Distributed process modeling for regional assessment of coastal vulnerability to sea-level rise

Sea-level rise involves increases in the coastal processes of inundation and erosion which are affected by a complex interplay of physical environmental parameters at the coast. Many assessments of coastal vulnerability to sea-level rise have been detailed and localised in extent. There is a need for regional assessment techniques which identify areas vulnerable to sea-level rise. Four physical environmental parameters – elevation, exposure, aspect and slope, are modeled on a regional scale for the Northern Spencer Gulf (NSG) study area using commonly available low-resolution elevation data of 10 m contour interval and GIS-based spatial modeling techniques. For comparison, the same parameters are modeled on a fine-scale for the False Bay area within the NSG using high-resolution elevation data. Physical environmental parameters on the two scales are statistically compared to coastal vulnerability classes as identified by Harvey et al. [1] using the Spearman rank-correlation test and stepwise linear regression. Coastal vulnerability is strongly correlated with elevation and exposure at both scales and this relationship is only slightly stronger for the high resolution False Bay data. The results of this study suggest that regional scale distributed coastal process modeling may be suitable as a first cut in assessing coastal vulnerability to sea-level rise in tide-dominated, sedimentary coastal regions. Distributed coastal process modeling provides a suitable basis for the assessment of coastal vulnerability to sea-level rise of sufficient accuracy for on-ground management and priority-setting on a regional scale.     

SO2 modeling in İzmit Gulf, Turkey during the winter of 1997: 3 cases

In this study, SO₂ dispersion over İzmit Gulf is simulated by California Puff (CALPUFF) model for three air pollution cases, which occurred on January 28, February 12, and February 26, 1997. These days are generally characterized by dominant high-pressure systems – pressure values reaching 1032 mb, low wind speeds and sometimes calm conditions, and low temperatures with a minimum of 0°C. Hourly simulations during those critical cases were carried out and results revealed very high concentrations of SO₂ transported to the downwind regions of Tüpraş and Gebze, and values sometimes exceeded 1,000 μg/m³. Nighttime and morning simulations associated with inversion produced considerably higher values of SO₂ than the afternoon simulations associated with breeze. Model verification was conducted by comparing the measured daily average values of eight stations with the model predicted values at the same receptor points. Results showed that the model well predicted the values at station Gebze in all three cases. The model sometimes underestimated and sometimes overestimated the concentrations at other receptor stations.     

Evaluating response surface designs for uncertainty analysis and prescriptive applications of a large-scale water quality model

Environmental models are often too large and cumbersome for effective use in regulatory decision making or in the characterization of uncertainty. This paper describes and compares four response surfaces that could complement a large-scale water quality model, the U.S. National Water Pollution Control Assessment Model (NWPCAM), in simulation and regulatory decision support applications. Results show that a physically based reduced-form model that exploits the mathematical structure of the underlying water quality model is a better predictor of policy-relevant outputs than the polynomial expansions that are frequently used in response surface studies.     

Stream Temperature Modeling Using Functional Regression Models

Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual curve of temperatures as one entity, rather than multiple daily or weekly values in classical models. Three types of functional models were explored in the study and compared to two classical models (Generalized Additive Model and Logistic Model) for six rivers from the United States The results show the functional models have the best performance for all the considered rivers. When comparing functional models between them, one variant of the historical functional model performs better than the two other models and is the most parsimonious. Functional regression leads to encouraging results to model the complete annual stream temperature curve as one entity compared to other classical approaches.     

Toward Explaining Nitrogen and Phosphorus Trends in Chesapeake Bay Tributaries, 1992–2012

Understanding trends in stream chemistry is critical to watershed management, and often complicated by multiple contaminant sources and landscape conditions changing over varying time scales. We adapted spatially referenced regression (SPARROW) to infer causes of recent nutrient trends in Chesapeake Bay tributaries by relating observed fluxes during 1992, 2002, and 2012 to contemporary inputs and watershed conditions. The annual flow‐normalized nitrogen flux to the bay from its watershed declined by 14% to 127,000 Mg (metric tons) between 1992 and 2012, due primarily (more than 80% of the decline) to reduced point sources. The remainder of the decline was due to reduced atmospheric deposition (13%) and urban nonpoint sources. Agricultural inputs, which contribute most nitrogen to the bay, changed little, although trends in the average nitrogen yield (flux per unit area) from cropland and pasture to streams in some settings suggest possible effects of evolving nutrient applications or other land management practices. Point sources of phosphorus to local streams declined by half between 1992 and 2012, while nonpoint inputs were relatively unchanged. Annual phosphorus delivery to the bay increased by 9% to 9,570 Mg between 1992 and 2012, however, due mainly to reduced retention in the Susquehanna River at Conowingo Reservoir.     

Modeling the probability of freeway lane-changing collision occurrence considering intervehicle interaction

Objective: To better capture the relationships between lane-changing collisions and explanatory variables, a microscopic model is developed for freeway lane-changing collisions based on the interactions between lane-changing vehicles.

Methods: The model applies an intervehicle interaction structure to account for the occurrence mechanism of lane-changing collisions. The occurrence mechanism can be described as the failure of a vehicle driver of an adjacent lane in avoiding the lane-changing vehicle, which disturbs the smooth movement of the adjacent lane vehicle and requires the driver's brake action to avoid an angle collision. This model is examined using data collected from freeways in Washington State during 2010 to 2011 and validated using lane-changing collision data for the SR 520 freeway.

Results: The findings of this study show that generalized truck percentage has a significant decreasing effect on lane-changing collision risk, whereas average spacing and several roadway characteristics have significant increasing effects. The frequency of slight collisions during peak hours is higher than that during off-peak hours. Young female drivers are more likely to be involved in collisions during lane-changing than young male drivers, but the result for senior drivers is opposite, with older male drivers having a higher probability of lane-changing collisions than female drivers in the same age group.

Conclusion: The process of lane-changing collisions is a complicated maneuver. Truck percentage, average spacing, and good roadway characteristics, such as straight and level segment, in the target lane have a significant effect on the occurrence of lane-changing collisions. Age and gender are also 2 important factors contributing to the relationship between lane-changing collisions and explanatory variables.     

基于OPMSE的畜禽养殖行业排放限值仿真

通过对清河流域畜禽养殖行业产生污水的各指标(BOD5,COD,NH_3-N)浓度进行统计,得出了排放污水中各污染物指标的浓度值范围。通过OPMSE的仿真计算,得出了排放污水经过BAT处理后,污染物浓度正态分布均值在90%,95%,99%置信水平下的置信区间。在置信水平为99%时,畜禽养殖行业的COD置信区间为(42.00,48.19),BOD_5置信区间为(7.47,8.74),NH_3-N置信区间为(6.78,7.88)。同时,依据仿真计算结果还得出了处理后污染物浓度的极小值和极大值,畜禽养殖行业的最佳出水COD,BOD_5,NH3_-N指标质量浓度分别为22.0,2.1,3.09 mg/L,最差出水COD,BOD_5,NH_3-N指标质量浓度分别为84.4,14.2,13.29 mg/L。将仿真结果与现有排放标准对比,拟定畜禽养殖行业的COD,BOD_5,NH_3-N直接排放限值分别为50,9,8 mg/L。     

响应面法对PBGA封装元器件的有限元模型修正

目的利用特殊设计夹具来模仿PCB板的典型插入式固定方式,通过响应面法对安装有PBGA的菊花链PCB板有限元模型进行修正。方法有限元模型经过三次响应面修正,每一个修正阶段都计算仿真前三阶频率与相对应的模态试验结果相对比,建立两个目标函数,并利用多目标遗传算法来缩小仿真分析与模态试验结果。结果有限元模型得到了有效的改善。结论响应面法可以从实际出发来提升有限元模型准确度。     

Historical and Future Stream Temperature Change Predicted by a Lidar‐Based Assessment of Riparian Condition and Channel Width

Riparian forests attenuate solar radiation, thereby mediating an important component of the thermal budget of streams. Here, we investigate the relationship between riparian degradation, stream temperature, and channel width in the Chehalis River Basin, Washington State. We used lidar data to measure canopy opening angle, the angle formed between the channel center and trees on both banks; we assumed historical tree heights and calculated the change in canopy angle relative to historical conditions. We then developed an empirical relationship between canopy angle and water temperature using existing data, and simulated temperatures between 2002 and 2080 by combining a tree growth model with climate change scenarios from the NorWeST regional prediction. The greatest change between historical and current conditions (~7°C) occurred in developed portions of the river network, with the highest values of change predicted at channel widths less than ~40 m. Tree growth lessened climate change increases in maximum temperature and the length of river exceeding biologically critical thresholds by ~50%–60%. Moreover, the maximum temperature of channels with bankfull widths less than ~50 m remained similar to current conditions, despite climate change increases. Our findings are consistent with a possible role for the riparian landscape in explaining the low sensitivity of stream temperatures to air temperatures observed in some small mountain streams.     

Cyberinfrastructure and Web Apps for Managing and Disseminating the National Water Model

Hydrologic modeling can be used to provide warnings before, and to support operations during and after floods. Recent technological advances have increased our ability to create hydrologic models over large areas. In the United States (U.S.), a new National Water Model (NWM) that generates hydrologic variables at a national scale was released in August 2016. This model represents a substantial step forward in our ability to predict hydrologic events in a consistent fashion across the entire U.S. Nevertheless, for these hydrologic results to be effectively communicated, they need to be put in context and be presented in a way that is straightforward and facilitates management‐related decisions. The large amounts of data produced by the NWM present one of the major challenges to fulfill this goal. We created a cyberinfrastructure to store NWM results, “accessibility” web applications to retrieve NWM results, and a REST API to access NWM results programmatically. To demonstrate the utility of this cyberinfrastructure, we created additional web apps that illustrate how to use our REST API and communicate hydrologic forecasts with the aid of dynamic flood maps. This work offers a starting point for the development of a more comprehensive toolset to validate the NWM while also improving the ability to access and visualize NWM forecasts, and develop additional national‐scale‐derived products such as flood maps.