Multi-model analysis of historical runoff changes in the Lancang-Mekong River Basin – Characteristics and uncertainties

Li, Yuxin; Chen, Aifang; Mao, Ganquan; Chen, Penghan; Huang, Han Hsiang; Yang, Hong; Wang, Zifeng; Wang, Kai; Chen, He; Meng, Ying; Zhong, Rui; Wang, Pengfei; Wang, Hong; Liu, Junguo

doi:10.1016/j.jhydrol.2023.129297

Cited by 5 publications

(2 citation statements)

References 67 publications

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“…Meanwhile, anthropogenic factors are obtained from the two datasets (i.e., WaterGAP2 and H08) from Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, https://www.isimip.org//) datasets archive [70]. The datasets of CMIP6 and ISIMIP are commonly employed in previous studies, and they are proven to be reliable [71][72][73]. Three GCMs datasets (i.e., GFDL-CM4, MIROC6, IPSL-CM6A-LR) are chosen in this study, and the reason for selecting them is that the driver datasets of WaterGAP2 and H08 are GFDL-ESM2M, IPSL-CM5A-LR, MIROC5, respectively.…”

Section: Case Study and Datamentioning

confidence: 99%

Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought

Wang,

Li,

Huang

et al. 2024

NSO

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In the context of global climate warming, the propagation of meteorological drought (MD) may aggravate the devastating impact of hydrological drought (HD) on water security and sustainable development. There are challenges in accurately predicting the propagation of drought and effectively quantifying the effects of uncertainty, especially in datadeficient regions. In this study, a novel method called RFCFA is developed through integrating random forest (RF), copula, and factorial analysis (FA) into a general framework as well as applied to the Aral Sea Basin (a typical arid and data-scarce basin in Central Asia) under considering the impact of climate change. Several findings can be summarized: (1) the projected future drought propagation probability of ASB is 39.2%, which is about 8% higher than historical level; (2) drought propagation is mainly affected by mean climate condition, catchment characteristics (i.e., elevation, LUCC, and slope), and human activities (i.e., irrigation and reservoir operation); (3) the lower propagation probability in spring is expected under SSP1-2.6 due to increased snow meltwater, and the drought propagation probability in autumn is the highest (reaching 45.4%) under the influence of reservoir operation; (4) the combined effects of meteorological conditions and agricultural irrigation can lead to a higher probability of future propagation in the upper river basin in summer. Findings are valuable for predicting drought propagation risk, revealing main factors and inherent uncertainties, as well as providing support for drought management and disaster prevention.

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Section: Case Study and Datamentioning

confidence: 99%

Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought

Wang,

Li,

Huang

et al. 2024

NSO

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“…Runoff is a crucial foundation for the sustainable and healthy development of human society and ecosystems. However, quantifying the effects of climate and land-use changes on runoff in a scientific manner remains a challenging task for current research [8][9][10][11]. Commonly used quantification methods include empirical approaches [12], statistical methods [13], and hydrological modeling methods [14].…”

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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Rescaled range/vector autoregressive-based changing characteristics of dry season streamflow in the Yujiang River Basin, Southern China

Dong,

Li,

Liu

2024

Stoch Environ Res Risk Assess

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Multi-model analysis of historical runoff changes in the Lancang-Mekong River Basin – Characteristics and uncertainties

Cited by 5 publications

References 67 publications

Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought

Development of a random-forest-copula-factorial analysis (RFCFA) method for predicting propagation between meteorological and hydrological drought

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

Rescaled range/vector autoregressive-based changing characteristics of dry season streamflow in the Yujiang River Basin, Southern China

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