Stochastic modelling of daily rainfall in Nigeria: intra-annual variation of model parameters

Jimoh, O. D.; Webster, Peter J.

doi:10.1016/s0022-1694(99)00088-8

Cited by 25 publications

(17 citation statements)

References 12 publications

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“…Also the relationships between these predictors and local variables may vary strongly within the annual cycle. In the case of precipitation, statistical models especially designed for a particular month (such as the start or end of rainy season) may be needed (Jimoh and Webster, 1999).…”

Section: Empirical Downscalingmentioning

confidence: 99%

A review of climate risk information for adaptation and development planning

Wilby¹,

Troni²,

Biot³

et al. 2009

Intl Journal of Climatology

258

155

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ABSTRACT:Although the use of climate scenarios for impact assessment has grown steadily since the 1990s, uptake of such information for adaptation is lagging by nearly a decade in terms of scientific output. Nonetheless, integration of climate risk information in development planning is now a priority for donor agencies because of the need to prepare for climate change impacts across different sectors and countries. This urgency stems from concerns that progress made against Millennium Development Goals (MDGs) could be threatened by anthropogenic climate change beyond 2015. Up to this time the human signal, though detectable and growing, will be a relatively small component of climate variability and change. This implies the need for a twin-track approach: on the one hand, vulnerability assessments of social and economic strategies for coping with present climate extremes and variability, and, on the other hand, development of climate forecast tools and scenarios to evaluate sector-specific, incremental changes in risk over the next few decades. This review starts by describing the climate outlook for the next couple of decades and the implications for adaptation assessments. We then review ways in which climate risk information is already being used in adaptation assessments and evaluate the strengths and weaknesses of three groups of techniques. Next we identify knowledge gaps and opportunities for improving the production and uptake of climate risk information for the 2020s. We assert that climate change scenarios can meet some, but not all, of the needs of adaptation planning. Even then, the choice of scenario technique must be matched to the intended application, taking into account local constraints of time, resources, human capacity and supporting infrastructure. We also show that much greater attention should be given to improving and critiquing models used for climate impact assessment, as standard practice. Finally, we highlight the over-arching need for the scientific community to provide more information and guidance on adapting to the risks of climate variability and change over nearer time horizons (i.e. the 2020s). Although the focus of the review is on information provision and uptake in developing regions, it is clear that many developed countries are facing the same challenges.

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Section: Empirical Downscalingmentioning

confidence: 99%

A review of climate risk information for adaptation and development planning

Wilby¹,

Troni²,

Biot³

et al. 2009

Intl Journal of Climatology

258

155

View full text Add to dashboard Cite

show abstract

“…It was concluded that caution is needed with the use of AIC and BIC for determining the optimum order of the Markov model and the use of frequency duration curves can provide a robust alternative method of model identification. Jimoh and Webster (1999) investigated the intra-annual variation of the Markov chain parameters for seven sites in Nigeria. They found that there was a systematic variation in P 01 (probability of a wet day following a dry day) as one moves northwards and a limited regional variation in P 11 .…”

Section: Markov Chainsmentioning

confidence: 99%

Stochastic generation of annual, monthly and daily climate data: A review

Srikanthan

McMahon²

2001

Hydrol. Earth Syst. Sci.

293

175

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The generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed.

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“…The outcome, however, does depend to some extent upon the previous day's events. To account for these dependencies, daily rainfall has already been simulated by stochastic models (Jimoh and Webster, 1999). The probability of a wet or dry day is also dependent on the prevailing climatic regime of the region in question.…”

Section: Model Background and Sample Applicationmentioning

confidence: 99%

A Markov chain simulation model for predicting critical wet and dry spells in Kenya: analysing rainfall events in the Kano Plains

Ochola

Kerkides

2003

Irrigation and Drainage

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The occurrence of wet and dry spells is a phenomenon most often used to identify the arid and semi-arid lands (ASAL) in Kenya. The use of first-order Markov processes that are embedded into a computer model to determine the critical climate extremes is presented. The model uses the concepts of conditional probability, Poisson probability distribution function and chi-square testing to predict the critical spells. The daily rainfall data (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000) for two weather stations in the Kano Plains (Kenya) have been used to illustrate model application. For example, based upon the bimodal rainfall pattern in the study area, the model revealed the length of the critical dry spell to be 14 days in the long rainy season and 12 days in the short rainy season, while the critical wet spell was found to be 12 and 8 days, for the long and short rains respectively for Ahero Irrigation Scheme. It is recommended that a climate and environmental audit in the Kano Plains for the determination of land and water management strategies include critical dry and wet spell determination. This will enhance more sustainable planning and utilization of the crucial water and land resources in the region and in other tropical rangelands.

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Stochastic modelling of daily rainfall in Nigeria: intra-annual variation of model parameters

Cited by 25 publications

References 12 publications

A review of climate risk information for adaptation and development planning

A review of climate risk information for adaptation and development planning

Stochastic generation of annual, monthly and daily climate data: A review

A Markov chain simulation model for predicting critical wet and dry spells in Kenya: analysing rainfall events in the Kano Plains

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