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