Requirements for radar rainfall data in urban catchment modelling and control

1997 ◽  
Vol 36 (8-9) ◽  
pp. 13-18
Author(s):  
Georg Johann ◽  
Hans-Reinhard Verworn
Keyword(s):  
Radar Data ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Radar Rainfall ◽  
Urban Catchment ◽  
Space Resolution ◽  
Time Space ◽  
Urban Catchments ◽  
The University ◽  
And Control

The use of radar rainfall data as input for storm runoff models can procure a real benefit if the hydrologic response of the watershed studied is strongly dependent on the spatial and temporal heterogeneity of precipitation over its area. In dynamic management of urban catchments rainfall runoff simulations are sensitive to the resolution of the input data. In this study the influence of varios time/space resolutions of radar rainfall data on the results of rainfall-runoff simulations is inverstigated. Therefore, X-band radar rainfall data with high resolution in space (0.5 × 0.5 km) and time (360° scan every second minute), are compared with radar data with 14 min time and 1 × 1 km space resolution as input for the HYSTEM/EXTRAN hydrodynamic model of a small urban catchment. The presented invstigations are realized within the research project “realtime control of a combined sewer system by radar estimates of precipitataon” carried out by the University of Hannover and the Emschergenossenschaft/Lippeverband.

scholarly journals Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 850 ◽  
Author(s):  
Lee ◽  
Kang ◽  
Joo ◽  
Kim ◽  
Kim ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Radar Data ◽  
Rainfall Data ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Radar Rainfall ◽  
Topographical Effects ◽  
Reservoir Regulation ◽  
Regulation Model ◽  
Runoff Model

The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808–3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models.

scholarly journals Sampling errors in rainfall measurements by weather radar

2005 ◽  
Vol 2 ◽  
pp. 151-155 ◽  
Author(s):  
F. Piccolo ◽  
G. B. Chirico
Keyword(s):  
Radar Data ◽  
Sampling Interval ◽  
Rainfall Data ◽  
Time Interval ◽  
Random Errors ◽  
Sampling Errors ◽  
Radar Rainfall ◽  
Accumulated Rainfall ◽  
Sampling Intervals ◽  
Types Of Error

Abstract. Radar rainfall data are affected by several types of error. Beside the error in the measurement of the rainfall reflectivity and its transformation into rainfall intensity, random errors can be generated by the temporal spacing of the radar scans. The aim of this work is to analize the sensitivity of the estimated rainfall maps to the radar sampling interval, i.e. the time interval between two consecutive radar scans. This analysis has been performed employing data collected with a polarimetric C-band radar in Rome, Italy. The radar data consist of reflectivity maps with a sampling interval of 1min and a spatial resolution of 300m, covering an area of 1296km2. The transformation of the reflectivity maps in rainfall fields has been validated against rainfall data collected by a network of 14 raingauges distributed across the study area. Accumulated rainfall maps have been calculated for different spatial resolutions (from 300m to 2400m) and different sampling intervals (from 1min to 16min). The observed differences between the estimated rainfall maps are significant, showing that the sampling interval can be an important source of error in radar rainfall measurements.

scholarly journals Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 49-56
Author(s):  
S.JOSEPHINE VANAJA ◽  
B.V. MUDGAL ◽  
S.B. THAMPI
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Runoff ◽  
Cyclonic Storm ◽  
Rain Gauges ◽  
Runoff Modeling

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

scholarly journals Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models

2017 ◽  
Vol 21 (2) ◽  
pp. 1077-1091 ◽  
Author(s):  
Manoranjan Muthusamy ◽  
Alma Schellart ◽  
Simon Tait ◽  
Gerard B. M. Heuvelink
Keyword(s):  
Measurement Error ◽  
Stochastic Simulation ◽  
Rainfall Intensity ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Model Parameters ◽  
Urban Catchment ◽  
Geostatistical Approach ◽  
Temporal Averaging ◽  
Urban Catchments

Abstract. In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a 400 m  ×  200 m urban catchment are used in combination with spatial stochastic simulation to obtain optimal predictions of the spatially averaged rainfall intensity at any point in time within the urban catchment. The uncertainty in the prediction of catchment average rainfall intensity is obtained for multiple combinations of intensity ranges and temporal averaging intervals. The two main challenges addressed in this study are scarcity of rainfall measurement locations and non-normality of rainfall data, both of which need to be considered when adopting a geostatistical approach. Scarcity of measurement points is dealt with by pooling sample variograms of repeated rainfall measurements with similar characteristics. Normality of rainfall data is achieved through the use of normal score transformation. Geostatistical models in the form of variograms are derived for transformed rainfall intensity. Next spatial stochastic simulation which is robust to nonlinear data transformation is applied to produce realisations of rainfall fields. These realisations in transformed space are first back-transformed and next spatially aggregated to derive a random sample of the spatially averaged rainfall intensity. Results show that the prediction uncertainty comes mainly from two sources: spatial variability of rainfall and measurement error. At smaller temporal averaging intervals both these effects are high, resulting in a relatively high uncertainty in prediction. With longer temporal averaging intervals the uncertainty becomes lower due to stronger spatial correlation of rainfall data and relatively smaller measurement error. Results also show that the measurement error increases with decreasing rainfall intensity resulting in a higher uncertainty at lower intensities. Results from this study can be used for uncertainty analyses of hydrologic and hydrodynamic modelling of similar-sized urban catchments as it provides information on uncertainty associated with rainfall estimation, which is arguably the most important input in these models. This will help to better interpret model results and avoid false calibration and force-fitting of model parameters.

scholarly journals Multi-hydro hydrological modelling of a complex peri-urban catchment with storage basins comparing C-band and X-band radar rainfall data

2018 ◽  
Vol 63 (11) ◽  
pp. 1619-1635 ◽  
Author(s):  
Bianca Alves de Souza ◽  
Igor da Silva Rocha Paz ◽  
Abdellah Ichiba ◽  
Bernard Willinger ◽  
Auguste Gires ◽  
...  
Keyword(s):  
Hydrological Modelling ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Urban Catchment ◽  
X Band

scholarly journals Weather radar rainfall data in urban hydrology

2017 ◽  
Vol 21 (3) ◽  
pp. 1359-1380 ◽  
Author(s):  
Søren Thorndahl ◽  
Thomas Einfalt ◽  
Patrick Willems ◽  
Jesper Ellerbæk Nielsen ◽  
Marie-Claire ten Veldhuis ◽  
...  
Keyword(s):  
Urban Areas ◽  
Numerical Models ◽  
Climate Change Impacts ◽  
Weather Radar ◽  
Radar Data ◽  
Rainfall Data ◽  
Urban Hydrology ◽  
Radar Rainfall ◽  
The Past ◽  
Hydrological Applications

Abstract. Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology necessitate an updated review of the state of the art in such radar rainfall data and applications. Three key areas with significant advances over the past decade have been identified: (1) temporal and spatial resolution of rainfall data required for different types of hydrological applications, (2) rainfall estimation, radar data adjustment and data quality, and (3) nowcasting of radar rainfall and real-time applications. Based on these three fields of research, the paper provides recommendations based on an updated overview of shortcomings, gains, and novel developments in relation to urban hydrological applications. The paper also reviews how the focus in urban hydrology research has shifted over the last decade to fields such as climate change impacts, resilience of urban areas to hydrological extremes, and online prediction/warning systems. It is discussed how radar rainfall data can add value to the aforementioned emerging fields in current and future applications, but also to the analysis of integrated water systems.

scholarly journals Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

2010 ◽  
Vol 7 (5) ◽  
pp. 7995-8043 ◽  
Author(s):  
A. Atencia ◽  
M. C. Llasat ◽  
L. Garrote ◽  
L. Mediero
Keyword(s):  
Hydrological Model ◽  
Radar Data ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Distributed Hydrological Model ◽  
Probability Matching ◽  
Radar Rainfall ◽  
Distributed Hydrologic Model ◽  
Surface Data ◽  
Distributed Hydrological Models

Abstract. The performance of distributed hydrological models depends on the resolution, both spatial and temporal, of the rainfall surface data introduced. The estimation of quantitative precipitation from meteorological radar or satellite can improve hydrological model results, thanks to an indirect estimation at higher spatial and temporal resolution. In this work, composed radar data from a network of three C-band radars, with 6-minutal temporal and 2 × 2 km2 spatial resolution, provided by the Catalan Meteorological Service, is used to feed the RIBS distributed hydrological model. A Window Probability Matching Method (gage-adjustment method) is applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation in both convective and stratiform Z/R relations used over Catalonia. Once the rainfall field has been adequately obtained, an advection correction, based on cross-correlation between two consecutive images, was introduced to get several time resolutions from 1 min to 30 min. Each different resolution is treated as an independent event, resulting in a probable range of input rainfall data. This ensemble of rainfall data is used, together with other sources of uncertainty, such as the initial basin state or the accuracy of discharge measurements, to calibrate the RIBS model using probabilistic methodology. A sensitivity analysis of time resolutions was implemented by comparing the various results with real values from stream-flow measurement stations.

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