Flood flow simulation using CMAX radar rainfall estimates in orographic basins

2013 ◽  
Vol 21 (3) ◽  
pp. 596-604 ◽  
Author(s):  
Sukmin Yoon ◽  
Changsam Jeong ◽  
Taesam Lee
Keyword(s):  
Flow Simulation ◽  
Radar Rainfall ◽  
Flood Flow ◽  
Rainfall Estimates

scholarly journals Statistical analysis of error propagation from radar rainfall to hydrological models

2013 ◽  
Vol 17 (4) ◽  
pp. 1445-1453 ◽  
Author(s):  
D. Zhu ◽  
D. Z. Peng ◽  
I. D. Cluckie
Keyword(s):  
Stream Flow ◽  
Flow Simulation ◽  
Statistical Error ◽  
Generation Model ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Lumped Model ◽  
Flow Simulations ◽  
Error Generation ◽  
Rainfall Estimates

Abstract. This study attempts to characterise the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation uncertainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realisations of possible combinations of normalised errors and normalised bias to reflect the identified radar error and temporal dependence. These realisations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi-distributed, lumped model TOPMODEL and the unit hydrograph model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2) in South-East England. The results show that the normalised bias of the radar rainfall estimates was enhanced in the simulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This preliminary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.

scholarly journals Statistical analysis of error propagation from radar rainfall to hydrological models

2012 ◽  
Vol 9 (9) ◽  
pp. 10277-10302
Author(s):  
D. H. Zhu ◽  
D. Z. Peng ◽  
I. D. Cluckie
Keyword(s):  
Stream Flow ◽  
Flow Simulation ◽  
Statistical Error ◽  
Distributed Model ◽  
Generation Model ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Lumped Model ◽  
Flow Simulations ◽  
Rainfall Estimates

Abstract. This study attempts to characterize the manner with which inherent error in radar rainfall estimates input influence the character of the stream flow simulation uncertainty in validated hydrological modelling. An artificial statistical error model described by Gaussian distribution was developed to generate realizations of possible combinations of normalized errors and normalized bias to reflect the identified radar error and temporal dependence. These realizations were embedded in the 5 km/15 min UK Nimrod radar rainfall data and used to generate ensembles of stream flow simulations using three different hydrological models with varying degrees of complexity, which consists of a fully distributed physically-based model MIKE SHE, a semi-distributed model TOPMODEL and a lumped model PRTF. These models were built for this purpose and applied to the Upper Medway Catchment (220 km2) in South-East England. The results show that the normalized bias of the radar rainfall estimates was enhanced in the simulated stream flow and also the dominate factor that had a significant impact on stream flow simulations. This preliminary radar-error-generation model could be developed more rigorously and comprehensively for the error characteristics of weather radars for quantitative measurement of rainfall.

scholarly journals Evaluating the accuracy of C- and X-band weather radars and their application for stream flow simulation

2012 ◽  
Vol 15 (4) ◽  
pp. 1121-1136 ◽  
Author(s):  
N. K. Shrestha ◽  
T. Goormans ◽  
P. Willems
Keyword(s):  
Conceptual Model ◽  
Stream Flow ◽  
Flow Simulation ◽  
Winter Storms ◽  
Weather Radars ◽  
X Band ◽  
Rainfall Estimates

This paper investigates the accuracy of rainfall estimates from C- and X-band weather radars and their application for stream flow simulation. Different adjustment procedures are applied to raw radar estimates using gauge readings from a network of 12 raingauges. The stream flow is simulated for the 48.17 km2 Molenbeek/Parkbeek catchment located in the Flemish region of Belgium based on a lumped conceptual model. Results showed that raw radar estimates can be greatly improved using adjustment procedures. The gauge-radar residuals however, remain large even after adjustments. The adjusted X-band radar estimates are observed to be better estimates than corresponding C-band estimates. Their application for stream flow simulation showed that raingauges and radars can simulate spatially more uniform winter storms with almost the same accuracy, whereas differences are more evident on summer events.

Radar Rainfall Estimates for Debris-Flow Early Warning Systems

2017 ◽  
pp. 453-465
Author(s):  
F. Marra ◽  
E. I. Nikolopoulos ◽  
J. D. Creutin ◽  
M. Borga
Keyword(s):  
Debris Flow ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Radar Rainfall ◽  
Rainfall Estimates

Estimation of Rainfall Based on the Results of Polarimetric Echo Classification

2008 ◽  
Vol 47 (9) ◽  
pp. 2445-2462 ◽  
Author(s):  
Scott E. Giangrande ◽  
Alexander V. Ryzhkov
Keyword(s):  
Type A ◽  
New Method ◽  
Polarimetric Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Different Types ◽  
Rms Error ◽  
Radar Resolution ◽  
Rainfall Estimates

Abstract The quality of polarimetric radar rainfall estimation is investigated for a broad range of distances from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D). The results of polarimetric echo classification have been integrated into the study to investigate the performance of radar rainfall estimation contingent on hydrometeor type. A new method for rainfall estimation that capitalizes on the results of polarimetric echo classification (EC method) is suggested. According to the EC method, polarimetric rainfall relations are utilized if the radar resolution volume is filled with rain (or rain and hail), and multiple R(Z) relations are used for different types of frozen hydrometeors. The intercept parameters in the R(Z) relations for each class are determined empirically from comparisons with gauges. It is shown that the EC method exhibits better performance than the conventional WSR-88D algorithm with a reduction by a factor of 1.5–2 in the rms error of 1-h rainfall estimates up to distances of 150 km from the radar.

Quantifying the potential of AQPI gap-filling radar network for streamflow simulation through a WRF-Hydro experiment

2021 ◽  
Author(s):  
Yingzhao Ma ◽  
V. Chandrasekar ◽  
Haonan Chen ◽  
Robert Cifelli
Keyword(s):  
Water Model ◽  
Urban Region ◽  
Gap Filling ◽  
Radar Rainfall ◽  
Bay Area ◽  
Radar Network ◽  
X Band ◽  
The Impact ◽  
Streamflow Simulation ◽  
Rainfall Estimates

AbstractIt remains a challenge to provide accurate and timely flood warnings in many parts of the western United States. As part of the Advanced Quantitative Precipitation Information (AQPI) project, this study explores the potential of using the AQPI gap-filling radar network for streamflow simulation of selected storm events in the San Francisco Bay Area under a WRF-Hydro modeling system. Two types of watersheds including natural and human-affected among the most flood-prone region of the Bay Area are investigated. Based on the high-resolution AQPI X-band radar rainfall estimates, three basic routing configurations, including Grid, Reach, and National Water Model (NWM), are used to quantify the impact of different model physics options on the simulated streamflow. It is found that the NWM performs better in terms of reproducing streamflow volumes and hydrograph shapes than the other routing configurations when reservoirs exist in the watershed. Additionally, the AQPI X-band radar rainfall estimates (without gauge correction) provide reasonable streamflow simulations, and they show better performance in reproducing the hydrograph peaks compared with the gauge-corrected rainfall estimates based on the operational S-band Next Generation Weather Radar network. Also, sensitivity test reveals that surficial conditions have a significant influence on the streamflow simulation during the storm: the discharge increases to a higher level as the infiltration factor (REFKDT) decreases, and its peak goes down and lags as surface roughness coefficient (Mann) increases. The time delay analysis of precipitation input on the streamflow at the two outfalls of the surveyed watersheds further demonstrates the link between AQPI gap-filling radar observations and streamflow changes in this urban region.

scholarly journals Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

2008 ◽  
Vol 8 (3) ◽  
pp. 445-460 ◽  
Author(s):  
M. P. Mittermaier
Keyword(s):  
Weather Prediction ◽  
Forecast Skill ◽  
Forecast Errors ◽  
Intensity Scale ◽  
Radar Rainfall ◽  
Scale Method ◽  
Skill Scores ◽  
Forecast Lead Time ◽  
The Uk ◽  
Rainfall Estimates

Abstract. A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP) verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS) and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used. The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

scholarly journals Simulation of flood flow in a river system using artificial neural networks

2005 ◽  
Vol 9 (4) ◽  
pp. 313-321 ◽  
Author(s):  
R. R. Shrestha ◽  
S. Theobald ◽  
F. Nestmann
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Flow Simulation ◽  
Simulation Models ◽  
River System ◽  
Training Data ◽  
Data Sets ◽  
Network Training ◽  
Artificial Neural ◽  
Flood Flow

Abstract. Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.

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