Projecting climate change impacts on hydrological processes on the Tibetan Plateau with model calibration against the glacier inventory data and observed streamflow

Zhao, Qiudong; Ding, Yongjian; Wang, Jian; Gao, Hongkai; Zhang, Shiqiang; Zhao, Chuancheng; Xu, Junli; Han, Hua; Shangguan, Donghui

doi:10.1016/j.jhydrol.2019.03.043

Cited by 151 publications

(111 citation statements)

References 109 publications

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“…Hydrological modeling is commonly used to obtain insights into the hydrology of glacierized catchments and to predict future changes (e.g., Akhtar, Ahmad, & Booij, 2008; Chen et al, 2018; Farinotti, Usselmann, Huss, Bauder, & Funk, 2012; Frans, Istanbulluoglu, Lettenmaier, Fountain, & Riedel, 2018; Zhao et al, 2019). A prerequisite for modeling to provide useful insights and analysis of future changes is that models can simulate the main processes that are relevant for the spatial and temporal scale of the modeling exercise.…”

Section: Introductionmentioning

confidence: 99%

Glacio‐hydrological model calibration and evaluation

et al. 2020

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Glaciers are essential for downstream water resources. Hydrological modeling is necessary for a better understanding and for future projections of the water resources in these rapidly changing systems, but modeling glacierized catchments is especially challenging. Here we review a wealth of glacio-hydrological modeling studies (145 publications) in catchments around the world. Major model challenges include a high uncertainty in the input data, mainly precipitation, due to scarce observations. Consequently, the risk of wrongly compensating input with model errors in competing snow and ice accumulation and melt process parameterization is particularly high. Modelers have used a range of calibration and validation approaches to address this issue. The review revealed that while a large part ($35%) of the reviewed studies used only streamflow data to evaluate model performances, most studies ($50%) have used additional data related to snow and glaciers to constrain model parameters. These data were employed in a variety of calibration strategies, including stepwise and multi-signal calibration. Although the primary aim of glaciohydrological modeling studies is to assess future climate change impacts, longterm changes have rarely been taken into account in model performance evaluations. Overall, a more precise description of which data are used how for model evaluation would facilitate the interpretation of the simulation results and their uncertainty, which in turn would support water resources management. Moreover, there is a need for systematic analyses of calibration approaches to disentangle what works best and why. Addressing this need will improve our system understanding and model simulations of glacierized catchments.

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Section: Introductionmentioning

confidence: 99%

Glacio‐hydrological model calibration and evaluation

et al. 2020

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“…In addition to climate change, recent works reveal the importance of ecological factors (e.g., CO 2 physiological forcing and land cover change) in modulating streamflow and its extremes. For example, increasing CO 2 concentration is found to alleviate the decreasing trend of streamflow in the future at the global scale through decreasing the stomatal conductance and vegetation transpiration (known as CO 2 physiological forcing) (Fowler et al, 2019;Wiltshire et al, 2013;Yang et al, 2019;Zhu et al, 2012). Contrary to CO 2 physiological forcing, vegetation greening in a warming climate is found to play a significant role in exacerbating hydrological drought, as it enhances transpiration and dries up the land (Yuan et al, 2018b).…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

et al. 2020

Hydrol. Earth Syst. Sci.

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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.

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“…这些研究基本没有考虑冻土融化对径流的影响. Zhao 等人 [48] [56,57] . 大型冰川将延迟"拐点"的到来时间, 而以小冰川为主的低纬度冰川流域, "拐点"往往已经或即将到来 [58] .…”

Section: 未来径流变化趋势预估unclassified

“…在青藏高原及其周边(印度河、黄河、澜沧江、怒江和雅鲁藏布江等)大部分河流源区, 冰川融水径流预计在 2050年之前都将持续增长, 到达"拐点"之后将逐渐减少. Luo等人 [49] 基于4个CMIP5模式和SWAT水文模型的预估结果则显示, 塔里木河流域的冰川融水径流的 "拐点"即将出现(在2016~2045年期间). Zhao等人 [48] "淡化" [46] . 但是在内陆干旱区, 下游降水较少, 河流源区径流变化对下游的贡献与影响较大.…”

Section: 流域冰川径流对气候变化的响应结果显示目前45%unclassified

“…3 [70] . 尽管有很大的不确定性, 已有的径流预估研究表明, 我国大部分冰川流域的冰川融水径流峰值近十几年内出现, 之后冰川融水径流将减少 [48,49] ; 大部分冰川流域的夏初冰川融水增加, 夏末冰川融水减少, 部分月份径流量减少10%以上 [57] . 总的来说, 目前源区冰川非突变性的退缩引起的径流时空变化尚难以对源区下游干流径流变化造成根本性的影响 [67] .…”

Section: 量增加约19×10 7 Munclassified

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