The effects of riparian forest restoration on catchment scale flood risk and flood hydrology
Simon J. Dixon Birmingham Institute of Forest Research, GEES, University of Birmingham [email protected]
David Sear, Nick Odoni, Tim Sykes & Stuart Lane
Context - Flooding
Increasing exposure
• Climate change predictions are for increasing extreme
precipitation events (IPCC 5th Assesment report, 2013).
• Events very likely to be more intense and more frequent.
• High confidence intensity of extreme precipitation events will increase.
• Increasing encroachment of properties & infrastructure onto
floodplain – 87 developments of 560 houses built in areas of
“high flood risk” & opposed by EA (FOI by Independent on
Sunday)
• Pressure on funding for flood protection
Flood mitigation
• Conventional approach is construction of structural defences to
protect at risk areas.
• With increasing risk exposure this approach is recognised as both
unsustainable and undesirable.
• Non-structural flood mitigation strategies can form a part of
future flood defence.
– Land use/afforestation
– Floodplain connectivity
– Channel complexity
• Slowing the passage of water
through the upper catchment.
River Restoration
• UK Environment Agency were particularly keen to see if they
could link river restoration programmes with flood mitigation.
• Focused on two river restoration scenarios:
– Restoring patches of forest in a catchment
– Adding “Engineered Logjams” (ELJs) to sections of river channel
• Questions
– Can river restoration projects affect flood hydrology?
– If so; can we make recommendations for how river restoration schemes
could be tailored/optimised to mitigate flood risk in the catchment.
Millington, 2007
Forest Restoration
• Land cover/land use exerts a strong effect on flow paths &
temporary storage capacity within a catchment.
• Increasing forest cover could mitigate flood risk through:
– Increasing interception (Robinson et al., 2003)
– Increasing infiltration (Bracken & Croke, 2007)
– Increasing temporary storage(Ghavasieh et al., 2006)
– Slowing conveyance of water (Lane et al., 2007, Thomas & Nisbet, 2007)
– Attenuating runoff (Hundecha & Bárdossy, 2004; Broadmeadow &
Nisbet, 2009)
Slowing flow
• Thomas & Nisbet (2007), modelled
a 1% recurrence flood in both
HEC-RAS (1D) and River2D.
• Woodland slowed flows and a 50ha
block of floodplain woodland
slowed flood wave travel by 30 mins
and increased storage by 15%
Similar results found by Ghavasieh
et al. (2006)
Logjam hydrology
• The mechanism by which logjams alter flood hydrology is
through increasing local hydraulic resistance/roughness, which
slows velocity and increases water levels locally.
• Where water levels are raised sufficiently this may trigger
overbank flow during a flood event where it may not otherwise
occur. Provided the floodplain surface is complex (especially
forests) this can then act as additional temporary storage.
Large wood & Logjams
• Very little large wood in UK rivers – legacy of management
• Installation of Engineered Logjams (ELJs) is widespread in river
restoration. Typically installation is piecemeal and opportunistic
Image from Brooks, 2006: Design guideline for the reintroduction of
wood into Australian streams
Artificial logjam, Thomas & Nisbet, 2012
• Empirical evidence
from river
restoration in the
New Forest UK
showed logjam
installation can
increase flood
wave travel time.
• Similar effects also
found by Thomas
& Nisbet (2012)
Logjam hydrology
Dixon et al, (In Review)
modelling the introduction of ELJs in a 9.2km2 catchment during a
1% flood event.
Catchment hydrology
• There are some confounding factors which contribute to a lack
of consensus on the effects of forests, particularly at a
catchment scale
• Variability in climatic factors are the same order of magnitude as
variations in land use changes, so hard to conclusively unpick
paired catchment data.
• Majority of data comes from catchments <10km2 or from
modelling of discrete reaches, rather than whole catchments
• Spatial configuration of land cover can be more important than
area
OVERFLOW
• Modelling conducted using the spatially distributed flood model OVERFLOW to explore effects of variety of restoration scenarios on flood hydrology.
• Uses spatially distributed unit hydrograph approach (see Maidment, 1993; Maidment et al., 1996; Olivera and Maidment, 1999; Saghafian et al., 2002; Liu et al., 2003; Du et al., 2009)
• Focus is on very wet events with runoff >70% - contribution to peak flow of rapid runoff pathways.
• More details on the model in Odoni & Lane, 2010, Dixon, 2013 and Dixon et al. (in review)
• Although based/calibrated on a gauged event, modelling is primarily a heuristic exercise.
Study Catchment/Event
• Lymington River catchment (98km2)
• 20m resolution DEM from lidar
• Moderate flood event
Scenarios
Scenario
Number
Scenario Channel Hydraulic
Resistance
Floodplain Hydraulic
Resistance
1 Engineered Logjams n=0.20 (in individual
grid cells)
n=0.07 (calibration
value)
2 25 year forest growth
(forest only)
n=0.05 (calibration
value)
n=0.10
3 50 year forest
growth(forest only)
n=0.05 (calibration
value)
n=0.12
4 100 year forest growth
(forest only)
n=0.05 (calibration
value)
n=0.15
5 25 year forest growth
n=0.06 n=0.10
6 50 year forest growth
n=0.075 n=0.12
7 100 year forest growth
n=0.10 n=0.15
• Channel Manning’s
n calculated using
variable power
equation (Ferguson,
2007, 2010).
• Logjam values
based on fieldwork
(Kitts, 2010; Dixon,
2013)
• 435 simulations per
scenario = 3045
model runs
Scenarios
• Forest restoration
scenarios based on
conceptual model of
Dixon, 2013. Based on
numerical modelling of
riparian forest growth
Results - ELJs
• Results for single
segments with ELJs show
high spatial variability in
response, but no clear
spatial pattern.
Dixon et al, (In Review)
Results - Forest
• In contrast to ELJs there is a much clearer spatial pattern for segment based forest restoration, which is reinforced as the forest ages.
• More distal reaches tend to decrease peak discharge, whereas hydrologically proximal reaches tend to increase peak discharge
Dixon et al, (In Review)
Explanatory Variables
• Slope: kinematic wave >0.01m/m
• Weak correlation between segment
length & area vs change in peak
Slope
• Slope relationship easier to see when absolute values of change used.
• No change at slopes steeper than 0.008m/m. (0.005m/m with ELJs). Slightly steeper than the 0.001m/m suggested by Sholtes & Doyle (2011) for transfer to kinematic flood wave
Dixon et al, (In Review)
Results - ELJs
• As proportion of network restored increases the magnitude of change in peak discharge tends to increase.
• The directionality of change however is not predictable.
• The hydrograph responds in a variety of ways; some model runs lead to simple scaling, whereas as other result in broader, shallower peaks.
Results - Forests
• Much clearer relationship between extent of restoration and flood response than for ELJs.
• Below 20% there is only a small change in peak discharge.
• Between 20-50% there is a large, predictable decrease in peak discharge, proportional to area restored.
• Above 50% a smaller reduction in peak magnitude.
Conceptual Model
Dixon et al, (In Review)
Possible Restoration Scenario
• Example of a sub-catchment
15% of total catchment area
(~14.5km2).
• Model of 25yr forest growth
suggests a 6% reduction in
peak magnitude.
• The reduction in peak
discharge becomes gradually
larger as the forest ages.
Conclusions
• Although both increases to in-channel large wood loads and floodplain complexity at the reach scale is capable of attenuating local flood waves, the effect of this local attenuation at the catchment scale is highly variable
• Restoration hydrologically proximal to the catchment outflow tends to increase peak discharge.
• Floodplain forest restoration in the upper and middle parts of the catchment tend to either decrease peak discharge at the catchment outflow, or have no effect
• Results indicate that engineered logjams applied to reaches of 1-5km will result in changes to flood peak magnitude of up to ±4%.
Restoration design implications
• The insertion of engineered logjams cannot be counted on to reduce flood risk at the catchment scale
• Where engineered logjams are planned in a flood sensitive area, detailed hydrological modelling should be conducted to determine the potential effects
• The most promising scenarios for balancing substantial reductions in flood risk with a practicality of implementation, is to restore floodplain forests and allow these to naturally increase the loading of large wood to the channel over time.
• Where floodplain forest restoration is modelled at the sub-catchment scale with 10-15% of the catchment restored, reductions of up to 6% in peak discharge can be seen at the catchment outflow after 25 years of forest growth
Bracken, L.J., Croke, J., 2007. The concept of hydrological connectivity and its contribution to understanding runoff-dominated geomorphic systems. Hydrological processes, 21(13),
1749-1763.
Broadmeadow, S., Nisbet, T.R., 2009. Opportunity Mapping for Woodland to Reduce Flooding in the Yorkshire & the Humber Region, Forest Research.
Brooks, A.P., Abbe, T.B., Cohen, T., Marsh, N., Mika, S., Boulton, A., Broderick, T., Borg, D., Rutherfurd, I.D., 2006. Design guideline for the reintroduction of wood into
Australian streams. Land & Water Australia, Canberra, Australia.
Dixon, S.J., 2013. Investigating the effects of large wood and forest management on flood risk and flood hydrology. Ph.D., University of Southampton, Southampton, 406 pp.
Du, J., Xie, H., Hu, Y., Xu, Y., Xu, C., 2009. Development and testing of a new storm runoff routing approach based on time variant spatially distributed travel time method. Journal
of Hydrology, 369(1-2), 44-54.
Ferguson, R.I., 2007. Flow resistance equations for gravel-and boulder-bed streams. Water resources research., 43, W05427.
Ferguson, R.I., 2010. Time to abandon the Manning equation? Earth Surface Processes and Landforms, 35(15), 1873-1876.
Ghavasieh, A.R., Poulard, C., Paquier, A., 2006. Effect of roughened strips on flood propagation: Assessment on representative virtual cases and validation. Journal of Hydrology,
318(1), 121-137.
Hundecha, Y., Bárdossy, A., 2004. Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model.
Journal of Hydrology, 292(1), 281-295.
IPCC, 2013. Climate Change 2013 The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
Cambridge University Press, Cambridge, UK and New York, NY, USA.
Kitts, D.R., 2010. The Hydraulic and Hydrological Performance of Large Wood Accumulations in a Low-order Forest Stream. Ph.D, University of Southampton, Southampton, 367
pp.
Lane, S.N., Morris, J., O'Connell, P.E., Quinn, P.F., 2007. 18 Managing the rural landscape. Future flooding and coastal erosion risks, 297.
Liu, Y.B., Gebremeskel, S., De Smedt, F., Hoffmann, L., Pfister, L., 2003. A diffusive transport approach for flow routing in GIS-based flood modeling. Journal of Hydrology,
283(1-4), 91-106.
Maidment, D.R., 1993. Developing a spatially distributed unit hydrograph by using GIS, HydroGIS 93: Application of Geographic Information Systems in Hydrology and Water
Resources. IAHS, Vienna, pp. 181-192.
Maidment, D.R., Olivera, F., Calver, A., Eatherall, A., Fraczek, W., 1996. Unit hydrograph derived from a spatially distributed velocity field. Hydrological Processes, 10, 831-844.
Millington, C.E., 2007. The Geomorphological dynamics of a restored forested floodplain. PhD, University of Southampton, Southampton, 328 pp.
Odoni, N.A., Lane, S.N., 2010. Assessment of the impact of upstream land management measures on flood flows in Pickering Beck using OVERFLOW, Durham University,
Durham, UK.
Olivera, F., Maidment, D.R., 1999. Geographic Information Systems (GIS)-based spatially distributed model for runoff routing. Water Resources Research, 35(4), 115-1164.
Robinson, M., Cognard-Plancq, A.L., Cosandey, C., David, J., Durand, P., Führer, H.W., Hall, R., Hendriques, M.O., Marc, V., McCarthy, R., McDonnell, M., Martin, C., Nisbet,
T.R., O’Dea, P., Rodgers, M., Zollner, A., 2003. Studies of the impact of forests on peak flows and baseflows: a European perspective. Forest Ecology and
Management, 186(1–3), 85-97.
Saghafian, B., Julien, P.Y., 1995. TIme to equilibrium for spatially variable watersheds. Journal of Hydrology, 172(1-4), 231-245.
Saghafian, B., Julien, P.Y., Rajaie, H., 2002. Runoff hydrograph simulation based on time variable isochrone technique. Journal of Hydrology, 261(1-4), 193-203.
Sholtes, J.S., Doyle, M.W., 2011. Effect of channel restoration on flood wave attenuation. Journal of Hydraulic Engineering, 137(2), 196-208.
Thomas, H., Nisbet, T.R., 2007. An assessment of the impact of floodplain woodland on flood flows. Water and Environment Journal, 21(2), 114-126.
Thomas, H., Nisbet, T.R., 2012. Modelling the hydraulic impact of reintroducing large woody debris into watercourses. Journal of Flood Risk Management, 5(2), 164-174.
