Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis.     

Return period of bivariate distributed extreme hydrological events

 Extreme hydrological events are inevitable and stochastic in nature. Characterized by multiple properties, the multivariate distribution is a better approach to represent this complex phenomenon than the univariate frequency analysis. However, it requires considerably more data and more sophisticated mathematical analysis. Therefore, a bivariate distribution is the most common method for modeling these extreme events. The return periods for a bivariate distribution can be defined using either separate single random variables or two joint random variables. In the latter case, the return periods can be defined using one random variable equaling or exceeding a certain magnitude and/or another random variable equaling or exceeding another magnitude or the conditional return periods of one random variable given another random variable equaling or exceeding a certain magnitude. In this study, the bivariate extreme value distribution with the Gumbel marginal distributions is used to model extreme flood events characterized by flood volume and flood peak. The proposed methodology is applied to the recorded daily streamflow from Ichu of the Pachang River located in Southern Taiwan. The results show a good agreement between the theoretical models and observed flood data. The author wishes to thank the two anonymous reviewers for their constructive comments that improving the quality of this work.     

Investigation on the properties of the relationship between rare and extreme rainfall and flood volumes, under some distributional restrictions

The fact that rainfall data are usually more abundant and more readily regionalized than streamflow data has motivated hydrologists to conceive methods that incorporate the hydrometeorologial information into flood frequency analyses. Some of them, particularly those derived from the French GRADEX method, involve assumptions concerning the relationship between extreme rainfall and flood volumes, under some distributional restrictions. In particular, for rainfall probability distributions exhibiting exponential-like upper tails, it is possible to derive the shape and scale of the probability distribution of flood volumes by hypothesizing the basic properties of such a relationship, under rare and/or extreme conditions. This paper focuses on a parametric mathematical model for the relationship between rare and extreme rainfall and flood volumes under exponentially-tailed distributions. The model is analyzed and fitted to rare and extreme events derived from hydrological simulation of long stochastically-generated synthetic series of rainfall and evaporation for the Indaiá River basin, located in south-central Brazil. The paper also provides a sensitivity analysis of the model parameters in order to better understand flood events under rare and extreme conditions. By working with hydrologically plausible hypothetical events, the modeling approach proved to be a useful way to explore extraordinary rainfall and flood events. The results from this exploratory analysis provide grounds to derive some conclusions regarding the relative positions of the upper tails of the probability distributions of rainfall and flood volumes.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.     

Influence of rainfall spatial variability on flood prediction

This paper deals with the sensitivity of distributed hydrological models to different patterns that account for the spatial distribution of rainfall: spatially averaged rainfall or rainfall field. The rainfall data come from a dense network of recording rain gauges that cover approximately 2000 km² around Mexico City. The reference rain sample accounts for the 50 most significant events, whose mean duration is about 10 h and maximal point depth 170 mm. Three models were tested using different runoff production models: storm-runoff coefficient, complete or partial interception. These models were then applied to four fictitious homogeneous basins, whose sizes range from 20 to 1500 km². For each test, the sensitivity of the model is expressed as the relative differences between the empirical distribution of the peak flows (and runoff volumes), calculated according to the two patterns of rainfall input: uniform or non-uniform. Differences in flows range from 10 to 80%, depending on the type of runoff production model used, the size of the basin and the return period of the event. The differences are generally moderate for extreme events. In the local context, this means that uniform design rainfall combining point rainfall distribution and the probabilistic concept of the areal reduction factor could be sufficient to estimate major flood probability. Differences are more significant for more frequent events. This can generate problems in calibrating the hydrological model when spatial rainfall localization is not taken into account: a bias in the estimation of parameters makes their physical interpretation difficult and leads to overestimation of extreme flows.     

A probabilistic framework for floodplain mapping using hydrological modeling and unsteady hydraulic modeling

ABSTRACT

Prediction of design hydrographs is key in floodplain mapping using hydraulic models, which are either steady state or unsteady. The former, which require only an input peak, substantially overestimate the volume of water entering the floodplain compared to the more realistic dynamic case simulated by the unsteady models that require the full hydrograph. Past efforts to account for the uncertainty of boundary conditions using unsteady hydraulic modeling have been based largely on a joint flood frequency–shape analysis, with only a very limited number of studies using hydrological modeling to produce the design hydrographs. This study therefore presents a generic probabilistic framework that couples a hydrological model with an unsteady hydraulic model to estimate the uncertainty of flood characteristics. The framework is demonstrated on the Swannanoa River watershed in North Carolina, USA. Given its flexibility, the framework can be applied to study other sources of uncertainty in other hydrological models and watersheds.     

Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios

ABSTRACT

Discharge observations and reliable rainfall forecasts are essential for flood prediction but their availability and accuracy are often limited. However, even scarce data may still allow adequate flood forecasts to be made. Here, we explored how far using limited discharge calibration data and uncertain forcing data would affect the performance of a bucket-type hydrological model for simulating floods in a tropical basin. Three events above thresholds with a high and a low frequency of occurrence were used in calibration and 81 rainfall scenarios with different degrees of uncertainty were used as input to assess their effects on flood predictions. Relatively similar model performance was found when using calibrated parameters based on a few events above different thresholds. Flood predictions were sensitive to rainfall errors, but those related to volume had a larger impact. The results of this study indicate that a limited number of events can be useful for predicting floods given uncertain rainfall forecasts.     

Model and parameter uncertainty in IDF relationships under climate change

Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty.     

Flood simulation using different sources of rainfall in the Huong River,Vietnam / Simulation d'inondation à l'aide de différentes sources d'information pluviométrique dans le bassin de la Rivière Huong,Vietnam

Abstract

The possibility of simulating flooding in the Huong River basin, Vietnam, was examined using quantitative precipitation forecasts at regional and global scales. Raingauge and satellite products were used for observed rainfall. To make maximum use of the spatial heterogeneity of the different types of rainfall data, a distributed hydrological model was set up to represent the hydrological processes. In this way, streamflow simulated using the rainfall data was compared with that observed in situ. The forecast on a global scale showed better performance during normal flow peak simulations than during extreme events. In contrast, it was found that during an extreme flood peak, the use of regional forecasts and satellite data gives results that are in close agreement with results using raingauge data. Using the simulated overflow volumes recorded at the control point downstream, inundation areas were then estimated using topographic characteristics. This study is the first step in developing a future efficient early warning system and evacuation strategy.     

Impact of climate change on 24‐h design rainfall depth estimation in Qiantang River Basin,East China

The frequency and magnitude of extreme meteorological or hydrological events such as floods and droughts in China have been influenced by global climate change. The water problem due to increasing frequency and magnitude of extreme events in the humid areas has gained great attention in recent years. However, the main challenge in the evaluation of climate change impact on extreme events is that large uncertainty could exist. Therefore, this paper first aims to model possible impacts of climate change on regional extreme precipitation (indicated by 24‐h design rainfall depth) at seven rainfall gauge stations in the Qiantang River Basin, East China. The Long Ashton Research Station‐Weather Generator is adopted to downscale the global projections obtained from general circulation models (GCMs) to regional climate data at site scale. The weather generator is also checked for its performance through three approaches, namely Kolmogorov–Smirnov test, comparison of L‐moment statistics and 24‐h design rainfall depths. Future 24‐h design rainfall depths at seven stations are estimated using Pearson Type III distribution and L‐moment approach. Second, uncertainty caused by three GCMs under various greenhouse gas emission scenarios for the future periods 2020s (2011–2030), 2055s (2046–2065) and 2090s (2080–2099) is investigated. The final results show that 24‐h design rainfall depth increases in most stations under the three GCMs and emission scenarios. However, there are large uncertainties involved in the estimations of 24‐h design rainfall depths at seven stations because of GCM, emission scenario and other uncertainty sources. At Hangzhou Station, a relative change of ?16% to 113% can be observed in 100y design rainfall depths. Copyright © 2012 John Wiley & Sons, Ltd.     

Assessment on the function of reservoirs for flood control during typhoon seasons based on a distributed hydrological model

A grid‐based distributed hydrological model, PDTank model, is used to simulate hydrological processes in the upper Tone River catchment. The Tone River catchment often suffers from heavy rainfall events during the typhoon seasons. The reservoirs located in the catchment play an important role in flood regulation. Through the coupling of the PDTank model and a reservoir module that combines the storage function and operation function, the PDTank model is used for flood forecasting in this study. By comparing the hydrographs simulated using gauging and radar rainfall data, it is found that the spatial variability of rainfall is an important factor for flood simulation and the accuracy of the hydrographs simulated using radar rainfall data is slightly improved. The simulation of the typhoon flood event numbered No. 9 shows that the reservoirs in the catchment attenuate the peak flood discharge by 423·3 m³/s and validates the potential applicability of the distributed hydrological model on the assessment of function of reservoirs for flood control during typhoon seasons. Copyright © 2011 John Wiley & Sons, Ltd.     

Assessing the significance of wet-canopy evaporation from forests during extreme rainfall events for flood mitigation in mountainous regions of the United Kingdom

There is increased interest in the potential of tree planting to help mitigate flooding using nature-based solutions or natural flood management. However, many publications based upon catchment studies conclude that, as flood magnitude increases, benefit from forest cover declines and is insignificant for extreme flood events. These conclusions conflict with estimates of evaporation loss from forest plot observations of gross rainfall, through fall and stem flow. This study explores data from existing studies to assess the magnitudes of evaporation and attempts to identify the meteorological conditions under which they would be supported. This is achieved using rainfall event data collated from publications and data archives from studies undertaken in temperate environments around the world. The meteorological conditions required to drive the observed evaporation losses are explored theoretically using the Penman–Monteith equation. The results of this theoretical analysis are compared with the prevailing meteorological conditions during large and extreme rainfall events in mountainous regions of the United Kingdom to assess the likely significance of wet canopy evaporation loss. The collated dataset showed that event Ewc losses between approximately 2 and 38% of gross rainfall (1.5 to 39.4 mm day⁻¹) have been observed during large rainfall events (up to 118 mm day⁻¹) and that there are few data for extreme events (>150 mm day⁻¹). Event data greater than 150 mm (reported separately) included similarly high percentage evaporation losses. Theoretical estimates of wet-canopy evaporation indicated that, to reproduce the losses towards the high end of these observations, relative humidity and the aerodynamic resistance for vapour transport needed to be lower than approximately 97.5% and 0.5 to 2 s m⁻¹ respectively. Surface meteorological data during large and extreme rainfall events in the United Kingdom suggest that conditions favourable for high wet-canopy evaporation are not uncommon and indicate that significant evaporation losses during large and extreme events are possible but not for all events and not at all locations. Thus the disparity with the results from catchment studies remains.     

New estimates of future changes in extreme rainfall across the UK using regional climate model integrations. 2. Future estimates and use in impact studies

Under enhanced greenhouse conditions, climate models suggest an increase in rainfall intensities in the northern Hemisphere. Major flood events in the UK during autumn 2000 and central Europe in August 2002, have focussed attention on the dramatic impacts these changes may have on many sectors of society. In the companion paper [Fowler et al., J. Hydrol. (2004) this issue], we suggested that the HadRM3H model may be used with some confidence to estimate extreme rainfall distributions, showing good predictive skill in estimating statistical properties of extreme rainfall during the baseline period, 1961–1990. In this study, we use results from the future integration of HadRM3H (following the IPCC SRES scenario A2 for 2070–2100) to assess possible changes in extreme rainfall across the UK using two methods: regional frequency analysis and individual grid box analysis. Results indicate that for short duration events (1–2 days), event magnitude at a given return period will increase by 10% across the UK. For longer duration events (5–10 days), event magnitudes at given return periods show large increases in Scotland (up to +30%), with greater relative change at higher return periods (25–50 years). In the rest of the UK, there are small increases in the magnitude of more frequent events (up to +10%) but reductions at higher return periods (up to −20%). These results provide information to alter design storm depths to examine climate change impacts on various structures. The uncertainty bounds of the estimated changes and a ‘scaling’ methodology are additionally detailed. This allows the estimation of changes for the 2020s, 2050s and 2080s, and gives some confidence in the use of these estimates in impact studies.     

Investigating the application of climate models in flood projection across the UK

Regional climate models (RCMs) have emerged as the preferred tool in hydrological impact assessment at the catchment scale. The direct application of RCM precipitation output is still not recommended; instead, a number of alternative methods have been proposed. One method that has been used is the change factor methodology, which typically uses changes to monthly mean or seasonal precipitation totals to develop change scenarios. However, such simplistic approaches are subject to significant caveats. In this paper, 18 RCMs covering the UK from the ENSEMBLES and UKCP09 projects are analysed across different catchments. The ensembles' ability in capturing monthly total and extreme precipitation is outlined to explore how the ability to make confident statements about future flood risk varies between different catchments. The suitability of applying simplistic change factor approaches in flood impact studies is also explored. We found that RCM ensembles do have some skill in simulating observed monthly precipitation; however, seasonal patterns of bias were evident across each of the catchments. Moreover, even apparently good simulations of extreme rainfall can mis‐estimate the magnitude of flood‐generating rainfall events in ways that would significantly affect flood risk management. For future changes in monthly mean precipitation, we observe the clear ‘drier summers/wetter winters’ signal used to develop current UK policy, but when we look instead at flood‐generating rainfall, this seasonal signal is less clear and greater increases are projected. Furthermore, the confidence associated with future projections varies from catchment to catchment and season to season as a result of the varying ability of the RCM ensembles, and in some cases, future flood risk projections using RCM outputs may be highly problematic. Copyright © 2013 John Wiley & Sons, Ltd.     

太湖1889洪水年的流域水文模拟

由于受到水文观测资料时间短的限制,目前难以认识百年遇机率的极端洪水.为此,本文根据19世纪末历史文献的洪水灾害记录,利用流域水文模型,对太湖1889洪水年的流域产流、入湖汇流等水文特征和过程进行模拟.本研究设计了三套模拟实验:首先在现代气候控制实验基础上对1988-2002年时间系列和特大洪水年进行水文模拟和模型率定校验;其次,采用长江下游19世纪末的气候观测资料驱动,对极端年份1889年逐日洪水过程模拟;最后,为减少1 a洪水年模拟的不确定性,还采用蒙特卡罗Bootstrap法模拟了15 a的流域气候场,在5475 d样本下进行特征年份的水文模拟.模拟结果表明,1889年洪水期间产流在当年6月底达到最大,1%频率的径流深达8.6 mm/d,95%CI的误差在-2.94～3.26 mm/d之间.汇入太湖径流同期达到最大,1%频率的洪水流量达到1286.9 m3/s,95%CI的误差在-128.3～165.7 m3/s之间.根据洪水Log-Normal概率分布,计算1889洪水年的重现期为149 a.经Bootstrap法对误差置信区的模拟,95%CI检验在70～175 a间的重现期可信.该研究为延长20世纪洪水序列、拓展对百年时间尺度的特大洪水的认识提供了动力学模拟方面的科学依据.     

Radar rainfall uncertainty modelling influenced by wind

Radar‐based estimates of rainfall are affected by many sources of uncertainties, which would propagate through the hydrological model when radar rainfall estimates are used as input or initial conditions. An elegant solution to quantify these uncertainties is to model the empirical relationship between radar measurements and rain gauge observations (as the ‘ground reference’). However, most current studies only use a fixed and uniform model to represent the uncertainty of radar rainfall, without consideration of its variation under different synoptic regimes. Wind is such a typical weather factor, as it not only induces error in rain gauge measurements but also causes the raindrops observed by weather radar to drift when they reach the ground. For this reason, as a first attempt, this study introduces the wind field into the uncertainty model and designs the radar rainfall uncertainty model under different wind conditions. We separate the original dataset into three subsamples according to wind speed, which are named as WDI (0–2 m/s), WDII (2–4 m/s) and WDIII (>4 m/s). The multivariate distributed ensemble generator is introduced and established for each subsample. Thirty typical events (10 at each wind range) are selected to explore the behaviours of uncertainty under different wind ranges. In each time step, 500 ensemble members are generated, and the values of 5th to 95th percentile values are used to produce the uncertainty bands. Two basic features of uncertainty bands, namely dispersion and ensemble bias, increase significantly with the growth of wind speed, demonstrating that wind speed plays a considerable role in influencing the behaviour of the uncertainty band. On the basis of these pieces of evidence, we conclude that the radar rainfall uncertainty model established under different wind conditions should be more realistic in representing the radar rainfall uncertainty. This study is only a start in incorporating synoptic regimes into rainfall uncertainty analysis, and a great deal of more effort is still needed to build a realistic and comprehensive uncertainty model for radar rainfall data. Copyright © 2014 John Wiley & Sons, Ltd.     

Peak runoff contributing area as hydrological signature of the probability distribution of floods

For the analysis of hydrological extremes and particularly in flood prediction, deeper investigation is needed on the relative effects of different hydrological processes acting at the basin scale in different hydroclimatic areas of the world. In this framework, the theoretical derivation of flood distribution shows a great potential for development and knowledge advancement. In addition, another promising path of investigation is represented by the use of distributed hydrological models via simulation modelling (including Monte Carlo, discrete event and continuous simulation). In this paper results of a theoretically derived flood frequency distribution are analyzed and compared with the results of a simulation scheme that uses a distributed hydrological model (DREAM) in cascade with a rainfall generator (IRP). The numerical simulation allows the reproduction of a large number of extreme events and provides insight into the main control for flood generation mechanisms with particular emphasis to the peak runoff contributing areas, highlighting the relevance of soil texture and morphology in different climatic environments. The proposed methodology is applied here to the Agri and the Bradano basin, in Southern Italy.     

Hydrological appraisal of rainfall estimates from radar,satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin,China

ABSTRACT

Multisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellite–gauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km². All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.     

Channel–floodplain connectivity during an extreme flood event: implications for sediment erosion,deposition, and delivery

The term connectivity has emerged as a powerful concept in hydrology and geomorphology and is emerging as an innovative component of catchment erosion modeling studies. However, considerable confusion remains regarding its definition and quantification, especially as it relates to fluvial systems. This confusion is exacerbated by a lack of detailed case studies and by the tendency to treat water and sediment separately. Extreme flood events provide a useful framework to assess variability in connectivity, particularly the connection between channels and floodplains. The catastrophic flood of January 2011 in the Lockyer valley, southeast Queensland, Australia provides an opportunity to examine this dimension in some detail and to determine how these dynamics operate under high flow regimes. High resolution aerial photographs and multi‐temporal LiDAR digital elevation models (DEMs), coupled with hydrological modeling, are used to assess both the nature of hydrologic and sedimentological connectivity and their dominant controls. Longitudinal variations in flood inundation extent led to the identification of nine reaches which displayed varying channel–floodplain connectivity. The major control on connectivity was significant non‐linear changes in channel capacity due to the presence of notable macrochannels which contained a > 3000 average recurrence interval (ARI) event at mid‐catchment locations. The spatial pattern of hydrological connectivity was not straight‐forward in spite of bankfull discharges for selected reaches exceeding 5600 m³ s^–1. Data indicate that the main channel boundary was the dominant source of sediment while the floodplains, where inundated, were the dominant sinks. Spatial variability in channel–floodplain hydrological connectivity leads to dis‐connectivity in the downstream transfer of sediments between reaches and affected sediment storage on adjacent floodplains. Consideration of such variability for even the most extreme flood events, highlights the need to carefully consider non‐linear changes in key variables such as channel capacity and flood conveyance in the development of a quantitative ‘connectivity index’. Copyright © 2013 John Wiley & Sons, Ltd.     

Seasonal ensemble generator for radar rainfall using copula and autoregressive model

Uncertainty analysis of radar rainfall enables stakeholders and users have a clear knowledge of the possible uncertainty associated with the rainfall products. Long-term empirical modeling of the relationship between radar and gauge measurements is an efficient and practical method to describe the radar rainfall uncertainty. However, complicated variation of synoptic conditions makes the radar-rainfall uncertainty model based on historical data hard to extend in the future state. A promising solution is to integrate synoptic regimes with the empirical model and explore the impact of individual synoptic regimes on radar rainfall uncertainty. This study is an attempt to introduce season, one of the most important synoptic factor, into the radar rainfall uncertainty model and proposes a seasonal ensemble generator for radar rainfall using copula and autoregressive model. We firstly analyze the histograms of rainfall-weighted temperature, the radar-gauge relationships, and Box and Whisker plots in different seasons and conclude that the radar rainfall uncertainty has strong seasonal dependence. Then a seasonal ensemble generator is designed and implemented in a UK catchment under a temperate maritime climate, which can fully model marginal distribution, spatial dependence, temporal dependence and seasonal dependence of radar rainfall uncertainty. To test its performance, 12 typical rainfall events (4 for each season) are chosen to generate ensemble rainfall values. In each time step, 500 ensemble members are produced and the values of 5th to 95th percentiles are used to derive the uncertainty bands. Except several outliers, the uncertainty bands encompass the observed gauge rainfall quite well. The parameters of the ensemble generator vary considerably for each season, indicating the seasonal ensemble generator reflects the impact of seasons on radar rainfall uncertainty. This study is an attempt to simultaneously consider four key features of radar rainfall uncertainty and future study will investigate their impacts on the outputs of hydrological models with radar rainfall as input or initial conditions.