Rainfall Trend Analysis Using Nonparametric Test: A Case Study of Malegaon City

2018 ◽  
pp. 25-33
Author(s):  
Preeti Ramkar ◽  
S. M. Yadav
Keyword(s):  
Trend Analysis ◽  
Nonparametric Test ◽  
Rainfall Trend

scholarly journals Rainfall Trend Analysis using Box Plot Method: Case Study UMP Campus Gambang and Pekan

2020 ◽  
Vol 712 ◽  
pp. 012021
Author(s):  
A A G Nadiatul Adilah ◽  
M Mohamad Zarif ◽  
A Mohamad Idris
Keyword(s):  
Trend Analysis ◽  
Rainfall Trend ◽  
Box Plot

Estimation and Long-term Trend Analysis of Surface Solar Radiation in Antarctica: A Case Study of Zhongshan Station

2021 ◽  
Author(s):  
Zhaoliang Zeng ◽  
Zemin Wang ◽  
Minghu Ding ◽  
Xiangdong Zheng ◽  
Xiaoyu Sun ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Trend Analysis ◽  
Surface Solar Radiation ◽  
Term Trend ◽  
Zhongshan Station ◽  
Long Term Trend

scholarly journals Rainfall Trend Analysis by ANN Method

2020 ◽  
Vol 9 (6) ◽  
pp. 1437-1447
Author(s):  
Bhupendra Dhankar ◽  
Gunja Dhruw
Keyword(s):  
Trend Analysis ◽  
Rainfall Trend

scholarly journals Rainfall Trend Analysis over Bihar: A Parametric Method

2021 ◽  
Vol 10 (2) ◽  
pp. 2851-2856
Author(s):  
Sushanta Sarkar Birendra Kumar ◽  
Sanjay Kumar
Keyword(s):  
Trend Analysis ◽  
Parametric Method ◽  
Rainfall Trend

scholarly journals Assessing potential storm tide inundation hazard under climate change: a case study of Southeast China coast

2019 ◽  
Author(s):  
Bingchuan Nie ◽  
Qingyong Wuxi ◽  
Jiachun Li ◽  
Feng Xu
Keyword(s):  
Climate Change ◽  
Tropical Cyclone ◽  
Trend Analysis ◽  
Sea Level ◽  
Coupling Effect ◽  
Storm Tide ◽  
Southeast China ◽  
China Coast ◽  
Water Elevation

Abstract. A methodology for assessing the storm tide inundation under TCI (tropical cyclone intensification) and SLR (sea level rise) is proposed, which integrates the trend analysis, numerical analysis and GIS-based analysis. In the trend analysis, the potential TCI and SLR can be estimated based on the long-term historical data of TC (tropical cyclone) and MSL (mean sea level) considering the non-stationary and spatially non-uniform effect; the numerical simulation is relied on the ADCIRC+SWAN model, which is capable of taking into account the tide-surge-wave coupling effect to improve the precision of water elevation prediction; the water elevation is then analyzed on the GIS platform, the potential inundation regions can be identified. Based on this methodology, a case study for the Southeast China coast, one of the storm surge prone areas in China, is presented. The results show that the high water elevation tends to occur in the bays and around the estuaries, the maximal water elevations caused by the typhoon wind of 100-year recurrence period can reach as high as 6.06 m, 5.82 m and 5.67 m around Aojiang, Feiyunjiang and Oujiang river estuaries, respectively. Non-stationary TCI and SLR due to climate change can further deteriorate the situation and enhance the risk of inundation there, i.e. the potential inundation area would expand by 108 % to about 798 km2 compared with the situation without considering TCI and SLR. In addition, the remotely sensed maps and inundation durations of the hardest hit regions are provided, which will aid the prevention and mitigation of storm tide inundation hazard and future coastal management there.

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