Vesta Afzali; Masoud Reza Hessami Kermani
Abstract
Introduction: The phenomenon of climate change and its consequences is a familiar topic which is associated with natural disasters such as, flooding, hurricane, drought that cause water crisis and irreparable damages. Studying this phenomenon is a serious warning regarding the earth’s weather change ...
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Introduction: The phenomenon of climate change and its consequences is a familiar topic which is associated with natural disasters such as, flooding, hurricane, drought that cause water crisis and irreparable damages. Studying this phenomenon is a serious warning regarding the earth’s weather change for a long period of time.
Materials and Methods: In order to understand and survey the impacts of climate change on water resources, Global Circulation Models, GCMs, are used; their main role is analyzing the current climate and projecting the future climate. Climate change scenarios developing from GCMs are the initial source of information to estimate plausible future climate. For transforming coarse resolution outputs of the GCMs into finer resolutions influenced by local variables, there is a need for reliable downscaling techniques in order to analyze climate changes in a region. The classical statistical methods run the model and generate the future climate just with considering the time variable. Multi-site daily rainfall and temperature time series are the primary inputs in most hydrological analyses such as rainfall-runoff modeling. Water resource management is directly influenced by the spatial and temporal variation of rainfall and temperature. Therefore, spatial-temporal modeling of daily rainfall or temperature including climate change effects is required for sustainable planning of water resources.
Results and Discussion: For the first time, in this study by ASD model (Automated regression-based Statistical Downscaling tool) developed by M. Hessami et al., multi-site downscaling of temperature and precipitation was done with CGCM3.1A2 outputs and two synoptic stations (Rasht and Bandar Anzali) simultaneously by considering the correlations of multiple sites. The model can process conditionally on the occurrence of precipitation or unconditionally for temperature. Hence, the modeling of daily precipitation involves two steps: one step, precipitation occurrence and the other step precipitation amounts and the modeling of daily temperature is performed in one step. The choice of predictor variables is one of the most influential steps in the development of statistical downscaling scheme because the decision largely determines the character of the downscaling results. It is essential to remember that predictors relevant to the local predict and should be adequately reproduced by the host climate model at the spatial scales used to condition the downscaled response. To test this approach over the current period and to compare the results with observed data, temperature and precipitation, from 2 stations, model is evaluated and calibrated by using NCEP (National Center for Environmental Prediction) reanalysis data before the use of GCMs as input variables. Then climate was predicted for three periods which each period consist of thirty years in the future, 2011-2040, 2041-2070 and 2071-2100. ASD reduces the problem of predictor selection and it is capable of performing all steps of statistical downscaling automatically. In order to select dominant predictors in multi-site modeling backward stepwise regression method was used; so that some predictors like 850 hPa geopotential, 850 hPa specific humidity, 1000 hPa specific humidity, screen air temperature (2m) were the most important variables for temperature models, and 850 hPa geopotential, 1000 hPa zonal velocity, 1000 hpa specific humidity, and screen air temperature (2m) played a main role in temperature modeling. For downscaling of precipitation, the amount of explained variance (R2) is 0.336 for NCEP data and it is 0.89, 0.922 and 0.855for maximum, mean and minimum temperature, respectively. The results underlined certain limitations to downscale the precipitation regime and its strength compared to downscale the temperature regime. To evaluate the performance of the multi-site statistical downscaling approach, several climatic and statistical indices were developed. For instance, based on daily total precipitation, two precipitation indices was used including percentage of wet days, maximum number of consecutive dry days. The results showed the increase of the average temperature and precipitation decreases for future mainly. In this case study, the decrease of 0.30 mm day-1 in the average rainfall on the second period of future, 2041-2070, and the reduction of 7.25% on the 3rd period, 2071-2100, in climate index of the percentage of wet days was predicted, compared to the based period. However, the results illustrated an increase in the mean of maximum, mean, and minimum temperature, 3.17, 2.5 and 2.8 °C, for the 3rd period of future from 2071 to 2100, respectively.
Conclusions: The aim of applying this new method is to demonstrate the importance of multi-site models and developing a suitable context to calculate the uncertainty of climate predictions. Further works are needed to evaluate in depth the fundamental assumption of multi- site statistical downscaling, i.e. the stability of the relationships between predictors and predict and in altered climate and test their plausibility and consistency.
N. Sayari; A. Alizadeh; M. Bannayan Awal; A.R. Farid Hossaini; M.R. Hessami Kermani
Abstract
Abstract
The climate change was known to force local hydrology, through changes in the pattern of precipitation, temperature and the other hydrological variables. In this research, the impact of global warming on maximum and minimum temperature, precipitation and evapotranspiration (wheat, corn, tomato ...
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Abstract
The climate change was known to force local hydrology, through changes in the pattern of precipitation, temperature and the other hydrological variables. In this research, the impact of global warming on maximum and minimum temperature, precipitation and evapotranspiration (wheat, corn, tomato and sugar beet) of Kashafrood basin under two climate change scenarios (A2 and B2), and the output of two GCM models (HadCM3 and CGCM2) for three period of times (2010-2039, 2040-2069 and 2070-2099), were investigated. For evaluation two scenarios were downscaled into local level with Automated Statistical Downscaling (ASD) model. Precipitation was expected to decrease and/or increase, depends on applied GCM. The results indicated that the annual precipitation decreased for three periods under CGCM2 model and also for two scenarios (A2 and B2) as much as 13%-16% decreasing, the annual precipitation for three periods under HadCM3 model and two scenarios (A2 and B2) as much as 2%-8% increasing. The maximum and minimum temperatures in the Kashafrood basin was predicted, which increased by CGCM2 and HadCM3 models with two scenarios. Based on the HadCM3 model, maximum and minimum temperatures were expected to increase 2.4 0C to 5.8 0C and 0.6 0C to 3.8 0C, respectively; for 2070-2099 periods. For CGCM2 model, maximum and minimum temperatures were expected to increase 0.06 0C to 2.59 0C and 0.1 0C to 1.9 0C respectively; for 2070-2099. Evapotranspiration under A2 and B2 scenarios and HadCM3 model was increased but increasing in evapotranspiration with CGCM2 model under both scenarios was not significant in many cases. The comparison of two models and also two scenarios indicated that more critical status for A2 scenario by using two GCM models for this basin.
Keywords: Climate change, General circulation model, Downscaling, HadCM3, CGCM2, Kashaf rood basin, Evapotranspiration
A. Alizadeh; N. Sayari; M.R. Hessami Kermani; M. Bannayan Aval; A.R. Farid-Hosseini
Abstract
چکیده
تغییر اقلیم دارای اثرات مستقیمی بر فرآیندهای هیدرولوژیکی نظیر تبخیر از سطح آب، تعرق از گیاه، تغذیه آبهای زیرزمینی، رواناب یا ذوب برف دارد. در این مقاله اثرات احتمالی ...
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چکیده
تغییر اقلیم دارای اثرات مستقیمی بر فرآیندهای هیدرولوژیکی نظیر تبخیر از سطح آب، تعرق از گیاه، تغذیه آبهای زیرزمینی، رواناب یا ذوب برف دارد. در این مقاله اثرات احتمالی تغییراقلیم بر تبخیر و تعرق در آینده بررسی شده است. به همین دلیل تأثیر تغییراقلیم بر دما (حداقل، حداکثر و میانگین) و بارش تحت سناریوی A2 و برای سه دوره 2039-2010، 2069-2040 و 2099-2070 و با استفاده از ریزمقیاس نمایی آماری و خروجی های مدل گردش عمومی جو HadCM3 در حوضه کشف رود مورد بررسی قرار گرفت. در مرحله بعدی با استفاده از پارامترهای پیش بینی شده، تبخیر و تعرق گیاهان الگوی کشت این حوضه شامل گندم، چغندرقند، گوجه فرنگی، سیب و ذرت با استفاده از روش هارگریوز و سامانی محاسبه و برای دوره های مختلف مورد مقایسه قرار گرفتند. نتایج حاصل نشان داد که دما (حداقل، حداکثر و میانگین) در هر سه دوره پیش بینی نسبت به دوره پایه 1990-1961 افزایش خواهد یافت. میانگین سالانه بارش پیش بینی شده در دوره های مذکور تفاوت معنی داری نداشت ولی توزیع آن در فصلهای مختلف تغییر خواهد کرد. بدینصورت که مقدار بارش برای ماههای زمستان و تابستان کاهش و برای ماههای پائیز و بهار افزایش خواهد یافت. میزان تبخیر و تعرق محاسبه شده برای تمامی ماهها و برای تمامی دوره ها تحت تأثیر دما افزوده خواهد شد. نتایج نشان می دهد که در صورت افزایش دمای هوا به میزان 1، 2 و 4 درجه سانتی گراد نیاز آبی الگوی کشت گیاهان در دشت کشف رود به ترتیب 6، 10 و 16 درصد افزایش پیدا خواهد کرد.
واژه های کلیدی: مدلهای گردش عمومی جو، ریزمقیاس نمائی آماری، تبخیر وتعرق گیاه، حوضه کشف رود، تغییراقلیم
