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Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities : Volume 9, Issue 3 (14/03/2012)

By Liu, Y.

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Book Id: WPLBN0004013266
Format Type: PDF Article :
File Size: Pages 58
Reproduction Date: 2015

Title: Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities : Volume 9, Issue 3 (14/03/2012)  
Author: Liu, Y.
Volume: Vol. 9, Issue 3
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Kumar, S., Smith, P., Seo, D., Rakovec, O., Moradkhani, H., Clark, M.,...Velzen, N. V. (2012). Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities : Volume 9, Issue 3 (14/03/2012). Retrieved from

Description: Earth System Science Interdisciplinary Center, the University of Maryland, College Park, USA. 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 into operational forecast systems to improve the skill of forecasts to better inform 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, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical considerations 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 modelling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

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

Ackermann, T., Loucks, D. P., Schwanenberg, D., and Detering, M.: Real-time modeling for navigation and hydropower in the River Mosel, J. Water Res. Pl.-ASCE, 126, 298, doi:10.1061/(ASCE)0733-9496(2000)126:5(298), 2000.; Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, doi:200710.1029/2005WR004745, 2007.; Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, doi:10.5194/hess-12-1323-2008, 2008.; Alsdorf, D. E., Rodríguez, E., and Lettenmaier, D. P.: Measuring surface water from space, Rev. Geophys., 45, RG2002, doi:10.1029/2006RG000197, 2007.; Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Arellano, A.: The Data Assimilation Research Testbed: a community facility, B. Am. Meteorol. Soc., 90, 1283–1296, doi:10.1175/2009BAMS2618.1, 2009.; Andreadis, K. M. and Lettenmaier, D. P.: Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886, 2006.; Andreadis, K. M., Clark, E. A., Lettenmaier, D. P., and Alsdorf, D. E.: Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model, Geophys. Res. Let., 34, 10403, doi:10.1029/2007GL029721, 2007.; Bárdossy, A. and Das, T.: Influence of rainfall observation network on model calibration and application, Hydrol. Earth Syst. Sci., 12, 77–89, doi:10.5194/hess-12-77-2008, 2008.; Blanco, T. B., Willems, P., Chiang, P. K., Haverbeke, N., Berlamont, J., and De Moor, B.: Flood regulation using nonlinear model predictive control, Control Eng. Pract., 18, 1147–1157, 2010.; Clark, M. P. and Slater, A. G.: Probabilistic quantitative precipitation estimation in complex terrain, J. Hydrometeorol., 7, 3–22, 2006.; Bauser, G., Hendricks Franssen, H. J., Kaiser, H. P., Kuhlmann, U., Stauffer, F., and Kinzelbach, W.: Real-time management of an urban groundwater well field threatened by pollution, Environ. Sci. Tech., 44, 6802–6807, doi:10.1021/es100648j, 2010.; Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, 2006.; Beven, K. and Binley, A.: The future of distributed models: calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992.; Breckpot, M., Blanco, T. B., and De Moor, B.: Flood control of rivers with nonlinear model predictive control and moving horizon estimation, 49th IEEE Conference on Decision and Control, 6107–6112, 2010.; Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., and Hasenauer, S.: Improving runoff prediction through the assimilation of the ASCAT soil moisture product, Hydrol. Earth Syst. Sci., 14, 1881–1893, doi:10.5194/hess-14-1881-2010, 2010.; Broersen, P. M. and Weerts, A. H.: Automatic error correction of rainfall-runoff models in flood forecasting systems, in Instrumentation and Measurement Technology Conference, IMTC 2005, Proc. IEEE, 2, 963–968, 2005.; Bulygina, N. and Gupta, H.: How Bayesian data assimilation can be used to estimate the mathematical structure of a model, Stoch. Env. R


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