**Model description paper**
24 Jul 2018

**Model description paper** | 24 Jul 2018

# SHAKTI: Subglacial Hydrology and Kinetic, Transient Interactions v1.0

Aleah Sommers Harihar Rajaram and Mathieu Morlighem

^{1},

^{1},

^{2}

**Aleah Sommers et al.**Aleah Sommers Harihar Rajaram and Mathieu Morlighem

^{1},

^{1},

^{2}

^{1}Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, Colorado, USA^{2}Department of Earth System Science, University of California, Irvine, California, USA

^{1}Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, Colorado, USA^{2}Department of Earth System Science, University of California, Irvine, California, USA

**Correspondence**: Aleah Sommers (aleah.sommers@colorado.edu)

**Correspondence**: Aleah Sommers (aleah.sommers@colorado.edu)

Received: 23 Feb 2018 – Discussion started: 27 Mar 2018 – Revised: 04 Jul 2018 – Accepted: 07 Jul 2018 – Published: 24 Jul 2018

Subglacial hydrology has a strong influence on glacier and ice sheet dynamics, particularly through the dependence of sliding velocity on subglacial water pressure. Significant challenges are involved in modeling subglacial hydrology, as the drainage geometry and flow mechanics are constantly changing, with complex feedbacks that play out between water and ice. A clear tradition has been established in the subglacial hydrology modeling literature of distinguishing between channelized (efficient) and sheetlike (inefficient or distributed) drainage systems or components and using slightly different forms of the governing equations in each subsystem to represent the dominant physics. Specifically, many previous subglacial hydrology models disregard opening by melt in the sheetlike system or redistribute it to adjacent channel elements in order to avoid runaway growth that occurs when it is included in the sheetlike system. We present a new subglacial hydrology model, SHAKTI (Subglacial Hydrology and Kinetic, Transient Interactions), in which a single set of governing equations is used everywhere, including opening by melt in the entire domain. SHAKTI employs a generalized relationship between the subglacial water flux and the hydraulic gradient that allows for the representation of laminar, turbulent, and transitional regimes depending on the local Reynolds number. This formulation allows for the coexistence of these flow regimes in different regions, and the configuration and geometry of the subglacial system evolves naturally to represent sheetlike drainage as well as systematic channelized drainage under appropriate conditions. We present steady and transient example simulations to illustrate the features and capabilities of the model and to examine sensitivity to mesh size and time step size. The model is implemented as part of the Ice Sheet System Model (ISSM).

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One of the significant consequences of contemporary climate change is rising sea level. A large component of sea level rise is the transfer of ice from glaciers and ice sheets into the ocean via melt, runoff, and iceberg calving (Church et al., 2013). Future ice dynamics remain a major uncertainty in sea level rise predictions involving many uncertain factors, including basal lubrication and effects on sliding velocities from subglacial drainage (e.g., Church et al., 2013; Shannon et al., 2013).

Although massive outlet glaciers of West Antarctica may be on the verge of irreversible collapse in the next 200 to 1000 years (Joughin et al., 2014; DeConto and Pollard, 2016), the Greenland ice sheet is currently the single largest contributor to sea level rise (Shepherd et al., 2012). Considering the substantial amount of water held in this frozen reservoir, it is important to improve understanding of its behavior, including the subtleties of its drainage, which affects ice velocity through sliding. Since 1990, many Greenland outlet glaciers have displayed dramatic accelerations and frontal retreats, yielding substantial changes on the rapid timescale of decades or years (Joughin et al., 2010). Other glaciers, however, have accelerated less rapidly or even decelerated over the same period (McFadden et al., 2011), and the mechanisms driving these contrasting responses are still not entirely understood. The recent accelerations observed in marine-terminating outlet glaciers, which exhibit some of the greatest accelerations and are highly sensitive to changes in terminus conditions, may be in response to changing ocean temperatures (Nick et al., 2009; Rignot et al., 2010; Andresen et al., 2012), but their diverse behaviors have been found to depend on more factors than ocean temperature alone, such as bed topography and subglacial discharge distribution (Slater et al., 2015; Rignot et al., 2016). In land-terminating glaciers, the observed accelerations are likely driven largely by water inputs to the ice sheet from the surface via crevasses and moulins, similar to alpine glaciers (e.g., Anderson et al., 2004; Bartholomaus et al., 2008). Meltwater inputs have been shown to drive variation in ice velocities on the Greenland ice sheet (e.g., Zwally et al., 2002; Bartholomew et al., 2012), as well as seasonal changes in the efficiency of the subglacial drainage system (e.g., Bartholomew et al., 2010; Chandler et al., 2013; Cowton et al., 2013; Andrews et al., 2014).

The hydrology of meltwater on the surface, within, and beneath glaciers and ice sheets should ideally be viewed and modeled as a complex system of processes considering the interconnectedness of surface mass balance, meltwater retention, discharge at the ice margin, and feedbacks between hydrology and ice dynamics (e.g., Rennermalm et al., 2013; Nienow et al., 2017). Water delivered to the bed through englacial conduits drives basal sliding, which has important effects on flow in some regions (Vaughan et al., 2013), and year-round sliding can occur with temperate bed conditions (Colgan et al., 2011). Increased meltwater input to the bed, however, does not necessarily imply increased basal sliding, contrary to what might seem intuitive. For example, as meltwater input increases, water pressure under the ice increases, leading to enhanced basal lubrication and higher sliding velocity (Zwally et al., 2002). But with sustained meltwater input over a melt season, more efficient drainage channels can develop, decreasing the water pressure (Schoof, 2010). Characteristics of individual outlet glaciers such as bed topography, ice geometry, surface temperature, and other factors all play into the intricate choreography of the seasonal evolution of the subglacial drainage system and its influence on ice velocity. Subglacial hydrology models have had success in simulating realistic drainage behavior, but challenges still remain.

The goal of this modeling effort is to see if a single set of governing equations can produce systematic, self-organized channelization where it should occur. In this paper, we describe the model formulation of SHAKTI (Subglacial Hydrology and Kinetic, Transient Interactions), which allows for flexible evolution of the subglacial drainage system configuration and flow regimes using a single set of governing equations over the entire domain. The model aims to represent the complex interactions due to (kinetic) movement of ice and water and (transient) changes in the subglacial system through time. We hope this unified formulation may be used to facilitate an exploration of the conditions under which different drainage system types form and persist and the flow regimes experienced in different areas of a domain. With upcoming application to actual glaciers, this type of model could provide useful insights into the seasonal evolution of real subglacial drainage systems and their influence on mass loss from the Greenland ice sheet, with the potential for broader application to Antarctica and alpine glaciers.

The paper is structured as follows: in Sect. 1.1–1.2, we provide a brief summary and review of historical and recent subglacial hydrology modeling progress to put our model in context. We then present the model's governing equations and the numerical framework in Sect. 2, with illustrative simulations to demonstrate key model features and capabilities in Sect. 3 and a discussion of implications and model limitations in Sect. 4.

## 1.1 Subglacial hydrology modeling context

Subglacial hydrology has long been an area of interest, initially in the context of geomorphology, groundwater, and surface hydrology from alpine glaciers and more recently in the context of its influence on ice sheet dynamics. Below is a brief and selective summary of previous subglacial hydrology modeling work motivated by glacier sliding. We direct readers to Flowers (2015) for a comprehensive review of the full subject history, recent advancements, and current challenges.

The first major efforts to quantitatively model subglacial hydrology began in the 1970s. Shreve (1972) described a system of arborescent subglacial channels, and Röthlisberger (1972) formulated equations for semicircular channels melted into the base of the ice sheet in a state of equilibrium between melt opening and creep closure. Nye (1973) expanded the work of Röthlisberger to consider channels incised into bedrock or subglacial sediments and more fully developed the equations into models for explaining outburst floods (Nye, 1976). In a different approach, Weertman (1972) considered subglacial drainage through a water sheet of approximately uniform thickness. In the following decade, different plausible drainage configurations were also proposed, such as a system of “linked cavities”, spaces that open behind bedrock bumps as a result of glacier sliding (Walder, 1986; Kamb, 1987). By the mid-1980s, it was recognized that the major components of subglacial hydrology could be classified as either efficient (channels or canals) or inefficient (thin sheets, flow through porous till, or distributed systems of linked cavities, often represented in continuum models as a sheet). While channels themselves emerge as a result of self-organized selective growth from a linked cavity system, a clear distinction between these two subsystems was established.

Since 2000, a renewed surge of interest in subglacial hydrology has been sparked as mass loss increases from glaciers and ice sheets and sea level rise is increasingly perceived as an imminent reality, generating a flurry of new observations and modeling advances. Although the effects of surface melt on ice sheet dynamics are not yet entirely understood (e.g., Clarke, 2005; Joughin et al., 2008), observations have reinforced the fact that surface meltwater significantly influences flow behavior in alpine glaciers and ice sheets (e.g., Mair et al., 2002; Zwally et al., 2002; Bartholomaus et al., 2008; Howat et al., 2008; Shepherd et al., 2009; Bartholomew et al., 2010, 2012; Hoffman et al., 2011; Sundal et al., 2011; Meierbachtol et al., 2013; Andrews et al., 2014). Along with more detailed observations, several efforts were made in the early 2000s to accurately simulate subglacial hydrology. Some of these studies treated the subglacial system as a water sheet of uniform thickness (e.g., Flowers and Clarke, 2002; Johnson and Fastook, 2002; Creyts and Schoof, 2009; Le Brocq et al., 2009). Arnold and Sharp (2002) presented a model with both distributed and channel flow, but only one configuration could operate at a time. Kessler and Anderson (2004) introduced a model using discrete drainage pathways that could transition between distributed and channelized modes, and Flowers et al. (2004) used a combination of a distributed sheet in parallel with a network of efficient channels. Schoof (2010) developed a 2-D network of discrete conduits that could behave like either channels or cavities and found that with sufficiently large discharge an arborescent network of channel-like conduits would form, although the resulting geometry was highly dependent on the rectangular grid used. Hewitt (2011) developed a model that used a water sheet to represent evolving linked cavities averaged over a patch of bed (an effective porous medium) coupled to a single channel.

More recent studies tied together key elements of subglacial drainage to form increasingly realistic 2-D models. Hewitt (2013) introduced a linked-cavity continuum sheet integrated with a structured channel network. In that model, channels open by melt, while the distributed sheet opens only by sliding over bedrock bumps (neglecting opening by melt from dissipative heat). Melt from dissipative heat contributes only to opening in channels. Werder et al. (2013) presented a model that involves water flow through a sheet (representative of averaged linked cavities) along with channels that are free to form anywhere along edges of the unstructured numerical mesh, exchanging water with the surrounding distributed sheet. Approaching the problem in a different way, Bougamont et al. (2014) reproduced seasonal ice flow variability through the hydromechanical response of soft basal sediment in lieu of simulating the evolution of a subglacial drainage system. To capture broad characteristics of subglacial drainage without resolving individual elements, de Fleurian et al. (2014) employed a 2-D dual-layer porous medium model, and Bueler and van Pelt (2015) formulated equations for a 2-D model that combines water stored in subglacial till with linked cavities. To help explain observations of high water pressure in late summer and fall, recent observations and modeling efforts have highlighted the importance of representing hydraulically isolated or “weakly connected” regions of the bed (Hoffman et al., 2016; Rada and Schoof, 2018) and addressed the problem by facilitating seasonal changes in the hydraulic conductivity (Downs et al., 2018).

## 1.2 Distinction between channelized and sheetlike drainage, and the problem of dissipation

A common theme in the subglacial hydrology modeling literature is a distinction between channelized (efficient) and sheetlike (inefficient or distributed) drainage systems or components. In most existing 2-D models, either only one of these forms is considered, or else slightly different equations are applied to coupled channel and sheet components. For the sheetlike system, these models only consider opening (i.e., growth of the sheet thickness) due to sliding over bedrock bumps, disregarding opening by melting of the upper ice surface. Melt is generated by the thermal energy obtained from dissipated mechanical energy (commonly referred to as energy loss or head loss). However, these models redirect the generated thermal energy into adjacent channel components that are allowed to melt and grow. Channel components are allowed to form in prespecified locations or to evolve along the edges of sheetlike elements, as in Werder et al. (2013). The main reason that most of these models disregard melt opening in the sheetlike system is to avoid the unstable behavior that has been found to occur when it is included, leading to unstable growth in which the melt opening rate exceeds the closure rate, sparking channelization (Hewitt, 2011) or driving initiation of glacial floods (Schoof, 2010). The transition to a channelized state has been described elegantly in previous work (e.g., Walder, 1986; Kamb, 1987; Schoof, 2010; Hewitt, 2011; Schoof et al., 2012; Werder et al., 2013; Hoffman and Price, 2014).

In reality, the subglacial hydrologic system is comprised of a wide array of drainage features, of which the sheet and channel are two end-members. Imposing a sharp distinction between the treatment of the melt opening term and dividing the governing equations between different model components may not allow for the full array of drainage features to arise. It is also a bit artificial to redirect the opening by melt in sheetlike elements to nearby channels. In the model formulation described in this paper, a single set of governing equations is applied over the entire domain, including the melt opening term everywhere. In our formulation, the hydraulic transmissivity of the subglacial domain is allowed to vary spatially and temporally, allowing for a continuum of drainage features. We also account for laminar, turbulent, and intermediate flow regimes based on an experimentally verified flow law for rough-walled rock fractures (Zimmerman et al., 2004). The gap thickness of each computational element in a discretization of the governing equations is allowed to evolve flexibly, and sequential elements with high gap growth rates typically link up to produce channelized features. The ability to represent coexisting turbulent, laminar, and intermediate regimes appears to be a promising approach to overcoming the previously mentioned instability that occurs when the melt generated by mechanical energy dissipation is retained in the sheet system equations. Even with the melt opening term included everywhere in the domain, we are able to generate steady and transient drainage configurations that include channel-like efficient drainage pathways. Our model does not aim to simulate every individual cavity or specific channel cross section, but rather captures the homogenized effects of these elements on a discrete mesh. As we demonstrate in Sect. 3, although the resolution of subglacial geometry in our approach is mesh and grid sensitive, the patterns of simulated basal water pressure and effective pressure (which are most relevant for calculating sliding velocities in ice dynamics models) are relatively robust with coarse resolutions (∼400 m).

This flexible subglacial hydrology model can handle transient meltwater inputs, both spatially distributed and localized, and allows the basal water flux and geometry to evolve according to these inputs to produce flow and drainage regimes across the spectrum from sheetlike to channelized. The subglacial drainage system is represented as a sheet with variable gap height, and we employ a flux formulation based on fracture flow equations. Channelized locations are not prescribed a priori, but can arise and decay naturally as reflected in the self-organized formation of connected paths of large gap height (calculated across elements) and lower water pressure (calculated at vertices) than their surroundings. In contrast, previous models allow efficient channels to arise along element or grid edges and calculate a specific cross-sectional channel area (e.g., Schoof, 2010; Hewitt et al., 2013; Werder et al., 2013).

The parallelized, finite-element SHAKTI model is currently implemented as part of the Ice Sheet System Model (ISSM; Larour et al., 2012; http://issm.jpl.nasa.gov, last access: 14 July 2018), with full two-way coupling with the ice dynamics model planned for upcoming work. Below, we present the equations involved in the SHAKTI formulation. The governing equations are similar to those used in Werder et al. (2013), with some key differences that enable the application of the same set of equations everywhere in the domain.

## 2.1 Summary of model equations

The SHAKTI model is based upon governing equations that describe the conservation of water and ice mass, the evolution of the gap height, water flux (approximate momentum equation for water velocity integrated over the gap height), and internal melt generation (approximate energy equation for heat produced at the bed). All variables used in the equations are summarized in Table 1, with constants and parameters summarized in Table 2.

In general, a complete set of governing equations for subglacial hydrology models should include acceleration terms in the momentum equation, and advection and in-plane conduction terms should be included in the energy equation. The most general form of the conservation equations for subglacial hydrology would be a multidimensional extension of the equations described by Spring and Hutter (1981) and Clarke (2003), with augmentation to account for opening by sliding. Our model formulation and most existing subglacial hydrology models typically neglect the acceleration terms in the momentum equation and employ an approximate energy equation in which all dissipated mechanical energy is locally used to produce melt; the equations presented here should be viewed as an approximation to the more general equations.

The water mass balance equation is written as

where *b* is subglacial gap height, *b*_{e} is the volume of water
stored englacially per unit area of bed, ** q** is basal water flux,
$\dot{m}$ is basal melt rate, and

*i*

_{e→b}represents the input rate of surface meltwater from the englacial to subglacial system. This water balance assumes that the subglacial gap is always filled with water and that water is incompressible.

Evolution of the gap height (subglacial geometry) involves opening due to melt and sliding over bumps on the bed, as well as closing due to ice creep:

where *A* is the ice flow-law parameter, *n* is the flow-law exponent,
*p*_{i} is the overburden pressure of ice, *p*_{w} is water
pressure, *β* is a dimensionless parameter governing opening by sliding,
and *u*_{b} is the magnitude of the sliding velocity. Equation (2) may be
viewed as a generalized ice mass balance equation augmented to consider
opening by sliding. In most existing 2-D models that include both channel and
distributed sheetlike drainage components (e.g., Werder et al., 2013), melt
opening is typically considered “channel opening” and opening by sliding
over bumps on the bed is considered “cavity opening”, with the different
terms applied to the appropriate components within the model. Our model
differs from other existing models in that we include both opening terms
everywhere in the domain, similar to the conduit model of Schoof (2010). The
opening by sliding parameter *β* is a function of typical bed bump height
(*b*_{r}) and bump spacing (*l*_{r}), as well as local gap height (so that
opening by sliding only occurs where the gap height is less than the typical
bump height). In defining *β*, we follow Werder et
al. (2013).

The horizontal basal water flux (approximate momentum equation) is described based on equations developed for flow in rock fractures (e.g., Zimmerman et al., 2003; Rajaram et al., 2009; Chaudhuri et al., 2013):

where *g* is gravitational acceleration, *ν* is kinematic viscosity of
water, *ω* is a dimensionless parameter controlling the nonlinear
transition from laminar to turbulent flow, *R**e* is the Reynolds number, and
*h* is hydraulic head defined as
$h={p}_{\mathrm{w}}/\left({\mathit{\rho}}_{\mathrm{w}}g\right)+{z}_{\mathrm{b}}$ (where *z*_{b} is bed
elevation). Note that the dimensions of the basal water flux are
m^{2} s^{−1}, i.e., a flow rate per unit width, obtained as an integral of
the velocity profile across the gap thickness. The momentum Eq. (5) is
approximate in the sense that acceleration terms are neglected and the flow
is approximated as a locally plane shear flow. Equation (5) is a key piece of
our model formulation in that it allows for a spatially and temporally
variable hydraulic transmissivity in the system and facilitates
the representation of the simultaneous coexistence of laminar, transitional, and
turbulent flow in subregions of the domain. Many existing subglacial
hydrology models prescribe a hydraulic conductivity parameter and assume the
flow to be turbulent everywhere. Equation (5) has been employed extensively
for modeling flow in rock fractures, especially in the laminar flow regime
(*ω**R**e*≪1), wherein it is commonly referred to as the local cubic
law. The extension of the local cubic law to transitional and turbulent
flows, by incorporating a Reynolds number dependence as in Eq. (5), has also
been employed in previous work on rock fractures
(Zimmerman et al., 2004; Rajaram et al., 2009; Chaudhuri et al., 2013) and was
experimentally verified by Zimmerman et al. (2009).

In the laminar flow regime, Eq. (5) derives from assuming locally plane Poiseuille flow and integrating the Stokes equations twice across the gap thickness to obtain

where *ν* is the kinematic viscosity of water. The definition of the Reynolds
number follows the precedent in fracture literature using the gap height *b*
as a characteristic length scale:

where *v* is the average velocity across the gap. Note that for laminar flow,
the flux in Eq. (6) is proportional to the hydraulic gradient ∇*h*. The
flux equation in the laminar regime (Eq. 6) is modified to allow for
transition to a turbulent regime by introducing the additional term in the
denominator to account for Reynolds number dependence. For fully developed
turbulent flow with a high Reynolds number (*ω**R**e*≫1), the magnitude
of the flux ** q** given by Eq. (5) is proportional to the square root of
the magnitude of the hydraulic gradient.

Equation (8) is analogous to the Darcy–Weisbach equation with a constant
(i.e., not dependent on Reynolds number) friction factor for flow in ducts.
For intermediate Reynolds numbers, Eq. (5) captures a nonlinear dependence
between flux and hydraulic gradient that is in between the linear and square
root dependences corresponding to laminar and turbulent flow regimes. The
parameter *ω* controls the Reynolds number at which the deviation from
the linear dependence becomes significant and is also related to the
friction factor. For example, with *ω*=0.001, *ω**R**e* is of order
10 at *R**e*=10 000, representing the value at which the friction factor
becomes independent of the Reynolds number. For comparison, in pipe flow, fully
developed turbulent flow with a constant friction factor occurs at *R**e*∼10 000 in very rough pipes (relative roughness >0.02).

Internal melt generation is calculated through an energy balance at the bed:

where *L* is latent heat of fusion of water, *G* is geothermal flux,
*u*_{b} is the ice basal velocity vector,
*τ*_{b} is the stress exerted by the bed onto the ice,
*c*_{t} is the change in pressure melting point with temperature, and
*c*_{w} is the heat capacity of water. Melt is therefore produced
through a combination of geothermal flux, frictional heat due to sliding, and
heat generated through internal dissipation (whereby mechanical kinetic energy
is converted to thermal energy) minus the heat consumed or released in
maintaining the water at the pressure melting temperature in the presence of
changing water pressure. We note that this form of the energy equation
assumes that all heat produced is converted locally to melt and neglects
the advective transport and storage of dissipative heat. We assume that the ice
and liquid water are isothermal and consistently at the pressure melting point
temperature. These assumptions may not be strictly valid under certain real
conditions that may have interesting heat transfer implications, such as heat
advection (Clarke, 2003), supercooling (Creyts and Clarke, 2010), or where
meltwater enters a system of cold ice (below the pressure melting point), but
we leave these potential model extensions for future work. As mentioned
previously in Sect. 1.2, Werder et al. (2013) and similar models do not
include the internal dissipation term in their sheetlike drainage components,
but assign any melt from dissipation to contribute to opening in the nearest
channel component.

For the sake of versatility, we also include an option to parameterize storage in the englacial system (note that this is not necessary for numerical stability; we use zero englacial storage in the example simulations presented in Sect. 3 of this paper). Following Werder et al. (2013), the englacial storage volume is defined as a function of water pressure:

where *e*_{v} is the englacial void ratio (*e*_{v}=0 for no
englacial storage).

Equations (1), (2), (5), and (9) are combined to form a parabolic, nonlinear
partial differential equation (PDE) in terms of hydraulic head, *h*.

With no englacial storage (*e*_{v}=0), Eq. (11) takes the form of an
elliptic PDE.

Defining a hydraulic transmissivity tensor,

Eq. (13) can be written more compactly as

Although we employ an isotropic representation of the hydraulic
transmissivity tensor in Eq. (12), our model formulation can be readily
generalized to incorporate anisotropy. The source terms on the right side of
the Eq. (13) and the conductivity depend on *h*, as a result of which
Eq. (13) is nonlinear, and solving for *h* requires iterative methods.

## 2.2 Boundary conditions

Boundary conditions can be applied as either prescribed head (Dirichlet) conditions or as flux (Neumann) conditions. To represent land-terminating glaciers, we typically apply a Dirichlet boundary condition of atmospheric pressure at the edge of the ice sheet:

To represent marine-terminating glaciers, the outlet boundary condition can be set to the overlying fjord water pressure. Prescribed flux boundary conditions are imposed on the other boundaries of the subglacial drainage domain:

where *f* can be set to represent no flux (*f*=0) or a prescribed flux, which can be constant or time varying.

In our current formulation, there is no lower limit imposed on the water pressure; this means that unphysical negative pressures can be calculated in the presence of steep bed slopes, as in Werder et al. (2013). While suction and cavitation may occur in these situations, the flow most likely transitions to free-surface flow with the subglacial gap partially filled by air or water vapor. At high water pressure, we restrict the value to not exceed the ice overburden pressure, which would in reality manifest as uplift of the ice or hydrofracturing at the bed. These extreme “underpressure” and “overpressure” regimes are important situations that have been considered in other studies (e.g., Tsai and Rice, 2010; Hewitt et al., 2012; Schoof et al., 2012), but are quite complex in 2-D and remain to be addressed carefully in future developments.

## 2.3 Computational strategy and implementation in the Ice Sheet System Model (ISSM)

The overall computational strategy employed is semi-implicit with an implicit
backward Euler discretization of Eq. (13) to solve for the head field (*h*),
combined with an explicit treatment of Eq. (2) for the evolution of the gap
height (*b*). Within each time step, the nonlinear Eq. (13) is solved using
Picard iteration to obtain the head (*h*) field. From *h*, we calculate
*p*_{w}, ** q**,

*R*

*e*, and $\dot{m}$ to be used in the subsequent iteration (in each iteration,

*p*

_{w},

**,**

*q**R*

*e*, and $\dot{m}$ are lagged from the previous iteration). Once the Picard iteration has successfully converged to a solution for

*h*, the gap height geometry (

*b*) is then updated explicitly based on basal gap dynamics using Eq. (2) to advance to the next time step. A schematic of this numerical procedure is presented in Fig. 1. Due to the explicit treatment of Eq. (2), there is a time step limitation, which will be discussed further in Sect. 4.

SHAKTI is implemented within ISSM, an open source ice dynamics model for Greenland and Antarctica developed by NASA's Jet Propulsion Laboratory and University of California at Irvine (Larour et al., 2012; http://issm.jpl.nasa.gov, last access: 14 July 2018). ISSM uses finite-element methods and parallel computing technologies, and includes sophisticated data assimilation and sensitivity analysis tools, to support numerous capabilities for ice sheet modeling applications on a variety of scales. The SHAKTI hydrology model solves the equations presented above in a parallel architecture using linear finite elements (i.e., P1 triangular Lagrange finite elements), which can be based on a structured or unstructured mesh. The source code is written in C++ and we rely on data structures and solvers provided by the Portable, Extensible Toolkit for Scientific Computation (PETSc; http://www.mcs.anl.gov/petsc, last access: 14 July 2018). The user interface in MATLAB is the same as for other solutions implemented in ISSM designed to facilitate model setup and post-processing (see Documentation; https://issm.jpl.nasa.gov/documentation/hydrologyshakti/, last access: 14 July 2018). The iterative solution of Eq. (13) for hydraulic head employs the direct linear solver MUMPS in PETSc in each iteration, but other solvers provided by PETSc could be easily tested in future work.

Model inputs include spatial fields of bed elevation, ice surface elevation, initial hydraulic head, initial basal gap height, ice sliding velocity, basal friction coefficient, typical bed bump height and spacing, englacial input to the bed (which can be constant or time varying and can be spatially distributed or located at discrete points to represent moulin input), and appropriate boundary conditions. Parameters that can either be specified or rely on a default value are geothermal flux, the ice-flow-law parameter and exponent, and the englacial storage coefficient.

Model outputs include spatiotemporal fields of hydraulic head, effective pressure, subglacial gap height (the effective geometry representative of an entire element), depth-integrated water flux, and “degree of channelization” (the ratio of opening by melt in each element to the total rate of opening in that element by both melt and sliding). Head and effective pressure are calculated at each vertex on the mesh; gap height, water flux, and degree of channelization are calculated over each element (these quantities are based on the head gradient). Instructions for setting up, running a simulation, and plotting outputs can be found in the SHAKTI model documentation (https://issm.jpl.nasa.gov/documentation/hydrologyshakti/ last access: 14 July 2018) and in an example tutorial (https://issm.jpl.nasa.gov/documentation/tutorials/shakti/, last access: 14 July 2018).

To demonstrate the capabilities of SHAKTI, here we present simple illustrative simulations that highlight some of its features. These test problems are designed to show the formation of sheetlike and channelized drainage in the context of different input scenarios (steady input, transient input, moulin point inputs, and distributed input) in simple model domains. We explore the mesh dependence of the model for the more complex examples in Sect. 3.2 and 3.3, with further discussion of this and other limitations included below in Sect. 4.

## 3.1 Channel formation from discrete moulin input

In this first example, we consider a 1 km square, 500 m thick tilted ice
slab with a surface and bed slope of 0.02 along the *x* direction. Steady input
of 4 m^{3} s^{−1} is prescribed at a single moulin at the center of the
square (*x*=500 m, *y*=500 m). Water pressure at the outflow (left edge of
the domain, *x*=0) is set to atmospheric pressure, with zero flux boundary
conditions at the other three sides of the domain. All other constants and
parameters are as described in Table 2. We use an unstructured triangular
mesh with a typical edge length of 20 m (with 4004 elements). The model is run
to a steady configuration (steady state is reached by 12 days) starting from
an initial gap height of 0.01 m. A channelized drainage pathway emerges from
the moulin to the outflow, with higher effective pressure (i.e., lower head
and water pressure), larger gap height, and higher basal flux than its
surroundings (Fig. 2). The degree of channelization metric also indicates a
value close to 1 (indicating that opening by melt dominates opening by
sliding) within the channelized drainage path. Note that the precise
configuration of the channelized pathway is somewhat influenced by the
unstructured mesh. Mesh sensitivity will be examined below in Sect. 3.2.

Scripts for running this example are included as a tutorial in ISSM (https://issm.jpl.nasa.gov/documentation/tutorials/shakti/, last access: 14 July 2018) and can serve as a template for more sophisticated simulations. Run times will vary by machine and number of processors, but to run this simulation on 24 processors for 30 days with a time step of 1 h, the entire simulation has a run time of approximately 38 s.

## 3.2 Channelization with multiple moulins

For the next example, we consider a rectangular domain 10 km long and 2 km
wide, with a flat bed (*z*_{b}=0 everywhere) and parabolic surface
profile with a minimum thickness of 300 m and a maximum of 610 m. Ten
moulins are located at arbitrarily chosen locations in the domain, each with
a steady input of 10 m^{3} s^{−1}. The model is run to 365 days with a
time step of 1 h (steady state is reached before 50 days), starting from an
initial gap height of 0.01 m. The resulting steady distributions shown in
Fig. 3 on five different meshes show a clear channelized drainage structure.
Rather than each moulin forming a unique channel to the outflow, the moulin
inputs influence each other, warping the pressure field and forming
arborescent efficient pathways that combine downstream. For this specific
arrangement of moulin inputs, a single principal drainage channel emerges.
The unique drainage configuration that evolves in a particular circumstance
and setting is affected by many factors, including bed topography, ice
thickness, sliding velocity, meltwater input location, and input intensity.

The exact configuration of self-organizing channels also depends to some
extent on the mesh. The five unstructured meshes used in this example have
typical edge lengths ranging from 50 m (12 714 elements) to 400 m
(205 elements). Using an unstructured mesh reduces bias in the channel direction
compared to a structured mesh, but the orientation and size of the elements
still affect the resulting geometry. Most subglacial hydrology models
that resolve individual channels are mesh dependent (e.g., Werder et al.,
2013). The different cases shown in Fig. 3 provide a qualitative view of
the dependence of channelization structure on mesh size. Specifically, the gap
height field on the coarsest mesh does not show a clear channel, and a
well-defined narrow channel is evident for larger distances upstream from the
outflow boundary as the mesh is refined. The general structure of the channel
is quite similar in the two finest meshes, but differences in alignment
persist due to the unstructured nature of the mesh. From the viewpoint of
coupling to ice motion and sliding calculations, the subglacial head and
effective pressure fields obtained from the subglacial hydrology model are
most important. The head and effective pressure fields shown in Fig. 3 are
much smoother than the gap height field and appear to show less sensitivity
to the mesh size. To evaluate this sensitivity further, Fig. 4 presents
quantitative plots of the mean head and effective pressure (averaged in the
*y* direction) for the five meshes. Across much of the domain, they converge
remarkably well, but diverge slightly in the region of significant
channelization.

## 3.3 Seasonal variation and distributed meltwater input

Next we consider a transient example involving a seasonal input cycle of
meltwater, with input distributed uniformly across a rectangular domain 4 km
long and 8 km wide. The bed is flat (*z*_{b}=0 everywhere). The ice
surface follows a parabolic profile, with ice thickness ranging from 550 m
at *x*=0 to 700 m at *x*=4 km and is uniform across the *y* direction. We
begin with an initial subglacial gap height of 0.01 m perturbed with random
variations drawn from a normal distribution with a standard deviation of 1 %.
The purpose of these random variations in the initial gap height is to serve
as triggers for potential instability and channelization, which is an
important phenomenon in subglacial hydrologic systems (Walder, 1986; Kamb,
1987; Schoof, 2010; Hewitt et al., 2011). Even in nature, the gap height is
unlikely to be uniform and the ubiquitous irregular variations in the gap
height and bedrock surface will act as natural perturbations to initiate
instabilities and channelization. As the ice slides over bedrock, abrasion
processes may also serve to generate irregularities. In the literature on the
self-organized formation of dissolution channels in rock fractures in karst
formations (e.g., Cheung and Rajaram, 2002; Szymczak and Ladd, 2006; Rajaram
et al., 2009), it has been established that under conditions that lead to
self-organized channel formation, the specific nature of the initial random
variations does not influence the structure and spacing of the channels; rather
it serves as a trigger for the initiation of channels. In unstructured
meshes, it is also possible for mesh-related asymmetries to introduce
perturbations that can serve as triggers for this instability. In stable
regimes, however, the same perturbations will not produce channelization.

The model is first run with steady distributed input of 1 m a^{−1} in a
spin-up stage with a time step of 1 h (steady state achieved in 4 days).
After a steady configuration is achieved, a cycle of meltwater input
variation is imposed and run for 1 year (365 days), also with a time step of
1 h. Seasonal meltwater input in m a^{−1} is approximated by a cosine
function between 0.4 and 0.7 a (days 146 and 255).

This yields a maximum meltwater input at the peak of the summer of
986 m a^{−1}, with a winter minimum of 1 m a^{−1} and annual mean
input of 149 m a^{−1}. The peak melt input corresponds to approximately
1000 m^{3} s^{−1} for the entire domain. Note that the values used here
are unrealistically high and are designed intentionally to show stable
behavior of the system across a variety of input magnitudes, even when
subjected to extreme forcing. Figure 5 shows time series plots of this
“seasonal” input forcing over one full annual cycle, with the corresponding
minimum, mean, and maximum gap height and head. Snapshots of the subglacial
hydrologic variable fields at intervals through the annual cycle are shown in
Fig. 6, and an animation of this simulation is included in the Supplement. As
melt increases, the maximum gap height increases, corresponding to growth of
the subglacial system and emergence of self-organized efficient channels. The
maximum gap height increases with increasing meltwater input until the peak
of the melt season, then decreases simultaneously as melt input decreases
(note that we use zero englacial storage in this simulation, so there is no
lag due to water storage in the system). The hydraulic head initially
increases with increased input (meaning an increase in subglacial water
pressure as additional water is added to the system), then decreases as
efficient low-pressure channels form, then increases again as melt starts to
decrease and the channels collapse. We hold the sliding velocity constant,
but in reality ice sheet sliding velocity generally increases with increased
water pressure (i.e., lower effective pressure) and decreases with lower water
pressure. With two-way coupling between the subglacial system and ice
dynamics (e.g., Hoffman and Price, 2014; Koziol and Arnold, 2018), the
sequence of hydraulic head or basal water pressure variation seen here would
likely result in a mid-to-late summer decline in sliding velocity, after
which the sliding velocity would increase again. Subsequently, as melt input
decreases to the winter minimum, the hydraulic head decreases to low values,
which would correspond to a decrease in sliding velocity. As shown in Fig. 6
for the early and late parts of the year, the system essentially behaves as a
one-dimensional system because the melt inputs are not large enough to take
the system into a regime in which channelization can occur. During the melt
season when inputs increase substantially, self-organized, regularly spaced
channels emerge, seen in Fig. 6 as having lower heads than their immediate
surroundings in the *y* direction. These channelized structures collapse and
disappear entirely as the meltwater input drops off and returns to the winter
minimum. The simulation results shown here demonstrate the ability of our
modeling framework to represent both stable regimes, in which the subglacial
system takes on a relatively smooth quasi-one-dimensional configuration, and
unstable regimes with self-organized efficient pathways when high meltwater
inputs and discharge trigger the transition to channelization.

To examine mesh dependence in this case of self-organized channelization,
Fig. 7 presents gap height and head distributions on three unstructured
meshes with typical edge lengths of 50, 100, and 200 m. At 100 m
resolution, the channelization effects are obvious, with similar spacing as
on the finer 50 m mesh. At 200 m resolution, the channels are still
apparent but the head and effective pressure fields are more smoothed than
with the finer meshes, especially in the upstream portions of the domain. In
the early and late parts of the cycle, the behavior obtained with different
mesh sizes is in good agreement for sheetlike drainage. The mesh dependence
is evaluated more quantitatively in Fig. 8 with *y*-averaged quantities for
day 1 (sheetlike drainage everywhere), day 200 (peak melt input and
extreme channelization), and day 250 (near the end of the melt input cycle as
channelization collapses). We see that the solutions obtained with different
mesh resolutions converge well for sheetlike drainage, but they show some
variation with channelization. These local differences are more pronounced in
the quantities calculated over elements (gap height and degree of
channelization), while differences are relatively small in the smooth
pressure distributions calculated at mesh vertices.

The flexible geometry and flow regimes of the SHAKTI model allow for various
drainage configurations to arise naturally. We conserve mass and energy in
all parts of the domain, in contrast to several existing models that neglect
the role of melt opening in sheetlike drainage systems or redistribute
dissipated mechanical energy in the sheet system to adjacent channels.
Previous studies found that with similar equations, including the melt term
in a distributed system leads to an instability and runaway growth, which
initiates channelization (Schoof, 2010; Hewitt, 2011). In our formulation,
even including melt from internal dissipation, we are able to achieve stable
configurations of subglacial geometry, basal water flux, and pressure fields
with steady and transient input forcing. Channelized pathways with lower
water pressure than their surroundings form from moulin inputs (Figs. 2 and
3) as well as self-organized configurations with high distributed melt input
(Fig. 6). A feature of our formulation that contributes to this behavior is
the way we calculate the basal water flux (approximate momentum equation,
Eq. 5), which allows for a transient, spatially variable transmissivity that
transitions naturally between laminar and turbulent flow regimes locally,
while allowing both types of flow regime to coexist in the model domain, as
well as flow that exhibits attributes along the wide transition between
laminar and turbulent flow. To illustrate this behavior more clearly, Fig. 9
presents the distribution of the Reynolds number through the initiation of
channelization for days 145–175 of the transient example in Sect. 3.3. On
day 145 (just before the onset of increased melt input; see Fig. 5), the
Reynolds number is low throughout the domain (the maximum Reynolds number is
only about 70), corresponding to laminar flow. On day 155, the Reynolds number
has increased, particularly near the outflow at the left, transitioning into
the turbulent regime in much of the domain with *R**e*>1000. As the
self-organized channelized structure emerges through days 165 and 175,
the Reynolds number becomes increasingly higher in the channelized pathways than
their surroundings. If we were to use a purely laminar or purely turbulent
flux formulation, the nature of the flow and the mechanical energy
dissipation rate would not be accurately represented across this range of
Reynolds numbers. If the flux is simulated as laminar everywhere (using a
very small value of *ω* in Eq. (5) so that *ω**R**e*≪1 and the
flux is always linearly proportional to the head gradient), channelization
still occurs with high inputs, but the flow mechanics are not correctly
represented for regions with large Reynolds numbers. If we force the flux to
be turbulent everywhere (by using a large value for *ω* in Eq. (5) so
that *ω**R**e*≫1 and the flux is always proportional to the square root
of the head gradient), the nonlinear iteration to solve Eq. (15) encounters
non-convergence with large oscillations between Picard iterations for the
same model problems that behave well when we employ the flux Eq. (5), which
allows for laminar, transitional, and turbulent flow regimes. The concept of
laminar–turbulent transition is well established in hydraulics and fluid
mechanics, and our representation of the nonlinear flux–gradient relationship
(Eq. 5) is consistent with this concept and is also consistent with
the experimental studies of Zimmerman et al. (2004) on rock fractures with
non-smooth walls.

The transient example in Sect. 3.3 illustrates one possible pattern of idealized seasonal evolution of the subglacial drainage system, in which channels emerge with increased melt and collapse to a sheetlike system again in the winter. The higher water pressure during the melt season would imply increased sliding velocity in a two-way coupled system, with a decrease in mid-to-late summer with well-established channelized drainage, followed by an increase as the efficient system initiates its shutdown and a decrease as meltwater input returns to the background winter rate. This seasonal pattern is reminiscent of observations of some Greenland outlet glaciers (Moon et al., 2014), and subglacial hydrology may indeed play a key role in shaping the seasonal velocity behavior of some glaciers, both land-terminating and marine-terminating. In future work on real glacier topography, we aim to investigate other velocity signatures, such as those that experience an annual minimum velocity in the late melt season, which is thought to be a result of highly efficient channel development (Moon et al., 2014), or those with high winter sliding velocities, which may be indicative of hydraulically isolated or poorly connected regions of the bed that maintain high water pressure through winter (e.g., Hoffman et al., 2016; Downs et al., 2018; Rada and Schoof, 2018). To accurately capture the influence of transient sliding velocities on the evolution of subglacial hydrology, two-way coupling between subglacial hydrology and ice dynamics is important.

## 4.1 Model limitations

This paper is intended to present a description of the SHAKTI model formulation with illustrative simulations under simple scenarios. Application to real glaciers remains for upcoming work, but we wish to clearly address the limitations of the model and acknowledge challenges faced by this and other subglacial hydrology models.

Time stepping is an important factor in numerical models of the highly
transient subglacial hydrologic system, such as SHAKTI. To illustrate the
influence of time step size, Fig. 10 presents the evolution of maximum head in
the single-moulin example (see Sect. 3.1 and Fig. 2) for different time step
sizes. In this example, the model converges properly to the same steady
configuration for time step sizes d*t*=0.25 h to d*t*=3 h. Note that as the
time step increases to about 3 h, small but stable fluctuations are seen.
With d*t*=4 h, however, the model never converges to the solution, but
instead enters a large systematic oscillation between incorrect values. For
larger time steps than d*t*=4 h, the nonlinear iteration itself has
difficulty converging and the amplitude of the oscillations becomes very
large with water pressure exceeding ice overburden pressure, which is
accompanied by very large dissipation rates. Difficulties in convergence
during numerical solutions of nonlinear PDEs with larger time steps is a
well-known issue in a variety of contexts. The appropriate time step size is
dependent on various parameters specific to a simulation such as topography,
ice thickness, and meltwater input rates. Due to the highly nonlinear nature
of the equations, it is unfortunately not straightforward to establish a time
step criterion for stable model behavior. As a general guideline we suggest
conducting an initial test with a time step of 1 h and adjusting
accordingly. We plan to implement adaptive time stepping in future
developments of SHAKTI. Note that the time steps required in subglacial
hydrology models are typically much smaller than the time steps frequently used
in long-term ice dynamics simulations, which may be on the order of years or
decades. Although it is desirable to maintain longer time steps in subglacial
hydrology models, the essential physics operates on much smaller timescales
and using a smaller time step of the order of hours may be unavoidable.
Coupling with ice sheet models may rely on spatiotemporally integrated basal
water and effective pressures.

We calculate basal gap height over each element, which means that the geometry is dependent on mesh size. It is not our aim to necessarily capture each individual cavity or channel cross section, but rather to obtain the effective geometry over each element and its effect on the pressure field, which has an important influence on ice sheet sliding velocity. In Sect. 3.2–3.3, we examined mesh sensitivity in example simulations (see Figs. 3 and 7). With very large elements (kilometer scale), the effects of channelized drainage may be smoothed out. For large-scale simulations, a variable mesh should be used with coarser resolution in the ice sheet interior away from the margins and finer resolution at lower elevations at which the bulk of meltwater is produced and enters the subglacial system (in which channelized networks are likely to form and sliding velocities are higher). The typical edge length scale should be selected according to the particular application depending on the resolution of bed topography, sliding velocities, modeling goals, and practical concerns of computing power. As a rough guideline to capture the formation of channelization in decent detail, we suggest an edge length of 150 m or less in the domain area of most interest (e.g., the few kilometers nearest the terminus of a glacier).

As stated in Sect. 2.2, the current formulation does not handle high water pressures that exceed overburden (we cap water pressure at overburden pressure and do not represent uplift) or low water pressures at which the system would transition to free surface flow (we assume the subglacial gap is always filled with water and allow unphysical negative water pressures to be calculated in the presence of steep slopes). The sample simulations presented in Sect. 3 do not involve either of these extreme pressure ranges in their solutions, so the results included here are unaffected by the upper limit imposed on water pressure or by allowing negative water pressures in lieu of transitioning to a partially filled system.

The examples in Sect. 3 do not involve complex bed topography, which is beyond the scope of this initial model description paper. The model has been successfully tested on real ice and bed geometry, however, and results will be included in forthcoming work.

Under thick ice with low meltwater input, the nonlinear iteration may have trouble converging to a head solution, entering a stable oscillation. This can frequently be resolved by decreasing the time step and/or employing under-relaxation to help the nonlinear iteration converge.

The SHAKTI model is not currently coupled to ice dynamics in a two-way manner. We prescribe a constant ice sliding velocity, and this sliding velocity does not evolve according to the influence of subglacial water pressure. With this one-way coupling, we are able to infer only qualitatively how the ice velocity would be affected by the changing subglacial system. In upcoming work, we plan to implement two-way coupling with the ice dynamics of ISSM to test different sliding laws and the behavior of the fully coupled system.

In this paper, we presented the SHAKTI model formulation with simple illustrative simulations to highlight some of the model features under different conditions. The model is similar to previous subglacial hydrology models, but employs a single set of “unified” governing equations over the entire domain, including opening by melt from internal dissipation everywhere, without imposing a distinction between channelized or sheetlike systems. The geometry is free to evolve; efficient, low-pressure channelized pathways can and do form as the subglacial system adjusts and facilitates transitions between different flow regimes. We find that with high meltwater input (via moulins or distributed input), self-organized channelized structures emerge with higher effective pressure (i.e., lower water pressure) than their surrounding areas. As meltwater input decreases, these channelized drainage structures collapse and disappear.

To understand the overall mass balance and behavior of glaciers and ice sheets, it is crucial to understand different observed seasonal velocity patterns and the corresponding enigmatic drainage systems hidden beneath the ice. Combined with advances in remote and field-based observations and the modeling of other processes involved in the hydrologic cycle of ice sheets and glaciers (such as surface mass balance, meltwater percolation and retention, and englacial transport of water), subglacial hydrology modeling may help close a gap in ice dynamics models to inform predictions of future mass loss and sea level rise. Forthcoming work will focus on the application of the SHAKTI model to real glaciers and coupling the model to an ice dynamics model (ISSM, into which SHAKTI is already built).

The SHAKTI model is freely available as part of the open source Ice Sheet System Model (ISSM), which is hosted in a subversion repository at https://issm.jpl.nasa.gov/download/ (Larour et al., 2012; last access: 14 July 2018).

The supplement related to this article is available online at: https://doi.org/10.5194/gmd-11-2955-2018-supplement.

HR and AS formulated the model equations. AS wrote the stand-alone versions of the finite-volume and finite-element models. MM built the parallel model into ISSM and assisted AS with further model development. AS performed simulations and compiled the paper with contributions from HR and MM.

The authors declare that they have no conflicts of interest.

This work was primarily supported by a NASA Earth and Space Science
Fellowship award (NNX14AL24H) to Aleah Sommers. A version of this model was
originally presented in a 2010 proposal by Harihar Rajaram and Robert
Anderson. We thank Robert Anderson for his continued encouragement. Special
thanks to Matthew Hoffman for many helpful conversations about subglacial
hydrology modeling, to Basile DeFleurian and Mauro Werder for including our
model in the Subglacial Hydrology Model Intercomparison Project (SHMIP, de Fleurian et al., 2018;
https://shmip.bitbucket.io/, last access: 14 July 2018)
and providing useful insights along the way, and to Eric Larour for his
initial enthusiasm that facilitated our collaboration with ISSM. We also
thank two anonymous reviewers for their constructive comments to improve the
clarity and strength of this paper.

Edited
by: Jeremy Fyke

Reviewed by: two anonymous referees

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fastand

slowdrainage with different equations for each system. The SHAKTI model allows for the ice–water drainage arrangement to transition naturally between different types of flow. This model can be used to understand how drainage affects glacier speeds and the associated ice loss to further inform predictions of sea level rise.