JWSS - Journal of Water and Soil Science

The purpose of this study was to investigate the relationship between time and spatial features of meteorological, hydrological and agricultural droughts in Karoon 1 Dam basin. Meteorological and statistical data were accordingly selected to evaluate the drought situation between 1993 and 2016. The results showed that hydrological droughts occurred in the meteorological drought and had a very high correlation with this year's meteorological drought. The most severe droughts occurred between 2006 and 2011. Studies also showed that every three years, the basin was accompanied by a meteorological drought and then a hydrological drought. The results also showed that the highest correlation was observed with the 12-month meteorological index, with a delay of 3 months, and the 6-month meteorological and hydrological index with a delay of 3 months and a three-month hydrological drought index with a delay of two months. Therefore, it could be concluded that hydrological droughts showed a delay of almost two to three months in the entire catchment area; since this period was 4 months or more, the correlation between these two indicators was eliminated and decreased. Also, due to drought zones, during the period from 1993 to 2009, most of the droughts were caused by rainfall reduction in the southwest of the province, and this was associated with a reduction in runoff in its hydrometric stations. Of course, in 2009-2012, the runoff status had been temporarily improved, and from 2012 to 2017, the drought situation had again returned spatially to the previous routine.

In the present study, the spatial and temporal changes of climate variables such as pan evaporation (Ep), temperature (T), relative humidity (RH), sunshine duration (SD), wind speed (W) and precipitation (P), as well as their relationship with altitude, were investigated. For this purpose, 68 meteorological stations with 30 years of data (1987-2016) throughout Iran on both seasonal and annual time scales were selected. Trend analysis of climate variables showed that over the past 30 years, most areas of Iran have become warmer and drier although all trends have not been significant. Investigation of the relationship between the trend slope of climate variables and altitude illustrated that there was no significant relationship between them during the study period on the annual time scale (p>0.1). However, in winter, the rate of increase in T (minimum, maximum and mean temperatures) and SD (p<0.1), as well as the rate of decrease in P (p<0.01), was significantly enhanced by increasing the altitude. The increase in mean and maximum T (p<0.1) and SD rates (p<0.001) in summer were significantly lower in the highlands than in the lowlands. In autumn, the trend slopes of minimum and mean T (p<0.05) were negatively correlated with altitude; in addition, the rates of increase in P and RH (p<0.05) in the highlands demonstrated a sharper increase. It seems, therefore, that most changes in climate variables have occurred in both autumn and winter. The results also showed that in winter, the highest rates of increase in Ts were related to the altitude of 1500-2000 m; however, the highest decrease in P belonged to the altitude of 2000-2500 m. In autumn, the highest rates of decrease in minimum and mean Ts had occurred in the altitude of 2000-2500 m; as well, he highest rate of increase in P was observed in the altitudes of both 0-500 m and 2000-2500 m.

An accurate and reliable prediction of groundwater level in a region is very important for sustainable use and management of water resources. In this study, the generalized feedforward (GFF) and radial basis function (RBF) of artificial neural networks (ANNs) have been evaluated for monthly predicting groundwater levels in the Dezful-Andimeshk plain in southwestern Iran. The partial mutual information (PMI) algorithm was used to determine efficient input variables in ANNs. The results of using the PMI algorithm showed that efficient input variables for monthly predicting groundwater level for piezometers affected by water discharge and recharge include only water level in the current month. Also, efficient input variables for predicting the water level for piezometers affected only by water discharge include the water level in the current month, the water level in the previous month, the water level in the previous two months, transverse coordinates of piezometers to UTM, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months and longitudinal coordinates of piezometers to UTM. In addition, efficient input variables of monthly predicting groundwater level for piezometers neither affected by water discharge nor water recharge, respectively, include the water level in the current month, the water level in the previous month, the water level in the previous two months, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months, the water level in the previous six months, transverse coordinates of piezometer to UTM and longitudinal coordinates of piezometer to UTM. The results indicated that the GFF network is more accurate than the RBF network for monthly predicting groundwater level for piezometers including water discharge and recharge and piezometers including only water discharge. Also, the RBF network is more accurate for monthly predicting groundwater levels for piezometers that include neither water discharge nor recharge than the GFF network.