Statistics for Spatial Data.

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

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Climate Change 2007: The Physical Science Basis.

Dissolved organic matter (DOM) in soils plays an important role in the biogeochemistry of carbon, nitrogen, and phosphorus, in pedogenesis, and in the transport of pollutants in soils. The aim of this review is to summarize the recent literature about controls on DOM concentrations and fluxes in soi

Controls on the dynamics of dissolved organic matter in soils: a review.

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

Conceptual modelling requires the identification of a suitable model structure and the estimation of parameter values through calibration against observed data. A lack of objective approaches to evaluate model structures and the inability of calibration procedures to distinguish between the suitability of different parameter sets are major sources of uncertainty in current modelling procedures. This paper presents an approach analysing the performance of the model in a dynamic fashion resulting in an improved use of available information. Model structures can be evaluated with respect to the failure of individual components, and periods of high information content for specific parameters can be identified. The procedure is termed dynamic identifiability analysis (DYNIA) and is applied to a model structure built from typical conceptual components. Copyright © 2003 John Wiley & Sons, Ltd.

Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis

. Many existing hydrological modelling procedures do not make best use of available information, resulting in non-minimal uncertainties in model structure and parameters, and a lack of detailed information regarding model behaviour. A framework is required that balances the level of model complexity supported by the available data with the level of performance suitable for the desired application. Tools are needed that make optimal use of the information available in the data to identify model structure and parameters, and that allow a detailed analysis of model behaviour. This should result in appropriate levels of model complexity as a function of available data, hydrological system characteristics and modelling purpose. This paper introduces an analytical framework to achieve this, and tools to use within it, based on a multi-objective approach to model calibration and analysis. The utility of the framework is demonstrated with an example from the field of rainfall-runoff modelling. Keywords: hydrological modelling, multi-objective calibration, model complexity, parameter identifiability

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A framework for development and application of hydrological models

Land use, water management and future flood risk.

https://www.engr.psu.edu/ce/Divisions/Hydro/Wagener/PublicationsPDFs/JoH2006%20Wagener%20and%20Wheater.pdf

Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty

This important monograph is based on the results of a study on the identification of conceptual lumped rainfall-runoff models for gauged and ungauged catchments. The task of model identification remains difficult despite decades of research. A detailed problem analysis and an extensive review form the basis for the development of a Matlab[registered] modelling toolkit consisting of two components: a Rainfall-Runoff Modelling Toolbox (RRMT) and a Monte Carlo Analysis Toolbox (MCAT). These are subsequently applied to study the tasks of model identification and evaluation. A novel dynamic identifiability approach has been developed for the gauged catchment case. The theory underlying the application of rainfall-runoff models for predictions in ungauged catchments is studied, problems are highlighted and promising ways to move forward are investigated. Modelling frameworks for both gauged and ungauged cases are developed. This book presents the first extensive treatment of rainfall-runoff model identification in gauged and ungauged catchments.

Howard Wheater

Papers

Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis

A framework for development and application of hydrological models

Land use, water management and future flood risk.

Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty

Rainfall-runoff modelling in gauged and ungauged catchments