# Impacts of peak-flow events on hyporheic denitrification potential

Tanu Singh Corresponding author<EMAIL_ADDRESS>Department of Numerical
Mathematics, Technical University of Munich, Garching, Germany  Shubhangi
Gupta  Marine Geodynamics, GEOMAR Helmholtz Center for Ocean Research Kiel,
Germany  Gabriele Chiogna  Department of Hydrology and River Basin Management,
Technical University of Munich, Germany  Institute of Geography, University of
Innsbruck, Innsbruck, Austria  Stefan Krause  School of Geography, Earth and
Environmental Sciences, University of Birmingham, Birmingham, UK  Barbara
Wohlmuth  Department of Numerical Mathematics, Technical University of Munich,
Garching, Germany  Department of Mathematics, University of Bergen, Bergen,
Norway

###### Abstract

Subsurface flows, particularly hyporheic exchange fluxes, driven by streambed
topography, permeability, channel gradient and dynamic flow conditions provide
prominent ecological services such as nitrate removal from streams and
aquifers. Stream flow dynamics cause strongly nonlinear and often episodic
contributions of nutrient concentrations in river-aquifer systems. Using a
fully coupled transient flow and reactive transport model, we investigated the
denitrification potential of hyporheic zones during peak-flow events. The
effects of streambed permeability, channel gradient and bedform amplitude on
the spatio-temporal distribution of nitrate and dissolved organic carbon in
streambeds and the associated denitrification potential were explored.
Distinct peak-flow events with different intensity, duration and hydrograph
shape were selected to represent a wide range of peak-flow scenarios. Our
results indicated that the specific hydrodynamic characteristics of individual
flow events largely determine the average positive or negative nitrate removal
capacity of hyporheic zones, however the magnitude of this capacity is
controlled by geomorphological settings (i.e. channel slope, streambed
permeability and bedform amplitude). Specifically, events with longer duration
and higher intensity were shown to promote higher nitrate removal efficiency
with higher magnitude of removal efficiency in the scenarios with higher slope
and permeability values. These results are essential for better assessment of
the subsurface nitrate removal capacity under the influence of flow dynamics
and particularly peak-flow events in order to provide tailored solutions for
effective restoration of interconnected river-aquifer systems.

##  1 Introduction

Multiple physical, chemical and biogeochemical processes occurring in
hyporheic zones (the transition zones between surface water and groundwater)
determine the ecological functioning and health of interconnected aquifer-
river interfaces (Krause et al., 2011b, ; Boano et al.,, 2014; Krause et al.,,
2017). The hyporheic zone acts as natural biogeochemical reactor and is
crucial for the self-purification capacity of the river-aquifer systems.
Several drivers and controls such as channel gradient, streambed permeability,
pressure gradient at the sediment-water interface affect the hyporheic flow
paths and nutrient transformation and distribution in the hyporheic zone
(Zarnetske et al.,, 2011; Krause et al., 2011a, ; Gomez-Velez et al.,, 2014;
Krause et al., 2014a, ). More recently, we have started to better understand
the role of hydrodynamic variability and in particular of peak-flow conditions
on hyporheic exchange flow and residence time distributions (Malzone et al.,,
2016; Wu et al.,, 2018; Singh et al.,, 2019, 2020). Considering the impact of
change to these dynamic factors on hyporheic exchange flow and biogeochemical
functioning is becoming even more relevant given the increasing frequency,
duration and severity of extreme (low and high) flow conditions due to climate
change (intense precipitation or snow-melting) or anthropogenic disturbances
(e.g., dam operations) and their implications for solute transport, mixing and
reactions in the hyporheic zone (Bruno et al.,, 2009, 2013; Jones,, 2014;
Casas-Mulet et al.,, 2015; Krause et al.,, 2017).

Catchment nutrient dynamics and their resulting loads in rivers are affected
by the hydro-meteorological conditions and runoff generation processes,
varying seasonally and also by land use practices, farming strategies,
increased urbanization and water resource management (Blaen et al.,, 2017;
Abbott et al.,, 2019; Lin et al.,, 2021). Precipitation events can flush large
proportion of nutrients from nearby agricultural lands and activate more
distant sources of nutrient ”hotspots” that are disconnected and do not
contribute to nutrient loads in rivers under baseflow conditions (Krause et
al., 2014b, ). Episodic peak-flow events can be dominating inﬂuences on
catchment nutrient export patterns and dynamics (Malcolm et al.,, 2004; Boano
et al.,, 2007; Blaen et al.,, 2017; Khamis et al.,, 2017). For instance, Lin
et al., (2019) showed that the nitrate flux decreased to less than half of
spring loads in the higher order stream of an agricultural catchment and
Comer-Warner et al., (2020) found in the experimental studies that finer-
grained streambeds can play a major role in nitrate removal from agricultural
catchments.

Amongst the many biogeochemical processes occurring in the hyporheic zone,
detailed understanding of the hyporheic denitrification efficiency is
essential to understand the capacity and limitations of the natural
attenuation potential of hyporheic zones as solution to high nitrate loads in
surface waters and groundwater (Findlay,, 1995; Gomez-Velez et al.,, 2017).
Eutrophication due to excess nutrients (such as nitrogen) availability leads
to low dissolved oxygen concentrations, high turbidity, and low pH with
detrimental effects on stream ecology and aquatic ecosystems (Smith and
Schindler,, 2009). Besides its impacts on the ecological functioning of
interconnected river-aquifer systems, increased nutrient loads do also cause
severe effects to socio-economic activities including drinking water supply
and recreational activities (Bennett et al.,, 2009; Abbott et al.,, 2019).
Lower order rural catchments dominated by agricultural land use are often
important sources of downstream river nutrient pollution, particularly
nitrogen. Flow variability can cause increase as well as decrease in nutrient
concentrations in hyporheic zones under different hydrodynamic and
hydrogeological conditions. The interactions between nutrient exchange fluxes,
mixing, including travel and reactions times are controlled by
geomorphological settings involving channel slopes, streambed permeability and
topography. Implications of these interacting dynamics need to be understood
in order to make the prediction of the natural capacity of the river-aquifer
systems to act as a natural biogeochemical reactor. Predictive dynamic
modeling has been a helpful tool to gain insights into the impacts of peak-
flow events not only concerning the improved assessment of resilience in
stream ecosystems but also widen the basis to design locally optimized
management and restoration strategies.

Recent advances in understanding the impacts of transient flow conditions on
nitrate removal in river-aquifer systems include Trauth and Fleckenstein,
(2017) who investigated the impact of single discharge events on flow
exchange, and reactions in the hyporheic zone below a natural in‐stream gravel
bar using linear relationship between discharge and nutrient loads. The
authors showed that hyporheic exchange ﬂux, solute transport, and consumption
increase during individual flow events. Boso et al., (2018) adopted a
probabilistic approach to quantify the impact of uncertainty in both the
temporally-variable river water and the rate constants on predictions of
$N_{2}O$ emissions at the bedform scale. On the other hand, Zheng and
Cardenas, (2018) focused on the effects of temperature on nitrogen cycling and
nitrate removal‐production efficiency in bedform-induced hyporheic zones under
steady flow conditions. Harvey et al., (2013) demonstrated that different
combinations of local hydraulic and biogeochemical characteristics in these
geomorphic units can contribute substantially to whole‐stream denitrification.
Gomez-Velez et al., (2015) presented numerical simulations of the Mississippi
river network using a hydrogeomorphic model and found that the vertical
exchange with sediments, driven by submerged bedforms, has denitrification
potential exceeding that of lateral hyporheic exchange with sediments
alongside river channels, driven by river bars and meandering banks. Hester et
al., (2019) showed that river stage and sediment heterogeneity pose the
strongest control on mixing-dependent denitrification. Despite the above
advances, very little is known about the hyporheic denitrification potential
during dynamic flow conditions, taking into consideration non-linear
observations of nutrient concentrations and under the influence of different
geomorphological settings.

To address this knowledge gap, we develop robust flow, solute, and reactive
transport models for simulation of transient two-dimensional reactive
transport to estimate the hydrodynamic impacts of hyporheic denitrification
potential during peak-flow events. We evaluate the impacts of peak-flow events
by taking into consideration (i) peak-flow events of different magnitude,
duration and event hydrograph shape (ii) streambed sediment permeability (iii)
stream channel slope (iv) bedform amplitude. Furthermore, we use the analysis
of observed stream flow nitrate and dissolved organic carbon concentration
relationships across a wide range of conditions to define the scenarios for
these simulations. This approach is designed to provide enhanced mechanistic
process understanding of impacts of peak-flow events on the fate of nitrate in
hyporheic environments, information that will assist both industry and
regulators in the design of measures to prevent the disruption of self-
purification in river ecosystems.

##  2 Materials and Methods

A physics-based model was developed and implemented for performing scenario
simulations in this study. The following approach was followed (1) setup of
simulation framework including streambed sediment geometry and
parameterization of material and hydrodynamic properties; (2) generation of
scenario hydrographs using prescribed head distribution across the sediment-
water interface; (3) estimation of Darcy velocity; (4) reactive transport
modeling of aerobic respiration and denitrification along hyporheic exchange
flowpaths using Monod-kinetics; and (5) simulation of model scenarios.

###  2.1 Description of the model domain

We use a simplified conceptualization of a streambed-river interface to
systematically explore the impact of peak-flow events on bedform-driven
hyporheic exchange and reactive transport. For this purpose, we implement
transient flow, transport and reactive transport models for porous matrix flow
in streambed sediments. The modeling domain ($\Omega$); represents homogeneous
and isotropic streambed sediments (See Figure 1). It is bounded at the top by
the sediment-water interface (SWI; $\partial\Omega_{SWI}$), which is assumed
sinusoidal $Z_{\mathrm{SWI}}=\frac{\Delta}{2}\sin(\frac{2\pi x}{L})$ where
$\Delta$ [L] and $L$ [L] are the characteristic amplitude and wavelength of
the bedform, respectively (see Figure 1). The total length and depth of the
modelling domain are $L_{t}=3\,L$ and $d_{b}$ [L], respectively. At the
bottom, the model domain is bounded by a horizontal boundary
($\partial\Omega_{b}$) and at sides by the lateral boundaries
($\partial\Omega_{u}$ and $\partial\Omega_{d}$). These dimensions are selected
to avoid boundary effects in the numerical simulations.

Figure 1: Schematic representation of the model. The sediment domain
($\Omega$) is assumed homogeneous and isotropic. Hyporheic exchange is driven
by streambed topography, channel gradient and time-varying discharge ($Q(t)$).
Prescribed head distribution is imposed along the SWI
($\partial\Omega_{SWI}$). Periodic (pressure drop) boundary conditions are
assumed for the lateral boundaries ($\partial\Omega_{u}$ and
$\partial\Omega_{d}$) representing an infinite domain, horizontal ambient flow
is assumed proportional to the channel slope, and the base of the model domain
($\partial\Omega_{b}$) is assumed impervious. Flow direction is from left to
right. Concentrations of nitrate and dissolved organic carbon are temporally
variable depending on the event scenarios.

###  2.2 Head distribution at the sediment-water interface and hyporheic flow
field

The pressure distribution at the sediment water-interface
($\partial\Omega_{SWI}$) assumes a linear combination of head fluctuations
induced by large- and small-scale bed topography Wörman et al., (2006);
Stonedahl et al., (2010):

$h(x,t)=-S\,x+H_{s}(t)+\dfrac{2\,h_{d}(t)}{\Delta}\,Z_{SWI}\bigg{(}x+\dfrac{L}{4}\bigg{)}$
(1)

where $S$ is channel slope, $H_{s}(t)$ [L] is the time-varying river stage,
$Z_{SWI}(x)$ is the function describing the bed topography [L], and $h_{d}(t)$
is the intensity of the dynamic head fluctuations Elliott and Brooks, (1997),

$h_{d}(t)=0.28\dfrac{U_{s}(t)^{2}}{2g}\begin{cases}\Bigg{(}\dfrac{\Delta}{0.34\,H_{s}(t)}\Bigg{)}^{3/8}&\mbox{for}\quad\dfrac{\Delta}{H_{s}(t)}\leqslant
0.34,\\\\[12.0pt]
\Bigg{(}\dfrac{\Delta}{0.34\,H_{s}(t)}\Bigg{)}^{3/2}&\mbox{for}\quad\dfrac{\Delta}{H_{s}(t)}>0.34,\end{cases}$
(2)

where the mean velocity [L$T^{-1}$] is estimated with the Chezy equation for a
rectangular channel as $U_{s}(t)=M^{-1}H_{s}(t)^{2/3}S^{1/2}$ with $M$ is the
Manning coefficient [L-1/3T] Dingman, (2009).

Finally, the horizontal and the vertical components of the Darcy velocity can
be expressed as Marzadri et al., (2016)

$\displaystyle v_{x}(x,y,t)=v_{0}(t)sin(\lambda x)[tanh(\lambda
d_{b})sinh(\lambda y)+cosh(\lambda y)]+v_{s},$ (3a) $\displaystyle
v_{y}(x,y,t)=-v_{0}(t)cos(\lambda x)[tanh(\lambda d_{b})cosh(\lambda
y)+sinh(\lambda y)]+v_{gw}$ (3b)

where $v_{0}=K\lambda h_{d}(t)$ is the maximum seepage velocity component due
to the bedform morphology [L$T^{-1}$], and $v_{s}=KS$ is the underflow seepage
velocity due to the channel gradient [L$T^{-1}$] and $\lambda=\frac{2\pi}{L}$
is the dune wave number. As we consider neutral groundwater conditions, i.e.
the absence of groundwater gaining or losing conditions, we get in our case
$v_{gw}=0$.

###  2.3 Reactive transport model

The reactive transport model for transient reactions Trauth et al., (2014);
Trauth and Fleckenstein, (2017) is given by

$\theta\frac{\partial c_{s}}{\partial t}=\nabla\cdot(\mathbf{D}\nabla
c_{s})-\nabla\cdot(\mathbf{q}c_{s})+R_{s}$ (4)

where $R_{s}$ is the reaction rate (source/sink term) is the solute species s,
$c_{s}$ is the concentration [ML-3] of that species, $\mathbf{q}$ is the Darcy
flux [LT-1], and $\mathbf{D}=\\{D_{ij}\\}$ is the hydrodynamic dispersion
tensor.

Infiltration of river-borne nitrate, dissolved organic carbon and oxygen
occurs across the sediment-water interface and the following reactions take
place

Aerobic respiration: CH2O + O2 -¿ CO2 + H2O

Denitrification: 5CH2O + 4NO3- + 4H+ -¿ 5CO2 + 2N2 + 7H2O

The reactions are simulated using Monod-kinetics formulation with an
additional inhibition term for the denitrification reaction for the conditions
when $O_{2}$ concentrations are high. The highest nitrate consumption occur
under low oxygen concentrations i.e. anoxic conditions. The general Monod-
kinetic is in the form Trauth et al., (2014)

$R=\mu_{max}I\bigg{(}\dfrac{C_{D}}{K_{D}+C_{D}}\bigg{)}\bigg{(}\dfrac{C_{A}}{K_{A}+C_{A}}\bigg{)}$
(5)

where R is the reaction rate, $\mu_{max}$ represents the maximum reaction
rate, $C_{D}$ and $C_{A}$ are the concentrations of the electron donor
($CH_{2}0$) and acceptors ($O_{2}$ and $NO_{3}$), and $K_{D}$ and $K_{A}$ are
the half saturation constants of electron donors and acceptors, respectively.
An inhibition factor I is required for the denitriﬁcation kinetic, as
described by

$I=\bigg{(}\dfrac{K_{I}}{K_{I}+C_{O_{2}}}\bigg{)}$ (6)

where $K_{I}$ is the inhibition constant and $C_{O_{2}}$ is the $O_{2}$
concentration, while I = 1 for aerobic respiration. The reaction rate of
denitrification is inhibited in the presence of $O_{2}$, because it is the
primary electron acceptor for organic matter oxidation in microbial-remediated
reactions. See Table 1 for the full description of how the parameters used for
reactive transport have been deﬁned.

Table 1: Input Parameters for reactive transport model Trauth and Fleckenstein, (2017) Parameters |  Value
---|---
$K_{O_{2}}$ |  6.25e-03 mmol/L
$K_{NO_{3}}$ |  3.23e-02 mmol/L
$K_{DOC}$ |  1.07e-01 mmol/L
$K_{I}$ |  3.13e-02 $O_{2}$ mmol/L
$\mu_{max,AR}$ |  4.78e-01 mmol/L/d
$\mu_{max,DN}$ |  8.64e-02 mmol/L/d
Table 2: Parameterisation of the numerical model. Parameters |  Value |  Description
---|---|---
Constant model parameters
$\lambda$ |  1 m |  Bedform wavelength
$d_{b}$ |  5 m |  Depth of the domain
M |  0.05 |  Manning’s coefficient
$\alpha_{L}$ |  0.05 m |  Longitudinal dispersivity
$\alpha_{T}$ |  0.005 m |  Transverse dispersivity
$\rho$ |  1000 kg m-3 |  Fluid density
$\mu$ |  1.002$\times$10-3 Pa s |  Fluid dynamic viscosity
$g$ |  9.81 ms-2 |  Acceleration due to gravity
Varied model parameters
$\Delta/\lambda$ |  0.1, 0.05 |  Bedform aspect ratio
$S$ |  0.05, 0.01, 0.001 |  Channel slope
$\kappa$ |  10-11 m2, 10-12 m2, 10-13 m2 |  Permeability

###  2.4 Numerical methods and solution scheme

The mathematical model is strongly coupled and highly nonlinear. The model is
discretized in space using a locally mass-conservative cell-centered finite
volume scheme, and in time using an implicit Euler scheme. The fluxes are
approximated using an orthogonal two-point stencil. The discrete model is
linearized using a standard Newton method and the resulting linear system is
solved using a SuperLU linear solver Demmel et al., (1999). The numerical
scheme is implemented within the DUNE-PDElab framework Bastian et al., (2010)
which is based on C++. The numerical simulator is versatile in terms of the
geometry of the computational domain, dimensionality of the problem (2D, 3D),
and the implementation of the periodic boundary conditions.

###  2.5 Scenarios and parametrisation of the model

Figure 2: Time series of stream discharge, nitrate concentrations, and
dissolved organic carbon concentrations. The grey shaded regions denote the
selected peak-flow events for the presented study. We use the data presented
in Blaen et al., (2017) for the numerical simulations.

As the relationships between stream flow and solute concentrations can vary
significantly spatially between rivers, catchments, geographic and landuse
contexts as well as based on the pre-event conditions, we here use the
continuous high-frequency observations of discharge and concentrations of
dissolved organic carbon and nitrate in an agricultural stream from previous
work by Blaen et al., (2017) as an example for demonstrating some of the event
dynamics that can be observed in intensively managed mixed-landuse catchments.
With the emergence of reliable in-situ water quality sensors high-frequency
observations of discharge and nutrients in rivers are becoming more frequent
now and extending the scenarios to other site conditions is likely to extend
the insights into event-driven nutrient dynamics in hyporheic zones in
different systems in the future. Therefore, in this study, high resolution
data of streamflow, nitrate and dissolved organic carbon for the Wood Brook at
the Birmingham Institute of Forest Research were analyzed (see Figure 2).

In this study, we considered five example peak-flow events selected for their
varying intensity, duration and shape of the hydrograph (see Figure 2 grey
shaded regions). The events are selected so that they have isolated shape and
consistently data availability for flow and nutrient concentrations. As
studied by Blaen et al., (2017) and see in Figure 2, nitrate concentrations
exhibited no clear long-term trend over the entire monitoring period whereas
the highest dissolved organic carbon concentrations were observed in late
summer. We then performed parametric studies analyzing the effects of varying
channel slope, sediment permeability and bedform amplitude of the streambed on
hyporheic exchange flow and nutrient turnover for the selected five peak-flow
and concentration events .

##  3 Results and Discussion

###  3.1 Hyporheic flow field

Figure 3: Snapshots of the dynamic flow field (black arrows represent
direction and are not proportional to the magnitude) in the sediment domain.
Surface represents the magnitude of Darcy flux vector (in log scale $m/s$).
Top row represents the streambed with $AR=0.1$ and bottom row with $AR=0.05$.
The first columns are for the times t = 0 (baseflow conditions), second is for
peak-flow conditions and for the Event 2 scenarios with $S=0.05$ and
$\kappa=10^{-11}$, third column is for peak flow conditions and for the
scenarios with $S=0.001$ and $\kappa=10^{-11}$ and finally fourth is for
$S=0.05$ and $\kappa=10^{-13}$.

Figure 3 illustrates the Darcy velocity magnitude in log scale in the sediment
domain with variable channel gradient, permeability, and aspect ratio of the
streambed. Interplay of factors such as channel gradient (proportional to
slope), sediment permeability, streambed topography, and event-induced
pressure at the sediment-water interface results in bi-directional circulation
systems in the streambed domain (depicted in Figure 3). This bidirectional
flow exchange leads to formation of areas or zones where the velocities are
zero or nearly zero. The lower aspect ratio scenario leads to formation of
smaller hyporheic zones due to smaller pressure gradient at the sediment-water
interface. Similar effect can be observed by the larger channel slope
magnitude due to increase in the horizontal ambient groundwater flow from
upstream to downstream direction. Larger slopes induce stronger underflow
velocities that lead to counteracting force against higher local pressure
gradient at the sediment-water interface and prevent hyporheic zones from
expanding.

Stagnation zones are of particular importance in biogeochemical
transformations and are often referred to as _biogeochemical hotspots_. The
fluctuation of these stagnation points is largely determined by the pressure
variations at the sediment water interface. However, the location and the size
of stagnation zones are modulated by the hydrogeological properties of the
streambed and river flow dynamics. As indicated by the log scale
representation of Darcy velocity magnitude, the higher aspect ratios of the
streambed lead to stronger velocities and larger hyporheic zones. Similar
trends can also be observed for higher permeabilities and higher slope values.
As the flow model assumes negligible storage, the pressure variations and
propagation are instantaneous within the domain.

###  3.2 Spatio-temporal distribution of hyporheic denitrification and DOC
consumption

Figure 4: Snapshots of the spatio-temporal distribution of denitrification and
DOC consumption (%) compared to the baseflow conditions in the sediment domain
for different time points of the Event 2 scenarios. The vertical black dashed
line delineates the scenarios with $S=0.05$ and $\kappa=10^{-11}$ (left) with
$S=0.001$ and $\kappa=10^{-12}$ (right). The first row illustrates the
reaction rates for early rising limb of the event, the second row for the
peak-flow, the third and fourth rows during the recession of the event and
finally the fifth row displays the rates right after the event.

Figure 4 illustrates the spatial and temporal distribution of dissolved
organic carbon (DOC) consumption and denitrification rates with respect to
baseflow conditions for the Event 2 and for the $AR=0.05$. The black dashed
line delineates the scenarios with $S=0.05$ and $\kappa=10^{-11}$ with
$S=0.001$ and $\kappa=10^{-12}$. The first row illustrates the reaction rates
for the early rising limb of the event, the second row for the peak-flow, the
third and fourth during the recession of the event and finally the fifth row
displays rates right after the event, indicating a dynamic change of reaction
rates during the peak-flow event.

The spatial and temporal variations of the reaction rates within the streambed
vary considerably over the duration of the event. The first row illustrates
the rates during the first hour of the event when reactivity rates are low
since the system only begins to be dynamic with flow and solute
concentrations. The results indicate that for the scenarios with $S=0.05$ and
$\kappa=10^{-11}$, high reactivity occurs primarily along the deepest
subsurface flow paths as well as the stagnation points. Since the results are
obtained with respect to the baseflow conditions, we observe that in the
regions of stagnation points, when close to the peak-flow time, the DOC
consumption and denitrification are lowered. However, this behaviour changes
sometime after the peak is attained. On the other hand, for scenarios with
$S=0.001$ and $\kappa=10^{-12}$, high reactivity occurs closer to the sediment
water interface as well as along the deep subsurface flow paths, particularly
evident during and after peak-flow conditions. These results demonstrate that
under different hydrogeological conditions, removal rates can occur at
different depths along the subsurface flow paths for the same peak-flow event.
Depending from the event-specific conditions, high reactivity may be
encountered at different depths of the streambed as the availability of
favourable conditions varies significantly under variable flow and solute
input conditions. It is also evident from Figure 3 and 4 that the flow and
transport processes can have differing sensitivities to transience Singh et
al., (2019). This is due to consideration of diffusion-dispersion processes in
the reactive model which are enhanced by transient flow conditions Gomez-Velez
et al., (2017).

Studies (e.g. Harvey et al., (2013); Boano et al., (2014)) have shown that
denitrifical and DOC consumption due to hyporheic exchange exhibit significant
variations at meter or even sub-meter scales. The spatial variability depends
on the differences in biogeochemical layering of water and solutes such as
nitrate, DOC and dissolved oxygen moving along distinct subsurface flow paths.
Efficient denitrification requires the following conditions: 1) anaerobic
conditions as otherwise oxygen has a higher affinity to act as electron
acceptor during respiration and 2) availability of labile DOC. Peak-flow
events can drive variable amounts of concentrations of different solutes
within different depths of streambed. However, it is important to note that
the patterns and dynamics of the contributions of the nutrients fluxes is not
necessarily proportional to the events and is dependent on factors such as
land-use factors, seasons and sediment-type Blaen et al., (2017) and usually
not taken into consideration.

###  3.3 Impacts of channel gradient, streambed permeabilities and topography
on solute concentration in sediment domain

Figure 5: Temporal evolution of the relative change in concentrations of
nitrate (blue), dissolved organic carbon (pink) and dissolved oxygen (orange)
as compared to baseflow conditions, as a function of time in the sediment
domain. The grey solid line indicates the shape of the hydrograph for the
event 4 with channel gradient (proportional to channel slope) decreasing
(0.05, 0.01 and 0.001) from left to right and streambed permeabilities
decreasing ($10^{-11}$, $10^{-12}$, $10^{-13}$) from top to bottom. Figure 6:
Temporal evolution of the relative change in concentrations of nitrate (blue),
dissolved organic carbon (pink) and dissolved oxygen (orange) as compared to
baseflow conditions, as a function of time in the sediment domain. The grey
solid line indicates the shape of the hydrograph for the event 5 with channel
gradient (proportional to channel slope) decreasing (0.05, 0.01 and 0.001)
from left to right and streambed permeabilities decreasing ($10^{-11}$,
$10^{-12}$, $10^{-13}$) from top to bottom. Figure 7: Temporal evolution of
the relative change in concentrations of nitrate (blue), dissolved organic
carbon (pink) and dissolved oxygen (orange) as compared to baseflow
conditions, as a function of time in the sediment domain. The grey solid line
indicates the shape of the hydrograph for the Event 2. The channel gradient
(proportional to channel slope) is decreasing (0.05, 0.01 and 0.001) from left
to right and streambed permeabilities are decreasing ($10^{-11}$, $10^{-12}$,
$10^{-13}$) from top to bottom. The solid coloured lines represent the
scenarios with aspect ratio of 0.1 and dashed lines with aspect ratio of 0.05.

Geomorphological properties of the streambed have a considerable impact on
nutrient concentrations in the streambed during both the events (Figure 5 and
6). Nitrate concentrations within the streambed domain is reduced to below
baseflow conditions for all scenarios, i.e., with varying channel gradient and
streambed permeabilities for the Event 4. However, for the Event 5, which is
larger in both duration and magnitude compared to Event 4, depicts above
baseflow concentrations. In terms of the input boundary concentrations, Event
4 is with an increasing trend in dissolved organic carbon (DOC) concentrations
with onset of the storm event however nitrate concentrations with a decreasing
trend. In the case of Event 5 for both DOC and nitrate the concentrations
almost follow the trend of the storm event. Please note that stream dissolved
oxygen (DO) concentrations for all the events is constant. For Event 1 and
Event 3 (not shown) both display a general increasing input of DOC whereas
nitrate observed a decreasing trend.

Scenarios with higher slopes (i.e. 0.05) and larger sediment permeability
($10^{-11}$) were associated with higher temporal variability in nitrate and
DOC concentrations. Both geomorphological settings when combined, due to their
large underflow velocities and higher hydraulic conductivity, lead to
considerable variations in flow and transport of nutrients in the streambed.
Increased underflow velocity causes the hyporheic zone not to expand and
causes faster interactions between surface water and ambient groundwater flow,
even with high local pressure profiles caused by Event 5. The solute transport
is therefore constrained to shallow fast-flowing subsurface circulation paths.

The scenario with a low magnitude and a short duration (Event 4) primarily led
to dilution of nitrate in the hyporheic zone because of the decrease of
nitrate influx into the streambed with increasing stream discharge.
Furthermore, the increased availability of DOC during this event leads to a
rise in both aerobic respiration and denitrification as both reactions require
supply of DOC. Contrary to the Event 4, Event 5 is the scenario with a high
magnitude and a long duration and is characterized by concentrations of
nitrate and DOC following the event, i.e., peaked concentrations coincide with
peak-flow of the event. This is because the relative change in streamﬂow
during peak-flow event was substantially higher than the associated change in
solute concentration. Our results highlight that that the discharge-
concentrations relationships can strongly determine the transport and
availability of solutes in the streambed.

In addition to the shape of peak-flow hydrographs and associated solute
fluxes, the transport of the water and solutes in the streambed is furthermore
affected by streambed topography (Figure 7). For diﬀerent bedform aspect
ratios of the streambed, the spatial distribution of the hydraulic head at the
sediment-water interface varies. Under the influence of transient flow
conditions, for high streambed aspect ratios (i.e. bedform amplitude) the
effects on nutrient distribution in the sediment domain are enhanced as the
local pressure is amplified due to larger advective pumping effect. As shown
in Figure 7, the temporal change in nutrient concentrations are generally
similar for both aspect ratios (AR), however the change in concentrations is
enhanced for higher AR compared to low AR.

Our results demonstrate that spatial and temporal distribution of
concentrations of nitrate and dissolved organic carbon within the streambed is
highly dependent on the changes in local pressure profiles caused by the
streambed topography, ﬂow intensity and ambient groundwater ﬂow. Our results
clearly indicated greater magnitude of solutes for higher aspect ratio of
streambed, higher slope values as well as higher streambed permeability.
Higher streambed aspect ratios induce a higher pressure head at the sediment-
water interface ($\Delta/H_{s}$) resulting in deeper streambed subsurface flow
paths. On the other hand, steeper streambed slopes induce higher flow
velocities across lateral boundaries leading to faster exchange between
surface water and ambient groundwater flow. Higher permeabilities of streambed
sediment which affect both, the horizontal ambient groundwater flow and the
infiltration potential of water and solutes in the streambed, also affect
nutrient transport in the streambed. These streambed conditions are
particularly important to consider during drier periods when the soil moisture
conditions are lower causing greater infiltration.

###  3.4 Temporal variability and hydrogeological controls on hyporheic
denitrification potential

Figure 8: Temporal dynamics of relative changes in nitrate concentration
fluxes with respect to baseflow conditions, as a function of time for event 4.
The grey solid curve corresponds to the shape of the hydrograph. From the left
to the right the channel gradient (proportional to channel slope) is
decreasing (0.05, 0.01 and 0.001) and from the top to the bottom streambed
permeability values are decreasing ($10^{-11}$, $10^{-12}$, $10^{-13}$).
Figure 9: Temporal dynamics of relative changes in nitrate concentration
fluxes with respect to baseflow conditions, as a function of time for event 5.
The grey solid curve corresponds to the shape of the hydrograph. From the left
to the right the channel gradient (proportional to channel slope) is
decreasing (0.05, 0.01 and 0.001) and from the top to the bottom streambed
permeability values are decreasing ($10^{-11}$, $10^{-12}$, $10^{-13}$).

In order to compare to estimate hyporheic nitrate removal potential for the
considered events, we estimated the relative change in nitrate exchange (i.e.
difference between influx and outflux) fluxes during the course of the event.
The influx and outflux of nitrate is strongly associated with the channel
slope, permeabilities as well as the event hydrograph shape along with input
solute concentrations (Figure 8 and 9). In Event 4, we observe that the
difference between the influx and outflux of nitrate is positive, primarily
for certain geomorphological settings ($S=0.05,0.01$ and $\kappa=10^{-11}$).
In fact, for conditions with lower streambed permeability, the outflux is
higher compared to the influx. However, if we compare these results to Event
5, different behaviour can be observed, i.e., the influx of nitrate is higher
than the outflux. This implies that nitrate entering the streambed are largely
reduced to molecular nitrogen. The difference between the outflux and influx
is largely increasing with decreasing slopes and the relative change is
decreasing with decreasing permeabilities. We can also observe that the width
between the rising and recession limb for the nitrate exchange fluxes is
widening for scenarios with lower permeability values.

Furthermore, we estimated the cumulative nitrate removal and is given by the
following mathematical statement

$R_{NO_{3}}(t)=\frac{\int_{0}^{t}(m_{in}-m_{out})\ \mathrm{d}t}{M_{in}}$ (7)

where $M_{in}$, the total mass that enters through inflow boundary at the
sediment-water interface ($\partial\Omega_{IN}$) and $m_{out}$ is the mass
leaving the hyporheic zone through the outflow boundary
$\partial\Omega_{OUT}$).

Figure 10: Cumulative nitrate concentration with respect to baseflow
conditions, as a function of time for the all the considered events and
geomorphological settings.

Observations from the breakthrough analysis provided further into the complex
reactive transport behaviour on nitrate within a sediment domain. It is
evident from the results that shape of the peak-flow event is a dominant
factor in the denitrification process (Figure 10). Highest cumulative removal
rate is observed for the Event 5 which is the longest duration event. This is
followed by the Event 3 with a duration of 25% less than the Event 5. This is
in line with the findings of Singh et al., (2019) where hyporheic zone
efficiency, a proxy for the oxygen consumption and denitriﬁcation potential
was used, and it was demonstrated that the events with longer duration and
higher intensity increase the potential of denitrification. Furthermore, it is
noteworthy to observe that the geomorphological settings only become dominant
factors for the events with long duration and high intensity and do not play a
crucial part in nitrate removal for other events. These ﬁndings are similar to
Trauth and Fleckenstein, (2017) where the simulation results for an in-stream
gravel bar showed higher rates of aerobic respiration and denitriﬁcation for
higher peak-ﬂow intensities and longer duration of the event. Additionally, in
particular, scenarios with higher channel slope values and higher permeability
depicted higher cumulative nitrate removal. The latter is also in line with
the findings of Hester et al., (2019) where the authors showed that the
increasing hydraulic conductivity increased mixing-dependent denitrification.

The denitriﬁcation in anaerobic zones of the HZ is limited by the supply of
labile DOC Zarnetske et al., (2011). During peak-flow events, the availability
of dissolved organic carbon in deeper parts of streambed can be increased,
which is a requisite for the denitrification process. As favourable conditions
may be formed deeper within the streambed during peak-flow events, transported
labile DOC supports denitrifying bacteria to complete the process of
denitriﬁcation. However, our results indicated that any peak-flow event alone
cannot induce increased denitrification, instead specific flood
characteristics combined with favourable geomorphic conditions within the
streambed determine hyporheic denitrification potential.

##  4 Conclusions and outlook

The denitrification potential under the influence of peak-flow events was
studied using a two-dimensional transient reactive transport model. Distinct
peak-flow events with characteristic peak-flow intensity, duration and shape
were selected from high frequency observations. The impacts of peak-flow
events on the denitrification potential were evaluated by considering a range
of geomorphological settings, defined by combinations of sediment
permeability, channel slope and bedform amplitude. In addition, we showed that
the denitrification potential is sensitive to the input nutrient fluxes and
high frequency field observations are important for improving the predictions
of denitrification efficiency. Spatio-temporal variations of the flow field
and reaction rates within the streambed varied significantly during the course
of the peak-flow event. Higher reactivity primarily occurred within deeper
subsurface flow paths and near stagnation zones. Furthermore, the the average
positive or negative nitrate removal capacity of hyporheic zones was strongly
associated with the values of channel slope and permeabilities. Specifically,
the hydrograph characteristics emerged as the dominant control for the nitrate
removal efficiency, where events with longer duration and higher intensity
promoted higher efficiency. The geomorphological parameters additionally
modulate the magnitude of the nitrate removal efficiency, such that within the
longer duration and higher intensity events, the scenarios with higher slope
and permeability values led to marginally higher magnitudes of removal
efficiency.

Peak-flow events are ubiquitous, occurring naturally due to intense
precipitation and snow melting, and also due human activities such as periodic
release from dam operations and waste water treatment plants. Peak-flow events
originating from human activities are typically controlled and hence any
cascading effect on water quality or aquatic life can be predicted and
managed. However, the natural factors of peak-flow events can be difficult to
predict and can have significant implications on overall ecosystem
functioning. Our results improve the understanding of the nitrate removal
capacity of hyporheic zones during peak-flow events and therefore help predict
the after-effects of such events on chemical status of the rivers which can be
beneficial for river restoration managers for designing locally optimized
restoration measures. Therefore, results presented in this study can be
relevant to achieve a good aquatic chemical and ecological health as well as
to develop strategies for a sustainable, efficient execution of river
management and restoration plans during and after peak-flow events.

This study assumes that conditions in the streambed favour complete
denitrification. However, depending on the residence times of water and
solutes in the streambed, nitrogen cycling can involve intermediate reactions.
For instance, incomplete denitrification can lead to release of $N_{2}0$ which
is a greenhouse gas and an ozone-depleting substance Briggs et al., (2015);
Comer-Warner et al., (2020), and therefore, may be of particular significance
for climate change impacts and related peak-flow events. Exploring the
scenarios leading to such conditions under peak-flow events need separate
attention and will be taken into consideration in our future work.

## Acknowledgments

This work is supported by the German Research Foundation under the grant
WO671/11-1. G.Chiogna is supported by the German Research Foundation under the
grant CH 981/4-1. All data required to reproduce the figures in this paper is
available through this link https://git.geomar.de/shubhangi-gupta/hyporheic-
denitrification-under-peak-flow-events.git.

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# F*** workflows: when parts of FAIR are missing
††thanks: Notice: This manuscript has been authored by UT-Battelle, LLC under
Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The
publisher, by accepting the article for publication, acknowledges that the
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others to do so, for U.S. Government purposes. The DOE will provide public
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(http://energy.gov/downloads/doe-public-access-plan).

Sean R. Wilkinson  0000-0002-1443-7479 Oak Ridge Leadership Computing Facility
Oak Ridge National Laboratory
Oak Ridge, TN, USA
0000-0002-1443-7479 Greg Eisenhauer  0000-0002-2070-043X College of Computing
Georgia Institute of Technology
Atlanta, GA, USA
0000-0002-2070-043X Anuj J. Kapadia  Kathryn Knight  0000-0003-2755-4495
Computational Sciences and Engineering
Oak Ridge National Laboratory
Oak Ridge, TN, USA
0000-0003-2755-4495 0000-0003-2976-0049 Oak Ridge Leadership Computing
Facility
Oak Ridge National Laboratory
Oak Ridge, TN, USA
0000-0003-2976-0049 Jeremy Logan  0000-0003-1529-3048 Computer Science and
Mathematics
Oak Ridge National Laboratory
Oak Ridge, TN, USA
0000-0003-1529-3048 Patrick Widener  Matthew Wolf  0000-0002-5882-0816 Oak
Ridge Leadership Computing Facility
Oak Ridge National Laboratory
Oak Ridge, TN, USA
0000-0002-5882-0816 0000-0002-8393-4436 Computer Science and Mathematics
Oak Ridge National Laboratory
Oak Ridge, TN, USA
0000-0002-8393-4436

###### Abstract

The FAIR principles for scientific data (Findable, Accessible, Interoperable,
Reusable) are also relevant to other digital objects such as research software
and scientific workflows that operate on scientific data. The FAIR principles
can be applied to the data being handled by a scientific workflow as well as
the processes, software, and other infrastructure which are necessary to
specify and execute a workflow. The FAIR principles were designed as
guidelines, rather than rules, that would allow for differences in standards
for different communities and for different degrees of compliance. There are
many practical considerations which impact the level of FAIR-ness that can
actually be achieved, including policies, traditions, and technologies.
Because of these considerations, obstacles are often encountered during the
workflow lifecycle that trace directly to shortcomings in the implementation
of the FAIR principles. Here, we detail some cases, without naming names, in
which data and workflows were Findable but otherwise lacking in areas commonly
needed and expected by modern FAIR methods, tools, and users. We describe how
some of these problems, all of which were overcome successfully, have
motivated us to push on systems and approaches for fully FAIR workflows.

###### Index Terms:

data science, FAIR principles, high performance computing, workflows

##  I Introduction

The FAIR principles – Findable, Accessible, Interoperable, and Reusable –
represent a set of guidelines for management and stewardship of scientific
data [1]. They were designed and endorsed by a diverse set of stakeholders
because of the urgent need to improve the infrastructure supporting the reuse
of scholarly data. Additionally, the FAIR Principles put specific emphasis on
enhancing the ability of machines to automatically find and use the data, in
addition to supporting its reuse by human scholars. This was not common among
other similar data initiatives.

The FAIR principles chiefly concern metadata, which means that they can be
applied to data as well as anything that can be described by data. For
example, FAIR can be applied to the data being handled by a scientific
workflow as well as to software and other infrastructure necessary to specify
and execute that workflow. In fact, the application of the FAIR principles
beyond scientific data is an active research topic in areas such as research
software [2], computational workflows [3], and even Jupyter notebooks [4].
There are needs to continue to adapt research data management to make data
more machine-actionable. Methods for increasing automation, such as model-
driven approaches, promise better efficiency while reducing sources of human-
generated errors, helping to promote reuse and reproducibility of science data
[5]. Because the FAIR principles were designed for broad applicability, they
are deliberately non-prescriptive, because the meaning of “necessary and
sufficient metadata” varies from one domain to the next. As a result, they are
more like guidelines than actual rules, and different communities have
different standards for what it means to be FAIR-compliant.

Many scientists do agree, however, that “Science is better when it adopts
FAIR” [6], and as a result, FAIR has grown from its initial publication into
an international movement, but it has not been without growing pains. FAIR is
not prescriptive, which means that there is no checklist to follow when
working toward compliance, and some of the principles are easier to implement
than others. Naturally, there are also practical considerations like Return on
Investment and cost optimization that must be taken into account [7].

Here, we detail some cases, without naming names, in which data and workflows
were Findable but otherwise lacking in the other areas commonly needed and
expected by modern FAIR methods, tools, and users. We describe how some of
these problems have motivated us to push on systems and approaches for fully
FAIR workflows. Fortunately, these examples are compiled from projects that
ultimately completed successfully, but where FAIR-related problems led to
delays, confusion, or lost opportunities.

As a convention, we will simply use “data” in the text rather than
“(meta)data”, “meta/data”, or other shorthand notations which might distract
the reader from our purpose. We focus here on both data consumed and produced
by workflows as well as a broad variety of differently structured metadata
about those workflows and their data. All of this metadata is also data in its
own right, of course, and for simplicity, we will simply use “data” to refer
to all of it.

We emphasize that our purpose here is to draw attention to the practical
realities involved in creating different types of shared scientific data,
software, and workflows. Such efforts are difficult enough in a world of
competing and sometimes contradictory priorities and constraints. Identifying
the people and organizations that have shared their science with us does not
serve that purpose, and thus we have obscured those names in the following
sections. Our personal experiences are properly used as a lens to see
opportunities more clearly for a better ecosystem.

##  II Missing Letters

The level of adherence to the FAIR principles varies across implementations,
and here we relate our experiences when different letters of FAIR are
“missing”. In other words, we describe examples of obstacles we have
encountered, grouped in terms of the FAIR principle which was most lacking.
All of these examples satisfied expectations for what FAIR considers Findable,
but each shows weaknesses in satisfying criteria for being Accessible,
Interoperable, or Reusable.

###  II-A Accessible

The first step in using data is, of course, to find them, and having found
them, the user needs to know how they can be accessed, possibly including
authentication and authorization. To that end, there are two FAIR sub-
principles regarding Accessibility. The first is that the data are retrievable
by their identifier using a standardized communications protocol which is
open, free, universally implementable, and able to support authentication and
authorization procedures, where necessary. The second is that metadata are
Accessible even when the data are no longer available. The example we describe
here exemplifies a situation in which, according to the FAIR principles, the
data are available and seem to fit the definition for being Accessible, but
they still are not machine-actionable in the ways that the FAIR principles
were intended to enable.

While gathering data for this project, we encountered a variety of
Accessibility issues, many of which likely resulted from good intentions that
had unforeseen consequences. One frequent hindrance involved cases where the
data, though available, was not readily processable because the authors had
favored making data human-readable at the expense of making it machine-
actionable. As a result, we encountered tables on web pages that were not
easily disentangled from the markup used to structure and style the page.
Another case included data that could only be downloaded as a PDF, and the
data were presented there in tabular form. Both of these cases are
representative of the same difficult problem, namely that there is no simple
way to infer structure from these visual presentation modes. Even if data
elements can be extracted, the tabular structure is lost, and so leveraging
such a dataset comes with the cost of manual extraction to a structured format
such as a spreadsheet or a CSV file.

One dataset, which we intentionally refrain from identifying here, offered a
sort of “perfect storm” of Accessibility challenges. First, as the data
required a Data Usage Agreement to be in place, access to the data was tightly
restricted. We were given a password to access an FTP server and instructed to
use a particular client (Filezilla). Indeed, we found that other FTP clients
were blocked, so access required the additional step of downloading and
installing Filezilla rather than simply using an already-installed client.

Once we were able to connect to the FTP site, we found we could see not only
our data, but also datasets that had been prepared for others. Keeping our
eyes on our own work, we downloaded our designated directory, which contained
roughly 50 sub-directories, each with a separate encrypted, password-protected
file. The encryption mechanism turned out to be specific to Microsoft Windows;
we therefore had to move the data to a separate Windows machine, decrypt each
of the files separately, and then move the entire file tree back to the Linux
machine where the data would be processed.

Processing the data continued to present challenges. First, the naming scheme
across the sub-directories’ files, which had been collected over a number of
years, was not consistent. This made scripting more difficult, as we needed to
provide an explicit mapping from the underlying structure to the actual
filenames.

As a final challenge, the individual data files had been encoded using a
fixed-width format which required a custom reader to be created just to load
and understand the content of the files. Fortunately, the required information
to implement the reader was provided, but it required reading through a
significant portion of the somewhat lengthy documentation. This stood out in
contrast to other datasets that were being gathered for the project, which
were mostly CSV, netCDF, and Microsoft Excel files; none of the others
provided such a steep barrier to entry.

###  II-B Interoperable

Data usually need to be integrated with other data. In addition, data need to
interoperate with applications or workflows for analysis, storage, and
processing. For this reason, there are three FAIR sub-principles that target
Interoperability. First, the data should use a formal, Accessible, shared, and
broadly applicable language for knowledge representation. Second, the data
should use vocabularies that also follow FAIR principles. Third, the data
should include qualified references to other data.

One solution posed to enable Interoperability of data is to remodel data
according to a concept model or ontology, and then either to map that data
into an existing system or to build a new system that can leverage this new
model, e.g., [8]. However, there are some difficulties with this solution, as
it partially assumes that the data at hand easily map to a data model, both
conceptually and in terms of the labels provided for the data. It also assumes
that the original data are labeled in such a way that a person or group of
persons can translate them into broadly applicable language that uses FAIR
vocabularies and includes qualified references.

In our experiences, invisible context abounds in data stored in various
institutional data silos, especially regarding policy changes which may have
seemed “obvious” at the time and therefore not worth recording. One concrete
example is the complete set of telemetry data from a decommissioned computer
system. During the system’s active lifetime, all contextual information about
what the telemetry messages meant was contained in the system’s manual, or
“man” pages. Once the system disappeared, so did the man pages, along with all
of the relevant context about how the messages in the various telemetry files
connected with one another and, crucially, if they would pose any security
risks should they be made available outside of the institution. Today, the
data, which are comprised of approximately 3 terabytes of mostly unstructured
text files, exist in a kind of limbo, where they cannot be destroyed but also
cannot be shared widely, since the cyber security staff cannot say with
certainty that the data are safe to release to the wider public. Because
crucial contextual information about the requisite qualified metadata
references between the files has been lost with the decommissioning of the
system, there is no way to render this dataset Interoperable with itself or
other systems.

Another tricky facet of Interoperability is the means of knowledge
representation, which is asserted in the first Interoperability principle.
Both “shared” and “broadly applicable” seem like reasonable conditions for
language at first. Instead, it turns out that this is actually quite
complicated because domains often disagree within themselves about what is
“shared” and/or “broadly applicable.” Star and Griesemer [9] describe the
heterogeneity inherent in scientific work, even within one domain, and develop
the concept of the boundary object, where a single object can be used for
different purposes by different people, even by people working toward a
similar research goal.

Crucially, the concept of a boundary object is predicated on standardization,
where something with a standardized or predetermined structure, such as a map
or a bird, can maintain a common identity, but may have different conceptual
meanings in different communities of practice. In a later paper clarifying how
the concept of a boundary object has evolved in the research community, Star
underscores that scientists are able to cooperate even when consensus is
rarely reached on how to describe a boundary object [10].

For instance, one concrete example appears in medical procedures within the
domain of a hospital, where there may be both local and shared meaning about
these procedures. Local meaning might be the work of nursing staff, where
perceived granularity in various procedures (captured in the Nursing
Interventions Classification, or NIC) differs with what might be coded as a
Current Procedural Terminology (CPT) code. For instance, the NIC includes the
category of “Spiritual Support” (5420). This same concept does show up in CPT
codes, but is specific to the work of Chaplains (Q9001-Q9003). If a hospital
system opted for CPT codes as the “broadly applicable” _lingua franca_ for
procedures, this granular concept for the specific nature of nursing work is
completely lost, as there is only one CPT code for nursing care in a hospital
(99211). The boundary object, “nursing care”, is understood both by nurses and
the rest of the hospital staff, but it is conceptually different, and
therefore rendered differently, depending on what classification system codes
this object. Using terms like “shared” and/or “broadly applicable” ignores
that subgroups within a domain may need to maintain a vague identity for a
common object, and for this reason obscures how this principle can erase
potentially essential granularity in how concepts get represented. This, in
turn, affects Interoperability with systems that use classifications of
varying granularity.

A recent study describes the roadblocks encountered when a clinical research
team attempted to retrieve 23,186 abdominal CT exams from radiology systems
[11]. One of the first difficulties they encountered were the Accession
Numbers (ACCs) used across different hospitals, which were not only
inconsistent from one system to the next, but also linked to resulting images
differently “such that the images were most often linked to the abdomen ACC,
but they could also be linked to the pelvis ACC, or even the chest ACC if
chest, abdomen, and pelvis CT exams were acquired together. These linkages
varied over time due to changing systems and policies.”

It is that final sentence which reveals the tremendous difficulty in applying
the third Interoperability principle to this example. It is unclear exactly
how changing systems and policies should be handled or described via qualified
references to other data. In fact, their discussion of these roadblocks
includes evolving policies as a major obstacle to research, because the
policies reflected in the data were only understood when the team members were
able to find individuals whose memories spanned these policy changes.

###  II-C Reusable

Ultimately, the goal of FAIR is to optimize the reuse of data. To achieve this
goal, metadata and data should be well-described so that they can be
replicated and/or combined in different settings. As a result, FAIR also
includes a Reusability principle, which somewhat confusingly has one sub-
principle comprised of three sub-sub-principles. Broadly speaking, the
Reusability principle says that data should be richly described with a
plurality of accurate and relevant attributes. More specifically, it says that
data should be released with a clear and Accessible data usage license, that
data should be associated with detailed provenance, and that data should meet
domain-relevant community standards.

There are a number of open problems in workflow science that make it difficult
to apply the Reusability principle to scientific workflows themselves. One of
these problems is that the word “workflow” does not have a consensus community
definition [12]. Without community agreement on precisely what constitutes a
workflow, there can be no agreement on precisely what metadata are necessary
to describe a workflow fully. Goble et al. [3] propose two areas in which the
FAIR principles apply to workflows: FAIR data both for and from workflows, and
criteria for FAIR digital objects. In the case of FAIR data for and from
workflows, workflows would include descriptive metadata about the data
produced as well as metadata that helps trace that data’s provenance. When
considering workflows as FAIR digital objects, as in the second case, a
workflow is seen as an “object” describing methodology that may be
subsequently distributed, used, cited, and modified. Without a community
agreement on what a workflow “is”, however, these two areas for applying FAIR
remain ill-defined.

For our purposes here, then, in applying the Reusability principle to
workflows, we will consider scientific workflows to include all of the data,
processes, and infrastructure necessary to follow the steps of the Scientific
Method for a given experiment. Reusability obstacles then arise for scientific
workflows at several levels.

One source of Reusability applies to tracking the provenance of the data being
manipulated by workflows. “Provenance” is both subjective and tricky, even
within the context of a domain (more on the complications of the word “domain-
relevant” later). Provenance is often an afterthought or is handled
inconsistently – not a standard practice or priority. Furthermore, standards
for recording provenance are not widely known (e.g., the W3C’s PROV-O ontology
[13] or PROV-JSON serialization [14]). For instance, some researchers lack
support for post-experiment metadata documentation, resulting in ad-hoc result
management like naming output files with experiment parameter values; this is,
in fact, a practice we have encountered among colleagues and collaborators
working in certain domains at Oak Ridge National Laboratory (ORNL). There is
great need for institutional support for systems that provide a convenient
means to record domain-specific experimental metadata like inputs, outputs,
and context of processes linked to a given workflow. Without such support, the
burden then falls on the researcher to invest time into recording this
information somewhere and either assuming the technical debt of maintaining a
system-of-record for this provenance, or, more practically, using whatever is
reasonably available at the time, such as a file or directory name.

Provenance is further complicated when data are aggregated from external
sources, where information related to collection methods, coding standards and
vocabularies (whether local or more widely-used), modification history, etc.
is entirely up to an external group or organization to document and make
available. Several sizeable datasets stewarded by ORNL arrive from external
organizations containing their own local representations that, when
aggregated, show inconsistencies at the data level that cannot be clearly
traced to a singular process or set of documentation practices (e.g., variable
coding standards for medical laboratory tests).

As mentioned earlier, “domain-relevant” is also a complicated assertion which
is also related to the discussion of boundary objects in the Interoperability
section. What, specifically, is a domain? What are the boundaries of a domain,
and how are they determined? Beyond finding a clear way to define a domain,
many concepts within domains are hotly debated by researchers, so pointing to
a standard is essentially meaningless in some cases. Alternatively, they would
at least require the ability to record two simultaneous standards for certain
concepts depending on the scientific stance of the researcher. An example here
would be that they share the same ontology, such as the ontic versus epistemic
views of quantum physics, or perhaps asking researchers in electron microscopy
to clarify the difference between “high-tension” and “acceleration voltage”,
which may or may not be used interchangeably, depending on whom you ask.

Another set of obstacles we have encountered while applying Reusability to
workflows appears in the important step of packaging the workflow for
consumption by others, such as for reproducing the experiment. Specifically,
it is difficult if not impossible to package a workflow for reuse when it
contains unwritten, human-centric operations. As a concrete example, we have
encountered a workflow in which one particular step involves column-wise
pasting of a large number of individual tabular files into a single large
file. This is a simple concatenation that could be accomplished with the UNIX
“paste” command in theory, but the datasets are generally too large for a
single paste to suffice without significant performance degradation. The
solution requires a series of smaller pastes to be performed over subsets of
files until a final paste can merge the pasted subsets. This requires careful
planning from a human in order to divide the pasting into parallelizable sub-
jobs that will each have a reasonably short runtime and avoid filesystem
bottlenecks from working with too large a number of files simultaneously. The
scientist also must monitor the jobs after launching them to make sure that
they are completing successfully and to keep track of remaining jobs. These
tasks can be accomplished by modern scripting languages, but scientists often
forego automation for various reasons. How, then, can this workflow be
packaged, if so much of it was unwritten and performed by a human?

##  III Discussion

These examples are compiled from issues and projects that proceeded to
success, but where the lack of, or idiosyncratic interpretation of, the FAIR
principles has led to delays, confusion, or lost opportunities. These sorts of
scenarios are almost assuredly not a surprise to most practitioners in the
sciences; in fact, it is generally considered a solved problem. That solution,
unfortunately, is to throw people at it – to hire a graduate student, a post-
doc, or a technical data engineer, for example, and just tell them to go away
and work on it for six months. Our purpose in revisiting and talking about
these problems is as a reminder that it doesn’t have to be this way.

Science is ultimately driven not just by the discovery of new information, but
also by the communication of that information in a way that it can be reused
and built upon and accumulated. With some more attention to the systems and
management, we could be generating datasets, software, and workflows that are
more Reusable and Accessible in both the context of science and in the context
of modern data and cloud architectures. We need to include the human costs and
the fragility of the current process in any Return on Investment calculation
for developing and adopting new approaches, and in that light, we believe
there is a clear case and need for such investment.

The hidden costs of human investment in reuse is known as “technical debt” in
the software engineering field. Some of us have been working to make this
connection between a more generalized concept of technical debt for scientific
data and workflows and the need for better and more FAIR infrastructures [15].
In the broader context, this problem is hardly new or unexpected. It is
particularly important now, however, as we see the rise of data-intensive
science applications that leverage the explosion in interest in Artificial
Intelligence (AI) and Machine Learning (ML) techniques. Feeding such
algorithms so that they can generate reasonable, unbiased, scientifically
valid results means that the Reusability, Interoperability, Accessibility, and
Findability of scientific results and the provenance of the processes and
workflows used to generate them must all be readily available.

Awareness of the FAIR principles among scientists grows every day, but for
adherence and adoption to keep up, incentive systems will need to improve.
Research Funding Organizations (RFOs) can be strong drivers, for example, by
acting as both the proverbial carrot and the corresponding stick. RFOs can
combine their funding requirements and funding policies (“stick”) with
guidance and financial support (“carrot”) for researchers to incorporate tools
and standards that make their data FAIR while fitting their research
objectives [16]. In this way, RFOs also encourage research institutions to
improve support and facilities for their researchers to create FAIR data,
because they need to comply with the funder’s requirements. As another
example, publication of quality-assured datasets with standardized metadata
should be rewarded in the same way that research papers in high-impact
journals are [17]. The hope in this approach is two-fold: that researchers
would be as eager to share their data as they are to publish their results,
and that they would prioritize the quality of the published data. This same
idea also extends to the publication of software and workflow tools, which are
often available only upon request.

Finally, we should emphasize that the research efforts we have discussed have
all led to quality scientific contributions, in spite of the issues and
problems we have identified. We are excited by the possibilities of a research
enterprise in which not only these kinds of efforts are made easier, but also
the process of extending them is made more straightforward.

##  IV Conclusion

At the same time that the FAIR principles “hit a chord” [18], workflows have
quickly grown and become the default abstraction in large-scale computational
science. As a result, the FAIR principles are also becoming more popular as a
common vocabulary for discussing and comparing workflows. We observe that the
community’s understanding of the interplay between FAIR’s components – and how
that interplay is best realized in operating system, runtime, and application
software – has lagged FAIR’s increased usage as a _lingua franca_. Until that
understanding is better developed, situations such as the ones we have
described here are likely to continue to arise. In the near term, we hope that
discussions like this help scientists avoid similar time-consuming roadblocks
in the pursuit of their scientific objectives. We hope that these personal
examples of datasets and opportunities where open science could have used a
little more FAIR-ness are not read as a condemnation of any one collaborator,
provider, or discipline. Instead, for the longer term, we see this as a call
to arms for all of us across the community to remember that these sunk costs
exist. There is no reason for them to remain an assumed part of business as
usual.

## Acknowledgments

This research used resources of the Oak Ridge Leadership Computing Facility at
the Oak Ridge National Laboratory, which is supported by the Office of Science
of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

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## Acknowledgments

The work of A. Höhl was funded by the project ML4Earth by the German Federal
Ministry for Economic Affairs and Climate Action under grant number 50EE2201C.
The work of I. Obadic is funded by the Munich Center for Machine Learning.
M.Á. Fernández-Torres acknowledges the support from the European Research
Council (ERC) under the ERC Synergy Grant USMILE (grant agreement 855187) and
the European Union’s Horizon 2020 research and innovation program within the
project ‘XAIDA: Extreme Events - Artificial Intelligence for Detection and
Attribution,’ under grant agreement No 101003469. The work of M.Á. Fernández-
Torres and X. Zhu is supported by the German Federal Ministry of Education and
Research (BMBF) in the framework of the international future AI lab ”AI4EO –
Artificial Intelligence for Earth Observation: Reasoning, Uncertainties,
Ethics and Beyond” (grant number: 01DD20001). H. Najjar acknowledges support
through a scholarship from the University of Kaiserslautern-Landau. The
authors are responsible for the content of this publication.

## Competing interests

The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported
in this paper.