The Effect of Large-Scale Climatic Signals on Rainfall in Mazandaran Province

    Rainfall prognosis plays an important role in drought management and planning of drinking water and agricultural water resources. Also, Future policies can be tailored to optimize spending and maximum productivity. In this study, the effect of large-scale climatic signals on rainfall in Mazandaran province was investigated. The first, the effect of climatic signals on precipitation simultaneously and with delay was studied by statistical methods (Pearson correlation coefficient) and then, using the M5Tree model, monthly rainfall was compared with related indices. Generally, the correlation coefficient between signals and precipitation showed that correlation with delay was greater than the coincidence. The results of the correlation study between climate indices and monthly precipitation with a one-step delay showed that there was a significant correlation with, AMM, NINO1 + 2, NINO3, NINO4, TNA and WHWP indices and rainfall in the Babolsar station. Also, AMM, NINO1 + 2, NINO3, ONI, TNA and WHWP indices had a significant correlation with rainfall at the Gharakhil station. Based on the findings, correlation between climatic signals and rainfall in Noshahr station, was significantly different with AMM, NINO1 + 2, NINO3, NINO3.4, TNA and WHWP indices. The correlation between Ramsar precipitation and climatic signals showed a significant difference with AMM, NINO1 + 2, NINO3, TNA and WHWP indices. The results of the M5Tree model indicated that, generally, in one-step delay, the best simulation performance will occur. Also, comparing the simulation results with Taylor's diagram showed that at all stations, the one-step delay values were closer to the observations. The advantage of delayed prediction is that the rainfall situation can be pre-determined and used to manage the water resources of the watershed.