事故车辆影响下的城市三车道道路交通流仿真

为探究事故车辆对城市三车道道路交通影响机制,首先,分析考虑事故车辆的城市三车道道路交通流特征;然后,构建左车道优先且考虑主动抢道和被动抢道行为的元胞自动机交通流模型;最后;研究事故持续时间td变化时事故车辆分别位于M道和R道的城市道路交通流演化。结果表明:事故车辆会形成交通瓶颈,呈现上游车辆聚集下游车辆稀疏的时空特征,诱发交通拥堵,事故车辆对三车道交通影响明显小于两车道情形;且事故车辆位于R车道对道路交通流影响比事故车辆位于M车道更小,但这种影响的差异随进车率pe增加而减小。     

考虑驾驶员反应时间的车辆碰撞预警模型

为了保证车辆在行驶过程中的安全性,提出了一种考虑驾驶员反应时间的车辆碰撞预警模型,改进了传统模型中驾驶员反应时间定值化的缺点。首先,依据车辆的制动过程分析了驾驶员反应时间对制动距离的影响。其次,设计驾驶员反应时间的模糊推理算法,选取驾龄、疲劳强度和应变能力3个主要因素作为评价指标来计算反应时间。最后,采用分等级的预警策略建立考虑驾驶员反应时间的碰撞预警模型,并通过Carsim-Matlab/Simulink联合仿真与传统模型进行对比分析。结果表明,设计的预警模型可以对不同类型的驾驶员进行差异化碰撞预警,在30 km/h和80 km/h两种车速下实际停车距离与理论值的最大误差为8%。     

The Effects of Irrigation and Climate on the High Plains Aquifer: A County‐Level Econometric Analysis

The High Plains Aquifer (HPA) underlies parts of eight states and 208 counties in the central area of the United States (U.S.). This region produces more than 9% of U.S. crops sales and relies on the aquifer for irrigation. However, these withdrawals have diminished the stock of water in the aquifer. In this paper, we investigate the aggregate county‐level effect on the HPA of groundwater withdrawal for irrigation, of climate variables, and of energy price changes. We merge economic theory and hydrological characteristics to jointly estimate equations describing irrigation behavior and a generalized water balance equation for the HPA. Our simple water balance model predicts, at average values for irrigation and precipitation, an HPA‐wide average decrease in the groundwater table of 0.47 feet per year, compared to 0.48 feet per year observed on average across the HPA during this 1985–2005 period. The observed distribution and predicted change across counties is in the (?3.22, 1.59) and (?2.24, 0.60) feet per year range, respectively. The estimated impact of irrigation is to decrease the water table by an average of 1.24 feet per year, whereas rainfall recharges the level by an average of 0.76 feet per year. Relative to the past several decades, if groundwater use is unconstrained, groundwater depletion would increase 50% in a scenario where precipitation falls by 25% and the number of degree days above 36°C doubles. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Changes in Hydrology of the Ganges Delta of Bangladesh and Corresponding Impacts on Water Resources

The Ganges Delta in Bangladesh is an example of water‐related catastrophes in a major rural river basin where limitations in quantity, quality, and timing of available water are producing disastrous conditions. Water availability limitations are modifying the hydrologic characteristics especially when water allocation is controlled from the upstream Farakka Barrage. This study presents the changes and consequences in the hydrologic regime due to climate‐ and human‐induced stresses. Flow duration curves (FDCs), rainfall elasticity, and temperature sensitivity were used to assess the pre‐ and post‐barrage water flow patterns. Hydrologic and climate indices were computed to provide insight on hydro‐climatic variability and trend. Significant increases in temperature, evapotranspiration, hot days, heating, and cooling degree days indicate the region is heading toward a warmer climate. Moreover, increase in high‐intensity rainfall of short duration is making the region prone to extreme floods. FDCs depict a large reduction in river flows between pre‐ and post‐barrage periods, resulting in lower water storage capacity. The reduction in freshwater flow increased the extent and intensity of salinity intrusion. This freshwater scarcity is reducing livelihood options considerably and indirectly forcing population migration from the delta region. Understanding the causes and directions of hydrologic changes is essential to formulate improve water resources management in the region.     

Climate and Land Use Effects on Hydrologic Processes in a Primarily Rain‐Fed,Agricultural Watershed

Anticipating changes in hydrologic variables is essential for making socioeconomic water resource decisions. This study aims to assess the potential impact of land use and climate change on the hydrologic processes of a primarily rain‐fed, agriculturally based watershed in Missouri. A detailed evaluation was performed using the Soil and Water Assessment Tool for the near future (2020–2039) and mid‐century (2040–2059). Land use scenarios were mapped using the Conversion of Land Use and its Effects model. Ensemble results, based on 19 climate models, indicated a temperature increase of about 1.0°C in near future and 2.0°C in mid‐century. Combined climate and land use change scenarios showed distinct annual and seasonal hydrologic variations. Annual precipitation was projected to increase from 6% to 7%, which resulted in 14% more spring days with soil water content equal to or exceeding field capacity in mid‐century. However, summer precipitation was projected to decrease, a critical factor for crop growth. Higher temperatures led to increased potential evapotranspiration during the growing season. Combined with changes in precipitation patterns, this resulted in an increased need for irrigation by 38 mm representing a 10% increase in total irrigation water use. Analysis from multiple land use scenarios indicated converting agriculture to forest land can potentially mitigate the effects of climate change on streamflow, thus ensuring future water availability.     

Perceptions of climate change in Thunder Bay,Ontario: towards a place-based understanding

Climate change is a global phenomenon that it is experienced and understood in places. This research examined the ways in which community members understand, perceive, and experience climate change in the context of Thunder Bay Ontario; a mid-size and remote city located in Northern Ontario, using semi-structured walking interviews (N?=?18). Themes that emerged from the interview data are presented and discussed in relation to the literature. Results emphasise that participants conceptualise climate change as a complex ethical issue that is caused by greenhouse gas emissions and a range of underlying social, economic, and political factors. Participants identified numerous changes in weather, seasonality, and extreme events and anticipate future impacts on local and regional food, water, and forests primarily. Emotional impacts of climate change, ranging from worry to feeling hopeful, emerged as an important theme. The data illustrate that the observed, experienced, and anticipated impacts of climate change are shaped by experiences on the land and water within the community of Thunder Bay and the region of Northern Ontario. Finally, the interview data illustrate that participants believe that transformative action, by a range of actors, is called for to address the problem of climate change. This study highlights the importance of place-based and context-specific climate change research and the utility of walking interviews.     

Environmental change of coastal Sundarbans: Impact on livelihood and standard of living status of indigenous people

There is overwhelming scientific consensus that environmental change is currently having ecological and socioeconomical impacts at the micro and macrolevel. Over the coming decades, the impact of development, climate change, and urbanization on the ecosystem is likely to become even more ruthless in the Sundarbans. Like many other ecologically sensitive areas, the Sundarbans of the Indian state of West Bengal and of Bangladesh are being stressed climatically to the extreme due to their geographical location. This study explores both the ways in which residents of communities in the West Bengal and Bangladesh Sundarbans perceive changes in the environment, as well as intergenerational changes in livelihoods to be driven in a large part by environmental changes. Persons from a total of 368 households were interviewed using a structured interview tool. As an example of differences in perception between residents of the two areas, survey respondents from communities of the Sundarbans of Bangladesh were more likely to perceive that rainfall amounts are changing than did the residents interviewed from the Sundarbans of West Bengal. From the sample data, it is shown that in the Sundarbans of Bangladesh, 59% of the respondents, as compared to 63% of the respondents in West Bengal, reported that they had chosen to enter their parents’ occupations. From the multivariate logistic regression analysis, it was observed that, especially in Bangladesh when compared to West Bengal, the state of the environment plays a vital role in whether respondents adopt occupations other than those of their parents.     

Efficient Delineation of Nested Depression Hierarchy in Digital Elevation Models for Hydrological Analysis Using Level‐Set Method

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill‐merge‐spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level‐set method based on graph theory. The proposed level‐set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level‐set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine‐resolution Light Detection and Ranging‐derived DEMs show that the raster‐based level‐set algorithm is much more efficient (~150 times faster) than the vector‐based contour tree method. The proposed level‐set algorithm has great potential for being applied to large‐scale ecohydrological analysis and watershed modeling.     

Modeling Hydrological and Environmental Consequences of Climate Change and Urbanization in the Boise River Watershed,Idaho

The potential impacts driven by climate variability and urbanization in the Boise River Watershed (BRW), located in southwestern Idaho, are evaluated. The outcomes from Global Circulation Models (GCMs) and land use and land cover (LULC) analysis have been incorporated into a hydrological and environmental modeling framework to characterize how climate variability and urbanization can affect the local hydrology and environment at the BRW. The combined impacts of future climate and LULC change are also evaluated relative to the historical baseline conditions. For modeling exercises, Hydrological Simulation Program‐Fortran (HSPF) is used in parallel computing and statistical techniques, including spatial downscaling and bias correlation, are employed to evaluate climate consequences derived from GCMs as well. The implications of climate variability and land use change driven by urbanization are then observed to evaluate how these overall global challenges can affect water quantity and quality conditions at the BRW. The results show the combined impacts of both climate change and urbanization can lead to more seasonal variability of streamflow (from ?27.5% to 12.5%) and water quality, including sediment (from ?36.5% to 49.3%), nitrogen (from ?24% to 124.2%), and phosphorus (from ?13.3% to 21.2%) during summer and early fall over the next several decades.     

Application of response surface methodology for the optimization of biodiesel production from yellow mustard (Sinapis alba L.) seed oil

In the present study, response surface methodology (RSM) involving central composite design (CCD) was applied to optimize the reaction parameters of biodiesel production from yellow mustard (Sinapis alba L.) seed oil during the single-step transesterification process. A total of 30 experiments were designed and performed to determine under the effects of variables on the biodiesel yield such as methanol to oil molar ratio (2:1–10:1), catalyst concentration (0.2–1.0 wt.% NaOH), reaction temperature (50–70°C), and reaction time (30–90 min). The second order polynomial model was used to predict the biodiesel yield and coefficient of determination (R²) was found to be at 0.9818. The optimum biodiesel yield was calculated as 96.695% from the model with the following reaction conditions: 7.41:1 of methanol to oil molar ratio, 0.63 wt. % NaOH of catalyst concentration, 61.84°C of reaction temperature, and 62.12 min of reaction time. It is seen that the regression model results were in agreement with the experimental data. The results showed that RSM is a suitable statistical technique for optimizing the reaction parameters in the transesterification process in order to maximize the biodiesel yield.