The availability of timely, accessible and well documented data plays a central role in the process of digital transformation in our societies and businesses. Considering this, the European Commission has established an ambitious agenda that aims to leverage on the favourable technological and political context and build a society that is empowered by data-driven innovation. Within this context, geospatial data remains critically important for many businesses and public services. The process of establishing Spatial Data Infrastructures (SDIs) in response to the legal provisions of the European Union INSPIRE Directive has a long history. While INSPIRE focuses mainly on ’unlocking’ data from the public sector, there is need to address emerging technological trends, and consider the role of other actors such as the private sector and citizen science initiatives. The objective of this paper, given those bounding conditions is twofold. Firstly, we position SDI-related developments in Europe within the broader context of the current political and technological scenery. In doing so, we pay particular attention to relevant technological developments and emerging trends that we see as enablers for the evolution of European SDIs. Secondly, we propose a high level concept of a pan-European (geo)data space with a 10-year horizon in mind. We do this by considering today’s technology while trying to adopt an evolutionary approach with developments that are incremental to contemporary SDIs.

The immense dependence of the growing population on groundwater has resulted in depletion at a fast pace can be seen nowadays. Identifying a groundwater potential zone can be proved as an aid to provide insight to the decision-makers and local authorities for planning purposes. This study evaluated the delineation of groundwater potential zones using integrated remote sensing and GIS approach. Various thematic layers such as geology, geomorphology, lineament, slope, drainage, soil, land use/land cover, and rainfall were considered in this study as these have influence on the occurrence of groundwater and its cycle, and maps have been prepared in GIS domain. Afterward, appropriate weights were assigned to these layers based on multi-criteria decision analysis, i.e., Analytical Hierarchy Process (AHP). Groundwater potentiality has been delineated in different zones (low, moderate, high, and very high) in the study region based on weighted overlay analysis. The study reveals zones with different groundwater prospects viz. low (1.27%), moderate (15.65%), high (75.54%), and very high (7.29%). The ground survey data provided by CGWB (Central Ground Water Board) of nearly 100 wells/dug wells/borewells/piezometers have been used for validation purposes, showing comparable results with the groundwater prospects zones. It also confirms that the majority of these wells fall under very high or high groundwater potential zones. They were also found to be thereby indicating that there is the existence of a permeable reservoir with considerable water storage in the subsurface. One of the most important issues for users and governments is groundwater depletion. Planning for the available groundwater resource is made easier by identifying the potential for groundwater (low to high).

Human activities and climate change increasingly threaten wetlands worldwide, yet their hydrochemical properties and water quality are often inadequately studied. This research focused on the Ghodaghodi Lake Complex (GLC) and associated lakes in Nepal, a Ramsar-listed site known for its biodiversity and ecological significance. The study was conducted to assess seasonal water quality, investigate the factors influencing hydrochemistry, and assess the lakes’ suitability for irrigation. Forty-nine water samples were collected from the GLC in pre-monsoon and post-monsoon periods. Nineteen physicochemical parameters, such as dissolved oxygen (DO), total dissolved solids (TDS), and major ions (calcium ‘Ca²⁺’, magnesium ‘Mg²⁺’, and bicarbonate ‘HCO₃⁻’), were analyzed using standard on-site and laboratory methods. Statistical methods, including analysis of variance (ANOVA), T-tests, and hydrochemical diagrams, e.g., Piper, were adopted to explore spatial and seasonal variations in water quality, revealing significant fluctuations in key hydrochemical indicators. Results showed marked seasonal differences, with pre-monsoon TDS levels averaging 143.1 mg/L compared to 78.9 mg/L post-monsoon, underscoring evaporation and dilution effects. The hydrochemical analysis identified Ca²⁺-HCO₃⁻ as the dominant water type, highlighting the influence of carbonate weathering on GLC’s water composition. Gibbs, mixing, and Piper diagram analysis supported these findings, confirming the predominance of HCO₃⁻, with Ca²⁺ and Mg²⁺ as the main cations. Additionally, sodium adsorption ratio (SAR) values were consistently below 1, confirming excellent irrigation quality. These findings provided critical data for policymakers and stakeholders, supporting sustainable wetland management and aligning with the United Nations’ Sustainable Development Goals relevant to environmental conservation, i.e., clean water and life on land.

Chilika Lake, a RAMSAR site, is an environmentally and ecologically pivotal coastal lagoon in India facing significant emerging environmental challenges due to anthropogenic activities and natural processes. Traditional in situ water quality monitoring methods are often labor intensive and time consuming. This study presents a novel approach for ex situ water quality monitoring in Chilika Lake, located on the east coast of India, utilizing Google Earth Engine (GEE) and spectral indices, such as the Normalized Difference Turbidity Index (NDTI), Normalized Difference Chlorophyll Index (NDCI), and total suspended solids (TSS). The methodology involves the integration of multi-temporal satellite imagery and advanced spectral indices to assess key water quality parameters, such as turbidity, chlorophyll-a concentration, and suspended sediments. The NDTI value in Chilika Lake increased from 2019 to 2021, and the Automatic Water Extraction Index (AWEI) method estimated the TSS concentration. The results demonstrate the effectiveness of this approach in providing accurate and comprehensive water quality assessments, which are crucial for the sustainable management of Chilika Lake. Maps and visualization are presented using GIS software. This study can effectively detect floating algal blooms, identify pollution sources, and determine environmental changes over time. Developing intuitive dashboards and visualization tools can help stakeholders engage with data-driven insights, increase community participation in conservation, and identify pollution sources.

Access to high quality data is essential in order to better understand the environmental and health impact of noise in an increasingly urbanised world. This paper analyses how recent developments of spatial data infrastructures in Europe can significantly improve the utilization of data and streamline reporting on a pan-European scale. The Infrastructure for Spatial Information in the European Community (INSPIRE), and Environmental Noise Directive (END) described in this manuscript provide principles for data management that, once applied, would lead to a better understanding of the state of environmental noise. Furthermore, shared, harmonised and easily discoverable environmental spatial data, required by the INSPIRE, would also support the data collection needed for the assessment and development of strategic noise maps. Action plans designed by the EU Member States to reduce noise and mitigate related effects can be shared to the public through already established nodes of the European spatial data infrastructure. Finally, data flows regarding reporting on the state of environment and END implementation to the European level can benefit by applying a decentralised e-reporting service oriented infrastructure. This would allow reported data to be maintained, frequently updated and enable pooling of information from/to other relevant and interrelated domains such as air quality, transportation, human health, population, marine environment or biodiversity. We describe those processes and provide a use case in which noise data from two neighbouring European countries are mapped to common data specifications, defined by INSPIRE, thus ensuring interoperability and harmonisation.