Desert
Volume:20 Issue: 2, Summer - Autumn 2015

One of the world's major mineral dust source regions lies along the border between Iran and Afghanistan. In this study it is hypothesized that a low-level jet may play in role in generating the intensity of this source region. The presence of a low-level jet east of the Seistan mountains is documented here for the first time. The jet exists mainly from May toSeptember and has a core at 850 mb. Maximum speeds are at 18 UTC and 0 UTC and occur in July-August. In the mean the core speed attains 20 ms-1 in those months. The jet attains a maximum at night, a minimum during the day. However, the vertical motions associated with it do not fluctuate greatly over the course of the day. The development of the jet during a dust outbreak in 2002 is also described. It arises a day or so before the outbreak and disappears as the outbreak ends.

Two statistical downscaling models, the non-homogeneous hidden Markov model (NHMM) and the Statistical Down– Scaling Model (SDSM) were used to generate future scenarios of both mean and extremes in the Tarim River basin, which were based on nine combined scenarios including three general circulation models (GCMs) (CSIRO30, ECHAM5, and GFDL21) predictor sets and three special report on emission scenarios (SRES) (SRES 1B, SRES A2, and SRES B1). Local climate change scenarios generated from statistical downscaling models was also compared with that projected by raw GCMs outputs. The results showed that the magnitude of changes for annual precipitation projected by raw GCMs outputs was greater than that generated by using statistical downscaling model. The difference between changes of annual maximum air temperature projected by statistical downscaling model and raw GCMs outputs was not as significant as that for annual precipitation. In total, the magnitude of these increasing trends projected by both statistical downscaling models and raw GCMs outputs was the greatest under SRES A2 scenario and the smallest under B1 scenario, with A1B scenario in–between. Generally, the magnitude of these increasing trends in the period of 2081 to 2100 was greater than that in the period of 2046 to 2065. The magnitude of standard deviation changes for daily precipitation projected by raw GCMs outputs was greater than that generated by statistical downscaling model under most of combined scenarios in both periods.

The invertebrate communities of the northern Chilean rivers are characterised by their marked endemism and specificity of their community structure in different basins. The river systems located in the Atacama Desert are endorheic and are affected by the rainy period of January-February commonly known as the “Bolivian winter“. The present study is the first report on the observations of arthropods in the ephemeral Atacama River during a period of the “Bolivian winter“. The Atacama River is characterised by species with poor invertebrate assemblage dominated by diapausing crustaceans (cladocera, copepod, ostracoda) and dispersing aquatic insects (ephemeroptera and diptera larvae).

Parameters such as future precipitation, temperature, snowfall, and runoff were modeled using PRECIS regional climate modeling system in Iran with the horizontal resolutions of 0.44×0.44°C in latitude and longitude under SRES A2 and B2 scenarios. The dataset was based on HadAM3p during the periods of 1961 1990 and 2071-2100. The overall precipitation error of the model in the period of 1961-1990 was 5.3%. Minimum errors were found to be over Azari, north-central, and Kordi regions. Maximum and minimum monthly precipitation errors were found in September and May, respectively; but, minimum and maximum seasonal biases were found in spring and winter with -0.1 and -17.2% errors, respectively. Results revealed a decrease in mean annual precipitation toward the end of the 21st century by 7.8 mm in B2 scenario and 10.1 mm in A2 Scenario with maximum regional decrease of 100 mm in the southeast of the Caspian Sea. The decrease in precipitation was higher for A2 scenario, whereas it was minimum for B1 scenario. Mean annual temperature of Iran during 2071-2100 would be projected to increase by 4.5-5.5°C in A2 scenario and 3-4°C in B2 scenarios compared to 1961-1990. It was shown that mean annual changes in runoff over the country were negligible both in A2 and B2 scenarios. Maximum annual amount of runoff increase was found over western part of the Caspian Sea, Zagros and Alborz mountain chains by 6.4-15. mm. Results also indicated that annual snowfall would decrease by the maximum amount of 22.9-23.7 cm over Zagros and Alborz mountain chains.

Managers always consider the precise estimation of sediments in watersheds due to various conditions, such as soil and water resources management, construction, infrastructure and economical and social issues. In this condition, an optimized determination of sediment rating equation (typical method until now for sediment yield estimation) is essential to investigate sediment yield in rivers. In this study, the best sediment rating equation was determined for four hydrometric stations of Pol-Doab watershed in Markazi province using sediment rating curves types (singlelinear, multi-linear, mean loads) together with bias correction factors (FAO, Quasi-Maximum Likelihood Estimator [QMLE], Smearing, Minimum Variance Unbiased Estimator [MVUE] and β). The results showed that the optimized equation in stations is the mean loads (MVUE), which can used for prediction of sediment yield in annual scale. Moreover, FAO factor is more accurate for the estimation of sediment yield in high variability conditions for monthly, weekly and daily scales. According to the obtained results, accurate representation of variability intensity of sediment yield is associated with the rating curves types, since the monthly rating curve is more accurate. Also, the results indicated that the watershed average slope has direct relation with b coefficient of rating equation, and when using this parameter, the rate of sediment yield can be determined for month, season and hydrological periods. Based on the obtained results, with increase in the watershed average slope, the slope of suspended sediment concentration (SSC) equation is also increased.

Zygophyllum atriplicoides is one of the important species of Iran rangelands that has special importance owing to some properties like high distribution rate, coverage percentage, density, and plant biomass that make it possible to supply a part of forage needed by livestock during spring and winter as well as to avoid soil degradation against water and wind erosions. This research tries to study the most important climatic factors affecting distribution of Z. atriplicoides using multivariate statistical methods and selecting ecological important 69 variables in Isfahan province. Four factors, such as cooling temperature, precipitation, cloudiness, and wind were determined by the factor analysis method and variables variance of 34.45, 29.43, 11.79, and 9.06 were obtained, respectively totally represents 84.74 of the changes. The mean of factor scores and climatic variables in three populations of the Z. atriplicoides species as the dominant species, Z. atriplicoides species as the associated species and regions without the Z. atriplicoides species were determined. Furthermore, the factor score matrix was used as the input of the hierarchical cluster analysis and 6 climate zones were identified in Isfahan province. The most important climatic factors affecting the species distribution were determined by incorporating the vegetation map, factors map, and climatic zones. The effect of altitude was also analyzed in the species distribution. Results showed that the temperature factor is the most important climatic factor affecting distribution of the species in the Isfahan province and the precipitation factor influences the species distribution. Also, the effect of altitude and soil salinity on analysis of Z. atriplicoides populations could not be ignored.

There has been some decline in the land potential capacity in many developing countries, and depending on location, the multi objective management strategy of Agroforestry can make effective use of natural resources to be feasible. Environmental principles which are effective on Agroforestry systems and their components, together with climatic factors, are the important parameters which been evaluated in this study. Two cities, Kazeroun (with warm climate) and Sepidan (with cold climate) in Fars province, were chosen for the purpose of this research, and after proper identification and classification of the Agroforestry systems, the effects of climatic factors were analyzed. Nineteen points were identified in these two cities by field investigations, with each type of Agroforestry systems and their corresponding components determined in each point. From the results, most of the systems in Kazeroun, characterized with warm climate, were Agrosilvopastoral, while Sepidan with a characteristic cold climate was the Agrosilvicultural system. Also, the components of each system clearly changed with change in climate, owing to the great importance of the livestock and crop components in Kazeroun and Sepidan, respectively. In general, it can be concluded that the impact of climatic factors on Agroforestry systems and their components has been approved in this study, and the results can be applied in developing these systems in other regions.

Knowledge about low flow statistics is essential for effective water resource planning and management in ungauged or poorly gauged catchment areas, especially in arid and semi-arid regions such as Iran. We employed a data set of 20 river flow time-series from the Iranian Central Plateau River Basin, Iran to evaluate the low-flow series using several frequency analysis methods and compared the result of these methods in their ability to set low flows for the conservation of instream water uses. Theoretical frequency distributions including the log-normal, three parameter lognormal, Gumbel, Pearson type III, and log Pearson type III were applied with the low-flow series. Goodness-of-fit tests including Lmoment and conventional moment methods for the observed data were applied to identify the best distributions. For each distribution, the calculated values of the residual sum of squares (RSS) was applied to compare between the conventional moment and L-moment methods, and the best method was selected to determine the most appropriate probability distribution. The lowest RSS values were used to select the best distribution for each station. Then, T-year low-flow series was estimated using the best probability distribution. Our results suggested that, for annual low flows, based on the computed RSS, Pearson type III, log Pearson type III, Gumbel distributions, and L moment method were suitably distinguished for 85%, 10%, and 5% of the stations, respectively. Finally, Compared to the conventional moment method, L-moments method was found to be more adequate to identify low-flow series probability distributions in the Iranian Central Plateau River Basin, while Pearson type III was found to be the best probability distribution for modeling minimum flow series in the study area.

Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to its essential application to water resources management. Recently, artificial intelligence has gained much popularity owing to its application in calibrating the nonlinear relationships inherent in the stream flow suspended sediment relationship. This study made us of adaptive neuro-fuzzy inference system (ANFIS) techniques and three artificial neural network approaches, namely, the Feed-forward back-propagation (FFBP), radial basis function based neural networks (RBF), geomorphology-based artificial neural network (GANN) to predict the streamflow suspended sediment relationship. To illustrate their applicability and efficiency,, the daily streamflow and suspended sediment data of Dalaki River station in south of Iran were used as a case study. The obtained results were compared with the sediment rating curve (SRC) and regression model (RM). Statistic measures (RMSE, MAE, and R2) were used to evaluate the performance of the models. From the results, adaptive neuro-fuzzy (ANFIS) approach combined capabilities of both Artificial Neural Networks and Fuzzy Logic and then reflected more accurate predictions of the system. The results showed that accuracy of estimations provided by ANFIS was higher than ANN approaches, regression model and sediment rating curve. Additionally, relating selected geomorphologic parameters as the inputs of the ANN with rainfall depth and peak runoff rate enhanced the accuracy of runoff rate, while sediment loss predictions from the watershed and GANN model performed better than the other ANN approaches together witj regression equations in Modeling of stream flow suspended sediment relationship.

Climate change has direct and indirect consequences on crop production and food security. Agriculture and crop production is one of the factors which depend on the weather conditions and it provides the human requirements in many aspects. The objective of this study is to assess the impacts of future climatic change on irrigated rice yield using the CERES-Rice model in the Southern Coast of Caspian Sea under three climate change scenarios of Sra1b, Sra2 and Srb1. Required data for this research includes the meteorological, soil and crop management data. The meteorological data include the daily data of minimum temperature, maximum temperature, solar radiation and precipitation during 1981-2010 and Global Climate Models (HADCM3, ECHAM5, IPCM4, GFCM2, NCCCSM and INCM3) during 1971-2000. Soil and product management data provided from field experiment was conducted from 2008 to 2009 at the Rice Research Institute in Rasht. Validating of Global Climate Models show that ECHAM5 climate model has the highest correlation with the lowest error to simulate the future temperature and precipitation. We used ECHAM5 climate model coupled with a crop growth model for simulating of the effects of climate change on rice protection. The results of prediction of climate change scenarios show that minimum and maximum temperature will be ascending and precipitation will be decreasing in the Rasht station. Results of simulated yield and biomass of the rice crop base on scenarios of Sra1b, Sra2 and Srb1 show that rice crop yield and biomass decrease with increasing of mean temperature and decreasing of precipitation.

Salinization is the main characteristic of soils in arid and semi-arid regions which reduce the agricultural potential of irrigated lands. Therefore, soil reclamation as well as determination of the leaching requirement for salt control is very important for better plant growth. In this study, the effects of leaching on saline soils of Sistan region, southeast of Iran were examined using unsaturated disturbed soil columns. The experiment was conducted on four texture types (loam, sandy clay loam, sandy loam and clay loam) and three replications. Soil samples were purred in polyvinyl chloride (PVC) cylinders and leaching procedures were conducted in 10 stages with up to 5 pore volumes. Effluent from each column was collected and evaluated in terms of Na, K, Ca2, Mg2 and EC. At the end of the study, soil columns were cut and their corresponding samples were analyzed for Na, K, Ca2, Mg2 and EC. The results of leaching experiments showed that the water used in this study could reduce solute concentration and thus, this soil does not need any amendment. For most soil textures, it was also observed that almost 85% of the salts were leached after the fifth stage of the leaching process. According to the results, ions entry into the effluent solution is fast in the coarse textured soils. So, the difference between the amounts of irrigation water needed to transport the salts and leach the saline soils can be attributed to the soil texture. It seems that the main reason for these reactions is the cation exchange.

Water supply of green spaces in arid areas is a major challenge. A high percentage of green spaces create lawns, which are high water consumer landscapes. Due to the environmental, recreational and athletic values of lawns, they are considered as non-removable elements in urban green space development. This paper reviews and discusses strategies for efficient water usage in lawn areas using library study methods. According to the results, first, it was recommended that water demands of turfgrasses are calculated using precise scientific methods. Eleven strategies including selecting appropriate plant species, clipping from appropriate height, removing the thatch layer, using wastewater as an irrigation water source, the use of superabsorbents, application of regulated deficit irrigation, the use of subsurface irrigation systems, replacement of lawns with appropriate ground cover plants, the use of surfactants and other chemicals such as paclobutrazol and endophyte fungi, as individual or combined strategies were suggested for efficient water usage in turfgrass areas. These results, in some cases, can be used as executive guidelines by green space professionals in order to reduce water usage in this sector and in other cases, they can be used as preliminary studies for research in the field of sustainable management of turfgrasses in arid and semi-arid areas.

Agricultural sustainability refers to the ability of a cropping system to produce, without causing irreversible damage to the ecosystem. There is an increasing need to view cropping systems and identify management practices in a holistic indicator-based impact assessment. The main objective of this study was to compare and rank the cropping systems of the Marvdasht plain in Fars Province; in order to show the gap between them. To achieve this aim, sustainability were divided into four levels based on Composite Index(CI),which is useful for the identification of sustainability and includes three dimension: (1) economical, (2) social, and (3) environmental and consist of 11 indicators. Required data were collected by questionnaire from 200 cropping farmers who were selected through a stratified sampling design from six regions located in Marvdasht plain. The computer software of SPSS was used to analyse the data. Indicators were normalized using the division by means technique and were weighted. The weightings were derived from Principal Component Analysis (PCA). CI was used to map the sustainability levels at the plain. According to classifying CI, four categories were identified, which zone 6 identified as being unsustainable, while two zones 2 and 3 were considered as belonging to the relatively unsustainable and the zone 5 was identified as relatively sustainable and the remaining zones (1,4) were classified as sustainable. We conclude that the usage of multidimensional and holistic CI for analysing sustainability of complex cropping systems is extremely important.

It has been more than half a century that plant geneticists and breeders have been trying to assemble a combination of genes in crop plants, in order to make them as suitable and productive as possible. Plant transformation technology in crop plants was first undertakenin the 1980s based on the ability of foreign gene integration into host plant genome and regeneration of transformed plant cells into whole plants. Soon after, transgenic plants were to be grown by farmers. Statistics show that farmers have started to cultivate genetically modified plants (GMPs) commercially since 1996. Between 1996 and 2012, the total surface area of land cultivated with GM crops has increased from 2 million hectares to more than 170 million hectares in 29 countries. To this extent, some concerns have been raised by ecologists and consumer organizations in West European countries based on the possibilities of horizontal and vertical gene flow of antibiotic or herbicide resistance from transgenic plants into human intestinal bacteria and some weeds via outcrossing, respectively. Due to consumer and ecologist concerns, different approaches have been developed to eliminate marker (and/or reporter) genes from the nuclear or chloroplast genome after selection. Some of these proposed methods are:1. Replacing selectable markers with screenable ones.
2. Elimination of marker genes by co-transformartion followed by classic recombination and selection.
3. Excision of marker gene by some site-specific recombinases.
4. Separation of the transgene and selectable marker by transposable elements.
5. Avoiding gene pollution by chloroplasts genetic engineering followed by elimination of selectable marker.

Since there is limited information on the simultaneous effect of drought stress and salicylic acid on yield and mucilage content of the medicinal herb, Plantago ovata Forssk is available, a pot experiment was conducted in factorial form based on a randomized complete block design with three replications in Jiroft, Kerman Province, south of Iran. As the first factor, drought stress included four levels of irrigation: 100℅ field capacity (FC) (no stress), 75℅ FC (low stress), 50℅ FC (medium stress) and 25℅ FC (high stress). In the second factor, salicylic acid had four levels: 0, 0.01, 0.5 and 1 mM. The results showed that maximum yield and yield components of P. ovata Forssk were obtained with 100℅ FC and simultaneous application of 1 mM salicylic acid. Minimum amounts of yield and yield components resulted from irrigation based on 25℅ FC with no application of salicylic acid. Such an outcome revealed the significant role of salicylic acid in increasing the tolerance of Plantago ovata Forssk to drought stress.