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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]

Our study focuses on the hydrologic implications of resolving and modeling atmospheric processes at different spatial scales. Here we use heavy precipitation events from an atmospheric model that was run at different horizontal grid spacings (i.e., 250 m, 500 m, 1 km, 2 km 4 km, and 12 km) and able to resolve different processes. Within an idealized simulation framework, these rainfall events are used as input to an operational distributed hydrologic model to evaluate the sensitivity of the hydrologic response to different forcing grid spacings. We consider the finest scale (i.e., 250 m) as reference, and compute event peak flows and volumes across a wide range of basin sizes. We find that the use of increasingly-coarser inputs leads to changes in the distribution of event peak flows and volumes, with the strongest sensitivity at the smallest catchment sizes. Our results show the compromise between computational cost and hydrologic performance, providing basic information for future endeavors geared towards regional downscaling. [ABSTRACT FROM AUTHOR]

This study assesses streamflow predictions generated by two distributed hydrologic models, the Hillslope Link Model (HLM) and the National Water Model (NWM), driven by three radar-based precipitation forcing datasets. These forcing data include the Multi-Radar Multi-Sensor (MRMS), and the Iowa Flood Center's single-polarization-based (IFC-SP) and dual-polarization-based (IFC-DP) products. To examine forcing- and model-dependent aspects of the representation of hydrologic processes, we mixed and matched all forcing data and models, and simulated streamflow for 2016–18 based on six forcing–model combinations. The forcing product evaluation using independent ground reference data showed that the IFC-DP radar-only product's accuracy is comparable to MRMS, which is rain gauge corrected. Streamflow evaluation at 140 U.S. Geological Survey (USGS) stations in Iowa demonstrated that the HLM tended to perform slightly better than the NWM, generating streamflow with smaller volume errors and higher predictive power as measured by Kling–Gupta efficiency (KGE). The authors also inspected the effect of estimation errors in the forcing products on streamflow generation and found that MRMS's slight underestimation bias led to streamflow underestimation for all simulation years, particularly with the NWM. The less biased product (IFC-DP), which has higher error variability, resulted in increased runoff volumes with larger dispersion of errors compared to the ones derived from MRMS. Despite its tendency to underestimate, MRMS showed consistent performance with lower error variability as reflected by the KGE. The dispersion observed from the evaluation metrics (e.g., volume error and KGE) seems to decrease as scale becomes larger, implying that random errors in forcing are likely to average out at larger-scale basins. The evaluation of simulated peaks revealed that an accurate estimation of peak (e.g., time and magnitude) remains challenging, as demonstrated by the highly scattered distribution of peak errors for both hydrologic models. [ABSTRACT FROM AUTHOR]

Despite efforts to detect and mitigate wind farm clutter in weather radar observations of rainfall, these signatures propagate to quantitative precipitation estimates. In this study, the authors investigate the hydrologic impact of wind farm clutter in the Multi‐Radar Multi‐Sensor rainfall products. The study uses the probability of detection method to identify wind farm clutter in data from Iowa for the years 2016 and 2017. Using the physically based distributed hydrologic model called the Hillslope‐Link Model, the authors show that streamflow (flood) prediction errors are generally significant at smaller basin communities where wind farms occupy a large portion of the upstream basins. These errors due to wind farm clutter show systematic decrease with the basin scales. The results from this study have implications for real‐time streamflow forecasts provided automatically by the National Water Model at the National Oceanic and Atmospheric Administration, particularly at small riverine communities. Plain Language Summary: Wind farms corrupt radar‐rainfall estimates used for flood forecasting. Erroneous streamflow forecasts affect small communities where wind farms occupy a large portion of the upstream basins. The effect is difficult to detect at larger scales typically monitored by the USGS streamflow gauging network as the wind farms occupy a small fraction of the basins at those scales. Key Points: Impact of wind farm clutter on streamflow predictions is higher at communities where wind farms cover a large portion of the upstream basinsDetection of wind farm effect is difficult at the USGS‐monitored basin scales as they occupy a small fraction of the drainage areaWind farm effect on streamflow predictions shows systematic decrease with increasing basin scales [ABSTRACT FROM AUTHOR]

This study demonstrates an implementation of the prototype quantitative precipitation R estimation algorithm using specific attenuation A for S-band polarimetric radar. The performance of R(A) algorithm is assessed, compared to the conventional algorithm using radar reflectivity Z, at multiple temporal scales. Because the factor a, defined as the net ratio of A to specific differential phase, is a key parameter of the algorithm characterized by drop size distributions (e.g., differential reflectivity Zdr dependence on Z), the estimation equations of a and a proper number of Zdr-Z samples required for a reliable a estimation are examined. Based on the dynamic estimation of a, the event-based evaluation using hourly rain gauge observations reveals that the performance of R(A) is superior to that of R(Z), with better agreement and lower variability. Despite its superiority, the study finds that R(A) leads to quite consistent overestimations of about 10%-30%. It is demonstrated that the application of uniform a over the entire radar domain yields the observed uncertainty because of the heterogeneity of precipitation in the domain. A climatological range-dependent feature of R(A) and R(Z) is inspected in the multiyear evaluation at yearly scale using rain totals for April-October. While R(Z) exposes a systematic shift and overestimation, each of which arise from the radarmiscalibration and bright band effects, R(A) combining with multiple R(Z) values for solid/mixed precipitation shows relatively robust performance without those effects. The immunity of R(A) to partial beam blockage (PBB) based on both qualitative and quantitative analyses is also verified. However, the capability of R(A) regarding PBB is limited by the presence of the melting layer and its application requirement for the total span of differential phase (e.g., 3°), which is another challenge for light rain. [ABSTRACT FROM AUTHOR]

Data analyses for the mobile Iowa X-band polarimetric (XPOL) radar from a long-duration rain event that occurred during the NASA Iowa Flood Studies (IFloodS) field campaign are presented. A network of six 2D video disdrometers (2DVDs) is used to derive four rain-rate estimators for the XPOL-5 radar. The rain accumulation validations with a collocated network of twin and triple tipping-bucket rain gauges have highlighted the need for combined algorithms because no single estimator was found to be sufficient for all cases considered. A combined version of weighted and composite algorithms is introduced, including a new R( Ah, Zdr) rainfall estimator for X band, where Ah is the specific attenuation for horizontal polarization and Zdr is the differential reflectivity. Based on measurement and algorithm errors, the weights are derived to be as piecewise constant functions over reflectivity values. The weights are later turned into continuous functions using smoothing splines. A methodology to derive the weights in near-real time is proposed for the composite-weighted algorithm. Comparisons of 2-h accumulations and 8-h event totals obtained from the XPOL-5 with 12 rain gauges have shown 10%-40% improvement in normalized bias over individual rainfall estimators. The analyses have enabled the development of rain-rate estimators for the Iowa XPOL. [ABSTRACT FROM AUTHOR]

Summary: The authors developed an approach for deriving a statistical model of range-dependent error (RDE) in radar-rainfall estimates by parameterizing the structure of the non-uniform vertical profile of radar reflectivity (VPR). The proposed parameterization of the mean VPR and its expected variations are characterized by several climatological parameters that describe dominant atmospheric conditions related to vertical reflectivity variation. We have used four years of radar volume scan data from the Tulsa weather radar WSR-88D (Oklahoma) to illustrate this approach and have estimated the model parameters by minimizing the sum of the squared differences between the modeled and observed VPR influences that were computed using radar data. We evaluated the mean and standard deviation of the modeled RDE against rain gauge data from the Oklahoma Mesonet network. No rain gauge data were used in the model development. The authors used the three lowest antenna elevation angles to demonstrate the model performance for cold (November–April) and warm (May–October) seasons. The RDE derived from the parameterized models shows very good agreement with the observed differences between radar and rain gauge estimates of rainfall. For the third elevation angle and cold season, there are 82% and 42% improvements for the RDE and its standard deviation with respect to the no-VPR case. The results of this study indicate that VPR is a key factor in the characterization of the radar range-dependent bias, and the proposed models can be used to represent the radar RDE in the absence of rain gauge data. [Copyright &y& Elsevier]

Abstract: There is a significant spatial sampling mismatch between radar and rain gauge data. The use of rain gauge data to estimate radar-rainfall error variance requires partitioning of the variance of the radar and rain gauge difference to account for the sampling mismatch. A key assumption in the literature pertaining to the error variance separation method used to partition the variance is that the covariance between radar-rainfall error and the error of rain gauges in representing radar sampling domain is negligible. Our study presents the results of an extensive test of this assumption. The test is based on empirical data and covers temporal scales ranging from 0.25 to 24h and spatial scales ranging from 1 to 32km. We used a two-year data set from two high quality and high density rain gauge networks in Oklahoma and excluded the winter months. The results obtained using a resampling procedure show that covariance can be considerable at large scales due to the significant variability. As the variability of the covariance rapidly increases with larger spatial and shorter temporal scales, applications of the error variance separation method at those scales require more caution. The variability of the covariance and one of its constituting variables, the variance ratio of radar and gauge errors, shows simple scaling behavior well characterized by a power-law. [Copyright &y& Elsevier]

A significant scale gap between radar and in situ measurements of rainfall using rain gauges and disdrometers indicates a pressing need for improved knowledge of rainfall variability at the spatial scales below those of today's operational radar rainfall products, that is, ∼1–4 km. Lidar technology has the potential to fulfill this need, but there has been inconsistency in the literature pertaining to quantitative observations of rain using lidar. Several publications have stated that light scattering properties of raindrops could not be correlated with rain rates, while other papers have demonstrated the existence of such relationships. This note provides empirical evidence in support of the latter claim. The authors conducted a simple experiment using a near-horizontal-pointing elastic lidar to observe rain in Iowa City, Iowa, in the fall of 2005. The lidar signal was used to estimate rainfall quantities that were subsequently compared with independent estimates of the same quantities obtained from an optical disdrometer that was placed about 370 m from the lidar, ∼10 m below the lidar beam. To perform the conversion from the raw lidar signal, the authors used an optical geometry-based procedure to estimate optical extinction data. A theoretical relationship between extinction coefficients and rain rates was derived based on a theoretical drop size distribution. The parameters of the relationship were found through a best-fit procedure using lidar and disdrometer data. The results show that the lidar-derived rain rates correspond to those obtained from the optical disdrometer with a root-mean-square difference of 55%. The authors conclude that although a great deal remains to be done to improve the inversion algorithm, lidar measurements of rain are possible and warrant further studies. Lidars deployed in conjunction with disdrometers can provide high spatial (<5 m) and temporal (<1 min disdrometer, ∼1 s lidar) resolution data over a relatively long distance for rainfall measurements (1–2 km in the case of the University of Iowa lidar). [ABSTRACT FROM AUTHOR]

Abstract: In the quantitative evaluation of radar-rainfall products (maps), rain gauge data are generally used as a good approximation of the true ground rainfall. However, rain gauges provide accurate measurements for a specific location, while radar estimates represent areal averages. Because these sampling discrepancies could introduce noise into the comparisons between these two sensors, they need to be accounted for. In this study, the spatial sampling error is defined as the ratio between the measurements by a single rain gauge and the true areal rainfall, defined as the value obtained by averaging the measurements by an adequate number of gauges within a pixel. Using a non-parametric scheme, the authors characterize its full statistical distribution for several spatial (4, 16 and 36km2) and temporal (15min and hourly) scales. To accomplish this task, a large dataset (more than six years) of rain gauge measurements obtained through a highly dense rain gauge network deployed in the Brue catchment in southwest England is used. The authors show that the standard deviation of the spatial sampling error decreases with increasing rainfall intensity and accumulation time and increases with increasing pixel size. Additionally, the authors show how the Laplace distribution could be used to model the distribution of spatial sampling errors for the spatial and temporal scales considered in this study. [Copyright &y& Elsevier]

Abstract: Wind is responsible for systematic errors that affect rain gauge measurements. The authors investigate the use of computational fluid dynamics (CFD) to calculate airflow around rain gauges by applying a high-resolution large eddy simulation (LES) model to determine the flow fields around a measuring system of two rain gauges. The simulated air flow field is characterized by the presence of massive separation which induces the formation and shedding of highly unsteady eddies in the detached shear layers and wakes. Parts of these detached structures occur over the orifice of the rain gauges and may substantially affect the dynamics of the raindrops in this critical region. Non-dissipative LES methods used with fine enough meshes can successfully predict these eddies and their associated fluctuations. The authors compare statistics from LES with steady-state Reynolds averaged Navier–Stokes (RANS) simulations using the k–ε and shear stress transport k–ω turbulence models. They find that both RANS and LES models predict similar mean velocity distributions around the rain gauges. However, they determine the distribution of the resolved turbulent kinetic energy (TKE) to be strongly dependent on the RANS model used. Neither RANS model predictions of TKE are close to those of LES. The authors conclude that the failure of RANS to predict TKE is an important limitation, as TKE is needed to scale the local velocity fluctuations in stochastic models used to calculate the motion of raindrops in the flow field. [Copyright &y& Elsevier]

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based rainfall intensities (R) are calculated from the observed radar reflectivities (Z). Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the Z–R transformation equation. The scale dependency of the power-law Z–R parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. The multiplicative (a) and exponent (b) parameters are said to be “scale dependent” if applying the observed and calculated rainfall intensities to objective function at different scale results in different “optimal” parameters. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Increased time- and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors; and 2) model uncertainties. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It is concluded that identification of optimal scale for Z–R relationship determination requires further knowledge of reflectivity and rain-intensity error structure. [ABSTRACT FROM AUTHOR]

• The study examines multi-scale aspects of radar-rainfall (R-R) conditional error. • The behavior of R-R standardized error varies with the magnitude of rainfall and shows conditional tendency. • The distribution of R-R standardized error approaches the Gaussian distribution as temporal scale increases. • The error feature regarding spatial scale appears to be almost scale-invariant. This study examines multi-scale aspects of radar-rainfall (R-R) conditional error, defined as the difference between a given R-R value and the conditional average of corresponding reference observations. To decompose the systematic and random error components, the authors adopted the second-order separation method while previous studies relied on addressing the first-order moment (i.e., conditional bias) only. The authors applied a non-parametric kernel regression approach to characterize the conditional mean and standard deviation and thus to derive a distribution of random component, standardized error. This empirical study is based on data from two rain gauge networks, consisting of 66 and 115 gauges, and two R-R estimates (the Iowa Flood Center and Multi-Radar Multi-Sensor products) over the Iowa domain in the United States. The authors explored the effect of multiple temporal (1–24 h) and spatial (0.5–32 km) scales on the conditional error structure. They found that the error distribution gradually approaches the Gaussian distribution with longer temporal scale while the error feature regarding spatial scale appears to be almost scale-invariant. [ABSTRACT FROM AUTHOR]

The most common rainfall measuring sensor for validation of radar-rainfall products is the rain gauge. However, the difference between area-rainfall and rain gauge point-rainfall estimates imposes additional noise in the radar-rain gauge difference statistics, which should not be interpreted as radar error. A methodology is proposed to quantify the radar-rainfall error variance by separating the variance of the rain gauge area-point rainfall difference from the variance of radar-rain gauge ratio. The error in this research is defined as the ratio of the 'true' rainfall to the estimated mean-areal rainfall by radar and rain gauge. Both radar and rain gauge multiplicative errors are assumed to be stochastic variables, lognormally distributed, with zero covariance. The rain gauge area-point difference variance is quantified based on the areal-rainfall variance reduction factor evaluated in the logarithmic domain. The statistical method described here has two distinct characteristics: first, it proposes a range-dependent formulation for the error variance, and second, the error variance estimates are relative to the mean rainfall at the radar product grids. Two months of radar and rain gauge data from the Melbourne, Florida, WSR-88D are used to illustrate the proposed method. The study concentrates on hourly rainfall accumulations at 2- and 4-km grid resolutions. Results show that the area-point difference in rain gauge rainfall contributes up to 60% of the variance observed in radar-rain gauge differences, depending on the radar grid size, the location of the sampling point in the grid, and the distance from the radar. [ABSTRACT FROM AUTHOR]

Real-time radar-rainfall bias adjustment procedures for weather surveillance Doppler radar (WSR-88D) are investigated. Statistical analysis of the mean-field bias is performed on a 2-year record of WSR-88D observations from Tulsa, Okla., and rain gauge measurements from a dense network under the radar umbrella. The analysis shows strong seasonal effect. A data-based Monte Carlo simulation experiment is performed on the same data to quantify the sampling error of estimated bias for varying rain gauge network densities. Simulation results show (1) that the sampling error decreases proportionally to the square of the rain gauge network density; and (2) that the sampling error is higher in the warm season. The performance of three mean-field radar-rainfall bias estimation and prediction algorithms is investigated. The algorithms include the WSR-88D precipitation adjustment procedure, the adaptive error parameter technique, and the maximum likelihood autoregressive model. A Monte Carlo simulation experiment is used to assess the algorithms' error statistics for the two seasons, three accumulation timescales, and two modes of operation (prediction and update). Results show significant seasonal and time-scale effects. [ABSTRACT FROM AUTHOR]

This study describes an automated system that generates a statewide real-time quantitative precipitation estimation (QPE) product for flood forecasting in Iowa. The QPE system comprises, real-time data acquisition, processing, and product visualization subsystems. Combined with information retrieved from numerical weather prediction, the system processes data from multiple radars using various algorithms accounting for precipitation microphysics and radar remote sensing uncertainties. The system generates a composite rainfall map covering the entire state of Iowa at a resolution of 0.5 km, updated every 5 min. With the help of the system's flexible modular configuration, we have recently added a new polarimetric algorithm based on specific attenuation. Independent evaluations based on comparisons with rain gauge data and hydrologic model prediction of streamflow demonstrate that the new implementation significantly improves the rainfall estimation accuracy. The new QPE product shows performance comparable to the Multi-Radar Multi-Sensor product that contains a rain gauge correction. • An automated QPE system generates a statewide real-time rainfall product for flood forecasting in Iowa. • The system processes NWP and radar data using algorithms accounting for precipitation microphysics and QPE uncertainties. • The system generates a rainfall map covering the entire state of Iowa at a resolution of 0.5 km, updated every 5 min. • The evaluation demonstrates that a new algorithm implementation significantly improves the rainfall estimation accuracy. [ABSTRACT FROM AUTHOR]

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

This study proposes a new estimation method for vertically integrated liquid water content (VIL) using radar reflectivity volume data and temperature sounding retrieved from the numerical weather model analysis. This method addresses uncertainty factors in conventional VIL estimation associated with the effects from the bright band (BB) and radar beam geometry near the radar site. The new VIL is then used for precipitation classification (convective/stratiform) and wind turbine clutter detection in the hope that the estimated VIL indicating vertical activities or development of precipitation systems will account for the two independent subjects together, in opposite ways. The non-precipitation radar echoes returned from wind turbines do not likely generate significant degree of VIL, compared to the one estimated from actual convective cells, which contain comparable reflectivity strength. We tested the proposed VIL estimation, precipitation classification, and wind turbine clutter detection methods using various Iowa cases and illustrated their successful application. We also performed a quantitative evaluation of precipitation classification using ground reference data from a dense rain gauge network over the Turkey River basin in Iowa. The evaluation results show improved performance for most non-convective event cases estimated by the stratiform estimator (Z = 200 R 1.6) because we applied the convective estimator (Z = 300 R 1.4) to all event cases without classification. In addition, we demonstrated the potential of the new classification to mitigate significant BB effects in quantitative precipitation estimation using a correction method based on the vertical profile of reflectivity. • The study proposes a new estimation method for vertically integrated liquid water content (VIL) that can be used for precipitation classification and wind turbine clutter detection. • The new method mitigates VIL estimation uncertainties affected by the effects from the melting layer and radar beam geometry near the radar site. • The performance evaluation demonstrated the utility of the VIL application to the classification of precipitation types and the detection of wind turbine effects present in radar observations. [ABSTRACT FROM AUTHOR]

• Proposed model captures range of observed soil moisture (SM) in time. • SMAP estimes are drier than modeled & observed SM during summer. • SM model was able to generate observed SM spatial statistics. • Observed and modeled average SM has highest variability at intermediate range. • SM skewness is positive and negative for dry and wet average SM, respectively. This study focused on the utility of coarse surface soil moisture observations for applications that require high resolution surface soil moisture information. This was accomplished by quantifying the information content of average soil moisture for three different spatial scales of 81 km2, 790 km2, and 4400 km2. In situ point observations of soil moisture from 31 stations in Iowa were used to develop a spatial stochastic model that assumes hillslope-scale model parameters are independent. Soil moisture dry-downs and wetting regimes were analyzed using rain gauge and soil moisture sensor data. The statistical nature of dry-downs were parameterized using a power-law decay, and soil moisture increases due to rainfall were parameterized using a non-dimensional logistic curve that is a function of soil moisture deficit. The resulting stochastic model is used to quantify the magnitude of the standard deviation, σ (θ) , and skewness G (θ) as a function of the areal average. We show that the greatest information content (small spatial standard deviation) of average observation corresponded to values near the minimum or the maximum soil moisture with σ (θ) < 5 % , while average observations for intermediate soil moisture values had the lowest information content with σ (θ) > 20 %. The differences in information content as a function of the areal average were consistent with the statistical nature of soil moisture that can be interpreted as small range bounded variable. However, this study provides quantitative estimates for the magnitude of the sub-grid and basin scale variability, documenting the utility for applications that require high resolution information. These results form the basis for the investigation of spatial runoff production in response to rainfall and to inform plot scale agriculture applications. [ABSTRACT FROM AUTHOR]

The study presents a conditional distribution transformation (CDT) method for improving radar rainfall (RR) verifications that use sparse raingauge networks as the ground reference (GR). Large differences between the sampling areas of radar and raingauge measurements render direct comparisons problematic. The purpose of the CDT method is to filter out the raingauge representativeness errors from radar–raingauge verification samples. Our objective is to test the validity and evaluate the accuracy of this method. These analyses are based on two large data samples from high-density research networks covering the Goodwin Creek watershed in Mississippi and the Little Washita watershed in Oklahoma. An example implementation in a quasi operational situation is also presented, and sample size requirements are investigated using Monte Carlo simulations. Our tests indicate that the CDT method performs with satisfactory accuracy and can considerably improve on the currently applied RR verification practices. [Copyright &y& Elsevier]