Temporal disaggregation of daily rainfall data in SuDS design: a case study in Tuscany

2021 ◽  
Author(s):  
Matteo Pampaloni ◽  
Alvaro Sordo Ward ◽  
Paola Bianucci ◽  
Ivan Gabriel Martin ◽  
Luis Garrote ◽  
...  
Keyword(s):  
Daily Rainfall ◽  
Rainfall Data ◽  
Design Parameters ◽  
Rainfall Regime ◽  
Rainfall Time Series ◽  
Storm Events ◽  
Time Step ◽  
Daily Rainfall Data ◽  
Hourly Rainfall ◽  
Temporal Disaggregation

<p>Sustainable urban Drainage Systems (SuDS), by themselves or combined with grey traditional infrastructures, help to diminish the runoff volume and peak flow, as well as to improve the water quality. Hydrological design of SuDS is usually based on rainfall volumetric percentiles as the number of rainfall events, N<sub>x</sub>, or the accumulated volume of the rainfall series, V<sub>x</sub>, to be managed. Sub-index x refers to common qualities used in SuDS design, like 80, 85, 90 and 95%. Usually, only daily rainfall data are available. Nevertheless, due to the characteristics of the urban watershed involved in the SuDS implementation, the quantification of design parameters for these facilities needs sub-hourly rainfall time series. To overcome this issue, a temporal disaggregation methodology was proposed based on the use of a stochastic rainfall generator model (RainSim V3). We analysed the case of Florence University rain gauge (Tuscany, Italy), by collecting 20 years (in the period from 1998 to 2018) of observed data at 15 minutes time step. First, we verified the ability of RainSim model to reproduce observed rainfall patterns at 15 minutes time-step. The parameters of the stochastic model were estimated using observed data with 24 hours time-step. We generated 100 series of 20 years each with a time step of 15 minutes. We accounted two variables to implement the storm events extraction: a) the Minimum Inter-event Time (MIT) between storm events; 2) the storm volume threshold. We obtained a better characterization of the rainfall regime by applying the temporal disaggregation methodology than using daily-observed data. Second, we compared the SuDS design parameters N<sub>x</sub> and V<sub>x</sub>, obtained by using the stochastically generated rainfall, the observed daily and 15 minutes data. Moreover, the effect of different MITs and different thresholds on N<sub>x</sub> and V<sub>x </sub>were evaluated. In all the cases, results show that N<sub>x</sub> and V<sub>x</sub> obtained with the median of the simulated series were closer to the actual observed parameters based on 15 minutes time step than the ones calculated with the observed daily data. Therefore, the proposed temporal disaggregation method arises as an efficient technique to overcome the lack of sub-hourly rainfall data, necessary to adequately design SuDS.</p>

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 Posterior Predictive of Bayesian Vector Autoregressive (BVAR) and Adjusting Transformation on the Spatio Temporal Disaggregation Method: Predict Hourly rainfall data at the outsampled Locations

2019 ◽  
pp. 357-369
Author(s):  
Suci Astutik ◽  
Umu Sa’adah ◽  
Supriatna Adhisuwignjo ◽  
Rauzan Sumara
Keyword(s):  
State Space ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Bayesian Var ◽  
Vector Autoregressive ◽  
Daily Rainfall Data ◽  
Hourly Rainfall ◽  
The Future ◽  
Temporal Disaggregation ◽  
Spatio Temporal

This research is a development from previous research that has studied the method of spatio temporal disaggregation with State space and adjusting procedures for predicting hourly rainfall based on daily rainfall (Astutik et al, 2013). However, this study is limited to predicting hourly rainfall in some sampled locations in the future. Astutik et al (2017, 2018) have modeled hourly and daily rainfall using posterior predictive bayesian VAR at the Sampean watershed of Bondowoso. This study aims to predict hourly rainfall data based on daily rainfall data in the future at the outsampled locations using posterior predictive bayesian VAR and adjusting procedures in the method of spatio temporal disaggregation.

scholarly journals Subdaily Rainfall Estimation through Daily Rainfall Downscaling Using Random Forests in Spain

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 125 ◽  
Author(s):  
Javier Diez-Sierra ◽  
Manuel del Jesus
Keyword(s):  
Daily Rainfall ◽  
Rainfall Data ◽  
Machine Learning Techniques ◽  
Explanatory Variables ◽  
Rain Gauges ◽  
Daily Rainfall Data ◽  
Learning Techniques ◽  
Hourly Rainfall ◽  
Rainfall Statistics ◽  
Poisson Cluster

Subdaily rainfall data, though essential for applications in many fields, is not as readily available as daily rainfall data. In this work, regression approaches that use atmospheric data and daily rainfall statistics as predictors are evaluated to downscale daily-to-subdaily rainfall statistics on more than 700 hourly rain gauges in Spain. We propose a new approach based on machine learning techniques that improves the downscaling skill of previous methodologies. Results are grouped by climate types (following the Köppen–Geiger classification) to investigate possible missing explanatory variables in the analysis. The methodology is then used to improve the ability of Poisson cluster models to simulate hourly rainfall series that mimic the statistical behavior of the observed ones. This approach can be applied for the study of extreme events and for daily-to-subdaily precipitation disaggregation in any location of Spain where daily rainfall data are available.

scholarly journals Cascade Rainfall Disaggregation Application in U.S. Central Plains

2017 ◽  
Vol 7 (4) ◽  
pp. 30 ◽  
Author(s):  
Jurgen D. Garbrecht ◽  
Rabi Gyawali ◽  
Robert W. Malone ◽  
John C. Zhang
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Rainfall Time Series ◽  
Random Component ◽  
Rainfall Characteristics ◽  
Hourly Rainfall ◽  
Replicated Observations

Long-term observations of daily rainfall are common and routinely available for a variety of hydrologic applications. In contrast, observations of 10 or more years of continuous hourly rainfall are rare. Yet, sub-daily rainfall data are required in rainfall-runoff models. Rainfall disaggregation can generate sub-daily time-series from available long term daily observations. Herein, the performance of Multiplicative Random Cascade (MRC) model at disaggregating daily-to-hourly rainfall was investigated. The MRC model was parameterized and validated with 15 years of continuous observed daily and hourly rainfall data at three weather stations in Oklahoma. Model performance, or degree to which the disaggregated rainfall time series replicated observations, was assessed using 46 variables of hourly rainfall characteristics, such as longest wet spell duration, average number of rainfall hours per year, and largest hourly rainfall. Findings include: a) average-type hourly rainfall characteristics were better replicated than single value characteristics such as longest, maximum, or peak hourly rainfall; b) the large number of sub-trace hourly rainfall values (<0.254 mm h-1) generated by the MRC model were not supported by observations; c) the random component of the MRC model led to a variation under 15% of the average value for most rainfall characteristics with the exceptions of the “longest wet spell duration” and “maximum hourly rainfall”; and d) the MRC model produced fewer persistent rainfall events compared to those in the observed rainfall record. The large number of generated trace rainfall values and difficulties to replicate reliably extreme rainfall characteristics, reduces the number of potential hydrologic applications that could take advantage of the MRC disaggregated hourly rainfall. Nevertheless, in most cases, the disaggregated rainfall generated by the MRC model replicated observed average-type rainfall characteristics well.

Disaggregation of Daily Rainfall into Hourly Rainfall in an Ungauged Urban Catchment

2021 ◽  
Author(s):  
Ashutosh Pati ◽  
Ravindra Kale ◽  
Bhabagrahi Sahoo
Keyword(s):  
Time Series ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Rainfall Time Series ◽  
Urban Flooding ◽  
Validation Period ◽  
Calibration And Validation ◽  
Method Of Fragments ◽  
Hourly Rainfall ◽  
Daily Scale

&lt;p&gt;Nowadays, most of the urban cities and their surrounding ambiances are facing increasing flooding issues. Many times, the cause of urban flooding is improper drainage under increasing rainfall intensity. To properly monitor and manage the drainage system in urban areas, high-resolution rainfall data is required to model the flooding scenarios a priori. However, the high-resolution rainfall data in urban regions to address the urban flooding issues are rarely available, especially in developing countries. To overcome this problem, many studies suggest the use of hourly scale IMERG-FR (Integrated Multi-satellitE Retrievals for GPM-Final Run) data which exhibits good agreement with the ground-truth rainfall measurements. Therefore, this study attempts to utilize area-averaged IMERG-FR hourly data over Bhubaneswar, a data-scarce urban area of eastern India as a benchmark for assessing the performance of six parametric (Bartlett-Lewis Model, BL) and a nonparametric (Method of Fragments, MOF) approaches disaggregating daily scale IMD (India Meteorological Department) rainfall data into hourly scale data. The performance of the considered approaches is evaluated by disaggregating the monsoon months (June-October) rainfall timeseries data for the period 2001-2015 by adopting performance criteria such as root mean square error (RMSE) and percent bias (PBIAS). The rainfall time series data from 2001-2010 and 2011-2015 were used for calibration and validation of the proposed approaches, respectively.&lt;/p&gt;&lt;p&gt;The obtained RMSE values in the case of the BL approach during calibration and validation period were 2.53 mm and 2.04 mm, respectively. Similarly, RMSE values in the case of the MOF approach during the calibration and validation period were 2.5 mm and 1.87 mm, respectively. This comparison suggests the both of these approaches exhibit nearly the same performance during the calibration period whereas the MOF approach was slightly better than BL during the validation period. The PBIAS estimates for the MOF approach were around -6.6% and 17.3% during the calibration and validation period, respectively, whereas the PBIAS estimates for the BL approach were around 11.25% for calibration and -11.25% for the validation period. From the present evaluation, it could be concluded that though the MOF approach exhibits slightly better performance in terms of RMSE, the BL approach can provide a more balanced performance in terms of PBIAS. As the MOF is a non-parametric approach, it can be applied to a lesser length of daily rainfall time series for disaggregation whereas the BL approach can perform well when its parameters are derived using a good length of rainfall series. Conclusively, this study summarizes the applicability of the BL and MOF approaches for disaggregating course resolution daily scale rainfall to hourly rainfall for the monsoon months in Bhubaneswar using IMERG-FR hourly rainfall data as a benchmark.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;Rainfall; Rainfall disaggregation; Bartlett-Lewis Model (BL); Method of Fragments (MOF); IMERG-FR; IMD.&lt;/p&gt;

scholarly journals The Daily and Hourly Rainfall Data Modeling Using Vector Autoregressive (VAR) with Maximum Likelihood Estimator (MLE) and Bayesian Method (Case Study in Sampean Watershed of Bondowoso, Indonesia)

2018 ◽  
Author(s):  
Suci Astutik ◽  
Umu Sa'adah ◽  
Supriatna Adhisuwignjo ◽  
Rauzan Sumara
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Var Model ◽  
Likelihood Estimator ◽  
Vector Autoregressive ◽  
Daily Rainfall Data ◽  
Run Off ◽  
Hourly Rainfall

The hourly and daily rainfall data which is spatially distributed are required as an input for run-off rain model. Furthermore, the run-off rain model is used to detect early flooding. The daily and hourly rainfall data have characteristics that most of data are zero. Therefore we need a model which can capture the phenomenon. A time series model involving location, which is a model that can be developed to approach the daily and hourly rainfall data, we can call Vector Autoregressive (VAR) model. The VAR model allows us for modeling rainfall data in several areas. However, in certain conditions the VAR model often occurs over-parameterization and reduces degrees of freedom. The aim of this study is to compare the VAR model with Maximum Likelihood Estimator (MLE) and Bayesian to hourly and daily rainfall data in SampeanWatershed of Bondowoso. The results showed that the hourly and daily rainfall data are fitted to VAR process of orde 5 and 1 respectively. Based on the AIC and SBC values indicate that the Bayesian is better than the MLE method. The Bayesian is able to predict parameters by producing a smaller variance covariance matrix than the MLE.

The effect of weather, drainage efficiency and duration of spring cultivations on barley yields in England

1972 ◽  
Vol 7 (2) ◽  
pp. 79-83 ◽  
Author(s):  
L P Smith
Keyword(s):  
Yield Loss ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Work Schedule ◽  
Average Yield ◽  
Daily Rainfall Data ◽  
Arable Farming ◽  
Work Distribution

Daily rainfall data for twenty years in arable farming areas are analysed with respect to four standards of drainage and for three lengths of schedule of spring work. Distribution and frequency in time of available work days are interpreted in terms of lateness of sowing and of barley yield. Formulae are established to calculate average yield loss in terms of drainage standard and work schedule, enabling estimates to be made of the effect of planned improvements.

scholarly journals Bayesian Time Series Modelling of the Italian Daily Rainfall Data using Mixed Distribution

2019 ◽  
Vol 8 (4) ◽  
pp. 2279-2288
Keyword(s):  
Time Series ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Seasonal Effects ◽  
Model Parameters ◽  
Daily Rainfall Data ◽  
Mixed Distribution ◽  
Time Series Modelling ◽  
Bayesian Time Series ◽  
The Ideal

A combination of continuous and discrete elements is referred to as a mixed distribution. For example, daily rainfall data consist of zero and positive values. We aim to develop a Bayesian time series model that captures the evolution of the daily rainfall data in Italy, focussing on directly linking the amount and occurrence of rainfall. Two gamma (G1 and G2) distributions with different parameterisations and lognormal distribution were investigated to identify the ideal distribution representing the amount process. Truncated Fourier series was used to incorporate the seasonal effects which captures the variability in daily rainfall amounts throughout the year. A first-order Markov chain was used to model rainfall occurrence conditional on the presence or absence of rainfall on the previous day. We also built a hierarchical prior structure to represent our subjective beliefs and capture the initial uncertainties of the unknown model parameters for both amount and occurrence processes. The daily rainfall data from Urbino rain gauge station in Italy were then used to demonstrate the applicability of our proposed methods. Residual analysis and posterior predictive checking method were utilised to assess the adequacy of model fit. In conclusion, we clearly found that our proposed method satisfactorily and accurately fits the Italian daily rainfall data. The gamma distribution was found to be the ideal probability density function to represent the amount of daily rainfall.

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