This study investigates and compares soil moisture and
hydrology projections of broadly used land models with permafrost processes
and highlights the causes and impacts of permafrost zone soil moisture
projections. Climate models project warmer temperatures and increases in
precipitation (
Hydrology plays a fundamental role in permafrost landscapes by modulating complex interactions among biogeochemical cycling (Frey and Mcclelland, 2009; Newman et al., 2015; Throckmorton et al., 2015), geomorphology (Grosse et al., 2013; Kanevskiy et al., 2017; Lara et al., 2015; Liljedahl et al., 2016), and ecosystem structure and function (Andresen et al., 2017; Avis et al., 2011; Oberbauer et al., 2007). Permafrost has a strong influence on hydrology by controlling surface and subsurface distribution and the storage, drainage and routing of water. Permafrost prevents vertical water flow, which often leads to saturated soil conditions in continuous permafrost while confining subsurface flow through perennially unfrozen zones (a.k.a. taliks) in discontinuous permafrost (Jafarov et al., 2018; Walvoord and Kurylyk, 2016). However, with the observed (Streletskiy et al., 2008) and predicted (Slater and Lawrence, 2013) thawing of permafrost, there is a large uncertainty in the future hydrological state of permafrost landscapes and in the associated responses such as the permafrost carbon–climate feedback.
The timing and magnitude of the permafrost carbon–climate feedback is, in
part, governed by changes in surface hydrology, through the regulation by
soil moisture of the form of carbon emissions from thawing labile soils and
microbial decomposition as either
Earth system models project an intensification of the hydrological cycle characterized by a general increase in the magnitude of water fluxes (e.g., precipitation, evapotranspiration and runoff) in northern latitudes (Rawlins et al., 2010; Swenson et al., 2012). In addition, intensification of the hydrological cycle is likely to modify the spatial and temporal patterns of water in the landscape. However, the spatial variability, timing and reasons for future changes in hydrology in terrestrial landscapes in the Arctic are unclear, and variability in projections of these features by current terrestrial hydrology applied in the Arctic has not been well documented. Therefore, there is an urgent need to assess and better understand hydrology simulations in land models and how differences in process representation affect projections of permafrost landscapes.
Upgrades in permafrost representation such as freeze and thaw processes in the land component of Earth system models have improved understanding of the evolution of hydrology in high northern latitudes. Particularly, soil thermal dynamics and active-layer hydrology upgrades include the effects of unfrozen water on phase change, insulation by snow (Peng et al., 2016), organic soils (Jafarov and Schaefer, 2016; Lawrence et al., 2008) and the hydraulic properties of frozen soils (Swenson et al., 2012). Nonetheless, large discrepancies in projections remain as the current generation of models substantially differ in soil thermal dynamics (e.g., Peng et al., 2016; Wang et al., 2016). In particular, variability among current models' simulations of the impact of permafrost thaw on soil water and hydrological states is not well documented. Therefore, in this study we analyze the output of a collection of widely used permafrost-enabled land models. These models participated in the Permafrost Carbon Network Model Intercomparison Project (PCN-MIP; McGuire et al., 2018, 2016) and contained the state-of-the-art representations of soil thermal dynamics in high latitudes at that time. In particular, we assess how changes in active-layer thickness and permafrost thaw influence near-surface soil moisture and hydrology projections under climate change. In addition, we provide comments on the main gaps and challenges in permafrost hydrology simulations and highlight the potential implications for the permafrost carbon–climate feedback.
This study assesses a collection of terrestrial simulations from models that participated in the PCN-MIP (McGuire et al., 2018, 2016; Table 1). The analysis presented here is unique as it focuses on the hydrological component of these models. Table 2 describes the main hydrological characteristics for each model. Additional details on participating models regarding soil thermal properties, snow, soil carbon and forcing trends can be found in previous PCN-MIP studies (e.g., McGuire et al., 2016; Koven et al., 2015; Wang et al., 2016; Peng et al., 2016). It is important to note that the versions of the models presented in this study are from McGuire et al. (2016, 2018) and some additional improvements to individual models may have been made since then.
The simulation protocol is described in detail in McGuire et al. (2016, 2018). In brief,
models' simulations were conducted from 1960 to 2299, partitioned by
historic (1960–2009) and future simulations (2010–2299), where future
simulations were forced with a common projected climate derived from a fully
coupled climate model simulation (CCSM4; Gent et al., 2011). Historic
atmospheric forcing datasets (Table 1; e.g., climate, atmospheric
The PCN model intercomparison uses the output from a single Earth system model climate projection and was motivated by a desire to keep the experimental design simple and computationally tractable. Clearly, using just one climate projection does not allow us to explore the impact of the broad range of potential climate outcomes that are seen across the CMIP5 (Coupled Model Intercomparison Project Phase 5) models. Instead, the PCN suite of simulations allows for a relatively controlled analysis of the spread of model responses to a single representative climate trajectory. The selection of CCSM4 as the climate projection model was motivated partly by convenience and also because it was one of the only models that had been run out to the year 2300 at the time of the PCN experiments. Further, as noted in McGuire et al. (2018), CCSM4 late 20th-century climate biases in the Arctic were among the lowest across the CMIP5 model archive. It should be noted that the use of a single climate projection means that the results presented here should be viewed as indicative of just one possible permafrost hydrologic trajectory. As we will show, even under this single climate trajectory, the range of hydrologic responses in the models is broad, indicating high structural uncertainty across models with respect to this particular aspect of the Arctic system response to global climate change.
Our analysis focused on the permafrost regions in the Northern Hemisphere
north of 45
Soil hydrologically active column configuration for each participating model. Numbers and arrows indicate full soil configuration of nonhydrologically active bedrock layers. Colors represent the number of layers.
Model descriptions and driving datasets.
We compared model simulations with long-term (1970–1999) mean monthly
discharge data from Dai et al. (2009). We computed model total annual discharge
(sum of surface and subsurface runoff) for the main river basins in the
permafrost region of North America (Mackenzie, Yukon) and Russia (Yenisey,
Lena). In particular, we compared (i) annual runoff anomalies, (ii) correlation coefficients, and (iii) distributions of annual discharge between
gauge data and models' simulations for the 30-year period of 1970–1999.
Gauge stations from major permafrost river basins used for simulation
comparison include (i) Arctic Red, Canada (67.46
Hydrology and soil thermal characteristics of participating models.
Air temperature forcing from greenhouse-gas emissions shows an increase of
Simulated annual mean changes in air temperature,
near-surface permafrost area, near-surface soil moisture and hydrology
variables relative to 1960 (RCP 8.5). Annual mean is computed from monthly
output values. The black line represents the models' ensemble mean, and the
gray area is the ensemble standard deviation. Panels
Spatial variability of projected changes in surface soil moisture (%) among models. Depicted changes are calculated as the difference between the 2071 to 2100 average and the 1960 to 1989 average. Colored area represents the initial simulated permafrost domain of 1960 for each model.
To understand why models projected upper soil drying despite increases in
the net precipitation (
Responses of August near-surface (0–20 cm) soil moisture to
ALT changes. Each box represents a range of
Models may project surface soil drying, but the hydrological pathways through
which this drying occurs appear to differ across models. The diversity of
precipitation partitioning (Fig. 5) demonstrates that specific
representations and parameterizations for ET and runoff are not consistent
across models. Though some models maintain a similar
Precipitation partitioning between total runoff and
evapotranspiration for participating models. Markers and arrows indicate the
change from initial period (1960–1989 average) to final period (2270–2299
average). Diagonal dashed lines represent the ensemble rainfall mean for the
initial (0.74 mm d
Evapotranspiration from the permafrost area is projected to rise in all
models driven by warmer air temperatures and more productive vegetation, but
the amplitude of that trend varies widely. The average projected
evapotranspiration increase is
Runoff is also projected to increase with projections across models being
highly variable (Fig. 2g). The change in the models' ensemble mean between
1960 and 2299 was
Comparison between gauge station data and runoff simulations from the major
river basins in the permafrost region shows that most models agree on the
long-term timing (Fig. 6, Table 3), but the magnitude is generally
underestimated (Fig. 7). The gauge discharge mean for the four river
basins is
Runoff anomaly comparison between gauge data and models simulations for the period 1970–1999.
Discharge comparison between gauge station data and model output for each river basin. Dashed line indicates mean annual discharge at gauge station. Boxplots derived from mean annual-discharge (total runoff) simulations for the period of 1970 to 1999.
The net water balance (
Correlation coefficients between simulated annual total runoff and gauge mean annual discharge 1970 to 1999. SIBCASA correlations are for surface runoff.
This study assessed near-surface soil moisture and hydrology projections in the permafrost region using widely used land models that represent permafrost. Most models showed near-surface drying despite the externally forced intensification of the water cycle driven by climate change. Drying was generally associated with increases of active-layer thickness and permafrost degradation in a warming climate. We show that the timing and magnitude of projected soil moisture changes vary widely across models, pointing to an uncertain future in permafrost hydrology and associated climatic feedbacks. In this section, we review the role of projected permafrost loss and active-layer thickening on soil moisture changes and some potential sources of variability among models. In addition, we comment on the potential effects of soil moisture projections on the permafrost carbon–climate feedback. It is important to note that this study is more qualitative in nature and does not focus on the detail of magnitude or spatial patterns of model signatures.
Increases in net precipitation and the counterintuitive drying of the top
soil in the permafrost region suggest that soil column processes such as
changes in active-layer thickness (ALT) and activation of subsurface
drainage with permafrost thaw are acting to dry the top soil layers (Fig. 8a). In general, models represent impermeable soils when frozen. Then, as
soils thaw at progressive depths in the summer, liquid water infiltrates
further into the active layer, draining deeper into the thawed soil column
(Avis et al., 2011; Lawrence et al., 2015;
Swenson et al., 2012). However, relevant soil column processes related to
thermokarst by thawing of excess ground ice (Lee et al., 2014)
are limited in these simulations despite their significant occurrence in the
permafrost region (Olefeldt et
al., 2016). As permafrost thaws, ground ice melts, potentially reducing the
volume of the soil column and changing the hydrological properties of the
soil (Aas et al., 2019; Nitzbon et
al., 2019). This would occur where soil surface elevation drops through
sudden collapse or slow deformation by an amount equal to or greater than
the increased depth of annual thaw (Fig. 8b). This mechanism, not
represented in current large-scale models, could result in projected
increases or no change in the water table over time as observed by long-term
studies (Andresen and
Lougheed, 2015; Mauritz et al., 2017; Natali et al., 2015). Subsidence of
12–13 cm has been observed in northern Alaska over a 5-year period, which
represents a volume loss of about 25 % of the average ALT for that region
(
Schematic of changes in the soil column moisture
Recent efforts have been made to address the high subgrid heterogeneity of fine-scale mechanisms including soil subsidence (Aas et al., 2019), hillslope hydrology, talik and thermokarst development (Jafarov et al., 2018), ice wedge degradation (Abolt et al., 2018; Liljedahl et al., 2016; Nitzbon et al., 2019), vertical and lateral heat transfer on permafrost thaw and groundwater flow (Kurylyk et al., 2016), and lateral water fluxes (Nitzbon et al., 2019). These processes are known to have a major role on surface and subsurface hydrology, and their implementation in large-scale models is needed. Other important challenges in land models' hydrology include representation of the significant area dynamics of the ubiquitous smaller, shallow water bodies observed over recent decades (Andresen and Lougheed, 2015; Jones et al., 2011; Roach et al., 2011; Smith et al., 2005). These systems are either lacking in simulations (polygon ponds and small lakes) or assumed to be static systems in simulations (large lakes). The implementation of surface hydrology dynamics and permafrost processes in large-scale land models will help reduce uncertainty in our ability to predict the future hydrological state of the Arctic and the associated climatic feedbacks. It is important to note that all these processes require data for model calibration, verification and evaluation that are commonly absent at large scales. Permafrost hydrology will only advance through synergistic efforts between field researchers and modelers.
Differences in representations of soil thermal dynamics can directly affect hydrology through timing of the freezing–thawing cycle and by altering the rates of permafrost loss and subsurface drainage (Finney et al., 2012). McGuire et al. (2016) and Peng et al. (2016) show that these models exhibit considerable differences in permafrost quantities such as active-layer thickness and the mean and trends in near-surface (0–3 m) permafrost extent even though all the models are forced with observed climatology. However, these differences are smaller than those seen across the CMIP5 models (Koven et al., 2013). All models except ORCHIDEE employ a multilayer finite-difference heat diffusion for soil thermal dynamics (Table 2). Organic soil insulation, snow insulation and unfrozen-water effects on phase change are the most common structural differences among models for soil thermal dynamics but do not explain the variability in the simulated changes in ALT and permafrost area as shown by McGuire et al. (2016). Half of the participating models include organic matter in the soil properties (CLM, ORCHIDEE, SIBCASA and UWVIC), which can significantly impact soil thermal properties and lead to an increase in the hydraulic conductivity of the soil column, thereby enhancing drainage and redistribution of water in the soil column. Soil vertical characterization is another important aspect for soil thermal dynamics and hydrology (Chadburn et al., 2015; Nicolsky et al., 2007). Lawrence et al. (2008) indicated that a high-resolution soil column representation is necessary for accurate simulation of long-term trends in active-layer depth. However, McGuire et al. (2016) showed that soil column depth did not clearly explain variability of the simulated loss of permafrost area across models.
Water table representation can result in a first-order effect on soil moisture. Most models (CLM, CoLM, SIBCASA and ORCHIDEE) use some version of TOPMODEL (Niu et al., 2007), which employs a prognostic water table where subgrid-scale topography is the main driver of soil moisture variability in the cell. However, the water table is not explicitly represented in other models such as LPJGUESS, which has a uniform water table which is only applied for wetland areas. In addition to the water table, the storage and transmission of water in soils is a fundamental component of an accurate representation of soil moisture (Niu and Yang, 2006). The representation of soil water storage and transmission varies across models from Richards equations based on Clapp–Hornberger (1978) and/or van Genuchten (1980) functions (e.g., CLM, CoLM, SIBCASA and ORCHIDEE) to a simplified one-layer bucket (e.g., TEM). It is also important to note that most models differ in their numerical implementations of processes such as water movement through frozen soils (Gouttevin et al., 2012; Swenson et al., 2012) and in the use of iterative solutions and vertical discretization of water transmission (De Rosnay et al., 2000).
Differences in representation of vertical fluxes through evapotranspiration
(ET) are also likely adding to the high variability in soil moisture
projections. ET sources (e.g., interception loss, plant transpiration and soil
evaporation) were similar across models but had different formulations
(Table 2). The diversity of ET implementations (e.g., evaporative resistances
from fractional areas) and of vegetation maps used by the modeling
groups (Ottlé et al., 2013) can also contribute to
the big spread on the temporal simulations for ET and soil moisture. Along
with projected increases in ET, net precipitation (
Despite runoff improvements (Swenson et al., 2012),
underestimation of river discharge has been a challenge in previous versions
in models (Slater et al., 2007). The differences between models and observations in mean annual
discharge may stem from several sources, particularly the substantial
variation in the precipitation forcing for these models (Fig. 2e). This is
attributed, in part, to the sparse observational networks in high latitudes.
River discharge at high latitudes can differ substantially when different
reanalysis forcing datasets are used. For example, river discharge for
Arctic rivers differs substantially in CLM4.5 simulations when forced with
GSWP3v1 compared to CRUNCEPv7 reanalysis datasets (not shown is runoff for
Mackenzie,
If drying of the permafrost region occurs, carbon losses from the soil will
be dominated by
The simulation data analyzed in this paper are available through the
National Snow and Ice Data Center (NSIDC;
This paper is a collective effort of the modeling groups of the
Permafrost Carbon Network (
The authors declare that they have no conflict of interest.
This paper is dedicated to the memory of Andrew G. Slater (1971–2016) for his scientific contributions in advancing Arctic hydrology modeling. This work was performed under the Next-Generation Ecosystem Experiments (NGEE Arctic, DOE ERKP757) project supported by the Biological and Environmental Research program in the Office of Science, U.S. Department of Energy. The study was also supported by the National Science Foundation through the Research Coordination Network (RCN) program and through the Study of Environmental Arctic Change (SEARCH) program in support of the Permafrost Carbon Network. We also acknowledge the joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101) and the European Union FP7-ENVIRONMENT project PAGE21.
This research has been supported by the Office of Science, U.S. Department of Energy (grant no. ERKP757).
This paper was edited by Ylva Sjöberg and reviewed by two anonymous referees.