Radar Rainfall Data Research Articles

Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data

Combined sewer overflows (CSOs) represent a common feature in combined urban drainage systems and are used to discharge excess water to the environment during heavy storms. To better understand the performance of CSOs, the UK water industry has installed a large number of monitoring systems that provide data for these assets. This paper presents research into the prediction of the hydraulic performance of CSOs using artificial neural networks (ANN) as an alternative to hydraulic models. Previous work has explored using an ANN model for the prediction of chamber depth using time series for depth and rain gauge data. Rainfall intensity data that can be provided by rainfall radar devices can be used to improve on this approach. Results are presented using real data from a CSO for a catchment in the North of England, UK. An ANN model trained with the pseudo-inverse rule was shown to be capable of predicting CSO depth with less than 5% error for predictions more than 1 hour ahead for unseen data. Such predictive approaches are important to the future management of combined sewer systems.

Hydrological appraisal of operational weather radar rainfall estimates in the context of different modelling structures

Abstract. Radar rainfall estimates have become increasingly available for hydrological modellers over recent years, especially for flood forecasting and warning over poorly gauged catchments. However, the impact of using radar rainfall as compared with conventional raingauge inputs, with respect to various hydrological model structures, remains unclear and yet to be addressed. In the study presented by this paper, we analysed the flow simulations of the upper Medway catchment of southeast England using the UK NIMROD radar rainfall estimates, using three hydrological models based upon three very different structures (e.g. a physically based distributed MIKE SHE model, a lumped conceptual model PDM and an event-based unit hydrograph model PRTF). We focused on the sensitivity of simulations in relation to the storm types and various rainfall intensities. The uncertainty in radar rainfall estimates, scale effects and extreme rainfall were examined in order to quantify the performance of the radar. We found that radar rainfall estimates were lower than raingauge measurements in high rainfall rates; the resolutions of radar rainfall data had insignificant impact at this catchment scale in the case of evenly distributed rainfall events but was obvious otherwise for high-intensity, localised rainfall events with great spatial heterogeneity. As to hydrological model performance, the distributed model had consistent reliable and good performance on peak simulation with all the rainfall types tested in this study.

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.

Long‐Term High‐Resolution Radar Rainfall Fields for Urban Hydrology

AbstractAccurate records of high‐resolution rainfall fields are essential in urban hydrology, and are lacking in many areas. We develop a high‐resolution (15 min, 1 km2) radar rainfall data set for Charlotte, North Carolina during the 2001‐2010 period using the Hydro‐NEXRAD system with radar reflectivity from the National Weather Service Weather Surveillance Radar 1988 Doppler weather radar located in Greer, South Carolina. A dense network of 71 rain gages is used for estimating and correcting radar rainfall biases. Radar rainfall estimates with daily mean field bias (MFB) correction accurately capture the spatial and temporal structure of extreme rainfall, but bias correction at finer timescales can improve cold‐season and tropical cyclone rainfall estimates. Approximately 25 rain gages are sufficient to estimate daily MFB over an area of at least 2,500 km2, suggesting that robust bias correction is feasible in many urban areas. Conditional (rain‐rate dependent) bias can be removed, but at the expense of other performance criteria such as mean square error. Hydro‐NEXRAD radar rainfall estimates are also compared with the coarser resolution (hourly, 16 km2) Stage IV operational rainfall product. Stage IV is adequate for flood water balance studies but is insufficient for applications such as urban flood modeling, in which the temporal and spatial scales of relevant hydrologic processes are short. We recommend the increased use of high‐resolution radar rainfall fields in urban hydrology.

Bias adjustment and advection interpolation of long-term high resolution radar rainfall series

Summary It is generally acknowledged that in order to apply radar rainfall data for hydrological proposes adjustment against ground observations are crucial. Traditionally, radar reflectivity is transformed into rainfall rates applying a fixed reflectivity – rainfall rate relationship even though this is known to depend on the changing drop size distribution of the specific rain. This creates a transient bias between the radar rainfall and the ground observations due to seasonal changes of the drop size distribution as well as other atmospheric effects and effects related to the radar observational technology. In this study different bias adjustment techniques is investigated, developing a complete 10-year dataset (2002–2012) of high spatio-temporal resolution radar rainfall based on a radar observations from a single C-band radar from Denmark. Results show that hourly adjustment mean field bias adjustment outperform daily mean field bias adjustment with regards to estimation of rainfall totals and peak rain rates. Furthermore, it is demonstrated that radar rainfall estimates can be improved significantly by implementation of a novel advection interpolation technique.

레이더 강우자료와 분포형 S-RAT모형을 활용한 홍수유출모의

This study simulated flood runoff of the Gamcheon Basin on the Nakdong River and Ohsipcheon Basin in Samcheok, Gangwon-do, where they frequently recently suffered from flash flood damages. First, consolidating radar rainfall data to consider time and spacial variabilities and development situations is necessary for an analysis of the flash floods. However, radar rainfall data are not reliable enough that it is difficult to use them as measurements of actual ground rainfall. Hence, this paper compared and analyzed the single polarization S-band data of the Gangneung meteorological radar and dual polarization S-band data of the Mt. Biseul radar to grasp their level of contribution to the runoff simulation. Second, a distributed hydrological model is necessary to effectively use GIS information and radar rainfall data of the grid format. Hence, in this paper, we used the S-RAT model as a distributed hydrologic model. The results of this study showed that the accuracy of peak discharge and time when using the distributed hydrologic model and radar rainfall data were similar to the observed values.

Rainfall-triggered shallow landslides at catchment scale: Threshold mechanics-based modeling for abruptness and localization

[1] Rainfall-induced shallow landslides may occur abruptly without distinct precursors and could span a wide range of soil mass released during a triggering event. We present a rainfall-induced landslide-triggering model for steep catchments with surfaces represented as an assembly of hydrologically and mechanically interconnected soil columns. The abruptness of failure was captured by defining local strength thresholds for mechanical bonds linking soil and bedrock and adjacent columns, whereby a failure of a single bond may initiate a chain reaction of subsequent failures, culminating in local mass release (a landslide). The catchment-scale hydromechanical landslide-triggering model (CHLT) was applied to results from two event-based landslide inventories triggered by two rainfall events in 2002 and 2005 in two nearby catchments located in the Prealps in Switzerland. Rainfall radar data, surface elevation and vegetation maps, and a soil production model for soil depth distribution were used for hydromechanical modeling of failure patterns for the two rainfall events at spatial and temporal resolutions of 2.5 m and 0.02 h, respectively. The CHLT model enabled systematic evaluation of the effects of soil type, mechanical reinforcement (soil cohesion and lateral root strength), and initial soil water content on landslide characteristics. We compared various landslide metrics and spatial distribution of simulated landslides in subcatchments with observed inventory data. Model parameters were optimized for the short but intense rainfall event in 2002, and the calibrated model was then applied for the 2005 rainfall, yielding reasonable predictions of landslide events and volumes and statistically reproducing localized landslide patterns similar to inventory data. The model provides a means for identifying local hot spots and offers insights into the dynamics of locally resolved landslide hazards in mountainous regions.

EPIGONE: the argus on the daily operation of throttle structures

This study presents the development of an Early Warning System (EWS) called EPIGONE focusing on the detection of dry weather overflows in the vicinity of throttle structures in sewer systems. Throttle structures are considered as vital parts of a sewer system as they are control sections limiting flow rates to a designed operational value. Because these structures are by definition prone to potential clogging or blockages, a close follow-up of the daily operation by an EWS facilitates increased vigilance or even alarm. Primary goal of EPIGONE is to alert operators and thus allow fast intervention in case of suspected failures of these structures within a settled timeframe. EPIGONE combines overflow water level measurements with rainfall radar information to determine Combined Sewer Overflow (CSO) activity during dry weather as this dual condition will indicate malfunctioning. This combination of measurements was found to be the most cost effective set-up to deploy on a large scale. Water level data are recorded and logged on-site and sent to a central controller via GSM/GPRS, where an algorithm determines dry weather overflow conditions. Rainfall radar data are used as criterion to decide on dry weather conditions. From there on alarms are sent out to multiple recipients via e-mail and/or text messages (SMS). Next to this, it is obvious that this system can also be used for ‘regular’ wet weather CSO monitoring.

State-space adjustment of radar rainfall and skill score evaluation of stochastic volume forecasts in urban drainage systems

Merging of radar rainfall data with rain gauge measurements is a common approach to overcome problems in deriving rain intensities from radar measurements. We extend an existing approach for adjustment of C-band radar data using state-space models and use the resulting rainfall intensities as input for forecasting outflow from two catchments in the Copenhagen area. Stochastic grey-box models are applied to create the runoff forecasts, providing us with not only a point forecast but also a quantification of the forecast uncertainty. Evaluating the results, we can show that using the adjusted radar data improves runoff forecasts compared with using the original radar data and that rain gauge measurements as forecast input are also outperformed. Combining the data merging approach with short-term rainfall forecasting algorithms may result in further improved runoff forecasts that can be used in real time control.

A spatial-temporal rainfall generator for urban drainage design

The work presented here is a contribution to the Thames Water project of improving the Counters Creek catchment sewerage system in London. An increase in the number of floods affecting basements in the area has indicated the need for improvements to the system. The cost of such improvements could be very high, and as such it is important to determine whether the traditional approach of applying 30-year spatially uniform design storms results in substantial overestimation. The first step in this is to generate simulations of spatially distributed rainfall events, from which 30-year storms can be extracted. Storms are modelled as clusters of Gaussian rainfall cells, extending the earlier Willems method to radar rainfall data. The parameters describing the cells and their motion are sampled from probability distributions derived from parameter estimates gained from 45 historical storm events within the catchment for the period 2000-2011. This spatial-temporal stochastic rainfall generator produces a two-dimensional time series of simulated storm events, from which events of given return period can be identified.

Critical Examination of Area Reduction Factors

AbstractArea reduction factors (ARFs), which are used to convert estimates of extreme point rainfall to estimates of extreme area-averaged rainfall, are central to conventional flood risk assessment. Errors in the estimation of ARFs can result in large errors in subsequent estimates of design rainfall and discharge. This paper presents a critical examination of commonly used ARFs, particularly those from the U.S. Weather Bureau TP-29, demonstrating that they do not adequately represent the true properties of extreme rainfall. This lack of representativeness is due mainly to formulations that mix rainfall observations from different storms and different storm types. Storm catalogs developed from a 10-year high-resolution radar rainfall data are used set to estimate storm-centered ARFs for Charlotte, North Carolina. Storms are classified as either tropical or nontropical to demonstrate that storm type strongly influences spatial rainfall structure. While there appears to be some relationship between ARF str...

Statistical analysis of error propagation from radar rainfall to hydrological models

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.

Optimal Rainfall Estimation by Considering Elevation in the Han River Basin, South Korea

AbstractMore than 70% of South Korea has mountainous terrain, which leads to significant spatiotemporal variability of rainfall. The country is exposed to the risk of flash floods owing to orographic rainfall. Rainfall observations are important in mountainous regions because flood control measures depend strongly on rainfall data. In particular, radar rainfall data are useful in these regions because of the limitations of rain gauges. However, radar rainfall data include errors despite the development of improved estimation techniques for their calculation. Further, the radar does not provide accurate data during heavy rainfall in mountainous areas. This study presents a radar rainfall adjustment method that considers the elevation in mountainous regions. Gauge rainfall and radar rainfall field data are modified by using standardized ordinary cokriging considering the elevation, and the conditional merging technique is used for combining the two types of data. For evaluating the proposed technique, the Han River basin was selected; a high correlation between rainfall and elevation can be seen in this basin. Further, the proposed technique was compared with the mean field bias and original conditional merging techniques. Comparison with kriged rainfall showed that the proposed method has a lesser tendency to oversmooth the rainfall distribution when compared with the other methods, and the optimal mean areal rainfall is very similar to the value obtained using gauges. It reveals that the proposed method can be applied to an area with significantly varying elevation, such as the Han River basin, to obtain radar rainfall data of high accuracy.

Short-term forecasting of urban storm water runoff in real-time using extrapolated radar rainfall data

Model-based short-term forecasting of urban storm water runoff can be applied in real-time control of drainage systems in order to optimize system capacity during rain and minimize combined sewer overflows, improve wastewater treatment or activate alarms if local flooding is impending. A novel online system, which forecasts flows and water levels in real-time with inputs from extrapolated radar rainfall data, has been developed. The fully distributed urban drainage model includes auto-calibration using online in-sewer measurements which is seen to improve forecast skills significantly. The radar rainfall extrapolation (nowcast) limits the lead time of the system to 2 hours. In this paper, the model set-up is tested on a small urban catchment for a period of 1.5 years. The 50 largest events are presented.

Special Issue on Radar Rainfall Data Analyses and Applications

Special Issue on Radar Rainfall Data Analyses and Applications

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.

Identifying and Resolving the Barriers and Issues in Using Radar-Derived Rainfall Estimating Technology

This paper was developed to document the barriers and issues in using radar rainfall estimating technology by the water resources engineering community. It also outlines shortand long-term resolutions of those barriers and other issues that were identified. The authors’ intent is that the ASCE Environmental and Water Resources Institute’s (EWRI) Surface Water Hydrology Technical Committee, Radar Rainfall Data and Application Task Committee, will seek to adopt, improve upon, and implement these suggested shortand long-term resolutions.

Quantitative Comparison of the Spatial Distribution of Radar and Gauge Rainfall Data

Abstract The common statement that a rain gauge network usually provides better observation at specific points while weather radar provides more accurate observation of the spatial distribution of rain field over a large area has never been subjected to quantitative evaluation. The aim of this paper is to evaluate the statement by using some statistical criteria. The Monte Carlo simulation experiment, inverse distance weighting (IDW) interpolation method, and cross-validation technique are used to investigate the relation between the accuracy of the interpolated rainfall and the rain gauge density. The radar reflectivity–rainfall intensity (Z–R) relationship is constructed by the least squares fitting method from observation data of radar and rain gauges. The variation in this relationship and the accuracy of the radar rainfall with rain gauge density are evaluated by using the Monte Carlo simulation experiment. Three storm events are selected as the case studies. The obtained results show that the accuracy of interpolated and radar rainfall increases nonlinearly with increasing gauge density. The higher correlation coefficient (γ) value of radar-rainfall estimation, compared to gauge interpolation, especially in the convective storm, proves that radar observation provides a more accurate spatial structure of the rain field than gauge observation does.

Automated Bayesian quality control of streaming rain gauge data

Radar-rainfall data are being used in an increasing number of real-time applications because of their wide spatial and temporal coverage. Because of uncertainties in radar measurements and the relationship between radar measurements and rainfall on the ground, radar-rainfall data are often combined with rain gauge data to improve their accuracy. However, while rain gauges can provide accurate estimates of rainfall, their data are sometimes corrupted with errors caused by the environment in which the gauges are deployed. This study develops a real-time method for identifying measurement errors in rain gauge data streams. This method employs a dynamic Bayesian network (DBN) model of the rain gauge data stream to sequentially forecast the next rain gauge measurement from both the rain gauge and weather radar data streams and a decision rule-based classifier to identify data errors. Because of the uncertainty in the relationship between the radar and rainfall measurements, this method uses a statistical learning method (expectation maximization) to determine the best parameters for this relationship, given an adaptively sized moving window of previous measurements. The performance of the error detector developed in this study is demonstrated using a precipitation sensor network composed of five telemetered tipping bucket rain gauges and a WSR-88D weather radar. Through an analysis using synthetic errors, the false alarm rate and false negative rate were calculated to be 0.90% and 1.5%, 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.