صادق واله

Erosion is primarily caused by precipitation and runoff, while erodibility results from the type of the formations at the catchment scale, which is subject of this study. The objective of this study was to investigate the relationship between geologic units and erosion intensity. Initially, two models of MPSIAC and Fargas were used to study erosion intensity. The Fargas model was presented based on the theoretical principles of two erosion and the erodibility factors, while MPSIAC model was based on 9 factors. BLM method was used to separate the intensities of a type of erosion according to the S.S.F table. RUSLE model was used to estimate soil erosion in the studied sub-basins. To determine the risk of erosion using the value coefficient, Fargas model was used. In the MPSIAC model, coefficients were considered for each of the nine factors. Based on the results of MPSIAC model, 79% of the basin surface had high erosion intensity and 21% had very high erosion intensity, while according to Fargas model, 0.3% had moderate erosion intensity, 10% had high erosion intensity, and 4.7% and 85% were characterized with severe erosion and very severe erosion, respectively. Moreover, in both models, 18% corresponded to very high erosion intensity (very severe), while 7.2% had high erosion intensity. In Fargas model, E1t1, E1t2, E2ig3, Q2f, Qt1, and Qt2 had very strong erosion intensity, whereas, in MPSIAC model, E1t1, Qt1, and Qt2 had high erosion intensity on their surfaces. One reason for this phenomenon could be the erodibility of the formations. Most of E1t2 and Q2t are highly eroded, which contributes to a large amount of drainage in addition to the erodibility of the formations.

One of the major challenges affecting the natural ecosystems and various aspects of human life is climate change. The effects of global warming on the hydrology and water cycle in nature are very serious, and the quantitative recognition of these effects creates more readiness to deal with its consequences. In the present study, the black box model (artificial neural network) and the semi-distributed model (SWAT) were selected and examined according to error and uncertainty. This paper employed the large-scale model (CanESM2) under scenarios RCP2.6 and RCP8.5 to investigate the effect of climate change. The results of climate change in this study showed that runoff (3-59%) and temperature (1.53-6.93 °C) have an increasing trend. In particular, this increasing trend is further exhibited by extreme values and severe floods. Also, the amount of runoff will increase by 7-11% in the upcoming period. For this reason, it is necessary to pay attention in urban studies to the increase of flood occurrence in the future.