مهدی قمقامی

Evapotranspiration (ET) as a major component of the hydrological cycle and an important variable in the calculation of the earth's surface water and energy balance has a key role in agricultural water management. Under limited meteorological data, ET estimations using empirical models are less accurate and require adjustments based on water balance approach or lysimetric measurements. Combining ground observations with remote sensing information (such as satellite images) can improve the accuracy of these estimations. In this study, SENTINEL-2 satellite products were used to estimate actual evapotranspiration values in 8 stations of the Gorganrud-Gharehsoo basin, north of Iran, during the period 2016 to 2018. Considering the number of required images and significate variations of ET during warm season, a four months period of April to June was selected for comparisons. The Penman Monteith equation estimations using observed data of study stations was chosen as an evaluation metric of satellite products.Comparisons were evaluated using statistical indices of R2, RMSE. The coefficient of determination (R2) values between Penman-Monteith equation (PM) estimations and SENTINEL-2 outputs for the study stations of Rezvan, Bandar Turkman, Aliabad-e-katol, Gonbadkavos, Kalaleh, Gorgan, Gorgan Hashem-Abad, and Minoodasht, were 0.95, 0.76, 0.86, 0.87, 0.80, 0.86 and 0.84, respectively. The highest correlation of SENTINEL-2 and PM equation was obtained in Rezvan station (RMSE: 0.21, R2 = 0.95), while the lowest correlation was observed in Ghonbad kavoos stations, (RMSE = 0.37, R2 = 0.87). According to results, the SENTINEL-2 evapotranspiration estimations can be used in regions with in adequate observed data. The finer spatial resoulution of SENTINEL-2 improves the ET estimations accuracy. This satellite products may be recommended for regions with inadequate weather stations especially in semi arid regions

Adopting novel policies in the agricultural sector as the largest consumer of water should undeniably lead to increased water use efficiency and reduced water loss. New technologies including nuclear techniques can be of great help in improving irrigation management through optimized water use efficiency. Study has shown that instrumental monitoring of soil moisture and biomass can be exploited to determine the required irrigation timing and volume. In this regard, such nuclear tools as neutron meter and cosmic ray neutron sensor (CRNS) as novel technologies, installed on farms either in spot installations or in networks, are capable of estimating soil moisture through detecting hydrogen within soil H2O molecules and decelerated neutrons. It has been confirmed that these technologies are beneficially effective in scheduling irrigation by predicting the incidence of imminent moisture stress and, thereby, determining proper irrigation timing, which will evidently enhance both water use efficiency and crop yield. This article provides an overview of the development and application of nuclear and isotopic techniques (NITs) in irrigation management and introduces methods of NITs tool calibration.

Since Iran has always faced fluctuations and decreases in the rainfall, and on the other hand, the uncontrolled harvest of water resources have led to a more severe decline in the groundwater resources, the medicinal plants cultivation can help overcome the water deficit crisis. This study aimed at determining the ecological suitability of cultivation of four medicinal plants including damask rose (Rosa damascena Mill.), saffron (Crocus sativus L.), thyme (Thymus vulgaris L.), and lavender (Lavandula angustifolia L.) in 36 plains located in the Salt Lake (Namak Lake) catchment. The research methodology included the collection, reconstruction, and quality control of the information, climatic simulation, calculation of the agro-climatological indicators, and estimation of the agriculture share of water resources separately for each plain, estimation of the plant water needs, determination of the moisture balance, preparation of the suitability layers of cultivation, and finally overlaying the layers to produce the suitable/unsuitable agricultural lands for the cultivation of these plants. The climate simulation was performed by the strategic re-sampling technique to generate the different scenarios during the period 2011-2035. All the models under the different scenarios predicted a decrease in the rainfall and an increase in the maximum and minimum temperatures. The EC-EARTH model under the RCP2.6 scenario predicted the highest percentage of rainfall decrease (about 23%) and temperature increase. Due to the salinity and heat stress, the agricultural lands located at Sharifabad plain, northern parts of Qom-Kahak plain agricultural lands, eastern lands of Saveh plain, northern lands of Kashan plain, and southern lands of Varamin plain were unsuitable for the cultivation of all four medicinal species. The cultivation of saffron and damask rose in most of the plains of Salt Lake catchment was suitable or very suitable.

Regional estimation of evapotranspiration (ET) is crucial in agricultural water management, cropping pattern and crop yield forecasting.The main purpose of this study is evaluation of actual evapotranspiration estimations of Global Land Evaporation Amsterdam (GLEAM) model of Karkhe basin, southwest of Iran. GLEAM outputs include different set of RS-based estimations, i.e. transpiration, interception, bare soil evaporation, open water evaporation and snow sublimation. In current research, the GLEAM estimation of actual evapotranspiration were compared by water balance approach (assuming a watertight basin) using coefficient of determination (R2). The corresponding values of R2 for the each study stations across the Karkhe basin namely Aran-e-Gharb,Pirsalman,Nazarabad,Polchehr,Paypol,Poldokhtar,Tangsazbon, Pole-e-kaskhan were 0.66, 0.62,0.77,0.84,0.59,0.49,0.56 and 0.66 Similarly, the RMSE values were 0.7, 0.62, 0.74, 0.25 0.45, 0.87, 0.73 and 0.51. The MAE of same comparisons were found to be 0.52, 0.43, 0.58, 0.18 0.3, 0.71, 0.53 and 0.36. Besides the relative deviation of GLEAM dataset estimations and those obtained from water balance method were calculated as -21,-3.73,-4.96,-4.19,-19.61,-2.39,-3.64,-25.18. The best agreement by all statistical indices was observed in Polchehr sub-basin. The results of this study indicated that RS-based estimations of GLEAM model can be used in Karkhe basin, especially in regions with lack of sufficient observed data.

The Crop Progress Percentage (CPP) in a given phenology stage reflects growth status in life cycle. Generally, routine field measurements of this parameter are lacking, hence various alternative approaches have been proposed for its estimation. The statistical methods such as Bayesian approaches and hidden Markov models (HMMs) have appropriate structural skills for solving problems with variety of continuous or discrete data and can be combined with remotely sensed data also. The aim of this study is evaluation of hidden Markov models’ skill in real time prediction of maize progress percentage in research field of university of Tehran located in Karaj. The HMMs follow the Bayesian structure in which, there are usually two layers; hidden and observable. Different phenological stages including Emergence to Milky were considered as the hidden layer and both Normalized Difference Vegetation Index (NDVI) and Accumulated Growth Degree-Day (AGDD) features, extracted from air temperature and LANDSAT7 ETM images, as a vector variable of observable layer. Calibration and evaluation of the model was performed using a 9 years (2002-2010) data set of the field phenology observations and meteorological data. According to the results, in general, for all phenological stages, the HMM was able to estimate the CPPs with average RMSE of 14%, which confirms the applicability of this approach as a suitable tool. Further studies in other climatic regions of the country are recommended for more scrutiny of phenological prediction models using remotes sensing and statistical approaches.

Crop phonological stages are commonly predicted by using accumulated growth degree days(AGDD).In this study a combined model of AGDD and remotely sensed NDVI has been developed for prediction of maize (cv. K407) phenology in Karaj using a nine year (2002 to 2010) dataset. For smoothing the existing noises of image processing, a combination of double logistic and weighing average (DL-WLS) approaches was employed. The results of combined phenology model were compared by two frequently used methods based on AGDD and date of sowing. The findings showed that in general, the developed model predicted the first 7 phenological stages of emergence to milky, more accurately comparing to other approaches (with average 2 and 2.5 days difference with observed dates, respectively) but was inaccurate for maturity stage. Our study highlights the need for further improvements in observations in the region.

The aim of this study is a comparison among two multi-site stochastic weather generators for simulation of winter rainfall occurrence across Iran using data of a selected network consisting of 130 rain gauge stations with a historical data of 21 years. The applied approaches included Hidden Markov Model (HMM) as a parametric approach and K-nearest neighbor (KNN) as non-parametric approach. Six stations namely, Bandar Anzali, Sari, Gharakhil Ghaemshahr, Gorgan, Shiraz and Zahedan were chosen respectively as the representative of different climates including very humid, humid, semi humid, Mediterranean, semi dry and dry climates. In comparison of first and second order momentums, results indicated that HMM performed well in almost every station. Data dispersion was examined using box plot and confidence interval analysis. The results revealed better performance for HMM. Regarding probabilities spaces, HMM showed a better performance in simulation of extreme events and higher percentiles of empirical distribution but KNN approach provided better estimations for middle percentiles values. LEPS Score index was used for comparison of cumulative distribution of observed and simulated series which showed more agreement in case of HMM. The spatial correlation was evaluated using Log-odds ratio index, which indicated that KNN model did better. Both approaches performed well in estimation of duration of wet and dry spells though a tendency to overestimate was observed at HMM and a tendency to underestimate viewed at KNN in simulating of wet spells. In general, HMM has more skill in simulation of daily rainfall series which might be attributed to its complex mathematical structure, however relatively good results of KNN approach showed that it can be recommended for less complicated applications.

Multi site modeling of rainfall is one of the most important issues in environmental sciences especially in watershed management. For this purpose, different statistical models have been developed which involve spatial approaches in simulation and modeling of daily rainfall values. The hidden Markov is one of the multi-site daily rainfall models which in addition to simulation of daily rainfall values, explores the spatial and temporal pattern of rainfall events. In this study, the winter (January to April) rainfall pattern of 130 rain gauges have been modeled using hidden Markov approach during a 21 years period (1990-2010). The aim of this study was finding temporal and spatial distribution of weather patterns and stochastic simulation of occurrence and amount of rainfall, simultaneously. To achieve this goal, different hidden Markov algorithms including, Viterbi decoding algorithm, Expectation-Maximization (EM) algorithm and a stochastic simulation approach with the probability transformation were applied. It is expected that extracted patterns, using hidden Markov model, are consistent with synoptic patterns and accordingly eight different weather pattern as the definite set of possible cases were recognized. The most frequent rainfall pattern extracted from hidden Markov model was the dry pattern (stable condition) in which the rainfall occurrence probability is low in most of the stations. This pattern has the maximum initial probability of 0.429 and maximum Markov transfer probability of 0.637 Besides, multi-site simulation of winter rainfall keeping the basic statistic of mean, standard deviation of total seasonal rainfall and percentile values in each station and also spatial correlation of occurrence or non-occurrence of rainfall produced reasonable result. In general this approach can be recommended for regional studies.

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.

Drought management is very important for optimal water resources application in arid and semi-arid regions. One strategy to manage drought is to predict drought conditions by probabilistic tools. In this study, total monthly precipitation records related to 33 synoptic stations of Iran during 1976-2005 were used to monitor and predict future drought conditions. Regarding the dry periods greater than six months in the arid regions of the country, the Standardized Precipitation Index (SPI) at 6-month timescale was used for drought monitoring. The first-order Markov chain model was employed to predict drought condition up to 3-step ahead. This model was fitted on the SPI series at all stations of interest, and it was identified that can represent the probabilistic behavior of drought over Iran. The results obtained from drought prediction at 1, 2, and 3-step ahead over Iran showed that the occurrenceof the severe drought (9 percent of stations) or normal conditions (87 percent of stations) is most probable in the future months, regardless of drought condition at current month. Also, drought monitoring based on aerial mean of monthly total precipitation time series over country showed that the trend of drought severity has been increasing in recent years.

Introduction In absence of long-term time series of meteorological variables required for different type of studies, simulation models of these variables, generally known as weather generators (WGs), have been developed in recently. Taking into account the significant climatic variations defined in climate change scenarios, it seems necessary to define these changes upon simulated series. Since conventional weather generators do not have such a capability, new methods should be taken into consideration. Models that have been used in empirical downscaling studies include multiple linear regression, canonical correlation analysis, nearest neighbor and artificial neural network approaches. These models can account for variability in the surface variables to an acceptable level but extrapolation beyond the historical conditions may be unreliable. The ability of the downscaling models to predict climate change impacts is greatly reduced due to the coarse spatial resolution of GCMs. This paper describes the application of a non-parametric approach called strategic re-sampling, in simulation of series along with the climate change scenarios. Using this method, these changes were conditioned upon observed series and then by using a weather generator, transferred to simulated series. The basic concept of the models is the non-parametric method of Nearest Neighborhood.Material and MethodsThe study was performed in 5 synoptic stations namely Tehran-Mehrabad, Mashhad, Tabriz, Qazvin and Bushehr stations. The target variables were maximum temperature, minimum temperature and rainfall. In this non-parametric simulation method, by defining a moving window with central feature vector and The atmospheric Aerosol Optical Depth (AOD) of the city of Tehran in each station was calculated using MODIS images. The calculated AODs were compared with pollution data collected at the station where a correlation coefficient of about 0.7 through a linear equation of (PM10 (μgr/m3) = 222.78*AOD - 65.091) was achieved. This is shown in figure (1) below: Fig. 1. A Model for extraction of PM10 from MODIS AODResults and Discussion Generally it seems that this amount of correlation for low spatial resolution data such as MODIS is acceptable. It should be noted that the AOD's pixel size are usually 10*10 km2 and therefore the ground underneath is covered only with a few of these pixels in each satellite passage, while the ground based measurement data are only valid in a limited distance around the station. This in many cases renders the accuracy of comparing satellite data and ground-based data difficult. One of the main sources of uncertainties was the MODIS AOD product low spatial resolution of 10 by 10 kilometers. While the results of measurement in the stations can hardly represents 100 square kilometers. ConclusionA positive relationship between aerosol optical depth and particulate matter of less than 10 nanometer size is discovered by many workers. To supply a model for this relationship use has been made from MODIS AOD products as well as in situ measurements of pollutions specially PM10. Despite low resolution of MODIS products, a correlation of about 0.7 was found between AOD and PM10 in Tehran. The results seem acceptable in situation of lack of continuous observation due to the present financial problems.

Weather data generators (WGs) have been developed for an extension of time series of such weather variables as rainfall, temperature and relative humidity to provide better understanding of systems affected by climatic factors. Different algorithms have been applied in these generators, broadly divided into parametric & non-parametric ones. In this study, the performance of non-parametric generator of K-nearest neighbor (KNN) with the capability of extrapolating data, has been evaluated in six synoptic stations of Iran namely; Tehran, Qazvin, Mashhad, Bushehr, Tabriz and Rasht for the period of 1961-2005. Besides, some of the obtained results have been compared with parametric generator of LARS-WG to show the priority of this approach to parametric methods. The results revealed that in most cases the KNN approach presents a better performance in simulating the parameters of observed series; however, LARS-WG better performs in simulating length of wet and dry spells but with minor differences.