Abstract
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, 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.
Highlights
It is essential to properly characterize and communicate uncertainty in weather, climate, and hydrologic forecasts to be able to effectively support emergency management and water resources decision making (National Research Council, 2006)
These data assimilation (DA) applications were developed for a variety of models ranging from physically-based land-surface models (e.g., Albergel et al, 2008; Nagarajan et al, 2010) to distributed hydrologic models (e.g., Clark et al, 2008a; Rakovec et al, 2012a, b) and conceptual rainfall-runoff models (e.g., Aubert et al, 2003; Seo et al, 2003, 2009; Moradkhani et al, 2005a, b; Weerts and El Serafy, 2006), hydraulic models (e.g., Shiiba et al, 2000; Madsen et al, 2003; Neal et al, 2007; Schumann et al, 2009; Weerts et al, 2010; Ricci et al, 2011), groundwater models (e.g., Valstar et al, 2004; Franssen et al, 2011), coupled surface-subsurface models (e.g., Camporese et al, 2009), biogeochemical models (e.g., Chen et al, 2009), and sediment transport models (e.g., Stroud et al, 2009)
The need for transitioning of hydrologic DA research into effective operations has become increasingly recognized in the wake of frequent occurrences of extreme events in recent years and increasing availability of new observations
Summary
It is essential to properly characterize and communicate uncertainty in weather, climate, and hydrologic forecasts to be able to effectively support emergency management and water resources decision making (National Research Council, 2006). The assimilation of various types of observations into operational hydrologic forecasting offers ample research opportunities and poses substantial challenges such as satellite retrieval algorithm development, bias correction, error estimation, downscaling, model diagnosis and improvement, new DA algorithm development, efficient or effective performance evaluation, computational efficiency enhancement, among others. To address these challenges and identify potential opportunities in the context of improving operational hydrologic forecasting and water resources management via DA, an international workshop was held in Delft, the Netherlands on 1–3 November 2010 (Weerts and Liu, 2011).
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