Selecting Indicators and Optimizing Decision Rules for Long‐Term Water Resources Planning

Murgatroyd, Anna

doi:10.1029/2020wr028117

Cited by 8 publications

(5 citation statements)

References 93 publications

(196 reference statements)

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“…distributions of SF MD and RCSL. This enables robust decision making under various types of uncertainties (He et al, 2021;Moallemi et al, 2021;Murgatroyd & Hall, 2021).…”

Section: Discussionmentioning

confidence: 99%

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A Physics‐Informed Bayesian Storyline Approach to Assess Sediment Transport in the Mekong

2022

Water Resources Research

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The Mekong Delta, home to 20 million people, is experiencing significant land loss due to rising sea levels, accelerating land subsidence, and declining sediment supply. Robust estimates of the sediment flux delivered to Mekong Delta (SFMD) and the relative contribution of sediment load (RCSL) from individual subbasins are key to designing future adaptation strategies, such as strategic dam planning, sand mining, and delta groundwater management. However, existing estimates of SFMD and RCSL are largely deterministic without uncertainty quantification or using a uniform sampling to represent uncertainty. They also remain questionable due to data inconsistency and methodological biases caused by overlooked physical processes. Here, we develop a hybrid physics‐based data‐driven modeling framework to constrain the probability distribution of SFMD and RCSL and explore how they change under plausible climate change and land‐use change scenarios, leveraging recent advances in Bayesian inference (i.e., reasoning by refutation). We find that pure yield‐based approaches, which typically ignore sediment retention, can lead to higher estimates of RCSL from upstream regions compared with the physics‐based approach. Our best estimate of historical (1962–2005) SFMD combining multiple lines of evidence shows a median of 106 Mt/yr with a 5%–95% range of 66–160 Mt/yr. Over the analyzed range of land‐use and climate change scenarios, future changes in SFMD seem to be more sensitive to the latter, especially changes in wet‐season precipitation. Our estimate of RCSL from the Upper Mekong River Basin is likely (>66% probability) in the range of 0.25–0.39 with a mean of 0.34 in all plausible scenarios.

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“…distributions of SF MD and RCSL. This enables robust decision making under various types of uncertainties (He et al, 2021;Moallemi et al, 2021;Murgatroyd & Hall, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Compared with existing point estimate using deterministic approaches, our probabilistic framework can provide the full distributions of SF MD and RCSL. This enables robust decision making under various types of uncertainties (He et al., 2021; Moallemi et al., 2021; Murgatroyd & Hall, 2021).…”

Section: Discussionmentioning

confidence: 99%

A Physics‐Informed Bayesian Storyline Approach to Assess Sediment Transport in the Mekong

2022

Water Resources Research

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“…In this study, the selection of evaluation indexes is carried out through two aspects. A large portion of the literature on water resources carrying capacity evaluation was consulted, and the indicators that were used more frequently were selected [30][31][32]. For the regional characteristics and water resources conditions and status quo of Zhangye City, and following the principles of scientificity, representativeness, reliability, and accessibility [33], the corresponding 12 indicators were selected.…”

Section: Construction Of Water Resources Carrying Capacity Index Systemmentioning

confidence: 99%

Evaluation of Water Resources Carrying Capacity of Zhangye City Based on Combined Weights and TOPSIS Modeling

Yang,

Qu,

Shen

et al. 2023

Water

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According to the natural condition of water resources and the economic, social, and ecological environment status of Zhangye City, the water resources carrying capacity of Zhangye City is evaluated by using the water resources carrying capacity Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model with combination assignment. The results show that: (1) From 2010 to 2020, the water resources carrying capacity of Zhangye City was generally stable at the macro level, ranking at grades III and IV. However, from the micro level, the water resources carrying capacity fluctuates to a certain extent and shows an increasing trend year by year. (2) The steady improvement of economic and social conditions is the main driving force for the improvement of the comprehensive water resources carrying capacity of Zhangye City, and the changes in the ecological environment are also important factors affecting the carrying capacity of water resources. The results provided a decision basis for future comprehensive development and utilization of water resources in Zhangye City and a reference for water resource carrying capacity and water resource security assessment in other arid and semi-arid areas in our country.

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“…The outcomes of these actions can inform infrastructure investment and sequence mitigative actions in planning for future extreme climate events (Haasnoot et al., 2019; Kwakkel et al., 2015). More recently, the DAPP framework has been extended to water supply policy pathways management and investment pathways where dynamic risk‐of‐failure (ROF) focused policies are identified that use state‐aware action triggers for short‐term drought mitigation (Gold et al., 2019; Palmer & Characklis, 2009; Zeff et al., 2016) and long‐term infrastructure investments (Gorelick et al., 2022; Hyun et al., 2021; Murgatroyd & Hall, 2021; Trindade et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Lau

Reed

Gold

2023

Water Resources Research

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Urban water utilities worldwide face supply reliability and financial stability challenges stemming from climate change and growing water demands (Farmani & Sweetapple, 2022;Pörtner et al., 2022), which are aggravated by the additional challenge of aging infrastructure. In the United States (U.S.), an estimated $434 billion of investment in maintaining and development water supply infrastructure is required by 2029 (ASCE, 2021). The 2022 Infrastructure Investment and Jobs Act allocates over $55 billion of federal funding for drinking water infrastructure (DeFazio, 2021), but the remaining investment burden will be borne by local governments (Smull et al., 2022). The provision of drinking water in the U.S. is thus dominantly a local issue-balancing the reliable

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Selecting Indicators and Optimizing Decision Rules for Long‐Term Water Resources Planning

Cited by 8 publications

References 93 publications

A Physics‐Informed Bayesian Storyline Approach to Assess Sediment Transport in the Mekong

A Physics‐Informed Bayesian Storyline Approach to Assess Sediment Transport in the Mekong

Evaluation of Water Resources Carrying Capacity of Zhangye City Based on Combined Weights and TOPSIS Modeling

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

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