Se presenta la comparación entre dos metodologías para el diseño de un tanque de almacenamiento de agua lluvia; La primera utiliza un análisis estadístico y determina el volumen óptimo de la estructura, a partir de, la aplicación de estadística inferencial sobre los datos de precipitación. La segunda metodología combina dos tipos de análisis: determinista y estadístico, en esta, el volumen óptimo se determina a través de la estadística inferencial, aplicada sobre los datos de tipo determinista. | The comparison between two methodologies for the design of a rainwater storage tank is presented; The first uses a statistical analysis and determines the optimal volume of the structure, from the application of inferential statistics on the precipitation data. The second methodology combines two types of analysis: deterministic and statistical, in this, the optimal volume is determined through inferential statistics, applied to deterministic data.

Water protection is one of the most important goals in environmental protection. The Clean Water Act in the USA and the Water Framework Directive (WFD) in Europe are the legal frameworks to facilitate the achievement of this goal. The question is raised of whether more information can be extracted from WFD-related groundwater data. To answer it, a methodology has been developed that is easy to use and could be implemented into official practice. A case study is presented in which the groundwater data of a sodic area in Austria (Seewinkel) is assessed. Eighteen parameters in groundwater sampled from 23 wells (1991–2011) were analyzed. With basic statistics, trend-, cluster-, Wilks’ λ and spatial sampling density analysis, local phosphorus and boron phenomena were described, along with the determining role of sulphate, groundwater flow, and the oxygen gradient in the area. As a final step, the spatial sampling density was determined. Regarding the current set of parameters, all the sampling sites are necessary and only in the case of certain parameters (Ca²⁺, Mg²⁺, K⁺, NO₃⁻, pH) could one sampling site be abandoned. The methodology applied brings a new perspective to exploring groundwater data collected according to the requirements of the WFD.

The rapid decline of groundwater levels (GWL) due to pervasive groundwater abstraction in the densely populated (~1 billion) Indus-Ganges-Brahmaputra-Meghna (IGBM) transboundary river basins of South Asia, necessitates a robust framework of prediction and understanding. While few localized studies exist, three-dimensional regional-scale characterization of GWL prediction is yet to be implemented. Here, ‘support vector machine’, a machine-learning-based method, is applied to data from the Gravity Recovery and Climate Experiment (GRACE) and data on land-surface-model-based groundwater storage and meteorological variables, to predict the GWL anomaly (GWLA) in the IGBM. The study has three main objectives, (1) to understand the spatial (observation well locations) and subsurface (shallow vs. deep observation wells) variability in prediction results for in-situ GWLA data for a large number of observation wells (n = 4,791); (2) to determine its relationship with groundwater abstraction, and; (3) to outline the advantages and limitations of using GRACE data for predicting GWLAs. The findings, based on individual observation well results, suggest significant prediction efficiency (median statistics: r > 0.71, NSE > 0.70; p < 0.05) in most of the IGBM; however, the study identifies hotspots, mostly in the agriculture-intensive regions, having relatively poor model performance. Further analysis of the subsurface depth-wise prediction statistics reveals that the significant dominance of pumping in the deeper depths of the aquifer is linked to the relatively poor model performance for the deep observation wells (screen depth > 35 m) compared with the shallow observation wells (screen depth < 35 m), thus, highlighting the limitation of GRACE in representing spatial and depth-dependent local-scale pumping.

Influences of hydraulic conductivity (K) heterogeneities on bedrock groundwater (BG) flow systems in mountainous topography are investigated using a conceptual 2D numerical modelling approach. A conceptual model for K heterogeneity in crystalline bedrock mountainous environments is developed based on a review of previous research, and represents heterogeneities due to weathering profile, bedrock fracture characteristics, and catchment-scale (∼0.1–1 km) structural features. Numerical groundwater modelling of K scenarios for hypothetical mountain catchment topography indicates that general characteristics of the BG flow directions are dominated by prominent topographic features. Within the modelled saturated BG flow system, ∼90 % or more of total BG flux is focussed within a fractured bedrock zone, extending to depths of ∼100–200 m below the ground surface, overlying lower-K bedrock. Structural features and heterogeneities, represented as discrete zones of higher or lower K relative to surrounding bedrock, locally influence BG flow, but do not influence general BG flow patterns or general positions of BG flow divides. This result is supported by similar BG transit-time distribution shapes and statistics for systems with and without structural features. The results support the development of topography-based methods for predicting general locations of BG flow-system boundaries in mountain regions.

Size and shape of individual flow-features, and not their ‘organization’ in sets of predominant orientation, are the major influences on the ability of groundwater to percolate through sparse channel networks. Measurements in background fractured crystalline rocks proposed for nuclear waste repositories provide useful insight. Flow-features are observed as locations of increased transmissivity during packer or flow testing in boreholes. They are conceived here as channels on fracture surfaces. Findings are based on numerical modelling and a general formula by Barker (2018) for the percolation of two-dimensional (2D) objects in 3D space. Equidimensional shapes are found to be the least efficient at forming percolating networks. As discs are evolved into highly eccentric ellipses, percolation thresholds for number, area and intersection density decrease. At the same time, the percentage of features forming the active flow path declines from about 10% for discs to a few per cent for 50:1 ellipses. Compiling recent field measurements of area density of flow-features reveals low values within a limited range (0.01–0.8 m⁻¹). When this range is combined with practical values of likely channel width, long narrow flow-features are the only reasonable components of a sparse percolating network. Conventional equidimensional discrete fracture networks are considered unlikely. Innovative field investigation and modelling methods based only on hydrogeological measurements are suggested. It is concluded that this consideration of shape supports the approach, broadly termed the ‘long channel’ concept. Barker J.A. (2018) Intersection statistics and percolation criteria for fractures of mixed shapes and sizes. Comput Geosci 112:47–53.