An investigation of trends in precipitation volume for the last three decades in different regions of Fars province, Iran

Under condition of climate changes as global warming, monitoring and detecting trend of precipitation volume is essential and will be useful for agricultural sections. Considering the fact that there were not enough research related to precipitation volume, this study aimed to determine trends in precipitation volume, monthly and annually in different regions of Fars province for the last three decades (33?years period; 1978–2010). Fars province is located in arid and semi-arid regions of Iran, and it plays an important role in agricultural production. Inverse distance weighting interpolation method was used to provide precipitation data for all regions. To analyze the trends of precipitation volume, Mann–Kendall test, Sen’s slope estimator, and 10-year moving average low-pass filter (within time series) were used. The negative trends were identified by the Sen’s slope estimator as well as Mann–Kendall test. However, all the trends were insignificant at the surveyed confidence level (95%). With regards to the application of 10-year moving average low-pass filter, a considerable decreasing trend was observed after around year 1994. Since one of the most important restrictions in agricultural development of the Fars province is lack of sufficient water resources, any changes onward to lack of sufficient precipitation impose impressive pressure and stress on valuable resources and subsequently agricultural production.     

Assessing building vulnerability to tsunami using the PTVA-3 model: A case study of Chabahar Bay,Iran

Chabahar Bay, in southeastern Iran, lies at the north of the Gulf of Oman and close to the Makran Subduction Zone, which makes it a region that is susceptible to tsunamis. This bay has an increasingly important role in Iran’s international trade, and therefore the assessment of the regional vulnerability to the effects of a tsunami is vital. Based on both the details of historical events and the results of numerical modeling of the propagation pattern of a tsunami in this region, this study assessed the vulnerability of buildings within the Chabahar Bay region to a tsunami event. The Papathoma Tsunami Vulnerability Assessment (PTVA) model was used to calculate a relative vulnerability index (RVI) for the affected buildings based on their physical and structural characteristics. The results showed that in a postulated worst-case-scenario tsunami event in the Chabahar Bay area, approximately 60 % of the residential buildings would be affected, a level of damage that is categorized as “Average” in the RVI classification. Overall, the economic losses related to the damage of residential buildings due to a tsunami in the Chabahar Bay area are anticipated to be the equivalent of US$ 16.5 million.     

PM10 Source Apportionment in Ahvaz,Iran, Using Positive Matrix Factorization

Source apportionment of particulate matter <10 µm in diameter (PM₁₀), having considerable impacts on human health and the environment, is of high priority in air quality management. The present study, therefore, aimed at identifying the potential sources of PM₁₀ in an arid area of Ahvaz located in southwest of Iran. For this purpose, we collected 24‐h PM₁₀ samples by a high volume air sampler. The samples were then analyzed for their elemental (Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, Se, Si, Sn, Sr, Li, Ti, V, Zn, Mo, and Sb) and ionic (NH, Cl^?, NO, and SO) components using inductively coupled plasma optical emission spectrometry and ion chromatography instruments, respectively. Eight factors were identified by positive matrix factorization: crustal dust (41.5%), road dust (5.5%), motor vehicles (11.5%), marine aerosol (8.0%), secondary aerosol (9.5%), metallurgical plants (6.0%), petrochemical industries and fossil fuel combustion (13.0%), and vegetative burning (5.0%). Result of this study suggested that the natural sources contribute most to PM₁₀ particles in the area, followed closely by the anthropogenic sources.     

Probabilistic life cycle analysis model for evaluating electric power infrastructure risk mitigation investments

One effect of climate change may be increased hurricane frequency or intensity due to changes in atmospheric and geoclimatic factors. It has been hypothesized that wetland restoration and infrastructure hardening measures may improve infrastructure resilience to increased hurricane frequency and intensity. This paper describes a parametric decision model used to assess the tradeoffs between wetland restoration and infrastructure hardening for electric power networks. We employ a hybrid economic input–output life-cycle analysis (EIO-LCA) model to capture: construction costs and life-cycle emissions for transitioning from the current electric power network configuration to a hardened network configuration; construction costs and life-cycle emissions associated with wetland restoration; and the intrinsic value of wetland restoration. Uncertainty is accounted for probabilistically through a Monte Carlo hurricane simulation model and parametric sensitivity analysis for the number of hurricanes expected to impact the project area during the project cycle and the rate of wetland storm surge attenuation. Our analysis robustly indicates that wetland restoration and undergrounding of electric power network infrastructure is not preferred to the “do-nothing” option of keeping all power lines overhead without wetland protection. However, we suggest a few items for future investigation. For example, our results suggest that, for the small case study developed, synergistic benefits of simultaneously hardening infrastructure and restoring wetlands may be limited, although research using a larger test bed while integrating additional costs may find an enhanced value of wetland restoration for disaster loss mitigation.     

Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran

Natural Hazards - The forest fire hazard mapping using the accurate models in the fire-prone areas has particular importance to predict the future fire occurrence and allocate the resources for...     

Sediment yield assessment at basin scale using geospatial technique

Soil erosion and sediment yield from catchments are key limitations to achieving sustainable land use and maintaining water quality in nature. One of the important aspects in protecting the watershed is evaluation of sediment produced by statistical methods. Controlling sediment loading in protecting the watershed requires knowledge of soil erosion and sedimentation. Sediment yield is usually not available as a direct measurement but is estimated using geospatial models. One of the geospatial models for estimating sediment yield at the basin scale is sediment delivery ratio (SDR). The present study investigates the spatial SDR model in determining the sediment yield rate considering climate and physical factors of basin in geographic information system environment. This new approach was developed and tested on the Amammeh catchments in Iran. The validation of the model was evaluated using the Nash Sutcliffe efficiency coefficient. The developed model is not only conceptually easy and well suited to the local data needs but also requires less parameter, which offers less uncertainty in its application while meeting the intended purpose. The model is developed based on local data. The results predict strong variations in SDR from 0 in to 70 % in the uplands of the Basin.     

Forecasting hurricane-induced power outage durations

Accurate estimates of the duration of power outages caused by hurricanes prior to landfall are valuable for utility companies and government agencies that wish to plan and optimize their restoration efforts. Accurate pre-storm estimates are also important information for customers and operators of other infrastructures systems, who rely heavily on electricity. Traditionally, utilities make restoration plans based on managerial judgment and experience. However, skillful outage forecast models are conducive to improved decision-making practices by utilities and can greatly enhance storm preparation and restoration management procedures of power companies and emergency managers. This paper presents a novel statistical approach for estimating power outage durations that is 87 % more accurate than existing models in the literature. The power outage duration models are developed and carefully validated for outages caused by Hurricanes Dennis, Katrina, and Ivan in a central Gulf Coast state. This paper identifies the key variables in predicting hurricane-induced outage durations and their degree of influence on predicting outage restoration for the utility company service area used as our case study.     

Evaluation of Cretaceous–Paleogene boundary based on calcareous nannofossils in section of Pol Dokhtar, Lorestan, southwestern Iran

The Pol Dokhtar section of southern Lorestan, faulted Zagros range of southwestern Iran, contains one of the most complete Early Campanian to Danian sequences. The lack of a good fundamental paleontological study is a strong motivation for investigating calcareous nannofossils in southwestern Iran. The majority of the section is made of shale, marl, and partly of marly limestone and clay limestone, respectively. As a result of this study, 24 genera and 45 species of nannofossils have been identified and presented for the first time. This confirms the existence of biozone CC18 of zonation scheme of Sissingh (Geologie en Minjbouw 56:37–65, 1977) to NP1 of zonation of Martini, which suggests the age of Early Campanian to Danian. All Early Campanian to Danian calcareous nannofossil biozones from CC18 (equivalent to the Aspidolithus parcus zone) to NP1 (equivalent to the Markalius inversus zone) are discussed. Also, the zonal subdivision of this section based on calcareous nannofossils has shown continuity in Cretaceous/Paleocene boundary in south part of Lorestan Province. We can also learn about the predominant conditions of the studied sedimentary basin that was in fact part of the Neotethys basin with the existence of indexed species calcareous nannofossils that indicate warm climate and high water depths of the basin in low latitudes.     

Modeling and Prediction of Habitat Suitability for Ferula gummosa Medicinal Plant in a Mountainous Area

Natural Resources Research - Habitat suitability modeling and mapping are important aspects of long-term strategies for sustaining plant ecosystems. In this study, seven state-of-the-art machine...     

Numerical simulation of wave generated by landslide incidents in dam reservoirs

In this work, a two-dimensional fourth-order Boussinesq-type numerical model is applied to estimate the impact of landslide-generated waves in dam reservoirs. This numerical model has recently been extended for simulating subaerial landslides. The extended model is validated using available three-dimensional experimental data, and a good agreement is obtained. The numerical model is then employed to investigate the impact of landslide-generated waves in two real cases, the Maku and Shafa-Roud dam reservoirs in the northwestern and the north of Iran, respectively. Generated wave heights, wave run-up, maximum wave height above dam crest, and dam overtopping volume have been estimated for each case. The amplitude of generated waves about 18 and 31 m and the volume of dam overtopping up to 80,000 m³ emphasize the importance of the estimation of the landslide-generated waves in dam sites.