Search

In this study, the impact of varying soil moisture heterogeneity (spatial variance and structure) on the development of the convective boundary layer (CBL) and shallow cumulus clouds was investigated. Applying soil moisture heterogeneity generated via spatially correlated Gaussian random fields based on a power law model and idealized atmospheric vertical profiles as initial conditions, three sets of large‐eddy simulations provide insight in the influence of soil moisture heterogeneity on the ensuing growth of the CBL and development of shallow cumulus clouds. A sensitivity on the strongly‐, weakly‐, and unstructured soil moisture heterogeneity is investigated. The simulation results show that domain‐averaged land surface sensible heat and latent heat flux change strongly with changing soil moisture variance because of the interactions between surface heterogeneity and induced circulations, while domain means of soil moisture are identical. Vertical profiles of boundary layer characteristics are strongly influenced by the surface energy partitioning and induced circulations, especially the profiles of liquid water and liquid water flux. The amount of liquid water and liquid water flux increase with increasing structure. In addition, the liquid water path (LWP) is higher in case of strongly‐structured heterogeneity because more available energy is partitioned into latent heat and more intensive updrafts exist. Interestingly, the increase of LWP with increasing soil moisture variance only occurs in the strongly‐structured cases, which suggests that soil moisture variance and structure work conjunctively in the surface energy partitioning and the cloud formation.

Human activities, such as human water use, have been shown to directly influence terrestrial water fluxes and states. Simulations of soil moisture, river discharge, evapotranspiration, and groundwater storage are significantly improved, if human interactions, such as irrigation and groundwater abstraction, are incorporated. Yet improvements through the incorporation of human water use on the simulation of local and remote precipitation are rarely studied but may contribute to the skill of land surface fluxes. In this study, we evaluate the impact of human water use on the skill of evapotranspiration and precipitation in a fully coupled bedrock-to-atmosphere modeling platform. The results show that human water use can potentially increase the skill of the simulations across scales. However, observational uncertainty at the watershed scale limits the identification of model deficiencies and added value related to human water use. Locally, daily precipitation statistics potentially benefit from the incorporation of human water use. Although the incorporation of human water use does not remove the wet bias, it can increase the model skill.

In the hydrologic cycle, continental landmasses constitute a sink for atmospheric moisture as annual terrestrial precipitation commonly exceeds evapotranspiration. Simultaneously, humans intervene in the hydrologic cycle and pump groundwater to sustain, for example, drinking water and food production. Here we use a coupled groundwater‐to‐atmosphere modeling platform, set up over the European continent, to study the influence of groundwater pumping and irrigation on the net atmospheric moisture import of the continental landmasses, which defines the strength of the continental sink. Water use scenarios are constructed to account for uncertainties of atmospheric feedback during the heatwave year 2003. We find that human water use induces groundwater‐to‐atmosphere feedback, which potentially weaken the continental sink over arid watersheds in southern Europe. This feedback is linked to groundwater storage, which suggests that atmospheric feedbacks to human water use may contribute to drying of watersheds, thereby raising water resources and socio‐economic concerns beyond local sustainability considerations.

The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.

The remarkable complexity of soil and its importance to a wide range of ecosystem services presents 133 major challenges to the modeling of soil processes. Although major progress in soil models has 134 occurred in the last decades, models of soil processes remain disjointed between disciplines or 135 ecosystem services, with considerable uncertainty remaining in the quality of predictions and several 136 challenges that remain yet to be addressed. Firstly, there is a need to improve exchange of knowledge 137 and experience amongst the different disciplines in soil science and to reach out to other Earth science 138 communities. Secondly, the community needs to develop a new generation of soil models based on a 139 systemic approach comprising relevant physical, chemical, and biological processes to address critical 140 knowledge gaps in our understanding of soil processes and their interactions. Overcoming these 141 challenges will facilitate exchanges between soil modeling and climate, plant, and social science 142 modeling communities. It will allow us to contribute to preserve and improve our assessment of 143 ecosystem services and advance our understanding of climate-change feedback mechanisms, amongst 144 others, thereby facilitating and strengthening communication among scientific disciplines and society. 145 In this paper we review the role of modeling soil processes in quantifying key soil processes that shape 146 ecosystem services with focus on provisioning and regulating services. We then identify key 147 challenges in modeling soil processes including the systematic incorporation of heterogeneity and 148 uncertainty, the integration of data and models, and strategies for effective integration of knowledge 149 on physical, chemical and biological soil processes. We discuss how the soil modeling community 150 could best interface with modern modeling activities in other disciplines such as climate, ecology, and 151 plant research and how to weave novel observation and measurement techniques into soil models. We 152 propose to establish an international soil modeling consortium to coherently advance soil modeling 153 activities and foster communication with other Earth science disciplines. Such a consortium should 154 promote soil modeling platforms and data repository for model development, calibration and inter-155 comparison essential for addressing contemporary challenges.

Data assimilation has become a popular method to integrate observations from multiple sources with land surface models to improve predictions of the water and energy cycles of the soil-vegetation-atmosphere continuum. Multivariate data assimilation refers to the simultaneous assimilation of observation data from multiple model state variables into a simulation model. In recent years, several land data assimilation systems have been developed in different research agencies. Because of the software availability or adaptability, these systems are not easy to apply for the purpose of multivariate land data assimilation research. We developed an open source multivariate land data assimilation framework (DasPy) which is implemented using the Python script language mixed with the C++ and Fortran programming languages. LETKF (Local Ensemble Transform Kalman Filter) is implemented as the main data assimilation algorithm, and uncertainties in the data assimilation can be introduced by perturbed atmospheric forcing data, and represented by perturbed soil and vegetation parameters and model initial conditions. The Community Land Model (CLM) was integrated as the model operator. The implementation allows also parameter estimation (soil properties and/or leaf area index) on the basis of the joint state and parameter estimation approach. The Community Microwave Emission Modelling platform (CMEM), COsmic-ray Soil Moisture Interaction Code (COSMIC) and the Two-Source Formulation (TSF) were integrated as observation operators for the assimilation of L-band passive microwave, cosmic-ray soil moisture probe and land surface temperature measurements, respectively. DasPy has been evaluated in several assimilation studies of neutron count intensity (soil moisture), L-band brightness temperature and land surface temperature. DasPy is parallelized using the hybrid Message Passing Interface and Open Multi-Processing techniques. All the input and output data flows are organized efficiently using the commonly used NetCDF file format. Online 1-D and 2-D visualization of data assimilation results is also implemented to facilitate the post simulation analysis. In summary, DasPy is a ready to use open source parallel multivariate land data assimilation framework.

Continental-scale hyper-resolution simulations constitute a grand challenge in characterizing non-linear feedbacks of states and fluxes of the coupled water, energy, and biogeochemical cycles of terrestrial systems. Tackling this challenge requires advanced coupling and supercomputing technologies for earth system models that are discussed in this study, utilizing the example of the implementation of the newly developed Terrestrial Systems Modeling Platform (TerrSysMP) on JUQUEEN (IBM Blue Gene/Q) of the Jülich Supercomputing Centre, Germany. The applied coupling strategies rely on the Multiple Program Multiple Data (MPMD) paradigm and require memory and load balancing considerations in the exchange of the coupling fields between different component models and allocation of computational resources, respectively. These considerations can be reached with advanced profiling and tracing tools leading to the efficient use of massively parallel computing environments, which is then mainly determined by the parallel performance of individual component models. However, the problem of model I/O and initialization in the peta-scale range requires major attention, because this constitutes a true big data challenge in the perspective of future exa-scale capabilities, which is unsolved.

Monitoring Earth’s terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth’s terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface‐subsurface interactions due to fine‐scale topography and vegetation; improved representation of land‐atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

We present the results of a unique, parallel scaling study using a 3-D variably saturated flow problem including land surface processes that ranges from a single processor to a maximum number of 16,384 processors. In the applied finite difference framework and for a fixed problem size per processor, this results in a maximum number of approximately 8 x 10(9) grid cells (unknowns). Detailed timing information shows that the applied simulation platform ParFlow exhibits excellent parallel efficiency. This study demonstrates that regional scale hydrologic simulations on the order of 10(3) km(2) are feasible at hydrologic resolution (similar to 10(0)-10(1) m laterally, 10(-2)-10(-1) m vertically) with reasonable computation times, which has been previously assumed to be an intractable computational problem.