Accelerated hydrological cycle over the Sanjiangyuan region induces more streamflow extremes at different global warming levels

Abstract

Abstract. Serving source water for the Yellow, Yangtze and Lancang-Mekong rivers, the Sanjiangyuan region affects 700 million people over its downstream areas. Recent research suggests that the Sanjiangyuan region will become wetter in a warming future, but future changes of streamflow extremes remain unclear due to the complex hydrological processes over high-land areas and limited knowledge of the influences of land cover change and CO2 physiological forcing. Based on high-resolution land surface modeling during 1979–2100 driven by the climate and ecological projections from 11 newly released Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models, we show that different accelerating rates of precipitation and evapotranspiration at 1.5 ∘C global warming level induce 55 % more dry extremes over Yellow River and 138 % more wet extremes over Yangtze River headwaters compared with the reference period (1985–2014). An additional 0.5 ∘C warming leads to a further nonlinear and more significant increase for both dry extremes over Yellow River (22 %) and wet extremes over Yangtze River (64 %). The combined role of CO2 physiological forcing and vegetation greening, which used to be neglected in hydrological projections, is found to alleviate dry extremes at 1.5 and 2.0 ∘C warming levels but to intensify dry extremes at 3.0 ∘C warming level. Moreover, vegetation greening contributes half of the differences between 1.5 and 3.0 ∘C warming levels. This study emphasizes the importance of ecological processes in determining future changes in streamflow extremes and suggests a “dry gets drier, wet gets wetter” condition over the warming headwaters.

Impacts of Global Climate Warming on Meteorological and Hydrological Droughts and Their Propagations

“…In this study, the 1985-2014 period with global average surface air temperature being 0.66°C above that of the pre-industrial era of 1850-1900 was set as the baseline period (Ji et al, 2020). Thus, 1.5°C, 2.0°C, 2.5°C, and 3.0°C warming levels were defined by the periods that the global average temperature first reaches the additional 0.84°C, 1.34°C, 1.84°C, and 2.34°C, respectively.…”

“…In the context of global warming-dominated climate change, it is vital and urgent to investigate the impacts of global warming on drought-like hydrological extreme events (Chen et al, 2018;Ji et al, 2020;Shi et al, 2021). The impacts of global warming are reflected in the aggravated frequency and intensity of drought-included extreme hydrological events, causing enormous socioeconomic and ecological losses (Allen et al, 2012;CRED, 2020;Shukla et al, 2019).…”

Bias Correction for Precipitation Simulated by RegCM4 over the Upper Reaches of the Yangtze River Based on the Mixed Distribution Quantile Mapping Method

“…On the other hand, the QM method that is based on the mixed binary distribution assumes a time-invariant bias (or transfer function) such as those present in other statistical downscaling techniques [2,13,28]. However, more and more evidence shows that the current global hydrological cycle is intensifying, which may cause this hypothesis to be untenable under the severe impact of climate change [34,35]. For example, if the region of interest experiences a different rainfall regime in the future as a result of changes in large-scale circulation, then the future bias would be controlled by different processes and the behavior of the bias may change.…”

High‐Resolution Land Surface Modeling of the Effect of Long‐Term Urbanization on Hydrothermal Changes Over Beijing Metropolitan Area

“…Compared with CSSP, CSSPv2 includes the Variable Infiltration Capacity runoff scheme and hydrological influences of soil organic matter (Yuan et al, 2018). The CSSPv2 model has been applied to attribute the influence of climate change, land cover change, and human water use on terrestrial energy and water cycle over high land areas Ji, Yuan, Jiao, et al, 2020) and to project future changes in streamflow extremes with the consideration of CO 2 physiological forcing and vegetation greening (Ji, Yuan, Ma, et al, 2020).…”

“…Dry adiabatic lapse rate and barometric height formula were used to adjust air temperature and surface pressure to the height of the lowest atmospheric model level (here set to 30 m), respectively. Other model inputs (e.g., leaf area index, soil texture, and elevation) are the same as Ji, Yuan, Ma, et al (2020).…”