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

Ochola, Washington O.; Kerkides, P.

doi:10.1002/ird.94

Cited by 62 publications

(43 citation statements)

References 23 publications

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“…Rainfall concentrates in one rainy season, largely controlled by the Inter-Tropical Convergence Zone, which means that most of the rainfall is received in the months December, January and February (DJF). The precipitation in Limpopo is very dependent on ENSO giving warm and dry years during strong ENSO events (Ogallo, 1988). Its water resources are shared by South Africa, Botswana, Zimbabwe and Mozambique.…”

Section: Study Basinmentioning

confidence: 99%

“…However, if the rainfall amount or temporal distribution is inadequate, crops may fail, thus compromising food security. While a lower total amount of rainfall over the crop growing season will influence the crop yield, it is often the poor temporal distribution of rainfall resulting in dry spells and wet spells that is the cause of reduced crop yields (Rockstrom, 2000;Ingram et al, 2002;Ochola and Kerkides, 2003;Barron, 2004;Usman and Reason, 2004;Barron and Okwach, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

Wetterhall

Winsemius

Dutra

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. The rainfall in southern Africa has a large interannual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December-February (DJF) with lead times from 0 to 4 months. The seasonal forecasts were evaluated against ERA-Interim reanalysis data, which in turn were corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile mapping as well as post-processed using a precipitation threshold to define a dry day. The results indicate that the forecasts show skill in predicting dry spells in comparison with a climatological ensemble based on previous years. Quantile mapping in combination with a precipitation threshold improved the skill of the forecast. The skill in prediction of dry spells was largest over the most droughtsensitive region. Seasonal forecasts have the potential to be used in a probabilistic forecast system for drought-sensitive crops, though these should be used with caution given the large uncertainties.

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Section: Study Basinmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

Wetterhall

Winsemius

Dutra

et al. 2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Therefore, the model has to be further improved when the distributions of the rainfall totals over consecutive days is considered to be important. This could be done by, for example, fitting a probability distribution model to predict the lengths of wet and dry spells (e.g., Ochola and Kerkides, 2003).…”

Section: Discussionmentioning

confidence: 99%

Stochastic single‐site generation of daily and monthly rainfall in the Middle East

Mhanna

Bauwens

2011

Meteorological Applications

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An important limitation of commonly used single site daily rainfall models is their inability to represent monthly characteristics of observed rainfall, whereas impact studies often demand the accommodation of such statistics of rainfall. In this study, a single-site model for simulations of daily and monthly rainfall in arid and semi-arid areas in the Middle East was developed. The rainfall generator is a Markov-Gamma model in which a first-order two-state Markov chain was used to determine the occurrence of rainfall and a two-parameter Gamma distribution was applied to simulate the rainfall amount on wet days. The basic structure of the generation process comprises the generation of a sequence of daily rainfall amounts and the adjustment of this daily rainfall by implementing the Thomas-Fiering monthly model. This procedure guarantees that important statistical characteristics of the daily and monthly rainfall are reproduced.

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“…Markov chains [48] are commonly used to assess drought occurrence probability, and to evaluate and predict the time of occurrence of a drought event [49][50][51][52]. In this study, this method was used to examine changes in drought severity at different time scales, and to predict the occurrence probability for each degree of severity.…”

Section: Markov Chains Evaluation Methodsmentioning

confidence: 99%

SDI and Markov Chains for Regional Drought Characteristics

et al. 2015

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Abstract:In recent years, global climate change has altered precipitation patterns, causing uneven spatial and temporal distribution of precipitation that gradually induces precipitation polarization phenomena. Taiwan is located in the subtropical climate zone, with distinct wet and dry seasons, which makes the polarization phenomenon more obvious; this has also led to a large difference between river flows during the wet and dry seasons, which is significantly influenced by precipitation, resulting in hydrological drought. Therefore, to effectively address the growing issue of water shortages, it is necessary to explore and assess the drought characteristics of river systems. In this study, the drought characteristics of northern Taiwan were studied using the streamflow drought index (SDI) and Markov chains. Analysis results showed that the year 2002 was a turning point for drought severity in both the Lanyang River and Yilan River basins; the severity of rain events in the Lanyang River basin increased after 2002, and the severity of drought events in the Yilan River basin exhibited a gradual upward trend. In the study of drought severity, analysis results from periods of three months (November to January) and six months (November to April) have shown significant drought characteristics. In addition, analysis of drought occurrence probabilities using the method of Markov chains has shown that the occurrence probabilities of drought events are higher in the Lanyang River basin than in the Yilan River basin; particularly for extreme events, the occurrence probability of an extreme drought event is 20.6% during the dry season (November to April) in the Lanyang River basin, and 3.4% in the Yilan River basin. This study shows that for analysis of drought/wet occurrence probabilities, the results obtained for the drought frequency and occurrence OPEN ACCESSSustainability 2015, 7 10790 probability using short-term data with the method of Markov chains can be used to predict the long-term occurrence probability of drought/wet events.

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

Cited by 62 publications

References 23 publications

Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin

Stochastic single‐site generation of daily and monthly rainfall in the Middle East

SDI and Markov Chains for Regional Drought Characteristics

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