Growth of the Decision Tree: Advances in Bottom‐Up Climate Change Risk Management

Ray, Patrick A.; Taner, M. U.; Schlef, K.; Wi, Sungwook; Khan, Hassaan F.; Freeman, Sarah; Brown, Casey

doi:10.1111/1752-1688.12701

Cited by 19 publications

(11 citation statements)

References 143 publications

(240 reference statements)

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“…The decision tree is a classic decision-making tool to help make sequential decisions under uncertainty. It is a well-used tool in water resources management decision-making (e.g., see Lund (1991); and Ray et al (2019)). Decision trees are also a very common data mining approach used for classification and prediction (Nefeslioglu et al, 2010).…”

Section: Decision Tree Analysismentioning

confidence: 99%

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Shen

Tolson

Mai

2022

Water Resources Research

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Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large‐sample SST assessment study empirically assesses how different data splitting methods influence post‐validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of continuous calibration sub‐periods (CSPs). A full‐period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35‐year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split‐sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split‐sample approach in hydrological modeling.

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Section: Decision Tree Analysismentioning

confidence: 99%

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Shen

Tolson

Mai

2022

Water Resources Research

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“…Decision making under uncertainty has been in focus in the water sector since climate change has been identified as a major challenge. Robust Decision Making (RDM) seeks to find solutions that perform acceptably under a wide range of potential futures (Lempert et al, 2003;Lempert and Groves, 2010;Kasprzyk et al, 2013;Ray and Brown, 2015;Ray et al, 2019), while adaptive planning (Fletcher et al, 2019;Herman et al, 2020) explores how the flexibility of measures can increase their robustness and which indicators might be useful to trigger adaptation. These methods are relevant to any decision making process linking complex human-natural systems (Moallemi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Nexus vs. Silo Investment Planning Under Uncertainty

Payet-Burin¹,

Kromann²,

Pereira-Cardenal³

et al. 2021

Front. Water

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Water, energy, and agricultural infrastructure investments have important inter-relations fulfilling potentially competing objectives. When shaping investment plans, decision makers need to evaluate those interactions and the associated uncertainties. We compare planning infrastructure under uncertainty with an integrated water-energy-food nexus framework and with sector-centered (silo) frameworks. We use WHAT-IF, an open-source hydroeconomic decision support tool with a holistic representation of the power and agriculture sectors. The tool is applied to an illustrative synthetic case and to a complex planning problem in the Zambezi River Basin involving reservoirs, hydropower, irrigation, transmission lines and power plant investments. In the synthetic case, the nexus framework selects investments that generate more synergies across sectors. In sector-centered frameworks, the value of investments that impact multiple sectors (like hydropower, bioenergy, and desalinization) are under- or overestimated. Furthermore, the nexus framework identifies risks related to uncertainties that are not linked to the investments respective sectors. In the Zambezi river case, we find that most investments are mainly sensitive to parameters related to their respective sectors, and that financial parameters like discount rate, capital costs or carbon taxes are driving the feasibility of investments. However, trade-offs between water for irrigation and water for hydropower are important; ignoring trade-offs in silo frameworks increases the irrigation expansion that is perceived as beneficial by 22% compared to a nexus framework that considers irrigation and hydropower jointly. Planning in a nexus framework is expected to be particularly important when projects and uncertainties can considerably affect the current equilibrium.

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“…Some authors refer to it when using local knowledge through participative approaches to foresight future scenarios and define locally relevant adaptation strategies (e.g., Bhave et al 2014;Girard et al 2015a), view adopted herein. Other authors consider BU as a scenario-free, robustness-based planning process; for example, in the "decision-scaling" approach (Brown et al 2012;Poff et al 2016;Ray et al 2019). As for the later view, unlike the top-down method, the BU approach relies more on possibilities than on probabilities (Blöschl et al 2013).…”

Section: Top-down Versus Bottom-up Adaptation Strategiesmentioning

confidence: 99%

A Top-Down Meets Bottom-Up Approach for Climate Change Adaptation in Water Resource Systems

Pulido-Velázquez

Marcos-García

Girard³

et al. 2022

Springer Climate

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The adaptation to the multiple facets of climate/global change challenges the conventional means of water system planning. Numerous demand and supply management options are often available, from which a portfolio of adaptation measures needs to be selected in a context of high uncertainty about future conditions. A framework is developed to integrate inputs from the two main approaches commonly used to plan for adaptation. The proposed “top–down meets bottom–up” approach provides a systematic and practical method for supporting the selection of adaptation measures at river basin level by comprehensively integrating the goals of economic efficiency, social acceptability, environmental sustainability, and adaptation robustness. The top-down approach relies on the use of a chain of models to assess the impact of global change on water resources and its adaptive management over a range of climate projections. Future demand scenarios and locally prioritized adaptation measures are identified following a bottom-up approach through a participatory process with the relevant stakeholders and experts. Cost-effective combinations of adaptation measures are then selected using a hydro-economic model at basin scale. The resulting adaptation portfolios are climate checked to define a robust program of measures based on trade-offs between adaptation costs and reliability. Valuable insights are obtained on the use of uncertain climate information for selecting robust, reliable, and resilient water management portfolios. Finally, cost allocation and equity implications are analyzed through the comparison of economically rational results (cooperative game theory) and the application of social justice principles.

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Growth of the Decision Tree: Advances in Bottom‐Up Climate Change Risk Management

Cited by 19 publications

References 143 publications

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Time to Update the Split‐Sample Approach in Hydrological Model Calibration

Nexus vs. Silo Investment Planning Under Uncertainty

A Top-Down Meets Bottom-Up Approach for Climate Change Adaptation in Water Resource Systems

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