A statistical model for karst spring discharge estimation under extensive groundwater development and extreme climate change

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     

Simulation of spring flows from a karst aquifer with an artificial neural network

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.     

Trivariate generalized extreme value distribution in flood frequency analysis

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.     

A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology

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.     

The role of anthropogenic activities in karst spring discharge volatility

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.     

Generalized extreme value shape parameter and its nature for extreme precipitation using long time series and the Bayesian approach

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. Koutsoyiannis

ASSOCIATE EDITOR not assigned     

基于广义极值分布的巴颜喀拉块体中部地震危险性分析

本文对广义极值分布模型的构建机理进行了深入详细的阐述,给出了逻辑意义更加合理的的重现期和重现水平定义,以及相关的地震危险性评价指标。在此基础上,应用构建模型对巴颜喀拉块体中部的地震危险性做了客观的评价,得出巴颜喀拉块体中部每年的平均最大发震为M_s5.1,每20年发生M_s6.0以上强震可能性超过97%,M_s7.5左右的超强震约100年一遇,块体内部孕育地震的能量积累迅速。     

Variation of karst spring discharge in the recent five decades as an indicator of global climate change: A case study at Shanxi,northern China

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…     

The two-component extreme value distribution for flood frequency analysis: Derivation of a new estimation method

The two component extreme value (TCEV) distribution has recently been shown to account for most of the characteristics of the real flood experience. A new method of parameter estimation for this distribution is derived using the principle of maximum entropy (POME). This method of parameter estimation is suitable for application in both the site-specific and regional cases and appears simpler than the maximum likelihood estimation method. Statistical properties of the regionalized estimation were evaluated using a Monte Carlo approach and compared with those of the maximum likelihood regional estimators.     

A high‐altitude temporary spring in a compartmentalized carbonate aquifer: the role of low‐permeability faults and karst conduits

The aim of this research was to refine the actual conceptual model related to the activation of high‐altitude temporary springs within the carbonate Apennines in southern Italy. The research was carried out through geophysical, hydrogeological, hydrochemical and isotopic investigations at the Acqua dei Faggi experimental site during five hydrologic years. The research demonstrated that, in carbonate aquifers where low‐permeability faults cause the aquifer system to be compartmentalized, high‐altitude temporary springs may be recharged by groundwater. In such settings, neither surface water infiltration in karst systems nor perched temporary aquifers play a role of utmost importance. The rare (once or a few time a year) activation of such springs is due to the fact that groundwater unusually reach the threshold head that allows the spring to flow. The activation of the studied high‐altitude temporary spring also depended on relationships between a low‐permeability fault core and a karst system that locally interrupts the low‐permeability barrier. In fact, when the hydraulic head did not reach the karst system, the concentrated head loss within the fault core did not allow the spring to flow, because the groundwater entirely flowed through the fault towards the downgradient compartment. Copyright © 2009 John Wiley & Sons, Ltd.     

位场总水平导数极值位置空间变化规律研究

通过对位场总水平导数函数性质的研究表明,位场总水平导数不是位函数,因而利用位场总水平导数构造新的边缘识别方法时会出现"奇点",使得计算结果的稳定性下降.对单一边界、双边界、多边界模型重力异常总水平导数和重力异常垂向导数总水平导数极值位置的空间变化规律研究表明,重力异常垂向导数总水平导数和化极磁力异常总水平导数的极值位置相同,与重力异常总水平导数的极值位置空间变化规律相似.利用位场总水平导数极大值位置能够准确识别单一直立边界地质体的边缘位置,但不能准确识别其它任何形体的边缘位置,其识别结果的偏移量大小随地质体的埋深、水平尺寸以及倾斜程度等变化,但能收敛于某一固定值;重力异常垂向导数总水平导数比重力异常总水平导数的峰值更加尖锐、横向识别能力更强,其极大值位置更靠近地质体上顶面边缘位置,但存在"次极大值"的影响.     

位场解析信号振幅极值位置空间变化规律研究

通过对单一边界、双边界、多边界以及点(线)质量模型重力异常解析信号振幅和重力异常垂向导数解析信号振幅的极值位置空间变化规律研究表明,重力异常垂向导数解析信号振幅和化极磁力异常解析信号振幅的极值位置相同,且与重力异常解析信号振幅的极值位置空间变化规律相似.利用位场解析信号振幅极大值位置能够准确识别单一直立边界地质体的边缘位置,但不能准确识别其它任何形体的边缘位置,其识别结果的偏移量大小随地质体的埋深、水平尺寸以及倾斜程度等变化.虽然重力异常垂向导数解析信号振幅比重力异常解析信号振幅的峰值更加尖锐、横向识别能力更强,其极大值位置更靠近地质体上顶面边缘位置,但均受地质体埋深的影响较大;随着埋深的增加,位场解析信号振幅的极大值位置会快速收敛到形体的"中心位置",其轨迹类似"叉子状";且对多边界模型会出现"极大值位置盲区"而无法识别其边缘位置.通过这些理论研究表明,位场解析信号振幅只能识别单一边界地质体的边缘位置;而不宜用来识别多边界地质体的边缘位置,但可以用来识别多边界地质体的"中心位置".     

An ensemble approach for the analysis of extreme rainfall under climate change in Naples (Italy)

In the present paper, an ensemble approach is proposed to estimate possible modifications caused by climate changes in the extreme precipitation regime, with the rain gauge Napoli Servizio Idrografico (Naples, Italy) chosen as test case. The proposed research, focused on the analysis of extremes on the basis of climate model simulations and rainfall observations, is structured in several consecutive steps. In the first step, all the dynamically downscaled EURO‐CORDEX simulations at about 12 km horizontal resolution are collected for the current period 1971–2000 and the future period 2071–2100, for the RCP4.5 and the RCP8.5 concentration scenarios. In the second step, the significance of climate change effects on extreme precipitation is statistically tested by comparing current and future simulated data and bias‐correction is performed by means of a novel approach based on a combination of simple delta change and quantile delta mapping, in compliance with the storm index method. In the third step, two different ensemble models are proposed, accounting for the variabilities given by the use of different climate models and for their hindcast performances. Finally, the ensemble models are used to build novel intensity–duration–frequency curves, and their effects on the early warning system thresholds for the area of interest are evaluated.