The quality of drinking water source remains as a major concern in areas of developing and underdeveloped countries worldwide. The treatment and supply of drinking water in Rwanda are carried out by Water and Sanitation Corporation, a state-owned public company. However, it is not able to supply water to all households. Consequently, the non-serviced households depend on natural water sources, like springs, to meet their water requirements. Nevertheless, the water quality in these springs is scarcely known. Therefore, this study assessed and compared metal elements in drinking water sources in the dry and rainy seasons in 2017 using the contamination degree, metal index, and geographic information systems to reveal the spatial distribution of water quality within the considered water sources of springs in Rwanda. The samples were collected monthly from nine water sources of springs and the measured elements are aluminium, calcium, copper, iron, manganese, and zinc. The metal index indicated that during the dry season and rainy season, the sites of Kibungo (1.10 and 1.26) and Kinigi (1.01 and 1.54) have assessed a metal index which is higher than 1. Thus, the water quality of those sites was getting the threshold of warning. The analysis indicated that pollutants are easily transported into water bodies during the rainy season in urban and rural areas to a greater extent than during the dry season .

Water is important for human health, industry, agriculture and ensuring the integrity and sustainability of the ecosystem. The water resources are the top affected by climate variability and population growth. The current population of Rwanda is about 12 million heading to about 25 million in 2050 under the changing climate, where since 1970 temperature rose by 1.4°C and is predicted that in 2050 to be about 2.5°C with severe effects on water resources in Rwanda. Thereby, this study reviewed the status and causes of water quality problems and suggested appropriate options to undertake for sustainable water resources access, employ and management in Rwanda. It was noticed that among others, the key threats to water quality in Rwanda, include not limited to climate change causing rainfall patterns which generated flooding, landslides and periodic droughts, which loaded pollutants into water. In addition, water quality is jeopardized by the rapid population growth, agrochemicals, industrialization, urbanization, soil steepness and land mismanagement. Accordingly, the reviewed water quality indicate that the water quality pollution likelihood is increasing over time. These facts reveal that the water quality soon or late will be highly polluted and calls for further adaptation and management measures.

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda.

National geospatial infrastructures increasingly connect Earth Observation (EO), geospatial artificial intelligence (GeoAI), interoperable services, and public-sector decision processes. However, an explicit method for assessing whether these technical functions operationalize the nine pathways of the United Nations Integrated Geospatial Information Framework (UN-IGIF) remains underdeveloped. This Communication develops a pathway-to-function-to-evidence assessment framework and applies it illustratively to the Rwanda Space Agency (RSA) GeoHub case. Using the UN-IGIF pathway definitions as normative criteria and the available GeoHub description as case material, we classify reported functions, map their institutional and technical dependencies, and identify the records required to substantiate operational performance and decision uptake. Worked mappings cover EO data stewardship, GeoAI processing, interoperable delivery, and sectoral decision workflows. The contribution is a transparent analytical procedure that distinguishes reported capabilities, proposed applications, and independently verified outcomes; it does not constitute an audit or quantitative impact evaluation. The framework offers a transferable basis for planning and evaluating EO-enabled geospatial infrastructures in emerging space ecosystems.