Combining modelling and direct measurement to quantify ...€¦ · Combining modelling and direct...
Transcript of Combining modelling and direct measurement to quantify ...€¦ · Combining modelling and direct...
Combining modelling and direct measurement to quantify nutrient surface runoff from flat fields Spatiotemporal variability of saturation excess surface runoff in flat fields due to interactions between meso- and micro-topography
I. Noij, W. Appels, H. Massop & C. van der Salm
What is the problem?
Surface runoff (SR) is known to be a fast and important transport route for P to surface water
The Netherlands are dominated by flat, well drained permeable soils, and surface runoff was assumed to be negligible
But...
Surface runoff is a very heterogeneous process
Measurements (at local hot spots) inevitably overestimate SR
Interactions between processes that control SR generation in these areas are poorly understood
By consequence we do not know the (relative) contribution of SR to P loads from agricultural fields to surface water, and...
We do not know the relevance of measures to reduce SR
Our approach
1. Analysis of low spots and connectivity, assess SR risk (2006; IPW5)
2. Measurements on high risk fields/spots (2007-2011)
3. Analysis of the meso- and micro- surface topography (2008/9; PhD Appels)
4. Modelling SR generation from fields (2008-2012; PhD Appels)
5. Compare 2 and 4 for up-scaling (2013)
@1: Low spots puddles SR?
@1: Big puddle with low connectivity
ditch border or buffer strip
@1: assess runoff risk: low spot distribution
15% lowest spots per separate field
Surface elevation
area
Disconnected spot
@1: assess runoff risk: include connectivity
Risk = ƒ (low spot area, connectivity) Connectivity = ƒ (overlap area) Risk index = low spot area x overlap area
FIELD
Ditch
Low spot
Overlap area
ditch border
0
1
4
Index value
@1: assess runoff risk: index value
Field A
Field H
Field G
@2 measurements of three fields A, G, H
At each field a hot spot was selected for measurements
A Measurement gutter has been installed
Automatic measurements of runoff volume and composition
@3 Surface topography (Field A, Arable)
Large low spot, small amplitude (SH) tillage rills (maize)
@3 Surface topography (G)
Slight elevation in the middle of the field, random microtopography (grass)
@3 Surface topography (H, Horticulture)
Very flat, lower spots in field corners, high amplitude tillage rills (carrots)
meso scale
DEMs
0.5x0.5 m2
corresponding
flow route
maps
Superimposed
micro relief
Small horizontal (SH)
Random (RA)
Large horizontal (LH)
corresponding
flow route
maps
Field A Field G Field H
@3:
Anal
ysis
of m
eso-
and
mic
ro-
topogra
phy
Surface runoff events
@4 Modelling SR generation
Redistribution model for surface runoff through microtopography (DEM cell size 10 cm: 1x106-3x106 cells per field)
Database of sinks and puddles: fill, overflow, connect, and/or merge
No flow equations: instantaneous redistribution
Area contributing to runoff is a dynamic function of storage at the soil surface1
1Appels et al., 2011, Advances in Water Resources
@4 Assumptions
Constant groundwater level in space
Estimate of soil water storage based on Van Genuchten hydrostatic equilibrium profile (parameters from Staringreeks) and daily groundwater data from nearby station.
Runoff calculations based on excess precipitation (week sum – soil water storage change).
@5 Measured and modelled SR
@5 Measured and calculated annual SR and P loss
Substantial deviations between measured and calculated SR
Neglecting ground water dynamics might be an important factor
Estimated loss field scale
Large variability in estimated P loss
At grass site substantial loss, similar to leaching losses
At other sites losses are small
Uncertainty in the modelling is still large
SR (mm) P loss (kg/ha/yr)Grass 25 2.0Maize 25 0.0Horticulture 7 0.1
Conclusions
• Meso-micro topography interactions create unique runoff pattern
• Quantification of this interaction is required for upscaling measurements from plot to field
• DEM plus tillage patterns may partly predict SR behaviour
• Dynamic groundwatermodel is required to correctly simulate fluxes
• Estimated P loss due to SR at the examined (high risk) sites varied from 0-2 kg/ha/yr