Balancing Open Science and Data Privacy in the Water Sciences

Abstract

Open science practices such as publishing data and code are transforming water science by enabling synthesis and enhancing reproducibility. However, as research increasingly bridges the physical and social science domains (e.g., socio-hydrology), there is the potential for well-meaning researchers to unintentionally violate the privacy and security of individuals or communities by sharing sensitive information. Here, we identify the contexts in which privacy violations are most likely to occur, such as working with high-resolution spatial data (e.g., from remote sensing), consumer data (e.g., from smart meters), and/or digital trace data (e.g., from social media). We also suggest practices for identifying and addressing privacy concerns at the individual, institutional, and disciplinary levels. We strongly advocate that the water science community continue moving toward open science and socio-environmental research and that progress toward these goals be rooted in open and ethical data management.

The importance of open science for biological assessment of aquatic environments

“…A common argument against open science is the protection of data that an individual research team may view as proprietary or sensitive. There are reasonable arguments to treat data as personal property, particularly if exceptional effort was spent to secure funding for a project and if the data were hard-earned or sensitive, e.g., detailed location data on endangered species or medical/socioeconomic data ( Zipper et al, 2019 ). These issues are less of a concern for bioassessment where many datasets are collected by institutions that are publicly funded and data accessibility may be mandated by law.…”

Quantifying Streamflow Depletion from Groundwater Pumping: A Practical Review of Past and Emerging Approaches for Water Management

“…While there have been substantial recent improvements in open-source tools to enable reproducible hydrological modeling workflows (Bakker et al, 2016;Fienen et al, 2021;White, Hemmings, et al, 2021), in practice true reproducibility remains rare in hydrological science (Stagge et al, 2019), indicating that significant improvements are needed with regards to reproducibility. However, in some settings, in particular at smaller spatial scales where there are fewer pumping wells, care should be taken to ensure that individual privacy is not compromised during data sharing by anonymizing or aggregating data to coarser scales Zipper, Stack Whitney, et al, 2019).…”

Satellite‐Based Monitoring of Irrigation Water Use: Assessing Measurement Errors and Their Implications for Agricultural Water Management Policy

“…Conversely, consistent underestimation of water use could allow farmers to overexploit water resources, limiting effectiveness of policies designed to manage social and environmental impacts of irrigation water use whether from groundwater or surface water. However, at present, measurement error properties (e.g., distribution, magnitudes, and biases) remain challenging to characterize given the lack of objective validation of satellite‐based water use estimates in the literature (section 2.3) and limited accessibility of public disaggregated water use data sets for ground truthing (Foster et al, 2019; Zipper et al, 2019).…”

“…Crucially, improving standards of model validation will require researchers and policymakers to work with farmers to enable greater collection and sharing of in situ water use data due to the limited availability of disaggregated water use records at present in the public domain. This will require significant efforts to build trust and apply appropriate data privacy safeguards around use of such data for development and testing of remote monitoring approaches (Zipper et al, 2019), in particular given historic concerns and resistance to the deployment of in situ monitoring in many irrigated agricultural regions.…”