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Stochastic processes in hydrology / by Vujica Yevjevich
Abstract
Incluye bibliografái e índice
... This method has been applied to numerous karst aquifer systems and continues to be developed with improved approaches. The most widely used in this context are correlation and spectral analysis, which are based on a systems approach and contribute significantly to the understanding of karst systems (Eisenlohr et al., 1997;Jemcov and Petrič, 2010;Jukić et al., 2022;Kovačič, 2010;Labat et al., 2000;Larocque et al., 1998;Mangin, 1984;Padilla and Pulido-Bosch, 1995;Panagopoulos and Lambrakis, 2006;Tagne and Dowling, 2018;Yevjevich, 1972). ...
... The correlation and spectral analyses involve the determination of autocorrelation functions (ACF), and cross-correlation functions (CCF) in the time domain, as well as the determination of spectral density functions (SDF), coherence functions (COF), gain functions (GAF) and phase functions (PHF) in the frequency domain. These functions find wide application in the analysis of karst hydrological systems, and the theoretical background and application reports can be found in several publications (e.g., Eisenlohr et al., 1997;Labat et al., 2000;Larocque et al., 1998;Mangin, 1984;Padilla and Pulido-Bosch, 1995;Panagopoulos and Lambrakis, 2006;Yevjevich, 1972). In the following, only a short overview of the theory of correlation and spectral analyses is given. ...
Karst aquifers have very heterogeneous structure and complex hydrodynamics through conduits, fractures and
matrix, therefore characteristics of groundwater flow and solute transport differ from those in intergranular and
fissured aquifers. Appropriately adapted techniques of hydrogeological research are required for understanding
of their functioning. This paper presents an advanced application of time series analysis in a binary karst aquifer
known for its complex hydrodynamics and mixing of water from various sources of recharge. A classical
approach with high temporal data resolution was extended to a spatial domain with simultaneous monitoring of
precipitation, sinking streams, water flow in cave systems, and springs. The main objectives of this study were to
define and compare the flow characteristics and storage capacity of selected springs and their catchments, and to
determine the influence of different types of recharge (autogenic versus allogenic). The time series of recharge
were analysed as input functions in the correlation and spectral analysis. The results undoubtedly show differences
in water transfer through the system, storage capacity, and recharge characteristics. However, they also
highlight the limitations of using cross-correlation functions to distinguish the influence of autogenic and allogenic
recharge. Due to their interference, the interpretation of the results in complex karst systems can be
ambiguous, therefore a method of partial cross-correlation analysis was additionally used. Although it has been
used before in karst aquifers to spatially characterise groundwater circulation and autogenic recharge, in this
study it was used for the first time to investigate the mutual influences of allogenic and autogenic recharge. The
results confirmed that this approach provides additional insight into the functioning of binary karst aquifers.
... Naveen et al., (1991) [49] have presented a detailed analysis of various climatological parameters like rainfall, temperature, wind speed, humidity, evaporation, solar radiation, etc., of Ananthapuramu. Rao et al., (2009) [50] have investigated the trends in heavy rainfall events of Ananthapuramu district using the rainfall data for the period 1971-2008. Reddy et al., (2008 [51] have presented a comprehensive study of drought conditions in Ananthapuramu district. The entire district of Ananthapuramu is declared as a hot-arid zone and several watershed management programs were initiated to mitigate the drought conditions. ...
... Similarly, negative serial correlation leads to accepting the null hypothesis of no trend when it is false (type II error). To test for serial correlation in the data, lag-k serial correlation coefficients are calculated [69][70][71][72][73]. In several of the trend studies, the time series is tested for serial correlation by calculating lag-1 serial correlation coefficient ρ 1 [74][75][76][77]. ...
Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the sub-district level and aggregated to monthly, annual and seasonal rainfall totals and the number of rainy days. The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. Non-Parametric Mann-Kendall test and Spearman's rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen's slope method. For the data influenced by serial correlation, various modified versions of Mann-Kendall tests (Pre-Whitening, Trend Free Pre-Whitening, Bias Corrected Pre-Whitening and two variants of Variance Correction Approaches) were applied. A significant increasing summer rainfall trend is observed in 6 out of 27 stations. Significant decreasing trends are observed at two stations in the southwest monsoon season and at two stations in the northeast monsoon season. To identify the trend change-points in the time series, distribution-free Cumulative SUM test and sequential Mann-Kendall tests were applied. Two open-source library packages were developed in R language namely, 'modifiedmk' and 'trendchange' to implement the statistical tests mentioned in this paper. The study will benefit water resource management, drought mitigation, socioeconomic development and sustainable agricultural planning in the region.
... The stochastic models of hydrological variables are usually developed by decomposing the hydrological time series into a number of components, e.g. linear or nonlinear trend, jumps, periodic component, stochastic component and noise component (Yevjevich 1972). The model structure is determined primarily by the temporal discretization scale. ...
... The significant low-frequency harmonics are identified from the relative cumulative periodogram as those outside the 95% confidence interval (Stojković et al. 2015). The macroperiodic component Q P is then obtained by summing the significant low-frequency harmonics (Yevjevich 1972): ...
Climate change projections of precipitation and temperature suggest that Serbia could be one of the most affected regions in southeastern Europe. To prepare adaptation measures, the impact of climate changes on water resources needs to be assessed. Pilot research is carried out for the Lim River basin, in southeastern Europe, to predict monthly flows under different climate scenarios. For estimation of future water availability, an alternative approach of developing a deterministic-stochastic time series model is chosen. The proposed two-stage time series model consists of several components: trend, long-term periodicity, seasonality and the stochastic component. The latter is based on a transfer function model with two input variables, precipitation and temperature, as climatic drivers. The Nash-Sutcliffe model efficiency for the observed period 1950–2012 is 0.829. The model is applied for the long-term hydrological prediction under the representative concentration pathway (RCP) emissions scenarios for the future time frame 2013–2070.
... FA uses correlation coefficient and covariance-variance matrices, to recognize variables within a set of observed variables that discriminate the pattern of correlations. Usually, it is used in data reduction to highlight a small number of factors that reveal most of the variance observed in a much larger number of variables, as in this study, so those selected for retention can be more meaningful (Yevjevich, 1972;Goddard and Kirby, 1976). Goddard and Kirby (1976) recommended that components with eigenvalues more than 1 should be used. ...
... Such single individual components do not offer any significant grouping of factors. That aspect coincides with the studies of Goddard and Kirby (1976), as their eigenvalues less than 1 are excluded from further interpretation (Yevjevich, 1972). Each of the final four components is associated with characteristic information that is provided by the contained indices (Table 3 in supplementary material). ...
Geomorphic indices can be used to examine the geomorphological and tectonic processes responsible for the development of the drainage basins. Such indices can be dependent on tectonics, erosional processes and other factors that control the morphology of the landforms. The inter-relationships between geomorphic indices can determine the influence of regional tectonic activity in the shape development of drainage basins. A Multi-Criteria Decision Analysis (MCDA) procedure has been used to perform an integrated cluster analysis that highlights information associated with the dominant regional tectonic activity. Factor Analysis (FA) and Analytical Hierarchy Process (AHP) were considered within that procedure, producing a representation of the distributed regional tectonic activity of the drainage basins studied. The study area is western Crete, located in the outer fore-arc of the Hellenic subduction zone, one of the world's most tectonically active regions. The results indicate that in the landscape evolution of the study area (especially the western basins) tectonic controls dominate over lithological controls.
... El modelo matemático es el más importante en Hidrología, a lo largo del presente trabajo de tesis cuando se hable de modelo hidrológico se entenderá que es un modelo matemático. Yevjevich (1972) describe que los modelos markovianos pueden tener parámetros constantes, parámetros variando con el tiempo o una combinación de ambos. El primer caso es típicamente usado para modelar series de tiempo de valores anuales mientras que los últimos pueden ser aplicadas para series de tiempo de intervalos que son una fracción del año. ...
En el presente estudio, se trata de generar series sintéticas medias mensuales del río Chira mediante el uso de modelos markovianos y luego se determina la capacidad de embalse óptimo, para lo cual se emplea el método Range, ya que de los estudios realizados, la represa Poechos tiene actualmente una vida útil de 20 años aproximadamente, como alternativa se propone el diseño de un embalse en la quebrada de San Francisco, ubicada en la cuenca del Río Chira. Se inicia, con el análisis de consistencia de la información de las series de descargas medias mensuales del Río Chira, sobre la base de las estaciones hidrométricas Ardilla y Ciruelo, ubicadas aguas arriba del embalse Poechos, resultando ambas series de tiempo consistentes, luego, se efectuo el modelamiento estocástico, mediante la utilización de los modelos markovianos ampliamente usados en la hidrología estocástica. De la calibración y validación de las series de tiempo simuladas, se determinó que el modelo markoviano de segundo orden AR (2), para la estación Ardilla y el modelo markoviano de tercer orden AR (3), para la estación Ciruelo, son las que generan los mejores estadísticos históricos de las series analizadas. Para la calibración y validación se usó el programa de cómputo SAMS 2000 (Análisis Estocástico, Modelamiento y Simulación), versión 98.1. Basados en los modelos antes mencionados se generaron 20 series sintéticas de 50 años cada una, para cada estación, con la ayuda del programa de cómputo SAMS 2000. Luego se determinó la capacidad de almacenamiento por el método Range, con las series generadas, con los datos de la estación Ardilla debido a que se ubica aguas abajo y registra dos tributarios más respecto a la estación Ciruelo. Finalmente se determinan experimentalmente la distribución de probabilidades de la capacidad de embalse, las cuales se ajustan a una distribución log-normal de dos parámetros.
... of serial independence is then tested by the lag-1 autocorrelation coefficient as H 0 : 1 0 r = against H 1 : 1 0 r > using the test of significance of serial correlation (Yevjevich, 1971) following Rai et al. (2010), ...
... Many methods and statistical approaches have been proposed in the past to describe the karst spring hydrograph (Maillet 1905;Boussinesq 1904;Drogue 1972;Atkinson 1977;Schöeller 1967), and especially since the 1970s with the application and adaptation of time series analysis techniques (e.g. Jenkins and Watts 1968;Box and Jenkins 1976) to hydrology (e. g. Yevjevich 1972) or karst hydrology (Mangin 1984) purposes. There are a lot of recent studies that still acknowledge these works, even if other methods accounting for non-linearity or non-stationarity (Labat et al. 2000) have since been proposed. ...
Karst aquifers are complex hydrogeological systems that require numerous in-situ measurements of hydrological and physico-chemical parameters to characterize transfer processes from the recharge area to the karst spring. Numerous graphical, statistical or signal processing methods have been developed for decades to interpret these measurements, but there is no simple and standardized tool that can be used for this purpose, which is necessary for a rigorous comparison of results between case studies. This Technical Note presents XLKarst, which has been developed to provide a simple and easy-to-use tool to process a selection of proven methods that characterize the functioning of karst systems. This tool allows (i) time series analysis based on correlation and spectral analysis and, for flow measurements, the use of other statistics and base flow separation, (ii) calculation of the cumulative distribution function to build a spring flow probability plot, and (iii) analysis of spring flow recession and expression of the results in a karst system classification scheme. These methods are first described by providing the key elements of their use and interpretation in the scientific literature. Then, an application to the Fontaine de Nîmes karst system (southern France) is used to highlight the complementarity of the methods proposed by XLKarst to describe the hydrodynamic behavior of a karst system based on daily data of rainfall and discharge over 22 years.
... The serial correlation coefficient is denoted by r k . The hypothesis of the serially independent then tested by lag-1 against null hypothesis H0 using significance of serial correlation (Rai et al., 2010;Yevjevich, 1971) ...
Climate change poses a significant global challenge, impacting rainfall and temperature patterns worldwide. To assess regional and temporal changes, we conducted a trend analysis on mean monsoon rainfall and mean summer temperature in four Indian states with diverse climates: Karnataka, Gujarat, Rajasthan, and Maharashtra. The selection of these states as study areas was based on the monsoon's arrival time from the Arabian Sea. Using nonparametric statistical trend analysis techniques such as the Mann-Kendall test, Sen's slope estimator, Kendall tau and Mann-Whitney-Pettitt (MWP). we examined trends from 1951 to 2000 at a significance level of 5%. Additionally, we employed linear regression to identify climatic patterns. Our findings revealed both positive and negative trends in mean monsoon rainfall and mean summer temperature across all four states. Rainfall trends exhibited a decreasing pattern in all states, except for Maharashtra, which displayed a slightly negative trend despite an overall positive annual temperature trend. Conversely, temperature trends showed an increasing pattern in all states except Maharashtra. To further explore the relationship between summer temperature and monsoon precipitation, we investigated several urban centers within these four states. The results indicated varying trends, including increasing, decreasing, and no discernible trend across different stations. Our analysis demonstrated a general decline in yearly monsoon precipitation across most regions in the four states, coupled with recorded temperature changes. Notably, Karnataka exhibited a stronger positive correlation between rainfall and temperature trends. Maharashtra and Gujarat also exhibited a positive correlation, albeit at a moderate level. Conversely, Rajasthan displayed a very weak correlation (tau = 0.079), indicating no significant relationship between these two climatic parameters.
... These periodic functions are estimated from the column vectors of the statistics of the observed daily flow series. For any parameter υ, its periodic component υτ,per may be expressed using the Fourier series [35,37]: ...
Changes in the hydrological regime are widely investigated using a variety of approaches. In this study, we assess changes in annual and seasonal flow characteristics based on a probabilistic representation of the seasonal runoff regime at the daily time scale. The probabilistic seasonal runoff pattern is constructed by determining quantiles from marginal distributions of daily flows for each day within the year. By applying Fourier transformation on the statistics of the daily flow partial series, we obtain smooth periodical functions of distribution parameters over the year and consequently of the quantiles. The main findings are based on the comparison of the dry, average, and wet hydrologic condition zones as defined by the daily flow quantiles of selected probabilities. This analysis was conducted for ten catchments in Serbia by considering changes between two 30-year nonoverlapping periods, 1961–1990 and 1991–2020. It was found that the relative change in runoff volume is the most pronounced in the extreme dry condition zone in the winter season (−33% to 34%). The annual time shift is the largest in the dry and average condition zones, ranging from −11 to 12 days. The applied methodology is not only applicable to the detection of hydrologic change, but could also be used in operational hydrology and extreme flow studies via drought indices such as the Standardized Streamflow Index.
... Some methods and models have been developed to estimate rainfall in past decades. Examples of these methods and models include the use of soil moisture to estimate rainfall accumulations (e.g., Crow 2007;Kucera et al. 2013), the use of the Soil Moisture to Rain (SM2RAIN) algorithm, and the estimation of rainfall by using the inversion of the water balance equation (Ciabatta et al. 2015), autoregressive moving-average (ARMA) models (Burlando et al. 1993), the fractional Gaussian noise model (Matalas and Wallis 1971), the autoregressive (AR) model (Yevjevich 1972), first-order approximations (Kirchner 2009), and the disaggregation models (Stedinger and Vogel 1984;Socolofsky et al. 2001). ...
It is necessary to select appropriate rainfall series as input to the hydrologic model to access more accurate hydrologic predictions and estimate reliable parameters in the modeling process. For achieving this aim, in the present study, the rainfall multipliers with a combination of Discrete Wavelet Transform (DWT) and principal component analysis (PCA) are applied to select effective rainfall series for modeling. DREAM(ZS) algorithm based on the Markov chain Monte Carlo (MCMC) scheme is used to estimate posterior parameters and investigate prediction uncertainties of a five-parameter hydrologic model, HYMOD. The model's results are then compared to those obtained from the other methods that use only the rainfall multipliers or the raw rainfall data. This study reveals the advantages of using a combined application of DWT and PCA methods to estimate hydrological prediction uncertainty and model parameters accurately. Considering the occasional flash flood incident that occurred in the study region (Tamar basin, which is situated in the Gorganroud river basin, Golestan province, Iran), the results of this research can be useful for forecasting floods accurately and planning for flood control management.
... Variations in the form of trends and abrupt changes are hard to distinguish in statistical tests [37,38]. This study presented a four-step framework for detecting trends and abrupt changes based on Hurst and correlation coefficients. ...
Rainfall erosivity is commonly used to estimate the probability of soil erosion caused by rainfall. The accurate detection of temporal changes in rainfall erosivity and the identification of abrupt changes and trends are important for understanding the physical causes of variation. In this study, a detection framework is introduced to identify temporal changes in rainfall erosivity time series as follows :(i) The significance of time series variation of rainfall erosivity is assessed based on the Hurst coefficient and divided into three levels: None, medium, and high. (ii) The detection of abrupt changes (Mann–Kendall, Moving T, and Bayesian tests) and trends (Spearman and Kendall rank correlation tests) of variate series and the correlation coefficient between the variation component and the original series is calculated. (iii) The modified series is obtained by preferentially eliminating the variation component (trend or change point) with larger correlation coefficients. (iv) We substituted the modified series into steps i to iii until the correlation coefficient was not significant. This framework is used to analyze the variation of rainfall erosivity in the Three Gorges Reservoir, China. The results showed that by using traditional methods, both an increasing trend and an upward change point were observed in Zigui station. However, after the upward change point was deducted from the annual rainfall erosivity series R(t), the resultant Rm(t) showed no statistically significant trend. Trend analysis should be performed considering the existence of an abrupt change to assess the long-term changes in rainfall erosivity series; otherwise, it would result in the wrong conclusion. In addition, the change points detected in the Rm(t) varied with the methods. Compared with the single-test method, the proposed framework can effectively reduce uncertainty.
... Low data requirements make precipitation-based indices, such as the SPI, simple to use for drought monitoring and analysis (Vicente-Serrano et al. 2010, Karavitis et al. 2011, 2012a which may explain why SPI is extensively used to identify and categorize drought events. Yet precipitation-based indices rely on the assumption that the variability of precipitation is much higher than that of temperature and evapotranspiration (Yevjevich 1972, Vicente-Serrano et al. 2010. These indices were developed considering that droughts are highly dependent on the temporal variability in precipitation, which is partially justified by the fact that SPI reasonably depicts the actual drought events (Karavitis et al. 2012a(Karavitis et al. , 2012b. ...
Droughts are mainly caused by rainfall deficiencies and high evapotranspiration rates. This article introduces a Factual Drought Index (FDI) as a composite index, based on precipitation and potential evapotranspiration (PET). Its innovation lies in the fact that it does not consider the total PET, but only the amount of PET that exceeds or falls behind the corresponding average PET, for a certain time period. So far, the PET-based indices take into account the total amount of PET, which in semiarid areas, may lead to overestimation of drought events. FDI was tested in drought-prone Greece, for the year 1990, when the country experienced an extreme drought event, and its results were compared to the corresponding Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). FDI constitutes a useful drought-management tool, as it satisfactorily identifies and categorizes drought events and is sensitive to precipitation and evapotranspiration changes.
... The hydrological drought analyses have been the subject of considerable investigations from the decade of the 1970s, and the pioneering models are well documented in Yevjevich [11,12]. Two main parameters of the hydrological drought viz., duration (length, L) and magnitude (M, also termed as severity), have been the subject of study. ...
On a global basis, there is trend that a majority of reservoirs are sized using a draft of 75% of the mean annual flow (0.75 MAF). The reservoir volumes based on the proposed drought magnitude (DM) method and the sequent peak algorithm (SPA) at 0.75 MAF draft were compared at the annual, monthly and weekly scales using the flow sequences of 25 Canadian rivers. In our assessment, the monthly scale is adequate for such analyses. The DM method, although capable of using flow data at any time scale, has been demonstrated using monthly standardized hydrological index (SHI) sequences. The moving average (MA) smoothing of the monthly SHI sequences formed the basis in the DM method for estimating the reservoir volume through the use of the extreme number theorem, and the hypothesis that drought magnitude is equal to the product of the drought intensity and drought length. The truncation level in the SHI sequences was found as SHIo [ = (0.75 ‒ 1) µo/σo], where µo and σo are the overall mean and standard deviation of the monthly flows. The DM-based estimates for the deficit volumes and the SPA-based reservoir volumes were found comparable within an error margin of ±18%.
... It is thus rational to expect the seasons in time series of di erent climate variables would be similar. Hence, this work estimates the seasonal boundaries using harmonic analysis, which has already been tested in various applications in the eld of geoscience [6,39,50,54,67]. The extension of harmonic analysis has also been implemented in meteorology and climatology; for instance, VanLoon et al. (1973) [59] used harmonic analysis to describe zonal standing waves in the atmosphere-pressure waves that described the ridges and troughs exhibited by the isobars on weather maps. ...
... It is thus rational to expect the seasons in time series of di erent climate variables would be similar. Hence, this work estimates the seasonal boundaries using harmonic analysis, which has already been tested in various applications in the eld of geoscience [6,39,50,54,67]. The extension of harmonic analysis has also been implemented in meteorology and climatology; for instance, VanLoon et al. (1973) [59] used harmonic analysis to describe zonal standing waves in the atmosphere-pressure waves that described the ridges and troughs exhibited by the isobars on weather maps. ...
Seasons are the divisions of the year into months or days according to the changes in weather, ecology and the intensity of sunlight in a given region. The temperature cycle plays a major role in de ning the meteorological seasons of the year. This study aims at investigating seasonal boundaries applying harmonic analysis in daily temperature for the duration of 30 years, recorded at six stations from 1988 to 2017, in northwest part of Bangladesh. Year by year harmonic analyses of daily temperature data in each station have been carried out to observe temporal and spatial variations in seasonal lengths. Periodic nature of daily temperature has been investigated employing spectral analysis, and it has been found that the estimated periodicities have higher power densities of the frequencies at 0.0027 and 0.0053 cycles/day. Some other minor periodic natures have also been observed in the analyses. Using the frequencies between 0.0027 to 0.0278 cycles/day, the observed periodicities in spectral analysis, harmonic analyses of minimum and maximum temperatures have found four seasonal boundaries every year in each of the stations. The estimated seasonal boundaries for the region fall between 19-Since seasonal variability results in imbalance in water, moisture and heat, it has the potential to signi cantly a ect agricultural production. Hence, the seasons and seasonal lengths presented in this research may help the concerned authorities take measures to reduce the risks for crop productivity to face the challenges arise from changing climate. Moreover, the results obtained are likely to contribute in introducing local climate calendar.
... It is thus rational to expect the seasons in time series of di erent climate variables would be similar. Hence, this work estimates the seasonal boundaries using harmonic analysis, which has already been tested in various applications in the eld of geoscience [6,39,50,54,67]. The extension of harmonic analysis has also been implemented in meteorology and climatology; for instance, VanLoon et al. (1973) [59] used harmonic analysis to describe zonal standing waves in the atmosphere-pressure waves that described the ridges and troughs exhibited by the isobars on weather maps. ...
Seasons are the divisions of the year into months or days according to the changes in weather, ecology and the intensity of sunlight in a given region. The temperature cycle plays a major role in defining the meteorological seasons of the year. This study aims at investigating seasonal boundaries applying harmonic analysis in daily temperature for the duration of 30 years, recorded at six stations from 1988 to 2017, in northwest part of Bangladesh. Year by year harmonic analyses of daily temperature data in each station have been carried out to observe temporal and spatial variations in seasonal lengths. Periodic nature of daily temperature has been investigated employing spectral analysis, and it has been found that the estimated periodicities have higher power densities of the frequencies at 0.0027 and 0.0053 cycles/day. Some other minor periodic natures have also been observed in the analyses. Using the frequencies between 0.0027 to 0.0278 cycles/day, the observed periodicities in spectral analysis, harmonic analyses of minimum and maximum temperatures have found four seasonal boundaries every year in each of the stations. The estimated seasonal boundaries for the region fall between 19-25 February, 19-23 May, 18-20 August and 17-22 November. Since seasonal variability results in imbalance in water, moisture and heat, it has the potential to significantly affect agricultural production. Hence, the seasons and seasonal lengths presented in this research may help the concerned authorities take measures to reduce the risks for crop productivity to face the challenges arise from changing climate. Moreover, the results obtained are likely to contribute in introducing local climate calendar.
... For example, streamflow in a river is analyzed and used for planning, quality and designing studies of dams, channels, rivers and basins. In this context, there are many studies to determine trends of hydrological, meteorological and climatological variables, and these issues have been investigated by many scientists and organizations by using different methodologies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] up to now. For instance, trend of streamflow (daily, monthly and annual recorded) was achieved by using parametric and non-parametric statistical tests in some relevant studies [18][19][20][21][22][23][24][25][26][27]. ...
Low, medium and high values of a variable are a significant issue in hydrological, meteorological and climatological events; moreover, these values are used to decide various design parameters based on scientific aspects and real applications around the world. In this context, a new trend method recently proposed by Şen was used for monthly streamflow data recorded at two different stations (2174- Murat River (Akkonak) and 2634-Garzan Creek (Kozluk)) in Euphrates-Tigris Basin. The Mann-Kendall trend test was also applied to the same data. It was seen that the stations had statistically significant decreasing trend according to the Mann-Kendall and Şen trend test at 95% confidence level; moreover, the proposed method provided an important advantage in terms of graphically evaluation of low, medium and high values of streamflow data.
... (2.2)]. The average values of these data and their periodic mean (Yevjevich, 1982) are presented in Figure 2. This figure exhibits the range and periodicity of ET o . ...
The present research work investigates the effects of climate change on crop water requirements in relation to soil water balance of the Pinios delta (Thessaly-Greece) in the frame of the 11SYN_3_1913/AGROCLIMA research project. In this deltaic plain, the dominant activities are related to irrigated agriculture, whose sustainability is depended upon the available surface water resources and the quality of coastal aquifers. Presently, the main cultivations of the area are kiwi and olive tree plantations, maize, alfalfa, sunflowers and cotton. Pinios deltaic plain is part of the NATURA network and is expected to be very sensitive to climate variations and its sustainability will depend on how fresh water supplies meet crop water needs. Therefore, crop water requirements are evaluated utilizing rainfall data, evapotranspiration estimates and soil water balance equations. For the evaluation of evapotranspiration rates the modified FAO 56 Penman-Monteith formula is applied, based on daily meteorological data (temperature, humidity, wind speed, solar radiation), from the recently installed weather station at Palaeopyrgos village. Furthermore, future simulations of crop water requirements are estimated Ψηφιακή Βιβλιοθήκη Θεόφραστος-Τμήμα Γεωλογίας. Α.Π.Θ. utilizing the essential crop and climatic data sets derived by the simulations of the RAMCO-2 (KNMI) regional climate model from the ENSEMBLES project, concerning the near future 2021-2050 and the far future 2071-2100 with respect to the reference period 1961-1990 are presented and analyzed. The results of the present study are expected to contribute to sustainable development strategies and mitigate the consequences of climate change.
... The equation could be formulated by balancing the change in volume V(t) with time taking the difference of the inflow I(t) and outflow Q(t) as indicated in Equation (1), considering the approach by Willen (1979) and Yevjevich (1972). Because the variables in Equation (1) have their respective mean and noise, the perturbation approach takes the form shown in Equation (2). ...
... Short records of the suspended sediment discharge prevent their use in the determination of the volume of sediment accumulation in the reservoir design. Therefore, stochastic modelling techniques, particularly the ARMA processes, have been important tools in hydrology since the pioneering studies going back to the 1960s (Thomas and Fiering 1962, Yevjevich 1972, Salas et al. 1980, and they are still in use for hydrological problems (Wang et al. 2018). Analytical expressions for the expected value and variance of sediment accumulation in river reservoirs can be derived for ARMA processes. ...
Sediment accumulation in a river reservoir is studied by stochastic time series models and analytical approach. The first-order moving average process is found the best for the suspended sediment discharge time series of the Juniata River at Newport, Pennsylvania, USA. Synthetic suspended sediment discharges are first generated with the chosen model after which analytical expressions are derived for the expected value and variance of sediment accumulation in the reservoir. The expected value and variance of the volume of sediment accumulation in the reservoir are calculated from a thousand synthetic time series each 38 years long and compared to the analytical approach. Stochastic and analytical approaches perfectly trace the observation in terms of the expected value and variability. Therefore, it is concluded that the expected value and variance of sediment accumulation in a reservoir could be estimated by analytical expressions without the cost of synthetic data generation mechanisms.
... Οι στατιστικές αναλύσεις των χρονοσειρών βασίζονται στις εργασίες των Jenkins and Watts (1968), Hannan (1970), Brillinger (1975), Box and Jenkins (1976) και εφαρμόστηκαν αρχικά στην υδρολογία του καρστ από τους Delleur (1971), Yevjevich (1972), Spolia and Chander (1973), Spolia et al. (1980), Ledolter (1978), Lettenmaier (1980) και άλλους, με στόχο τη συμπλήρωση των χρονοσειρών αλλά και την εκτίμηση των παραμέτρων των στοχαστικών μοντέλων. Οι Mangin (1981), Mangin and Pulido-Bosch (1983), Mangin (1984), , Larocque et al. (1998), Lambrakis et al. (2000), Panagopoulos & Lambrakis (2006), κ.ά., βασιζόμενοι στις αναλύσεις των χρονοσειρών εφάρμοσαν μεθοδολογίες για την περιγραφή και τη λειτουργία των καρστικών υδροφόρων. ...
THE HELLENIC KARST. PROPOSITIONS FOR RESEARCH METHODOLOGIES AND APPLICATION EXAMPLES
LAMBRAKIS N., Professor of Hydrogeology,
Panepistimioupoli Patron, 265 04, nlambrakis@upatras.gr
Abs tract
The carbonate rocks of the Hellenic geotectonic zones are mostly karstified and host important aquifers with hydraulic transmissivity values varying between 10 -3 και 10 -2
m2/s. The Hellenic karst is described as mature and morphological characteristics like karents, dolines, polges, caves etc. are abudant in such areas. The epikarstic zone, where present, controls the groundwater infiltration and thus the aquifer recharge. Difficulties in research due to inaccessibility of mostly of the karst areas can be compensated satisfactorily by applying statistical analysis to the spring’s time series, and by evaluating the groundwater’s quality. For a ll available springs,s and the corresponding aquifers that are presented in the current manuscript, data processing clearly showed that karst aquifers display an important storage that could be explained by the presence of a dense network of small voids characterized by slow laminar flow or/and the presence of the epikarst zone that controls the water infiltration into the phreatic zone. Although karst displays all elements of a telogenetic formation, yet the data processing indicated that it has not reached maturity. Properties such as the complexity, the homogeneity, the degree of the karst structure development are also obvious. Individual karst units may be simple or complex, some exhibiting the characteristics of homogeneous formations, however for all of them, the phreatic zone is relatively well drained. Moreover, the hydrochemical and isotopic approach decisively contributed to the delimitation of karst aquifers, the emergence of their relationship with others, and the presence of the dominant minerals and hydrochemical processes that regulate the groundwater quality.
2
/s. The Hellenic
... Similarly, a negative serial correlation leads to accepting the null hypothesis of no trend when it is false (type II error). To test for serial correlation in the data, lag−1 serial correlation coefficients are calculated [77][78][79][80][81]. In several of the trend studies, the time series is tested for serial correlation by calculating the lag−1 serial correlation coefficient ρ [82][83][84][85]. ...
The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the subdistrict level and aggregated to monthly, annual, seasonal rainfall totals, and the number of rainy days. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. NonParametric Mann–Kendall test and Spearman′s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen′s slope method. For the data influenced by serial correlation, various modified versions of Mann–Kendall tests (pre-whitening, trend-free pre-whitening, bias-corrected pre-whitening, and two variants of variance correction approaches) were applied. A significant increasing summer rainfall trend is observed in six out of 27 stations. Significant decreasing trends are observed at two stations during the southwest monsoon season and at two stations during the northeast monsoon season. To identify the trend change points in the time series, distribution−free cumulative sum test, and sequential Mann–Kendall tests were applied. Two open−source library packages were developed in R language namely, ”modifiedmk” and ”trendchange” to implement the statistical tests mentioned in this paper. The study results benefit water resource management, drought mitigation, socio−economic development, and sustainable agricultural planning in the region.
... Finalmente, a partir de la recurrencia observada, se ha analizado la probabilidad de aparición de al menos un evento torrencial en un año para cada uno de los umbrales analizados. Esta probabilidad se ha estudiado a través del análisis de la distribución de Poisson, (de parámetro μ≥1) (Yevjevich, 1972): ...
El incremento de la frecuencia de la torrencialidad en ámbitos mediterráneos es una cuestión preocupante y no solo desde el punto de vista hidrogeomorfológico, sino también desde el humano, socioeconómico e infraestructural. La definición del umbral de torrencialidad es clave para determinar la peligrosidad de un territorio determinado. En este estudio ajustamos diferentes clasificaciones de torrencialidad con los problemas e impactos identificados en dos áreas mediterráneas contrastadas de la provincia de Málaga, una de ellas caracterizada por un clima mediterráneo subhúmedo (Guadalhorce), y otra por un clima mediterráneo seco-semiárido (Axarquía). A partir del análisis de los datos pluviométricos de 18 observatorios (Red SAIH), los resultados determinan una escasa recurrencia de los eventos torrenciales predefinidos (>100 mm 24 h-1; >60 mm h-1), razón por la que proponemos el uso de un nuevo umbral de torrencialidad de menor duración, pero mucha más elevada intensidad (>10 mm 10 min-1), debido a que son muchos los episodios producidos por debajo de los umbrales anteriores, pero con consecuencias territoriales, como hemos constatado en el análisis mediático. La precipitación que se ajusta a este criterio ha sido definida como “precipitación geomorfológica” por su elevada capacidad de generar alteraciones en los componentes superficiales del territorio. Este tipo de precipitación presenta en ambos ambientes una alta recurrencia, un periodo de retorno prácticamente anual y una elevada erosividad.
... From a methodological point of view, the relations between input and output variables are generally investigated, in fractured and/or karst (Padilla and Pulido-Bosch, 1995;Larocque et al., 1998) and porous aquifer systems (Lo Russo et al., 2014;Chiaudani et al., 2017), by means of univariate and bivariate time-series analyses, such as the Autocorrelation and Cross-correlation functions (Yevjevich, 1972;Mangin, 1984;Box et al., 1994;Mangin, 1994). The study of these relations provides useful information to get a deeper insight into ...
The wide regional aquifer system springs are one of the most important sources of freshwater, especially because of the ever-increasing global water demand. Thus, these springs must be protected and managed in a sustainable manner. To this purpose, a detailed knowledge about the corresponding aquifer systems hydrodynamic is vital.
In this general framework, the Majella carbonate aquifer is one of the most important as well as heterogenous aquifer systems in Central Italy. It is characterized by high elevation, a heterogeneous hydraulic conductivity distribution and groundwater flow, and its recharge is mainly due to both rainfall and snowmelt. Its most important spring is the Verde spring, located in the eastern side, exploited for drinking and hydroelectric purposes.
The main objectives of this research are (1) to get a deeper insight into the hydrogeological conceptual model of the Verde spring, combining multiparameter input and output time-series, and (2) to quantify each inflow effect on the Verde spring parameters by a methodological approach that is quite uncommon in literature: comparing univariate and bivariate statistical analyses of both raw and residual (non-systematic component) time-series.
The results obtained highlighted that the snowmelt is the most important inflow of the Majella aquifer system. The snow cover melting leaks slowly large amount of water into the aquifer and creates smoothly significative modifications to the spring discharge, electrical conductivity and temperature, in terms of groundwater volume increase and dilution. Contrariwise, although the rainfall inflow volume and the corresponding spring parameters changes are very limited, the transient behavior of the rainfall inflow allowed identifying different recharge modes. These recharge modes depend on several flow paths in the unsaturated zone, characterized by different water volumes brought toward the saturated zone, sizes, hydraulic conductivities, and distances from the Verde spring.
... Here it is assumed that annual hydrological time series X t is composed of deterministic (Y t ) and stochastic (S t ) components (Yevjevich 1972;Guttman and Plantico 1989;Xie et al. 2005;Stojković et al. 2017). This is also called an additive model and can be expressed as: ...
Conventional methods to design the lowest navigable water level (LNWL) in inland waterways are usually based on stationary time series. However, these methods are not applicable when nonstationarity is encountered, and new methods should be developed for designing the LNWL under nonstationary conditions. Accordingly, this article proposes an approach to design the LNWL in nonstationary conditions, with a case study at the Yunjinghong station in the Lancang River basin in Southwest China. Both deterministic (trends, jumps and periodicities) and stochastic components in the hydrological time series are considered and distinguished, and the rank version of the von Neumann’s ratio (RVN) test is used to detect the stationarity of observed data and its residue after the deterministic components are removed. The stationary water level series under different environments are then generated by adding the corresponding deterministic component to the stationary stochastic component. The LNWL at the Yunjinghong station was estimated by this method using the synthetic duration curve. The results showed that the annual water level series at the Yunjinghong station presented a significant jump in 2004 with an average magnitude decline of − 0.63 m afterwards. Furthermore, the difference of the LNWL at certain guaranteed rate (90%, 95% and 98%) was nearly − 0.63 m between the current and past environments, while the estimated LNWL under the current environment had a difference of − 0.60 m depending on nonstationarity impacts. Overall, the results clearly confirmed the influence of hydrological nonstationarity on the estimation of LNWL, which should be carefully considered and evaluated for channel planning and design, as well as for navigation risk assessment.
... Thus, for any selected dyadic time scale (e.g., for the semi-diurnal time scale of earth tides), we isolated and extracted the corresponding "wavelet component" of the original signal. Also, we used wavelets in some cases as a filter, decomposing the original signal into a filtered quantity (the wavelet "approximation") and its residual signal (the wavelet residual) at any The correlation and cross-correlation function analyses versus time lag (1), as well as the corresponding Fourier spectral analyses in frequency space (2), are both based on the theory of random processes: the reader is referred to Bras et al. (1985); Box et al. (1976); Papoulis et al. (2002), Yaglom (1987), Yevjevich (1972). ...
Statistical analyses of meteorological, crack aperture and water content time series measured at the Tournemire Underground Research Laboratory : The MuStat algorithm: Tools, methods and application (short version of February 2014).
... Otherwise, the H 0 hypothesis is accepted that the trend is not statistically significant. In this study, α=10% and α=5% two-tailed confidence levels are used for the MK trend test (Mann, 1945;Yevjevich, 1972;Kendall, 1975;Kottegoda, 1980;Helsel and Hirsch, 2002). ...
In this study an energy consumption modelling for long term (December 2006- March 2016) forecasting of monthly natural gas consumption in households and industry area for Yozgat city, Turkey was presented. In this context, it can be said that this paper has two purposes. One of them is the application and accuracy of the artificial neural networks. Estimate performances are compared with each other, and the estimates of the optimal models are evaluated with the monthly recorded natural gas consumption according to root mean square error, mean absolute error, and correlation coefficient. The other purpose of the study is to analysis trend of monthly natural gas consumption of Yozgat by using Mann-Kendall and a new method recently proposed by Şen. The results showed that the artificial neural networks gave satisfactory results in estimating monthly natural gas consumption. In the trend analysis, it was seen that both Mann-Kendall and Şen trend tests gave statistically significant increasing trend at 95% confidence level for monthly natural gas consumption of Yozgat.
... The MK test is one of the non-parametric tests to detect trend in a time series especially for climatological, meteorological, and hydrological data. Commonly used MK trend test is not described here because it can be found in related studies [27][28][29][30][31][32][33]. ...
... The statistical technique used to discover cyclic components in a time series is known as spectral analysis (Jenkins and Watts 1968;Yevjevich 1972;Bras and Rodríguez-Iturbe 1985). The signal component represents the structured part of the time series, made up of a small number of embedded periodicities or cycles repeated over a long time. ...
In the present work, spectral analysis has been applied to determine the presence and statistical significance of climate cycles in long-term data series from different rainfall and gauging stations located in the Tramuntana Range, in the north-western sector of the island of Majorca. Climate signals recorded previously in the Mediterranean region have been identified: the ENSO, NAO, HALE, QBO and Sun Spot cycles as well as others related to solar activity; the most powerful signals correspond to the annual cycle, followed by the 6-month and NAO cycles. The incorporation of data derived from gauging stations contributes to better climate signal detection as local and exceptional influences are eliminated. Simulations have been performed for each rainfall/gauging station, using the most significant climate cycles obtained by means of the power spectrum. A good correlation between rainfall/flow values and simulated cycles has been obtained. The NAO and ENSO cycles are the most influential in the rainy periods, and specifically the NAO cycle, where a good correlation between episodes of high rainfall/flow and high values of ANAOI can be observed. At a second stage, landslides dated and recorded in the Tramuntana Range since 1954 (174 events) have been correlated with the simulated cycles obtaining good results, as the landslide events match rainfall peaks well. The correlation for the past decade (since 2005), when a detailed landslide inventory is available, also reveals a coincidence between landslide events and climate cycles, and specifically NAO and ENSO cycles. That is the case of the period 2008-2010, when numerous mass movements took place, and when the largest movement of the inventory was recorded. Results show a potential rainy period in the Tramuntana Range for the coming years (with maximum values around year 2021) when conditions similar to those related to the 2008-2010 event, could take place again. The methodology presented in this work can contribute to the prediction of temporal, extreme hydrological events in order to design short/medium-term mitigation strategies on a regional scale.
Drought is a natural phenomenon causing disasters and its period of occurrence can be predicted in recent times based on several methods using the same or different variables. The prediction is usually associated with the climate interactions in the form of rainfall or discharge patterns which can be analyzed using the return period. Therefore, this research was conducted in four different stages of data acquisition and validation, drought analysis method based on the data, drought prediction method based on hydrology, and sample applications to determine the debit availability in other watersheds. Historical rainfall data converted to dependable rainfall at 80% probability were used as input for the rainfall-discharge analysis while the hydrological drought analysis was conducted using the drought threshold value. Moreover, the drought was predicted using an artificial neural network model while historical data were used to verify the hydrological character of the prediction model. The results of the analysis conducted were further used to predict the water balance in different river areas due to the fact that each area has a different hydrological character. Meanwhile, the watersheds used as case research showed that the model has reliability of up to 80%.
The Lowest navigable water level (LNWL) is an important indicator used for navigation design to balance the relationship between navigation safety and economic benefits of a waterway. However, it is a challenge of accurately estimating LNWLs due to the nonstationary characteristics of observed water level data series. In this study, a comprehensive framework was developed for handling this issue. In this framework, inter-annual variabilities in both the mean and variance of water level series were described by decomposing original series and were eliminated by composing new series. Intra-annual variability was determined by detecting indicators describing intra-annual water level distributions. Considerations of inter- and intra-annual variabilities were combined by designing annual water level processes for the past and current environments. Shipping risks during both annual and multi-annual periods were considered in the framework as well. The framework was demonstrated in estimating LNWLs at the Gaodao and Shijiao stations in the North River basin, southern China. The recommended LNWLs at the Gaodao station were 22.32 m for 95% guaranteed rate and 21.84 m for 98% guaranteed rate; LNWLs at the Shijiao station were 0.27 m for 95% guaranteed rate and 0.15 m for 98% guaranteed rate. The impact of variance variability on estimations of LNWLs was also evaluated. Results indicated that the recommended LNWLs would have errors of 0.11 ~ 0.48 m at the Gaodao station and 0.03 ~ 0.04 m at the Shijiao station if the variance variability was not considered. The proposed framework was then compared with Nonstationary synthetic duration curve (NSDC) method, and results illustrated that the duration curves plotted by NSDC method were unreasonable, leading to inaccurate design values. Overall, the developed framework is more reasonable and suitable for designing LNWLs of waterways where the variabilities of the water levels at different time scales are different or where the historical water level data contain various variations .
One of the most well-known uses of Artificial Intelligence which has noticed an enormous development within the digital era is actually Machine Learning Techniques in which the method scientific studies and also increases the overall performance of its via progressive learning with no explicit programming. It is popular within many programs certainly one of them becoming a weather condition prediction. Image distinction, as well as feature extraction, are regarded as to become essentially the most popularly pre-owned techniques finished utilizing machine mastering procedure. With this proposed method, a hybrid model is developed for predicting rainfall by using feature extraction methods that have been proposed by us. The unit was created in such a manner it fetches a sequence of pictures originating from a data source as well as different info regarding earlier rainfalls wearing a particular region. The pictures are actually pre-processed as well as additional segmented for option extraction. The segmented pictures are then categorized via the Random Forest algorithm in which the sequence of pictures is actually validated frame by frame. The effectiveness of the suggested design is actually evaluated and it is kept in a sent out HADOOP File Systems (HDFS) for faster retrieval of information. It’s found that this suggested model provides greater results. The functionality of this unit tends to be more precise because the unit has an iterative method for characteristic extraction inside classifying pictures. The suggested item is actually incorporated by using an aware process to be able to attain a warning or an alert to the individuals in a space properly prior to a flood really hits.
Evaluation of drought duration risk using annual secular precipitation data
in ancient stations of Iran
Abstract
The aim of the study is to evaluate the return period and risk of drought duration in five ancient and unique stations of Iran, having more than one-hundred annual precipitation data. For determining the return period and risk of drought in the stationsof interest (including Jask, Bushehr, Isfahan, Mashhad and Tehran) the following stages were performed: a) Applying a homogeneity test (i.e., Run test) on annual precipitation series was indicated the Jask station unlike other stations which were homogenous, is nonhomogenous, b) Using a goodness-of-fit test ( 2 χ ), the most suitable distribution function for annual precipitation data was selected from among fourteen functions, c) Based on the best-fit function obtained for each station, the amount of precipitation for seven probability levels (Thresholds) from 0.1 to 0.7 (with step 0.1) were determined, d) With respect to the precipitation of each probability level and subtracting it from annual precipitation data during record period, dry periods were indicated and drought duration for each dry period was computed, and e) The two statistical characteristics of drought including return period and risk were calculated with different mathematical equations. The results based on the annual precipitation series of four aforesaid stations showed: a) For a given drought duration, as the probability of occurrence of precipitation increases the return period decreases, or with a certain probability of occurrence of precipitation, as increasing drought duration the return period also increases, b) For the probability of occurrence of drought 30 %, the longest drought in Isfahan (i.e., six-year duration) and other stations (i.e., three-year duration) occur once each 2000 and 51 years, respectively, and d) for a certain probability of drought occurrence, as increasing the basic period, the risk of drought increases. For instance, for the probability of occurrence of drought 50 %, it was identified that the drought risk in Bushehr and Tehran station is more than Isfahan and Mashhad.
Keywords: Meteorological Drought, Drought Return Period, Drought Risk, Drought Duration, Ancient Meteorological Stations of Iran.
Black-box model: The black box is the aquifer; precipitation or useful rain is the input function and the output function is a spring’s flow.
This paper includes a study which applied homogeneity and trend tests on time series of monthly mean temperature and monthly total rainfall recorded in the provinces of Bartın, Bolu, Düzce, Kastamonu, Sinop, and Zonguldak of western Black Sea region in Turkey. The Runs test was used to check the homogeneity of both variables. Analyses were made using Mann-Kendall (MK) trend test, Şen trend test, and linear trend test for the time period 1960–2017. According to the results of all tests, although monthly total rainfall of Bartın, Kastomonu, Sinop, and Zonguldak stations has inhomogeneous time series, Bolu and Düzce have homogeneous time series. The monthly mean temperature of all stations has no homogeneity. Although Kastamonu and Sinop have statistically significant increasing trends according to the linear trend test, other provinces have no statistically significant trends for both variables. There are no statistically significant trends for both variables in all stations according to the MK trend test; however, there are statistically significant increasing trends for monthly mean temperature in all stations according to the Şen trend test. Monthly total rainfall of Düzce (17072) has a statistically significant decreasing trend at 95% confidence interval; other stations have statistically significant increasing trends according to the Şen trend test.
The importance of a law that, for an assigned porous medium, gives the variability of the hydraulic conductivity with the effective porosity is well known. Commonly this variability is represented by a power law at different scales, especially those of laboratory and field. Often it can be useful to have a scaling law valid for a single scale range, comprising both those of laboratory and field. Therefore investigation about the reliability of the laws representing the scaling behavior in the various intervals to which reference was made could be suitable.The purpose of this study is to provide evidence about the reliability of the scaling laws for laboratory, field and global (laboratory plus field) scaling ranges, verifying consistency with the expected proportionality between hydraulic conductivity and effective porosity for each of these. This verification was carried out using power-type scaling laws to two and four parameters respectively, and performing suitable moving averages of original data sets, in an attempt to reduce the inevitable measurement uncertainties. The experimental results, obtained with reference to the confined aquifer of Montalto Uffugo test field, show that there are no significant differences between the scaling laws considered and highlight the need to reduce the measurement uncertainties, which weigh heavily on the reliability of scaling laws. Keywords: Scaling law; Hydraulic conductivity; Effective porosity; Laboratory measurement, Field measurement. INFLUENCIA DE LA AMPLITUD DEL INTERVALO DE ESCALA EN LA DETERMINACIÓN DE LA CONDUCTIVIDAD HIDRÁULICA Y LA POROSIDAD EFECTIVA. EL CASO DE UN ACUÍFERO POROSA DEL SUR DE ITALIA La importancia de una ley que, para un dado medio poroso, describa la variabilidad de la conductividad hidráulica en función de la porosidad efectiva es bien conocida. Frecuentemente esta variabilidad está representada por una ley de potencia a diferentes escalas, especialmente las de laboratorio y de campo. A menudo es útil tener una ley de escalamiento válida para un cierto rango de escala incluyendo los de laboratorio y campo. Por esta razón, es oportuno investigar la confiabilidad de las leyes que representan este comportamiento de escalamiento en los diferentes intervalos a los cuales típicamente se hace referencia. El propósito de este estudio es proporcionar evidencia sobre la confiabilidad de las leyes de escalamiento para intervalos de escala válidos para laboratorio, campo y globales (laboratorio + campo), verificando la consistencia con la proporcionalidad esperada entre la conductividad hidráulica y la porosidad efectiva para cada uno de estos. Esta verificación se llevó a cabo utilizando leyes de escalamiento de potencia para dos y cuatro parámetros respectivamente, y tratando de reducir las inevitables incertidumbres de medición. Los resultados experimentales obtenidos con referencia al acuífero confinado del campo de prueba Montalto Uffugo, muestran que no hay diferencias significativas entre las leyes de escalamiento consideradas y se destaca la necesidad de reducir las incertidumbres de medición, las cuales tienen un alto peso sobre la fiabilidad de las leyes de escala. Palabras clave: ley de escalamiento; Conductividad hidráulica; porosidad efectiva; mediciones de laboratorio, medición de campo.
Water quality evaluation is critically important for the protection and sustainable management of groundwater resources, which are variably vulnerable to ever-increasing human-induced physical and chemical pressures (e.g., overexploitation and pollution of aquifers) and to climate change/variability. Preceding studies have applied a variety of tools and techniques, ranging from conventional to modern, for characterization of the groundwater quality worldwide. Recently, geographic information system (GIS) technology has been successfully integrated with the advanced statistical/geostatistical methods, providing improved interpretation capabilities for the assessment of the water quality over different spatial scales. This review intends to examine the current standing of the GIS-integrated statistical/geostatistical methods applied in hydrogeochemical studies. In this paper, we focus on applications of the time series modeling, multivariate statistical/geostatistical analyses, and artificial intelligence techniques used for groundwater quality evaluation and aquifer vulnerability assessment. In addition, we provide an overview of salient groundwater quality indices developed over the years and employed for the assessment of groundwater quality across the globe. Then, limitations and research gaps of the past studies are outlined and perspectives of the future research needs are discussed. It is revealed that comprehensive applications of the GIS-integrated advanced statistical methods are generally rare in groundwater quality evaluations. One of the major challenges in future research will be implementing procedures of statistical methods in GIS software to enhance analysis capabilities for both spatial and temporal data (multiple sites/stations and time frames) in a simultaneous manner.
Briefly tracing the history of hydrologic modeling, this paper discusses the progress that has been achieved in hydrologic modeling since the advent of computer and what the future may have in store for hydrologic modeling. Hydrologic progress can be described through the developments in data collection and processing, concepts and theories, integration with allied sciences, computational and analysis tools, and models and model results. It is argued that with the aid of new information gathering and computational tools, hydrology will witness greater integration with both technical and non-technical areas and increasing applications of information technology tools. Furthermore, hydrology will play an increasingly important role in meeting grand challenges of the twenty-first century, such as food security, water security, energy security, health security, ecosystem security, and sustainable development.
El objetivo de este trabajo es presentar una metodología de Redes Neuro-Fuzzy aplicadas al problema de la previsión de caudales afluentes mensuales en centrales hidroeléctricas, cuyo resultado puede emplearse como una de las entradas en los modelos de planificación de largo plazo en sistemas hidrotérmicos de potencia. Se procuró emplear una herramienta que combine los Sistemas de Inferencia Fuzzy y las Redes Adaptables; se escogió el modelo ANFIS (Adaptive Neuro-Based Fuzzy Inference System). Para construir la arquitectura del modelo ANFIS, se emplearon muestras de caudales de meses consecutivos en forma de pares entrada-salida. El proceso de aprendizaje corresponde a un algoritmo híbrido de aprendizaje supervisado que combina métodos de mínimos cuadrados y métodos de optimización descendientes, y para encontrar la mejor configuración ANFIS se empleó un método heurístico que combina la cantidad y tipo de funciones de pertenencia de las variables de entrada. Con el fin de evaluar el desempeño del modelo ANFIS se estableció una comparación frente a modelos de series de tiempo estocásticas, empleados para la previsión de caudales afluentes a la central hidroeléctrica Paute, a través de medidas de error medio cuadrático, error porcentual medio y error absoluto medio, aplicadas a los modelos propuestos.
The theory of linear reservoirs is applied to discharge data from Storglaciären and Isfallsglaciären, near Kebnekaise in northern Sweden, during 1 to 17 August 1969. This period is almost without precipitation.
Time series analysis is used to describe storage parameters for both of the glaciers. These parameters describe the characteristics of the internal drainage from each glacier. The analysis shows that Storglaciären has two and Isfallsglaciären has three storages in their internal drainage systems. The impulse delay to maximum discharge is within three and four hours.
The input of the model is based on the energy balance at a melting snow and ice surface. The meteorological parameters used are recorded at Tarfala Research Station. The model seems to describe the internal drainage satisfactorily.
During the time series analysis we found that an outburst of water from Isfallsglaciären had taken place. Different attempts are made to calculate the released volume of water.
In simple terms, hydrology is the study of the waters of the Earth, including their occurrence, distribution, and movement. The constant circulation of water and its change in physical state is called the hydrologic cycle. The study of water started at least a few thousands years ago, but the modern scientific approach to the hydrologic cycle started in the seventeenth century. Since then, hydrology has witnessed a tremendous growth, especially over the last century, with significant advances in computational power and hydrologic data measurements. This chapter presents a general and introductory account of hydrology. First, the concept of the hydrologic cycle is described. Next, a brief history of the scientific development of hydrology is presented. Then, the concept of hydrologic system is explained, followed by a description of the hydrologic system model and model classification. Finally, the role of hydrologic data and time series modeling as well as the physical basis of time series modeling are highlighted.
Geomorphic indices can be used to examine the geomorphological and tectonic processes responsible for the development of the drainage basins. Such indices can be dependent on tectonics, erosional processes and other factors that control the morphology of the landforms. The inter-relationships between geomorphic indices can determine the influence of regional tectonic activity in the shape development of drainage basins. A Multi-Criteria Decision Analysis (MCDA) procedure has been used to perform an integrated cluster analysis that highlights information associated with the dominant regional tectonic activity. Factor Analysis (FA) and Analytical Hierarchy Process (AHP) were considered within that procedure, producing a representation of the distributed regional tectonic activity of the drainage basins studied. The study area is western Crete, located in the outer fore-arc of the Hellenic subduction zone, one of the world's most tectonically active regions. The results indicate that in the landscape evolution of the study area (especially the western basins) tectonic controls dominate over lithological controls.
In this research the Flow -Precipitation fluctuations in 9 Station in Dinevar basin were analyzed by spectral density functions and Autocorrelation Functions in the frequency and time domain. First fluctuations in the correlogram of each time series were analyzed. Then Spectral density functions based on the Fourier series were used to check the time series in the frequency domain. In this regard, the Spectrum value of each flow and rainfall series was calculated and the periodogram of each series was produced. The Periodograms showed fluctuations in several frequencies. These were seasonal fluctuations with frequency 0.083 (return period of 12 months), short term fluctuations with frequency 0.25 (return period of 4 months), and midterm fluctuations with frequency 0.167 (return period of 4 months). Fluctuations were not observed for return period of 6 months in Precipitation data. Cross spectral analysis of data showed high match levels in frequencies 0.083 and 0.25. Also the estimated values of delay by cross spectral analysis for precipitation seasonal fluctuations (0.083) and its effect on the seasonal flow fluctuations was estimated as about 1.5 months. The delay was determinate as 0.4, 0.5, and 0.14 months for fluctuations with smaller return periods. According to the spectral analysis results for precipitation data in all stations, after removing the frequency and trends, a long term fluctuation (cycle) was observed in frequency 0.01 (100 months) that can be associated to the Wet and dry periods in this region.
Die Korrelationsanalyse untersucht stochastische Zusammenhänge zwischen gleichwertigen (normal verteilten) Zufallsvariablen. Maß für die Straffheit des linearen Zusammenhanges ist der Korrelationskoeffizient; dabei kann sowohl von der ersten auf die zweite Variable geschlossen werden als auch umgekehrt. Eine Abhängigkeit nach Ursache und Wirkung wird bei voneinander abhängigen Kollektiven angenommen und durch die Regression ausgedrückt. Wird die Variable y als abhängige und die andere x als unabhängig aufgefasst, handelt es sich um eine Regression von y bezüglich x. Eine Abgrenzung der Begriffe Korrelation als Zusammenhang der Grundgesamtheit und Regression als Zusammenhang der Stichprobe wird nicht vorgenommen. Durch die Regression kann über die Eintrittshäufigkeit der Werte keine Aussage gemacht werden.
Wenn der Ablauf eines hydrologischen Prozesses bzw. die Ergebnisse von Beobachtungen als Folge von Zufallsvariablen aufgefasst werden, unterliegen alle Beobachtungen einer Wahrscheinlichkeitsverteilung. Der Umfang aller Beobachtungen wird als Stichprobe oder Kollektiv bezeichnet, z. B. die (täglichen) Wasserstandsablesungen an einem Pegel, die aus der Grundgesamtheit, also den unendlich vielen Wasserständen entnommen sind. Dabei wird ein wesentliches Merkmal (hier: Wasserstand) benutzt, um Stichprobe und Grundgesamtheit zu beschreiben. Wird die Zeitabhängigkeit der hydrologischen Größe berücksichtigt, spricht man meist von einer stochastischen Größe, sonst von einer statistischen Größe. Eine Stichprobe umfasst N verfügbare Beobachtungen aus einer Grundgesamtheit, die alle Beobachtungen umfasst und erwartungstreue Parameter (unbiased parameters) liefert. Eine Zufallsvariable ist z. B. der nächste Beobachtungswert einer hydrologischen Größe.
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