Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities
Liu, Y. ; Weerts, A. ; Clark, M. ; Franssen, H.J. ; Moradkhani, S. ; Seo, D.J. ; Schwanenberg, D. ; Smith, P. ; Dijk, A.I.J.M. van; Velzen, N. ; He, M. ; Lee, H. ; Noh, S.J. ; Rakovec, O. ; Restrepo, P. - \ 2012
Hydrology and Earth System Sciences 16 (2012). - ISSN 1027-5606 - p. 3863 - 3887.
ensemble kalman filter - variational data assimilation - numerical weather-prediction - soil-moisture retrievals - land data assimilation - stochastic hydrometeorological model - sequential data assimilation - improving runoff prediction - state-parameter estimat
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, as well 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.
Observation uncertainty of satellite soil moisture products determined with physically-based modeling
Wanders, N. ; Karssenberg, D. ; Bierkens, M.F.P. ; Parinussa, R. ; Jeu, R. de; Dam, J.C. van; Jong, S. de - \ 2012
Remote Sensing of Environment 127 (2012). - ISSN 0034-4257 - p. 341 - 356.
passive microwave measurements - improving runoff prediction - vegetation optical depth - ers scatterometer - amsr-e - retrieval - assimilation - validation - algorithm - index
Accurate estimates of soil moisture as initial conditions to hydrological models are expected to greatly increase the accuracy of flood and drought predictions. As in-situ soil moisture observations are scarce, satellite-based estimates are a suitable alternative. The validation of remotely sensed soil moisture products is generally hampered by the difference in spatial support of in-situ observations and satellite footprints. Unsaturated zone modeling may serve as a valuable validation tool because it could bridge the gap of different spatial supports. A stochastic, distributed unsaturated zone model (SWAP) was used in which the spatial support was matched to these of the satellite soil moisture retrievals. A comparison between point observations and the SWAP model was performed to enhance understanding of the model and to assure that the SWAP model could be used with confidence for other locations in Spain. A timeseries analysis was performed to compare surface soil moisture from the SWAP model to surface soil moisture retrievals from three different microwave sensors, including AMSR-E, SMOS and ASCAT. Results suggest that temporal dynamics are best captured by AMSR-E and ASCAT resulting in an averaged correlation coefficient of 0.68 and 0.71, respectively. SMOS shows the capability of capturing the long-term trends, however on short timescales the soil moisture signal was not captured as well as by the other sensors, resulting in an averaged correlation coefficient of 0.42. Root mean square errors for the three sensors were found to be very similar (± 0.05 m3m- 3). The satellite uncertainty is spatially correlated and distinct spatial patterns are found over Spain.