Daily rainfall occurrence

2017 ◽  
pp. 207-211
Author(s):  
Walter Zucchini ◽  
Iain L. MacDonald ◽  
Roland Langrock
Keyword(s):  
Daily Rainfall ◽  
Rainfall Occurrence

Stochastic multi-site generation of daily rainfall occurrence in south Florida

2007 ◽  
Vol 22 (6) ◽  
pp. 705-717 ◽  
Author(s):  
Tae-woong Kim ◽  
Hosung Ahn ◽  
Gunhui Chung ◽  
Chulsang Yoo
Keyword(s):  
Daily Rainfall ◽  
South Florida ◽  
Rainfall Occurrence

scholarly journals Spatial Coherence of Tropical Rainfall at the Regional Scale

2007 ◽  
Vol 20 (21) ◽  
pp. 5244-5263 ◽  
Author(s):  
Vincent Moron ◽  
Andrew W. Robertson ◽  
M. Neil Ward ◽  
Pierre Camberlin
Keyword(s):  
Regional Scale ◽  
Spatial Coherence ◽  
Daily Rainfall ◽  
Frequency Of Occurrence ◽  
Tropical Regions ◽  
Mean Intensity ◽  
Northwestern India ◽  
Rainfall Occurrence ◽  
Rainfall Frequency ◽  
Daily Time

Abstract This study examines the spatial coherence characteristics of daily station observations of rainfall in five tropical regions during the principal rainfall season(s): the Brazilian Nordeste, Senegal, Kenya, northwestern India, and northern Queensland. The rainfall networks include between 9 and 81 stations, and 29–70 seasons of observations. Seasonal-mean rainfall totals are decomposed in terms of daily rainfall frequency (i.e., the number of wet days) and mean intensity (i.e., the mean rainfall amount on wet days). Despite the diverse spatiotemporal sampling, orography, and land cover between regions, three general results emerge. 1) Interannual anomalies of rainfall frequency are usually the most spatially coherent variable, generally followed closely by the seasonal amount, with the daily mean intensity in a distant third place. In some cases, such as northwestern India, which is characterized by large daily rainfall amounts, the frequency of occurrence is much more coherent than the seasonal amount. 2) On daily time scales, the interstation correlations between amounts on wet days always fall to insignificant values beyond a distance of about 100 km. The spatial scale of daily rainfall occurrence is larger and more variable among the networks. 3) The regional-scale signal of the seasonal amount is primarily related to a systematic spatially coherent modulation of the frequency of occurrence.

scholarly journals Seasonal Predictability and Spatial Coherence of Rainfall Characteristics in the Tropical Setting of Senegal

2006 ◽  
Vol 134 (11) ◽  
pp. 3248-3262 ◽  
Author(s):  
Vincent Moron ◽  
Andrew W. Robertson ◽  
M. Neil Ward
Keyword(s):  
Interannual Variability ◽  
General Circulation ◽  
Degrees Of Freedom ◽  
Regional Scale ◽  
Spatial Coherence ◽  
Daily Rainfall ◽  
Occurrence Frequency ◽  
Seasonal Rainfall ◽  
Mean Intensity ◽  
Rainfall Occurrence

Abstract This study examines space–time characteristics of seasonal rainfall predictability in a tropical region by analyzing observed data and model simulations over Senegal. Predictability is analyzed in terms of the spatial coherence of observed interannual variability at the station scale, and within-ensemble coherence of general circulation model (GCM) simulations with observed sea surface temperatures (SSTs) prescribed. Seasonal mean rainfall anomalies are decomposed in terms of daily rainfall frequency and daily mean intensity. The observed spatial coherence is computed from a 13-station network of daily rainfall during the July–September season 1961–98 in terms of (i) interannual variability of a standardized anomaly index (i.e., the average of the normalized anomalies of each station), (ii) the external variance (i.e., the fraction of common variance among stations), and (iii) the number of spatiotemporal degrees of freedom. Spatial coherence of interannual anomalies across stations is found to be much stronger for seasonal rainfall amount and daily occurrence frequency, compared with daily mean intensity of rainfall. Combinatorial analysis of the station observations suggests that, for occurrence and seasonal amount, the empirical number of spatial degrees of freedom is largely insensitive to the number of stations considered, and is between 3 and 4 for Senegal. For daily mean intensity, by contrast, each station is found to convey almost independent information, and the number of degrees of freedom would be expected to increase for a denser network of stations. The GCM estimates of potential predictability and skill associated with the SST forcing are found to be remarkably consistent with those inferred from the observed spatial coherence: there is a moderate-to-strong skill at reproducing the interannual variations of seasonal amounts and rainfall occurrence, whereas the skill is weak for the mean intensity of rainfall. Over Senegal during July–September, it is concluded that (i) regional-scale seasonal amount and rainfall occurrence frequency are predictable from SSTs, (ii) daily mean intensity of rainfall is spatially incoherent and largely unpredictable at the regional scale, and (iii) point-score estimates of seasonal rainfall predictability and skill are subject to large sampling variability.

scholarly journals Theoretical framework to estimate spatially averaged rainfalls conditional on river discharges and point rainfall measurements from a single location: an application to Western Greece

2012 ◽  
Vol 9 (11) ◽  
pp. 12463-12522
Author(s):  
A. Langousis ◽  
V. Kaleris
Keyword(s):  
River Basin ◽  
Daily Rainfall ◽  
Theoretical Framework ◽  
Climatic Conditions ◽  
Rainfall Runoff ◽  
Flow Conditions ◽  
Suggested Approach ◽  
Rainfall Occurrence ◽  
Single Location ◽  
Western Greece

Abstract. We focus on the special case of catchments covered by a single raingauge, and develop a theoretical framework to obtain estimates of spatial rainfall averages conditional on rainfall measurements from a single location, and the flow conditions at the catchment outlet. In doing so we use: (a) statistical tools to identify and correct inconsistencies between daily rainfall occurrence and amount and the flow conditions at the outlet of the basin, (b) concepts from multifractal theory to relate the fraction of wet intervals in point rainfall measurements and that in spatial rainfall averages, while accounting for the shape and size of the catchment, the size, lifetime and advection velocity of rainfall generating features and the location of the raingauge inside the basin, and (c) semi-theoretical arguments to assure consistency between rainfall and runoff volumes at an inter-annual level, implicitly accounting for spatial heterogeneities of rainfall caused by orographic influences. In an application study, using point rainfall records from Glafkos river basin in Western Greece, we find the suggested approach to demonstrate significant skill in resolving rainfall-runoff incompatibilities at a daily level, while reproducing the statistics of spatial rainfall averages at both monthly and annual time scales, independently of the location of the raingauge and the magnitude of the observed deviations between point rainfall measurements and spatial rainfall averages. The developed scheme should serve as an important tool for the effective calibration of rainfall-runoff models in basins covered by a single raingauge and, also, improve hydrologic impact assessment at a river basin level under changing climatic conditions.

scholarly journals Development and evaluation of a stochastic daily rainfall model with long-term variability

2017 ◽  
Vol 21 (12) ◽  
pp. 6541-6558 ◽  
Author(s):  
A. F. M. Kamal Chowdhury ◽  
Natalie Lockart ◽  
Garry Willgoose ◽  
George Kuczera ◽  
Anthony S. Kiem ◽  
...  
Keyword(s):  
Gamma Distribution ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Primary Objective ◽  
Generation Model ◽  
Gamma Model ◽  
Rainfall Occurrence ◽  
Rainfall Model ◽  
Two Parameters

Abstract. The primary objective of this study is to develop a stochastic rainfall generation model that can match not only the short resolution (daily) variability but also the longer resolution (monthly to multiyear) variability of observed rainfall. This study has developed a Markov chain (MC) model, which uses a two-state MC process with two parameters (wet-to-wet and dry-to-dry transition probabilities) to simulate rainfall occurrence and a gamma distribution with two parameters (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. Starting with the traditional MC-gamma model with deterministic parameters, this study has developed and assessed four other variants of the MC-gamma model with different parameterisations. The key finding is that if the parameters of the gamma distribution are randomly sampled each year from fitted distributions rather than fixed parameters with time, the variability of rainfall depths at both short and longer temporal resolutions can be preserved, while the variability of wet periods (i.e. number of wet days and mean length of wet spell) can be preserved by decadally varied MC parameters. This is a straightforward enhancement to the traditional simplest MC model and is both objective and parsimonious.

scholarly journals Intensity-Duration-Frequency relationships: stochastic modeling and disaggregation of daily rainfall in the lagoa Mirim watershed, Rio Grande do Sul, Brazil

2016 ◽  
Vol 36 (3) ◽  
pp. 492-502 ◽  
Author(s):  
Rita de C. F Damé ◽  
Claudia F. A. Teixeira-Gandra ◽  
Hugo A. S. Guedes ◽  
Gisele M. da Silva ◽  
Suélen C. R. da Silveira
Keyword(s):  
Stochastic Modeling ◽  
Mean Squared Error ◽  
Transition Probabilities ◽  
Information Gain ◽  
Daily Rainfall ◽  
Gumbel Distribution ◽  
Rain Gauge ◽  
Rainfall Occurrence ◽  
Intensity Duration Frequency ◽  
Annual Maximum Daily Rainfall

ABSTRACT This study aimed to investigate information gain by using rainfall intensity-duration-frequency (IDF) relationships, with data gathered within N+M years from seven rain gauge stations located in the Lagoa Mirim Watershed (South Atlantic basin). After N years of daily rainfall, the transition probabilities of a time homogeneous two-state Markov chain were defined to simulate rainfall occurrence, as well as gamma distribution to measure it; for that, daily rainfall series were composed of N+M years, with M being the generated series. The series were adjusted to Gumbel distribution, being used in annual maximum daily rainfall disaggregation for durations of 10, 20, 30, 40, 50, 60, 120, 360, 720 and 1440 min. Daily rainfall disaggregation was validated through IDF relationships taken from pluviograph records of N years and from N+M years, using the “t” test of relative mean squared error. We can infer that there was information gain using IDF relationships of rainfall occurrence when using N years of observed data and M years of generated data by stochastic modeling compared to those obtained from a composed series of N years.

A stochastic description of temporal daily rainfall patterns

1984 ◽  
Vol 11 (2) ◽  
pp. 234-238 ◽  
Author(s):  
Van-Thanh-Van Nguyen
Keyword(s):  
Markov Chain ◽  
Stochastic Model ◽  
Exponential Distribution ◽  
Daily Rainfall ◽  
Stochastic Approach ◽  
Rainfall Event ◽  
Event Duration ◽  
Rainfall Occurrence ◽  
Rainfall Patterns

A stochastic approach to characterization of temporal rainfall patterns is proposed wherein a rainfall event is defined as an unbroken sequence of consecutive daily rainfalls. A stochastic model is developed to determine the probability distribution of cumulative rainfall amounts at the end of each day within a total rainfall event duration of n days. The model is structured such that the distribution of daily rainfall depths can be approximated by an exponential distribution and the rainfall occurrence process can be described by a first-order stationary Markov chain. An illustrative example was presented, using a 32-year daily rainfall record at Dorval Airport on Montreal Island. The results of this example have demonstrated the adequacy and descriptive capabilities of the model. It can be concluded that the methodology proposed here seems to be more general and more flexible than those that have been used in previous investigations. Key words: daily rainfall process, temporal rainfall pattern, stochastic model, stochastic hydrology, exponential distribution, Markov chain.

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