S. Esmailian, M. Pajouhesh, N. Gharahi, Kh. Abdollahi, Gh. Shams,
Volume 28, Issue 2 (8-2024)
Abstract
Studying the process of soil erosion and evaluating its effective factors is one of the most important prerequisites for proper management of soil and water resources. This study was conducted to investigate the production of surface and pipe runoff and sediment using artificial rainfall on silt loam soil in the laboratory. So, the soil was collected from the study area and transported to the laboratory. Laboratory experiments were performed on a soil bed in a rectangular flume with three pipes, at slopes of 2%, 6%, 10%, 14%, and 18% under simulated rain (30 mm/h) for one hour. Related graphs were drawn in Excel to analyze the results, and Spearman's correlation test was used in SPSS software to check the correlation between runoff and sediment values in each slope. The results showed that with the increase in slope, the sum of surface and pipe runoff and sediment increased over time. For example, in a slope of 2%, the runoff and sediment in the initial moments of the experiment increased from 0 to 1.3 liters and 26.2 g m-2 at the end of the experiment. Also, the correlation coefficient between runoff and sediment in the slopes was 0.98, 0.62, 0.4, 0.93, and 0.15, respectively, which was significant in some, but in others, it was not significant because of soil loss.
Homa Chegini, Chooghi Bairam Komaki, Majid Owneq, Hamidreza Asgari, Khalil Ghorbani,
Volume 30, Issue 1 (3-2026)
Abstract
This study aimed to analyze the spatial–temporal correlation between the Vegetation Health Index (VHI) and climatic variables, including precipitation, potential evapotranspiration (PET), and mean temperature, in Golestan Province during the period 2000–2024. MODIS satellite products were used for vegetation and land surface temperature data, while the TerraClimate dataset provided precipitation and PET variables. After spatial–temporal alignment, the Cross-Correlation Function (CCF) was applied to identify optimal time lags, and the Random Forest model was employed to assess the relative importance of the climatic drivers. Turning to the results, increasing trends in mean temperature and PET were observed, alongside a significant decrease in precipitation, which led to intensified climatic stress and reduced VHI across the province, especially during summer in croplands and rangelands. The relationship between VHI and precipitation was positive (maximum correlation of 0.299 in croplands), negative with PET (−0.287), and non-linear with temperature (0.275). Notably, VHI responded to precipitation with short-term lags (0–1 month), whereas PET and temperature effects emerged with longer lags (2–4 months). The Random Forest analysis highlighted precipitation as the most influential factor on VHI, followed by PET and temperature, achieving strong predictive performance (R² = 0.78, RMSE = 0.09). Overall, these findings emphasize precipitation as the immediate driver of vegetation health, while PET and temperature act as secondary, cumulative stressors. The results provide valuable insights for developing climate adaptation and sustainable resource management strategies in agriculture and natural ecosystems of Golestan Province.
