Abstract: Saturated hydraulic conductivity of Soil is an
important property in processes involving water and solute flow in
soils. Saturated hydraulic conductivity of soil is difficult to measure
and can be highly variable, requiring a large number of replicate
samples. In this study, 60 sets of soil samples were collected at
Saqhez region of Kurdistan province-IRAN. The statistics such as
Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean
Bias Error (MBE) and Mean Absolute Error (MAE) were used to
evaluation the multiple linear regression models varied with number
of dataset. In this study the multiple linear regression models were
evaluated when only percentage of sand, silt, and clay content (SSC)
were used as inputs, and when SSC and bulk density, Bd, (SSC+Bd)
were used as inputs. The R, RMSE, MBE and MAE values of the 50
dataset for method (SSC), were calculated 0.925, 15.29, -1.03 and
12.51 and for method (SSC+Bd), were calculated 0.927, 15.28,-1.11
and 12.92, respectively, for relationship obtained from multiple
linear regressions on data. Also the R, RMSE, MBE and MAE values
of the 10 dataset for method (SSC), were calculated 0.725, 19.62, -
9.87 and 18.91 and for method (SSC+Bd), were calculated 0.618,
24.69, -17.37 and 22.16, respectively, which shows when number of
dataset increase, precision of estimated saturated hydraulic
conductivity, increases.
Abstract: Droughts are complex, natural hazards that, to a
varying degree, affect some parts of the world every year. The range
of drought impacts is related to drought occurring in different stages
of the hydrological cycle and usually different types of droughts,
such as meteorological, agricultural, hydrological, and socioeconomical
are distinguished. Streamflow drought was analyzed by
the method of truncation level (at 70% level) on daily discharges
measured in 54 hydrometric stations in southwestern Iran. Frequency
analysis was carried out for annual maximum series (AMS) of
drought deficit volume and duration series. Some factors including
physiographic, climatic, geologic, and vegetation cover were studied
as influential factors in the regional analysis. According to the results
of factor analysis, six most effective factors were identified as area,
rainfall from December to February, the percent of area with
Normalized Difference Vegetation Index (NDVI)