Analysis of future drought characteristics in China using the regional climate model CCLM

Huang, Jinlong; Zhai, Jun; Jiang, Tong; Wang, Yanjun; Li, Xiucang; Wang, Run; Xiong, Ming; Su, Buda; Fischer, Thomas

doi:10.1007/s00382-017-3623-z

Cited by 100 publications

(55 citation statements)

References 70 publications

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“…Extensive studies have focused on changes in extreme climate events under global climate change (Hirabayashi et al, 2013;Huang et al, 2017;Kharin et al, 2013). Currently, with the 1.5 • C target, the socioeconomic impacts of 1.5 • C global warming on factors, such as population exposed to disasters, need to be further studied.…”

Section: Discussionmentioning

confidence: 99%

Population exposure to droughts in China under the 1.5 °C global warming target

et al. 2018

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Abstract. The Paris Agreement proposes a 1.5 ∘C target to limit the increase in global mean temperature (GMT). Studying the population exposure to droughts under this 1.5 ∘C target will be helpful in guiding new policies that mitigate and adapt to disaster risks under climate change. Based on simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), the Standardized Precipitation Evapotranspiration Index (SPEI) was used to calculate drought frequencies in the reference period (1986–2005) and 1.5 ∘C global warming scenario (2020–2039 in RCP2.6). Then population exposure was evaluated by combining drought frequency with simulated population data from shared socioeconomic pathways (SSPs). In addition, the relative importance of climate and demographic change and the cumulative probability of exposure change were analyzed. Results revealed that population exposure to droughts in the east of China is higher than that in the west; exposure in the middle and lower reaches of the Yangtze River region is the highest, and it is lowest in the Qinghai-Tibet region. An additional 12.89 million people will be exposed to droughts under the 1.5 ∘C global warming scenario relative to the reference period. Demographic change is the primary contributor to exposure (79.95 %) in the 1.5 ∘C global warming scenario, more than climate change (29.93 %) or the interaction effect (−9.88 %). Of the three drought intensities – mild, moderate, and extreme – moderate droughts contribute the most to exposure (63.59 %). Probabilities of increasing or decreasing total drought frequency are roughly equal (49.86 % and 49.66 %, respectively), while the frequency of extreme drought is likely to decrease (71.83 % probability) in the 1.5 ∘C global warming scenario. The study suggested that reaching the 1.5 ∘C target is a potential way for mitigating the impact of climate change on both drought hazard and population exposure.

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Section: Discussionmentioning

confidence: 99%

Population exposure to droughts in China under the 1.5 °C global warming target

et al. 2018

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“…SPEI estimation requires monthly water deficit (MWD), that is, the difference of monthly precipitation (P) and monthly potential evapotranspiration (PET). Hargreaves (Rhee et al, 2016;Dibike et al, 2017;Oguntunde et al, 2017;Spinoni et al, 2018), Thornthwaite (Törnros and Menzel, 2014;Smirnov et al, 2016;Wu et al, 2016;Bonsal et al, 2017;Chen and Sun, 2017;Feng et al, 2017;Khan et al, 2017), and Penman-Monteith (Wang et al, 2014;Feng et al, 2017;Gao et al, 2017;Huang et al, 2018;Zhang et al, 2018) are the three most commonly used PET estimation methods employed in SPEI based drought studies under projected scenarios. Penman-Monteith method is the best reported method and data intensive, Hargreaves method utilizes daily maximum and minimum temperature whereas Thonthwaite requires monthly mean temperature.…”

Section: Drought Characteristicsmentioning

confidence: 99%

Drought characterization over India under projected climate scenario

Bisht

Sridhar

Mishra

et al. 2018

Intl Journal of Climatology

109

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The study evaluates the drought characteristics in India over projected climatic scenarios in different time frames, that is, near‐future (2010–2039), mid‐future (2040–2069), and far‐future (2070–2099) in comparison with reference period (1976–2005). Standardized Precipitation Evapotranspiration Index (SPEI), a multi‐scalar drought index was used owing to its robustness in capturing drought conditions while accounting the temperature. Gridded rainfall and temperature data provided by India Meteorological Department (IMD) was used to perform bias correction of nine Global Climate Models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5) project. Quantile mapping was used to correct the daily rainfall data at seasonal scale whereas daily temperature data was corrected at monthly scale. Multi‐Model Ensemble (MME) was prepared for different homogeneous monsoon regions of India, namely Hilly Regions (HR), Central Northeast (CNE), Northeast (NE), Northwest (NW), West Central (WC), and Peninsula (PS). Taylor diagram statistics were used for the preparation of MME. The regional climate cycle obtained from MME was found to be in good agreement with observed cycle derived from IMD data. The Mann–Kendal trend test was employed to detect the trend in drought severity and magnitude whereas L‐moments based frequency analysis was used to assess the magnitude of extreme drought severity under different time frames. The study reveals an increasing trend in drought severity, duration, occurrences, and the average length of drought under warming climate scenarios. Furthermore, the area under “above moderate drought” (i.e., severe and extreme drought combined) condition was also found to be increasing in projected climate.

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“…The frequency of extreme climatic events will greatly increase for the next 40 years in Northern China, most notably in semi-arid regions [3,4]. There will probably be an enhancement of the drought sensitivity of ecosystems [5], reduction in productivity and biodiversity, and change of vegetation species composition [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

The Response of Vegetation Phenology and Productivity to Drought in Semi-Arid Regions of Northern China

2018

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A major disturbance in nature, drought, has a significant impact on the vulnerability and resilience of semi-arid ecosystems by shifting phenology and productivity. However, due to the various disturbance mechanisms, phenology and primary productivity have remained largely ambiguous until now. This paper evaluated the spatio-temporal changes of phenology and productivity based on GIMMS NDVI3g time series data, and demonstrated the responses of vegetation phenology and productivity to drought disturbances with the standardized precipitation evapotranspiration index (SPEI) in semi-arid ecosystems of northern China. The results showed that (1): vegetation phenology exhibited dramatic spatial heterogeneity with different rates, mostly presented in the regions with high chances of land cover type variation. The delayed onset of growing season (SOS) and advanced end of growing season (EOS) occurred in Horqin Sandy Land and the eastern Ordos Plateau with a one to three days/decade (p < 0.05) rate and in the middle and east of Inner Mongolia with a two days/decade rate, respectively. Vegetation productivity presented a clear pattern: south increased and north decreased. (2) Spring drought delayed SOS in grassland, barren/sparsely vegetated land, and cropland, while autumn drought significantly advanced EOS in grassland and barren/sparsely vegetated lands. Annual drought reduced vegetation productivity and the sensitivity of productivity regarding drought disturbance was higher than that of phenology.

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Analysis of future drought characteristics in China using the regional climate model CCLM

Cited by 100 publications

References 70 publications

Population exposure to droughts in China under the 1.5 °C global warming target

Population exposure to droughts in China under the 1.5 °C global warming target

Drought characterization over India under projected climate scenario

The Response of Vegetation Phenology and Productivity to Drought in Semi-Arid Regions of Northern China

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