## 2 Methods
### Lars-Wg Model  
Different methods are used for the generation of climate change scenarios. In this study, general circulation models were used to simulate climate change. These models are three-dimensional and based on physical and mathematical relations. First, tributary models were simulated; then, all the tributary models of oceans and atmosphere were combined. One of the weaknesses of these models is related to the large scale of computational cells in the temporal and spatial domains. Thus, the associated variables did not have sufficient accuracy for simulating climate change. Different methods, such as the LARS-WG model, were used to downscale computational cells. Additionally, emission scenario A\({}_{1}\)B and the HAD-CM3 model were selected to simulate climate change. The A\({}_{1}\)B scenario considered rapid future economic and population growth. Specifically, maximum population growth occurs in the current half-century, and the population increase will then decelerate. Additionally, technological advancements will rapidly occur in the future period. A previous study of climate change in the Aidoghmoush basin showed that the A\({}_{1}\)B scenario and HAD-CM3 model exhibited good performance compared to other models and scenarios. [PERSON] et al. [34] investigated the uncertainty of different climate models and computed the weights of the different climate change models and scenario models presented. They found that the HAD-CM3 model and A\({}_{1}\)B scenario had the highest weights and lowest uncertainty for computations of temperature and precipitation. Additionally, other previous studies in the basin found that the HAD-CM3 model and A\({}_{1}\)B scenario yielded the best results among various models and scenarios [24,34]. Two significant steps should be carried out to set up the LARS-WG model considering future periods with downscaling of the GCM data. The first step is to create a data file that defines the estimated behavior of the historical climatic parameters during particular previous periods (*.wg). The second step is to adapt to the selected climate change scenario (*.sta). To complete this step, the three different climate parameters--precipitation, temperature, and radiation--should be computed utilizing Atmosphere-Ocean General Circulation Model (AOGCM) and considering the following features:  
* Once all of the above parameter data files have been completed using (*.sec), all the required data are ready to use the LARS-WG model;
* The average monthly rate of changes of wet and dry events;
* Rate of change of the average monthly precipitation considering the long-term period;
* Rate of change of the daily temperature (fluctuations) considering the long-term period;
* The absolute difference of maximum and minimum monthly average temperature;
* Absolute change of the monthly average radiation (long-term). The LARS-WG model can be used to produce daily values of maximum and minimum temperature, precipitation, and radiation, or the number of sunny hours. The first version of this model was used to evaluate agricultural risk [35]. The Markov chain is used for modelling precipitation in the LARS-WG model. LARS-WG uses complex statistical distributions to predict meteorological variables. Specifically, the LARS-WG model receives climate data from the climate model (HAD-CM3); then, the specific scenario for each network in the HAD-CM3 model is considered. A model run requires information from the previous, or base period. This climate change information for the basin and the input from the HAD-CM3 model are subsequently used to simulate climate change conditions for future periods. Thus, the LARS-WG model is considered a black box model [36, 37, 38]. Main variables in these models are the numbers of wet and dry days and daily precipitation; associated distances are divided equally in the empirical distributions. Radiation in this model is simulated independently from temperature.