On the scale-dependent propagation of hydrologic uncertainty using high-resolution X-band radar rainfall estimates (original) (raw)
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Hydrological Evaluation of X-band Polarimetric Radar Rainfall Estimation in a Mountainous Region
DESCRIPTION A study comparing XPOL and two C-band polarimetric estimates in a NE Italian Alps basin. In situ rainfall observations reported by a dense raingauge network and two disdrometers. XPOL estimates show high correlations (0.70-0.99) and low MRE (21%) against in situ data. The two C-band radar estimates gave higher MREs (50-70%) and lower correlations (0.48-0.81). Runoff simulations based on XPOL estimates are very close to the gaugebased simulations. The ones obtained by the C-band estimates resulted in underestimate runoff response. (UNDER REVISION)
Abstract[1] The goal of this study is to diagnose the manner in which radar-rainfall input affects peak flow simulation uncertainties across scales. We used the distributed physically based hydrological model CUENCAS with parameters that are estimated from available data and without fitting the model output to discharge observations. We evaluated the model's performance using (1) observed streamflow at the outlet of nested basins ranging in scale from 20 to 16,000 km2 and (2) streamflow simulated by a well-established and extensively calibrated hydrological model used by the US National Weather Service (SAC-SMA). To mimic radar-rainfall uncertainty, we applied a recently proposed statistical model of radar-rainfall error to produce rainfall ensembles based on different expected error scenarios. We used the generated ensembles as input for the hydrological model and summarized the effects on flow sensitivities using a relative measure of the ensemble peak flow dispersion for every link in the river network. Results show that peak flow simulation uncertainty is strongly dependent on the catchment scale. Uncertainty decreases with increasing catchment drainage area due to the aggregation effect of the river network that filters out small-scale uncertainties. The rate at which uncertainty changes depends on the error structure of the input rainfall fields. We found that random errors that are uncorrelated in space produce high peak flow variability for small scale basins, but uncertainties decrease rapidly as scale increases. In contrast, spatially correlated errors produce less scatter in peak flows for small scales, but uncertainty decreases slowly with increasing catchment size. This study demonstrates the large impact of scale on uncertainty in hydrological simulations and demonstrates the need for a more robust characterization of the uncertainty structure in radar-rainfall. Our results are diagnostic and illustrate the benefits of using the calibration-free, multiscale framework to investigate uncertainty propagation with hydrological models.
Hydrological response to radar rainfall maps through a distributed model
Natural Hazards, 1994
Weather radars in investigating physical characteristics of precipitation are becoming essential instruments in the field of short term meteorological investigation and forecasting. To analyze the radar signal impact in hydrological forecasting, precipitation input fields, generated through a statistical mathematical model, are supplied to a distributed hydrological model. Such a model would allow the control of the basin response to precipitation measurements obtained by a meteorological radar and, in the meanwhile, to evaluate the influence of distributed input. The distributed model describes the basin hydrological behavior, subdividing it into distinct geometrical cells and increasing the physical significance by reproducing the distributed hydrographic basins characteristics, such as infiltration capacity, runoff concentration time, network propagation speed, soil moisture influence. Each basin cell is characterized by its geological, pedological and morphological status, and m...
Remote Sensing
In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsiv...
Hydrology and Earth System Sciences, 2011
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 5 this work, composed radar data from a network of three C-band radars, with 6-minutal temporal and 2 × 2 km 2 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 10 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 un-15 certainty, 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.
Ability of a dual polarized X-band radar to estimate rainfall
The aim of this study is to assess rainfall estimates by a dual polarized X-band radar. This study was part of the European project FRAMEA (Flood forecasting using Radar in Alpine and Mediterranean Areas). Two radars were set up near the small town of Collobrières in South Eastern France. The first radar was a dual polarized X-band radar (Hydrix Ò) associated with a ZPHI Ò algorithm while the second one was an S-band radar (Météo France). We compared radar rainfall data with measurements obtained by two rain gauge networks (Météo France and Cemagref). During the experiments from February 2006 to June 2007, four significant rainfall events occurred. The accuracy of the rain rate obtained with both S-band and X-band radars decreased significantly beyond 60 km, in particular for the X-band radar. At closer ranges, such as 30–60 km from the radars, the X-band and the S-band radar retrievals showed similar performance with Nash criteria around 0.80 for the X-band radar and 0.75 for the S-band radar. Furthermore, the X-band radar did not require calibration on rainfall records, which tends to make it a useful method to assess rainfall in areas without a rain gauge network.
Effect of Radar-Rainfall Errors on Rainfall-Runoff Modeling
Recent years have witnessed significant advances in development of operational radarrainfall products. These products are desirable for several hydrologic applications such as flood forecasting and rainfall-runoff modeling. It is recognized that radar-rainfall estimates are associated with unknown uncertainties. The nature of these uncertainties and their impact on the prediction accuracy of hydrologic models is not fully understood. The present study presents an analysis of uncertainties of operational radar-rainfall products and how they propagate into rainfall-runoff models. The study uses NWS Multi-sensor Precipitation Estimator (MPE) radar-rainfall products over the Goodwin Creek experimental watershed. Surface rainfall observations from a dense rain gauge network in the watershed are used to analyze error characteristics of radar products. MPE radar data are used as input to a fully distributed hydrologic model (GSSHA) to simulate runoff response during 11 storms recorded in 2002. The study focuses on effect of three different radar error characteristics: systematic error (bias), random error, and temporal and spatial correlations of radar the error filed.
High-Resolution Rainfall Estimation from X-Band Polarimetric Radar Measurements
Journal of Hydrometeorology, 2004
An improved algorithm based on the self-consistent principle for rain attenuation correction of reflectivity Z H and differential reflectivity Z DR are presented for X-band radar. The proposed algorithm calculates the optimum coefficients for the relation between the specific attenuation coefficient and the specific differential phase, every 1 km along a slant range. The attenuation-corrected Z DR is calculated from reflectivity at horizontal polarization and from reflectivity at vertical polarization after attenuation correction. The improved rain attenuation correction algorithm is applied to the range-height indicator (RHI) scans as well as the plan position indicator (PPI) volume scan data observed by X-band wavelength (MP-X) radar, as operated by the National Research Institute for Earth Science and Disaster Prevention (NIED) in Japan. The corrected Z H and Z DR values are in good agreement with those calculated from the drop size distribution (DSD) measured by disdrometers.
2010
The advantages of X-band polarimetric radar over the conventional radar in estimating rainfall and simulating flood in urban river basin using distributed hydrological model were investigated. Classical radar-rainfall algorithm R(ZH) and composite polarimetric radar-rainfall algorithm R(ZH) R(KDP) which have been corrected from rain attenuation were used to estimate the rainfall intensity and simulate storm event. Performance of the hydrological model using high spatial resolution rainfall estimated from Xband was compared to that of using lower resolution. The comparison demonstrated the advantages of Xband observation over the observation with lower spatial resolution for detecting a heavy rainfall in small area that led to runoff underestimation. Integrating polarimetric algorithm to the model allowed for more accurate rainfall estimates and gave an improved model performance. The effects of systematic and random error in radar-rainfall data to hydrological model performance were...