# The effect of riparian woodland cover on ecosystem service delivery by river floodplains: a scenario assessment

Jan E Vermaat, Martin Palt, Jeremy Piffady, Arturs Putnins, Jochem Kail

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Jan E Vermaat, Martin Palt, Jeremy Piffady, Arturs Putnins, Jochem Kail. The effect of riparian woodland cover on ecosystem service delivery by river floodplains: a scenario assessment. Ecosphere, 2021, 12 (8), pp.e03716. 10.1002/ecs2.3716. hal-04051769

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# The effect of riparian woodland cover on ecosystem service delivery by river floodplains: a scenario assessment

JAN E. VERMAAT ID<fnref n="1" />,<fnref n="1" />† MARTIN PALT ID<fnref n="2" /> JEREMY PIFFADY ID<fnref n="3" /> ARTURS PUTNINS,<fnref n="1" />,⁴ AND JOCHEM KAIL ID<fnref n="2" />

**Citation:** Vermaat, J. E., M. Palt, J. Piffady, A. Putnins, and J. Kail. 2021. The effect of riparian woodland cover on ecosystem service delivery by river floodplains: a scenario assessment. Ecosphere 12(8):e03716. 10.1002/ecs2.3716

**Abstract.** Sixteen ecosystem services were quantified for the riverine landscapes of the Nahe, Stever (Germany), Bresse plain, and Azergues (France), to assess the effects of riparian woodland cover. Future woodland cover in 2050 was modeled to reflect contrasting scenarios of river management aligned to the well-established shared socioeconomic pathways. The scenarios are labeled as current, pessimistic, best practice, and ambitious riparian management practices (RMPs). We linked services to floodplain land use and river morphology and quantified them separately for spatial segments (0.5–1 km in length, *n* = 118–3419, depending on river length), using an analytical framework, the “Mononen cascade.” Conservative monetary value estimates were based on net producer income before tax and subsidy, a shadow market price for carbon, flood damage functions, or willingness to pay for recreation and non-use. Most services were linked to land use, some affected the value of other services through simple rules (woodland shade affected trout survival hence angling benefit, a minimum of woodland affected pest regulation, hence crop productivity). In the current landscape state, provisioning, regulating, and cultural services all showed optimum curves with woodland cover: Provisioning services and cultural services were maximal around 45%, whereas this was around 30% for regulating services. More woodland was present in steeper near-source segments. Averaged across rivers, mean total service provision was estimated at 1084 ± 4 €·ha⁻<fnref n="1" />·yr⁻<fnref n="1" />, with 40%, 36%, and 24% contributed by, respectively, provisioning, regulating, and cultural services. The three scenarios led to a limited change in total ecosystem service delivery, even if mean woodland cover was reduced from 27% to 17% in the pessimistic RMP and increased to 70% in the ambitious RMP for the most extreme case of the Stever. Provisioning services declined with increased woodland cover and cultural services increased. Regulating services did not change that much, because they are dominated by flood prevention in our assessment. The “best practice” scenario appeared to combine a modest increase in cultural services with a slight increase in provisioning service. An ambitious nature conservation objective as in the ambitious RMP appears possible without seriously compromising overall societal benefit.

**Key words:** ecosystem services cascade; riparian woodland; river restoration; shared socioeconomic pathways; Strahler river order; water quality.

**Received** 4 August 2020; revised 6 April 2021; accepted 20 April 2021. Corresponding Editor: Whitney S. Beck.

**Copyright:** © 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution
License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

⁴Present address: Latvian State Forests (LVM), Vaiņodes 1 Riga LV-1004 Latvia.

† E-mail: jan.vermaat@nmbu.no

<footnote n="1">¹Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås 1432 Norway</footnote>

<footnote n="2">²Department of Aquatic Ecology, University of Duisburg-Essen, Essen 45117 Germany</footnote>

<footnote n="3">³INRAE, UR Riverly, EcoFlows, Villeurbanne F-69625 France</footnote>
INTRODUCTION

The presence or absence of riparian woodland
is thought to have a major influence on biodiver-
sity and ecosystem functioning of streams and
adjacent floodplains (Sweeney and Newbold
2014). Under natural conditions, most European
rivers would be accompanied by woodland
(Brown et al. 2018; a recent North American
example in Whited et al. 2007). The establish-
ment of woodland buffers is generally consid-
ered an effective restoration measure (Bernhardt
et al. 2005, Stutter et al. 2012). The effect on flood
buffering, however, is not straightforward (Leyer
et al. 2012), and local conditions may determine
a balance between biodiversity benefits and pos-
sibly adverse flooding effects upstream. Similar
unforeseen trade-offs may occur among other
functions as well, which calls for a comprehen-
sive assessment of all possible effects of a mea-
sure, such as woodland restoration, across the
whole extent of the current or historical flood-
plain of a river (Tockner et al. 2000).

The ecosystem services approach can be used
as an integrating framework for such a compre-
hensive assessment, as it can link floodplain land
use as well as river characteristics (together
reflecting the ecosystem) to an exhaustive list of
societal benefits (Burkhard et al. 2009, Bateman
et al. 2013, Vermaat et al. 2020). A priori, it is
important that critical methodological concerns
are considered. This implies that the quantified
services should be “final,” hence directly con-
tribute to human well-being (Boyd and Banzhaf
2007), that double counting is carefully checked,
that different underlying assumptions for mone-
tary value estimates or other rankings are under-
stood (Wallace 2007, Bateman et al. 2011, Bouma
and van Beukering 2015) and that its anthro-
pocentricity is understood (i.e., “the benefits
people obtain from ecosystems” or “nature’s con-
tribution to people”; MEA 2005; Braat 2018; Díaz
et al. 2018; Kenter 2018).

Variation in woodland cover in the floodplain
and the riparian area of rivers can thus be linked
to variation in the provision of different services
by the river and its floodplain. We used a modifi-
cation of the ecosystem services "cascade" of
Boerema et al. (2017) proposed by Vermaat et al.
(2020) for this purpose. The view of ecosystem
services as a cascade that flows from an

ecosystem with structural components via inter-
mediate functions to a final service that is of
benefit to humans and thus can be valued
economically is presented originally by Haines-
Young and Potschin (2010). Mononen et al.
(2016) and Boerema et al. (2017) summarize the
debate on how the different elements of such a
cascade can be understood. Variants of this "cas-
cade" framework have been applied in decision
support for the multiple use of landscapes (Dick
et al. 2017), in regional and national assessments
of the manifold of ways in which whole land-
scapes contribute to human well-being through
ecosystem services (Martín-López et al. 2012,
Mononen et al. 2016, Maseyk et al. 2018), and in
integrated assessments of ecosystem restoration
success (Vermaat et al. 2016).

In Europe, floodplain woodland cover varies
substantially among and within river; it is often
highest in the upper reaches, although this is
under strong control of geomorphology and
land-use patterns (Petts and Foster 1985). To
explore the possible interactive effects of a future
increase in woodland as a possible consequence
of environmental policy or ongoing demographic
processes such as the depopulation of the coun-
tryside, scenarios can be used. Scenarios are a
common tool to systematically study the poten-
tial consequences of differences in policy focus
and societal development (Lorenzoni et al. 2000;
Berkhout et al. 2002, Busch 2006; O'Neill et al.
2017). We used the Shared Socioeconomic Path-
way (SSP) scenarios of societal development
developed by O'Neill et al. (2017) as they have
become widely used benchmarks. The SSPs
describe contrasting trajectories of societal
change in terms of demography, economic devel-
opment, technological advances, and national
and global policy focus on issues of international
cooperation, sustainability, and climate change.
These SSPs have been used for projections of
future land use, world energy markets, and cli-
mate modeling (O'Neill et al. 2017). We down-
scaled these SSP scenarios into a set of specific
riparian management practices (RMPs) that
describe measures taken by river management
expressed as changes in floodplain land use and
other river characteristics. Their effect was
assessed with our ecosystem services assessment
framework. Since the framework allows the trac-
ing of separate services, identifying trade-offs
among services (Martín-López et al. 2012) as a
consequence of different scenarios is possible.

The underlying generic assumption would be
that river restoration with increased woodland
has an overall positive ecological effect, also mea-
surable in ecosystem service provision, although
the objective of a restoration effort is often impli-
cit and inarticulate (Bernhardt et al. 2005, Jähnig
et al. 2011). Gilvear et al. (2013) proposed that
river restoration would generally lead to a
decrease in provisioning services, whereas regu-
lating and cultural services would increase.
Increasing woodland cover can be seen as a form
of restoration and then should lead to the same
general pattern. However, different scenarios
that involve substantial variation in the cover of
riparian woodland may have opposing effects:
More woodland will be negative for agricultural
productivity in the floodplain but positive for in-
stream water temperature mitigation and hence
trout survival, as one trade-off (Broadmeadow
et al. 2011). Since several such potential trade-
offs may occur, phrasing a zero hypothesis is not
straightforward, and we therefore chose to
phrase more open questions:

1. What is the effect of riparian woodland cover on the suite of ecosystem services provided by rivers and their floodplains? Can we identify systematic patterns?

2. How do different riparian management practices (RMPs, linked to SSPs) perform in terms of ecosystem service delivery?

3. Can we generalize on trade-offs among dif-
ferent services that occur as a function of
variable riparian woodland cover? Does the
effect of increased woodland cover follow
the prediction by Gilvear et al. (2013)?

MATERIALS AND METHODS

Case study rivers

We selected two lower-mountain and two low-
land river systems in, respectively, Germany and
France: The Nahe, Stever, Azergues, and the
Bresse plain. The latter actually combines three
smaller rivers in a homogeneous landscape, and
its riparian network has been studied by Van
Looy et al. (2017). These mid-sized rivers are part
of the drainage network of, respectively, the

Rhine and Rhone. They differ among others in
slope, underlying geology, land cover pattern,
human population density, and intensity of agri-
cultural land use (Table 1) and thus are consid-
ered to reflect a variety of riverine landscapes in
the Northwest of Central Europe, with the low-
land Stever and Bresse being under the most
intensive agriculture.

Ecosystem service assessment framework

We used the ecosystem services framework
that Vermaat et al. (2020) adopted from Mono-
nen et al. (2016) and Boerema et al. (2017) and
labeled the “Mononen cascade.” Briefly, it is
based on the ecosystem services classification
CICES 5.1 and specifies the subsequent steps in
the cascade for each service linking these to land
cover and river morphology (Fig. 1). It uses the
three MEA (2005) categories of provisioning, reg-
ulating, and cultural services to group the differ-
ent final services. Each service is quantified in
terms of biophysical units (benefit sensu Mono-
nen et al. 2016; such as kg·ha⁻¹·yr⁻¹) and subse-
quent monetary units (societal benefit sensu
Mononen et al. 2016, €·ha⁻¹·yr⁻¹), which can be
summed as an estimate of total economic value
(TEV; 1 € ≈ 1.25 US$ median midmarket 2011–
2021). The “Mononen cascade” originally con-
sists of four elements: ecosystem structure,
ecosystem function, societal benefit, and societal
value. We use land-use cover as proxy for ecosys-
tem structure. Then, we merge the two steps
function and benefit into one element, the service
in biophysical units since all underlying ecosys-
tem functions that potentially contribute to a
final service can be seen as intermediate and the
final function is thus also the final service (as in
Boerema et al. 2017 and Vermaat et al. 2020).
Finally, we use a range of valuation methods
from environmental economics (Brander et al.
2006, Bouma and van Beukering 2015) to arrive
at a monetary estimate of societal “value,” the
third element in our adapted cascade. We want
to stress that we use such monetary value esti-
mates for final services and an aggregation of
these into an estimate of TEV (as a rate per area
and year) as a tangible indicator for comparative
use in scenario evaluations and in communica-
tion with policymakers. They should not be
interpreted as directly convertible to market
prices or absolute “values.”
<table><caption>Table 1. Characteristics of the four study rivers.</caption><thead><tr><th>Characteristics</th><th>Nahe</th><th>Stever</th><th>Azergues<br>(including Brevenne<br>and Turdine)</th><th>Bresse<br>(combines<br>Chalaronne, Veyle,<br>Reyssouze)</th></tr></thead><tbody><tr><td>Drains into</td><td>Rhine</td><td>Lippe</td><td>Saone</td><td>Saone</td></tr><tr><td>Segment slope (%)†</td><td>3.66 ± 3.72 (0–35.83)</td><td>0.46 ± 0.59 (0–6.09)</td><td>4.84 ± 3.33 (0.25–15.03)</td><td>0.59 ± 0.53 (0.03–3.70)</td></tr><tr><td>(Sub-)segment width (m)‡</td><td>101.1 ± 79.0</td><td>90.1 ± 94.0</td><td>149.7 ± 142.7</td><td>112.8 ± 126.4</td></tr><tr><td>River length quantified (km)</td><td>3303</td><td>942</td><td>424</td><td>663</td></tr><tr><td>No. segments (sub-segments)</td><td>3499 (5638)</td><td>445 (1696)</td><td>119</td><td>171</td></tr><tr><td>Percentage woodland§ in floodplain (current)</td><td>39</td><td>27</td><td>36</td><td>19</td></tr><tr><td>Percentage agriculture in floodplain (pasture and cropland, current)</td><td>40</td><td>63</td><td>37</td><td>65</td></tr><tr><td>Percentage built-up in floodplain (current)</td><td>12</td><td>4</td><td>18</td><td>8</td></tr><tr><td>Nitrogen surplus (kg N·ha<sup>-1</sup>·yr<sup>-1</sup>, ~2000–2010)¶</td><td>30</td><td>120</td><td>30</td><td>40</td></tr><tr><td>Human population density in catchment (No./km<sup>2</sup> ~2010)#</td><td>170</td><td>194</td><td>131</td><td>147</td></tr></tbody></table>

† Values are expressed as mean ± standard deviation (SD) with range in parentheses.

‡ Values are expressed as mean ± SD.

§ Percentages land cover are averages across the (sub)segments.

¶ Based on Grizetti et al. (2007) and Poisvert et al. (2017).

# From regional statistics.

For all 16 quantified services, the assumptions and data sources are summarized in Table 2. A worked-out spreadsheet including all steps in the cascade is available as Data S1.

Deriving riparian management practices from benchmark shared socioeconomic pathway scenarios of societal change

We use a set of scenarios of societal change that have been derived from the benchmark SSPs (O'Neill et al. 2017) and were articulated for our specific purpose to reflect plausible, contrasting trajectories of riparian management in Europe (our RMPs). This articulation is documented separately in Vermaat et al. (2018). We set the starting year or baseline at approximately 2015 and label it as "current." Our chosen horizon in the future is 2050, as a compromise between a relevant time span for current policymakers and the time needed for policy to be fully implemented in landscapes. Furthermore, it is likely that by that time the trajectories of geophysical climate change grasped with the different Representative Concentration Pathways will not yet be markedly different beyond the projected uncertainty

bands (IPCC 2014). This allows us to focus on the societal aspects of plausible futures described in the SSPs which simplifies the number of alternatives to be compared. It also excludes the possibly confounding uncertainty in, for example, the future hydrology of our study streams. As a consequence, our estimate of flood damage prevention is based on current flow regimes, which may well be a conservative underestimate.

We selected three out of the five SSPs (respectively SSP1, SSP2, and SSP3, also labeled "sustainability," "middle of the road," and "regional rivalry" in the literature; O'Neill et al. 2017; Popp et al. 2017). We downscaled these to reflect three different, contrasting overall pathways of change in society which then led us to three corresponding plausible ways in which European riparian management would develop: either with a stronger focus on environmental sustainability, or continuing along current lines, or moving away from and ignoring environmental concerns (Riparian Management Practices or RMPs labeled as "ambitious," "best practice," or "pessimistic"; Vermaat et al. 2018; characteristics in Table 3). Current inland water management in
<figure><img src="image_3.png" /><figcaption>Fig. 1. Flow of 16 different ecosystem services from ecosystem structure (expressed as different types of land use, floodplain, and river metrics in the green box) to annual service flow in biophysical benefit and monetary value estimates. The elements benefit and value in the “Mononen cascade” are pooled here for simplicity. The box “riparian human population” provides population estimates from riparian municipalities through which the valley runs, used for the estimation of a number of services. Provisioning, regulating, and cultural services are indicated with different colors. Three small “knowledge rule” step diagrams indicate the effect of intermediate services, respectively, that of woodland cover on pest regulation expressed in crop revenue, that of riparian woodland cover on stream temperature, brown trout survival and hence value for angling, and that of landscape heterogeneity as the percentage woodland on attractiveness for recreation. Axis units of these diagrams are dimensionless fractions. Further explanation in Table 2.</figcaption></figure>

the European Union is governed by the Water Framework Directive (WFD), and we aligned our RMPs with the currently known policy cycle and measures of this WFD (White and Howe 2003). Our first draft RMPs have been discussed with panels of institutional stakeholders for each of the four study river systems and adjusted when necessary. For the Nahe, we met on 20 November 2017 at the office of the federal state government of Rhineland-Palatinate in Mainz with eight

participants; for the Stever, we met on 2 June 2018 in the office of the district government in Münster with 10 participants; and for the Bresse and Azergues, this occurred at IRSTEA in Lyon on 5 July 2018, with 13 participants from both rivers. In each workshop, at least two of the authors were present. An additional purpose of the workshops was to be informed of possible local sources of information for the quantification of the 16 ecosystem services.
<table><caption>Table 2. Relevant ecosystem services selected and aggregated when necessary from CICES 5.1 and quantified in the four river systems.</caption><thead><tr><th>Service (CICES 5.1 codes)</th><th>Description</th><th>Explanation, sources</th></tr></thead><tbody><tr><td><strong>Provisioning</strong></td><td></td><td></td></tr><tr><td>Crops (1.1.1.1)</td><td>Net farm gate revenue arable farms (154–1152 €·ha<sup>-1</sup> [cropland]·yr<sup>-1</sup>)</td><td>Income of farmer minus costs, but before taxes and subsidy, a benchmark statistic that is not market consumer price of a product, hence excludes any increases along the value chain. Data are from Mueller and Mueller (2017) from a standard set of representative and intensively monitored farms in Rheinland-Pfalz, Germany; from Boerman et al. (2015) for the Stever and from Agreste (2017) for the two French systems. In the Nahe, vineyards have not been included as they are outside the floodplain</td></tr><tr><td>Dairy, meat, hides, wool fleeces (1.1.3.1 and 1.1.3.2)</td><td>Net farm gate revenue dairy farms (201–1054 €·ha<sup>-1</sup> [grassland]·yr<sup>-1</sup>)</td><td>Based on the same sources as crops. We have assumed dairy products to be the final service, and not cattle fodder. Sheep stocks reportedly are limited in the study areas and the value is based on a world market estimate per fleece of 19.5 €</td></tr><tr><td>Fish from ponds, mainly trout (1.1.4.1)</td><td>Gross income minus costs per km stream length (0–14 €·ha<sup>-1</sup>·yr<sup>-1</sup>)</td><td>Several fish farms occur along the Stever and in the Bresse; productivity and net revenue estimated from Hiller and Wichmann (2010); values normalized per area floodplain</td></tr><tr><td>Timber (1.1.5.2)</td><td>Conservative annualized net present value estimate based on annual beech or fir productivity for Northern and Central Europe (138–218 €·ha<sup>-1</sup> [forest]·yr<sup>-1</sup>)</td><td>We use a conservative low-end value for Germany based on Duncker et al. (2012, different scenarios with different rates of interest, range of 0–800 €·ha<sup>-1</sup>·yr<sup>-1</sup>), Hastreiter (2017, 130 €·ha<sup>-1</sup>·yr<sup>-1</sup>, net revenue small scale forestry) and Boesch et al. (2018, 300 €·ha<sup>-1</sup>·yr<sup>-1</sup>). For France, the values were adjusted from Societe Forestiere (2018)</td></tr><tr><td>Berries and mushrooms, game (1.1.5.1 and 1.1.6.1)</td><td>Conservative estimate from a comparative European review, mainly Germany and France (12–24 €·ha<sup>-1</sup> [woodland]·yr<sup>-1</sup>, 90% due to game)</td><td>French and German data adopted from Schulp et al. (2014), which has a similar estimate as Boesch et al. (2018) for Germany</td></tr><tr><td>Hydropower (4.2.1.3)</td><td>Reported current locally generated hydropower (0–11 €·ha<sup>-1</sup> [floodplain]·yr<sup>-1</sup>)</td><td>Values are normalized from length of 3rd-order streams to floodplain area. Consumer price is halved to reduce the benefits accumulating in the value chain and remain comparable with net farm gate revenues as for crops and dairy. Based on Anderer et al. (2009) for the Nahe, on LANUV (2017) for the Stever, and stakeholder reporting on the Bresse. The Azerques currently has no hydropower generation</td></tr><tr><td>Drinking water (4.2.1.1)</td><td>Reported local extraction and use of surface water (0–5 €·ha<sup>-1</sup> [floodplain]·yr<sup>-1</sup>)</td><td>This can be river water infiltrated into aquifers and then extracted again, or direct use. Market price is halved to reduce the benefits accumulated in the value chain and remain comparable to net farm gate revenue. Values are normalized to floodplain area. In the Nahe and the Bresse drinking water is mainly extracted from deep aquifers and no river water is used. A substantial fraction (crude estimate 40%) of the Stever flow is infiltrated at Haltern into a sandy aquifer, together with water from the Muehlenbach and natural groundwater recharge, at to produce drinking water for parts of the Ruhrgebiet region (data drinking water company Gelsenwasser AG and information service of Nordrhein-Westfalen www.elwasweb.nrw.de)</td></tr><tr><td><strong>Regulating</strong></td><td></td><td></td></tr><tr><td>Greenhouse gas reduction (2.2.6.1)</td><td>Carbon sequestration in coniferous, deciduous woodland and riparian bushes at, respectively, 6, 5 and 4 ton C·ha<sup>-1</sup>·yr<sup>-1</sup> (based on Paul et al. 2009); we assume that mixed woodland is similar to deciduous</td><td>For the “current” state of riparian management, a low price of 5 euro per ton C is used (Elsasser et al. 2010, Loeschel et al. 2013). For the ambitious RMP, we assume a moderate increase due to the further development of a carbon credit market to 20 euro (Vermaat et al. 2016, Boesch et al. 2018). For “best practice,” we use 10, and for “pessimistic,” we use 1 euro per ton C</td></tr></tbody></table>
<table><caption>(Table 2. Continued.)</caption><thead><tr><th>Service (CICES 5.1 codes)</th><th>Description</th><th>Explanation, sources</th></tr></thead><tbody><tr><td>Erosion control: lateral sediment retention (2.2.1.1 and 2.2.1.2)</td><td>Expressed as riparian woodland P-loss prevention for erosion-derived material from the lateral zone adjacent to the stream (kg P·ha<sup>-1</sup> [floodplain]·yr<sup>-1</sup>)</td><td>P is used as simple proxy for top-soil to avoid any possible double counting. Median low-end potential P loads for grassland and arable land (from Venohr et al. 2017) are reduced relative to the proportion of the river length that has riparian woodland. If this proportion is 1, all the potential load is retained. Grassland has 1 kg P·ha<sup>-1</sup>·yr<sup>-1</sup> available for erosion, cropland 2 kg P·ha<sup>-1</sup>·yr<sup>-1</sup>. A low-end conservative value estimate for P is derived from an artificial fertilizer market price of 1.1322 €/kg P from a 2010 median market price at www.indexmundi.com</td></tr><tr><td>Flood prevention (2.2.1.3)</td><td>Damage function based on the risk of a 1/100 yr flood and a median distribution of different land-use types over the river corridor (0–7 €·ha<sup>-1</sup> [floodplain]·yr<sup>-1</sup>)</td><td>Assumption is that one flooded upstream reach prevents the damage of flooding a median downstream reach of equivalent area, hence with the median distribution of land use across the whole river. Value of built-up land is particularly high (252 €/m<sup>2</sup>, agricultural land has 7, and woodland has 1). This is conservatively down-adjusted to the height of the flood wave relative to property or crop (we use 0.2), normalized to an annual value with a factor 1/100. Based on De Moel and Aerts (2011) and then normalized to floodplain area. Duration and height of the 1/100 flood was estimated from locally available water authority data repositories and reports: for the Nahe from: http://www.gda-wasser.rlp.de, for the Stever from: www.elwasweb.nrw.de and www.luadb.it.nrw.de; for the Bresse from https://www.vigicrues.gouv.fr/niv2-bassin.php?CdEntVigiCru=18; and for the Azergues from the same website and the Plans Prevention des Risques d'Inondation at www.rhone.gouv.fr. A median flood duration of 7 d was used for all rivers except for the Azergues where we reduced it based on expert judgment of JP</td></tr><tr><td>Pest regulation (2.2.3.1 and 2.2.3.2)</td><td>Expressed as a modulation of crop productivity (provisioning service 1.1.1.1 above) linked to the presence of woodland and hedges as source of pest control. Modulation is a simple knowledge rule: if woodland cover <25%, then crop productivity reduced to 80%</td><td>Based on Tscharntke et al. (2012), who present a rule of thumb on a minimum woodland and hedge cover for central European landscapes</td></tr><tr><td>Water quality improvement: nutrient retention (2.2.5.1)</td><td>Waterborne phosphorus retention in stream and in riparian floodplain during a flood</td><td>Only phosphorus is used to conservatively prevent double counting. Different forms of nitrogen, BOD, or toxic substances are not addressed separately, and hence, this is likely a conservative underestimate. From load reduction per stream km as well as P sedimentation during a flood event and combined with a conservative low market price for P of 1.1322 €/kg P derived from artificial fertilizers in the same way as for erosion control. Load reduction per km of stream length is derived from De Klein and Koelmans (2011), and Olde Venterink et al. (2003) at around 200 kg P/km river length for low land rivers and conservatively reduced to 10 kg P/km river length for the steep Nahe and to 100 kg P/km for the other three rivers, because of a higher slope and flow in the current systems, and in accordance with unpublished MONERIS model estimates by Gericke and Venohr for the Nahe. Load reduction during flood is estimated at 0.14 kg P·ha<sup>-1</sup>·yr<sup>-1</sup> for the Nahe, 0.05 for the Stever, 0.50 for the Azergues and 0.01 for the Bresse from local reported flood events and concentrations. The two retention mechanisms are normalized to floodplain area. Concentrations and loads for the Nahe from Ittel and Saelzer (2015), for the Stever from the ELWAS database (www.elwa.sweb.nrw.de), for the Azergues from Barry and Faure (2011), and for the Bresse from Gay Environnement (2016)</td></tr></tbody></table>
<table><caption>(Table 2. Continued.)</caption><thead><tr><th>Service (CICES 5.1 codes)</th><th>Description</th><th>Explanation, sources</th></tr></thead><tbody><tr><td>Water temperature regulation through riparian shading (2.2.6.2)</td><td>Shading affects the probability of trout survival and is expressed as a modulating effect on the cultural service angling. Knowledge rule: if 50% of the main river length is shaded by woodland, then 100% survival, else a stepwise decline in survival to a residual survival of 10%</td><td>The fish survival knowledge rule is directly linked to the value estimate for the cultural service recreative angling (Fig. 1), because trout is the most favored species for angling (Arlinghaus 2004). The trout survival knowledge rule is based on Broadmeadow et al (2011) who showed that in a stream in S England periods with water temperature over 25°C were effectively prevented if woodland cover of the stream exceeded 50% of its length. This temperature is the “incipient lethal water temperature” which, if maintained over 7 d, will cause 50% mortality</td></tr><tr><td>Cultural</td><td></td><td></td></tr><tr><td>Recreative angling (taken separate from hunting, 1.1.6.1)</td><td>Angling days per km of stream</td><td>This is based on the proportion of households with one angler and the number of households in a catchment, and a low-end conservative estimate of their reported willingness to pay for angling per year from Arlinghaus (2004:275) and Federation Nationale de la Peche en France (2014) and Le Goffe and Salanie (2004; 130 €/yr per angler); value is normalized to river length and then floodplain area. Household numbers are derived from regional population statistics</td></tr><tr><td>Active recreation in the river and its floodplain corridor (all in CICES category 3.1 pooled)</td><td>Separate local estimates for the number of local and residents and tourist visitors that use and appreciate the area per year from local statistics. Multiplied with their willingness to pay for this and modulated by a knowledge rule on the appreciation of a scenic landscape: if forest cover declines below 20% tourist appreciation drops to 60%, if it is above 70% then appreciation drops to 80% (95–138 €·ha<sup>-1</sup> [non-urban floodplain]·yr<sup>-1</sup>)</td><td>Knowledge rule on scenic landscape is based on Frank et al. (2013); willingness to pay of residents and visitors based on Elsasser et al. (2010) and Boesch et al (2018). Resident population and tourism data for the Nahe have been obtained from the public statistics of Rheinland-Pfalz: https://www.staistik.rlp.de/, those for the Stever from Wittkampf (2016), and those for Bresse and Azergues from Barry and Faure (2011)</td></tr><tr><td>Nature conservation non-use (all in CICES category 3.2 pooled)</td><td>Willingness to pay per valley household (5–162 €·ha<sup>-1</sup> [non-urban floodplain]·yr<sup>-1</sup>)</td><td>Based on nationwide studies in Germany on household willingness to pay for nature conservation (Wuestemann et al. 2014; Boesch et al. 2018; 231 €·yr<sup>-1</sup>·household<sup>-1</sup>; 27% of households willing to pay, estimated household size 2 persons) and for France on Garcia et al. (2011), 50 €·yr<sup>-1</sup>·household<sup>-1</sup>; 58% of households willing to pay, household size 3 persons). Estimates adjusted to local population sizes from municipality national statistics and then normalized to floodplain area</td></tr></tbody></table>

Notes: Value estimates are expressed as euro per ha catchment per year, and monetary values can be considered approximately 2010–2015 values. An estimated biophysical service flow (e.g., kg·ha<sup>-1</sup>·yr<sup>-1</sup>), or a range for the monetary value estimate (€·ha<sup>-1</sup>·yr<sup>-1</sup>) is reported wherever it is a simple link to land use. A fully worked-out example of our data spreadsheet is provided as Data S1. In the Descriptions, the values in parentheses are ranges of monetary value estimates across the four catchments for the “current” state; or biophysical flow. RMP, riparian management practice.

## Land use in floodplain segments

The four river networks were divided into river segments. These are homogenous with respect to national river types, Strahler order, and valley slope (based on the official river networks of the federal states in Germany and from the SYRAH CE network of Valette et al. 2012 in France). This resulted in river segments of different lengths up to several kilometers. To ensure comparability between segments, all segments

with a length larger 1 km were subdivided into sub-segments with a length of 0.5–1.0 km and segments with a length less than 0.5 km were excluded from the analysis. The riparian area along the river sub-segments was demarcated using information on the river corridor or alluvial floodplain from local agencies or assuming it to be 12 times bank-full channel width, but at least 30 m on each side of the river. This corresponds to the functional definition of riparian
<table><caption>Table 3. Articulation of four Riparian Management Practices (RMPs) derived from the respective Shared Socioeconomic Pathways (SSPs, O'Neill et al. 2017; full downscaling of SSPs for the four study rivers in Vermaat et al. 2018).</caption><thead><tr><th>Riparian management practice</th><th>Corresponding SSP label (from O'Neill et al. 2017)</th><th>In brief</th><th>Details: choices for implementation</th></tr></thead><tbody><tr><td>Current, baseline</td><td>—</td><td>—</td><td>Current, the present situation in the four river systems, which approximately reflects the situation in 2015</td></tr><tr><td>Pessimistic</td><td>SSP3: "regional rivalry—a rocky road"</td><td>WFD no longer pursued, intensity of non-ecological agriculture is increased</td><td><ul><li>No additional WFD measures implemented, maintenance of structural measures stopped.</li></ul></td></tr><tr><td>Best practice</td><td>SSP2: "middle of the road"</td><td>River management is continued in the period toward 2050 according to the current WFD regulations</td><td><ul><li>Woody vegetation along cropland removed</li><li>All woody buffer measures as planned in the first and second River Basin Management Plan† cycle are implemented.</li><li>In addition, similar measures were assumed to be implemented after the end of the WFD in 2027 to 2050: In the Nahe, 10 m wide woody buffers are developed along each side of all river segments that are classified as priority (Schwerpunktgewaesser) in the Nahe catchment. This is feasible for "best practice" since already between 2000 and 2015, about 1000 of the 8000 river km in Rhineland-Palatinate have been restored. In the Stever, all measures presently considered necessary to reach good ecological status are implemented. For French catchments, all the restoration programs involving riparian buffer management planned by the local stakeholders have been implemented. Furthermore, a sub-basin of Azergues and a sub-basin of Bresse had a dedicated management program for the riparian corridor, which also served as a basis for this scenario</li></ul></td></tr><tr><td>Ambitious</td><td>SSP1: "sustainability—taking the green road"</td><td>A further development of the WFD toward a more sustainable water use</td><td><ul><li>Woody vegetation is developed in the whole riparian area, approximately corresponding to the meander belt width or active floodplain, at least a buffer of 30 m on each side of the river.</li><li>Except for the following areas: urban areas, transport lines (e.g., roads, railroads), electricity transmission corridors, open, non-forested nature reserves</li></ul></td></tr></tbody></table>

areas of Ilhardt et al. (2000) and Verry et al. (2004). Since riparian woodlands provide ecosystem services such as nutrient retention and recreation in a larger landscape context, the whole floodplain was considered in addition, which was technically implemented by demarcating the official 100-yr flooding area, covering large parts of the valley floor, but at least including the riparian area. Land use in the riparian area and floodplain was described by quantifying the area covered by the following land cover classes: urban, urban green spaces, open mining, arable land, grassland, non-woody natural

vegetation, woodland-shrubs, woodland-coniferous, woodland-mixed, woodland-deciduous, lakes, wetlands, rivers, and transport lines (roads, railroads). For the two German catchments, the most detailed official land-use data set ATKIS (covering woody vegetation up from a minimum size of 0.1 ha) was complemented by woody vegetation in the riparian area down to single lines of trees along rivers identified on orthophotos using remote sensing. For the two French catchments, a land-use data set with an even higher spatial resolution was already available (0.004 ha; Decherf et al. 2014).

<footnote>† RMBP cycle is the policy cycle of the Water Framework Directive (WFD), water quality legislation across the European Union (White and Howe 2003).</footnote>
In addition, land-use data were changed according to the RMPs (Table 3) and the area covered by the land-use classes was recalculated.

## RESULTS

In the current situation, the mean percentage of woodland in a floodplain segment was found to vary between 25% and 50%, but differences among individual rivers as well as river segments are substantial (Figs. 2, 3; Appendix S1: Fig. S1). Pooled across rivers, the three groups of services each show an “optimum curve” pattern with the available woodland in the floodplain (Fig. 2). River type, expressed as Strahler order, corresponds with the percentage woodland in the floodplain, with more woodland in lower order segments (Fig. 2). Visually estimated optima in woodland cover appears to be somewhat different for the three service groups (Fig. 2): Regulating services are maximal around 30% woodland cover (Strahler orders 4 and 5), provisioning, and cultural services around 45% (the mean woodland cover for Strahler order 2). In an overall analysis of variance (Table 4), segment area was the covariate explaining least, whereas the percentage woodland was more important than Strahler order for provisioning and cultural services, but not for regulating services. Here, Strahler order was more important, likely through the predominance of flood risk prevention. Also, both Strahler order and percentage woodland were independently significant, suggesting that they affect service delivery differently, despite the apparent underlying parallel trend in Fig. 2. Total explained variance of the model was particularly high for cultural services (48%), and this is likely due to the underlying optimum curve in the knowledge rule for the relation between recreation and woodland cover, which is supported when the individual segment estimates are inspected (Fig. 3).

For clarity, we have grouped the 16 services in the three MEA classes. Among the provisioning services, agricultural production and timber were generally most important in the current situation; among the regulating services, this was flood prevention; and among cultural services, recreation was predominant (Table 5). An exception was the Stever, where drinking water production was an important provisioning service,

and non-use for biodiversity conservation was in the same order of magnitude as active recreation. The Stever was also distinctly higher in estimated agricultural value per ha than the other three rivers, likely reflecting the more intensive agricultural practice of lowland farming in Northwestern Europe (cf. Table 1).

The RMPs we outlined as plausible alternative future states of river management led to substantial differences in woodland cover in the river floodplain (Fig. 4). In all, we implemented the largest increases in woodland for the ambitious RMP. The overall effect for each river, however, was quite variable. Whereas for the Stever total ecosystem provision declined in the ambitious RMP, it increased for the other three rivers. However, within each river these differences in TEV among the RMPs are modest (maximal effect ratio ambitious/current = 1.15 for the Bresse). The effect ratio was often higher for cultural services, but this could coincide with a decline for provisioning services (e.g., 1.57 and 0.60 for the Stever, but 1.55 and 1.05 for the Bresse, see also Fig. 4). Overall, the absolute patterns were strongest for the Stever (Fig. 4), revealing a trade-off between provisioning and cultural services underlying the apparent flat response in TEV. Regulating services did not change very much across the different RMPs, particularly because they are dominated by our flood prevention estimate. Slight increases with the ambitious RMP (Fig. 4) are due to the increase in carbon sequestration with increasing woodland, and an assumed higher carbon price, and also due to a higher lateral sediment retention with increased woodland (Table 2).

## DISCUSSION

Our analysis suggests that in the current landscape configuration, all three service categories showed optimum curves with increasing woodland cover: Provisioning services and cultural services were maximal around 45%, whereas this was around 30% for regulating services. This apparent systematic pattern is more variable in the individual rivers (Appendix S1: Fig. S1). The river management scenarios (RMPs) we implemented led to major differences in riparian woodland cover, but the overall effect on total ecosystem service provision (TEV) was limited.
In three of the four rivers, an increase in cultural services was accompanied with a decrease in provisioning services. Among the predictions made by Gilvear et al. (2013), only the trade-off between cultural and provisioning services was supported by our findings, which we take as an argument for caution in generalizations.

When addressing the potential effect of woodland cover on ecosystem service provision, we must keep in mind that woodland cover in the studied river systems is not low (Table 1: 19–39%), compared to the riverine landscapes such as the one studied by Vermaat et al. (2016; average 25%, range 0–81%) or Maseyk et al. (2018). The latter authors found that an increase in wooded riparian buffers from zero to 7% led to only marginal changes in dairy production, landscape amenity, and three water quality variables. Actually, a substantial proportion of the segments of the lowland rivers Stever and Bresse have woodland cover below 25% (Fig. 3), but the large spatial variability in woodland cover along the stream becomes invisible in our aggregate means (compare Figs. 2, 3). Strahler order and woodland cover covaried, so that lower order upland stream segments have more woodland.

The estimated optimum in total ecosystem service provision at intermediate woodland cover (around 45%) and intermediate Strahler order (2–3) is comfortably close to the advice of 50% from an earlier qualitative review of multiple benefits of riparian woodland (Broadmeadow and Nisbett 2004). Overall patterns in regulating, provisioning, and cultural services suggest an increase in regulating services with increasing stream order, and a decrease in provisioning services and cultural services. This is likely the consequence of the geomorphological landscape configuration in these river networks, where floodplains become larger with higher stream order, and thus have more space for flood retention, but also for competing land-use forms other than agriculture. Steeper, first-order parts of the network often have more woodland due to the combination of suitability and demand for land, as in Tomscha et al. (2017). These are also the landscapes preferred for recreation and nature conservation. We interpret this as an overall, systematic pattern, which of course is subject to substantial local variation (Fig. 3). It must be noted

<figure><img src="image_4.png" /></figure>

Fig. 2. Effect of the current percentage of woodland in the river corridor on ecosystem service delivery aggregated over all segments in the four river systems. The 16 services are pooled into the three MEA categories provisioning, regulating, and cultural. Strahler order is used as an ordering indicator of river type, with headwater streams having order 1. All polynomial fits are significant ($r^2$ > 0.93, *P* < 0.01), but only the one for total services is displayed. Note that vertical and horizontal standard errors are included but these are generally too small to be depicted due to the high number of segments included. Percentage woodland declined significantly with Strahler order but a regression explained a limited proportion of the variance ($y = -5x + 53$, $r^2$ = 0.05, *P* < 0.001, *n* = 7622). A similar figure broken down for the most important services and the four individual rivers is given in Appendix S1: Fig. S1.

that we did include first-order streams, contrary to Tomscha et al. (2017), because even though a floodplain may not be apparent in the landscape, these small upland streams do flood and the riparian woodland does provide all services we considered here. Our pattern in TEV does not correspond with the findings of Felipe-Lucia et al. (2014) for a Spanish river-and-floodplain system, who report a maximum in the diversity of services provided by the floodplain with a full riparian woodland coverage, but very different approaches make a direct comparison difficult.

The major change in woodland cover we realized in the ambitious RMP, which is based on the sustainability-oriented SSP1, did not lead to equally major shifts in total ecosystem service
<figure><img src="image_5.png" /><figcaption>Fig. 3. Individual segment estimates of total economic value as sum of all provisioning, regulating, and cultural services quantified for all four rivers vs. current woodland cover.</figcaption></figure>

delivery, but it led to an increase in cultural ser-
vices at the expense of provisioning services
(Fig. 4). For the Stever, it led to a decline in our
value estimate of total ecosystem services, due to
the replacement of intensively used agricultural
land by woodland that has lower net returns and
the predominance of these two provisioning ser-
vices in the total value estimate (cf. Table 5).
However, for the other three rivers total ecosys-
tem service provision increased with woodland
cover, particularly due to cultural services. The
overall higher value of regulating services for the
Nahe and the Bresse is due to a combination of
absolute floodplain area (largest in the Nahe,
Table 1) and the higher proportion of built-up
areas (largest in the Bresse), as these contribute
most to the value estimate of flood prevention
(Table 2). The second most conspicuous pattern
in our scenario outcomes is the limited difference
between the remaining three RMPs. Both the

pessimistic and the best practice RMP led to only
slight changes in woodland cover with similar
effects on the patterns in ecosystem service deliv-
ery. Notably in the Stever, the best practice RMP
would already lead to an increase in cultural ser-
vices without negatively affecting provisioning
services, that is, farming output and drinking
water production. It must be noted that the value
estimate for nature conservation is derived from
an overall appreciation of German citizens for
nature protection, rather than a local appraisal of
such a landscape change derived from choice
experiment surveys as in, for example, Vermaat
et al. (2016). Hence, this most likely is a low, con-
servative estimate, since local valuation studies
for charismatic species, such as trout, may well
elicit higher value estimates (cf. Martín-López
et al. 2007). A third issue is the limited response
in regulating services (Fig. 4c), which are domi-
nated by our flood prevention estimate. This is
<table><caption>Table 4. Analysis of variance of the effect of Strahler order (1–6) with segment area and area woodland in the segment as covariates on total, provisioning, regulating, and cultural services value estimates.</caption><thead><tr><th>Factor in the model</th><th>Total services</th><th>Provisioning</th><th>Regulating</th><th>Cultural</th></tr></thead><tbody><tr><td>Intercept</td><td>42</td><td>9</td><td>55</td><td>20</td></tr><tr><td>Segment area</td><td>3</td><td>2</td><td>1</td><td>3</td></tr><tr><td>Area woodland in the segment</td><td>42</td><td>64</td><td>10</td><td>75</td></tr><tr><td>Strahler order</td><td>13</td><td>25</td><td>34</td><td>2</td></tr><tr><td>Total variance explained by the model (%)</td><td>19</td><td>21</td><td>23</td><td>48</td></tr></tbody></table>

Notes: Data pooled over the four river systems. Total degrees of freedom 7624. Presented are the percentage in the model sums of squares attributed to each factor and the total variance explained by the corrected model. All three factors included explained a highly significant part of the variance (P < 0.001).

<table><caption>Table 5. Most important (contributing >5 €·ha⁻¹·yr⁻¹) ecosystem services for each of the four study rivers under the current situation.</caption><thead><tr><th rowspan="2">River (TEV)</th><th colspan="3">Provisioning (€·ha⁻¹·yr⁻¹)</th><th colspan="3">Regulating (€·ha⁻¹·yr⁻¹)</th><th colspan="3">Cultural (€·ha⁻¹·yr⁻¹)</th></tr><tr><th>Service</th><th>Mean</th><th>%</th><th>Service</th><th>Mean</th><th>%</th><th>Service</th><th>Mean</th><th>%</th></tr></thead><tbody><tr><td rowspan="4">Nahe (935)</td><td>Dairy</td><td>140</td><td>53</td><td>Flood prevention</td><td>412</td><td>97</td><td>Recreation</td><td>115</td><td>46</td></tr><tr><td>Timber</td><td>63</td><td>24</td><td>Carbon sequestration</td><td>10</td><td>2</td><td>Angling</td><td>70</td><td>28</td></tr><tr><td>Crops</td><td>42</td><td>16</td><td></td><td></td><td></td><td>Conservation non-use</td><td>63</td><td>25</td></tr><tr><td>Subtotal</td><td>264</td><td></td><td></td><td>423</td><td></td><td></td><td>248</td><td></td></tr><tr><td rowspan="4">Stever (1590)</td><td>Crops</td><td>464</td><td>44</td><td>Flood prevention</td><td>299</td><td>91</td><td>Recreation</td><td>78</td><td>37</td></tr><tr><td>Drinking water</td><td>353</td><td>34</td><td>Water quality: P-retention</td><td>13</td><td>3</td><td>Conservation non-use</td><td>73</td><td>35</td></tr><tr><td>Dairy</td><td>168</td><td>16</td><td>Carbon sequestration</td><td>7</td><td>4</td><td>Angling</td><td>59</td><td>28</td></tr><tr><td>Subtotal</td><td>1062</td><td></td><td></td><td>319</td><td></td><td></td><td>210</td><td></td></tr><tr><td rowspan="4">Bresse (538)</td><td>Dairy</td><td>52</td><td>36</td><td>Flood prevention</td><td>279</td><td>96</td><td>Recreation</td><td>72</td><td>70</td></tr><tr><td>Crops</td><td>51</td><td>35</td><td>Water quality: P-retention</td><td>6</td><td>2</td><td>Conservation non-use</td><td>17</td><td>17</td></tr><tr><td>Timber</td><td>25</td><td>18</td><td>Carbon sequestration</td><td>5</td><td>2</td><td>Angling</td><td>13</td><td>13</td></tr><tr><td>Fish culture</td><td>14</td><td>9</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Azergues (787)</td><td>Subtotal</td><td>146</td><td></td><td></td><td>290</td><td></td><td></td><td>102</td><td></td></tr><tr><td>Dairy</td><td>50</td><td>33</td><td>Flood prevention</td><td>487</td><td>97</td><td>Recreation</td><td>85</td><td>65</td></tr><tr><td>Crops</td><td>48</td><td>31</td><td>Carbon sequestration</td><td>9</td><td>2</td><td>Angling</td><td>33</td><td>25</td></tr><tr><td>Timber</td><td>42</td><td>28</td><td>Water quality: P-retention</td><td>8</td><td>2</td><td>Conservation non-use</td><td>13</td><td>10</td></tr><tr><td></td><td>Subtotal</td><td>152</td><td></td><td></td><td>504</td><td></td><td></td><td>131</td><td></td></tr></tbody></table>

Note: Presented are mean estimated monetary value per ha, percentage contributed to its MEA class, as well as subtotals and grand totals, the latter an estimate of total economic value (TEV).

likely due to the fact that we have not varied population density, settlement policy or land and house pricing, or the location of settlements in our RMPs because we chose these RMPs to be limited to measures within the remit of European water management institutions. Thus, our estimate of flood damage value is likely both a conservative low-end value and unrealistically stable, but we think it is justified to limit the number of assumptions in our scenario articulations. Finally, Fig. 4 suggests a trade-off between provisioning and cultural services, in contrast to what we deduce from the pattern in the current situation in Fig. 2. So, if the current situation is

pushed toward the occupation of agricultural land with woodland (ambitious RMP), this leads to a decline in overall value—an obvious “trade-off.” Martín-López et al. (2012) also found a trade-off between provisioning and cultural services in an extensive study of societal preferences in eight areas across Spain. The apparent contradiction in our data is due to the fact that in the “current” situation we see a changing pattern along the length of the four rivers pooled, whereas when the comparison with the ambitious RMP is made, we see a change over time, and the separate pattern for each river is not equally intense.
<figure><img src="image_6.png" /></figure>

Fig. 4. Effect of the different Riparian Management Practices (RMPs) on (a) total ecosystem service delivery expressed as total economic value, (b) regulating, (c) provisioning, and (d) cultural services, all plotted against the percentage of woodland for each RMP. P, pessimistic; C, current; B, best practice and A, ambitious. The order of woodland cover of these four RMPs is the same for each river, from low to high: P, C, B, A. Different symbols indicate the four different rivers (see legend).

The "Mononen cascade" framework applied here was developed from the cascade model proposed by Mononen et al. (2016), which again has its roots in the cascade presented by among others Haines-Young and Potschin (2010). We will not reiterate the discussion whether "nature can be valued at all" (Gómez-Baggethun et al. 2010, Hermelingmeier and Nicholas 2017), but important premises of our approach are that one can attribute final services to land-use cover types and that monetary estimates of these services are consistent and "valid," though not necessarily "accurate" or "precise." Our compilation of different value estimates each with its underlying approaches and assumptions is a seriously disputed aspect of TEV estimates (among others Schröter et al. 2014). We think it allows

comparison across scenarios or policy alternatives and services, if only framed carefully in a consistent study design (Boerema et al. 2017, Hanna et al. 2018), and thus can be used to inform policy. The valuation step, in principle, is not different from using a ranking scale which is summed, as applied in, for example, Burkhard et al. (2009) or Newton et al. (2012), but the monetary valuation causes a weighing of the different services, rather than treating all individual services as equal. Our weighing with a monetary ruler is equally traceable as using ranks or scores (Table 2), but it is based on expressed societal preferences, which indeed may lead to lower value estimates for nature conservation non-use than for active recreation (Table 5), although in three of our four rivers these are remarkably
close. Vermaat et al. (2020) discuss the method-
ological strengths and weaknesses of the current
framework in more detail.

Briefly, we see two important limitations of our
current study. First, our approach would have
benefited from a spatial linking of stream seg-
ments in the river network, so that we could have
estimated flood prevention, but also nutrient and
sediment retention in a more realistic way. We are
not aware of a study that has succeeded in com-
bining such hydrological realism with an assess-
ment of the full suite of ecosystem services.
Second, we have not done a formal uncertainty or
sensitivity analysis, because estimating uncertain-
ties without empirical basis would be mere guess-
work. For example, a sensitivity analysis on the
effect of our flood prevention estimate in the sum
of regulating services and also that of our knowl-
edge rule on the effect of woodland cover on
recreational appreciation would have been useful.
Vermaat et al. (2016) assessed changes in ecosystem service provision due to restoration of European rivers and their floodplains with a similar
though less formalized approach. Our current
TEV estimates are similar in order of magnitude
(their median unrestored TEV 1000 €·ha⁻¹·yr⁻¹;
ours 843 €·ha⁻¹·yr⁻¹). A final methodological
point is our consistent choice for the most conservative low-end estimate, wherever we had the
choice. Our justification is that we want to remain
far from optimistic advocacy (Bouma and van
Beukering 2015) and that we combine estimates
based on highly different underlying approaches,
but the consequence is that some of our estimates
indeed are low. An obvious example is carbon
sequestration: Other work, such as the natural
capital accounting exercise for the UK (Trenbirth
and Dutton 2020), uses 20 €/t C for non-traded
carbon equivalents as a mid-level for 2010 which
increases in the subsequent years, compared to
our baseline of 5 €/t (Table 2). Overall, this
implies that our value estimates best can be seen
as indicative, but internally consistent, and then
for an approximate time window of 2010–2015 for
the baseline scenario.

If we equate our ambitious RMP to a major
restoration effort, we can test the hypothesis of
Gilvear et al. (2013). Increased woodland cover,
however, only led to a substantial decrease in pro-
visioning services in one of the four rivers, regu-
lating services increased in two, whereas cultural

services indeed increased in all cases. Hence, we
cannot simply generalize along the lines of Gil-
vear et al. (2013) but must revert to more service-
or landscape-specific hypotheses. For example,
the market value of woodland linked to timber
and an increasing demand for biomass to replace
fossil fuel (Trømborg et al. 2020) should not be
ignored, but also the intensity of adjacent land
use, and hence, the land rent (cf. the Stever and
Vermaat et al. 2016) is relevant when monetary
value estimates of all possible services are of
interest. At the landscape scale of a river and its
floodplain, we see that greatly increasing the per-
centage covered with woodland, as in the ambi-
tious RMP, may well lead to an increase in
cultural services, hence appreciation by recreation
including anglers, at the expense of provisioning
services, here particularly agriculture.

Compared to previous assessments of ecosystem services provision along rivers, our study combines high spatial detail, a comprehensive and well-defined set of ecosystem services that includes a final monetary value estimate, and a verification stage with stakeholder representatives, rather than a limited selection of services or a rank-based scoring system. This largely corresponds with the five recommendations made by Hanna et al. (2018): assess multiple services, use reproducible data and methods, include service interactions, select extent study area relevant to question, and engage with stakeholders. The latter has been important in the verification of our scenario's, without these reflective workshops, our scenario articulations as RMPs would have been less realistic to river managers and land-use planners. At the same time, we experienced that we had to maintain a balance with our basis in the benchmark SSPs to ensure comparability with other scientific work on scenarios.

In conclusion, we have shown that our set of
seven provisioning, six regulating, and three cul-
tural services, as quantified with the “Mononen
cascade” for four central European river systems,
currently all show optimum curves with increasing
woodland cover: Provisioning services and cultural
services were maximal around 45%, whereas this
was around 30% for regulating services. On aver-
age, river type, expressed as Strahler order, was
found to correspond quite closely with the percent-
age woodland in the floodplain, with more wood-
land in steeper lower order segments.
Geomorphological and land cover variation among and within individual river systems is pooled into this average, but can be substantial (Table 1, Fig. 3; Appendix S1: Fig. S1). The three different modeled woodland cover scenarios led to a remarkably limited change in total ecosystem service delivery, even if mean woodland cover was reduced from 27% to 17% in the pessimistic RMP and increased to 70% in the ambitious RMP for the most extreme case of the Stever. We did, however, see a clear decline of provisioning service with increased woodland cover and an increase in cultural services. Regulating services did not change that much, because they are dominated by flood prevention in our assessment. It appears that the “best practice” scenario combines a modest increase in cultural services with a slight increase in provisioning services. Also, the outcome suggests that very ambitious nature conservation objectives can be met with a limited decrease in total societal benefit (TEV) only, and despite the low-end monetary value estimates for nature conservation non-use.

## ACKNOWLEDGMENTS

We thank all our colleagues in OSCAR for the positive collegial cooperation, and the workshop participants in Mainz, Münster, and Lyon for their constructive contributions. Work on this paper funded through the 2015–2016 BiodivERsA COFUND call for research proposals, which is supported by the national funders German Federal Ministry of Education and Research (01LC1618A), Agence National de Recherche (ANR-16-EBI3-0015-01), and Research Council of Norway (268704/E50). The first author also wants to acknowledge his colleagues in the ecosystem services team of the Nordic Centre of Excellence BIOWATER, which is funded by Nordforsk under project number 82263: Discussions with Bart Immerzeel, Eija Pouta, and Arttu Juutinen were very useful. Erik Aschehoug helped with the writing style.

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SUPPORTING INFORMATION

Additional Supporting Information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/ecs2.3716/full