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The integrated distributed hydrological model ParFlow-CLM was used to predict water and energy transport between subsurface, land surface, and atmosphere for the Stettbach headwater catchment, Germany. Based on this model, global sensitivity analysis was performed using the Latin-Hypercube (LH) sampling strategy followed by the One-factor-Ata-Time (OAT) method to identify the most influential and interactive parameters affecting the main hydrologic processes. total 12 parameters were evaluated including soil hydraulic properties, storage, Manning coefficient, leaf area index, stem area index, and aerodynamic resistance that characterize water and energy fluxes in soil and vegetation. In addition, the sensitivity analysis was also carried out for different slopes and meteorological conditions to test the transferability of the results regions with other topographies and climates. Our results show that the simulated energy fluxes, i.e. latent heat flux and sensible heat flux are sensitive to the parameters such as wilting point, leaf area index, and stem area index, especially for steep slope and subarctic climate conditions. The simulated soil evaporation, plant transpiration, infiltration, and runoff, are most sensitive to soil porosity, the van Genuchten parameter n representing the soil pore size distribution, soil wilting point, and leaf area index. The subsurface soil water storage and groundwater storage are most sensitive to soil porosity, while the surface water storage was most sensitive to the soil roughness parameter. For the different slope and climate conditions, the rank order of input parameter sensitivity is consistent, but the magnitude of parameter sensitivity is very different. The strongest deviation in parameter sensitivity occurred for sensible heat flux under the different slope conditions as well as for transpiration under different climate conditions. Overall, this study provides an insight into the most important input parameters that control hydrological fluxes and how the simulated variables vary with the change in parameter values, which can improve our understanding of the key processes in the model and help us to reduce the computational demands of completing multiple simulations of expensive domains. [ABSTRACT FROM AUTHOR]

The inclusion of perennial, woody crops in land surface models is crucial to address their role in carbon (C) sequestration, food production, and water requirements under climate change. To help quantifying the biogeochemical and biogeophysical processes associated with these agro-ecosystems, we developed and tested a new sub-model, CLM-FruitTree, for deciduous fruit orchards within the framework of the Community Land Model version 5 (CLM5). The model development included (1) a new perennial crop phenology description, (2) an adapted C and nitrogen allocation scheme, considering both storage and photosynthetic growth of annual and perennial plant organs, (3) typical management practices associated with fruit orchards, and (4) the parameterization of an apple plant functional type. CLM-FruitTree was tested using extensive field measurements from an apple orchard in South Tyrol, Italy. Growth and partitioning of biomass to the individual plant components was well represented by CLM-FruitTree and average yield was predicted within 2.3 % of the observed values despite low simulated inter-annual variability compared to observations. The simulated seasonal course of C, energy, and water fluxes was in good agreement with the EC measurements owing to the accurate representation of the prolonged growing season and typical leaf area development of the orchard. We found that gross primary production, net radiation, and latent heat flux were highly correlated (r > 0.94) with EC measurements and showed little bias (< ±5 %). Simulated respiration components, sensible heat, and ground heat flux were less consistent with observations. This was attributed to simplifications in the orchard structure and to the presence of additional management practices that are not yet represented in CLM-FruitTree. Finally, the results suggested that the representation of microbial and autotrophic respiration, and energy partitioning in complex, heterogeneous canopies in CLM5 requires further attention. The new sub-model CLM-FruitTree improved the representation of agricultural systems in CLM5 and can be used to study land surface processes in fruit orchards at the local, regional or larger scale. [ABSTRACT FROM AUTHOR]

The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site specific field data focussing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields as well as water, energy and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines after Lu et al. (2017) in CLM5; (2) implementing plant specific parameters for sugar beet, potatoes and winter wheat, thereby adding these crop functional types (CFT) to the list of actively managed crops in CLM5; (3) introducing a cover cropping subroutine that allows multiple crop types on the same column within one year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is a common agricultural management technique in humid and sub-humid regions. We compared simulation results with field data and found that both the parameterization of the CFTs, as well as the winter wheat subroutines, led to a significant simulation improvement in terms of energy fluxes, leaf area index (LAI), net ecosystem exchange (RMSE reduction for latent and sensible heat by up to 57 % and 59 % respectively) and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI curve and latent heat flux (reduction of winter time RMSE for latent heat flux by 42 %). We anticipate that our model modifications offer opportunities to improve yield predictions, to study the effects of large-scale cover cropping on energy fluxes, soil carbon and nitrogen pools, and soil water storage in future studies with CLM5. [ABSTRACT FROM AUTHOR]