Future projections of flooding characteristics in the Lancang-Mekong River Basin under climate change

Wang, Shuxia; Zhang, Liping; She, Dunxian; Wang, Gangsheng; Zhang, Qin

doi:10.1016/j.jhydrol.2021.126778

Cited by 52 publications

(27 citation statements)

References 74 publications

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“…Among high-latitude rivers in northwest and southwest China, similar results of increasing and decreasing trends in the magnitude of river floods due to climate change have been obtained in the Tien Shan Mountains, Aksu, Tarim and Lancang-Mekong river basins (Jiang et al, 2021, Mao et al, 2012, Wang et al, 2021, Zhang et al, 2016. In Central Asia, climate change is leading to an increased risk of flooding and landslides, and the likelihood of glacial lake outburst flooding is expected to increase with rising temperatures and an increase in the number of glacial lakes (Reyer et al, 2017).…”

Section: Attribution Of Climate Change On Variability In Flood Magnitudesupporting

confidence: 53%

Trends and variability in flood magnitude under climate change in the Qilian Mountains, Northwest China

Wang

Chen

et al. 2022

Preprint

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Analyzing trends in flood magnitude changes and its driving factors under climate change is a key challenge for effective water resources management in arid and semi-arid regions, especially for inland rivers originating in the Qilian Mountains (QMs). Based on the annual maximum peak discharge (AMPD) and Peaks-Over-Threshold (POT3M) flood series of twelve typical rivers from 1970 to 2021, spatial and temporal trends in flood magnitude were investigated using Sen’s slope estimator and Mann-Kendall test. The results showed that in AMPD series, 42% of the rivers were significantly decreasing trends while 8% was significantly increasing; the POT3M series differed in that 25% of the rivers were significantly decreasing trends while 8% was significantly increasing. The regional differences in the QMs from east to west were that rivers in the eastern region (e.g., Gulang, Zamu, and Xiying rivers) showed significantly decreasing trends in AMPD and POT3M; most rivers in the central region had no significant trend, while the Shule river in the western region showed a significantly increasing trend. Temperatures and precipitation showed a fluctuating increasing trend after 1987, which were the main factor contributing to the change in flood magnitude trends of AMPD and POT3M flood series in the QMs. Regional differences in precipitation, precipitation intensity, and the ratio of glacial meltwater in the rivers of the eastern, central and western regions resulted in differences in flood magnitude trends between the east and west.

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Section: Attribution Of Climate Change On Variability In Flood Magnitudesupporting

confidence: 53%

Trends and variability in flood magnitude under climate change in the Qilian Mountains, Northwest China

Wang

Chen

et al. 2022

Preprint

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“…Precipitation and snowmelt are the primary sources of water, and the Lancang-Mekong River basin is known to be largely a rain-fed rather than snow-fed river [42]. In the Lancang River Basin, snowmelt mainly comes from the Tibetan plateau, while precipitation is the main discharge source of the Mekong River [39,43].…”

Section: Study Areamentioning

confidence: 99%

Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

et al. 2021

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Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data.

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“…Climate change largely affects the regional and global water cycle (Etchevers et al, 2002;Arnell et al, 2003;Yu et al, 2019;Pakmehr et al, 2020;Douville et al, 2021;Gudmundsson et al, 2021), especially raising concerns on extreme weather and climate events that can cause severe damage to water resources management, ecosystem, and regional socio-economic development (Tong et al, 2019;Wang et al, 2021). This highlights the significance of investigating the historical and future climate change and its probable impact on the hydrological cycle (Li et al, 2018;Wang et al, 2021). Generally, the global climate models (GCMs) are considered to be the primary tools that simulate multidecadal climate dynamics and generate global climate change projections under different future scenarios (Sharma et al, 2007;Taylor et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Climate change largely affects the regional and global water cycle (Etchevers et al ., 2002; Arnell et al ., 2003; Yu et al ., 2019; Pakmehr et al ., 2020; Douville et al ., 2021; Gudmundsson et al ., 2021), especially raising concerns on extreme weather and climate events that can cause severe damage to water resources management, ecosystem, and regional socio‐economic development (Tong et al ., 2019; Wang et al ., 2021). This highlights the significance of investigating the historical and future climate change and its probable impact on the hydrological cycle (Li et al ., 2018; Wang et al ., 2021). Generally, the global climate models (GCMs) are considered to be the primary tools that simulate multidecadal climate dynamics and generate global climate change projections under different future scenarios (Sharma et al ., 2007; Taylor et al ., 2012).…”

Section: Introductionmentioning

confidence: 99%

Piecewise‐quantile mapping improves bias correction of global climate model daily precipitation towards preserving quantiles and extremes

Zhang

Gan

Zhang

et al. 2022

Intl Journal of Climatology

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Bias correction is a vital technique to correct the climate model outputs for regional studies. Prior bias correction methods such as quantile mapping (QM) may be problematic in correcting extreme values. Herein we proposed a novel bias correction method, that is, the piecewise‐quantile mapping (PQM), by combing piecewise mapping with QM, which implements bias corrections on both extreme and nonextreme data. We compared the PQM method and other six commonly used methods in correcting daily precipitation in the Mekong River Basin (MRB) and the Yangtze River Basin (YRB) from two global climate models (GCMs) (i.e., EC‐Earth3 and MPI‐ESM1‐2‐HR) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our results show that the PQM perform better in bias correction pertaining to both the precipitation percentiles and most of the extremes indices. For example, the mean absolute error of R20mm (the number of days when precipitation ≥20 mm) is significantly reduced from 21.64 to 9.03 days and from 10.96 to 5.71 days over MRB and YRB, respectively. In addition, the PQM could capture the spatial distributions of extremes indices reasonably well. The PQM developed in this study provides more accurate bias correction of the GCMs outputs, which will reduce the uncertainty in the subsequent analyses, such as climate change impacts on hydrological and biogeochemical cycles, particularly under extreme conditions.

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Future projections of flooding characteristics in the Lancang-Mekong River Basin under climate change

Cited by 52 publications

References 74 publications

Trends and variability in flood magnitude under climate change in the Qilian Mountains, Northwest China

Trends and variability in flood magnitude under climate change in the Qilian Mountains, Northwest China

Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

Piecewise‐quantile mapping improves bias correction of global climate model daily precipitation towards preserving quantiles and extremes

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