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Modern Applied Statistics With S

The Intergovernmental Panel on Climate Change (IPCC) Technical Paper Climate Change and Water draws together and evaluates the information in IPCC Assessment and Special Reports concerning the impacts of climate change on hydrological processes and regimes, and on freshwater resources – their availability, quality, use and management. It takes into account current and projected regional key vulnerabilities, prospects for adaptation, and the relationships between climate change mitigation and water. Its objectives are:

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Climate change and water.

There is now a large published literature on the strengths and weaknesses of downscaling methods for different climatic variables, in different regions and seasons. However, little attention is given to the choice of downscaling method when examining the impacts of climate change on hydrological systems. This review paper assesses the current downscaling literature, examining new developments in the downscaling field specifically for hydrological impacts. Sections focus on the downscaling concept; new methods; comparative methodological studies; the modelling of extremes; and the application to hydrological impacts.

Consideration is then given to new developments in climate scenario construction which may offer the most potential for advancement within the ‘downscaling for hydrological impacts’ community, such as probabilistic modelling, pattern scaling and downscaling of multiple variables and suggests ways that they can be merged with downscaling techniques in a probabilistic climate change scenario framework to assess the uncertainties associated with future projections. Within hydrological impact studies there is still little consideration given to applied research; how the results can be best used to enable stakeholders and managers to make informed, robust decisions on adaptation and mitigation strategies in the face of many uncertainties about the future. It is suggested that there is a need for a move away from comparison studies into the provision of decision-making tools for planning and management that are robust to future uncertainties; with examination and understanding of uncertainties within the modelling system. Copyright © 2007 Royal Meteorological Society

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Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

Changes in climate extremes and their impacts on the natural physical environment: An overview of the IPCC SREX report

Understanding the variability of the terrestrial hydrologic cycle is central to determining the potential for extreme events and susceptibility to future change. In the absence of long-term, large-scale observations of the components of the hydrologic cycle, modeling can provide consistent fields of land surface fluxes and states. This paper describes the creation of a global, 50-yr, 3-hourly, 1.0° dataset of meteorological forcings that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature, and radiation. Corrections are also made to the ra...

https://iri.columbia.edu/~blyon/REFERENCES/P30.pdf

Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling

Yearly global mean temperature and ocean levels, daily share prices, and the signals transmitted back to Earth by the Voyager space craft are all examples of sequential observations over time known as time series. This book gives you a step-by-step introduction to analysing time series using the open source software R. Each time series model is motivated with practical applications, and is defined in mathematical notation. Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data.Finally, the model is used to analyse observeddata taken from apractical application. By using R, the whole procedure can be reproduced by the reader. All the data sets used in the book are available on the website http://www.massey.ac.nz/~pscowper/ts. The book is written for undergraduate students of mathematics, economics, business and finance, geography, engineering and related disciplines, and postgraduate students who may need to analyse time series as part of their taught programme or their research.

https://www.springer.com/productFlyer_978-0-387-88697-8.pdf?SGWID=0-0-1297-173882905-0

Introductory Time Series with R

RainSim V3 is a robust and well tested stochastic rainfall field generator used successfully in a broad range of climates and end-user applications. Rainfall fields or multi-site time series can be sampled from a spatial-temporal Neyman-Scott rectangular pulses process: storm events occur as a temporal Poisson process; each triggers raincell generation using a stationary spatial Poisson process; raincells are clustered in time lagging the storm event; each raincell contributes rainfall uniformly across its circular extent and throughout its lifetime; raincell lag, duration, radius and intensity are random variables; orographic effects are accounted for by non-uniform scaling of the rainfall field. Robust and efficient numerical optimization schemes for model calibration are identified following the evaluation of five schemes with optional log-transformation of the parameters. The log-parameter Shuffled Complex Evolution (lnSCE) algorithm with a convergence criterion is chosen for single site applications and an effort limited restarted lnSCE algorithm is selected for spatial applications. The new objective function is described and shown to improve model calibration. Linear and quadratic expressions are identified which can reduce the bias between the fitted and simulated probabilities of both dry hours and dry days as used in calibration. Exact fitting of mean rainfall statistics is also implemented and demonstrated. An application to the Dommel catchment on the Netherlands/Belgian border illustrates the ability of the improved model to match observed statistics and extremes.

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RainSim: A spatial-temporal stochastic rainfall modelling system

Stochastic point process modelling of rainfall. I. Single-site fitting and validation

This paper provides some useful results for modeling rainfall. It extends work on the Neyman-Scott cluster model for simulating rainfall time series. Several important properties have previously been found for the model, for example, the expectation and variance of the amount of rain captured in an arbitrary time interval (Rodriguez-Iturbe et al., 1987a), In this paper additional properties are derived, such as the probability of an arbitrary interval of any chosen length being dry. In applications this is a desirable property to have, and is often used for fitting stochastic rainfall models to field data. The model is currently being used in rainfall time series research directed toward improving sewage systems in the United Kingdom. To illustrate the model's performance an example is given, where the model is fitted to 10 years of hourly data taken from Blackpool, England.

Further developments of the neyman‐scott clustered point process for modeling rainfall

The objective in this paper is to present and fit a relatively simple stochastic spatial-temporal model of rainfall in which the arrival times of rain cells occur in a clustered point process. In the x - y plane, rain cells are represented as discs; each disc having a random radius; the locations of the disc centres being given by a two-dimensional Poisson process. The intensity of each cell is a random variable that remains constant over the area of the disc and throughout the lifetime of the cell, the lifetime being an exponential random variable. The cells are randomly classified from 1 to n with different parameters for the different cell types, so that the random variables of an arbitrary cell, e. g. radius and intensity, are correlated. Multi-site second-order properties are derived and used to fit the model to hourly rainfall data taken from six sites in the Thames basin, UK.

Paul S. P. Cowpertwait

Papers

Introductory Time Series with R

RainSim: A spatial-temporal stochastic rainfall modelling system

Stochastic point process modelling of rainfall. I. Single-site fitting and validation

Further developments of the neyman‐scott clustered point process for modeling rainfall

A Generalized Spatial-Temporal Model of Rainfall Based on a Clustered Point Process