A flow-weighted ensemble strategy to assess the impacts of climate change on watershed hydrology

Dong, Feifei; Javed, Aisha; Saber, Ali; Neumann, Alex; Arnillas, Carlos Alberto; Kaltenecker, Georgina; Arhonditsis, George B.

doi:10.1016/j.jhydrol.2020.125898

Cited by 26 publications

(13 citation statements)

References 81 publications

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“…A wealth of studies claim that multimodel combinations such as the ensemble method are more robust than single modeling (Arsenault et al, 2015;Wan et al, 2021;. The LWE applied in this study is developed based on the works of Dong et al (2021). This study further improves low flow performance by adding runoff signatures to the calibration scheme and further enlarging the low flow difference with log transformation compared to the original method.…”

Section: Log-based Weighted Ensemble Method: a Tradeoff For Proper Ru...mentioning

confidence: 99%

“…Since the SWM method (Dong et al., 2021) improves the simulated FDC by using the entire discrepancy between the observed and simulated FDCs, the skill can be further improved by dividing them into several parts. Therefore, we further developed the LWE method based on SWM by dividing daily runoff into two parts according to the FDC.…”

Section: Methodsmentioning

confidence: 99%

“…For example, Dong et al. (2021) developed a flow‐based weighting strategy to recreate empirical flow frequency distributions. Meanwhile, Wan et al.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, five extensively used hydrological conceptual models are improved with an evapotranspiration model (Penman‐Monteith‐Leuning V2, PML‐V2) to incorporate vegetation inputs (LAI and LC). Three ensemble methods, including the simple ensemble mean method (SEM), the flow‐based weight method (abbreviated as the SWM, hereafter (Dong et al., 2021)) and LWE, are then applied based on the five improved models in this study.…”

Section: Introductionmentioning

confidence: 99%

“…Because runoff simulated by a single model simulation may be biased and the related model uncertainty is difficult to quantify (Gudmundsson et al, 2012;Neuman, 2003), multimodel ensemble approaches have been applied to reduce the uncertainties for improved model performance (Ajami et al, 2006;Kaune et al, 2020;Neto et al, 2018;Velazquez et al, 2010;Yan et al, 2021). For example, Dong et al (2021) developed a flow-based weighting strategy to recreate empirical flow frequency distributions. Meanwhile, Wan et al (2021) suggested that the runoff performance can be improved by using less than the ensemble size of six, along with poor performance in terms of low flow.…”

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confidence: 99%

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Estimating Vegetation Greening Influences on Runoff Signatures Using a Log‐Based Weighted Ensemble Method

Huang

Zhang

et al. 2022

Water Resources Research

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Vegetation greening profoundly impacts the water cycle, and recent concerns about greening impacts have focused on various hydrological cycle components. However, the impacts of greening on catchment runoff signatures reflecting magnitude, low/high flow frequency, low/high flow duration and flow dynamics remain poorly understood. To properly simulate these runoff signatures, we use five modified hydrological models incorporating vegetation dynamics and further derive three ensemble approaches to obtain eight runoff time series outputs in a major tributary of the Yellow River Basin. Multiple validations suggest that the log‐based weighted ensemble (LWE) approach is robust for depicting the impact of greening on selected runoff signatures. This is especially true for the low flow part of the runoff time series and the overall performance of the selected signatures since LWE explicitly reduces the low flow bias. With this approach, five experiments were designed to isolate the impact of vegetation greening on runoff signatures, and the comparisons among the experiments indicate that greening noticeably decreases runoff magnitude, increases low flow frequency/duration and decreases high flow frequency/duration signatures. However, greening has little influence on runoff dynamic signatures. Each percent increase in leaf area index results in (a) changes of −0.2 ± 0.1% for magnitude signatures; (b) changes of −0.34 ± 0.30% and 0.56 ± 0.28% with wide ranges for annual high flow days and annual low flow days, respectively; and (c) marginal change on flow dynamic signatures. This study provides new insights by disentangling greening impacts on various runoff signatures using a trade‐off ensemble method.

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Section: Log-based Weighted Ensemble Method: a Tradeoff For Proper Ru...mentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…For example, Dong et al. (2021) developed a flow‐based weighting strategy to recreate empirical flow frequency distributions. Meanwhile, Wan et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Estimating Vegetation Greening Influences on Runoff Signatures Using a Log‐Based Weighted Ensemble Method

Huang

Zhang

et al. 2022

Water Resources Research

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Improving drought monitoring using climate models with bias‐corrected under Gaussian mixture probability models

Naz,

Ali,

Kartal

et al. 2024

Intl Journal of Climatology

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Global climate models (GCMs) are extensively used to calculate standardized drought indices. However, inaccuracies in GCM simulations and uncertainties inherent in the standardization methodology limit the precision of drought evaluations. The objective of this research is to remove bias in GCMs for improving drought monitoring and assessment. Consequently, this article proposes a new framework for drought index under the ensemble of GCMs—Multi‐Model Quantile Mapped Standardized Precipitation Index (MMQMSPI). In accordance of Standardized Precipitation Index (SPI), the second stage derives a new index by assessing the feasibility of parametric and nonparametric models during standardization. In the application, we used 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) data of precipitation across 32 grid points within the Tibetan Plateau region. The comparative findings reveal that the integration of KCGMD is the most suitable choice compared to other best‐fitted univariate distributions in both features of the proposed framework. In this research, we assess the implications of evaluating future patterns of drought for the years 2015–2100 using seven different time periods and three different future scenarios. Temporal behavior clearly shows monthly variations in the pattern of MMQMSPI, and these variations differ on each time scale, but a drastic change can be seen over the long term, i.e., extreme dry and wet conditions, with a higher probability in all scenarios.

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A new Monte Carlo Feature Selection (MCFS) algorithm-based weighting scheme for multi-model ensemble of precipitation

Baseer,

Ali,

Ilyas

et al. 2023

Theor Appl Climatol

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A flow-weighted ensemble strategy to assess the impacts of climate change on watershed hydrology

Cited by 26 publications

References 81 publications

Estimating Vegetation Greening Influences on Runoff Signatures Using a Log‐Based Weighted Ensemble Method

Estimating Vegetation Greening Influences on Runoff Signatures Using a Log‐Based Weighted Ensemble Method

Improving drought monitoring using climate models with bias‐corrected under Gaussian mixture probability models

A new Monte Carlo Feature Selection (MCFS) algorithm-based weighting scheme for multi-model ensemble of precipitation

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