Agricultural Meteorology
M. Amirabadizadeh; Mahdieh Frozanmehr; M. Yaghoobzadeh; Saeideh Hosainabadi
Abstract
IntroductionNowadays, climate change is one of the human challenges in the exploitation and management of water resources. Temperature along with precipitation is one of the most important climatic elements and is one of the main factors in zoning and climatic classification. Due to location of ...
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IntroductionNowadays, climate change is one of the human challenges in the exploitation and management of water resources. Temperature along with precipitation is one of the most important climatic elements and is one of the main factors in zoning and climatic classification. Due to location of Iran within the drought belt and proximity to the high-pressure tropical zone, this country has an arid and semi-arid climate and suffers from drought in majority of years. Therefore, temperature fluctuations and variability are important issues, and make the study of temperature changes a necessity. In the current study, four data mining algorithms in selecting predictors for downscaling of maximum temperature in Birjand synoptic station have been studied, compared and the superior algorithm has been introduced. As the number of large scale features are high, selection of machine learning algorithm will play as an important role in statistical downscaling of climatic variables such as maximum temperature. Materials and MethodsToday, the data set is such that many variables are used to describe the climatic phenomenon in environmental studies. As the number of data is huge, choosing the predictors is one of the most important steps in preprocessing machine learning. In this study, four machine learning methods including stochastic approximation of simultaneous turbulence (SPSA), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Gradient Boosting Method (GBM) in selecting important features in downscaling of maximum temperature in Birjand synoptic station during the statistical period of 1961-2019 were studied and compared. It is a mechanism to find a combination of predictors that with a minimum number of predictors can produce an acceptable evaluation index in estimating the variable under study. For the present study, the weather information of Birjand Synoptic Meteorological Station has been prepared by the Meteorological Organization of Iran. In order to calibrate and validate the machine learning algorithms, 70% and 30% of the available monthly data, respectively, were allocated for this purpose. To conduct this research, coding in R-Studio environment and Caret and Fscaret packages were used. In this study, to evaluate the performance of the algorithms, three indices includes relative Nash-Sutcliffe Efficiency (rNSE), Volume Efficiency (VE) and Kling-Gupta Efficiency (KGE) were used.Results and DiscussionBefore using the algorithms in selecting large-scale predictors, the correlation between these variables and the maximum observational temperature at Birjand station was investigated. Large scale variables mslp, P1_v, P8_v, P8_u, P850 Temp, with a maximum correlation temperature of 0.6 showed that the correlation is acceptable given the complexity of the climate change phenomenon. In addition, these results show that all the algorithms used the important factors including F1, F2, F15, F16, F18, F20 and F26 by more than 50% and the first variable (mean pressure at the ocean surface) was the most important parameter in downscaling of maximum temperature. Also, the highest importance was for P1_v and the lowest value related to P5_u, as 73.2% and 15%, respectively. Violin plots of downscaled maximum temperature in validation step of different algorithms along with the observed maximum temperature in Birjand synoptic station in each of the algorithms showed that the values of the first and third quartiles in the output data of SPSA algorithm compared to other algorithms were closer to the observed data. According to the evaluation criteria, SPSA algorithm has a higher performance than other algorithms in reproducing the maximum monthly temperature values in Birjand synoptic station. Also, based on the volumetric efficiency evaluation criteria and relative Nash-Sutcliffe, GBM algorithm was more successful in selecting predictors than Ridge and LASSO algorithms. It is also observed that SPSA algorithm shows different results than other algorithms. In comparison of mean and variance of downscaled and observed maximum temperature, the results of t-test and F-test showed that SPSA algorithm has higher efficiency than other algorithms in regenerating mean and variance of observed maximum temperature in Birjand synoptic station at the 5% significance level.ConclusionThe data used in this study included large scale atmospheric variables and the maximum observed temperature at Birjand station. The algorithms were used to select important predictors and the performance of these methods in the validation part. According to the results of this study, the highest importance among large-scale variables is related to P1_v and the lowest value is related to P5_u, the values of which were 73.2% and 15%, respectively. The SPSA algorithm also performs better than other algorithms in selecting predictors and consequently the maximum temperature.
Agricultural Meteorology
M. Fashaee; S.H. Sanaei Nejad; M. Quchanian
Abstract
Introduction Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote ...
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Introduction Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote sensing data are often provided from three sources: microwave, visible and thermal. Most satellite soil moisture-based algorithms rely on passive microwave images, active microwaves, or a combination of data from several different sensors. Among the various remote sensing methods, the microwave electromagnetic spectrum has fewer physical limitations than other spectrum in measuring soil moisture. However, microwave soil moisture data often have very large pixel dimensions (more than 10 km), making it difficult to use them on a small scale.Materials and Methods In this study, in order to calculate the agricultural drought index at the field-scale, AMSR2 Retrieval data were calibrated first using field moisture measurement data in the Neishabour plain during 2017 to 2019. During the research period, 560 soil samples (20 samples in 28 shifts) were collected and soil moisture was measured in the laboratory of the Department of Water Science and Engineering, Ferdowsi University of Mashhad. LPRM_AMSR2_ SOILM3_001 is one of the third level products of the AMSR2 sensor, which is produced on a daily basis with a spatial resolution of 25 × 25 km2. Land surface parameters including surface temperature, surface soil moisture and plant water availability were obtained by passive microwave data using the Land parameter Retrieval Method (LPRM). Then, by using Modis sensor images (NDVI and LST), linear downscaling equations were extracted. The dimensions of the AMSR2 images were reduced from 25 kilometers to 1000 meters using these equations. In next step, SMADI Agricultural Drought Index, which is a combination of vegetation characteristics, soil moisture and land surface temperature, was used to monitor agricultural drought at the field-scale. Statistical indicators such as coefficient of determination (R^2), mean absolute error (MAE) and root mean square error (RMSE) were also used to evaluate the statistical performance.Results and DiscussionBy visual analysis of the role of vegetation and land unevenness, it was found that these two factors affect the regression relationships extracted for calibration of remote sensing data. The RMSE and MAE values for the regression equations used in the calibration process were calculated in the range of 1.6 to 4%, which can be considered acceptable in comparison with the mean values of the soil moisture data (15 to 20). The results showed that changes in SMADI index in three land use zones including rainfed cultivation (R1), medium rangeland (R2) and poor rangeland (R3) have experienced a similar trend to precipitation changes, illustrating that precipitation is one of the most effective factors in major changes in SMADI agricultural drought index fluctuations. It was also observed that SMADI index changes with a delay of 1 to 8 days compared to the precipitation changes in all three zones. In all three zones, the SMADI index followed a similar trend to in-situ soil moisture changes. At mot 80% of the changes in SMADI-R1 index can be explained by in-situ SM-R1, and the rest of the changes were related to other environmental factors or measurement error. This decreases to 68% in the R3 zone. It should be noted that soil moisture monitoring can more accurately reflect the impact of environmental factors on the changes in agricultural drought index such as SMADI than other variables; because the rainfall recorded at the meteorological station does not necessarily occur uniformly throughout the study area. On the other hand, any amount of precipitation will not necessarily lead to an effective change in soil moisture storage. This also renders assessment of the performance of agricultural drought indicators difficult.Conclusion Examination of statistical indices of coefficient of determination (R2), mean absolute error value (MAE) and root mean square error (RMSE) showed that the algorithm used in downscaling as well as estimating SMADI agricultural drought index is well able to reflect the interactions between precipitation, soil moisture, vegetation and changes in canopy temperature profile. This feature justifies and strengthens its application in agrometeorological analysis.
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 ...
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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.
shima tajabadi; Bijan Ghahraman; Ali Naghi Ziaei
Abstract
Introduction: The range of meteorological parameters, such as temperature, are different at different scales. Fractal geometry is a branch of mathematics that has many applications in the field of discrete and continuous domains. Downscaling may be done by different methods, including univariate, multivariate ...
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Introduction: The range of meteorological parameters, such as temperature, are different at different scales. Fractal geometry is a branch of mathematics that has many applications in the field of discrete and continuous domains. Downscaling may be done by different methods, including univariate, multivariate regression functions, splined function and fractal function. Finding the best model for fractal downscaling, is needed to implement the distance between measured and modeled data sets. This distance may be estimated by different methods, including Euclidian. For temporal downscaling, the data are two-dimensional, i.e. time and that of principal variable (e.g. temperatures).In such a case, the dimensionality problem arises in Euclidean space. In these cases, data are usually changed to non-dimensional forms which are referred to standardization, normalization, scaling, or non-dimensionalizing. So, in addition to imbalance of data calculating distance between two sets, we are also considering the impact of standardized data on the number of interpolation points, run time, and accuracy of downscaling the temperature of Mashhad synoptic station.
Materials and Methods: In this paper, fractal model was used for modeling and downscaling temperature datasets for the period of 2007- 2009 at Mashhad Synoptic stations with two approaches of Hasdurf distance to determine the interpolation points (first approach: in first approach original data was used. Second approach: in second approach the data were standardized). We adopted some criteria, such as root mean squared error, correlation, and Akaike information criteria to assess the accuracy of fractal downscaling.
Mashhad is the second most populous city in Iran and capital of Razavi Khorasan Province. It is located in the northeast of the country, close to the borders of Turkmenistan and Afghanistan. It is built-up (or metro) area was home to 2,782,976 inhabitants including Mashhad Taman and Torqabeh cities. It was a major oasis along the ancientSilk Road connecting with Merv in the East. The city is located at 36.20º North latitude and 59.35º East longitude, Mashhad features a steppe climate with hot summers and cool winters. The city only receives about 250 mm of precipitation per year, summers are typically hot and dry, with high temperatures sometimes exceeds 35 °C (95 °F). Winters are typically cool to cold and somewhat humid, with overnight lows routinely dropping below freezing.
At first, fractal method was used to produce daily temperature from daily datasets with two attitude and different interval interpolation (5, 10, 15days). Then the same process was applied to produce 3-hours temperature.
Results and Discussion:
1. Downscaling for daily temperature: In this part, we considered that which standardizing approach and which interval interpolation, will carry the best accuracy for the fractal modeling. Although RMSE, R2, AIC, show that standardized approach is not better, but the difference is not substantial.
Results from fractal modeling from 5-day interval interpolation and 10-day interval interpolation with daily measured temperature in Mashhad compared based on 1:1 line of perfect agreement, and showed acceptable (=5%) behavior. In both approaches and two interval interpolation with both 5 and 10 days, predicted temperatures imitate the behavior of the measured temperatures. However, simulation with no standardization approach show better results for both distance interpolation compared to the second approach with standardization.
2. Downscaling daily temperature to 3-hour interval: We compared downscaled 3-hour temperature from two standardizing approaches and two timesinterpolation based on daily temperature with 3-hour measured temperature and compared the results with respect to 1:1 line of perfect agreement. It is clear that the results of the three-hour downscaling show the same results with daily downscaling, because temperature shows the fractal behavior. Although both approaches perform well but un-standardizing is better, yet the difference is not pronounced.
Conclusion: Overall, in both approaches, three-hour and daily downscaling is done precisely and with high quality. The number of interpolation points was reduced by 30% under the second standardizing approach, which followed by considerable computer runtime. However, the result shows that the first approach had better modeling.
The comparison results of the modeling with 5 intervals interpolation and with 10, the 10 intervals interpolation were more acceptable, such that correlation coefficient was between (first approach: 0.98 and 0.7, second approach: 0.98 and 0.65) while RMSE was between (first approach: 1.33 and 3.27 ° C and second approach: 1.44 and 6.02 ° C), and AICc was between (first approach: 0.55-3.27 and second approach: 2.87-3.51).The intercepts and slopes of regression lines between measured and predicted temperatures were not statistically (5% level of significant) different from 0 and 1, respectively.
shahrokh fatehi; jahangard mohammadi; Mohammad Hassan Salehi; aziz momeni; Norair Toomanian; Azam Jafari
Abstract
Introduction: Spatial scale is a major concept in many sciences concerned with human activities and physical, chemical and biological processes occurring at the earth’s surface. Many environmental problems such as the impact of climate change on ecosystems, food, water and soil security requires not ...
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Introduction: Spatial scale is a major concept in many sciences concerned with human activities and physical, chemical and biological processes occurring at the earth’s surface. Many environmental problems such as the impact of climate change on ecosystems, food, water and soil security requires not only an understanding of how processes operates at different scales and how they can be linked across scales but also gathering more information at finer spatial resolution. This paper presents results of different downscaling techniques taking soil organic matter data as one of the main and basic environmental piece of information in Mereksubcatchment (covered about 24000 ha) located in Kermanshah province. Techniques include direct model and point sampling under generalized linear model, regression tree and artificial neural networks. Model performances with respect to different indices were compared.
Materials and Methods: legacy soil data is used in this research, 320 observation points were randomly selected. Soil samples were collected from 0-30 cm of the soil surface layer in 2008 year. After preliminary data processing and point pattern analysis, spatial structure information of organic carbon determined using variography. Then, the support point data were converted to block support of 50 m by using block ordinary kriging. Covariates obtained from three resources including digital elevation model, TM Landsat imagery and legacy polygon maps. 23 relief parameters were derived from digital elevation model with 10m × 10m grid-cell resolution. Environmental information obtained from Landsat imagery included, clay index, normalized difference vegetation index, grain size index. The image data were re-sampled from its original spatial resolution of 30*30m to resolution of 10m*10m. Geomorphology, lithology and land use maps were also included in modelling process as categorical auxiliary variables. All auxiliary variables aggregated to 50*50 grid resolutions using mean filtering. In this study Direct and point sampling downscaling techniques were used under different statistical and data mining algorithms, including generalized linear models, regression trees and artificial neural networks. The direct approach was implemented here using generalized linear models, regression trees and artificial neural networks in following three steps, (i) creating the spatial resolution of 50m*50m averaged over 10m*10m grid resolution environmental variables within each coarse grid resolution, (ii) establishing relationships between these coarse grid resolutions of 50m*50m environmental variables and soil organic carbon using GLMs, regression tree and neural networks and (iii) using parameter values gained in step 2 in combination with the original 10m*10mgrid resolution environmental variables to produce predictions of soil organic carbon with10m*10m grid resolution. In point sampling approach, within each coarse resolution (50m*50m), a fixed number of fine grid resolution (10m*10m) were randomly selected to calibrate models at high resolution. In this study, 5 fine grid resolutions (20% fine grid cell within each coarse grid cell) randomlywere sampled at. Then, each selected point overlied on an underlying fine-resolution grid and recorded its environmental variables and averaged fine grid resolution (10m*10m) within their corresponding coarse grid resolution (50m*50m). To calibrate model parameters, these averaged environmental variables were used. The calibrated parameters applied to fine-resolution environmental data in order to predict soil organic carbon at spatial resolution of 10m*10m. The prediction accuracy of the resulting soil organic carbon maps was evaluated using a K-fold validation approach. For this purpose, the entire dataset was divided into calibration (n = 240) and validation (n = 80) datasets four times at random. Prediction of soil organic carbon using calibration datasets and their validation was conducted for each split, and the average validation indices are reported here. The obtained values of the observed and predicted SOC were interpreted by calculating Adjusted R2 and the root mean square error (RMSE).
Results and Discussion: Point pattern analysis showed the sampling design is, generally, representative relative to geographical space .A semi-variogram was used to drive the spatial structure information of soil organic carbon. We used an exponential model to map soil organic carbon using block kriging. Grid resolution block kriging map was 50m*50m. Since the distribution of organic carbon variable and covariates were normal or close to normal for run generalized linear models selected Gaussian families and identity link function. The validation results of this model in point sampling was slightly (Adjusted R2=0.57 and RMSE=0.22) better than the direct method (Adjusted R2 =0.47 and RMSE=0.26).The results of modelling using regression tree in point sampling approach (Adjusted R2 =0.57and RMSE=0.22) is very close to the direct method (Adjusted R2 =0.57 and RMSE=0.23).In implementation of neural networks, the combination of the number of neurons and learning rate for direct downscaling method were obtained 10 and 0.10, respectively and for point sampling downscaling method were, 20 and 0.1 The results of validation obtained from the implementation of this model in point sampling approach (Adjusted R2 =0.45 and RMSE=0.27) is very close to the direct method (Adjusted R2 =0.47 and RMSE=0.28).Validation results indicated that in both downscaling approaches, regression tree (Adjusted R2=0.57, root mean square root (RMSE) =0.22-0.23) has higher accuracy and efficiency better than generalized linear models (Adjusted R2=0.49-0.57, RMSE=0.22-0.26) and neural network (Adjusted R2=0.45-0.47, RMSE=0.27-0.28).
Conclusion: In general, the results showed that the efficiency and accuracy of the sampling point approach is slightly better than the direct approach. Validation results indicated that in both downscaling approaches, regression tree has higher accuracy and performed better than neural network and generalized linear models. However, it is required to perform more research on the different ways of downscaling digital soil maps in the future.
M. E. Banihabib; K. Hasani; A. R. Massah Bavani
Abstract
Introduction: Forecasting the inflow to the reservoir is important issues due to the limited water resources and the importance of optimal utilization of reservoirs to meet the need for drinking, industry and agriculture in future time periods. In the meantime, ignoring the effects of climate change ...
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Introduction: Forecasting the inflow to the reservoir is important issues due to the limited water resources and the importance of optimal utilization of reservoirs to meet the need for drinking, industry and agriculture in future time periods. In the meantime, ignoring the effects of climate change on meteorological and hydrological parameters and water resources in long-term planning of water resources cause inaccuracy. It is essential to assess the impact of climate change on reservoir operation in arid regions. In this research, climate change impact on hydrological and meteorological variables of the Shahcheragh dam basin, in Semnan Province, was studied using an integrated model of climate change assessment.
Materials and Methods: The case study area of this study was located in Damghan Township, Semnan Province, Iran. It is an arid zone. The case study area is a part of the Iran Central Desert. The basin is in 12 km north of the Damghan City and between 53° E to 54° 30’ E longitude and 36° N to 36° 30’ N latitude. The area of the basin is 1,373 km2 with average annual inflow around 17.9 MCM. Total actual evaporation and average annual rainfall are 1,986 mm and 137 mm, respectively. This case study is chosen to test proposed framework for assessment of climate change impact hydrological and meteorological variables of the basin. In the proposed model, LARS-WG and ANN sub-models (7 sub models with a combination of different inputs such as temperature, precipitation and also solar radiation) were used for downscaling daily outputs of CGCM3 model under 3 emission scenarios, A2, B1 and A1B and reservoir inflow simulation, respectively. LARS-WG was tested in 99% confidence level before using it as downscaling model and feed-forward neural network was used as raifall-runoff model. Moreover, the base period data (BPD), 1990-2008, were used for calibration. Finally, reservoir inflow was simulated for future period data (FPD) of 2015-2044 and compared to BPD. The best ANN sub-model has minimum Mean Absolute Relative Error (MARE) index (0.27 in test phases) and maximum correlation coefficient (ρ) (0.82 in test phases).
Results and Discussion: The tested climate change scenarios revealed that climate change has more impact on rainfall and temperature than solar radiation. The utmost growth of monthly rainfall occurred in May under all the three tested climate change scenarios. But, rainfall under A1B scenario had the maximum growth (52%) whereas the most decrease occurred (–21.5%) during January under the A2 climate change scenario. Rainfall dropped over the period of June to October under the three tested climate change scenarios. Furthermore, in all three scenarios, the maximum temperature increased about 2.2 to 2.6°C in May but the lowest increase of temperature occurred in January under A2 and B1 scenarios as 0.3 and 0.5°C, respectively. The maximum temperature usually increased in all months compared to the baseline period. Minimum and maximum temperatures enlarged likewise in all months, with 2.05°C in September under A2 climate change scenario. Conversely, solar radiation change was comparatively low and the most decreases occurred in February under A1B and A2 climate change scenarios as –4.2% and –4.3% , respectively, and in August under the B1 scenario as –4.2%. The greatest increase of solar radiation occurs in April, November, and March by 3.1%, 3.2%, and 4.9% for A1B, A2, and B1 scenarios, respectively. The impact of climate change on rainfall and temperature can origin changes on reservoir inflow and need new strategies to adapt reservoir operation for change inflows. Therefore, first, reservoir inflow in future period (after climate change impact) should be anticipated for the adaptation of the reservoir.
A Feed-Forward (FF) Multilayer-Perceptron (MLP) Artificial Neural Network (ANN) model was nominated for the seven tested ANN models based on minimization of error function. The selected model had 12 neurons in the hidden layer, and two delays. The comparison of forecasted flow hydrograph by selecting an ANN model and observed one proved that forecasted flow hydrograph can follow observed one closely. By comparison with the IHACRES model, this model displayed a 54% and 46% lower error functions for validation data. The selected model was used to forecast flow for the climate change scenarios of the future period.
Conclusions: The results show a reduction of monthly flow in most months and annual flow in all studied scenarios. The following main points can be concluded:
• By climate change, flow growths in dry years and it declines in wet and normal years.
• The studied climate change scenarios showed that climate change has more impact on rainfall and temperature than solar radiation.
Gh. Ghandhari; J. Soltani; M. Hamidian Pour
Abstract
Introduction: The rapid population growth in Iran and the corresponding increases in water demands, including drinking water, industry, agriculture and urban development and existing constraints necessitate optimal scheduling necessity in use of this crucial source. Furthermore, the phenomenon of climate ...
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Introduction: The rapid population growth in Iran and the corresponding increases in water demands, including drinking water, industry, agriculture and urban development and existing constraints necessitate optimal scheduling necessity in use of this crucial source. Furthermore, the phenomenon of climate change as a major challenge for humanity can be considered in future periods. Climate change is caused by human activity have also been identified as significant causes of recent climate change, referred to as "global warming". Climate change indicates an unusual change in the Earth's atmosphere and climate consequences of the different parts of planet Earth. Climate change may refer to a change in average weather conditions, or in the time variation of weather around longer-term average conditions. A Warmer climate exacerbates the hydrologic cycle, altering precipitation, magnitude and timing of runoff. The purpose of this study was to evaluate the effect of climate change on water consumption and demand in Bar river basin of Neighbor. Climate change affects precipitation and temperature patterns and hence, may alter on water requirements and demand at three sectors; agriculture, industry and urban water.
Materials and Methods: At present, Global coupled atmosphere-ocean general circulation models (AOGCMs) are the most frequently used models for projection of different climatic change scenarios. AOGCMs models represent the pinnacle of complexity in climate models and internalize as many processes as possible. These models are based on physical laws that are provided by mathematical relations. AOGCMs models used for climate studies and climate forecast are run at coarse spatial resolution and are unable to resolve important sub-grid scale features such as clouds and topography. As a result AOGCMs output cannot be used for local impact studies. Therefore, downscaling methods were developed to obtain local-scale weather and climate, particularly at the surface level, from regional-scale atmospheric variables that are provided by AOGCMs. Four different downscaling methods exist: regression methods, weather pattern-based approaches, stochastic weather generators, which are all statistical downscaling methods, and limited-area modeling. For this research, HadCM3 and statistical downscaling model (SDSM), precipitation and temperature variations were simulated under A2 scenario. Then the impacts of these variations on Bar River discharge were analyzed, i.e. water resources at three sectors of agriculture, industrial and potable water under climate change during 2011-2040 using WEAP. Results at first part of simulation showed that temperature is increasing and precipitation is decreasing resulted in decreasing of Bar discharge. According to the decreasing on Bar discharge, water allocation was simulated under these conditions of agricultural and industrial development and increasing of population with WEAP. Simulation showed that watershed will face increasing of water demand for all three sectors; agriculture, industry and drinking water, so the highest water shortage would be in agricultural demand and then industry and drinking water respectively. IWRM is the basic managerial need to rest the demands especially for drought periods. Current allocation process is based on steady state conditions while allocation pattern would be done under climate change conditions so we need to be reinvestigat the last allocations for all three sectors. Another challenge for this watershed refers to the gardens and steel factory of Khorasan that they need to use new technologies for reduction of their water needs.
Results Discussion: In this study, the outputs of General Circulation Models (HadCM3) and statistical downscaling model (SDSM) have been used to investigate the changes of rainfall and temperature under A2 scenario in Bar river basin of Neishaboor and assess the impacts of this changes on the Bar river’s discharge. Finally, using WEAP model under climate change conditions for the period of 2011-2040, the status of basin water resources was evaluated for the three sectors (agricultural, domestic and industrial). The results indicated increased temperature in the Arie station amounting to 16 percent and rainfall reduction in the Arie and Taghan stations amounting to 3.9 and 8.75 percent respectively. Under these conditions, according to the increasing water demands of agricultural and industrial sectors in the future, there will be a shortage of water supply resources in the region. So the agricultural sector with 12 percent will have the highest percentage of water shortage and water scarcity and of the industrial sector will be 2%. However, the drinking water or domestic demand will not face a shortage of supplies.
Conclusion: Therefore given that the most part of agriculture sector’s share of basin is allocated to orchards and on the other hand the most shortages are related to agriculture, then while creating an integrated management of water resources, development and use of modern methods of irrigation during the period of 2011 - 2040 would seem to be necessary.
Nooshin Ahmadibaseri; A. Shirvani; mohammad jafar nazemosadat
Abstract
In this study, the artificial neural networks (ANNs) and regression models were used to downscale the simulated outputs of the general circulation models (GCMs). The simulated precipitation for 25.18 º N to 34.51 º N and 45 º E to 60 º E, geopotential height at 850 mb and zonal wind at 200 mb for ...
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In this study, the artificial neural networks (ANNs) and regression models were used to downscale the simulated outputs of the general circulation models (GCMs). The simulated precipitation for 25.18 º N to 34.51 º N and 45 º E to 60 º E, geopotential height at 850 mb and zonal wind at 200 mb for 12.56° N to 43.25° N and 19.68° E to 61.87° E data sets as the predictors were extracted from ECHAM5 GCM for the period 1960-2005. The observed monthly precipitation data of Abadan, Abadeh, Ahwaz, Bandar Abbas, Bushehr, Shiraz and Fasa stations as the predictand were extracted for the period 1960-2005. The principal components (PCs) of the simulated data sets were extracted and then six PCs were considered as the input file of the ANN and multiple regression models. Also the combinations of the simulated data sets were used as the input file of these models. The periods 1960-2000 and 2001-2005 were considered as the train and test data in the ANN, respectively. The Pearson correlation coefficient and normalized root mean square error results indicated that ANN predicts precipitation more accurate than multiple regression. For the monthly time scale, the geopotential height is the best predictor and for the seasonal time scale (winter) the simulated precipitation is the best predictor in ANN based standardized precipitation principal components.
N. Validi; Alinaghi Ziaei; B. Ghahraman; H. Ansari
Abstract
For optimal management of a catchment, the time and space downscaling of hydrological properties is essential. To achieve accurate energy and water budget equations in every time or space resolution, spatial and temporal downscaled information of water budget's components are used. The fractal geometry ...
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For optimal management of a catchment, the time and space downscaling of hydrological properties is essential. To achieve accurate energy and water budget equations in every time or space resolution, spatial and temporal downscaled information of water budget's components are used. The fractal geometry is a branch of mathematics which has been utilized in discrete and periodic fields to generate data with different scales from observed data. In this research, the fractal interpolation functions were used for temporal downscaling of daily temperature data. The fractal dimension was used to express the rate of irregularities or fluctuations in the quatity. The fractal dimension of Mashhad daily temperature datasets for the period of 1992- 2007 was calculated. The mean of the fractal dimension was obtained 1.54. Moreover, using the fractal interpolation functions and the midday temperature dataset with 15 days resolution, hourly temperature dataset has been estimated and compared with observed dataset. It was shown that despite the considerable time interval between two consecutive measurements (as 15 days), the temperature time series with 3 hours resolution were obtained. The determination coefficient and the root mean square error of the model are 0.77 and 7, respectively.
hadi sanikhani; yaghoub dinpazhoh; sarvin zamanzad ghavidel
Abstract
Changes in temperature and precipitation patterns have serious impacts on the quantity and quality of water resources, especially in arid regions such Iran. In recent years, frequent droughts have threatened the water resources in Iran. Because of the increasing demand for water, studying the impacts ...
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Changes in temperature and precipitation patterns have serious impacts on the quantity and quality of water resources, especially in arid regions such Iran. In recent years, frequent droughts have threatened the water resources in Iran. Because of the increasing demand for water, studying the impacts of climate change on water resources is necessary. In this study, the impacts of climate changes on run-off in Ajichay watershed, located in East Azerbaijn were considered. To predict the climate change based on the General Circulation Models (GCM), the LARS-WG tool for downscaling was used. By using LARS-WG, climate change in Ajichay watershed by applying HADCM3 model and three emission scenarios, A1B, A2 and B1 in 2055 horizon was investigated. The results show a rise in temperature and reduction in precipitation. In the other part of the research, for simulationofthe impacts of climate change on watershed run-off, Gene Expression Programming (GEP) was used. The results indicated that significant reduction in run-off. With regarding the results of this research, for adaptation with climate change, it is necessary to consider suitable management action in this watershed.
H. Seyyed Kaboli; A.M. AkhodAli; A.R. Masah Bavani; F. Radmanesh
Abstract
General Circulation Models (GCMs) have been identifiedas asuitable tool for studying climate change. Butthese models simulate climatic parametersinthe large-scale which has poor performance in the simulation of processes such asrain fall-run off. There fore, several of down scaling methods were developed. ...
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General Circulation Models (GCMs) have been identifiedas asuitable tool for studying climate change. Butthese models simulate climatic parametersinthe large-scale which has poor performance in the simulation of processes such asrain fall-run off. There fore, several of down scaling methods were developed. This researchis presented down scaling model based onk-nearest neighbor (K-NN) non-parametric method. The modelis used to simulate daily precipitation data in Ahvaz station for the next period (2015-2044) under climate change scenarios based on out puts of three General Circulation Models, including HADCM3, NCARPC Mand CSIROMK3.5. The results indicate that them odelhasa high capacity for down scaling data. It is predicted that the frequency of storm is increased with high intensity on future period in Ahvaz station while dry spells will be prolonged.
