جستجوی مقالات مرتبط با کلیدواژه "downscaling" در نشریات گروه "فیزیک"

Extreme weather conditions have an important role on strategic planning of water resource and developing adaptation plans and natural disaster management. Therefore, it is necessary to present a detailed perspective of upcoming extreme patterns of rainfall events. In the context of climate change, pattern extraction of extreme events can only be achieved by using of daily downscaling methods. In the current paper, two single site downscaling methods SDSM, DMDM and a multisite approach based on Singular Value Decomposition (SVD) technique are used and their results are evaluated. The case study is located on Tehran province with over 10 precipitation and synoptic stations in the period 1985 to 2005. The used climate change scenarios were generated for (2021-2050) period. In addition, the daily NCEP/NCAR dataset and results of climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) were achieved from the Canadian Centre for Climate Modeling and Analysis. For each downscaling models, based on their own concepts, suitable predictors have been selected via backward stepwise regression as a preprocessing step (Hessami et al. 2008). The implemented multisite approach is based on combination of two multiple regression models to simulate precipitation amount and occurrence and also using SVD to capture stochastic behavior of precipitation to preserve accurately the space–time statistical properties of daily precipitation (Khalili and Nguyen, 2017). Beside the SDSM as a regression based downscaling method (Wilby et al. 2002), DMDM as a regression based tool box including Multiple Linear Regression (MLR), Ridge Regression (RR), Multivariate Adaptive Regression Splines (MARS) and Model Tree (MT) have been used as well (Tavakol et al. 2013b). To achieve the goal of the current research, temporal downscaling method to simulate extreme precipitation values is needed. In this regard, numerical model based on scaling invariant concept is used to do temporal downscaling (Nguyen et al. 2007). The sub daily extreme rainfall, are estimated from daily downscaled rainfalls by analyzing the non-central moments of observed rainfalls, single time regime (from 6 h to 24 h) and using scaling factor. Finally, as the major output of this study, Intensity Duration Frequency (IDF) curve is calculated affected by climate change in the period 2020 to 2050. The results based on statistical assessment both in calibration and validation periods of daily precipitation show the effectiveness of SDSM and DMDM models, respectively, in terms of long-term monthly average, and multisite model in preserving the trend of computed information in comparison with observed values. Based on uncertainty assessments results, DMDM provided the most precsion results versus the other methods over the study area. In addition, the models performance rank in estimating unseen station belong to the DMDM and multisite and SDSM methods, respectively. In the second step, quantities of IDF curves for return periods of (2-100) years and durations of (6, 12, 24 hour) at Mehrabad station are estimated using the results of coupled three different spatial downscaling and GEV distribution. Results show higher accuracy of DMDM and SDSM models respectively in comparison with multisite model. Based on the linear structure of SDSM and Multisite downscaling models versus the complex structure of DMDM, it seems that limitations of linear methods cause DMDM to be superior to the other ones. In addition, evaluation of the results of extreme values by three different climate change scenarios based on the DMDM downscaling model indicates an increase in rainfall intensity using scenario RCP8.5 and a decrease under scenarios RCP4.5 and RCP2.6.

The aim of this research is to evaluate a statistical method for downscaling the precipitation output of a number of Coupled General Circulation Models issuing seasonal forecasts 9 month in advance. Canonical Correlation Analysis (CCA) is applied for post-processing precipitation from the North American Multi-model Ensemble (NMME) project. The analysis is done for a long-term period (1986-2015) in the west of Iran. The area under study includes Karkheh River Basin where a significant reduction in renewable water resources has faced policymakers with challenges in water resources allocation and provision of environmental requirements to Hoor-al-Azim marshland downstream. PERSIANN-CDR biases are computed and corrected against in-situ observations by applying the multiplicative method. Bias corrected Satellite-based rainfall data merged with 23 gauge-based data. The approach for merging station-satellite-based rainfall estimation includes a spatio-temporal LM method which fits linear regression to the deterministic part of universal variation. It exhibits appropriate performance in terms of Correlation, Nash-Sutcliffe Efficiency and mean absolute error and multiplicative bias. After merging, correlation coefficients between the merged data and gauge-based rainfall are between 0.92 and 0.98 for all stations whereas it was between 0.7-0.95 for PERSIANN-CDR. The merged precipitation grided dataset is then used as the reference to evaluate NMME seasonal forecasting systems October-December being the target season. Forecasts initialized on the early October, September and August (lead time-0, lead-time-1 and lead-time-2 months, respectively) are evaluated for individual raw model outputs. Multi-Model Ensemble is also developed by assigning equal weights to individual models. Multi-model Ensemble which consists the 3 best individual models (CCSM4, CMC2 and CFSv2) outperforms all other MME which consist 2 to 8 models (ρ=0.560). It also outperforms CCSM4 which has the highest Spearman correlation of 0.486 among all models. Canonical Correlation Analysis (CCA) is then applied to individual and MME seasonal mean precipitation forecasts to correct biases in the position. Probabilistic forecasts are produced based on the best-guess forecast estimated by regression model (CCA). Predictand is transformed to normal distribution before performing the calculations. Then the forecast is transformed back to the empirical distribution. By assuming that the errors in the best-guess forecast are normally distributed, the variance of the errors is defined by the sampling errors in the regression parameters, and by the variance of the errors in the cross-validated predictions. Then the probabilities of exceeding the various thresholds (below normal, normal and above normal terciles) are calculated for issuing probabilistic forecast from 1986-2015. The goodness index is improved for all models after performing CCA especially for GFDL-aer04 and CMC1 having the most correctable systematic biases. 3 model-based MME is recognized to have highest skill (Spearman correlation=0.623) at 0-month lead time. The models also show high skill for initializations made in the early August and early September. ROC-area for below-normal precipitation is more than 0.5 for almost all models which shows the skill of NMME seasonal forecast systems in meteorological drought prediction. The skill of NMME in forecasting October-December precipitation in the west of Iran can help decision makers in real-time water resources and agricultural planning before water-year starts (In the late September).

Due to climate changes, precipitation forecast average in time scale is one of the most important challenges for specialists in the recent years. The purpose of this research is to investigate the capabilities of the dynamic model RegCM4 in precipitation forecast in cold period of Fars province. In this study, September to February or 6 month is considered as the cold period. Several variable statistical periods 1990-2010 are selected. In this study, two data sets are used by post-processing methods using statistical regression techniques. 1 - Data needed for implementation of Dynamic Model RegCM4 was taken from the Centre ICTP with a NetCDF format including weather data on a daily scale (6 hours) with a horizontal grid 5/2 × 5/2 degrees, sea level data, with 1 ° grid surface data. 2 - Monthly precipitation data (mm) watch of seven synoptic stations which have been received from the Meteorological province. In order to implement dynamic model, the test of scheme determination is performed and investigation showed that Darrell’ scheme in comparison with the two other schemes, Koo and Emmanuel, has fewer errors in the modeling the rainfall during the cold season in Fars stations. After running the model, outputs were processed using multivariate regression methods. In order to enhance the efficiency of RegCM4 model, 20 km horizontal resolution model output (dynamic) using multiple regression the statistical post-processing (dynamic-data) groups. Double precipitation data with a resolution of 20 km and precipitation observations of monthly precipitation data were compared with the post-processed to determine the performance of the statistical processing on the output RegCM4 model.The results with comparing the data showed that in 43% of the stations in autumn the use of raw output of climate model precipitation RegCM4 and dynamic-statistic method have had the same efficiencies and in about 14% of cases neither of the two options have preference to the other. In winter many more stations represent the efficiency of using the raw climate model RegCM4 as 4 out of 7 stations have confirmed the superiority of the model in this season (57 percent) while the number of successful stations using the dynamic-Statistics is 2 (29 percent). Also 1 station (14%) in applying the above two cases do not have a specific preference. In the cold period of Fars Province, the number of the stations adapted with raw output of climate model (rainfall) RegCM4, and the output of dynamic-statistic method is respectively 5 cases (71%) and 2 cases (29%). in the study of rainfall in cold period no cases has been found that none of the options is not superior to the other in it. In general we can say that in 1.57% of cases the output of RegCM4 model and in 3.33% of cases the output of dynamic-statistic method has better ability to predict rainfall of Fars in cold period.Therefore, we can conclude that the small-scale dynamic view of 20 × 20 km horizontal resolution needed to apply statistical post-processing or dynamic-data to enhance the accuracy of the data is not mentioned.

Climate change that the human faces is a somewhat unavoidable phenomenon. Successful management of water resources needs recognition and perception of climate change in order to cope with water scarcity. The water scarcity is created by natural forcings such as drought، which is affected by regional climate. In other words، variation of climate variables as a result of climate change leads to variations in drought severity and frequency. Since climate change scenarios are based on assumption of increasing، decreasing or non-significant trend in climatic means، It is expected that the effects of these assumptions would be reflected in the prediction of meteorological phenomena like drought. In Markov analysis، these variations are determined as change in transfer probability function values or shift in drought severity class، which are both important in management decisions. For instance، by increasing the temperature or decreasing the rainfall it is expected that occurrence of a drought event under certain conditions would be more probable. In this study، the outputs of three General Circulation Models (GCMs) namely; ECHO-G، CGCM3T63 HADCM3 under three climate change scenarios were downscaled using a non-parametric approach for simulation of rainfall and temperature series during 2011-2040 in northwest of Iran. This downscaling approach is combination of two techniques i. e. Kernel probability density function estimator (KDE) and Strategic Re-sampling method by which predicted variations of GCM outputs are extended and transformed to generated time series of a given future period. In KDE method، A probability density function is defined with center value of ith observation from series (xi، i=1،…،n). Contribution of each observation in estimation of probability density function of ith observation is estimated by this Kernel function. The main parameter of this function is the bandwidth which is، by mathematical definition، a distance on x-axis in which the function variation is insignificant. Firstly a random normal kernel is selected and its average is considered as the base vector. Selection probability of each vector is 1/n. Then by calculation of cumulative probability and comparison with a random number between 0 and 1، one of the normal kernels is selected for rest of the simulations. The strategic re-sampling method uses a rule for generating series with specific feature such as increasing frequency of warmer or more rainy days. The criteria for such features are selected by the user based on the outputs of GCMs. Considering its semi random nature، this approach cannot be used alone for regional climate change simulations and should be combined with a weather generator such that the applied rule should be run on observed or historical series. Then، the outputs are feed in weather generator for generating a completely random series coincide with climatic scenario. After simulation of climate، Reconnaissance Drought Index (RDI) was used for monitoring drought during two periods 1971-2000 and 2011 to 2040 in northwest of Iran. This index uses the ratio of precipitation and evapotranspiration (calculated by Thorntwait method)، hence as the index becomes smaller، more severe would be the drought. Thus، the necessary variables for RDI estimation are monthly mean temperature and total rainfall. For RDI calculation، firstly، the precipitation (prec) and potential evapotranspiration (PET) are calculated cumulatively with determination of the moving window value، and then، RDI values are obtained as logarithm of cumulative prec to PET ratio. Four classes are considered for RDI including: normal class (larger than -1)، moderately drought class (-1 to -1. 5)، severe drought class (-1. 5 to -2) and very severe drought (less than -2). Taking into account the length of the dry periods in the arid regions of the country، the Reconnaissance Drought Index in 6-month timescale was used for drought monitoring. Markov analysis was applied for calculation of transfer probability and corresponding drought severity classes with three steps forward to assess the sequences of climatic assumptions on management early warnings. Behavior of a Markov model is determined by a series of probabilities in transition from one state to another namely transition probabilities. These probabilities may vary by climate change. The first-order Markov chain model was employed to predict drought condition up to 3-step ahead. This model was fitted on the RDI series at all stations of interest، and it was identified that it can represent the probabilistic behavior of drought over northwest of Iran. Research findings are presented in three parts of downscaling method implementation، RDI monitoring and Markov analysis. The weather generator model was successful for simulation of monthly normals including means and standard deviation. Also، strategic re-sampling technique as aligning method was successful for simulation of deviations from normal. Drought monitoring with RDI showed a water tension resonance in second 15 years of 1971-2000 periods. Likewise، in part of Markov analysis، findings of this study revealed that under conditions of increasing temperature and decreasing rainfall، as the worst case، the effect of climate change on meteorological drought would appear as the class shift، and in most of the study stations under this scenario، increased duration of extremely drought (class 4) was forecasted، even 2 steps ahead، which is important in water resource management.

Global warming caused by human activity and climate change is one of the issues attracted that has attention of many climate scientists. The relationship between climate parameters should be used in climate change studies to understand the complex nature of the environment and predict changes in the future. The reliable tool to investigate climate change effects on different systems is using the climate simulations by coupled general circulation of atmosphere and ocean. These models are capable to model the oceanic and atmospheric parameters for a long time period using IPCC scenarios. Due to the low spatial resolution of down scaled phenomena, in general circulation climate models, these models cannot provide accurately approximation of climate conditions of study areas. Therefore, outputs of these models should be down scale to weather station. The use of statistical methods especially when lower cost and faster assessment of climatic factors is required, have more advantages and capabilities. These models downscale the large scale circulation data by using outputs of GSM models and applying specific scenarios that produce climate data. In this study a type of GCM model as HADCM3 for the period 2046-2065 was used. To simulate climatic parameters in Tamar Basin, the HADCM3 data downscaled using LARS-WG mode under A2 and A1B scenarios. Tamar river basin is located in Golestan Province north-east of Iran that have 1525.3 km2 area. There are a few climatology and rain gauges in Tamar river basin. Most of these gauges except Tamar station that have more than 40 years precipitation and temperature data have short inventory period data (15 years rainfall data and 8 temperature data). According to the International organization WMO standards which at least thirty years considered as reference period, therefore, in this study the Tamar climatology data that were recorded for 30 years were used. For this purpose the temperature and rainfall data of Tamar station In a 30-year period (1981-2011) Was extracted. Due to the lack of sunshine data in stations, the Maravehtappe synoptic data, located at 30 km from the centre of the basin, was used. According to the Tamar basin area and variation in hypsometry of basin and also Tamar station located at outlet of basin, the rainfall and temperature data collected in this station cannot present the whole of basin changes. To solve the mentioned problem the temperature data was generalized for the whole of basin using a gradient equation with the differences between altitude of the station and the average altitude of the basin. The rainfall data also after the hydrologic processing, was transfered to the average altitude of basin using gradient equation. So the 30 day data in the month was randomly selected and the minimum and the maximum temperature data based on Tamar, Rebat-e-Ghrabil and Cheshmekhan station that located at the outside of the basin was extracted. Also the rainfall data of Tamar station with Tangrah, Rebat-e-Gharabil and cheshmekhan that are located at the outside of the basin were used. Then according to the obtained data, the gradient related to 30 days for each year was plotted, and a relationship was obtained. Totally, 2700 gradient relationship for 30 year also for maximum and minimum temperature and rainfall data were generated. Then, 30 gradient relationships for the maximum temperature and the minimum temperature and the rainfall data were selected with the gradient relationship of each year with higher correlation coefficient. Then the gradient relationship for each year and according to the highest percentage of watershed area that was located in the same altitude of centroid of the basin was acquired. Maximum and minimum temperature data for each year were moved to the center of the basin and data corresponding to the height of centroid the basin for log to climate models were obtained. In this study, in order to down scale of the atmospheric general circulation model data HADCM3, the LARS-WG model which is one of the weather generator models was used. To run this model in this research, calibration period was selected between 1981-2011, years then the model was run after preprocessing the input data.In the next step the model was assessed with NSE and RMSE and MAE indices. Results show that the simulation data for this period are in good agreement with observation data. To evaluate climate fluctuations in the Tamar basin, general circulation model data were down scaled using LARS-WG model according to both A1B and A2 scenarios and thus the daily values of the parameters were generated. The results showed that the average temperature will increase under A2 scenario about 2.48 ° C and under A1B scenario about 2.43 ° C. Meanwhile the maximum temperature change will be higher than the minimum temperature change. From this subject we can conclude that the changes (increases) in the average air temperature in the future will be most affected by the minimum temperature. The results show that 16% increase in precipitation under A2 scenario and 2% rainfall under A1B scenario during 2046-2065 periods. Also, sunshine hours in the study period will be reduced under both scenarios. The results indicate that for the A2 scenario has the highest emissions of carbon dioxide, methane and nitrous oxide, higher temperatures and more rain are expected.

One of the most important impact of climate change is reduction of precipitation in some areas including Iran. Hence, climate change studies are essential in these areas. Besides, according to IPCC, some meteorological stations of Iran, such as Tabriz (capital of East Azerbaijan Province) have showed a downward trend in precipitation. Therefore, East Azerbaijan Province was selected as the study area in this survey. It is one of the north-western provinces with cold dry climate. Firstly, monthly temperature and precipitation observed data over 1981-2012 were gathered from 15 meteorological stations of the region, and they were produced for 0.5-degree cells by interpolation methods and selecting the most appropriate one based on the amount of corresponding errors (RMSE and ME). Thereafter, monthly precipitation and temperature data for 2013-2022 were projected using 16 Atmosphere-Ocean General Circulation Models (AOGCMs) under A2B, A2 and B1 SRES scenarios, and downscaled by Bias Correction/Spatial Downscaling technique at 0.5-degree cells. After applying pattern scaling method on monthly temperature and precipitation data, in order to produce future data under more scenarios, monthly climatic variables were calculated for 10, 25 and 50 percent risk, and risk analysis was done based on the computed parameters. The pattern scaling technique used in this study calculates the variable under a desired scenario, from the base scenario (A2 in this study) with a linear equation in which the global temperature rise was calculated by a model named MAGICC. Assessing observed climatic variables showed that western parts of the province had lower precipitation and higher temperature, while eastern parts had higher precipitation. However, south-western cells also experienced a better situation. Mean annual temperature over 1981-2012 was between 7.5-13.5 degrees Centigrade, and annual precipitation was 260 to more than 310 millimeters. Moreover, despite precipitation fluctuations over 1981-2012, annual precipitation of the first years is higher than the last years. After applying pattern scaling method and accessing future monthly precipitation and temperature data under 49 scenarios for 16 AOGCMs, temperature and precipitation boxplots of each month were produced for each month. Results showed that precipitation is right-skewed in all months and all cells. The outliers of March and April are less than others, while August outliers are numerous. Comparing boxplots of temperature and precipitation indicated that outliers of temperature data are much less than precipitation, i.e. uncertainties of AOGCMs and downscaling to project temperature are less than precipitation. The monthly precipitation and temperature data were calculated for 10, 25 and 50 percent risk and the monthly temperature-risk and precipitation-risk line charts were produced for each cell. The amount of monthly temperature and precipitation with higher and lower risk showed a significant difference. Furthermore, projections with lower risk have less difference and they indicate almost one prediction. According to the areal interpolated maps of the future mean annual precipitation and temperature, the least temperature will be around Sarab station, and the highest temperature will be near Malekan and Bonab stations. Furthermore, maps showed that the amount of temperature will increase by moving west. Moreover, by moving from high risk to lower risks, the amount of temperature increases about one degree Centigrade. Western regions will experience lower precipitation with all levels of risk, and the maximum annual precipitation will be seen in north-eastern spots. The difference between the predicted and observed temperature and precipitation with 10, 25 and 50 percent risk for each cell was calculated and their spatial distribution maps were produced by applying different interpolation methods and selecting the best method. It is predicted that temperature will increase 2.9-3.15 degrees Centigrade with 10 percent risk, and the rise amount is bigger in the western areas. Precipitation will decrease about 75 to 150 millimeters. Temperature will increase 2.1-2.25 degree Centigrade with 25 percent risk, and the amount of precipitation in some areas will be lower and in some others will rise even up to 50 millimeters. The temperature with 50 percent risk is projected to increase about 1.2 degree Centigrade, and precipitation will also aggrandize. In conclusion, the temperature increase in the next decade will be bigger in the southern areas of the province, and precipitation amount of north-western and western areas will experience higher precipitation. The results of this study confirm other research done by others before, indicating the least amount of observed precipitation was in Sarab station. By having these results for future periods the decision makers of this field will have a better vision, ad so they will be able to sufficiently plan for the future. In addition to this research, some suggestions are proposed as follows to improve and strengthen the (i) past and future drought assessment in the area with different drought indexes, (ii) presenting a more logic relationship between temperature and precipitation because of relatively low correlation between temperature and precipitation and so not being linear, or applying models ensemble and comparing the results with this survey, (iii) using daily temperature and precipitation instead of monthly data to improve the results.