Search

This paper comparatively assesses the performance of five data assimilation techniques for three-parameter Muskingum routing with a spatially lumped or distributed model structure. The assimilation techniques used include direct insertion (DI), nudging scheme (NS), Kalman filter (KF), ensemble Kalman filter (EnKF) and asynchronous ensemble Kalman filter (AEnKF), which are applied to river reaches in Texas and Louisiana, USA. For both lumped and distributed routing, results from KF, EnKF and AEnKF are sensitive to the error specification. As expected, DI outperformed the other models in the case of lumped modelling, while in distributed routing, KF approaches, particularly AEnKF and EnKF, performed better than DI or nudging, reflecting the benefit of updating distributed states through error covariance modelling in KF approaches. The results of this work would be useful in setting up data assimilation systems that employ increasingly abundant real-time observations using distributed hydrological routing models. [ABSTRACT FROM AUTHOR]

This paper presents a simple yet effective weakly-constrained (WC) data assimilation (DA) approach for hydrologic models which accounts for model structural inadequacies associated with rainfall-runoff transformation processes. Compared to the strongly-constrained (SC) DA, WC DA adjusts the control variables less while producing similarly or more accurate analysis. Hence the adjusted model states are dynamically more consistent with those of the base model. The inadequacy of a rainfall-runoff model was modeled as an additive error to runoff components prior to routing and penalized in the objective function. Two example modeling applications, distributed and lumped, were carried out to investigate the effects of the WC DA approach on DA results. For distributed modeling, the distributed Sacramento Soil Moisture Accounting (SAC-SMA) model was applied to the TIFM7 Basin in Missouri, USA. For lumped modeling, the lumped SAC-SMA model was applied to nineteen basins in Texas. In both cases, the variational DA (VAR) technique was used to assimilate discharge data at the basin outlet. For distributed SAC-SMA, spatially homogeneous error modeling yielded updated states that are spatially much more similar to the a priori states, as quantified by Earth Mover’s Distance (EMD), than spatially heterogeneous error modeling by up to ∼10 times. DA experiments using both lumped and distributed SAC-SMA modeling indicated that assimilating outlet flow using the WC approach generally produce smaller mean absolute difference as well as higher correlation between the a priori and the updated states than the SC approach, while producing similar or smaller root mean square error of streamflow analysis and prediction. Large differences were found in both lumped and distributed modeling cases between the updated and the a priori lower zone tension and primary free water contents for both WC and SC approaches, indicating possible model structural deficiency in describing low flows or evapotranspiration processes for the catchments studied. Also presented are the findings from this study and key issues relevant to WC DA approaches using hydrologic models. [ABSTRACT FROM AUTHOR]

Summary An ensemble data assimilation (DA) procedure is developed and evaluated for the Hydrologic Simulation Program – Fortran (HSPF), a widely used watershed water quality model. The procedure aims at improving the accuracy of short-range water quality prediction by updating the model initial conditions (IC) based on real-time observations of hydrologic and water quality variables. The observations assimilated include streamflow, biochemical oxygen demand (BOD), dissolved oxygen (DO), chlorophyll a (CHL-a), nitrate (NO 3 ), phosphate (PO 4 ) and water temperature (TW). The DA procedure uses the maximum likelihood ensemble filter (MLEF), which is capable of handling both nonlinear model dynamics and nonlinear observation equations, in a fixed-lag smoother formulation. For evaluation, the DA procedure was applied to the Kumho Catchment of the Nakdong River Basin in the Republic of Korea. A set of performance measures was used to evaluate analysis and prediction of streamflow and water quality variables. To remove systematic errors in the model simulation originating from structural and parametric errors, a parsimonious bias correction procedure is incorporated into the observation equation. The results show that the DA procedure substantially improves predictive skill for most variables; reduction in root mean square error ranges from 11% to 60% for Day-1 through 3 predictions for all observed variables except DO. It is seen that MLEF handles highly nonlinear hydrologic and biochemical observation equations very well, and that it is an effective DA technique for water quality forecasting. [ABSTRACT FROM AUTHOR]

Summary Various data assimilation (DA) methods have been used and are being explored for use in operational streamflow forecasting. For ensemble forecasting, ensemble Kalman filter (EnKF) is an appealing candidate for familiarity and relative simplicity. EnKF, however, is optimal in the second-order sense, only if the observation equation is linear. As such, without an iterative approach, EnKF may not be appropriate for assimilating streamflow data for updating soil moisture states due to the strong nonlinear relationships between the two. Maximum likelihood ensemble filter (MLEF), on the other hand, is not subject to the above limitation. Being an ensemble extension of variational assimilation (VAR), MLEF also offers a strong connection with the traditional single-valued forecast process through the control, or the maximum likelihood, solution. In this work, we apply MLEF and EnKF as a fixed lag smoother to the Sacramento (SAC) soil moisture accounting model and unit hydrograph (UH) for assimilation of streamflow, mean areal precipitation (MAP) and potential evaporation (MAPE) data for updating soil moisture states. For comparative evaluation, three experiments were carried out. Comparison between homoscedastic vs. heteroscedastic modeling of selected statistical parameters for DA indicates that heteroscedastic modeling does not improve over homoscedastic modeling, and that homoscedastic error modeling with sensitivity analysis may suffice for application of MLEF for soil moisture updating using streamflow data. Comparative evaluation with respect to the model errors associated with soil moisture dynamics, the ensemble size and the number of streamflow observations assimilated per cycle showed that, in general, MLEF outperformed EnKF under varying conditions of observation and model errors, and ensemble size, and that MLEF performed well with an ensemble size as small as 5 while EnKF required a much larger ensemble size to perform closely to MLEF. Also, MLEF was not very sensitive to the uncertainty parameters and performed reasonably well over relatively wide ranges of parameter settings, an attribute desirable for operational applications where accurate estimation of such parameters is often difficult. [ABSTRACT FROM AUTHOR]

This study examines the utility of satellite-based quantitative precipitation estimates (QPEs) from the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for hydrologic prediction. In this work, two sets of SCaMPR QPEs, one without and the other with Tropical Rainfall Measurement Mission (TRMM) version 6 data integrated, were used as input forcing to the lumped National Weather Service hydrologic model to retrospectively generate flow simulations for 10 Texas catchments over 2000-07. The year 2000 was used for the model spinup, 2001-04 for calibration, and 2005-07 for validation. The results were validated using observed streamflow alongside similar simulations obtained using interpolated gauge QPEs with varying gauge network densities, and still others using the operational radar-gauge multisensor product (MAPX). The focus of the evaluation was on the high-flow events. A number of factors that could impact the relative utility of SCaMPR satellite QPE and gauge-only analysis (GMOSAIC) for flood prediction were examined, namely, 1) the incremental impacts of TRMM version 6 data ingest, 2) gauge density, 3) effects of calibration approaches, and 4) basin properties. Results indicate that ground-sensor-based QPEs in a broad sense outperform SCaMPR QPEs, while SCaMPR QPEs are competitive in a minority of catchments. TRMM ingest helped substantially improve the SCaMPR QPE-based simulation results. Change in calibration forcing, that is, calibrating the model using individual QPEs rather than the MAPX (the most accurate QPE), yielded overall improvements to the simulation accuracy but did not change the relative performance of the QPEs. [ABSTRACT FROM AUTHOR]

Highlights: [•] The HMOS produces ensemble streamflow forecasts from single-valued forecasts. [•] Here, the single-valued forecasts are 6-h operational forecasts out to 5days. [•] The HMOS uses the single-valued forecast, QPF and the recent observed streamflow. [•] The HMOS ensemble forecast provides an estimate of the total predictive uncertainty. [•] In general, the HMOS ensemble forecasts are reliable and skillful. [Copyright &y& Elsevier]

This paper evaluates a nonparametric technique for estimating the conditional probability distribution of a predictand given a vector of predictors. In the current application, the predictors are formed from a multimodel ensemble of simulated streamflows, such that the hydrologic uncertainties are modelled independently of any forcing uncertainties. The technique is based on Bayesian optimal linear estimation of indicator variables and is analogous to indicator cokriging (ICK) in geostatistics. By developing linear estimators for the conditional probability that the observed variable does not exceed several thresholds, ICK provides a discrete approximation of the full conditional probability distribution. The weights of the predictors can be chosen to minimize the expected error variance at each threshold (the Brier score) or, without loss of analytical tractability, a combination of the error variance and the expected square bias conditional upon the observation, i.e. the Type-II conditional bias (CB). The latter is referred to as CB-penalized ICK (CBP-ICK) and is an important enhancement to ICK. Indeed, the biases in atmospheric and hydrologic predictions generally increase towards the tails of their probability distributions. The performance of CBP-ICK is evaluated for selected basins in the eastern USA using a range of probabilistic verification metrics and associated confidence intervals for the sampling uncertainties. Overall, CBP-ICK produces unbiased and skillful estimates of the hydrologic uncertainties, with some sensitivity to the calibration data period at high flow thresholds. More generally, we argue that the common aim in statistical post-processing of 'maximizing sharpness subject to reliability (Type-I CB)' should be recast to accommodate both the Type-I and Type-II CBs, as both are important for practical applications of hydrologic predictions. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Precipitation forecasts from the Short-Range Ensemble Forecast (SREF) system of the National Centers for Environmental Prediction (NCEP) are verified for the period April 2006-August 2010. Verification is conducted for 10-20 hydrologic basins in each of the following: the middle Atlantic, the southern plains, the windward slopes of the Sierra Nevada, and the foothills of the Cascade Range in the Pacific Northwest. Mean areal precipitation is verified conditionally upon forecast lead time, amount of precipitation, season, forecast valid time, and accumulation period. The stationary block bootstrap is used to quantify the sampling uncertainties of the verification metrics. In general, the forecasts are more skillful for moderate precipitation amounts than either light or heavy precipitation. This originates from a threshold-dependent conditional bias in the ensemble mean forecast. Specifically, the forecasts overestimate low observed precipitation and underestimate high precipitation (a type-II conditional bias). Also, the forecast probabilities are generally overconfident (a type-I conditional bias), except for basins in the southern plains, where forecasts of moderate to high precipitation are reliable. Depending on location, different types of bias correction may be needed. Overall, the northwest basins show the greatest potential for statistical postprocessing, particularly during the cool season, when the type-I conditional bias and correlations are both high. The basins of the middle Atlantic and southern plains show less potential for statistical postprocessing, as the type-II conditional bias is larger and the correlations are weaker. In the Sierra Nevada, the greatest benefits of statistical postprocessing should be expected for light precipitation, specifically during the warm season, when the type-I conditional bias is large and the correlations are strong. [ABSTRACT FROM AUTHOR]

Abstract: We assess the potential of updating soil moisture states of a distributed hydrologic model by assimilating streamflow and in situ soil moisture data for high-resolution analysis and prediction of streamflow and soil moisture. The model used is the gridded Sacramento (SAC) and kinematic-wave routing models of the National Weather Service (NWS) Hydrology Laboratory’s Research Distributed Hydrologic Model (HL-RDHM) operating at an hourly time step. The data assimilation (DA) technique used is variational assimilation (VAR). Assimilating streamflow and soil moisture data into distributed hydrologic models is new and particularly challenging due to the large degrees of freedom associated with the inverse problem. This paper reports findings from the first phase of the research in which we assume, among others, perfectly known hydrometeorological forcing. The motivation for the simplification is to reduce the complexity of the problem in favour of improved understanding and easier interpretation even if it may compromise the goodness of the results. To assess the potential, two types of experiments, synthetic and real-world, were carried out for Eldon (ELDO2), a 795-km2 headwater catchment located near the Oklahoma (OK) and Arkansas (AR) border in the U.S. The synthetic experiment assesses the upper bound of the performance of the assimilation procedure under the idealized conditions of no structural or parametric errors in the models, a full dynamic range and no microscale variability in the in situ observations of soil moisture, and perfectly known univariate statistics of the observational errors. The results show that assimilating in situ soil moisture data in addition to streamflow data significantly improves analysis and prediction of soil moisture and streamflow, and that assimilating streamflow observations at interior locations in addition to those at the outlet improves analysis and prediction of soil moisture within the drainage areas of the interior stream gauges and of streamflow at downstream cells along the channel network. To assess performance under more realistic conditions, but still under the assumption of perfectly known hydrometeorological forcing to allow comparisons with the synthetic experiment, an exploratory real-world experiment was carried out in which all other assumptions were lifted. The results show that, expectedly, assimilating interior flows in addition to outlet flow improves analysis as well as prediction of streamflow at stream gauge locations, but that assimilating in situ soil moisture data in addition to streamflow data provides little improvement in streamflow analysis and prediction though it reduces systematic biases in soil moisture simulation. [Copyright &y& Elsevier]

Summary: Reliable and skillful precipitation ensemble forecasts are necessary to produce reliable and skilful hydrologic ensemble forecasts. It is well known that, in general, raw precipitation ensemble forecasts from the numerical weather prediction (NWP) models are not very reliable and that, for short-range prediction, human forecasters add significant skill to the NWP-generated single-valued quantitative precipitation forecasts (QPF). In this paper, we describe and evaluate a statistical procedure for producing precipitation ensemble forecasts from single-valued QPFs. The procedure is based on the bivariate probability distribution between the observed precipitation and the single-valued QPF. The distribution is modeled as a mixed-type in which the relationship between the positive observed precipitation and positive forecast precipitation is assumed to be bivariate meta-Gaussian. We also describe and comparatively evaluate a generalized meta-Gaussian model in which the model parameter is optimized by minimizing the mean Continuous Ranked Probability Score. The performance of these procedures is assessed through dependent and cross validation using data for selected river basins in the service areas of the Arkansas-Red Basin, California-Nevada and Middle-Atlantic River Forecast Centers of the National Weather Service. The validation results show that, overall, the precipitation ensembles generated by the proposed procedures are reliable and capture the skill in the conditioning single-valued forecasts very well. [ABSTRACT FROM AUTHOR]

Summary: In operational hydrologic forecasting, to account for errors in the initial and boundary conditions, and in parameters and structures of the hydrologic models, the forecasters routinely make adjustments in real-time to the hydrometeorological input, hydrologic model states and, in certain cases, model parameters based on streamflow observations. Though a great deal of effort has been made in recent years to automate such “run-time modifications” (MOD) by human forecasters to a possible extent, automatic state updating of hydrologic models is yet to be widely accepted or routinely practiced in operational hydrology for a range of reasons. In this paper, we describe a state updating procedure intended specifically for operational streamflow forecasting for gauged headwater basins, and compare its performance with human forecaster MOD through a real-time forecasting experiment. The procedure is based on variational assimilation (VAR) of streamflow, precipitation and potential evaporation (PE) data into lumped soil moisture accounting and routing models operating at a 1-h timestep. The procedure has been in experimental operation since 2003 at the National Weather Service’s (NWS) West Gulf River Forecast Center (WGRFC) in Fort Worth, TX. Also described is a novel parameter estimation and optimization tool, the Adjoint-Based OPTimizer (AB_OPT), used for lumped hydrologic modeling at a 1-h timestep necessary for VAR. [Copyright &y& Elsevier]

Summary: Routine availability of hydrological, geological, and other physiographic data today allows us to obtain a priori estimates of hydrologic model parameters prior to explicit model calibration. When informative a priori estimates of model parameters are available, the problem of hydrologic model calibration becomes one of filtering, i.e. improving the a priori estimates based on observations of input and output to and from the hydrologic system, respectively, rather than one of bounded global optimization based solely on the input and output data as in traditional model calibration. Given that global optimization is computationally very expensive and does not, in general, transfer the spatial patterns of soil and land surface characteristics to the model parameters, the filtering approach is particularly appealing for automatic calibration of distributed hydrologic models. Toward that ultimate goal, we explore in this work calibration of a lumped hydrologic model via limited optimization of a priori estimates of the model parameters. The technique developed for the purpose is a simple yet effective and efficient pattern search algorithm called the Stepwise Line Search (SLS). To evaluate the methodology, calibration and validation experiments were performed for 20 basins in the US National Weather Service West Gulf River Forecast Center’s (NWS/WGRFC) service area in Texas. We show that SLS locates the posterior parameter estimates very efficiently in the vicinity of the a priori estimates that are comparable, in terms of reducing the objective function value, to those from global minimization. A cross validation experiment indicates that, when parametric uncertainty due to lack of calibration data is considered, limited optimization of a priori parameters using SLS may be preferred to global optimization. [Copyright &y& Elsevier]

Summary For monitoring and prediction of water-related hazards in urban areas such as flash flooding, high-resolution hydrologic and hydraulic modeling is necessary. Because of large sensitivity and scale dependence of rainfall–runoff models to errors in quantitative precipitation estimates (QPE), it is very important that the accuracy of QPE be improved in high-resolution hydrologic modeling to the greatest extent possible. With the availability of multiple radar-based precipitation products in many areas, one may now consider fusing them to produce more accurate high-resolution QPE for a wide spectrum of applications. In this work, we formulate and comparatively evaluate four relatively simple procedures for such fusion based on Fisher estimation and its conditional bias-penalized variant: Direct Estimation (DE), Bias Correction (BC), Reduced-Dimension Bias Correction (RBC) and Simple Estimation (SE). They are applied to fuse the Multisensor Precipitation Estimator (MPE) and radar-only Next Generation QPE (Q2) products at the 15-min 1-km resolution (Experiment 1), and the MPE and Collaborative Adaptive Sensing of the Atmosphere (CASA) QPE products at the 15-min 500-m resolution (Experiment 2). The resulting fused estimates are evaluated using the 15-min rain gauge observations from the City of Grand Prairie in the Dallas–Fort Worth Metroplex (DFW) in north Texas. The main criterion used for evaluation is that the fused QPE improves over the ingredient QPEs at their native spatial resolutions, and that, at the higher resolution, the fused QPE improves not only over the ingredient higher-resolution QPE but also over the ingredient lower-resolution QPE trivially disaggregated using the ingredient high-resolution QPE. All four procedures assume that the ingredient QPEs are unbiased, which is not likely to hold true in reality even if real-time bias correction is in operation. To test robustness under more realistic conditions, the fusion procedures were evaluated with and without post hoc bias correction of the ingredient QPEs. The results show that only SE passes the evaluation criterion consistently. The performance of DE and BC are generally comparable; while DE is more attractive for computational economy, BC is more attractive for reducing occurrences of negative estimates. The performance of RBC is poor as it does not account for magnitude-dependent biases in the QPE products. SE assumes that the higher-resolution QPE product is skillful in capturing spatiotemporal variability of precipitation at its native resolution, and that the lower-resolution QPE product provides skill at its native resolution. While the above assumptions may not always be met, the simplicity and robustness observed in this work make SE an extremely attractive choice as a simple post-processor to the QPE process. Also, unlike the other procedures considered in this work, it is extremely easy to update the statistical parameters of SE in real time, similarly to the real-time bias correction currently used in MPE, for improved performance via self-learning. [ABSTRACT FROM AUTHOR]

Summary This study explores mitigating bias in satellite quantitative precipitation estimates (SQPE) and improving hydrologic predictions at ungauged locations via adjustment of the mean field bias (MFB) in SQPE and data assimilation (DA) of streamflow observations in a distributed hydrologic model. In this study, a variational procedure is used to adjust MFB in Climate Prediction Center MORPHing (CMORPH) SQPE and assimilate streamflow observations at the outlet of Elk River Basin in Missouri into the distributed Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic wave routing models. The benefits of assimilation are assessed by comparing the streamflow predictions with or without DA at both the outlet and an upstream location, and by comparing the soil moisture grids forced by CMORPH SQPE against those forced by higher-quality multisensor quantitative precipitation estimates (MQPE) from National Weather Service. Special attention is given to the dependence of the efficacy of DA on the quality and latency of the SQPE, and the impact of dynamic correction of MFB in the SQPE via DA. The results show that adjusting MFB in CMORPH SQPE in addition to assimilating outlet flow reduces 66% of the bias in the CMORPH SQPE analysis and the RMSE of 12-h streamflow predictions by 81% at the outlet and 34–62% at interior locations of the catchment. Compared to applying a temporally invariant MFB for the entire storm, the DA-based, dynamic MFB correction reduces the RMSE of 6-h streamflow prediction by 63% at the outlet and 39–69% at interior locations. It is also shown that the accuracy of streamflow prediction deteriorates if the delineation of the precipitation area by CMORPH SQPE is significantly different, as measured by the Hausdorff distance, from that by MQPE. When compared with adjusting MFB in the CMORPH SQPE over the entire assimilation window, adjusting the MFB for all but the latest 18 h (i.e., the latency of CMORPH SQPE) within the assimilation window reduces the mean square error (MSE)-based skill score of streamflow predictions at the outlet by up to 0.08 and at interior locations by up to 0.13. [ABSTRACT FROM AUTHOR]

Summary Urban flash flooding is a serious problem in large, highly populated areas such as the Dallas–Fort Worth Metroplex (DFW). Being able to monitor and predict flash flooding at a high spatiotemporal resolution is critical to providing location-specific early warnings and cost-effective emergency management in such areas. Under the idealized conditions of perfect models and precipitation input, one may expect that spatiotemporal specificity and accuracy of the model output improve as the resolution of the models and precipitation input increases. In reality, however, due to the errors in the precipitation input, and in the structures, parameters and states of the models, there are practical limits to the model resolution. In this work, we assess the sensitivity of streamflow simulation in urban catchments to the spatiotemporal resolution of precipitation input and hydrologic modeling to identify the resolution at which the simulation errors may be at minimum given the quality of the precipitation input and hydrologic models used, and the response time of the catchment. The hydrologic modeling system used in this work is the National Weather Service (NWS) Hydrology Laboratory’s Research Distributed Hydrologic Model (HLRDHM) applied at spatiotemporal resolutions ranging from 250 m to 2 km and from 1 min to 1 h applied over the Cities of Fort Worth, Arlington and Grand Prairie in DFW. The high-resolution precipitation input is from the DFW Demonstration Network of the Collaborative Adaptive Sensing of the Atmosphere (CASA) radars. For comparison, the NWS Multisensor Precipitation Estimator (MPE) product, which is available at a 4-km 1-h resolution, was also used. The streamflow simulation results are evaluated for 5 urban catchments ranging in size from 3.4 to 54.6 km 2 and from about 45 min to 3 h in time-to-peak in the Cities of Fort Worth, Arlington and Grand Prairie. The streamflow observations used in evaluation were obtained from water level measurements via rating curves derived from 1-D steady-state non-uniform hydraulic modeling. The results indicate that a spatiotemporal resolution of 500 m and 15 min or higher is a good choice for streamflow prediction using HLRDHM and CASA QPE in the study area, but that, due to the nonlinear accretion of random errors in QPE and imperfect model dynamics, there are trade-offs to consider among resolution, timeliness of prediction and prediction accuracy. [ABSTRACT FROM AUTHOR]

Summary Retrospective forecasts of precipitation, temperature, and streamflow were generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) for a 20-year period between 1979 and 1999. The hindcasts were produced for two basins in each of four River Forecast Centers (RFCs), namely the Arkansas–Red Basin RFC, the Colorado Basin RFC, the California–Nevada RFC and the Middle Atlantic RFC. In a companion paper, temperature and precipitation hindcasts were produced with the Meteorological Ensemble Forecast Processor (MEFP) and verified against observed temperature and precipitation, respectively. Inputs to the MEFP comprised raw precipitation and temperature forecasts from the frozen (circa 1997) version of the NWS Global Forecast System (MEFP-GFS) and a conditional or “resampled” climatology (MEFP-CLIM). For this paper, streamflow hindcasts were produced with the Community Hydrologic Prediction System and were bias-corrected with the Ensemble Post-processor (EnsPost). In order to separate the meteorological and hydrologic uncertainties, the raw streamflow forecasts were verified against simulated streamflows, as well as observed flows. Also, when verifying the bias-corrected streamflow forecasts, the total skill was decomposed into contributions from the MEFP-GFS and the EnsPost. In general, the streamflow forecasts are substantially more skillful when using the MEFP-GFS together with the EnsPost than using the MEFP with resampled climatology alone. However, both the raw and bias-corrected streamflow forecasts have lower biases, stronger correlations and are more skillful in CB- and CN-RFCs than AB- and MA-RFCs. In addition, there are strong variations in forecast quality with streamflow amount, forecast lead time, season and aggregation period. The relative importance of the meteorological and hydrologic uncertainties also varies between basins and is modulated by the same controls on forecast quality. For example, the MEFP-GFS accounts for the majority of skill in the CNRFC basins. This is associated with the greater predictability of large storms in the North Coast Ranges during the winter months. In CBRFC, much of the skill in the streamflow forecasts originates from the hydrologic modeling and the EnsPost, particularly during the snowmelt period. In AB- and MA-RFCs, the contributions from the MEFP and the EnsPost are more variable. This paper summarizes the verification results, describes the expected performance and limitations of the HEFS for short- to medium-range streamflow forecasting, and provides recommendations for future research. [ABSTRACT FROM AUTHOR]

• Impact on urban flood prediction of different modeling resolutions is assessed using WRF-Hydro. • Selective calibration for simulation of highly peaked urban floods is described. • Low-dimensional data assimilation for event-based prediction is described. We assess the impact of increasing the resolution of hydrologic modeling, calibration of selected model parameters and assimilation of streamflow observation toward event-based urban flood modeling and prediction using WRF-Hydro in the Dallas-Fort Worth area (DFW). We use quantitative precipitation estimates at 500-m 1-min resolution from the Collaborative Adaptive Sensing of the Atmosphere radar network for observed rainfall, Stepwise Line Search for calibration, and fixed-lag smoothing for data assimilation (DA). The model domain is a 144.6 km2 area comprising 3 urban catchments in Arlington and Grand Prairie in the middle of DFW. It is shown that event-specific calibration of 6 WRF-Hydro parameters is largely successful in simulating hydrographs at the catchment outlets particularly for the most important rising limbs, but less so for attenuated peaks or fast-receding falling limbs. A spatial resolution of at least 250 m was necessary for the land surface model (LSM) to delineate small catchments and hence to capture catchment-wide rainfall with acceptable accuracy. Simulations at selected combinations of resolutions, 250 and 125 m for the LSM and 250, 125, 50 m for the routing models, showed mixed results. The overall results indicate that, in the absence of resolution-specific prescription and calibration of channel routing parameters, a resolution of 250 m for both the LSM and routing models is a good choice in terms of performance and computational requirements, and that, in the absence of high-quality calibration and continuous simulation of streamflow, DA is necessary to initialize WRF-Hydro for event-based high-resolution urban flood prediction. [ABSTRACT FROM AUTHOR]