Markov Chain Decomposition of Monthly Rainfall into Daily Rainfall: Evaluation of Climate Change Impact

Yoo, Chulsang; Lee, Jinwook; Ro, Yonghun

doi:10.1155/2016/7957490

Cited by 7 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Semenov (2008) and Martin et al (2007) state that stochastic models applied in hydrology are often used to complement daily climatological data. In addition, the models assess the effect of climate change on daily precipitation (Yoo et al, 2016). Markov Chain (MC) models are often proposed to rapidly obtain weather forecasts (dry or rainy) and their transition throughout the year (Lennartsson et al, 2008;Breinl et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Markov Chain (MC) models are often proposed to rapidly obtain weather forecasts (dry or rainy) and their transition throughout the year (Lennartsson et al, 2008;Breinl et al, 2013). In the present work, the MC method is used to model the occurrence of daily precipitation, as occurs often in the literature (Sharif et al, 2007, Damé et al, 2007, Selvaraj and Selvi, 2010, Sukla et al, 2016, Yoo et al, 2016. The emphasis on the application of the Markov chain derives from the use of the information from the previous day (dry or rainy) to provide a prognosis about the possible occurrence of a dry or rainy day for a given region (Carvalho et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Água vol. 14 n. 3, e2284 -Taubaté 2019 has more complex structures and greater difficulties in parameter estimation (Yoo et al, 2016), while MC basically involves two components: the occurrence of binary precipitation (precipitation or absence of precipitation) and the quantity of precipitation on wet days (Breinl et al, 2013). The MC are also the most-indicated models for the study of rainy-and dry-day sequences, since other models for probabilities have difficulty in describing the daily persistence of the occurrence conditions (Sukla et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

Santos¹,

Blanco²,

Júnior³

2019

Rev. ambiente água

View full text Add to dashboard Cite

Studies on the probability of rainfall and its spatiotemporal variations are important for the planning of water resources and optimization of the calendar of agricultural activities. This study identifies the occurrence of rain by first-order Markov Chain (MC) and by two states in the Tapajos River Basin (TRB), Amazon, Brazil. Cluster analysis (CA), based on the Ward method, was used to classify homogeneous regions and select samples for checking the probability of rainfall occurrence by season. The historical series of daily rainfall data of 80 stations were used for the period 1990-2014. The CA technique identified 8 homogeneous regions and their probability of occurrence of rainfall, helping to determine which regions and periods have greater need of irrigation. Results of the probability of occurrence of dry and rainy periods in the TRB were used to define the dry (May thru September) and rainy seasons (October thru April). Elements of the matrix transition probabilities showed variability in relation to time and, in addition, the influence of geographical position of seasonal rainfall in determining dry and rainy periods at specific sites in the TRB.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

Santos¹,

Blanco²,

Júnior³

2019

Rev. ambiente água

View full text Add to dashboard Cite

show abstract

“…For instance the Markov chain is one of those tools that compute the probability of occurrence of an event knowing that it has occurred previously (Thirriot, 1986;Arnaud 1985). Many studies using the Markov chain in other regions exist in the scientific literature (Gabriel and Newman 1962;Todorovic and Woolhiser 1975;Katz 1977;Shahraki et al, 2013;Halder et al 2016;Yoo et al 2016;Bojar et al 2018). For example Halder et al (2016) used the Markov chain process to analyze the rainfall distribution in Eastern India.…”

Section: Introductionmentioning

confidence: 99%

Probability of dry and wet spells over West Africa during the summer monsoon season

Basse¹,

Camara²,

Diba³

et al. 2021

Sci. Res. Essays

View full text Add to dashboard Cite

This work aims at characterizing the conditional probability of single or consecutive dry and wet days in West Africa using the first-order Markov chain approach during the monsoon season (June to October). The results show that the probabilities of having a wet day (P W ), a wet day preceded by another wet day (P WW ) and a wet day preceded by a dry day (P DW ) are stronger in regions where the rainfall is maximum (mountain regions). In contrast, the probabilities of having a dry day (P D ), a dry day preceded by a wet day (P WD ) and a dry day preceded by another dry day (P DD ) are lower in the regions with higher precipitation. The seasonal cycle of P WW (P DD ) is consistent with that of P W (P D ), respectively in Western Sahel and Central Sahel regions. At the interannual timescale, MK test results show that P W and P DW (P D and P DD ) exhibit statistically significant increasing (decreasing) trends the Western Sahel and the Central Sahel. Besides, the shorter dry spells (3 days) show statistically significant decreasing trends only in the Western Sahel. The longer dry spells (5, 7 and 10 days) show statistically significant downward trends over the Western and Central Sahel. Wet spells probabilities show non-significant decreasing trends in all sub-domains, except in the Western Sahel for the 10 days spells. Knowledge of theses probabilities will contribute to develop efficient strategies for water resources management and agricultural decision making in West African countries.

show abstract

“…Consequently, adopting climate change projections derived from climate model chains (GCMs-RCMs combinations) at the local scale (Kélibia) may induce more uncertainties in addition to those cited above. For this reason, Yoo et al (2016) considered the GCM outputs can be interpreted as alternative climate scenarios rather than predictions.…”

Section: Introductionmentioning

confidence: 99%

Impact of rainfall structure and climate change on soil and groundwater salinization

2020

View full text Add to dashboard Cite

Irrigated areas, mainly in arid regions, are threatened by salinization processes. Climate change inducing temperature rise and rainfall depletion are expected to enhance these processes. Numerical models are often used to predict salinization in the root zone as well as water and solute fluxes reaching groundwater. Climatic data, mainly rainfall, have an important influence on the estimation of those fluxes. The present paper studies the impact of rainfall structure and climate change on soil and groundwater salinization. Soil samples were collected in three drip-irrigated plots in Korba semi-arid coastal plain in Tunisia during dry and wet seasons. Collected field data (water contents and soil salinities) are used to define the initial solute and flow conditions and to estimate the soil hydraulic parameters for numerical modeling. Daily rainfall structure and annual rainfall transition under both Markov Chain and climate change (RCP4.5 and RCP8.5) are assessed. Different climatic scenarios are then introduced as boundary conditions in HYDRUS-1D, to test the influence of rainfall on the salinity evolution in the soil profile and groundwater. Results show that both annual rainfall amounts and daily structure have an impact on soil concentrations and solute fluxes quantities reaching groundwater. The influence of rainfall paths is more important for larger unsaturated zone thickness when considering the dry and median rainfall conditions. Climate change scenarios show significant accumulation of salts in the root zone implying the imperative use of adequate irrigation practices.

show abstract

Markov Chain Decomposition of Monthly Rainfall into Daily Rainfall: Evaluation of Climate Change Impact

Cited by 7 publications

References 36 publications

Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

Probability of dry and wet spells over West Africa during the summer monsoon season

Impact of rainfall structure and climate change on soil and groundwater salinization

Contact Info

Product

Resources

About