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

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 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. <br><br> 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

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

“…Statistical correction is typically implemented to directly correct errors in output variables. Other data assimilation strategies, including hard updating, sequential and variational assimilation, are normally applied to reduce short-term errors in state variables; nevertheless, more advanced updating approaches can also be extended for updating input variables and/or parameters by treating them as state variables [15]. Figure 2 illustrate a schematic of the batch calibration, the extended Kalman filter, and the ensemble Kalman filter as examples of data-model integration.…”

“…Although various remote sensing products, hydrologic models, and integration approaches can be used, the implementation of remote sensing data to constrain streamflow forecasting is under-researched and great opportunities and improvements are expected in future studies [15]. …”

“…Remotely sensed variables such as soil moisture can be used to address long-term systematic errors through batch calibration and short-term random errors through data assimilation. The batch calibration methods can be further classified into deterministic calibration [24][25][26] and stochastic calibration [27][28][29], while the data assimilation algorithms can be further classified into hard updating (direct insertion or initialization), statistical correction, nudging, sequential assimilation, and variational assimilation [15,[30][31][32]. Related to those data-model fusing approaches, remote sensing data can be used to address uncertainties in different sources, including input data, output data, state variables, and model parameters [1].…”

“…Precipitation products are generally utilized to remedy the insufficient gauged rainfall information [22], e.g., the ungauged areas, while soil moisture and snow products are commonly used to improve the key storage variables in rainfall-runoff and snowmelt-runoff models [15], respectively, so as to improve streamflow forecasts. There have also been attempts to integrate other types of remote sensing products into forecasting models, e.g., remotely sensed terrestrial water storage (TWS) [16,23], leaf area index (LAI) and/or evapotranspiration (ET) [18]; however, they have not been used for operational purposes.…”

“…While a number of reviews on hydrologic model data integration [14][15][16][17][18][19] and the use of remote sensing data for flood monitoring and mapping [20,21] have referred to this topic; there has not been a review article specifically on the use of remote sensing in operational flood forecasting applications, which is an important research area and has its own specific challenges and opportunities. Although floods can be driven by either rainfall or snowmelt, these types of processes are quite different in runoff generation mechanism.…”