Z. Nouri; A. Talebi; B. Ebrahimi
Abstract
Introduction: In the past century, the climate has been changing on both regional and global scales over the earth. It is also expected that such changes will continue in the near future. Climate change is due to increased greenhouse gas emissions in the atmosphere. The concentration of these gases is ...
Read More
Introduction: In the past century, the climate has been changing on both regional and global scales over the earth. It is also expected that such changes will continue in the near future. Climate change is due to increased greenhouse gas emissions in the atmosphere. The concentration of these gases is directly related to the temperature increase. Climate change affects the hydrological cycle through changes in time, amount, the shape of precipitation, evaporation rates and transfer, soil moisture, runoff, etc. Today, the use of hydrological models have been developed to have the factors affecting the hydrological cycle in the watershed. The Soil and Water Assessment Tool (SWAT) is an example of these models. The common method of assessing the effects of climate change on flow is using hydrological models along with general circulation models (GCMS) or regional weather models (RCMS). The purpose of this study is to investigate the effect of climate change on runoff and evapotranspiration (real and potential) of Mehrgerd Watershed using the SWAT hydrologic model and the CanESM2 climatic model.
Materials and Methods: For modeling the change rate of regional climate parameters in the future period (2017-2030) and the effect of these changes on hydrological parameters, the daily data of minimum and maximum temperature of the Borujen station and precipitation of the Tange Zardaloo station for the base period (1984-2005) were used as inputs of the CanESM2 model. Accordingly, using the model of SDSM5.2 under the scenario of RCP8.5 was performed the downscaling operation. To evaluate the efficiency of the SDSM model were used statistical criteria R2, RMSE, and NS. In the next step, the SWAT 2012 model was used to simulate the hydrologic conditions. After introducing the DEM map with a precision of 20 meters, the region was divided into 18 sub-basins. From the combination of land use maps, soil, and slope, 54 units of hydrological response (HRU) were obtained. Then, climatic data including precipitation, minimum and maximum temperature, relative humidity, wind speed, and solar radiation were introduced to the model. Due to the presence of the dam and the two water transfer lines in the area, physical data and discharge were calculated and introduced into the model. The calibration and validation of the model were done by Sufi-2 algorithm. The calibration process was conducted for the period 2004 to 2012 while the validation process was from 2013 to 2016. In order to evaluate the performance of the model, coefficients NS, R2, P-Factor and R-Factor were used. For this purpose, the model was restarted to obtain the appropriate range for each parameter. After calibrating the hydrological model was introduced the simulated climate to the SWAT model. Finally, the effect of climate change was investigated on runoff and evapotranspiration (real and potential) of Mehrgerd Watershed.
Results and Discussion: The results of the downscaling of the climatic model in this region indicate a decrease of 53.48% of precipitation and increase minimum and maximum temperatures for a future period (2017-2030), 0.84 and 3.99%, respectively. Based on the results of the sensitivity analysis of the SWAT model, 10 parameters were identified as the most sensitive parameters. In the hydrological section, the statistical criteria of R2, NS, P-Factor and R-Factor were obtained for the calibration period 0.73, 0.69, 0.52 and 0.24, respectively and for the validation period, 0.71, 0.58, 0.45 and 0.29, respectively. Comparing runoff simulation in the future period under the influence of climate change and comparison of its values with the base period showed a decrease of 23.82% in an annual average of runoff. Climate change will also reduce actual evapotranspiration by 26.03% and increase potential evapotranspiration by 10.20%.
Conclusion: Based on the results of the SDSM model, it was determined that the precipitation is strongly reduced in comparison with the observation period, and the minimum and maximum temperatures increase with a slight difference compared to the observation period. According to statistical criteria, the SDMS model has succeeded in simulating the parameters for the future period. Accordingly, the values of R2, RMSE, and NS for precipitation, were equal to 0.92, 5.81 and 0.39, respectively, and for the minimum and maximum temperatures were obtained 0.99, 0.16, 0.99 and 0.99, 0.21, 0.99, respectively. In the hydrological section, the statistical criteria were acceptable values for the calibration period and the validation. Finally, it was found that under the influence of climate change, runoff decreases. Real evapotranspiration is also declining due to a lack of available water, but potential evapotranspiration is increasing due to the close relationship with temperature.
azam habibipoor; Ali Talebi; Ali Akbar Karimian; Farhad Dehghani; Mohammad Hosain Mokhtari
Abstract
Introduction: Salinity is one of the problems of arid and semi-arid soils. Identification and classification of saline/alkaline soils is necessity for dealing with difficult situations and correct management. Considering the nature of salinity data and selection of befitting methods to process data before ...
Read More
Introduction: Salinity is one of the problems of arid and semi-arid soils. Identification and classification of saline/alkaline soils is necessity for dealing with difficult situations and correct management. Considering the nature of salinity data and selection of befitting methods to process data before use artificial neural network, can result in better simulations. The aim of this study was to investigate the optimal method for data processing to enhance the accuracy of surface soil salinity simulation and improve the efficiency of decision tree algorithm.
Materials and Methods: The study area was 88940.4 hectares of Marvast plain located in central Iran (54° 5´to 54° 18´ east longitude and 30° 10´to 30° 35´north latitude). This region faces with problems of soil and water resources salinity. In this study, the effect of data processing on increasing accuracy of simulation of soil surface salinity was assessed in Marvast region using decision tree algorithm. For this purpose, the decision tree algorithm was applied and simulation was performed using three approaches i.e. original data, logarithmic data and standardized data. Finally, five statistics including R، Rmse، %Rmse، MAE and Bias were calculated to evaluate the performance of used simulation methods.
Results and Discussion: In this study, when the logarithmic data was used, the composition of band 7 – elevation was identified as the most appropriate condition. The created tree can estimate the soil salinity by five laws:
If elevation is less than 1519, then the average of surface soil salinity will be 147.9 ds/m.
If elevation is between 1519 to 1569.9, then the average of surface soil salinity will be 43.6 ds/m.
If elevation is between 1569.9 to 1609.8, then the average of surface soil salinity will be 17.5 ds/m.
If elevation is more or equal to 1609.8 and pixel value of band 7 (ETM+ sensor) in selected point is less than 0.295, then the average of surface soil salinity will be 4.7 ds/m.
If elevation is higher or equal to 1609.8 and pixel value of band 7 (ETM+ sensor) in selected point is more than or equal to 0.295, then the average of surface soil salinity will be 1.4 ds/m.
For the approach of using the logarithmic data, decision tree algorithm used two parameters out of 46 independent variables introduced into the model. R، Rmse، %Rmse، MAE and Bias for this method was computed to be 0.76, 0.49, 38.57, 0.37 and -0.14, respectively. The application of logarithmic data was recognized as the best method considering the lower calculated error and its less input requirement. Using Easy fit software, the distribution of salinity data was found to be Log Pearson 3. Thus, the use of logarithmic data improved model performance. Our findings were in agreement with those of Afkhami et al (2015) who increased the simulation accuracy of suspended sediment with artificial intelligence methods (Artificial neural networks and ANFIS) using logarithmic data.
Conclusions: As effective factors for soil salinity simulation vary in different regions, application of a unique method and indicator to estimate soil salinity in deferent region may not be possible.. The application of semi intelligent algorithm which limits user intervention and selects effective parameters for simulation would increase the simulation accuracy. Furthermore, considering the nature of salinity data and selection of befitting methods to process before using decision tree algorithm can effectively improve model performance. The current study was conducted to select an appropriate approach to enhance the simulation accuracy of surface soil salinity. The results demonstrate that the performance of decision tree algorithm as one of the artificial intelligence models can be affected by input data. In this study, Log-Pearson3 distribution was defined as the distribution of salinity data. Moreover, despite existence of significant correlation coefficients for three simulation methods, the error was lower when logarithmic data was used. Since the probability distribution of salinity data in the studied area was logarithmic (Log-Pearson 3), the reduction in error rate can be attributed to the probability distribution of salinity data.
