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Areal rainfall is routinely estimated based on the observed rainfall data using distributed point rainfall gauges. However, the data collected are sparse and cannot represent the continuous rainfall distribution (or field) over a large watershed due to the limitations of weather station networks. Recent improvements in remote-sensing-based rainfall estimation facilitate more accurate and effective hydrological modeling with a continuous spatial representation of rainfall over a watershed of interest. In this study, we conducted a systematic stepwise comparison of the areal rainfalls estimated by a synoptic weather station and radar station networks throughout South Korea. The bias in the areal rainfalls computed by the automated synoptic observing system and automatic weather system networks was analyzed on an hourly basis for the year 2021. The results showed that the bias increased significantly for hydrological analysis; more importantly, the identified bias exhibited a magnitude comparable to that of the low flow. This discrepancy could potentially mislead the overall rainfall-runoff modeling process. Moreover, the areal rainfall estimated by the radar-based approach significantly differed from that estimated by the existing Thiessen Weighting approach by 4%–100%, indicating that areal rainfalls from a limited number of weather stations are problematic for hydrologic studies. Our case study demonstrated that the gauging station density must be within 10 km2 on average for accurate areal rainfall estimation. This study recommends the use of radar rainfall networks to reduce uncertainties in the measurement and prediction of areal rainfalls with a limited number of ground weather station networks. [ABSTRACT FROM AUTHOR]

Study region: eastern part of the French Mediterranean coastal area. Study focus: This work focuses on the heavy precipitation events that have affected the eastern part of the French Mediterranean coastal area over the last decade and the river discharges associated with these events. The aim is to make a throughout analysis of the spatio-temporal characteristics of heavy precipitation events, and to evaluate whether a high flow event is associated with them when looking at river discharges on affected catchments. New hydrological insights for the region: Based on radar-based precipitation grids, 158 heavy precipitation events (HPEs) affected the study area over the period 2007–2020. In addition, 65 % of HPEs were associated with a high flow event (HFE) on one or more gauged catchments. Results show that HPEs were more frequent during the beginning of summer and autumn. A large portion of autumn and winter HPEs were associated with a HFE. Looking at the spatio-temporal characteristics of the HPEs, their mean duration was 20 hours, with 42 % of the events lasting less than 6 hours. Some common characteristics among the HPEs that caused HFEs were identified: all HPEs lasting more than 30 hours and all HPEs with catchment precipitation accumulation above 150 mm were associated with a HFE.

River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological stations. A prediction model incorporating a two-dimensional convolutional neural network (2D-CNN) and long short-term memory (LSTM) is constructed to exploit geographical and temporal features of radar rainfall data, and a transfer learning method using a newly defined flow–distance matrix is presented. The results of our evaluation of the Oyodo River basin in Japan show that the presented transfer learning model using radar rainfall instead of upstream measurements has a good prediction accuracy in the case of torrential rain, with a Nash–Sutcliffe efficiency (NSE) value of 0.86 and a Kling–Gupta efficiency (KGE) of 0.83 for 6-h-ahead forecast for the top-four peak water-level height cases, which is comparable to the conventional model using upstream measurements (NSE = 0.84 and KGE = 0.83). It is also confirmed that the transfer learning model maintains its performance even when the amount of training data for the prediction site is reduced; values of NSE = 0.82 and KGE = 0.82 were achieved when reducing the training torrential-rain-period data from 12 to 3 periods (with 105 periods of data from other rivers for transfer learning). The results demonstrate that radar rainfall data and a few torrential rain measurements at the prediction location potentially enable us to predict river water levels even if hydrological stations have not been installed at the prediction location. [ABSTRACT FROM AUTHOR]

Precipitation frequency analysis is important to the design of infrastructure. While analysis has traditionally been conducted using rain gauge data, quantitative precipitation estimates (QPEs) based on multi-sensor radar offers an opportunity for improvement. This study seeks to evaluate the implications of windfarm locations and weather radar coverage areas on radar rainfall frequency estimation. The analyses are based on 19 years of hourly Stage-IV radar data over the state of Iowa in the Midwestern United States. The data were compiled using an annual maximum series approach and a generalized extreme value (GEV) distribution to estimate pixel return quantiles. Results showed that windfarm locations positively correlate to elevated GEV shape parameters, resulting in a wider upper tail causing possible overestimation of extreme events. Probability of detection analysis revealed that areas roughly equidistant from multiple radars were more likely to record rainfall accumulations over all hourly thresholds tested. Radar based quantile estimates at windfarm locations and distances far from WSR-88D radar sites tended to be greater than gauge derived values while radar quantiles underestimated those based on observed values across the Iowa domain. This underestimation has been outlined as "conditional bias" by previous studies. While our analysis shows that these issues are overcome with sufficient expansion of reference windows, it strengthens the concerns of earlier studies suggesting radar-rainfall alone is not yet adequate for the determination of rainfall recurrence intervals used in engineering design. [ABSTRACT FROM AUTHOR]

The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep-learning approach that augments rainfall data with increased time resolutions to complement relatively lower-resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs), namely TempNet, to improve the temporal resolution of radar-based rainfall products and compare the proposed model with an optical flow-based interpolation method and CNN-baseline model. While TempNet achieves a mean absolute error of 0.332 mm/h, comparison methods achieve 0.35 and 0.341, respectively. The methodology presented in this study could be used for enhancing rainfall maps with better temporal resolution and imputation of missing frames in sequences of 2D rainfall maps to support hydrological and flood forecasting studies. HIGHLIGHTS This paper improves the temporal resolution of radar rainfall products from 10 to 5 min.; A residual Convolutional Neural Network architecture, namely TempNet, is proposed for the temporal downscaling of radar rainfall.; Results show that the TempNet outperforms the optical flow-based temporal interpolation method in the study area.;

Weather radar is a form of alternative indirect rainfall measurement for use in mitigating flash flood hazards. It is a challenging task to obtain accurate radar rainfall data without integration with automatic rain gauge networks. This paper investigated transformation equations to convert the calibrated daily Z–R relationship to the sub-hourly scale and proposed optional schemes for downscaling the daily bias adjustment factor into 15 min resolution scale to produce a high-resolution radar rainfall product for flash flood modelling. Radar reflectivity data from three radar stations in Thailand and their corresponding daily gauge rainfall data were used in the analysis. Two bias adjustment schemes (DMFB and DS_DMFB), accounting for the temporal variation, and one spatiotemporal scheme (SPTB_IDS) were used to generate three corresponding rainfall datasets for the unified river basin simulator (URBS) model to simulate flood hydrographs in the Tubma basin, Thailand. The results showed that combining the proposed 15-min Z–R scaling equation and the SPTB_IDS produced the most reliable radar rainfall amount leading to an increase in the accuracy of flood modelling with the lowest uncertainty. This indicated that the temporal downscaling solution together with spatial interpolation technique for sub-hourly radar rainfall assessment could benefit flash flood simulation in a data-scarce basin. HIGHLIGHTS Daily gauge rainfall cannot detect a variation of sub-hourly intense rainfall. This paper proposed alternative techniques to downscale the Z-R relationship and daily data and combine it with radar observations to synthesize 15-min radar rainfall data.; Results show the benefit of the downscaled Z-R relationship and the most complicated radar bias adjustment method substantially improved the accuracy of flood estimates and reduced uncertainties in the flash flood modelling.;

Flash floods are a major weather-related risk, as they cause more than 5000 fatalities annually, according to the World Meteorological Organization. Quantitative Precipitation Estimation is a method used to approximate the rainfall over locations where direct field observations are not available. It represents one of the most valuable information employed by meteorologists and hydrologists for issuing early warnings concerning flash floods. The current study is in line with the efforts to improve radar-based rainfall estimates through the use of machine learning techniques applied on radar data. With this aim, as a proof of concept, six machine learning models are evaluated to make estimations of the radar-based hourly accumulated rainfall using reflectivity data collected on the lowest radar elevation angles, and we employ a new data model for representing these radar data. The data were collected by a WSR-98D weather radar of the Romanian Meteorological Administration, located in the central region of Romania, during 30 non-consecutive days of the convective seasons, between 2016 and 2021. We obtained encouraging results using a stacked machine learning model. In terms of the Root Mean Squared Error evaluation metric, the results of the proposed stacked regressor are better than the radar estimated accumulated rainfall by about 33% and also outperform the baseline computed using the Z-R relationship by about 13%. [ABSTRACT FROM AUTHOR]

Frequency analysis of rainfall data is essential in the design and modelling of hydrological systems but is often statistically limited by the total observation period. With advances in weather radar technology, frequency analysis of areal rainfall data is possible at a higher spatial resolution. Still, the observation periods are short relative to established rain gauge networks. A stochastic framework, "stochastic storm transposition" shows great promise in recreating rainfall statistics from radar rainfall products, similar to rain gauge-derived statistics. This study estimates intensity–duration–frequency (IDF) relationships at both point and urban catchment scales. We use the stochastic storm transposition framework and a single high-resolution, 17-year long (however, discontinuous), radar rainfall dataset. The IDF relations are directly compared to rain gauge statistics with more than 40 years of observation, and rainfall extremes derived from the original, and untransposed, radar dataset. An overall agreement is discovered, however, with some discrepancies in short-duration storms due to scaling errors between gauge and radar. [ABSTRACT FROM AUTHOR]

A storm on August 5, 2012, triggered a soil slip that mobilized into a debris flow in a relatively remote part of Great SmokyMountains National Park, 2 km southwest of Mt. LeConte, TN. A rain gauge at Mt. LeConte Resort, recorded manually at 07:00 Eastern Time each day, reported 92.46 mm of rainfall on August 6, 2012, to the National Weather Service. Near Composite Re- flectivity (NCR) and Digital Precipitation Array (DPA) products from the Knoxville, TN, NEXRAD Doppler weather radar station for August 5 and 6, 2012, were used to infer the storm's rainfall intensity and duration details. NCR products consist of the maximum radar re- flectivity value from all 14 elevation angles scanned for each 1.09 km2 cell, which users must convert to precipitation depth. DPA products are rain rate (in./hr) calculated with a National Weather Service model that uses radar reflectivity and automated rain gauge measurements to assign regional bias factors to each complete radar scan of the atmosphere surrounding the radar station using a national grid system composed of 17.12 km2 cells. Cumulative NCR inferred rain depth was 90.65 mm at the Mt. LeConte gauge location and 69.23 mm at the soil slip location. The cumulative DPA rain depth was 47.64 mm at the gauge. The maximum 15 minute NCR precipitation intensity was 39.61 mm/hr at the Mt. LeConte gauge location and 63.24 mm/hr at the soil slip location. The timing of the soil slip probably coincided with a 20-minute-long interval of maximum intensity precipitation. [ABSTRACT FROM AUTHOR]

Flash floods are a major weather-related risk, as they cause more than 5000 fatalities annually, according to the World Meteorological Organization. Quantitative Precipitation Estimation is a method used to approximate the rainfall over locations where direct field observations are not available. It represents one of the most valuable information employed by meteorologists and hydrologists for issuing early warnings concerning flash floods. The current study is in line with the efforts to improve radar-based rainfall estimates through the use of machine learning techniques applied on radar data. With this aim, as a proof of concept, six machine learning models are evaluated to make estimations of the radar-based hourly accumulated rainfall using reflectivity data collected on the lowest radar elevation angles, and we employ a new data model for representing these radar data. The data were collected by a WSR-98D weather radar of the Romanian Meteorological Administration, located in the central region of Romania, during 30 non-consecutive days of the convective seasons, between 2016 and 2021. We obtained encouraging results using a stacked machine learning model. In terms of the Root Mean Squared Error evaluation metric, the results of the proposed stacked regressor are better than the radar estimated accumulated rainfall by about 33% and also outperform the baseline computed using the Z-R relationship by about 13%.

Reliable hourly flood forecasting using weather radar rainfall data for early warning system is essential for reducing natural disaster risk during extreme typhoon events. This study proposed a novel approach integrated with physics-based WASH123D and HEC-HMS models to forecast 1 h ahead flood level in the Fengshan Creek basin, northern Taiwan. The comparison was done with data-driven support vector machine (SVM) model, and performances were assessed by using statistical indicators (root mean square error, correlation coefficient, the error of time to peak flood level, the error of peak flood). Four typhoons and two plum rain events (with 620 data sets) were selected for the process of model calibration and validation. The model performs better when it used quantitative precipitation estimate radar data rather than rain gauge data. Results of using 1 h ahead quantitative precipitation forecast (QPF) as input for flood forecasting were encouraging but not feasible to use directly for early flood warning system due to errors in peak flood levels and timing. Therefore, the improvement in accuracy of 1 h ahead flood forecasting was done using physics-based approach and SVM model. The systematic comparison revealed that the SVM model is an attractive way out to improve the accuracy of QPF forecasted flood levels but unable to fully describe the flood level patterns in terms of timings and flood peaks, while the results obtained by the physics-based approach were accurate and much better than the SVM model. The approach fully described the physics of hydrograph patterns and outputs have exactly the same 1 h ahead predictions, in excellent agreement with observations. The reliable and accurate reflections of timing and amount of flood peaks in all selected typhoons by a newly developed physics-based approach with its operational nature are recommended to use by the government in the future for early warning to reduce the flood impacts during typhoon events. [ABSTRACT FROM AUTHOR]

Efficient quantification of conditional errors in radar rainfall (RR) products requires a realistic model of random error distribution. Nonparametric estimate of the probability density function (pdf) of standardized RR errors is obtained using a large data sample. The standardization is based on a second‐order separation of systematic and random effects. The estimated empirical distribution is skewed and has exponential shapes of its tails with two different scales. It cannot be modeled with the Gaussian law that was used in several previous studies. A good representation of the tails of the empirical distribution of RR errors is obtained with a three‐parameter modified Laplace model with a shift and unequal slopes of its two sides. Plain Language Summary: Reliable measurement of rainfall is important for hydrology, water management, agriculture, and other users. It is a challenging task because rainfall is highly variable and networks of rain gauges are too sparse to capture this variability. Thus, rainfall estimates based on weather radars are becoming indispensable because they provide continuous coverage of the area up to about 100 km around the radars. However, they are ridden by many uncertainties that must be known in order to avoid dangerous mistakes in all applications of the estimates. In this paper, we contribute an important new piece to this knowledge—the empirically based information about the frequency of occurrence of different magnitudes of the errors that is called the error probability distribution. It allows to evaluate their impact on different applications of radar rainfall. It also enables more reliable comparisons of different rainfall estimation methods in order to monitor progress in this area. The new information was obtained by applying a new mathematical modeling method to a large sample of radar rainfall estimates combined with rain gauge measurements of true rainfall. Many previous applications assumed that radar rainfall errors were distributed according to the commonly used bell curve called normal distribution. Our results show that the actual error distribution differs considerably from normality and that using the normal model leads to misrepresentation of the uncertainties. We now have the strongly supported mathematical tool to treat them in a much more realistic way. Key Points: Empirical distribution of conditional random errors in radar rainfall is estimated using second‐order separation of systematic and random effectsThe error distribution cannot be described with Gaussian model used beforeAsymmetric shifted Laplace model fits well the tails of the empirical distribution [ABSTRACT FROM AUTHOR]

Short-term rainfall prediction with high spatial and temporal resolution is of great importance to rainfall-triggered natural hazards such as landslide, flood, and debris flow. An analogue-based forecasting method may be able to provide short-term radar rainfall predictions, or "nowcasts," in which the probability distribution of the atmospheric state at a given location in the future is estimated based on a set of past observations. Expensive computations and diverse atmospheric interactions across multiple spatial scales, however, limit the application of this method. This study employs a physically based, empirical ensemble rainfall nowcasting model to obtain ensembles from historical rainfall fields. To do this, we consider meteorological factors, rainfall spatial distributions, and the temporal evolution of rainfall patterns. Cross-correlation is used as a measure of the similarity between the spatial distributions of two rainfall patterns, and the movement rate of rain, with respect to both direction and distance, is used to compare the patterns temporally. A moving window scheme is used to reduce the computational load, with rainfall data providing most reference values. The relationship between different scales and rainfall forecasts is further explored by cascade divisions. While smaller analogue scales indicate better forecasts in this study, the optimal analogue scale will vary for different rainfall events. This study is only a start in incorporating multi-scaled analogue into rainfall nowcasting analysis, and a great deal of more effort is still needed to build a realistic and comprehensive analogue-based rainfall prediction model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High-resolution rainfall fields are a crucial tool for many hydrological and hydrodynamic applications, including flood forecasting and urban drainage design. The aim of this study is to explore and exploit the space–time properties of rainfall using Fast-Fourier transforms, to provide a new method for the generation of high-resolution synthetic rainfall grids. These fields have realistic spatio-temporal properties, parametrised using historical radar rainfall events, matching the resolution of weather radar data (1km, 5 min), for events with a duration of 0.5–6 h. Utilising spectral random field theory, simulated rainfall fields are generated with a prescribed correlation structure, anisotropy, advection and marginal rainfall rate proportions and distributions. A model for rainfall generation is demonstrated, with an enriched model parameter sampling architecture using meaningful event clustering, based on space–time event properties. This model framework performs well at recreating short-duration spatio-temporal rainfall events, both visually and statistically. The extension of a clustered rainfall model allows for larger-scale sampling of synthetic event parameters, with specific rainfall event types. There are numerous potential uses for this rainfall model, such as design storms or test cases for applications of radar rainfall estimates. These include but are not limited to nowcasting, numerical weather prediction, flash flood forecasting and machine learning model training data generation. • A new method for the generation of high-resolution synthetic rainfall grids. • Utilises Fast Fourier transform and spectral random field theory. • Simulated events have realistic prescribed spatio-temporal properties. • Model framework performs well, both visually and statistically. • An enriched model parameter sampling architecture uses meaningful event clustering. [ABSTRACT FROM AUTHOR]

The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar reflectivity data to estimate monsoon rainfall using parametric Z-R models. The 10-min reflectivity data recorded in Kota Bahru radar station (in Malaysia) and hourly rain record in nearby 58 gauge stations during 2013–2015 were used. The three-dimensional nearest neighbor interpolation with altitude correction was applied for pixel matching. The non-linear Levenberg Marquardt (LM) regression, integrated with ANN regression minimized the spatiotemporal variability of the proposed Z-R model. Results showed an improvement in the statistical indicator, when LM and ANN overestimated (6.6%) and underestimated (4.4%), respectively, the mean total rainfall. For all rainfall categories, the ANN model has a positive efficiency ratio of >0.2.

Radar‐gauge rainfall discrepancies are considered to originate from radar rainfall measurements while ignoring the fact that radar observes rain aloft while a rain gauge measures rainfall on the ground. Observations of raindrops observed aloft by weather radars consider that raindrops fall vertically to the ground without changing in size. This premise obviously does not stand because raindrop location changes due to wind drift and raindrop size changes due to evaporation. However, both effects are usually ignored. This study proposes a fully formulated scheme to numerically simulate both raindrop drift and evaporation in the air and reduces the uncertainties of radar rainfall estimation. The Weather Research and Forecasting model is used to simulate high‐resolution three‐dimensional atmospheric fields. A dual‐polarization radar retrieves the raindrop size distribution for each radar pixel. Three schemes are designed and implemented using the Hameldon Hill radar in Lancashire, England. The first considers only raindrop drift, the second considers only evaporation, and the last considers both aspects. Results show that wind advection can cause a large drift for small raindrops. Considerable loss of rainfall is observed due to raindrop evaporation. Overall, the three schemes improve the radar‐gauge correlation by 3.2%, 2.9%, and 3.8% and reduce their discrepancy by 17.9%, 8.6%, and 21.7%, respectively, over eight selected events. This study contributes to the improvement of quantitative precipitation estimation from radar polarimetry and allows a better understanding of precipitation processes. Key Points: The impact of raindrop drift and evaporation on radar‐gauge rainfall comparison was studiedThe proposed scheme considers both variables and can reduce this discrepancy by as much as 21.7%The three‐dimensional atmospheric fields downscaled by the WRF can effectively simulate raindrop evolution [ABSTRACT FROM AUTHOR]

In applied hydrology, estimating the peak flood discharge in ungauged or poorly gauged river sections is vital for urbanized areas. Spatially distributed rainfall data such as weather radar data may be a good choice to represent the driving force in hydrologic models for ungauged regions. However, it is important to examine the accuracy of this product, especially over mountainous regions. The bias between radar rainfall and rain gauge rainfall can be progressively removed by using information provided by rain gauges. The Kalman Filter algorithm is applied for the mean field bias correction of radar rainfall data using past estimates and observations. Regarding the bias-correction methods, two filtering approaches are developed from 8 events observed at 13 rain gauge stations, and the bias-corrected radar (BCR) rainfall data are used to compare simulated and observed hydrographs for the three flood events that caused severe consequences in Samsun–Terme. It is found out that in frontal type rainfall, BCR rainfall estimates improve the Nash–Sutcliffe efficiency from 0.56 to 0.80 in runoff simulation of the event occurred on 22 November 2014; however, simulations of the event occurred on 2 August 2015 and 28 May 2016 have poorer statistical results probably owing to the effect of convective type rainfall and snow melting, respectively. [ABSTRACT FROM AUTHOR]

• Two flash flood early warning systems are compared analysing three extreme events. • Hazard is assessed in terms of return period for the whole Liguria region. • Weather radar rainfall is used as input for the warning systems. The anticipation of flash flood events is crucial to issue warnings to mitigate their impact. This work presents a comparison of two early warning systems for real-time flash flood hazard forecasting at regional scale. The two systems are based in a gridded drainage network and they use weather radar precipitation inputs to assess the hazard level in different points of the study area, considering the return period (in years) as the indicator of the flash flood hazard. The essential difference between the systems is that one is a rainfall-based system (ERICHA), using the upstream basin-aggregated rainfall as the variable to determine the hazard level, while the other (Flood-PROOFS) is a system based on a distributed rainfall-runoff model to compute the streamflows at pixel scale. The comparison has been done for three rainfall events in the autumn of 2014 that resulted in severe flooding in the Liguria region (Northwest of Italy). The results obtained by the two systems show many similarities, particularly for larger catchments and for large return periods (extreme floods). [ABSTRACT FROM AUTHOR]

Conditional daily rainfields were generated using collocated raingauge radar data by a kriging interpolation method, and disaggregated into hourly rainfields using variants of the method of fragments. A geographic information system (GIS)-based distributed rainfall–runoff model was used to convert the hourly rainfields into hydrographs. Using the complete radar rainfall as input, the rainfall–runoff model was calibrated based on storm events taken from nested catchments. Performance statistics were estimated by comparing the observed and the complete radar rainfall simulated hydrographs. Degradation in the hydrograph performance statistics by the simulated hourly rainfields was used to identify runoff error propagation. Uncertainty in daily rainfall amounts alone caused higher errors in runoff (depth, peak, and time to peak) than those caused by uncertainties in the hourly proportions alone. However, the degradation, which reduced with runoff depth, caused by the combined uncertainties was not significantly different from that caused by the uncertainty of amounts alone. [ABSTRACT FROM AUTHOR]

Abstract The Northeast United States, particularly New York State has experienced an increase in extreme daily precipitation during the past 50 years. Recent events such as Hurricane Irene and Superstorm Sandy, have revealed vulnerability to the intense precipitation within the transportation sector. In the scale of New York City, where transit system is the most dominant mode of transportation and daily mobility of millions of passengers depends on it, any disruption in the transit service would result in gridlocks and massive delays. To assess the impacts of rainfall on the subway ridership, we merged high resolution radar rainfall and subway ridership data to conduct a detailed analysis for each of the 116 subway stations at the borough of Manhattan. The analysis is carried out on both hourly and daily resolution level, where a spatial-temporal Bayesian multi-level regression model is used to capture the underlying dependency between the parameters. The estimation results are obtained through Markov Chain Monte Carlo sampling method. The results for daily analysis indicate that during weekdays, transit ridership in the stations located in commercial zones are less sensitive to the rainfall compared to the ones in residential zones. Highlights • Impacts of spatial variability of rainfall on subway ridership is assessed using large scale ridership and rainfall data. • A unified multi scale model, combining time and spatial scales for ridership is proposed. • The study framework can be used by agencies to improve system efficiency and resilience at the station level. [ABSTRACT FROM AUTHOR]

Extreme rainfall has caused severe road damage and landslide disasters in mountainous areas. Rainfall forecasting derived from remote sensing data has been widely adopted for disaster prevention and early warning as a trend in recent years. By integrating high-resolution radar rain data, for example, the QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system provides a great opportunity to establish the extreme climate-based landslide susceptibility model, which would be helpful in the prevention of hillslope disasters under climate change. QPESUMS was adopted to obtain spatio-temporal rainfall patterns, and further, multi-temporal landslide inventories (2003–2018) would integrate with other explanatory factors and therefore, we can establish the logistic regression method for prediction of landslide susceptibility sites in the Laonong River watershed, which was devastated by Typhoon Morakot in 2009. Simulations of landslide susceptibility under the critical rainfall (300, 600, and 900 mm) were designed to verify the model’s sensitivity. Due to the orographic effect, rainfall was concentrated at the low mountainous and middle elevation areas in the southern Laonong River watershed. Landslide change analysis indicates that the landslide ratio increased from 1.5% to 7.0% after Typhoon Morakot in 2009. Subsequently, the landslide ratio fluctuated between 3.5% and 4.5% after 2012, which indicates that the recovery of landslide areas is still in progress. The validation results showed that the calibrated model of 2005 is preferred in the general period, with an accuracy of 78%. For extreme rainfall typhoons, the calibrated model of 2009 would perform better (72%). This study presented that the integration of multi-temporal landslide inventories in a logistic regression model is capable of predicting rainfall-triggered landslide risk under climate change.

Regional Distributed Hydrological models are being adopted around the world for prediction of streamflow fluctuations and floods. However, the details of the hydraulic geometry of the channels in the river network (cross sectional geometry, slope, drag coefficients, etc.) are not always known, which imposes the need for simplifications based on scaling laws and their prescription. We use a distributed hydrological model forced with radar-derived rainfall fields to test the effect of spatial variations in the scaling parameters of Hydraulic Geometric (HG) relationships used to simplify routing equations. For our experimental setup, we create a virtual watershed that obeys local self-similarity laws for HG and attempt to predict the resulting hydrographs using a global self-similar HG parameterization. We find that the errors in the peak flow value and timing are consistent with the errors that are observed when trying to replicate actual observation of streamflow. Our results provide evidence that local self-similarity can be a more appropriate simplification of HG scaling laws than global self-similarity.

Highlights • Applied an ensemble rainfall error model to radar-based rainfall fields. • Generated an ensemble of rainfall with improved statistical metrics. • The ensemble of rainfall was used as input in a hydrological model. • The use of the rainfall ensemble reduced the overestimation of streamflow volumes. Abstract This study investigates the efficiency of correcting radar rainfall estimates using a stochastic error model in the upper Iguaçu river basin in Southern Brazil for improving streamflow simulations. The 2-Dimensional Satellite Rainfall Error Model (SREM2D) is adopted here and modified to account for topographic complexity, seasonality, and distance from the radar. SREM2D was used to correct the radar rainfall estimates and produce an ensemble of equally probable rainfall fields, that were then used to force a distributed hydrological model. Systematic and random errors in simulated streamflow were evaluated for a cascade of sub-basins of the Iguaçu catchment, with drainage area ranging from 1,808 to 21,536 km2). Results showed an improvement in the statistical metrics when the SREM2D ensemble was used as input to the hydrological model in place of the radar rainfall estimates in most sub-basins. Specifically, SREM2D was able to remove the relative bias (up to 50%) in the radar rainfall dataset regardless of the basin dimension, whereas the random error was reduced more prominently in the larger basins (up to 100 m3 s−1). An event scale evaluation was also performed for nine selected flood events in three sub-basins. SREM2D reduced the overestimation in the cumulative rainfall and streamflow volumes during these events. [ABSTRACT FROM AUTHOR]

Highlights • New grid-based model is used to compute flood and runoff-contributing area. • New proposed IRCA index combines runoff-contributing area and rain intensity on it. • IRCA for a characteristic duration is well correlated with flood peak discharge. • Land-uses with low runoff potential become dominant in large–extreme flood events. • Pre-peak soil moisture differs between flood magnitudes while pre-event does not. Abstract During the complex dynamic interactions between rainfall and basin properties, different portions of the basin produce runoff at different moments. Capturing this spatiotemporal variability is important for flood analysis, but knowledge of this subject is limited. The presented research aims at improving the understanding of runoff-contributing areas (RCA; hillslope sections from which water flows, reaches the stream network, and consequently the basin outlet) and at examining their relationship with the magnitude of a flash flood's peak discharge. A distributed hydrological model (GB-HYDRA) that enables computing RCA and flood discharge was developed. The model was applied to four medium-size basins (18–69 km2) in a Mediterranean climate and 59 flash flood events were analyzed. The correlation between basin input flux (basin area multiplied by the basin maximal rain intensity averaged over the time of concentration) and output flux (observed peak discharge) was poor (R2 = 0.16). However, using a newly developed index, termed IRCA, to calculate the input flux accounting only for the RCA extent and rainfall intensity over it, resulted in a substantially higher correlation (R2 = 0.64) across a wide range of flood magnitudes. The highest correlation was found using a 50-min time window, which is shorter than the time of concentration. Flood events were categorized according to their magnitude and the differences of several factors among the groups were examined. Pre-storm soil moisture content was found to be similar for all event magnitudes; however, pre-peak soil moisture content was substantially different between moderate and large–extreme events. Other important properties that differed between magnitudes were: RCA extent and its averaged rain intensity and ratio of convective rainfall. Finally, areas with land-uses characterized by low runoff potential became dominant and contributed mainly during large and extreme events. Although the RCA and its extent full potential is yet to be fulfilled, it is proposed as a significant tool for understanding processes of flash flood generation at the basin scale in future research. [ABSTRACT FROM AUTHOR]

Highlights • Bias correction approach is proposed for radar rainfall estimates within a Bayesian framework. • This study considers correlations of both parameters and residuals over stations. • Substantial shrinkage of the uncertainty of the parameters is noticeable in the PPM. • We also note a decline in the bias of radar rainfall estimates. • Rainfall ensembles can satisfactorily reproduce key statistical properties. Abstract The use of radar rainfall estimates has been limited by the lack of a reliable method of obtaining operationally accurate radar-based rainfall estimates and the associated potential errors embedded in the retrieval process. Moreover, the existing approaches have difficulty merging ground- and radar-based measurements due to spatial and temporal variations in bias as well as the uncertainties in model parameters that also can affect the overall bias. This study proposes a novel approach for radar rainfall estimates using a hierarchical Bayesian model (HBM) in the context of bias correction. In particular, three different variations (i.e. the partial-pooling model (PPM), complete-pooling model (CPM), no-pooling model (NPM)) of the HBM are explored to better characterize the bias correction factor and the associated uncertainty. In the case of the CPM, rain gauges are assumed to have a constant bias level over the spatial domain, which results in a significant increase in RMSE. Conversely, in the NPM approach, the parameters are independently estimated for each station, resulting in an increase in the uncertainty of the parameters caused by compensating for the estimates of other parameters; this approach also leads to a substantial increase in the RMSE. Here, we introduce the PPM approach, which aims to jointly estimate correction factors across all gauging stations, while considering the covariance structure of both parameters and model errors. The results obtained for the PPM show a noticeable reduction in the uncertainty of the parameters when compared to that of the NPM. We also note a decline in the bias of radar rainfall estimates. Finally, we further utilize the proposed PPM-based bias correction approach as an ensemble generator for simulation of radar rainfall estimates. The simulated rainfall ensembles can satisfactorily reproduce key statistical properties retrieved from ground reference measurements. [ABSTRACT FROM AUTHOR]

Interaction between adverse weather conditions and motor vehicle crashes is an important topic for traffic engineers and hydrometeorologists. With the recent availability of high resolution precipitation products (hourly, 4 × 4 km), it is possible to evaluate crash risk during rainfall events more accurately. Texas, the second largest state in the U.S., with a relatively high population density in its eastern part that receives significant rainfall from tropical events, experiences many hazardous traffic conditions every year. This study investigates temporal and spatial variability of the Relative Accident Risk (RAR) due to rainy conditions across Texas during the year 2015 using a Crash-Based Matched Pairs Analysis (CB-MPA) approach for every 4 × 4 km grid using an hourly time scale. The overall findings show that rainfall increases crash risk across the state by about 57%, while seasonal-based analysis confirms the role of precipitation patterns on crash rates. Although eastern and central counties (wetter and more urbanized) have remarkably higher rates of crash occurrence, the western counties (mainly rural and dry) show higher RAR values. Moreover, higher rainfall intensity can increase RAR up to three-fold while directly having an adverse effect on crash injury type. There is a relatively high correlation between rainfall intensity and RAR values (R 2  = 0.76). The analysis also shows higher RAR values on high-speed interstate highways and tollways compared to urban local streets. RAR values also vary according to the gender and age of drivers. The study findings shed light on future paths toward more detailed applications of high-resolution environmental data in crash risk analysis. [ABSTRACT FROM AUTHOR]

The growth of urban population, combined with an increase of extreme events due to climate change call for a better understanding and representation of urban floods. The uncertainty in rainfall distribution is one of the most important factors that affects the watershed response to a given precipitation event. However, most of the investigations on this topic have considered theoretical scenarios, with little reference to case studies in the real world. This paper incorporates the use of spatially-variable precipitation data from a long-range radar in the simulation of the severe floods that impacted the city of Hull, U.K., in June 2007. This radar-based rainfall field is merged with rain gauge data using a Kriging with External Drift interpolation technique. The utility of this spatially-variable information is investigated through the comparison of computed flooded areas (uniform and radar) against those registered by public authorities. Both results show similar skills at reproducing the real event, but differences in the total precipitated volumes, water depths and flooded areas are illustrated. It is envisaged that in urban areas and with the advent of higher resolution radars, these differences will be more important and call for further investigation. [ABSTRACT FROM AUTHOR]

Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.

The spatial variability of watershed characteristics has to be considered in detail when developing a flood prediction model because it has great influence on the generation of runoff and other hydrologic processes. Physically based, distributed-parameter models can use detailed watershed data that are becoming more readily available to improve hydrologic simulations. However, the computational penalty can be very high for simulations over large catchments. In this study, a physically based, distributed-parameter hydrologic model was used to simulate different flooding events over an 11,285 $$\hbox {km}^{2}$$ basin at 150 m $$\times $$ 150 m grid resolution. To overcome the computational demand, the model was run on nine sub-catchments separately, without losing hydrologic connectivity among the sub-catchments. The outflow from an upstream sub-basin was used as the inflow into the next sub-basin, allowing this inflow to join the flow generated in the downstream sub-basin in a dynamic mode. Rainfall data from weather radar were used as input. This approach also allowed excluding certain sub-basins from the model simulations without affecting the hydrologic connectivity. A calibration was performed on one sub-basin only, and then, the model was validated over the entire watershed. The simulation results were reasonable and similar to results from single catchment simulations. By using this approach, it was possible to employ observed stream discharge at interior points of a catchment as part of the model inputs. It will also make it possible to run fully distributed, physically based models over very large catchments at very high resolution. [ABSTRACT FROM AUTHOR]

This paper addresses a regional-scale analysis of the rainfall-induced landslides for 2014 storm events based on detailed hydro-meteorological data set in the Ialomita Subcarpathians. This area is located in the western part of the Curvature Subcarpathians, a complex geological and geomorphic unit in Romania. The high temporal frequency of landslide events in the last decades (1997, 1998, 2005, 2006, 2010, 2012 and 2014) leads us to considerer that these processes play a major role in the evolution of this area's landscape where the most frequent landforms are rotational slides, translational slides, mudslides and complex movements. The rainy period between April and August 2014 induced numerous flash floods and landslides in this specific area that resulted to severe economic losses estimated to 8 million euros in Dambovita County. Spatially distributed rainfall during the main storm events estimated from adjusted radar-based precipitation was used to investigate the hydro-meteorological conditions that triggered or not landslides in the Ialomita Subcarpathians. Hydrological pre-conditions were assessed by hourly in situ soil moisture measurements at local scale and hydrological modelling at regional scale. ModClark semi distributed model implemented in HEC HMS software that integrates radar data was used to analyse catchment response to the main rainfall event that resulted to landslides in 2014. Analysis between rainfall, soil moisture conditions and direct runoff was performed for identifying the contribution of the hydro-meteorologic conditions to landsliding process in the Ialomita Subcarpathians. A detailed landslide inventory based on field mapping and visual interpretation of satellite and aerial images was completed with information from local authorities and mass media. Despite the limited number of landslide events, this study allows a detailed insight of understanding the influence of rainfall in landslide occurrence in this specific area. [ABSTRACT FROM AUTHOR]

This study is about use of spatially distributed rain in physically based hydrological models. In recent years, spatially distributed radar rainfall data have become available. The distributed radar rain is used to precisely model hydrologic processes and it is more realistic than the past practice of distribution methods like Thiessen polygons. Radar provides a highly accurate spatial distribution of rainfall and greatly improves the basin average rainfall estimates. However, quantification of the exact amount of rainfall from radar observation is relatively difficult. Thus, the fundamental idea of this study is to apply hourly gauge and radar rainfall data in a distributed hydrological model to simulate hydrological parameters. Hence the comparison is made between the outcomes of the WetSpa model from radar rainfall distribution and gauge rainfall distributed by the Thiessen polygon technique. The comparative plots of the hydrograph and the results of hydrological components such as evapotranspiration, surface runoff, soil moisture, recharge and interflow, reflect the spatially distributed radar input performing well for model outflow simulation. EDITOR D. Koutsoyiannis ASSOCIATE EDITOR F. Pappenberger [ABSTRACT FROM AUTHOR]

This study presents a novel approach of areal rainfall estimation based on radar precipitation imageries that is capable of reflecting spatial variability in rainfall while maintaining methodological simplicity. We named this methodology 'the radar polygon method' (RPM). In this approach, gages in the study area compete against each other to gain the grid cells of the radar precipitation field under its territory. The criterion of the competition between the gages is the similarity between the precipitation of the target grid cell and that of the gage location. We tested the applicability of RPM on four watersheds in Korean Peninsula in which 41 rain gage locations, used as the basis of polygon formation, exist. Even though RPM uses only the information regarding the spatial variability of rainfall extracted from the radar rainfall imageries without any geographical information, it obtains the polygon-like (or just polygon) shape of the governing territory for each of the gages in all four study watersheds. The difference between the radar polygon and the corresponding Thiessen polygon was especially notable for the areas where orography significantly affects the spatial variability of rainfall. Furthermore, the spatial variability of elevation within radar polygons was generally smaller than that within Thiessen polygons indicating that Radar polygon captures the topographic impact on rainfall that Thiessen polygon cannot. The major contribution of this study is that it suggested a novel field of using radar rainfall by providing an effective way of areal rainfall estimation which retains the simplicity of Thiessen polygon approach and is not affected by the relatively low accuracy of radar rainfall imagery. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study the quality of radar rainfall with respect to temporal and spatial resolution was examined. Gwangdeoksan Radar and the Korea Institute of Civil Engineering and Building Technology ( KICT) Radar were used for this purpose, along with the automatic weather systems data within Seoul. In addition, several quantitative quality measures were applied to examine the resolution. A negative bias of more than 40% in the radar rainfall of Gwangdeoksan Radar occurred with changes in the temporal and spatial resolution. However, the negative bias in the radar rainfall of KICT Radar was less than 10%. The mean square error in the radar rainfall of Gwangdeoksan Radar ranged from 5.5 to 0.4 mm2 h−2 and the mean square error in the radar rainfall of KICT Radar ranged from 30.2 to 1.5 mm2 h−2. The correlation co-efficient of the accumulated time of 30 min was more than 0.7, which increased by 30% from the correlation co-efficient of the accumulated time of 10 min. The bias in the radar rainfall of Gwangdeoksan Radar was still large with changes in the temporal and spatial resolution. However, the radar rainfall of KICT Radar was quantitatively similar to the gauge rainfall, and so the radar rainfall did not need any further quantitative correction. [ABSTRACT FROM AUTHOR]

Merging radar and gauge rainfall estimates is an area of active research. Since rain gauges alone are often limited at representing the complete spatial distribution of rainfall, a combination of radar-derived rainfall with spatially interpolated gauge estimates using alternate weighting approaches is investigated. This paper examines several merging methods that differ in the consideration of correlation among the estimation errors, their distribution, and the application of dynamic and static weighting. The merging process has been applied to the radar data from Terrey Hills radar located in Sydney, Australia, and spatially interpolated gauge rainfall on the same area. The performance of the merging methods is assessed by comparing the combined estimate with the gauge observation. It is however clear from our findings that rainfall estimation from any of the combination approaches assessed contains less error than any of the noncombination approaches. The results show that the correlation between these two rainfall estimation errors plays a significant role in the performance of the merging methods. The combination method should be chosen depending on the purpose, accuracy of the estimate, and complexity of the method. [ABSTRACT FROM AUTHOR]