Radar Rainfall Input Research Articles

Integrated Flood Modeling for Klang River Basin Using HEC-HMS and Radar Rainfall Input

Integrated Flood Modeling for Klang River Basin Using HEC-HMS and Radar Rainfall Input

A Comparative Evaluation of Using Rain Gauge and NEXRAD Radar-Estimated Rainfall Data for Simulating Streamflow

Ascertaining the spatiotemporal accuracy of precipitation is a challenge for hydrologists and planners for flood protection measures. The objective of this study was to compare streamflow simulations using rain gauge and radar data from a watershed in Southern Ontario, Canada, using the Hydrologic Engineering Center’s event-based distributed Hydrologic Modeling System (HEC-HMS). The model was run using the curve number (CN) and the Green and Ampt infiltration methods. The results show that the streamflow simulated with rain gauge data compared better with the observed streamflow than the streamflow simulated using radar data. However, when the Mean Field Bias (MFB) corrections were applied, the quality of the streamflow results obtained from radar rainfall data improved. The results showed no significant difference between the simulated streamflow using the SCS and the Green and Ampt infiltration approach. However, the SCS method is reasonably more appropriate for modeling the runoff at the sub-basin-scale than the Green and Ampt infiltration approach. With the SCS method, the simulated and observed runoff amount obtained using rain gauge rainfall showed an R2 value of 0.88 and 0.78 for MFB-corrected radar and 0.75 for radar only. For the Green and Ampt modeling option, the R2 value for the simulated and observed runoff amounts were 0.87 with rain gauge, 0.66 with radar only, and 0.68 with MFB-corrected radar rainfall inputs. The NSE values for rain gauge input ranged from 0.65 to 0.35. Overall, three values were less than 0.5 for streamflow for both the methods. For seven radar rainfall events, the NSE was greater than 0.5, with a range of very good to satisfactory. The analysis of RSR showed a very good comparison of stream flow using the SCS curve number method and Green and Ampt method using different rainfall inputs. Only one value, the 2 November 2003 event, was above 0.7 for rain gauge-based streamflow. The other RSR values were in the range of “very good”. Overall, the study showed better results for the simulated runoff with the MFB-corrected radar rainfall when compared with the simulations obtained using radar rainfall only. Therefore, MFB-corrected radar could be explored as a substitute rainfall source.

Streamflow Predictions in a Small Urban–Rural Watershed: The Effects of Radar Rainfall Resolution and Urban Rainfall–Runoff Dynamics

The authors predicted streamflow in an urban–rural watershed using a nested regional–local modeling approach for the community of Manchester, Iowa, which is downstream of a largely rural watershed. The nested model coupled the hillslope-link model (HLM), used to simulate the upstream rural basins, and XPSWMM, which was used to simulate the more complex rainfall–runoff dynamics and surface and subsurface drainage in the urban areas, making it capable of producing flood maps at the street level. By integrating these models built for different purposes, we enabled fast and accurate simulation of hydrological processes in the rural basins while also modeling the flows in an urban environment. Using the model, we investigated how the spatial and temporal resolution of radar rainfall inputs can affect the modeled streamflow. We used a combination of three radar rainfall products to capture the uncertainty of rainfall estimation in the model results. Our nested model was able to simulate the hydrographs and timing and duration above the threshold known to result in nuisance flooding in Manchester. The spatiotemporal resolution the radar rainfall input to the model impacted the streamflow outputs of the regional, local, and nested models differently depending on the storm event.

Flood Estimation for SMART Control Operation Using Integrated Radar Rainfall Input with the HEC-HMS Model

Accurate estimates of intense rainfall that induce flash floods in urban areas are necessary for flood hazard preparedness. The city of Kuala Lumpur is frequently hit by flash floods after prolonged heavy storms, causing substantial damage to property and infrastructure, and disruptions to the socioeconomic activities of urbanites. The flood mitigation measures that have been implemented include the 2007 construction of the Stormwater Management and Road Tunnel (SMART), which diverts the flood water coming out of the Upper Klang Ampang catchment from entering the city prime areas. This study developed an integrated hydrological model of the catchment with input from radar rainfall to estimate the flood volume threshold value for SMART operation. The hydrologic and hydraulic modeling were performed using HEC-HMS with the HEC-GeoHMS tool to extract the hydrologic parameter information using GIS as input data for the catchment model. A radar processing system named RAINRATE AUTO V2 was developed to efficiently process raw radar data from the Subang radar and produce calibrated and gridded rainfall to the HEC-HMS flood estimation model for the Upper Klang Ampang urban catchment area. The results show promising performance regarding the use of radar rainfall, and there was an acceptable percent error in the peak discharge simulations. The advanced integrated system can be applied in managing flood risks at the catchment scale by efficiently estimating incoming flood events in the future.

Representing radar rainfall uncertainty with ensembles based on a time-variant geostatistical error modelling approach

The application of radar quantitative precipitation estimation (QPE) to hydrology and water quality models can be preferred to interpolated rainfall point measurements because of the wide coverage that radars can provide, together with a good spatio-temporal resolutions. Nonetheless, it is often limited by the proneness of radar QPE to a multitude of errors. Although radar errors have been widely studied and techniques have been developed to correct most of them, residual errors are still intrinsic in radar QPE. An estimation of uncertainty of radar QPE and an assessment of uncertainty propagation in modelling applications is important to quantify the relative importance of the uncertainty associated to radar rainfall input in the overall modelling uncertainty. A suitable tool for this purpose is the generation of radar rainfall ensembles. An ensemble is the representation of the rainfall field and its uncertainty through a collection of possible alternative rainfall fields, produced according to the observed errors, their spatial characteristics, and their probability distribution. The errors are derived from a comparison between radar QPE and ground point measurements. The novelty of the proposed ensemble generator is that it is based on a geostatistical approach that assures a fast and robust generation of synthetic error fields, based on the time-variant characteristics of errors. The method is developed to meet the requirement of operational applications to large datasets. The method is applied to a case study in Northern England, using the UK Met Office NIMROD radar composites at 1km resolution and at 1h accumulation on an area of 180km by 180km.The errors are estimated using a network of 199 tipping bucket rain gauges from the Environment Agency. 183 of the rain gauges are used for the error modelling, while 16 are kept apart for validation. The validation is done by comparing the radar rainfall ensemble with the values recorded by the validation rain gauges. The validated ensemble is then tested on a hydrological case study, to show the advantage of probabilistic rainfall for uncertainty propagation. The ensemble spread only partially captures the mismatch between the modelled and the observed flow. The residual uncertainty can be attributed to other sources of uncertainty, in particular to model structural uncertainty, parameter identification uncertainty, uncertainty in other inputs, and uncertainty in the observed flow.

Evaluation of Maximum a Posteriori Estimation as Data Assimilation Method for Forecasting Infiltration-Inflow Affected Urban Runoff with Radar Rainfall Input

High quality on-line flow forecasts are useful for real-time operation of urban drainage systems and wastewater treatment plants. This requires computationally efficient models, which are continuously updated with observed data to provide good initial conditions for the forecasts. This paper presents a way of updating conceptual rainfall-runoff models using Maximum a Posteriori estimation to determine the most likely parameter constellation at the current point in time. This is done by combining information from prior parameter distributions and the model goodness of fit over a predefined period of time that precedes the forecast. The method is illustrated for an urban catchment, where flow forecasts of 0–4 h are generated by applying a lumped linear reservoir model with three cascading reservoirs. Radar rainfall observations are used as input to the model. The effects of different prior standard deviations and lengths of the auto-calibration period on the resulting flow forecast performance are evaluated. We were able to demonstrate that, if properly tuned, the method leads to a significant increase in forecasting performance compared to a model without continuous auto-calibration. Delayed responses and erratic behaviour in the parameter variations are, however, observed and the choice of prior distributions and length of auto-calibration period is not straightforward.

Probabilistic online runoff forecasting for urban catchments using inputs from rain gauges as well as statically and dynamically adjusted weather radar

• Rainfall nowcasts from rain gauges and 2 types of adjusted radar data are compared. • Probabilistic runoff forecasts are generated in 2 urban catchments in an on-line mode. • Time-statically adjusted radar data as model input yield best runoff forecasts. • Radar adjustment and online runoff forecast should be considered as a whole. • Improved spatial resolution in on-line rainfall runoff models improves forecasts. We investigate the application of rainfall observations and forecasts from rain gauges and weather radar as input to operational urban runoff forecasting models. We apply lumped rainfall runoff models implemented in a stochastic grey-box modelling framework. Different model structures are considered that account for the spatial distribution of rainfall in different degrees of detail. Considering two urban example catchments, we show that statically adjusted radar rainfall input improves the quality of probabilistic runoff forecasts as compared to input based on rain gauge observations, although the characteristics of these radar measurements are rather different from those on the ground. Data driven runoff forecasting models can to some extent adapt to bias of the rainfall input by model parameter calibration and state-updating. More detailed structures in these models provide improved runoff forecasts compared to the structures considering mean areal rainfall only. A time-dynamic adjustment of the radar data to rain gauge data provides improved rainfall forecasts when compared with rainfall observations on the ground. However, dynamic adjustment reduces the potential for creating runoff forecasts and in fact also leads to reduced cross correlation between radar rainfall and runoff measurements. We conclude that evaluating the performance of radar rainfall adjustment against rain gauges may not always be adequate and that adjustment procedure and online runoff forecasting should ideally be considered as one unit.

The use of weather radar for rainfall-runoff modeling, case of Seybouse watershed (Algeria)

The use of radar for rainfall and runoff estimation is beneficial because of the high spatial and temporal resolution and large areal coverage; the main objective of this research is the calibration of the weather radar of Annaba for hydrological applications. To improve rainfall distribution estimation for the maritime watershed (1,129 km2) (Seybouse, Annaba), located in the north eastern of Algeria, a new technique was used based on the creation of six virtual rainfall stations uniformly throughout the area. The rainfall data for these virtual stations are estimated from raw weather radar data using a newly developed program called “Rain-Data ver1.0.” The calibrated radar-derived rainfall is used as input data in the Gridded Surface Subsurface Hydrologic Analysis model; the results show that all radar rainfall input data tend to produce more accurate runoff hydrographs than rain gauge data. Finally, the use of radar rainfall data to estimate runoff gives encouraging results, especially in regions where continuous gauge rainfall measurements are not available and rain gauges are sparsely distributed.

Radar Rainfall Application in Distributed Hydrologic Modeling for Cypress Creek Watershed, Texas

AbstractRecent advances in hydrologic models have depended on the use of radar rainfall input in physically based, fully distributed models. Previous research conducted for case studies near Houston, Texas, have focused on the use of radar rainfall for large storm events such as tropical storms and hurricanes. A fully distributed model, Vflo, was used to model streamflow during small storm events in the Cypress Creek watershed, near Houston, Texas. Two events were simulated both with rain gauge—corrected radar data and exclusively with rain gauges. Then a third event was modeled exclusively with rain gauge data to assess model performance with only rain gauge data. The modeled streamflow was then compared to the USGS observed streamflow, using peak streamflow, time to peak, and volume streamflow, to evaluate the model performance between radar and rain gauge input. A comparison of the models for the events shows that the radar input results better match the observed streamflow for the streamflow volume an...

Radar-rainfall errors and their effect on runoff simulation in Huaihe River basin

将雷达测雨数据与分布式水文模型相耦合进行径流过程模拟,分析雷达测雨误差及其径流过程模拟效果,研究雷达测雨误差对径流过程模拟的影响效应.在对淮河流域气象中心业务化的5种淮河流域雷达测雨数据进行误差分析的基础上,采用雷达测雨数据驱动HEC-HMS水文模型,模拟分析淮河息县水文站以上流域2007年7月1-10日强降雨集中期的径流过程.结果表明:利用雷达测雨数据的径流模拟结果与实测资料的模拟结果基本吻合,各种雷达测雨数据误差经过HEC-HMS水文模型传递后,误差明显减小.联合校准法对应的模拟效果最好,过程流量相对误差NBs'和洪峰流量相对误差Z'分别为-20.2%和-13.3%.;Coupling radar-rainfall data with distributed hydrological model was important in improving flood forecast precision and leading time.However,radar-rainfall error propagation in hydrological model largely affected runoff process simulation.In this study,the operational radar-rainfall data,provided by Huaihe River basin Meteorological Center,was analyzed.With HEC-HMS hydrological model input,radar-rainfall data was used to simulate and analyze the runoff process of rainfall concentration period,in the Xixian hydrological station of Huaihe River basin,during July 1 10,2007.The results showed that runoff simulation based on radar-rainfall data was consistent with the simulation based on rain gauge data.The radar-rainfall input error had been decreased obviously after the propagation of the HEC-HMS model.Runoff simulation based on radar-rainfall data by union calibration was the best,whose relative error were-20.2% and-13.3% for flow process and peak flow,respectively.

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

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.

Flood modelling with a distributed event-based parsimonious rainfall-runoff model: case of the karstic Lez river catchment

Abstract. Rainfall-runoff models are crucial tools for the statistical prediction of flash floods and real-time forecasting. This paper focuses on a karstic basin in the South of France and proposes a distributed parsimonious event-based rainfall-runoff model, coherent with the poor knowledge of both evaporative and underground fluxes. The model combines a SCS runoff model and a Lag and Route routing model for each cell of a regular grid mesh. The efficiency of the model is discussed not only to satisfactorily simulate floods but also to get powerful relationships between the initial condition of the model and various predictors of the initial wetness state of the basin, such as the base flow, the Hu2 index from the Meteo-France SIM model and the piezometric levels of the aquifer. The advantage of using meteorological radar rainfall in flood modelling is also assessed. Model calibration proved to be satisfactory by using an hourly time step with Nash criterion values, ranging between 0.66 and 0.94 for eighteen of the twenty-one selected events. The radar rainfall inputs significantly improved the simulations or the assessment of the initial condition of the model for 5 events at the beginning of autumn, mostly in September–October (mean improvement of Nash is 0.09; correction in the initial condition ranges from −205 to 124 mm), but were less efficient for the events at the end of autumn. In this period, the weak vertical extension of the precipitation system and the low altitude of the 0 °C isotherm could affect the efficiency of radar measurements due to the distance between the basin and the radar (~60 km). The model initial condition S is correlated with the three tested predictors (R2 > 0.6). The interpretation of the model suggests that groundwater does not affect the first peaks of the flood, but can strongly impact subsequent peaks in the case of a multi-storm event. Because this kind of model is based on a limited amount of readily available data, it should be suitable for operational applications.

An assessment of distributed flash flood forecasting accuracy using radar and rain gauge input for a physics-based distributed hydrologic model

Summary One approach to reducing societal impacts from flooding is to minimize the public’s exposure by closing flooded intersections, and warning stakeholders. Emergency responders must know when and where flooding is likely to occur. This article describes the real-time performance of a flash flood forecasting system for a significant flood event (September 7–8, 2010) in Austin, Texas. The system uses a physics-based distributed (PBD) hydrologic model, V flo , together with radar rainfall input to predict stage and discharge at 222 locations in real-time. A comparison of model forecast accuracy using the operational rain gauge-adjusted radar rainfall input (GARR) is made against rain gauge only (RGO) input for a recent flash flood. A collection of calibrated hydrologic models for flash flood prone basins, within the City of Austin, is used for the comparison. A 1.9 h reduction in timing error was achieved using GARR as input rather than RGO. The RMSE of peak stage forecasts with GARR was 0.89 m, but with RGO input, the peak stage RMSE increased to 1.77 m. The use of GARR as input to the PBD model not only increases the forecast lead-time accuracy, but also the accuracy of forecast peak stage across a range of basin sizes. Rain gauge density over the forecast basins was one of the main determinants of forecast accuracy during an extreme event that resulted in significant flooding in a major metropolitan area.

Distributed hydrologic and hydraulic modelling with radar rainfall input: Reconstruction of the 8–9 September 2002 catastrophic flood event in the Gard region, France

On 8–9 September 2002, an extreme rainfall event caused by a stationary mesoscale convective system (MCS) occurred in the Gard region, France. Distributed hydrologic and hydraulic modelling has been carried out to assess and compare the various sources of data collected operationally and during the post-event field surveys. Distributed hydrological modelling was performed with n-TOPMODELs and assessed for ungauged basins with the discharge estimates of the post-event surveys. A careful examination of the occurrence in time and space of the flash floods over the head watersheds indicates that flooding was controlled by the trajectory of the convective part of the MCS. Stationarity of the MCS over the Gardon watershed (1858 km 2 at Remoulins) for 28 h was responsible for the exceptional magnitude of the flood at this scale. The flood dynamics were characterized by an extensive inundation of the Gardonnenque plain upstream of the Gardon Gorges resulting in a significant peak flow reduction downstream. One-dimensional unsteady-flow hydraulic modelling was found to be required to reproduce these dynamics. Hydraulic modelling also proved to be potentially useful for the critical analysis and extrapolation of operational discharge rating curves.

Distributed Hydrologic Prediction: Sensitivity to Accuracy of Initial Soil Moisture Conditions and Radar Rainfall Input

The aim of this study is to evaluate prediction accuracy and sensitivity of a distributed hydrologic model. Accurate predictions of runoff are needed where reservoir operations are used to control flooding and to manage water resources. The study area consists of watershed areas that are influent to reservoirs in the 967 km2 Yongdam basin, and the 2,293 km2 Namgang basin located on the Korean Peninsula. For these basins with complex terrain, a physics-based distributed hydrologic model is set up with geospatial data, calibrated, and used to test sensitivity to accuracy of radar and rain gauge input and initial conditions. The events studied range in magnitude from 86 to over 249 mm and include two typhoons and two heavy rainfall events. Radar reflectivity is converted to rainfall rates using Z-R relationships, and then corrected for bias using a spatially variable correction derived from the rain gauge networks that cover both basins. Adjustment of assumed model parameters for the Namgang and Yongdam wate...

On the propagation of uncertainty in weather radar estimates of rainfall through hydrological models

AbstractThe generation of flow forecasts using rainfall inputs to hydrological models has been developed over many years. Unfortunately, errors in input data to models may vary considerably depending upon the different sources of data such as raingauges, radar and high resolution Numerical Weather Prediction (NWP) models. This has hampered the operational use of radar for quantitative flow forecasting. The manner with which radar rainfall input and model parametric uncertainty influence the character of the flow simulation uncertainty in hydrological models has been investigated by several authors. In this paper an approach to this problem based upon a stochastic hydrological model is considered. The errors in the input data, although they may be constrained, do propagate through the model to the flow predictions. Previous work on this error propagation through a fully distributed model is described, and a similar analysis for a stochastic hydrological model implemented in a mixed rural and urban area in north‐west England carried out. Results are compared with those previously published for an American catchment. A possible approach to selecting flow forecast ensemble members is proposed. Copyright © 2009 Royal Meteorological Society

Understanding uncertainty in distributed flash flood forecasting for semiarid regions

Semiarid flash floods pose a significant danger for life and property in many dry regions around the world. One effective way to mitigate flood risk lies in implementing a real‐time forecast and warning system based on a rainfall‐runoff model. This study used a semiarid, physics‐based, and spatially distributed watershed model driven by high‐resolution radar rainfall input to evaluate such a system. The predictive utility of the model and dominant sources of uncertainty were investigated for several runoff events within the U.S. Department of Agriculture Agricultural Research Service Walnut Gulch Experimental Watershed located in the southwestern United States. Sources of uncertainty considered were rainfall estimates, watershed model parameters, and initial soil moisture conditions. Results derived through a variance‐based comprehensive global sensitivity analysis indicated that the high predictive uncertainty in the modeled response was heavily dominated by biases in the radar rainfall depth estimates. Key model parameters and initial model states were identified, and we generally found that modeled hillslope characteristics are more influential than channel characteristics in small semiarid basins. We also observed an inconsistency in the parameter sets identified as behavioral for different events, which suggests that model calibration to historical data is unlikely to consistently improve predictive performance for different events and that real‐time parameter updating may be preferable.

Applicability of the Neural Network Based Radar Rainfall Estimation in Streamflow Modeling

This study evaluates performance of the neural network based radar rainfall estimation and examines the applicability of the radar rainfall input estimated from the neural network (RNN) in streamflow modeling. The Uono River basin is selected as the study basin. A distributed hydrologic model is driven by using RNN, the radar rainfall input obtained from the Z-R relationship (RZ-R), and the gauge rainfall (RG) respectively. The statistical results of radar rainfall estimation indicate that the radar rainfall product using the neural network is more accurate than that using the operational Z-R relationship. In addition, the streamflow simulation results show that the simulated hydrographs obtained from RNN are more accurate than those obtained from RZ-R. The study concluded that the neural network technique outperforms the existing operational Z-R relationship. The appropriately trained network at the rain gauge sites is accurate, stable, and robust for estimating radar rainfall over the whole basin. The study also suggested that the RNN is an alternative input for hydrologic modeling when the gauge rainfall data is unavailable or insufficient.

Discretization scale dependencies of the ensemble flow range versus catchment area relationship in distributed hydrologic modeling

Summary Earlier research results have shown that the range of the ensemble flow simulations from a well-validated operational hydrologic model possesses a well-defined log-linear relationship to catchment area for which the ensembles are generated. The ensemble range is a function of both parametric and rainfall input uncertainty. In this paper the sensitivity of this scaling relationship for simulation uncertainty to the numerical discretization scale of the distributed model is quantified when the model is driven by WSR-88D (NEXRAD) radar data and for two catchments in the south-central United States. The distributed model used is subcatchment (rather than pixel) based, has components that are based on operational models employed in the US National Weather Service, and has been validated for the two application catchments of this work as part of the Distributed Model Intercomparison Project (DMIP). The approach taken develops a new parsimonious model for spatially correlated radar-rainfall errors with radar-pixel error variance that depends on the magnitude of the observed rainfall. Monte Carlo methods are used to translate parametric and radar-rainfall input uncertainty to ensemble flow simulations at a number of subcatchments of varying size. Selected events in the period from May 1993 to July 1999 were analyzed. The results confirm that the ensemble flow range is dependent on subcatchment scale with a well-defined log-linear relationship for both application catchments. This finding holds for all uncertainty scenarios examined and across all the scales of distributed model discretization. It is further found that a higher model discretization leads to a shorter range of simulation uncertainty for subcatchment spatial scales that are substantially larger than the average subcatchment size. Flow simulation uncertainty that is due to either parametric or input uncertainty alone scale in a way that is similar to the combined uncertainty scaling.

레이더 추정강우의 수문학적 활용 (II): 불확실성 해석

본 연구에서는 관악산 레이더 자료로부터 소양강 유역에서 최적 추정된 레이더 강우로 준분포형 TOPMODEL의 적용성을 평가하고 Monte Carlo난수발생 기법과 연계한 앙상블 유량모의 기법을 구축하여 레이더 추정강우가 가질 수 있는 불확실성이 수문모형에 미치는 영향을 모형의 매개변수와 연계하여 분석하였다. 레이더 추정강우를 활용하여 소양강 유역의 유량계산을 2003년 7월의 사례기간 동안 수행한 결과 실시간 편차보정 기법으로 수정된 레이더 입력강우의 경우 관측유량과 비교하여 매우 잘 일치하는 것으로 나타나 레이더 추정강우의 수문학적 활용성은 매우 높은 것으로 판단된다. 불확실성 분석에서는 레이더 강우가 가질 수 있는 불확실성이 수문모형의 매개변수 불확실성보다 덜 민감하게 영향을 미치는 것으로 검토되었고 레이더 강우와 매개변수가 혼합된 불확실성이 유량모의에서 가장 큰 오차를 보일 수 있는 것으로 검토되었다. The present study analyzes hydrologic utilization of optimal radar-derived rainfall by using semi-distributed TOPMODEL and evaluates the impacts of radar rainfall and model parametric uncertainty on a hydrologic model. Monte Carlo technique is used to produce the flow ensembles. The simulated flows from the corrected radar rainfalls with real-time bias adjustment scheme are well agreed to observed flows during 22-26 July 2003. It is shown that radar-derived rainfall is useful for simulating streamflow on a basin scale. These results are diagnose with which radar-rainfall Input and parametric uncertainty influence the character of the flow simulation uncertainty. The main conclusions for this uncertainty analysis are that the radar input uncertainty is less influent than the parametric one, and combined uncertainty with radar and Parametric input can be included the highest uncertainty on a streamflow simulation.