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Monitoring and estimation of snow depth in alpine catchments is needed for a proper assessment of management alternatives for water supply in these water resources systems. The distribution of snowpack thickness is usually approached by using field data that come from snow samples collected at a given number of locations that constitute the monitoring network. Optimal design of this network is required to obtain the best possible estimates. Assuming that there is an existing monitoring network, its optimization may imply the selection of an optimal network as a subset of the existing one (if there are no funds to maintain them) or enlarging the existing network by one or more stations (optimal augmentation problem). We propose an optimization procedure that minimizes the total variance in the estimate of snowpack thickness. The novelty of this work is to treat, for the first time, the problem of snow observation network optimization for an entire mountain range rather than for small catchments as done in the previous studies. Taking into account the reduced data available, which is a common problem in many mountain ranges, the importance of a proper design of these observation networks is even larger. Snowpack thickness is estimated by combining regression models to approach the effect of the explanatory variables and kriging techniques to consider the influence of the stakes location. We solve the optimization problems under different hypotheses, studying the impacts of augmentation and reduction, both, one by one and in pairs. We also analyse the sensitivity of results to nonsnow measurements deduced from satellite information. Finally, we design a new optimal network by combining the reduction and augmentation methods. The methodology has been applied to the Sierra Nevada mountain range (southern Spain), where very limited resources are employed to monitor snowfall and where an optimal snow network design could prove critical. An optimal snow observation network is defined by relocating some observation points. It would reduce the estimation variance by around 600 cm2 (15%). 相似文献
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Carolina Guardiola‐Albert Sergio Martos‐Rosillo Eulogio Pardo‐Igúzquiza Juan José Durán Valsero Antonio Pedrera Pablo Jiménez‐Gavilán Cristina Liñán Baena 《Ground water》2015,53(6):885-895
Management of water resources, implying their appropriate protection, calls for a sound evaluation of recharge. Such assessment is very complex in karst aquifers. Most methods are developed for application to detrital aquifers, without taking into account the extraordinary heterogeneity of porosity and permeability of karst systems. It is commonly recommended to estimate recharge using multiple methods; however, differences inherent to the diverse methods make it difficult to clarify the accuracy of each result. In this study, recharge was estimated in a karst aquifer working in a natural regime, in a Mediterranean‐type climate, in the western part of the Sierra de las Nieves (southern Spain). Mediterranean climate regions are characterized by high inter‐annual rainfall variability featuring long dry periods and short intense wet periods, the latter constituting the most important contribution to aquifer water input. This paper aims to identify the methods that provide the most plausible range of recharge rate during wet periods. Six methods were tested: the classical method of Thornthwaite‐Mather, the Visual Balan code, the chloride balance method, and spatially distributed methods such as APLIS, a novel spatiotemporal estimation of recharge, and ZOODRM. The results help determine valid methods for application in the rest of the unit of study and in similar karst aquifers. 相似文献
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Juan Antonio Luque-Espinar Rosa María Mateos Inmaculada García-Moreno Eulogio Pardo-Igúzquiza Gerardo Herrera 《Natural Hazards》2017,89(3):985-1007
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. 相似文献
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In studies that involve a finite sample size of spatial data it is often of interest to test (statistically) the assumption that the marginal (or univariate) distribution of the data is Gaussian (normal). This may be important per se because, for example, a data transformation may be desired if the normality hypothesis is rejected, or it may provide a way of testing other hypotheses, such as lognormality, by testing the normality of the logarithms of the observations. The most commonly used tests, such as the Kolmogorov–Smirnov (K–S), chi-square (2), and Shapiro–Wilks (S–W) tests, are designed on the assumption that the observations are independent and identically distributed (iid). In geostatistical applications, however, this is not usually the case unless the spatial covariance (semivariogram) function is a pure nugget variance. If the covariance structure has a (practical) range greater than the minimum distance between observations, the data are correlated and the standard tests cannot be applied to the probability density function (pdf) or cumulative probability function (cdf) estimated directly from the data. The problem with correlated data arises not from the correlation per se but from cases in which correlated data are clustered rather than being located on a regular grid. In these cases inferences requiring iid assumptions may be seriously biased because of the spatial correlation among the observations. If unbiased (i.e., de-clustered) estimates of the pdf or cdf are obtained, then normality tests, such as K-S, 2, or S–W, can be applied using the unbiased estimates and an effective number of samples equivalent to the iid case. There are three questions to be addressed in these cases: Is the distribution ergodic? 相似文献
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Eulogio Pardo-Igúzquiza 《Mathematical Geology》1999,31(1):47-65
This paper shows the application of the Bayesian inference approach in estimating spatial covariance parameters. This methodology is particularly valuable where the number of experimental data is small, as occurs frequently in modeling reservoirs in petroleum engineering or when dealing with hydrodynamic variables in groundwater hydrology. There are two main advantages of Bayesian estimation: firstly that the complete distribution of the parameters is estimated and, from this distribution, it is a straightforward procedure to obtain point estimates, confidence regions, and interval estimates; secondly, all the prior information about the parameters (information available before the data are collected) is included in the inference procedure through their prior distribution. The results obtained from simulation studies are discussed. 相似文献
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Assessment of the sampling variance of the experimental variogram is an important topic in geostatistics as it gives the uncertainty of the variogram estimates. This assessment, however, is repeatedly overlooked in most applications mainly, perhaps, because a general approach has not been implemented in the most commonly used software packages for variogram analysis. In this paper the authors propose a solution that can be implemented easily in a computer program, and which, subject to certain assumptions, is exact. These assumptions are not very restrictive: second-order stationarity (the process has a finite variance and the variogram has a sill) and, solely for the purpose of evaluating fourth-order moments, a Gaussian distribution for the random function. The approach described here gives the variance–covariance matrix of the experimental variogram, which takes into account not only the correlation among the experiemental values but also the multiple use of data in the variogram computation. Among other applications, standard errors may be attached to the variogram estimates and the variance–covariance matrix may be used for fitting a theoretical model by weighted, or by generalized, least squares. Confidence regions that hold a given confidence level for all the variogram lag estimates simultaneously have been calculated using the Bonferroni method for rectangular intervals, and using the multivariate Gaussian assumption for K-dimensional elliptical intervals (where K is the number of experimental variogram estimates). A general approach for incorporating the uncertainty of the experimental variogram into the uncertainty of the variogram model parameters is also shown. A case study with rainfall data is used to illustrate the proposed approach. 相似文献
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Erten Oktay Pardo-Igúzquiza Eulogio Olea Ricardo A. 《Natural Resources Research》2020,29(2):1087-1099
Natural Resources Research - The semivariogram, which measures the spatial variability between experimental data, is generally used as a structural input in all two-point geostatistical procedures.... 相似文献
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One important handicap when working with stratigraphic sequences is the discontinuous character of the sedimentary record, especially relevant in cyclostratigraphic analysis. Uneven palaeoclimatic/palaeoceanographic time series are common, their cyclostratigraphic analysis being comparatively difficult because most spectral methodologies are appropriate only when working with even sampling. As a means to solve this problem, a program for calculating the smoothed Lomb-Scargle periodogram and cross-periodogram, which additionally evaluates the statistical confidence of the estimated power spectrum through a Monte Carlo procedure (the permutation test), has been developed. The spectral analysis of a short uneven time series calls for assessment of the statistical significance of the spectral peaks, since a periodogram can always be calculated but the main challenge resides in identifying true spectral features. To demonstrate the effectiveness of this program, two case studies are presented: the one deals with synthetic data and the other with paleoceanographic/palaeoclimatic proxies. On a simulated time series of 500 data, two uneven time series (with 100 and 25 data) were generated by selecting data at random. Comparative analysis between the power spectra from the simulated series and from the two uneven time series demonstrates the usefulness of the smoothed Lomb-Scargle periodogram for uneven sequences, making it possible to distinguish between statistically significant and spurious spectral peaks. Fragmentary time series of Cd/Ca ratios and ??18O from core AII107-131 of SPECMAP were analysed as a real case study. The efficiency of the direct and cross Lomb-Scargle periodogram in recognizing Milankovitch and sub-Milankovitch signals related to palaeoclimatic/palaeoceanographic changes is demonstrated. As implemented, the Lomb-Scargle periodogram may be applied to any palaeoclimatic/palaeoceanographic proxies, including those usually recovered from contourites, and it holds special interest in the context of centennial- to millennial-scale climatic changes affecting contouritic currents. 相似文献