Recent developments in fast and scalable inverse modeling and data assimilation methods in hydrology
The last twenty years have brought significant advances in hydrology and hydrogeology,
especially in the area of data availability and predictive modeling capabilities. Remote …
especially in the area of data availability and predictive modeling capabilities. Remote …
Review of the Kalman-type hydrological data assimilation
There is great potential in Data Assimilation (DA) for the purposes of uncertainty
identification, reduction and real-time correction of hydrological models. This paper reviews …
identification, reduction and real-time correction of hydrological models. This paper reviews …
Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting
CM DeChant, H Moradkhani - Water Resources Research, 2012 - Wiley Online Library
In hydrologic modeling, state‐parameter estimation using data assimilation techniques is
increasing in popularity. Several studies, using both the ensemble Kalman filter (EnKF) and …
increasing in popularity. Several studies, using both the ensemble Kalman filter (EnKF) and …
Sequential data assimilation for streamflow forecasting: assessing the sensitivity to uncertainties and updated variables of a conceptual hydrological model at basin …
G Piazzi, G Thirel, C Perrin… - Water Resources …, 2021 - Wiley Online Library
Skillful streamflow forecasts provide key support to several water‐related applications.
Because of the critical impact of initial conditions (ICs) on forecast accuracy, ever‐growing …
Because of the critical impact of initial conditions (ICs) on forecast accuracy, ever‐growing …
Ensemble Kalman methods with constraints
Ensemble Kalman methods constitute an increasingly important tool in both state and
parameter estimation problems. Their popularity stems from the derivative-free nature of the …
parameter estimation problems. Their popularity stems from the derivative-free nature of the …
Data‐driven model uncertainty estimation in hydrologic data assimilation
The increasing availability of earth observations necessitates mathematical methods to
optimally combine such data with hydrologic models. Several algorithms exist for such …
optimally combine such data with hydrologic models. Several algorithms exist for such …
Identifying time-varying hydrological model parameters to improve simulation efficiency by the ensemble Kalman filter: A joint assimilation of streamflow and actual …
M Xiong, P Liu, L Cheng, C Deng, Z Gui, X Zhang… - Journal of …, 2019 - Elsevier
Hydrological model parameters are essential for model simulation, which may vary with time
owing to climatic variations and human activities. As a result, the implementation of …
owing to climatic variations and human activities. As a result, the implementation of …
Regularized ensemble Kalman methods for inverse problems
Inverse problems are common and important in many applications in computational physics
but are inherently ill-posed with many possible model parameters resulting in satisfactory …
but are inherently ill-posed with many possible model parameters resulting in satisfactory …
Modeling and state estimation for dynamic systems with linear equality constraints
The problem of modeling and estimation for linear equality constrained (LEC) systems is
considered. The exact constrained dynamic model usually is not readily available or is too …
considered. The exact constrained dynamic model usually is not readily available or is too …
Simultaneous state and parameter estimation: the role of sensitivity analysis
State and parameter estimation is essential for process monitoring and control. Observability
plays an important role in both state and parameter estimation. In simultaneous state and …
plays an important role in both state and parameter estimation. In simultaneous state and …