Hydrologic Verification: A Call for Action and Collaboration

Welles, E.; Sorooshian, Soroosh; Carter, Gary; Olsen, Billy G.

doi:10.1175/bams-88-4-503

Cited by 61 publications

(40 citation statements)

References 15 publications

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“…For example, Welles et al (2007) reported that the hydrologic forecasting skill for some river basins at the US National Weather Service (NWS) River Forecast Centers has hardly improved over the past decade, with above flood-stage hydrologic forecasts beyond three days having very poor skill. This highlights the potential, as well as the need, of assimilating new observations into the operational hydrologic forecasting process to improve the predictive skill and extend the forecast lead time.…”

Section: Introductionmentioning

confidence: 99%

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Liu

Weerts²,

Clark³

et al. 2012

Hydrol. Earth Syst. Sci.

390

296

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Abstract. Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters.The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, asPublished by Copernicus Publications on behalf of the European Geosciences Union. Y. Liu et al.: Advancing data assimilation in operational hydrologic forecastingwell as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

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Section: Introductionmentioning

confidence: 99%

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Liu

Weerts²,

Clark³

et al. 2012

Hydrol. Earth Syst. Sci.

390

296

View full text Add to dashboard Cite

show abstract

“…In contrast, there have been renewed calls for the study of hydrologic forecasting science (Welles et al 2007). Welles et al were concerned that decisions surrounding investments to improve forecast accuracy were largely being done based on qualitative impressions from subject matter experts.…”

Section: A a Case For The Science Of Hydrologic Forecastingmentioning

confidence: 99%

Challenges of Operational River Forecasting

Pagano

Wood

Ramos³

et al. 2014

Journal of Hydrometeorology

Self Cite

156

116

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Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human-natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using humangenerated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.

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“…When floods occur and more information is needed, the local river forecast center (RFC) can decide to publish river-stage forecasts more frequently and for more locations. Welles et al (2007) provides a detailed description of the forecasting process.…”

Section: Datamentioning

confidence: 99%

Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables

Hoss

Fischbeck

2015

Hydrol. Earth Syst. Sci.

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Abstract. This study applies quantile regression (QR) to predict exceedance probabilities of various water levels, including flood stages, with combinations of deterministic forecasts, past forecast errors and rates of water level rise as independent variables. A computationally cheap technique to estimate forecast uncertainty is valuable, because many national flood forecasting services, such as the National Weather Service (NWS), only publish deterministic singlevalued forecasts. The study uses data from the 82 river gauges, for which the NWS' North Central River Forecast Center issues forecasts daily. Archived forecasts for lead times of up to 6 days from 2001 to 2013 were analyzed. Besides the forecast itself, this study uses the rate of rise of the river stage in the last 24 and 48 h and the forecast error 24 and 48 h ago as predictors in QR configurations. When compared to just using the forecast as an independent variable, adding the latter four predictors significantly improved the forecasts, as measured by the Brier skill score and the continuous ranked probability score. Mainly, the resolution increases, as the forecast-only QR configuration already delivered high reliability. Combining the forecast with the other four predictors results in a much less favorable performance. Lastly, the forecast performance does not strongly depend on the size of the training data set but on the year, the river gauge, lead time and event threshold that are being forecast. We find that each event threshold requires a separate configuration or at least calibration.

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Hydrologic Verification: A Call for Action and Collaboration

Cited by 61 publications

References 15 publications

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Challenges of Operational River Forecasting

Performance and robustness of probabilistic river forecasts computed with quantile regression based on multiple independent variables

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