USING HERBARIUM DATA TO INCREASE THE LIKELIHOOD OF FINDING FERTILE PLANTS IN THE FIELD

The Phenological Predictability Index (PPI) is an algorithm incorporated into Brahms, one of the most widely used herbarium database management systems. PPI uses herbarium specimen data to calculate the probability of the occurrence of various phenological events in the field. Our hypothesis was that use of PPI to quantify the likelihood that a given species will be found in flower bud, flower or fruit in a particular area in a specific period makes field expeditions more successful in terms of finding fertile plants. PPI was applied to herbarium data for various angiosperm species locally abundant in Central Brazil to determine the month in which they were most likely to be found, in each of five areas of the Distrito Federal, with flower buds, flowers or fruits (i.e. the ‘maximum probability month’ for each of these phenophases). Plants of the selected species growing along randomised transects were tagged and their phenology was monitored over 12 months (method 1), and two one-day field excursions to each area were undertaken, by botanists with no prior knowledge of whether the species had previously been recorded at these sites, to record their phenological state (method 2). The results showed that field excursions in the PPI-determined maximum probability month for flower buds, flowers or fruits would be expected to result in a > 90% likelihood of finding individual plants of a given species in each of these phenophases. PPI may fail to predict phenophase for species with supra-annual reproductive events or with high event contingency. For bimodal species, the PPI-determined maximum probability month is that in which a specific phenophase is likely to be most intense. In planning an all-purpose collecting trip to an area with seasonal plant fertility, PPI scores are useful when selecting the best month for travel.

Trends, Variability, and Seasonality of Maximum Annual Daily Precipitation in the Upper Vistula Basin, Poland

The aim of this study was to detect trends in maximum annual daily precipitation in the Upper Vistula Basin. We analyzed data from 51 weather stations between 1971 and 2014. Then we used the Mann–Kendall test to detect monotonical trends of the precipitation for three significance levels: 1, 5, and 10%. Our analysis of weather conditions helped us describe the mechanism behind the formation of maximum annual daily precipitation. To analyze precipitation seasonality, we also used Colwell indices. Our study identified a significant trend of the highest daily precipitation for the assumed significance levels (0.01, 0.05, 0.1) for 22% of the investigated weather stations at different elevations. The significant trends found were positive and an increase in precipitation is expected. From 1971 to 2014, the maximum daily total precipitation most often occurred in the summer half-year, i.e., from May until September. These months included a total of 88% of days with the highest daily precipitation. The predictability index for the highest total precipitation within the area was high and exceeded 5%. It was markedly affected by the coefficient of constancy (C) and to a lesser degree by the seasonality index (M). Our analysis demonstrated a convergence of the Colwell indices and frequency of cyclonic situation and, therefore, confirmed their usability in the analysis of precipitation seasonality.

The ENSO Transition Probabilities

ENSO is investigated here by considering it as a transition from different states. Transition probability matrices can be defined to describe the evolution of ENSO in this way. Sea surface temperature anomalies are classified into four categories, or states, and the probability to move from one state to another has been calculated for both observations and a simulation from a GCM. This could be useful for understanding and diagnosing general circulation models elucidating the mechanisms that govern ENSO in models. Furthermore, these matrices have been used to define a predictability index of ENSO based on the entropy concept introduced by Shannon. The index correctly identifies the emergence of the spring predictability barrier and the seasonal variations of the transition probabilities. The transition probability matrices could also be used to formulate a basic prediction model for ENSO that was tested here on a case study.

Time-Varying Return Predictability in the Chinese Stock Market

China’s stock market is the largest emerging market in the world. It is widely accepted that the Chinese stock market is far from efficiency and it possesses possible linear and nonlinear dependencies. We study the predictability of returns in the Chinese stock market by employing the wild bootstrap automatic variance ratio test and the generalized spectral test. We find that the return predictability vary over time and a significant return predictability is observed around market turmoils. Our findings are consistent with the Adaptive Markets Hypothesis (AMH) and have practical implications for market participants and policy makers. A predictability index can be constructed for each asset, which might help warn a crisis is in store, ease the development of the ongoing bubble, and stabilize the market.

Fractal Dimensional Analysis in Financial Time Series

A predictability index for time series of a financial market vector consisting of chosen market parameters is suggested providing a measure of long range predictability of the market. It is based on fractional Brownian motion that includes Brownian motion as a particular case followed by the time series of financial market parameters. By analyzing respective time series, these indices are computed for parameters like volatility, FII investments in the local market, IIP numbers, CPI numbers, Dow Jones Index, different stock market indices, currency rates, and gold prices.

Landslide forecasting and factors influencing predictability

Forecasting a catastrophic collapse is a key element in landslide risk reduction, but it is also a very difficult task owing to the scientific difficulties in predicting a complex natural event and also to the severe social repercussions caused by a false or missed alarm. A prediction is always affected by a certain error; however, when this error can imply evacuations or other severe consequences a high reliability in the forecast is, at least, desirable. In order to increase the confidence of predictions, a new methodology is presented here. In contrast to traditional approaches, this methodology iteratively applies several forecasting methods based on displacement data and, thanks to an innovative data representation, gives a valuation of the reliability of the prediction. This approach has been employed to back-analyse 15 landslide collapses. By introducing a predictability index, this study also contributes to the understanding of how geology and other factors influence the possibility of forecasting a slope failure. The results showed how kinematics, and all the factors influencing it, such as geomechanics, rainfall and other external agents, are key concerning landslide predictability.

Correlation between predictability index and the error performance of customer baseline load (CBL) calculation

This paper attempts to explore the correlation between the content of high frequency component of customers' historical consumption data (measured by a proposed index called predictability index) and the accuracy of Customer Baseline Load (CBL) calculation methods. In this paper, the customer's consumption signal is transformed from time-domain to frequency domain to separate the high and low frequency components of the consumption signal. Then, after reconstructing the time-domain equivalent of both of these signals, the predictability index for all customers are calculated. The data employed by this study belong to Australian Energy Market Operation (AEMO), and is the hourly consumption of 189 customers for the time span of a year (2012). This index is proposed to be used for the purpose of clustering the customers into different bins by K-means clustering algorithm. Then the CBL for customers of each bin is calculated by two methods of CAISO and Randomized Controlled Trial (RCT), and then the average error in each bin is computed. Afterwards, the correlation between the average P_index of each bin, and its normalized average error is calculated. It is found that there is a strong correlation between the P_index and the error performance of the CBL calculation methods.