Comparison of Algorithms and Parameterisations for Infiltration into Organic-Covered Permafrost Soils

Infiltration into frozen and unfrozen soils is critical in hydrology, controlling active layer soil water dynamics and influencing runoff. Few Land Surface Models (LSMs) and Hydrological Models (HMs) have been developed, adapted or tested for frozen conditions and permafrost soils. Considering the vast geographical area influenced by freeze/thaw processes and permafrost, and the rapid environmental change observed worldwide in these regions, a need exists to improve models to better represent their hydrology. In this study, various infiltration algorithms and parameterisation methods, which are commonly employed in current LSMs and HMs were tested against detailed measurements at three sites in Canada’s discontinuous permafrost region with organic soil depths ranging from 0.02 to 3 m. Field data from two consecutive years were used to calibrate and evaluate the infiltration algorithms and parameterisations. Important conclusions include: (1) the single most important factor that controls the infiltration at permafrost sites is ground thaw depth, (2) differences among the simulated infiltration by different algorithms and parameterisations were only found when the ground was frozen or during the initial fast thawing stages, but not after ground thaw reaches a critical depth of 15 to 30 cm, (3) despite similarities in simulated total infiltration after ground thaw reaches the critical depth, the choice of algorithm influenced the distribution of water among the soil layers, and (4) the ice impedance factor for hydraulic conductivity, which is commonly used in LSMs and HMs, may not be necessary once the water potential driven frozen soil parameterisation is employed. Results from this work provide guidelines that can be directly implemented in LSMs and HMs to improve their application in organic covered permafrost soils

Simulation of Water and Energy Fluxes in an Old-Growth Seasonal Temperate Rain Forest Using the Simultaneous Heat and Water (SHAW) Model

In the Pacific Northwest (PNW), concern about the impacts of climate and land cover change on water resources and flood-generating processes emphasizes the need for a mechanistic understanding of the interactions between forest canopies and hydrologic processes. Detailed measurements during the 1999 and 2000 hydrologic years were used to modify the Simultaneous Heat and Water (SHAW) model for application in forested systems. Major changes to the model include improved representation of rainfall interception and stomatal conductance dynamics. The model was developed for the 1999 hydrologic year and tested for the 2000 hydrologic year without modification of the site parameters. The model effectively simulated throughfall, soil water content profiles, and shallow soil temperatures for both years. The largest discrepancies between soil moisture and temperature were observed during periods of discontinuous snow cover due to spatial variability that was not explicitly simulated by the model. Soil warming at bare locations was delayed until most of the snow cover ablated because of the large heat sink associated with the residual snow patches. During the summer, simulated transpiration decreased from a maximum monthly mean of 2.2 mm day⁻¹ in July to 1.3 mm day⁻¹ in September as a result of decreasing soil moisture and declining net radiation. The results indicate that a relatively simple representation of the vegetation canopy can accurately simulate seasonal hydrologic fluxes in this environment, except during periods of discontinuous snow cover

Estimating Actual Evapotranspiration from Stony-Soils in Montane Ecosystems

Quantification of evapotranspiration (ET) is crucial for understanding the water balance and for efficient water resources planning. Agricultural settings have received most attention regarding ET measurements while less knowledge is available for actual ET (ETA) in natural ecosystems, many of which have soils containing significant amounts of stones. This study is focused on modelling ETA from stony soil, particularly in montane ecosystems where we estimate the contribution of stone content on water retention properties in soil. We employed a numerical model (HYDRUS-1D) to simulate ETA in natural settings in northern Utah and southern Idaho during the 2015 and 2016 growing seasons based on meteorological and soil moisture measurements at a range of depths. We simulated ETA under three different scenarios, considering soil with (i) no stones, (ii) highly porous stones, and (iii) negligibly porous stones. The simulation results showed significant overestimation of ETA when neglecting stones in comparison to ETA measured by eddy covariance. ETA estimates with negligibly porous stones were lower for all cases due to the decrease in soil water storage compared with estimates made considering highly porous stones. Assumptions of highly porous or negligibly porous stones led to reductions in simulated ETA of between 10% and 30%, respectively, compared with no stones. These results reveal the important role played by soil stones, which can impact the water balance by altering available soil moisture and thus ETA in montane ecosystems

A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies

This work advances a unified approach to process-based hydrologic modeling, which we term the ‘‘Structure for Unifying Multiple Modeling Alternatives (SUMMA).’’ The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty.Keywords: unified model, scaling behavior, hydrometeorologyKeywords: unified model, scaling behavior, hydrometeorolog

A Unified Approach for Process-Based Hydrologic Modeling: 2. Model Implementation and Case Studies

This work advances a unified approach to process-based hydrologic modeling, which we term the “Structure for Unifying Multiple Modeling Alternatives (SUMMA).” The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty

Examining Interactions Between and Among Predictors of Net Ecosystem Exchange: A Machine Learning Approach in a Semi-arid Landscape

Abstract Net ecosystem exchange (NEE) is an essential climate indicator of the direction and magnitude of carbon dioxide (CO2) transfer between land surfaces and the atmosphere. Improved estimates of NEE can serve to better constrain spatiotemporal characteristics of terrestrial carbon fluxes, improve verification of land models, and advance monitoring of Earth’s terrestrial ecosystems. Spatiotemporal NEE information developed by combining ground-based flux tower observations and spatiotemporal remote sensing datasets are of potential value in benchmarking land models. We apply a machine learning approach (Random Forest (RF)) to develop spatiotemporally varying NEE estimates using observations from a flux tower and several variables that can potentially be retrieved from satellite data and are related to ecosystem dynamics. Specific variables in model development include a mixture of remotely sensed (fraction of photosynthetically active radiation (fPAR), Leaf Area Index (LAI)) and ground-based data (soil moisture, downward solar radiation, precipitation and mean air temperature) in a complex landscape of the Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho, USA. Predicted results show good agreement with the observed data for the NEE (r2 = 0.87). We then validate the temporal pattern of the NEE generated by the RF model for two independent years at the two sites not used in the development of the model. The model development process revealed that the most important predictors include LAI, downward solar radiation, and soil moisture. This work provides a demonstration of the potential power of machine learning methods for combining a variety of observational datasets to create spatiotemporally extensive datasets for land model verification and benchmarking

Hydrologic response and recovery to prescribed fire and vegetation removal in a small rangeland catchment

Prescribed fire can be used to return wild lands to their natural fire cycle, control invasive weeds, and reduce fuel loads, but there are gaps in the understanding of post-disturbance responses of vegetation and hydrology. The impact of a prescribed fire and subsequent aspen cutting on evapotranspiration (ET) and streamflow was assessed for the Upper Sheep Creek catchment, a 26-ha headwater catchment dominated by low sagebrush, mountain big sagebrush, and aspen within the Reynolds Creek Experimental Watershed. The 2007 prescribed fire consumed 100% of the mountain big sagebrush and approximately 21% of the low sagebrush. The aspen, which were mostly untouched by the fire, were cut in the fall of 2008. Post-disturbance ET and vegetation recovery were related to the loss of rooting depth. ET recovered within 2 years on the low sagebrush area with limited rooting depth, while that on the deeper-rooted mountain big sagebrush area took 4 years to recover. ET from the aspen trees, which can sprout from existing roots, recovered within 2 years. The influence of vegetation disturbance on streamflow was assessed using both empirical time trend analysis and process-based modelling. Although both approaches suggested approximately a 20% increase in streamflow during the 6 years post-disturbance, results from the empirical time trend analysis were marginally significant (p = 0·055), while those from the process-based modelling were not statistically significant. Marginal streamflow response can be attributed to rapid post-disturbance recovery of the aspen where most of the streamflow originates

Past and projected future changes in snowpack and soil frost at the Hubbard Brook Experimental Forest, New Hampshire, USA

Long-term data from the Hubbard Brook Experimental Forest in New Hampshire show that air temperature has increased by about 1°C over the last half century. The warmer climate has caused significant declines in snow depth, snow water equivalent and snow cover duration. Paradoxically, it has been suggested that warmer air temperatures may result in colder soils and more soil frost, as warming leads to a reduction in snow cover insulating soils during winter. Hubbard Brook has one of the longest records of direct field measurements of soil frost in the United States. Historical records show no long-term trends in maximum annual frost depth, which is possibly confounded by high interannual variability and infrequency of major soil frost events. As a complement to field measurements, soil frost can be modelled reliably using knowledge of the physics of energy and water transfer. We simulated soil freezing and thawing to the year 2100 using a soil energy and water balance model driven by statistically downscaled climate change projections from three atmosphere-ocean general circulation models under two emission scenarios. Results indicated no major changes in maximum annual frost depth and only a slight increase in number of freeze–thaw events. The most important change suggested by the model is a decline in the number of days with soil frost, stemming from a concurrent decline in the number of snow-covered days. This shortening of the frost-covered period has important implications for forest ecosystem processes such as tree phenology and growth, hydrological flowpaths during winter, and biogeochemical processes in soil