فصلنامه جغرافیای طبیعی
پیاپی 54 (زمستان 1400)

Low-precision rainfall forecasting leads to significant losses in various sectors such as agriculture and the environment. In this regard, the effect of support vector regression (SVR), gene expression programming (GEP) and group data modeling (GMDH) models on improving the performance of the hybrid model was examined, which is based on station rainfall data. Urmia and Isfahan with two different climates were used in the period 1964-2019. In nonlinear section modeling, the third combination with the linear section combination, residuals and observational data in the previous time step had less error, for example in Isfahan station, the rate of RMSE reduction from combination 1 to 3.73 / 62 And the rate of SMAPE reduction from 2 to 3 was equal to 62.79%. The hybrid model had better performance than the stochastic model, so that the amount of RMSE from the stochastic model to the hybrid model with SVR, GEP and GMDH at Urmia station decreased by 79.46, 68.34 and 75.77%, respectively. . The gene expression programming model was less accurate than the other models studied (in Urmia station, the rate of UII reduction from GEP to SVR model was 32.5 and 15.62%, respectively, and in Isfahan station, the rate of increase in Nash coefficient was Sutcliffe from GEP to GMDH was 22.38). The amount of Nash-Sutcliffe coefficient in all three models in Urmia station was higher than Isfahan (the average rate of decrease in Nash-Sutcliffe coefficient from Urmia station to Isfahan was 6.22%) but the value of coefficient in both stations is within acceptable range. Therefore, choosing an efficient model with the right combination in nonlinear modeling will have a significant effect on increasing the efficiency of the hybrid model.

This study analyzes the climatic-agricultural zoning of rain-fed corn in Lorestan province and its role in reducing environmental risks. The study method in this research is citation-descriptive in which the information (precipitation, temperature, etc.) of a 20-year statistical period (1377-1397) has been gathered on a daily, monthly, seasonal and annual basis from 53 synoptic stations, climatology and rain gauge, inside and outside of this province. Then, considering the required conditions for this crop such as heat thresholds, growth period and the amount of water requirement, suitable growth zones has been determined by GIS and after that the zoning of agricultural climate of corn crop in this province has been mapped. On the base of the final obtained map, this province has 8588.15 kilometers of potential lands, 13938.13 square kilometers of semi-potential lands and 5631.57 square kilometers of non-potential lands. Kriging interpolation method has been used to create coordination in data integration and also auxiliary points to find the regions with the same amount of temperature and rainfall.

The purpose of this study is to evaluate the temporal and spatial changes of vegetation in Mullah Fahleh of Firoozabad region of Fars province. The standard vegetation index data of Landsat 5, 7 and 8 satellites, the precipitation data and Palmer index, which are available as a remote sensing product, were extracted for 1992 to 2020 from the Google Earth Engine. Markov chain method was used to predict future vegetation changes. Using data from 1992 and 2002, the model was first run for 2020 and based on its acceptable performance (the kappa index of 75%), prediction was extended to 2030. The results showed an improving trend in vegetation status, so that the dense cover will increase from 4% in the initial period to 19% in 2030. The results of this study can help rangeland managers in the region to better exploit the region's natural resources and prevent the destruction of this ecosystem.

In this regard, the present study investigated the climatic of the Zahedan snow days since 1976 over a period of 40 years. To achieve this goal, two databases were used. First databases; the present weather codes are related to the snow phenomenon, which was collected daily from the Meteorological Organization. The second database, relates to the daily data of sea level pressure and geopotential height in the troposphere from National Center for Environmental Prediction (NCEP). With Environmental to Circulation Technic and cluster analysis, weather patterns causing snow days, identified. The results of the analysis showed that, snowfall in Zahedan is concentrated in autumn and winter seasons (December, January, February and March). At sea level pressure, integration patterns of Siberian-Caspian high-pressure migrants with 32.5% and Siberian-Caucasian highs with 20%, play the most roles in snowfall event. Also in the middle troposphere, Turkmenistan's ridge and Blocks (cut-off high) account for 42.5% and the European omega block pattern accounts for 25% of the snowfall frequency. It seems that the simultaneous activity of atmospheric disturbances in the immigrant high pressure systems of the Siberian, European, Caucasian, Russian and Caspian, deep ridge polar latitudes and its extension to the lower latitudes, It will exacerbate showers and temperatures, cold weather and snowfall.

Drought is one of the most costly natural disasters in Iran and the need for a study to determine vulnerability and identify the factors affecting it in different parts of the country.The aim of this study was to compare CVI and AHP methods in assessing drought and measuring climatic vulnerability in Guilan province in order to identify its dimensions.The research method is spatial and applied in terms of purpose. In the AHP model, the importance of the resource component (R) with a weight of 0.293 is the highest and the weight of the component of geographical features (G) with a weight of 0.045 is the lowest. In the study of variables in the AHP model, the variable of total rainfall with a weight of 0.750 was the most effective variable and the variety and quality of agricultural products with a weight of 0.088 was the least effective variable according to experts. In the study of CVI components, the CVI index of the whole province was 44.5%. The CVI index had the highest rating in the resource component (R) at 56.12% and the lowest rating in the 36.94% component. The zoning of the AHP model showed that the middle part of the province, including Rasht, Saravan, Kasma, Anzali, has the highest area with an area of 42.11%. In the CVI model,the highest vulnerability is in the whole city of Talesh with an area of 15.57%.Astara and Rezvanshahr cities with an area of 20.88% were observed in a very low vulnerability class.

In this study, the perspects of evapotranspiration changes in Karun catchment were investigated using Hargreaves- Samani method, LARS-WG6 downscaling model, and output of HadGEM2-ES model, under scenarios of RCPs for three periods of 2021-2040, 2041-2060 and 2061-2080 as compared to the period 1996-2015. The results show that the minimum average annual temperature in the catchment increases between 1.2 to 3.4 °C and the maximum temperature increases between 1.3 to 3.7 °C in future periods compared to the base period. The amount of evapotranspiration will increase in monthly, seasonal and annual time scales in the future climate, compared to the current conditions, so that the maximum increase in annual evapotranspiration in the distant future period and under the RCP8.5 scenario will be equal to 12.3% compared to the base period. The twenty-year box plot in future periods under different scenarios show that the highest evapotranspiration uncertainty occurs in the period 2061-2080 and under the scenarios RCP8.5 and RCP4.5 respectively. According to zoning maps of evapotranspiration changes in the basin, the highest amount of evapotranspiration changes will occur during the period 2061-2080 and under the RCP8.5 scenario in the western regions of the basin. Considering that Karun River originates from the mountainous areas on the north of the basin, increasing the temperature and in turn, increasing evapotranspiration in future periods will reduce the level of snow coverage in the area and will have significant negative effects on the flow of Karun River and can thus reduce the water resources of the basin.

The satellite-based precipitation products are one of the sources of rainfall estimation. Nonetheless, for usage in the local regions and, or for parameterizing of meteorological and hydrological models at basin scales, their spatial resolution is often coarse. Therefore, in this study, a downscaling– calibration method was developed for global precipitation measurement (GPM) satellite estimates (at 0.1° spatial resolution), for one year from 01/04/2014 to 31/03/2015, by considering the spatial heterogeneity of the relationship between precipitation and the environmental variables using the mixed geographically weighted regression (MGWR) model for Golestan province. In obtaining improved precipitation data with 1 km spatial resolution at an annual scale, the results showed that (1) the proposed method not only improved the spatial resolution of precipitation but also increased accuracy; (2) the downscaled and calibrated precipitation data (CC = 0.74, bias = 0.23) performed better than the original data (CC = 0.58, bias = 0.35) against ground observations.