Assessing the correlation between climate signals and monthly mean and extreme precipitation and discharge of Golestan Dam Watershed

Earth sciences research journal

Golian, Saeed¹  Ruigar, Hossein¹ 

[1] University of Shahrood, Shahrood, Iran

关键词: Climate signals;    Sea surface temperature;    Sea level pressure;    Precipitation;    Discharge- Correlation;   

DOI  :  10.15446/esrj.v19n1.40996

学科分类：天文学(综合)

来源: Universidad Nacional de Colombia * Departamento de Geociencias

【 摘 要 】

Climate teleconnection signals are one of the main factors influencing the earth's climate oscillations in global and regional scales. In the present research, the effect of these signals on precipitation and discharge of Madarsoo Watershed at the upstream of Golestan Dam was investigated. For this purpose, three raingauges and hydrometric stations with respectively 40 and 38 years of daily rainfall and discharge data were selected. Pearson-correlation coefficient was used to consider the correlation between climate signals and monthly mean and extreme precipitation and discharge. The results showed strong correlation between monthly total precipitation of Tangrah and Tamer with Sea Level Pressure (SLP) of Caspian Sea with 7 and 9 months of lag, respectively, and Galikesh with Sea Surface Temperature (SST) of Caspian Sea with 9 months of lag. For monthly mean discharge of Tangrah and Galikesh, the maximum correlation was calculated for SLP of Caspian Sea with a lag of 9 months and Tamer with SST of Greenland with a lag of 7 months. For the extreme monthly data, very strong correlation was detected between the precipitations of all raingauges in June with Greenland SST with 8 months of lag. In contrast, for maximum monthly discharge of Tangrah and Galikesh hydrometric stations, the maximum correlation coefficients were calculated for SST of Black Sea with 4 months of lag in August and for Tamer with SLP of Caspian Sea with 4 months of lag in August. With regard to the results, the utilization of these signals, especially SLP and SST, is strongly suggested to predict the maximum and mean monthly precipitation and discharge over this region.

【 授权许可】

CC BY