共查询到20条相似文献，搜索用时 31 毫秒
1.
We develop a doubly stochastic point process model with exponentially decaying pulses to describe the statistical properties of the rainfall intensity process. Mathematical formulation of the point process model is described along with second-order moment characteristics of the rainfall depth and aggregated processes. The derived second-order properties of the accumulated rainfall at different aggregation levels are used in model assessment. A data analysis using 15 years of sub-hourly rainfall data from England is presented. Models with fixed and variable pulse lifetime are explored. The performance of the model is compared with that of a doubly stochastic rectangular pulse model. The proposed model fits most of the empirical rainfall properties well at sub-hourly, hourly and daily aggregation levels. 相似文献
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3.
A multifractal analysis of hourly and daily rainfall data recorded at four locations of Andalusia (southern Spain) was carried out in order to study the temporal structure of rainfall and to find differences between both time resolutions. The results show that an algebraic tail is required to fit the probability distribution of the extreme rain events for all the cases. The presence of a multifractal phase transition associated with a critical moment in the empirical moments scaling exponent function was also detected. Both facts indicate that the rainfall process is a case of self‐organized criticality (SOC) dynamics, although the results differ for each place according to the time resolution and the nature of the rainfall, either convective or frontal. This SOC behaviour is related to a statistically steady state that implies the presence of clusterization in the time‐occurrence sequence of rain events. Such fluctuations have been shown by performing the analysis of the Fano and Allan factors and the count‐based periodogram. The values for the “synoptic maximum”, the typical lifetime of planetary scale atmospheric structures, have been obtained for each place and some important periodicities have been detected when dealing with extremes. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
4.
A. W. Jayawardena P. C. Xu W. K. Li 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(7):863-877
In many regions, monthly (or bimonthly) rainfall data can be considered as deterministic while daily rainfall data may be
treated as random. As a result, deterministic models may not sufficiently fit the daily data because of the strong stochastic
nature, while stochastic models may also not reliably fit into daily rainfall time series because of the deterministic nature
at the large scale (i.e. coarse scale). Although there are different approaches for simulating daily rainfall, mixing of deterministic
and stochastic models (towards possible representation of both deterministic and stochastic properties) has not hitherto been
proposed. An attempt is made in this study to simulate daily rainfall data by utilizing discrete wavelet transformation and
hidden Markov model. We use a deterministic model to obtain large-scale data, and a stochastic model to simulate the wavelet
tree coefficients. The simulated daily rainfall is obtained by inverse transformation. We then compare the accumulated simulated
and accumulated observed data from the Chao Phraya Basin in Thailand. Because of the stochastic nature at the small scale,
the simulated daily rainfall on a point to point comparison show deviations with the observed data. However the accumulated
simulated data do show some level of agreement with the observed data. 相似文献
5.
S. Yue M. Hashino B. Bobée P. F. Rasmussen T. B. M. J. Ouarda 《Stochastic Environmental Research and Risk Assessment (SERRA)》1999,13(5):317-326
This work presents the derivation of general streamflow cumulants from daily rainfall time series. The general streamflow
cumulants can be used to compute basic streamflow statistics such as mean, variance, coefficient of skewness, and correlation
coefficient. Streamflow is considered as a filtered point process where the input is a daily rainfall time series assumed
to be a marked point process. The marks of the process are the daily rainfall amounts which are assumed independent and identically
distributed. The number of rainfall occurrences is a counting process represented by either the binomial, the Poisson, or
the negative binomial probability distribution depending on its ratio of mean to variance. The first three cumulants and the
covariance function of J-day averaged streamflows are deduced based on the characteristic function of a filtered point process. These cumulants are
functions of the stochastic properties of the daily rainfall process and the basin-response function representing the causal
relationship between rainfall and runoff. 相似文献
6.
AbstractIn a recent development in the literature, a new temporal rainfall model, based on the Bartlett-Lewis clustering mechanism and intended for sub-hourly application, was introduced. That model replaced the rectangular rain cells of the original model with finite Poisson processes of instantaneous pulses, allowing greater variability in rainfall intensity over short intervals. In the present paper, the basic instantaneous pulse model is first extended to allow for randomly varying storm types. A systematic comparison of a number of key model variants, fitted to 5-min rainfall data from Germany, then generates further new insights into the models, leading to the development of an additional model extension, which introduces dependence between rainfall intensity and duration in a simple way. The new model retains the original rectangular cells, previously assumed inappropriate for fine-scale data, obviating the need for the computationally more intensive instantaneous pulse model.
Editor D. Koutsoyiannis 相似文献
7.
Abstract A cluster point process model is considered for the analysis of fine-scale rainfall time series. The model is based on three Poisson processes. The first is a Poisson process of storm origins, where each storm has a random (exponential) lifetime. The second is a Poisson process of cell origins that occur during the storm lifetime, terminating when the storm finishes. Each cell has a random lifetime that follows an exponential distribution (or terminates when the storm terminates, whichever occurs first). During cell lifetimes, a third Poisson process of instantaneous pulses occurs. The model is essentially an extension of the well-known Bartlett-Lewis rectangular pulses model, with the rectangular profiles replaced with a Poisson process of instantaneous pulse depths to ensure more realistic rainfall profiles for fine-scale series. Model equations, derived in Cowpertwait et al. (2007), are used to fit different sets of properties to a 60 year record of 5-min data taken from Kelburn, New Zealand. As in the previous work, two superposed processes are used to account for two main and distinct precipitation types (convective and stratiform). By treating the within-cell pulses as dependent random variables, it is found, by simulation, that improved fits to extreme values and the proportion of dry intervals are obtained. Citation Cowpertwait, P. S. P., Xie, G., Isham, V., Onof, C. & Walsh, D. C. I. (2011) A fine-scale point process model of rainfall with dependent pulse depths within cells. Hydrol. Sci. J. 56(7), 1110–1117. 相似文献
8.
Koichi Unami Felix Kofi Abagale Macarius Yangyuoru Abul Hasan M. Badiul Alam Gordana Kranjac-Berisavljevic 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(5):725-733
Droughts and floods are two opposite but related hydrological events. They both lie at the extremes of rainfall intensity
when the period of that intensity is measured over long intervals. This paper presents a new concept based on stochastic calculus
to assess the risk of both droughts and floods. An extended definition of rainfall intensity is applied to point rainfall
to simultaneously deal with high intensity storms and dry spells. The mean-reverting Ornstein–Uhlenbeck process, which is
a stochastic differential equation model, simulates the behavior of point rainfall evolving not over time, but instead with
cumulative rainfall depth. Coefficients of the polynomial functions that approximate the model parameters are identified from
observed raingauge data using the least squares method. The probability that neither drought nor flood occurs until the cumulative
rainfall depth reaches a given value requires solving a Dirichlet problem for the backward Kolmogorov equation associated
with the stochastic differential equation. A numerical model is developed to compute that probability, using the finite element
method with an effective upwind discretization scheme. Applicability of the model is demonstrated at three raingauge sites
located in Ghana, where rainfed subsistence farming is the dominant practice in a variety of tropical climates. 相似文献
9.
B. Sirangelo T. Caloiero R. Coscarelli E. Ferrari 《Stochastic Environmental Research and Risk Assessment (SERRA)》2015,29(1):143-155
In the present paper a stochastic approach which considers the arrival of rainfall events as a Poisson process is proposed to analyse the sequences of no rainy days. Particularly, among the different Poisson models, a non-homogeneous Poisson model was selected and then applied to the daily rainfall series registered at the Cosenza rain gauge (Calabria, southern Italy), as test series. The aim was to evaluate the different behaviour of the dry spells observed in two different 30-year periods, i.e. 1951–1980 and 1981–2010. The analyses performed through Monte Carlo simulations assessed the statistical significance of the variation of the mean expected values of dry spells observed at annual scale in the second period with respect to those observed in the first. The model has then been verified by comparing the results of the test series with the ones obtained from other three rain gauges of the same region. Moreover, greater occurrence probabilities for long dry spells in 1981–2010 than in 1951–1980 were detected for the test series. Analogously, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 resulted less than half of the corresponding ones evaluated with the data generated for the previous 30-year period. 相似文献
10.
Alemseged Tamiru Haile Tom RientjesAmbro Gieske Victor JettenMekonnen Gebremichael 《Advances in water resources》2011,34(1):26-37
Motivated by the need for rainfall prediction models in data scarce areas, we adapted a simple storage based cloud model to use routinely available thermal infrared (TIR) data. The data is obtained from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the Meteosat Second Generation (MSG-2) satellite. Model inputs are TIR cloud top temperatures at 15-min intervals and observations of pressure, temperature, and dew point temperatures from ground-based stations at 30-min intervals. The sensitivity of the parsimonious cloud model to its parameters is evaluated by a regional sensitivity analysis (RSA) which suggests that model performance is sensitive to few parameters. The model was calibrated and tested for four convective events that were observed during the wet season in the source basin of the Upper Blue Nile River. The difference between the simulated and the observed depth of the selected rain events varies between 0.2 and 1.8 mm with a root mean square error of smaller than 0.5 mm for each event. It is shown that the updraft velocity characteristic can provide relevant information for rainfall forecasting. The simulation results suggest the effectiveness of the model approach as evaluated by selected performance measures. The various characteristics of the rainfall events as simulated generally match to observed counter parts when ground-based and remote sensing observations are combined. 相似文献
11.
Bellie Sivakumar Ashish Sharma 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(4):451-459
High-resolution temporal rainfall data sequences serve as inputs for a range of applications in planning, design and management
of small (especially urban) water resources systems, including continuous flow simulation and evaluation of alternate policies
for environmental impact assessment. However, such data are often not available, since their measurements are costly and time-consuming.
One alternative to obtain high-resolution data is to try to derive them from available low-resolution information through
a disaggregation procedure. This study evaluates a random cascade approach for generation of high-resolution rainfall data
at a point location. The approach is based on the concept of scaling in rainfall, or, relating the properties associated with
the rainfall process at one temporal scale to a finer-resolution scale. The procedure involves two steps: (1) identification
of the presence of scaling behavior in the rainfall process; and (2) generation of synthetic data possessing same/similar
scaling properties of the observed rainfall data. The scaling identification is made using a statistical moment scaling function,
and the log–Poisson distribution is assumed to generate the synthetic rainfall data. The effectiveness of the approach is
tested on the rainfall data observed at the Sydney Observatory Hill, Sydney, Australia. Rainfall data corresponding to four
different successively doubled resolutions (daily, 12, 6, and 3 h) are studied, and disaggregation of data is attempted only
between these successively doubled resolutions. The results indicate the presence of multi-scaling behavior in the rainfall
data. The synthetic data generated using the log–Poisson distribution are found to exhibit scaling behaviors that match very
well with that for the observed data. However, the results also indicate that fitting the scaling function alone does not
necessarily mean reproducing the broader attributes that characterize the data. This observation clearly points out the extreme
caution needed in the application of the existing methods for identification of scaling in rainfall, especially since such
methods are also prevalent in studies of the emerging satellite observations and thus in the broader spectrum of hydrologic
modeling. 相似文献
12.
Simplified, vertically-averaged soil moisture models have been widely used to describe and study eco-hydrological processes in water-limited ecosystems. The principal aim of these models is to understand how the main physical and biological processes linking soil, vegetation, and climate impact on the statistical properties of soil moisture. A key component of these models is the stochastic nature of daily rainfall, which is mathematically described as a compound Poisson process with daily rainfall amounts drawn from an exponential distribution. Since measurements show that the exponential distribution is often not the best candidate to fit daily rainfall, we compare the soil moisture probability density functions obtained from a soil water balance model with daily rainfall depths assumed to be distributed as exponential, mixed-exponential, and gamma. This model with different daily rainfall distributions is applied to a catchment in New South Wales, Australia, in order to show that the estimation of the seasonal statistics of soil moisture might be improved when using the distribution that better fits daily rainfall data. This study also shows that the choice of the daily rainfall distributions might considerably affect the estimation of vegetation water-stress, leakage and runoff occurrence, and the whole water balance. 相似文献
13.
D.L. De Luca 《水文科学杂志》2013,58(8):1536-1558
AbstractThe spatial and temporal variability of the scaling properties and correlation structure of a data set of rainfall time series, aggregated over different temporal resolutions, and observed in 70 raingauges across the Basilicata and Calabria regions of southern Italy, is investigated. Two types of random cascade model, namely canonical and microcanonical models, were used for each raingauge and selected season. For both models, different hypotheses concerning dependency of parameters on time scale and rainfall height can be adopted. In particular, a new approach is proposed which consists of several combinations of models with a different scale dependence of parameters for different temporal resolutions. The goal is to improve the modelling of the main features of rainfall time series, especially for cases where the variability of rainfall changes irregularly with temporal aggregation. The results obtained with the new methodology showed good agreement with the observed data, in particular, for the summer months. In fact, during this season, rainfall heights aggregated at fine temporal resolutions (from 5 to 20 min) are more similar (relative to the winter season) to the values cumulated on 1 or 3 h (due to convective phenomena) and, consequently, the process of rainfall breakdown is nearly stationary for a range of finer temporal resolutions.
Editor D. Koutsoyiannis; Associate editor A. Montanari 相似文献
14.
The variation in point precipitation with elevation is investigated using an event-based stochastic model of thunderstorm rainfall and empirical data. Parameters of the model correspond to the number of events per unit of time and the depth of rainfall per event. An increase in precipitation with elevation may be due to an increase in the number of events, in the amount of rainfall per event or to some combination of both possibilities. The distribution of the number of events per season is assumed to be a Poisson variate while the distribution of point rainfall depths may be taken as geometric. The summation of a random number of random variables is used to represent seasonal point precipitation. Assuming that the two parameters of the model increase linearly with elevation, then total seasonal rainfall increases as a quadratic polynomial with elevation. The use of the model allows one to obtain the return period of storm rainfall of a given magnitude despite a short historical record. An independent set of data was used to verify the procedure. 相似文献
15.
Guillaume Evin Anne-Catherine Favre 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(4):831-847
Stochastic point processes for rainfall are known to be able to preserve the temporal variability of rainfall on several levels of aggregation (e.g. hourly, daily), especially when the cluster approach is used. One major assumption in most of the applications todate is the stationarity of the rainfall properties in time, which must be reconsidered under a climate change hypothesis. Here, we propose new theoretical developments of a Poisson-based model with cluster, namely the Neyman–Scott Rectangular Pulses Model, which treats storm frequency as a nonstationary function. In this paper, storm frequency is modelled as a linear function of time in order to reproduce an increase (or decrease) of the mean annual precipitation. The basic theory is reconsidered and the moments are derived up to the third order. Then, a calibration method based on the generalized method of moments is proposed and discussed. An application to a rainfall time series from Uccle illustrates how this model can reproduce a trend for the average rainfall. This work opens new avenues for future developments on transient stochastic rainfall models and highlights the major challenges linked to this approach. 相似文献
16.
Fabio Russo Federico Lombardo Eugenio Gorgucci 《Physics and Chemistry of the Earth》2006,31(18):1252-1261
Rainfall fields estimation over a catchment area is an important stage in many hydrological applications. In this context, weather radars have several advantages because a single-site can scan a vast area with very high temporal and spatial resolution. The construction of weather radar systems with dual polarization capability allowed progress on radar rainfall estimation and its hydro-meteorological applications. For these applications of radar data it is necessary to remove the ground clutter contamination with an algorithm based on the backscattering signal variance of the differential reflectivity. The calibration of the GDSTM model (Gaussian Displacements Spatial-Temporal Model), a cluster stochastic generation model in continuous space and time, is herewith presented. In this model, storms arrive in a Poisson process in time with cells occurring in each storm that cluster in space and time. The model is calibrated, using data collected by the weather radar Polar 55C located in Rome, inside a square area of 132 × 132 km2, with the radar at the centre. The GDSTM is fitted to sequences of radar images with a time interval between the PPIs scans of 5 min. A generalized method of moment procedure is used for parameter estimation. For the validation of the ability of the model to reproduce internal structure of rain event, a geo-morphological rainfall-runoff model, based on width function (WFIUH), was calibrated using simulated and observed data. Several rainfall fields are generated with the stochastic model and later they are used as input of the WFIUH model so that the forecast discharges can be compared to the observed ones. 相似文献
17.
The presence of scaling statistical properties in temporal rainfall has been well established in many empirical investigations during the latest decade. These properties have more and more come to be regarded as a fundamental feature of the rainfall process. How to best use the scaling properties for applied modelling remains to be assessed, however, particularly in the case of continuous rainfall time‐series. One therefore is forced to use conventional time‐series modelling, e.g. based on point process theory, which does not explicitly take scaling into account. In light of this, there is a need to investigate the degree to which point‐process models are able to ‘unintentionally’ reproduce the empirical scaling properties. In the present study, four 25‐year series of 20‐min rainfall intensities observed in Arno River basin, Italy, were investigated. A Neyman–Scott rectangular pulses (NSRP) model was fitted to these series, so enabling the generation of synthetic time‐series suitable for investigation. A multifractal scaling behaviour was found to characterize the raw data within a range of time‐scales between approximately 20 min and 1 week. The main features of this behaviour were surprisingly well reproduced in the simulated data, although some differences were observed, particularly at small scales below the typical duration of a rain cell. This suggests the possibility of a combined use of the NSRP model and a scaling approach, in order to extend the NSRP range of applicability for simulation purposes. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
18.
Federico Murrone Fabio Rossi Pierluigi Claps 《Stochastic Environmental Research and Risk Assessment (SERRA)》1997,11(6):483-510
A conceptual-stochastic approach to short time runoff data modelling is proposed, according to the aim of reproducing the
hydrological aspects of the streamflow process and of preserving as much as possible the dynamics of the process itself. This
latter task implies preservation of streamflow characteristics at higher scales of aggregation and, within a conceptual framework,
involves compatibility with models proposed for the runoff process at those scales. At a daily time scale the watershed response
to the effective rainfall is considered as deriving from the response of three linear reservoirs, respectively representing
contributions to streamflows of large deep aquifers, with over-year response lag, of aquifers which run dry by the end of
the dry season and of subsurface runoff. The surface runoff component is regarded as an uncorrelated point process. Considering
the occurrences of effective rainfall events as generated by an independent Poisson process, the output of the linear system
represents a conceptually-based multiple shot noise process. Model identification and parameter estimation are supported by
information related to the aggregated runoff process, in agreement to the conceptual framework proposed, and this allows parameter
parsimony, efficient estimation and effectiveness of the streamflow reproduction. Good performances emerged from the model
application and testing made with reference to some daily runoff series from Italian basins. 相似文献
19.
Federico Murrone Fabio Rossi Pierluigi Claps 《Stochastic Hydrology and Hydraulics》1997,11(6):483-510
A conceptual-stochastic approach to short time runoff data modelling is proposed, according to the aim of reproducing the
hydrological aspects of the streamflow process and of preserving as much as possible the dynamics of the process itself. This
latter task implies preservation of streamflow characteristics at higher scales of aggregation and, within a conceptual framework,
involves compatibility with models proposed for the runoff process at those scales. At a daily time scale the watershed response
to the effective rainfall is considered as deriving from the response of three linear reservoirs, respectively representing
contributions to streamflows of large deep aquifers, with over-year response lag, of aquifers which run dry by the end of
the dry season and of subsurface runoff. The surface runoff component is regarded as an uncorrelated point process. Considering
the occurrences of effective rainfall events as generated by an independent Poisson process, the output of the linear system
represents a conceptually-based multiple shot noise process. Model identification and parameter estimation are supported by
information related to the aggregated runoff process, in agreement to the conceptual framework proposed, and this allows parameter
parsimony, efficient estimation and effectiveness of the streamflow reproduction. Good performances emerged from the model
application and testing made with reference to some daily runoff series from Italian basins. 相似文献
20.
We present a web application named Let-It-Rain that is able to generate a 1-h temporal resolution synthetic rainfall time series using the modified Bartlett–Lewis rectangular pulse (MBLRP) model, a type of Poisson stochastic rainfall generator. Let-It-Rain, which can be accessed through the web address http://www.LetItRain.info, adopts a web-based framework combining ArcGIS Server from server side for parameter value dissemination and JavaScript from client side to implement the MBLRP model. This enables any desktop and mobile end users with internet access and web browser to obtain the synthetic rainfall time series at any given location at which the parameter regionalization work has been completed (currently the contiguous United States and Republic of Korea) with only a few mouse clicks. Let-It-Rain shows satisfactory performance in its ability to reproduce observed rainfall mean, variance, auto-correlation, and probability of zero rainfall at hourly through daily accumulation levels. It also shows a reasonably good performance in reproducing watershed runoff depth and peak flow. We expect that Let-It-Rain can stimulate the uncertainty analysis of hydrologic variables across the world. 相似文献