Climate variability impacts on runoff projection under quantile mapping bias correction in the support CMIP6: An investigation in Lushi basin of China

Xue, Peipei; Zhang, Chenguang; Zhao, Wen; Park, Eungyu; Jakada, Hamza

doi:10.1016/j.jhydrol.2022.128550

Cited by 11 publications

(2 citation statements)

References 92 publications

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“…Quantile mapping (QM) is a statistical method used for adjusting the cumulative distribution function (CDF) of a model's output to match that of observed data [53]. The main idea is to correct biases and discrepancies in the simulated data by aligning their quantiles with those of the observed data.…”

Section: Bias Correction and Output Validationmentioning

confidence: 99%

Large-Scale Hydrological Models and Transboundary River Basins

Skoulikaris

2024

Water

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Large-scale hydrological modeling is an emerging approach in river hydrology, especially in regions with limited available data. This research focuses on evaluating the performance of two well-known large-scale hydrological models, namely E-HYPE and LISFLOOD, for the five transboundary rivers of Greece. For this purpose, discharge time series at the rivers’ outlets from both models are compared with observed datasets wherever possible. The comparison is conducted using well-established statistical measures, namely, coefficient of determination, Percent Bias, Nash–Sutcliffe Efficiency, Root-Mean-Square Error, and Kling–Gupta Efficiency. Subsequently, the hydrological models’ time series are bias corrected through scaling factor, linear regression, delta change, and quantile mapping methods, respectively. The outputs are then re-evaluated against observations using the same statistical measures. The results demonstrate that neither of the large-scale hydrological models consistently outperformed the other, as one model performed better in some of the basins while the other excelled in the remaining cases. The bias-correction process identifies linear regression and quantile mapping as the most suitable methods for the case study basins. Additionally, the research assesses the influence of upstream waters on the rivers’ water budget. The research highlights the significance of large-scale models in transboundary hydrology, presents a methodological approach for their applicability in any river basin on a global scale, and underscores the usefulness of the outputs in cooperative management of international waters.

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Section: Bias Correction and Output Validationmentioning

confidence: 99%

Large-Scale Hydrological Models and Transboundary River Basins

Skoulikaris

2024

Water

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“…Climate and land-use changes have varying impacts on the hydrological cycle processes in watersheds, particularly leading to the significant spatiotemporal changes in runoff [1][2][3]. Climate change has a relatively long impact on runoff, primarily through direct effects of precipitation and indirect effects of temperature and evaporation [4,5], while land-use change has a relatively short impact, mainly affecting runoff through alterations in hydrological elements such as surface vegetation retention, infiltration, evaporation, and puddle filling [3,6]. With the effects of integrated climate and land-use change on runoff, runoff can either increase or decrease simultaneously, or display opposite trends with one factor increasing while the other decreases [7].…”

Section: Introductionmentioning

confidence: 99%

CMADS and CFSR Data-Driven SWAT Modeling for Impacts of Climate and Land-Use Change on Runoff

Du,

Wu,

Ruan

et al. 2023

Water

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Climate and land-use change significantly impact hydrological processes and water resources management. However, studies of runoff simulation accuracy and attribution analysis in large-scale basins based on multi-source data and different scenario projections are limited. This study employed the Soil and Water Assessment Tool (SWAT) model in conjunction with spatial interpolation techniques to evaluate the accuracy of Climate Forecast System Reanalysis (CFSR), China Meteorological Assimilation Driven Dataset (CMADS), and observation (OBS) in runoff simulations, and configured various scenarios using the Patch-generating Land-use Simulation (PLUS) model to analyze effects of climate and land-use changes on runoff in the Jing River Basin from 1999 to 2018. Results demonstrated the superior performance of the CMADS+SWAT model compared to than CFSR+SWAT model, as the latter underestimated peak runoff. Changes in precipitation had a stronger impact on runoff than temperature, with increased flow from farmland and strong interception effects from forestland. Integrated climate and land-use changes led to an average annual runoff reduction of 1.24 m3/s (I2), primarily attributed to climate change (1.12 m3/s, I3), with a small contribution from land-use change (0.12 m3/s, I4). CMADS exhibited robust applicability under diverse scenarios, effectively enhancing runoff simulation accuracy. The findings provide invaluable guidance for water resources management in semi-arid regions.

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