Persistence and Corresponding Time Scales of Soil Moisture Dynamics During Summer in the Babao River Basin, Northwest China

Shen, Shi; Ye, Sijing; Cheng, Changxiu; Song, Changchun; Gao, Jianbo; Yang, Jing; Ning, Lixin; Su, Kuo-Lan; Zhang, Ting

doi:10.1029/2018jd028414

Cited by 18 publications

(20 citation statements)

References 50 publications

(65 reference statements)

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“…The study area in the Babao River Basin is the upstream of the Heihe River (Figure 1). The Babao River Basin, lying approximately between 37°4315N-38°1824N and 100°0035E -101°0904E, covers a total area of 2450 km 2 in Qinghai Province (Shen et al, 2018). This area is characterized by scant precipitation, strong evaporation, frequent winds, and long sunshine hours.…”

Section: Study Areamentioning

confidence: 99%

Spatial-temporal variability of the fluctuation of soil temperature in the Babao River Basin, Northwest China

2019

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Journal of Geographical Sciences sites are 0.863 at a depth of 4 cm > 0.818 at 10 cm > 0.744 at 20 cm. 5) For the variation of ST at different elevations, it appears that Max, Ranges, Average, Standard Deviation and ST fluctuation decrease with increasing elevation at the same soil depth. And with the increase of elevation, the decrease rates of Max, Range, Average, Standard Deviation at 4 cm are-0.89 ℃/100 m,-0.94℃/100 m,-0.43℃/100 m, and-0.25℃/100 m, respectively. In addition, this correlation decreased with the increase of soil depth. 6) Significant correlation between PE values of ST at depths of 4, 10 and 20 cm can easily be found. This finding implies that temperature can easily be transmitted within soil at depths between 4 and 20 cm. 7) For the variation of ST on shady slope and sunny slope sides, it appears that the PE values of ST at 4, 10 and 20 cm for 8 sites located on shady slope side are 0.868, 0.824 and 0.776, respectively, whereas they are 0.858, 0.810 and 0.716 for 9 sites located on sunny slope side.

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Section: Study Areamentioning

confidence: 99%

Spatial-temporal variability of the fluctuation of soil temperature in the Babao River Basin, Northwest China

2019

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“…It was a time-consuming process to complete the optimization in Lark's applied case. In practice, the geographical sampling space is generally much more complex [18][19][20], and the time taken by the optimization process is significantly longer. However, it is important to provide optimal sampling schemes in time by optimizing efficiently.…”

Section: Introductionmentioning

confidence: 99%

An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II

Gao

Zhang

et al. 2020

IJGI

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Complex geographical spatial sampling usually encounters various multi-objective optimization problems, for which effective multi-objective optimization algorithms are much needed to help advance the field. To improve the computational efficiency of the multi-objective optimization process, the archived multi-objective simulated annealing (AMOSA)-II method is proposed as an improved parallelized multi-objective optimization method for complex geographical spatial sampling. Based on the AMOSA method, multiple Markov chains are used to extend the traditional single Markov chain; multi-core parallelization technology is employed based on multi-Markov chains. The tabu-archive constraint is designed to avoid repeated searches for optimal solutions. Two cases were investigated: one with six typical traditional test problems, and the other for soil spatial sampling optimization applications. Six performance indices of the two cases were analyzed—computational time, convergence, purity, spacing, min-spacing and displacement. The results revealed that AMOSA-II performed better which was more effective in obtaining preferable optimal solutions compared with AMOSA and NSGA-II. AMOSA-II can be treated as a feasible means to apply in other complex geographical spatial sampling optimizations.

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“…Furthermore, a large amount of data on the HRB has been collected and made available. This includes climatic data, hydrological data, remote sensing data, soil moisture and soil type data (Li et al, 2013;Xiong et al, 2013;Cheng et al, 2014;Song et al, 2017;Shen et al, 2018;Zhang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatial associations between NDVI and environmental factors in the Heihe River Basin

et al. 2019

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The Heihe River Basin is located in the arid and semi-arid regions of Northwest China. Here, the terrestrial ecosystem is vulnerable, making it necessary to identify the factors that could affect the ecosystem. In this study, MODIS-NDVI data with a 250-m resolution were used as a proxy for the terrestrial ecosystem. By combining these with environmental factors, we were able to explore the spatial features of NDVI and identify the factors influencing the NDVI distribution in the Heihe River Basin during the period of 2000-2016. A geographical detector (Geodetector) was employed to examine the spatial heterogeneity of the NDVI and to explore the factors that could potentially influence the NDVI distribution. The results indicate that: (1) the NDVI in the Heihe River Basin appeared high in the southeast while being low in the north, showing spatial heterogeneity with a q-statistic of 0.38. The spatial trend of the vegetation in the three sub-basins generally increased in the growing seasons from 2000 to 2016; (2) The results obtained by the Geodetector (as denoted by the q-statistic as well as the degree of spatial association between the NDVI and environmental factors) showed spatial heterogeneity in the associations between the NDVI and the environmental factors for the overall basin as well as the sub-basins. Precipitation was the dominant factor for the overall basin. In the upper basin, elevation was found to be the dominant factor. The dominant factor in the middle basin was precipitation, closely followed by the soil type. In the lower basin, the dominant factor was soil type with a lower q-statistic of 0.13, and the dominant interaction between the elevation and soil type was nonlinearly enhanced (q-statistic = 0.22).

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Persistence and Corresponding Time Scales of Soil Moisture Dynamics During Summer in the Babao River Basin, Northwest China

Cited by 18 publications

References 50 publications

Spatial-temporal variability of the fluctuation of soil temperature in the Babao River Basin, Northwest China

Spatial-temporal variability of the fluctuation of soil temperature in the Babao River Basin, Northwest China

An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II

Spatial associations between NDVI and environmental factors in the Heihe River Basin

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