Historical precipitation time series for applications in urban hydrology

1998 ◽  
Vol 37 (11) ◽  
pp. 147-153 ◽  
Author(s):  
G. Johann ◽  
I. Papadakis ◽  
A. Pfister
Keyword(s):  
Time Series ◽  
Water System ◽  
West Germany ◽  
Urban Hydrology ◽  
Rainfall Runoff ◽  
Precipitation Time Series ◽  
Time Intervals ◽  
Hydraulic Simulation ◽  
North West

The quality of results of rainfall runoff modelling depends strongly on the hydrologic input data. In particular, for urban hydrology applications long-term rainfall series without gaps and of high quality and reliability are required in rainfall runoff and hydraulic simulation of the investigated drainage and receiving water system. The presented study discusses a method for filling gaps in precipitation time series provided by the Emschergenossenschaft and Lippeverband (EG/LV) in north west Germany. Various time intervals based on deterministic and statistical approaches are investigated. Intervals between 5 and 120 min will be discussed in particular. Several neighbour stations of the EG/LV raingauge network are considered. On the basis of representative examples it is shown how the time intervals mentioned above influences the quality of the estimated gap-filling rainfall data.

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2016 ◽  
Vol 20 (4) ◽  
pp. 1387-1403 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Ole Bøssing Christensen ◽  
Karsten Arnbjerg-Nielsen ◽  
Peter Steen Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as input to the WG are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 km  ×  60 km model domain. The WG simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from regional climate models (RCMs) with spatial resolutions of 25 and 8 km, respectively. Six different RCM simulation pairs are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

scholarly journals Conjugate fluctuation analysis for a set of 41 magnetic clouds measured by the ACE spacecraft

2014 ◽  
Vol 1 (1) ◽  
pp. 583-613 ◽  
Author(s):  
A. Ojeda González ◽  
W. D. Gonzalez ◽  
O. Mendes ◽  
M. O. Domingues ◽  
R. R. Rosa
Keyword(s):  
Time Series ◽  
Plasma Sheath ◽  
Magnetic Clouds ◽  
Fluctuation Analysis ◽  
Threshold Values ◽  
Time Intervals ◽  
Rescaled Range ◽  
Distribution Of Values ◽  
Detrended Fluctuation

Abstract. The statistical distribution of values in the signal and the autocorrelations (interpreted as the memory or persistence) between values are attributes of a time series. The autocorrelation function values are positive in a~time series with persistence, while it are negative in a time series with anti persistence. The persistence of values with respect to each other can be strong, weak, or nonexistent. A strong correlation implies a "memory" of previous values in the time series. The long-range persistence in time series could be studied using semivariograms, rescaled-range, detrended fluctuation analysis and Fourier spectral analysis, respectively. In this work the persistence analysis has been used to study IMF time series. We use data from the IMF GSM-components with time resolution of 16 s. Time intervals corresponding to distinct processes around 41 MCs in the period between March 1998 and December 2003 were selected. In this exploratory study the purpose with this selection is to deal with the cases presenting the three periods: plasma sheath, MC and post-MC. We calculated one exponent of persistence (e.g., α, β, Hu, Ha) over the previous three time intervals. The persistence exponent values increased inside cloud regions, and it was possible select the following threshold values: 〈α(j)〉 =1.392; 〈Ha(j)〉 = 0.327; 〈Hu(j)〉 =0.875. These values are useful as another test to evaluate the quality of the identification. If the cloud is well-structured, then the persistence exponents values exceed thresholds. In 80.5% of the cases studied, these tools were able to separate the region of the cloud from neighboring regions. The Hausdorff exponent (Ha) provides the best results.

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2015 ◽  
Vol 12 (2) ◽  
pp. 2561-2605 ◽  
Author(s):  
H. J. D. Sørup ◽  
O. B. Christensen ◽  
K. Arnbjerg-Nielsen ◽  
P. S. Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a Spatio-Temporal Neyman–Scott Rectangular Pulses weather generator (WG). Precipitation time series for fitting the model are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 by 60 km model domain. The model simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from Regional Climate Models (RCMs) with spatial resolutions of 25 and 8 km respectively. Six different RCM simulations are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

A method for automatic validation of long time series of data in urban hydrology

2002 ◽  
Vol 45 (4-5) ◽  
pp. 263-270 ◽  
Author(s):  
M. Mourad ◽  
J.-L. Bertrand-Krajewski
Keyword(s):  
Time Series ◽  
Reliable Data ◽  
Urban Hydrology ◽  
Gradient Material ◽  
Long Time Series ◽  
Analytical Redundancy ◽  
Long Time ◽  
Validation Procedure ◽  
Automatic Validation

Modelling in urban hydrology is largely based on the analysis of long time series of data. The quality of the results strongly depends on the quality of the data used. Doubtful or wrong data must be detected and eventually substituted by reliable ones when it is feasible before any further exploitation. This paper deals with the development of an automatic pre-validation procedure that detects doubtful and not reliable data, in order to facilitate their interpretation. This procedure consists in applying a set of seven tests based on the following criteria : the functioning state of the sensor, the physical range of the quantity, the locally realistic range, the duration since the last maintenance of the sensor, the signal's gradient, material redundancy and analytical redundancy. The results of the tests are coded with the letter A for reliable values, B for doubtful values and C for wrong values. After this automatic prevalidation, the ultimate validation of values marked B and C is carried out manually by the operator, with the assistance of specifically developed visual and graphical tools.

scholarly journals Modelling the frequency distribution of inter-arrival times from daily precipitation time-series in North-West Italy

2018 ◽  
Vol 50 (1) ◽  
pp. 339-357 ◽  
Author(s):  
Giorgio Baiamonte ◽  
Luca Mercalli ◽  
Daniele Cat Berro ◽  
Carmelo Agnese ◽  
Stefano Ferraris
Keyword(s):  
Time Series ◽  
Frequency Distribution ◽  
Daily Precipitation ◽  
Regional Scale ◽  
Precipitation Time Series ◽  
Precipitation Regime ◽  
Arrival Times ◽  
North West ◽  
Pettitt Test ◽  
Precipitation Regimes

Abstract The discrete three-parameter Lerch distribution is used to analyse the frequency distribution of inter-arrival times derived from 26 daily precipitation time-series, collected by stations located throughout a 28,000 km2 area in North-West Italy (altitudes ranging from 113 m to 2,170 m a.s.l.). The precipitation regime of these Alpine regions is very different (latitude 44.5 to 46.5 N) from the typical Mediterranean precipitation regime of the island of Sicily (latitude 37 to 38 N), where the Lerch distribution has already been tested and whose results are compared. In order to verify the homogeneity of the precipitation time series, the Pettitt test was preliminarily performed. In this work, a good fitting of the Lerch distribution to NW Italy is shown, thus evidencing the wide applicability of this kind of distribution, also allowing to jointly model dry spells and wet spells. The three parameters of the Lerch distribution showed rather different values than the Sicily ones, likely due to the very different precipitation regimes. Finally, a relevant spatial variability of inter-arrival times in the study area was revealed from the regional scale application of the probability distribution here described. The outcomes of this study could be of interest in different hydrologic applications.

scholarly journals Nonlinear fluctuation analysis for a set of 41 magnetic clouds measured by the Advanced Composition Explorer (ACE) spacecraft

2014 ◽  
Vol 21 (5) ◽  
pp. 1059-1073 ◽  
Author(s):  
A. Ojeda González ◽  
W. D. Gonzalez ◽  
O. Mendes ◽  
M. O. Domingues ◽  
R. R. Rosa
Keyword(s):  
Time Series ◽  
Plasma Sheath ◽  
Magnetic Clouds ◽  
Fluctuation Analysis ◽  
Time Intervals ◽  
Rescaled Range ◽  
Distribution Of Values ◽  
Detrended Fluctuation ◽  
Nonlinear Fluctuation

Abstract. The statistical distribution of values in the signal and the autocorrelations (interpreted as the memory or persistence) between values are attributes of a time series. The autocorrelation function values are positive in a time series with persistence, while they are negative in a time series with anti-persistence. The persistence of values with respect to each other can be strong, weak, or nonexistent. A strong correlation implies a "memory" of previous values in the time series. The long-range persistence in time series could be studied using semivariograms, rescaled range, detrended fluctuation analysis and Fourier spectral analysis, respectively. In this work, persistence analysis is to study interplanetary magnetic field (IMF) time series. We use data from the IMF components with a time resolution of 16 s. Time intervals corresponding to distinct processes around 41 magnetic clouds (MCs) in the period between March 1998 and December 2003 were selected. In this exploratory study, the purpose of this selection is to deal with the cases presenting the three periods: plasma sheath, MC, and post-MC. We calculated one exponent of persistence (e.g., α, β, Hu, Ha) over the previous three time intervals. The persistence exponent values increased inside cloud regions, and it was possible to select the following threshold values: α(j) = 1.392, Ha(j) = 0.327, and Hu(j) = 0.875. These values are useful as another test to evaluate the quality of the identification. If the cloud is well structured, then the persistence exponent values exceed thresholds. In 80.5% of the cases studied, these tools were able to separate the region of the cloud from neighboring regions. The Hausdorff exponent (Ha) provides the best results.

Assessing the adequacy of catchment streamflow yield estimates

Soil Research ◽  
1993 ◽  
Vol 31 (5) ◽  
pp. 665 ◽  
Author(s):  
FHS Chiew ◽  
TA Mcmahon
Keyword(s):  
Time Series ◽  
Rainfall Runoff ◽  
Climate Data ◽  
Statistical Parameters ◽  
Model Adequacy ◽  
Required Quality ◽  
Level Of Agreement

Rainfall-runoff models are frequently used by hydrologists to estimate runoff from rainfall and climate data, with the model adequacy assessed by comparing the level of agreement between flows simulated by the model and the recorded flows. This paper describes simple methods (visual plots, statistical parameters and dimensionless coefficients) which are commonly used to compare estimated and recorded streamflow time series and discusses their advantages and limitations. Results of a survey conducted to ascertain the required quality of flow estimates before they are considered to be satisfactory, as well as to identify preferred methods used by hydrologists in Australia to determine the adequacy of streamflow estimates, are also discussed in this paper. Information from the survey is also used to suggest objective criteria based on dimensionless coefficients that can be used as guides in assessing the adequacy of flows estimated by rainfall-runoff models. In particular, the coefficient of efficiency is a very useful indicator in assessing model adequacy.

scholarly journals Time series of ⸹ 26 Mg variability in precipitation of north‐west Germany

2021 ◽  
Author(s):  
Sylvia Riechelmann ◽  
Christoph Spötl ◽  
Adrian Immenhauser
Keyword(s):  
Time Series ◽  
West Germany ◽  
North West

Analysis of rainfall time series with regard to temporal disaggregation for the use in urban hydrology

1998 ◽  
Vol 37 (11) ◽  
pp. 65-72 ◽  
Author(s):  
S. Burckhardt-Gammeter ◽  
R. Fankhauser
Keyword(s):  
Time Series ◽  
Temporal Trend ◽  
Urban Hydrology ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Constant Ratio ◽  
Specific Location ◽  
Temporal Disaggregation ◽  
Rain Data ◽  
Meteorological Institute

The temporal resolution of rain data recorded by national weather services is often lower (10-min resolution) than the data needed for rainfall-runoff simulations (1-5 min resolution). However, the time series are sufficiently long for statistical analysis. The goal of the project presented here is to develop a procedure that disaggregates the 10-min rainfall data of the Swiss Meteorological Institute (SMI) to I-min time resolution. The aim is to implement the procedure in a computer program which can be used by practitioners for generating high resolution rainfall time series at a specific location. In a first step the 1-min data and the 10-min data of two rainfall time series in Switzerland (Heiden, Lucerne) were compared to find characteristic patterns and correlations. The position of 1-min peak within the 10-min interval, the ratio of 1-min peak to 10-min intensity as well as the distribution of the 1-min values within the 10-min interval were investigated. The analysis showed that the positions of the 1-min peaks within a 10-min interval were not uniformly distributed. The distribution depended on the temporal trend of the 10-min values. The ratio of 1-min peak to 10-min intensity seemed to tend towards a constant ratio for high intensities. The results for the two series (Heiden and Lucerne) were not significantly different. These first findings are encouraging with regard to developing a disaggregation to 1-min values which satisfy the accuracy needed in urban hydrology.

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