Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

Abstract

More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required.

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

“…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.…”

Multivariate hydrological data assimilation of soil moisture and groundwater head

“…Similarly, the state vector can be augmented to include all relevant state variables, and possibly model parameters. The covariance matrix is thereby expanded to a block matrix where each block presents the cross-covariance between variables in the state vector (Montzka et al, 2012). A potential challenge in this respect is that implementing EnKF techniques like localization no longer becomes straightforward.…”

Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

“…An adequate bias correction of CCI-SM data for high resolutions may require more elaborated methods and techniques to account for the increased variability over time. A further possibility is the multiscale assimilation of the CCI-SM data, which would allow to update various model grid cells covered by a satellite observation (Montzka et al, 2012). In multiscale assimilation, the average soil moisture content for the group of grid cells covered by the satellite measurement is compared 20 with the satellite-based soil moisture content which may result in slightly improved CLM simulation results, but was beyond the scope of this study.…”