Agricultural Meteorology
Nasrin Ebrahimi; Azar Zarrin; Abbas Mofidi; Abbasali Dadashi-Roudbari
Abstract
IntroductionClimate change has led to changes in the frequency, intensity, duration, and spatial distribution of climate extremes. During the last decade (2011-2020), the average global temperature was 0.1 ± 1.1 oC higher than in the preindustrial era. Iran and especially the Urmia Lake ...
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IntroductionClimate change has led to changes in the frequency, intensity, duration, and spatial distribution of climate extremes. During the last decade (2011-2020), the average global temperature was 0.1 ± 1.1 oC higher than in the preindustrial era. Iran and especially the Urmia Lake basin is one of the most vulnerable areas to climate change. Urmia lake basin has received the special attention of policymakers and planners since it is the location of Lake Urmia, and it also holds nearly 7% of Iran's water resources. A huge program of dam construction and irrigation networks has been started in this basin in the northwest of Iran since the late 1960s. Despite the increasing attention to Lake Urmia since 1995, the water level of this lake has decreased. During the drought of 1990-2001, Lake Urmia experienced a decrease in its level without any recovery and is decreasing at an alarming rate. Therefore, it is necessary to project the future climate of the Urmia Lake basin and especially extreme precipitation based on the latest climate change models. Materials and MethodsThe CMIP6 models were used to investigate the future projection of extreme precipitation in the Lake Urmia basin. Considering the horizontal resolution, availability of daily data, and climate sensitivity, we selected five models including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. The horizontal resolution of all five models is 0.5o. The 25-year historical period (1990-2014) and the 25-year projection period for the near future (2026-2050) were chosen to analyze the extreme precipitation in the Urmia Lake Basin. The future projection was considered under three shared socioeconomic pathways (SSPs) scenarios. These scenarios include SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Mean bias error (MBE) and Normalized Root Mean Square Error (NRMSE) were computed to evaluate the individual models and the multi-model ensemble generated by Bayesian Model Average (BMA) method. To assess extreme precipitation, we used four indices including the Number of heavy precipitation days (R10mm), the number of very heavy precipitation days (R20mm), the Maximum 1-day total precipitation (Rx1day), and the Simple Daily Intensity Index (SDII). Results and DiscussionThe performance of five CMIP6 individual models and the multi-model ensemble in the Lake Urmia basin during the period of 1990 to 2014 was evaluated against eight ground stations. The investigation of the annual precipitation showed that this variable is underestimated in CMIP6 models in the basin averaged. The maximum and minimum bias values model was seen in Saqez station by -9.64 mm for the MRI-ESM2-0 and -0.43 mm for the UKESM1-0-LL, respectively. The highest average MBE in the Urmia Lake basin was respectively obtained for GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL models. Among the examined models, MPI-ESM1-2-HR has shown the highest efficiency among the examined individual models.Variations in the number of heavy precipitation days during the historical period (1990-2014) have distinguished three main areas for the Lake Urmia basin. The main hotspot of heavy precipitations in the Urmia Lake basin is located in the southwest of Kurdistan province with a long-term average of 25.4 days. The next hotspots are the northwest and the northeast of the basin. In the historical period (1990-2014), the precipitation intensity index Rx1day experienced considerable variability. Based on CMIP6-MME, the value of the Rx1day index in the Urmia Lake basin is estimated between a minimum of 16.3 mm and a maximum of 63.3 mm. The maximum variation of this index is seen in the southern areas of the basin, especially on the border with Iraq. ConclusionEvaluation of individual CMIP6 models showed that these models underestimated precipitation in the Lake Urmia basin during the historical period (1990-2014). The CMIP6-MME has significantly improved precipitation estimation. The results of the investigation of days with heavy and very heavy precipitation showed that the two indices R10mm and R20mm are increasing in most areas of the Lake Urmia basin by the middle of the 21st century. Trend analysis showed that the days with heavy and very heavy precipitation will increase under different SSP scenarios in most areas of the Lake Urmia basin, especially in the northern and western regions. Also, days with heavy and very heavy precipitation will have a greater contribution than normal precipitation days in the future. It is expected that the intensity of precipitation will increase in the coming decades in the Lake Urmia basin, and this increase is more for the western and northern regions than for other regions of the basin. This result may potentially increase the flood risk in Lake Urmia.
Agricultural Meteorology
Kh. Javan; A. Movaghari
Abstract
Introduction
The most important effect of global warming is the increase in extreme weather events. According to AR5 reports, between 1951 and 2010, the number of warm days and nights increased and the number of cold days and nights has declined globally. In addition, the duration and frequency of hot ...
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Introduction
The most important effect of global warming is the increase in extreme weather events. According to AR5 reports, between 1951 and 2010, the number of warm days and nights increased and the number of cold days and nights has declined globally. In addition, the duration and frequency of hot periods, including thermal waves, have increased since the middle of the twentieth century. The trend analysis of temperature extreme indices is important in estimating the trend of global warming. Temperature Changes are affected by many complex factors. A significant part of these changes is due to the elements of the general circulation of the atmosphere and the sea surface temperature. Given that extreme weather events are one of the most devastating natural hazards and have harmful effects on different parts of society, therefore, many researchers have studied the changes in the past and future of extreme events and the mechanisms that trigger these changes. This research attempts to study the trend of changes in extreme temperature indices in North-West of Iran, and also their relation with general circulation of atmosphere.
Materials and Methods
At first, diurnal data of minimum and maximum temperature of 20 synoptic stations of the Northwest of Iran, which have long-term and reliable statistics, extracted for the period of 1986-2010 and quality control and data homogeneity of them were investigated. afterwards, 16 Extreme temperature indices introduced by ETCCDMI were applied. In general, these indices are categorized into five categories of absolute indices, based on percentiles, based on thresholds, periodic, and amplitudes that measure the frequency, severity and duration of the temperature. These indices are estimated by RClimDex software and the trend rate of the changes in indices was shown through maps. To measure the changes in the general circulation of atmosphere the annual mean circulation composites extracted for the periods of 1961-1985 and 1986 -2016 based on the reanalysis data of the NCEP / NCAR. Then the difference maps plotted using grads software.
Results
The regional trend of extreme indices and the percentage of stations with a positive and negative trend were identified and the spatial distribution of the gradient of each of the indices was mapped. The results show that all absolute temperature indices have an increasing trend. On average, the maximum temperature (TXx and TXn) has increased by about 0.04 degrees over the decade. The increase rate of TNx is about 0.03 degrees, while the TNn increased by about 0.1 degrees Celsius per decade during the study period. Therefore, in the north-west of Iran, temperature increase has mainly occurred at night. The values of cold days (TX10) and cold nights (TN10) decreased with a gradient of -0.46 and -0.42 days in the decade. The warm days (TX90) and warm nights (TN90) have an increasing trend in 95% of the stations in the area. Frost days (FD) and icing days (IDs) have a decreasing trend, whereas, summer days (SU25) and tropical nights (TR20) have an increasing trend. The number of frost days with a gradient of -0.95 and the number of icing days with a gradient of -0.63 days in decade are decreasing. While, the number of summer days with a gradient of 0.81 and the number of tropical nights with gradient of 0.31 days in decade are increasing. In the northwest of Iran, all stations have been experiencing the increasing trend in Warm Spell Duration Index (WSDI), but the Cold Spell Duration Index (CSDI) in 70% of the stations in the region has decreased. Growing season length, as an effective index especially in agriculture, is increasing by an average of 1.1 days per decade. Based on the results of research carried out globally and at Iran, the trend of Daily Temperature Range (DTR) is negative, while this index has a positive and increasing trend in 65% of North-West stations in Iran. Except TNx and TNn indices that have positive trend in most stations in the region, Comparison of warm and cold extreme indices indicates that warm indices have a positive and incremental trend, while cold indicators show a decreasing trend. The positive gradient of these indices also corresponds to the decreasing trend of cold day and night indices, which indicates an increase in temperature and a decrease in cold days and nights. The study of large-scale changes in atmospheric circulation shows that the study area has got warmer in the spring and summer and colder in autumn and winter.
Conclusion
In this study, the trend of temperature extreme indices in North-West of Iran and its relation with the large-scale general circulation of the atmosphere have been investigated. The results show that all absolute temperature indices (TXx, TXn, TNx and TNn) are incremental. The indices of cold days (TX10) and nights (TN10) decreased with a gradient of -0.46 and -0.42 days in the decade and the indices of warm days (TX90) and warm nights (TN90) are increasing in 95% of the stations in the area. Frost days and icing days (IDs) show declining trend and summer days (SU25) and tropical nights (TR20) have an increasing trend. In the north-west of Iran, all stations have experienced an increasing trend in warm spell duration index (WSDI), but the cold spell duration index (CSDI) has been decreasing in 70% of the stations in the area. Growing season length (GSL) is increasing by an average of 1.1 days in every decade. Daily temperature range (DTR) has a positive and increasing trend in 65% of stations in north-west Iran. Comparison of warm and cold extreme indices indicates that warming indices have a positive and incremental trend, while cold indices show a decreasing trend. Study of the general circulation of atmosphere of the region by drawing and analyzing difference maps indicates that the study area has been warmer in spring and summer and colder in autumn and winter.
Agricultural Meteorology
S. Mirshekari; F. Yaghoubi; S.A. Hashemi
Abstract
Introduction
The 21st century is witnessing the increase of climate change as an important challenge due to its destructive environmental and socio-economic effects. Extreme climatic conditions have become frequent and more intense in recent decades as a result of human activities. Iran, as one of the ...
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Introduction
The 21st century is witnessing the increase of climate change as an important challenge due to its destructive environmental and socio-economic effects. Extreme climatic conditions have become frequent and more intense in recent decades as a result of human activities. Iran, as one of the countries in the Middle East with a different climate in each region of the country, has suffered significant adverse effects of climate change. Considering the importance of the climate change, it is important to investigate the changes in climate variables to know the future conditions and make management decisions. In the field of climate research, global climate models are useful tools that are often used to investigate the global climate system, including historical and projected periods. Since the use of the CMIP6 dataset provides improved clarity and accuracy for predicting future climate forecasts, the main objective of the present study is to predict the temperature and precipitation changes in the near, mid, and far future in Sistan-va-Baluchestan province.
Materials and Methods
The minimum temperature, maximum temperature, and precipitation data of 10 general circulation models (GCMs) of the 6th IPCC report for the baseline (1990-2014) were downloaded from the Global Climate Research Program database (https://esgf-node.llnl.gov). Then GCMs were including ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1-HR, CNRM-ESM2-1, EC-Earth3-CC, EC-Earth3-Veg-LR, INM-CM4-8, INM-CM5-0, MIROC6, and NorESM2-MM. Four statistical indicators including correlation coefficient (R2), RMSE, Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE) were used to evaluate the performance of 10 GCMs. Based on the results obtained from the these indicators, the models that had higher performance in predicting the temperature and precipitation data were selected as the best models for forecasting in the future. The ensemble of these models under two SSP2-4.5 and SSP5-8.5 scenarios for the near, middle, and far future (2026-2050, 2051-2075, and 2076-2100) were extracted from the World Climate Research Program database.
CMhyd (Climate Model data for hydrologic modeling) tool was used to bias correction climate data of the selected models. In order to choose the best bias correction method, the R2, RMSE, NSE, and MAE were estimated.
After bias correction, the climate data of selected models were ensembled and then the changes in precipitation and maximum and minimum temperature in three future periods compared to the baseline was estimated.
Results and Discussion
The results showed that out of 10 GCMs, seven models had good performance (R2 > 0.40, 4.23 < RMSE < 12.02°C, 0.12 < NSE < 0.74, and 3.36 < MAE < 9.59°C) in simulating daily minimum and maximum temperature. However, the performance of all models in simulated daily precipitation was poor (R2 > 0.19, 1.24 < RMSE < 3.70 mm, -7.41 < NSE < -0.57, and 0.23 < MAE < 0.85 mm).
Among the different bias correction methods of temperature and precipitation available in CMhyd, the distribution mapping method had the best performance.
In all three regions, compared to the baseline, the average annual minimum and maximum temperature under two scenarios will increase in the future periods and precipitation will decrease in most periods and scenarios. These changes will be mainly in the SSP5-8.5 scenario compared to SSP2-4.5 and also in the far future period compared to the middle and near future. Averaged across all locations, annual maximum temperature showed increases in near, middle, and far projected periods of 1.3, 2.1, and 2.8°C under SSP2-4.5 and 1.6, 3.1, and 5.1°C under SSP5-8.5, respectively (Fig. 2), while for minimum temperature, the increases will be of 1.6, 2.6, and 3.4°C for SSP2-4.5 and 1.9, 3.9, and 6.3°C for SSP5-8.5. The range of annual precipitation among all sites was from –58.22 to 49.33% under SSP5-8.5 in the near and far future periods in Zabol and Iranshahr, respectively.
The annual increase in the average maximum and minimum temperature will be mainly due to the increase in air temperature in the months of January, February, August, September, October, November and December. The annual decrease in precipitation will mainly result from the decrease in precipitation in January, February, March, November, and December, and the annual increase in precipitation will result from the significant increase in precipitation in May and October compared to the baseline.
Conclusion
The results showed that under different scenarios of climate change, the maximum and minimum temperatures in the near, middle, and far future periods will face an increase compared to the baseline. However, the precipitation changes in the future time periods are not the same as compared to the baseline, and in some periods the precipitation will decrease and in others it will increase. But in general, the decrease in precipitation will be more than its increase. Therefore, it is very important to formulate and implement appropriate management programs for the needs of each region, in order to properly manage water resources and adapt to extreme temperatures and their consequences.
Agricultural Meteorology
S.F. Ziaei Asl; A.A. Sabziparvar
Abstract
Introduction: It is possible to guide the agricultural experts to achieve a suitable genotype and adapt to climatic conditions in proportion to the length of the modified growing season by identifying the impact of climate change in recent years on the cumulative rate of degree-days of plant growth. ...
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Introduction: It is possible to guide the agricultural experts to achieve a suitable genotype and adapt to climatic conditions in proportion to the length of the modified growing season by identifying the impact of climate change in recent years on the cumulative rate of degree-days of plant growth. This will prevent the waste of capital and agricultural inputs and ultimately prevent the reduction of the final crop due to the mismatch of genotype-crop with the current climate. In the present study, an attempt has been made to study and compare the trend in the start and end of the growing season, the growing season length (GSL), and growing degree-days(GDD) during 1959-2018 in the elevated and coastal areas of Iran.Materials and Methods: For this study, the daily temperature of 27 synoptic stations were used including 19 stations in elevated areas and 8 stations in coastal areas during 1959-2018. The first day with a minimum daily temperature equal to or greater than 0, 5, and 10 °C was considered as the start of the growing season (SGS). Moreover, the first day after the start of the growing season which has a minimum daily temperature of less than 0, 5, and 10 °C was considered as the end of the growing season (EGS). Trend analysis was performed in time series of GSL and GDD based on thresholds of 0, 5, and 10 °C using the Mann-Kendall test. To compare the results, the statistical period of 60 years was divided into two periods of 30 years (1959-1988 and 1989-2018). In both periods, the statistical characteristics of the GSL and GDD based on the three thresholds mentioned in coastal and elevated areas were surveyed and compared. In this study, deviation from the mean was used to complete the study of changes in the GSL. This index shows the scatter of data around the mean.Results and Discussion: The GSL extension came from both the advance in SGS and the delay in EGS. Comparison results of the two 30-year periods (1959-1988 and 1989-2018) showed that during 1989-2018, in most stations the GSL has increased. During this period, based on 0 °C, the earliest and latest SGS were on February 24 and April 30 in Yazd and Shahrekord, respectively. Accordingly, the earliest and latest EGS were on October 15 and December 11 in Shahrekord and Gorgan, respectively. Based on 5 °C, the earliest and latest SGS were on February 10 and June 2 in Abadan and Gorgan, respectively. Accordingly, the earliest and latest EGS on September 17 and December 6 were at Shahrekord, Bam, and Abadan, respectively. Based on 10 °C, the earliest and latest SGS was on February 11 and June 20 at stations, respectively. Accordingly, the earliest and latest EGS were on August 27 and December 8 in Shahrekord and Bushehr, respectively. The shortest and longest GSLs based on all three thresholds of 0, 5, and 10 °C were Shahrekord and Bandar Abbas, respectively. The highest and lowest coefficient of variation of GSL were 20.8% in Zanjan and 4.9% in Bandar Abbas, respectively. Based on 0, 5, and 10 °C, the lowest GDDs in GSL are 3233, 1767, and 880 °C.d, respectively, and all of them were obtained at Shahrekord. On the other hand, the highest GDD0, GDD5, and GDD10 in GSL were 6783, 7372, and 5761 °C.d, respectively, in Yazd, Abadan, and Bandar Abbas. The most significant trend in GSL was in Zanjan, Zahedan, and Khorramabad.Conclusion: Examination of changes in the GSL indicates the existence of a significant trend in a limited number of stations. Also, with increasing the threshold from 0 to 5 and from 5 to 10 °C, there is a significant decreasing trend in more stations. At the threshold of 10 °C a significant and decreasing trend of GSL was observed in Urmia, Sanandaj, Khorramabad, Birjand, and Bandar Abbas stations, In following, a significant increasing trend was observed in Tabriz, Tehran, Kermanshah, Isfahan, Yazd, and Bushehr stations. The results of the studies showed fewer changes in the time series of the GSL based on thresholds of 0 and 5 °C in the statistical period of 1989-2018. On the other hand, the results showed that the GSL trend is significant in more stations in the recent period based on the threshold of 10 °C. Deviation from the average GSL in coastal areas was greater than the elevated areas so that the GSL based on 10 °C in both areas increased with greater slope and continuity. This increasing trend of deviation from the average in the coastal areas from the early '70s and the elevated areas from the early '90s and continues until now. In this regard, Bandar Abbas station and then Bushehr station had the longest GSL, and Shahrekord station had the shortest GSL among other stations which has been studied. Comparison of GDDs of the GSL during 1989-2018 showed the decrease of GDDs from south to north and from west to east of the country. Accordingly, in the southern stations of the country, the conditions for tropical plants (threshold of 10 °C) have become more suitable than the cold stations of the west and northwest, Time series analysis of the average annual GDDs based on the three thresholds during 1989-2018 showed a significant increasing (positive) trend in 93% of the stations. During the second 30-years period, Shahrekord and Shiraz stations did not show a significant trend in all three mentioned thresholds. However, the analysis of the annual average of GDDs during 1959-1988 showed the trend in 41% of the stations. According to the results of this study, it can be concluded that in cold regions, due to the increase in GDDs, the supply of cooling units for plants with certain cooling needs is more difficult. In the south of the country, as the total required GDD is achieved earlier, the GSL gets shorter, and therefore less dry biomass will accumulate in the product. This likely leads to a reduction in crop yields in this part of the country.
S.M.J. Nazemosadat; L. Abbasi; S. Mehravar
Abstract
Introduction:Based on the research and assessment carried out during the Climate Change Enabling Activity Project under United Nations Framework Convention on Climate Change (UNFCCC) and using the scenarios proposed by IPCC, it is estimated that if the CO2 concentration doubles by the year 2100, ...
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Introduction:Based on the research and assessment carried out during the Climate Change Enabling Activity Project under United Nations Framework Convention on Climate Change (UNFCCC) and using the scenarios proposed by IPCC, it is estimated that if the CO2 concentration doubles by the year 2100, the average temperature in Iran will increase by 1.5-4.5°C which will cause significant changes in water resources, energy demand, agricultural products and coastal zones. The present study is aimed to investigate the characteristics of climate change in Iran and some parts of the neighboring countries. Identifying the spatio-temporal changes in three atmospheric variables comprising perceptible water (PW), specific humidity (SH) and vector wind (VW, U and V components) over 1960-2017 was the main themes of the study. Materials and Methods: Monthly values of these variables during wintertime (January to March) were extracted from the CDC/ Reanalysis 2/ NOAA in 2.5 * 2.5 grids for the period of 1960-2017. The study area locates between 20o to 45o N and 30o to 70o E. After averaging monthly data into seasonal series, as first step, significant changes in the considered series were investigated between two equal periods having 29 years of data (1960-1988 and 1989-2017). In the second step, the 58 years of the study period were divided into five successive decades (1960-2009) and a period with eight years (2010-2017). The Kolmogorov-Smirnov (K-S) field significant test was used for assessing the spatio-temporal difference between the obtained maps associated with various decades. Results and Discussion: According to Figures 1 and 2, for both of the 29-year time-scales (1960-1988 and 1989-2017), PW was maximum (12 to 17 kg/m2) alongside the northern coasts of the Persian Gulf and the Oman Sea. After this, PW had the highest values over the southern coasts of the Caspian Sea (10 to 12 kg/m2). Oppose to these coastal areas, minimum values of this variable with about 6 to 10 kg/m2 were associated with the Zagros mountains. In general, PW exhibited an inverse relationship with elevation. In contrast to PW, SH maximized (4.2 to 5 g/kg) over the Zagros ranges and its relationship with elevation was generally positive. The lowest value of the SH data was about 3.5 g/kg suggesting relatively low variation in the SH data within the country. Compared to the 1960-1988 period, a significant decline was observed in the values of PW and SH in 1989-2017. Although this decline was obvious over all parts of the country, it was slightly significant for the southwestern (northwestern) districts. Compared to the first half of the study period (1960-1989), PW (or SH) decreased by about 2.5 kg/m2 (or 0.6 g/kg) in southwestern and 0.3 kg/m2 (or 0.15 g/kg) in northwestern parts of Iran for the recent half (1989-2017). Differences between wind data during these two time-periods were mostly either northerly or easterly suggesting a significant decrease in the rain-bearing southerly or westerly circulation over 1989-2017. Anomalies of the near-surface wintertime winds were mostly found to be southerly or westerly during 1960-1988 implicating the possibility of moisture transport from the Persian Gulf, the Oman Sea, the Mediterranean Sea, and the Red Sea into the most parts of Iran. Conversely, the anomalies were either northerly or easterly in1989-2017 suggesting less moisture transport into Iran for this recent period. In the decadal time-scale, maximum values of PW, SH, as well as southerly or westerly circulations, were observed during 1960-1969. The given results suggest that the enhanced (or suppressed) values of PW and SH are generally harmonized with the strengthened southerly and westerly (or northerly and easterly) wind anomalies. For this period, prevailing of southeasterly winds over the Caspian Sea enhanced or suppressed the measure of PW, SH over the western or eastern coasts of the Sea, respectively. Even though the mentioned atmospheric circulation patterns were generally similar for the 1960-1969 and 1970-1979 decades, positive anomalies of PW and SH, as well as the westerly and southerly airflows, were slightly suppressed for the second decade. The anomalies of westerly and southerly winds decreased by about one-fifth for 1980-1989 as compared with that in 1960-1969 resulting in a significant decrease in the PW and SH data for this decade. Although these anomalies were slightly positive over most parts of Iran, their weakness did not allow significant improvement in the PW and SH values. The period of 2000-2009 was evaluated as the driest decade of the study period for which the negative anomalies of PW and SH, as well as westerly and southerly circulations, were maximized (in absolute values). In spite of the fact that these undesirable conditions have recovered during the period of 2010-2017, PW and SH were still very low for this recent period. With the exception of the 1990-1999 decade, PW and SH have continuously decreased for the decades after 1970. The rain-bearing southerly and westerly winds have been gradually replaced with dry northerly or easterly wind during the recent periods. Conclusion: The findings showed that the PW and SH distribution patterns are close together in the 29-year periods, the measures were, however, significantly smaller in the second period than in the first. The wind anomalies, which were mostly southerly and westerly in 1960-1988, have been changed to northerly and easterly in 1989-2017. Since the southerly and westerly winds play an influential role in moisture transfer to Iran, their reduction in the second period is consistent with the observed decrease in PW and SH. Among the ten-year periods, the highest positive PW and SH abnormalities are associated with the 1960 and 1969 decade. This positive anomaly decreased over the time. Since a positive trend is observed for 2010-2017, it can be concluded that 2000-2010 is the driest decade of the study period. The positive anomalies of westerlies (easterlies) and southerlies (northerlies) increased (decreased) the magnitudes of PW and SH.
S. Kouzegaran; M. Mousavi Baygi; iman babaeian
Abstract
Introduction: Global warming causes alteration of climate extreme indices and increased severity and frequency of incidence of meteorological extreme events. In most climate change studies, only the potential trends or fluctuations in the average long run of climatic phenomena have been examined. However, ...
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Introduction: Global warming causes alteration of climate extreme indices and increased severity and frequency of incidence of meteorological extreme events. In most climate change studies, only the potential trends or fluctuations in the average long run of climatic phenomena have been examined. However, the study of affectability and pattern change of extreme atmospheric events is also important. Changes in climatic elements especially extreme temperature factors have a significant influence on the performance of farming systems. Accordingly, understanding changes in temperature parameters and extreme temperature indices is the prerequisite to sustainable development in agriculture and should be considered in management processes. Investigation of extreme values for planning and policy for the agricultural sector, water resource, environment, industry, and economic management is important. Materials and Methods: To evaluate the extreme temperature indices trend, some indices of temperature, recommended by the CCl/CLIVAR Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI), were considered using Rclimdex software. In this study, daily minimum and maximum temperature data retrieved from MPI-ESM-LR global climate model were used to predict future climate extreme events over the next three periods of 2026-2050, 2051-2075, and 2076-2100 based on IPCC scenarios of RCP4.5 and RCP8.5 of the studied area, covering South Khorasan province and southern part of Razavi Khorasan province, located in the east of Iran. The modified BCSD method was used to downscale extreme temperature data. Results and Discussion: Results showed an increasing trend of warm climate extreme. According to the output of Rclimdex for RCP4.5 scenario in the period of 2026-2050, it was observed that SU25 index, summer days, has a positive trend at all studied stations. This index was found to be significant and increased at all stations in the mid-term future period, and it had an increasing trend in the far future period, which was not significant. The number of Tropical Nights (TR20) index had a positive trend at all. In the mid-term future period, there was a significant increasing trend for some stations, while there were some negative and insignificant trends at some stations in the far future. The maximum monthly daily maximum temperature (TXx) and the maximum monthly daily minimum temperature (TNx) indices also had an increasing trend at all stations, and the mid-term future period had a significant increasing trend, while the trend was decreasing in the far future period. Results for temperature extreme indices under RCP8.5 scenario showed that SU25 index had a positive trend at all stations studied in the near future, mid-term, and far future period. Index of tropical nights (TR20) had an upward trend, which was significant in mid-term and far future periods at most stations. Percentage of days in which maximum temperature is below than 10th percentile (TX10P), indicating a decrease in cold days, had a negative trend for all stations in the near future period. In the mid-term and far future periods, this trend was significant at all stations. The maximum monthly daily maximum temperature (TXx) and the maximum monthly daily minimum temperature (TNx) indices also had an increasing trend at all stations and all three periods, and the trend was significant in the mid-term future. Conclusion: Minimum and maximum daily temperatures of MPI-ESM-LR global climate model were used to predict climatic extreme events during three future periods of 2026-2050, 2051-2075, and 2076-2100 under RCP4.5 and RCP8.5 scenarios at some stations located in South Khorasan province and southern part of Khorasan Razavi province. During the three studied future periods, extreme temperature indices changed significantly. The results showed that in both periods over the future years under the both scenarios, hot extreme indices would increase and cold extreme indices would decrease. It was observed that hot extreme indices, such as summer day index, the number of tropical nights, warm days and nights increased, while cold extreme indices had a decreasing trend in the period of study, which shows a decrease in the severity and frequency of cold events.
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.
Ahmad Reza Razavi; Mahdi Nassiri Mahallati; Alireza Koocheki; Alireza Beheshti
Abstract
Introduction: Climate change (CC) is one of the most important concerns for mankind in the current century. Increasing CO2 concentration and the proof of the greenhouse effect theory in which the type and composition of atmospheric gases which influence the earth temperature, are among undeniable facts ...
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Introduction: Climate change (CC) is one of the most important concerns for mankind in the current century. Increasing CO2 concentration and the proof of the greenhouse effect theory in which the type and composition of atmospheric gases which influence the earth temperature, are among undeniable facts makes the future climate change more possible. Impacts of Global warming on hydrological cycles and precipitation patterns would be more prominent in arid and semi-arid regions of the earth. For the arid and semi-arid nature and the poverty more fraction of Afghanistan suffer from, it is likely that the impacts of CC on the country will be more intense. This is while there is no credible and reliant research addressing the impacts of CC on agriculture and food security sector of Afghanistan. Studying the impacts of CC on agriculture, future changes in agroclimatic indices and application of crop growth simulation models intensively require a precise and adequate sets of meteorological data. Because of many reasons, Afghanistan's historical meteorological data coverage is really weak. In this research the applicability of AgMERRA as a gauge-satellite based dataset for filling the Afghanistan in-situ meteorological gaps is evaluated via goodness of fit measures, patterns of seasonal changes and the probability distribution functions.
Materials and Methods: This study is conducted on four major stations of Afghanistan (Kabul, Herat, Mazar Sharif and Qandahar in the east, west, north and south of the country, respectively) (Fig. 1 and table 1) which had the best in-situ meteorological data coverage. Observed Maximum (Tmax) and Minimum temperature (Tmin) and precipitation (PRCP) data is collected via Afghanistan Meteorological Authority (AMA) or other sources. AgMERRA database downloaded with .nc4 format and extracted with R statistical software or Panoply ver. 4.8.4, dependently. Then five goodness of fit (GOF) measures (RMSE, NRMSE, MBE, R2 and d) are calculated according to the equations 1 to 5. There are different norms and indices to measure the validity of a models, some based on Pearson correlation coefficient (R and R2) which indicate the degree of correlation between observed and predicted data but have some amounts of sensitivity to extreme values (outliers). Although, many other measures are considered to overcome the weaknesses but it is hard to distinguish the best.
Results and Discussion: The results of this research indicated the good potency, effectiveness and ability of AgMERRA for gap-filling of in-situ meteorological data and producing spatiotemporal data series. Several studies in this area have almost the same results. It is reported that AgMERRA is the most applicable dataset for reflecting precipitation data comparing with ERA-Interim, ERA-Interim/Land and JRA-55 datasets. Comparisons via NRMSE shows great (>10%) and good (>20%) amounts in all stations and temporal scales. Among other stations, Mazar Shrif showed the best conformity between AgMERRA and observed data, while Kabul station had the weakest, probably due to complex topographic situation of the Kabul airport station. The amounts of R2 for predicting temperature (Tmax and Tmin) were more than 0.86 in daily, 14-days and monthly temporal scales. The lowest amount of the coefficient of determination was obtained at Qandahar station for Tmean in daily temporal scale (R2=0.8) and the highest amount obtained for daily Tmax at Mazar Sharif station (R2=0.947). R2 for daily PRCP were inadequate, but increasing to adequate amounts in 14-days and monthly temporal scales. The highest spatiotemporal amount of Tmax,Tmin and Tmean was obtained in daily scale and the lowest amount was obtained for Tmean (1.8 and 0.9, respectively). The Index of agreement (d), also had adequate amounts for 14-days and monthly PRCP (>0.87). The amount of MBE for precipitation in Herat, Mazar Sharif and Kabul stations were negative, while it was positive in Qandahar station with a hot and dry climate. AgMERRA could show a good compliance with changes of observed seasonal patterns, however, some amount of over and under-estimates are obvious especially for Kabul station. This compliance with in-situ observed patterns was acceptable for daily temporal scale, although AgMERRA was unable to predict some of the fluctuations in probability distribution composition (with the range of 1 °C), especially fot Tmax and Tmin, but fot Tmean the fluctuations simulated well.
Conclusion: According to the results of the study, AgMERRA showed an acceptable potency to simulate the in-situ meteorological data in four major studied stations of Afghanistan. According to the stochastic nature of PRCP, the variable showed the weakest results in daily temporal scale but acceptable in 14-days and monthly. Given the weak coverage of in-situ meteorological data of Afghanistan, AgMERRA could be a valid dataset for producing well scaled spatiotemporal data series to be used in agroclimatic, CC and crop growth modeling studies.
ommolbanin bazrafshan; zahra gerkani nezhad moshizi
Abstract
Introduction: Agriculture sector, as the key consumer of fresh water resources throughout the world, is progressively more squeezed by the requirements ofother contemporary society areas and threatened by potential climatic change. Irrigation is the major part of agricultural water usage in Iran, which ...
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Introduction: Agriculture sector, as the key consumer of fresh water resources throughout the world, is progressively more squeezed by the requirements ofother contemporary society areas and threatened by potential climatic change. Irrigation is the major part of agricultural water usage in Iran, which consumes 90% of total agricultural water use. Increasing competition for water resources use, in conjunction with climate factors change may have significant effects on water availableness for agricultural production. Climate change has already affected components of the hydrologic cycle, such as precipitation redistribution, runoff and groundwater cycling. The water footprint of a crop is the volume of freshwater both consumed during the crop production process, and it has three components consist of green water footprint (the volume of the precipitation consumed in crop production); blue water footprint (the volume of runoff or groundwater consumed in crop production); grey water footprint (the volume of freshwater that is required to assimilate the load of pollutants during the crop production process) and white water footprint (the volume of water losses during the irrigation process).
Materials and Methods: The Hormozgan is located in a hyper -arid region that is impressionable to the potential impact of climate. The data used in this research consist of climate data and agriculturaldata. The climate data (2002-2016) was taken from the Iran Islamic Republic Meteorological Organization including monthly average maximum temperature, monthly average minimum temperature, relative humidity, precipitation, wind speed and sunshine hours. The agricultural data consists of, cultivation area, crop yield and soil type weretaken from the Agricultural-Jihad Bureau of Hormozgan Province. CROPWAT model is used to estimate crop water and crop irrigation requirements using meteorological, crop and soil dates. Effective precipitation (Pe) values were calculated by USDA method and crop evapotranspiration (ETc) was calculated by FAO-Penman-Montieth method. The WFGreen (effective precipitation), WFBlue (net irrigation requirement) and WFWhite (irrigation water losses) water footprints (WF) of potato production were estimated for Hormozgan. The Mann-Kendall (M-K) trend test is used to analyze the trends and abrupt changes of the climatic factors.
Results and Discussion: The total tomato WF was estimated 0.639 m3/kg in the Hormozgan province that Jask and Bastak have maximum and minimum with 1.54 and 0.66 m3/kg, respectively. The share of green, blue and white water footprint estimated 5, 18 and 77 percent, respectively. The largest shares of water footprint were observed in Bandar- Abbas (27%). The sum of the water footprint it is 19.2 MCM, which is more than 95% of the total water footprint (70.2 MCM) in the whole province. In Bashagard a large share of water footprint is related to the blue water footprint despite having a considerable amount of seasonal precipitation. Regarding the dominance of autumn precipitation in it, changing the vegetation genotype and cultivation of varieties resistant to water deficit will increase the plausibility of dry farming and increases share of the green water footprint. The white water footprint has the largest shares (77%) of while subsidence is so serious in more than 36 plains. Hormozgan province has low precipitation and high water demand. On the other hand, improper irrigation management (number of events and the volume of irrigation) has led to decreased tomato performance in these regions and larger water footprint. The share of blue water footprint is 18% that 4 times more than from WFGreen. The considerable amounts of precipitation in this province, strategies such as cultivation of new genotypes more adapted to the wet periods, shortening the flowering period of Saffron with the aim of avoiding the dry period at the end of the growing season can be considered to reduce the share of the blue water footprint and reduce the share of the green and white water footprints. Total consumed and exported virtual water volume from the region are 10.8 MCM to 28 million Rials per year. The export of these crops imports the most pressure on groundwater and surface water resources of the region. The M-K test results of climatic factors throughout the 2002–2016 study periods in Hormozgan showed that sunshine hours during the tomato growth period experienced downward trends for the M-K statistics values were less than zero and the downward temperature trend reached statistical significance. The declining temperature and sunshine hours would result in lower crop evapotranspiration (ETc) and agricultural water consumption, while CWR donot have any trend. The trend analysis shows that the green, blue and white water footprint had significant increasing trends in the central part. Increasing theyield would result in lower water footprint.
Conclusion: Ground water depletion and water shortage are two problems in Hormozgan province which have occurred due to the irregular use and inappropriate management of demand and supply of water in agricultural sector. The water footprint (WF) of crop production is a comprehensive indicator that can reflect water consumption types, quantities and environmental impacts during the crop growth period. This study assesses interannual variability of green, blue and white WFs of tomato production in Hormozgan from 2001 to 2015. The share of green, blue and white WFs in the region is 5, 17 and 77 percent and 10.5 MCM year-1. Under the combined influence of climate change and water footprint variation, WFCs weredecreasing trends. In contrast, sunshine hours had decreasing trend. The statistical analysis revealed that interannual variabilities of WFCs were caused by both climatic and non-climatic factors.
Mohammad Nazeri Tahrudi; Farshad Ahmadi; Keivan Khalili
Abstract
Introduction: Given the fact that Iran is located in the center of the dryland of earth and is significantly influenced by the deserts of Central Asia and hot dry deserts of Arabia and Africa, is one of the most arid and low rainfall land areas.So is the proper management of water resources is of critical ...
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Introduction: Given the fact that Iran is located in the center of the dryland of earth and is significantly influenced by the deserts of Central Asia and hot dry deserts of Arabia and Africa, is one of the most arid and low rainfall land areas.So is the proper management of water resources is of critical importance. The first step in the proper management of water resources is studying the factors that affected these resources including climate change. In fact climate change is a dynamic process in terms of time and place. Large parts of the Earth's climate as part of their normal variability in short-term and long-term experience. Short-term climate changes due to the difference in terms of average annual values of specific climate variables in average periods such as 30 years. Causes and effects of regional climate change in several parts of the world have been widely studied from various aspects. Among hydrological parameters, precipitation is the most important parameter in the complex hydrologic cycle. Follow the phenomenon of global warming on the Earth's surface, the rainfall pattern has changed.Trends of rainfall in different parts of the world have been studied by many researchers. Due to climate change in Iran and climate change in the Basin of Urmia Lake it seems that evaluation the trend of monthly and annual precipitation and its time of change point in the basin of Urmia Lake changes is important. The goal of this study is evaluatingthe trend and time of the change point trend of monthly and annual precipitation of rain gage stations in Urmia Lake basin.
Material and methods: Lake Urmia is the focus of surplus accumulation of surface currents all the rivers of the basin, with an area of approximately 5750 square kilometers and the average elevation of 1276 m above sea level and is located in the middle of the northern basin. Around of Lake Urmia there are 16 wetlands with an area of 5 to 120 hectares (some have dried up) that mostly have sweet or salty and fresh water and a high value of ecosystems.Urmia Lake Basin is situated in eastern of 44-14 to 47-53 and north of 40-35 to 30-38 coordinates. Urmia Lake Basin rainfall changes is 220 to 900 mm and have mean precipitation about 263 mm that added in central parts of the basin to the highlands.
Trend analysis: The aim of process test is to specify whether an ascending or a descending trend exists in data series. Since parametric tests have some assumptions including normality, stability, and independence of variables, where most of these assumptions do not apply to hydrologic variables, the nonparametric methods are more preferred in meteorological and hydrological studies. The nonparametric methods are less sensitive to extreme values compared to parametric tests in the examination of trends. Nonparametric tests can also be utilized for data time series regardless of linearity or nonlinearity of the trend (Khalili et al. 2014). One of the most well-known nonparametric tests is Mann-Kendall test (Mann 1945; Kendall 1975).
The modified Mann-Kendall test (MMK): The main assumption of Mann-Kendall test is that the sample data has no significant autocorrelation. However, some hydrological series might have a significant autocorrelation coefficient. When a series has a positive autocorrelation coefficient, there is an increased chance for Mann-Kendall test to reveal the existence of a trend in this series. In this case, the null hypothesis i.e. lack of trend is rejected, yet this hypothesis should not actually be. The modified Mann-Kendall test was presented by Hamed and Rao (1998) and has been used by Kumar et al (2009) for the analysis of the trend of Indian rivers. In this method, the effect of all significant autocorrelation coefficients is removed from the time series and is appliesto a series whose autocorrelation coefficients are significant in one or more cases.
Change point test: Pettittest is a non-parametrictest that was developedin 1979byPettit. Themethod is used in order tofind change points ina time series(Salarijazi et al 2012).In this study,thestatisticwas usedtofind asudden change intemperaturedata.Thisstatistic isatest with rank basis and without a distributionin orderto detectsignificantchangesin the mean of the time seriesanditis importantwhenthereis noassumptionabout the change time.
Results and discussion: In this study the trend of monthly and annual precipitation of rain gage stations that located in Urmia Lake basin were investigated using modified Mann-Kendall test. Z values of case study were calculated in two monthly and annual scales. The results of evaluation the trend of precipitation of rain gage stations of Urmia Lake basin showed that in October, December, January, February and March (five months of the year) the trend of precipitation is decreasing and the mean of Z values showed the less than zero values. In April and May there is no sensible changing in precipitation trend. Also the results showed that the March, April and May have a low failure rate and February, December and July have a most of change point of monthly precipitation data. About 60 percentages of the time of change point in precipitation trend are between 1992 and 1998. Also the results showed that two months of May and November there is no changing point in west Urmia Lake rain gage stations. In annual scale the time of changing trend is between 1992 and 1998.
Conclusion: The results of evaluation the trend of Lake Urmia precipitations showed that the Urmia Lake basin has a combination of decreasing and increasing trend in studied time period. The decreasing trend in precipitation often seen in west stations of the basin and west and south-west of Urmia Lake. The increasing trend also seen in south and north-east of Urmia Lake basin. Also the results of zoning the Z values of Mann-Kendall test showed that in annual scale the regions that influenced by polar-continental air mass that they entered Iran have a decreasing trend.
Amirhosein Aghakhani Afshar; Yousef Hassanzadeh; Ali Asghar Besalatpour; Mohsen Pourreza Bilondi
Abstract
Introduction: Hydrology cycle of river basins and water resources availability in arid and semi-arid regions are highly affected by climate changes, so that recently the increase of temperature due to the increase of greenhouse gases have led to anomaly in the Earth’ climate system. At present, General ...
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Introduction: Hydrology cycle of river basins and water resources availability in arid and semi-arid regions are highly affected by climate changes, so that recently the increase of temperature due to the increase of greenhouse gases have led to anomaly in the Earth’ climate system. At present, General Circulation Models (GCMs) are the most frequently used models for projection of different climatic change scenarios. Up to now, IPCC has released four different versions of GCM models, including First Assessment Report models (FAR) in 1990, Second Assessment Report models (SAR) in 1996, Third Assessment Report models (TAR) in 2001 and Fourth Assessment Report models (AR4) in 2007. In 2011, new generation of GCM, known as phase five of the Coupled Model Intercomparison Project (CMIP5) released which it has been actively participated in the preparation of Intergovernmental Panel on Climate Change (IPCC) fifth Assessment report (AR5). A set of experiments such as simulations of 20th and projections of 21st century climate under the new emission scenarios (so called Representative Concentration Pathways (RCPs)) are included in CMIP5. Iran is a country that located in arid and semi-arid climates mostly characterized by low rainfall and high temperature. Anomalies in precipitation and temperature in Iran play a significant role in this agricultural and quickly developing country. Growing population, extensive urbanization and rapid economic development shows that Iran faces intensive challenges in available water resources at present and especially in the future. The first purpose of this study is to analyze the seasonal trends of future climate components over the Kashafrood Watershed Basin (KWB) located in the northeastern part of Iran and in the Khorsan-e Razavi province using fifth report of Intergovernmental Panel on climate change (IPCC) under new emission scenarios with Mann-Kendall (MK) test. Mann-Kendall is one of the most commonly used nonparametric tests to detect climatic changes in time series and trend analysis. The second purpose of this study is to compare CMIP5 models with each other and determine the changes in rainfall and temperature in the future periods in compare with base period on seasonal scale in all parts of this basin.
Materials and Methods: In this research, keeping in view the importance of precipitation and temperature parameters, fourteen models obtained from the General Circulation Models (GCMs) of the newest generation in the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to forecast the future climate changes in the study area. In historical time (1992-2005), simulated data of these models were compared with observed data (34 rainfall and 12 temperature stations) using four evaluation criteria for goodness-of-fit including Nash-Sutcliffe (NS), Percent of Bias (PBIAS), coefficient of determination (R2) and the ratio of the root mean square error to the standard deviation of measured data (RSR). Furthermore, all models have a very good rating performance for all of the evaluation criteria and therefore investigation is done for precipitation data as an important component in survey of climate subject to select the optimum models in kashafrood watershed basin.
Results and Discussion: By comparing four evaluation criteria for fourteen models of CMIP5 during historical time, finally, four climate models, including GFDL-ESM2G, IPSL-CM5A-MR, MIROC-ESM and NorESM1-M which indicated more agreement with observed data according to the evaluation criteria were selected. Furthermore, four Representative Concentration Pathways (RCPs) of new emission scenario, namely RCP2.6, RCP4.5, RCP6.0 and RCP8.5 were extracted, interpolated and then under three future periods, including near-century (2006-2037), mid-century (2037-2070) and late-century (2070-2100) were investigated and compered.
Conclusions: The results of Mann-Kendall test which was applied to examine the trend, revealed that the precipitation have variable positive and negative trends which were statistically significant. In addition, mean temperature have a significant positive trend with 90, 99 and 99.9% confidence level. In seasonal scale, survey of climatic variable (rainfall and mean temperature) showed that the maximum and minimum of precipitations occur during spring and summer and mean temperature in all seasons is higher than historical baseline, respectively. Maximum and minimum of mean temperature occur in summer and winter, and the amount of seasonal precipitation in these seasons will be reduced. Finally, across all parts of kashafrood watershed basin, rainfall and mean temperature will be reduced and increased, respectively. In conclusion, models of CMIP5 can simulate the future climate change in this region and four models of CMIP5 can be used for this region.
Parisa Farzamnia; Shahram Manafi; Hamidreza Momtaz
Abstract
Introduction: Minerals are one of the main components of soils which play different roles in the soils. Minerals make up about 50% of the volume of most soils. They provide physical support for plants, and create the water- and air-filled pores that make plant growth possible. Mineral weathering releases ...
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Introduction: Minerals are one of the main components of soils which play different roles in the soils. Minerals make up about 50% of the volume of most soils. They provide physical support for plants, and create the water- and air-filled pores that make plant growth possible. Mineral weathering releases plant nutrients which are retained by other minerals through adsorption, cation exchange, and precipitation. Minerals are indicators of the amount of weathering that has taken place, and the presence or absence of particular minerals gives clues to how soils have been formed. The physical and chemical characteristics of soil minerals are important consideration in planning, constructing, and maintaining of buildings, roads, and airports. Clay minerals can be used for understanding of soil formation, optimum management of dry and wet lands and interpretation of paleo environments. Moreover, clay minerals can provide some valuable information such as the origin of sediments, transportation and precipitation of sediments and also some information about intercontinental weathering regimes. Quaternary sediments have occupied most of the agricultural and natural resources of Urima plain and recognition of mineralogical of these soils is essential to optimum and stabile use of these soils. Additionally, caly mineralogical investigation can provide some information about the intensity of weathering processes and climate change in this area. Thus, in this study clay minerals of quaternary sediments in northeast of Urmia and the mechanisms of their formation and also tracing probable climate change in this area were investigated.
Materials and Methods: This study was performed in theUrmia plain in west Azerbaijan Province. The study area is located on quaternary sediments and physiographically, this area is a part of a river alluvial plain with the gentle slope toward Urmia Lake. The mean annual precipitation and temperature of this area are 345.37 mm and 10.83 °C respectively and the soil moisture and temperature regimes are dry xeric and mesic respectively. In this study, eight soil profiles in quaternary sediments were dug and sampled and the morphological, physical, chemical and mineralogical properties were determined using standard methods.
Results and Discussion: According to the results, Illite, smectite, Kaolinite, chlorite, vermiculite and hydroxy interlayer vermiculite (HIV) were the dominant clay minerals in these soils. The origin of illite, chlorite and kaolinite were related to inheritance from parent material. Regarding to the present of some smectite in the parent material of these soils, some of smectites have been inherited from parent material. Nevertheless it seems that, the most of smectites in these soils have pedogenic origin. Based on mineralogical results and trends variation of smectite and illite along studied profiles, we concluded that some of smectites in these soils have been formed from illite transformation. In profiles 4 and 6, regarding to low depth water table and consequently poor drainage, high pH and high values of calcium and magnesium cations, provide suitable conditions for the neoformation of smectit and so, some of smectites have been formed via neoformation from soil solution. In these soils, vermiculites were pedogenic and have been formed during transformation of illite to smectite. Small amounts of hydroxy interlayer vermiculites were present in buried horizons and regarding that they were not present in parent material, it might be because these minerals are pedogenic and have been formed in a past wetter climate. The transformation of illite to smectite in lower horizons needs high moisture and regarding to recent semiarid climate of study area, the suitable amount of moisture for this transformation, especially in lower depths and also in buried horizons, is not present. Thus, it seems the transformation of illite to smectite in lower depths and buried horizons has been taken place in a wetter past climate. So we concluded that smectite and hydroxy interlayer vermiculite are evidences of a wetter past climate in this area.
Conclusion: In this study the origin of smectite in buried horizons was related to transformation of illite. According to high moisture condition which is necessary for the weathering of illite, the occurrence of this process related to more humid climate of the past. Additionally, the presence of hydroxy interlayer vermiculites was related to previously wetter climate as well. So results of this study can be used for recognition of climatic change in the study area.
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.
Z. Dehghan; F. Fathian; S. Eslamian
Abstract
Introduction: According to the fifth International Panel on Climate Change (IPCC) report, increasing concentrations of CO2 and other greenhouse gases resulting from anthropogenic activities have led to fundamental changes on global climate over the course of the last century. The future global climate ...
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Introduction: According to the fifth International Panel on Climate Change (IPCC) report, increasing concentrations of CO2 and other greenhouse gases resulting from anthropogenic activities have led to fundamental changes on global climate over the course of the last century. The future global climate will be characterized by uncertainty and change, and this will affect water resources and agricultural activities worldwide. To estimate future climate change resulting from the continuous increase of greenhouse gas concentration in the atmosphere, general circulation models (GCMs) are used. Resolution of the output of the GCM models is one of the problems of these models. Using downscaling tools to convert global large-scale data to climate data for the study area is essential. These techniques are used to convert the coarse spatial resolution of the GCMs output into a fine resolution, which may involve the generation of station data of a specific area using GCMs climatic output variables. The objectives of this study are, therefore, to investigate and evaluate the statistical downscaling approaches.
Materials and Methods: Different models and methods have been developed which the uncertainty and validation of results in each of them in the study area should be investigated to achieve the more real results in the future. In the present study, the performance of SDSM, IDW and LARS-WG models for downscaling of the temperature and precipitation data of Pars Abad synoptic station were compared and investigated. IDW technique is based on the functions of the inverse distances in which the weights are characterized by the inverse of the distance and normalized, so their aggregate equivalents one. SDSM is categorized as a hybrid model, which utilized a linear regression method and a stochastic weather generator. The GCM’s outputs (named as predictors) are used to a linearly condition local-scale weather generator parameters at single stations. LARS-WG is a stochastic weather generator and it is widely used for the climate change assessment. This model uses the observed daily weather data, to compute a set of parameters for probability distributions of weather variables, which are used to generate synthetic weather time series of arbitrary length by randomly selecting values from the appropriate distributions. In this study, data from the Pars Abad meteorological station, which was used as the data for the baseline period, was also used to predict climate variables. The record of data is 30 years (1971-2000), and the mean temperature and precipitation are 13.7 and 283 mm per year, respectively. The driest month is August, which receives less than 5 mm of rain. Most of the rainfall occurs in April, averaging at 47 mm. July is the warmest month of the year, with an average temperature of 28.9 oC, and January is the coldest, with an average temperature of -2.3 °C. Precipitation differs by 42.8 mm between the driest and wettest months of the year and the average temperature varies by 31.2 °C.
Results and Discussion: The calibration and validation results of the SDSM and LARS-WG models in the case of temperature showed that two models have better abilities for temperature simulation in comparison with precipitation data and, in all models, the increasing temperature was observed for most of the warm months. In the case of precipitation, the results of three models have considerable different towards each other and changes intensity of decreasing and increasing precipitation compared to the baseline in IDW model is higher and in LARS-WG model is lower than two other models. But, in case of calculated evapotranspiration, the results of SDSM and IDW models indicate the increasing evapotranspiration in the all months even modest and its maximum value is in last spring and summer. While, calculated evapotranspiration by using LARS-WG model has showed the lower estimation than the baseline period which implies the low ability of model to calculate this model. In general, scenario A2 resulted in more increases in temperature than B2 in each time period. Whereas, in the case of rainfall, the results for each time period were different. For ETo, in comparison to the baseline, both A2 and B2 scenarios showed an increase during both time periods.
Conclusion: In general, the results showed that all three models have similar and good performance for simulating and downscaling of temperature and precipitation data. Therefore, these three models can be adopted to study climate change impacts on natural phenomenon.
S. Babaei Hessar; R. Ghazavi
Abstract
Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, ...
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Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Various methods, such as time series and artificial neural network models, have been proposed to predict the level of rainfall. But there is not enough attention to global warming and climate change issues. The main aim of this study is to investigate the conformity of artificial neural network and time series models with climate scenarios.
Materials and Methods: For this study, 50 years of daily rainfall data (1961 to 2010) of the synoptic station of Urmia, Tabriz and Khoy was investigated. Data was obtained from Meteorological Organization of Iran. In the present study, the results of two Artificial Neural Network (ANN) and Time Seri (TS) methods were compared with the result of the Emission Scenarios (A2 & B1). HadCM3 model in LARS-WG software was used to generate rainfall for the next 18 years (2011-2029). The results of models were compared with climate scenarios over the next 18 years in the three synoptic stations located in the basin of the Lake Urmia. At the first stage, the best model of time series method was selected. The precipitation was estimated for the next 18 years using these models. For the same period, precipitation was forecast using artificial neural networks. Finally, the results of two models were compared with data generated under two scenarios (B1 and A2) in LARS-WG.
Results and Discussion: Different order of AR, MA and ARMA was examined to select the best model of TS The results show that AR(1) was suitable for Tabriz and Khoy stations .In the Urmia station MA(1) was the best performance. Multiple Layer Perceptron with a 10 neurons in hidden layer and the output layer consists of five neurons had the lowest MSE and the highest correlation coefficient in modeling the values of annual precipitation. So MLP was determined as the best structure of neural network for rainfall prediction. According to results, precipitation predicted by the ANN model was very close to the results of A2 and B1 scenario, whereas TS has a significant difference with these scenarios. Average rainfall predicted by two A2 and B1 scenarios in Urmia station has more difference than other stations. Based on the B1 scenario, precipitation will increase 11 percent over the next two decades. It will decrease 10.7 percent according to A2 emissions scenario. According to ANN models and two A2 and B1 scenarios, the rates of rainfall will increase in Tabriz and Khoy stations. However, according to TS model, rainfall will decline 5.94 and 3.63 percent for these two stations, respectively.
Conclusion: Global warming and climate change should have adverse effects on groundwater and surface water resources. Different models are used for simulating of thes effects. But, conformity of these models with the results of climate scenarios is an issue that has not been addressed. In the present research coincidence of TS model, ANN model and climate change scenarios was investigated. Results show under emissions scenarios, during the next two decades in Tabriz and Khoy stations, precipitation will increase. In Urmia station B1 and A2 scenario percent increase by 11 percent and 10.5 percent decline predicted, respectively. The results of Roshan and et al (4) and Golmohammad and et al, (7) investigations show increasing trend in the rainfall rate and confirming the results of this study According to results, the performance of ANN model is better than TS model for rainfall prediction and its result is similar to climate change scenarios. Similar results have been reported by Wang et al (29) and the Norani et al (20). Due to the significant difference between the TS and climate scenarios used in the study area, is not recommended, though it can be used as a plausible climate scenario to predict the precipitation of stations in the future studied. At the end, it is suggested that the similar studies carried out in a larger number of stations in the country with respect to global warming and climate change, to determine the validity of the methods used to the predicted rainfall.
H. Zareabyaneh; M. GHobaeisoogh; Abolfazl Mosaedi
Abstract
Introduction: Drought is a natural and recurrent feature of climate. The characterizations of it may change under the effect of climate change in future periods. During the last few decades a number of different indices have been developed to quantify drought probabilities. Droughts are caused by disruptions ...
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Introduction: Drought is a natural and recurrent feature of climate. The characterizations of it may change under the effect of climate change in future periods. During the last few decades a number of different indices have been developed to quantify drought probabilities. Droughts are caused by disruptions to an expected precipitation pattern and can be intensified by unusually high temperature values. Precipitation-based drought indices, including the Standardized precipitation index (SPI), cannot identify the role of temperature increase in drought condition and in addressing the consequences of climate change. Recently, two new standardized drought indices have been proposed for drought variability analysis on multiple time scales, the Reconnaissance Drought Index (RDI, Tsakiris et al., 2007) and the Standardized Precipitation Evapotranspiration Index (SPEI, Vicente-Serrano et al., 2010). The objective of this study is to evaluate the characterization of wet and dry periods under the effect of climate change according to SPEI index in synoptic station of Hamedan for the next thirty years (2011-2040).
Materials and Methods: In this study, the indices of SPEI, SPI and RDI were investigated and the SPEI index as a multiscalar and suitable index was used to detect, monitor, and explore the consequences of global warming on drought conditions in synoptic station of Hamedan (airport). For this purpose, the period of 1981-2010 was chosen as the base period and the simulation of the future climate variables were done based on A1B, A2 and B2 emissions scenarios and performance of multi model ensemble via LARS-WG5 model for the period of 2011-2040. The performance of the multi model ensemble was done by using five global climate models including IPCM4, MPEH5, HADCM3, GFCM21, and NCCCS in the IPCC Fourth Assessment Report (Semenov and Stratonovitch, 2010). By simulating the values of precipitation ,and the values of temperature and the values of estimated evapotranspiration , the values of SPEI, RDI and SPI indices were calculated annually and 1, 3 and 6 months (short- term period) and 12, 18 and 24 months (long- term period) time scales for the base period and the three next decades. Then, the relation among them was computed and investigated via correlation coefficient. Then, by monitoring the humidity condition via SPEI index, the characterization of wet and dry periods including period numbers, longest period, total deficit or surplus, and maximum deficit or surplus were derived based on Run theory and were comprised for the base period and three future decades.
Results and Discussion: Evaluation of LARS-WG5 model for base period showed that the model was able to simulate minimum and maximum temperatures and precipitation data with high accuracy based on statistic error and can be used to generate data for future years according to emission scenario. According to the simulated results of performance of multi model ensemble, the average values of mean temperature and precipitation will increase by 0.820C and 2.5 % for A2 scenario, respectively. In addition, the minimum and maximum temperatures have increased in all of the months according to the three scenarios in comparison with the base period. The correlation results between the investigated indices showed that the maximum and minimum of correlation can be observed between SPI & RDI and SPEI & SPI indices in the base period and future decade for each scenario, respectively. Drought assessment based on the SPEI index in the base period shows that the main drought episodes occurred in the 1999 to 2001 that were consistent with FAO report (2006). Comparison of wet and dry periods in relation to the base period showed that the number of dry periods will increase in time scales of 1 and 3 months and will decrease in other long-term time scales.
Conclusion: Climate change and its effects are among the main challenges of water resources management in the present century. In this study, the effects of this phenomenon on drought monitoring and change of characterizations were investigated. For this purposes, we used daily meteorological variables during thirty years (1981-2010) from Hamedan Synoptic station. The results of drought monitoring were based on SPEI index, and it revealed the high variability of humidity condition in the first decade of simulation in comparison with the second and third decades. This issue indicated that this decade requires more attention and management measurements. Also, according to the results of the derived characterization via Run theory, the number of dry periods will decrease and persistence of the longest dry period and consequently the volume of deficit will increase in the next three decades. In addition, the total volume surplus of wet periods will decrease in relation to the base period that can be interpreted as the increasing of moisture deficit in future decades The SPEI is based on precipitation and temperature data, and it has the advantage of combining multiscalar character with the capacity to include the effects of temperature variability on drought assessment. Thus, we recommend SPEI, as a suitable index for studying and identifying the effect of climate change on drought conditions.
Rooholla Moradi; Alireza Koocheki; Mehdi Nassiri; Hamed Mansoori
Abstract
Introduction: The latest report of the Intergovernmental Panel on Climate Change (IPCC) states that future emissions of greenhouse gases (GHGs) will continue to increase and cause climatic change (16). These conditions are also true for Iran. The three greenhouse gases associated with agriculture are ...
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Introduction: The latest report of the Intergovernmental Panel on Climate Change (IPCC) states that future emissions of greenhouse gases (GHGs) will continue to increase and cause climatic change (16). These conditions are also true for Iran. The three greenhouse gases associated with agriculture are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). The three GHGs associated with agriculture CO2, CH4, and N2O differ in their effectiveness in trapping heat and in their turnover rates in the atmosphere. This environmental change will have serious impacts on different growth and development processes of crops. Increasing temperature could affect physiological processes such as photosynthesis, respiration and partitioning of photoassimilates. Farmers are not able to change or manage the climatic conditions, but some factors such as soil, water, seed and agricultural practices can be managed to reduce the adverse impacts of climate change (32). Mitigation and adaptation are two known ways for reducing the negative impacts of climate change. Mitigation strategies are associated with decreasing greenhouse gas (GHG) emissions through management practices such as reducing chemical fertilizer application, mechanization, increasing carbon storage in agroecosystems, planting biofuel crops and moving towards organic farming (42), etc.
Material and Methods: This study was carried out at the experimental field of the Ferdowsi University of Mashhad in 2011 and was repeated in 2012. The Research Station (36°16´N, 59°36´E) is located at about 985 m a.s.l. Average temperature and precipitation rate of the research station in two years are shown in Figure. 1. The three-factor experiment was set up in a strip-split-plot arranged in a randomized complete block design with three replications. The experimental treatments were tillage systems (conventional and reduced tillage) and residual management (remaining and leaving of maize residual) assigned to main plots and different levels of N fertilizer (0, 150, 300 and 450 kg urea ha-1) was randomized as a subplot in tillage treatment. The seedbed preparation was made based on common practices at the location. Plot size under the trial was 4 m × 3 m so as to get 70 cm inter row spacing. Maize seeds (single-cross 704 cultivar) were hand sown in May for two years. The ideal density of the crops was considered as spacing 20 cm inter plant. As soon as the seeds were sown, irrigation continued every 10 days. No herbicides or chemical fertilizers were applied during the course of the trials and weeding was done manually when necessary. Measurement of CO2 emissions was performed by the closed chamber method. For this purpose, PVC plastic rings (20 cm in diameter and 30 cm height) were scattered on each of the plots. The chambers were placed in soil for two hours and the gathered air was collected by 10 ml vacuum syringe. Then, the samples were transferred to the laboratory and CO2 was measured using GC-mass.
Results and Discussion: The results showed that CO2 emissions for conventional tillage was about 15 and 10% higher than the reduced tillage in 2011 and 2012, respectively. The CO2 emissions can be taken as indicators of soil tillage effects on the soil ecosystem, because CO2 emissions are closely connected to the microbial turnover and the physical accessibility of organic matter to microbes. These parameters were more available in the conventional tillage than the reduced tillage. CO2 emissions were strongly higher in the remaining residual condition rather than leaving condition in two years. CO2 emissions in the remaining residual condition was about 4.36 and 5.37 times higher than that of the leaving residual condition in 2011 and 2012, respectively. The microbial respiration and humidity of soil in the remaining residual condition is higher than that of the leaving residual condition. CO2 emission was elevated with increasing the rate of N fertilizer. The N fertilizer can increase the microbial activity of the soil. Cover cropping and N fertilization can increase CO2 emissions in full and reduced tilled soils by increasing the amount of crop residue returned to the soil. The results showed that CO2 emissions in 2011 were higher than 2012 in all treatments. The residual treatment had more effect on daily CO2 emission in comparison with tillage and N fertilizer treatments in both years. The trait was higher under conventional tillage, residue remaining and higher N fertilizer levels compared to reduced tillage, residue leaving and lower N fertilizer application. Linear regression for air temperature and mean CO2 emission illustrated that there was a positive correlation between air temperature and CO2 emission.
Conclusion: In essence, the results showed that CO2 emissions for conventional tillage were higher than that of reduced tillage in two years. Remaining residual condition had strongly higher CO2 emission rather than leaving condition. CO2 emission was elevated with increasing the rate of N fertilizer.
B. Mansouri; H. Ahmadzadeh; A. Massah Bavani; saeed morid; M. Delavar; S. Lotfi
Abstract
This paper evaluate impacts of climate change on temperature, rainfall and runoff in the future Using statistical model, LARS-WG, and conceptual hydrological model, SWAT. In order to the Zarrinehrud river basin, as the biggest catchment of the Lake Urmia basin was selected as a case study. At first, ...
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This paper evaluate impacts of climate change on temperature, rainfall and runoff in the future Using statistical model, LARS-WG, and conceptual hydrological model, SWAT. In order to the Zarrinehrud river basin, as the biggest catchment of the Lake Urmia basin was selected as a case study. At first, for the generation of future weather data in the basin, LARS-WG model was calibrated using meteorological data and then 14 models of AOGCM were applied and results of these models were downscaled using LARS-WG model in 6 synoptic stations for period of 2015 to 2030. SWAT model was used for evaluation of climate change impacts on runoff in the basin. In order to, the model was calibrated and validated using 6 gauging stations for period of 1987-2007 and the value of R2 was between 0.49 and 0.71 for calibration and between 0.54 and 0.77 for validation. Then by introducing average of downscaled results of AOGCM models to the SWAT, runoff changes of the basin were simulated during 2015-2030. Average of results of LARS-WG model indicated that the monthly mean of minimum and maximum temperatures will increase compared to the baseline period. Also monthly average of precipitation will decrease in spring season but will increase in summer and autumn. The results showed that in addition to the amount of precipitation, its pattern will change in the future period, too. The results of runoff simulation showed that the amount of inflow to the Zarrinehrud reservoir will reduce 28.4 percent compared to the baseline period.
B. Ashraf; A. Alizadeh; M. Mousavi Baygi; M. Bannayan Awal
Abstract
Scince climatic models are the basic tools to study climate change and because of the multiplicity of these models, selecting the most appropriate model for the studying location is very considerable. In this research the temperature and precipitation simulated data by BCM2, CGCM3, CNRMCM3, MRICGCM2.3 ...
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Scince climatic models are the basic tools to study climate change and because of the multiplicity of these models, selecting the most appropriate model for the studying location is very considerable. In this research the temperature and precipitation simulated data by BCM2, CGCM3, CNRMCM3, MRICGCM2.3 and MIROC3 models are downscaled with proportional method according A1B, A2 and B1 emission scenarios for Torbat-heydariye, Sabzevar and Mashhad initially. Then using coefficient of determination (R2), index of agreement (D) and mean-square deviations (MSD), models were verified individually and as ensemble performance. The results showed that, based on individual performance and three emission scenarios, MRICGCM2.3 model in Torbat-heydariye and Mashhad and MIROC3.2 model in Sabzevar had the best performance in simulation of temperature and MIROC3.2, MRICGCM2.3 and CNRMCM3 models have provided the most accurate predictions for precipitation in Torbat-heydariye, Sabzevar and Mashahad respectively. Also simulated temperature by all models in Torbat-heydariye and Sabzevar base on B1 scenario and, in Mashhad based on A2 scenario had the lowest uncertainty. The most accuracy in modeling of precipitation was resulted based on A2 scenario in Torbat-heydariye and, B1 scenario in Sabzevar and Mashhad. Investigation of calculated statistics driven from ensemble performance of 5 selected models caused notable reduction of simulation error and thus increase the accuracy of predictions based on all emission scenarios generally. In this case, the best fitting of simulated and observed temperature data were achieved based on B1 scenario in Torbat-heydariye and Sabzevar and, A2 scenario in Mashhad. And the best fitting simulated and observed precipitation data were obtained based on A2 scenario in Torbat-heydariye and, B1 scenario in Sabzevar and Mashhad. According to the results of this research, before any climate change research it is necessary to select the optimum GCM model for the studying region to simulate climatic parameters.
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.
mostafa rezaee; saeed morid
Abstract
Lake Urmia is faced with problematic drawdown trend in the recent years that climate change can exacerbate this phenomena. This research work applies HadCM3 climate change data (scenarios A2 and B2) for the period of 2010-39 to evaluate changes in temperature and rainfall of the basin. To downscale these ...
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Lake Urmia is faced with problematic drawdown trend in the recent years that climate change can exacerbate this phenomena. This research work applies HadCM3 climate change data (scenarios A2 and B2) for the period of 2010-39 to evaluate changes in temperature and rainfall of the basin. To downscale these data a new approach is used, which is a combination of SDSM model and TOPSIS approach. Furthermore, the climate change data was introduces to the Soil and Water Assessment Tools model (SWAT) that was calibrated using local data and information to evaluate future changes in the basin’s runoff. Of course at this stage, only the Simineh Rud subbasin as one of the basin’s main subbasin was investigated. The results showed that climate change will reduce monthly rainfalls up to 40% and increase the temperatures up to 2 °C. Also, the basin’s runoffs will face with reduction of about 25%. Consequently, the inflows to lake may decrease about 30%. In general, the results of this research work emphasize on more attentions to the problems of the lake that will be booster under climate change condition
M. Rezaei; M. Nohtani; A. Moghaddamnia; A. Abkar; M. Rezaei
Abstract
One of the most important problems in the management and planning of water resources is to forecast long-term precipitation in arid region and hyper arid regions. In this study, statistical downscaling model (SDSM) is used for study of climate change effects on precipitation. The data used as input to ...
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One of the most important problems in the management and planning of water resources is to forecast long-term precipitation in arid region and hyper arid regions. In this study, statistical downscaling model (SDSM) is used for study of climate change effects on precipitation. The data used as input to the Model are daily precipitation of Kerman and Bam synoptic stations, NCEP (National Centers for Environmental Prediction) data and the A2 and B2 emission scenarios HadCM3 for the reference period (1971-2001). Using HadCM3 A2, B2 data the precipitation for three period (2010-2039), (2040-2069) and (2070-2099) are predicted and compared with the reference period. We used the first 15 years data (1971-1985) for the calibration and the second 15 years data (1986-2001) for model validation. Research results showed that the precipitation will change and Change directions are positive in some months and negative in other months. After the examination function Indexes results from SDSM model shown that this model has better accuracy and a high ability to predict precipitation in arid region than hyper arid region.
M. Azari; Hamid Reza Moradi; B. Saghafian; M. Faramarzi
Abstract
Climate change is one of the most significant challenges that all living matters on the Earth needs to face. A warmer climate will accelerate the hydrologic cycle, altering rainfall, magnitude and timing of runoff.Iran might become one of the most vulnerable areas in the world regarding climate change. ...
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Climate change is one of the most significant challenges that all living matters on the Earth needs to face. A warmer climate will accelerate the hydrologic cycle, altering rainfall, magnitude and timing of runoff.Iran might become one of the most vulnerable areas in the world regarding climate change. To study the effects of climatic variations, the Soil and Water Assessment Tool (SWAT) model was implemented to simulate the hydrologic regime on Gorganroud basin. The SUFI-2 algorithm in the SWAT-CUP program was used for parameter optimization using the monthly river discharge. The climatechange scenarios were constructed using outcomes of three General Circulation Models (CGCM2, HadCM3 and SCIRO)for low and high levels of greenhouse gas emission scenarios (A1F1 and B1). The calibration andvalidation results of the SWAT model agreedwell with the observed data. The study result showed that Compared with the present climate, the climate models predicted a −3.2% to -6.5% changes in annual precipitation, 3 to 5.7°C rises in maximum temperature, and 2.8 to 5.2°C rises in minimum temperature. SWAT predicted a −0.4% to -7.7% changefor runoff, −5.2% to -13% change for soil water content, 5.3% to 10.2% change for water yield, and -1.6% to -3.6% decrease for evapotranspiration during 2010–2039 under all climate scenarios.These results highlight the strong impact of climate change in regional water resources and reflect the importance of incorporating these analysis into adaptive management in the future watershed management strategies.
J. Fallahi; P. Rezvani Moghaddam; M. Nassiri Mahallati; Mohammad Ali Behdani
Abstract
Climate change by increasing concentrations of greenhouse gases, particularly carbon dioxide, has led to increase attention to the carbon sequestration through the restoration and protection of vegetation cover. In this regards, ecosystems of arid regions have a special importance. In this study the ...
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Climate change by increasing concentrations of greenhouse gases, particularly carbon dioxide, has led to increase attention to the carbon sequestration through the restoration and protection of vegetation cover. In this regards, ecosystems of arid regions have a special importance. In this study the effects of reconstruction and conservation, on soil carbon sequestration of the region of the International Carbon Sequestration Project in Hussein Abad, South Khorasan province of Iran was investigated by a simulation approach using RothC model. In addition, the effects of climate change (increasing temperature and decreasing rainfall) on soil carbon sequestration potential was studied. In the studied area, replanting was done in 2004 and then soil samples were taken every two months during 2010-2011. After collecting the required input data for RothC model (climate, soil and management input data), the model was evaluated and validated for the study area. Moreover, soil carbon sequestration was studied under climate change condition. The simulation results revealed that the RothC model is applicable in rangelands of dry and warm regions, because it estimated the soil carbon changes over the time with proper accuracy. The amounts of model performance index, R2 and RMSE were 0.98, 98% and 0.01, respectively. Simulation study indicated that soil carbon storage will increase from 2011 to 2050 and will be affected by climate change and protection programs. Based on model estimation the amounts of soil carbon in preotected areas will be higher than non-protected areas. Moreover, in non-climate change scenario the amounts of soil carbon will be higher than climate change scenario in 2050.
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.
