Risk‐Based Wellhead Protection Decision Support: A Repeatable Workflow Approach

Abstract

Environmental water management often benefits from a risk-based approach where information on the area of interest is characterized, assembled, and incorporated into a decision model considering uncertainty. This includes prior information from literature, field measurements, professional interpretation, and data assimilation resulting in a decision tool with a posterior uncertainty assessment accounting for prior understanding and what is learned through model development and data assimilation. Model construction and data assimilation are time consuming and prone to errors, which motivates a repeatable workflow where revisions resulting from new interpretations or discovery of errors can be addressed and the analyses repeated efficiently and rigorously. In this work, motivated by the real world application of delineating risk-based (probabilistic) sources of water to supply wells in a humid temperate climate, a scripted workflow was generated for groundwater model construction, data assimilation, particle-tracking and post-processing. The workflow leverages existing datasets describing hydrogeology, hydrography, water use, recharge, and lateral boundaries. These specific data are available in the United States but the tools can be applied to similar datasets worldwide. The workflow builds the model, performs ensemble-based history matching, and uses a posterior Monte Carlo approach to provide probabilistic capture zones describing source water to wells in a risk-based framework. The water managers can then select areas of varying levels of protection based on their tolerance for risk of potential wrongness of the underlying models. All the tools in this workflow are open-source and free, which facilitates testing of this repeatable and transparent approach to other environmental problems.

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

“…Actionable. For management purposes, the method must be able to provide an estimate within an acceptable margin of error with input data that either already exist and/or can be obtained, and provide sufficient information about prediction uncertainty so that a water manager can weigh costs, benefits, and risks of their decision options (Doherty and Simmons 2013;Fienen et al 2021). Implicit within actionability are numerous practical considerations, including whether there is sufficient in-house expertise to implement the method or whether analysis must be contracted, and the related issue of whether the cost of obtaining streamflow depletion estimates is affordable.…”

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

“…(2) Actionable. For management purposes, the method must be able to provide an estimate within an acceptable margin of error with input data that either already exist and/or can be obtained, and provide sufficient information about prediction uncertainty so that a water manager can weigh costs, benefits, and risks of their decision options (Doherty and Simmons, 2013;Fienen et al, 2021). Implicit within actionability are numerous practical considerations, including whether there is sufficient in-house expertise to implement the method or whether analysis must be contracted, and the related issue of whether the cost of obtaining streamflow depletion estimates is affordable.…”

“…Necessary steps to ensure reproducibility would likely include archiving raw and processed data files, model input files, calibration datasets, and code necessary to run any analyses or models and version used (Lowndes et al, 2017;Wilkinson et al, 2016). 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).…”

“…As numerical models are based on the physical representation of hydrological processes and simulate both the storage and flux of water throughout the groundwater and interconnected surface water system, they are more flexible than analytical models. Processes such as vadose zone dynamics, phreatophytic evapotranspiration, and surface water management can be directly included within a numerical modeling framework to estimate their separate or combined impact on streamflow (Brookfield and Gnau, 2016;Condon and Maxwell, 2013;Markstrom et al, 2008;Tolley et al, 2019;Zipper et al, 2017), and data associated with each of these processes can be assimilated into the model during the history matching process (Camporese et al, 2010;Naz et al, 2019;Fienen et al, 2021).…”

“…The most widely used numerical modeling platform (MODFLOW) as well as many emerging approaches (i.e., GSFLOW, ParFlow) are open source and are well-suited for streamflow depletion in decision making. There are also many emerging open-source tools for the reproducible creation and analysis of numerical models (Bakker et al, 2016;Fienen et al, 2021;Gardner et al, 2018;Ng et al, 2018;White et al, 2016White et al, , 2018.…”