An agent-based evacuation model for the 2011 Brisbane City-scale riverine flood

In this study, an agent-based model is proposed in order to provide new insights into the policy analysis and strategy assessment of city-scale evacuation management. The proposed model is suitable for assessment of the influence of different departure times and communications among peer evacuees on the number of residents at risk who arrive at official shelters. A case study is applied to build a simulation model for the coastal city of Brisbane in Australia. The Brisbane River catchment experiences regular flooding almost every year; the second severest flood since the twentieth century occurred in 2011. During that event, over 15,000 properties were inundated and around 3600 households evacuated in metropolitan Brisbane alone. Making use of high-performance computing clusters, the evacuation simulation was coupled with results from a validated hydrodynamic model to test a variety of escaping scenarios based on the 2011 flood situation. This case study demonstrates the proposed model’s capacity to represent the dynamic evacuation process and also shows that the model is able to help develop flood emergency plans and evaluate response measures through exploring key elements in a range of scenarios.     

Three-dimensional numerical and analytical study of horizontal group of square anchor plates in sand

In this paper, numerical and analytical methods are used to evaluate the ultimate pullout capacity of a group of square anchor plates in row or square configurations, installed horizontally in dense sand. The elasto-plastic numerical study of square anchor plates is carried out using three-dimensional finite element analysis. The soil is modeled by an elasto-plastic model with a Mohr–Coulomb yield criterion. An analytical method based on a simplified three-dimensional failure mechanism is developed in this study. The interference effect is evaluated by group efficiency η, defined as the ratio of the ultimate pullout capacity of group of N anchor plates to that of a single isolated plate multiplied by number of plates. The variation of the group efficiency η was computed with respect to change in the spacing between plates. Results of the analyses show that the spacing between the plates, the internal friction angle of soil and the installation depth are the most important parameters influencing the group efficiency. New equations are developed in this study to evaluate the group efficiency of square anchor plates embedded horizontally in sand at shallow depth (H = 4B). The results obtained by numerical and analytical solutions are in excellent agreement.     

Prediction of fog/visibility over India using NWP Model

Frequent occurrence of fog in different parts of northern India is common during the winter months of December and January. Low visibility conditions due to fog disrupt normal public life. Visibility conditions heavily affect both surface and air transport. A number of flights are either diverted or cancelled every year during the winter season due to low visibility conditions, experienced at different airports of north India. Thus, fog and visibility forecasts over plains of north India become very important during winter months. This study aims to understand the ability of a NWP model (NCMRWF, Unified Model, NCUM) with a diagnostic visibility scheme to forecast visibility over plains of north India. The present study verifies visibility forecasts obtained from NCUM against the INSAT-3D fog images and visibility observations from the METAR reports of different stations in the plains of north India. The study shows that the visibility forecast obtained from NCUM can provide reasonably good indication of the spatial extent of fog in advance of one day. The fog intensity is also predicted fairly well. The study also verifies the simple diagnostic model for fog which is driven by NWP model forecast of surface relative humidity and wind speed. The performance of NWP model forecast of visibility is found comparable to that from simple fog model driven by NWP forecast of relative humidity and wind speed.     

Geochemistry and distribution of sediments in the East Indian shelf,SW Bay of Bengal: implications on weathering,transport and depositional environment

Forty-two substrate sediment samples and three cores procured from the shelf region between Chennai and Cuddalore were analyzed to understand the spatial and vertical sediment distribution. Samples subjected to grain size, bulk and clay mineralogical analyses, REE and trace element geochemistry yielded interesting results about the sediment characteristics with respect to the modern day environment. The present study revealed that the study area is characterized by high energy environment marked by predominant composition of medium sand. Results confirmed the evidence of more illite than kaolinite, smectite, and chlorite in the clay mineral assemblage indicating a terrigenous source. Geochemical data also revealed that the enrichment of light rare earth elements (LREE) in the sediments is due to continental source of Precambrian times. High feldspar content in the sediments which is emphasized by bulk mineralogical data displayed positive Eu anomaly. By using the paleoredox index, the area of interest is considered to have undergone no major changes in its depositional settings.     

Reliability of reanalyses products in simulating precipitation and temperature characteristics over India

Various reanalyses have been utilized in numerous climate related researches around the globe, however, there exists considerable biasedness in these products, especially in precipitation and temperature data. The ability of these reanalysis products to simulate the precipitation and temperature patterns is observed to be satisfactory at global scale, while it differs significantly at regional scale, especially over regions of high spatio-temporal heterogeneity such as India. Therefore, it is essential to evaluate the applicability and robustness of reanalyses in climate related research. The annual and seasonal variability in spatio-temporal patterns and trends of precipitation and temperature data, with respect to the IMD gridded data over 34 yrs, are evaluated for six global reanalyses namely, NCEP/NCAR Reanalysis (NCEP R1), NCEP-DOE AMIP-2 Reanalysis (NCEP R2), Climate Forecast System Reanalysis (CFSR), ECMWF Interim Reanalysis (ERA-Interim), Modern Era Retrospective Analysis for Research and Application Land only model (MERRA-Land) and JMA 55-year Reanalysis (JRA-55). The ability of the reanalyses was tested based on several factors such as statistical and categorical indices, spells and trends, for annual and seasonal daily values. Several regional and seasonal differences were observed, particularly over high rainfall regions such as Western Ghats and northeastern India. MERRA-Land is found to give the best results for precipitation over India, which is attributed to the updated forcing data using gauge-based precipitation observations. Similarly, ERA-Interim and JRA-55 exhibit better performance for temperature than other datasets. All reanalyses failed to correctly reproduce the trends in IMD data, for both precipitation and temperature. These observations will provide a better perception on the reliability and applicability of reanalyses for climate and hydrological studies over India.     

Tracing submarine groundwater discharge flux in Tolo Harbor,Hong Kong (China)

Submarine groundwater discharge (SGD) is an important pathway for groundwater and associated chemicals to discharge to the sea. Groundwater levels monitored along a transect perpendicular to the shoreline are used to calculate SGD flux from the nearshore aquifer to Tolo Harbor, Hong Kong (China). The calculated SGD flux—recharge/discharge measured with Darcy’s Law methods—agrees well with estimates based on geo-tracer techniques and seepage meter in Tolo Harbor during previous studies. The estimated freshwater SGD is 1.69–2.0 m²/d at the study site and 0.3?±?0.04 cm/d for the whole of Tolo Harbor, which is comparable to the river discharge (0.25?±?0.07 cm/d) and precipitation (0.45?±?0.15 cm/d). The tide-driven SGD in the intertidal zone is 13.98–17.59 m²/d at the study site and 2.42?±?0.56 cm/d for the whole of Tolo Harbor. The SGD occurring in the subtidal zone and the bottom of Tolo Harbor is 3.12?±?4.63 cm/d. Fresh SGD accounts for ~5% of the total SGD, while the rest (~95%) is contributed by saline SGD driven by various forces. About 96% of the tide-driven SGD in the intertidal zone occurs in the ebbing tide period because the head difference between the groundwater level and sea level is great during this period. Tide-driven SGD in the spring tide is ~1.2 times that during neap tide. The tidal fluctuation amplitude and tide-driven SGD in the intertidal zone are positively correlated to each other; thus, a spring neap variation of the tide-driven SGD is observed.     

Were merely storm-landslides driven by the 2015-2016 Niño in the Mendoza River valley?

Since Holocene time, above-mean precipitations recorded during the El Niño warm ENSO phase have been linked to the occurrence of severe debris flows in the arid Central Andes. The 2015–2016 El Niño, for its unusual strength, began driving huge and dangerous landslides in the Central Andes (32°) in the recent South Hemisphere summer. The resulting damages negatively impacted the regional economy. Despite this, causes of these dangerous events were ambiguously reported. For this reason, a multidisciplinary study was carried out in the Mendoza River valley. Firstly, a geomorphological analysis of affected basins was conducted, estimating morphometric parameters of recorded events such as velocity, stream flow, and volume. Atmospheric conditions during such events were analyzed, considering precipitations, snow cover, temperature range, and the elevation of the zero isotherm. Based on our findings, the role of El Niño on the slope instability in the Central Andes is more complex in the climate change scenario. Even though some events were effectively triggered by intense summer rainstorm following expectations, the most dangerous events were caused by the progressive uplifting of the zero isotherm in smaller basins where headwaters are occupied by debris rock glaciers. Our research findings give light to the dynamic coupled system ENSO–climate change–landslides (ECCL) at least in this particular case study of the Mendoza River valley. Landslide activity in this Andean region is driven by wetter conditions linked to the ENSO warm phase, but also to progressive warming since the twentieth century in the region. This fact emphasizes the future impact of the natural hazards on Andean mountain communities.     

Ensemble clustering for efficient robust optimization of naturally fractured reservoirs

In the development of naturally fractured reservoirs (NFRs), the existence of natural fractures induces severe fingering and breakthrough. To manage the flooding process and improve the ultimate recovery, we propose a numerical workflow to generate optimal production schedules for smart wells, in which the inflow control valve (ICV) settings can be controlled individually. To properly consider the uncertainty introduced by randomly distributed natural fractures, the robust optimization would require a large ensemble size and it would be computationally demanding. In this work, a hierarchical clustering method is proposed to select representative models for the robust optimization in order to avoid redundant simulation runs and improve the efficiency of the robust optimization. By reducing the full ensemble of models into a small subset ensemble, the efficiency of the robust optimization algorithm is significantly improved. The robust optimization is performed using the StoSAG scheme to find the optimal well controls that maximize the net-present-value (NPV) of the NFR’s development. Due to the discrete property of a natural fracture field, traditional feature extraction methods such as model-parameter-based clustering may not be directly applicable. Therefore, two different kinds of clustering-based optimization methods, a state-based (e.g., s _w profiles) clustering and a response-based (e.g., production rates) clustering, are proposed and compared. The computational results show that the robust clustering optimization could increase the computational efficiency significantly without sacrificing much expected NPV of the robust optimization. Moreover, the performance of different clustering algorithms varies widely in correspondence to different selections of clustering features. By properly extracting model features, the clustered subset could adequately represent the uncertainty of the full ensemble.