Delineation of flooding risk hotspots based on digital elevation model,calculated and historical flooding extents: the case of Ouagadougou

Delineation of flood risk hotspots can be considered as one of the first steps in an integrated methodology for urban flood risk management and mitigation. This paper presents a step-by-step methodology in a GIS-based framework for identifying flooding risk hotspots for residential buildings. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. The novelty of this paper consists in the fact that the flood-prone areas (the TWI thresholds) are identified through a maximum likelihood method (MLE) based both on inundation profiles calculated for a specific return period (T_R), and on information about the extent of historical flooding in the area of interest. Furthermore, Bayesian parameter updating is employed in order to estimate the TWI threshold by employing the historical extent as prior information and the inundation map for calculating the likelihood function. For different statistics of the TWI threshold, the map of potentially flood-prone areas is overlaid with the map of residential urban morphology units in order to delineate the residential flooding risk urban hotspots. Overlaying the delineated urban hotspots with geo-spatial census datasets, the number of people affected by flooding is estimated. These kind of screening procedures are particularly useful for locations where there is a lack of detailed data or where it is difficult to perform accurate flood risk assessment. In fact, an application of the proposed procedure is demonstrated for the identification of urban flooding risk hotspots in the city of Ouagadougou, capital of Burkina Faso, a city for which the observed spatial extent of a major flood event in 2009 and a calculated inundation map for a return period of 300 years are both available.     

Seismic reliability assessment and the nonergodicity in the modelling parameter uncertainties

Modelling uncertainty can significantly affect the structural seismic reliability assessment. However, the limit state excursion due to this type of uncertainty may not be described by a Poisson process as it lacks renewal properties with the occurrence of each earthquake event. Furthermore, considering uncertainties related to ground motion representation by employing recorded ground motions together with modelling uncertainties is not a trivial task. Robust fragility assessment, proposed previously by the authors, employs the structural response to recorded ground motion as data in order to update prescribed seismic fragility models. Robust fragility can be extremely efficient for considering also the structural modelling uncertainties by creating a dataset of one-to-one assignments of structural model realizations and as-recorded ground motions. This can reduce the computational effort by more than 1 order of magnitude. However, it should be kept in mind that the fragility concept itself is based on the underlying assumption of Poisson-type renewal. Using the concept of updated robust reliability, considering both the uncertainty in ground motion representation based on as-recorded ground motion and non ergodic modelling uncertainties, the error introduced through structural reliability assessment by using the robust fragility is quantified. It is shown through specific application to an existing RC frame that this error is quite small when the product of the time interval and the standard deviation of failure rate is small and is on the conservative side.     

Using magnetic susceptibility measurements to differentiate soil drainage classes in central Iran

Studia Geophysica et Geodaetica - We examine the potential of magnetic susceptibility measurements to discriminate different soil drainage classes in the Gandoman region, central Iran. Four soil...     

The relationship among environmental variables,jellyfish and non‐gelatinous zooplankton: A case study in the north of the Gulf of Oman

Processes underlying the temporal and spatial variations observed in the distribution of jellyfish and non‐gelatinous zooplankton in the Gulf of Oman are not well understood. This information gap is clearly a major issue in controlling the harmful blooms of jellyfish and non‐gelatinous zooplankton. Samples of jellyfish and non‐gelatinous zooplankton were collected from six stations in Chabahar Bay and three stations in Pozm Bay within four seasons. At each station, environmental variables were also recorded from bottom and surface water. A total of 83 individuals of medusae representing four species of Scyphozoa (i.e., Cyanea nozakii, Chrysaora sp., Pelagia noctiluca, Catostylus tagi) and species of Hydrozoa (i.e., Diphyes sp., Rhacostoma sp., Aequorea spp.) were observed in the study area. A total of 70,727.25 individuals/m^?3 of non‐gelatinous zooplankton dominated by copepods and cladocerans were collected in nine stations within the four seasons. The results of a RELATE analysis yielded no significant association between species composition for jellyfish and non‐gelatinous zooplankton. Among environmental variables, water transparency, nitrite concentration, water depth and temperature were better associated with the total variation in jellyfish species composition than with that of non‐gelatinous zooplankton. Dissolved oxygen, pH, and phosphate concentration were significant environmental variables associated with the variation in the spatial and temporal distribution patterns of non‐gelatinous zooplankton assemblages. Although some jellyfish species (i.e., Rhacostoma sp., Pelagia noctiluca, Catostylus tagi) occur independently of non‐gelatinous zooplankton assemblages, other jellyfish (i.e., Chrysaora sp., Aequorea spp., Cyanea nozakii, Diphyes sp.) are strongly correlated with non‐gelatinous zooplankton assemblages.     

Disaggregation-based response weighting scheme for seismic risk assessment of structures

The results of seismic hazard disaggregation can be used to assign relative weights to a given ground motion record based on its corresponding magnitude, distance and deviation from the ground motion prediction model (epsilon) in order to make probability-based seismic assessments using non-linear dynamic analysis. In this paper, the implications of using the weighted ground motion records are investigated in terms of the mean annual frequency of exceedance of the critical component-based demand to capacity ratio in an existing reinforced concrete structure using both the peak ground acceleration and the first-mode spectral acceleration as intensity measures. It is demonstrated how site-specific seismic hazard disaggregation can be used in order to obtain the conditional probability distribution for a relevant ground motion characteristic given the chosen intensity measure. Distinguished by the amount of structural analysis required, two alternative non-linear dynamic analysis procedures, namely the cloud and the stripes method are implemented. The weighted cloud and the weighted stripes methods are then introduced as analysis procedures which modify the structural response to the selected ground motion records by employing the information provided from the seismic hazard analysis. It is demonstrated that the resulting annual frequencies based on weighted records are comparable to those obtained by using vector-valued intensity measures, while requiring less computational effort.     

Frequency analysis of climate extreme events in Zanjan, Iran

In this study, generalized extreme value distribution (GEV) and generalized Pareto distribution (GPD) were fitted to the maximum and minimum temperature, maximum wind speed, and maximum precipitation series of Zanjan. Maximum (minimum) daily and absolute annual observations of Zanjan station from 1961 to 2011 were used. The parameters of the distributions were estimated using the maximum likelihood estimation method. Quantiles corresponding to 2, 5, 10, 25, 50, and 100 years return periods were calculated. It was found that both candidate distributions fitted to extreme events series, were statistically reasonable. Most of the observations from 1961 to 2011 were found to fall within 1–10 years return period. Low extremal index (θ) values were found for excess maximum and minimum temperatures over a high threshold, indicating the occurrence of consecutively high peaks. For the purpose of filtering the dependent observations to obtain a set of approximately independent threshold excesses, a declustering method was performed, which separated the excesses into clusters, then the de-clustered peaks were fitted to the GPD. In both models, values of the shape parameters of extreme precipitation and extreme wind speed were close to zero. The shape parameter was less negative in the GPD than the GEV. This leads to significantly lower return period estimates for high extremes with the GPD model.     

Climate-resilient agricultural water management to alleviate negative impacts of global warming in rice production systems

Theoretical and Applied Climatology - Improving the economic productivity of limited available freshwater through producing more rice with less water is essential to sustain paddy production...     

Late Santonian–Early Maastrichtian calcareous nannofossils from marly deposits near Kerman (Central Iran)

For the first time, the calcareous nannofossils of marly deposits near Kerman (Bardsir area) have been studied. This study presents the integrated (calcareous nannofossils) biostratigraphy of the Bardsir section in the Kerman basin, Central Iran. In most parts of Central Iran, the Upper Cretaceous sequence is complete and continuous and is divided into two groups: Cenomanian–Touronian flysch and Campanian–Maastrichtian flysch. Flyschs composed of sets of green marl sequences (Coniacian–Santonian) have been separated to reduce the basin depth and refer to the relative calm. Bardsir is located 57.6 km from Kerman (Central Iran). The lithology of this area includes light green marl with layers of calcareous siltstone, limestone, and flysch rocks. In this study, 24 samples were taken and prepared with smear slide. Most species were photographed with a light microscope. As a result of this study, 30 genera and 42 species of nannofossils have been identified. A high-resolution calcareous nannofossil biostratigraphic study has been carried out, allowing the division of the studied section into eight biozones of Late Santonian to Early Maastrichtian age (CC17–CC24).