scholarly journals Baseline geographic information on wildfire-watershed risk in Canada: Needs, gaps, and opportunities

2021 ◽  
Author(s):  
François-Nicolas Robinne ◽  
Catherine Paquette ◽  
Dennis Hallema ◽  
Kevin D. Bladon ◽  
Marc Parisien

As the pressures on water supply from shifting forest disturbance regimes continue to escalate, researchers are being asked to answer increasingly complex questions. However, many questions in wildfire-watershed risk (WWR) research remained unaddressed due to a paucity of relevant datasets. There are, indeed, many fundamental processes we do not understand that require additional data collection to develop risk management frameworks. As such, WWR researchers and managers face a paradox in their need to address critical questions important for the sustainability of socio-hydrological systems while dealing with incomplete information. In many cases, this leads to valuable research ideas being discarded on the account of limited data availability. However, imperfect, incomplete, or limited data should not deter researchers and managers from performing analyses to assess risk. In fact, such analyses improve the research benefit-to-cost ratio of existing data, help unravel gaps in data sources, enable generation of new hypotheses, and highlight where data availability and openness can be improved. If we do not use what we have, how can we know what we need? This issue is of particular interest in Canada, where baseline WWR information for the entire country is generally missing, despite growing concerns about water security in the face of a shifting wildfire regimes. In this commentary, we (a) identify several relevant open geospatial datasets, (b) illustrate how these datasets can be leveraged to produce simple yet relevant risk information, (c) identify some high priority data gaps that require immediate attention, and (d) discuss future avenues towards the creation of baseline Pan-Canadian WWR information.

Author(s):  
Alex van Dulmen ◽  
Martin Fellendorf

In cases where budgets and space are limited, the realization of new bicycle infrastructure is often hard, as an evaluation of the existing network or the benefits of new investments is rarely possible. Travel demand models can offer a tool to support decision makers, but because of limited data availability for cycling, the validity of the demand estimation and trip assignment are often questionable. This paper presents a quantitative method to evaluate a bicycle network and plan strategic improvements, despite limited data sources for cycling. The proposed method is based on a multimodal aggregate travel demand model. Instead of evaluating the effects of network improvements on the modal split as well as link and flow volumes, this method works the other way around. A desired modal share for cycling is set, and the resulting link and flow volumes are the basis for a hypothetical bicycle network that is able to satisfy this demand. The current bicycle network is compared with the hypothetical network, resulting in preferable actions and a ranking based on the importance and potentials to improve the modal share for cycling. Necessary accompanying measures for other transport modes can also be derived using this method. For example, our test case, a city in Austria with 300,000 inhabitants, showed that a shift of short trips in the inner city toward cycling would, without countermeasures, provide capacity for new longer car trips. The proposed method can be applied to existing travel models that already contain a mode choice model.


2012 ◽  
Vol 87 (3) ◽  
pp. 839-865 ◽  
Author(s):  
Daniel A. Bens ◽  
Theodore H. Goodman ◽  
Monica Neamtiu

ABSTRACT This study examines whether managers alter their financial reporting decisions in the face of investment-related pressure. We define investment-related pressure as the increased pressure managers feel to retain their job following an M&A poorly received by the market. We hypothesize that managers attempt to assuage pressure by delivering strong performance post-merger, creating incentives for misreporting. Our findings indicate that acquirers with more negative M&A announcement returns are more likely to misstate financial statements in the post-investment period and the issuance of misstated financials mitigates this pressure, at least in the near term. Our study contributes to the literature on the relation between corporate investing and financial reporting by showing how investment-related pressure leads to misreporting, even in a setting where the costs (e.g., greater probability of detection) are high. Our study also has implications for the large body of research that evaluates various consequences of M&As using post-merger performance. Specifically, researchers should be careful to distinguish real from misstated financial performance in the post-investment period. Data Availability: Data are available from the public sources indicated in the text.


2015 ◽  
Vol 17 (5) ◽  
pp. 789-804 ◽  
Author(s):  
Marius Møller Rokstad ◽  
Rita Maria Ugarelli

Ensuring reliable structural condition of sewers is an important criterion for sewer rehabilitation decisions. Deterioration models applied to sewer pipes support the rehabilitation planning by means of prioritising pipes according to their current and predicted structural status. There is a benefit in applying such models if sufficient inspection data for calibration, an appropriate deterioration model, and adequate covariates to explain the variability in the conditions are available. In this paper it is discussed up to what level the application of sewer deterioration models can be beneficial under limited data availability. The findings show that the indirect nature of the explanatory covariates which are commonly used in sewer deterioration models makes it difficult to harness any benefit from modelling sewer conditions at a network level, but that the deterioration model application still may be beneficial for prioritising inspection candidates. The prediction power of the current sewer deterioration models is limited by the adequacy of the explanatory variables available, and by the fact that different failure modes are mixed in the aggregated condition class, and not modelled explicitly.


2008 ◽  
Vol 53 (3) ◽  
pp. 588-601 ◽  
Author(s):  
ALEJANDRA STEHR ◽  
PATRICK DEBELS ◽  
FRANCISCO ROMERO ◽  
HERNAN ALCAYAGA

2020 ◽  
Vol 69 (2) ◽  
pp. 85-107
Author(s):  
Christoph Duden

The analysis of income risk is the basis for successful whole farm risk management. The measurement of risks helps to objectively assess the farms’ individual risk exposure. However, due to limited data availability, comprehensive overall risk analyses are often scarce, e.g. for Germany. The present study analyses risk exposure for more than 3,000 farms in Germany in the period 1996/97-2015/16 on the basis of the national Farm Accountancy Data Network (FADN). Our results show that (i) risk exposure is heterogeneous and that fluctuations and particularly large decreases in farm income are rarely attributable to individual risk components (e. g. prices or yields), (ii) farm income risk has been higher in the period after 2007 for many farms, especially arable and dairy farms, (iii) while the income risk in dairy farming increased, it is still lower than that of most other farm types in the period 2006/07-2015/16, (iv) the for-mation of expected values has a significant influence on the absolute level of the measured risk and should be given more attention in future research.


2021 ◽  
pp. 21-32
Author(s):  
Peter Joo Hee Ng ◽  
Sharon Zheng

2010 ◽  
Vol 9 (2) ◽  
Author(s):  
Maria Nieswand ◽  
Astrid Cullmann ◽  
Anne Neumann

We empirically demonstrate a practical approach of efficiency evaluation with limited data availability in some regulated industries. We apply PCA-DEA for radial efficiency measurement to U.S. natural gas transmission companies in 2007. PCA-DEA reduces dimensions of the optimization problem while maintaining most of the variation in the original data. Our results suggest that the methodology reduces the probability of over-estimation of individual firm-specific performance.


2019 ◽  
Vol 131 ◽  
pp. 01064 ◽  
Author(s):  
Guang Deng ◽  
Peng Zhang ◽  
Zhiyong Li ◽  
xin Tian

GF-6 satellite is a kind of high-resolution satellites launched by China in recent years. Its sensors have the characteristics of multispectrals, wide field of view, high spatial resolution and high frequency imaging. In order to carry out fine identification of forest types, this paper proposes a method to improve data screening efficiency and data availability rate in GF-6 satellite data selection stage. This paper describes the selection process and key technical methods of GF-6 satellite data, and gives a verification program. It has been proved that the program meets the design objectives and can quickly scree out the required fast screening technologies in the face of massive data and large-area business applications, thus increasing the degree of automation and reducing the workload of manual visual selection.


Sign in / Sign up

Export Citation Format

Share Document