Recharge and spatial distribution of groundwater hydrochemistry in the Geum River basin, South Korea

2021 ◽  
Author(s):  
Hanna Choi ◽  
Chung-Mo Lee ◽  
Dong Chan Koh ◽  
Yoon Yeol Yoon
Keyword(s):  
Spatial Distribution ◽  
South Korea ◽  
River Basin ◽  
Groundwater Hydrochemistry

scholarly journals Modelling the Spatial Distribution of ASF-Positive Wild Boar Carcasses in South Korea Using 2019–2020 National Surveillance Data

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1208
Author(s):  
Jun-Sik Lim ◽  
Timothée Vergne ◽  
Son-Il Pak ◽  
Eutteum Kim
Keyword(s):  
Spatial Distribution ◽  
Risk Factor ◽  
South Korea ◽  
Wild Boar ◽  
African Swine Fever Virus ◽  
Significant Risk ◽  
African Swine Fever ◽  
Credible Interval ◽  
Imperfect Detection ◽  
Wild Boars

In September 2019, African swine fever (ASF) was reported in South Korea for the first time. Since then, more than 651 ASF cases in wild boars and 14 farm outbreaks have been notified in the country. Despite the efforts to eradicate ASF among wild boar populations, the number of reported ASF-positive wild boar carcasses have increased recently. The purpose of this study was to characterize the spatial distribution of ASF-positive wild boar carcasses to identify the risk factors associated with the presence and number of ASF-positive wild boar carcasses in the affected areas. Because surveillance efforts have substantially increased in early 2020, we divided the study into two periods (2 October 2019 to 19 January 2020, and 19 January to 28 April 2020) based on the number of reported cases and aggregated the number of reported ASF-positive carcasses into a regular grid of hexagons of 3-km diameter. To account for imperfect detection of positive carcasses, we adjusted spatial zero-inflated Poisson regression models to the number of ASF-positive wild boar carcasses per hexagon. During the first study period, proximity to North Korea was identified as the major risk factor for the presence of African swine fever virus. In addition, there were more positive carcasses reported in affected hexagons with high habitat suitability for wild boars, low heat load index (HLI), and high human density. During the second study period, proximity to an ASF-positive carcass reported during the first period was the only significant risk factor for the presence of ASF-positive carcasses. Additionally, low HLI and elevation were associated with an increased number of ASF-positive carcasses reported in the affected hexagons. Although the proportion of ASF-affected hexagons increased from 0.06 (95% credible interval (CrI): 0.05–0.07) to 0.09 (95% CrI: 0.08–0.10), the probability of reporting at least one positive carcass in ASF-affected hexagons increased from 0.49 (95% CrI: 0.41–0.57) to 0.73 (95% CrI: 0.66–0.81) between the two study periods. These results can be used to further advance risk-based surveillance strategies in the Republic of Korea.

scholarly journals Optimal Rainfall Estimation by Considering Elevation in the Han River Basin, South Korea

2013 ◽  
Vol 52 (4) ◽  
pp. 802-818 ◽  
Author(s):  
Seong-Sim Yoon ◽  
Deg-Hyo Bae
Keyword(s):  
South Korea ◽  
River Basin ◽  
Flood Control ◽  
Rainfall Data ◽  
Spatiotemporal Variability ◽  
Radar Rainfall ◽  
Mountainous Regions ◽  
Han River ◽  
Orographic Rainfall ◽  
Han River Basin

AbstractMore than 70% of South Korea has mountainous terrain, which leads to significant spatiotemporal variability of rainfall. The country is exposed to the risk of flash floods owing to orographic rainfall. Rainfall observations are important in mountainous regions because flood control measures depend strongly on rainfall data. In particular, radar rainfall data are useful in these regions because of the limitations of rain gauges. However, radar rainfall data include errors despite the development of improved estimation techniques for their calculation. Further, the radar does not provide accurate data during heavy rainfall in mountainous areas. This study presents a radar rainfall adjustment method that considers the elevation in mountainous regions. Gauge rainfall and radar rainfall field data are modified by using standardized ordinary cokriging considering the elevation, and the conditional merging technique is used for combining the two types of data. For evaluating the proposed technique, the Han River basin was selected; a high correlation between rainfall and elevation can be seen in this basin. Further, the proposed technique was compared with the mean field bias and original conditional merging techniques. Comparison with kriged rainfall showed that the proposed method has a lesser tendency to oversmooth the rainfall distribution when compared with the other methods, and the optimal mean areal rainfall is very similar to the value obtained using gauges. It reveals that the proposed method can be applied to an area with significantly varying elevation, such as the Han River basin, to obtain radar rainfall data of high accuracy.

scholarly journals Study on the spatial distribution of water resource value in the agricultural system of the Yellow River Basin

Water Policy ◽  
2021 ◽  
Author(s):  
Huiliang Wang ◽  
Shuoqiao Huang ◽  
Danyang Di ◽  
Yu Wang ◽  
Fengyi Zhang
Keyword(s):  
Spatial Distribution ◽  
Water Resources ◽  
River Basin ◽  
Water Resource ◽  
Yellow River ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Agricultural System ◽  
Resource Value ◽  
The Yellow River Basin

Abstract To analyze the spatial distribution characteristics of water resource value in the agricultural system of the Yellow River Basin, this paper takes the Yellow River Basin as its research object and studies the spatial distribution characteristics and influencing factors of water resource value in the agricultural system using the emergy theory and method, the spatial autocorrelation analysis method, and the spatial regression model. The results show that (1) the value of water resources in the agricultural system ranges from 0.64 to 0.98$/m3, and the value in the middle and lower reaches of the basin is relatively high; (2) the Moran index of the water resource value in the agricultural system is 0.2772, showing a positive spatial autocorrelation feature. Here, ‘high-high (high value city gathering)’ is the main aggregation mode, which is mainly concentrated in the middle and lower reaches of the basin. (3) The spatial error model, moreover, has the best simulation effect. The cultivated land area, total agricultural output value, agricultural labor force, and total mechanical power have a significant positive impact on the agricultural production value of water resources in the Yellow River Basin; the altitude, annual average temperature, and agricultural water consumption have a negative impact. Overall, this study shows that guiding the distribution of water resources according to their value and increasing agricultural water use in the middle and lower reaches of the basin will help improve the overall agricultural production efficiency of water resources in the basin.

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