Learning and flexibility for water supply infrastructure planning under groundwater resource uncertainty

Abstract

Water supply infrastructure planning in groundwater-dependent regions is often challenged by uncertainty in future groundwater resource availability. Many major aquifer systems face long-term water table decline due to unsustainable withdrawals. However, many regions, especially those in the developing world, have a scarcity of groundwater data. This creates large uncertainties in groundwater resource predictions and decisions about whether to develop alternative supply sources. Developing infrastructure too soon can lead to unnecessary and expensive irreversible investments, but waiting too long can threaten water supply reliability. This study develops an adaptive infrastructure planning framework that applies Bayesian learning on groundwater observations to assess opportunities to learn about groundwater availability in the future and adapt infrastructure plans. This approach allows planners in data scarce regions to assess under what conditions a flexible infrastructure planning approach, in which initial plans are made but infrastructure development is deferred, can mitigate the risk of overbuilding infrastructure while maintaining water supply reliability in the face of uncertainty. This framework connects engineering options analysis from infrastructure planning to groundwater resources modeling. We demonstrate a proof-of-concept on a desalination planning case for the city of Riyadh, Saudi Arabia, where poor characterization of a fossil aquifer creates uncertainty in how long current groundwater resources can reliably supply demand. We find that a flexible planning approach reduces the risk of over-building infrastructure compared to a traditional static planning approach by 40% with minimal reliability risk (<1%). This striking result may be explained by the slow-evolving nature of groundwater decline, which provides time for planners to react, in contrast to more sudden risks such as flooding where tradeoffs between cost and reliability risk are heightened. This Bayesian approach shows promise for many civil infrastructure domains by providing a method to quantify learning in environmental modeling and assess the effectiveness of adaptive planning.

Managing Groundwater to Ensure Ecosystem Function

“…Adherence to all of the minimum provisions described in this paper is particularly crucial to reduce the risk of deleterious impacts to these systems. Moreover, precaution would dictate that if new groundwater uses are allowed, they be smaller from the outset and then monitored to affirm no unacceptable impact (Fletcher et al 2019) before allowing use to ratchet up, rather than approving larger initial uses based on the supposition they could be curtailed later if need be. Such an approach would also provide an opportunity to test and verify the adequacy of the monitoring regime and adaptive management mechanisms necessary to achieve sustainability goals, while also reducing the risk of over-building water infrastructure and incurring extra implementation costs (Fletcher et al 2019).…”

“…Clarity about who is responsible for monitoring, reporting, and implementation costs is key, and should include what funding sources or mechanisms will be used. Verification that funds are available, have been set aside, or are accounted for in bonding mechanisms, can increase incentives and certainty in the implementation of sustainable groundwater management (Cody et al 2015;Newman et al 2018;Fletcher et al 2019). Some mechanisms for generating funds include administrative, water use, or replenishment fees for water districts (Newman et al 2018) as well as seeking assistance from federal programs like the U.S. Department of Agriculture's Conservation Reserve Enhancement Program (Cody et al 2015).…”

Model emulators and complexity management at the environmental science-action interface

“…People have also recognized the potential of model emulators to aid in the context of environmental decision-making to speed scenario testing (Carnevale et al, 2012). In integrated assessment models, which include interdependent submodels of environmental and social systems, emulators can help reduce computational barriers in model coupling and characterize systems' uncertainty (Fletcher et al, 2019;Haasnoot et al, 2014;Little et al, 2019).…”

Application of multi-model ensemble averaging techniques for groundwater simulation: synthetic and real-world case studies

“…The successful application and widespread use of MODFLOW have made this model a benchmark approach to simulate groundwater systems (e.g., Guiguer & Franz 1996). So far, MODFLOW has been applied in various case studies such as stream depletion simulation of the aquifer (Ou et al 2016;Minderhoud et al 2017;Fletcher et al 2019), calibration of hydrodynamic components (Xu et al 2017), evaluation of the interaction effects of aquifer recharge and evapotranspiration (Maquin et al 2017), simulation of drawdown in the inclined wells (Batu 2015), assessment of aquifer vulnerability (Pacheco et al 2018), flood propagation and infiltration (Li et al 2019), among others. However, the MODFLOW model can perform best in regular geometry with proper grid flexibility, whereas, for real-world applications with irregular geometry, this approach may poorly perform due to its governing flow approximation which leads to structural uncertainty in simulation (e.g., Strack 2017).…”