Water-Energy Data & Data Infrastructure

Team Members

Caroline Adkins

MR

Meerashree Sundara Raju

MH

Madeline Hodge

Emily Winn

Overview

Increasing water stress is forcing state and local water resource managers to evaluate non-traditional water supplies. Complicating this process is the fact that local political, economic, social, technical, legal, and environmental (PESTLE) contexts for tapping alternative source waters vary widely by geographic location. The screening of alternative water sources to secure American water supplies (SAWS) tool aims to provide a centralized data repository, analysis, and mapping tool for factors influencing adoption of non-traditional water supplies at the county scale. SAWS integrates these previously disparate PESTLE datasets into a centralized, uniform viability assessment framework to enable comparisons across different water supplies and geographic regions. SAWS supports a regional supply portfolio optimization, comparative supply viability assessments, and goal prioritization for technological development, among other analyses. Finally, the ability to import new datasets, combined with native flexibility in how viability metrics are calculated and weighted, allows users to perform sensitivity analysis and assess the value of additional data collection. SAWS was designed to provide policy makers, water resource managers, consulting engineers, and other stakeholders with a common platform for gathering, interpreting, and visualizing pathways to enhanced water security, resilience, and affordability.

Links

Supported By

• National Science Foundation
• ExxonMobil

Team Members

Fletcher Chapin

Yin-Li Liu

Overview

Digital twins and other digital solutions are transforming the planning, design, operation, and maintenance of water assets. Implementing these solutions is often slowed by data management activities including cleaning, storage, and querying. We identify three limitations of existing data management platforms: data inaccessibility, inadequate integration of data and metadata, and the absence of embedded data analysis capabilities. We introduce Python for Process Engineering Schema (PyPES), an object-oriented, open-source schema for water data management, to address these shortcomings.

Links

Supported By

• Center for Integrated Facility Engineering (CIFE)

Team Members

Fletcher Chapin

Overview

Recent advances in machine learning and artificial intelligence show great promise for process automation. The data-driven nature of many state-of-the-art solutions presents challenges along the data pipeline from collection to validation, such as unavailability, misidentification, fragmentation, and low quality of data. We propose a water treatment ontology, WaTr, coupled with a data management platform, Acquirium, to address these challenges. A formal ontology (e.g., WaTr) can automatically enforce metadata rules and enable querying by logical relationships instead of esoteric tag names to increase data availability and avoid misidentification. Acquirium leverages the power of a formal ontology with a more user-friendly experience, providing built-in data reconciliation and validation tools to reconcile fragmented data and improve data quality. WaTr and Acquirium can help to accelerate the adoption of cutting-edge digital water solutions by alleviating data management headaches.

Links

Supported By

• National Alliance for Water Innovation (NAWI)

Team Members

Daly Wettermark

Fletcher Chapin

Constance Rouffet

Overview

Data on wastewater treatment plant’s (WWTP’s) installed processes enables regional and national emissions inventories and infrastructure planning, yet existing datasets like EPA's Clean Watershed Needs Survey (CWNS) suffer from data sparseness and update frequency. This project develops and validates a methodology combining large language models (LLMs) with formal ontologies to extract WWTP unit process configurations from unstructured regulatory permit documents. Applied to 609 California facilities, our ontology-structured LLM approach achieves an F1 score above 0.9 and reduces unit-process category-level error from 49% (CWNS baseline) to 13%. The methodology enables repeatable, automated extraction as permits are renewed and can be extended to other states and infrastructure types.

Links

Supported By

• National Alliance for Water Innovation (NAWI)
• Woods Institute for the Environment
• Stanford SURGE Summer Program