عنوان مقاله [English]
Introduction: Direct monitoring of soil moisture and the extraction of moisture data by point method is not only costly and time-consuming, but in a large scale, is impractical; while drought is a regional phenomenon and requires extensive and regional data to monitor it. Therefore, providing a simple method for monitoring soil moisture on a regional scale is of fundamental importance. Climatological or climatic indicators that are continuously measured and recorded at weather stations can be used as information that is readily available to determine soil surface properties such as temperature and soil moisture. The main objective of this study was to estimate soil moisture under forest cover, using climatic parameters that were recorded from a nearby station. Satellite imagery is currently used to estimate the temperature and moisture status of soil, but it must also be matched with accurate ground data and sufficient weather data stations, so it will not be applicable everywhere.
Materials and Methods: Mele-Siah forest habitat in the northwest of Ilam city was selected as the study area in this research. In this regard, in both the southern and northern slopes of Mele-Siah forest site, 18 pairs of humidity sensor at depths of 50, 70 and 110 cm were installed and soil moisture using TDR device was measured monthly. Monthly measurements of soil moisture were performed for three consecutive years and recorded as soil water content. In order to determine the relationship between soil moisture data and meteorological variables, the following 7 variables were extracted from the climatic data available at Ilam Weather Station: rainfall, relative humidity, average temperature, average maximum temperature, average minimum temperature, minimum absolute temperature and maximum absolute temperature. Multiple regression analysis and Pearson correlation coefficient in SPSS software were used to analyze the data and the relationship between soil moisture and climate indices.
Results and Discussion: The results indicated that the moisture variations at the soil surface, in comparison to the other depths, are more severe in all the months of the year. Therefore, the humidity drops at a distance of 20 cm between the depths of 70 to 50 cm, much more than 40 cm between the depths of 110 to 70 centimeters. The average moisture content in the months of the year in the direction of the north was more than that one was in the direction of the south. Climatic parameters of each month had high correlations with soil moisture levels of the same month. In this regard, average temperature and relative air humidity showed the highest correlations. Soil moisture in the area is minimized in September. The determination coefficient (R2) for the regression equation was about 0.93, which represented a very good estimation of soil moisture by the model. The highest average humidity was observed in early December and March and its lowest was observed in September. Correlation coefficients between soil moisture content and climatic indices of each month with two months before of them were not significant. However, the correlation coefficients between soil moisture content and climatic indices of the same month and previous month, except for rainfall, was significant. The results showed that we can use meteorological parameters to estimate soil moisture balance in the forests, which continuous and extensive measurement of soil moisture in them is difficult.
Conclusion: According to the study, the weather indicators such as average temperature and relative humidity of air whose data are available at weather stations or easy to measurable in remote areas, can be used to estimate soil moisture content under forests cover that do not have the possibility of continuous and extensive soil moisture measurement. Although rainfall was expected to be more strongly correlated with soil moisture content, but it should be noted that rainfall in some dry months is negligible or zero. Incidentally, we often want to estimate the moisture content of the soil, especially in dry months, through measurable parameters, thus the data of rainfall is not desirable. However, considering the topography of the study area, the distance from the meteorological station and the effect of elevation on soil moisture content may be effective on the accuracy of the results, but the use of data from meteorological stations near area provides the right comparison.