Deep-sea sediments in the eastern equatorial Atlantic off the African coast and meteorological flow patterns over the Sahel

Deep-sea mineral dust deposits are a powerful tool allowing to deduct atmospheric flow patterns. Here deep-sea sediments from the eastern tropical Atlantic were used to derive climatic conditions over the African continent where in summer the dust is lifted in the transition zone of the Sahara and the Sahel. The meteorological process causing this aeolian dust injection are the squall lines, the rain bearing disturbances of the Sahel. They produce a high surface wind velocity area in their northern vicinity. The deep-sea deposits of sediments and their spatial patterns allowed to conclude that the dust raising mechanism connected with the squall lines were similar for present day conditions and for the time of the last glacial maximum. The Saharan trades and the African monsoon had about the same extension in either time slice coupled with an identical travel belt of the squall lines. However, the intensity and the frequency of the squall lines were lower in glacial times.

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Zusammenfassung Tiefseesedimente liefern interessante und wertvolle Aufschlüsse über atmosphärische Strömungsbedingungen. Hier werden die Sedimente aus dem Gebiet des östlichen tropischen Nordatlantiks verwendet, um klimatische Angaben über die Strömungsverhältnisse über dem afrikanischen Kontinent zu gewinnen, da die Quelle des Staubes auf dem Kontinent im Übergangsbereich zwischen der Sahara und dem Sahel zu suchen ist. Die meteorologische Ursache des systematischen Staubeintrages in die Atmosphäre sind die regenbringenden sommerlichen Störungen des Sahel («>squall lines»<), die in ihrem nördlichen Randbereich ein Gebiet hoher Windgeschwindigkeit am Boden aufweisen, so daß es hier regelmäßig zu Staubaufwirbelungen kommt. Mit Hilfe der räumlichen und zeitlichen Verteilung der Tiefseesedimente konnte schon früher gezeigt werden, daß während der Periode des letzten Hochglazials die Lage der Grenzzone zwischen Passat und afrikanischem Monsun sich im Vergleich zur Gegenwart nicht verändert hat. Hier wird nun zusätzlich deutlich, daß sowohl die Intensität als auch die Frequenz der «>squall lines»< zum Höhepunkt der Vereisung geringer was als heute.

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Résumé Les sédiments amenés par le vent dans les mers profondes fournissent de précieuses indications sur la distribution des courants atmosphériques. Dans cette note, les sédiments de la partie orientale de l'Atlantique tropical sont examinés en vue de déduire les conditions climatiques régnant sur le continent africain, où la source de la poussière est située entre le Sahara et le Sahel. En effet, l'origine météorologique des apports éoliens sont les pertubations pluvieuses (lignes de grains) qui sont accompagnées sur leur bordure nord de vents de grande vitesse au sol. La distribution des sédiments sur le fond océanique a déjà permis de conclure que le mécanisme de transport était, lors de la dernière glaciation (18.000 ans), semblable à ce qu'il est aujourd'hui. Toutefois, l'intensité et la frequence des perturbations étaient plus faibles qu'aujourd'hui.

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Mapping susceptibility of rainfall-triggered shallow landslides using a probabilistic approach

To prepare a landslide susceptibility map is essential to identify hazardous regions, construct appropriate mitigation facilities, and plan emergency measures for a region prone to landslides triggered by rainfall. The conventional mapping methods require much information about past landslides records and contributing terrace and rainfall. They also rely heavily on the quantity and quality of accessible information and subjectively of the map builder. This paper contributes to a systematic and quantitative assessment of mapping landslide hazards over a region. Geographical Information System is implemented to retrieve relevant parameters from data layers, including the spatial distribution of transient fluid pressures, which is estimated using the TRIGRS program. The factor of safety of each pixel in the study region is calculated analytically. Monte Carlo simulation of random variables is conducted to process the estimation of fluid pressure and factor of safety for multiple times. The failure probability of each pixel is thus estimated. These procedures of mapping landslide potential are demonstrated in a case history. The analysis results reveal a positive correlation between landslide probability and accumulated rainfall. This approach gives simulation results compared to field records. The location and size of actual landslide are well predicted. An explanation for some of the inconsistencies is also provided to emphasize the importance of site information on the accuracy of mapping results.     

Axial service limit state analysis of drilled shafts using probabilistic approach

Drilled shafts are, typically, designed by considering the axial ultimate limit state. In this design methodology, the axial displacement requirements are verified once the design is completed. As an alternative, drilled shafts may be designed by considering the axial service limit state. Service limit state foundation design is more efficient when done using the load and resistance factor design (LRFD) approach. Furthermore, reliability may be rationally incorporated into the design process that utilizes the LRFD method. In this paper, we develop probabilistic approaches for axial service limit state analysis of drilled shafts. The variability of shaft-soil interface properties is modeled by lognormal probability distribution functions. The probability distributions are combined with a closed-form analytical relationship of axial load-displacement curves for drilled shafts. The closed-form analytical relationship is derived based upon the “t–z” approach. This analytical relationship is used with the Monte Carlo simulation method to obtain probabilistic load-displacement curves, which are analyzed to develop methods for determining the probability of drilled shaft failure at the service limit state. The developed method may be utilized to obtain resistance factors that can be applied to LRFD based service limit state design.     

A neurocomputing approach to the forecasting of monthly maximum temperature over Kolkata, India using total ozone concentration as predictor

The association between the monthly total ozone concentration and monthly maximum temperature over Kolkata (22.56° N, 88.30° E), India, has been explored in this paper. For this, the predictability of monthly maximum temperature based on the total ozone as predictor is investigated using Artificial Neural Network. The presence of persistence and similar cyclic patterns are revealed through autocorrelation and cross-correlation coefficients. Common cycles of length 12 and 6 have been identified through periodogram. Hence, a predictive model has been generated by Artificial Neural Network in the form of Multi Layer Perceptron (MLP) using scaled conjugate gradient learning with sigmoid non-linearity. After training and testing the network, an MLP with total ozone of month n as predictor and maximum temperature of month (n + 1) as the target output is found as the best model. Performance of the model has been judged statistically. Finally, the MLP model has been compared with linear and non-linear regressions and the efficiency of MLP has been established over the regression models.     

Multi-model approach to petroleum resource appraisal using analytic methodologies for probabilistic systems

The geologic appraisal model that is selected for a petroleum resource assessment depends upon purpose of the assessment, basic geologic assumptions of the area, type of available data, time available before deadlines, available human and financial resources, available computer facilities, and, most importantly, the available quantitative methodology with corresponding computer software and any new quantitative methodology that would have to be developed. Therefore, different resource assessment projects usually require different geologic models. Also, more than one geologic model might be needed in a single project for assessing different regions of the study or for cross-checking resource estimates of the area. Some geologic analyses used in the past for petroleum resource appraisal involved play analysis. The corresponding quantitative methodologies of these analyses usually consisted of Monte Carlo simulation techniques. A probabilistic system of petroleum resource appraisal for play analysis has been designed to meet the following requirements: (1) includes a variety of geologic models, (2) uses an analytic methodology instead of Monte Carlo simulation, (3) possesses the capacity to aggregate estimates from many areas that have been assessed by different geologic models, and (4) runs quickly on a microcomputer. Geologic models consist of four basic types: reservoir engineering, volumetric yield, field size, and direct assessment. Several case histories and present studies by the U.S. Geological Survey are discussed.This paper was presented at Emerging Concepts. MGLIS-87, Redwood City, California, 13–15 April 1987.     

Identification of wet-prone regions over Northwest Himalaya using high-resolution satellite seasonal estimates

Natural Hazards - The present study aims to evaluate four satellite estimates, namely CMORPH v0.x, PERSIANN-CDR, TMPA-V7 and TMPA-V7-RT, over four seasons (during 2003–2017) against ground...     

Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA–GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years’ worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA–GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA–ANN models. The results indicate that the SARIMA–GEP model (R ²=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA–ANN (R ²=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA–GEP over the SARIMA–ANN model.     

Toward the probabilistic forecasting of cyclone-induced marine flooding by overtopping at Reunion Island aided by a time-varying random-forest classification approach

Natural Hazards - In 2017, Irma and Maria highlighted the vulnerability of small islands to cyclonic events and the necessity of advancing the forecast techniques for cyclone-induced marine...     

Holocene environmental change in the West African Sahel: sedimentological and mineral‐magnetic analyses of lake sediments from Jikariya Lake,northeastern Nigeria

Previous studies have demonstrated long‐term changes in effective moisture in sub‐Saharan Africa. Here, we reconstruct Holocene environments using a ～7 m lake‐sediment sequence recovered from the northeastern Nigerian Sahel and attempt to distinguish basin‐specific changes from regional climatic variations. The sequence was analysed for sedimentological properties, mineral magnetism and pollen, and dated by ¹³⁷Cs, ²¹⁰Pb excess and ¹⁴C. Extremely arid conditions of the terminal Pleistocene ended ca. 11 500 cal. BP (calendar years) when climate ameliorated and a lake developed until the occurrence of an arid event leading to lake desiccation at ～11 200 cal. BP. Following this, climate ameliorated and a water body re‐emerged. Very wet conditions predominated 11 200–5600 cal. BP, followed by drought between 5600 and 5500 cal. BP and a return to moderate humidity from 5500 to 4000 cal. BP. After 4000 cal. BP, a marked deterioration occurred, culminating in lake desiccation at ca. 800 cal. BP. After this time the climate remained generally dry and the re‐emerging lake was shallow. Comparison of these results with other well‐dated sequences in the region demonstrates the importance of basin‐specific influences on the palaeolimnological records in addition to regional climatic controls. Disentangling these different controls, as well as the reconstruction of Holocene climate, therefore requires a multiple‐basin approach. Copyright © 2008 John Wiley & Sons, Ltd.     

Spatial probabilistic modeling of slope failure using an integrated GIS Monte Carlo simulation approach

Spatial probabilistic modeling of slope failure using a combined Geographic Information System (GIS), infinite-slope stability model and Monte Carlo simulation approach is proposed and applied in the landslide-prone area of Sasebo city, southern Japan. A digital elevation model (DEM) for the study area has been created at a scale of 1/2500. Calculated results of slope angle and slope aspect derived from the DEM are discussed. Through the spatial interpolation of the identified stream network, the thickness distribution of the colluvium above Tertiary strata is determined with precision. Finally, by integrating an infinite-slope stability model and Monte Carlo simulation with GIS, and applying spatial processing, a slope failure probability distribution map is obtained for the case of both low and high water levels.     

Location specific forecasting of maximum and minimum temperatures over India by using the statistical bias corrected output of global forecasting system

The output from Global Forecasting System (GFS) T574L64 operational at India Meteorological Department (IMD), New Delhi is used for obtaining location specific quantitative forecast of maximum and minimum temperatures over India in the medium range time scale. In this study, a statistical bias correction algorithm has been introduced to reduce the systematic bias in the 24–120 hour GFS model location specific forecast of maximum and minimum temperatures for 98 selected synoptic stations, representing different geographical regions of India. The statistical bias correction algorithm used for minimizing the bias of the next forecast is Decaying Weighted Mean (DWM), as it is suitable for small samples. The main objective of this study is to evaluate the skill of Direct Model Output (DMO) and Bias Corrected (BC) GFS for location specific forecast of maximum and minimum temperatures over India. The performance skill of 24–120 hour DMO and BC forecast of GFS model is evaluated for all the 98 synoptic stations during summer (May-August 2012) and winter (November 2012–February 2013) seasons using different statistical evaluation skill measures. The magnitude of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for BC GFS forecast is lower than DMO during both summer and winter seasons. The BC GFS forecasts have higher skill score as compared to GFS DMO over most of the stations in all day-1 to day-5 forecasts during both summer and winter seasons. It is concluded from the study that the skill of GFS statistical BC forecast improves over the GFS DMO remarkably and hence can be used as an operational weather forecasting system for location specific forecast over India.     

Assessment of shallow landslide susceptibility using the transient infiltration flow model and GIS-based probabilistic approach

This study proposes a probabilistic analysis method for modeling rainfall-induced shallow landslide susceptibility by combining a transient infiltration flow model and Monte Carlo simulations. The spatiotemporal change in pore water pressure over time caused by rainfall infiltration is one of the most important factors causing landslides. Therefore, the transient infiltration hydrogeological model was adopted to estimate the pore water pressure within the hill slope and to analyze landslide susceptibility. In addition, because of the inherent uncertainty and variability caused by complex geological conditions and the limited number of available soil samples over a large area, this study utilized probabilistic analysis based on Monte Carlo simulations to account for the variability in the input parameters. The analysis was performed in a geographic information system (GIS) environment because GIS can deal efficiently with a large volume of spatial data. To evaluate its effectiveness, the proposed analysis method was applied to a study area that had experienced a large number of landslides in July 2006. For the susceptibility analysis, a spatial database of input parameters and a landslide inventory map were constructed in a GIS environment. The results of the landslide susceptibility assessment were compared with the landslide inventory, and the proposed approach demonstrated good predictive performance. In addition, the probabilistic method exhibited better performance than the deterministic alternative. Thus, analysis methods that account for uncertainties in input parameters are more appropriate for analysis of an extensive area, for which uncertainties may significantly affect the predictions because of the large area and limited data.     

Population balance in confined comminution using a physically based probabilistic approach for polydisperse granular materials

A novel, simplified approach is presented in order to compute variations of grading in granular assemblies during confined comminution under quasi‐static compression. The method is based on a population balance equation and requires a breakage probability, considered here as a probabilistic phenomenon that takes into account the particle strength and the loading condition of individual grains. Under basic assumptions, a simple breakage probability can be defined in order to get a valuable result for engineering applications and powder technology. The size effect in the strength of individual particles is introduced according to Weibull's theory. The particle loading and the cushioning effect in the granular packing are accounted for by considering the orientations of the contact forces obtained from 3D discrete element method simulations of highly polydisperse materials. The method proposed could have a value for engineering purposes in powder technology and geomechanics and gives a general framework for further research developments based on population balance. Copyright © 2016 John Wiley & Sons, Ltd.     

Seasonal forecasting of 2014 summer heat wave over Beijing using GRAAP and other statistical methods

Natural Hazards - Heat waves have taken place more and more frequently in Beijing due to the rapid urban expansion since the mid-1970s, which has been greatly affecting people’s life and...     

Modeling and mapping of water erosion in northeastern Algeria using a seasonal multicriteria approach

Aurès region remains one of the most exposed areas to water erosion phenomenon in Algeria, because of the strong climatic aggressiveness, the rugged relief, the predominance of sensitive land, and a vegetative cover that does not play its protective role. This article is a part of studies performed to protect agricultural and water infrastructure in this region. The main objective of this study is the cartographic modeling of an erosion hazard at the Oued Chemoura watershed, representative of the Aurès. The modeling approach uses a geographic information system and incorporates the following six factors controlling erosion: slope, friability of substrate, erodibility of soils, land cover, rainfall erosivity, and support practices. Result shows a synthetic map of the soil erosion hazard which locates the most threatened areas and priorities for possible planning interventions. A statistical study on the relationship solid–liquid flow was developed. Measurements conducted at the station of Chemoura, over the period 1969–1994, were exploited for this purpose. The results show a high specific degradation varying between 50 and 360 tons km^?2 season^?1.     

Impact of vegetation on the simulation of seasonal monsoon rainfall over the Indian subcontinent using a regional model

The change in the type of vegetation fraction can induce major changes in the local effects such as local evaporation, surface radiation, etc., that in turn induces changes in the model simulated outputs. The present study deals with the effects of vegetation in climate modeling over the Indian region using the MM5 mesoscale model. The main objective of the present study is to investigate the impact of vegetation dataset derived from SPOT satellite by ISRO (Indian Space Research Organization) versus that of USGS (United States Geological Survey) vegetation dataset on the simulation of the Indian summer monsoon. The present study has been conducted for five monsoon seasons (1998–2002), giving emphasis over the two contrasting southwest monsoon seasons of 1998 (normal) and 2002 (deficient). The study reveals mixed results on the impact of vegetation datasets generated by ISRO and USGS on the simulations of the monsoon. Results indicate that the ISRO data has a positive impact on the simulations of the monsoon over northeastern India and along the western coast. The MM5-USGS has greater tendency of overestimation of rainfall. It has higher standard deviation indicating that it induces a dispersive effect on the rainfall simulation. Among the five years of study, it is seen that the RMSE of July and JJAS (June–July–August–September) for All India Rainfall is mostly lower for MM5-ISRO. Also, the bias of July and JJAS rainfall is mostly closer to unity for MM5-ISRO. The wind fields at 850 hPa and 200 hPa are also better simulated by MM5 using ISRO vegetation. The synoptic features like Somali jet and Tibetan anticyclone are simulated closer to the verification analysis by ISRO vegetation. The 2 m air temperature is also better simulated by ISRO vegetation over the northeastern India, showing greater spatial variability over the region. However, the JJAS total rainfall over north India and Deccan coast is better simulated using the USGS vegetation. Sensible heat flux over north-west India is also better simulated by MM5-USGS.