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

<p>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.</p><p>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.</p><p><strong>Keywords: </strong>Rainfall; Rainfall disaggregation; Bartlett-Lewis Model (BL); Method of Fragments (MOF); IMERG-FR; IMD.</p>

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.

scholarly journals Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series

2013 ◽  
Vol 17 (7) ◽  
pp. 2487-2500 ◽  
Author(s):  
D. Lisniak ◽  
J. Franke ◽  
C. Bernhofer
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Global Climate ◽  
Statistical Properties ◽  
Rainfall Data ◽  
Time Slice ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Rainfall Characteristics ◽  
Hourly Rainfall

Abstract. The use of multiplicative random cascades (MRCs) for temporal rainfall disaggregation has been extensively studied in the past. MRCs are appealing for rainfall disaggregation due to their formal simplicity and the possibility to extract the model parameters directly from observed high resolution rainfall data. These parameters, however, represent the rainfall characteristics of the observation period. Since rainfall characteristics of different time slices are changing due to climate variability, we propose a parameterization approach for MRCs to adjust the parameters according to past (observed) or future (projected) time series. This is done on the basis of circulation patterns (CPs) by extracting a distinct MRC parameterization from high resolution rainfall data, as observed on days governed by each individual CP. The parameterization approach is tested by comparing the statistical properties of disaggregated rainfall time series of two time slices, 1969–1979 and 1989–1999, to the results obtained by two other disaggregation methods (a conceptually similar MRC without CP-based parameterization and a recombination approach) and to the statistical properties of observed hourly rainfall data. In this context, all three approaches use rainfall data of the time slice 1989–1999 for parameterization. We found that the inclusion of CPs into the parameterization of a MRC yields hourly time series that better reproduce the properties of observed rainfall in time slice 1989–1999, as compared to the simple MRC. Despite similar results of both MRCs in the validation period of 1969–1979, we can conclude that the CP-based parameterization approach is applicable for temporal rainfall disaggregation in time slices distinct from the parameterization period. This approach accounts for changes in rainfall characteristics due to changes in the frequency of occurrence of the CPs and allows generating hourly rainfall from daily data, as often provided by a statistical downscaling of global climate change.

scholarly journals Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of daily rainfall under nonstationary climatic conditions

2012 ◽  
Vol 9 (9) ◽  
pp. 10115-10149 ◽  
Author(s):  
D. Lisniak ◽  
J. Franke ◽  
C. Bernhofer
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Climatic Conditions ◽  
Statistical Properties ◽  
Rainfall Data ◽  
Time Slice ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Rainfall Characteristics ◽  
Hourly Rainfall

Abstract. The use of multiplicative random cascades (MRCs) for temporal rainfall disaggregation has been extensively studied in the past. MRCs are appealing for rainfall disaggregation due to their formal simplicity and the possibility to extract the model parameters directly from observed high resolution rainfall data. These parameters, however, represent the rainfall characteristics of the observation period. Since rainfall characteristics of different time slices are changing due to climate variability, we propose a parameterization approach for MRCs to adjust the parameters according to past (observed) or future (projected) time series. This is done on the basis of circulation patterns (CPs) by extracting a distinct MRC parameterization from high resolution rainfall data, as observed on days governed by each individual CP. The parameterization approach is tested by comparing the statistical properties of disaggregated rainfall time series of two time slices, 1969–1979 and 1989–1999, to the results obtained by two other disaggregation methods (a conceptually similar MRC without CP-based parameterization and a recombination approach) and to the statistical properties of observed hourly rainfall data. In this context, all three approaches use rainfall data of the time slice 1989–1999 for parameterization. We found that the inclusion of CPs into the parameterization of a MRC yields hourly time series that better reproduce the properties of observed rainfall in time slice 1989–1999, as compared to the simple MRC. Despite similar properties of both MRCs for the time slice 1969–1979, we can conclude that the CP-based parameterization approach is applicable for temporal rainfall disaggregation in time slices distinct from the parameterization period. This approach accounts for changes in rainfall characteristics due to changes in the frequency of occurrence of the CPs and allows generating hourly rainfall from daily data, as often provided by a statistical downscaling of global climate change.

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

&lt;p&gt;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&lt;sub&gt;x&lt;/sub&gt;, or the accumulated volume of the rainfall series, V&lt;sub&gt;x&lt;/sub&gt;, 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&lt;sub&gt;x&lt;/sub&gt; and V&lt;sub&gt;x&lt;/sub&gt;, 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&lt;sub&gt;x&lt;/sub&gt; and V&lt;sub&gt;x &lt;/sub&gt;were evaluated. In all the cases, results show that N&lt;sub&gt;x&lt;/sub&gt; and V&lt;sub&gt;x&lt;/sub&gt; 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.&lt;/p&gt;

Spatial Variability of the Hurst Exponent for the Daily Scale Rainfall Series in the State of Zacatecas, Mexico

2013 ◽  
Vol 52 (12) ◽  
pp. 2771-2780 ◽  
Author(s):  
M. A. Velásquez Valle ◽  
G. Medina García ◽  
Ignacio Sánchez Cohen ◽  
L. Klaudia Oleschko ◽  
J. A. Ruiz Corral ◽  
...  
Keyword(s):  
Time Series ◽  
Hurst Exponent ◽  
Fractal Theory ◽  
Daily Rainfall ◽  
The State ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Rainfall Time Series ◽  
Wavelet Techniques ◽  
Hurst Exponents

AbstractThe structural pattern of rainfall data exhibits random fluctuations over time and space. Utilizing concepts of fractal theory, it has been possible to identify characteristics of rainfall data beyond simple statistical indicators of their randomness. The objective of this research was to identify the spatial variation of the Hurst exponent, extracted through standard wavelet techniques from time series of daily rainfall data in the state of Zacatecas, Mexico. The Hurst exponent was extracted for 26 locations using the reference techniques for auto-affine traces—in particular, the wavelets method. Results have shown that the Hurst exponents of rainfall time series are negatively influenced by altitude; thus, stations located at higher altitudes were characterized by Hurst exponents indicating more nonpersistent behavior. The trends among geographical variables (west longitude and latitude) and climatic parameters (annual rainfall and number of rainy days) and their relationship with the Hurst exponent were also analyzed.

Simulating reference rainfall scenarios for hydrological applications using a multifractal approach

2021 ◽  
Author(s):  
Arun Ramanathan ◽  
Pierre-Antoine Versini ◽  
Daniel Schertzer ◽  
Ioulia Tchiguirinskaia ◽  
Remi Perrin ◽  
...  
Keyword(s):  
Time Series ◽  
Moment Analysis ◽  
Specific Reference ◽  
Rainfall Time Series ◽  
Time Series Dataset ◽  
Hourly Rainfall ◽  
Spatial Dimensions ◽  
Multifractal Theory ◽  
Intensity Duration Frequency ◽  
Hydrological Applications

&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Hydrological applications such as flood design usually deal with and are driven by region-specific reference rainfall regulations, generally expressed as Intensity-Duration-Frequency (IDF) values. The meteorological module of hydro-meteorological models used in such applications should therefore be capable of simulating these reference rainfall scenarios. The multifractal cascade framework, since it incorporates physically realistic properties of rainfall processes such as non-homogeneity (intermittency), scale invariance, and extremal statistics, seems to be an appropriate choice for this purpose. Here we suggest a rather simple discrete-in-scale multifractal cascade based approach. Hourly rainfall time-series datasets (with lengths ranging from around 28 to 35 years) over six cities (Paris, Marseille, Strasbourg, Nantes, Lyon, and Lille) in France that are characterized by different climates and a six-minute rainfall time series dataset (with a length of around 15&amp;#160; years) over Paris were analyzed via spectral analysis and Trace Moment analysis to understand the scaling range over which the universal multifractal theory can be considered valid. Then the Double Trace Moment analysis was performed to estimate the universal multifractal parameters &amp;#945;,C&lt;sub&gt;1&lt;/sub&gt; that are required by the multifractal cascade model for simulating rainfall. A renormalization technique that estimates suitable renormalization constants based on the IDF values of reference rainfall is used to simulate the reference rainfall scenarios. Although only purely temporal simulations are considered here, this approach could possibly be generalized to higher spatial dimensions as well.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Multifractals, Non-linear geophysical systems, Cascade dynamics, Scaling, Hydrology, Stochastic rainfall simulations.&lt;/p&gt;

scholarly journals Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

2014 ◽  
Vol 18 (1) ◽  
pp. 353-365 ◽  
Author(s):  
U. Haberlandt ◽  
I. Radtke
Keyword(s):  
Time Series ◽  
Frequency Analysis ◽  
Model Calibration ◽  
Hydrological Model ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Rainfall Data ◽  
Flood Frequency Analysis ◽  
Peak Flows ◽  
Hourly Rainfall

Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

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.

scholarly journals A multiple threshold method for fitting the generalized Pareto distribution and a simple representation of the rainfall process

2010 ◽  
Vol 7 (4) ◽  
pp. 4957-4994 ◽  
Author(s):  
R. Deidda
Keyword(s):  
Time Series ◽  
Distribution Function ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Daily Rainfall ◽  
Rainfall Time Series ◽  
Threshold Method ◽  
Generalized Pareto ◽  
Multiple Threshold ◽  
Optimum Threshold

Abstract. Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution function to reliably describe the exceedances of daily rainfall records above a proper optimum threshold, which should be selected as small as possible to retain the largest sample while assuring an acceptable fitting. Such an optimum threshold may differ from site to site, affecting consequently not only the GPD scale parameter, but also the probability of threshold exceedance. Thus a first objective of this paper is to derive some expressions to parameterize a simple threshold-invariant three-parameter distribution function which is able to describe zero and non zero values of rainfall time series by assuring a perfect overlapping with the GPD fitted on the exceedances of any threshold larger than the optimum one. Since the proposed distribution does not depend on the local thresholds adopted for fitting the GPD, it will only reflect the on-site climatic signature and thus appears particularly suitable for hydrological applications and regional analyses. A second objective is to develop and test the Multiple Threshold Method (MTM) to infer the parameters of interest on the exceedances of a wide range of thresholds using again the concept of parameters threshold-invariance. We show the ability of the MTM in fitting historical daily rainfall time series recorded with different resolutions. Finally, we prove the supremacy of the MTM fit against the standard single threshold fit, often adopted for partial duration series, by evaluating and comparing the performances on Monte Carlo samples drawn by GPDs with different shape and scale parameters and different discretizations.

scholarly journals A regional similarity-based approach for sub-daily rainfall nonparametric generation

RBRH ◽  
2020 ◽  
Vol 25 ◽  
Author(s):  
Milena Guerra de Aguilar ◽  
Veber Afonso Figueiredo Costa
Keyword(s):  
Daily Rainfall ◽  
High Temporal Resolution ◽  
Rainfall Time Series ◽  
Storm Events ◽  
Runoff Simulation ◽  
K Nearest Neighbors ◽  
Method Of Fragments ◽  
Block Maxima ◽  
Regional Similarity ◽  
Doce River

ABSTRACT Rainfall time series with high temporal resolution are required for estimating storm events for the design of urban drainage systems, for performing rainfall-runoff simulation in small catchments and for modeling flash-floods. Nonetheless, large and continuous sub-daily rainfall samples are often unavailable. For dealing with the limited availability of high-resolution rainfall records, in both time and space, this paper explored an alternative version of the k-nearest neighbors algorithm, coupled with the method of fragments (KNN-MOF model), which utilizes a state-based logic for simulating consecutive wet days and a regionalized similarity-based approach for sampling fragments from hydrologically similar nearby stations. The proposed disaggregation method was applied to 40 rainfall gauging stations located in the São Francisco and Doce river catchments. Disaggregation of daily rainfall was performed for the durations of 60, 180 and 360 minutes. Results indicated the model presented an appropriate performance to disaggregate daily rainfall, reasonably reproducing sub-daily summary statistics. In addition, the annual block-maxima behavior, even for low exceedance probabilities, was relatively well described, although not all expected variability in the quantiles was properly summarized by the model. Overall, the proposed approach proved a sound and easy to implement alternative for simulating continuous sub-daily rainfall amounts from coarse-resolution records.

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