Modeling Climate Variables of Rivers Basin using Time Series Analysis (Case Study: Karkheh River Basin at Iran)

Abstract

Stochastic models (time series models) have been proposed as one technique to generate scenarios of future climate change. Precipitation, temperature and evaporation are among the main indicators in climate study. The goal of this study is the simulation and modeling of climatic parameters such as annual precipitation, temperature and evaporation using stochastic methods (time series analysis). The 40-year data of precipitation and 37-year data of temperature and evaporation at Jelogir Majin station (upstream of Karkheh dam reservoir) in western of Iran has been used in this study and based on ARIMA model, The auto-correlation and partial auto-correlation methods, assessment of parameters and types of model, the suitable models to forecast annual precipitation, temperature and evaporation were obtained. After model validation and evaluation, the Predicting was made for the ten future years (2006 to 2015). In view of the Predicting made, the precipitation amounts will be decreased than recent years. As regards the mean of annual temperature and evaporation, the findings of the Predicting show an increase in temperature and evaporation.

Spatiotemporal Changes in Air Temperature and Precipitation Extremes over Iran

“…Equation ( 15) is a special case of the quantile regression with 0.5 quantiles. The linear quantile regression model is the analogy to Equation (13), except that the regression coefficients are quantile-dependent. That is,…”

“…Given the central role of precipitation in the water cycle, the spatiotemporal analysis of extreme precipitation is crucial to enhancing the resilience of socioecological systems and their infrastructure under climate change [5]. The investigation of the main characteristics of extreme precipitation, as well as its spatiotemporal change and trend, has received growing attention in recent decades [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. It is also notable that, except trends in climate precipitation (variability), future climate change has raised the concern of the scientific community.…”

Copula Based Spatial Analysis of Drought Return Period in Southwest of Iran

“…Researches acknowledge worried conditions regarding the drought growing trend in Iran, particularly in extreme state happened in recent years [9,10]. In southwest of Iran, Khuzestan province which holds major water resources of the country [11], has revealed a frequent drought occurrence during the last decades [12]. At least one extremely severe drought took place in the last fifteen years in Khuzestan [13,14].…”

Effects of Automatic Hyperparameter Tuning on the Performance of Multi‐Variate Deep Learning‐Based Rainfall Nowcasting

“…Researches on rainfall forecasting and its influencing factors have been conducted for decades, and many approaches have been proposed. There are various traditional forecasting models in the literature, which have been developed using classic time series algorithms such as linear regression, autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural networks (ANNs), numerical weather predictions (NWPs), and Fuzzy logic algorithm (Burlando et al., 1993; Felfelani & Kerachian, 2016; Hamidi Machekposhti et al., 2018; Muka et al., 2017; Musarat et al., 2021).…”