Multi-parametric variational data assimilation for hydrological forecasting

Alvarado-Montero, Rodolfo; Schwanenberg, Dirk; Krahe, Peter; Helmke, Peer; Klein, Bastian

doi:10.1016/j.advwatres.2017.09.026

Cited by 8 publications

(7 citation statements)

References 33 publications

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Section: Data Assimilation Methodsmentioning

confidence: 99%

“…This type of methods is also applied in Model Prediction Control applications when the assimilation window is shifted to the predictive horizon [150]. Recently, [151] extended this approach to consider multiparametric conditions in consideration with snow DA. Despite the previous drawbacks, EnKF techniques have been widely implemented to process snow observation data [19,20,62,88,111,[152][153][154][155][156].…”

Section: Data Assimilation Methodsmentioning

confidence: 99%

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Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

et al. 2018

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The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments.

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Section: Data Assimilation Methodsmentioning

confidence: 99%

Section: Data Assimilation Methodsmentioning

confidence: 99%

Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

et al. 2018

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“…The method, which can be classified as a variational DA (VDA) approach (Bannister, 2017; Ercolani & Castelli, 2017; Liu & Gupta, 2007), is data driven to circumvent the problems related to mischaracterization of error structures as suggested by Pathiraja et al (2018). It uses a moving window approach to sequentially update the model states as in Alvarado‐Montero et al (2020); however, in the proposed approach, the state variables to update are selected using a systematic method rather than an empirical one as described by Liu et al (2012) and Seo et al (2009). Likewise, Abaza et al (2015) choose four variables for all forecast dates and order them from the most to least persistent variables.…”

Section: Introductionmentioning

confidence: 99%

Application of Parameter Screening to Derive Optimal Initial State Adjustments for Streamflow Forecasting

Mai

Arsenault

Tolson

et al. 2020

Water Resources Research

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Streamflow forecasting is essential in many applications, including the management of hydropower reservoirs and of flood risks. To increase the performance of these hydrologic forecasts, a hydrological model is typically updated with optimal initial model states. These states are usually selected a priori and adjusted to obtain the optimal initial setup. The model states to adjust are generally selected by experts with deep knowledge of the model's behavior. These relationships are rarely documented and formalized, leading to difficulties in reproducibility and transferability to other models. The method here provides a fully automatized system to find the model states based on the parameter-free sensitivity method of efficient elementary effects (EEE), which identifies the most informative variables of the hydrologic model CEQUEAU. The analysis is applied to 1,826 model setups for four Canadian basins. The identified states are formalized using clustering techniques to obtain adjustment "recipes" conditioned on given meteorological conditions. Several recipes are tested and compared to expert-based recipes. The recipes are tested according to their ability to (a) obtain optimal initial states and (b) their forecast performance. The results show that (1) the EEE-based recipes perform similarly or better than the expert-based recipes in both objectives; (2) the recipes are similar across the four basins, highlighting their potential of spatial transferability; and (3) the adjustment of hydrologic model inputs significantly decreases forecast performance. The proposed approach to use EEE-based recipes to obtain optimal initial states for forecasting is reproducible, objective, and suitable to be applied to other models and basins.

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“…Hydrological forecasting plays an important role in basin flood control systems, which include engineering and non-engineering measures (Hao et al 2012). The prediction of hydrological variables is susceptible to various uncertainties, such as the climate, model structure and parameters, and initial prediction conditions (Kavetski et al 2006;Chen et al 2011;Renard et al 2011;Madadgar et al 2014;Alvarado-Montero et al 2017;Jiang et al 2018). The uncertainty of hydrological forecasting is helpful to reveal the basin hydrological characteristics and provide support to decision makers in the formulation of water resources management schemes (Jia et al 2019;Zhou et al 2019).…”

Section: Introductionmentioning

confidence: 99%

A Hydrologic Uncertainty Processor Using Linear Derivation in the Normal Quantile Transform Space

Zhou

Feng

Liu

et al. 2020

Water Resour Manage

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Hydrological forecasting plays an important role in basin flood control systems, and the uncertainty of hydrological forecasting is helpful to reveal basin hydrological characteristics and provide support to decision makers in formulating water resources management schemes. The hydrologic uncertainty processor (HUP) has been widely employed in hydrological uncertainty prediction. However, in the HUP normal quantile transform (NQT) space, the posteriori distribution is derived from the Bayesian theory. This increases the difficulty of the theory and calculations. In this paper, a new method is proposed to deduce the posterior residual equation, and the HUP-Gaussian mixture model (HUP-GMM) is adopted to simplify the calculations. By maintaining the original hypothesis, since the posterior residual is known to follow a normal distribution, the posterior linear correlation equation can be directly assumed without prior and likelihood inferences. In particular, the complex Bayesian inference is replaced with simple linear equations. By converting the linear equation into the original space, we obtain a new method consisting of the HUP linear GMM (HUP-LG). In the study area, the parameters of the HUP-LG and HUP-GMM in the NQT space are calculated, and corresponding expressions of the probability density in the original space are obtained. The results reveal that the HUP-LG simplifies the calculation process in the NQT space, and attains the same performance as that of the HUP-GMM.

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Multi-parametric variational data assimilation for hydrological forecasting

Cited by 8 publications

References 33 publications

Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey

Application of Parameter Screening to Derive Optimal Initial State Adjustments for Streamflow Forecasting

A Hydrologic Uncertainty Processor Using Linear Derivation in the Normal Quantile Transform Space

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