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Stochastic modeling of daily precipitation in China

LIAO Yaoming,ZHANG Qiang,CHEN Deliang,

地理学报 , 2004,
Abstract: A stochastic model for daily precipitation simulation in China was developed based on the framework of a 'Richardson-type' weather generator that is an important tool in studying impacts of weather/climate on a variety of systems including ecosystem and risk assessment. The purpose of this work is to develop a weather generator for applications in China. The focus is on precipitation simulation since determination of other weather variables such as temperature is dependent on precipitation simulation. A framework of first order Markov Chain with Gamma Distribution for daily precipitation is adopted in this work. Based on this framework, four parameters of precipitation simulation for each month at 672 stations all over China were determined using daily precipitation data from 1961 to 2000. Compared with previous works, our estimation for the parameters was made for more stations and longer observations, which makes the weather generator more applicable and reliable. Spatial distributions of the four parameters are analyzed in a regional climate context. The seasonal variations of these parameters at five stations representing regional differences are discussed. Based on the estimated monthly parameters at 672 stations, daily precipitations for any period can be simulated. A 30-year simulation was made and compared with observations during 1971-2000 in terms of annual and monthly statistics. The results are satisfactory, which demonstrates the usefulness of the weather generator.
A Comparison of Four Precipitation Distribution Models Used in Daily Stochastic Models

LIU Yonghe,ZHANG Wanchang,SHAO Yuehong,ZHANG Kexin,

大气科学进展 , 2011,
Abstract: Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrological simulations. For modeling daily precipitation in weather generators, first-order Markov chain--dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the performance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian information criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August.
Downscaling of daily precipitation with a stochastic weather generator for the subtropical region in South China
Y. D. Chen,X. Chen,C.-Y. Xu,Q. Shao
Hydrology and Earth System Sciences Discussions , 2006,
Abstract: Daily precipitation series at station or local scales is a critical input for rainfall-runoff modelling which, in turn, plays a vital role in the assessment of climate change impact on hydrologic processes and many other water resource studies. Future climate projected by General Circulation Models (GCMs) presents averaged values in large scales. Therefore, downscaling techniques are usually needed to transfer GCM-derived climate outputs into station-based values. In this study, a statistical downscaling model is investigated and its applicability in generating daily precipitation series is tested in the subtropical region of South China, which has not been investigated before. The model includes the first-order Markov chain for modeling wet day probability, Gamma distribution function for describing variation of wet-day precipitation amounts, and a statistical downscaling approach to transferring large-scale (in both space and time) future precipitation series from GCM climate change scenarios to station or local scales. A set of observed daily precipitation series of 32 years from 17 rainfall stations in and around a grid of 2.5° in latitude by 3.75° in longitude in Guangdong province of China is used to evaluate the model accuracy and validate the downscaling results. The downscaled daily precipitation series and the extreme precipitation features (including maximum, maximum 3-day average and maximum 7-day average) are compared with the observed values. The results show that the proposed model is capable of reproducing the mean daily amount and model parameters of the daily precipitation series at station or local scales in the study region.
Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation  [PDF]
S. Hwang,W. D. Graham
Hydrology and Earth System Sciences Discussions , 2013, DOI: 10.5194/hessd-10-2141-2013
Abstract: There are a number of statistical techniques that downscale coarse climate information from global circulation models (GCM). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data which can be an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (bias-correction and stochastic analog method, BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve the spatial autocorrelation structure of observed daily precipitation sequences. This approach was designed to reproduce observed spatial and temporal variability as well as mean climatology. We used the BCSA method to downscale 4 GCM precipitation predictions from 1961 to 1999 over the state of Florida and compared the skill of the method to the results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, bias-correction and constructed analog (BCCA) method, and a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC). Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean climatology of daily precipitation while the BCSD, SDBC and BCSA methods accurately reproduced it, (2) the BCSD and BCCA methods underestimated temporal variability because of the interpolation and regression schemes used for downscaling and thus, did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in precipitation resulting in under-prediction of spatial variance and over-prediction of spatial correlation, whereas the new stochastic technique (BCSA) accurately reproduces observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be accurately predicted. For low-relief, rainfall-dominated watersheds where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommende
Stochastic bias-correction of daily rainfall scenarios for hydrological applications  [PDF]
I. Portoghese,E. Bruno,N. Guyennon,V. Iacobellis
Natural Hazards and Earth System Sciences (NHESS) & Discussions (NHESSD) , 2011, DOI: 10.5194/nhess-11-2497-2011
Abstract: The accuracy of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes. In fact, the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows and groundwater recharge. In this study, a comparison between statistical properties of rainfall observations and model control simulations from a Regional Climate Model (RCM) was performed through a robust and meaningful representation of the precipitation process. The output of the adopted RCM was analysed and re-scaled exploiting the structure of a stochastic model of the point rainfall process. In particular, the stochastic model is able to adequately reproduce the rainfall intermittency at the synoptic scale, which is one of the crucial aspects for the Mediterranean environments. Possible alteration in the local rainfall regime was investigated by means of the historical daily time-series from a dense rain-gauge network, which were also used for the analysis of the RCM bias in terms of dry and wet periods and storm intensity. The result is a stochastic scheme for bias-correction at the RCM-cell scale, which produces a realistic representation of the daily rainfall intermittency and precipitation depths, though a residual bias in the storm intensity of longer storm events persists.
Stochastic characteristics of daily rainfall at Purajaya region
Ahmad Zakaria
Journal of Engineering and Applied Sciences , 2011,
Abstract: Aim of this research is to study stochastic characteristics of daily rainfall series. The study was undertaken using 25 years (1977-2001) data of Purajaya region. The series of the daily rainfall data assumed was trend free. The periodic components of daily rainfall series were represented by using 253 harmonic expressions and stochastic components of daily rainfall series were presented using second order autoregressive parameters. Validation of generated daily rainfall series was done by comparing between generated with measured daily rainfall series. For periodic modeling, mean of the correlation coefficient between generated and measured daily rainfall series with the number of the data N is equal to 512 days for 25 years was found to be 0,9576. For periodic and stochastic modeling, mean of the correlation coefficient was found to be 0.9999. Therefore, developed periodic and stochastic model could be used for future prediction of daily rainfall time series.
A Stochastic Precipitation Generator Based on Generalized Linear Models and NCEP Reanalysis Data

LIU Yonghe,ZHANG Wanchang,ZHU Shiliang and,

大气科学 , 2010,
Abstract: Weather generators were useful for generating incomplete history weather records and weather scenarios in future. Recently they were used to study downscaling climate variables and provide weather forcings for hydrological and ecological simulations. Generalized linear models (GLM) are powerful tools for relating large-scale weather variables with high resolution variables at the earth surface. The application of generators based on GLM is promising. In this paper, by using five variables derived from single grid NCEP reanalysis data, such as air temperature, 500-hPa geopotential height, potential temperature, relative humidity, sea-level pressure, as large-scale independent variables that affect precipitation variations, GLM for downscaling and simulating daily precipitation were constructed. For determination of precipitation occurrence probability, the logistic model was used, and for simulating daily precipitation amounts, the Gamma, exponential, normal, and lognormal distributions were used respectively. The observed daily precipitation series and the selected NCEP reanalysis data were used for parameter-estimation and simulation. The maximum likelihood estimate of the model parameters was performed by genetic algorithms. The results of estimation showed that the fitting effect of the gamma distribution-based models was the best, the lognormal distribution was the second, the exponential distribution was the third, and the fitting effect of the normal distribution-based models was the worst; on the other hand, the fitting effect of the models with their parameters estimated for each month separately was a little better than that without separation of months. Both the observed and simulated daily precipitation-occurrence probabilities were added for each month and the corresponding precipitation amounts were also averaged for each month in order to verify the models ability. Results showed that the precipitation-occurrence probability was underestimated slightly by the logistic model, and precipitation-amounts expectations were underestimated by the exponential and gamma distribution-based GLM but were overestimated by the lognormal distribution-based GLM when monthly total precipitation amounts were large. The stochastic simulation of precipitation showed that the lognormal distribution-based GLM overestimated the yearly averaged monthly total precipitation and the exponential or gamma distribution-based GLM had a good simulation effect. On the whole, these stochastic models based on GLM and the NCEP reanalysis data are powerful tools for explaining and simulating the occurrence probability and amounts of precipitation.
Precipitation downscaling using random cascades: a case study in Italy
B. Groppelli, D. Bocchiola,R. Rosso
Advances in Geosciences (ADGEO) , 2010,
Abstract: We present a Stochastic Space Random Cascade (SSRC) approach to downscale precipitation from a Global Climate Model (hereon, GCMs) for an Italian Alpine watershed, the Oglio river (1440 km2). The SSRC model is locally tuned upon Oglio river for spatial downscaling (approx. 2 km) of daily precipitation from the NCAR Parallel Climate Model. We use a 10 years (1990–1999) series of observed daily precipitation data from 25 rain gages. Scale Recursive Estimation coupled with Expectation Maximization algorithm is used for model estimation. Seasonal parameters of the multiplicative cascade are accommodated by statistical distributions conditioned upon climatic forcing, based on regression analysis. The main advantage of the SSRC is to reproduce spatial clustering, intermittency, self-similarity of precipitation fields and their spatial correlation structure, with low computational burden.
Aerul ?i Apa : Componente ale Mediului , 2012,
Abstract: Efficiency of gumbel analyses for determining extreme daily precipitation in Switzerland. Gumbel analyses were carried out on rainfall time-series at 151 locations in Switzerland for 4 different periods of 30 years in order to estimate daily extreme precipitation for a return period of 100 years. Those estimations were compared with maximal daily values measured during the last 100 years (1911-2010) to test the efficiency of these analyses. This comparison shows that these analyses provide good results for 50 to 60% locations in this country from rainfall time-series 1961-1990 and 1980-2010. On the other hand, daily precipitation with a return period of 100 years is underestimated at most locations from time-series 1931-1960 and especially 1911-1940. Such underestimation results from the increase of maximal daily precipitation recorded from 1911 to 2010 at 90% locations in Switzerland.
Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand)  [PDF]
Abazar Solgi,Vahid Nourani,Amir Pourhaghi
Advances in Civil Engineering , 2014, DOI: 10.1155/2014/279368
Abstract: Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN) as a nonlinear interextrapolator is extensively used by hydrologists for precipitation modeling as well as other fields of hydrology. In the present study, wavelet analysis combined with artificial neural network and finally was compared with adaptive neurofuzzy system to predict the precipitation in Verayneh station, Nahavand, Hamedan, Iran. For this purpose, the original time series using wavelet theory decomposed to multiple subtime series. Then, these subseries were applied as input data for artificial neural network, to predict daily precipitation, and compared with results of adaptive neurofuzzy system. The results showed that the combination of wavelet models and neural networks has a better performance than adaptive neurofuzzy system, and can be applied to predict both short- and long-term precipitations. 1. Introduction Estimation and forecasting of precipitation and its runoff have played effective and critical role in the watershed management and proper utilization of watershed, dams, and reservoirs and finally minimizing the damage caused by floods and drought. Therefore, this subject is the hydrologist’s interest. Predicting any event forms the basis of crisis management, and when this goal can be achieved, the predicting model could be accessed. Several methods are used for predicting hydrological events such as precipitation. Using each of these methods is always with some error in results. Accurate prediction of hydrological signals such as precipitation can provide useful information to predict amount of precipitation for water resources and soil management in a basin. In addition, correct prediction of hydrological signals plays an important role in reducing the effects of drought on water resources systems. Hydrological systems are affected by many factors such as climate, land cover, soil infiltration rates, evapotranspiration which is dependent on stochastic components, multitemporal scales, and above-mentioned nonlinear characteristics. Despite nonlinear relationships, uncertainty, and high lack of precision and variables temporal and spatial characteristics in water circulation system, none of the statistical and conceptual models which are proposed for accurate precipitation and runoff modeling were
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