TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model

Abstract

Abstract. Modelling of terrestrial systems is continuously moving towards more integrated modelling approaches, where different terrestrial compartment models are combined in order to realise a more sophisticated physical description of water, energy and carbon fluxes across compartment boundaries and to provide a more integrated view on terrestrial processes. While such models can effectively reduce certain parameterisation errors of single compartment models, model predictions are still prone to uncertainties regarding model input variables. The resulting uncertainties of model predictions can be effectively tackled by data assimilation techniques, which allow one to correct model predictions with observations taking into account both the model and measurement uncertainties. The steadily increasing availability of computational resources makes it now increasingly possible to perform data assimilation also for computationally highly demanding integrated terrestrial system models. However, as the computational burden for integrated models as well as data assimilation techniques is quite large, there is an increasing need to provide computationally efficient data assimilation frameworks for integrated models that allow one to run on and to make efficient use of massively parallel computational resources. In this paper we present a data assimilation framework for the land surface–subsurface part of the Terrestrial System Modelling Platform (TerrSysMP). TerrSysMP is connected via a memory-based coupling approach with the pre-existing parallel data assimilation library PDAF (Parallel Data Assimilation Framework). This framework provides a fully parallel modular environment for performing data assimilation for the land surface and the subsurface compartment. A simple synthetic case study for a land surface–subsurface system (0.8 million unknowns) is used to demonstrate the effects of data assimilation in the integrated model TerrSysMP and to assess the scaling behaviour of the data assimilation system. Results show that data assimilation effectively corrects model states and parameters of the integrated model towards the reference values. Scaling tests provide evidence that the data assimilation system for TerrSysMP can make efficient use of parallel computational resources for > 30 k processors. Simulations with a large problem size (20 million unknowns) for the forward model were also efficiently handled by the data assimilation system. The proposed data assimilation framework is useful in simulating and estimating uncertainties in predicted states and fluxes of the terrestrial system over large spatial scales at high resolution utilising integrated models.

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

“…Kurtz et al (2016) recently provided a framework to couple PDAF with the land surface-subsurface part of the Terrestrial Systems Modelling Platform (TerrSysMP; Gasper et al, 2014;Shrestha et al, 2014). They showed the efficient use of parallel computational resources by TerrSysMP-PDAF, which is needed to simulate predicted states and fluxes over large 30 spatial domains and long simulations.…”

“…stand-alone CLM3.5 for soil moisture assimilation. Readers are referred to Kurtz et al (2016) for technical descriptions of coupling and model performance.…”

“…For a detailed description of CLM3.5, the readers are referred to Oleson et al (2008). CLM3.5 was used in this study, instead of its most recent version, to keep the modeling framework consistent to Kurtz et al (2016).…”

“…Soil moisture (SM) constitutes a key variable in major processes of the hydrologic cycle related to infiltration and runoff generation, root water uptake and plant transpiration, and evaporation (Vereecken et al, 2016). Thus, soil moisture strongly influences the partitioning of incoming radiative energy into latent and sensible heat and significantly affects the land surface energy and water budgets.…”

“…We used CLM3.5 coupled to the parallel data assimilation framework (PDAF) library (Kurtz et al, 2016;Nerger and Hiller, 2013). PDAF is computationally efficient due to its parallelization of data assimilation routines and is suitable for applications at large spatial scales and high-resolution over long time periods (Kurtz et al, 2016).…”