Desert
Volume:25 Issue: 2, Summer - Autumn 2020

Nowadays, rainwater harvesting systems, particularly on the roof of the buildings in residential areas could be considered as a managerial procedure to reduce water crisis. This efficient method is being investigated and implemented in different countries all around the world. This study was conducted on the estimation of rainfall, runoff coefficient, and the calculation of the optimized tank volume and the total harvested amount of rainwater on the roofs of the residential areas in Torbat-e Jam. It is noteworthy to mention that the roofs’ surfaces are covered by waterproofing. This study aimed to estimate the amount of the harvested rainwater in the study area, and to apply this method in different areas of Torbat-e Jam, Razavi Khorasan Province, Iran. According to the results, the values of the roofs’ runoff coefficient and average rainfall were 0.9 and 154 mm, respectively. The results of the tank volume sensitivity analysis showed that the average peak 24-hour rainfall is the best rainfall index to calculate the tank volume. The total harvested amount of rainwater was 5,606 m3 considering the total surface of the roofs and the annual rainfall in the study area. Applying this procedure in other areas of Torbat-e Jam, the harvested rainwater was estimated as 772,806 m3. This amount of harvest resulted in 15.5% water saving.

Due to the rising drought-severity all around the world, one of the most important goals of arid agricultural systems is to increase wheat yield as a strategic crops in these areas. Improving the yield components is believed to be an efficient and vonventional strategy for increasing wheat yield. This study was carried out on 61 advanced lines and five Iranian commercial cultivars in order to identify the most effective components of grain yield (GY) under late-season drought stress conditions. The experiment was carried out based on an augment design during 2013-14 and 2014-15 growing seasons. Fertile spikes number m-2 (FSN), spike weight m-2 (SPW), grain number per spike (GNS) and plant harvest index, as the most effective variables, explained 94.06% of GY variance. FSNand SPWrevealed the maximum direct and positive effect on GY enhancement. The first and second factors, as “yield and yield-components” and “vegetative growth” factors, respectively, explained 76.4% of the data on the total variance. The highest alignment with GY belonged to SPW and FSN . The genotypes were grouped in four different clusters. Bi-plot and cluster results revealed a remarkable genetic diversity among the genotypes; therefore, these results might be helpful to identify donor parents in wheat breeding crosses for yield increscent. Finally, FSN and SPW, the main indicators for increasing grain weight m-2, were proposed as the most important grain yield-components under terminal drought stress conditions.

Soil texture is variable through space and controls most of the soil’s Physico-chemical, biological and hydrological characteristics and governs agricultural production and yield. Therefore, determining its variability and generating accurate soil texture maps have a key role in soil management and sustainable agriculture. The purpose of this study is to introduce a numerical algorithm named Least Square Support Vector Machine for Regression (LS-SVR) as a predictive model in Digital Soil Mapping (DSM) of soil texture fractions and evaluating its performances based on modeling evaluation criteria. In this study, the soil texture data of 49 soil profiles in Tabriz plain, Iran, was used. The important covariates were selected using Genetic Algorithm (GA). The model evaluation results based on ME, MAE, RMSE, and R2 indicate the high performance of LS-SVR in predicting soil texture components. The prediction RMSE for sand, silt, and clay was 6.82, 5.08 and 6.06, respectively. Silt prediction had the highest ME and the lowest MAE and RSME values. The algorithm simulated the complex spatial patterns of soil texture fractions and provided high accuracy predictions and maps. Therefore, the LS-SVR algorithm has the capability to be used as predictive models in soil texture digital mapping. This study highlighted the potential of the LS-SVR algorithm in high precision soil mapping. The generated maps can be used as basic information for environmental management and modeling.

Considering a large number of cities located in arid and semi-arid climatic regions of the world with limited water resources, reducing water consumption and maintenance costs is an important research and implementation priority in urban landscaping. In other words, reducing the high costs of irrigation, increasing water use efficiency and reducing weed competition are important factors in achieving sustainable green spaces in arid and semiarid regions. Application of mulches is one of the suggested strategies for maintaining bed moisture and weed control. In this research, the effects of organic and inorganic mulches on the performance of the flowering plant of Blanket flower (Gaillardia sp.) were investigated in the arid climate city of Mashhad located in the northeast of Iran. The experiment was conducted as a randomized complete block design with three replications. The four mulch treatments included wood chips, scoria, pine leaves, polyethylene as a layer mulch, and no mulch as the control. The polyethylene mulch inhibited the weed growth up to 100%, and other mulch types also significantly reduced the percentage of the weed coverage (p≤0.01). Application of pine leaves delayed the flowering for seven days while polyethylene caused an early flowering to six days compared to the control. Using different mulch types in water shortage conditions of urban landscapes in arid and semi-arid regions is recommended.

This study was conducted to investigate the impacts of organic fertilizers on soil water holding capacity in four different suctions (0, 0.05, 0.33 and 15 bar) and their impacts on water retention curve in three different soil textures in five governing climates of Iran, which were cultivated under wheat and maize for two consecutive years. Furthermore, the role of organic materials in aggregation process was surveyed. The influence of organic materials on soil water holding capacity was evaluated in five treatments, including 10000, 20000 kg/ha of animal manures; 10000, 20000 kg/ha of compost, and control treatment in factorial and completely randomized statistical design. The results revealed that the addition of materials resulted into an increase in the mean weight diameter of soils aggregates with more concentration on 250-500 and 500-1000µ diameter. Additionally, the improvement of soil aggregation was more in sandy loam soil and less in clay loam soil. Interestingly, the role of organic materials in increasing soil water holding capacity in different plants and consecutive years was not significant.  By adding organic materials in field capacity and permanent wilting point, soil volumetric moisture increased far better than other studied soil moisture points. Sandy loam texture and semi-arid climates indicated the greatest variability to the additional organic materials. In conclusion, compost fertilizer in arid and semi-arid climates with sandy loam texture had the most influence on soil water holding capacity, particularly in FC and PWP moisture points

Modeling and characterization of the geometry and distribution of Rock Facture Networks (RFNs) are essential in applications such as hydrogeological or environmental evaluations. It is widely accepted that RFNs are potentially associated with the hydrogeological (thus salinity) characteristics of the surrounding environments. Despite the complexity and inaccessibility of RFNs, stochastic methods provide a functional framework to predict their characteristics in the subsurface. An efficient tool for modeling RFNs is the Discrete Fracture Network (DFN) which also includes a number of geostatistical techniques that consider spatial variability structure. The advantages of these techniques are: realistic results, ease of application, and uncertainty assessments. Multiple-point geostatistics/statistics (MPS) is a modern and effective geostatistical tool for realistically simulating RFNs. In the present study, we modeled the RFNs in a location near the Qarabagh area, in the western Urmia Lake; in this regard, we used the Single Normal Equation Simulation (SNESIM) algorithm of the MPS geostatistical method using Training Images (TIs) instead of variograms. The required datasets and information for this modeling was provided using the field measurements of the fracture orientations and dips, as well as the outcrop photographs. The outcomes of these models can be used in predicting the salinity distribution in the surrounding area . Therefore, through the SNESIM algorithm, TIs obtained from the outcrop photographs, and direct measurements, 100 RFN realizations were generated at each station. These realizations were then averaged to predict the locations with higher and lower fracture probabilities and to assess the general trend of the fracture distributions.

Soil Temperature (ST) is critical for environmental applications. While its measurement is often difficult, estimation from environmental parameters has shown promise. The purpose of this study was to model ST in cold season  from soil properties and environmental parameters. This study was conducted as a pot experiment in Ardebil, Iran. Automatic thermal sensors were installed at 5 and 10 cm depths. Besides, soil properties and environmental parameters were determined based on field and laboratory works. Machine learning methods including Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Interface System (ANFIS) were used for modeling ST. The air temperature was observed as the most effective factor in ST modeling. The relationship between soil and air temperature was stronger at 5 cm depth compared to 10 cm. The R2 between soil and air temperature was higher in the absence of sunlight than in its presence. The prediction of ANFIS (R2= 0.96 and MAPE= 10.5) was closer to the observed ST values compared to the ANN (R2= 0.91 and MAPE= 35) and MLR (R2= 0.57 and MAPE= 41). The results revealed the advantage of ANFIS method for ST modeling. This approach can be applied for soil depths and locations with data gap.

Species diversity is a combination of species richness with species evenness. It helps us differentiate between communities or areas that have the same number of different species, but not in the same abundance. The spatial distribution pattern of plant species is an important topic in plant ecology, the assessment of which is an essential part of research into plant communities. This study aimed to investigate the differences between richness, diversity, and evenness indices obtained for random, uniform, and clumped distribution patterns. For this investigation, three plant distribution patterns were simulated and then random sampling was performed with 10 plots of the size 1 m2 for each pattern, each with five repeats for greater accuracy. Finally, the number of species, the Margalef index, and the Menhinick index for richness, the Simpson index and the Shannon-Wiener index for diversity, and the Simpson index, the Shannon-Wiener index, and the Pielou index for evenness were computed and compared. The results of the analysis of variance showed a significant difference between richness, diversity, and evenness indices in different distribution patterns. Accordingly, Shannon-Wiener diversity is the best index when the management objective is more concerned with rare species. Also, Simpson’s diversity, would be more appropriate where dominant species are more important.

The present study was conducted to assess the changes in Iran's drought severity for the duration of 1964 to 2014. For this purpose, the spatial distribution of drought was annually and seasonally evaluated using climate data from 26 synoptic stations over Iranian territory based on standardized precipitation index (SPI). In this regard, the climate classification in the study area was performed applying Dermartone method. Moreover, the annual and seasonal values of SPI were calculated for the whole Iranian territory and each climate region. The SPI index for monotonic trend was calculated in each climate region utilizing Mann-Kendall and Theil Sen estimators. Our results implied that the minimum and maximum values of SPI (-3.86 and 2.89, respectively) appeared during spring in dry and Mediterranean climate regions. In addition, the maximum and minimum values of annual continuous SPI appeared in 1999-2004 and 1974-1982, respectively. The maximum and minimum values of seasonal continuous SPI also appeared for a duration of 9 years during summers respectively in the period of 1977 to 1985 and springs in the period of 2006 to 2014. The application of Mann-Kendall and Theil Sen estimator analyses revealed that 9 out of 26 stations had a significant decreasing SPI trend. Moreover, the annual and seasonal time series in moderately dry regions indicated a meaningfully decreasing trend in winter and annual SPI. Additionally, winter, spring, autumn and annual values of SPI had a meaningful decreasing trend in the Mediterranean climate region. In dry and very wet climate regions, no obvious trend was detected for the annual or seasonal SPI index.

Soil quality indicators are measurable characteristics of the soil affecting the soil capacity for crop production or environmental performance. Among these indicators, air capacity (AC) and relative field capacity (RFC) are believed to be the most important ones. To select the best combination that affects soil physical quality indicators (AC and RFC), we employed a hybrid algorithm: an ant colony organization (ACO) in combination with an artificial neural network (ANN). Multiple linear regression and support vector regression models were constructed for the comparison of performances. The results obtained from running ACO-ANN to select the best combination revealed that a combination with four input variables, including soil organic matter, clay, carbonate calcium equivalent, and bulk density, had the lowest error. The R2 values in the ACO-ANN model for the AC and RFC predictions were respectively 0.91 and 0.95 whereas they were 0.75 and 0.53 respectively in support vector regression model, and 0.54 and 0.53 in the multiple linear regression model. Since the results obtained from the ACO-ANN algorithm are acceptable, this algorithm could be applied to other locations of the world in order to tackle environmental problems.  The results form sensitivity analysis for the ANN model showed that carbonate calcium equivalent and clay content had the highest and the lowest effects on AC and RFC indicators, respectively.

The effects of climate changes are generally expected to reduce the growth and survival of forests, particularly in semiarid regions. This study was conducted to demonstrate the effects of runoff harvesting technique on the reduction in forest tree dieback phenomenon in the Zagros forests, Iran. In order to evaluate this hypothesis, runoff was harvested through the crescent shapedtrench (CST) affecting soil moisture storage. The selected forest site is located in Kalehzard, Kermanshah, in Zagros region, western Iran. The experiment was a randomized complete block design with four treatment plots: trench with protection (T+PT), protection treatment (PT), trench without protection (T-PT), and control treatment (CT). Three years of comparative monitoring explored that dieback rate increased followed by the reduction in the average annual precipitation and worsening temperature conditions. Hence, T+PT treatment led into a significant reduction in dieback rate (37.7 tree ha-1) and re-growing of certain stands (including total 18.0 tree ha-1) compared to CT. Furthermore, our results demonstrated that T-PT contributed to lower level on dieback reduction (6 tree ha-1) revealing the importance of protection measure which is so effective for the built trench. As a result, micro-catchment could provide soil moisture for the enhancement of forest in semiarid regions, such as Zagros areas.

The present study was conducted to identify the synoptic patterns that could display the origin of dust-storm over southern provinces of Iran. In order to design these patterns, we selected 17 weather stations whose data-sets of visibility per meter for one decade (2000 to 2009) were provided from Meteorological Organization of Iran. This paper used daily data of Sea Level Pressure (SLP) from NCEP/NCAR for designing the synoptic patterns as composite maps for each group. The extraction of dust records from the stations and consequently the evaluation of dust-storms frequency were our primitive aims. According to results, there were totally 345 dust-storms from 2000 to 2009 in the study area. Moreover, our results revealed that the dust-storms could be classified to three groups, including pervasive, semi-pervasive, and small ones based on Dust Stations (DS) frequency. All the dust-storms comprise 2 to 41 days. This paper illustrated the patterns for all the peak dusty days of the above-mentioned groups by extracting the sea level pressure data. According to the findings, the synoptic patterns demonstrated that the Pakistan Low is an important thermal low in Southern Asia, which pumped dust from 5 routes originated from Sahel, Southern Hijaz, and Mesopotamia Plain, toward the study area, particularly during the pervasive ones. This low appeared weak and disappeared during semi-pervasive and small dust-storms.