Investigating the influence of two different flow routing algorithms on soil–water–vegetation interactions using the dynamic ecosystem model LPJ‐GUESS

Abstract

This paper compares two flow routing algorithms' influences on ecohydrological estimations in a northern peatland catchment, within the framework of an arctic‐enabled version of the dynamic ecosystem model LPJ‐GUESS. Accurate hydrological estimations are needed to fully capture vegetation dynamics and carbon fluxes in the subarctic peatland enviroment. A previously proposed distributed hydrological method based on the single flow (SF) algorithm extracted topographic indices has shown to improve runoff estimations in LPJ‐GUESS. This paper investigates model performance differences caused by two flow routing algorithms, and importantly both permafrost processes and peatland hydrology are included in the model. The newly developed triangular form‐based multiple flow (TFM) is selected due to its improved consideration of flow continuity and more realistic flow estimation over flat surfaces. A variety of measured data is included to assess both hydrological and ecological accuracy, and the results demonstrate that the choice of flow algorithm does matter for mesoscale ecohydrology applications. The allowance of flow convergence and consideration of flow partition differences from different terrain forms in the TFM algorithm yield better correspondence with the observed hydrological processes and also carbon fluxes. By directing flow to only one downslope cell together with its poorer depiction of flow over flat areas, the SF algorithm can result in too high runoff estimations for low‐flat regions and overestimate carbon uptake and release in the peatland. The results of this study also highlight the need for care when selecting flow routing algorithms for biogeochemical estimations, especially within hydrologically and climatically sensitive environments. Copyright © 2014 John Wiley & Sons, Ltd.

Modelling Holocene peatland dynamics with an individual-based dynamic vegetation model

“…In Stordalen, the southern and western parts of the mire are normally fed from higher areas centrally and to the east (Johansson et al, 2006), and recent warming has resulted in the runoff rate increasing from the elevated sites to the low lying areas that have slowly become increasingly waterlogged. Tang et al (2015) showed the importance of including the 550 slope and drainage area in order to distribute water within the catchment area, and demonstrated how these factors influence vegetation distribution and carbon fluxes in LPJ-GUESS.…”

“…The increase in wet areas at Stordalen is however associated with peat soil subsidence during permafrost thaw and the resultant change in hydrological networks across the mire landscape (Åkerman & Johansson 2008), a complex physical process not included in our model. Another factor that contributed to the recent dynamics of the site is the influence of the underlying topography on the sub-surface flow and the 545 addition of water through run-on from the surrounding catchment (Tang et al, 2015). Though we incorporated lateral exchange of water between the simulated patches, we ignored the effect of underlying topography that affects the water movement.…”

“…global scales, together with associated biogeochemical cycles and climate feedbacks, in particular through the carbon cycle (Friedlingstein et al, 2006;Sitch et al, 2008;Smith et al, 2001;Strandberg et al, 2014;Zhang et al, 2014). Only a few DGVMs include representations of the unique vegetation, biophysical and biogeochemical characteristics of peatland ecosystems (Kleinen et al, 2012;Tang et al, 2015;Wania et al, 2009a, b). Model formulations of peat accumulation and decay have been proposed and demonstrated at the site scale (Frolking et al, 2010) but have not yet, to our knowledge, been implemented within the framework of a DGVM, or applied at larger spatial scales than a single study site or landscape.…”

Parameter interactions and sensitivity analysis for modelling carbon heat and water fluxes in a natural peatland, using CoupModel v5

“…Although while the existence of interactions between the processes and their parameters is supposed to be less dependent on site conditions and model structure, the exact shape of the connections, constraint parameter ranges, as well as the relevance of the specific processes and the specific interactions might strongly depend on these factors. Still, one or more of the following parameters that we identified as most influential, correspond to key parameters in other studies using other models and partly different ecosystems; the respiration rate coefficients, radiation use efficiency, transpiration coefficients or the soil water retention capacity were among the most sensitive parameters for NEE, its components or yield, respectively, in, e.g., the PCARS (Frolking et al, 2002) and the GUESS-ROMUL (Yurova et al, 2007) model on peatland, the SiB v2.5 model on a forest area including some wetlands (Prihodko et al, 2008), the LPJ-GUESS model on forest and herbaceous vegetation (Pappas et al, 2013), the EPIC model on cropland (Wang et al, 2005), the BIOME-BGC model for different tree species (Tatarinov and Cienciala, 2006), or the ACASA (Staudt et al, 2010), the 3-PG (Esprey et al, 2004;Xenakis et al, 2008), the FORUG (Verbeeck et al, 2006) or the DRAINMOD-FOREST (Tian et al, 2014) model on forest. These sensitivities seem to be therefore quite independent of model structure, included processes and parameters used for calibration and apply to different types of ecosystems.…”

Geomorphometry: Today and Tomorrow

“…A number of recent applications have also tackled sources of error, the various ways uncertainty can be estimated and handled in terrain modeling workflows, how this knowledge can be used to assess 'fitness-for-use' in specific applications, and the new opportunities for multi-scale analysis and cross-scale inference afforded by the increasing availability of DEMs across a broad range of scales. A series of stellar case studies shows how the measurement of error and uncertainty accompanying terrain modeling workflows might be used to improve our understanding of predictive vegetation modeling [9], soil redistribution resulting from water erosion [10], how catchment area calculations, slope estimates and numerical simulations of landscape development [11] and the soil-water-vegetation interactions in the LPJ-GUESS dynamic ecosystem model [12] are influenced by the choice of flow direction algorithm, and how a new sub-grid TOPMODEL parameterization and the associated uncertainties influence the modeling of the spatiotemporal dynamics of global wetlands [13]. More applications like these will be needed in the future.…”