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

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, 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.

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

“…In contrast to the previous sequential methods, variational assimilation methods have been widely used in weather forecasting and costal engineering applications (Li and Navon, 2001;Seo et al, 2003;Valstar et al, 2004;Fischer et al, 2005;Lorenc and Rawlins, 2005;Seo et al, 2009;Lee et al, 2011aLee et al, , 2012Liu et al, 2012). In these methods, the cost function that measures the difference between the error in the initial conditions and the error between model predictions and observations over time is minimised to identify the best estimate of the initial state condition (Seo et al, 2009;Lee et al, 2011a).…”

“…In these methods, the cost function that measures the difference between the error in the initial conditions and the error between model predictions and observations over time is minimised to identify the best estimate of the initial state condition (Seo et al, 2009;Lee et al, 2011a). A detailed review of the status, progress, challenges and opportunities in advancing DA for operational hydrologic predictions is provided in Liu et al (2012).…”

“…It is in fact assumed that uncertainties in the input data are the main source of uncertainty in operational flood forecasting (Sittner and Krouse, 1979;Canizares et al, 1998;Todini et al, 2005). Liu et al (2012) pointed out that the need for implementing reliable data assimilation methods in operational forecast is increasing in order to fill this gap between the scientific world and practice.…”

“…Walker and Houser (2005) gave a brief overview about the history of hydrological data assimilation and discussed different assimilation methods. Recently, Liu et al (2012) presented a comprehensive literature review about the latest advanced of data assimilation procedures in operational flood forecasting by means of water observations coming from physical sensors or remote sensing in a distributed fashion.…”

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

“…However, ESPs depend heavily on the model's initial conditions (Franz et al, 2008). Presently, many water resources managers still use a manual approach to adjust the initial state of the watershed based on available observations and the user's experience (Liu et al, 2012).…”