An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil

Ground-reflected global positioning system signals measured by a geodetic-quality GPS system can be used to infer temporal changes in near-surface soil moisture for the area surrounding the antenna. This technique, known as GPS-interferometric reflectometry, analyzes changes in the interference pattern of the direct and reflected signals, which are recorded in signal-to-noise ratio (SNR) data, as interferograms. Temporal fluctuations in the phase of the interferogram are indicative of changes in near-surface volumetric soil moisture content. However, SNR phase is also highly sensitive to changes in overlying vegetation, and thus, the effects of seasonal vegetation changes on the ground-reflected signal must be considered. Here a method is described for determining whether SNR data are significantly corrupted by vegetation and for correcting these effects. Absolute soil moisture content must be determined for each site using ancillary data for the residual moisture content. Accounting for vegetation effects significantly improves the agreement between GPS-derived soil moisture and in situ measurements.     

The socio-environmental data explorer (SEDE): a social media–enhanced decision support system to explore risk perception to hazard events

ABSTRACT

Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to social, environmental, and technological risk and hazard events. We introduce a prototype SME-DSS entitled socio-environmental data explorer (SEDE) to explore the opportunities and challenges of leveraging social media for decision support. We use a winter storm during 25–28 January 2015 that accumulated record amounts of snow along the East Coast of the United States as a case study to evaluate SEDE in helping assess social response to environmental risk and hazard events as well as evaluate social media as a theoretical component within the social amplification of risk framework (SARF) that serves as a theoretical foundation for SME-DSS.     

Understanding the peak growing season ecosystem water-use efficiency at four boreal fens in the Athabasca oil sands region

Ecosystem water-use efficiency (WUE), a ratio between gross ecosystem production (GEP) and water loss through evapotranspiration (ET) can be helpful for the assessment of coupled peatland carbon and water cycles under anthropogenic changes in the Athabasca oil sands region (AOSR) where extensive oil sands development has been occurring since the 1960's. As such, this study assessed multiyear peak growing season variability of WUE at four fens (poor treed, poor open, treed moderate-rich, open saline) near Fort McMurray using the eddy covariance technique combined with a set of environmental variables. Freshwater fens were characterized by WUE values within the range reported from other boreal wetlands while a saline fen had significantly lower values of WUE. Negative correlation (R_s < −0.55, p < 0.05) between WUE and net radiation was observed. Moisture conditions were responsible for interannual differences in WUE, whereby increasing WUE under wetter conditions was observed. However, such a pattern was offset by decreased air temperature (T_air) resulting in moisture oversupply. This study also revealed a negative effect of wildfire on WUE due to a prominent decline in GEP and a moderate decrease in ET. WUE can be useful for monitoring the functioning of natural and constructed fens, but a better understanding of WUE variability under a wide range of climatic conditions with respect to differences in vegetation is required.     

Antimony deposits in the Variscan Armorican belt,a link with mafic intrusives?

The Variscan Armorican belt was the World leading producer of antimony at the beginning of the twentieth century. However, geological controls on the deposits remain unconstrained. Here, we illustrate the field setting of mineralisations and perform a statistical analysis using Geographical Information System software, geophysical and geological data. The analysis shows that Sb deposits are spatially correlated with high‐density and magnetic zones. This may reflect the presence of mafic/ultramafic bodies at depth – a suggestion that is further supported by numerous occurrences of doleritic dykes throughout the region. Future antimony prospecting should focus on the high‐density and high‐magnetic zones which comprise about 18% of the Armorican massif. Links between Sb deposits and mafic intrusions are recognised elsewhere in the West European Variscan belt, emphasising that these may be key for antimony prospecting in deformation belts.     

Estimating source regions for snowmelt runoff in a Rocky Mountain basin: tests of a data‐based conceptual modeling approach

In many mountain basins, river discharge measurements are located far away from runoff source areas. This study tests whether a basic snowmelt runoff conceptual model can be used to estimate relative contributions of different elevation zones to basin‐scale discharge in the Cache la Poudre, a snowmelt‐dominated Rocky Mountain river. Model tests evaluate scenarios that vary model configuration, input variables, and parameter values to determine how these factors affect discharge simulation and the distribution of runoff generation with elevation. Results show that the model simulates basin discharge well (NSCE and R >0.90) when input precipitation and temperature are distributed with different lapse rates, with a rain‐snow threshold parameter between 0 and 3.3 °C, and with a melt rate parameter between 2 and 4 mm °C^?1 d^?1 because these variables and parameters can have compensating interactions with each other and with the runoff coefficient parameter. Only the hydrograph recession parameter can be uniquely defined with this model structure. These non‐unique model scenarios with different configurations, input variables, and parameter values all indicate that the majority of basin discharge comes from elevations above 2900 m, or less than 25% of the basin total area, with a steep increase in runoff generation above 2600 m. However, the simulations produce unrealistically low runoff ratios for elevations above 3000 m, highlighting the need for additional measurements of snow and discharge at under‐sampled elevations to evaluate the accuracy of simulated snow and runoff patterns. Copyright © 2013 John Wiley & Sons, Ltd.     

Global sensitivity analysis of DRAINMOD‐FOREST,an integrated forest ecosystem model

Global sensitivity analysis is a useful tool to understand process‐based ecosystem models by identifying key parameters and processes controlling model predictions. This study reported a comprehensive global sensitivity analysis for DRAINMOD‐FOREST, an integrated model for simulating water, carbon (C), and nitrogen (N) cycles and plant growth in lowland forests. The analysis was carried out for multiple long‐term model predictions of hydrology, biogeochemistry, and plant growth. Results showed that long‐term mean hydrological predictions were highly sensitive to several key plant physiological parameters. Long‐term mean annual soil organic C content and mineralization rate were mainly controlled by temperature‐related parameters for soil organic matter decomposition. Mean annual forest productivity and N uptake were found to be mainly dependent upon plant production‐related parameters, including canopy quantum use efficiency and carbon use efficiency. Mean annual nitrate loss was highly sensitive to parameters controlling both hydrology and plant production, while mean annual dissolved organic nitrogen loss was controlled by parameters associated with its production and physical sorption. Parameters controlling forest production, C allocation, and specific leaf area highly affected long‐term mean annual leaf area. Results of this study could help minimize the efforts needed for calibrating DRAINMOD‐FOREST. Meanwhile, this study demonstrates the critical role of plants in regulating water, C, and N cycles in forest ecosystems and highlights the necessity of incorporating a dynamic plant growth model for comprehensively simulating hydrological and biogeochemical processes. Copyright © 2013 John Wiley & Sons, Ltd.     

Prediction-based parking allocation framework in urban environments

ABSTRACT

Finding a parking space is usually challenging in urban areas. The literature shows that 30% of traffic congestion is caused by searching for parking spaces, which results in unnecessary energy consumption and environmental pollution. With the development of sensor technologies, smart parking guidance systems provide users with a variety of real-time parking space information. However, users cannot know whether the target parking space remains available upon arrival. Moreover, parking resources may be under competition when multiple users target the same open parking space. In this research, we develop a new framework named prediction-based parking allocation (PPA) that provides smart parking services to users. In PPA, we first construct a prediction model of parking occupancy and predict the subsequent parking availabilities. Then, we design a matching-based allocation strategy to assign users to selected parking spaces. To the best of our knowledge, this is the first study that combines occupancy prediction and space allocation simultaneously to address smart parking issues. Finally, we collect a real dataset from the SFPark on-street parking system for performance evaluation. According to experimental results, PPA can effectively increase the parking success rate and reduce costs, fuel consumption, and carbon emissions.