Search

Implementing optimized irrigation strategies to achieve acceptable wheat yield while conserving groundwater resources is critical for extensively irrigated regions. Field experiments regarding winter wheat irrigation management were conducted for two growing seasons (2013–2014 and 2014–2015) to calibrate and validate the AquaCrop model. To determine proper parameterization of the AquaCrop model for various data availability conditions, the model performance was tested for calibration based on three different datasets, including a) calibration based on a full irrigation condition (CA 1), b) calibration based on a dryland condition (CA 2), and c) calibration based on diverse irrigation conditions, which included full irrigation, different deficit irrigation levels, and dryland (CA 3). The CA 3 calibration scenario resulted in the model's highest accuracy in simulating biomass, grain yield, total soil water, and seasonal evapotranspiration during the calibration and validation process. However, the model performance was also convenient when calibration based on full irrigation conditions was pursued. The calibrated and validated AquaCrop model based on the CA3 was used to analyze long-term (36-year) irrigation management scenarios for identifying the optimized water-conservative winter wheat irrigation strategies. The long-term analysis emphasized that increasing irrigation intervals from 7 to 15 or 20 days could reduce groundwater withdrawal by 52–64 % while expecting a 14–19 % reduction in grain yield. By implementing one 25 mm irrigation after planting and two 25 mm depth irrigation events at jointing or leaf growth stages, 91 % of Kansas's average winter wheat yield (2.88 tons/ha) could be achieved. During wet years, the 1.2 ton/ha reduction in biomass and 0.27–0.62 ton/ha reduction in grain yield is expected irrespective of irrigation management strategies. Considerable uncertainty was detected in grain yield production during dry years under dryland (rainfed) conditions. The maximum winter wheat production could be achieved under normal years, establishing a balance between precipitation and heat units. Our agro-hydrological analysis revealed that the highest irrigation water use efficiency could be attained by the application of two 25 mm irrigation events at jointing or flag leaf growth stages during normal years, the application of two irrigation events during jointing or heading during wet years, and two 25 mm irrigations at heading or flowering during dry years. The results of this research could be used as a baseline for producers of the U.S. Central High Plains and semi-arid regions with similar climate characteristics to cope with water scarcity in winter wheat production.

IntroductionPredicting river flow is one of the most crucial aspects in water resources management. Improving forecasting methods can lead to a reduction in damages caused by hydrological phenomena. Studies indicate that artificial neural network models provide better predictions for river flow compared to physical and conceptual models. However, since these models may not offer reliable performance in estimating unstable data, using preprocessing techniques is necessary to enhance the accuracy and performance of artificial neural networks in estimating hydrological time series with nonlinear relationships. One of these methods is wavelet transformation, which utilizes signal processing techniques. Materials and MethodsIn this study, to evaluate the efficiency of discrete and continuous wavelet types in the Wavelet-Artificial Neural Network (WANN) hybrid model for monthly flow prediction, a case study was conducted on the Kardeh Dam watershed in the northeast of Iran, serving as a water source for part of Mashhad city and irrigation downstream agricultural lands. Monthly streamflow estimates for the upstream sub-basin of the Kardeh Dam were obtained from the meteorological and hydrometric stations' monthly statistics over a 30-year period (1991-2020). The WANN model is a hybrid time series model where the output of the wavelet transform serves as a data preprocessing method entering an artificial neural network as the predictive model. The combination of wavelet analysis and artificial neural network implies using wavelet capabilities for feature extraction, followed by the neural network to learn patterns and predict data, potentially enhancing the models' performance by leveraging both methods. The 4-fold cross-validation method was employed for the artificial neural network model validation, where the model underwent validation and accuracy assessment four times, each time using 75% of the data for training and the remaining 25% for model validation. The final results were presented by averaging the validation and accuracy results obtained from each of the four model runs. To evaluate and compare the performance of the models used in this study, three evaluation indices, Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Pearson correlation coefficient (R), were employed. Results and DiscussionThe analysis of meteorological and hydrometric data in this study revealed that monthly streamflow in two time steps, T-1 and T-2, were the most effective predictive variables. Each of the two runoff variables of the previous month (Qt-1) and the previous two months (Qt-2) were analyzed by each of the Haar and Fejer-Korovkin2 discrete wavelet transforms and the two continuous Symlet3 and Daubechies2 wavelets at three levels. The results of each level of decomposition was given as input to the ANN model. The presented results at each decomposition level indicated that hybrid models could accurately predict lower flows compared to the single ANN model, and the estimation of maximum values also significantly improved in the hybrid models. Among the wavelets used, Haar wavelets exhibited the weakest performance, and the less commonly employed Kf2 wavelet showed a moderate performance. Since the Haar and Fk2 wavelets, with their discrete structure, did not perform well in decomposing continuous monthly streamflow data, continuous wavelet models outperformed discrete wavelet models. The hybrid models, combining wavelet analysis and artificial neural networks, demonstrated up to an 11% improvement over the performance of the single neural network model. ConclusionStreamflow is a crucial element in the hydrological cycle, and predicting it is vital for purposes such as flood prediction and providing water for consumption. The objective of this research was to evaluate the performance of different types of discrete and continuous wavelet models at various decomposition levels in enhancing the efficiency of artificial neural network (ANN) models for streamflow prediction. Since climate and watershed characteristics can influence the nature of data fluctuations and, consequently, the results of the wavelet model decomposition, choosing an appropriate wavelet model is essential for obtaining the best results. Considering the existing variations in the results of different studies regarding the selection of the best wavelet type, it is suggested to use both continuous and discrete wavelet types in modeling to achieve the best predictions and select the optimal results. Given that a lower number of input variables in neural network models lead to higher accuracy in modeling results, it is recommended to perform decomposition at a two-level depth to reduce input components to the neural network model, thereby reducing the model execution time.

Flood management is a technique and strategy to reduce the risk and damage caused by floods, and it specifies the necessity of using efficient solutions through short-term and long-term plans to minimize losses and damages during an accident. Effective flood management requires a coordinated and integrated system for dealing with this natural phenomenon. In this article, the causes of floods and corrective strategies for structural and non-structural (defensive structures, forecasting, rescue operations, and determining residential areas) flood control and integrated flood management are explained. Also, by examining flood risk management strategies in France, England, Netherlands, and Malaysia, flood governance measures in Iran are explained through five approaches of prevention, defense measures, reduction of event effects, disaster preparedness, and recovery, and organizational and non-organizational bodies and institutions effective in risk management are determined. Finally, the factors that aggravate the flood damage in Iran, the legal and practical challenges related to the river bed, and the legal vacuum of universal flood insurance are discussed.

IntroductionThe world population has grown rapidly over the last 150 years and continues to do so, resulting in impacts on hydrologic resources at both a local and global scale (Yang et al., 2012). The competition for water between humans and ecosystems leads to complex interactions between hydrologic and social systems (liu et al., 2015). From the beginning of human history, it is located in floodplains. Floods can have large societal impacts, such as severe damage to urban areas, which are expected to grow around the world (Alfieriet al., 2018). In traditional hydrology, humans are either conceptualized as an external force to the system under study or taken into account as boundary conditions (Peel and Blöschl, 2011). Sivapalan et al. (2012) proposed a new model for investigating the interactions of the hydrological system and the social system. It explores the procedure coupled human-water system evolves and possible trajectories of its co-evolution, including the possibility of generating emergent, even unexpected, behaviors. Socio-hydrology must strive to be a quantitative science. There are several methods to control and mitigate flood risk, one of these methods is flood zoning (Jha et al., 2012). In last two decates, The Kalat city is flooded almost every year and many houses and historical sites in the city are damaged. Therefore, the main purpose of thisWe paper is to show investigated how changing human behavior with nature can affect the behavior of the natural system.Method and MaterialsKalat city located in 59° 43' 23" to 59° 47' 41" northern latitude and 36° 59' 35" to 37° 00' 05" eastern longitude. The city is divided into 11 sub-basins. The city has experienced fast and inappropriate urbanization over the past few years. To collect our data, the annual reports of the Regional Water Organization and the Environment Organization of Khorasan Province were used.SCS method was used to estimate the runoff peak discharge. Precipitation has been estimated for seven return periods: 2, 5, 10, 25, 50, 100, and 200 years. In this study, to analyze the sensitivity of runoff, we considered precipitation and curves number from 20% less to 20% more than the actual values in the study basin (at intervals of 5 %). We used the Cowan method to determine the roughness coefficient in this study. HEC-RAS model has been used for flood zoning. To determine the impact of various factors on the intensification of floods in Kalat city, we obtained questionnaires from relevant authorities. Likert scale was used to measure the results of the questionnaires. We prepared two questionnaires; first one is related to the inner city zone and includes the factors that intensify the occurrence of floods inside the city of Kalat, and it was classified into the following parts: 1) Local community 2) Managerial 3) Physical; and the second one includes the factors that intensify the flood in the upper part of Kalat city. We classified these factors into three parts: 1) Non-local community 2) Managerial 3) Environmental .Results and DiscussionResults of sensitivity analyzes demonstrated that land-use and land cover change had a further effect on peak discharge. In sub-basin 1, by 20% increase in the curve number, the level of peak dumping increased by more than 111%, with a return period of 2 year; while a 20% increase in precipitation, in the same return period, rises the peak discharge only 3%. The peak discharge time in some sub-basins was brief due to the presence of impermeable surfaces, so that in sub-basins 4, 6, 7, and 8, the peak discharge time was less than 30 minutes. These results highlight the dangers of these floods and the need for proper flood planning and management in these sub-basins. The results of the Manning coefficient demonstrated that we can reduce flood damage by applying management measures in the future, as well as paying attention to the feedback between urbanization and the flood zone. Roughness control by applying management programs can reduce the area of flood zones to 0.1 square kilometers. In this case, buildings should be removed from the river, and there should be no structure in the path of the river. According to the questionnaires in the inner city part, the most fundamental factor in intensifying the flood damage was related to “activities of local people” with the average of 3.59. In the upper part of the city, the most influential factors were ascribed to “managerial factors” with the average of 3.79.ConclusionIn a general conclusion, it can be concluded that the role of human factors in the occurrence and intensification of floods was much greater than rainfall. Therefore, in order to manage and control floods, it is necessary to prevent the change of land use and the reduction of permeability. And management programs should be aimed at increasing surface permeability. We suggest that more research be done on the role of economic and social factors in increasing flood risk in other climate zones.

In recent decades, population growth, urban sprawl, urban environmental changes, and related issues are one of the significant issues in proper planning to manage the urban environment. One of the issues in urban development is the occurrence of floods and flooding due to heavy rains. In this research, flood modeling was studied in Mashhad Zarkash watercourses. The amount of rainfall for the return period of 10, 25, 50, 100, and 200 years were extracted by CumFreq software using the maximum 24-hour rainfall statistics of three rain gauge stations closer to the Zarkesh, Jagharq, Sar-e-Asyab, and Torqabeh watercourses basins during the statistical years 1364 to 1390. The peak discharge was calculated using the US Soil Protection Organization (SCS) rainfall-runoff method. Zarkesh watercourse is located on the outskirts of Mashhad. River and flood flow modeling was performed using Arc GIS, HEC-GEORAS, and HEC-RAS software in two conditions including structure (bridge) and no structure. Due to urban marginalization, urban development and land use change have greatly expanded in this region. The results of flood simulation showed that flood levels with a return period of 50 years increased by 50000 m2 equal to 22% in the presence of a structure compared to the state without a structure. The results of this research show that the construction of bridges on the river, the roughness coefficient by land use change, and the number of curves due to land permeability changes are effective in the flood zone.

The comparative efficacy of silicon (Si) and zinc (Zn) nanoparticles (NPs) in mitigating drought stress in fennel (Foeniculum vulgare) remains largely unexplored. This study evaluated the impact of Si NPs and Zn NPs on enhancing plant growth and physiological-biochemical attributes of fennel under varying irrigation regimes. The 2-year study was a split-pot design with irrigation at three irrigation levels (100, 75, and 50% field capacity, FC) and five treatments of foliar application of Si and Zn NPs (control, 1 mM Si NP, 2 mM Si NP, 1 mM Zn NP, 2 mM Zn NP). Results showed that drought stress reduced plant performance. Increases in superoxide dismutase (SOD, 131%) and catalase (CAT, 276%) were seen after a 50% FC drought without the use of Si and Zn NPs. Conversely, biological yield (34%), seed yield (44%), chlorophyll a + b (26%), relative water content (RWC, 21%), and essential oil (EO) yield (50%) were all reduced. However, application of Zn and Si, particularly 1 mM Si and 2 mM Zn, greatly mitigated drought stress via lowering CAT and SOD activity and enhancing plant yield, chlorophyll content, RWC, and EO. The composition of the EO consisted primarily of anethole, followed by limonene, fenchone, and estragole. During drought conditions, monoterpene hydrocarbons increased while oxygenated monoterpenes decreased. The opposite trend was observed for Si and Zn NPs. Our results suggest that applying Zn NPs at 2 mM followed by Si NPs at 1 mM improved plant resilience and EO yield in fennel plants under water stress. This study investigated the physiological and biochemical effects of foliar application of Si and Zn nanoparticles (NPs) on fennel (Foeniculum vulgare) plants under different irrigation levels. Si NPs and Zn NPs improved chlorophyll and water content of plants, and superoxide dismutase activity under drought stress, but essential oil content generally decreased by severe drought (50% field capacity). Superoxide dismutase and catalase activities were sensitive to drought, making them potential markers for monitoring fennel under drought stress. [ABSTRACT FROM AUTHOR]

Background and purpose: Oral squamous cell carcinoma (OSCC) is the most common tumor of the head and neck. XRCC1 is a DNA repair gene and there are controversies about the association between XRCC1 gene polymorphism (RS1799782) in pathogenesis and susceptibility to OSCC. The purpose of this study was to investigate the association between XRCC1 (rs1799782) gene polymorphism and its dominant allele frequency in OSCC patients and healthy subjects. Materials and methods: In this case-control study, 50 patients and 59 healthy individuals enrolled. Paraffin blocks of patients with OSCC were used to prepare 5-10 µm sections and 5 cc peripheral blood were collected from healthy individuals. Genomic DNA was extracted and evaluated using the PCR-restriction fragment length polymorphism (RFLP). Results: There was a significant difference in the allele and genotype frequency of XRCC1 (rs1799782) between patients with OSCC and control individuals (X2 = 6.530, P = 0.0342). Frequency of T allele was 34% and 19% in OSCC patients and control group, respectively (OR= 2.1962, P= 0.0175). Smoking was strongly associated with TT genotype (OR=20.65: 95% CI: 4.49-10.1) Conclusion: According to this study, XRCC1 (rs1799782) is effective in pathogenesis of OSCC and the TT genotype has a role in susceptibility to OSCC.

Introduction: Soil erosion and sediment transportation decrease water resources, and cause many social and economic problems. On the other hand, sediment transportation by rivers causes problems such as water quality degradation, reservoirs sedimentation, redirect of rivers, or decrease in their transportability. Therefore, finding the proper methods in sediment yield study in watersheds is essential in planning and management of land and water resources. Climatic characteristics, physiography, geology, and hydrology of basins are the most effective factors in producing and transporting sediments according to several sources, but the role and impact of some factors are more pronounced than the others in different areas. As a result, the objective of this study was to investigate and identify the most important climatic, physiographic, geological, and hydrological factors in several watersheds of the northeastern part of Iran, by applying Gamma Test (GT) and principal component analysis (PCA) techniques.Materials and Methods: In this study, the data of discharge flow and suspended sediment concentration, and daily flow discharge recorded in 15 hydrometric stations in Mashhad and Neyshbour restricts and required maps were provided from the Regional Water Company of Khorasan Razavi, Iran. After drawing statistical bar graph period of suspended sediment, daily discharge, annual precipitation, and relatively adequate data, stations with the longest period and with the lowest deficit data were selected to determine the common statistical periods. Therefore, in this study, the time period of 1983-1984 to 2011-2012 was selected, and the run test was applied to control data quality and homogeneity. Then, the most effective factors of sediment yield were determined by principal component analysis (PCA) and Gamma Test (GT).Results and Discussion: The results of the principal component analysis showed that 90 percent of the first five components justify the changes. Among the factors, area and gross gradient of the mainstream from the first component, the average annual flow rate of mainstream, meandering waterways of the mainstream from second component, and drainage density of third component were identified as the most important influencing factors on suspended sediment production. Ninety superior combinations of 1500 proposed combinations were obtained by Gamma Test to evaluate the effects of each parameter on suspended sediment yield. To determine the order of importance of the entered parameters, first, Gamma Test was performed on all 12 parameters. Gamma values of all cases for each proposed combination were compared. The results showed that the impact of these statistics was lowered by eliminating high gamma parameters and the removal of low values. The data analysis revealed that the low levels of gamma and high accuracy of ratio to find the desired outputs from entries. By lowering the gradient, the complexity of the model was lowered and more suitable model was provided. As a result, high levels of gradient represented the complexity of the final model. The results of the percentage values of each of the 12 variables were considered among the superior equations for estimating the suspended sediment composition. In this regard, the mean annual discharge, main channel length, area, average annual rainfall, and percentage of the outcrop of erosion sensitive rocks with a total of 63 percent of the proposed equations were the most important factors affecting the sediment yield in the study area. The average height parameter of area, the average and gross slope of the mainstream had the lowest presence among the optimized compounds.Conclusion: Based on the results of the principal component analysis, the two factors of basin area and gross slope of the mainstream were selected as the most important factors affecting the amount of annual suspended sediment load, respectively. Based on the results of the Gamma Test, 12 main variables affecting suspended sediment load were identified and the effect of each of them on the production and transport of suspended sediment was determined. Based on the comparison of the results of the two methods of PCA and GT, it can be concluded that if the purpose of research or study is to prepare a model with the highest accuracy in estimating suspended sediment load, the 12-variable model of GT includes factors related to physiographical, geological, climatic and hydrological factors are suggested. However, if the preparation of a model with appropriate accuracy and a limited number of input variables is considered, a 5-variable model derived from the PCA method is proposed. At the same time, if the purpose is to prepare a model with the least input variables and their easy access and calculation and initial estimation of suspended sediments, a bivariate model (based on basin area and gross slope of the mainstream factors) resulting from PCA is proposed. According to the results of the present study, it can be concluded that the study of more parameters has provided grounds for evaluating their importance in sediment yield. Finally, due to the correlation of many parameters with each other, a limited number of parameters that have a more important role in suspended sediment estimation, were selected. Another finding of this study is the increase in the accuracy of the sediment model’s preparation due to achieving more important and effective parameters in sediment yield and identifying them in order to investigate the best sediment management measures in watersheds. It is suggested that similar research should be done in other watersheds with different conditions in terms of climatic conditions, topography, geology, and so on.

Soil salinization is one of the significant concerns regarding irrigation with saline waters as an alternative resource for limited freshwater resources in arid and semi-arid regions. Thus, the investigation of proper management methods to control soil salinity for irrigation with saline waters is inevitable. The HYDRUS-1D model is a well-known numerical model that can facilitate the exploration of management scenarios to mitigate the consequences of irrigation with saline waters, especially soil salinization. However, before using the model as a decision support system, it is crucial to calibrate the model and analyze the model’s parameters and outputs’ uncertainty. Therefore, the generalized likelihood uncertainty estimation (GLUE) algorithm was implemented for the HYDRUS-1D model in the R environment to calibrate the model and assess the uncertainty aspects for simulating soil salinity of corn root zone under saline irrigation with linear move sprinkle irrigation system. The results of the study have detected a lower level of uncertainty in the α, n, and θs (saturated soil water content) parameters of water flow simulations, dispersivity (λ), and adsorption isotherm coefficient (Kd) parameters of solute transport simulations comparing to the other parameters. A higher level of uncertainty was found for the diffusion coefficient as its corresponding posterior distribution was not considerably changed from its prior distribution. The reason for this phenomenon could be the minor contribution of diffusion to the solute transport process in the soil compared with advection and hydrodynamic dispersion under saline water irrigation conditions. Predictive uncertainty results revealed a lower level of uncertainty in the model outputs for the initial growth stages of corn. The analysis of the predictive uncertainty band also declared that the uncertainty in the model parameters was the predominant source of uncertainty in the model outputs. In addition, the excellent performance of the calibrated model based on 50% quantiles of the posterior distributions of the model parameters was observed in terms of simulating soil water content (SWC) and electrical conductivity of soil water (ECsw) at the corn root zone. The ranges of NRMSE for SWC and ECsw simulations at different soil depths were 0.003 to 0.01 and 0.09 to 0.11, respectively. The results of this study have demonstrated the authenticity of the GLUE algorithm to seek uncertainty aspects and calibration of the HYDRUS-1D model to simulate the soil salinity at the corn root zone at field scale under a linear move irrigation system.

Utilizing degraded quality waters such as saline water as irrigation water with proper management methods such as leaching application is a potential answer to water scarcity in agricultural systems. Leaching application requires understanding the relationship between the amount of irrigation water and its quality with the dynamic of salts in the soil. The HYDRUS-1D model can simulate the dynamic of soil salinity under saline water irrigation conditions. However, these simulations are subject to uncertainty. A study was conducted to assess the uncertainty of the HYDRUS-1D model parameters and outputs to simulate the dynamic of salts under saline water irrigation conditions using the Markov Chain Monte Carlo (MCMC) based Metropolis-Hastings algorithm in the R-Studio environment. Results indicated a low level of uncertainty in parameters related to the advection term (water movement simulation) and water stress reduction function for root water uptake in the solute transport process. However, a higher level of uncertainty was detected for dispersivity and diffusivity parameters, possibly because of the study’s scale or some error in initial or boundary conditions. The model output (predictive) uncertainty showed a high uncertainty in dry periods compared to wet periods (under irrigation or rainfall). The uncertainty in model parameters was the primary source of total uncertainty in model predictions. The implementation of the Metropolis-Hastings algorithm for the HYDRUS-1D was able to conveniently estimate the residual water content (θr) value for the water simulation processes. The model’s performance in simulating soil water content and soil water electrical conductivity (ECsw) was good when tested with the 50% quantile of the posterior distribution of the parameters. Uncertainty assessment in this study revealed the effectiveness of the Metropolis-Hastings algorithm in exploring uncertainty aspects of the HYDRUS-1D model for reproducing soil salinity dynamics under saline water irrigation at a field scale.

The present study aimed to use remote sensing technology to estimate the concentration of particulate materials in the water entering the reservoirs of dams and consequently investigate the possibility of estimating the amount of sediment carried to the reservoir by flood during the life of the dam and its annual estimate. Using an advanced spectrometer device (ASD), the reflectance values of water containing different amounts of particulate sediments were measured in the range of 400–2500 nm; then, these reflectance values were represented for the Landsat 8 satellite OLI bands using their spectral response functions. In the study of interband correlation with the number of particulate materials, band 2 (blue) and band 5 (near-infrared) were identified to prepare a specific and appropriate model. The specificity of the reflectance difference in the two abovementioned bands was presented as an exponential relationship between the concentration of particulate materials and spectral reflectance. In this model, the RMSE parameter for the maximum possible sediment concentration was equal to 1.57 and the parameter R2 was equal to 0.91. In the second step, at the same time as the satellite passed, the area was visited and the sediments of the Ardak dam reservoir were sampled by recording their location. To complete this research, two measures were performed simultaneously, calculating the concentration of particulate materials sampled in the laboratory environment and their location on the image. Then, the number of particulate materials is estimated by taking into account the coordinates recorded from the images on which the relevant corrections have been made. According to the extracted exponential model, the results of estimating the concentration of particulate matter obtained from the model and Landsat satellite images with the concentration of particulate matter obtained from sampling showed its complete compatibility with field surveys to validate this research.

Introduction: Drought is a recurrent feature of climate that caused by deficiency of precipitation over time. Due to the rise in water demand and alarming climate change, recent year’s observer much focus on drought and drought conditions. A multiple types of deficits and relevant temporal scales can be achieved through the construction of a joint indicator that draws on information from multiple sources and will therefore enable better assessment of drought characteristics including return period, persistent and severity. The Standardized Precipitation Evapotranspiration Index (SPEI) combines information from precipitation and temperature in the form of water surplus or deficit according to Standardized Precipitation Index (SPI). Rainfall over some regions of Iran during some resent year was below average while mean and maximum temperatures were very high during this period, as was evaporation. This would suggest that drought conditions were worse than in previous recent periods with similarly low rainfall. The main objective of this study is to assess the influences of humidity on the SPEI index and investigate its relation with SPI and Reconnaissance Drought Index (RDI) over six different climatic regions in Iran. Materials and Methods: Iran has different climatic conditions which vary from desert in central part to costal wet near the Caspian Sea. In this study the selection of stations was done based on Alijani et al (2008) climatic classification. We chose 11 synoptic stations from six different climatic classes including costal wet (Rasht and Babolsar), semi mountains (Mashhad and Tabriz), mountains (Shiraz and Khoram Abad), semi-arid (Tehran and Semnan), arid (Kerman and Yazd) and costal desert (Bandar Abas). The Meteorological datasets for the aforementioned stations were obtained from the Iran Meteorological Organization (IRIMO) for the period 1960-2010. The compiled data included average monthly values of precipitation, minimum and maximum air temperature, mean relative humidity, sunshine hours) and wind speed at 2 m height. A probability-based overall water deficit assessment was achieved from multiple drought-related indices (i.e. SPEI, SPI and RDI). The humidity conditions were monitored for given stations based on each index during annual, short term (1, 3 and 6 months) and long term (9 , 12, 18 and 24 months) periods. This research further examine the Locally Weighted Scatter plot Smoothing (LOWESS) graphical method and nonparametric Man- Kendal test to evaluate the trends associated with humidity deficiency in annual and monthly time scales during 51 years period (i.e. 1960-2010). Results and Discussion: Our results revealed that the maximum correlation between SPEI index with indices of SPI and RDI was achieved in the coastal wet region and with a declining trend in relative humidity condition in the rest of the regions, this correlation is down over both short- and long-term periods. A comparison between SPI and SPEI also performed that the SPI index was able to reflect prolonged drought over the costal wet region where it showed significant inconstancy in desert and semi desert regions. SPEI result suggested substantial deficiencies in relative humidity at the beginning of 1997 during long term period which indicated an increasing trend of drought statues during last decades. Overall, according to the results of SPEI index in 1month periods monthly drought assessment showed a declining trend in drought magnitude during autumn, winter and spring season months (October to June) at investigated stations excepts Tehran and Shiraz stations and with a potential deficiency in relative humidity conditions. Unlikely, annual trend showed increasing trends in drought frequency and persistent over last decade. Conclusion: Our results can be summarized as below: Focusing on various types of deficits, the result of humidity based deficiencies indicated that for semi-mountains, mountains, semi-arid, arid and costal desert regions the period of 1997 to 2010 has a large total moisture shortage over all climatic regions. Most of the climate stations showed moisture deficits (decline trends) during October to June (9-month) at many stations expect Tehran and Shiraz stations which revealed a significant increasing over 51 years. We recommend using SPEI index for arid and semi-arid regions because it includes temperature variability in drought model so it reflects drought conditions better than other indices. Furthermore, three drought indices (i.e. SPEI, SPI and RDI) have similar sensitivity to water deficits over wet climatic regions; therefore, each of those indices can be used.

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.

Introduction: Weather features and their variations have an important role in the yield of agricultural products, especially in rain-fed conditions. The main metrological variables that affected yields consist of precipitation, temperature, soil moisture and solar radiation. Also, drought is one of the major constraints to production, especially the mid-season drought which occurs during the podand seed formation stages and the terminal drought which occurs during the pod filling stage. The results of investigating the relation between drought indices such as Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Crop Moisture index (CMI) and Z index with crop yields indicated the capability of these indices to estimate variations in crop yields. The objective of this study in the first step is investigation of relations among wheat and barley crop yields with climatic variables and SPI and RDI drought indices based on Principle Component Analysis (PCA) method at Bojnourd, Mashhad and Birjand stations. In addition, by selecting the prominent variables via PCA method, the best models of estimating each crop’s yield based on multivariate regression methods at selected stations were determined. Materials and Methods: In this study, the relationship between yields of rain-fed wheat and barley with weather variables consisting of minimum, mean and maximum temperature, precipitation, evapotranspiration and drought indices including SPI and RDI were investigated and modeled at Bojnourd, Mashhad and Birjand stations. For this purpose, the values of each variable were calculated for 34 time scales of 1, 2, 3, 4, 6, and 9 months and wet periods (nine 1-month periods, eight 2- month periods, seven 3- month periods, six 4- month periods, two 6- month periods, one wet period (5 or 7-month) and one 9-month period). After that, the main influencing variables were chosen among investigated time periods for each variable by using the method of principal component analysis (PCA). In continuation, the selected variables via PCA technique were used in the multivariate regression methods to create the best model of predicting wheat and barley yields based on each mentioned variable and combination of them. The performance of the established model was evaluated based on Ideal Point Error (IPE) criteria and the best predicting model of wheat and barley was selected for each region. Results and Discussion: The results showed that applying PCA technique as a powerful statistical tool leads to decrease of the error and inflation of constructed models. This is done by reducing the volume of data and selecting influencing variables. Based on the PCA results by choosing only four components the 90 percent and greater than variation of crop yields are estimated and the first component includes time periods of spring and winter months. Investigation of the results of the best model at the given stations based on IPE criteria show that the constructed models based on variables of SPI index have more accuracy for predicting yields of wheat and barley at station of Bojnourd, at Mashhad station the created models based on a combination of variables and at Birjand station a model based on a combination of variables and a created model according to RDI variables was used that has more accuracy for predicting yields of wheat and barley, respectively. Comparing the estimated and actual values of wheat and barley yields indicate that the correlation coefficients of the models when applied to estimate the yield of wheat and barley at Bojnourd station resulted in 68 and 69 percent, at Mashhad station 89 and 86 percent and at Birjand station 66 and 74 percent, respectively.The performance evaluation graph shown in Fig. 1 can be used to illustrate model performance and to diagnose model bias. Conclusion: According to the results, a relation between crop yields and combination of metrological variables and drought indices is more positive and stronger than only metrological variables combination. The results showed that the variables of temperature, precipitation and evapotranspiration are to be considered. Also, the evaluation model indicated that the RDI index is more suitable for predicting rain-fed wheat and barley yields.

Characteristics of precipitation and the regionalization major role in the efficient use of water resources and soil and management of environmental hazards. Regionalization of rainfall can help to better use of water resources and to correct manage of environmental hazards. According to the analysis of climate phenomena such as precipitation, all data should be related to a homogeneous region, on the basis in this study, homogenous regions using data from long-term annual precipitation in Golestan province and the appropriate number of stations determined using the newer methods. Precipitation monthly data from 29 rain-gauge stations and evaporation poll in Golestan province from 1361 to 1391 were used to testing of homogeneity, the random and outlier data that 25 stations remained. Then using Wards hierarchicalclustering and with different variables was evaluated segmentation varies. Clustering in two clusters have higher average silhouette 0.48, accordingly, the province was divided into two regions. Homogeneity investigated by heterogeneity test for each region. according to investigations was performed by L- moments coefficient of skewness (τ_3^R) was smaller 0.23, The result Hosking and Wallis test was used to examine the homogeneity region. For this two region, the test statistic H11>, which is confirmed by the homogeneity of the two areas, Finally was divided into two regions. The high correlation coefficient between stations in each cluster and low correlation coefficient between two different cluster is another reason for separation of areas from each other.Characteristics of precipitation and the regionalization major role in the efficient use of water resources and soil and management of environmental hazards. Regionalization of rainfall can help to better use of water resources and to correct manage of environmental hazards. According to the analysis of climate phenomena such as precipitation, all data should be related to a homogeneous region, on the basis in this study, homogenous regions using data from long-term annual precipitation in Golestan province and the appropriate number of stations determined using the newer methods. Precipitation monthly data from 29 rain-gauge stations and evaporation poll in Golestan province from 1361 to 1391 were used to testing of homogeneity, the random and outlier data that 25 stations remained. Then using Wards hierarchicalclustering and with different variables was evaluated segmentation varies. Clustering in two clusters have higher average silhouette 0.48, accordingly, the province was divided into two regions. Homogeneity investigated by heterogeneity test for each region. according to investigations was performed by L- moments coefficient of skewness (τ_3^R) was smaller 0.23, The result Hosking and Wallis test was used to examine the homogeneity region. For this two region, the test statistic H11>, which is confirmed by the homogeneity of the two areas, Finally was divided into two regions. The high correlation coefficient between stations in each cluster and low correlation coefficient between two different cluster is another reason for separation of areas from each other.

Drought is a natural creeping event that starts due to lower moisture compared to normal condition. This phenomenon impacts all aspects of human activities. However there is neither any detailed definition nor a general and proper index for drought monitoring In the present study using the Drought indices SPI and RDI to monitor drought in 10 synoptic stations in the province were studied over a period of 24 years(1991-2010). After using panel data analysis of annual and seasonal drought tried to detecte effective the parameters above were measured using two indicators. Based on the results of monitoring Drought was found a severe drought that the 2008 in the province. Also, analyse of Panel data was show all six parameters mean of maximume tempretuer, mean of minimum tempreture, sun shine, precipitation, relative humidity and mean wind speed in 2 meters that to calculate the drought index RDI, not required to calculate Drought in time scale of annual and seasonal in 10 stations; due time scale, only of some these parameters are required. Based on SPI, precipitation is necessary for time scale annual and seasonal droghut.

Introduction: Drought is a climatic phenomenon and an integral part of climate fluctuations that occurs periodically and intermittently throughout the world and across all climates. However, the magnitude of this natural hazard in arid and semi-arid regions, such as most parts of Iran, is more acute due to the high sensitivity and weakness of these areas, and its effects may persist for years after the occurrence of drought. Drought is a multifaceted phenomenon as precipitation, temperature, evaporation, wind and relative humidity play important roles in the drought characteristics such as occurrence, severity, and magnitude. Climate change and global warming, and in some cases displacement of meteorological stations cause heterogeneity in time series of meteorological data. Therefore, the purpose of this study was to investigate the homogeneity and break point in precipitation time series data and the effects of a break point in drought characteristics in some synoptic stations in Iran. Materials and Methods: In this study, homogeneity of rainfall time series data at two time scales of annual (water year) and plant growth periods in some selected synoptic stations of Iran with different climatic conditions was investigated. For this purpose, four tests including Standardized Normal Homogeneity test (SNH), Buishand's Range test (BHR), Buishand's U test (BUR) and Petite's test were applied and the break points were determined. Then, at the stations with break points in the precipitation data series, the drought severity values were determined using four indices of SPI, SPEI, RDI and eRDI, for two periods, (before and after of the break points). Then drought characteristics based on Markov Chain Model and Transition probability matrix including vulnerability, reliability, reversibility and stationary of three condition of droughts (dry, normal and/or wet condition) were determined for the two time scales periods (annual and plant growth periods). Then, the differences between the characteristics for the two periods were investigated. Also, the characteristics of drought-free time intervals for the two periods based on Run's theory were determined and compared. Results and Discussion: Based on the homogeneity tests, precipitation data of Arak and Tabriz stations for two scales of annual and plant growth periods have break points. According to the results, in the most cases, the second period's reversibility was lower than the first period. Reliability and vulnerability also decreased and increased in all cases in the second period, respectively, compared with the first period. In most cases, there was an increase in stationary of drought in the second period relative to the first period. The rate of change in the probability of survival of the normal and wet condition in both periods was increasing and in some cases decreasing. Regarding the results of Run's theory at the growth periods scale, the average and maximum duration of drought periods increased in all cases in the second period. The minimum, average and maximum severity of drought periods also increased in most cases in the second period. The minimum, average, and maximum values increased in most cases in the second period. On an annual basis, the number of drought periods in most cases has increased in the second period. The average and maximum duration of drought periods increased in all cases in the second period. The minimum, average, and maximum severity of drought periods also increased almost in all cases in the second period. Minimum, average, and maximum of drought magnitude values increased in most cases in the second period with respect to the first one. The minimum, average and maximum values of the drought-free durations (interval time without drought conditions) in most cases were lower in the second period. At the annual scale, the minimum duration of drought was one year in all cases and no change was found between the time slices. The average duration in most cases was lower in the second period. Conclusion: The results show that the rainfall data of Arak and Tabriz stations have break points in the time scales of plant growth period and annual periods. The reliability was decreasing while the vulnerability of drought was increasing in the second period, indicating an increase in drought occurrence in recent decades. Moreover, the probability of drought stability (stationary) in the second period increased in most cases. The average and maximum duration of drought periods also increased in the second period. The minimum, average, and maximum drought severity, and the minimum, average, and maximum of magnitude of drought periods were higher during the second period. In most cases, the minimum, average, and maximum of severity and magnitude of drought-free time intervals were lower in the second period. In general, difference in the characteristics of drought before and after of precipitation break point can be due to increased evapotranspiration, as a result of global warming, intensifying the effects of drought. Moreover, based on the results of the eRDI index, the climatic conditions became drier in both stations and time periods. In other words, it can be stated that the effective rainfall has decreased to some extent in recent years compared to the early years of the study period. Further studies are needed to assess the changes in drought characteristics in all synoptic stations in the country having long-term data. [ABSTRACT FROM AUTHOR]