Model-wise uncertainty decomposition in multi-model ensemble hydrological projections

Ohn, Ilsang; Kim, Seonghyun; Seo, Seung Beom; Kim, Young-Oh; Kim, Yongdai

doi:10.1007/s00477-021-02039-4

Cited by 4 publications

(6 citation statements)

References 35 publications

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“…In the analysis, the rainfall duration is a key factor to compute the rainfall intensity and is closely related to evaluate the acoustic impacts on rainfall stimulation. Therefore, to assess the impact efficiency of acoustic interference on rainfall with different durations, the single-factor analysis of variance model (ANOVA) (Ohn et al, 2021;Zhao et al, 2012) was employed, because the acoustic rainfall enhancement is a continuous process.…”

Section: Analysis Methodsmentioning

confidence: 99%

Statistic evaluation of low-frequency acoustic wave impact on rainfall stimulation

Wang

Chen

et al. 2022

Stoch Environ Res Risk Assess

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For securing sustainable water resources, traditional solutions including seawater desalination are limited due to high cost and restricted operating conditions.Plateau. Statistical analysis was applied to evaluate the effect of acoustic wave on rainfall stimulation. The results of average rainfall intensity distribution using ordinary least square analysis disclose that acoustic interference has a considerable effect on rainfall enhancement. Such a rainfall enhancement effect increases significantly along with the increase of rainfall duration which is the index of cloud precipitation potential.For rainfall events with the duration more than two hours, the average rainfall intensity was improved by 72% with the acoustic wave effect over the experiment site. The phenomena are consistent with the fact that clouds with larger precipitation potential contain more cloud droplets which are beneficial to the acoustic coagulation process.

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Section: Analysis Methodsmentioning

confidence: 99%

Statistic evaluation of low-frequency acoustic wave impact on rainfall stimulation

Wang

Chen

et al. 2022

Stoch Environ Res Risk Assess

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“…Relative O2 impacts introduced by the three climate scenario factors (ESM, downscaling method, and watershed model) were determined by applying an analysis of variance (ANOVA) approach to average ΔAHV estimates for each climate scenario. This method has been previously applied to the quantification of uncertainty sources in climate and hydrological applications (Hawkins and Sutton, 2009;Yip et al, 2011;Bosshard et al, 2013;Ohn et al, 2021). To use this method in this study, an average annual metric is first calculated for an outcome of interest (i.e., change in discharge, nitrogen loading, or hypoxic volume) within the Multi-Factor experiment.…”

Section: Climate Scenario Analysesmentioning

confidence: 99%

“…Then, the relative uncertainty is determined by calculating the sum of squares due to individual effects for each experimental factor (ESM, downscaling method, or watershed model). Following Ohn et al (2021), the cumulative uncertainty is quantified for successive uncertainties introduced by each factor as well as their interactions, removing the unexplained interaction term described in Bosshard et al (2013). The two additional ESM scenarios described previously (Table 1, Table S3) were used due to the inexact matches between MACA and BCSD ESMs selected by KKZ.…”

Section: Climate Scenario Analysesmentioning

confidence: 99%

“…The two additional ESM scenarios described previously (Table 1, Table S3) were used due to the inexact matches between MACA and BCSD ESMs selected by KKZ. Despite five ESMs being used in combination with only two downscaling methods and two watershed models in this analysis, the approach outlined (Bosshard et al, 2013;Ohn et al, 2021) accounts for this factor imbalance (five vs. two) by repeatedly subsampling combinations of two ESM scenarios from the five available.…”

Section: Climate Scenario Analysesmentioning

confidence: 99%

“…When directly applying such projections to study regional coastal oxygen responses, dynamically or statistically downscaled estimates of atmospheric and marine variables are typically used to continuously simulate climate impacts or to calculate and apply a change factor (Carter et al, 1994;Anandhi et al, 2011) to a shorter historical time period. Quantifying the relative uncertainties from various sources including ESM, downscaling methodology, internal variability, and hydrological model is not new to the field of climate research (Hawkins and Sutton, 2009;Yip et al, 2011;Northrop and Chandler, 2014) or watershed applications (Bosshard et al, 2013;Vetter et al, 2017;Wang et al, 2020;Ohn et al, 2021). Questions of uncertainty due to climate effects in past marine ecosystem impact studies have often been addressed by selecting some combination of ESMs and/or emissions scenarios (Meier et al, 2011a;Ni et al, 2019;Saraiva et al, 2019b;Meier et al, 2019;Meier et al, 2021;Pozo Buil et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Impacts and uncertainties of climate-induced changes in watershed inputs on estuarine hypoxia

Hinson

Friedrichs²,

Najjar³

et al. 2022

Preprint

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Abstract. Multiple climate-driven stressors, including warming and increased nutrient delivery, are exacerbating hypoxia in coastal marine environments. Within coastal watersheds, environmental managers are particularly interested in climate impacts on terrestrial processes, which may undermine the efficacy of management actions designed to reduce eutrophication and consequent low-oxygen conditions in receiving coastal waters. However, substantial uncertainty accompanies the application of Earth System Model (ESM) projections to a regional modeling framework when quantifying future changes to estuarine hypoxia due to climate change. In this study, two downscaling methods are applied to multiple ESMs and used to force two independent watershed models for Chesapeake Bay, a large coastal-plain estuary of the eastern United States. The projected watershed changes are then used to force a coupled 3-D hydrodynamic-biogeochemical estuarine model to project climate impacts on hypoxia, with particular emphasis on projection uncertainties. Results indicate that all three factors (ESM, downscaling method, and watershed model) are found to contribute significantly to the uncertainty associated with future hypoxia, with the choice of ESM being the largest contributor. Overall, in the absence of management actions, there is a high likelihood that climate change impacts on the watershed will expand low-oxygen conditions by 2050, relative to a 1990s baseline period; however, the projected increase in hypoxia is quite small (4 %) because only climate-induced changes in watershed inputs are considered and not those on the estuary itself. Results also demonstrate that the attainment of established nutrient reduction targets will reduce annual hypoxia by about 50 % compared to the 1990s. Given these estimates, it is virtually certain that fully implemented management actions reducing excess nutrient loadings will outweigh hypoxia increases driven by climate-induced changes in terrestrial runoff.

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Statistic evaluation of low-frequency acoustic wave impact on rainfall stimulation

WANG

Chen

et al. 2022

Preprint

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For securing sustainable water resources, traditional solutions including seawater desalination are limited due to high cost and restricted operating conditions. Thus, acoustic stimulation of precipitation is a potential alternative method due to its low cost and convenient operation. Low-frequency acoustic field can be used to stimulate precipitation through evoking wavy motion of air particles in clouds which will significantly enhance the process of collision coalescence of cloud droplets and lead to their volume increase. Nevertheless, there is still a lack of rational methods to evaluate the effect of acoustic enhancement of rainfall in field experiments. To this end, in this study, nearly two-month field experiments of acoustic rainfall enhancement with 39 trials from May to July 2020 were carried out by our research team in the Tibetan Plateau. Statistical analysis was applied to evaluate the effect of acoustic wave on rainfall stimulation. The results of average rainfall intensity distribution using ordinary least square analysis disclose that acoustic interference has a considerable effect on rainfall enhancement. Such a rainfall enhancement effect increases significantly along with the increase of rainfall duration which is the index of cloud precipitation potential. For rainfall events with the duration more than two hours, the average rainfall intensity was improved by 72% with the acoustic wave effect over the experiment site. The phenomena are consistent with the fact that clouds with larger precipitation potential contain more cloud droplets which are beneficial to the acoustic coagulation process.

show abstract

Model-wise uncertainty decomposition in multi-model ensemble hydrological projections

Cited by 4 publications

References 35 publications

Statistic evaluation of low-frequency acoustic wave impact on rainfall stimulation

Statistic evaluation of low-frequency acoustic wave impact on rainfall stimulation

Impacts and uncertainties of climate-induced changes in watershed inputs on estuarine hypoxia

Statistic evaluation of low-frequency acoustic wave impact on rainfall stimulation

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