Spatial Variance Research Articles

Analyzing the Mediterranean Tropical-like Cyclone Ianos Using the Moist Static Energy Budget

This paper presents a detailed analysis of the energy dynamics of the Mediterranean tropical-like cyclone, Medicane Ianos, by using a moist static energy (MSE) budget framework. Medicanes are hybrid cyclonic systems that share characteristics of both extratropical and tropical cyclones, making their classification and prediction challenging. Using high-resolution ERA5 reanalysis data, we analyzed the life cycle of Ianos, which is one of the strongest recorded medicanes, employing the vertically integrated MSE spatial variance budget to quantify the contributions of different energy sources to the cyclone’s development. The chosen study area was approximately 25002 km2, covering the entire track of the cyclone. The budget was calculated after tracking Ianos and applying Hart phase space analysis to assess the cyclone phases. The results show that the MSE budget can reveal that the cyclone development was driven by a delicate balance between convection and dynamical factors. The interplay between vertical and horizontal advection, in particular the upward transport of moist air and the lateral inflow of warm, moist air and cold, dry air, was a key mechanism driving the evolution of Ianos, followed by surface fluxes and radiative feedback. By analyzing what process contributes most to the increase in MSE variance, we concluded that Ianos can be assimilated in the tropical framework within a radius of 600 km around the cyclone center, but only during its intense phase. In this way, the budget can contribute as a diagnostic tool to the ongoing debate regarding medicanes classification.

Characteristics of Tree Growth at the Early Stage of Natural Succession on Abandoned Farmland in Southwest China’s Karst Region

Southwest China’s karst region represents a global hotspot for ecological restoration, with natural succession on abandoned farmland emerging as a pivotal mechanism under recent land-use transitions. Despite its ecological significance, empirical data remain scarce regarding tree growth characteristics in this fragile ecosystem. This seven-year study (2018–2024) at Puding Karst Ecosystem Research Station quantified the spatiotemporal patterns of tree growth through monthly diameter at breast height (DBH) measurements for dominant species, coupled with microhabitat characterization (rock exposure, competition indices, and canopy architecture). Key findings revealed that the mean annual DBH increment was 5.74 mm/a, while biomass accumulation averaged 9.38 kg/a; growing-season drought duration significantly modulated interannual growth variation; and microhabitat heterogeneity and tree size significantly influenced the spatial variance of tree growth. These results substantiate natural succession as an effective carbon sequestration strategy, particularly in nutrient-depleted karst terrains. We advocate for the policy prioritization of passive restoration over active afforestation in marginal croplands.

Landscape Spatiotemporal Heterogeneity Decreased the Resistance of Alpine Grassland to Soil Droughts

Alpine grasslands face increasing threats from soil droughts due to climate change. While extensive research has focused on the direct impacts of drought on vegetation, the role of landscape fragmentation and spatiotemporal heterogeneity in shaping the response of these ecosystems to drought remains inadequately explored. This study aims to fill this gap by examining the Gannan alpine grassland in the northeastern Qinghai-Tibet Plateau. Using remote sensing data, indicators of spatial and temporal heterogeneity were derived, including spatial variance (SCV), spatial autocorrelation (SAC), and temporal autocorrelation (TAC). Two soil drought thresholds (Tr: threshold of rapid resistance loss and Tc: threshold of complete resistance loss) representing percentile-based drought intensities were identified to assess NDVI decline under drought conditions. Our findings indicate that the grassland has low resistance to soil droughts, with mean Tr and Tc of 8.93th and 7.36th percentile, respectively. Both increasing and decreasing spatiotemporal heterogeneity reduced vegetation resistance, with increasing SCV having a more pronounced effect. Specifically, increasing SCV increased Tr and Tc 1.4 times faster and 2.6 time slower than decreasing SCV, respectively. These results underscore the critical role of landscape heterogeneity in modulating grassland responses to drought, suggesting that managing vegetation patches could enhance ecosystem resilience.

Potential tree cover under current and future climate scenarios

Forests play a key role in the global commitments to reach carbon neutrality in the coming decades. Global maps of potential tree cover at high spatial resolution for current and future climate scenarios are needed to assess the risk of future forest carbon loss and carbon storage potential through afforestation/reforestation projects. Here, we present data integrating satellite-based tree cover observations into a machine learning framework to estimate tree cover carrying capacity (percentage of tree coverage), which reflects the maximum potential tree cover, accounting for natural disturbances. Our model improves upon previous estimates by reducing prediction errors, better aligning with tree cover observations in intact areas, and lowering spatial variance in areas without topographical variation. However, uncertainties remain, particularly in regions where human activity has significantly altered landscapes. The tree cover carrying capacity provides an estimate of potential tree cover based on climatic and soil conditions. This serves as an initial step in identifying afforestation/reforestation opportunities but should be further assessed for land-use competition, ecological feasibility, and other limitations.

AAV MiniSAR Long-Integration-Time Imaging by Modeling Spatial Variance as Sinusoidal Series

AAV MiniSAR Long-Integration-Time Imaging by Modeling Spatial Variance as Sinusoidal Series

Disparities in Markers of Child Deprivation for Health and Nutrition in Al-Qurna District for the Year 2023

This research seeks to examine the regional variance of deprivation evidence and indicators related to health and nutrition for children across different age groups. The health sector encompasses three age categories: (5 years and younger), (6-14 years), and (15-17 years). This domain has 6 indicators for the category of 5 years and under, and 5 indicators for the categories of 6-14 years and 15-17 years. The nutrition field included six variables, suggesting that the research aimed to assess the magnitude of child poverty and its prevalence, as well as to examine the spatial variance of poverty levels within the study area. The study utilized a descriptive and analytical methodology. Upon evaluating the data, a disparity in deprivation rates between the domains of health and nutrition was identified. The health sector was valued at 217.8 per thousand, whilst the nutrition sector was valued at 201.0 per thousand.

Effect of Satellite Spatial Resolution on Submesoscale Variance in Ocean Color and Temperature

AbstractThe capability of moderate‐spatial‐resolution satellites to accurately resolve submesoscale variations in surface tracers remains an open question, one with relevance to observing physical‐biological interactions in the surface ocean. In this study, we address this question by comparing the variance of two tracers, chlorophyll concentration (Chl) and sea surface temperature (SST), resolved by two satellites—MODIS Aqua, with a resolution of 1.5 km, and Landsat 8/9, with a resolution of 30 m. We quantify tracer variance resolved by both satellites on the submesoscale using spatial variance spectral slopes. We find that MODIS measures significantly higher variance compared to Landsat, in both Chl and SST. This is because, despite higher signal‐to‐noise ratio for MODIS per pixel, Landsat signal‐to‐noise ratio increases considerably when aggregating pixels. Furthermore, by comparing Chl to SST variance for each satellite we find Landsat to be better match to theory for resolving submesoscale physical‐biological interactions.

Evaluation of initial geostress field of underground powerhouse in the complex alteration area of western Sichuan based on back-propagation neural network method

Rock alteration leads to the degradation of the physical and mechanical properties of original rock. The spatial variance in the degree of rock alteration significantly influences regional ground stress distribution characteristics. The key problem in the process of ground stress inversion of 3-d geological model of underground powerhouse is that the geological model is difficult to divide reasonable alteration region, and the mechanical parameters of altered rock mass have strong non-uniformity. To solve these problems, a rapid and efficient classification criterion based on exploration-derived geological information was proposed. Additionally, this classification was used to analyze the distribution function of mechanical parameters in surrounding rocks across different alteration grades. The geological data obtained through exploration tunnels reveal that the spatial distribution characteristics of joint lineament density and altered rock development features are closely related to the fault. Based on the development mechanism of altered rocks, the distribution law of alteration is revealed from the perspective of engineering geology. And with the application of the back-propagation neural network method, a new three-dimensional ground stress inversion model that considers the distribution of altered rock was proposed. The inversion results indicate that the ground stress values obtained from the model, which considers the distribution of various alteration grades, correspond closely with the observed ground stress distribution characteristics. Moreover, this result proves that this inversion method is reasonable when obtaining the geostress field at engineering scale across the alteration region as evinced by a comparative study. This inversion method has good applicability to obtain the initial ground stress distribution in the area of structural alteration where the lithology difference is large but there is no obvious lithology boundary.

Evaluation of the coordinated development of health resources, health service utilization, and the regional economy in China and analysis of influencing factors

ObjectiveThis study investigates the coupling coordination relationships and influencing factors of health resources, health service utilization, and regional economy in China, aiming to provide reasonable suggestions for promoting the coordinated development of medical and health services and regional economy.MethodsThe panel data from 2016 to 2021 on China’s health resources, health service utilization were analyzed concerning China’s health resources, health service utilization, and regional economy. These included the entropy weight method for index weight calculation, the synthesis level index for subsystem synthesis level calculation, a coupled coordinated development model, exploratory spatial data analysis, and a geographic detector model. These analytical approaches facilitated the examination of spatial and temporal variances, alongside the identification of driving factors influencing the coupled and coordinated development of health resources, health service utilization, and regional economy in China.ResultsThe findings reveal that (1) a high level of coupling exists among the three subsystems across all provinces in China. (2) Concerning the spatial pattern of coupling coordination, notable regional differences are observed in the degree of coupling coordination between health resource utilization, health service utilization, and regional economic development, with a noticeable decline from east to west and central regions positioned in between. (3) Regarding spatial evolution characteristics, an overall trend of higher levels is observed in the east and north compared to the west and south, indicating localized spatial clustering. In the south‒north direction, there is a parabolic rise followed by a decline from north to south. In the east‒west direction, there is an inverted “U” shaped curve with gradual strengthening from east to west. (4) The spatial disparities in synergetic development among health resource utilization, health service utilization, and the regional economy arise from multiple influencing factors interacting with each other. (5) Spatial variations are noted in the impacts of driving factors on coordinated development among health resource utilization, health service utilization, and the regional economy during different time periods.ConclusionRegional disparities primarily arise from various factors, such as healthcare visit frequency, healthcare human resource availability, and healthcare material resource allocation; moreover, these factors also vary geographically across different time periods. Strategic measures for medical care services and economic development should be formulated by each region based on its own circumstances to enhance economic benefits and achieve coordinated high-quality development among health resource utilization, health service utilization, and the regional economy.

Drivers of mesoscale convective aggregation and spatial humidity variability in the tropical western Pacific

We examine mesoscale convective organisation in the tropical western Pacific using a multivariate analysis of column humidity, precipitation and sea surface temperature (SST) observations. We demonstrate that in boreal summer and autumn, convection remains spatially random despite radiative-feedbacks acting to aggregate convection, which we attribute to the high density of convective moisture sources and the role of wind shear. Instead, in winter and spring, a weak meridional SST gradient exists and convection is usually clustered over the regions of warmer SSTs, with significant meridional humidity gradients. However, this is sporadically interrupted by episodes of convection migration to the coldest SSTs and limited spatial humidity variance. These episodes are the result of westward propagating equatorial waves, which remove meridional humidity gradients. It appears that the drivers of mesoscale convective clustering and humidity variability in the Pacific warm pool are the SST gradients, shear, and equatorial wave dynamics.

A general framework quantifying variability in spatial inhomogeneity of global precipitation and its contribution

The spatial inhomogeneity of changes in global precipitation, which is directly related to extreme flooding or drought events, represents a significant feature of climate change. To date, limited studies quantified the spatial inhomogeneity of global precipitation and its long-term change. Aiming at solving the above challenge, this study introduces a novel but simple methodological framework that is able to (1) quantify the spatial inhomogeneity of global precipitation and its variability, (2) estimate contributions of different precipitation intensities and (3) assess contributions of different regions. In this framework, the spatial inhomogeneity is quantified by the spatial variance of gridded precipitation normalized by the global mean precipitation, namely spatial coefficient of variation (SCV). Then, the spatial probability density function (PDF) of precipitation intensity is introduced to identify contributions of different precipitation intervals that lead to changes in global precipitation inhomogeneity. Finally, the global inhomogeneity can be decomposed into intra-regional and inter-regional inhomogeneity components to estimate the contributions from different regions. The result shows that the inhomogeneity of global annual precipitation has increased consistently across multiple datasets in the satellite era (1979–2021), attributed to the increasing area of both extremely high and low precipitation. Based on the Global Precipitation Climatology Project (GPCP V2.3) dataset, the increase in inhomogeneity of global annual precipitation is primarily contributed by the intra-regional inhomogeneity component of Northern Hemispheric tropical ocean (+ 60.2%) and Southern Hemispheric tropical ocean (+ 40.3%), and is partly offset by the inter-regional inhomogeneity component of Northern Hemispheric mid-latitude ocean (− 4.5%). This study demonstrates how the spatial inhomogeneity of global precipitation can be easily estimated, which is implications for quantifying, monitoring, and understanding changes in climate extremes. Our framework offers a tool for dataset or model assessment, particularly useful for the regions susceptible to extreme precipitation events.

Discrimination of Spatial and Temporal Variabilities in the Analysis of Groundwater Databases: Application to the Bourgogne-Franche-Comté Region, France

This study highlights the importance of distinguishing the mechanisms driving spatial and temporal variances in groundwater database analyses, with a particular focus on bacteriological contamination processes. Groundwater quality data from the Bourgogne-Franche-Comté region of France forms the basis of this investigation. Specifically, the SISE-EAUX database includes 3569 groundwater samples collected over various dates from 989 monitoring points. The methodology involves structuring the data into three distinct sets: (1) A spatio-temporal dataset without any conditioning; (2) A spatial dataset that assigns the mean values of each parameter to each sampling point; (3) A temporal dataset that captures deviations from the mean for each sampling point and parameter. These datasets enable a separate analysis of spatial and temporal variances. Principal component analysis (PCA) and parameter hierarchical clustering were used to compare the results, yielding valuable insights into the underlying processes. This analysis helps distinguish between factors related to geological or pedological spatial distributions and those associated with climatic events, such as intense rainfall episodes exhibiting seasonal patterns. Such differentiation enhances the understanding of fecal contamination vectors and nitrate pollution, which are often linked to surface and subsurface runoff in vulnerable catchment areas. While conceptually clear, the practical separation of spatial and temporal variability presents challenges. For example, catchments sensitive to surface water inflows during rainfall events are unevenly distributed across the region, correlating with specific natural environments. As a result, areas of high temporal variability are also well-structured spatially, underscoring the interdependence of these two types of variability. This complexity is exemplified by the behavior of iron, which varies in association with surface and subsurface parameters depending on spatial and temporal contexts. Additionally, asynchronous sampling and varying frequencies across sites lead to discrepancies in the average temporal data acquisition between points. These disparities can influence spatial variability calculations, as temporal variability effects are not entirely removed. Despite these challenges, the distinction between spatial and temporal components is essential for a deeper understanding of groundwater quality mechanisms. This refined approach improves diagnostic precision and supports more targeted and effective water resource management strategies.

Multi-environment field trials for wheat yield, stability and breeding progress in Germany

Multi-environmental trials (MET) with temporal and spatial variance are crucial for understanding genotype-environment-management (GxExM) interactions in crops. Here, we present a MET dataset for winter wheat in Germany. The dataset encompasses MET spanning six years (2015–2020), six locations and nine crop management scenarios (consisting of combinations for three treatments, unbalanced in each location and year) comparing 228 cultivars released between 1963 and 2016, amounting to a total of 526,751 data points covering 24 traits. Beside grain yield, ten agronomic traits, four baking quality traits, plant height, heading date, maturity date and six fungal disease infection indices are included. Additionally, we provide management records, including fertilizer use, plant protection measures, irrigation, and weather data. We demonstrate how this dataset can address four agronomic questions related to GxExM interactions. Further potential applications of the dataset include empirical analyses, genomic and enviromic analyses for breeding targets, or development of decision-supporting models for agricultural management and policy decisions.

Using ESPEN data for evidence-based control of neglected tropical diseases in sub-Saharan Africa: A comprehensive model-based geostatistical analysis of soil-transmitted helminths.

The Expanded Special Project for the Elimination of Neglected Tropical Diseases (ESPEN) was launched in 2019 by the World Health Organization and African nations to combat Neglected Tropical Diseases (NTDs), including Soil-transmitted helminths (STH), which still affect over 1.5 billion people globally. In this study, we present a comprehensive geostatistical analysis of publicly available STH survey data from ESPEN to delineate inter-country disparities in STH prevalence and its environmental drivers while highlighting the strengths and limitations that arise from the use of the ESPEN data. To achieve this, we also propose the use of calibration validation methods to assess the suitability of geostatistical models for disease mapping at the national scale. We analysed the most recent survey data with at least 50 geo-referenced observations, and modelled each STH species data (hookworm, roundworm, whipworm) separately. Binomial geostatistical models were developed for each country, exploring associations between STH and environmental covariates, and were validated using the non-randomized probability integral transform. We produced pixel-, subnational-, and country-level prevalence maps for successfully calibrated countries. All the results were made publicly available through an R Shiny application. Among 35 countries with STH data that met our inclusion criteria, the reported data years ranged from 2004 to 2018. Models from 25 countries were found to be well-calibrated. Spatial patterns exhibited significant variation in STH species distribution and heterogeneity in spatial correlation scale (1.14 km to 3,027.44 km) and residual spatial variation variance across countries. This study highlights the utility of ESPEN data in assessing spatial variations in STH prevalence across countries using model-based geostatistics. Despite the challenges posed by data sparsity which limit the application of geostatistical models, the insights gained remain crucial for directing focused interventions and shaping future STH assessment strategies within national control programs.

Pedestrian thermal comfort mapping for evidence-based urban planning; an interdisciplinary and user-friendly mobile approach for the case study of Dresden, Germany

AbstractIn the face of climate change and increasing urbanisation, ensuring outdoor thermal comfort is becoming an increasingly crucial consideration for sustainable urban planning. However, informed decision-making is limited by the challenge of obtaining high-resolution thermal comfort data. This study introduces an interdisciplinary, low-resource, and user-friendly methodology for thermal comfort mapping, employing a self-built low-cost meteorological device for mobile climate monitoring. This device was utilised in the city center of Dresden, Germany to collect air temperature, humidity, pressure, surface temperature, global radiation, and globe temperature data as key inputs for the calculation of thermal comfort indices. These measurements were then used to calculate the Universal Thermal Climate Index (UTCI) using the RayMan Pro urban climate modelling program. Due to the limited resource capacities of urban planning departments, clear priority areas must be identified. Therefore, an exemplary approach for the prioritisation of consistent hotspots using the highest 5% of UTCI values was developed. The spatial variances in UTCI were validated through mobile pedestrian thermal comfort questionnaires, which allowed for the comparison of objective and subjective estimates of thermal comfort and gave the basis on which to make holistic and practical suggestions for urban planning interventions. This paper demonstrates an accessible and interdisciplinary approach to thermal comfort mapping which can empower urban planning stakeholders with scientifically informed and cost-effective decision-making tools for climate-adapted urban development.

Automated fast label-free quantification of cardiomyocyte dynamics with raw holograms for cardiotoxicity screening.

Traditional cell analysis approaches based on quantitative phase imaging (QPI) necessitate a reconstruction stage, which utilizes digital holography. However, phase retrieval processing can be complicated and time-consuming since it needs numerical reconstruction and then phase unwrapping. For analysis of cardiomyocyte (CM) dynamics, it was reported that by estimating the spatial variance of the optical path difference from QPI, the spatial displacement of CMs can be quantified, thereby enabling monitoring of the excitation-contraction activity of CMs. Also, it was reported that the Farnebäck optical flow method could be combined with the holographic imaging information from QPI to characterize the contractile motion of single CMs, enabling monitoring of the mechanical beating activity of CMs for cardiotoxicity screening. However, no studies have analyzed the contractile dynamics of CMs based on raw holograms. In this paper, we present a fast, label-free, and high throughput method for contractile dynamic analysis of human-induced pluripotent stem cell-derived CMs using raw holograms or the filtered holograms, which are obtained by filtering only The proposed approach obviates the need for time-consuming numerical reconstruction and phase unwrapping for CM's dynamic analysis while still having performance comparable to that of the previous methods. Accordingly, we developed a computational algorithm to characterize the CM's functional behaviors from contractile motion waveform obtained from raw or filtered holograms, which allows the calculation of various temporal metrics related to beating activity from contraction-relaxation motion-speed profile. To the best of our knowledge, this approach is the first to analyze drug-treated CM's dynamics from raw or filtered holograms without the need for numerical phase image reconstruction. For one hologram, the reconstruction process itself in the existing methods takes at least three times longer than the process of tracking the contraction-relaxation motion-speed profile using optical flow in the proposed method. Furthermore, our proposed methodology was validated in the toxicity screening of two drugs (E-4031 and isoprenaline) with various concentrations. The findings provide information on CM contractile motion and kinetics for cardiotoxicity screening.

Large Differences in Herbivore Performance Emerge From Simple Herbivore Behaviours and Fine-Scale Spatial Heterogeneity in Phytochemistry.

Patterns of phytochemistry localisation in plant tissues are diverse within and across leaves. These spatial heterogeneities are important to the fitness of herbivores, but their effects on herbivore foraging and dietary experience remain elusive. We manipulated the spatial variance and clusteredness of a plant toxin in a synthetic diet landscape on which individual caterpillars fed. We monitored caterpillars with cameras across most of their larval development. Caterpillars that fed on diets with a lower spatial variance and more clustered arrangement of toxins had overall worse performance, mostly because those caterpillars ate less, moved more, ingested more toxin, or failed to physiologically acclimate. Using empirically parameterised individual-based models, we found that differences in movement away from, not towards, less toxic food drove a body size-dependent effect of clusteredness. Hence, the spatial pattern of phytochemicals itself, beyond mean concentration, can have important consequences for herbivores through complex interactions with herbivore foraging.

АНАЛИЗ ПРОСТРАНСТВЕННО-ВРЕМЕННОЙ ИЗМЕНЧИВОСТИ СПЕКТРОВ ВЕТРОВОГО ВОЛНЕНИЯ В ЧЕРНОМ И АЗОВСКОМ МОРЯХ

The study addresses the spatiotemporal variability of the wind wave frequency spectra in the Black Sea and the Sea of Azov. Using the WAVEWATCH III model, a database of the spectra for the period from 1983 to 2020 with a time step of 3 hours on an irregular high-resolution computational grid has been created. To describe the variability of the spectra, a classification system consisting of 23 classes differing in peak frequency and spectral density has been developed. Each spectrum in the database is assigned to one of the classes. It has been found out that the class with the lowest spectral density predominates throughout the entire Black Sea and the Sea of Azov, but the percentage of this class varies depending on a region. The second and third most frequent classes also have a low spectral density (to 1 m2/Hz) and differ depending on the location of an analyzed point. On average, the Sea of Azov is characterized by spectra with a higher maximum frequency of the spectral peak as compared to those of the Black Sea. Interannual variability in the frequency of wave classes has varying spatial variance without any significant trends.

IMU-aided adaptive mesh-grid based video motion deblurring.

Motion blur is a problem that degrades the visual quality of images for human perception and also challenges computer vision tasks. While existing studies mostly focus on deblurring algorithms to remove uniform blur due to their computational efficiency, such approaches fail when faced with non-uniform blur. In this study, we propose a novel algorithm for motion deblurring that utilizes an adaptive mesh-grid approach to manage non-uniform motion blur with a focus on reducing the computational cost. The proposed method divides the image into a mesh-grid and estimates the blur point spread function (PSF) using an inertial sensor. For each video frame, the size of the grid cells is determined adaptively according to the in-frame spatial variance of blur magnitude which is a proposed metric for the blur non-uniformity in the video frame. The adaptive mesh-size takes smaller values for higher variances, increasing the spatial accuracy of the PSF estimation. Two versions of the adaptive mesh-size algorithm are studied, optimized for either best quality or balanced performance and computation cost. Also, a trade-off parameter is defined for changing the mesh-size according to application requirements. The experiments, using real-life motion data combined with simulated motion blur demonstrate that the proposed adaptive mesh-size algorithm can achieve 5% increase in PSNR quality gain together with a 19% decrease in computation time on the average when compared to the constant mesh-size method.

Multivariate and Spatial Study and Monitoring Strategies of Groundwater Quality for Human Consumption in Corsica

Groundwater, widely used for supplying drinking water to populations, is a vital resource that must be managed sustainably, which requires a thorough understanding of its diverse physico-chemical and bacteriological characteristics. This study, based on a 27-year extraction from the Sise-Eaux database (1993–2020), focused on the island of Corsica (72,000 km2), which is diverse in terms of altitude and slopes and features a strong lithological contrast between crystalline Corsica and metamorphic and sedimentary Corsica. Following logarithmic conditioning of the data (662 water catchments, 2830 samples, and 15 parameters) and distinguishing between spatial and spatiotemporal variances, a principal component analysis was conducted to achieve dimensionality reduction and to identify the processes driving water diversity. In addition, the spatial structure of the parameters was studied. The analysis notably distinguishes a seasonal determinism for bacterial contamination (rain, runoff, bacterial transport, and contamination of catchments) and a more strictly spatial determinism (geographic, lithological, and land use factors). The behavior of each parameter allowed for their classification into seven distinct groups based on their average coordinates on the factorial axes, accounting for 95% of the dataset’s total variance. Several strategies can be considered for the inventory and mapping of groundwater, namely, (1) establishing quality parameter distribution maps, (2) dimensionality reduction through principal component analysis followed by two sub-options: (2a) mapping factorial axes or (2b) establishing a typology of parameters based on their behavior and mapping a representative for each group. The advantages and disadvantages of each of these strategies are discussed.