Juan Antonio Luque-Espinar

Categorical Indicator Kriging for assessing the risk of groundwater nitrate pollution: The case of Vega de Granada aquifer (SE Spain)

Groundwater nitrate pollution associated with agricultural activity is an important environmental problem in the management of this natural resource, as acknowledged by the European Water Framework Directive. Therefore, specific measures aimed to control the risk of water pollution by nitrates must be implemented to minimise its impact on the environment and potential risk to human health. The spatial probability distribution of nitrate contents exceeding a threshold or limit value, established within the quality standard, will be helpful to managers and decision-makers. A methodology based on non-parametric and non-linear methods of Indicator Kriging was used in the elaboration of a nitrate pollution categorical map for the aquifer of Vega de Granada (SE Spain). The map has been obtained from the local estimation of the probability that a nitrate content in an unsampled location belongs to one of the three categories established by the European Water Framework Directive: CL. 1 good quality [Min - 37.5 ppm], CL. 2 intermediate quality [37.5-50 ppm] and CL. 3 poor quality [50 ppm - Max]. The obtained results show that the areas exceeding nitrate concentrations of 50 ppm, poor quality waters, occupy more than 50% of the aquifer area. A great proportion of the areas municipalities are located in these poor quality water areas. The intermediate quality and good quality areas correspond to 21% and 28%, respectively, but with the highest population density. These results are coherent with the experimental data, which show an average nitrate concentration value of 72 ppm, significantly higher than the quality standard limit of 50 ppm. Consequently, the results suggest the importance of planning actions in order to control and monitor aquifer nitrate pollution.

Using Semivariogram Parameter Uncertainty in Hydrogeological Applications

Geostatistical estimation (kriging) and geostatistical simulation are routinely used in ground water hydrology for optimal spatial interpolation and Monte Carlo risk assessment, respectively. Both techniques are based on a model of spatial variability (semivariogram or covariance) that generally is not known but must be inferred from the experimental data. Where the number of experimental data is small (say, several tens), as is not unusual in ground water hydrology, the model fitted to the empirical semivariogram entails considerable uncertainty. If all the practical results are based on this unique fitted model, the final results will be biased. We propose that, instead of using a unique semivariogram model, the full range of models that are inside a given confidence region should be used, and the weight that each semivariogram model has on the final result should depend on its plausibility. The first task, then, is to evaluate the uncertainty of the model, which can be efficiently done by using maximum likelihood inference. The second task is to use the range of plausible models in applications and to show the effect observed on the final results. This procedure is put forth here with kriging and simulation applications, where the uncertainty in semivariogram parameters is propagated into the final results (e.g., the prediction of ground water head). A case study using log-transmissivity data from the Vega de Granada aquifer, in southern Spain, is given to illustrate the methodology.

Compositional cokriging for mapping the probability risk of groundwater contamination by nitrates.

Contamination by nitrates is an important cause of groundwater pollution and represents a potential risk to human health. Management decisions must be made using probability maps that assess the nitrate concentration potential of exceeding regulatory thresholds. However these maps are obtained with only a small number of sparse monitoring locations where the nitrate concentrations have been measured. It is therefore of great interest to have an efficient methodology for obtaining those probability maps. In this paper, we make use of the fact that the discrete probability density function is a compositional variable. The spatial discrete probability density function is estimated by compositional cokriging. There are several advantages in using this approach: (i) problems of classical indicator cokriging, like estimates outside the interval (0,1) and order relations, are avoided; (ii) secondary variables (e.g. aquifer parameters) can be included in the estimation of the probability maps; (iii) uncertainty maps of the probability maps can be obtained; (iv) finally there are modelling advantages because the variograms and cross-variograms of real variables that do not have the restrictions of indicator variograms and indicator cross-variograms. The methodology was applied to the Vega de Granada aquifer in Southern Spain and the advantages of the compositional cokriging approach were demonstrated.

Methodological approach for the analysis of groundwater quality in the framework of the Groundwater Directive

All countries of the European Union are required to determine the evolution of groundwater quality, including trend assessment. With this aim, the Water Framework Directive advises using standardized statistical analysis, like least squares regression. But this methodology is not applicable to all situations and what’s more, does not offer a sound methodological framework. There are many statistical procedures to evaluate temporal behaviour of environmental data but, when applied unconnectedly, erroneous conclusions can be reached due to bias of assuming partial or particular conducts. In this paper, a methodology for studying such information is proposed, integrating most common methods for time series analysis. To provide a sound scientific basis to the methodology, statistic intervals combined with trend assessment are proposed, after adjusting a regression curve and applying smoothing techniques to select the baseline level. Confidence intervals have been used when a threshold value does exist. Whether it is not fixed or the baseline level exceeds the standard, prediction intervals were employed. The approach has been analysed at Plana de Vinaroz Groundwater Body (PV). As a result, PV is classed of poor chemical status in regard to diffuse pollution and sea water intrusion, and consequently a programme of measures is necessary. In relation with marine intrusion, a regional downward trend has been found, showing no further deterioration. An additional outcome of the procedure is a methodological framework for the systematic review of the relevant information for evaluation of Groundwater Body chemical status, which includes additional steps to check the effectiveness of the programme of measures and update the baseline level periodically. The proposed methodology, based on procedures usually applied separately, provides a comprehensive framework for groundwater quality data analysis. It will allow more rigorous implementation objectives of the Directive. Results obtained for the developed case are more robust from the statistical point of view, because all hypotheses have been contemplated.

Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods

Recognising the various sources of nitrate pollution and understanding system dynamics are fundamental to tackle groundwater quality problems. A comprehensive GIS database of twenty parameters regarding hydrogeological and hydrological features and driving forces were used as inputs for predictive models of nitrate pollution. Additionally, key variables extracted from remotely sensed Normalised Difference Vegetation Index time-series (NDVI) were included in database to provide indications of agroecosystem dynamics. Many approaches can be used to evaluate feature importance related to groundwater pollution caused by nitrates. Filters, wrappers and embedded methods are used to rank feature importance according to the probability of occurrence of nitrates above a threshold value in groundwater. Machine learning algorithms (MLA) such as Classification and Regression Trees (CART), Random Forest (RF) and Support Vector Machines (SVM) are used as wrappers considering four different sequential search approaches: the sequential backward selection (SBS), the sequential forward selection (SFS), the sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). Feature importance obtained from RF and CART was used as an embedded approach. RF with SFFS had the best performance (mmce=0.12 and AUC=0.92) and good interpretability, where three features related to groundwater polluted areas were selected: i) industries and facilities rating according to their production capacity and total nitrogen emissions to water within a 3km buffer, ii) livestock farms rating by manure production within a 5km buffer and, iii) cumulated NDVI for the post-maximum month, being used as a proxy of vegetation productivity and crop yield.

Karst massif susceptibility from rock matrix, fracture and conduit porosities: a case study of the Sierra de las Nieves (Málaga, Spain)

The potential contamination of a karst massif from surface sources can be evaluated and represented in a susceptibility map. In the case of a carbonate aquifer, the susceptibility assessment must take into account their very complex and heterogeneous nature. The complexity originates in the presence of three types of porosity: matrix rock, fracture and conduit porosity. This paper presents a method for karst susceptibility mapping by estimating the three porosity types and evaluates their integration in a single susceptibility index. Matrix rock porosity is measured in the laboratory from samples collected in the field and is well correlated with lithology. Fracture porosity is estimated from fracture mapping and field measurements of secondary fracture porosity. Geostatistical methods are used to obtain continuous fields of rock matrix porosity and fracture porosity. Conduit porosity is calculated from a power model fitted to speleological cave mapping data. However, because of the scarcity and sampling bias of conduit data, probabilistic models are conjectured. A fourth porosity factor evaluated is the detritic filling of karst depressions. The integration of the different porosities in a single susceptibility factor gives a quantitative map that is reclassified to provide a qualitative, easy to interpret susceptibility index map of the karst system. Porosity estimation may also be of interest in recharge estimation and mathematical modelling of flow and transport in karst systems. The case study used to illustrate this approach is the Sierra de las Nieves karstified rock mass, a high relief Mediterranean karst in the province of Málaga in southern Spain.

A Method for Automatic Detection and Delineation of Karst Depressions and Hills

Karst depressions of decametric scale (dolines, uvalas, poljes, and other endorheic basins) play an important role in the hydrogeology of karst aquifers. They are traps of sediment and when their detritic filling has an important thickness they can retain a large amount of water delaying their percolation towards the water table or towards the networks of conduits. Many times the delineation of the depressions may be difficult because the study area may be very large, or inaccessible or hidden by vegetation. In those circumstances, it is of great help to have an automatic method of depression detection and delineation. The proposed procedure uses the digital elevation model, a geographical information system, an algorithm of pit removal and basic operations of map algebra. The method provides the depth of each detected depression measured from its rim. This fact can be used to detect the center of maximum depth as well as for calculating morphometric parameters using depth. The final map of depressions can be characterized by altitude in order to have morphometric parameters related with elevation. The algorithm has been extended for detection and delineation of karst hills. The methodology is illustrated with the Sierra de las Nieves karst aquifer in the province of Malaga, Southern Spain, where the depressions and hills show a strong structural control.

Integral Porosity Estimation of the Sierra de Las Nieves Karst Aquifer (Málaga, Spain)

Karst aquifers are very complex and heterogeneous systems because of the presence of three kinds of porosity (matrix rock porosity, fracture porosity, and conduit porosity) that generally have a large spatial variability. In order to have realistic karst models the three kinds of porosity and their spatial variability must be taken into account. A quantitative model of a karst aquifer is proposed by integration of the three kinds of porosity in a three dimensional numeric model. Nevertheless, the main task of this work is restricted to the proposal of methods for their evaluation. Matrix rock porosity has been measured in the laboratory from samples collected in the field. Matrix rock porosity is well correlated with the lithology and with the structural position of the rock. Fracture porosity has been estimated from fracture mapping and field measurements. A geostatistical method is used to obtain a continuous field of fracture porosity. Conduit porosity has been calculated from a power model fitted to speleologic cave mapping data. However, because of the scarcity of conduit data, probabilistic models must be conjectured. The integration of the three kinds of porosity gives a three dimensional numerical model that can be used in vulnerability mapping, recharge estimation, and mathematical modeling of flow and transport in karst systems. The approach is illustrated with the Sierra de las Nieves karst aquifer in the province of Malaga in Southern Spain.

Analysis of Groundwater Monitoring Data Sets with Non-Detect Observations: Application to the Plana de Sagunto (Valencia, Spain) Groundwater Body

Under article 17 of the Water Framework Directive the European Union was required to establish a framework to prevent and control groundwater pollution. Taking into consideration distinctive characteristics of groundwater concentration data, appropriate statistical tests are required. In this regard, Annex IV of the Groundwater Directive sets that all measurements below the quantification limit have to be substituted by half of the value of the highest quantification limit, except for total pesticides. To set a methodological approach to evaluate censored data, the Plana de Sagunto Ground Water Body has been chosen. Several methods have been tested based upon application to different degrees of censoring to the available sample data. Censored estimation techniques, like Kaplan-Meier or Robust Regression have proved to be helpful in checking compliance with threshold values.

SAR interferometry monitoring of subsidence in a detritic basin related to water depletion in the underlying confined carbonate aquifer (Torremolinos, southern Spain)

This research underlines the need to improve water management policies for areas linked to confined karstic aquifers subjected to intensive exploitation, and to develop additional efforts towards monitoring their subsidence evolution. We analyze subsidence related to intensive use of groundwater in a confined karstic aquifer, through the use of the InSAR technique, by the southern coast of Spain (Costa del Sol). Carbonates are overlain by an unconfined detritic aquifer with interlayered high transmissivity rocks, in connection with the Mediterranean Sea, where the water level is rather stable. Despite this, an accumulated deformation in the line-of-sight (LOS) direction greater than -100 mm was observed by means of the ERS-1/2 (1992-2000) and Envisat (2003-2009) satellite SAR sensors. During this period, the Costa del Sol experienced a major population increase due to the expansion of the tourism industry, with the consequent increase in groundwater exploitation. The maximum LOS displacement rates recorded during both time spans are respectively -6 mm/yr and -11 mm/yr, respectively. During the entire period, there was an accumulated descent of the confined water level of 140 m, and several fluctuations of more than 80 m correlating with the subsidence trend observed for the whole area. Main sedimentary depocenters (up to 800 m), revealed by gravity prospecting, partly coincide with areas of subsidence maxima; yet ground deformation is also influenced by other factors, the main ones being the fine-grained facies distribution and rapid urbanization due to high touristic pressure.