scholarly journals Influence of Rainfall Data with Different Spatial Resolutions on Flood Forecasting Reliability

10.29007/74bp ◽  
2018 ◽  
Author(s):  
Mamoru Miyamoto ◽  
Kazuhiro Matsumoto
Keyword(s):  
River Discharge ◽  
Hydrological Model ◽  
Radar Data ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Error Assessment ◽  
Distributed Hydrological Model ◽  
Radar Rainfall ◽  
Convergence Results ◽  
Gauge Data

Recent advancements in precipitation observation technology make it possible to precisely describe the intensity and temporal-spatial distribution of heavy rainfall, which can cause severe floods and inundations. Such technologies have also increased the accuracy of flood forecasting. However, error factors in flood forecasting remain to be solved, originating in not only input data but also model structure and calibration. Thus, this study focused on convergence results of errors in parameter optimization of the PWRI Distributed Hydrological Model and the reproducibility of river discharge. The reliability of ground-gauge and C-band-radar rainfall is compared in terms of flood forecasting under the condition of the minimum error due to calibration. Although the convergence results showed that C-band radar rainfall was superior to ground gauge rainfall, both were equally effective in reproducing river discharge with a high NSE of 0.9 at a station with error assessment. On the other hand, the reproducibility of river discharge with C-band radar data was highly superior to that with ground gauge data at a station without error assessment. This indicates that grid-based high resolution rainfall data is necessary for basin-wide flood forecasting.

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.

scholarly journals Simulating Typhoon Floods with Gauge Data and Mesoscale-Modeled Rainfall in a Mountainous Watershed

2005 ◽  
Vol 6 (3) ◽  
pp. 306-323 ◽  
Author(s):  
Ming-Hsu Li ◽  
Ming-Jen Yang ◽  
Ruitang Soong ◽  
Hsiao-Ling Huang
Keyword(s):  
Goodness Of Fit ◽  
Hydrological Model ◽  
Mesoscale Model ◽  
Time Lag ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Time Lags ◽  
Distributed Hydrological Model ◽  
Mountainous Watershed ◽  
Gauge Data

Abstract A physically based distributed hydrological model was applied to simulate typhoon floods over a mountainous watershed in Taiwan. The meteorological forcings include the observed gauge rainfall data and the predicted rainfall data from a mesoscale meteorological model, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). This study investigates the flood responses of three Typhoons: Zeb (1998), Nari (2001), and Herb (1996), which possessed unique meteorological features and that all produced severe floods. The predicted basin-averaged rainfall hydrographs by the MM5 are compared with that interpreted by rain gauge data to reveal the discrepancies in rainfall peak amounts and time lags, and to explore their subsequent effects on flood generation. The simulated flood hydrographs at the Hsia-Yun station, which is upstream of the Shihmen Reservoir, are compared with observed flood discharges in terms of the amount and time lag of flood peaks. It is shown that the small discrepancy in rainfall peaks and phase lags could be significantly amplified in simulated flood responses of a mountainous watershed. The overall predictive skill of the distributed hydrological model with different rainfall inputs is examined with three parameters, which include the runoff ratio (RR), root-mean-square error (rmse), and goodness of fit (GOF). Although the runoff ratio for the MM5-predicted rainfall is superior to that for the observed gauge rainfall, the simulated hydrographs with observed gauge rainfall have smaller rmse and GOF values for three events. This study shows that the error in flood prediction with the mesoscale-modeled rainfall is mainly caused by the rainfall–peak difference, which arises from the inherent uncertainties in the mesoscale-modeled rainfalls over a mountainous terrain during the typhoon landfall periods.

scholarly journals Correcting the radar rainfall forcing of a hydrological model with data assimilation: application to flood forecasting in the Lez catchment in Southern France

2012 ◽  
Vol 16 (11) ◽  
pp. 4247-4264 ◽  
Author(s):  
E. Harader ◽  
V. Borrell-Estupina ◽  
S. Ricci ◽  
M. Coustau ◽  
O. Thual ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Assimilation Efficiency ◽  
Flood Forecasting ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Operational Environment ◽  
Radar Rainfall ◽  
Flood Peak ◽  
Orographic Rainfall

Abstract. The present study explores the application of a data assimilation (DA) procedure to correct the radar rainfall inputs of an event-based, distributed, parsimonious hydrological model. An extended Kalman filter algorithm was built on top of a rainfall-runoff model in order to assimilate discharge observations at the catchment outlet. This work focuses primarily on the uncertainty in the rainfall data and considers this as the principal source of error in the simulated discharges, neglecting simplifications in the hydrological model structure and poor knowledge of catchment physics. The study site is the 114 km2 Lez catchment near Montpellier, France. This catchment is subject to heavy orographic rainfall and characterised by a karstic geology, leading to flash flooding events. The hydrological model uses a derived version of the SCS method, combined with a Lag and Route transfer function. Because the radar rainfall input to the model depends on geographical features and cloud structures, it is particularly uncertain and results in significant errors in the simulated discharges. This study seeks to demonstrate that a simple DA algorithm is capable of rendering radar rainfall suitable for hydrological forecasting. To test this hypothesis, the DA analysis was applied to estimate a constant hyetograph correction to each of 19 flood events. The analysis was carried in two different modes: by assimilating observations at all available time steps, referred to here as reanalysis mode, and by using only observations up to 3 h before the flood peak to mimic an operational environment, referred to as pseudo-forecast mode. In reanalysis mode, the resulting correction of the radar rainfall data was then compared to the mean field bias (MFB), a corrective coefficient determined using rain gauge measurements. It was shown that the radar rainfall corrected using DA leads to improved discharge simulations and Nash-Sutcliffe efficiency criteria compared to the MFB correction. In pseudo-forecast mode, the reduction of the uncertainty in the rainfall data leads to a reduction of the error in the simulated discharge, but uncertainty from the model parameterisation diminishes data assimilation efficiency. While the DA algorithm used is this study is effective in correcting uncertain radar rainfall, model uncertainty remains an important challenge for flood forecasting within the Lez catchment.

scholarly journals On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment

2019 ◽  
Author(s):  
Maxime Jay-Allemand ◽  
Pierre Javelle ◽  
Igor Gejadze ◽  
Patrick Arnaud ◽  
Pierre-Olivier Malaterre ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Weather Forecasting ◽  
Flash Flood ◽  
Estimation Method ◽  
Distributed Hydrological Model ◽  
Initial State ◽  
Ungauged Catchments ◽  
Temporal Dimension ◽  
Radar Rainfall ◽  
Additional Constraints

Abstract. Flash flood alerts in metropolitan France are provided by SCHAPI (Service Central Hydrométéorologique et d’Appui à la Prévision des Inondations) through the Vigicrues Flash service, which is designed to work in ungauged catchments. The AIGA method implemented in Vigicrues Flash is designed for flood forecasting on small- and medium-scale watersheds. It is based on a distributed hydrological model accounting for spatial variability of the rainfall and the catchment properties, based on the radar rainfall observation inputs. Calibration of distributed parameters describing these properties with high resolution is difficult, both technically (in terms of the estimation method), and because of the identifiability issues. Indeed, the number of parameters to be calibrated is much greater than the number of spatial locations where the discharge observations are usually available. However, the flood propagation is a dynamic process, so observations have also a temporal dimension. This must be larger enough to comprise a representative set of events. In order to fully benefit from using the AIGA method, we consider its hydrological model (GRD) in combination with the variational estimation (data assimilation) method. In this method, the optimal set of parameters is found by minimizing the objective function which includes the misfit between the observed and predicted values and some additional constraints. The minimization process requires the gradient of the cost function with respect to all control parameters, which is efficiently computed using the adjoint model. The variational estimation method is scalable, fast converging, and offers a convenient framework for introducing additional constraints relevant to hydrology. It can be used both for calibrating the parameters and estimating the initial state of the hydrological system for short range forecasting (in a manner used in weather forecasting). The study area is the Gardon d’Anduze watershed where four gauging stations are available. In numerical experiments, the benefits of using the distributed against the uniform calibration are analysed in terms of the model predictive performance. Distributed calibration shows encouraging results with better model prediction at gauged and ungauged locations.

scholarly journals Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

2017 ◽  
Author(s):  
Igor Paz ◽  
Bernard Willinger ◽  
Auguste Gires ◽  
Laurent Monier ◽  
Christophe Zobrist ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Radar Data ◽  
Rain Gauge ◽  
Distributed Hydrological Model ◽  
Space Resolution ◽  
Rain Gauges ◽  
X Band ◽  
Distributed Simulations ◽  
Rain Gauge Network ◽  
Catchment Model

This paper presents a comparison between rain gauges, C-band and X-band radar data over an instrumented and regulated catchment of the Paris region, as well as their respective hydrological impacts with the help of flow observations and a semi-distributed hydrological model. Both radars confirm the high spatial variability of the rainfall down to their space resolution (respectively one kilometer and 250 m) and therefore underscore limitations of semi-distributed simulations. The use of the polarimetric capacity of the Météo-France C-band radar was limited to corrections of the horizontal reflectivity and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, neither calibration was performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (below called ENPC X-band radar), nor any optimization of its scans. In spite of that and the non-negligible fact that the catchment was much closer to the C-band radar than to the X-band radar (20 km vs. 40 km), the latter seems to perform at least as well as the former, but with a higher scale resolution. This characteristic was best highlighted with the help of a multifractal analysis of the respective radar data, which also shows that the X-band radar was able to pick up a few extremes that were smoothed out by the C-band radar.

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.

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