Scenario modelling of the sustainable development goals under uncertainty

Gao, Lei; Eker, Sibel; Bryan, Brett A.

doi:10.1002/essoar.10506498.2

Cited by 1 publication

(2 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

2022

Water Resources Research

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

“…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

View full text Add to dashboard Cite

show abstract

Scenario modelling of the sustainable development goals under uncertainty

Cited by 1 publication

References 70 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

Contact Info

Product

Resources

About