hojjatollah ghasemi

Evaporation and Transpiration (including soil water evaporation and transpiration in plants) account for about 60% of the annual atmospheric precipitation on the earth. This process is the backbone of the hydrological cycle and a key element in Water Resources Management and the agricultural sector. The Soil and Water Assessment Tool (SWAT) and the Surface Energy Balance Algorithm for Land (SEBAL) are specialized models that address this problem. A SEBAL model is a tool for estimating the spatial distribution of actual evaporation and transpiration using satellite images such as MODIS, and Landsat 8. Although Landsat 8 images have a higher Spatioal resolution than MODIS images (30 m vs. 1000 m), their Tempoural resolution is lower than that of MODIS images (every 16 days vs. every day). On the other hand, daily MODIS images may not always be usable because of problems such as cloudiness. In addition, the interpretation of numerous satellite images is very time-consuming. In the current study, the evaporation and transpiration values in the catchment area of Karun River were obtained in three dry, normal and wet years (2019, 2020, 2021) using the recalibrated SWAT model based on runoff and crop yield and the SEBAL algorithm. Then the results of the SEBAL algorithm and SWAT model were compared with each other based on the water year conditions. In the second stage of the research, the results of the SEBAL algorithm and the range of changes of the main parameters of this algorithm in the plains of Ahvaz-Mlathani and Al-Baji basins were presented according to the ground data and using the MODIS images from the Terra satellite with a suitable time resolution and OLI sensor from Landsat8 satellite, which has a good spatial resolution.

By developing GIS and remote sensing technology, the widespread access possibility and local distribution of hydrological management parameters and variables have become practical. Runoff rainfall modeling is always an important and continuous need for practical issues in the fields of water resources evaluation, flood forecasting, engineering canals designing, and many other goals (Bone, 2001). Calculation of runoff-rainfall results has been made practical and operationalized by using GIS techniques and a distributed hydrological model. The WetSpa runoff-rainfall model is a hydrological-distribution model that was developed in Brussels in 1997 (Wange et al, 1997) and also, had been used in various research and executive projects by developing in various models. This hydrological model has the capability of performing simulations at the pixel level and because of this, it provides the possibility of using aerial and satellite images with accurate information measured at the basin level, which has a local distribution (Hooshyarypor at el, 1397). Due to the high uncertainties of hydrological parameters, the calibration model is one of the most important part of modeling whose optimization techniques are mainly are used for such purposes. There are various methods for optimization, sensitivity analysis, and also evaluation of uncertainty of models. Accordingly, this paper’s purpose is to calibrate the WetSpa hydrological model with a multi-objective optimization approach that uses the Social Spider Algorithm (SSA) and the Black Widow Spider (BWO) techniques. By this point of view, to achieve a reliable prediction, in addition to the usual discharges, the model must be able to predict high and low discharges accurately (including maximum and minimum), so the objective functions are selected in a way that the best match between observational and computational values can be achieved during the Vasanji process.

WetSpa Model: WetSpa is a continuous local and temporal model in which all of the simulations are conducted continuously. The WetSpa model displays the water and energy balance for each calculation cell, considers rainfall processes, vegetation, snowmelt, wetting, infiltration, evapotranspiration, leakage, surface runoff, wall flow, and groundwater flow. The hydrological system simulated by this model consists of four layers: vegetation, soil surface, root zone, and saturated groundwater table.The Optimization algorithms: The Social Spider Optimization (SSO) algorithm is a new optimization approach which is proposed in 2013 by Kause et al. Another optimization approach used in this paper is the Black Widow Spider (BWO) optimization algorithm. During the day, the black widow spider is out of sight and is mostly nocturnal, and rotates its network during the night. Generally, the widow spends most of her adult life on the same site (Andrite and Banta, 2002).Objective functions and model evaluation: In this paper, to evaluate the model, five statistical indices have been used of correlation coefficient (r), Root Mean Square Error (RMSE), mean absolute error (MAE), Nash-Sutcliffe index, and Nash-Sutcliffe logarithmic index. To increase the accuracy of the model in predicting the minimum and maximum flows, two alogarithm Nash-Sutcliffe and Nash-Sutcliffe logarithmic criteria were used.Zashk basin and model data: Zashk Basin is located in Khorasan Razavi province and in the west of Mashhad with an area of 65.56 square kilometers.

In this part of the paper, the calibration results of the WetSpa model using social spider and black widow spider algorithms are presented. The problem decision variables are the 11 global parameters illustrated in Table 1. Objective functions must be selected in a way that at the end of the calibration process the best match between the observed and computational values can be obtained. Some of these functions give more weight in high flows, while others consider more weight in low flows and have more emphasis on them. In this regard, Nash-Sutcliffe (NS) criterion and its logarithmic form (NS-Log) have been used. In this paper, the results obtained based on these two functions are illustrated. After determining all the necessary local networks in basin modeling, precipitation, evaporation, temperature, and discharge information from 2009 to 2011 were used to calibrate the model and 2012 to 2014 data were used to validate the results. Each of the optimization algorithms was conducted with an initial population size of 100 people and over 100 generations (number of times the optimization model was executed). As it was observed, the answers obtained by BWO are better than the answers of SSO due to its higher NS values. According to the obtained results, NS and NS-Log values are generally from -2.3 to 0.75 and from -0.165 to -0.01, respectively. This problem illustrates that the calibrated model has been more successful in simulating low discharges. In fact, since the model has been executed continuously, the simulation results in 2008 are considered as the Warm-Up period.

This paper calibrated the WetSpa distributed rainfall-runoff model. To calibrate the model, two evolutionary optimization algorithms of social spider (SSO) and black widow spider (BWO) were used in the Zashk basin of Mashhad. The results obtained from the usage of the model in this basin illustrated the satisfactory capability of these algorithms in calibrating the WetSpa model. Comparing the results obtained from the SSO and BWO algorithms illustrated that in the multi-objective calibration problem, the BWO algorithm was slightly more successful than the SSO model. Also, the results indicated that the simulation quality of low discharges was higher than the high ones. The reason for this problem, firstly, can be due to the lower abundance of high discharges in the evaluated data set and secondly, the relatively slow response of the model to changes in hydrological conditions in flood conditions in the catchment basin, because the temporary groundwater storage coefficient (K5) allocated a large amount for itself that can directly affect the reduction of surface runoff. On the other hand, the weakness of the results in predicting some low discharges can be related to the optimal value of groundwater recession coefficient (K2), which has taken a small amount in the calibration process. The results of this study illustrated that the effect of the surface runoff coefficient on the model results is much greater than other parameters (about 36%).