Analysis and Characterization of Probability Distribution and Small-Scale Spatial Variability of Rainfall in Singapore Using a Dense Gauge Network

Mandapaka, Pradeep V.; Qin, Xiaosheng

doi:10.1175/jamc-d-13-0115.1

Cited by 45 publications

(39 citation statements)

References 41 publications

(61 reference statements)

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“…Diurnal variations of temperature and precipitation are more prominent than month-to-month variation (Fong and Ng, 2012). The average annual rainfall is 2430 mm during the period of 1980-2010, with the average of 51% rainy days (Mandapaka and Qin, 2013). Annual mean temperature in Singapore is around 26-27.5 ∘ C (Chow and Roth, 2006).…”

Section: Study Areamentioning

confidence: 99%

Spatio‐temporal variation of wet and dry spell characteristics of tropical precipitation in Singapore and its association with ENSO

Meshgi

2016

Intl Journal of Climatology

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Climatology and statistical variability of wet and dry spell characteristics of precipitation provide an effective tool to understand the impacts of natural climate variability such as El Niño/Southern Oscillation (ENSO) and climate change on the water availability over a region. However, to date, detailed studies about precipitation spell characteristics in the tropical urban context are still limited. Therefore, this study presents a comprehensive framework to characterize the wet and dry spells of precipitation in a tropical urban city-state (i.e. Singapore), based on daily data from 26 precipitation stations. In addition, 14 wet and dry spell indices are defined, and spatial and temporal variations of the indices are analysed based on ordinary kriging method and an improved Mann-Kendall (MK) test method, respectively. Lastly, the influence of ENSO condition on the wet and dry spell indices is investigated utilizing a regional regression method. Results indicate that: (1) the region over Singapore is predominantly characterized by 1-day wet and dry spells, while the main contributions to total duration of wet and dry spells come from 3-4-day wet spells and 3-6-day dry spells, respectively. The major contribution to total accumulated depth of wet spells comes from 3-4-day and 20-80 mm wet spells; (2) significant increasing trends are detected in general and extreme characteristics of wet spells at annual time scale, however, these trends are not significant during the two monsoon seasons. Spatial variation of wet and dry spell indices is also evident; (3) Singapore's precipitation spells are influenced by ENSO, but mainly during the southwest monsoon season. During the El Niño phase, precipitation spells generally become drier. The predicted changes of wet and dry spell characteristics in the contexts of ENSO and climate change have implications for water resources management in tropical urban cities.

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Section: Study Areamentioning

confidence: 99%

Spatio‐temporal variation of wet and dry spell characteristics of tropical precipitation in Singapore and its association with ENSO

Meshgi

2016

Intl Journal of Climatology

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“…Earlier studies have shown the usefulness of such analysis in gauge-satellite comparisons, hydrological and meteorological modeling and setting up gauge networks (Habib et al, 2001;Krajewski et al, 2003;Ciach and Krajewski, 2006;Villarini et al, 2008;Liechti et al, 2012;Luini and Capsoni, 2012;Mandapaka and Qin, 2013;Li et al, 2014;Chen et al, 2015). Spearman correlation coefficients have been computed between each pair of rain gauge locations for different rain accumulation periods.…”

Section: Spatial Correlationmentioning

confidence: 99%

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

Sunilkumar

Rao

Satheeshkumar

2016

Hydrol. Earth Syst. Sci.

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Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and subdaily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of 6-12 km. Morphological features of rainfall in two principal rainy seasons (southwest monsoon, SWM, and northeast monsoon, NEM) show marked differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale/long-lived systems (during wet spells), whereas the contribution from small-scale/short-lived systems is considerable during the SWM. Rain events with longer duration and copious rainfall are seen mostly in the western quadrants (a quadrant is 1/4 of the study region) in the SWM and northern quadrants in the NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits large spatial and seasonal variability with larger diurnal amplitudes at all the gauge locations (except for 1) during the SWM and smaller and insignificant diurnal amplitudes at many gauge locations during the NEM. On average, the diurnal amplitudes are a factor of 2 larger in the SWM than in the NEM. The 24 h harmonic explains about 70 % of total variance in the SWM and only ∼ 30 % in the NEM. During the SWM, the rainfall peak is observed between 20:00 and 02:00 IST (Indian Standard Time) and is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the correlation remains nearly the same after 12 h of integration in both monsoon seasons. The 1 h resolution data show the steepest reduction in correlation with intergauge distance and the correlation becomes insignificant after ∼ 30 km in both monsoon seasons.Validation of high-resolution rainfall estimates from various MPEs against the gauge rainfall data indicates that all MPEs underestimate the light and heavy rain. The MPEs exhibit good detection skills of rain at both 3 and 24 h resolutions; however, considerable improvement is observed at 24 h resolution. Among the different MPEs investigated, the Climate Prediction Centre morphing technique (CMORPH) performs better at 3-hourly resolution in both monsoons. The performance of Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) is much better at daily resolution than at 3-hourly, as evidenced by better statistical metrics than the other MPEs. All MPEs captured the basic shape of the diurnal cycle and the amplitude quite well, but failed to reproduce the weak/insign...

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“…Interestingly, the wettest period (December–January) and the driest period (February) are the times when there are high spatial correlations among stations. Previous research has indicated that rainfall patterns are the smoothest during wet periods and most variable during February and April in Singapore (Mandapaka and Qin, ). The average precipitation is 4.91 mm for February, 7.12 mm for April, 5.58 mm for July and 9.82 mm for December.…”

Section: Resultsmentioning

confidence: 99%

Multi‐site rainfall simulation at tropical regions: a comparison of three types of generators

Vallam

Qin

2016

Meteorological Applications

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ABSTRACT:Rainfall modelling is an essential component of different hydrological studies. However, rainfall modelling in tropical regions, especially urban areas, remains inadequate. To determine the applicability of different types of rainfall modelling approaches, simulations by two Markov models (Matlab-based weather generator (MulGETS) and multi-site rainfall simulator (MRS)) and a Neyman-Scott based Poisson cluster model (RainSim) were compared with a proposed modified k-nearest neighbour (KNN) model for 30 years rainfall in Singapore. The MRS model was determined to be suitable for single-site applications in tropical regions. However, for multi-site conditions, RainSim was adjudged the most suitable given its accuracy in preserving observed spatial information, despite its performance lagging in few statistical indicators. The KNN model was found to perform satisfactorily during the wet seasons, and was the only model that could repeat the extreme precipitation values closely. Although typical studies evaluate the performance of models based on a set of criteria, there exists a lacuna in understanding the quality of simulations by these models. Therefore, uncertainty analysis based on two different criteria was implemented to understand the performance of the stochastic processes within. Although the MulGETS model exhibited the lowest differences between Prediction Intervals, RainSim's Prediction Intervals were found to subsume observed data more often. The proposed study would be useful for users examining rainfall models for differing objectives.

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Analysis and Characterization of Probability Distribution and Small-Scale Spatial Variability of Rainfall in Singapore Using a Dense Gauge Network

Cited by 45 publications

References 41 publications

Spatio‐temporal variation of wet and dry spell characteristics of tropical precipitation in Singapore and its association with ENSO

Spatio‐temporal variation of wet and dry spell characteristics of tropical precipitation in Singapore and its association with ENSO

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

Multi‐site rainfall simulation at tropical regions: a comparison of three types of generators

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