, Gumbel and GEV). Prior distributions are specified by using regional prior knowledge about quantiles. Posterior distributions are used to estimate parameters, quantify the probability of models and derive a realistic frequency analysis, which takes into account estimation, distribution and stationarity uncertainties. MCMC methods are needed for this purpose, and are described in the article. Finally, an application to a POT discharge series is presented, with an analysis of both occurrence process and peak distribution.  相似文献   

10.
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   

11.
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   

12.
Abstract

Extreme flood events have been and continue to be one of the most important natural hazards responsible for deaths and economic losses. Extreme floods result in direct destructive effects during the time of the event, and they also may be followed by a related chain of indirect calamities such as famines and epidemics that produce additional damages and suffering. Extreme hydrological events that have occurred in the historical past may also occur in the future. Knowledge about magnitudes and recurrence frequencies of past extreme hydrological events in most regions are too short to adequately evaluate potential magnitudes and recurrence frequencies of extreme hydrological events. Stationary climate in which the mean and variance do not change over time is a basic underlying assumption of standard methodological procedures for estimating recurrence probabilities of extreme hydrological events. Palaeo-archives contained in river and lake sediments, fossil plant and animal matter, ice layers, and other natural archives show that the assumption of stationary climate is not valid when the time scale is extended beyond centuries and millennia. Records of past extreme floods that occurred long before the period of instrumentation can be reconstructed from the distribution of slackwater flood deposits or from derivation of water depths competent to transport the largest rocks found in flood deposited sediment. Palaeoflood records reconstructed from the Upper Mississippi and Lower Colorado River systems in the United States confirm nonstationary behaviour of the mean and variance in hydrological time series. These stratigraphic records have shown that even very modest climatic changes have resulted in very important changes in the magnitudes and recurrence frequencies of extreme floods. A close relationship was found between the palaeo-flood record of extreme floods in the Upper Mississippi River system and a palaeo-record of stable isotopes of oxygen and carbon preserved in speleothem calcite from a local cave. The relationship suggests that isotopic records elsewhere might be calibrated to provide insight about how future potential climate changes might impact extreme flood magnitudes and recurrence frequencies there. Atmospheric global circulation models (GCMs) mainly simulate average climatic conditions and are presently inadequate sources of information about how future climate changes might be represented at the extreme event scale. Palaeo-flood archives, however, provide basic information about how magnitudes and recurrence frequencies of extreme hydrological events responded to past climate changes and they also provide a reference base against which GCM simulations can be calibrated regionally and be better interpreted to decipher hydrological information at the extreme event scale.  相似文献   

13.
Ugo Moisello 《水文研究》2007,21(10):1265-1279
The use of partial probability weighted moments (PPWM) for estimating hydrological extremes is compared to that of probability weighted moments (PWM). Firstly, estimates from at‐site data are considered. Two Monte Carlo analyses, conducted using continuous and empirical parent distributions (of peak discharge and daily rainfall annual maxima) and applying four different distributions (Gumbel, Fréchet, GEV and generalized Pareto), show that the estimates obtained from PPWMs are better than those obtained from PWMs if the parent distribution is unknown, as happens in practice. Secondly, the use of partial L‐moments (obtained from PPWMs) as diagnostic tools is considered. The theoretical partial L‐diagrams are compared with the experimental data. Five different distributions (exponential, Pareto, Gumbel, GEV and generalized Pareto) and 297 samples of peak discharge annual maxima are considered. Finally, the use of PPWMs with regional data is investigated. Three different kinds of regional analyses are considered. The first kind is the regression of quantile estimates on basin area. The study is conducted applying the GEV distribution to peak discharge annual maxima. The regressions obtained with PPWMs are slightly better than those obtained with PWMs. The second kind of regional analysis is the parametric one, of which four different models are considered. The congruence between local and regional estimates is examined, using peak discharge annual maxima. The congruence degree is sometimes higher for PPWMs, sometimes for PWMs. The third kind of regional analysis uses the index flood method. The study, conducted applying the GEV distribution to synthetic data from a lognormal joint distribution, shows that better estimates are obtained sometimes from PPWMs, sometimes from PWMs. All the results seem to indicate that using PPWMs can constitute a valid tool, provided that the influence of ouliers, of course higher with censored samples, is kept under control. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

14.
Most natural disasters are caused by water‐/climate‐related hazards, such as floods, droughts, typhoons, and landslides. In the last few years, great attention has been paid to climate change, and especially the impact of climate change on water resources and the natural disasters that have been an important issue in many countries. As climate change increases the frequency and intensity of extreme rainfall, the number of water‐related disasters is expected to rise. In this regard, this study intends to analyse the changes in extreme weather events and the associated flow regime in both the past and the future. Given trend analysis, spatially coherent and statistically significant changes in the extreme events of temperature and rainfall were identified. A weather generator based on the non‐stationary Markov chain model was applied to produce a daily climate change scenario for the Han River basin for a period of 2001–2090. The weather generator mainly utilizes the climate change SRES A2 scenario driven by input from the regional climate model. Following this, the SLURP model, which is a semi‐distributed hydrological model, was applied to produce a long‐term daily runoff ensemble series. Finally, the indicator of hydrologic alteration was applied to carry out a quantitative analysis and assessment of the impact of climate change on runoff, the river flow regime, and the aquatic ecosystem. It was found that the runoff is expected to decrease in May and July, while no significant changes occur in June. In comparison with historical evidence, the runoff is expected to increase from August to April. A remarkable increase, which is about 40%, in runoff was identified in September. The amount of the minimum discharge over various durations tended to increase when compared to the present hydrological condition. A detailed comparison for discharge and its associated characteristics was discussed. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

15.
Hans Van de Vyver 《水文研究》2018,32(11):1635-1647
Rainfall intensity–duration–frequency (IDF) curves are a standard tool in urban water resources engineering and management. They express how return levels of extreme rainfall intensity vary with duration. The simple scaling property of extreme rainfall intensity, with respect to duration, determines the form of IDF relationships. It is supposed that the annual maximum intensity follows the generalized extreme value (GEV) distribution. As well known, for simple scaling processes, the location parameter and scale parameter of the GEV distribution obey a power law with the same exponent. Although, the simple scaling hypothesis is commonly used as a suitable working assumption, the multiscaling approach provides a more general framework. We present a new IDF relationship that has been formulated on the basis of the multiscaling property. It turns out that the GEV parameters (location and scale) have a different scaling exponent. Next, we apply a Bayesian framework to estimate the multiscaling GEV model and to choose the most appropriate model. It is shown that the model performance increases when using the multiscaling approach. The new model for IDF curves reproduces the data very well and has a reasonable degree of complexity without overfitting on the data.  相似文献   

16.
Extreme rainfalls in South Korea result mainly from convective storms and typhoon storms during the summer. A proper way for dealing with the extreme rainfalls in hydrologic design is to consider the statistical characteristics of the annual maximum rainfall from two different storms when determining design rainfalls. Therefore, this study introduced a mixed generalized extreme value (GEV) distribution to estimate the rainfall quantile for 57 gauge stations across South Korea and compared the rainfall quantiles with those from conventional rainfall frequency analysis using a single GEV distribution. Overall, these results show that the mixed GEV distribution allows probability behavior to be taken into account during rainfall frequency analysis through the process of parameter estimation. The resulting rainfall quantile estimates were found to be significantly smaller than those determined using a single GEV distribution. The difference of rainfall quantiles was found to be closely correlated with the occurrence probability of typhoon and the distribution parameters.  相似文献   

17.
The conventional approach to the frequency analysis of extreme precipitation is complicated by non-stationarity resulting from climate variability and change. This study utilized a non-stationary frequency analysis to better understand the time-varying behavior of short-duration (1-, 6-, 12-, and 24-h) precipitation extremes at 65 weather stations scattered across South Korea. Trends in precipitation extremes were diagnosed with respect to both annual maximum precipitation (AMP) and peaks-over-threshold (POT) extremes. Non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with model parameters made a linear function of time were applied to AMP and POT respectively. Trends detected using the Mann–Kendall test revealed that the stations showing an increasing trend in AMP extremes were concentrated in the mountainous areas (the northeast and southwest regions) of South Korea. Trend tests on POT extremes provided fairly different results, with a significantly reduced number of stations showing an increasing trend and with some stations showing a decreasing trend. For most of stations showing a statistically significant trend, non-stationary GEV and GPD models significantly outperformed their stationary counterparts, particularly for precipitation extremes with shorter durations. Due to a significant-increasing trend in the POT frequency found at a considerable number of stations (about 10 stations for each rainfall duration), the performance of modeling POT extremes was further improved with a non-homogeneous Poisson model. The large differences in design storm estimates between stationary and non-stationary models (design storm estimates from stationary models were significantly lower than the estimates of non-stationary models) demonstrated the challenges in relying on the stationary assumption when planning the design and management of water facilities. This study also highlighted the need of caution when quantifying design storms from POT and AMP extremes by showing a large discrepancy between the estimates from those two approaches.  相似文献   

18.
ABSTRACT

In cold region environments, any alteration in the hydro-climatic regime can have profound impacts on river ice processes. This paper studies the implications of hydro-climatic trends on river ice processes, particularly on the freeze-up and ice-cover breakup along the Athabasca River in Fort McMurray in western Canada, which is an area very prone to ice-jam flooding. Using a stochastic approach in a one-dimensional hydrodynamic river ice model, a relationship between overbank flow and breakup discharge is established. Furthermore, the likelihood of ice-jam flooding in the future (2041–2070 period) is assessed by forcing a hydrological model with meteorological inputs from the Canadian regional climate model driven by two atmospheric–ocean general circulation climate models. Our results show that the probability of ice-jam flooding for the town of Fort McMurray in the future will be lower, but extreme ice-jam flood events are still probable.  相似文献   

19.
As one of the largest international scientific pro- grams in geoscience and environmental science, global change studies were initiated in the early 1980s[1,2]. Noticeable achievements have been made in the stud- ies using indicators such as loess, marine sediment, permafrost, vermicular red earth, and even magmatic activity[2―6]. In recent years, the importance of ground- water as a new type of global change indicators has caused wide attention[7]. Stochastic, isotopic and hy- drochemical st…  相似文献   

20.
Abstract

The global climate change may have serious impacts on the frequency, magnitude, location and duration of hydrological extremes. Changed hydrological extremes will have important implications on the design of future hydraulic structures, flood-plain development, and water resource management. This study assesses the potential impact of a changed climate on the timing and magnitude of hydrological extremes in a densely populated and urbanized river basin in southwestern Ontario, Canada. An ensemble of future climate scenarios is developed using a weather generating algorithm, linked with GCM outputs. These climate scenarios are then transformed into basin runoff by a semi-distributed hydrological model of the study area. The results show that future maximum river flows in the study area will be less extreme and more variable in terms of magnitude, and more irregular in terms of seasonal occurrence, than they are at present. Low flows may become less extreme and variable in terms of magnitude, and more irregular in terms of seasonal occurrence. According to the evaluated scenarios, climate change may have favourable impacts on the distribution of hydrological extremes in the study area.  相似文献   

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