Robust data assimilation in hydrological modeling – A comparison of Kalman and H-infinity filters

Wang, Dingbao; Cai, Ximing

doi:10.1016/j.advwatres.2007.10.001

Cited by 27 publications

(19 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Increasingly, methods of data assimilation are being applied to both hydrological and hydrometeorological problems driven by prospects of better characterization of initial conditions and improved forecasting skill (Mecikalski et al 1999;Reichle et al 2001;Crosson et al 2002;Reichle et al 2002;Heathman et al 2003;Merlin et al 2006;Pan et al 2008;Wang and Cai 2008;Barrett et al 2008). The benefits afforded by the application of data assimilation approaches to hydrometeorological problems include better estimation of initial soil moisture and temperature in mesoscale climatological models (Jones et al 2004;Huang et al 2008), improved energy partitioning between latent and sensible heat fluxes (Pipunic et al 2008), and a concomitant higher skill in quantitative precipitation forecasts (Koster et al 2000).…”

Section: Introductionmentioning

confidence: 99%

On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation

Barrett

Renzullo

2009

Journal of Hydrometeorology

View full text Add to dashboard Cite

Data assimilation applications require the development of appropriate mathematical operators to relate model states to satellite observations. Two such ''observation'' operators were developed and used to examine the conditions under which satellite microwave and thermal observations provide effective constraints on estimated soil moisture. The first operator uses a two-layer surface energy balance (SEB) model to relate rootzone moisture with top-of-canopy temperature. The second couples SEB and microwave radiative transfer models to yield top-of-atmosphere brightness temperature from surface layer moisture content. Tangent linear models for these operators were developed to examine the sensitivity of modeled observations to variations in soil moisture. Assuming a standard deviation in the observed surface temperature of 0.5 K and maximal model sensitivity, the error in the analysis moisture content decreased by 11% for a background error of 0.025 m 3 m 23 and by 29% for a background error of 0.05 m 3 m 23 . As the observation error approached 2 K, the assimilation of individual surface temperature observations provided virtually no constraint on estimates of soil moisture. Given the range of published errors on brightness temperature, microwave satellite observations were always a strong constraint on soil moisture, except under dense forest and in relatively dry soils. Under contrasting vegetation cover and soil moisture conditions, orthogonal information contained in thermal and microwave observations can be used to improve soil moisture estimation because limited constraint afforded by one data type is compensated by strong constraint from the other data type.

show abstract

Section: Introductionmentioning

confidence: 99%

On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation

Barrett

Renzullo

2009

Journal of Hydrometeorology

View full text Add to dashboard Cite

show abstract

“…These include, for example, the one-, two-, three-and fourdimensional variational algorithms (1D-, 2D-, 3D-, and 4D-VAR, e.g., Seo et al, 2003Seo et al, , 2009Valstar et al, 2004), extended or ensemble Kalman filtering (EKF or EnKF, e.g., Moradkhani et al, 2005b;Slater and Clark, 2006;Weerts and El Serafy, 2006;Shamir et al, 2010), particle filtering (e.g., Moradkhani et al, 2005a;Weerts and El Serafy, 2006;Matgen et al, 2010;DeChant and Moradkhani, 2012), H-infinity filters (Wang and Cai, 2008), hybrid EnKF or 4D-VAR approaches (e.g., Zhang et al, 2009), and other Bayesian approaches (e.g., Reggiani and Weerts, 2008;Todini, 2008;Reggiani et al, 2009). While most DA applications have focused on updating hydrologic model states (e.g., soil moisture and snow water equivalent), recent research has also examined the benefits of estimating model states and model parameters simultaneously (e.g., Moradkhani et al, 2005a, b Vrugt et al, 2005;Lu et al, 2010;Leisenring and Moradkhani, 2011;Nie et al, 2011) as well as the possibility of model structure identification and uncertainty estimation (e.g., Neuman, 2003;Bulygina and Gupta, 2010;Hsu et al, 2009;Parrish et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Weerts²,

Clark³

et al. 2012

Hydrol. Earth Syst. Sci.

391

296

View full text Add to dashboard Cite

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.

show abstract

“…Although contemporary robust control approaches such as  -controller have been used in many fields [11,12], environmental engineering applications have not seen exhaustive use of the robust system theory tools. However, there are some limited applications such as the comparison of Kalman and H ∞ filters in hydrologic system modeling in which uncertainty is difficult to model [13], the control systems approach to generalized water quality control problems [14], and various convex optimization schemes [15]. In addition, robust control was extended to non-linear systems [16] such as the management of nonlinear soil aquifer treatment systems whose administration appears to be challenging due to its dynamic nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Modeling and robust optimization of an interconnected lake-aquifer system

Yilmaz

Uddameri

2010

2010 IEEE International Conference on Control Applications

View full text Add to dashboard Cite

This study develops a robust modeling and stabilization framework for an interconnected lake-aquifer system in the field of hydrology. The conventional lakeaquifer dynamics are transformed into a multi-input multioutput linear time-invariant model with a bounded uncertainty. The robust performance of the system is obtained through an H-infinity controller to address a bounded model uncertainty, satisfactory reference input tracking and disturbance rejection behaviour, and maximum profitability of the water extraction process. The robust optimization design results demonstrate that the designed robust controller maintains the desired aquifer level under a bounded model uncertainty and an exogenous input excitation while maximizing the corresponding environmental utility. Therefore, the proposed framework offers a new worst-case deterministic optimization algorithm for policy makers during efficient water management policy decisions, maximizing economical benefits while ensuring sustainable social, biological and environmental activities.

show abstract

Robust data assimilation in hydrological modeling – A comparison of Kalman and H-infinity filters

Cited by 27 publications

References 32 publications

On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation

On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation

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

Modeling and robust optimization of an interconnected lake-aquifer system

Contact Info

Product

Resources

About