Integrating Upstream–Downstream Connectivity and Analytical Methods to Prioritize a Potential High-Risk Watershed for Iodine Monitoring

Olumide Ajulo; Kelly Good; John Sivey; Alina Ebling; Shital Vaidya

Understanding how upstream activities influence downstream drinking-water quality is critical for watershed management and public health protection. This study builds on the sub-basin flow-path framework developed by Good et al. (2025) to identify a high-priority drinking water treatment plant (DWTP) and associated source water for iodine monitoring. Using watershed connectivity and flow path tracing, we identified watersheds with potential contributions that may affect disinfection byproduct (DBP) precursor inputs into DWTP intakes. Unlike existing iodine and bromide studies that relied on random site selection or existing concentration data, our study links hydrologic flow paths and watershed characteristics to source water vulnerability. The selected DWTP serves as the focus for ground-truthing this prioritization through field sampling and laboratory analysis. Analytical methods include ion chromatography (IC) for iodide and iodate speciation and inductively coupled plasma triple quadrupole mass spectrometry (ICP-QQQ) for total iodine quantification. Together, these measurements will evaluate whether upstream source contributions align with the predicted high-priority ranking. This in-progress work establishes a defensible, watershed-based framework that connects hydrologic source areas, chemical monitoring, and drinking water management. The approach demonstrates how flow-informed site selection and advanced analytical methods can be integrated to improve understanding of I-DBP precursor dynamics and support proactive source water protection and treatment strategies.

Using Mental Models to Bridge the Gap Between Human and Natural Systems: An Application to Land and Water Management in a Transboundary Arid Basin

Raquel Neri-Barranco; Saurav Kumar

Managing water resources in coupled human–natural systems (CHNS) remains a persistent challenge, especially in transboundary arid regions where social, political, and ecological boundaries overlap. To address this complexity, we introduce AIMM (Artificial Intelligence-driven Mental Modeler)—an open-source software platform that integrates stakeholder knowledge with empirical time-series data to support watershed management and decision-making. AIMM enables users to construct mental models representing system components and hypothesized causal relationships, while an embedded machine learning engine based on Dynamic Double Machine Learning (EconML) estimates the direction and magnitude of these connections using historical data.

An initial application focused on the El Paso–Ciudad Juárez region, where rapid expansion of pecan orchards is transforming land use and intensifying water demand in an already water-scarce basin. Five academic stakeholders constructed mental models identifying key drivers and feedbacks influencing agricultural expansion and water availability. AIMM quantified the structure of each model through graph-theoretic indices (e.g., node density, centrality, and hierarchy) and compared them using a modified distance ratio metric to assess areas of consensus and divergence. A composite model was generated through matrix aggregation, providing a transparent, data-grounded representation of stakeholder understanding.

This integrative framework demonstrates a scalable approach for linking qualitative stakeholder perspectives with quantitative hydrologic and land-use data, offering a novel pathway to co-produce actionable knowledge for watershed management. Future work will extend AIMM to include a broader group of regional actors—government agencies, NGOs, and producers—to identify shared priorities and intervention points for sustainable land and water governance. By combining cognitive and empirical insights, AIMM advances inclusive, evidence-based decision support for managing complex socio-environmental systems.

Modernizing Flood Early Warning Systems in Data-Sparse Urban Basins: Technical Strategies from the Odaw River, Ghana

Kingsley Afiadezor

In rapidly urbanizing West African watersheds, the "information recession" caused by declining physical monitoring infrastructure poses a severe threat to flood resilience. This paper presents the technical strategies being implemented by the Ghana Hydrological Authority (GHA) under the World Bank-funded Greater Accra Resilient and Integrated Development (GARID) project.

The study focuses on the Odaw River Basin, where a critical assessment revealed that out of a national network of 255 gauging sites, only 40% are currently operational, with 90% relying on manual staff gauges. To bridge this hydro-information gap, the author discusses the deployment of a new hybrid monitoring framework. This initiative integrates traditional manual readings with advanced automated technologies, including ultrasonic water level sensors, radar-based sensors, and automatic water level recorders (AWLR).

The paper analyzes the technical challenges of maintaining these high-frequency data streams, including equipment vulnerability and data transmission in a data-scarce environment. Furthermore, it demonstrates how these automated streams are utilized to validate HEC-RAS hydraulic models and update Intensity-Duration-Frequency (IDF) curves. By documenting the transition toward a modernized, sensor-driven Flood Early Warning System (FEWS), this case study provides a replicable roadmap for building climate resilience in urban watersheds facing significant infrastructure deficits.