Partial Information And Partial Weight - Two New Information Theoretic Metrics To Help Specify A Data-Based Natural System Model

How to define a system? This is a problem faced routinely in science and engineering, with solutions developed from our understanding of the processes inherent, to assessing the underlying structure based on observational evidence alone. In general, system specification involves identifying a few meaningful predictors (from a large enough set that is plausibly related to the response) and formulating a relation between them and the system response being modeled. For systems where physical relationships are less apparent, and sufficient observational records exist, a range of statistical alternatives have been investigated as a possible way of specifying the underlying form. Here, we introduce partial information (PI) as a new means for specifying the system, its key advantage being the relative lack of major assumptions about the processes being modeled in order to characterize the complete system. In addition to PI which offers a means of identifying the system predictors of interest, we also introduce the concept of partial weights (PW) which use the identified predictors to formulate a predictive model that acknowledges the relative contributions varied predictor variables make to the prediction of the response. We assess the utility of the PI-PW framework using synthetically generated datasets from known linear, non-linear and high-dimensional dynamic yet chaotic systems, and demonstrate the efficacy of the procedure in ascertaining the underlying true system with varying extents of observational evidence available. We highlight how this framework can be invaluable in formulating prediction models for natural systems which are modeled using empirical or semi-empirical alternatives, and discuss current limitations that still need to be overcome

Quantifying GCM Simulation Uncertainty And Incorporating Into Water Resources Assessment

Rainfall and temperature, simulated using Global Climate Models (GCMs), serve as key inputs for hydrological models in studying catchment responses to climate scenarios. GCM simulations of rainfall and temperature, however, are uncertain due to model structure, scenarios and initial conditions, which results in biased outcomes if used for hydrological models without due consideration of the uncertainties. In this study, we develop a novel uncertainty metric, square root error variance (SREV), to quantify uncertainties involved in GCM rainfall and temperature simulations as well as illustrate its application for water resources assessment. The uncertainty metric involves converting multiple GCM simulations into their percentile, estimating uncertainties at each quantile and translating these uncertainties into time-series. We apply the method to estimate uncertainties in rainfall and temperature simulations using multiple GCM, scenarios and ensemble runs. The utility of the uncertainty estimate for water resources assessment is illustrated through two case studies: (1) future drought analysis across the world; and (2) water availability study at the Warragamba catchment, Sydney, Australia. In the first case, future drought is estimated using Standard Precipitation Index (SPI) with simulation-extrapolation (an algorithm that reduces parameter bias when input errors are known) being used to reduce biases in SPI parameter. In the second case, an additive error model is proposed to generate rainfall and temperature realizations that are used to simulate streamflow. Future storage requirement of the reservoir is then evaluated with its associated uncertainty using behavior analysis. The results suggest that GCM uncertainty arises mainly from model structural errors, for both rainfall and temperature. Consideration of these uncertainties in drought analysis is vital, as drought values with and without considering the uncertainties are significantly different. It is also found that the existing storage capacity of the Warragamba reservoir suffices the future requirements, although large uncertainty exists in the storage estimates

Continuous rainfall simulation: 2. A regionalized daily rainfall generation approach

This paper is the second of two in the current issue that presents a framework for simulating continuous (uninterrupted) rainfall sequences at both gaged and ungaged locations. The ultimate objective of the papers is to present a methodology for stochastically generating continuous subdaily rainfall sequences at any location such that the statistics at a range of aggregation scales are preserved. In this paper we complete the regionalized algorithm by adopting a rationale for generating daily sequences at any location by sampling daily rainfall records from "nearby" gages with statistically similar rainfall sequences.The approach consists of two distinct steps: first the identification of a set of locations with daily rainfall sequences that are statistically similar to the location of interest, and second the development of an algorithm to sample daily rainfall from those locations. In the first step, the similarity between all bivariate combinations of 2708 daily rainfall records across Australia were considered, and a logistic regression model was formulated to predict the similarity between stations as a function of a number of physiographic covariates. Based on the model results, a number of nearby locations with adequate daily rainfall records are identified for any ungaged location of interest (the "target" location), and then used as the basis for stochastically generating the daily rainfall sequences. The continuous simulation algorithm was tested at five locations where long historical daily rainfall records are available for comparison, and found to perform well in representing the distributional and dependence attributes of the observed daily record. These daily sequences were then used to disaggregate to a subdaily time step using the rainfall state-based disaggregation approach described in the first paper, and found to provide a good representation of the continuous rainfall sequences at the location of interest. Copyright 2012 by the American Geophysical Union.Rajeshwar Mehrotra, Seth Westra, Ashish Sharma and Ratnasingham Srikantha

Rectifying low-frequency variability in future climate sea surface temperature simulations: are corrections for extreme change scenarios realistic?

Most procedures for redressing systematic bias in climate modeling are calibrated using current climate observations, and perform well. However, their performance in the future climate remains uncertain as no observations exist to compare against. In this context, we use the current and future climate outputs of an ultra-high resolution of Community Earth System Model (UHR-CESM) as the representative truth and bias correct monthly sea surface temperature (SST) simulations of eight Coupled Model Intercomparison Project 6 models over the Niño 3.4 region. A time-frequency bias correction approach is used to correct for bias in distributional, trend, and spectral attributes present in the models. This results in a near perfect power spectrum of the bias corrected current climate model simulations. Considering all correction procedures remain unchanged into the future, the overall representation of the corrected SST simulations shows improvement with consistency across models for the doubled CO _2 scenario, but higher variability and lower consistency in the quadrupled CO _2 concentration scenario

Incorporating nonstationarity in regional flood frequency analysis procedures to account for climate change impact

There now exists clear evidence of the impact of climate change on flooding, creating need for new methodologies for design flood estimation that account for warming induced nonstationarity. Current alternatives for nonstationary flood frequency analysis require the specification of a nonstationary probability model (often with time-varying parameters) that requires sufficient data to be stable. Reliance on the trend in observed data often ignores the dissimilar behaviour of frequent and rare quantiles under a warming climate. This, in turn, results in the direction of change to be consistent across both rare and frequent projected flood quantiles. However, results of multiple recent studies suggest that rarer floods are likely to increase while more frequent floods may decrease in magnitude into the future. In this study, we highlight this limitation of existing nonstationary flood frequency approaches and propose a novel nonstationary regional flood frequency analysis approach that captures the differing behaviour of more frequent and rare flood quantiles under a warming climate. The need for longer flood data to model nonstationarity is accommodated by pooling regional information which makes the projections more precise. Data for 105 Australian catchments are used to validate our proposed approach. Results show the effectiveness of the proposed method in capturing the variation of flood quantiles with varying average recurrence intervals in a changing climate. Finally, a few important statistics of future projections are also presented

Multisite rainfall stochastic downscaling for climate change impact assessment

This thesis presents the development and application of a downscaling framework for multi site simulation of daily rainfall. The rainfall simulation is achieved in two stages. First, rainfall occurrences at multiple sites are downscaled, which is followed by the generation of daily rainfall amounts at each site identified as wet. A continuous weather state based nonparametric downscaling model conditional on atmospheric predictors and a previous day average rainfall state is developed for simulation of multi site rainfall occurrences. A nonparametric kernel density approach is used for simulation of rainfall amounts at individual sites conditional on atmospheric variables and the previous day rainfall amount. The proposed model maintains spatial correlation of rainfall occurrences by simulating concurrently at all stations and of amounts by using random innovations that are spatially correlated yet serially independent. Temporal dependence is reproduced in the occurrence series by conditioning on previous day average wetness fraction and assuming the weather states to be Markovian, and in the amount series by conditioning on the previous day rainfall amount. The seasonal transition is maintained by simulating rainfall on a day-to-day basis using a moving window formulation. The developed downscaling framework is calibrated using the relevant atmospheric variables and rainfall records of 30 stations around Sydney, Australia. Results indicate a better representation of the spatio-temporal structure of the observed rainfall as compared to existing alternatives. Subsequently, the framework is applied to predict plausible changes in rainfall in warmer conditions using the same set of atmospheric variables for future climate obtained as a General Circulation Model simulation. While the case studies presented are restricted to a specific region, the downscaling model is designed to be useful in any generic catchment modelling and management activity and/or for investigating possible changes that might be experienced by hydrological, agricultural and ecological systems in future climates

Association of betel nut with carcinogenesis: revisit with a clinical perspective.

Betel nut (BN), betel quid (BQ) and products derived from them are widely used as a socially endorsed masticatory product. The addictive nature of BN/BQ has resulted in its widespread usage making it the fourth most abused substance by humans. Progressively, several additives, including chewing tobacco, got added to simple BN preparations. This addictive practice has been shown to have strong etiological correlation with human susceptibility to cancer, particularly oral and oropharyngeal cancers.The PUBMED database was searched to retrieve all relevant published studies in English on BN and BQ, and its association with oral and oropharyngeal cancers. Only complete studies directly dealing with BN/BQ induced carcinogenesis using statistically valid and acceptable sample size were analyzed. Additional relevant information available from other sources was also considered.This systematic review attempts to put in perspective the consequences of this widespread habit of BN/BQ mastication, practiced by approximately 10% of the world population, on oral cancer with a clinical perspective. BN/BQ mastication seems to be significantly associated with susceptibility to oral and oropharyngeal cancers. Addition of tobacco to BN has been found to only marginally increase the cancer risk. Despite the widespread usage of BN/BQ and its strong association with human susceptibility to cancer, no serious strategy seems to exist to control this habit. The review, therefore, also looks at various preventive efforts being made by governments and highlights the multifaceted intervention strategies required to mitigate and/or control the habit of BN/BQ mastication

Simplified flow chart of main events of BN induced carcinogenesis.

<p>The simplified flow chart is intended to highlight the complexity of BN and its constituents, and how they affect different metabolic components and systems of a cell to eventually lead to carcinogenic transformation. For more details see reviews in references 2–5.</p

p53 associated alterations in betel nut (BN) and/or betel quid (BQ) associated human precancerous lesions/cancers.

<p>p53 associated alterations in betel nut (BN) and/or betel quid (BQ) associated human precancerous lesions/cancers.</p

Flow chart of included studies.

<p>The flow chart depicts the number of citations and resource materials that have been screened, excluded and/or included in the systematic review.</p