Flood risk reduction and flow buffering as ecosystem services: A flow persistence indicator for watershed health

TL;DR: In this article, the dimensionless FlowPer parameter (Fp), representing predictability, is key to a parsimonious recursive model of river flow, Qt = FpQt-1 + (1-Fp)(Pt-Etx), with Q, P and E expressed in mm d-1.

Abstract: Flood damage depends on location and adaptation of human presence and activity to inherent variability of river flow. Reduced predictability of river flow is a common sign of degrading watersheds associated with increased flooding risk and reduced dry-season flows. The dimensionless FlowPer parameter (Fp), representing predictability, is key to a parsimonious recursive model of river flow, Qt = FpQt-1 + (1-Fp)(Pt-Etx), with Q, P and E expressed in mm d-1. Fp varies between 0 and 1, and can be derived from a time-series of measured (or modeled) river flow data. The spatially averaged precipitation term Pt and preceding cumulative evapotranspiration since previous rain Etx are treated as constrained but unknown, stochastic variables. A decrease in Fp from 0.9 to 0.8 means peak flow doubling from 10 to 20% of peak rainfall (minus its accompanying Etx) and, in a numerical example, an increase in expected flood duration by 3 days. We compared Fp estimates from four meso-scale watersheds in Indonesia and Thailand, with varying climate, geology and land cover history, at a decadal time scale. Wet-season (3-monthly) Fp values are lower than dry-season values in climates with pronounced seasonality. A wet-season Fp value above 0.7 was achievable in forest-agroforestry mosaic case studies. Interannual variability in Fp is large relative to effects of land cover change; multiple years of paired-plot data are needed to reject no-change null-hypotheses. While empirical evidence at scale is understandably scarce, Fp trends over time serve as a holistic scale-dependent performance indicator of degrading/recovering watershed health.

Summary

1 Introduction

• Floods can be the direct result of reservoir dams, log jams or protective dykes breaking, with water derived from unexpected heavy rainfall, rapid snowmelt, tsunamis or coastal storm surges.
• There is a problem with the credibility of assumed deforestation–flood relations (van Noordwijk et al., 2007; Verbist et al., 2010), beyond the local scales (< 10 km2) of paired catchments where ample direct empirical proof exists, especially in non-tropical climate zones (Bruijnzeel, 1990, 2004).
• This Malaysian study may be the first credible empirical evidence at this scale.
• Existing models differ in the number of explanatory variables and parameters they use, but are generally dependent on empirical data of rainfall that are available for specific measurement points but not at the spatial resolution that is required for a close match between measured and modelled river flow.

2.1 Recursive model

• One of the easiest-to-observe aspects of a river is its dayto-day fluctuation in water level, related to the volumetric flow via rating curves (Maidment, 1992).
• Without knowing details of upstream rainfall and the pathways the rain takes to reach the river, observation of the daily fluctuations in water level allows for important inferences to be made.
• It is also of direct utility; sudden rises can lead to floods without sufficient warning, while rapid decline makes water utilisation difficult.
• Indeed, a common local description of watershed degradation is that rivers become more “flashy” and less predictable, having lost a buffer or “sponge” effect (Joshi et al., 2004; Ranieri et al., 2004; Rahayu et al., 2013).

2.2 Base flow

• Clarifying the Qa,t contribution is equivalent in one of several ways to separate base flow from peak flows.
• Rearranging Eq. (3) the authors obtain (6) The ∑ Qa,t term reflects the sum of peak flows in millimetres.
• For Fp= 1 (the theoretical maximum), the authors conclude that all Qa,t must be zero, and all flow is “base flow”.

2.3 Low flows

• The groundwater reservoir that is drained, equalling the cumulative dry-season flow if the dry period is sufficiently long, is Qx /(1−Fp).
• It thus matters how low flows are evaluated: from the perspective of the lowest level reached, or as cumulative flow.
• The combination of climate, geology and land form are the primary determinants of cumulative low flows, but if land cover reduces the recharge of groundwater there may be impacts on dry-season flow, that are not directly reflected in Fp.
• If a single Fp value would account for both dry and wet season, the effects of changing Fp on low flows may well be more pronounced than those on flood risk.

2.4 Flow-pathway dependence of flow persistence

• The patch-level partitioning of water between infiltration and overland flow is further modified at hillslope level, with a common distinction between three pathways that reaches streams: overland flow, interflow and groundwater flow (Band, 1993; Weiler and McDonnell, 2004).
• An additional interpretation of Eq. (1), potentially adding to their understanding of results but not needed for analysis of empirical data, can be that three pathways of water through a landscape contribute to river flow (Barnes, 1939): groundwater release with Fp,g values close to 1.0, overland flow with Fp,o values close to 0 and interflow with intermediate Fp,i values.
• The effective Fp in the rainy season can be interpreted as indicating the relative importance of the other two flow pathways.
• Thus, the value of Fp,o can be substantially above zero if the rainfall has a significant temporal autocorrelation, with heavy rainfall on subsequent days being more likely than would be expected from general rainfall frequencies.
• If rainfall following a wet day is more likely to occur than following a dry day, as is commonly observed in Markov chain analysis of rainfall patterns (Jones and Thornton, 1997; Bardossy and Plate, 1991), the overland flow component of total flow will also have a partial temporal autocorrelation, adding to the overall predictability of river flow.

2.5 Relationship between flow persistence and flashiness index

• This suggests that FI= 2 (1−Fp) is a first approximation and becomes zero for Fp= 1.
• Where (part of) precipitation occurs as snow, the timing of snowmelt defines www.hydrol-earth-syst-sci.net/21/2321/2017/.
• It may not directly influence flow persistence, but will be accounted for in the flow description that uses flow persistence as a key parameter.

3.1 River-flow data for four tropical watersheds

• To test the applicability of the Fp metric and explore its properties, data from four Southeast Asian watersheds were used, which will be described and further analysed in Part 2 (van Noordwijk et al., 2017).
• River-flow data at the outflow of the Way Besai were also obtained from PU and PUSAIR (Centre for Research and Development on Water Resources), with an average of river flow of 16.7 m3 s−1.

3.2 Numerical examples

• For visualising the effects of stochastic rainfall on river flow according to Eq. (1), a spreadsheet model that is available from the authors on request was used in “Monte Carlo” simulations.
• Fixed values for Fp were used in combination with a stochastic Qa,t value.
• The latter was obtained from a random generator (rand) with two settings for a sinus-based daily rainfall probability: (a) one for situations that have approximately 120 rainy days, and an annual Q of around 160 mm, and (b) one that leads to around 45 rainy days and an annual total around 600 mm.

3.3 Flow persistence as a simple flood risk indicator

• From this model the effects of Fp (and hence of changes in Fp) on maximum daily flow rates, plus maximum flow totals assessed over a 2–5 days period, was obtained in a Monte Carlo process (without Markov autocorrelation of rainfall in the default case; see below).
• Relative flood protection was calculated as the difference between peak flows (assessed for 1– 5 days duration after a 1-year warm-up period) for a given Fp vs. those for Fp= 0, relative to those at Fp= 0.

3.4 An algorithm for deriving Fp from a time series of streamflow data

• Equation (3) provides a first method to derive Fp from empirical data if these cover a full hydrologic year.
• Where rainfall has clear seasonality, it is an attractive and indeed common practice to derive a groundwater recession rate from a semi-logarithmic plot of Q against time (Tallaksen, 1995).
• The authors cannot be sure, however, that this Fp,g estimate also applies in the rainy season, because overall wet-season Fp will include contributions by Fp,o and Fp,i as well (compare Eq. 9).
• Fp estimate can thus make use of the corresponding distribution of “apparent Qa” values as estimates of Fp,try, calculated as Qa,t,Fp,try =Qt −Fp,try Qt−1.
• The FlowPer Fp algorithm (van Noordwijk et al., 2011) derives the distribution of Qa,t,Fp,try estimates for a range of Fp,try values (Fig. 3b) and selects the value Fp,try that minimises the variance Var(Qa,Fp,try ) (or its standard deviation) (Fig. 3c).

3.5 Flashiness and flow separation

• Hydrographs analysed for Fp were also used for calculating the R–B flashiness index (Baker et al., 2004) by summing the absolute values of all daily changes in flow.
• Two com- mon flow separation algorithms (fixed and sliding interval methods; Furey and Gupta, 2001) were used to estimate the base-flow fraction at an annual basis.
• The average of the two was compared to Fp.

4.1 Numerical examples

• Figure 4 provides two examples, for annual river flows of around 1600 and 600 mm yr−1, of the way a change in Fp values (based on Eq. 1) influences the pattern of river flow for a unimodal rainfall regime with a well-developed dry season.
• The increasing “spikiness” of the graph as Fp is lowered, regardless of annual flow, indicates reduced predictability of flow on any given day during the wet season on the basis of the flow on the preceding day.
• A bi-plot of river flow on subsequent days for the same simulations (Fig. 5) shows two main effects of reducing the Fp value: the scatter increases, and the slope of the lower envelope containing the swarm of points is lowered (as it equals Fp).
• Both of these changes can provide entry points for an algorithm to estimate Fp from empirical time series, provided the basic assumptions of the simple model apply and the data are of acceptable quality.
• For the numerical examples shown in Fig. 4, the relative increase of the maximum daily flow when the Fp value de- www.hydrol-earth-syst-sci.net/21/2321/2017/.

4.2 Flood intensity and duration

• Two counteracting effects are at play here; a lower Fp means that a larger fraction (1−Fp) of the effective rainfall contributes to river flow, but the increased flow is less persis- Hydrol.
• In the example the flood protection in situations where the rainfall during 1 or 2 days causes the peak is slightly stronger than where the cumulative rainfall over 3–5 days causes floods, as typically occurs downstream.
• Higher initial Fp values will lead to more rapid increases in high flows for the same reduction in Fp (Fig. 6b).
• Flood duration rather responds to changes in Fp in a curvilinear manner, as flow persistence implies flood persistence (once flooding occurs), but the greater the flow persistence the less likely such a flooding threshold is passed (Fig. 6c).

4.3 Algorithm for Fp estimates from river-flow time series

• The algorithm has so far returned non-ambiguous Fp estimates on any modelled time series data of river flow, as well as for all empirical data set the authors tested (including all examples tested in Part 2, van Noordwijk et al., 2017), although there probably are data sets on which it can breakdown.
• Visual inspection of Qt−1/Qt bi-plots (as in Fig. 4) can provide clues to non-homogenous data sets, to potential situations where effective Fp depends on flow level Qt and where www.hydrol-earth-syst-sci.net/21/2321/2017/.
• Where river-flow estimates were derived from a model with random elements, however, variation in Fp estimates was observed, which suggests that specific aspects of actual rainfall, beyond the basic characteristics of a watershed and its vegetation, do have at least some effect.

4.4 Flow persistence compared to base flow and flashiness index

• Figure 7 compares results for a hydrograph of a single year for the Way Besai catchment, described in more detail in Part 2 (van Noordwijk et al., 2017).
• While there is agreement on most of what is indicated as base flow, the short-term response to peaks in the flow differ, with base flow in the Fp method more rapidly increasing after peak events.
• When compared across multiple years for four Southeast Asian catchments (Fig. 8a), there is partial agreement in the way inter-annual variation is described in each catchment, while numerical values are similar.
• Figure 8b and c compare numerical results for the R– B flashiness index with Fp for the four test catchments and for a number of hydrographs constructed as in Fig. 3a.

5.1 Salience

• To select the specific management actions that will maintain or increase Fp, a locally calibrated land use/hydrology model is needed, such as GenRiver (Part 2, van Noordwijk et al., 2017), DHV (Bergström, 1995) or SWAT (Yen et al., 2015).
• The definition of watershed health, like that of human health has evolved over time.

5.2 Credibility

• If rainfall data exist, and especially rainfall data that apply to each subcatchment, the Qa term does not have to be treated as a random variable and event-specific information on the flow pathways may be inferred for a more precise account of the hydrograph.
• The slope of the log–log relationship between flow recession (dQ/dt) and Q that Kirchner (2009) used is conceptually similar to the Fp metric the authors derived here, but the specific algorithm to derive the parameter from empirical data differs.
• The main advantage of continuing with the flashiness index is that there is an empirical basis for comparisons and the index has been included in existing “watershed health” monitoring programmes, especially in the USA.
• Their study indicated that results may differ significantly between catchments and critical tests of Fp across multiple situations are obviously needed, as Part 2 (van Noordwijk et al., 2017) will provide.

5.3 Legitimacy

• By focusing on observable effects at river level, rather than prescriptive recipes for land cover (“Reforestation”), the Fp parameter can be used to more effectively compare the combined effects of land cover change, changes in the riparian wetlands and engineered water storage reservoirs, in their effect on flow buffering.
• Therefore, it can be used as part of a negotiation support approach to natural resources management in which levelling off on knowledge and joint fact finding in blame attribution are key steps to negotiated solutions that are legitimate and seen to be so (van Noordwijk et al., 2013; Leimona et al., 2015).
• But the most challenging aspect of the management of flood, as any other environmental risk, is that the frequency of dis- www.hydrol-earth-syst-sci.net/21/2321/2017/.
• Hydrol. Earth Syst. Sci., 21, 2321–2340, 2017 asters is too low to intuitively influence human behaviour where short-term risk-taking benefits are attractive.

6 Conclusions

• In conclusion, the Fp metric appears to allow for an efficient way of summarising complex landscape processes into a single parameter that reflects the effects of landscape management within the context of the local climate.
• Flow persistence is the result of rainfall persistence and the temporal delay provided by the pathway water takes through the soil and the river system.
• Further tests for specific case studies can clarify how changes in tree cover (deforestation, reforestation and agroforestation) in different contexts influence river-flow dynamics and Fp values.
• Several colleagues contributed to the development and early tests of the Fp method.
• Edited by: J. Seibert Reviewed by: D. C. Le Maitre and two anonymous referees.

Figures

Figure 5. Panels (a) and (b) show the temporal autocorrelation of river flow for the same simulations as Fig. 4; the lower envelope of the points indicated slope Fp, the points above this line the effect of fresh additions to river flow.

Figure 1. (a) Multiple perspectives on the way flood risk is to be understood, monitored and handled according to different knowledge systems; (b) basic requirements for a “metric” to be used in public discussions of natural resource management issues that deserve to be resolved and acted upon (modified from van Noordwijk et al., 2016).

Figure 4. Effects of the Fp parameter on hydrographs of daily river flow generated by a random rainfall generator, with persistent and additional flow components indicated, for two settings with total rainfall of approximately 1600 and 600 mm yr−1 (NB river flow is here expressed as mm day−1 rather than as m3 s−1 as in Fig. 3).

Figure 8. (a) Comparison of yearly data for four Southeast Asian watersheds analysed with common flow separation methods (average of results in Fig. 7) and the flow persistence method and comparison of the Richards–Baker flashiness index (Baker et al., 2004) and the flow persistence metric Fp for (b) four Southeast Asian watersheds, (c) a series of hydrographs as in Fig. 4a, with five replicates per Fp value.

Figure 2. Steps in a causal pathway that relates the salience of “avoided flood damage as ecosystem service” to the interaction of exposure (1; being in the wrong place at critical times), hazard (2; spatially explicit flood frequency and duration) and human determinants of vulnerability (3); the hazard component depends, in common scientific analysis, on the pattern of river flow described in a hydrograph (4), which in turn is understood to be influenced by conditions along the river channel (5), precipitation and potential evapotranspiration (Epot) as climatic factors (6) and the condition in the watershed (7) determining evapotranspiration (Eact), temporary water storage (1S) and water partitioning over overland flow and infiltration; these watershed functions in turn depend on the interaction of terrain (topography, soils, geology), vegetation and human land use; current understanding of a two-way interaction between vegetation and rainfall adds further complexity (8).

Figure 6. (a) Effects of flow persistence on the relative flood protection (decrease in maximum flow measured over a 1–5 day period relative to a case with Fp= 0 (a few small negative points were replaced by small positive values to allow for the exponential fit); (b) and (c) effects of a decrease in flow persistence on the volume of water involved in peak flows (b; relative to the volume at Fp is 0.6–0.9) and in the duration (in days) of floods (c).

Figure 7. Comparison of base-flow separation of a hydrograph according to the flow persistence method (a) and two common flow separation methods, respectively, with fixed (b) and sliding intervals (c).

Figure 3. Example of the derivation of best-fitting Fp,try value for an example hydrograph (a) on the basis of the inferred Qa distribution (cumulative frequency in b), and three properties of this distribution (c): its sum, frequency of negative values and standard deviation; the Fp,try minimum of the latter is derived from the parameters of a fitted quadratic equation.

Table 1. Comparison of properties of the flashiness index and flow persistence Fp.

Full text

Hydrol. Earth Syst. Sci., 21, 2321–2340, 2017
www.hydrol-earth-syst-sci.net/21/2321/2017/
doi:10.5194/hess-21-2321-2017
© Author(s) 2017. CC Attribution 3.0 License.
Flood risk reduction and flow buffering as ecosystem services –
Part 1: Theory on flow persistence, flashiness and base flow
Meine van Noordwijk
1,2
, Lisa Tanika
1
, and Betha Lusiana
1
1
World Agroforestry Centre, Bogor, Indonesia
2
Plant Production System, Wageningen University, Wageningen, the Netherlands
Correspondence to: Meine van Noordwijk (m.vannoordwijk@cgiar.org)
Received: 15 December 2015 – Discussion started: 19 January 2016
Revised: 24 March 2017 – Accepted: 4 April 2017 – Published: 5 May 2017
Abstract. Flood damage reflects insufficient adaptation of
human presence and activity to location and variability of
river flow in a given climate. Flood risk increases when land-
scapes degrade, counteracted or aggravated by engineering
solutions. Efforts to maintain and restore buffering as an
ecosystem function may help adaptation to climate change,
but this require quantification of effectiveness in their spe-
cific social-ecological context. However, the specific role of
forests, trees, soil and drainage pathways in flow buffering,
given geology, land form and climate, remains controversial.
When complementing the scarce heavily instrumented catch-
ments with reliable long-term data, especially in the trop-
ics, there is a need for metrics for data-sparse conditions.
We present and discuss a flow persistence metric that re-
lates transmission to river flow of peak rainfall events to the
base-flow component of the water balance. The dimension-
less flow persistence parameter F
p
is defined in a recursive
flow model and can be estimated from limited time series of
observed daily flow, without requiring knowledge of spatially
distributed rainfall upstream. The F
p
metric (or its change
over time from what appears to be the local norm) matches
local knowledge concepts. Inter-annual variation in the F
p
metric in sample watersheds correlates with variation in the
“flashiness index” used in existing watershed health monitor-
ing programmes, but the relationship between these metrics
varies with context. Inter-annual variation in F
p
also corre-
lates with common base-flow indicators, but again in a way
that varies between watersheds. Further exploration of the
responsiveness of F
p
in watersheds with different character-
istics to the interaction of land cover and the specific reali-
sation of space–time patterns of rainfall in a limited obser-
vation period is needed to evaluate interpretation of F
p
as an
indicator of anthropogenic changes in watershed conditions.
1 Introduction
Floods can be the direct result of reservoir dams, log jams
or protective dykes breaking, with water derived from un-
expected heavy rainfall, rapid snowmelt, tsunamis or coastal
storm surges. We focus here on floods that are associated, at
least in the public eye, with watershed degradation. Degrada-
tion of watersheds and its consequences for river-flow regime
and flooding intensity and frequency are a widespread con-
cern (Brauman et al., 2007; Bishop and Pagiola, 2012; Win-
semius et al., 2013). Engineering measures (dams, reservoirs,
canalisation, dykes, and flow regulation) can significantly al-
ter the flow regime of rivers, and reduce the direct relation-
ship with landscape conditions in the (upper) catchment (Poff
et al., 1997). The life expectancy of such structures depends,
however, on the sediment load of incoming rivers and thus on
upper watershed conditions (Graf et al., 2010). Where “flow
regulation” has been included in efforts to assess an eco-
nomic value of ecosystem services, it can emerge as a major
component of overall value; the economic damage of floods
to cities build on floodplains can be huge and the benefits
of avoiding disasters thus large (Farber et al., 2002; Turner
and Daily, 2008; Brauman et al., 2007). The “counter fac-
tual” part of any avoided damage argument, however, de-
pends on metrics that are transparent in their basic concept
and relationship with observables. Basic requirements for a
metric to be used in managing issues of public concern in
a complex multi-stakeholder environment are that it (i) has
Published by Copernicus Publications on behalf of the European Geosciences Union.

2322 M. van Noordwijk et al.: Flood risk reduction and flow buffering as ecosystem services – Part 1
Figure 1. (a) Multiple perspectives on the way flood risk is to be understood, monitored and handled according to different knowledge
systems; (b) basic requirements for a “metric” to be used in public discussions of natural resource management issues that deserve to be
resolved and acted upon (modified from van Noordwijk et al., 2016).
a direct relationship with a problem that needs to be solved
(“salience”), (ii) is aligned with current science-based under-
standing of how the underpinning systems function and can
be managed (“credibility”) and (iii) can be understood from
local and public/policy perspectives (“legitimacy”) (Clark et
al., 2016). Figure 1 summarises these requirements, building
on van Noordwijk et al. (2016).
In the popular discussion on floods, especially in the trop-
ics, a direct relationship with deforestation and reforestation
is still commonly perceived to dominate, and forest cover
is seen as salient and legitimate metric of watershed quality
(or of urgency of restoration where it is low). A requirement
for 30 % forest cover, is for example included in the spatial
planning law in Indonesia in this context (Galudra and Sir-
ait, 2009). Yet, rivers are probably dominated by the other
70 % of the landscape. There is a problem with the credibil-
ity of assumed deforestation–flood relations (van Noordwijk
et al., 2007; Verbist et al., 2010), beyond the local scales
(< 10 km
2
) of paired catchments where ample direct em-
pirical proof exists, especially in non-tropical climate zones
(Bruijnzeel, 1990, 2004). Current watershed rehabilitation
programmes that focus on increasing tree cover in upper
watersheds are only partly aligned with current scientific
evidence of effects of large-scale tree planting on stream-
flow (Ghimire et al., 2014; Malmer et al., 2010; Palmer,
2009; van Noordwijk et al., 2015a). The relationship be-
tween floods and change in forest quality and quantity, and
the availability of evidence for such a relationship at vari-
ous scales has been widely discussed over the past decades
(Andréassian, 2004; Bruijnzeel, 2004; Bradshaw et al., 2007;
van Dijk et al., 2009). Measurements in Cote d’Ivoire, for
example, showed strong scale dependence of runoff from
30 to 50 % of rainfall at 1 m
2
point scale, to 4 % at 130 ha
watershed scale, linked to spatial variability of soil proper-
Hydrol. Earth Syst. Sci., 21, 2321–2340, 2017 www.hydrol-earth-syst-sci.net/21/2321/2017/

M. van Noordwijk et al.: Flood risk reduction and flow buffering as ecosystem services – Part 1 2323
ties plus variations in rainfall patterns (Van de Giesen et al.,
2000). The ratio between peak and average flow decreases
from headwater streams to main rivers in a predictable man-
ner; while mean annual discharge scales with (area)
1.0
, max-
imum river flow was found to scale with (area)
0.4
to (area)
0.7
on average (Rodríguez-Iturbe and Rinaldo, 2001; van Noord-
wijk et al., 1998; Herschy, 2002), with even lower powers for
area in flash floods that are linked to an extreme rainfall event
over a restricted area (Marchi et al., 2010). The determinants
of peak flow are thus scale dependent, with space–time cor-
relations in rainfall interacting with subcatchment-level flow
buffering at any point along the river. Whether and where
peak flows lead to flooding depends on the capacity of the
rivers to pass on peak flows towards downstream lakes or the
sea, assisted by riparian buffer areas with sufficient storage
capacity (Di Baldassarre et al., 2013). Reducing local flood-
ing risk by increased drainage increases flooding risk down-
stream, challenging the nested-scales management of water-
sheds to find an optimal spatial distribution, rather than min-
imisation, of flooding probabilities. Well-studied effects of
forest conversion on peak flows in small upper stream catch-
ments (Bruijnzeel, 2004; Alila et al., 2009) do not necessar-
ily translate to flooding downstream. With most of the pub-
lished studies still referring to the temperate zone, the sit-
uation in the tropics (generally in the absence of snow) is
contested (Bonell and Bruijnzeel, 2005). As summarised by
Beck et al. (2013), meso- to macroscale catchment studies
(> 1 and > 10 000 km
2
, respectively) in the tropics, subtrop-
ics and warm temperate regions have mostly failed to demon-
strate a clear relationship between river flow and change in
forest area. Lack of evidence cannot be firmly interpreted
as evidence for lack of effect, however. Detectability of ef-
fects depends on their relative size, the accuracy of the mea-
surement devices, length of observation period, and back-
ground variability of the signal. A recent econometric study
for Peninsular Malaysia by Tan-Soo et al. (2016) concluded
that, after appropriate corrections for space–time correlates
in the data set for 31 meso- and macroscale basins (554–
28 643 km
2
), conversion of inland rain forest to monocultural
plantations of oil palm or rubber increased the number of
flooding days reported, but not the number of flood events,
while conversion of wetland forests to urban areas reduced
downstream flood duration. This Malaysian study may be
the first credible empirical evidence at this scale. The differ-
ence between results for flood duration and flood frequency
and the result for draining wetland forests warrant further
scrutiny. Consistency of these findings with river-flow mod-
els based on a water balance and likely pathways of water
under the influence of change in land cover and land use has
yet to be shown. Two recent studies for southern China con-
firm the conventional perspective that deforestation increases
high flows, but are contrasting in effects of reforestation.
Zhou et al. (2010) analysed a 50-year data set for Guangdong
Province in China and concluded that forest recovery had not
changed the annual water yield (or its underpinning water
balance terms precipitation and evapotranspiration), but had
a statistically significant positive effect on dry-season (low)
flows. Liu et al. (2015), however, found for the Meijiang wa-
tershed (6983 km
2
) in subtropical China that while histori-
cal deforestation had decreased the magnitudes of low flows
(daily flows ≤ Q
95 %
) by 30.1 %, low flows were not signif-
icantly improved by reforestation. They concluded that re-
covery of low flows by reforestation may take a much longer
time than expected probably because of severe soil erosion
and resultant loss of soil infiltration capacity after deforesta-
tion. Changes in river-flow patterns over a limited period of
time can be the combined and interactive effects of variations
in the local rainfall regime, land cover effects on soil struc-
ture and engineering modifications of water flow that can be
teased apart with modelling tools (Ma et al., 2014).
Lacombe et al. (2016) documented that the hydrological
effects of natural regeneration differ from those of plantation
forestry, while forest statistics do not normally differentiate
between these different land covers. In a regression study of
the high- and low-flow regimes in the Volta and Mekong river
basins, Lacombe and McCartney (2016) found that in the
variation among tributaries various aspects of land cover and
land cover change had explanatory power. Between the two
basins, however, these aspects differed. In the Mekong basin,
variation in forest cover had no direct effect on flows, but
extending paddy areas resulted in a decrease in downstream
low flows, probably by increasing evapotranspiration in the
dry season. In the Volta River basin, the conversion of forests
to crops (or a reduction of tree cover in the existing parkland
system) induced greater downstream flood flows. This ob-
servation is aligned with the experimental identification of
an optimal, intermediate tree cover from the perspective of
groundwater recharge in parklands in Burkina Faso (Ilstedt
et al., 2016).
The statistical challenges of attribution of cause and effect
in such data sets are considerable with land use/land cover ef-
fects interacting with spatially and temporally variable rain-
fall, geological configuration and the fact that land use is not
changing in random fashion or following any pre-randomised
design (Alila et al., 2009; Rudel et al., 2005). Hydrologi-
cal analysis across 12 catchments in Puerto Rico by Beck
et al. (2013) did not find significant relationships between
the change in forest cover or urban area, and change in var-
ious flow characteristics, despite indications that regrowing
forests increased evapotranspiration.
These observations imply that the percent of tree cover (or
other forest-related indicators) is probably not a good met-
ric for judging the ecosystem services provided by a wa-
tershed (of different levels of “health”), and that a metric
more directly reflecting changes in river flow may be needed.
Here we will explore a simple recursive model of river flow
(van Noordwijk et al., 2011) that (i) is focused on (loss of)
flow predictability, (ii) can account for the types of results
obtained by the cited recent Malaysian study (Tan-Soo et al.,
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2324 M. van Noordwijk et al.: Flood risk reduction and flow buffering as ecosystem services – Part 1
Figure 2. Steps in a causal pathway that relates the salience of “avoided flood damage as ecosystem service” to the interaction of exposure
(1; being in the wrong place at critical times), hazard (2; spatially explicit flood frequency and duration) and human determinants of vulnera-
bility (3); the hazard component depends, in common scientific analysis, on the pattern of river flow described in a hydrograph (4), which in
turn is understood to be influenced by conditions along the river channel (5), precipitation and potential evapotranspiration (E
pot
) as climatic
factors (6) and the condition in the watershed (7) determining evapotranspiration (E
act
), temporary water storage (1S) and water partition-
ing over overland flow and infiltration; these watershed functions in turn depend on the interaction of terrain (topography, soils, geology),
vegetation and human land use; current understanding of a two-way interaction between vegetation and rainfall adds further complexity (8).
2016), and (iii) may constitute a suitable performance indi-
cator to monitor watershed health through time.
Before discussing the credibility dimension of river-flow
metrics, the way these relate to the salience and legitimacy
issues around “flood damage” as policy issues needs atten-
tion. The salient issue of “flood damage” is compatible with a
common dissection of risk as the product of exposure, hazard
and vulnerability (steps 1–3 in Fig. 2). Many aspects beyond
forests and tree cover play a role; in fact these factors are
multiple steps away (step 7a) from the direct river-flow dy-
namics that determine floods. Extreme discharge events plus
river-level engineering (steps 4 and 5) co-determine hazard
(step 2), while exposure (step 1) depends on topographic po-
sition interacting with human presence, and vulnerability can
be modified by engineering at a finer scale and be further re-
duced by advice to leave an area in high-risk periods. A re-
cent study (Jongman et al., 2015) found that human fatalities
and material losses between 1980 and 2010 expressed as a
share of the exposed population and gross domestic product
were decreasing with rising income. The planning needed to
avoid extensive damage requires quantification of the risk of
higher than usual discharges, especially at the upper tail end
of the flow frequency distribution.
The statistical scarcity, per definition, of “extreme events”
and the challenge of data collection where they do occur,
make it hard to rely on site-specific empirical data as such.
Inference of risks needs some trust in extrapolation methods,
as is often provided by use of trusted underlying mechanisms
and/or data obtained in a geographical proximity. Existing
data on flood frequency and duration, as well as human and
economic damage are influenced by topography, soils, hu-
man population density and economic activity, responding to
engineered infrastructure (step 5 in Fig. 2), as well as the ex-
treme rainfall events that are their proximate cause (step 6).
Subsidence due to groundwater extraction in urban areas of
high population density is a specific problem for a number of
cities built on floodplains (such as Jakarta and Bangkok), but
subsidence of drained peat areas has also been found to in-
crease flooding risks elsewhere (Sumarga et al., 2016). Com-
mon hydrological analysis of flood frequency (called 1 in 10-
, 1 in 100- and 1 in 1000-year flood events, for example) re-
lies on direct observations at step 4 in Fig. 2, but typically
requires spatial extrapolation beyond points of data collec-
tion through river-flow models that combine at least steps 5
and 6. Relatively simple ways of including the conditions in
the watershed (step 7) in such models rely on the runoff curve
number method (Ponce and Hawkins, 1996) and the SWAT
(soil water assessment tool) model that was built on its foun-
dation (Gassman et al., 2007). Applications on tropical soils
have had mixed success (Oliveira et al., 2016). Describing
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M. van Noordwijk et al.: Flood risk reduction and flow buffering as ecosystem services – Part 1 2325
peak flows as a proportion of the rainfall event that triggered
them has a long history, but where the proportionality factors
are estimated for ungauged catchments results may be unre-
liable (Efstratiadis et al., 2014). More refined descriptions of
the infiltration process (step 7b) are available, using recursive
models as filters on empirical data (Grimaldi et al., 2013), but
data for this approach may not be generally available. Ac-
cording to Van den Putte et al. (2013) the Green–Ampt infil-
tration equation can be fitted to data for dry conditions when
soil crusts limit infiltration, but not in wet winter conditions.
These authors argued that simpler models may be better.
Analysis of likely change in flood frequencies in the
context of climate change adaptation has been challenging
(Milly et al., 2002; Ma et al., 2014). There is a lack of
simple performance indicators for watershed health at its
point of relating precipitation P and river flow Q (step 4 in
Fig. 2) that align with local observations of river behaviour
and concerns about its change and that can reconcile local,
public/policy and scientific knowledge, thereby helping ne-
gotiated change in watershed management (Leimona et al.,
2015). The behaviour of rivers depends on many climatic
(step 6 in Fig. 2) and terrain factors (step 7a–d in Fig. 2) that
make it a challenge to differentiate between human-induced
ecosystem structural change and soil degradation (step 7b)
on the one hand and intrinsic variability on the other. Step 8
in Fig. 2 represents not only the direct influence of climate
on vegetation but also a possible reverse influence (van No-
ordwijk et al., 2015b). Hydrological models tend to focus on
predicting hydrographs at one or more temporal scales, and
are usually tested on data sets from limited locations. Despite
many decades (if not centuries) of hydrological modelling,
current hydrologic theory, models and empirical methods
have been found to be largely inadequate for sound predic-
tions in ungauged basins (Hrachowitz et al., 2013). Efforts
to resolve this through harmonisation of modelling strategies
have so far failed. Existing models differ in the number of
explanatory variables and parameters they use, but are gen-
erally dependent on empirical data of rainfall that are avail-
able for specific measurement points but not at the spatial
resolution that is required for a close match between mea-
sured and modelled river flow. Spatially explicit models have
conceptual appeal (Ma et al., 2010) but have too many de-
grees of freedom and too many opportunities for getting right
answers for wrong reasons if used for empirical calibration
(Beven, 2011). Parsimonious, parameter-sparse models are
appropriate for the level of evidence available to constrain
them, but these parameters are themselves implicitly influ-
enced by many aspects of existing and changing features of
the watershed, making it hard to use such models for scenario
studies of changing land use and change in climate forc-
ing. Here we present a more direct approach deriving a met-
ric of flow predictability that can bridge local concerns and
concepts to quantified hydrologic function: the “flow persis-
tence” parameter as directly observable characteristic (step 4
in Fig. 2), which can be logically linked to the primary points
of intervention in watershed management, interacting with
climate and engineering-based change.
In this contribution to the debate, we will first define the
metric “flow persistence” in the context of temporal autocor-
relation of river flow and then derive a way to estimate its
numerical value. In Part 2 (van Noordwijk et al., 2017) we
will apply the algorithm to river-flow data for a number of
contrasting meso-scale watersheds. In the discussion of this
paper, we will consider the new flow persistence metric in
terms of three groups of criteria for usable knowledge (Fig. 1,
Clark et al., 2016; Lusiana et al., 2011; Leimona et al., 2015)
based on salience (i, ii), credibility (ii, iv) and legitimacy (v–
vii):
i. Does flow persistence relate to important aspects of wa-
tershed behaviour, complementing existing metrics such
as the “flashiness index” and “base-flow separation”
techniques?
ii. Does its quantification help to select management ac-
tions?
iii. Is there consistency of numerical results?
iv. How sensitive is it to bias and random error in data
sources?
v. Does it match local knowledge?
vi. Can it be used to empower local stakeholders of water-
shed management?
vii. Can it inform local risk management?
2 Flow persistence in water balance equations
2.1 Recursive model
One of the easiest-to-observe aspects of a river is its day-
to-day fluctuation in water level, related to the volumetric
flow (discharge) via rating curves (Maidment, 1992). With-
out knowing details of upstream rainfall and the pathways the
rain takes to reach the river, observation of the daily fluctu-
ations in water level allows for important inferences to be
made. It is also of direct utility; sudden rises can lead to
floods without sufficient warning, while rapid decline makes
water utilisation difficult. Indeed, a common local descrip-
tion of watershed degradation is that rivers become more
“flashy” and less predictable, having lost a buffer or “sponge”
effect (Joshi et al., 2004; Ranieri et al., 2004; Rahayu et al.,
2013). A simple model of river flow at time t, Q
t
, is that it is
similar to that of the day before (Q
t−1
), multiplied with F
p
, a
dimensionless parameter called “flow persistence” (van No-
ordwijk et al., 2011), plus an additional stochastic term Q
a,t
:
Q
t
= F
p
Q
t−1
+ Q
a,t
. (1)
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Frequently asked questions

Q1. What are the primary determinants of cumulative low flows?

The combination of climate, geology and land form are the primary determinants of cumulative low flows, but if land cover reduces the recharge of groundwater there may be impacts on dry-season flow, that are not directly reflected in Fp. 

Q2. What is the main advantage of including Fp?

The main advantage of including Fp is that it can be estimated from incomplete flow records, has a clear link to peak flow events and has a more direct relationship with under-lying flow pathways, changes in rainfall (or snowmelt) and evapotranspiration, reflecting land cover change. 

Q3. What is the importance of planning to avoid extensive damage?

The planning needed to avoid extensive damage requires quantification of the risk of higher than usual discharges, especially at the upper tail end of the flow frequency distribution. 

Q4. What is the method for estimating groundwater recession rates?

Where rainfall has clear seasonality, it is an attractive and indeed common practice to derive a groundwater recession rate from a semi-logarithmic plot of Q against time (Tallaksen, 1995). 

Q5. What is the effect of rainfall on flashiness?

If Etx is negatively affected (either by a change in vegetation or by insufficient buffering, reducing water availability on non-rainfall days) flashiness will increase, beyond the main effects on Fp. 

Q6. What is the role of extreme discharge events in determining flood risk?

Extreme discharge events plus river-level engineering (steps 4 and 5) co-determine hazard (step 2), while exposure (step 1) depends on topographic position interacting with human presence, and vulnerability can be modified by engineering at a finer scale and be further reduced by advice to leave an area in high-risk periods. 

Q7. What is the relationship between the expected fraction of rainfall and the amount of rainfall?

While the expected fraction of rainfall that contributes to direct flow is linearly related to rainfall via (1−Fp), flooding risk as such will have a non-linear relationship with rainfall, which depends on topography and antecedent rainfall. 

Q8. What is the effect of a lower Fp on the flow of a river?

Two counteracting effects are at play here; a lower Fp means that a larger fraction (1−Fp) of the effective rainfall contributes to river flow, but the increased flow is less persis-Hydrol. 

Q9. What other watersheds were used to study the variation of the Fp metric?

Data from three other watersheds were used to explore the variation of Fp across multiple years and its relationship with the flashiness index: Bialo (111.7 km2) in South Sulawesi, Indonesia, with agroforestry as the dominant land cover type, Cidanau (241.6 km2) in West Java, Indonesia, dominated by mixed agroforestry land uses but with a peat swamp before the final outlet and Mae Chaem (3892 km2) in northern Thailand, part of the upper Ping Basin, and dominated by evergreen, deciduous and pine forest. 

Q10. What is the basis for differentiating Fp within a hydrologic year?

Analysis of the way an aggregate Fp depends on the dominant flow pathways provides a basis for differentiating Fp within a hydrologic year. 

Q11. What is the way to interpret the effects of land cover change on flow persistence?

With current methods, it seems that effects of land cover change on flow persistence that shift the Fp value by about 0.1 are the limit of what can be asserted from empirical data (with shifts of that order in a single year a warning sign rather than a firmly established change). 

Q12. Where did the flashiness index differ from the other two variables?

Where hydrographs were generated with a simple flow model with the Fp parameter as key variable, the flashiness index is more tightly related, especially for higher Fp values, than where both flashiness index and Fp were derivedHydrol. 

Q13. What is the main difference between the Fp parameter and the hydrograph?

As the Fp parameter captures the predictability of river flow that is a key aspect of degradation according to local knowledge systems, its results are much easier to convey than full hydrographs or exceedance probabilities of flood levels. 

Q14. What is the relationship between Fp and flood risk?

Catchment changes, such as increases or decreases in percentage tree cover, will generally have a non-linear relationship with Fp as well as with flooding risks. 

Q15. What can be the main reason for the change in river flow patterns?

Changes in river-flow patterns over a limited period of time can be the combined and interactive effects of variations in the local rainfall regime, land cover effects on soil structure and engineering modifications of water flow that can be teased apart with modelling tools (Ma et al., 2014).