Trend Analysis of Hydro-Climatic Variables in Lake Baikal Basin

Dorjsuren, Batsuren; Batsaikhan, Nyamdavaa; Yan, Denghua; Yadamjav, Otgonbayar; Chonokhuu, Sonomdagva; Enkhbold, Altanbold; Dorligjav, Sandelger; Wang, Kun; Weng, Baisha; Qin, Tianling

doi:10.1134/s0097807822010031

Cited by 4 publications

(1 citation statement)

References 51 publications

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“…The Outline of the Integrated Regional Development of the Yangtze River Delta released in December 2019 covered the whole territory of Shanghai, Jiangsu, Zhejiang and Anhui in the regional integration scope. In the studies of Chen et al [14][15][16] and Ying Wu et al, the geographical scope of the Yangtze River Delta region was expanded to 41 cities.…”

Section: Sample Selectionmentioning

confidence: 99%

Research on Evaluation and Spatial-Temporal Differentiation Laws of Regional Innovation Quality Based on Spatial Autocorrelation Model

Wang,

Ding

2024

Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December

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The significant improvement of the regional integration development in Yangtze River Delta and researches on its innovation efficiency have received increasing attention. This paper conducted spatial autocorrelation analysis on the innovation efficiency scores of 41 regions in the Yangtze River Delta from 2012 to 2020. Through the measurement of innovation quality by DEA-Malmquist model, it is found that the differentiation degree of innovation quality level in the Yangtze River Delta region is continuously improving. Further research on these scores through spatial autocorrelation analysis has showed that the spatial distribution of innovation quality level in the Yangtze River Delta region has a significant spatial positive correlation. With high-value regions as the center, the innovation quality level of their surrounding regions is also relatively high; and the regions with the same spatial relationship show obvious contiguous distribution characteristics according to the results of local spatial autocorrelation analysis.

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Section: Sample Selectionmentioning

confidence: 99%

Research on Evaluation and Spatial-Temporal Differentiation Laws of Regional Innovation Quality Based on Spatial Autocorrelation Model

Wang,

Ding

2024

Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December

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Hydrometeorological variation in the middle and upper reaches of the Yellow River Basin (1960–2019)

Liu,

Yang,

Cui

et al. 2024

HydroResearch

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Trend analysis of hydro-climatic variables in the Great Lakes Depression region of Mongolia

Dorjsuren,

Zemtsov,

Batsaikhan

et al. 2024

Journal of Water and Climate Change

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Arid and semi-arid regions are the first to be affected by hydro-climatic changes. The Great Lakes Depression Basin in western Mongolia is the most notable example of such a region. Therefore, analyzing hydro-climatic changes in the Great Lakes Depression region is essential for future climate, hydrological, eco-hydrological processes, and ecosystem studies in similar areas and basins. In this study, Mann–Kendall (MK), innovative trend analysis method (ITAM), and Sen's slope estimator test (SSET) were used to determine the interrelationship between climate and river discharge changes and lake water level changes through statistical analysis. During the last 30 years, the air temperature has increased by 1.2 °C (Z = 1.16). Total annual precipitation decreased by 23.44 mm, resulting in 134.16 mm (Z = −0.79). The river discharge of the major rivers, such as Khovd River (Z = −3.51) and Zavkhan River (Z = −6.01), has significantly decreased. In Uvs (Z = 0.30) and Khyargas (Z = 2.03) lakes, the water level has also dropped. This study confirms that the increase in air temperature in the depression area of the Great Lakes reduces the amount of precipitation, and the decrease in precipitation affects the decrease in river discharge, which further affects the water level of the inflowing lakes.

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Trend Analysis of Hydro-Climatic Variables in Lake Baikal Basin

Cited by 4 publications

References 51 publications

Research on Evaluation and Spatial-Temporal Differentiation Laws of Regional Innovation Quality Based on Spatial Autocorrelation Model

Research on Evaluation and Spatial-Temporal Differentiation Laws of Regional Innovation Quality Based on Spatial Autocorrelation Model

Hydrometeorological variation in the middle and upper reaches of the Yellow River Basin (1960–2019)

Trend analysis of hydro-climatic variables in the Great Lakes Depression region of Mongolia

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