Search

Hydrologic models operated by the National Weather Service call for an accurate, consistent, high‐resolution, multi‐decade, continental‐scale record of hydrometeorological fields to serve as forcing data for model calibration. To serve this purpose, the Analysis of Record for Calibration was developed, and version 1.1 of the dataset is described in this study. Geospatial and scientific requirements, methods used in dataset generation, and input data sources are described. Given the prominent role of precipitation in model calibration, accurate and consistent precipitation is a particularly high priority for the analysis. To evaluate the analysis from this perspective, its daily precipitation is compared with surface observing stations over 43 years. The analysis exhibits low bias compared with other similar products. It also displays nonstationary bias behavior after 2015 due to the lack of a climatological constraint, as well as frequent occurrences of heavy‐to‐extreme precipitation that are often difficult to verify. These findings should be taken into account when the product is used for model calibration. [ABSTRACT FROM AUTHOR]

The Office of Hydrologic Development (OHD) of the U.S. National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS) conducted the two phases of the Distributed Model Intercomparison Project (DMIP) as cost-effective studies to guide the transition to spatially distributed hydrologic modeling for operational forecasting at NWS River Forecast Centers (RFCs). Phase 2 of the Distributed Model Intercomparison Project (DMIP 2) was formulated primarily as a mechanism to help guide the U.S. NWS as it expands its use of spatially distributed watershed models for operational river, flash flood, and water resources forecasting. The overall purpose of DMIP 2 was to test many distributed models forced by high quality operational data with a view towards meeting NWS operational forecasting needs. At the same time, DMIP 2 was formulated as an experiment that could be leveraged by the broader scientific community as a platform for the testing, evaluation, and improvement of distributed models.DMIP 2 contained experiments in two regions: in the DMIP 1 Oklahoma basins, and second, in two basins in the Sierra Nevada Mountains in the western USA. This paper presents the overview and results of the DMIP 2 experiments conducted for the two Sierra Nevada basins. Simulations from five independent groups from France, Italy, Spain and the USA were analyzed. Experiments included comparison of lumped and distributed model streamflow simulations generated with uncalibrated and calibrated parameters, and simulations of snow water equivalent (SWE) at interior locations. As in other phases of DMIP, the participant simulations were evaluated against observed hourly streamflow and SWE data and compared with simulations provided by the NWS operational lumped model. A wide range of statistical measures are used to evaluate model performance on a run-period and event basis. Differences between uncalibrated and calibrated model simulations are assessed.Results indicate that in the two study basins, no single model performed best in all cases. In addition, no distributed model was able to consistently outperform the lumped model benchmark. However, one or more distributed models were able to outperform the lumped model benchmark in many of the analyses. Several calibrated distributed models achieved higher correlation and lower bias than the calibrated lumped benchmark in the calibration, validation, and combined periods. Evaluating a number of specific precipitation-runoff events, one calibrated distributed model was able to perform at a level equal to or better than the calibrated lumped model benchmark in terms of event-averaged peak and runoff volume error. However, three distributed models were able to provide improved peak timing compared to the lumped benchmark. Taken together, calibrated distributed models provided specific improvements over the lumped benchmark in 24% of the model-basin pairs for peak flow, 12% of the model-basin pairs for event runoff volume, and 41% of the model-basin pairs for peak timing. Model calibration improved the performance statistics of nearly all models (lumped and distributed). Analysis of several precipitation/runoff events indicates that distributed models may more accurately model the dynamics of the rain/snow line (and resulting hydrologic conditions) compared to the lumped benchmark model. Analysis of SWE simulations shows that better results were achieved at higher elevation observation sites.Although the performance of distributed models was mixed compared to the lumped benchmark, all calibrated models performed well compared to results in the DMIP 2 Oklahoma basins in terms of run period correlation and %Bias, and event-averaged peak and runoff error. This finding is noteworthy considering that these Sierra Nevada basins have complications such as orographically-enhanced precipitation, snow accumulation and melt, rain on snow events, and highly variable topography. Looking at these findings and those from the previous DMIP experiments, it is clear that at this point in their evolution, distributed models have the potential to provide valuable information on specific flood events that could complement lumped model simulations. © 2013.

Radar and multisensor quantitative precipitation estimates (QPEs) have played a critical role in real-time hydrologic and weather predictions. The utility of this data set for hydrologic model calibration, however, is hampered by the limited data archive and the presence of data gaps. In the research reported in this paper, the use of a composite reflectivity data set is investigated, namely the Weather Services International (WSI) National Operational Weather radar (NOWrad) data set, to create an hourly QPE that would complement the National Weather Service (NWS) Stage III archive by filling in the latter's gaps and potentially improving the latter's accuracy. The paper starts from an inventory analysis of Stage III and NOWrad products, and through which it is found that as much as 17% of the Stage III data were missing for some of the regions, and some of these gaps can be filled using the NOWrad data. Then, the paper presents two experimental products, as follows: (1) NOWrad-Raw, based on variable ZðreflectivityÞ - Rðrain rateÞ relationship derived for each month using monthly Cooperative Observer Porgram (COOP) station totals; and (2) NOWrad-bias-corrected mosaic (BMO), derived by bias-correcting the NOWrad-Raw using hourly rain gauge products. Then the two products along with Stage III products against independent hourly gauge reports over two locations [(1) Charlotte-Mecklenburg metropolitan area in North Carolina, and (2) Lower Colorado River drainage in central Texas]. The analyses over the two test sites reveal the following: (1) NOWrad-Raw product suffers from a negative overall and conditional bias, while the Stage III is closer to bias-neutral; and (2) NOWrad-Raw product, at least over the Charlotte-Mecklenburg area, can outperform the Stage III in terms of correlation with gauge data and skill in detecting light rainfall. Further analyses of the Raw and bias-corrected NOWrad suggest that negative conditional bias may be substantially improved, but there is a tendency to overcorrect the bias for the summer months, possibly due to the presence of false detections. The results show that NOWrad can be a viable source of high-resolution quantitative precipitation information and it complements the current NWS archive in several respects. Possible mechanisms for further improving the accuracy of the NOWrad QPE are discussed. [ABSTRACT FROM AUTHOR]