The dimensionless Leaf Area Index (LAI) is widely used to characterize vegetation cover. With recent remote sensing developments LAI is available for large areas, although not continuous. However, in practice, continuous spatial-temporal LAI datasets are required for many environmental models. We investigate the relationship between LAI and climatic variable rainfall and Growing Degree Days (GDD) on the basis of data of a cold semi-arid region in Southwest Iran. For this purpose, monthly rainfall and temperature data were collected from ground stations between 2003 and 2015; LAI data were obtained from MODIS for the same period. The best relationship for predicting the monthly LAI values was selected from a set of single- and two-variable candidate models by considering their statistical goodness of fit (correlation coefficients, Nash-Sutcliffe coefficients, Root Mean Square Error and mean absolute error). Although various forms of linear and nonlinear relationships were tested, none showed a statistically meaningful relationship between LAI and rainfall for the study area. However, a two-variable nonlinear function was selected based on an iterative procedure linking rainfall and GDD to the expected LAI. By taking advantage of map algebra tools, this relationship can be used to predict missing LAI data for time series simulations. It is also concluded that the relationship between MODIS LAI and modeled LAI on basis of climatic variables shows a higher correlation for the wet season than for dry season.

Groundwater is generally influenced by overexploitation and climatic stresses particularly in arid and semi-arid areas of the world. The present research was conducted to identify the relative contribution of drought and overexploitation to groundwater budget deficit in an unconfined aquifer system. In order to simulate groundwater, the simulated recharge from WetSpass-M model was applied in the MODFLOW model along with other required packages. Moreover, the groundwater budget deficit caused by stressors was quantified through the use of calibrated groundwater model predictions. In order to better understand how the stressors affect the groundwater deficit, the aquifer was divided into Clusters 1, 2, and 3. Locally, the results showed that the contribution of stressors to groundwater budget deficit was the highest in Cluster 1 due to the groundwater overexploitation and quick reaction of the groundwater level to the droughts. Overall, this research showed that both drought and overexploitation, with an average of 2.44 and 3.32 million cubic meters, respectively, played a significant role in groundwater storage deficit. Furthermore, the effect of groundwater overexploitation was approximately 36% more than droughts.

The remote sensing approach of Mapping Evapotranspiration with the Internalized Calibration (METRIC) model is one of the accurate evapotranspiration models but the its complexity motivated us to seek for a simplified version of the original model. This study was designed to propose and evaluate the performance of a new methodology called Simplified Mapping Evapotranspiration with the Internalized Calibration (S-METRIC) model to estimate Actual Evapotranspiration (AET). S-METRIC is a simpler technique to implement while it requires relatively limited input data without losing tangible precision. Thanks to the simpler equations, it is faster computationally. The election of the mathematical form of this formula is mainly based on two purpose: first, it was upon the simplification of the formulas and then, their precision to approximate the formulas of METRIC. Polyfitn function and the synthetic meteorological data were used to fit corresponding curves for the four formulas of S-METRIC ( $R_{\mathrm{s}\downarrow }$, ${\tau }_{\mathrm{s}\mathrm{w}}$, $\mathrm{C}\mathrm{o}\mathrm{s}{\theta }_{\mathrm{h}\mathrm{o}\mathrm{r}}$ and $\mathrm{C}\mathrm{o}\mathrm{s}{\theta }_{\mathrm{r}\mathrm{e}\mathrm{l}}$). The monthly AET calculated by S-METRIC was compared using a number of statistics indexes, including mean absolute error (MAE), root-mean-square error (RMSE), BIAS, relative error (RE), coefficient of determination (R²) and Nash-Sutcliffe efficiency coefficient (NSE). The results of the monthly AET comparison suggested that both models had similar performance to estimate monthly AET in the Vanak Basin with R² = 0.97–0.99, NS = 0.90–0.99, MAE = 0.49–8.7 mm month⁻¹ and RMSE = 0.8–13.1 mm month⁻¹. Also, the results of the monthly estimated $R_{\mathrm{s}\downarrow }$METRIC and $R_{\mathrm{s}\downarrow }$S-METRIC values comparison suggested that both models had showed a similar performance to estimate monthly $R_{\mathrm{s}\downarrow }$with R² = 0.97–0.99, MAE = 5.9–11.38 W m² and RMSE = 7.17–13.12 W m².