scholarly journals A Comparison of Design Storms for Urban Drainage System Applications

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 757 ◽  
Author(s):  
Balbastre-Soldevila ◽  
García-Bartual ◽  
Andrés-Doménech
Keyword(s):  
Temporal Pattern ◽  
Drainage System ◽  
Rainfall Data ◽  
Data Series ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Maximum Rainfall ◽  
Intensity Duration Frequency ◽  
Rainfall Runoff Model ◽  
Runoff Model

The present research develops a systematic application of a selected family of 11 well-known design storms, all of them obtained from the same rainfall data sample. Some of them are fully consistent with the intensity–duration–frequency (IDF) curves, while others are built according to typical observed patterns in the historical rainfall series. The employed data series consists on a high-resolution rainfall time series in Valencia (Spain), covering the period from 1990 to 2012. The goal of the research is the systematic comparison of these design storms, paying special attention to some relevant quantitative properties, as the maximum rainfall intensity, the total cumulative rainfall depth or the temporal pattern characterising the synthetic storm. For comparison purposes, storm duration was set to 1 hour and return period equal to 25 years in all cases. The comparison is enhanced by using each of the design storms as rainfall input to a calibrated urban hydrology rainfall–runoff model, yielding to a family of hydrographs for a given neighbourhood of the city of Valencia (Spain). The discussion and conclusions derived from the present research refer to both, the comparison between design storms and the comparison of resulting hydrographs after the application of the mentioned rainfall–runoff model. Seven of the tested design storms yielded to similar overall performance, showing negligible differences in practice. Among them, only Average Variability Method (AVM) and Two Parameter Gamma function (G2P) incorporate in their definition a temporal pattern inferred from empirical patterns identified in the historical rainfall data used herein. The remaining four design storms lead to more significant discrepancies attending both to the rainfall itself and to the resulting hydrograph. Such differences are ~8% concerning estimated discharges.

Effect of rainfall data errors on the optimized values of model parameters

Soil Research ◽  
1982 ◽  
Vol 20 (1) ◽  
pp. 15
Author(s):  
WC Boughton ◽  
FT Sefe
Keyword(s):  
Storage Capacity ◽  
Rainfall Data ◽  
Model Parameters ◽  
Similar Magnitude ◽  
Rainfall Runoff ◽  
Data Errors ◽  
Cast Doubt ◽  
Rainfall Runoff Model ◽  
Induced Changes ◽  
Runoff Model

The rainfall input to a rainfall-runoff model was arbitrarily increased and decreased in order to determine the magnitude of corresponding changes in optimized values of the model parameters. The optimized capacities of moisture stores representing surface storage capacity of a catchment changed by average amounts of +24% and -20% as rainfall input was changed by +10% and -10%, respectively. Values of other parameters showed changes of similar magnitude, but there was no uniformity in the magnitude of induced changes from catchment to catchment. The results cast doubt on the validity of relating optimized values of model parameters to physical characteristics of catchments.

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.

The effect of rainfall measurement uncertainties on rainfall–runoff processes modelling

2007 ◽  
Vol 55 (4) ◽  
pp. 103-111 ◽  
Author(s):  
D. Stransky ◽  
V. Bares ◽  
P. Fatka
Keyword(s):  
Measurement Errors ◽  
Dynamic Test ◽  
Systematic Errors ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Test Duration ◽  
Rainfall Runoff ◽  
Rainfall Volume ◽  
Rainfall Runoff Model ◽  
Runoff Model

Rainfall data are a crucial input for various tasks concerning the wet weather period. Nevertheless, their measurement is affected by random and systematic errors that cause an underestimation of the rainfall volume. Therefore, the general objective of the presented work was to assess the credibility of measured rainfall data and to evaluate the effect of measurement errors on urban drainage modelling tasks. Within the project, the methodology of the tipping bucket rain gauge (TBR) was defined and assessed in terms of uncertainty analysis. A set of 18 TBRs was calibrated and the results were compared to the previous calibration. This enables us to evaluate the ageing of TBRs. A propagation of calibration and other systematic errors through the rainfall–runoff model was performed on experimental catchment. It was found that the TBR calibration is important mainly for tasks connected with the assessment of peak values and high flow durations. The omission of calibration leads to up to 30% underestimation and the effect of other systematic errors can add a further 15%. The TBR calibration should be done every two years in order to catch up the ageing of TBR mechanics. Further, the authors recommend to adjust the dynamic test duration proportionally to generated rainfall intensity.

Perhitungan ketersediaan air permukaan di Indonesia berdasarkan data satelit

2018 ◽  
Vol 13 (2) ◽  
pp. 115-130 ◽  
Author(s):  
Radhika Radhika ◽  
Rendy Firmansyah ◽  
Waluyo Hatmoko
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Water Availability ◽  
River Flow ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Satellite Technology ◽  
Rainfall Runoff Model ◽  
The Tropics ◽  
Runoff Model

Information on water availability is vital in water resources management. Unfortunately, information on the condition of hydrological data, either river flow data, or rainfall data is very limited temporally and spatially. With the availability of satellite technology, rainfall in the tropics can be monitored and recorded for further analysis. This paper discusses the calculation of surface water availability based on rainfall data from TRMM satellite, and then Wflow, a distributed rainfall-runoff model generates monthly time runoff data from 2003 to 2015 for all river basin areas in Indonesia. It is concluded that the average surface water availability in Indonesia is 88.3 thousand m3/s or equivalent to 2.78 trillion m3/ year. This figure is lower than the study of Water Resources Research Center 2010 based on discharge at the post estimated water that produces 3.9 trillion m3/year, but very close to the study of Aquastat FAO of 2.79 trillion m3 / year. The main benefit of this satellite-based calculation is that at any location in Indonesia, potential surface water can be obtained by multiplying the area of the catchment and the runoff height.

scholarly journals Rainfall-Induced Landslide Susceptibility Using a Rainfall–Runoff Model and Logistic Regression

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1354 ◽  
Author(s):  
Hsun-Chuan Chan ◽  
Po-An Chen ◽  
Jung-Tai Lee
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Hydrological Cycle ◽  
Characteristic Curve ◽  
Rainfall Runoff ◽  
Maximum Rainfall ◽  
Susceptibility Analysis ◽  
Proposed Model ◽  
Rainfall Runoff Model ◽  
Runoff Model

Conventional landslide susceptibility analysis adopted rainfall depth or maximum rainfall intensity as the hydrological factor. However, using these factors cannot delineate temporal variations of landslide in a rainfall event. In the hydrological cycle, runoff quantity reflects rainfall characteristics and surface feature variations. In this study, a rainfall–runoff model was adopted to simulate the runoff produced by rainfall in various periods of a typhoon event. To simplify the number of factors in landslide susceptibility analysis, the runoff depth was used to replace rainfall factors and some topographical factors. The proposed model adopted the upstream area of the Alishan River in southern Taiwan as the study area. The landslide susceptibility analysis of the study area was conducted by using a logistic regression model. The results indicated that the overall accuracy of predicted events exceeded 80%, and the area under the receiver operating characteristic curve (AUC) closed to 0.8. The results revealed that the proposed landslide susceptibility simulation performed favorably in the study area. The proposed model could predict the evolution of landslide susceptibility in various periods of a typhoon and serve as a new reference for landslide hazard prevention.

scholarly journals Commercial microwave links as rainfall input data in urban hydrological modelling

2021 ◽  
Author(s):  
Greta Cazzaniga ◽  
Carlo De Michele ◽  
Cristina Deidda ◽  
Michele D'Amico ◽  
Antonio Ghezzi ◽  
...  
Keyword(s):  
River Discharge ◽  
Hydrological Model ◽  
Warning System ◽  
Signal Propagation ◽  
Rainfall Data ◽  
Data Availability ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Commercial Microwave Links

<p>Many studies in literature have showed that hydrological models are highly sensitive to spatial variability of the rainfall field. Limited and inaccurate rainfall observations can negatively affect flood forecasting and the decision-making processes based on warning system. This problem becomes much more evident in urban catchments which usually covers huge areas and where the runoff process is faster, due to the highly impervious surfaces. Given this, it is a priority to develop always new operational instruments which can improve rainfall data availability and accurately quantify rainfall variability in space. To face this challenge, in the recent years, it has been investigated the use of commercial microwave links (CML) as opportunistic rainfall sensors which could be integrated with traditional rainfall observations in areas lacking sensors. The technique relies on the well-established relationship between CML's signal attenuation and rainfall intensity across the signal propagation path. Here, we assess the operational potential of a CML network, located in the northern area of Lambro river (Lombardia region, Italy). This urbanized region is of great hydrological interest, since it is often subjected to flash floods, hence it requires a robust and accurate warning system. We considered a set of about 80 CMLs distributed quite uniformly over the entire study area and we assessed if and how rainfall data collected by them can improve river discharge predictions. To this aim, we implemented a semi-distributed rainfall-runoff model, which reproduces the river flow at the outlet section in Lesmo (Monza e Brianza), and we fed the hydrological model with CML rainfall data. We tested the use of CML rainfall data as input to the hydrological model. In particular, we used path-averaged rainfall intensities, calculated from CML path attenuation, as point measurements with a weight inversely proportional to CML length. To check the suitability of CML data as input to our urban rainfall-runoff model, we compared the observed river discharge with the predicted one, obtained using different rainfall data layouts. Indeed, we tested CML data but also rain gauges measurements and a combination of CML and rain gauge observations.</p>

scholarly journals Joint Optimization of Conceptual Rainfall-Runoff Model Parameters and Weights Attributed to Meteorological Stations

2019 ◽  
Vol 33 (13) ◽  
pp. 4509-4524 ◽  
Author(s):  
Adam P. Piotrowski ◽  
Marzena Osuch ◽  
Jarosław J. Napiorkowski
Keyword(s):  
Error Correction ◽  
Meteorological Data ◽  
Optimization Procedure ◽  
Model Error ◽  
Joint Optimization ◽  
Data Series ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract Conceptual lumped rainfall-runoff models are frequently used for various environmental problems. To put them into practice, both the model calibration method and data series of the area-averaged precipitation and air temperature are needed. In the case when data from more than one measurement station are available, first the catchment-averaged meteorological data series are usually obtained by some method, and then they are used for calibration of a lumped rainfall-runoff model. However, various optimization methods could easily be applied to simultaneously calibrate both the aggregation weights attributed to various meteorological stations to obtain a lumped meteorological data series and the rainfall-runoff model parameters. This increases the problem dimensionality but allows the optimization procedure to choose the data that are most important for the rainfall-runoff process in a particular catchment, without a priori assumptions. We test the idea using two conceptual models, HBV and GR4J, and three mutually different, relatively recently proposed Evolutionary Computation and Swarm Intelligence optimization algorithms, that are applied to three catchments located in Poland and northwestern USA. We consider two cases: with and without the model error correction applied to the rainfall-runoff models. It is shown that for the calibration period, joint optimization of the weights used to aggregate the meteorological data and the parameters of the rainfall-runoff model improves the results. However, the results for the validation period are inconclusive and depend on the model, error correction, optimization algorithm, and catchment.

Investigation of marginal distribution and dependence structure of simulated streamflow by a rainfall-runoff model

2020 ◽  
Author(s):  
Vasileios Kourakos ◽  
Theano Iliopoulou ◽  
Panayiotis Dimitriadis ◽  
Demetris Koutsoyiannis ◽  
Vassilios Kaleris ◽  
...  
Keyword(s):  
Marginal Distribution ◽  
Dependence Structure ◽  
Rainfall Time Series ◽  
State Variables ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rainfall Runoff Model ◽  
Stochastic Properties ◽  
Runoff Model

<p>Runoff simulation using hydrological models has a key role in water resources<br>management. Thus, there is a need to investigate how rainfall-runoff models preserve<br>the stochastic characteristics of real-world streamflow data. It is also useful to compare<br>the stochastic properties of output with those of input processes (rainfall) and internal<br>state variables (soil moisture), with focus on marginal distribution tails and long-term<br>persistence. To this aim, we perform a case study using the ENNS rainfall-runoff model<br>with real and synthetic rainfall time series, and for all processes we study the marginal<br>distributions and the dependence structures. In the analyses we use recently developed<br>stochastic tools such as K-moments and climacograms.</p>

Spatial representation of stochastically generated rainfall for derived flood frequency analysis

2021 ◽  
Author(s):  
Luisa-Bianca Thiele ◽  
Ross Pidoto ◽  
Uwe Haberlandt
Keyword(s):  
Time Series ◽  
Spatial Representation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Multiple Point ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Areal Rainfall ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>For derived flood frequency analyses, stochastic rainfall models can be linked with rainfall-runoff models to improve the accuracy of design flood estimations when the length of observed rainfall and runoff data is not sufficient. The stochastic rainfall time series, which are used as input for the rainfall-runoff model, can be generated with different spatial resolution: (a) Point rainfall, which is stochastically generated rainfall at a single site. (b) Areal rainfall, which is catchment rainfall averaged over multiple sites before using the single-site stochastic rainfall model. (c) Multiple point rainfall, which is stochastically generated at multiple sites with spatial correlation before averaging to catchment rainfall. To find the most applicable spatial representation of stochastically generated rainfall for derived flood frequency analysis, simulated and observed runoff time series will be compared based on runoff statistics. The simulated runoff time series are generated utilizing the rainfall-runoff model HBV-IWW with an hourly time step. The rainfall-runoff model is driven with point, areal and multiple point stochastic rainfall time series generated by an Alternating Renewal rainfall model (ARM). In order to take into account the influence of catchment size on the results, catchments of different sizes within Germany are considered in this study.  While point rainfall may be applicable for small catchments, it is expected that above a certain catchment size a more detailed spatial representation of stochastically generated rainfall is necessary. Here, it would be advantageous if the results based on areal rainfall are comparable to those of the multiple point rainfall. The stochastically generation of areal rainfall is less complex compared to the stochastically generation of multiple point rainfall and extremes at the catchment scale may also be better represented by areal rainfall.    </p>

scholarly journals Flood Simulations in Mid-Latitude Agricultural Land Using Regional Current and Future Extreme Weathers

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2421
Author(s):  
Nobuaki Kimura ◽  
Hirohide Kiri ◽  
Sachie Kanada ◽  
Iwao Kitagawa ◽  
Ikuo Yoshinaga ◽  
...  
Keyword(s):  
Agricultural Land ◽  
Climate Model ◽  
Flood Disaster ◽  
Rainfall Data ◽  
Extreme Weather Events ◽  
Rainfall Runoff ◽  
The Future ◽  
Flood Simulations ◽  
Rainfall Runoff Model ◽  
Runoff Model

Recent extreme weather events like the August 2016 flood disaster have significantly affected farmland in mid-latitude regions like the Tokachi River (TR) watershed, the most productive farmland in Japan. The August 2016 flood disaster was caused by multiple typhoons that occurred in the span of two weeks and dealt catastrophic damage to agricultural land. This disaster was the focus of our flood model simulations. For the hydrological model input, the rainfall data with 0.04° grid space and an hourly interval were provided by a regional climate model (RCM) during the period of multiple typhoon occurrences. The high-resolution data can take account of the geographic effects, hardly reproduced by ordinary RCMs. The rainfall data drove a conceptual, distributed rainfall–runoff model, embedded in the integrated flood analysis system. The rainfall–runoff model provided discharges along rivers over the TR watershed. The RCM also provided future rainfall data with pseudo-global warming climate, assuming that the August 2016 disaster could reoccur again in the late 21st century. The future rainfall data were used to conduct a future flood simulation. With bias corrections, current and future flood simulations showed the potential inundated areas along riverbanks based on flood risk levels. The crop field-based agricultural losses in both simulations were estimated. The future cost may be two to three times higher as indicated by slightly higher simulated future discharge peaks in tributaries.

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