In this study, we analyzed the effectiveness of a data visualization tool (dashboard) designed to provide insights to decision-makers about the vulnerability of water resources in the tri-national Upper Lempa River Basin (CARL) in the face of future climate and land use uncertainties. The effectiveness of the dashboard was assessed using three methods: (1) a user survey for evaluating dashboard clarity, completeness, and ease of use using seven parameters proposed by Stephen Few. The result of the survey overwhelmingly indicated a positive experience when interacting with the dashboard; (2) pre- and post-use tests were used to assess knowledge acquisition. The users’ correct answers increased by an average of 35%, and incorrect answers decreased by an average of 25% for questions assessing an understanding of water demands, the effects of climate change on the basin’s water security, land use trends, and the sub-basins with the highest vulnerability in the region; and, significantly, (3) user reports on insights drawn from their interaction with the dashboard. Users drew insights on the extent to which different regions will likely experience increased vulnerability regarding water resources and on strategies that could reduce this vulnerability. The key issues that need to be addressed to ensure that the dashboard fosters greater transparency and public participation in governance and is used by decision-makers to improve the management of water resources in this watershed are the following: (1) the incorporation of user feedback and the inclusion of adaptation strategies and their impacts on the dashboard; (2) the commitment and training of a local institution to host and maintain the dashboard and to make it available to the public; (3) the engagement of decision-makers from the three countries regarding the use of the dashboard to understand future uncertainties and the potential impact of adaptation strategies on performance metrics.

Climate change projections are studied using General Circulation Models (GCMs). GCMs are models that simulate climate on a broad scale, hence they cannot be directly used in local impact studies, such as, for example, hydrological studies. GCMs must go through a process of downscaling, to adjust their results in terms of spatial scale and reduce their bias before being used at the local scale. Quantile Mapping is one of the most widely used approaches for bias correcting GCM climate outputs. However, in its conventional formulation QM assumes a time-invariant correction function, which potentially results in additional biases. This has motivated the development of trend-preserving variations, accounting for a non-stationary correction function and aiming to preserve the raw GCM signal. Unfortunately, choosing which variation to use is not straight-forward. We present the climQMBC package (https://github.com/saedoquililongo/climQMBC or https://doi.org/10.5281/zenodo.18392900) as an easy-to-use tool to compare quantile mapping approaches. climQMBC is available in Python, R and MATLAB, and contains the classic QM method and four trend-preserving variations: Detrended Quantile Mapping (DQM), Quantile Delta Mapping (QDM), Unbiased Quantile Mapping (UQM) and Scaled Distribution Mapping (SDM). This package has a built-in summary report that allows comparing methods in terms of their capability of preserving raw GCM trends. A synthetic exercise showed that the most reliable methods are the UQM and DQM.

Water-related risks are increasing for resource-based livelihoods in the Southern Hemisphere and tropical regions. Water security as a concept has not been extensively reviewed and assessed for rural and urban livelihoods in this context. Although there are studies related to water security, the evidence and approaches to assess water security in transboundary basins are scarce, and several of these focus on defining levels of water security in terms of water scarcity, but does water availability alone guarantee water security? Historically, transboundary water resources management has been based on discourses of water security as a national security issue rather than a collaborative approach. This nationalistic use of water as a threat or power strategy weakens relations between nations and hinders cooperation. To meet these challenges, studies suggest the consolidation of transboundary institutions responsible for monitoring water conditions and serving as conflict mediators between riparian countries.An example of this transboundary framework is the tri-national cooperation process that has been developed for environmental management and sustainable development in the Upper Lempa River basin (ULRB), located in a key part of the Trifinio Region is made up of the countries of El Salvador, Guatemala, and Honduras. The legal framework of the Treaty between these three countries, called “Agua Sin Fronteras – 2006-2024”, recognized the relevance of community participation in landscape management. However, the characterization of the biophysical variables and processes in each country in the ULRB is different in terms of data availability (i.e., space and time) and its homogeneity (i.e., kind of variable), which difficult to assess water security as a transboundary tool. To solve these weaknesses and gaps, we proposed a Water Security Index (WSI) with an approach to measure multiple indicators of hydrological risk relative to context specific water needs, including water availability, quality and sustainability. We followed the logic of the Pressure-State-Response (PSR) model to select indices that can be spatialized in different time steps. The WSI estimates the level of water security on a scale of 0 to 1, where 0 corresponds to the least favorable condition in terms of adequate quantities of water of acceptable quality for sustaining livelihoods, human well-being, and development socioeconomic; and 1 is the most favorable condition. The WSI was evaluated based on the results of a hydrological model (WEAP) under different climatic conditions (i.e., wet, dry, and normal). This evaluation allowed us to identify critical sub-basins (i.e., hot spots) for each condition and prioritized sub-basins with a high degree of vulnerability in all three conditions combined to support ecosystem services and human well-being. Through the analysis of the WSI index, 25 priority hot spots were identified as priority for intervention considering the resulting WSI in the combined conditions of which 11 are recurrent in the three conditions.

Climate change poses increasing threats to water security in Colombia, particularly in regions where groundwater monitoring remains limited. This study evaluated groundwater potential as an adaptation measure across six strategic regions covering 16.5% of Colombian territory: Tolima-Huila, Boyacá-Santander, Caribe Oriental, Caribe Occidental, Cauca-Nariño, and Meta-Vichada. We integrated a multi-source methodological approach combining conceptual hydrogeological characterization, potential recharge estimation using the Thornthwaite–Mather method, and groundwater storage anomaly analysis through GRACE satellite data coupled with the GLDAS model for the period 1992–2022. Potential recharge was evaluated for the historical period (1992–2022) and projected under SSP2-4.5 and SSP5-8.5 climate change scenarios (2040–2070) using global circulation models. Results revealed significant regional heterogeneity, with critical recharge reductions reaching 230 mm/month in Cauca-Nariño and 202 mm/month in Meta-Vichada under SSP5-8.5 compared to the historical baseline. Positive groundwater storage anomalies identified sectors with exploitation potential even in low-recharge areas, particularly in northern Tolima-Huila. Priority zones were defined for implementing adaptation measures, including artificial recharge, strategic use of fractured media, and strengthened water governance. The proposed methodology provides replicable technical foundations for climate-resilient planning at regional and local scales, which is particularly relevant for contexts with scarce hydrogeological information.

Effective water resource management in hydrologically diverse regions requires the integration of advanced decision-support systems with collaborative approaches. This study presents the development and application of the ERA TOOL, a hydroinformatics platform designed to evaluate water resources across multiple jurisdictions in Colombia. Initially developed through collaboration between the Stockholm Environment Institute (SEI) and regional environmental authorities (CARs), the tool has been implemented for four CARs and is currently being adapted for a fifth, highlighting its scalability and adaptability to diverse hydrological and institutional contexts.The ERA TOOL builds on the framework of the Regional Water Assessment (ERA, by its acronym in Spanish), a process mandated by Colombian policy to assess water availability, quality, and vulnerability while addressing anthropogenic pressures. By integrating localized hydrological modeling using the Water Evaluation and Planning (WEAP) system and geospatial analyses, the tool provides a comprehensive Decision Support System (DSS) for evaluating current conditions and projecting future scenarios under climate variability and increasing demands.Key features of the ERA TOOL include dynamic visualizations of regional indicators such as flow duration curves, groundwater recharge, and surface water availability and quality indicators, all presented through an intuitive, interactive interface. The platform also facilitates the co-design of solutions by incorporating feedback from stakeholders during its development and deployment. This collaborative process ensures that the tool meets the specific needs of each CAR, enhancing institutional capacity and fostering a shared understanding of water resource dynamics.The implementation across multiple CARs has demonstrated the versatility of the ERA TOOL in addressing diverse regional climates and challenges, from hydrological extremes to water allocation and quality management. By linking data-driven insights with participatory processes, the tool has empowered decision-makers to implement evidence-based strategies that promote equitable and sustainable water governance. All platforms developed are freely accessible, with the latest version available for consultation at the following link: https://latinoamericasei.shinyapps.io/ERA_CARDER/.This ongoing initiative underscores the potential for replicating and adapting hydroinformatics solutions across regions with varying hydrological and institutional conditions. It provides a model for leveraging innovative tools and participatory research to bridge gaps between technical expertise and practical application in water resource management.

Climate change projections are studied using General Circulation Models (GCMs). GCMs are models that simulate climate on a broad scale, hence they cannot be directly used in local impact studies, such as, for example, hydrological studies. GCMs must go through a process of downscaling, to adjust their results in terms of spatial scale and reduce their bias before being used at the local scale. Quantile Mapping is one of the most widely used approaches for bias correcting GCM climate outputs. However, in its conventional formulation QM assumes a time-invariant correction function, which potentially results in additional biases. This has motivated the development of trend-preserving variations, accounting for a non-stationary correction function and aiming to preserve the raw GCM signal. Unfortunately, choosing which variation to use is not straight-forward. We present the climQMBC package (https://github.com/saedoquililongo/climQMBC or https://doi.org/10.5281/zenodo.18392900) as an easy-to-use tool to compare quantile mapping approaches. climQMBC is available in Python, R and MATLAB, and contains the classic QM method and four trend-preserving variations: Detrended Quantile Mapping (DQM), Quantile Delta Mapping (QDM), Unbiased Quantile Mapping (UQM) and Scaled Distribution Mapping (SDM). This package has a built-in summary report that allows comparing methods in terms of their capability of preserving raw GCM trends. A synthetic exercise showed that the most reliable methods are the UQM and DQM.