621 research outputs found
Interactions between thresholds and spatial discretizations of snow: insights from estimates of wolverine denning habitat in the Colorado Rocky Mountains
Thresholds can be used to interpret environmental data in a way
that is easily communicated and useful for decision-making purposes.
However, thresholds are often developed for specific data products and time
periods, changing findings when the same threshold is applied to datasets or
periods with different characteristics. Here, we test the impact of
different spatial discretizations of snow on annual estimates of wolverine
denning opportunities in the Colorado Rocky Mountains, defined using a snow
water equivalent (SWE) threshold (0.20 m) and threshold date (15 May) from
previous habitat assessments. Annual potential wolverine denning area (PWDA)
was thresholded from a 36-year (1985–2020) snow reanalysis model with
three different spatial discretizations: (1) 480 m grid cells (D480), (2) 90 m
grid cells (D90), and (3) 480 m grid cells with implicit representations of
subgrid snow spatial heterogeneity (S480). Relative to the D480 and S480
discretizations, D90 resolved shallower snow deposits on slopes between 3050
and 3350 m elevation, decreasing PWDA by 10 %, on average. In years with
warmer and/or drier winters, S480 discretizations with subgrid
representations of snow heterogeneity increased PWDA, even within grid cells
where mean 15 May SWE was less than the SWE threshold. These simulations
increased PWDA by upwards of 30 % in low-snow years, as compared to the
D480 and D90 simulations without subgrid snow heterogeneity. Despite PWDA
sensitivity to different snow spatial discretizations, PWDA was controlled
more by annual variations in winter precipitation and temperature. However,
small changes to the SWE threshold (±0.07 m) and threshold date
(±2 weeks) also affected PWDA by as much as 82 %. Across these
threshold ranges, PWDA was approximately 18 % more sensitive to the SWE
threshold than the threshold date. However, the sensitivity to the threshold
date was larger in years with late spring snowfall, when PWDA depended on
whether modeled SWE was thresholded before, during, or after spring snow
accumulation. Our results demonstrate that snow thresholds are useful but
may not always provide a complete picture of the annual variability in
snow-adapted wildlife denning opportunities. Studies thresholding
spatiotemporal datasets could be improved by including (1) information about
the fidelity of thresholds across multiple spatial discretizations and (2) uncertainties related to ranges of realistic thresholds.</p
Weak differentiability of product measures
In this paper, we study cost functions over a finite collection of random variables. For these types of models, a calculus of differentiation is developed that allows us to obtain a closed-form expression for derivatives where "differentiation" has to be understood in the weak sense. The technique for proving the results is new and establishes an interesting link between functional analysis and gradient estimation. The key contribution of this paper is a product rule of weak differentiation. In addition, a product rule of weak analyticity is presented that allows for Taylor series approximations of finite products measures. In particular, from characteristics of the individual probability measures, a lower bound (i.e., domain of convergence) can be established for the set of parameter values for which the Taylor series converges to the true value. Applications of our theory to the ruin problem from insurance mathematics and to stochastic activity networks arising in project evaluation review techniques are provided. © 2010 INFORMS
Entire curves avoiding given sets in C^n
Let be a proper closed subset of and
at most countable (). We give conditions
of and , under which there exists a holomorphic immersion (or a proper
holomorphic embedding) with .Comment: 10 page
Bergman kernel and complex singularity exponent
We give a precise estimate of the Bergman kernel for the model domain defined
by where
is a holomorphic map from to ,
in terms of the complex singularity exponent of .Comment: to appear in Science in China, a special issue dedicated to Professor
Zhong Tongde's 80th birthda
Drought impact in the Bolivian Altiplano agriculture associated with the El Niño–Southern Oscillation using satellite imagery data
Drought is a major natural hazard in the Bolivian Altiplano that causes large agricultural losses. However, the drought effect on agriculture varies largely on a local scale due to diverse factors such as climatological and hydrological conditions, sensitivity of crop yield to water stress, and crop phenological stage among others. To improve the knowledge of drought impact on agriculture, this study aims to classify drought severity using vegetation and land surface temperature data, analyse the relationship between drought and climate anomalies, and examine the spatio-temporal variability of drought using vegetation and climate data. Empirical data for drought assessment purposes in this area are scarce and spatially unevenly distributed. Due to these limitations we used vegetation, land surface temperature (LST), precipitation derived from satellite imagery, and gridded air temperature data products. Initially, we tested the performance of satellite precipitation and gridded air temperature data on a local level. Then, the normalized difference vegetation index (NDVI) and LST were used to classify drought events associated with past El Niño–Southern Oscillation (ENSO) phases. It was found that the most severe drought events generally occur during a positive ENSO phase (El Niño years). In addition, we found that a decrease in vegetation is mainly driven by low precipitation and high temperature, and we identified areas where agricultural losses will be most pronounced under such conditions. The results show that droughts can be monitored using satellite imagery data when ground data are scarce or of poor data quality. The results can be especially beneficial for emergency response operations and for enabling a proactive approach to disaster risk management against droughts
Improving the Feature Stability and Classification Performance of Bimodal Brain and Heart Biometrics
Electrical activities from brain (electroencephalogram, EEG) and heart (electrocardiogram, ECG) have been proposed as biometric modalities but the combined use of these signals appear not to have been studied thoroughly. Also, the feature stability of these signals has been a limiting factor for biometric usage. This paper presents results from a pilot study that reveal the combined use of brain and heart modalities provide improved classification performance and further-more, an improvement in the stability of the features over time through the use of binaural brain entrainment. The classification rate was increased, for the case of the neural network classifier from 92.4% to 95.1% and for the case of LDA, from 98.6% to 99.8%. The average standard deviation with binaural brain entrainment using all the inter-session features (from all the subjects) was 1.09, as compared to 1.26 without entrainment. This result suggests the improved stability of both the EEG and ECG features over time and hence resulting in higher classification performance. Overall, the results indicate that combining ECG and EEG gives improved classification performance and that through the use of binaural brain entrainment, both the ECG and EEG features are more stable over time
Reinstated p53 response and high anti-T-cell leukemia activity by the novel alkylating deacetylase inhibitor tinostamustine
Non peer reviewe
Copernicus Cal/Val Solution - D4.1 - Roadmap and Sustainability Analysis
This document analyses funding and schedule aspects of the Copernicus Cal/Val Solution
- …