Development of a Rainfall-Recharge Relationship for a Fractured Basaltic Aquifer in Central India

Groundwater being an important component of the hydrological cycle, estimation of its annual replenishment is essential to evolve a plan for optimum utilization. Groundwater balance approach, which is used extensively for the quantification of recharge and discharge components has been adopted for the rainfall-recharge estimation. Various inflow and outflow components have been identified and estimated for Sagar block in Sagar district of Madhya Pradesh, which faces acute water scarcity and continuous decline in groundwater levels. The computed recharge from rainfall varies between 122.45 and 183.71 MCM. The computed rainfall-recharge is compared with the Chaturvedi (1973), Kumar and Seethapathi (2002), Krishna (1987), and U.P. Irrigation Research Institute models. Models have also been developed to estimate rainfall-recharge for varying ranges of the annual rainfall and have been compared with the existing models. The relative error in estimation of rainfall-recharge from proposed models varies between 0.03 and 9.24%. The overall scenario is net decline in groundwater storage to an extent of ?31.31 MCM over a period of 16 years from 1985–1986 to 2000–2001. The trend analysis by Kendall’s rank correlation test, regression test for linear trend and Mann–Kendall test also clearly suggests falling trends in groundwater storage at 5% significant level, thereby demonstrating over-exploitation of the groundwater aquifer. This has subsequently led to progressive decline in groundwater table in the study area. Efforts should be initiated to tap the surface water by creating storages at suitable sites and artificial recharge practices should be encouraged after identifying suitable recharge zones. Conjunctive use of the surface and groundwater along with water conservation practices and groundwater management measures should be taken up to arrest the progressive decline in groundwater levels and over-exploitation of groundwater aquifer.     

SCS-CN Based Quantification of Potential of Rooftop Catchments and Computation of ASRC for Rainwater Harvesting

Rooftop rainwater harvesting, among other options, play a central role in addressing water security and reducing impacts on the environment. The storm or annual storm runoff coefficient (RC/ASRC) play a significant role in quantification of potential of rooftop catchments for rainwater harvesting, however, these are usually selected from generic lists available in literature. This study explores methodology/procedures based on one of the most popular and versatile hydrological model, Soil Conservation Service Curve Number (SCS-CN) (SCS 1986) and its variants, i.e., Hawkins SCS-CN (HSCS-CN) model (Hawkins et al. 2001), Michel SCS-CN (MSCS-CN) model (Michel et al. Water Resour Res 41:W02011, 2005), and Storm Water Management Model-Annual Storm Runoff Coefficient (SWMM-ASRC) (Heaney et al. 1976) and compares their performance with Central Ground Board (CGWB) (CGWB 2000) approach. It has been found that for the same amount of rainfall and same rooftop catchment area, the MSCS-CN model yields highest rooftop runoff followed by SWMM-ASRC?>?HSCS-CN?>?SCS-CN?>?CGWB. However, the SCS-CN model has close resemblance with CGWB approach followed by HSCS-CN model, SWMM-ASRC, and MSCS-CN model. ASRCs were developed using these models and it was found that MSCS-CN model has the highest value of ASRC (= 0.944) followed by SWMM-ASRC approach (=0.900), HSCS-CN model (=0.830), SCS-CN model (=0.801), and CGWB approach (=0.800). The versatility of these models lies to the fact that CN values (according to rooftop catchment characteristics) would yield rooftop runoff and therefore ASRC values based on sound hydrological perception and not just on the empiricism. The models have inherent capability to incorporate the major factors responsible for runoff production from rooftop/urban, i.e., surface characteristics, initial abstraction, and antecedent dry weather period (ADWP) for the catchments and would be better a tool for quantification rather than just using empirical runoff coefficients for the purpose.     

A Contribution to the Study of the Phenomenon of Horizontal Infiltration

In this work an attempt is made to compare experimental soil moisture profiles for a horizontal infiltration experiment (Poulovassilis, Water Resour Res 13:369?C374, 1977) with calculated profiles. These calculated profiles are obtained variously through the use of the computation code of HYDRUS 1D and through the semi-analytical solution of the appropriate diffusion equation when the soil water diffusivity is obtained directly from the experimental data. The necessary input data for using HYDRUS 1D are obtained in three different ways: First, the experimental data of the main wetting branch of the moisture retention curve (MRC) coupled with Ks (the hydraulic conductivity at saturation) are used. Second the predicted, according to the Parlange model (Parlange, Water Resour Res 12:224?C228, 1976) main wetting branch of the MRC, when experimental data points of the main drying branch of the MRC are used. Third the predicted, according to the Mualem model (Mualem, Water Resour Res 13:773?C780, 1977) main wetting branch of the MRC, again when experimental data points of the main drying branch of the MRC, are used. In the previous three cases predicting appropriate hydraulic conductivity K(??) relationship is obtained through the model of Mualem (Water Resour Res 12:513?C522, 1976). The comparison of the above soil moisture profiles leads to the following: The numerically calculated profiles are moving faster than the experimental ones. Calculated profiles exhibit a Green?CAmpt-like shape. Differences among the experimental and calculated profiles are more pronounced in larger ??-values. Closer to the experimental profiles are those obtained semi-analytically. From the cumulative infiltration versus square-root of time curves, it is evident that the HYDRUS 1D results are compatible with the requirements imposed by the Boltzmann transformation.     

Simulation of Water Distribution Network under Pressure-Deficient Condition

Pressure deficient condition occurs in the water distribution network (WDN) when the nodal demands are in excess of the design discharge as in the case of fire demand, pump failure, pipe breaks, valve failure etc. It causes either no-flow or partial-flow depending upon the available pressure head at the nodes. To evaluate the nodal flows in such condition, node flow analysis (NFA) gives reasonable results in comparison to demand-driven analysis (DDA) and head-dependent analysis (HDA). The NFA works on the predefined pressure-discharge relationship to evaluate the nodal flows. However, this approach requires human intervention and hence cannot be applied to large WDN. Recently, modified pressure-deficient network algorithm (M-PDNA) has been developed by Babu and Mohan (2012) for pressure-deficient analysis with EPANET toolkit. However, it requires modification of the source code of EPANET. In this study a relationship with the M-PDNA and node flow analysis (Gupta and Bhave 1996) has been investigated and it is found that M-PDNA is the simplified version of NFA. Further, the working principle of M-PDNA has been investigated with suitable examples of Babu and Mohan (2012). The theoretical basis of M-PDNA has not been investigated in terms of head-discharge relationship. Herein, a head-discharge relationship based on the working principal of M-PDNA is proposed. Some of the toolkits are also readily available to modify demand driven solver of EPANET 2 to suit for the pressure-driven analysis and then it can be used for analysing pressure deficient network. Also in this study, a modification in M-PDNA approach is proposed which does not require the use of EPANET toolkit which is found to be capable of simulating both pressure-sufficient and pressure-deficient conditions in a single hydraulic simulation. Using the proposed approach, pressure-deficient condition is analysed with constant and variable demand pattern.     

The Brazilian Water Resources Management Policy: Fifteen Years of Success and Challenges

As a result of the increasing global awareness about the importance of water, many developed and developing countries have reviewed their water resources management policies and laws. In Brazil, Law 9,433, enacted in (1997), establishes the National Water Resources Policy (NWRP) and the National Water Resource Management System (NWRMS), introducing a new integrated approach to water resources management through the application of planning and economic instruments. At the institutional level, this brought many changes. A new institutional framework was established with the creation of river basin committees and water agencies. Almost 15 years after the Law took effect, these changes are still being implemented, and some adjustments have been necessary. In light of the Brazilian NWRP, this paper presents and analyzes the legal and institutional reform that has been taking place in Brazil’s water resources sector since 1997. An initial analysis shows that today, the implementation process still faces many challenges, hindering the effective consolidation of the instruments set out by Law 9,433/1997. The paper concludes that although Brazil’s model is generally in line with international trends, and despite the major progress that has been made to date, in some hydrographic regions the instruments conceived in the country’s model are still in the incipient stage of implementation, indicating that greater efforts are necessary, some of which are suggested in this article.     

Spatial Determinants of Urban Residential Water Demand in Fortaleza,Brazil

This paper aims at estimating the residential water demand function for the city of Fortaleza, Brazil, considering the potential impact of including spatial effects in the model. The empirical evidence is a unique micro-data set obtained through a household water consumption survey carried out in 2007. We estimated three econometric models, which have as explanatory variables the average/marginal price, the difference, income, number of male and female residents and the number of bathrooms, under different spatial specifications: the Spatial Error Model (SEM), the Spatial Autoregressive model (SAR), and finally, the Spatial Autoregressive Moving Average model (SARMA). Results suggest that the SARMA model is the “best” as shown by a series of tests. Such results contradict conclusions drawn by Chang et al. (Urban Geogr 31(7):953–972, 2010), House-Peters et al. (JAWRA J Am Water Resour Assoc 46(3), 2010), and Ramachandran and Johnston (2011). This means, among other things, that not controlling spatial effects is a key specification error, underestimating the effect of almost all variables in the model. Sometimes, these differences can be as high as 24.66 % and 13.32 % for price elasticity in the Average Price and the McFadden models, respectively.     

Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree

This paper explored the potential of Backpropagation Neural Network (BNN) and M5 model tree based regression approach to estimate the mean annual flood. Data used in this study were taken from an earlier study by Swamee et al. (J Water Resour Plan Manage 121:403–407, 1995) for 93 Indian catchments spread over the entire country. The relationship proposed by Swamee et al. (J Water Resour Plan Manage 121:403–407, 1995) was compared with the predictive accuracy of a BNN and M5 model tree approach. The data were analyzed using a tenfold cross-validation. Comparison of the results showed that predictions with the backpropagation neural network fell within a scatter line of ±30% with a correlation coefficient of 0.975. Furthermore, predictions with the M5 model tree fell well within a scatter line of ±15% with a correlation coefficient as high as 0.994. The results also showed that predicted values with neural network and M5 model tree were within about 1.25 times the actual values. However, the predicted values obtained using the Swamee et al. (J Water Resour Plan Manage 121:403–407, 1995) approach fell much beyond the scatter line of ±50% and the predicted mean annual flood values were sometimes as high as eight times the actual values. The correlation coefficient with this approach was 0.897. The results from this study suggest that backpropagation neural network and M5 model tree-based modeling approaches are superior in accuracy to the model proposed by Swamee et al. (J Water Resour Plan Manage 121:403–407, 1995). This study also suggests that M5 model trees, being analogous to piecewise linear functions, have certain advantages over neural networks as they offer more insight into the generated model, are acceptable to decision makers and are very efficient in training, and always converge.     

A Comparison Between Conventional and M5 Model Tree Methods for Converting Pan Evaporation to Reference Evapotranspiration for Semi-Arid Region

In this study, the performance of M5 model tree and conventional method for converting pan evaporation data (E_p) to reference evapotranspiration (ET₀) were assessed in semi-arid regions. Conventional method uses pan coefficient (K_p) as a factor to convert E_p to ET₀. Two common K_p equations for pans with dry fetch (Allen et al. 1998; Abdel-Wahed and Snyder in J Irrig Drain Eng 134(4):425–429, 2008) were considered for the comparison. The values of ET₀ derived using these three methods were compared to those estimated using the reference FAO Penmane Monteith (FAO-PM) method under semi-arid conditions of the Khuzestan plain (Southwest Iran). The results showed that the M5 model is the best one to estimate ET₀ over test sites (0.5 mm d^?1 of root mean square error (RMSE) and 0.98 of coefficient of determination (R ²). Conversely, the performance of the two K_p equations was poor.     

Assessment of Environmental Flow Requirements by Entropy-Based Multi-Criteria Decision

Environmental flow methodologies based on hydrological indices have increasingly evolved, because they play an important role in a riverine ecosystem management. Moreover, human activities, such as dams, in many basins have altered their natural hydrologic processes and they also change habitat for aquatic and riparian species. Decision making by echo-managers considering these multi-variables has been subjective. Therefore, to provide eco-managers with guidelines, an eco-index with entropy related to the probability density function (pdf) and Ordered Weighted Averaging (OWA) operator is been suggested in this study. The index was applied to Geum River in South Korea with 33 Indicators of Hydrologic Alteration (IHA) by Richter et al. (1998) through IHA software (TNC 2009). The objective of this paper is to outline an entropy-based hydrologic alteration assessment of biologically relevant flow regimes using gauged flow data. On the Geum River, dams have affected river flow measured at four stations and the eco-index suggests that more attention needs to be paid to the tributaries between Gongju and Gyuam stations. Echo-Index with maximum entropy and OWA operator reflects the effect of every parameter and helps with determination considering multi variables. In future more temporal and spatial information needs to be examined for ecosystem alteration.     

City Blueprints: Baseline Assessments of Sustainable Water Management in 11 Cities of the Future

The necessity of Urban Water Cycle Services (UWCS) adapting to future stresses calls for changes that take sustainability into account. Megatrends (e.g. population growth, water scarcity, pollution and climate change) pose urgent water challenges in cities. In a previous paper, a set of indicators, i.e., the City Blueprint has been developed to assess the sustainability of UWCS (Van Leeuwen et al., Wat Resour Manage 26:2177–2197, 2012). In this paper this approach has been applied in 9 cities and regions in Europe (Amsterdam, Algarve, Athens, Bucharest, Hamburg, Reggio Emilia, Rotterdam, Oslo and Cities of Scotland) and in 2 African cities in Angola (Kilamba Kiaxi) and Tanzania (Dar es Salaam). The assessments showed that cities vary considerably with regard to the sustainability of the UWCS. This is also captured in the Blue City Index (BCI), the arithmetic mean of 24 indicators comprising the City Blueprint (Van Leeuwen et al., Wat Resour Manage 26:2177–2197, 2012). Theoretically, the BCI has a minimum score of 0 and a maximum score of 10. The actual BCIs in the 11 cities studied varied from 3.31 (Kilamba Kiaxi) to 7.72 (Hamburg). The BCI was positively correlated with the Gross Domestic Product (GDP) per person, the ambitions of the local authorities regarding the sustainability of the UWCS, the voluntary participation index (VPI) and all governance indicators according to the World Bank. The study demonstrated that the variability in sustainability among the UWCS of cities offers great opportunities for short-term and long-term improvements, provided that cities share their best practices.     

Identification of Homogeneous Rainfall Regimes in Northeast Region of India using Fuzzy Cluster Analysis

Regionalization methods are often used in hydrology for frequency analysis of floods. The hydrologically homogeneous regions should be determined using cluster analysis instead of the geographically close stations. In view of the ongoing environmental and climate changes in the Northeastern of India, regionalization of homogeneous rainfall region is essential to lay out an effective flood frequency analysis of this region. The choice of appropriate cluster approach used according to the data of the basin is also significant. In the context of this study, total precipitation data of stations operated by Indian Meteorological Department (IMD) in Northeastern of India basins for cluster analysis are used. Further, five cluster validity indices, namely Partition Coefficient, Partition Entropy, Extended Xie-Beni index, Fukuyama-Sugeno index and Kwon index have been tested to determine the effectiveness in identifying optimal partition provided by the fuzzy c mean clustering algorithm (FCM). A comparison is also performed using K- Mean clustering algorithm. Additionally, regional homogeneity tests based on l-moments approach are used to check homogeneity of regions identified by both cluster analysis approaches. It was concluded that regional homogeneity test results show that regions defined by FCM method are sufficiently homogeneous for regional frequency analysis.     

Estimating Temporal Changes in Extreme Rainfall in Sicily Region (Italy)

An intensification of extreme rainfall events have characterized several areas of peninsular and insular Italy since the early 2000s, suggesting an upward ongoing trend likely driven by climate change. In the present study temporal changes in 1-, 3-, 6-, 12- and 24-h annual maxima rainfall series from more than 200 sites in Sicily region (Italy) are examined. A regional study is performed in order to reduce the uncertainty in change detection related to the limited length of the available records of extreme rainfall series. More specifically, annual maxima series are treated according to a regional flood index - type approach to frequency analysis, by assuming stationarity on a decadal time scale. First a cluster analysis using at-site characteristics is used to determine homogeneous rainfall regions. Then, potential changes in regional L-moment ratios are analyzed using a 10-year moving window. Furthermore, the shapes of regional growth curves, derived by splitting the records into separate decades, are compared. In addition, a jackknife procedure is used to assess uncertainty in the fitted growth curves and to identify significant trends in quantile estimates. Results reveal that L-moment ratios show a general decreasing trend and that growth curves for the last decade (2000–2009) usually do not stand above the others, with the only exception of the ones related to the outer western part of Sicily. On the other hand, rainfall quantile estimates for the same period are the highest values almost all over the region. An explanation can be found in the increase of subregional average medians, largely caused by recent severe local storms.     

Regional Frequency Analysis of Extreme Rainfalls in the West Coast of Peninsular Malaysia using Partial L-Moments

This study was to reinstate the development of regional frequency analysis using L-moments approach. The Partial L-moments (PL-moments) method was employed and a new relationship for homogeneity analysis is developed. For this study, the PL-moments for generalized logistic (GLO), generalized pareto (GPA) and generalized value (GEV) distributions were derived based on the formula defined by Wang (Water Resour Res 32:1767?C1771, 1996). The three distributions are used to develop the regional frequency analysis procedures. As a case of study, the Selangor catchment that consists of 30 sites which located on the west coast of Peninsular Malaysia has chosen as sample. Based on L-moment and PL-moment ratio diagrams as well as Z-test statistics, the GEV and GLO were identified as the best distributions to represent the statistical properties of extreme rainfalls in Selangor. Monte Carlo simulation shows that the method of PL-moments would outperform L-moments method for estimation of large returns period event.     

A Numerical Model of the Wave that Overtopped the Vajont Dam in 1963

The Vajont landslide took place in northern Italy on October 9th 1963 and caused a huge impulse wave to travel along the artificial reservoir and overtop the dam, flooding and devastating a considerable area along the Piave riverbed. In this event about 1900 people died, 1700 of them in the Piave Valley alone. The water depth of the wave in some points exceeded 50 m. Although the phenomenon is clearly tridimensional, the application of a pure 3D hydraulic numerical model to this wide and natural territory is very complicated. Recently, the authors have presented a numerical model of the impulse wave that wasted the Vajont basin as a consequence of the landslide Bosa and Petti (Environ Model Softw 26:406–418, 2011). In this paper, a 2DH numerical model has been applied to study the effects of the overtopping wave in the Piave Valley, in order to verify if the simplifications assumed by a 2DH model still make it possible to describe the evolution of the wave in the proper manner.     

Application of the Innovative Trend Analysis Method for the Trend Analysis of Rainfall Anomalies in Southern Italy

In this paper, an investigation of the temporal rainfall variability, in a large area of southern Italy, has been carried out using a homogeneous monthly rainfall dataset of 559 rain gauges with more than 50 years of observation. The area under investigation is a large portion of the Italian peninsula, ranging from the Campania and the Apulia regions in the North, to Sicily in the South, and covering an area of about 85,000 km². Possible trends in seasonal and annual rainfall values have been detected by means of a new graphical technique, ?en’s method, which allows the trend identification of the low, medium and high values of a series. Moreover, the Mann–Kendall test has been also applied. As a result, different values and tendencies of the highest and of the lowest rainfall data have emerged among the five regions considered in the analysis. In particular, at seasonal scale, a negative trend has been detected especially in winter and in autumn in the whole study area, whereas not well defined trend signals have been identified in summer and spring.     

Modifiers and Amplifiers of High and low Flows on the Ping River in Northern Thailand (1921–2009): The Roles of Climatic Events and Anthropogenic Activity

We analyse an 89-year streamflow record (1921?C2009) from the Upper Ping River in northern Thailand to determine if anomalous flows have increased over time (Trenberth, Clim Res 47:123?C138, 1999; Trenberth, Clim Chang 42:327?C339, 2011). We also relate the temporal behavior of high and low flows to climatic phenomena and anthropogenic activities. Peak flows have not increased significantly since 1921. However, minimum flows showed a very significant downward trend over the study period (???=?0.01). Annual and wet season discharge show significant downward trends (???=?0.05). All flow variables appear to be more variable now than 90?years ago especially annual peak flows. Both annual peak and minimum flows are correlated with annual and wet season rainfall totals. Minimum flow is also sensitive to the length of the monsoon season and number of rainy days in the previous monsoon season. Peak flow activity is driven predominantly by climate phenomena, such as tropical storm activity and monsoon anomalies, but the relationship between peak flows and ENSO phenomena is unclear. In general, annual discharge variables did not correspond unequivocally with El Nin? or La Nin? events. Minimum flows show a major decline from the mid-1950s in line with major anthropogenic changes in the catchment. The plausible intensification of the hydrological cycle that may accompany global warming is of concern because of the potential to affect tropical storm activity and monsoon anomalies, phenomena that are linked with very high flows in this river system. The obvious effect of human activities such as reservoir management on low flows calls for careful management to prevent droughts in the future.     

Parameter Identification for a Slug Test in a Well with Finite-Thickness Skin Using Extended Kalman Filter

Yeh and Chen (J Hydro 342(3?C4):283-294, 2007) integrated a slug test solution for a well having a finite-thickness skin with the simulated annealing (SA) to determine the hydraulic parameters of the skin zone and formation zone. Some results obtained in positive-skin scenarios are however not accurate if compared with the target values of the parameters. This study first employs the sensitivity and correlation analyses to quantify the relationship between two normalized sensitivities and analyze the resulting errors in parameter estimates. It is found that the inaccuracy in parameter estimates can be attributed to following two problems: (1) the normalized sensitivities of the skin thickness and hydraulic conductivity are highly correlated and (2) the SA algorithm is very sensitive to round-off error in well-water-level (WWL) data. A parameter identification approach is thus developed based on the extended Kalman filter (EKF) coupled with the solution used by Yeh and Chen (J Hydro 342(3?C4):283-294, 2007) to determine the parameters in six positive-skin scenarios where the parameters were not accurately determined before. We show that previous two problems can be overcome by the proposed approach because it is designed to account for uncertainties of measurements. Moreover, the EKF can save 99.8% and 99.9% computing time when compared with the results using the SA in analyzing 20 WWL data and 47 WWL data, respectively.     

Probability Analysis of Crop Water Stress Index: An Application of Double Bounded Density Function (DB-CDF)

Soil moisture is an uncertain variable due to rainfall randomness. Furthermore, its density function is hybrid in nature, with spikes at maximum and minimum soil moisture (saturation and field capacity). Both of these properties are also considered for crop water stress index. The crop water stress index can be used to show the sensitivity of a crop to deficit irrigation. In this paper, a new methodology is proposed to probability analysis of water stress index using Double Bounded Density Function (DB-CDF) and moment analysis of crop water stress index. For this purpose, two equations were developed for the first and second moments of water stress index. To find out the value of the proposed moment equations, they are used as constraints in a stochastic model of crop water allocation as developed previously by Ganji and Shekarrizfard (Water Resour Manage 25:547–561, 2010). After verification of the model, the DB-CDF of soil moisture stress index was estimated using the value of proposed moments in the growing periods. The results show that in case of deficit irrigation, the probability of crop water stress occurrence is high and as a consequence, any unpredictable water shortage leads to yield reduction. The application of the proposed methodology is novel and has not been reported in the literature to date.     

Small Amplitude Sharp Pressure Waves to Diagnose Pipe Systems

In this paper the possibility of using transient tests, generated by the Portable Pressure Wave Maker (PPWM) device, as a powerful tool in the management of pipe systems, is demonstrated. Specifically, tests carried out in different experimental set-ups at the Water Engineering Laboratory of the University of Perugia, Italy show that small amplitude sharp pressure waves produced by the PPWM allow to locate and evaluate the entity of anomalies, such as leaks, illegal branches, partial blockages, and negligently partially closed in-line valves. To improve the precision of localization of anomalies, arrival times of pressure waves are detected by means of wavelet analysis. Simple relations based on the water hammer theory are proposed to evaluate the entity of the anomalies.     

Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain

Chemical and physical-chemical parameters define water quality and are involved in water body type and habitat determination. They support a biological community of a certain ecological status. Water quality controls involve a large number of measurements of variables and observations according to the European Water Framework Directive (Directive 2000/60/EC). In some cases, such as areas with especially critical uses or points in which potential pollution episodes are expected, the automatic monitoring is recommended. However, the chemical and physical-chemical measurements are costly and time consuming. Turbidity is shown as a key variable for the water quality control and it is also an integrative parameter. For this reason, the aim of this work is focused on this main parameter through the study of the influence of several water quality parameters on it. The artificial neural networks (ANNs) have been used in a wide range of biological problems with promising results. Bearing this in mind, turbidity values have been predicted here by using artificial neural networks (ANNs) from the remaining measured water quality parameters with success taking into account the synergistic interactions between the input variables in the Nalón river basin (Northern Spain). Finally, the main conclusions of this study are exposed.