Development of a Land Subsidence Forecasting Model Using Small Baseline Subset—Differential Synthetic Aperture Radar Interferometry and Particle Swarm Optimization—Random Forest (Case Study: Tehran-Karaj-Shahriyar Aquifer,Iran)

Doklady Earth Sciences - Land subsidence, as a dangerous environmental issue, causes serious damages to farms and urban infrastructure. In this regards, this research was conducted with aimed to...     

Uncertainty and sensitivity analysis of a travel-time distribution-based stream water electrical conductivity model

In this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2-year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute-balance approach and (2) a water age-based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age-based model than the solute-balance model; 4. using time-variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS-TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute-balance model, the rainfall concentration C_rain and in the age-based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute-balance model. Little change in parameter sensitivity over time was found for the age-based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute-balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.     

Causes and consequences of recent floods in the Golestan catchments and Caspian Sea regions of Iran

In August 2001, the worst flash flooding event of the Caspian Sea regions in over two centuries claimed over 300 lives after a weekend of heavy rainfall and brought about a devastating disaster in the Mother-Soo catchment, province of Golestan, Iran. As a result of this event, a series of site investigation were carried out to identify the pertinent factors that led to a flood of this magnitude. This paper identifies the fundamental causes of the frequent floods and debris flow occurrence in the area prone to flooding and analyzes the main runoff mechanism of these events. The maximum observed 24-h rainfall depths and maximum peak discharges at the existing gauges were compared with the depths of rainfall and the peak values corresponding to the August 2001 flood respectively. For the majority of the rain gauges, the rainfall depth exceeded those of historical recorded events. In Golestan dam, an increase of 7.5 times the maximum value observed in the past 20 years was noted. The flood height was 10–15 m while passing through the middle subcatchment area of Golestan National Park. The preliminary evaluation indicates the existence of bare soil in the catchment, movable material, steep slopes, high rainfall intensity, deterioration of pasture and forest land, and inappropriate agriculture and development practices as well as climate change were the main factors for the occurrence and the extent of the August 2001 disaster. Finally, due to the likelihood of flooding and debris flow events in future, some countermeasures are proposed.     

Palaeoecology of well-preserved coral communities in a siliciclastic environment from the Late Pleistocene (MIS 7), Kish Island, Persian Gulf (Iran): the development of low-relief reef frameworks (biostromes) in increasingly restricted environments

Major changes in community structure and depositional relief of high-latitude coral communities in the southern Persian Gulf between marine isotope stage (MIS) 7 and the present day suggest that the area has become increasingly restricted. Corals and bivalves from outcrops on Kish Island, Iran, were identified in order to interpret the Late Pleistocene palaeoenvironmental setting. U/Th disequilibrium dating was used to constrain the ages of the stratigraphic units. During MIS 7, two coral-bearing sequences were deposited on what is now Kish Island. The lower sequence is dated as MIS 7.5 and changes laterally from an assemblage dominated by Cyphastrea sp. and Platygyra daedalea in the west to one characterized by branching Montipora in the east. By contrast, the upper sequence, dated as MIS 7.1, transitions from an assemblage dominated by platy Montipora in the west to a diverse assemblage of Platygyra and other faviids in the east. The assemblages of both sequences are within a marl matrix and bounded by thin lithified mollusc-rich layers. Corals and bivalves indicate that the sequences were deposited on gentle slopes in sheltered environments less than 20 m deep. The MIS 7 deposits may be classified as coral carpets or biostromes that developed a low-relief framework. During MIS 5, coral communities were no longer framework building and are now limited to an Acropora-rich layer of coral rubble that covers large parts of the island, and two small incipient reefs with sparse faviids. Similarities between the MIS 5 and modern nearshore coral communities suggest that the environmental conditions during MIS 5 were comparable to those of today. The late Pleistocene coral carpets and non-framework coral communities of the southern Persian Gulf may serve as models for coral biostromes in the fossil record, which formed under restricted environmental conditions such as elevated terrigenous input, high turbidity, and strong seasonal changes in temperature and/or salinity.     

Seasonal variability of aerosol optical properties in Darwin,Australia

This paper investigates the annual cycle in aerosol optical thickness (AOT) and Angstrom exponent in Darwin, Australia, a coastal site in the Tropical Warm Pool where the major aerosol sources are biomass burning and sea salt. We have used radiometer measurements from the Tropical Western Pacific Atmospheric Radiation Measurement facility for the period March 2002–June 2003. Strong seasonal cycles in AOT and Angstrom exponent were observed, peaking during the burning season (May–November). Investigation of the spectral dependence of optical thickness showed that the Angstrom formula can be satisfactorily fitted to the AOT data during the burning season but not on summer and autumn afternoons due to the presence of sea salt aerosols.     

Bayesian spatio-temporal analysis of stomach cancer incidence in Iran, 2003–2010

Stomach cancer is the second most common cancer in Iranian men and the fourth most common cancer in Iranian women. The incidence rate of this cancer in Iranian men is almost 2.5 times that in women. The objective of this study was to investigate the trend of stomach cancer incidence rate in Iran in an 8-year time period (2003–2010) and also update the incidence estimates of stomach cancer. Data from a total number of 41,830 patients diagnosed with stomach cancer according to the International Classification of Diseases (C16) in 2003–2010 were analyzed. We used Bayesian spatial and Bayesian spatio-temporal models to study the relative risk and trend of stomach cancer incidence rate in Iran. Out of 41,830 stomach cancer registered patients, 72% were male. The average smoothed SIRs were 0.79, 0.82 and 0.78 for the general, female and male population, respectively. This shows a nearly stable incidence rate. The northwest of Iran had the highest incidence rate of stomach cancer. The trend of this rate was declining to the lowest rate in the southeast of the country. The estimated values of coefficient of the trend term for general, female and male population in this model were 0.0085, ? 0.018 and 0.0041, which indicate almost a stable fixed trend. The map of temporal trends also showed that although the incidence rate of this cancer is to some extent stable, in general, in the central and in the east of the country the incidence relative risk has increased over time. The prevalence of Helicobacter pylori infection, Lower socioeconomic status and Iodine deficiency were speculated to be relevant factors for the high incidence rates of stomach cancer in the northwest of Iran. Preventive measures in the north and northwest of Iran could have an effect on controlling this cancer in these areas.     

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.