Fundamentals of Data Assimilation and Theoretical Advances

Moradkhani, Hamid; Nearing, Grey; Abbaszadeh, Peyman; Pathiraja, Sahani

doi:10.1007/978-3-642-39925-1_30

Cited by 25 publications

(42 citation statements)

References 72 publications

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“…Current practice in ensemble flood forecasting is mostly based on generating ensemble forecasts from different meteorological inputs, different initial conditions, multiple hydrological models, or multiple parameter sets, or a combination of above (Cloke & Pappenberger, ; Duan et al, ; Roundy et al, ). The most common approach to ensemble flood forecasting is to generate flood forecasts by perturbed initial conditions for either NWP models, the output of which are used as forcing inputs to drive a hydrological model(s), or for the hydrological models themselves (Moradkhani et al, ). For the former approach, raw ensemble precipitation forecasts must be post‐processed via some type of statistical technique to remove the systematic and spread biases inherent in those forecasts (Li et al, ).…”

Section: Current Challenges and Future Opportunitiesmentioning

confidence: 99%

Ensemble flood forecasting: Current status and future opportunities

et al. 2020

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Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk-based decision-making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this article, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focusing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for example, flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydrometeorological model development, and real-world flood management using probabilistic ensemble forecasts, especially through effective communication.

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Section: Current Challenges and Future Opportunitiesmentioning

confidence: 99%

Ensemble flood forecasting: Current status and future opportunities

et al. 2020

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“…There are several factors which have made EnKF an attractive DA scheme among hydrometeorologists, including ease of application, efficiency of the framework, and the explicit handling of uncertainties through an ensemble approach [48]. EnKF stems from the development of the KF approach and was extended from the EKF procedure.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…This procedure is known as "filtering". DA filtering is specifically effective in case of real-time operational flow forecasting, in which updating the initial states of a forecasting system is necessary in consecutive time intervals [48]. The most commonly applied…”

Section: Introductionmentioning

confidence: 99%

Development and evaluation of a hydrologic data-assimilation scheme for short-range flow and inflow forecasts in a data-sparse high-latitude region using a distributed model and ensemble Kalman filtering

Samuel

Rousseau

Abbasnezhadi

et al. 2019

Advances in Water Resources

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“…They include the generalized likelihood uncertainty estimation (GLUE) method (Beven & Binley, 1992, 2014), and a wealth of techniques based on Markov Chain Monte Carlo and Bayesian inference (Abbaspour et al, 1997, 2004; Kuczera & Parent, 1998; Laloy & Vrugt, 2012; Vrugt, 2016; Vrugt et al, 2008, 2009, 2013). The problem of equifinality also makes the forecasting applications challenging since the different parameter combinations, together with other sources of errors, can lead to intangible uncertainties in state variables (Carrassi et al, 2018; Moradkhani et al, 2005, 2018). In this respect, various data assimilation algorithms have been designed to account for very large levels of uncertainties and to facilitate estimates that are consistent with both recent observations and previous conditions (Beven & Freer, 2001; Fisher, 2003; Hernández & Liang, 2018, 2019; Ning et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A Calibration Framework for High‐Resolution Hydrological Models Using a Multiresolution and Heterogeneous Strategy

Sun

Hernández

Liang

et al. 2020

Water Resources Research

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Increasing spatial and temporal resolution of numerical models continues to propel progress in hydrological sciences, but, at the same time, it has strained the ability of modern automatic calibration methods to produce realistic model parameter combinations for these models. This paper presents a new reliable and fast automatic calibration framework to address this issue. In essence, the proposed framework, adopting a divide and conquer strategy, first partitions the parameters into groups of different resolutions based on their sensitivity or importance, in which the most sensitive parameters are prioritized with the highest resolution in the parameter search space, while the least sensitive ones are initially explored with the coarsest resolution. This is followed by an optimization‐based iterative calibration procedure consisting of a series of subtasks or runs. Between consecutive runs, the setup configuration is heterogeneous with parameter search ranges and resolutions varying among groups. At the completion of each subtask, the parameter ranges within each group are systematically refined from their previously estimated ranges, which are initially based on a priori information. Parameters attain stable convergence progressively with each run. A comparison of this new calibration framework with a traditional optimization‐based approach was performed using a quasi‐synthetic double‐model setup experiment to calibrate 134 parameters and two well‐known distributed hydrological models: the Variable Infiltration Capacity (VIC) model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The results demonstrate statistically that the proposed framework can better mitigate equifinality problem, yields more realistic model parameter estimates, and is computationally more efficient.

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Fundamentals of Data Assimilation and Theoretical Advances

Cited by 25 publications

References 72 publications

Ensemble flood forecasting: Current status and future opportunities

Ensemble flood forecasting: Current status and future opportunities

Development and evaluation of a hydrologic data-assimilation scheme for short-range flow and inflow forecasts in a data-sparse high-latitude region using a distributed model and ensemble Kalman filtering

A Calibration Framework for High‐Resolution Hydrological Models Using a Multiresolution and Heterogeneous Strategy

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