Estimation of surface runoff using NRCS curve number in some areas in northwest coast, Egypt

The sustainable agricultural development in the northwest coast of Egypt suffers constantly from the effects of surface runoff. Moreover, there is an urgent need by decision makers to know the effects of runoff. So the aim of this work is to integrate remote sensing and field data and the natural resource conservation service curve number model (NRCS-CN).using geographic information systems (GIS) for spatial evaluation of surface runoff .CN approach to assessment the effect of patio-temporal variations of different soil types as well as potential climate change impact on surface runoff. DEM was used to describe the effects of slope variables on water retention and surface runoff volumes. In addition the results reflects that the magnitude of surface runoff is associated with CN values using NRCS-CN model . The average of water retention ranging between 2.5 to 3.9m the results illustrated that the highest value of runoff is distinguished around the urban area and its surrounding where it ranged between 138 - 199 mm. The results show an increase in the amount of surface runoff to 199 mm when rainfall increases 200 mm / year. The north of the area may be exposed to erosion hazards more than the south and a change in the soil quality may occur in addition to the environmental imbalance in the region

On the use of multivariate analysis and land evaluation for potential agricultural development of the northwestern coast of Egypt

The development of the agricultural sector is considered the backbone of sustainable development in Egypt. While the developing countries of the world face many challenges regarding food security due to rapid population growth and limited agricultural resources, this study aimed to assess the soils of Sidi Barrani and Salloum using multivariate analysis to determine the land capability and crop suitability for potential alternative crop uses, based on using principal component analysis (PCA), agglomerative hierarchical cluster analysis (AHC) and the Almagra model of MicroLEIS. In total, 24 soil profiles were dug, to represent the geomorphic units of the study area, and the soil physicochemical parameters were analyzed in laboratory. The land capability assessment was classified into five significant classes (C1 to C5) based on AHC and PCA analyses. The class C1 represents the highest capable class while C5 is assigned to lowest class. The results indicated that about 7% of the total area was classified as highly capable land (C1), which is area characterized by high concentrations of macronutrients (N, P, K) and low soil salinity value. However, about 52% of the total area was assigned to moderately high class (C2), and 29% was allocated in moderate class (C3), whilst the remaining area (12%) was classified as the low (C4) and not capable (C5) classes, due to soil limitations such as shallow soil depth, high salinity, and increased erosion susceptibility. Moreover, the results of the Almagra soil suitability model for ten crops were described into four suitability classes, while about 37% of the study area was allocated in the highly suitable class (S2) for wheat, olive, alfalfa, sugar beet and fig. Furthermore, 13% of the area was categorized as highly suitable soil (S2) for citrus and peach. On the other hand, about 50% of the total area was assigned to the marginal class (S4) for most of the selected crops. Hence, the use of multivariate analysis, mapping land capability and modeling the soil suitability for diverse crops help the decision makers with regard to potential agricultural development. © 2020 by the authors