Visualizations of flood maps from simulation models are widely used for assessing the likelihood of flood hazards in spatial planning. The choice of a suitable type of visualization as well as efficient color maps is critical to avoid errors or bias when interpreting the data. Based on a review of previous flood uncertainty visualization techniques, this paper identifies areas of improvements and suggests criteria for the design of a task-specific color scale in flood map visualization. We contribute a novel color map design for visualizing probabilities and uncertainties from flood simulation ensembles. A user study encompassing 83 participants was carried out to evaluate the effects of this new color map on user’s decisions in a spatial planning task. We found that the type of visualization makes a difference when it comes to identification of non-hazardous sites in the flood risk map and when accepting risks in more uncertain areas. In comparison with two other existing visualization techniques, we observed that the new design was superior both in terms of task compliance and efficiency. In regions with uncertain flood statuses, users were biased toward accepting less risky locations with our new color map design. 相似文献
This study developed a new paradigm for groundwater vulnerability assessment by modifying the standard DRASTIC index (DI) model based on catastrophe theory. The developed paradigm was called the catastrophe theory-based DI (CDI) model. The proposed model was applied to assess groundwater vulnerability to pollution index (GVPI) in Perak Province, Malaysia. The area vulnerability index was modeled by considering the DRASTIC multiple vulnerability causative factors (VCFs) obtained from different data sources. The weights and ranking of the VCFs were computed by using the inner fuzzy membership mechanism of the CDI model. The estimated vulnerability index values of the CDI model were processed in a geographic information system (GIS) environment to produce a catastrophe theory–DRASTIC groundwater vulnerability to pollution index (CDGVPI) map, which demarcated the area into five vulnerability zones. The produced CDGVPI map was validated by applying the water quality status–vulnerability zone relationship (WVR) approach and the relative operating characteristic (ROC) curve method. The performance of the developed CDI model was compared with that of the standard DI model. The validation results of the WVR approach exhibits 89.29% prediction accuracy for the CDI model compared with 75% for the DI model. Meanwhile, the ROC validation results for the CDI and DI models are 88.8% and 78%, respectively. The GIS-based CDI model demonstrated better performance than the DI model. The GVPI maps produced in this study can be used for precise decision making process in environmental planning and groundwater management. 相似文献
We present the thermal infrared (5-35 μm) spectrum of 956 Elisa as measured by the Spitzer Infrared Spectrograph (“IRS”; Houck, J.R. et al. [2004]. Astrophys. J. Suppl. 154, 18-24) together with new groundbased lightcurve data and near-IR spectra. From the visible lightcurve photometry, we determine a rotation period of 16.494 ± 0.001 h, identify the rotational phase of the Spitzer observations, and estimate the visible absolute magnitude (HV) at that rotational phase to be 12.58 ± 0.04. From radiometric analysis of the thermal flux spectrum, we find that at the time of observation 956 Elisa had a projected radius of 5.3 ± 0.4 km with a visible albedo pV = 0.142 ± 0.022, significantly lower than that of the prototype V-type asteroid, 4 Vesta. (This corresponds to a radius of 5.2 ± 0.4 km at lightcurve mean.) Analysis with the standard thermal model (STM) results in a sub-solar temperature of 292.3 ± 2.8 K and beaming parameter η = 1.16 ± 0.05. Thermophysical modeling places a lower limit of on the thermal inertia of the asteroid’s surface layer (if the surface is very smooth) but more likely values fall between 30 and depending on the sense of rotation.The emissivity spectrum, calculated by dividing the measured thermal flux spectrum by the modeled thermal continuum, exhibits mineralogically interpretable spectral features within the 9-12 μm reststrahlen band, the 15-16.5 μm Si-O-Si stretching region, and the 16-25 μm reststrahlen region that are consistent with pyroxene of diogenitic composition: extant diogenitic pyroxenes fall within the narrow compositional range Wo2±1En74±2Fs24±1. Spectral deconvolution of the 9-12 μm reststrahlen features indicates that up to ≈20% olivine may also be present, suggesting an olivine-diogenite-like mineralogy. The mid-IR spectrum is inconsistent with non-cumulate eucrite as the major component on the surface of 956 Elisa, although cumulate eucrite material may be present at abundances lower than that of the diogenite component.Analysis of new near-IR spectra of 956 Elisa with the Modified Gaussian Model (MGM; Sunshine, J.M., Pieters, C.M., Pratt, S.F. [1990]. J. Geophys. Res. 95 (May), 6955-6966) results in two pyroxene compositions: 75% magnesian low-Ca pyroxene and 25% high-Ca pyroxene. High-Ca pyroxene is not evident in the mid-IR data, but may belong to a component that is underrepresented in the mid-IR spectrum either because of its spatial distribution on the asteroid or because of its particle size. High-Ca pyroxenes that occur as exsolution lamellae may also be more evident spectrally in the NIR than in the mid-IR. In any case, we find that the mid-IR spectrum of 956 Elisa is dominated by emission from material of diogenite-like composition, which has very rarely been observed among asteroids. 相似文献
This paper investigates the stability of an automatic system for classifying kerogen material from images of sieved rock samples.
The system comprises four stages: image acquisition, background removal, segmentation, and classification of the segmented
kerogen pieces as either inertinite or vitrinite. Depending upon a segmentation parameter d, called “overlap”, touching pieces of kerogen may be split differently. The aim of this study is to establish how robust
the classification result is to variations of the segmentation parameter. There are two issues that pose difficulties in carrying
out an experiment. First, even a trained professional may be uncertain when distinguishing between isolated pieces of inertinite
and vitrinite, extracted from transmitted-light microscope images. Second, because manual labelling of large amount of data
for training the system is an arduous task, we acquired the true labels (ground truth) only for the pieces obtained at overlap
d=0.5. To construct ground truth for various values of d we propose here label-inheritance trees. With thus estimated ground truth, an experiment was carried out to evaluate the
robustness of the system to changes in the segmentation through varying the overlap value d. The average system accuracy across values of d spanning the range from 0 to 1 was 86.5%, which is only slightly lower than the accuracy of the system at the design value
of d=0.5 (89.07%). 相似文献
We explore the causes and predictability of extreme low minimum temperatures (Tmin) that occurred across northern and eastern Australia in September 2019. Historically, reduced Tmin is related to the occurrence of a positive Indian Ocean Dipole (IOD) and central Pacific El Niño. Positive IOD events tend to locate an anomalous anticyclone over the Great Australian Bight, therefore inducing cold advection across eastern Australia. Positive IOD and central Pacific El Niño also reduce cloud cover over northern and eastern Australia, thus enhancing radiative cooling at night-time. During September 2019, the IOD and central Pacific El Niño were strongly positive, and so the observed Tmin anomalies are well reconstructed based on their historical relationships with the IOD and central Pacific El Niño. This implies that September 2019 Tmin anomalies should have been predictable at least 1–2 months in advance. However, even at zero lead time the Bureau of Metereorolgy ACCESS-S1 seasonal prediction model failed to predict the anomalous anticyclone in the Bight and the cold anomalies in the east. Analysis of hindcasts for 1990–2012 indicates that the model's teleconnections from the IOD are systematically weaker than the observed, which likely stems from mean state biases in sea surface temperature and rainfall in the tropical Indian and western Pacific Oceans. Together with this weak IOD teleconnection, forecasts for earlier-than-observed onset of the negative Southern Annular Mode following the strong polar stratospheric warming that occurred in late August 2019 may have contributed to the Tmin forecast bust over Australia for September 2019.
This study assesses the regional-scale summer precipitation produced by the dynamical downscaling of analyzed large-scale fields. The main goal of this study is to investigate how much the regional model adds smaller scale precipitation information that the large-scale fields do not resolve. The modeling region for this study covers the southeastern United States (Florida, Georgia, Alabama, South Carolina, and North Carolina) where the summer climate is subtropical in nature, with a heavy influence of regional-scale convection. The coarse resolution (2.5° latitude/longitude) large-scale atmospheric variables from the National Center for Environmental Prediction (NCEP)/DOE reanalysis (R2) are downscaled using the NCEP/Environmental Climate Prediction Center regional spectral model (RSM) to produce precipitation at 20?km resolution for 16 summer seasons (1990?C2005). The RSM produces realistic details in the regional summer precipitation at 20?km resolution. Compared to R2, the RSM-produced monthly precipitation shows better agreement with observations. There is a reduced wet bias and a more realistic spatial pattern of the precipitation climatology compared with the interpolated R2 values. The root mean square errors of the monthly R2 precipitation are reduced over 93% (1,697) of all the grid points in the five states (1,821). The temporal correlation also improves over 92% (1,675) of all grid points such that the domain-averaged correlation increases from 0.38 (R2) to 0.55 (RSM). The RSM accurately reproduces the first two observed eigenmodes, compared with the R2 product for which the second mode is not properly reproduced. The spatial patterns for wet versus dry summer years are also successfully simulated in RSM. For shorter time scales, the RSM resolves heavy rainfall events and their frequency better than R2. Correlation and categorical classification (above/near/below average) for the monthly frequency of heavy precipitation days is also significantly improved by the RSM. 相似文献
The sustainable development in the ocean and costal areas has been an issue for the archipelago nation. Since two decades ago, some archipelago nations have attempted to implement the concept of both scientifically and politically sounding sustainability. The vulnerability assessment is one of the methods that are being used to measure the ocean and coastal sustainability in order to have better evaluation and redesign of the land development as well as policy making. Most of the vulnerability assessment has been conducted based on pressures, damages and changes that involve in the region. A common understanding of the vulnerability assessment is that there are three aspects to be considered: hazards, resilience and damages. These three aspects must be well defined at first in order to have better indicators or sub-indices for the vulnerability index. There are several issues and factors that should be considered before performing the vulnerability assessment. Firstly, each country has different coastal characteristics due to a different geologic process. Secondly, the three aspects of the vulnerability (i.e. hazards, resilience and damages) are impacting on each country at a different scale. Thirdly, the vulnerability of a small island region is different from that of a large island region. Finally, policies and regulations vary in each country.From the data analysis results, it is found that the urban settlement in Seribu Islands is one of the resilient factors in addition to the geological and geomorphological conditions. The resilience factors in Seribu Islands are classified into four categories: 1) settlements area, 2) population density, 3) hard infrastructure such as airfields, ports and roads, 4) geological process such as abrasion and erosion. Based on the island characteristics of Seribu Islands, a unique vulnerability index that fits to this locality is developed. It is shown that the vulnerability index developed in this study can measure the resilience of Seribu Islands. In addition to the aforementioned resilience factors, the unique geographical condition and the geological stability in Seribu Islands made the outer islands become a barrier from oceanographic conditions and made the inner islands protected. However, the population growth made significant changes in terms of ecology, water, sanitation and pollution within the region. 相似文献