JWSS - Journal of Water and Soil Science

Groundwater is a major source of drought. Karstic aquifers are important sources of groundwater in the West and Kermanshah province. This study was performed to investigate the effects of the meteorological drought on the karstic aquifer with different conditions of development. The studied areas in this research included two karstic aquifers, Bistoon-Parau and Patagh mountain in Kermanshah province. In this study, we used monthly precipitation and springs discharge during a period of 20 years.  Accordingly, the SPI and SDI indices were used to investigate the different states of meteorological and hydrological droughts, respectively. To determine the relationship between meteorological droughts and groundwater, Pearson correlation was used; aalso, to determine the time delay, the correlation between the different time conditions (no delay and delay 1 to 6 months) of the SDI index and the SPI index was investigated. The results of the relationship between the meteorological drought and groundwater showed that both had a significant correlation (p-value: 0.01). Also,  based on the results of the correlation between different time conditions (no delay and delay 1 to 6 months) ,the SDI index was compared to the SPI index, showing that the time delay between the occurrence of meteorological drought and groundwater in the studied areas without time delay or a maximum one-month delay had happened. Based on the results, Pearson correlation coefficients between the SPI and SDI indices in the Bistoon-Parav region were more than those of the Patagh mountain region indicating the development of the Bistoon-Parav karst region, as compared with the Patagh Mountain.

Saturated hydraulic conductivity (K_s) is one of the most important soil physical characteristics that plays a major role in the soil hydrological behaviour. It is mainly affected by the soil structure characteristics. Aggregate size distribution is a measure of soil structure formation that can affect Ks. In this study, variations of K_s were investigated in various aggregate size distributions in an agricultural soil sample. Toward this aim, eight different aggregate size distributions with the same mean weight diameter (MWD= 4.9 mm) were provided using different percentages of aggregate fractions consisting of (< 2, 2-4, 4-8 and 8-11mm). The Ks values along with other physicochemical properties were determined in different aggregate size distributions. Based on the results, significant differences were found among the aggregate size distributions in K_s, particle size distribution, porosity, aggregate stability, electrical conductivity (EC), organic matter and calcium carbonate. The aggregate size distributions with a higher percentage of coarse aggregates (4-8 and 8-11 mm) also showed higher K_s as well as clay percentage. A positive correlation was also observed between Ks and clay, aggregate stability and EC, whereas sand showed a negative correlation with K_s. No significant correlations were found between K_s and silt, porosity and organic matter. Further, multiple linear regression analysis showed that clay and aggregate stability were the two soil properties controlling K_s in the aggregate size distributions (R²=0.80, p<0.01). Aggregate stability was recognized as the most important indicator for evaluating the K_s variations in various aggregate size distributions.

Soil loss and extensive degradation caused by gully erosion have always caused serious damage. Because direct field measurement and monitoring of gully erosion are costly and time-consuming, it is very difficult to determine the amount of soil loss caused by gully erosion. The present research was conducted to calculate the volume of soil loss due to gully erosion using machine learning models in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province based on field studies. Machine learning models include random forest, support vector machine, artificial neural network, and adaptive neural fuzzy inference system. The location of 68 gullies in the area was recorded. Hence, initially, digital layers of factors affecting the expansion of gullies, including topography, pedology, lithology, and hydrology, were prepared as independent variables to model soil loss caused by gullies. Then, representative gullies were selected in the studied watershed, and the volume of soil loss due to gully erosion was directly measured in the field as a dependent variable. The measured gullies were randomly divided into two training and validation groups. The results of the models were evaluated using root mean square error (RMSE) and R2, and the models were compared. According to the results, gully erosion in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province is increasing every year. Also, the amount of erosion and soil loss will increase when the amount of rainfall and the frequency of intense rainfall (≥5mm) are high. Among the machine learning models used in the present research, the random forest (RF) model was selected as the best model to predict soil loss generated by gully erosion.

Gully erosion is one of the most important types of water erosion. Since the amount of soil loss due to this erosion is directly related to environmental factors, the amount of soil loss due to each gully can be modeled based on environmental conditions. According to the high ability of machine learning models based on artificial intelligence to analyze environmental information, in addition to determining soil loss due to gully erosion, modeling has been carried out using two random forest models, and artificial neural networks and evaluating their efficiency in the Mahurmilati watershed located in the southwest of Fars province in this study. The dimensional parameters of 70 gullies were measured over four years (2021-2024), and the volume and weight of soil lost were calculated. 15 environmental factors were selected as predictive variables, and modeling was performed with a cross-validation approach using these two models, and the accuracy of the models was evaluated using quantitative criteria. The amount of soil loss in gullies during the study period was 15300.94 tons. The accuracy evaluation of the models showed that the random forest model had better performance based on the coefficient of determination (R2=0.66-0.73). Also, this model had the lowest value in terms of the RSR error index evaluation criterion (RSR=0.66-1.03) and the highest accuracy. In terms of the fit evaluation index (D), the random forest model also had the highest fit between the observational and forecast data and had the highest value of this index (D=0.83), and therefore, it was introduced as the superior model for predicting soil loss due to gully erosion in this watershed.