Hydrology Laboratory Research Distributed Hydrologic Model Research Articles

Applying the NWS’s Distributed Hydrologic Model to Short-Range Forecasting of Quickflow in the Mahantango Creek Watershed

Abstract Accurate and reliable forecasts of quickflow, including interflow and overland flow, are essential for predicting rainfall–runoff events that can wash off recently applied agricultural nutrients. In this study, we examined whether a gridded version of the Sacramento Soil Moisture Accounting model with Heat Transfer (SAC-HT) could simulate and forecast quickflow in two agricultural watersheds in east-central Pennsylvania. Specifically, we used the Hydrology Laboratory–Research Distributed Hydrologic Model (HL-RDHM) software, which incorporates SAC-HT, to conduct a 15-yr (2003–17) simulation of quickflow in the 420-km2 Mahantango Creek watershed and in WE-38, a 7.3-km2 headwater interior basin. We directly calibrated HL-RDHM using hydrologic observations at the Mahantango Creek outlet, while all grid cells within Mahantango Creek, including WE-38, were calibrated indirectly using scalar multipliers derived from the basin outlet calibration. Using the calibrated model, we then assessed the quality of short-range (24–72 h) deterministic forecasts of daily quickflow in both watersheds over a 2-yr period (July 2017–October 2019). At the basin outlet, HL-RDHM quickflow simulations showed low biases (PBIAS = 10.5%) and strong agreement (KGE″ = 0.81) with observations. At the headwater scale, HL-RDHM overestimated quickflow (PBIAS = 69.0%) to a greater degree, but quickflow simulations remained satisfactory (KGE″ = 0.65). When applied to quickflow forecasting, HL-RDHM produced skillful forecasts (>90% of Peirce and Gerrity skill scores above 0.5) at all lead times and significantly outperformed persistence forecasts, although skill gains in Mahantango Creek were slightly lower. Accordingly, short-range quickflow forecasts by HL-RDHM show promise for informing operational decision-making in agriculture. Significance Statement Daily runoff forecasts can alert farmers to rainfall–runoff events that have the potential to wash off recently applied fertilizers and manures. To gauge whether daily runoff forecasts are accurate and reliable, we used runoff monitoring data from a large agricultural watershed and one of its headwater tributaries to evaluate the quality of short-term runoff forecasts (1–3 days ahead) that were generated by a National Weather Service watershed model. Results showed that the accuracy and reliability of daily runoff forecasts generally improved in both watersheds as lead times increased from 1 to 3 days. Study findings highlight the potential for National Weather Service models to provide useful short-term runoff forecasts that can inform operational decision-making in agriculture.

Evaluation of a Distributed Streamflow Forecast Model at Multiple Watershed Scales

Demand for reliable estimates of streamflow has increased as society becomes more susceptible to climatic extremes such as droughts and flooding, especially at small scales where local population centers and infrastructure can be affected by rapidly occurring events. In the current study, the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) (NOAA/NWS, Silver Spring, MD, USA) was used to explore the accuracy of a distributed hydrologic model to simulate discharge at watershed scales ranging from 20 to 2500 km2. The model was calibrated and validated using observed discharge data at the basin outlets, and discharge at uncalibrated subbasin locations was evaluated. Two precipitation products with nominal spatial resolutions of 12.5 km and 4 km were tested to characterize the role of input resolution on the discharge simulations. In general, model performance decreased as basin size decreased. When sub-basin area was less than 250 km2 or 20–40% of the total watershed area, model performance dropped below the defined acceptable levels. Simulations forced with the lower resolution precipitation product had better model evaluation statistics; for example, the Nash–Sutcliffe efficiency (NSE) scores ranged from 0.50 to 0.67 for the verification period for basin outlets, compared to scores that ranged from 0.33 to 0.52 for the higher spatial resolution forcing.

Skill of ensemble flood inundation forecasts at short- to medium-range timescales

We investigate the ability to improve flood inundation forecasts at short- to medium-range (1–7 days) timescales through weather ensembles and statistical water surface elevation (WSE) postprocessing. To generate the flood inundation forecasts, a one-dimensional hydraulic model, namely the Hydrologic Engineering Center’s River Analysis System (HEC-RAS), is coupled to a regional hydrological ensemble prediction system (RHEPS). The RHEPS is comprised of: i) hydrometeorological observations; ii) weather ensembles from the National Centers for Environmental Prediction Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); and iii) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) as the hydrological model. The coupled RHEPS-hydraulic system is evaluated along the tidal Delaware River near the City of Philadelphia, Pennsylvania, U.S. For the evaluation, emphasis is placed on the tidal-riverine transitional zone of the Delaware River and the downstream propagation of hydrometeorological uncertainty through the flood inundation forecasts. The coupled system is used to generate hourly flood inundation forecasts at lead times from 1 to 7 days, over the period 2008–2013. Additionally, WSEs from the coupled system are statistically postprocessed using quantile regression (QR). Results show that the raw flood inundation ensemble forecasts exhibit higher skill than the deterministic ones. We also find that statistical postprocessing improves the skill of the raw flood inundation ensemble forecasts, with greater improvements at the longer lead times (>3 days). Overall, we find that both weather ensembles and statistical WSE postprocessing can be used to enhance the skill of flood inundation forecasts at short- to medium-range timescales. In turn, this may serve to enhance the spatial representation of flood forecasts several days in advance, which could contribute in the future to making flood forecast communication and warnings more effective.

Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

Abstract. The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases, however, the differences between this scenario and the scenario with postprocessing alone are not as significant. We conclude that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.

Applying SMOS soil moisture data into the National Weather Service (NWS)’s Research Distributed Hydrologic Model (HL-RDHM) for flash flood guidance application

After rainfall, soil moisture is the most important factor dictating flash flooding, since rainfall infiltration and runoff are based on the saturation of the soil. However, continuous and regional soil moisture data acquisition is difficult by ground-based measurement. As such, soil moisture is often derived from land surface models and used by agencies such as the National Oceanic and Atmospheric Administration's National Weather Service (NOAA/NWS) as a proxy for estimates of soil moisture at the surface in order to support operational flood forecasting. The current Flash Flood Guidance (FFG) system at the Arkansas Red Basin River Forecast Center (ABRFC) provides gridded flash flood guidance (GFFG) by using the soil moisture from the NWS Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) to scale Natural Resources Conservation Service curve numbers. This study evaluates the contribution of remote sensing technology to quantifiable improvements in HL-RDHM soil moisture as well as adding a satellite-based soil moisture component to the NWS FFG Algorithm. The Soil Moisture and Ocean Salinity (SMOS) satellite of European Space Agency operates at an L-band (1.4GHz) wavelength which offers relatively deeper penetration and has lower sensitivity to vegetation impacts than other microwave satellite platforms. It has been shown to be well-suited for observing surface soil moisture. The purpose of this paper is to determine, execute, and assess a method of SMOS data assimilation applicable for use with the HL-RDHM modeling system. The value of remote sensing data in constraining modeled soil moisture states is evaluated. Results from the technique developed in this study imply a potential for SMOS-based improvement of the GFFG product. The technique is also expected to be useful for assimilating soil moisture data from the Soil Moisture Active Passive (SMAP).

Ensemble Streamflow Forecasting across the U.S. Mid-Atlantic Region with a Distributed Hydrological Model Forced by GEFS Reforecasts

Abstract The quality of ensemble streamflow forecasts in the U.S. mid-Atlantic region (MAR) is investigated for short- to medium-range forecast lead times (6–168 h). To this end, a regional hydrological ensemble prediction system (RHEPS) is assembled and implemented. The RHEPS in this case comprises the ensemble meteorological forcing, a distributed hydrological model, and a statistical postprocessor. As the meteorological forcing, precipitation, and near-surface temperature outputs from the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast, version 2 (GEFSRv2), are used. The Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) is used as the distributed hydrological model, and a statistical autoregressive model with an exogenous variable is used as the postprocessor. To verify streamflow forecasts from the RHEPS, eight river basins in the MAR are selected, ranging in drainage area from ~262 to 29 965 km2 and covering some of the major rivers in the MAR. The verification results for the RHEPS show that, at the initial lead times (1–3 days), the hydrological uncertainties have more impact on forecast skill than the meteorological ones. The former become less pronounced, and the meteorological uncertainties dominate, across longer lead times (>3 days). Nonetheless, the ensemble streamflow forecasts remain skillful for lead times of up to 7 days. Additionally, postprocessing increases forecast skills across lead times and spatial scales, particularly for the high-flow conditions. Overall, the proposed RHEPS is able to improve streamflow forecasting in the MAR relative to the deterministic (unperturbed GEFSRv2 member) forecasting case.

Case Studies of a MODIS-Based Potential Evapotranspiration Input to the Sacramento Soil Moisture Accounting Model

Abstract A satellite-based potential evapotranspiration (PET) estimate derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations was tested for input to the spatially lumped and gridded Sacramento Soil Moisture Accounting (SAC-SMA) model. The 15 forecast points within the National Weather Service (NWS) North Central River Forecast Center (NCRFC) forecasting region were the basis for this analysis. Through a series of case studies, the MODIS-derived PET estimate (M-PET) was evaluated for input to the SAC-SMA model by comparing streamflow simulations with those from traditional SAC-SMA evapotranspiration (ET) demand. Two prior studies have evaluated the M-PET data 1) to compute new long-term average ET demand values and 2) to input a time series (i.e., daily time-varying PET) to the NWS Hydrology Laboratory–Research Distributed Hydrologic Model (HL-RDHM), a spatially distributed version of the SAC-SMA model. This current paper presents results from a third test in which the M-PET time series is input to the lumped SAC-SMA model. In all cases, evaluation is between the M-PET data and the long-term average values used by the NWS. Similar to prior studies, results of the current analysis are mixed with improved model evaluation statistics for 4 of 15 basins tested. Of the three cases, using the time-varying M-PET as input to the distributed SAC-SMA model led to the most promising results, with model simulations that are at least as good as those when using the SAC-SMA ET demand. Analyses of the model-simulated ET suggest that the time-varying M-PET input may produce a more physically realistic representation of ET processes in both the lumped and distributed versions of the SAC-SMA model.

Assessing the Efficacy of High-Resolution Satellite-Based PERSIANN-CDR Precipitation Product in Simulating Streamflow

AbstractThis study aims to investigate the performance of Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) in a rainfall–runoff modeling application over the past three decades. PERSIANN-CDR provides precipitation data at daily and 0.25° temporal and spatial resolutions from 1983 to present for the 60°S–60°N latitude band and 0°–360° longitude. The study is conducted in two phases over three test basins from the Distributed Hydrologic Model Intercomparison Project, phase 2 (DMIP2). In phase 1, a more recent period of time (2003–10) when other high-resolution satellite-based precipitation products are available is chosen. Precipitation evaluation analysis, conducted against stage IV gauge-adjusted radar data, shows that PERSIANN-CDR and TRMM Multisatellite Precipitation Analysis (TMPA) have close performances with a higher correlation coefficient for TMPA (~0.8 vs 0.75 for PERSIANN-CDR) and almost the same root-mean-square deviation (~6) for both products. TMPA and PERSIANN-CDR outperform PERSIANN, mainly because, unlike PERSIANN, TMPA and PERSIANN-CDR are gauge-adjusted precipitation products. The National Weather Service Office of Hydrologic Development Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) is then forced with PERSIANN, PERSIANN-CDR, TMPA, and stage IV data. Quantitative analysis using five different statistical and model efficiency measures against USGS streamflow observation show that in general in all three DMIP2 basins, the simulated hydrographs forced with PERSIANN-CDR and TMPA have close agreement. Given the promising results in the first phase, the simulation process is extended back to 1983 where only PERSIANN-CDR rainfall estimates are available. The results show that PERSIANN-CDR-derived streamflow simulations are comparable to USGS observations with correlation coefficients of ~0.67–0.73, relatively low biases (~5%–12%), and high index of agreement criterion (~0.68–0.83) between PERSIANN-CDR-simulated daily streamflow and USGS daily observations. The results prove the capability of PERSIANN-CDR in hydrological rainfall–runoff modeling application, especially for long-term streamflow simulations over the past three decades.

Using Densely Distributed Soil Moisture Observations for Calibration of a Hydrologic Model

Abstract Calibration is a crucial step in hydrologic modeling that is typically handled by tuning parameters to match an observed hydrograph. In this research, an alternative calibration scheme based on soil moisture was investigated as a means of identifying the potentially heterogeneous calibration needs of a distributed hydrologic model. The National Weather Service’s (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) was employed to carry out such a calibration, along with concentrated in situ soil moisture observations from the Iowa Flood Studies (IFloodS) field campaign in Iowa’s Turkey River basin. Synthetic, single-pixel experiments were conducted in order to identify parameters relevant to soil moisture dynamics and to test the ability of three calibration procedures (discharge, soil moisture, and hybrid based) to recapture prescribed parameter sets. It was found that three storage parameters of HL-RDHM could be consistently identified using soil moisture RMSE as the objective function and that the addition of discharge-based calibration led to more consistent parameter identification for all 11 storage and release parameters. Expanding to full-basin experiments, these three calibration procedures were applied following an investigation to find the most advantageous method of distributing the point-based calibrations carried out at each pixel collocated with an IFloodS observation site. A method based on pixel similarity was deemed most appropriate for this purpose. Additionally, streamflow simulations calibrated with soil moisture showed improvement in RMSE and Nash–Sutcliffe efficiency (NSE) for all calibration–validation events despite a short calibration period, a promising result when considering calibration of ungauged basins. However, supplementary evaluation metrics show mixed results for streamflow simulations, suggesting further investigation is required.

Distributed Hydrologic Modeling Using Satellite-Derived Potential Evapotranspiration

Abstract Satellite-derived potential evapotranspiration (PET) estimates computed from Moderate Resolution Imaging Spectroradiometer (MODIS) observations and the Priestley–Taylor formula (M-PET) are evaluated as input to the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM). The HL-RDHM is run at a 4-km spatial and 6-h temporal resolution for 13 watersheds in the upper Mississippi and Red River basins for 2003–10. Simulated discharge using inputs of daily M-PET is evaluated for all watersheds, and simulated evapotranspiration (ET) is evaluated at two watersheds using nearby latent heat flux observations. M-PET–derived model simulations are compared to output using the long-term average PET values (default-PET) provided as part of the HL-RDHM application. In addition, uncalibrated and calibrated simulations are evaluated for both PET data sources. Calibrating select model parameters is found to substantially improve simulated discharge for both datasets. Overall average percent bias (PBias) and Nash–Sutcliffe efficiency (NSE) values for simulated discharge are better from the default-PET than the M-PET for the calibrated models during the verification period, indicating that the time-varying M-PET input did not improve the discharge simulation in the HL-RDHM. M-PET tends to produce higher NSE values than the default-PET for the Wisconsin and Minnesota basins, but lower NSE values for the Iowa basins. M-PET–simulated ET matches the range and variability of observed ET better than the default-PET at two sites studied and may provide potential model improvements in that regard.

Rainfall-runoff modeling in a flashy tropical watershed using the distributed HL-RDHM model

Summary Many watersheds in Hawai‘i are flash flood prone due to their small contributing areas and frequent intense rainfall. Motivated by the possibility of developing an operational flood forecasting system, this study evaluated the performance of the National Weather Service (NWS) model, the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) in simulating the hydrology of the flood-prone Hanalei watershed in Kaua‘i, Hawai‘i. This rural watershed is very wet and has strong spatial rainfall gradients. Application of HL-RDHM to Hanalei watershed required (i) modifying the Hydrologic Rainfall Analysis Project (HRAP) coordinate system; (ii) generating precipitation grids from rain gauge data, and (iii) generating parameters for Sacramento Soil Moisture Accounting Model (SAC-SMA) and routing parameter grids for the modified HRAP coordinate system. Results were obtained for several spatial resolutions. Hourly basin-average rainfall calculated from one HRAP resolution grid (4 km × 4 km) was too low and inaccurate. More realistic rainfall and more accurate streamflow predictions were obtained with the ½ and ¼ HRAP grids. For a one year period with the best precipitation data, the performance of HL-RDHM was satisfactory even without calibration for basin-averaged and distributed a priori parameter grids. Calibration and validation of HL-RDHM were conducted using four-year data set each. The model reasonably matched the observed peak discharges and time to peak during calibration and validation periods. The performance of model was assessed using the following three statistical measures: Root Mean Square Error (RMSE), Nash–Sutcliffe efficiency (NSE) and Percent bias (PBIAS). Overall, HL-RDHM’s performance was “very good (NSE > 0.75, PBIAS 3 s −1 ) than at the higher ones (140 and 248 m 3 s −1 ) as indicated by lower values of probability of detection and critical success index, and a higher value of the false alarm ratio. These results suggest that HL-RDHM may be suitable for flood forecasting applications in watersheds with steep terrain and strong spatio-temporal variability of precipitation, e.g. US Pacific North-West, and tropical islands.

From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models

Abstract. Distributed watershed models are now widely used in practice to simulate runoff responses at high spatial and temporal resolutions. Counter to this purpose, diagnostic analyses of distributed models currently aggregate performance measures in space and/or time and are thus disconnected from the models' operational and scientific goals. To address this disconnect, this study contributes a novel approach for computing and visualizing time-varying global sensitivity indices for spatially distributed model parameters. The high-resolution model diagnostics employ the method of Morris to identify evolving patterns in dominant model processes at sub-daily timescales over a six-month period. The method is demonstrated on the United States National Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) in the Blue River watershed, Oklahoma, USA. Three hydrologic events are selected from within the six-month period to investigate the patterns in spatiotemporal sensitivities that emerge as a function of forcing patterns as well as wet-to-dry transitions. Events with similar magnitudes and durations exhibit significantly different performance controls in space and time, indicating that the diagnostic inferences drawn from representative events will be heavily biased by the a priori selection of those events. By contrast, this study demonstrates high-resolution time-varying sensitivity analysis, requiring no assumptions regarding representative events and allowing modelers to identify transitions between sets of dominant parameters or processes a posteriori. The proposed approach details the dynamics of parameter sensitivity in nearly continuous time, providing critical diagnostic insights into the underlying model processes driving predictions. Furthermore, the approach offers the potential to identify transition points between dominant parameters and processes in the absence of observations, such as under nonstationarity.

Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models

Abstract. The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) over a six-month period in the Blue River watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly screen the most and least sensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. The method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models.

Hydrologic Evaluation of Rainfall Estimates from Radar, Satellite, Gauge, and Combinations on Ft. Cobb Basin, Oklahoma

AbstractThis study evaluates rainfall estimates from the Next Generation Weather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service’s (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) at 4-km/l-h and 0.25°/3-h resolutions. The unadjusted radar product was the overall worst product, while the stage IV radar product with hourly rain gauge adjustment had the best hydrologic skill with a Micronet relative efficiency score of −0.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANN-CCS-RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the model was calibrated at TRMM’s 0.25°/3-h resolution, thus highlighting the importance of considering rainfall product resolution during model calibration.

Modification of the National Weather Service Distributed Hydrologic Model for subsurface water exchanges between grids

To account for spatial variability of precipitation, as well as basin physiographic properties, the National Weather Service (NWS) has developed a distributed version of its hydrologic component, termed the Hydrology Laboratory‐Research Distributed Hydrologic Model (HL‐RDHM). Because channels are the only source of water exchange between neighboring computational elements, the absence of such exchange has been identified as a weakness in the model. The primary objective of this paper is to modify the model structure to account for subsurface water exchanges without dramatically altering the conceptual framework of the water balance module. The subsurface exchanges are established by partitioning the slow response components released from the lower layer storages into two parts: the first part involves the grid's conceptual channel, while the second is added to the lower layer storages of the downstream pixel. Realizing the deficiency of the water balance module to locate the lower zone layers in sufficient depths, a complementary study is conducted to test the feasibility of further improvement in the modified model by equally shifting downward the lower zone layers of all pixels over the basin. The Baron Fork at Eldon, Oklahoma, is chosen as the test basin. Ten years of grid‐based multisensor precipitation data are used to investigate the effects of the modification, plus shifting the lower zone layers on model performance. The results show that the modified‐shifted HL‐RDHM can markedly improve the streamflow simulations at the interior point, as well as very high peak‐flow simulations at the basin's outlet.

Impacts of Polarimetric Radar Observations on Hydrologic Simulation

Abstract Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary. To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.

Advancing the identification and evaluation of distributed rainfall‐runoff models using global sensitivity analysis

This study provides a step‐wise analysis of a conceptual grid‐based distributed rainfall‐runoff model, the United States National Weather Service (US NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL‐RDHM). It evaluates model parameter sensitivities for annual, monthly, and event time periods with the intent of elucidating the key parameters impacting the distributed model's forecasts. This study demonstrates a methodology that balances the computational constraints posed by global sensitivity analysis with the need to fully characterize the HL‐RDHM's sensitivities. The HL‐RDHM's sensitivities were assessed for annual and monthly periods using distributed forcing and identical model parameters for all grid cells at 24‐hour and 1‐hour model time steps respectively for two case study watersheds within the Juniata River Basin in central Pennsylvania. This study also provides detailed spatial analysis of the HL‐RDHM's sensitivities for two flood events based on 1‐hour model time steps selected to demonstrate how strongly the spatial heterogeneity of forcing influences the model's spatial sensitivities. Our verification analysis of the sensitivity analysis method demonstrates that the method provides robust sensitivity rankings and that these rankings could be used to significantly reduce the number of parameters that should be considered when calibrating the HL‐RDHM. Overall, the sensitivity analysis results reveal that storage variation, spatial trends in forcing, and cell proximity to the gauged watershed outlet are the three primary factors that control the HL‐RDHM's behavior.

Problems and prospects in long and medium range weather forecasting

We investigate the ability to improve flood inundation forecasts at short- to medium-range (1–7 days) timescales through weather ensembles and statistical water surface elevation (WSE) postprocessing. To generate the flood inundation forecasts, a one-dimensional hydraulic model, namely the Hydrologic Engineering Center’s River Analysis System (HEC-RAS), is coupled to a regional hydrological ensemble prediction system (RHEPS). The RHEPS is comprised of: i) hydrometeorological observations; ii) weather ensembles from the National Centers for Environmental Prediction Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); and iii) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) as the hydrological model. The coupled RHEPS-hydraulic system is evaluated along the tidal Delaware River near the City of Philadelphia, Pennsylvania, U.S. For the evaluation, emphasis is placed on the tidal-riverine transitional zone of the Delaware River and the downstream propagation of hydrometeorological uncertainty through the flood inundation forecasts. The coupled system is used to generate hourly flood inundation forecasts at lead times from 1 to 7 days, over the period 2008–2013. Additionally, WSEs from the coupled system are statistically postprocessed using quantile regression (QR). Results show that the raw flood inundation ensemble forecasts exhibit higher skill than the deterministic ones. We also find that statistical postprocessing improves the skill of the raw flood inundation ensemble forecasts, with greater improvements at the longer lead times (>3 days). Overall, we find that both weather ensembles and statistical WSE postprocessing can be used to enhance the skill of flood inundation forecasts at short- to medium-range timescales. In turn, this may serve to enhance the spatial representation of flood forecasts several days in advance, which could contribute in the future to making flood forecast communication and warnings more effective.