scholarly journals The sensitivity of catchment runoff models to rainfall data at different spatial scales

2000 ◽  
Vol 4 (4) ◽  
pp. 653-667 ◽  
Author(s):  
V. A. Bell ◽  
R. J. Moore
Keyword(s):  
Time Series ◽  
Spatial Scales ◽  
Rainfall Data ◽  
Scale Model ◽  
Distributed Model ◽  
Rainfall Time Series ◽  
Time Step ◽  
Stratiform Rain ◽  
Convective Rain ◽  
Catchment Runoff

Abstract. The sensitivity of catchment runoff models to rainfall is investigated at a variety of spatial scales using data from a dense raingauge network and weather radar. These data form part of the HYREX (HYdrological Radar EXperiment) dataset. They encompass records from 49 raingauges over the 135 km2 Brue catchment in south-west England together with 2 and 5 km grid-square radar data. Separate rainfall time-series for the radar and raingauge data are constructed on 2, 5 and 10 km grids, and as catchment average values, at a 15 minute time-step. The sensitivity of the catchment runoff models to these grid scales of input data is evaluated on selected convective and stratiform rainfall events. Each rainfall time-series is used to produce an ensemble of modelled hydrographs in order to investigate this sensitivity. The distributed model is shown to be sensitive to the locations of the raingauges within the catchment and hence to the spatial variability of rainfall over the catchment. Runoff sensitivity is strongest during convective rainfall when a broader spread of modelled hydrographs results, with twice the variability of that arising from stratiform rain. Sensitivity to rainfall data and model resolution is explored and, surprisingly, best performance is obtained using a lower resolution of rainfall data and model. Results from the distributed catchment model, the Simple Grid Model, are compared with those obtained from a lumped model, the PDM. Performance from the distributed model is found to be only marginally better during stratiform rain (R2 of 0.922 compared to 0.911) but significantly better during convective rain (R2 of 0.953 compared to 0.909). The improved performance from the distributed model can, in part, be accredited to the excellence of the dense raingauge network which would not be the norm for operational flood warning systems. In the final part of the paper, the effect of rainfall resolution on the performance of the 2 km distributed model is explored. The need to recalibrate the model for use with rainfall data of a given resolution, particularly for periods of convective rain, is highlighted. Again, best performance is obtained using lower resolution rainfall data. This is interpreted as evidence for the need to improve the distributed model structure to make better use of the higher resolution information on rainfall and topographic controls on runoff. Degrading the resolution of rainfall data, model or both to achieve the smoothing apparently needed is not seen as wholly appropriate. Keywords: rainfall, runoff, sensitivity, scale, model, flood

SLICES OF MULTIFRACTAL MEASURES AND APPLICATIONS TO RAINFALL DISTRIBUTIONS

Fractals ◽  
2000 ◽  
Vol 08 (04) ◽  
pp. 337-348 ◽  
Author(s):  
BIRGER LAMMERING
Keyword(s):  
Time Series ◽  
Field Data ◽  
Rainfall Data ◽  
Rainfall Time Series ◽  
Mathematical Ideas ◽  
Radar Images ◽  
The Usa ◽  
Self Similar ◽  
The Relationship ◽  
Plane Measure

We discuss the relationship between the multifractal functions of a plane measure and those of slices or sections of the measure with a line. Motivated by recent mathematical ideas about the relationship between measures and their slices, we formulate the "slice hypothesis," and consider the theoretical limitations of this hypothesis. We compute the multifractal functions of several standard self-similar and self-affine measures and their slices to examine the validity of the slice hypothesis. We are particularly interested in using the slice hypothesis to estimate multifractal properties of spatial rainfall fields by analyzing rainfall data representing slices of rainfall fields. We consider how rainfall time series at a fixed site and slices of composite radar images can be used for this purpose, testing this on field data from a radar composite in the USA and on appropriate time series.

Comparing runoff statistics simulated with a hydrological model utilizing stochastically generated lumped catchment rainfall and multiple point rainfall

2020 ◽  
Author(s):  
Luisa-Bianca Thiele ◽  
Ross Pidoto ◽  
Uwe Haberlandt
Keyword(s):  
Time Series ◽  
Hydrological Modelling ◽  
Flood Frequency ◽  
Multiple Point ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Design Flood ◽  
Catchment Scale ◽  
Time Step ◽  
Rainfall 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. In the past, when using stochastic rainfall time series for hydrological modelling purposes, catchment rainfall for use in hydrological modelling was calculated from the multiple point rainfall time series. As an alternative to this approach, it will be tested whether catchment rainfall can be modelled directly, negating the drawbacks (and need) encountered in generating spatially consistent time series. An Alternating Renewal rainfall model (ARM) will be used to generate multiple point and lumped catchment rainfall time series in hourly resolution. The generated rainfall time series will be used to drive the rainfall-runoff model HBV-IWW with an hourly time step for mesoscale catchments in Germany. Validation will be performed by comparing modelled runoff regarding runoff and flood statistics using stochastically generated lumped catchment rainfall versus multiple point rainfall. It would be advantageous if the results based on catchment rainfall are comparable to those using multiple point rainfall, so catchment rainfall could be generated directly with the stochastic rainfall models. Extremes at the catchment scale may also be better represented if catchment rainfall is generated directly.</p>

scholarly journals Temporal rainfall disaggregation using a micro-canonical cascade model: Possibilities to improve the autocorrelation

2019 ◽  
Author(s):  
Hannes Müller-Thomy
Keyword(s):  
Time Series ◽  
Extreme Event ◽  
A Priori ◽  
Extreme Rainfall ◽  
Cascade Model ◽  
Special Focus ◽  
Rainfall Time Series ◽  
Lower Saxony ◽  
Time Step ◽  
Daily Data

Abstract. In urban hydrology rainfall time series of high resolution in time are crucial. Such time series with sufficient length can be generated through the disaggregation of daily data with a micro-canonical cascade model. A well-known problem of time series generated so is the underestimation of the autocorrelation. In this paper two cascade model modifications are analysed regarding their ability to improve the autocorrelation. Both modifications are based on a state-of-the-art reference cascade model. In the first modification, a position-dependency is introduced in the first disaggregation step. In the second modification the position of a wet time step is redefined in addition. Both modifications led to an improvement of the autocorrelation, especially the position redefinition. Simultaneously, two approaches are investigated to avoid the generation of time steps with too small rainfall intensities, the conservation of a minimum rainfall amount during the disaggregation process itself and the mimicry of a measurement device after the disaggregation process. The mimicry approach shows slight better results for the autocorrelation and hence was kept for a subsequent resampling investigation using Simulated Annealing. For the resampling, a special focus was given to the conservation of the extreme rainfall values. Therefore, a universal extreme event definition was introduced to define extreme events a priori without knowing their occurrence in time or magnitude. The resampling algorithm is capable of improving the autocorrelation, independent of the previously applied cascade model variant. Also, the improvement of the autocorrelation by the resampling was higher than by the choice of the cascade model modification. The best overall representation of the autocorrelation was achieved by method C in combination with the resampling algorithm. The study was carried out for 24 rain gauges in Lower Saxony, Germany.

scholarly journals Assimilation of Piezometric Data to Calibrate Parsimonious Daily Hydrological Models

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2342
Author(s):  
Axel Flinck ◽  
Nathalie Folton ◽  
Patrick Arnaud
Keyword(s):  
Time Series ◽  
River Flow ◽  
Hydrological Model ◽  
Water Levels ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Time Step ◽  
Processing Methods ◽  
Analysis System ◽  
Set Up

Low water levels are a seasonal phenomenon, which can be long, short, and more or less intense, affecting entire watercourses. This phenomenon has become a concern for many countries who seek better understanding of the processes that affect it and learn how to optimally manage water resources (pumping, irrigation). Consequently, a lumped rainfall model at daily time step (GR) has been defined, calibrated, and regionalised over French territories. The input data come from SAFRAN, the distributed mesoscale atmospheric analysis system, which provides daily solid and liquid precipitation and temperature data throughout the French territory. This model could be improved, in particular to more accurately simulate the hydrological response of watersheds interacting with groundwater. The idea is to use piezometric data from the ADES bank, available in France, and to use it for the calibration phase of the hydrological model. The analysis was carried out across ten French catchments that are representative of various hydrometeorological behaviours and are located in a diverse hydrogeological context. Each catchment must be represented by a piezometer that closely represents the main aquifer that interacts with the basin. This piezometer is located on part of the watershed that is most covered in terms of its drainage network, and closest to its outlet. Different signal processing methods are used to characterise the relationship between the fluctuation of river flow, piezometric levels and rainfall time series. Potential processing methods will be carried out in the temporal domain. To quantify groundwater table inertia and that of the catchment area, correlograms were calculated from daily chronicles of flows and piezometric levels. A cross-correlatory analysis was set up to see, in more detail, the correlations between the flow rates (especially base flows) and piezometric level time series. This type of analysis makes it possible to study relationships between various observations, and tests were carried out to take this information into account during the phase of the calibration of hydrological model parameters. These different analyses will hopefully help us to use piezometric data to consolidate the quality and robustness of the modelling.

scholarly journals A Data-Driven Surrogate Modelling Approach for Acceleration of Short-Term Simulations of a Dynamic Urban Drainage Simulator

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1849 ◽  
Author(s):  
Mahmood Mahmoodian ◽  
Jairo Arturo Torres-Matallana ◽  
Ulrich Leopold ◽  
Georges Schutz ◽  
Francois H. L. R. Clemens
Keyword(s):  
Time Series ◽  
Storage Tank ◽  
Data Driven ◽  
Urban Drainage ◽  
Rainfall Time Series ◽  
Short Term ◽  
Time Step ◽  
Speed Up ◽  
Error Indicators

In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving force is a challenge in this regard due to the high dimensionality problem. However, this problem can be less relevant when the focus is only on short-term simulations. The novelty of this research is the consideration of short-term rainfall time series as training parameters for the GPE. Rainfall intensity at each time step is counted as a separate parameter. A method to generate synthetic rainfall events for GPE training purposes is introduced as well. Here, an emulator is developed to predict the upcoming daily time series of the total wastewater volume in a storage tank and the corresponding Combined Sewer Overflow (CSO) volume. Nash-Sutcliffe Efficiency (NSE) and Volumetric Efficiency (VE) are calculated as emulation error indicators. For the case study herein, the emulator is able to speed up the simulations up to 380 times with a low accuracy cost for prediction of the total storage tank volume (medians of NSE = 0.96 and VE = 0.87). CSO events occurrence is detected in 82% of the cases, although with some considerable accuracy cost (medians of NSE = 0.76 and VE = 0.5). Applicability of the emulator for consecutive short-term simulations, based on real observed rainfall time series is also validated with a high accuracy (NSE = 0.97, VE = 0.89).

scholarly journals Prediction of monsoon rainfall for a mesoscale Indian catchment based on stochastical downscaling and objective circulation patterns

2005 ◽  
Vol 2 (5) ◽  
pp. 1961-1993
Author(s):  
E. Zehe ◽  
A. K. Singh ◽  
A. Bárdossy
Keyword(s):  
Time Series ◽  
Classification Scheme ◽  
Decadal Variability ◽  
Monthly Rainfall ◽  
Temperate Climate ◽  
Time Variability ◽  
Rainfall Time Series ◽  
Time Step ◽  
North West ◽  
Circulation Patterns

Abstract. In this study a stochastical approach for generating rainfall time series based on objective circulation patterns (CP is applied to the mesoscale Anas catchment in North West India. This CP based approach was developed and successfully applied in the humid and temperate climate of Central Europe. The objective of the study was to find out whether this approach is transferable to a catchment in North West India with a totally different semi arid climate. For the Anas catchment it was possible to identify a CP classification scheme consisting of 12 CPs defined in a window between 5° N 40° E and 35° N 95° E, which explained the space-time variability of observed rainfall at 10 stations in the Anas catchment. Based on the classification scheme, NCAR pressure data from 500 hPa level were classified into a CP time series for the period of 1964–1994, which was in turn used as meteorological forcing for multivariate stochastical rainfall simulations with a daily time step. On the monthly time scale the model performed well. Except for stations Udaigarh and Bhabra the average annual cycle of monthly rainfall and rainy days in a month was matched well. The frequency distributions of monthly rainfall at different stations were also captured well. Correlation coefficients between simulated and observed monthly rainfall were larger than 0.85 at each station. Within a long term simulation of 30 years the model yielded promising predictions for monthly as well as for seasonal rainfall totals, but showed also clear deficiencies in capturing the very extremes and inter-decadal variability of monsoon strength. In this respect, the introduction of additional predictors such as SST anomalies and wind direction classes promised the most substantial model improvements.

scholarly journals Disaggregation of Rainfall Time Series using Artificial Neural Network In Case Of Limited Data

2019 ◽  
Vol 9 (1S3) ◽  
pp. 177-181
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Temporal Resolution ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Rainfall Time Series ◽  
Limited Data ◽  
Artificial Neural ◽  
Better Than

Temporal resolution of rainfall series needs to be necessarily less to use it in many engineering applications. But most of the simulated and observed rainfall series are coarser than 3hours. Hence, it is imperative to disaggregate coarser rainfall to finer. The quantum of necessary fineness depends on application in which the rainfall data is going to be used. In this paper, the competency of Artificial Neural Network to disaggregate 3 hour rainfall into hourly, in case of limited data is verified. It is found that the disaggregation is viable with the constraint of limited data also. The rainfall is disaggregated using three models, of which, performance of the second model is much better than the others. Nonetheless the constraint of limited data, the rationale behind the better performance of the second model, is clearly discussed

scholarly journals Temporal rainfall disaggregation using a micro-canonical cascade model: possibilities to improve the autocorrelation

2020 ◽  
Vol 24 (1) ◽  
pp. 169-188 ◽  
Author(s):  
Hannes Müller-Thomy
Keyword(s):  
Time Series ◽  
Extreme Event ◽  
Cascade Model ◽  
Special Focus ◽  
Rainfall Time Series ◽  
Lower Saxony ◽  
Step Method ◽  
Time Step ◽  
Previous Time ◽  
Previous Time Step

Abstract. In urban hydrology rainfall time series of high resolution in time are crucial. Such time series with sufficient length can be generated through the disaggregation of daily data with a micro-canonical cascade model. A well-known problem of time series generated in this way is the inadequate representation of the autocorrelation. In this paper two cascade model modifications are analysed regarding their ability to improve the autocorrelation in disaggregated time series with 5 min resolution. Both modifications are based on a state-of-the-art reference cascade model (method A). In the first modification, a position dependency is introduced in the first disaggregation step (method B). In the second modification the position of a wet time step is redefined in addition by taking into account the disaggregated finer time steps of the previous time step instead of the previous time step itself (method C). Both modifications led to an improvement of the autocorrelation, especially the position redefinition (e.g. for lag-1 autocorrelation, relative errors of −3 % (method B) and 1 % (method C) instead of −4 % for method A). To ensure the conservation of a minimum rainfall amount in the wet time steps, the mimicry of a measurement device is simulated after the disaggregation process. Simulated annealing as a post-processing strategy was tested as an alternative as well as an addition to the modifications in methods B and C. For the resampling, a special focus was given to the conservation of the extreme rainfall values. Therefore, a universal extreme event definition was introduced to define extreme events a priori without knowing their occurrence in time or magnitude. The resampling algorithm is capable of improving the autocorrelation, independent of the previously applied cascade model variant (e.g. for lag-1 autocorrelation the relative error of −4 % for method A is reduced to 0.9 %). Also, the improvement of the autocorrelation by the resampling was higher than by the choice of the cascade model modification. The best overall representation of the autocorrelation was achieved by method C in combination with the resampling algorithm. The study was carried out for 24 rain gauges in Lower Saxony, Germany.

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.

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