- Email: [email protected]
Review of soil phosphorus routines in ecosystem models
Journal Pre-proof Review of Soil Phosphorus Routines in Ecosystem Models
J. Pferdmenges, L. Breuer, S. Julich, P. Kraft PII:
S1364-8152(19)30495-5
DOI:
https://doi.org/10.1016/j.envsoft.2020.104639
Reference:
ENSO 104639
To appear in:
Environmental Modelling and Software
Received Date:
29 May 2019
Accepted Date:
20 January 2020
Please cite this article as: J. Pferdmenges, L. Breuer, S. Julich, P. Kraft, Review of Soil Phosphorus Routines in Ecosystem Models, Environmental Modelling and Software (2020), https://doi.org/10. 1016/j.envsoft.2020.104639
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Journal Pre-proof Review of Soil Phosphorus Routines in Ecosystem Models J. Pferdmengesa,*, L. Breuera,b, S. Julichc and P. Krafta a
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for
BioSystems, Land Use and Nutrition (iFZ), Heinrich-Buff-Ring 26, Justus Liebig University Giessen, 35392 Giessen, Germany b
Centre for International Development and Environmental Research (ZEU), Justus Liebig
University Giessen, Senckenbergstrasse 3, 35390 Giessen, Germany c
Institute of Soil Science and Site Ecology, Technische Universität Dresden, Pienner Str. 19,
01737 Tharandt, Germany *Corresponding author. Tel.: +49 641 9937397; E-mail: [email protected] Declarations of interest: none
Highlights: 1. We review phosphorus models, with a focus on terrestrial process representation 2. Transport of colloidal P is important in many soils, but neglected in most models 3. Most models lack processes to simulate preferential flow 4. A model blueprint is presented to overcome current limitations of P modeling Abstract. We compiled information on 26 numerical models, which consider the terrestrial phosphorus (P) cycle and compared them regarding process description, model structure and applicability to different ecosystems and scales. We address the differences in their hydrological components and between their soil P routines, the implementation of a preferential flow component in soils, as well as whether the model performance has been tested for P transport. The comparison of the models revealed that none offers the flexibility for a realistic representation of P transport through different ecosystems and on diverging scales. Especially the transport of P through macroporous soils (e.g. forests) is deficient. Five models represent macropores accurately, but all of them lack a validated P routine. We therefore present a model blueprint to be able to incorporate a physically realistic representation of macropore flow and particulate P transport in forested systems. Keywords: Phosphorus transport; Solute transport; Soil water leaching; Preferential Flow; Macropores; Hydrological models
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1. Introduction Phosphorus (P) is an important nutrient, but a surplus of P can lead to eutrophication of aquatic ecosystems. Therefore, it is important to study transport mechanisms and routes of P from terrestrial to aquatic ecosystems. For this purpose, it is essential to understand how water and particles are moved within soils in terrestrial ecosystems. Research still focuses mostly on P in agricultural soils (King et al., 2015), whereas P is often neglected in forests and other soils. Hence, current knowledge of P cycling in these ecosystems is still insufficient, although recent studies (e.g., Bol et al., 2016; D. Julich et al., 2017; Sohrt et al., 2017) have shown that P transport is relevant in undisturbed soils. A better understanding of the processes involved may lead to a better comprehensibility of the correlation between different factors, e.g. soil properties, nutrient status and plant health. This is important for forestry, but also a key requirement for predictions of future forest ecosystem changes (Bol et al., 2016), especially with regard to climate change and resulting shifts in precipitation behavior. It is necessary to consider different ecosystems separately because P pools and transport processes differ between them, especially due to dissimilar land management practices and thus differences in the hydrological cycles. The main difference between the hydrological cycles of agriculture and forests is caused by the soil structure. On arable land, plowing and secondary tillage such as harrowing or rotovating leads to a relatively homogeneous structure in the upper soil and thus to a disruption of flow paths (Geohring et al., 2001; Jarvis, 2007). Macropore flow can still be generated in conventionally tilled soils under intense or persistent rain, but studies have shown that macropore flow is more pronounced under no-till arable compared to conventional tillage management (Andersson et al., 2013; Jarvis, 2007). This predominance of macropores is similar in forests or other untilled soils. Animal burrows, fissures, cracks, and root channels lead to the development of a wide network of relatively stable macropores. These promote the formation of preferential flow paths (PFPs) (Bogner et al., 2012; Bundt et al., 2001), resulting in water bypassing large portions of soil without interaction with the matrix. In forests, these heterogeneous runoff characteristics are further facilitated by patchy throughfall and stem flow, which can result in the concentration of large amounts of precipitation water at relatively small areas (Levia and Frost, 2003). In general, P in soils is distributed over different pools, which can be classified based on the form (organic, inorganic) and attachment (dissolved, exchangeable, sorbed). The size of these pools can vary considerably between ecosystems. Prietzel et al. (2016) showed that other factors are even more important for the size of the P pools than the type of ecosystem, for
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Journal Pre-proof example the parent material and stage of pedogenesis. As stated by Frossard et al. (2000) and Julich et al. (2016), soils can contain between 100 and 3000 mg P kg-1 soil. In forest soils, P can be distributed highly variably, with respect to the content, speciation, availability, and source of P (Julich et al., 2016). However, a comparison of the P content between soil matrix and PFPs in a German forest showed no statistically significant differences (D. Julich et al., 2017). In tilled soils, the spatial variability of the P content is reduced compared to undisturbed soils due to the homogenization of the surface soil by plowing. Moreover, fertilization usually leads to a much higher input of P in agroecosystems than in forests (Bol et al., 2016; King et al., 2015). Fertilization often also decreases the diversity of P forms and increases orthophosphate concentrations (Cade-Menun, 2005). While the transport of dissolved P often dominates in the soil water of arable soils, particulate forms can account for a large proportion of total P transported in grassland (Heathwaite and Dils, 2000; Turner and Haygarth, 2000) and forest soils (Bol et al., 2016). This is mostly reasoned by the predominance of PFPs, which enables colloid transport (Beven and Germann, 2013; Vendelboe et al., 2011) and therefore the movement of particulate P. A variety of studies indicate that especially large runoff events lead to the mobilization of high amounts of P in macroporous soils (Bol et al., 2016; S. Julich et al., 2017; Kaiser et al., 2003; King et al., 2015; Ulen, 1995). Contrary, under baseflow conditions, often the amount of dissolved P in the soil solution of forest soils is below the detection limit, which in many laboratories is around 0.03 to 0.05 mg P L-1 (Bol et al., 2016; Mealy, 2011). As a result, movement of P is either only evident over long periods of time or during storm events. Historically, these relatively short extreme events have been mostly neglected when calculating annual P losses. To complement field studies on P pools and transport, computer models are convenient tools. These can be used, for example, for calculations of changes in P storage over time, P loss predictions and balancing, analysis of management or climate change scenarios, as well as for testing hypotheses regarding P cycling mechanisms. Although many hydrological and biogeochemical models exist, only some of them are able to simulate the transport of P through the soil, and a large share of nutrient fate models entirely omit P turnover. The state of P transport and turnover models has been subject to several reviews in the past (e.g. Lewis and McGechan, 2002; Qi and Qi, 2016; Radcliffe et al., 2015; Vadas et al., 2013; Wellen et al., 2015), but most of them only considered a small selection of currently available models. Lewis and McGechan (2002) summarize the state of four catchment models with regard to nitrogen (N) and P losses to groundwater and surface waters following the application of agricultural waste. Processes considered are the transport of soluble and particulate P, surface application (e.g., as fertilizer), mineralization/immobilization, adsorption/desorption, leaching, runoff, and uptake by plants in agricultural systems. Radcliffe et al. (2015) reviewed
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Journal Pre-proof eight models more recently. They examined their suitability for simulating P losses occurring in drainage waters from artificially drained fields. For this purpose, they also included information on macropore flow, but confined to agricultural soils. In another study, Wellen et al. (2015) compared the N and P components of five spatially distributed models. Since the transport through macropores is not considered in their review, the information contained is of limited use for forest soil applications. Qi and Qi (2016) focused in their review on P loss through subsurface tile drains in nine water quality models. Models that cannot simulate tile drainage were excluded. Vadas et al. (2013) also contains information about P transport, but here the emphasis is on the challenges in developing new models. The models considered are compared in regard for diffuse P losses from agricultural soils – preferential flow is not taken into account. These five reviews cover the processes of 17 models in total, but all lack important information with regard to forest ecosystems or other ecosystems with dominating PFPs. To close this gap, we review existing P transport models with a focus on their applicability for agricultural as well as forested ecosystems to simulate not only the transport of dissolved P, but also particulate P. The emphasis of this work is not the calculation of total P losses, but the documentation of processes involved as well as potential improvements in process representation. As an analytical framework, we focus on the representation of the following model features: -
Temporal and spatial scale
-
P pools and forms
-
Mechanisms of surface and subsurface transport with a focus on different flow paths for dissolved and particulate transport
-
Soil water solution interactions with the soil matrix
-
P uptake by vegetation.
We apply this framework to a large number of environmental models that simulate transport of P or generalized nutrients. Thereby we consider models of different scales (from plot to catchment scale) and complexity. In contrast to the previous reviews, we establish a broad overview of all available models for the simulation of P transport.
2. Scope of models included The most important criterion for a model to be included in this review is the ability to simulate P cycling or the transport of solutes in general through soils. To find suitable models we searched ISI Web of Science database, using keywords like “phosph*”, “solute(s)”, “transport”
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Journal Pre-proof or “leach*” in combination with “(hydrological) model(l)-ing”. Some models were also reported to us personally or found because they were referenced in scientific papers, instead of finding them via Web of Science. We included all dynamic numeric models found this way that were used for the modeling of P, regardless of the complexity of the transport mechanisms or whether a validation for P transport exists. In total, we found 26 models that could be used for the investigation of P transport in soils. For a first classification, we analyzed the model types. These can be divided into processbased, conceptual, and empirical. While for process-based and conceptual models a theoretical understanding of relevant processes is necessary (Cuddington et al., 2013), empirical models are based on empirical observations and do not make any statement about the underlying mechanisms and influencing variables. Both process-based and conceptual models are mechanistic models based on a biogeochemical background. While process-based models try to represent the processes as accurately as possible, conceptual models are created by a distinct conceptualization or generalization. Consequently, they are greatly abstracted and simplified. However, most models cannot be clearly assigned to one of these types, as they often contain components from more than one type (Addiscott and Wagenet, 1985). We summarized these models as ‘mixed type’ models. Despite this uniform term, these models can differ greatly from one another. While some models are mainly process-based, but with some conceptualized features, other models are contrary. As a clear guidance to differentiate process-based and conceptual models, we decided to define all models that include the solution of partial differential equations (PDEs) for transport simulation and a complex nutrient routine (see below) as process-based. Models that include only simplified transport and nutrient components are defined as conceptual, while models with either a simplified transport component or a simplified nutrient component are mixed types. In addition to this distinguishing feature, other important model characteristics are for example the spatial and temporal scales. The spatial scale determines the way individual processes are represented in the models. Micro scale models (i.e. soil profile and plot scales) only simulate vertical infiltration and transport processes, but no lateral processes. Large-scale approaches simulate the processes for example on hillslope or catchment level. The temporal scale of the models also differs significantly. While some models are only able to represent very short time periods, e.g. single precipitation events, other models can simulate periods over one or several years (see section 2.2). Another distinction is the amount of data required for parameterization of the model (Sharpley et al., 2002). This is closely related to the level of complexity of represented processes and therefore to the model type. Typical required data include land use, soil texture, topography,
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Journal Pre-proof and management practices. The amount of data needed and the number of parameters increase with increasing mechanization of the models. The data considered in these models can be a combination of collected field data and experimental data but also model results. Therefore, we first examined all models with regard to these differentiation criteria in order to provide an overview. The results of this initial classification and some additional general information are shown in Table 1. In the following, we will discuss the characteristics of the individual models in more detail.
Table 1. Overview of general model properties. Additional information on the spatial and temporal scale of each model is provided in section 2.2. A list with the most important references for each model as well as for P routine validation/testing can be found in Table-A 1 in the appendix. Model
Language
Model type
Model scale
Time step
Performance of P routine tested
ADAPT
Fortran
mixed
plot
day
yes
ANIMO
Fortran
mixed
plot
flexible
yes
AnnAGNPS
Fortran
mixed
catchment
day
yes
ANSWERS2000
NA1
mixed
plot, catchment
flexible
no
APEX
Fortran
mixed
plot, catchment
day
yes
CAMEL
Visual Basic
mixed
catchment
day
no
DAYCENT
Fortran
mixed
plot
day
no
DRAINMOD-P Fortran
mixed
plot
flexible (hour or day)
yes
EPIC
Fortran
mixed
plot
day
yes
GLEAMS
Fortran
mixed
plot
day
yes
HGS
Fortran
process
soil profile, plot, catchment
flexible
no
HSPF
Fortran
mixed
catchment
flexible
yes
HYDRUS
NA
process
soil profile, plot, catchment
flexible
yes
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Fortran
mixed
catchment
day
yes
ICECREAMDB
NA
mixed
plot
day
yes
INCA-P
C++
mixed
catchment
day
yes
LASCAM
NA
conceptual catchment
day
yes
MACRO
Fortran
process
soil profile
flexible
yes
PDP
Python
mixed
catchment
day
yes
PHREEQC
C,C++
process
soil profile
second
yes
PLEASE
NA
conceptual plot
day
yes
RZWQM2-P
Fortran
process
flexible
yes
SimplyP
C++
conceptual catchment
day
yes
SWAP
Fortran
process
plot
flexible
no
SWAT
Fortran
mixed
catchment, continent
day
yes
SWIM
NA
mixed
catchment
day
no
1NA
soil profile
means that we were not able to find this information.
2.1 Model overview The models in this review represent a wide range of different approaches, with many similarities and overlaps. In Table 2 we give a short overview of the main objectives of every model as well as the land use forms for which they were primarily developed. However, even those models that were developed explicitly for one ecosystem can often be transferred to another based on the processes included. At the same time, the fact that a model has been developed for a specific ecosystem does not guarantee that it includes all the important processes to simulate P transport there. In order to overcome this confinement, some models offer the possibility to be coupled with other models to complement missing processes. For example, ANIMO does not consider hydrological components, but it can be combined with SWATRE (Belmans et al., 1981) or WATBAL (Berghuijs van Dijk, 1990). Also, PDP is not able to simulate transport of particulate P in surface water and dissolved P in runoff from dry and paddy lands, but Huang et al. (2016a) solved this problem by coupling the model with USLE and INCA-P. PHREEQC is not able to simulate water flow and solute transport on its own, so Mao et al. (2006) coupled it with SEAWAT to close this gap. The resulting model, which is
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Journal Pre-proof called PHWAT, is not included in this review, since we were not able to find information on P transport. Further models exist that are not considered in this review, although they are able to simulate P transport through the environment. For example, SurPhos (Vadas et al., 2007) simulates the fate and transport of P in agricultural systems, but since it focuses on surface applied manure and dissolved P loss in surface runoff, it is not relevant for P loss through the soil. Another excluded model is APLE, which is a Microsoft Excel spreadsheet model (Vadas et al., 2012). It simulates P loss in runoff and soil P dynamics over ten years on annual time steps. There are often different versions of the models we present in this review. For example, different versions of HYDRUS exist for different scales, e.g. Hydrus-1D and Hydrus 2D/3D, which are summarized for this review under the term HYDRUS. Moreover, RZWQM2-P is a derivate of the whole system model RZWQM2 (Sadhukhan and Qi, 2018). The same applies for DRAINMOD-P, which is based on DRAINMOD (Askar, 2019). Tian et al. (2012) developed another DRAINMOD adaption named DRAINMOD-Forest to simulate water and nutrient dynamics in drained forest soils. However, this model is based on the official DRAINMOD release, which is why it lacks important features included in DRAINMOD-P. Other included models are built from one or more predecessors. For example, APEX is a derivative of EPIC, and ADAPT is an extension of GLEAMS with the hydrological component of DRAINMOD. ICECREAM-DB is based on the Finish model ICECREAM (Larsson et al., 2007) and the soil water and heat model SOIL. The models INCA-P and SimplyP have recently been reimplemented within the Mobius model building framework (Norling, 2019). PHREEQC, a geochemical reactive transport model, is based on reaction kinetics of chemical processes. It uses ion-association, Pitzer, or SIT (Specific ion Interaction Theory) equations for the calculations of solute activities, e.g. 1D transport (Parkhurst and Appelo, 2013).
Table 2. Overview of the land use for which the models where primarily developed, as well as a short overview of the main application scope. Model
Land use
Scope
ADAPT
agriculture
nutrient and pesticide transport
ANIMO
agriculture
nutrient transport
AnnAGNPS
mixed1
sediment and contaminant transport
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ANSWERS-2000
mixed
APEX
mixed
CAMEL
mixed
evaluation of best management practices on surface runoff and sediment loss routing water, sediment, nutrients and pesticides across complex landscapes studying of land use changes and water quality e.g., simulation of water quality, runoff
DAYCENT
agricultural and forest
generation, plant growth, nutrient cycling,
soils2
erosion, impact of land use, management practices, climate change
DRAINMOD-P
agricultural (and forest)
simulation of P transport
soils
evaluation of effect of various land
EPIC
agriculture
GLEAMS
agriculture
nutrient and pesticide transport
agricultural and forest
simulation of the entire terrestrial portion
soils
of the hydrological cycle
HGS
HSPF
HYDRUS
HYPE
ICECREAM-DB
INCA-P
management strategies on soil erosion
routing water, sediment, nutrients and
mixed
pesticides through complex landscapes
agricultural and forest
routing water, sediment, nutrients and
soils
pesticides through complex landscapes simulation of water flow and transport of
mixed
different substances through the soil
(Swedish) agricultural
simulation of P transport, water discharge
soils
and erosion simulation of P transport, water quality
mixed
and aquatic ecology
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LASCAM
simulation of water quality as well as land
mixed
use and climate changes
agricultural and forest
MACRO
contaminant transport
soils
PDP
lowland polder systems
simulation of P transport simulation of natural or polluted water,
PHREEQC
natural and artificial soils
laboratory experiments, and industrial processes
PLEASE
agriculture
simulation of P transport
RZWQM2-P
agriculture
simulation of P transport
SimplyP
mixed
SWAP
simulation of P transport and suspended sediment
agricultural and forest
simulation of water, solute and heat
soils
transport, as well as plant growth
mixed
simulation of water quality and quantity,
SWAT
impact of land use, management practices, and climate change simulation of water quality and quantity,
SWIM
mixed
impact of land use, management practices, and climate change
1Catchment
scale models are marked as “mixed”; 2for one-dimensional models, land uses are listed separately
2.2 Temporal and spatial scale The spatial and temporal scale has a large impact on the properties and functions of a model, whereby both can be influenced by the model type: while pure mechanistic models are mostly suitable for small spatial scales and rather short periods of time (usually less than a year or even less than a day), more empirical models can be used for annual or even multiyear simulations and at larger spatial scales (Radcliffe et al., 2015). Sharpley et al. (2002) and Haygarth et al. (2005) pointed out, which processes are important for P transport depends on
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Journal Pre-proof the model scale. For example, the representation of detachment, deposition, and resuspension of soil particles differ between plot and catchment scales. On small scales, processes are often described in detail, while on larger scales processes are represented by more simple empirical relations and soils often are grouped in associations. Savenije (2001) describes this as “averaging processes”. Scale dependency is evident not only in the spatial scale, but also in the temporal scale. In particular, the time steps of a model have a great influence on the degree of detail, e.g. a model with a resolution per second requires consideration of completely different processes than a model with daily time steps. The temporal and spatial scales of all 26 models are depicted in Table 1. For this overview, the spatial scale was subdivided into soil profile (one-dimensional), plot (larger areas, but homogeneous weather and soils; equivalent to a ‘field’ in agricultural models), and catchment, with increasing range. Soil profile and plot scaled models both focus on vertical fluxes and therefore often make similar assumptions. While only three of the models in this review are restricted to one-dimensional simulations of soil profiles, nine models are specialized for plot and ten for catchment scale applications. The remaining four models can be used flexibly for different scales. The model SWAP was developed for plot scaled modeling, but via the use of geographical information systems and definition of additional features upscaling to regional scale is possible. Another important factor is the spatial disaggregation, which varies strongly between the models. Catchment scale models use a variety of lateral spatial disaggregation. The Models AnnAGNPS, APEX, HYPE, INCA-P, SWAT, and SWIM split the area into Hydrologic Response Units (HRU), which are combinations of homogeneous land use, management, topographical, and soil characteristics (Arnold et al., 2012). HGS provides several options ranging from simple rectangular domains to irregular domains with complex geometry and layering (Aquanty Inc., 2016). Other models use grid cells (ANSWERS-2000, CAMEL, and HYDRUS), partitioning based on single features like land use (HSPF and PDP), subcatchments (LASCAM), or P content classes (SimplyP). Soil profile and plot scaled models (i.e., ANIMO, DAYCENT, DRAINMOD-P, EPIC, GLEAMS, ICECREAM-DB, MACRO, PHREEQC, PLEASE, RZWQM2-P, and SWAP) do not use lateral discretization. ADAPT is also designed for plot scale applications. However, Gowda et al. (2007) used the concept of HRUs to simulate whole watersheds. In general, plot scale models can also be used for larger areas, as long as they are homogeneous, for example for soil, weather, and management practice. The size of the plot depends on the desired resolution and precision (Gerik et al., 2015). Likewise, the disaggregation with depth differs between the models. For example, INCA-P and SimplyP simulate one soil layer and one deeper mineral soil/groundwater layer. In LASCAM
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Journal Pre-proof the soil is divided into three conceptual storages, namely a perched near stream aquifer, permanent groundwater, and an intermediate unsaturated store. Other models subdivide the soil into more layers, e.g. up to ten (DAYCENT, EPIC, RZWQM2-P), 12 (GLEAMS), 50 (ANIMO), or even 200 (MACRO), freely chosen by the user. Besides the spatial scale, the temporal scale also differs between the models. Typical time spans for many models are in the range of years. Only a few models are explicitly set up for the simulation of short periods, e.g. on the scale of single precipitation events (e.g., MACRO, PHREEQC, or HYDRUS). This does not mean that the models mentioned cannot simulate longer periods, but it is not recommended due to the absence of processes that are important over longer periods. The exact simulated period can usually be determined by the user, whereas the time steps are mostly fixed: 16 models use daily time steps, while only PHREEQC features a resolution per second. The model ANSWERS-2000 follows a unique approach, since it uses 30-second time steps during runoff and switches to daily time steps between runoff events. RZWQM2-P simulates crop growth, nutrient balance, and pesticide modules on daily time steps, and soil water, soil heat transfer, and surface energy balance on sub-hourly time steps. In MACRO, precipitation data can be either hourly or daily, while the output can be chosen freely. Still, the calculation steps are defined internally. HYDRUS also declares the time steps internally, but the user can choose time step controls, with which the time steps are automatic adjusted during computation. The remaining six models (ANIMO, DRAINMOD-P, HGS, HSPF, RZWQM2-P, and SWAP) allow the range to be chosen freely.
3. Phosphorus pools and forms With regard to the P routines, the 26 models reviewed here can be divided into several groups with different degrees of complexity. An often-used approach is the conceptual soil P model of Jones et al (1984a). In this approach, three interconnected inorganic P pools are modeled (labile P, active P and stable P), as well as two organic P pools (fresh organic P, stable organic P). An initial value for labile P is given by the user as an input parameter, and based on this active and stable P are calculated. Furthermore, some models follow similar structural approaches as Jones et al (1984a), although single pools and forms of P deviate. Other models only consider dissolved P and particulate P without further differentiation. And finally there are some models in this review that do not have readily available P routines at all, so for modeling of P transport the user has to parameterize a general solute transport component to represent different P forms. All the models without a readily available P routine (HGS, HYDRUS, MACRO, and SWAP) were used in the past for the simulation of P. The parameterization results mostly in very simple
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Journal Pre-proof P routines, including only basic pools, e.g. dissolved P or total P. It is possible to parameterize SWAP for the simulation of dissolved P, but Kroes et al. (2017) recommend coupling it with ANIMO to simulate P or N processes. To improve the P modeling of HYDRUS, Nahra (2006) coupled HYDRUS-1D with NICA and created a PO4 routine for soil water, but this is not included in the official release of HYDRUS. Although these models without readily available P routine can be used to simulate P transport via appropriate parameterization, the lack of P transformation and reaction processes results in the primary suitability for small periods of time (e.g. individual precipitation events). Still, since this review contains models of different complexity and applicability, we decided to include these models. Except for HGS, the performance for the simulation of P of all these general solute transport models was validated. Some models with P specific turnover routines, i.e. ANSWERS-2000, CAMEL, DAYCENT, and SWIM, miss any published validation with field data. The P routine of Jones et al. (1984a) was originally created for the model EPIC (Williams et al., 1983), but it is also used in ADAPT, AnnAGNPS, ANSWERS-2000, APEX, CAMEL, DRAINMOD-P, GLEAMS, ICECREAM-DB, RZWQM2-P, SWAT, and SWIM. While all of these models’ P routines are based on this foundation, they still comprise different modifications. For example, EPIC contains a constant P sorption parameter for an entire simulation, while AnnAGNPS calculates this parameter daily, based on changing soil properties (Vadas et al., 2013), and SWAT calculates it dynamically at the beginning of each simulation year (Collick et al., 2016). This distinction can lead to very different sizes of the P pools. In DRAINMOD-P, the organic P pools are based on the organic N routine from DRAINMOD-N II (Youssef et al., 2005). The model RZWQM2-P contains an additional routine for calculating the loss of particulate P via surface runoff and tile drainage. There is also a modification for GLEAMS to simulate the transport of particulate P, namely an extension called PARTLE (Shirmohammadi et al., 1998). Therefore, of these models only RZWQM2-P and GLEAMS allow for particulate P movement to be simulated, whereas all other models based on this approach by Jones et al. (1984a) are not capable of doing so (see Table 4). Other models with structurally very similar P routines to Jones et al. (1984a) are INCA-P (Jackson-Blake et al., 2016; Wade et al., 2002) and SimplyP (Jackson-Blake et al., 2017). Both simulate dissolved P, inactive soil P, and labile soil P. Another similar approach can be found in PLEASE (Schoumans et al., 2013; van der Salm et al., 2011), which depicts dissolved inorganic P, adsorbed P, and soluble P. Slightly more complex is the approach of ANIMO (Groenendijk and Kroes, 1999), which simulates in addition to three inorganic P stores (dissolved, adsorbed, and precipitated inorganic P) also three organic P stores (dissolved, stable, and fresh organic P). HYPE includes three immobile P pools (inorganic adsorbed to soil particles, organic with rapid turnover, and organic with slow turnover) and two mobile pools
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Journal Pre-proof (particulate and soluble P). The P routine of HSPF is based on the AGCHEM module (DiazRamirez et al., 2013). Here, P is simulated as sediment-attached, dissolved in surface runoff, and as concentrations in the interflow and groundwater compartments. The P routines of other models are simpler. For example, PDP (Huang et al., 2016b) simulates only dissolved P and particulate P. Likewise, according to Lewis and McGechan (2002), DAYCENT is able to simulate sorbed and labile soil P, but no applications or validations thereof could be found. LASCAM distinguishes only between organic/adsorbed P and soluble P (Viney et al., 2000). PHREEQC (Herrmann et al., 2013; Moharami and Jalali, 2014) takes a very different approach, since it is a hydro-geochemical transport model and therefore depicts the actual chemical compounds. The question of which of these methods produces the best results is not easy to answer, as all methods have advantages and disadvantages. As Qi and Qi (2016) pointed out, a simplified representation (e.g., HYDRUS, MACRO, and SWAP) can lead to less accurate prediction results. On the other hand, a simplified representation can also lead to more accurate prediction results, as improved
performance during calibration can be due to
overfitting rather than improved process representation(e.g. Perrin et al., 2001; Seibert, 2003).
4. Transport components For transport processes, a distinction can be made between dissolved and suspended transport, surface and subsurface transport, and transition between mobile and immobile forms. Not all models include every transport component, due to different spatial foci. PHREEQC is a 1D model of vadose zone transport and therefore lacks surface transport, while conceptual large-scale models like LASCAM simulate only dissolved P and no mobile-immobile transition. Table 3 summarizes which water compartments are included in the models, in order to provide an indicator for possible uses of the models.
Table 3. Overview of important hydrological compartments of the different models. Model ADAPT ANIMO
Matrix
Macropores
Darcy with Dupuitbypass flow Forchheimer assumptions yes (see on bypass flow the right)
AnnAGNPS
no leaching
no
ANSWERS2000
storage routing
no
Surface Infiltration Groundwater Streamflow Water Curve Number, yes yes (Darcy) no Green & Ampt coupling with external hydrological model necessary Curve yes no yes Number Green & yes no yes Ampt
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Journal Pre-proof
APEX
storage routing
bypass flow
CAMEL
storage routing
dualpermeability (in aquifer)
yes
Curve Number, Green & Ampt
yes
yes
yes
Green & Ampt
yes (Darcy)
yes
capacity based
no
no
Green & Ampt
yes (Darcy)
only as sink
yes
no
Richards no yes equation Darcy with DRAINMOD Dupuitbypass flow1 yes -P Forchheimer assumptions DAYCENT
Curve Number, Green & Ampt Curve Number
EPIC
storage routing
no
yes
GLEAMS
storage routing
no
yes
HGS
Richards equation
discretely, dualporosity, dualpermeability
yes
Richards equation
yes (variably yes saturated)
HSPF
empirical equation
no
yes
Stanford model
yes (empirical)
yes
HYDRUS
Richards equation
dualporosity, dualpermeability
yes (via Richards extensio equation n)
yes
only as sink
HYPE
storage routing
bypass flow
yes
capacity based
yes
yes
bypass flow
yes
Richards equation
bypass flow
yes
no
yes
MACRO
Richards equation
dualporosity, dualpermeability
no
Richards equation
PDP
no leaching
no
yes
constant infiltration no rate
no
PHREEQC
no matrix flow
dual-porosity no
no
no
no
PLEASE
no leaching
no
yes
NA
yes (empirical)
yes
bypass flow
yes
Green & Ampt
yes (Darcy)
only as sink
bypass flow
yes
unlimited
yes
yes
ICECREAM- Richards DB equation INCA-P LASCAM
RZWQM2-P SimplyP
storage routing storage routing
Richards equation storage routing
15
capacity based capacity based
yes (as sink) no
yes (based on storage capacity) yes (empirical) yes (empirical) yes
no yes yes only as sink
Journal Pre-proof SWAP
Richards equation
bypass flow2 yes
SWAT
storage routing
bypass flow
yes
SWIM
storage routing
no
yes
Green & Ampt Curve Number, Green & Ampt Curve Number
no
no
yes (empirical)
yes
yes
yes
1The
official release of DRAINMOD is not able to simulate macropores, but the derivate DRAINMOD-P contains a bypass routine; 2SWAP simulates no interaction between matrix and macropores, but the macropores reach to different depths and therefore can release water and nutrients into the matrix. 4.1 Surface transport Many models are capable of simulating the transport of P at the soil surface. As shown in Table 4, this can take place in various ways. While some models only represent the transport of dissolved P, other models simulate both the surface transport of suspended particulate P and dissolved P. Different mechanisms can be used for different transport routes. Dissolved P, for example, can either diffuse from soil solution of the upper soil layer, or be released from sorption as a function of the extractability of P in the near-surface soil (Sharpley et al., 2002). Suspended P is mostly simulated coupled with erosion. Most models simulate erosion using the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) or some modification of this, like the modified USLE (MUSLE) (Williams, 1975) or the revised USLE (RUSLE) (Renard et al., 1991). USLE is a simple empirical approach for predicting long-term average annual soil loss. It is based on various factors, like rainfall erosivity, soil erodibility, topography, and cropping management. A more complex technique is the process-oriented modeling of erosion, where detachment, transport, and deposition of sediment are simulated discretely (Wolfe, 2007). However, this technique is rarely used. Table 4 shows how the different models simulate erosion as well as surface transport of P. Models that are able to simulate surface transport of both dissolved and particulate P are ADAPT, Answers-2000, APEX, CAMEL, DAYCENT, EPIC, GLEAMS, HSPF, ICECREAM-DB, INCA-P, LASCAM, RZWQM2-P, SimplyP, SWAT, and SWIM. Many of these models are not able to simulate the transport of particulate P through the soil. Still, to represent the transport at the surface, this is circumvented by coupling the surface transport of P via a transport factor to the erosion of sediments. From all the models in this review, many models (e.g. ADAPT, AnnAGNPS, CAMEL, EPIC, LASCAM, and SWAT) calculate surface transport of dissolved P based on the concentration of labile P in the top soil layer, runoff volume, and an extraction coefficient. In PLEASE, P loads from a field to surface waters are estimated based on a function of depth and the distribution
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Journal Pre-proof of total annual horizontal water flux (Schoumans et al., 2013). For the transport of particulate P, ICECREAM-DB assumes the same distribution between the P pools in the eroded material as in the bulk soil (Yli-Halla et al., 2005). On the contrary, particulate P transport in overland flow is often simulated by a logarithmic relationship between P enrichment ratio and sediment discharge (Menzel, 1980). This is in accordance with the fact that eroded P is preferentially attached to the finer sediment particles, which tend to be first eroded (Viney et al., 2000). Therefore, eroded particulate material is mostly enriched with P compared to surface soil (Sharpley et al., 2002). For example, in LASCAM P concentrations decrease as the mass of eroded material increases. Similar enrichment ratios are also used for example in ADAPT and GLEAMS.
4.2 Subsurface transport Different approaches exist to simulate the infiltration and transport of water and solutes in soils. Infiltration is very often calculated using a conceptual curve number approach (NRCS, 2004) or the Green and Ampt equation. Alternatively, a constant infiltration rate can be assumed, or infiltration can be capacity based. Approaches that are more complex calculate infiltration based on the hydraulic gradient (Richards equation). Table 3 shows how this is implemented in the different models. For simulating water transport through soils, a commonly used conceptual approach is a storage routing representation. This approach (often described as “tipping bucket”) always fills one storage until field capacity is reached, before the excess water is routed to the next layer. This simple approach is used in most conceptual models, but also in many models classified as mixed type. Still, it has several drawbacks, e.g. it is not able to simulate upward flow. Therefore, they have certain disadvantages compared to physical-based approaches, for example when simulating shallow water table soils. For process-based models, the subsurface water flow and solute transport can, according to the classification of Šimůnek et al. (2003), be grouped in single-porosity, dual-porosity and dual-permeability approaches. The different concepts are shown in Figure 1. Single porosity approaches only simulate water flow and solute transport through the soil matrix. A variation of this approach is supplemented by a fast bypass flow component (Figure 1b). Here, the bypassed water and solutes are directly routed to the groundwater or catchment outlet, so there is no interaction between the slow and the fast water components. This differs in dual-porosity (Figure 1c) and dual-permeability (Figure 1d) models, where it is assumed that the porous medium consists of two interacting regions. One region represents the macropores, while the other comprises the micropores inside the matrix. In dual-porosity models, water in the matrix is stagnant, whereas dual-permeability
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Journal Pre-proof simulates water flow in both macropores and matrix. Additionally, some dual-permeability models simulate not only slow matrix and fast macropore transport, but also immobilization processes (Šimůnek et al., 2003; Figure 1e). In all these different approaches, the transport of solutes is predominantly simulated as relatively simple physical relations, e.g. via the convection-diffusion-dispersion equation (Gerke and van Genuchten, 1993; Šimůnek et al., 2003). An even simpler approach to calculate solute transport, which is frequently used in conceptual models, is to multiply the concentration of solute by the water flux.
Figure 1. Concepts of different water flow and solute transport approaches, taken from Šimůnek et al. (2009) and edited.
4.2.1 Matrix transport In single-porosity models (Figure 1a), saturated water flow is mainly simulated via storage routing or the Darcy equation (particularly for groundwater flow), whereas for unsaturated conditions the Richards equation is the most used equation. Single-porosity models are ANSWERS-2000, DAYCENT, EPIC, GLEAMS, HSPF, LASCAM, PDP, PLEASE, and SWIM (see Table 3). Of these models, ANSWERS-2000, EPIC, GLEAMS, LASCAM, and SWIM use the storage routing approach, while DAYCENT represents matrix transport using the Richards equation. PDP does not simulate the soil divided into soil layers, as it was developed specifically for polder systems. Instead, it describes the water balance in four land-use areas, namely residential area, surface water area, paddy and dry lands, without vertical transport of water and P through the soil. PLEASE also does not simulate the vertical transport of water and solutes, since it focuses on the total amount of leached P instead of its transport routes. Therefore, the soil is not divided in different layers, but the loss of P is calculated based on empirical assumptions, i.e. the concentration of P and the total groundwater outflow as a function of depth. In this model, only horizontal runoff is calculated. Matrix flow is also not included in the models AnnAGNPS and PHREEQC. Since PHREEQC is a dual-porosity model, water and solutes in the matrix are stagnant and only transported via macropores. AnnAGNPS focusses on surface runoff and does not consider vertical leachate. However, lateral
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Journal Pre-proof subsurface flow is calculated based on Darcy’s equation and assumed homogeneous through the entire soil profile (Bingner et al., 2015). The remaining models all have a second, faster transport route (see section 4.2.2). Still, their matrix transport components are mostly similar to single-porosity approaches, namely six models (i.e., APEX, CAMEL, HYPE, INCA-P, SimplyP, and SWAT) with storage routing and six models (HGS, HYDRUS, ICECREAM-DB, MACRO, RZWQM2-P, and SWAP) using the Richards equation. DRAINMOD-P uses the Darcy equation with Dupuit-Forchheimer assumptions. This approximation assumes that flow lines are parallel to the impermeable sublayer and thus that lateral flow processes can be separated from vertical processes. This is reasonable when the rate of subsurface flow is low (Clark et al., 2015a; Kampf and Burges, 2007). The model ADAPT is unable to simulate unsaturated water transport, but (since its hydrologic component is based on DRAINMOD) vertical transport through the matrix is simulated using the Darcy equation. ANIMO has to be coupled with another model to represent hydrological processes, since the model itself is not capable of simulating water transport components. The transport of solutes through the matrix differs little between the models. Mostly it is calculated by convection-diffusion equations (ANIMO, DRAINMOD-P, GLEAMS, HGS, HYDRUS, ICECREAM-DB, MACRO, and SWAP). Kroes et al. (2017) recommends combining SWAP with ANIMO for a more realistic simulation of P transport. In HSPF, subsurface transport of dissolved P is simulated by adjusting the ratio of surface to subsurface total P (Radcliffe et al., 2015). For many of the models, we were not able to find published information on the exact mechanisms of solute transport. However, apparently most models are able to simulate transport via water movement. Contrary, the transport of particulate P is restricted to macropores, so none of the single-porosity models is able to simulate this process explicitly.
4.2.2 Macropore transport Macropore transport is usually much faster than transport via matrix. In many models (i.e., ADAPT, ANIMO, APEX, DRAINMOD-P, HYPE, ICECREAM-DB, INCA-P, RZWQM2-P, SimplyP, SWAP, and SWAT) it is simply represented as an additional fast bypass component. In SimplyP this quick flow component is simplified even more, since it includes not only macropore flow, but also saturation excess overland flow, tile drainage, and runoff from impervious surfaces (Jackson-Blake et al., 2017). Many soils, especially those of forests, grasslands, and no-tillage agriculture, usually contain macropores. Hence, transport through PFPs as well as interactions between PFPs and the matrix are important for P translocation
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Journal Pre-proof (Bogner et al., 2012; Bundt et al., 2001; Julich et al., 2016). As these interactions cannot be represented by bypass flow, this transport route is mainly of interest for the modeling of agricultural soils with drainages, while dual-porosity and dual-permeability approaches are representations of macroporous soils that are more realistic. According to Qi and Qi (2016), macropore flow in dual-permeability models is mostly represented either by Richards equation, kinematic wave approach, or gravitational flow using Poiseuille’s law to calculate water infiltration into macropores. While macropore flow based on the Richards equation considers both gravitational flow and capillary-driven flow, the kinematic wave equation only includes the vertical gravity flow and ignores capillary. Since Poiseuille’s law premises that the macropores are cylindrical, this approach is less accurate when the pores deviate from this assumption (Šimůnek et al., 2012). Moreover, Poiseuille’s law assumes saturated conditions, which only rarely applies to macropores (Jarvis, 2007). Of all the models presented in this review, only five models contain dual-porosity or dualpermeability representations, namely CAMEL, HGS, HYDRUS, MACRO, and PHREEQC. While PHREEQC is restricted to dual-porosity, the models HGS, HYDRUS, and MACRO are able to simulate both dual-porosity and dual-permeability systems. CAMEL takes a special position because the unsaturated soil is simulated simply as a single porosity system, but it uses a dual permeability approach for the saturated aquifer. This groundwater flow is simulated via Darcian flow with two different hydraulic conductivities. The dual-permeability models HGS and HYDRUS use the Richards equation for both matrix and macropore water flow. In MACRO, water transport through macropores is simulated via kinematic wave approach. Since PHREEQC needs to be coupled with another model to simulate water flow, it is not possible to specify the mechanism. For example, Wissmeier and Barry (2010) created an extension for unsaturated flow in PHREEQC, which enables the use of the Richards equation. Generally, existing models with a second flow component follow various approaches for the representation of solute and particle transport through macropores and how the exchange between matrix and macropores is modeled (Djabelkhir et al., 2017; Šimůnek et al., 2003). For example, in HYDRUS this is solved similar to most single-porosity approaches: solute transport is calculated via convection-diffusion type equations (Gerke and van Genuchten, 1993). This is true for both fracture and matrix regions, but with different parameter values assigned (Šimůnek et al., 2003). In HYDRUS, the exchange of water between matrix and macropores is driven by the gradient of pressure heads. In MACRO, the exchange of water from macropores to micropores is calculated using a mass transfer expression, while the transport of solutes is calculated neglecting dispersion, since advection is assumed to dominate. In PHREEQC, various mechanisms are available: advective transport, advective-dispersion transport (1D), and diffuse transport (2D and 3D).
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Journal Pre-proof The model SWAT is only capable of simulating bypass flow and lacks a P transport component for the macropore pathways, which is why it is not applicable for the physically correct simulation of P transport processes in macroporous soils (Radcliffe et al., 2015). In RZWQM2-P, the water flux through macropores is simulated via Poiseuille’s law, which is based on gravitational flow. Dissolved reactive P and particulate P are directly routed from the first soil layer to the groundwater without interaction with the matrix (Sadhukhan and Qi, 2018). In SWAP, water can be infiltrated into macropores at the soil surface and then be transported into deeper soil layers. The model distinguishes between continuous, horizontal interconnected macropores, and discontinuous macropores ending at different depths, so water and nutrients can be transported to various soil layers. Additionally, the macropores are divided into static and dynamic (Kroes et al., 2017). For APEX, Ford et al. (2017) recently developed a new modification to implement macropores. They distinguish between matrixexcess macropores and matrix-desiccation macropores. Matrix-excess macropores appear when saturation exceeds the ‘water-entry’ pressure of the matrix, while matrix-desiccation macropores occur at low moisture conditions and form a draught crack network. The latter is modeled as a function of clay content and potential evapotranspiration demand deficiency. In this approach, macropore flow of dissolved P is assumed to equilibrate with the surface soil, so P is partitioned between adsorption on the soil surface and transportation through the macropores by using the Langmuir isotherm. Water transport takes place in the form of a bypass flow to the bottom soil layer, and when its capacity is exceeded, it is moved upwards to the next unsaturated layer.
4.2.3 Mobile-immobile transition In dual-porosity models, the exchange of P and solutes between macropores and matrix is typically associated with mobilization and immobilization processes. Aside from these processes, especially single-porosity models also often simulate immobilization of P via adsorption. Immobilized P is generally not congruent with particulate P, but in some model descriptions, these two terms are used synonymous: In most models that are able to simulate particulate P, it is considered as immobile, or it can only be transported by surface erosion. Jones et al. (1984a) created the concept of stable vs. dissolved P for EPIC and this has been used in all models building on EPICs P concept (see section 3). Here stable P is only transported by surface erosion and is therefore excluded from leaching. The immobilization of P in many other models works in similar ways. Heathwaite (2003) and Vadas et al. (2013) criticize this, because this P routine has seen very limited updates. Especially movement of particulate P within the soil is therefore missing in most models. Only the models GLEAMS
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Journal Pre-proof (via the extension PARTLE), HYPE, INCA-P, PDP, RZWQM2-P, and SimplyP (Table 4) are able to simulate the transport of particulate P. The transition between immobile and mobile P is often realized by an equilibrium function. For example, as described in section 3, models with P routines based on Jones et al. (1984a) simulate the mobile-immobile transition by creating an equilibrium between stable and active mineral P and between active mineral P and labile mineral P. This equilibrium between the three pools is based on the user-given value for labile P, and the relative sizes of the pools are soil specific. The equilibria can be disturbed by different processes, for example leaching, dissolved loss in run-off and plant uptake of labile P, or loss of stable or active P via erosion. When this happens, the imbalance is calculated and then P is moved between the pools to restore balance (Vadas et al., 2013). The equilibrium between labile and active pools can be restored within several days or weeks, while the equilibrium between active and stable pools is slower (Jones et al., 1984a). Despite this similarity between so many models, small differences exist between these approaches. For example, in ADAPT and GLEAMS the partitioning of mineral P between aqueous and solid phases is implemented via a partitioning coefficient according to the linear adsorption isotherm (Radcliffe et al., 2015). SimplyP also uses a simple linear relationship to equilibrate labile soil P and dissolved P (Jackson-Blake et al., 2017). The P routine of APEX is also based on Jones et al. (1984a), but this model uses the Langmuir isotherm for adsorption. Also in PLEASE, the dissolved inorganic P concentration is calculated using the Langmuir isotherm equation (Radcliffe et al., 2015; van der Zee and Bolt, 1991), while the amount of reversibly sorbed P in the top soil layer is related to the waterextractable P and oxalate-extractable Al and Fe content (Radcliffe et al., 2015; Schoumans and Groenendijk, 2000). In deeper layers, sorbed P decreases with depth based on a firstorder exponential expression. ANIMO provides the Langmuir isotherm and the Freundlich isotherm, which is also used for example by HYPE, INCA-P, MACRO, and SWAP. LASCAM is not able to simulate mobile-immobile transitions. Unfortunately, for many models it was not possible to find detailed descriptions of how they simulate mobilization and immobilization.
4.3 Plant P uptake and removal An important difference between arable land and forests is the annual harvesting of crops from fields, while forest trees are harvested irregularly or not at all. In addition, harvest and other management actions (such as thinning) often focus on selected trees, without removing the entire stand. This leads to differences in nutrient circulation. Examples of models that can simulate within-year variations in both annual and perennial plant P uptake are EPIC, INCA-P, SWAP, SWAT, and SWIM. In SWAP, the disparity between annual and permanent affects not
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Journal Pre-proof only plant growth, but also rainfall interception: for agricultural crops, infiltration is calculated using the Hoyninger-Braden equation, whereas for forests the Gash equation is used (Kroes et al., 2017). Models that appear to represent only annual crops are for example AnnAGNPS and RZWQM2-P. In both models, crop properties such as cultivating and harvest time, tillage, and fertilization need to be given as inputs. Another important factor is the method of calculation of plant P uptake. It can be divided into different approaches, namely supply-driven and demand-driven plant uptake, as well as combinations of both. While supply-driven approaches simulate P uptake by plants based on the P concentrations in soils, demand-driven plant uptake is mainly based on the demand of the plants directly. An example of demand-driven P uptake can be found in GLEAMS (Knisel and Davis, 2000). The uptake of labile P is estimated for each layer where transpiration occurs, with the total uptake from all layers equal to the plant P demand. The plant P demand is a function of the Optimum Leaf Area Index, which is tabulated for a large number of crops over a growing season. This approach is based on the formulation of the EPIC model, where the potential plant uptake of labile P is simulated as a linear function up to a user-specified critical concentration (Jones et al., 1984a). A similar approach was chosen in LASCAM, where plant P uptake depends on the rate of canopy biomass accumulation (instead of the Optimum Leaf Area Index), so that it varies seasonally (Viney et al., 2000). In DAYCENT, a combination of demand-driven and supply-driven P uptake is implemented. Plant P uptake is controlled by the size of the labile P pool, whereas in turn the labile P pool varies with the size of the mineral N pool. Additionally, the uptake of labile P is constrained by upper and lower limits for nutrient content in the shoots and roots, which in turn are considered as a function of plant biomass (Lewis and McGechan, 2002). In APEX, P uptake is also simulated as a combination of demand- and supply-driven, with the root weight as an confinement factor of the supply (Williams et al., 2015). Other models that simulate plant P uptake represented by a combination of both approaches are, for example, INCA-P (Jackson-Blake et al., 2016), RZWQM2-P (Sadhukhan and Qi, 2018), and DRAINMOD-P (Askar, 2019). HYDRUS can simulate chemical uptake of plants both passively and actively. Passive uptake is based on the Feddes equation and P in soil solution, while active uptake is based on the MichaelisMenten equation (Qi and Qi, 2016). In MACRO, plant chemical uptake is included via a source/sink term in the solute transport equation (Qi and Qi, 2016). For many models, it was not possible to find out how they simulate plant uptake exactly (see Table 4), but it is likely that in most cases it is implemented very simplistically, for example as a simple demand-driven uptake, based on C:N:P ratios in the plants.
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Journal Pre-proof Table 4. Overview of phosphorus forms (dissolved vs. particulate) and important related processes. Subsurface P processes Model
Surface processes Plant uptake
Dissolved P
Particulate P
ADAPT
yes
immobile2
demanddriven
USLE
suspended & dissolved
ANIMO
yes
immobile
yes1
no
dissolved
AnnAGNPS
yes
immobile
demanddriven
RUSLE, HUSLE
Answers2000
yes
immobile
demand- and empirical supplyfunction driven
suspended & dissolved
APEX
yes
immobile
demand- and USLE, MUSLE, supplyRUSLE driven
suspended & dissolved
CAMEL
yes
immobile
demand- and supplyyes1 driven
suspended & dissolved
DAYCENT
yes
immobile
demand- and supplyyes driven
suspended & dissolved
DRAINMODyes P
mobile3
yes
RUSLE
suspended & dissolved
EPIC
yes
immobile
demanddriven
USLE, MUSLE, RUSLE
suspended & dissolved
GLEAMS
yes
PARTLE
demanddriven
MUSLE
suspended & dissolved
HGS
no
no
yes
no
no (transport of solutes)
HSPF
yes
immobile
yes
process-based
suspended & dissolved
HYDRUS
no
no
demand- and supplyno driven
HYPE
yes
mobile
yes
Erosion
yes
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P transport
dissolved
no
suspended & dissolved
Journal Pre-proof ICECREAMyes DB
immobile
yes
INCA-P
mobile
demand- and supplyprocess-based driven
suspended & dissolved suspended & dissolved
yes
MUSLE
suspended & dissolved
LASCAM
yes
immobile
based on canopy based on biomass surface runoff accumulation
MACRO
no
no
empirical sink term
detachment of soil particles from surface
PDP
yes
mobile
yes
USLE
suspended & dissolved
PHREEQC
yes
NA
no
no
no
PLEASE
yes
immobile
no
no
no
mobile
demand- and based on supplyGLEAMS driven (MUSLE)
based on USLE suspended & and empirical dissolved relationship with streamflow
RZWQM2-P yes
no (transport of solutes & colloids)
suspended & dissolved
SimplyP
yes
mobile
annually constant input value
SWAP
no
no
fix user input
only surface runoff
SWAT
yes
immobile
demanddriven
MUSLE
suspended & dissolved
SWIM
yes
immobile
demanddriven
MUSLE
suspended & dissolved
dissolved
1In
the columns “Plant uptake” and “Erosion” a simple “yes” indicates that the processes are included, but we found no information on the exact processes; 2“mobile” signifies that the movement of particulate P through the soil is somehow included, i.e. via a bypass component, dual-porosity, or dual-permeability; 3“immobile” means that particulate P can be simulated, but only as an immobile sink.
5. Future developments in environmental process-based P modeling 5.1 General suggestions for improvements As shown in this review, there are a number of environmental models with P routines. Depending on the focus of the models, they show significant differences. In order to provide
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Journal Pre-proof an overview of the existing models, we created a decision tree (see Figure 2). There we present the different transport routes of the individual models (single porosity, dual-porosity, and dual-permeability) as well as the specific P forms that are included. The color code of the figure follows the traffic light principle, i.e. green boxes represent most accurate process representation (e.g., dual permeability and transport of dissolved and particulate P), orange boxes indicate less exact model structures (e.g., dual porosity and dissolved P transport with immobile particulate P component), and red boxes show the simplest representations (no macropores, no functioning P routine). However, this does not mean that the simpler models are generally unsuitable for calculating P fluxes through different ecosystems. Depending on the question and the complexity required, the models can still be very useful. It is noticeable that there is no model for which all processes are represented most accurately (green) to simulate P transport through forest soils. Many models simulate P transport based on a more than 30 years old approach by Jones et al. (1984a), and we could not find many improvements over the last decades in P modeling. Often, there is no reason against this established method, but – depending on the research question – the lack of a mobile particulate P component can be a major deficit. According to Heathwaite (2003) and Vadas et al. (2013), the focus on the development of data-intensive complex models instead of more generic models is a main reason for current deficits in modeling. In line with this, Radcliffe et al. (2015) recommend to revise P modeling and to develop new, improved routines. Considering our focus of interest, this might be especially the transport of particulate P, which is only implemented in very few models. Additionally, model performance needs to be tested specifically for P to make sure that simulation results of soil P dynamics are reliable. This is a flaw of many existing models, since simulated soil P dynamics are rarely presented. Besides this critique of the P routines and the often-found lack of dual-permeability approaches, there are many other potentially important processes, which might be worth considering for model development, for example: -
P uptake by trees and other plants might deviate from P uptake by crops
-
Surface transport, groundwater, and stream components are usually important components
-
Due to stem flow, nutrient inputs might be concentrated locally (Levia and Frost, 2003)
-
Atmospheric particulate deposition might be increased due to interception (Lequy et al., 2014).
Some of the named models include most of the processes we consider important. The newly improved RZWQM2-P deserves particular mention here, as it contains a complex P routine including mobile particulate P, as well as a fast bypass component (but no dual-permeability).
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Journal Pre-proof Still, the possibility to up- and downscale the model specific to the current research question would be an advantage. This is very hard to realize, since a change of scale results in a shift in the important processes. Moreover, such a high flexibility in the model structure might lead to very complex models with many parameters. For different reasons, a large number of parameters can increase the inaccuracy of a model. For example, often parameters have no physical basis, they might be impossible to measure in the field (Djabelkhir et al., 2017), or measured parameters can be scale-dependent and therefore not representative. Radcliffe et al. (2015) and Nelson and Parsons (2006) also pointed out that there is generally a lack of guidance on how to obtain independent values for additional parameters. In overparameterized models the values of individual parameters cannot be unequivocally identified by the data (so called “equifinality”, see Beven and Freer, 2001), so prediction uncertainty might increase. Moreover, small inaccuracies of individual parameters can lead to very large total errors. This is for example criticized by Buytaert et al. (2008) as an “over-complexity” of most models. They propose a number of directives that should be followed when developing new models: the model code should be fully accessible, modular, and portable. This way, the user is enabled to use a model in a highly flexible manner with improved control over the modeling assumptions. For example, modularity allows the user to choose whether particular processes should be represented either by mechanistic equations or by empirical relations. It also simplifies the addition of new independent routines to represent additional processes. This way, in modular and accessible approaches the spatial discretization and the temporal disaggregation can be adjusted more easily. All these suggestions are in accordance with the postulation of Clark et al. (2011) for the development of modular frameworks. Modeling frameworks are toolboxes that provide a variety of different processes, which can be assembled into a model by a user. The resulting model can thus be tailored to specific tasks. Although the compatibility of the individual process representations can be problematic, this enables a large number of different models to be created without high programming effort. Moreover, modeling frameworks are suitable for testing of multiple working hypotheses. Such an approach would be of great advantage for modeling P dynamics, as the comparison of different hypotheses would allow testing which processes are actually important. This way it could be analyzed which macropore transport component is sufficient (fast bypass, dualporosity, or dual-permeability), or to what extent the included process representations depend on the spatial and temporal scale. Existing modeling frameworks are, for example, the Catchment Modelling Framework (CMF) (Kraft et al., 2011), SUMMA (Clark et al., 2015a, 2015b), Raven (Craig and the Raven Development Team, 2019), and the more generic Mobius (Norling, 2019). Unfortunately, until now, no existing hydro-biogeochemical modeling framework is able to simulate P transport through soils.
27
1 2
Figure 2. Decision tree, which shows the most important features of all P models. The split on the first level differentiates models with and without
3
macropores, and the split on the second level further separates based on macropore flow representation. On the third level, the splits distinguish
4
between the ways the models simulate phosphorus transport. The colors (green, orange, red) indicate the accuracy of process representation.
28
5
The color of the models indicate whether the performance of P simulation was tested against field data: blue models contain tested P routines,
6
while red models indicate P routines of unknown quality.
29
Journal Pre-proof 7
5.2 A blueprint for a process-based P model for different ecosystems
8
In this section, we present a blueprint for a process-based and modular environmental P model.
9
As outlined in section 5.1, a major disadvantage of most existing models is that they are static,
10
i.e. the process representations are predefined and cannot be altered. Modularity provides an
11
option on how a model could be designed that allows the user to test and compare different
12
hypotheses. With regard to the results of this review, the presented blueprint could be used to
13
examine whether an explicit representation of macropores can improve modeling P dynamics
14
in different soils and on different scales. Additionally, different P routines could be compared
15
without other factors being modified. For this purpose, a model should contain only the most
16
important processes to simulate the transport of P in different environments, i.e. arable land,
17
grassland, and forests. Nevertheless, in order to be able to test hypotheses for different
18
landscapes and scales, a large number of processes must be representable:
19
1. The model should include different hydrological processes from which a user can
20
choose (see Figure 3, top), for example for infiltration (e.g., Green & Ampt, curve
21
number) and percolation (e.g., Richards equation, storage routing), or optional features
22
like snow storage.
23
2. There should be different options for the transport through the soil, i.e. parallel matrix
24
and explicit macropore transport (Figure 3) and, as a more simplified alternative, direct
25
infiltration into deeper layers via macropores. The horizontal transport via macropores
26
to the stream (Savenije, 2018) should also be included as an optional feature.
27
3. The possibility to connect different spatial entities (gridded cells, polygons) horizontally,
28
for example via Darcy, Richards, or kinematic wave equation, would allow for the
29
construction of models ranging from plot to regional scale.
30
4. For the simulation of P transport and turnover, we propose the development of a P
31
routine loosely based on Jones et al. (1984a), but including the transport of particulate
32
P. As depicted in Figure 3 (bottom), we differentiate between the transport of P through
33
the soil matrix and – if simulated explicitly – macropores. While processes in the matrix
34
are represented in more detail, the processes in the macropores are strictly simplified.
35
Since the transport processes in PFPs are based on gravitational flow, the distinction
36
between dissolved and particulate P will be sufficient. These pools will be in equilibrium
37
with the five pools in the soil matrix. As an alternative approach, a highly simplified P
38
routine could be integrated, which only distinguishes between dissolved and particulate
39
P in both matrix and macropores.
40
5. For the simulation of plants, different possibilities should be included, i.e. a
41
differentiation between permanent forest and grassland versus annual plants and crops.
30
Journal Pre-proof 42
In order to achieve this flexibility, we promote the implementation of this approach in a
43
modeling framework. Since the Catchment Modeling Framework (CMF) (Kraft et al., 2011)
44
offers a multitude of possibilities, including the representation of macroporous flow and
45
transport, this framework is ideal for implementing a P routine.
46
Obviously, the modular modeling approach presented here can be further extended in order
47
to be of use for even more tasks. For example, it could be possible to couple the P routines
48
with the C and N cycles, or to calculate the effect of nutrient states on the net primary
49
productivity. An extension to weathering processes is also possible. The accessibility and
50
modularity of modeling frameworks like CMF would allow such diverse approaches to be
51
realized. Therefore, while the blueprint presented here is certainly not a universally valid model
52
for the simulation of P transport, it qualifies as a good basis for hypothesis testing and provides
53
the possibility for further refinements and adjustments.
54
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Journal Pre-proof
55 56
Figure 3. Top: Blueprint of a P model with relevant hydrological storages and processes. Dark
57
blue boxes represent storages; light green boxes indicate boundary conditions. Bottom:
58
Blueprint for relevant P routines with P pools and transitions in the soil matrix based on Jones
59
et al. (1984a) (left) and within macropores (right). Orange boxes indicate which P pools in the
60
matrix are in equilibrium with particulate P in the macropores, while the yellow boxes show
61
which pools are in equilibrium with dissolved P.
32
Journal Pre-proof 62 63
6. Conclusion
64
We reviewed 26 models that are able to simulate P transport through the soil. Still, their foci
65
are very diverse, and therefore the representation of processes varies greatly. Most of the
66
models were originally created for the simulation of processes in agricultural soils. While some
67
of these models were extended for other ecosystems, it is likely that processes relevant in non-
68
agricultural environments are not well represented. These include, for example, the uptake of
69
P by plants other than crops, or the transport through PFPs. Moreover, while all models in this
70
review are able to simulate some sort of P transport and turnover, they have not necessarily
71
been established for this purpose. For this reason, the P routines of many models have only
72
been tested in very limited experiments or not been tested at all. Only PLEASE, INCA-P,
73
SimplyP and PDP focus especially on the simulation of P leaching from soils. Furthermore, the
74
movement ‘through-the-soil’ of particulate P is an important aspect of P leaching (Julich et al.,
75
2016), yet this process is only represented in seven of 26 models. In all these models, the
76
transport is represented by a fast bypass component. In order to be able to represent this
77
transport process more realistically, the simulation of a dual-permeability system could be
78
appropriate. Since no model fulfils all hypothesized demands, we developed a blueprint of a
79
modular model for the simulation of P leaching through different soils. Especially when it is
80
implemented as part of a modular hydrological framework, this approach could help to
81
compare different hypotheses, e.g. under which circumstances the explicit representation of a
82
dual-permeability system is appropriate. It could also be used for further developments in
83
modeling of P transport.
84
In order to improve the modeling of P processes in the environment, we think that a shift
85
towards the use of modeling frameworks is necessary. By enabling multiple hypothesis testing,
86
we envisage substantial improvements in P modeling quality. In addition, the transferability of
87
the models to different conditions (e.g., various land use forms) should be given greater
88
consideration. For this purpose, the agreement on well-established methods might be
89
necessary, also in the generation of experimental data. Only when sampling is comparable
90
(e.g., comparable depth, consideration of macropores, or same analysis methods for P) can
91
the data be used to develop comparable models, which are of interest for more than an
92
individual case study. If these suggestions are considered, we assume that an improvement
93
of the modeling of P is possible.
94 95
Acknowledgements
33
Journal Pre-proof 96
This project was carried out in the frame of the priority program 1685 “Ecosystem Nutrition:
97
Forest Strategies for limited Phosphorus Resources” funded by the DFG, subproject
98
“Quantification, modeling, and regionalization of seepage losses of phosphorus from forest
99
soils” (BR 2238/26-2).
34
Journal Pre-proof 100
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Journal Pre-proof Table-A 1. Overview of model references and references for performance testing of phosphorus simulation. Model
Model reference
Reference for P routine testing
ADAPT
Gowda et al. (2012)
Dalzell et al. (2004)
ANIMO
Groenendijk and Kroes (1999), Kroes and Roelsma (1998)
van der Salm and Schoumans (2000)
AnnAGNPS
Bingner et al. (2015), Yuan et al. (2005)
Pease et al. (2010), Yuan et al. (2005)
ANSWERS2000
Bouraoui and Dillaha (2000, 1996) NA1
APEX
Plotkin et al. (2013), Steglich et al. Bhandari et al. (2016), Francesconi et (2016) al. (2016), Saleh et al. (2001)
CAMEL
Koo et al. (2005, 2004)
NA
DAYCENT
Parton et al. (1998)
NA
DRAINMOD-P
Deal et al. (1986), Tian et al. (2012), Askar (2019)
Askar (2019)
EPIC
Jones et al. (1984a), Sharpley and Della Peruta et al. (2014), Jones et al. Williams (1990) (1984b), Richardson and King (1995)
GLEAMS
Leonard et al. (1987)
Knisel and Turtola (2000)
HGS
Brunner and Simmons (2012)
NA
HSPF
Bicknell et al. (1996), Grimsrud et al. (1982), Johanson et al. (1980)
Ribarova et al. (2008)
HYDRUS
Agah et al. (2016), Šimůnek et al. (2008)
Agah et al. (2016), Freiberger et al. (2013), Hassan et al. (2010), Naseri et al. (2011)
HYPE
Lindström et al. (2010)
Jiang and Rode (2012), Pers et al. (2016)
ICECREAMDB
Larsson et al. (2007)
Bärlund et al. (2008), Larsson et al. (2007), Liu et al. (2012)
INCA-P
Jackson-Blake et al. (2016), Wade Jackson-Blake et al. (2017, 2016) et al. (2002)
LASCAM
Viney et al. (2000)
Viney and Sivapalan (2001), Zammit et al. (2005)
MACRO
Jarvis (1991, 1995), McGechan et al. (2002)
McGechan (2003), McGechan et al. (2002)
45
Journal Pre-proof PDP
Huang et al. (2016b)
Huang et al. (2016a)
PHREEQC
Parkhurst and Appelo (2013, 1999)
Herrmann et al. (2013), Moharami and Jalali (2014)
PLEASE
Schoumans et al. (2013)
Schoumans et al. (2013), van der Salm et al. (2011)
RZWQM2-P
Ma et al. (2012), Sadhukhan and Qi (2018)
Sadhukhan et al. (2019a, 2019b)
SimplyP
Jackson-Blake et al. (2017)
Jackson-Blake et al. (2017)
SWAP
Gusev and Nasonova (1998), Kroes et al. (2017)
NA
SWAT
Arnold et al. (2012, 1998)
Grizzetti et al. (2003), Vadas and White (2010)
SWIM
Krysanova et al. (2005)
NA
1NA
means that we were not able to find this information.
46
Journal Pre-proof
Declaration of 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. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
J. Pferdmenges, L. Breuer, S. Julich, P. Kraft PII:
S1364-8152(19)30495-5
DOI:
https://doi.org/10.1016/j.envsoft.2020.104639
Reference:
ENSO 104639
To appear in:
Environmental Modelling and Software
Received Date:
29 May 2019
Accepted Date:
20 January 2020
Please cite this article as: J. Pferdmenges, L. Breuer, S. Julich, P. Kraft, Review of Soil Phosphorus Routines in Ecosystem Models, Environmental Modelling and Software (2020), https://doi.org/10. 1016/j.envsoft.2020.104639
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Journal Pre-proof Review of Soil Phosphorus Routines in Ecosystem Models J. Pferdmengesa,*, L. Breuera,b, S. Julichc and P. Krafta a
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for
BioSystems, Land Use and Nutrition (iFZ), Heinrich-Buff-Ring 26, Justus Liebig University Giessen, 35392 Giessen, Germany b
Centre for International Development and Environmental Research (ZEU), Justus Liebig
University Giessen, Senckenbergstrasse 3, 35390 Giessen, Germany c
Institute of Soil Science and Site Ecology, Technische Universität Dresden, Pienner Str. 19,
01737 Tharandt, Germany *Corresponding author. Tel.: +49 641 9937397; E-mail: [email protected] Declarations of interest: none
Highlights: 1. We review phosphorus models, with a focus on terrestrial process representation 2. Transport of colloidal P is important in many soils, but neglected in most models 3. Most models lack processes to simulate preferential flow 4. A model blueprint is presented to overcome current limitations of P modeling Abstract. We compiled information on 26 numerical models, which consider the terrestrial phosphorus (P) cycle and compared them regarding process description, model structure and applicability to different ecosystems and scales. We address the differences in their hydrological components and between their soil P routines, the implementation of a preferential flow component in soils, as well as whether the model performance has been tested for P transport. The comparison of the models revealed that none offers the flexibility for a realistic representation of P transport through different ecosystems and on diverging scales. Especially the transport of P through macroporous soils (e.g. forests) is deficient. Five models represent macropores accurately, but all of them lack a validated P routine. We therefore present a model blueprint to be able to incorporate a physically realistic representation of macropore flow and particulate P transport in forested systems. Keywords: Phosphorus transport; Solute transport; Soil water leaching; Preferential Flow; Macropores; Hydrological models
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1. Introduction Phosphorus (P) is an important nutrient, but a surplus of P can lead to eutrophication of aquatic ecosystems. Therefore, it is important to study transport mechanisms and routes of P from terrestrial to aquatic ecosystems. For this purpose, it is essential to understand how water and particles are moved within soils in terrestrial ecosystems. Research still focuses mostly on P in agricultural soils (King et al., 2015), whereas P is often neglected in forests and other soils. Hence, current knowledge of P cycling in these ecosystems is still insufficient, although recent studies (e.g., Bol et al., 2016; D. Julich et al., 2017; Sohrt et al., 2017) have shown that P transport is relevant in undisturbed soils. A better understanding of the processes involved may lead to a better comprehensibility of the correlation between different factors, e.g. soil properties, nutrient status and plant health. This is important for forestry, but also a key requirement for predictions of future forest ecosystem changes (Bol et al., 2016), especially with regard to climate change and resulting shifts in precipitation behavior. It is necessary to consider different ecosystems separately because P pools and transport processes differ between them, especially due to dissimilar land management practices and thus differences in the hydrological cycles. The main difference between the hydrological cycles of agriculture and forests is caused by the soil structure. On arable land, plowing and secondary tillage such as harrowing or rotovating leads to a relatively homogeneous structure in the upper soil and thus to a disruption of flow paths (Geohring et al., 2001; Jarvis, 2007). Macropore flow can still be generated in conventionally tilled soils under intense or persistent rain, but studies have shown that macropore flow is more pronounced under no-till arable compared to conventional tillage management (Andersson et al., 2013; Jarvis, 2007). This predominance of macropores is similar in forests or other untilled soils. Animal burrows, fissures, cracks, and root channels lead to the development of a wide network of relatively stable macropores. These promote the formation of preferential flow paths (PFPs) (Bogner et al., 2012; Bundt et al., 2001), resulting in water bypassing large portions of soil without interaction with the matrix. In forests, these heterogeneous runoff characteristics are further facilitated by patchy throughfall and stem flow, which can result in the concentration of large amounts of precipitation water at relatively small areas (Levia and Frost, 2003). In general, P in soils is distributed over different pools, which can be classified based on the form (organic, inorganic) and attachment (dissolved, exchangeable, sorbed). The size of these pools can vary considerably between ecosystems. Prietzel et al. (2016) showed that other factors are even more important for the size of the P pools than the type of ecosystem, for
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Journal Pre-proof example the parent material and stage of pedogenesis. As stated by Frossard et al. (2000) and Julich et al. (2016), soils can contain between 100 and 3000 mg P kg-1 soil. In forest soils, P can be distributed highly variably, with respect to the content, speciation, availability, and source of P (Julich et al., 2016). However, a comparison of the P content between soil matrix and PFPs in a German forest showed no statistically significant differences (D. Julich et al., 2017). In tilled soils, the spatial variability of the P content is reduced compared to undisturbed soils due to the homogenization of the surface soil by plowing. Moreover, fertilization usually leads to a much higher input of P in agroecosystems than in forests (Bol et al., 2016; King et al., 2015). Fertilization often also decreases the diversity of P forms and increases orthophosphate concentrations (Cade-Menun, 2005). While the transport of dissolved P often dominates in the soil water of arable soils, particulate forms can account for a large proportion of total P transported in grassland (Heathwaite and Dils, 2000; Turner and Haygarth, 2000) and forest soils (Bol et al., 2016). This is mostly reasoned by the predominance of PFPs, which enables colloid transport (Beven and Germann, 2013; Vendelboe et al., 2011) and therefore the movement of particulate P. A variety of studies indicate that especially large runoff events lead to the mobilization of high amounts of P in macroporous soils (Bol et al., 2016; S. Julich et al., 2017; Kaiser et al., 2003; King et al., 2015; Ulen, 1995). Contrary, under baseflow conditions, often the amount of dissolved P in the soil solution of forest soils is below the detection limit, which in many laboratories is around 0.03 to 0.05 mg P L-1 (Bol et al., 2016; Mealy, 2011). As a result, movement of P is either only evident over long periods of time or during storm events. Historically, these relatively short extreme events have been mostly neglected when calculating annual P losses. To complement field studies on P pools and transport, computer models are convenient tools. These can be used, for example, for calculations of changes in P storage over time, P loss predictions and balancing, analysis of management or climate change scenarios, as well as for testing hypotheses regarding P cycling mechanisms. Although many hydrological and biogeochemical models exist, only some of them are able to simulate the transport of P through the soil, and a large share of nutrient fate models entirely omit P turnover. The state of P transport and turnover models has been subject to several reviews in the past (e.g. Lewis and McGechan, 2002; Qi and Qi, 2016; Radcliffe et al., 2015; Vadas et al., 2013; Wellen et al., 2015), but most of them only considered a small selection of currently available models. Lewis and McGechan (2002) summarize the state of four catchment models with regard to nitrogen (N) and P losses to groundwater and surface waters following the application of agricultural waste. Processes considered are the transport of soluble and particulate P, surface application (e.g., as fertilizer), mineralization/immobilization, adsorption/desorption, leaching, runoff, and uptake by plants in agricultural systems. Radcliffe et al. (2015) reviewed
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Journal Pre-proof eight models more recently. They examined their suitability for simulating P losses occurring in drainage waters from artificially drained fields. For this purpose, they also included information on macropore flow, but confined to agricultural soils. In another study, Wellen et al. (2015) compared the N and P components of five spatially distributed models. Since the transport through macropores is not considered in their review, the information contained is of limited use for forest soil applications. Qi and Qi (2016) focused in their review on P loss through subsurface tile drains in nine water quality models. Models that cannot simulate tile drainage were excluded. Vadas et al. (2013) also contains information about P transport, but here the emphasis is on the challenges in developing new models. The models considered are compared in regard for diffuse P losses from agricultural soils – preferential flow is not taken into account. These five reviews cover the processes of 17 models in total, but all lack important information with regard to forest ecosystems or other ecosystems with dominating PFPs. To close this gap, we review existing P transport models with a focus on their applicability for agricultural as well as forested ecosystems to simulate not only the transport of dissolved P, but also particulate P. The emphasis of this work is not the calculation of total P losses, but the documentation of processes involved as well as potential improvements in process representation. As an analytical framework, we focus on the representation of the following model features: -
Temporal and spatial scale
-
P pools and forms
-
Mechanisms of surface and subsurface transport with a focus on different flow paths for dissolved and particulate transport
-
Soil water solution interactions with the soil matrix
-
P uptake by vegetation.
We apply this framework to a large number of environmental models that simulate transport of P or generalized nutrients. Thereby we consider models of different scales (from plot to catchment scale) and complexity. In contrast to the previous reviews, we establish a broad overview of all available models for the simulation of P transport.
2. Scope of models included The most important criterion for a model to be included in this review is the ability to simulate P cycling or the transport of solutes in general through soils. To find suitable models we searched ISI Web of Science database, using keywords like “phosph*”, “solute(s)”, “transport”
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Journal Pre-proof or “leach*” in combination with “(hydrological) model(l)-ing”. Some models were also reported to us personally or found because they were referenced in scientific papers, instead of finding them via Web of Science. We included all dynamic numeric models found this way that were used for the modeling of P, regardless of the complexity of the transport mechanisms or whether a validation for P transport exists. In total, we found 26 models that could be used for the investigation of P transport in soils. For a first classification, we analyzed the model types. These can be divided into processbased, conceptual, and empirical. While for process-based and conceptual models a theoretical understanding of relevant processes is necessary (Cuddington et al., 2013), empirical models are based on empirical observations and do not make any statement about the underlying mechanisms and influencing variables. Both process-based and conceptual models are mechanistic models based on a biogeochemical background. While process-based models try to represent the processes as accurately as possible, conceptual models are created by a distinct conceptualization or generalization. Consequently, they are greatly abstracted and simplified. However, most models cannot be clearly assigned to one of these types, as they often contain components from more than one type (Addiscott and Wagenet, 1985). We summarized these models as ‘mixed type’ models. Despite this uniform term, these models can differ greatly from one another. While some models are mainly process-based, but with some conceptualized features, other models are contrary. As a clear guidance to differentiate process-based and conceptual models, we decided to define all models that include the solution of partial differential equations (PDEs) for transport simulation and a complex nutrient routine (see below) as process-based. Models that include only simplified transport and nutrient components are defined as conceptual, while models with either a simplified transport component or a simplified nutrient component are mixed types. In addition to this distinguishing feature, other important model characteristics are for example the spatial and temporal scales. The spatial scale determines the way individual processes are represented in the models. Micro scale models (i.e. soil profile and plot scales) only simulate vertical infiltration and transport processes, but no lateral processes. Large-scale approaches simulate the processes for example on hillslope or catchment level. The temporal scale of the models also differs significantly. While some models are only able to represent very short time periods, e.g. single precipitation events, other models can simulate periods over one or several years (see section 2.2). Another distinction is the amount of data required for parameterization of the model (Sharpley et al., 2002). This is closely related to the level of complexity of represented processes and therefore to the model type. Typical required data include land use, soil texture, topography,
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Journal Pre-proof and management practices. The amount of data needed and the number of parameters increase with increasing mechanization of the models. The data considered in these models can be a combination of collected field data and experimental data but also model results. Therefore, we first examined all models with regard to these differentiation criteria in order to provide an overview. The results of this initial classification and some additional general information are shown in Table 1. In the following, we will discuss the characteristics of the individual models in more detail.
Table 1. Overview of general model properties. Additional information on the spatial and temporal scale of each model is provided in section 2.2. A list with the most important references for each model as well as for P routine validation/testing can be found in Table-A 1 in the appendix. Model
Language
Model type
Model scale
Time step
Performance of P routine tested
ADAPT
Fortran
mixed
plot
day
yes
ANIMO
Fortran
mixed
plot
flexible
yes
AnnAGNPS
Fortran
mixed
catchment
day
yes
ANSWERS2000
NA1
mixed
plot, catchment
flexible
no
APEX
Fortran
mixed
plot, catchment
day
yes
CAMEL
Visual Basic
mixed
catchment
day
no
DAYCENT
Fortran
mixed
plot
day
no
DRAINMOD-P Fortran
mixed
plot
flexible (hour or day)
yes
EPIC
Fortran
mixed
plot
day
yes
GLEAMS
Fortran
mixed
plot
day
yes
HGS
Fortran
process
soil profile, plot, catchment
flexible
no
HSPF
Fortran
mixed
catchment
flexible
yes
HYDRUS
NA
process
soil profile, plot, catchment
flexible
yes
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Fortran
mixed
catchment
day
yes
ICECREAMDB
NA
mixed
plot
day
yes
INCA-P
C++
mixed
catchment
day
yes
LASCAM
NA
conceptual catchment
day
yes
MACRO
Fortran
process
soil profile
flexible
yes
PDP
Python
mixed
catchment
day
yes
PHREEQC
C,C++
process
soil profile
second
yes
PLEASE
NA
conceptual plot
day
yes
RZWQM2-P
Fortran
process
flexible
yes
SimplyP
C++
conceptual catchment
day
yes
SWAP
Fortran
process
plot
flexible
no
SWAT
Fortran
mixed
catchment, continent
day
yes
SWIM
NA
mixed
catchment
day
no
1NA
soil profile
means that we were not able to find this information.
2.1 Model overview The models in this review represent a wide range of different approaches, with many similarities and overlaps. In Table 2 we give a short overview of the main objectives of every model as well as the land use forms for which they were primarily developed. However, even those models that were developed explicitly for one ecosystem can often be transferred to another based on the processes included. At the same time, the fact that a model has been developed for a specific ecosystem does not guarantee that it includes all the important processes to simulate P transport there. In order to overcome this confinement, some models offer the possibility to be coupled with other models to complement missing processes. For example, ANIMO does not consider hydrological components, but it can be combined with SWATRE (Belmans et al., 1981) or WATBAL (Berghuijs van Dijk, 1990). Also, PDP is not able to simulate transport of particulate P in surface water and dissolved P in runoff from dry and paddy lands, but Huang et al. (2016a) solved this problem by coupling the model with USLE and INCA-P. PHREEQC is not able to simulate water flow and solute transport on its own, so Mao et al. (2006) coupled it with SEAWAT to close this gap. The resulting model, which is
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Journal Pre-proof called PHWAT, is not included in this review, since we were not able to find information on P transport. Further models exist that are not considered in this review, although they are able to simulate P transport through the environment. For example, SurPhos (Vadas et al., 2007) simulates the fate and transport of P in agricultural systems, but since it focuses on surface applied manure and dissolved P loss in surface runoff, it is not relevant for P loss through the soil. Another excluded model is APLE, which is a Microsoft Excel spreadsheet model (Vadas et al., 2012). It simulates P loss in runoff and soil P dynamics over ten years on annual time steps. There are often different versions of the models we present in this review. For example, different versions of HYDRUS exist for different scales, e.g. Hydrus-1D and Hydrus 2D/3D, which are summarized for this review under the term HYDRUS. Moreover, RZWQM2-P is a derivate of the whole system model RZWQM2 (Sadhukhan and Qi, 2018). The same applies for DRAINMOD-P, which is based on DRAINMOD (Askar, 2019). Tian et al. (2012) developed another DRAINMOD adaption named DRAINMOD-Forest to simulate water and nutrient dynamics in drained forest soils. However, this model is based on the official DRAINMOD release, which is why it lacks important features included in DRAINMOD-P. Other included models are built from one or more predecessors. For example, APEX is a derivative of EPIC, and ADAPT is an extension of GLEAMS with the hydrological component of DRAINMOD. ICECREAM-DB is based on the Finish model ICECREAM (Larsson et al., 2007) and the soil water and heat model SOIL. The models INCA-P and SimplyP have recently been reimplemented within the Mobius model building framework (Norling, 2019). PHREEQC, a geochemical reactive transport model, is based on reaction kinetics of chemical processes. It uses ion-association, Pitzer, or SIT (Specific ion Interaction Theory) equations for the calculations of solute activities, e.g. 1D transport (Parkhurst and Appelo, 2013).
Table 2. Overview of the land use for which the models where primarily developed, as well as a short overview of the main application scope. Model
Land use
Scope
ADAPT
agriculture
nutrient and pesticide transport
ANIMO
agriculture
nutrient transport
AnnAGNPS
mixed1
sediment and contaminant transport
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ANSWERS-2000
mixed
APEX
mixed
CAMEL
mixed
evaluation of best management practices on surface runoff and sediment loss routing water, sediment, nutrients and pesticides across complex landscapes studying of land use changes and water quality e.g., simulation of water quality, runoff
DAYCENT
agricultural and forest
generation, plant growth, nutrient cycling,
soils2
erosion, impact of land use, management practices, climate change
DRAINMOD-P
agricultural (and forest)
simulation of P transport
soils
evaluation of effect of various land
EPIC
agriculture
GLEAMS
agriculture
nutrient and pesticide transport
agricultural and forest
simulation of the entire terrestrial portion
soils
of the hydrological cycle
HGS
HSPF
HYDRUS
HYPE
ICECREAM-DB
INCA-P
management strategies on soil erosion
routing water, sediment, nutrients and
mixed
pesticides through complex landscapes
agricultural and forest
routing water, sediment, nutrients and
soils
pesticides through complex landscapes simulation of water flow and transport of
mixed
different substances through the soil
(Swedish) agricultural
simulation of P transport, water discharge
soils
and erosion simulation of P transport, water quality
mixed
and aquatic ecology
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LASCAM
simulation of water quality as well as land
mixed
use and climate changes
agricultural and forest
MACRO
contaminant transport
soils
PDP
lowland polder systems
simulation of P transport simulation of natural or polluted water,
PHREEQC
natural and artificial soils
laboratory experiments, and industrial processes
PLEASE
agriculture
simulation of P transport
RZWQM2-P
agriculture
simulation of P transport
SimplyP
mixed
SWAP
simulation of P transport and suspended sediment
agricultural and forest
simulation of water, solute and heat
soils
transport, as well as plant growth
mixed
simulation of water quality and quantity,
SWAT
impact of land use, management practices, and climate change simulation of water quality and quantity,
SWIM
mixed
impact of land use, management practices, and climate change
1Catchment
scale models are marked as “mixed”; 2for one-dimensional models, land uses are listed separately
2.2 Temporal and spatial scale The spatial and temporal scale has a large impact on the properties and functions of a model, whereby both can be influenced by the model type: while pure mechanistic models are mostly suitable for small spatial scales and rather short periods of time (usually less than a year or even less than a day), more empirical models can be used for annual or even multiyear simulations and at larger spatial scales (Radcliffe et al., 2015). Sharpley et al. (2002) and Haygarth et al. (2005) pointed out, which processes are important for P transport depends on
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Journal Pre-proof the model scale. For example, the representation of detachment, deposition, and resuspension of soil particles differ between plot and catchment scales. On small scales, processes are often described in detail, while on larger scales processes are represented by more simple empirical relations and soils often are grouped in associations. Savenije (2001) describes this as “averaging processes”. Scale dependency is evident not only in the spatial scale, but also in the temporal scale. In particular, the time steps of a model have a great influence on the degree of detail, e.g. a model with a resolution per second requires consideration of completely different processes than a model with daily time steps. The temporal and spatial scales of all 26 models are depicted in Table 1. For this overview, the spatial scale was subdivided into soil profile (one-dimensional), plot (larger areas, but homogeneous weather and soils; equivalent to a ‘field’ in agricultural models), and catchment, with increasing range. Soil profile and plot scaled models both focus on vertical fluxes and therefore often make similar assumptions. While only three of the models in this review are restricted to one-dimensional simulations of soil profiles, nine models are specialized for plot and ten for catchment scale applications. The remaining four models can be used flexibly for different scales. The model SWAP was developed for plot scaled modeling, but via the use of geographical information systems and definition of additional features upscaling to regional scale is possible. Another important factor is the spatial disaggregation, which varies strongly between the models. Catchment scale models use a variety of lateral spatial disaggregation. The Models AnnAGNPS, APEX, HYPE, INCA-P, SWAT, and SWIM split the area into Hydrologic Response Units (HRU), which are combinations of homogeneous land use, management, topographical, and soil characteristics (Arnold et al., 2012). HGS provides several options ranging from simple rectangular domains to irregular domains with complex geometry and layering (Aquanty Inc., 2016). Other models use grid cells (ANSWERS-2000, CAMEL, and HYDRUS), partitioning based on single features like land use (HSPF and PDP), subcatchments (LASCAM), or P content classes (SimplyP). Soil profile and plot scaled models (i.e., ANIMO, DAYCENT, DRAINMOD-P, EPIC, GLEAMS, ICECREAM-DB, MACRO, PHREEQC, PLEASE, RZWQM2-P, and SWAP) do not use lateral discretization. ADAPT is also designed for plot scale applications. However, Gowda et al. (2007) used the concept of HRUs to simulate whole watersheds. In general, plot scale models can also be used for larger areas, as long as they are homogeneous, for example for soil, weather, and management practice. The size of the plot depends on the desired resolution and precision (Gerik et al., 2015). Likewise, the disaggregation with depth differs between the models. For example, INCA-P and SimplyP simulate one soil layer and one deeper mineral soil/groundwater layer. In LASCAM
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Journal Pre-proof the soil is divided into three conceptual storages, namely a perched near stream aquifer, permanent groundwater, and an intermediate unsaturated store. Other models subdivide the soil into more layers, e.g. up to ten (DAYCENT, EPIC, RZWQM2-P), 12 (GLEAMS), 50 (ANIMO), or even 200 (MACRO), freely chosen by the user. Besides the spatial scale, the temporal scale also differs between the models. Typical time spans for many models are in the range of years. Only a few models are explicitly set up for the simulation of short periods, e.g. on the scale of single precipitation events (e.g., MACRO, PHREEQC, or HYDRUS). This does not mean that the models mentioned cannot simulate longer periods, but it is not recommended due to the absence of processes that are important over longer periods. The exact simulated period can usually be determined by the user, whereas the time steps are mostly fixed: 16 models use daily time steps, while only PHREEQC features a resolution per second. The model ANSWERS-2000 follows a unique approach, since it uses 30-second time steps during runoff and switches to daily time steps between runoff events. RZWQM2-P simulates crop growth, nutrient balance, and pesticide modules on daily time steps, and soil water, soil heat transfer, and surface energy balance on sub-hourly time steps. In MACRO, precipitation data can be either hourly or daily, while the output can be chosen freely. Still, the calculation steps are defined internally. HYDRUS also declares the time steps internally, but the user can choose time step controls, with which the time steps are automatic adjusted during computation. The remaining six models (ANIMO, DRAINMOD-P, HGS, HSPF, RZWQM2-P, and SWAP) allow the range to be chosen freely.
3. Phosphorus pools and forms With regard to the P routines, the 26 models reviewed here can be divided into several groups with different degrees of complexity. An often-used approach is the conceptual soil P model of Jones et al (1984a). In this approach, three interconnected inorganic P pools are modeled (labile P, active P and stable P), as well as two organic P pools (fresh organic P, stable organic P). An initial value for labile P is given by the user as an input parameter, and based on this active and stable P are calculated. Furthermore, some models follow similar structural approaches as Jones et al (1984a), although single pools and forms of P deviate. Other models only consider dissolved P and particulate P without further differentiation. And finally there are some models in this review that do not have readily available P routines at all, so for modeling of P transport the user has to parameterize a general solute transport component to represent different P forms. All the models without a readily available P routine (HGS, HYDRUS, MACRO, and SWAP) were used in the past for the simulation of P. The parameterization results mostly in very simple
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Journal Pre-proof P routines, including only basic pools, e.g. dissolved P or total P. It is possible to parameterize SWAP for the simulation of dissolved P, but Kroes et al. (2017) recommend coupling it with ANIMO to simulate P or N processes. To improve the P modeling of HYDRUS, Nahra (2006) coupled HYDRUS-1D with NICA and created a PO4 routine for soil water, but this is not included in the official release of HYDRUS. Although these models without readily available P routine can be used to simulate P transport via appropriate parameterization, the lack of P transformation and reaction processes results in the primary suitability for small periods of time (e.g. individual precipitation events). Still, since this review contains models of different complexity and applicability, we decided to include these models. Except for HGS, the performance for the simulation of P of all these general solute transport models was validated. Some models with P specific turnover routines, i.e. ANSWERS-2000, CAMEL, DAYCENT, and SWIM, miss any published validation with field data. The P routine of Jones et al. (1984a) was originally created for the model EPIC (Williams et al., 1983), but it is also used in ADAPT, AnnAGNPS, ANSWERS-2000, APEX, CAMEL, DRAINMOD-P, GLEAMS, ICECREAM-DB, RZWQM2-P, SWAT, and SWIM. While all of these models’ P routines are based on this foundation, they still comprise different modifications. For example, EPIC contains a constant P sorption parameter for an entire simulation, while AnnAGNPS calculates this parameter daily, based on changing soil properties (Vadas et al., 2013), and SWAT calculates it dynamically at the beginning of each simulation year (Collick et al., 2016). This distinction can lead to very different sizes of the P pools. In DRAINMOD-P, the organic P pools are based on the organic N routine from DRAINMOD-N II (Youssef et al., 2005). The model RZWQM2-P contains an additional routine for calculating the loss of particulate P via surface runoff and tile drainage. There is also a modification for GLEAMS to simulate the transport of particulate P, namely an extension called PARTLE (Shirmohammadi et al., 1998). Therefore, of these models only RZWQM2-P and GLEAMS allow for particulate P movement to be simulated, whereas all other models based on this approach by Jones et al. (1984a) are not capable of doing so (see Table 4). Other models with structurally very similar P routines to Jones et al. (1984a) are INCA-P (Jackson-Blake et al., 2016; Wade et al., 2002) and SimplyP (Jackson-Blake et al., 2017). Both simulate dissolved P, inactive soil P, and labile soil P. Another similar approach can be found in PLEASE (Schoumans et al., 2013; van der Salm et al., 2011), which depicts dissolved inorganic P, adsorbed P, and soluble P. Slightly more complex is the approach of ANIMO (Groenendijk and Kroes, 1999), which simulates in addition to three inorganic P stores (dissolved, adsorbed, and precipitated inorganic P) also three organic P stores (dissolved, stable, and fresh organic P). HYPE includes three immobile P pools (inorganic adsorbed to soil particles, organic with rapid turnover, and organic with slow turnover) and two mobile pools
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Journal Pre-proof (particulate and soluble P). The P routine of HSPF is based on the AGCHEM module (DiazRamirez et al., 2013). Here, P is simulated as sediment-attached, dissolved in surface runoff, and as concentrations in the interflow and groundwater compartments. The P routines of other models are simpler. For example, PDP (Huang et al., 2016b) simulates only dissolved P and particulate P. Likewise, according to Lewis and McGechan (2002), DAYCENT is able to simulate sorbed and labile soil P, but no applications or validations thereof could be found. LASCAM distinguishes only between organic/adsorbed P and soluble P (Viney et al., 2000). PHREEQC (Herrmann et al., 2013; Moharami and Jalali, 2014) takes a very different approach, since it is a hydro-geochemical transport model and therefore depicts the actual chemical compounds. The question of which of these methods produces the best results is not easy to answer, as all methods have advantages and disadvantages. As Qi and Qi (2016) pointed out, a simplified representation (e.g., HYDRUS, MACRO, and SWAP) can lead to less accurate prediction results. On the other hand, a simplified representation can also lead to more accurate prediction results, as improved
performance during calibration can be due to
overfitting rather than improved process representation(e.g. Perrin et al., 2001; Seibert, 2003).
4. Transport components For transport processes, a distinction can be made between dissolved and suspended transport, surface and subsurface transport, and transition between mobile and immobile forms. Not all models include every transport component, due to different spatial foci. PHREEQC is a 1D model of vadose zone transport and therefore lacks surface transport, while conceptual large-scale models like LASCAM simulate only dissolved P and no mobile-immobile transition. Table 3 summarizes which water compartments are included in the models, in order to provide an indicator for possible uses of the models.
Table 3. Overview of important hydrological compartments of the different models. Model ADAPT ANIMO
Matrix
Macropores
Darcy with Dupuitbypass flow Forchheimer assumptions yes (see on bypass flow the right)
AnnAGNPS
no leaching
no
ANSWERS2000
storage routing
no
Surface Infiltration Groundwater Streamflow Water Curve Number, yes yes (Darcy) no Green & Ampt coupling with external hydrological model necessary Curve yes no yes Number Green & yes no yes Ampt
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Journal Pre-proof
APEX
storage routing
bypass flow
CAMEL
storage routing
dualpermeability (in aquifer)
yes
Curve Number, Green & Ampt
yes
yes
yes
Green & Ampt
yes (Darcy)
yes
capacity based
no
no
Green & Ampt
yes (Darcy)
only as sink
yes
no
Richards no yes equation Darcy with DRAINMOD Dupuitbypass flow1 yes -P Forchheimer assumptions DAYCENT
Curve Number, Green & Ampt Curve Number
EPIC
storage routing
no
yes
GLEAMS
storage routing
no
yes
HGS
Richards equation
discretely, dualporosity, dualpermeability
yes
Richards equation
yes (variably yes saturated)
HSPF
empirical equation
no
yes
Stanford model
yes (empirical)
yes
HYDRUS
Richards equation
dualporosity, dualpermeability
yes (via Richards extensio equation n)
yes
only as sink
HYPE
storage routing
bypass flow
yes
capacity based
yes
yes
bypass flow
yes
Richards equation
bypass flow
yes
no
yes
MACRO
Richards equation
dualporosity, dualpermeability
no
Richards equation
PDP
no leaching
no
yes
constant infiltration no rate
no
PHREEQC
no matrix flow
dual-porosity no
no
no
no
PLEASE
no leaching
no
yes
NA
yes (empirical)
yes
bypass flow
yes
Green & Ampt
yes (Darcy)
only as sink
bypass flow
yes
unlimited
yes
yes
ICECREAM- Richards DB equation INCA-P LASCAM
RZWQM2-P SimplyP
storage routing storage routing
Richards equation storage routing
15
capacity based capacity based
yes (as sink) no
yes (based on storage capacity) yes (empirical) yes (empirical) yes
no yes yes only as sink
Journal Pre-proof SWAP
Richards equation
bypass flow2 yes
SWAT
storage routing
bypass flow
yes
SWIM
storage routing
no
yes
Green & Ampt Curve Number, Green & Ampt Curve Number
no
no
yes (empirical)
yes
yes
yes
1The
official release of DRAINMOD is not able to simulate macropores, but the derivate DRAINMOD-P contains a bypass routine; 2SWAP simulates no interaction between matrix and macropores, but the macropores reach to different depths and therefore can release water and nutrients into the matrix. 4.1 Surface transport Many models are capable of simulating the transport of P at the soil surface. As shown in Table 4, this can take place in various ways. While some models only represent the transport of dissolved P, other models simulate both the surface transport of suspended particulate P and dissolved P. Different mechanisms can be used for different transport routes. Dissolved P, for example, can either diffuse from soil solution of the upper soil layer, or be released from sorption as a function of the extractability of P in the near-surface soil (Sharpley et al., 2002). Suspended P is mostly simulated coupled with erosion. Most models simulate erosion using the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) or some modification of this, like the modified USLE (MUSLE) (Williams, 1975) or the revised USLE (RUSLE) (Renard et al., 1991). USLE is a simple empirical approach for predicting long-term average annual soil loss. It is based on various factors, like rainfall erosivity, soil erodibility, topography, and cropping management. A more complex technique is the process-oriented modeling of erosion, where detachment, transport, and deposition of sediment are simulated discretely (Wolfe, 2007). However, this technique is rarely used. Table 4 shows how the different models simulate erosion as well as surface transport of P. Models that are able to simulate surface transport of both dissolved and particulate P are ADAPT, Answers-2000, APEX, CAMEL, DAYCENT, EPIC, GLEAMS, HSPF, ICECREAM-DB, INCA-P, LASCAM, RZWQM2-P, SimplyP, SWAT, and SWIM. Many of these models are not able to simulate the transport of particulate P through the soil. Still, to represent the transport at the surface, this is circumvented by coupling the surface transport of P via a transport factor to the erosion of sediments. From all the models in this review, many models (e.g. ADAPT, AnnAGNPS, CAMEL, EPIC, LASCAM, and SWAT) calculate surface transport of dissolved P based on the concentration of labile P in the top soil layer, runoff volume, and an extraction coefficient. In PLEASE, P loads from a field to surface waters are estimated based on a function of depth and the distribution
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Journal Pre-proof of total annual horizontal water flux (Schoumans et al., 2013). For the transport of particulate P, ICECREAM-DB assumes the same distribution between the P pools in the eroded material as in the bulk soil (Yli-Halla et al., 2005). On the contrary, particulate P transport in overland flow is often simulated by a logarithmic relationship between P enrichment ratio and sediment discharge (Menzel, 1980). This is in accordance with the fact that eroded P is preferentially attached to the finer sediment particles, which tend to be first eroded (Viney et al., 2000). Therefore, eroded particulate material is mostly enriched with P compared to surface soil (Sharpley et al., 2002). For example, in LASCAM P concentrations decrease as the mass of eroded material increases. Similar enrichment ratios are also used for example in ADAPT and GLEAMS.
4.2 Subsurface transport Different approaches exist to simulate the infiltration and transport of water and solutes in soils. Infiltration is very often calculated using a conceptual curve number approach (NRCS, 2004) or the Green and Ampt equation. Alternatively, a constant infiltration rate can be assumed, or infiltration can be capacity based. Approaches that are more complex calculate infiltration based on the hydraulic gradient (Richards equation). Table 3 shows how this is implemented in the different models. For simulating water transport through soils, a commonly used conceptual approach is a storage routing representation. This approach (often described as “tipping bucket”) always fills one storage until field capacity is reached, before the excess water is routed to the next layer. This simple approach is used in most conceptual models, but also in many models classified as mixed type. Still, it has several drawbacks, e.g. it is not able to simulate upward flow. Therefore, they have certain disadvantages compared to physical-based approaches, for example when simulating shallow water table soils. For process-based models, the subsurface water flow and solute transport can, according to the classification of Šimůnek et al. (2003), be grouped in single-porosity, dual-porosity and dual-permeability approaches. The different concepts are shown in Figure 1. Single porosity approaches only simulate water flow and solute transport through the soil matrix. A variation of this approach is supplemented by a fast bypass flow component (Figure 1b). Here, the bypassed water and solutes are directly routed to the groundwater or catchment outlet, so there is no interaction between the slow and the fast water components. This differs in dual-porosity (Figure 1c) and dual-permeability (Figure 1d) models, where it is assumed that the porous medium consists of two interacting regions. One region represents the macropores, while the other comprises the micropores inside the matrix. In dual-porosity models, water in the matrix is stagnant, whereas dual-permeability
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Journal Pre-proof simulates water flow in both macropores and matrix. Additionally, some dual-permeability models simulate not only slow matrix and fast macropore transport, but also immobilization processes (Šimůnek et al., 2003; Figure 1e). In all these different approaches, the transport of solutes is predominantly simulated as relatively simple physical relations, e.g. via the convection-diffusion-dispersion equation (Gerke and van Genuchten, 1993; Šimůnek et al., 2003). An even simpler approach to calculate solute transport, which is frequently used in conceptual models, is to multiply the concentration of solute by the water flux.
Figure 1. Concepts of different water flow and solute transport approaches, taken from Šimůnek et al. (2009) and edited.
4.2.1 Matrix transport In single-porosity models (Figure 1a), saturated water flow is mainly simulated via storage routing or the Darcy equation (particularly for groundwater flow), whereas for unsaturated conditions the Richards equation is the most used equation. Single-porosity models are ANSWERS-2000, DAYCENT, EPIC, GLEAMS, HSPF, LASCAM, PDP, PLEASE, and SWIM (see Table 3). Of these models, ANSWERS-2000, EPIC, GLEAMS, LASCAM, and SWIM use the storage routing approach, while DAYCENT represents matrix transport using the Richards equation. PDP does not simulate the soil divided into soil layers, as it was developed specifically for polder systems. Instead, it describes the water balance in four land-use areas, namely residential area, surface water area, paddy and dry lands, without vertical transport of water and P through the soil. PLEASE also does not simulate the vertical transport of water and solutes, since it focuses on the total amount of leached P instead of its transport routes. Therefore, the soil is not divided in different layers, but the loss of P is calculated based on empirical assumptions, i.e. the concentration of P and the total groundwater outflow as a function of depth. In this model, only horizontal runoff is calculated. Matrix flow is also not included in the models AnnAGNPS and PHREEQC. Since PHREEQC is a dual-porosity model, water and solutes in the matrix are stagnant and only transported via macropores. AnnAGNPS focusses on surface runoff and does not consider vertical leachate. However, lateral
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Journal Pre-proof subsurface flow is calculated based on Darcy’s equation and assumed homogeneous through the entire soil profile (Bingner et al., 2015). The remaining models all have a second, faster transport route (see section 4.2.2). Still, their matrix transport components are mostly similar to single-porosity approaches, namely six models (i.e., APEX, CAMEL, HYPE, INCA-P, SimplyP, and SWAT) with storage routing and six models (HGS, HYDRUS, ICECREAM-DB, MACRO, RZWQM2-P, and SWAP) using the Richards equation. DRAINMOD-P uses the Darcy equation with Dupuit-Forchheimer assumptions. This approximation assumes that flow lines are parallel to the impermeable sublayer and thus that lateral flow processes can be separated from vertical processes. This is reasonable when the rate of subsurface flow is low (Clark et al., 2015a; Kampf and Burges, 2007). The model ADAPT is unable to simulate unsaturated water transport, but (since its hydrologic component is based on DRAINMOD) vertical transport through the matrix is simulated using the Darcy equation. ANIMO has to be coupled with another model to represent hydrological processes, since the model itself is not capable of simulating water transport components. The transport of solutes through the matrix differs little between the models. Mostly it is calculated by convection-diffusion equations (ANIMO, DRAINMOD-P, GLEAMS, HGS, HYDRUS, ICECREAM-DB, MACRO, and SWAP). Kroes et al. (2017) recommends combining SWAP with ANIMO for a more realistic simulation of P transport. In HSPF, subsurface transport of dissolved P is simulated by adjusting the ratio of surface to subsurface total P (Radcliffe et al., 2015). For many of the models, we were not able to find published information on the exact mechanisms of solute transport. However, apparently most models are able to simulate transport via water movement. Contrary, the transport of particulate P is restricted to macropores, so none of the single-porosity models is able to simulate this process explicitly.
4.2.2 Macropore transport Macropore transport is usually much faster than transport via matrix. In many models (i.e., ADAPT, ANIMO, APEX, DRAINMOD-P, HYPE, ICECREAM-DB, INCA-P, RZWQM2-P, SimplyP, SWAP, and SWAT) it is simply represented as an additional fast bypass component. In SimplyP this quick flow component is simplified even more, since it includes not only macropore flow, but also saturation excess overland flow, tile drainage, and runoff from impervious surfaces (Jackson-Blake et al., 2017). Many soils, especially those of forests, grasslands, and no-tillage agriculture, usually contain macropores. Hence, transport through PFPs as well as interactions between PFPs and the matrix are important for P translocation
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Journal Pre-proof (Bogner et al., 2012; Bundt et al., 2001; Julich et al., 2016). As these interactions cannot be represented by bypass flow, this transport route is mainly of interest for the modeling of agricultural soils with drainages, while dual-porosity and dual-permeability approaches are representations of macroporous soils that are more realistic. According to Qi and Qi (2016), macropore flow in dual-permeability models is mostly represented either by Richards equation, kinematic wave approach, or gravitational flow using Poiseuille’s law to calculate water infiltration into macropores. While macropore flow based on the Richards equation considers both gravitational flow and capillary-driven flow, the kinematic wave equation only includes the vertical gravity flow and ignores capillary. Since Poiseuille’s law premises that the macropores are cylindrical, this approach is less accurate when the pores deviate from this assumption (Šimůnek et al., 2012). Moreover, Poiseuille’s law assumes saturated conditions, which only rarely applies to macropores (Jarvis, 2007). Of all the models presented in this review, only five models contain dual-porosity or dualpermeability representations, namely CAMEL, HGS, HYDRUS, MACRO, and PHREEQC. While PHREEQC is restricted to dual-porosity, the models HGS, HYDRUS, and MACRO are able to simulate both dual-porosity and dual-permeability systems. CAMEL takes a special position because the unsaturated soil is simulated simply as a single porosity system, but it uses a dual permeability approach for the saturated aquifer. This groundwater flow is simulated via Darcian flow with two different hydraulic conductivities. The dual-permeability models HGS and HYDRUS use the Richards equation for both matrix and macropore water flow. In MACRO, water transport through macropores is simulated via kinematic wave approach. Since PHREEQC needs to be coupled with another model to simulate water flow, it is not possible to specify the mechanism. For example, Wissmeier and Barry (2010) created an extension for unsaturated flow in PHREEQC, which enables the use of the Richards equation. Generally, existing models with a second flow component follow various approaches for the representation of solute and particle transport through macropores and how the exchange between matrix and macropores is modeled (Djabelkhir et al., 2017; Šimůnek et al., 2003). For example, in HYDRUS this is solved similar to most single-porosity approaches: solute transport is calculated via convection-diffusion type equations (Gerke and van Genuchten, 1993). This is true for both fracture and matrix regions, but with different parameter values assigned (Šimůnek et al., 2003). In HYDRUS, the exchange of water between matrix and macropores is driven by the gradient of pressure heads. In MACRO, the exchange of water from macropores to micropores is calculated using a mass transfer expression, while the transport of solutes is calculated neglecting dispersion, since advection is assumed to dominate. In PHREEQC, various mechanisms are available: advective transport, advective-dispersion transport (1D), and diffuse transport (2D and 3D).
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Journal Pre-proof The model SWAT is only capable of simulating bypass flow and lacks a P transport component for the macropore pathways, which is why it is not applicable for the physically correct simulation of P transport processes in macroporous soils (Radcliffe et al., 2015). In RZWQM2-P, the water flux through macropores is simulated via Poiseuille’s law, which is based on gravitational flow. Dissolved reactive P and particulate P are directly routed from the first soil layer to the groundwater without interaction with the matrix (Sadhukhan and Qi, 2018). In SWAP, water can be infiltrated into macropores at the soil surface and then be transported into deeper soil layers. The model distinguishes between continuous, horizontal interconnected macropores, and discontinuous macropores ending at different depths, so water and nutrients can be transported to various soil layers. Additionally, the macropores are divided into static and dynamic (Kroes et al., 2017). For APEX, Ford et al. (2017) recently developed a new modification to implement macropores. They distinguish between matrixexcess macropores and matrix-desiccation macropores. Matrix-excess macropores appear when saturation exceeds the ‘water-entry’ pressure of the matrix, while matrix-desiccation macropores occur at low moisture conditions and form a draught crack network. The latter is modeled as a function of clay content and potential evapotranspiration demand deficiency. In this approach, macropore flow of dissolved P is assumed to equilibrate with the surface soil, so P is partitioned between adsorption on the soil surface and transportation through the macropores by using the Langmuir isotherm. Water transport takes place in the form of a bypass flow to the bottom soil layer, and when its capacity is exceeded, it is moved upwards to the next unsaturated layer.
4.2.3 Mobile-immobile transition In dual-porosity models, the exchange of P and solutes between macropores and matrix is typically associated with mobilization and immobilization processes. Aside from these processes, especially single-porosity models also often simulate immobilization of P via adsorption. Immobilized P is generally not congruent with particulate P, but in some model descriptions, these two terms are used synonymous: In most models that are able to simulate particulate P, it is considered as immobile, or it can only be transported by surface erosion. Jones et al. (1984a) created the concept of stable vs. dissolved P for EPIC and this has been used in all models building on EPICs P concept (see section 3). Here stable P is only transported by surface erosion and is therefore excluded from leaching. The immobilization of P in many other models works in similar ways. Heathwaite (2003) and Vadas et al. (2013) criticize this, because this P routine has seen very limited updates. Especially movement of particulate P within the soil is therefore missing in most models. Only the models GLEAMS
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Journal Pre-proof (via the extension PARTLE), HYPE, INCA-P, PDP, RZWQM2-P, and SimplyP (Table 4) are able to simulate the transport of particulate P. The transition between immobile and mobile P is often realized by an equilibrium function. For example, as described in section 3, models with P routines based on Jones et al. (1984a) simulate the mobile-immobile transition by creating an equilibrium between stable and active mineral P and between active mineral P and labile mineral P. This equilibrium between the three pools is based on the user-given value for labile P, and the relative sizes of the pools are soil specific. The equilibria can be disturbed by different processes, for example leaching, dissolved loss in run-off and plant uptake of labile P, or loss of stable or active P via erosion. When this happens, the imbalance is calculated and then P is moved between the pools to restore balance (Vadas et al., 2013). The equilibrium between labile and active pools can be restored within several days or weeks, while the equilibrium between active and stable pools is slower (Jones et al., 1984a). Despite this similarity between so many models, small differences exist between these approaches. For example, in ADAPT and GLEAMS the partitioning of mineral P between aqueous and solid phases is implemented via a partitioning coefficient according to the linear adsorption isotherm (Radcliffe et al., 2015). SimplyP also uses a simple linear relationship to equilibrate labile soil P and dissolved P (Jackson-Blake et al., 2017). The P routine of APEX is also based on Jones et al. (1984a), but this model uses the Langmuir isotherm for adsorption. Also in PLEASE, the dissolved inorganic P concentration is calculated using the Langmuir isotherm equation (Radcliffe et al., 2015; van der Zee and Bolt, 1991), while the amount of reversibly sorbed P in the top soil layer is related to the waterextractable P and oxalate-extractable Al and Fe content (Radcliffe et al., 2015; Schoumans and Groenendijk, 2000). In deeper layers, sorbed P decreases with depth based on a firstorder exponential expression. ANIMO provides the Langmuir isotherm and the Freundlich isotherm, which is also used for example by HYPE, INCA-P, MACRO, and SWAP. LASCAM is not able to simulate mobile-immobile transitions. Unfortunately, for many models it was not possible to find detailed descriptions of how they simulate mobilization and immobilization.
4.3 Plant P uptake and removal An important difference between arable land and forests is the annual harvesting of crops from fields, while forest trees are harvested irregularly or not at all. In addition, harvest and other management actions (such as thinning) often focus on selected trees, without removing the entire stand. This leads to differences in nutrient circulation. Examples of models that can simulate within-year variations in both annual and perennial plant P uptake are EPIC, INCA-P, SWAP, SWAT, and SWIM. In SWAP, the disparity between annual and permanent affects not
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Journal Pre-proof only plant growth, but also rainfall interception: for agricultural crops, infiltration is calculated using the Hoyninger-Braden equation, whereas for forests the Gash equation is used (Kroes et al., 2017). Models that appear to represent only annual crops are for example AnnAGNPS and RZWQM2-P. In both models, crop properties such as cultivating and harvest time, tillage, and fertilization need to be given as inputs. Another important factor is the method of calculation of plant P uptake. It can be divided into different approaches, namely supply-driven and demand-driven plant uptake, as well as combinations of both. While supply-driven approaches simulate P uptake by plants based on the P concentrations in soils, demand-driven plant uptake is mainly based on the demand of the plants directly. An example of demand-driven P uptake can be found in GLEAMS (Knisel and Davis, 2000). The uptake of labile P is estimated for each layer where transpiration occurs, with the total uptake from all layers equal to the plant P demand. The plant P demand is a function of the Optimum Leaf Area Index, which is tabulated for a large number of crops over a growing season. This approach is based on the formulation of the EPIC model, where the potential plant uptake of labile P is simulated as a linear function up to a user-specified critical concentration (Jones et al., 1984a). A similar approach was chosen in LASCAM, where plant P uptake depends on the rate of canopy biomass accumulation (instead of the Optimum Leaf Area Index), so that it varies seasonally (Viney et al., 2000). In DAYCENT, a combination of demand-driven and supply-driven P uptake is implemented. Plant P uptake is controlled by the size of the labile P pool, whereas in turn the labile P pool varies with the size of the mineral N pool. Additionally, the uptake of labile P is constrained by upper and lower limits for nutrient content in the shoots and roots, which in turn are considered as a function of plant biomass (Lewis and McGechan, 2002). In APEX, P uptake is also simulated as a combination of demand- and supply-driven, with the root weight as an confinement factor of the supply (Williams et al., 2015). Other models that simulate plant P uptake represented by a combination of both approaches are, for example, INCA-P (Jackson-Blake et al., 2016), RZWQM2-P (Sadhukhan and Qi, 2018), and DRAINMOD-P (Askar, 2019). HYDRUS can simulate chemical uptake of plants both passively and actively. Passive uptake is based on the Feddes equation and P in soil solution, while active uptake is based on the MichaelisMenten equation (Qi and Qi, 2016). In MACRO, plant chemical uptake is included via a source/sink term in the solute transport equation (Qi and Qi, 2016). For many models, it was not possible to find out how they simulate plant uptake exactly (see Table 4), but it is likely that in most cases it is implemented very simplistically, for example as a simple demand-driven uptake, based on C:N:P ratios in the plants.
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Journal Pre-proof Table 4. Overview of phosphorus forms (dissolved vs. particulate) and important related processes. Subsurface P processes Model
Surface processes Plant uptake
Dissolved P
Particulate P
ADAPT
yes
immobile2
demanddriven
USLE
suspended & dissolved
ANIMO
yes
immobile
yes1
no
dissolved
AnnAGNPS
yes
immobile
demanddriven
RUSLE, HUSLE
Answers2000
yes
immobile
demand- and empirical supplyfunction driven
suspended & dissolved
APEX
yes
immobile
demand- and USLE, MUSLE, supplyRUSLE driven
suspended & dissolved
CAMEL
yes
immobile
demand- and supplyyes1 driven
suspended & dissolved
DAYCENT
yes
immobile
demand- and supplyyes driven
suspended & dissolved
DRAINMODyes P
mobile3
yes
RUSLE
suspended & dissolved
EPIC
yes
immobile
demanddriven
USLE, MUSLE, RUSLE
suspended & dissolved
GLEAMS
yes
PARTLE
demanddriven
MUSLE
suspended & dissolved
HGS
no
no
yes
no
no (transport of solutes)
HSPF
yes
immobile
yes
process-based
suspended & dissolved
HYDRUS
no
no
demand- and supplyno driven
HYPE
yes
mobile
yes
Erosion
yes
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P transport
dissolved
no
suspended & dissolved
Journal Pre-proof ICECREAMyes DB
immobile
yes
INCA-P
mobile
demand- and supplyprocess-based driven
suspended & dissolved suspended & dissolved
yes
MUSLE
suspended & dissolved
LASCAM
yes
immobile
based on canopy based on biomass surface runoff accumulation
MACRO
no
no
empirical sink term
detachment of soil particles from surface
PDP
yes
mobile
yes
USLE
suspended & dissolved
PHREEQC
yes
NA
no
no
no
PLEASE
yes
immobile
no
no
no
mobile
demand- and based on supplyGLEAMS driven (MUSLE)
based on USLE suspended & and empirical dissolved relationship with streamflow
RZWQM2-P yes
no (transport of solutes & colloids)
suspended & dissolved
SimplyP
yes
mobile
annually constant input value
SWAP
no
no
fix user input
only surface runoff
SWAT
yes
immobile
demanddriven
MUSLE
suspended & dissolved
SWIM
yes
immobile
demanddriven
MUSLE
suspended & dissolved
dissolved
1In
the columns “Plant uptake” and “Erosion” a simple “yes” indicates that the processes are included, but we found no information on the exact processes; 2“mobile” signifies that the movement of particulate P through the soil is somehow included, i.e. via a bypass component, dual-porosity, or dual-permeability; 3“immobile” means that particulate P can be simulated, but only as an immobile sink.
5. Future developments in environmental process-based P modeling 5.1 General suggestions for improvements As shown in this review, there are a number of environmental models with P routines. Depending on the focus of the models, they show significant differences. In order to provide
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Journal Pre-proof an overview of the existing models, we created a decision tree (see Figure 2). There we present the different transport routes of the individual models (single porosity, dual-porosity, and dual-permeability) as well as the specific P forms that are included. The color code of the figure follows the traffic light principle, i.e. green boxes represent most accurate process representation (e.g., dual permeability and transport of dissolved and particulate P), orange boxes indicate less exact model structures (e.g., dual porosity and dissolved P transport with immobile particulate P component), and red boxes show the simplest representations (no macropores, no functioning P routine). However, this does not mean that the simpler models are generally unsuitable for calculating P fluxes through different ecosystems. Depending on the question and the complexity required, the models can still be very useful. It is noticeable that there is no model for which all processes are represented most accurately (green) to simulate P transport through forest soils. Many models simulate P transport based on a more than 30 years old approach by Jones et al. (1984a), and we could not find many improvements over the last decades in P modeling. Often, there is no reason against this established method, but – depending on the research question – the lack of a mobile particulate P component can be a major deficit. According to Heathwaite (2003) and Vadas et al. (2013), the focus on the development of data-intensive complex models instead of more generic models is a main reason for current deficits in modeling. In line with this, Radcliffe et al. (2015) recommend to revise P modeling and to develop new, improved routines. Considering our focus of interest, this might be especially the transport of particulate P, which is only implemented in very few models. Additionally, model performance needs to be tested specifically for P to make sure that simulation results of soil P dynamics are reliable. This is a flaw of many existing models, since simulated soil P dynamics are rarely presented. Besides this critique of the P routines and the often-found lack of dual-permeability approaches, there are many other potentially important processes, which might be worth considering for model development, for example: -
P uptake by trees and other plants might deviate from P uptake by crops
-
Surface transport, groundwater, and stream components are usually important components
-
Due to stem flow, nutrient inputs might be concentrated locally (Levia and Frost, 2003)
-
Atmospheric particulate deposition might be increased due to interception (Lequy et al., 2014).
Some of the named models include most of the processes we consider important. The newly improved RZWQM2-P deserves particular mention here, as it contains a complex P routine including mobile particulate P, as well as a fast bypass component (but no dual-permeability).
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Journal Pre-proof Still, the possibility to up- and downscale the model specific to the current research question would be an advantage. This is very hard to realize, since a change of scale results in a shift in the important processes. Moreover, such a high flexibility in the model structure might lead to very complex models with many parameters. For different reasons, a large number of parameters can increase the inaccuracy of a model. For example, often parameters have no physical basis, they might be impossible to measure in the field (Djabelkhir et al., 2017), or measured parameters can be scale-dependent and therefore not representative. Radcliffe et al. (2015) and Nelson and Parsons (2006) also pointed out that there is generally a lack of guidance on how to obtain independent values for additional parameters. In overparameterized models the values of individual parameters cannot be unequivocally identified by the data (so called “equifinality”, see Beven and Freer, 2001), so prediction uncertainty might increase. Moreover, small inaccuracies of individual parameters can lead to very large total errors. This is for example criticized by Buytaert et al. (2008) as an “over-complexity” of most models. They propose a number of directives that should be followed when developing new models: the model code should be fully accessible, modular, and portable. This way, the user is enabled to use a model in a highly flexible manner with improved control over the modeling assumptions. For example, modularity allows the user to choose whether particular processes should be represented either by mechanistic equations or by empirical relations. It also simplifies the addition of new independent routines to represent additional processes. This way, in modular and accessible approaches the spatial discretization and the temporal disaggregation can be adjusted more easily. All these suggestions are in accordance with the postulation of Clark et al. (2011) for the development of modular frameworks. Modeling frameworks are toolboxes that provide a variety of different processes, which can be assembled into a model by a user. The resulting model can thus be tailored to specific tasks. Although the compatibility of the individual process representations can be problematic, this enables a large number of different models to be created without high programming effort. Moreover, modeling frameworks are suitable for testing of multiple working hypotheses. Such an approach would be of great advantage for modeling P dynamics, as the comparison of different hypotheses would allow testing which processes are actually important. This way it could be analyzed which macropore transport component is sufficient (fast bypass, dualporosity, or dual-permeability), or to what extent the included process representations depend on the spatial and temporal scale. Existing modeling frameworks are, for example, the Catchment Modelling Framework (CMF) (Kraft et al., 2011), SUMMA (Clark et al., 2015a, 2015b), Raven (Craig and the Raven Development Team, 2019), and the more generic Mobius (Norling, 2019). Unfortunately, until now, no existing hydro-biogeochemical modeling framework is able to simulate P transport through soils.
27
1 2
Figure 2. Decision tree, which shows the most important features of all P models. The split on the first level differentiates models with and without
3
macropores, and the split on the second level further separates based on macropore flow representation. On the third level, the splits distinguish
4
between the ways the models simulate phosphorus transport. The colors (green, orange, red) indicate the accuracy of process representation.
28
5
The color of the models indicate whether the performance of P simulation was tested against field data: blue models contain tested P routines,
6
while red models indicate P routines of unknown quality.
29
Journal Pre-proof 7
5.2 A blueprint for a process-based P model for different ecosystems
8
In this section, we present a blueprint for a process-based and modular environmental P model.
9
As outlined in section 5.1, a major disadvantage of most existing models is that they are static,
10
i.e. the process representations are predefined and cannot be altered. Modularity provides an
11
option on how a model could be designed that allows the user to test and compare different
12
hypotheses. With regard to the results of this review, the presented blueprint could be used to
13
examine whether an explicit representation of macropores can improve modeling P dynamics
14
in different soils and on different scales. Additionally, different P routines could be compared
15
without other factors being modified. For this purpose, a model should contain only the most
16
important processes to simulate the transport of P in different environments, i.e. arable land,
17
grassland, and forests. Nevertheless, in order to be able to test hypotheses for different
18
landscapes and scales, a large number of processes must be representable:
19
1. The model should include different hydrological processes from which a user can
20
choose (see Figure 3, top), for example for infiltration (e.g., Green & Ampt, curve
21
number) and percolation (e.g., Richards equation, storage routing), or optional features
22
like snow storage.
23
2. There should be different options for the transport through the soil, i.e. parallel matrix
24
and explicit macropore transport (Figure 3) and, as a more simplified alternative, direct
25
infiltration into deeper layers via macropores. The horizontal transport via macropores
26
to the stream (Savenije, 2018) should also be included as an optional feature.
27
3. The possibility to connect different spatial entities (gridded cells, polygons) horizontally,
28
for example via Darcy, Richards, or kinematic wave equation, would allow for the
29
construction of models ranging from plot to regional scale.
30
4. For the simulation of P transport and turnover, we propose the development of a P
31
routine loosely based on Jones et al. (1984a), but including the transport of particulate
32
P. As depicted in Figure 3 (bottom), we differentiate between the transport of P through
33
the soil matrix and – if simulated explicitly – macropores. While processes in the matrix
34
are represented in more detail, the processes in the macropores are strictly simplified.
35
Since the transport processes in PFPs are based on gravitational flow, the distinction
36
between dissolved and particulate P will be sufficient. These pools will be in equilibrium
37
with the five pools in the soil matrix. As an alternative approach, a highly simplified P
38
routine could be integrated, which only distinguishes between dissolved and particulate
39
P in both matrix and macropores.
40
5. For the simulation of plants, different possibilities should be included, i.e. a
41
differentiation between permanent forest and grassland versus annual plants and crops.
30
Journal Pre-proof 42
In order to achieve this flexibility, we promote the implementation of this approach in a
43
modeling framework. Since the Catchment Modeling Framework (CMF) (Kraft et al., 2011)
44
offers a multitude of possibilities, including the representation of macroporous flow and
45
transport, this framework is ideal for implementing a P routine.
46
Obviously, the modular modeling approach presented here can be further extended in order
47
to be of use for even more tasks. For example, it could be possible to couple the P routines
48
with the C and N cycles, or to calculate the effect of nutrient states on the net primary
49
productivity. An extension to weathering processes is also possible. The accessibility and
50
modularity of modeling frameworks like CMF would allow such diverse approaches to be
51
realized. Therefore, while the blueprint presented here is certainly not a universally valid model
52
for the simulation of P transport, it qualifies as a good basis for hypothesis testing and provides
53
the possibility for further refinements and adjustments.
54
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Journal Pre-proof
55 56
Figure 3. Top: Blueprint of a P model with relevant hydrological storages and processes. Dark
57
blue boxes represent storages; light green boxes indicate boundary conditions. Bottom:
58
Blueprint for relevant P routines with P pools and transitions in the soil matrix based on Jones
59
et al. (1984a) (left) and within macropores (right). Orange boxes indicate which P pools in the
60
matrix are in equilibrium with particulate P in the macropores, while the yellow boxes show
61
which pools are in equilibrium with dissolved P.
32
Journal Pre-proof 62 63
6. Conclusion
64
We reviewed 26 models that are able to simulate P transport through the soil. Still, their foci
65
are very diverse, and therefore the representation of processes varies greatly. Most of the
66
models were originally created for the simulation of processes in agricultural soils. While some
67
of these models were extended for other ecosystems, it is likely that processes relevant in non-
68
agricultural environments are not well represented. These include, for example, the uptake of
69
P by plants other than crops, or the transport through PFPs. Moreover, while all models in this
70
review are able to simulate some sort of P transport and turnover, they have not necessarily
71
been established for this purpose. For this reason, the P routines of many models have only
72
been tested in very limited experiments or not been tested at all. Only PLEASE, INCA-P,
73
SimplyP and PDP focus especially on the simulation of P leaching from soils. Furthermore, the
74
movement ‘through-the-soil’ of particulate P is an important aspect of P leaching (Julich et al.,
75
2016), yet this process is only represented in seven of 26 models. In all these models, the
76
transport is represented by a fast bypass component. In order to be able to represent this
77
transport process more realistically, the simulation of a dual-permeability system could be
78
appropriate. Since no model fulfils all hypothesized demands, we developed a blueprint of a
79
modular model for the simulation of P leaching through different soils. Especially when it is
80
implemented as part of a modular hydrological framework, this approach could help to
81
compare different hypotheses, e.g. under which circumstances the explicit representation of a
82
dual-permeability system is appropriate. It could also be used for further developments in
83
modeling of P transport.
84
In order to improve the modeling of P processes in the environment, we think that a shift
85
towards the use of modeling frameworks is necessary. By enabling multiple hypothesis testing,
86
we envisage substantial improvements in P modeling quality. In addition, the transferability of
87
the models to different conditions (e.g., various land use forms) should be given greater
88
consideration. For this purpose, the agreement on well-established methods might be
89
necessary, also in the generation of experimental data. Only when sampling is comparable
90
(e.g., comparable depth, consideration of macropores, or same analysis methods for P) can
91
the data be used to develop comparable models, which are of interest for more than an
92
individual case study. If these suggestions are considered, we assume that an improvement
93
of the modeling of P is possible.
94 95
Acknowledgements
33
Journal Pre-proof 96
This project was carried out in the frame of the priority program 1685 “Ecosystem Nutrition:
97
Forest Strategies for limited Phosphorus Resources” funded by the DFG, subproject
98
“Quantification, modeling, and regionalization of seepage losses of phosphorus from forest
99
soils” (BR 2238/26-2).
34
Journal Pre-proof 100
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Journal Pre-proof Table-A 1. Overview of model references and references for performance testing of phosphorus simulation. Model
Model reference
Reference for P routine testing
ADAPT
Gowda et al. (2012)
Dalzell et al. (2004)
ANIMO
Groenendijk and Kroes (1999), Kroes and Roelsma (1998)
van der Salm and Schoumans (2000)
AnnAGNPS
Bingner et al. (2015), Yuan et al. (2005)
Pease et al. (2010), Yuan et al. (2005)
ANSWERS2000
Bouraoui and Dillaha (2000, 1996) NA1
APEX
Plotkin et al. (2013), Steglich et al. Bhandari et al. (2016), Francesconi et (2016) al. (2016), Saleh et al. (2001)
CAMEL
Koo et al. (2005, 2004)
NA
DAYCENT
Parton et al. (1998)
NA
DRAINMOD-P
Deal et al. (1986), Tian et al. (2012), Askar (2019)
Askar (2019)
EPIC
Jones et al. (1984a), Sharpley and Della Peruta et al. (2014), Jones et al. Williams (1990) (1984b), Richardson and King (1995)
GLEAMS
Leonard et al. (1987)
Knisel and Turtola (2000)
HGS
Brunner and Simmons (2012)
NA
HSPF
Bicknell et al. (1996), Grimsrud et al. (1982), Johanson et al. (1980)
Ribarova et al. (2008)
HYDRUS
Agah et al. (2016), Šimůnek et al. (2008)
Agah et al. (2016), Freiberger et al. (2013), Hassan et al. (2010), Naseri et al. (2011)
HYPE
Lindström et al. (2010)
Jiang and Rode (2012), Pers et al. (2016)
ICECREAMDB
Larsson et al. (2007)
Bärlund et al. (2008), Larsson et al. (2007), Liu et al. (2012)
INCA-P
Jackson-Blake et al. (2016), Wade Jackson-Blake et al. (2017, 2016) et al. (2002)
LASCAM
Viney et al. (2000)
Viney and Sivapalan (2001), Zammit et al. (2005)
MACRO
Jarvis (1991, 1995), McGechan et al. (2002)
McGechan (2003), McGechan et al. (2002)
45
Journal Pre-proof PDP
Huang et al. (2016b)
Huang et al. (2016a)
PHREEQC
Parkhurst and Appelo (2013, 1999)
Herrmann et al. (2013), Moharami and Jalali (2014)
PLEASE
Schoumans et al. (2013)
Schoumans et al. (2013), van der Salm et al. (2011)
RZWQM2-P
Ma et al. (2012), Sadhukhan and Qi (2018)
Sadhukhan et al. (2019a, 2019b)
SimplyP
Jackson-Blake et al. (2017)
Jackson-Blake et al. (2017)
SWAP
Gusev and Nasonova (1998), Kroes et al. (2017)
NA
SWAT
Arnold et al. (2012, 1998)
Grizzetti et al. (2003), Vadas and White (2010)
SWIM
Krysanova et al. (2005)
NA
1NA
means that we were not able to find this information.
46
Journal Pre-proof
Declaration of 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. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: