Modeling, challenges, and strategies for understanding impacts of climate extremes (droughts and floods) on water quality in Asia: A review

Fabian, Pamela Sofia; Kwon, Hyun‐Han; Vithanage, Meththika; Lee, Joo-Heon

doi:10.1016/j.envres.2023.115617

Cited by 31 publications

(6 citation statements)

References 117 publications

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“…Key factors of water quality change have been identified using quantitative research where greater focus has been placed on turbidity and eutrophication. Eutrophication is the most prevalent problem in water quality worldwide [60]. Eutrophication is produced by excessive nutrient loads, especially phosphorus (P) and nitrogen (N), which have been the subject of several research on water quality measures.…”

Section: Evaluating Performance Of Lid In Enhancing Water Qualitymentioning

confidence: 99%

Assessing the Performance and Challenges of Low-Impact Development under Climate Change: A Bibliometric Review

Wang,

Feng,

Ikram

et al. 2023

Sustainability

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Low-Impact Development (LID) represents a cogent strategy designed to conserve or reestablish antecedent hydrological states through an array of innovative mechanisms and methodologies. Since the dawn of the millennium, LID-centric research has demonstrated a persistent upward trajectory, mainly focusing on its capacity to mitigate climate change repercussions, particularly runoff and peak flows. However, a standardized rubric and toolkit for LID evaluation remain elusive. While numerous studies have documented the hydrological and water quality benefits of LID, the impacts of climate change on its effectiveness remain uncertain due to varying spatial and temporal climate patterns. This comprehensive review examined 1355 peer-reviewed articles in English, comprising both research articles and reviews, indexed in the Web of Science up until 2022. Findings from the bibliometric analysis revealed significant contributions and emergent trends in the field. Notably, there is an increasing emphasis on performance evaluation and efficiency of LID systems, and on understanding their impact on hydrology and water quality. However, this review identified the lack of a standardized LID evaluation framework and the uncertainty in LID effectiveness due to varying climate patterns. Furthermore, this study highlighted the urgent need for optimization of current hydrological models, advancement of LID optimization, modeling, monitoring, and performance, and stakeholder awareness about LID functionality. This review also underscored the potential future research trajectories, including the need to quantify LID’s effectiveness in urban flooding and water quality management and refining LID simulation models. Cumulatively, this review consolidates contemporaneous and prospective research breakthroughs in urban LID, serving as an indispensable compendium for academics and practitioners in the discipline.

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Section: Evaluating Performance Of Lid In Enhancing Water Qualitymentioning

confidence: 99%

Assessing the Performance and Challenges of Low-Impact Development under Climate Change: A Bibliometric Review

Wang,

Feng,

Ikram

et al. 2023

Sustainability

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“…More broadly, the significance of streamflow extends beyond the context of human water demands. Terrestrial aquatic ecosystems are intricately linked to streamflow dynamics, and their health depends on consistent adequate flow [1]. Streamflow can impact the prevalence and transmission of illnesses in aquatic ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting

Wei,

Hashim,

Lai

et al. 2024

IEEE Access

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Deep learning excels at managing spatial and temporal time series with variable patterns for streamflow forecasting, but traditional machine learning algorithms may struggle with complicated data, including non-linear and multidimensional complexity. Empirical heterogeneity within watersheds and limitations inherent to each estimation methodology pose challenges in effectively measuring and appraising hydrological statistical frameworks of spatial and temporal variables. This study emphasizes streamflow forecasting in the region of Johor, a coastal state in Peninsular Malaysia, utilizing a 28-year streamflow-pattern dataset from Malaysia's Department of Irrigation and Drainage for the Johor River and its tropical rainforest environment. For this dataset, wavelet transformation significantly improves the resolution of lag noise when historical streamflow data are used as lagged input variables, producing a 6% reduction in the root-mean-square error. A comparative analysis of convolutional neural networks and artificial neural networks reveals these models' distinct behavioral patterns. Convolutional neural networks exhibit lower stochasticity than artificial neural networks when dealing with complex time series data and with data transformed into a format suitable for modeling. However, convolutional neural networks may suffer from overfitting, particularly in cases in which the structure of the time series is overly simplified.Using Bayesian neural networks, we modeled network weights and biases as probability distributions to assess aleatoric and epistemic variability, employing Markov chain Monte Carlo and bootstrap resampling techniques. This modeling allowed us to quantify uncertainty, providing confidence intervals and metrics for a robust quantitative assessment of model prediction variability.

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“…There are some review studies related to drought and floods considering their impacts, retrospective and prospective evaluations, modeling challenges, improvement of attribution changes, and risk reductions [ [40] , [41] , [42] , [43] , [44] , [45] , [46] ]. However, a comprehensive review on the research process that uses literature analysis approaches in East Asia related to contribution to SDGs remains lacking.…”

Section: Introductionmentioning

confidence: 99%

Floods and droughts research progress and its contributions toward sustainability

Faiz,

Ma,

Naz

2024

Heliyon

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Modeling, challenges, and strategies for understanding impacts of climate extremes (droughts and floods) on water quality in Asia: A review

Cited by 31 publications

References 117 publications

Assessing the Performance and Challenges of Low-Impact Development under Climate Change: A Bibliometric Review

Assessing the Performance and Challenges of Low-Impact Development under Climate Change: A Bibliometric Review

Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting

Floods and droughts research progress and its contributions toward sustainability

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