Trade-off informed adaptive and robust real options water resources planning

Cooperative adaptive management of the Nile River with climate and socio-economic uncertainties

“…Several approaches have been proposed to enable planning under deep uncertainty 26,30 ; these allow designing robust and flexible plans that maximize resilience and minimize investment costs on the basis of, for example, adaptation tipping points 31 , dynamic adaptive planning 32 and dynamic adaptive policy pathways 24,33 . Several recent studies have applied such adaptive methods to planning water resource systems in various contexts 25,[34][35][36][37][38] . For instance, a reservoir adaptive planning framework has been developed to explicitly consider learning about climate uncertainty over time 35 , and other studies have optimized the indicators, actions and/or thresholds in the design process of adaptive plans for water resource systems 33,36,39,40 .…”

Advancing Regional Water Supply Management and Infrastructure Investment Pathways That Are Equitable, Robust, Adaptive, and Cooperatively Stable

“…This framing mirrors approaches widely used in water supply planning literature that seek to balance infrastructure investment cost with tolerable drought risk (Borgomeo et al, 2016;Beh et al, 2015;Erfani et al, 2014;S. M. Fletcher et al, 2017;Pachos et al, 2022). Using the MEI framing, the utilities evaluate objectives in expectation across approximate DU optimization sampling (Figure 2f), reflecting a methodological choice to solely focus on the outcomes of a robust optimization that exploits approximate sampling strategies to discover policies that maintain performance across deeply uncertain futures (e.g., see examples in Eker & Kwakkel, 2018;Hall et al, 2020;Mortazavi-Naeini et al, 2014;Pachos et al, 2022;Watson & Kasprzyk, 2017).…”

Evaluating Implementation Uncertainties and Defining Safe Operating Spaces for Deeply Uncertain Cooperative Multi‐City Water Supply Investment Pathways

“…Regionalization presents benefits such as more cost‐efficient use of shared resources and lower operational costs (Silvestre et al., 2018), as well as achieving common reliability goals and reducing the risk of stranded assets (de Boer & Bressers, 2013). These benefits have been realized through financial instruments such as third‐party and self‐insurance (Brown & Carriquiry, 2007; Zeff & Characklis, 2013), regional water transfers agreements (Chang & Griffin, 1992; Characklis et al., 2006; Lund & Israel, 1995; Palmer & Characklis, 2009; Womble & Hanemann, 2020), and more recently, risk‐based water policy pathways infrastructure investment strategies (Beh et al., 2015a; Borgomeo et al., 2018; Pachos et al., 2022; Trindade et al., 2019; Zeff et al., 2016). Despite these benefits, regionalization can expose utilities to financial risks driven by the intermittent use of short‐term water transfer purchases (Zeff & Characklis, 2013).…”

“…The implementation of regional water policy pathways planning and management has been aided by the use of multiobjective evolutionary algorithms (MOEAs) under uncertainty to discover high‐performing design alternatives that represent optimal tradeoffs between the conflicting objectives of supply reliability and financial stability (Beh et al., 2015b; Borgomeo et al., 2016; Geressu & Harou, 2015; Gold et al., 2022; Huskova et al., 2016; Pachos et al., 2022; Trindade et al., 2019). Recent studies couple MOEAs with visual analytics that can aid in communicating the tradeoffs across alternative regional cooperative strategies in major water resources systems (Giuliani et al., 2022; Gonzalez et al., 2021; Maier et al., 2014; Matrosov et al., 2015; Seyedashraf et al., 2022; Smith et al., 2016; Watson & Kasprzyk, 2017).…”

“…Recent studies couple MOEAs with visual analytics that can aid in communicating the tradeoffs across alternative regional cooperative strategies in major water resources systems (Giuliani et al., 2022; Gonzalez et al., 2021; Maier et al., 2014; Matrosov et al., 2015; Seyedashraf et al., 2022; Smith et al., 2016; Watson & Kasprzyk, 2017). The ability to formulate, solve, and navigate challenging water supply policy pathways problems with MOEAs and visual analytics has enabled the inclusion of adaptive, state‐aware strategies that improve water‐use efficiency and financial risk management by accounting for the dynamics of long‐term infrastructure sequencing and their interactions with short‐term drought mitigation actions (Asefa et al., 2014; Erfani et al., 2018; Gold et al., 2022; Gorelick et al., 2023; Hall et al., 2019; Pachos et al., 2022; Ricalde et al., 2022; Zeff et al., 2016).…”