Spatial and temporal characteristics of droughts in Luanhe River basin, China

Wang, Yixuan; Zhang, Ting; Xu, Chen; Li, Jianzhu; Feng, Ping

doi:10.1007/s00704-017-2059-z

Cited by 22 publications

(18 citation statements)

References 90 publications

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“…Huang et al [6] found that the correlations between annual actual evaporation and monthly El Niño-Southern Oscillation (ENSO) and Arctic Oscillation (AO) values were significant at a long time scale in the Wei River Basin (WRB) in China and indirectly affected the propagation time from meteorological drought to hydrological drought. The same result was also found in different studies [30][31][32].…”

Section: Introductionsupporting

confidence: 89%

“…In recent years, the interrelationship between regional drought and large-scale atmospheric circulation has received great attention. A large number of studies have confirmed that the evolution of droughts in time and space can be explained by climate anomalies, such as the El Niño Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO) [30,31,[51][52][53][54][55][56]. Huang et al [6] used a wavelet transform to analyze the correlation between meteorological and hydrological variable time series.…”

Section: Links Between Climate Indices and Droughtmentioning

confidence: 99%

“…In addition, climate changes can seriously affect drought characteristics and propagation. In the past, some researchers used a wavelet analysis to determine whether there were periodical changes in the standardized drought index at different time scales [30,31], and to show that a larger wavelet coefficient could be found at a long-term scale, which reveals a significant periodical change. These results may have been influenced by large-scale atmospheric circulation, thus it is necessary to analyze whether climate factors are related to drought by using a cross wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

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Using the Markov Chain to Analyze Precipitation and Groundwater Drought Characteristics and Linkage with Atmospheric Circulation

Yeh

Hsu

2019

Sustainability

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In recent years, Taiwan has been facing water shortages due to the impact of climate change, which has resulted in many serious drought events, especially in southern Taiwan. Long-term records from 25 rainfall stations and 17 groundwater stations in the southern Taiwan basin were used in this study. We used the Standardized Precipitation Index (SPI) and the Standardized Groundwater Level Index (SGI) and employed the first-order Markov chain model and wavelet transform to determine the drought characteristics and propagation, including the steady-state probabilities of drought events and the mean duration for each station. The Drought Index (DI) was also used to investigate the effects of rainfall on groundwater drought. The results show that the steady-state probability of the meteorological drought in the Yanshui River basin in southern Taiwan is higher than that in other basins. The area with the longer mean duration is located in the Yanshui River basin and the Erren River basin, and overall, the mean duration ranges from 3 to 7 months. In addition, the results from the drought proneness analysis indicated that when rainfall causes a longer drought duration, there will be a higher degree of proneness to groundwater drought in the future. Finally, the results show that the mean duration of groundwater droughts are longer than those of meteorological droughts. The results of the wavelet analysis revealed a positive correlation at long-term scales, which may be related to large-scale atmospheric circulation. The information from this research could be used as a reference for water resource management in the future.

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Section: Introductionsupporting

confidence: 89%

Section: Links Between Climate Indices and Droughtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using the Markov Chain to Analyze Precipitation and Groundwater Drought Characteristics and Linkage with Atmospheric Circulation

Yeh

Hsu

2019

Sustainability

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“…The variability of sea surface temperature (SST) at different oscillation scales, such as interannual, decadal and multi-decadal, is considered one of the drivers behind changes in hydrologic variables (Kayano and Andreoli 2007, Kayano and Capistrano 2014, Tang et al 2014, Wang et al 2018. Understanding in detail the different periodic SST cycles and their effects on hydrologic variables can improve climate-based forecast models and water granting policies, and also provides better predictability to water resources system management, enhancing its resilience.…”

Section: Introductionmentioning

confidence: 99%

Mapping abrupt streamflow shift in an abrupt climate shift through multiple change point methodologies: Brazil case study

Rocha

Filho

2020

Hydrological Sciences Journal

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Three change-point methodologies were used to detect changes in the mean value of annual streamflow series and analyse simultaneous changes in large-scale global sea surface temperature (SST) oscillations. To verify the relationship between the variables we used wavelet coherence analysis. A preliminary detection skill test was performed using asynthetic series and Pruned Exact Linear Time (PELT) presented the best results among the methods used (Pettitt test, Bai and Perron algorithm) when combined with a penalty selection via the Changepoints for a Range of Penalties (CROPS) method. However, the use of classical penalty functions resulted in a poor performance of PELT. The three methods showed an extremely high convergence rate (> 90%) for the correct change points and a smaller rate for false positives (< 24%). Changes in the streamflow mean value coincided with phase shift of the low-frequency indices Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), also corroborated by the wavelet results. Most of the changes can be associated with phase shift impacts in the South Atlantic Convergence Zone.

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“…The Varimax rotation attempts to de-noise the column of loads so that each component is explained by a finite set of variables [39], [40], [43]. We found that the threshold value of RPCs from 0.5 to 0.6 was reasonable for spatial division of the sub-regions experiencing similar wetness / dryness changes during the study period [44], [45].…”

Section: ) Principal Components Analysismentioning

confidence: 87%

Regional Assessment of Climate Potential Productivity of Terrestrial Ecosystems and Its Responses to Climate Change Over China From 1980-2018

et al. 2020

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Evaluating the potential productivity of the terrestrial ecosystem is extremely important to ascertain the threshold of vegetation productivity, to maximize the utilization of regional climate resources, carbon sequestration and to mitigate climate warming caused by rising CO 2 concentrations. However, most previous studies neglected the optimum state of natural vegetation without human intervention and regional change trend of vegetation under future climate change. In this study, variations in spatio-temporal distributions of climate potential productivity (CPP) over China from 1980 to 2018 are analyzed with the synthetic estimating model. A comprehensive regionalization method (Principal components analysis, PCA) based on standardized precipitation evapotranspiration index (SPEI), and statistical analysis methods are adopted to assess CPP and its response to the climate change in different regions of China. The results demonstrate that the global temperature rising and precipitation decreasing have obvious effects on the productivity of terrestrial ecosystem and its spatio-temporal distribution in different sub-regions and ecosystems. Among them, precipitation is the dominant factor, and temperature significantly affects some regions such as Tibetan Plateau (TP) and Northeast China (NE) with high-altitude or high-latitude. The optimum temperature for the CPP in Xinjiang (XJ) region and Northwest China (NW) is 7.5 • C and 8 • C, respectively. With regards to the ecosystems, the CPP of grassland shows complex trends in XJ, Southwest China (SW), NE, and TP; especially in XJ (NE), the CPP shows a decreasing (an increasing) trend when the temperature is more than 7.5 • C (0 • C). Linear correlations occur between farmland CPP and temperature in each sub-region except for XJ. The same situation also exists at forest CPP, especially in TP, NE and NC regions. However, under the temperature increasing and precipitation decreasing, there are slight adverse impacts on the CPP of vegetation at the national scale, indicating that drier and warmer climate are detrimental for vegetation growth. INDEX TERMS Climate potential productivity (CPP), synthetic model, SPEI, spatio-temporal variation, climate change. The associate editor coordinating the review of this manuscript and approving it for publication was Jenny Mahoney.

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Spatial and temporal characteristics of droughts in Luanhe River basin, China

Cited by 22 publications

References 90 publications

Using the Markov Chain to Analyze Precipitation and Groundwater Drought Characteristics and Linkage with Atmospheric Circulation

Using the Markov Chain to Analyze Precipitation and Groundwater Drought Characteristics and Linkage with Atmospheric Circulation

Mapping abrupt streamflow shift in an abrupt climate shift through multiple change point methodologies: Brazil case study

Regional Assessment of Climate Potential Productivity of Terrestrial Ecosystems and Its Responses to Climate Change Over China From 1980-2018

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