Statistical framework to simulate daily rainfall series conditional on upper-air predictor variables

2014 ◽  
Vol 50 (5) ◽  
pp. 3907-3932 ◽  
Author(s):  
Andreas Langousis ◽  
Vassilios Kaleris
Keyword(s):  
Daily Rainfall ◽  
Rainfall Series ◽  
Predictor Variables ◽  
Statistical Framework

scholarly journals Conceptual Rainfall–Runoff Model Performance with Different Spatial Rainfall Inputs

2011 ◽  
Vol 12 (5) ◽  
pp. 1100-1112 ◽  
Author(s):  
J. Vaze ◽  
D. A. Post ◽  
F. H. S. Chiew ◽  
J.-M. Perraud ◽  
J. Teng ◽  
...  
Keyword(s):  
Model Simulation ◽  
Model Performance ◽  
Daily Rainfall ◽  
Grid Cell ◽  
Rainfall Series ◽  
Data Availability ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall–runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall–runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the “distributed” application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall–runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required.

scholarly journals Rainfall statistics changes in Sicily

2013 ◽  
Vol 10 (2) ◽  
pp. 2323-2352 ◽  
Author(s):  
E. Arnone ◽  
D. Pumo ◽  
F. Viola ◽  
L. V. Noto ◽  
G. La Loggia
Keyword(s):  
Extreme Events ◽  
Daily Rainfall ◽  
Negative Trend ◽  
Rainfall Series ◽  
Annual Maximum ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Annual Maximum Rainfall ◽  
Rainfall Statistics

Abstract. Changes in rainfall characteristics are one of the most relevant signs of current climate alterations. Many studies have demonstrated an increase in rainfall intensity and a reduction of frequency in several areas of the world, including Mediterranean areas. Rainfall characteristics may be crucial for vegetation patterns formation and evolution in Mediterranean ecosystems, with important implications, for example, in vegetation water stress or coexistence and competition dynamics. At the same time, characteristics of extreme rainfall events are fundamental for the estimation of flood peaks and quantiles which can be used in many hydrological applications, such as design of the most common hydraulic structures, or planning and management of flood prone areas. In the past, Sicily has been screened for several signals of possible climate change. Annual, seasonal and monthly rainfall data in the entire Sicilian region have been analyzed, showing a global reduction of total annual rainfall. Moreover, annual maximum rainfall series for different durations have been rarely analyzed in order to detect the presence of trends. Results indicated that for short durations, historical series generally exhibit increasing trends while for longer durations the trends are mainly negative. Starting from these premises, the aim of this study is to investigate and quantify changes in rainfall statistics in Sicily, during the second half of the last century. Time series of about 60 stations over the region have been processed and screened by using the non parametric Mann–Kendall test. Particularly, extreme events have been analyzed using annual maximum rainfall series at 1, 3, 6, 12 and 24 h duration while daily rainfall properties have been analyzed in term of frequency and intensity, also characterizing seasonal rainfall features. Results of extreme events analysis confirmed an increasing trend for rainfall of short durations, especially for one hour rainfall duration. Instead, precipitation of long durations have exhibited a decreased trend. With regard to the spatial distribution, increase in short duration precipitation has been observed especially in stations located along the coastline; however, no clear and well-defined spatial pattern have been outlined by the results. Outcomes of analysis for daily rainfall properties have showed that heavy-torrential precipitation tends to be more frequent at regional scale, while light rainfall events exhibited a negative trend at some sites. Values of total annual precipitations confirmed a significant negative trend, mainly due to the reduction during the winter season.

Hybrid of ARIMA-GARCH Modeling in Rainfall Time Series

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Fadhilah Yusof ◽  
Ibrahim Lawal Kane ◽  
Zulkifli Yusop
Keyword(s):  
Time Series ◽  
Daily Rainfall ◽  
Rainfall Series ◽  
Empirical Modeling ◽  
Dependence Structure ◽  
Rainfall Time Series ◽  
Arima Models ◽  
Starting Point ◽  
Garch Modeling ◽  
Squared Residuals

The dependence structure of rainfall is usually very complex both in time and space. It is shown in this paper that the daily rainfall series of Ipoh and Alorsetar are affected by nonlinear characteristics of the variance often referred to as variance clustering or volatility, where large changes tend to follow large changes and small changes tend to follow small changes. In most empirical modeling of hydrological time series, the focus was on modeling and predicting the mean behavior of the time series through conventional methods of an Autoregressive Moving Average (ARMA) modeling proposed by the Box Jenkins methodology. The conventional models operate under the assumption that the series is stationary that is: constant mean and either constant variance or season-dependent variances, however, does not take into account the second order moment or conditional variance, but they form a good starting point for time series analysis. The residuals from preliminary ARIMA models derived from the daily rainfall time series were tested for ARCH behavior. The autocorrelation structure of the residuals and the squared residuals were inspected, the residuals are uncorrelated but the squared residuals show autocorrelation, the Ljung-Box test confirmed the results. McLeod-Li test and a test based on the Lagrange multiplier (LM) principle were applied to the squared residuals from ARIMA models. The results of these auxiliary tests show clear evidence to reject the null hypothesis of no ARCH effect. Hence indicates that GARCH modeling is necessary. Therefore the composite ARIMA-GARCH model captures the dynamics of the daily rainfall series in study areas more precisely. On the other hand, Seasonal ARIMA model became a suitable model for the monthly average rainfall series of the same locations treated.

Quality control process of the daily rainfall series available in Catalonia from 1855 to the present

2019 ◽  
Vol 137 (3-4) ◽  
pp. 2715-2729 ◽  
Author(s):  
Alba Llabrés-Brustenga ◽  
Anna Rius ◽  
Raúl Rodríguez-Solà ◽  
M. Carmen Casas-Castillo ◽  
Angel Redaño
Keyword(s):  
Quality Control ◽  
Control Process ◽  
Daily Rainfall ◽  
Rainfall Series ◽  
Quality Control Process

scholarly journals The NAO signal in daily rainfall series over the Iberian Peninsula

2005 ◽  
Vol 29 ◽  
pp. 103-109 ◽  
Author(s):  
MC Gallego ◽  
JA García ◽  
JM Vaquero
Keyword(s):  
Iberian Peninsula ◽  
Daily Rainfall ◽  
Rainfall Series

DEVELOPING AND CALIBRATING A STOCHASTIC RAINFALL GENERATOR MODEL FOR SIMULATING DAILY RAINFALL BY MARKOV CHAIN APPROACH

2015 ◽  
Vol 76 (15) ◽  
Author(s):  
N. S. Dlamini ◽  
M. K. Rowshon ◽  
Ujjwal Sahab ◽  
A. Fikri ◽  
S. H. Lai ◽  
...  
Keyword(s):  
Markov Chain ◽  
Production Systems ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Rainfall Series ◽  
Climate Data ◽  
Management Tools ◽  
Stochastic Weather Generator ◽  
Markov Chain Approach

Rainfall is an important parameter in tropical humid regions for which paddy production systems depend. A significant portion of paddy water requirements is supplied by natural rainfall. Several studies have predicted changes in rainfall patterns and in the amount of rain that may be obtainable in future owing to climate change. There is increased concern about future water availability for an important crop such as rice. Need to develop new water management tools for sustainable production is inevitable, but such tools require long-term climate data that is credible and consistent with the time. This study concerns itself with evaluating a stochastic weather generator (WGEN) model for simulating daily rainfall series. The model is assessed using long-term historical rainfall data obtained from a rice growing irrigation schemes in Malaysia. The model is based on a first-order two-state Markov chain approach which uses two transition probabilities and random number to generate rainfall series. Selected statistical properties were computed for each station and compared against those retrieved from the model after model training and testing. The results obtained from these comparisons are quite satisfactory giving confidence about the performance and future outputs from the model. The model has shown good skill in describing the rainfall occurrence process and rainfall amounts for the area. The model will be adapted in a subsequent study for downscaling and simulating effective daily rainfall series corresponding to future climate scenarios.

scholarly journals A Stochastic Procedure for Temporal Disaggregation of Daily Rainfall Data in SuDS Design

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 403
Author(s):  
Matteo Pampaloni ◽  
Alvaro Sordo-Ward ◽  
Paola Bianucci ◽  
Ivan Gabriel-Martin ◽  
Enrica Caporali ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Design Parameters ◽  
Generation Process ◽  
Time Step ◽  
Rainfall Events ◽  
Daily Rainfall Data ◽  
Key Characteristics ◽  
Temporal Disaggregation

Hydrological design of Sustainable urban Drainage Systems (SuDS) is commonly achieved by estimating rainfall volumetric percentiles from daily rainfall series. Nevertheless, urban watersheds demand rainfall data at sub-hourly time step. Temporal disaggregation of daily rainfall records using stochastic methodologies can be applied to improve SuDS design parameters. This paper is aimed to analyze the ability of the synthetic rainfall generation process to reproduce the main characteristics of the observed rainfall and the estimation of the hydrologic parameters often used for SuDS design and by using the generally available daily rainfall data. Other specifics objectives are to analyze the effect of Minimum Inter-event Time (MIT) and storm volume threshold on rainfall volumetric percentiles commonly used in SuDS design. The reliability of the stochastic spatial-temporal model RainSim V.3 to reproduce observed key characteristics of rainfall pattern and volumetric percentiles, was also investigated. Observed and simulated continuous rainfall series with sub-hourly time-step were used to calculate four key characteristics of rainfall and two types of rainfall volumetric percentiles. To separate independent rainstorm events, MIT values of 3, 6, 12, 24, 48 and 72 h and storm volume thresholds of 0.2, 0.5, 1 and 2 mm were considered. Results show that the proposed methodology improves the estimation of the key characteristics of the rainfall events as well as the hydrologic parameters for SuDS design, compared with values directly deduced from the observed rainfall series with daily time-step. Moreover, MITs rainfall volumetric percentiles of total number of rainfall events are very sensitive to MIT and threshold values, while percentiles of total volume of accumulated rainfall series are sensitive only to MIT values.

scholarly journals Rainfall statistics changes in Sicily

2013 ◽  
Vol 17 (7) ◽  
pp. 2449-2458 ◽  
Author(s):  
E. Arnone ◽  
D. Pumo ◽  
F. Viola ◽  
L. V. Noto ◽  
G. La Loggia
Keyword(s):  
Extreme Events ◽  
Daily Rainfall ◽  
Rainfall Series ◽  
Annual Maximum ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Annual Maximum Rainfall ◽  
Rainfall Statistics ◽  
Precipitation Events

Abstract. Changes in rainfall characteristics are one of the most relevant signs of current climate alterations. Many studies have demonstrated an increase in rainfall intensity and a reduction of frequency in several areas of the world, including Mediterranean areas. Rainfall characteristics may be crucial for vegetation patterns formation and evolution in Mediterranean ecosystems, with important implications, for example, in vegetation water stress or coexistence and competition dynamics. At the same time, characteristics of extreme rainfall events are fundamental for the estimation of flood peaks and quantiles that can be used in many hydrological applications, such as design of the most common hydraulic structures, or planning and management of flood-prone areas. In the past, Sicily has been screened for several signals of possible climate change. Annual, seasonal and monthly rainfall data in the entire Sicilian region have been analyzed, showing a global reduction of total annual rainfall. Moreover, annual maximum rainfall series for different durations have been rarely analyzed in order to detect the presence of trends. Results indicated that for short durations, historical series generally exhibit increasing trends, while for longer durations the trends are mainly negative. Starting from these premises, the aim of this study is to investigate and quantify changes in rainfall statistics in Sicily, during the second half of the last century. Time series of about 60 stations over the region have been processed and screened by using the nonparametric Mann–Kendall test. In particular, extreme events have been analyzed using annual maximum rainfall series at 1, 3, 6, 12 and 24 h duration, while daily rainfall properties have been analyzed in terms of frequency and intensity, also characterizing seasonal rainfall features. Results of extreme events analysis confirmed an increasing trend for rainfall of short durations, especially for 1 h rainfall duration. Conversely, precipitation events of long durations have exhibited a decreased trend. Increase in short-duration precipitation has been observed especially in stations located along the coastline; however, no clear and well-defined spatial pattern has been outlined by the results. Outcomes of analysis for daily rainfall properties have showed that heavy–torrential precipitation events tend to be more frequent at regional scale, while light rainfall events exhibited a negative trend at some sites. Values of total annual precipitation events confirmed a significant negative trend, mainly due to the reduction during the winter season.

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