german irrigation project saphir

Faculty of Environmental Sciences, Institute of Hydrology and Meteorology

German irrigation project SAPHIR

Sabine SeidelICID Korea, 17 September 2014

Project overview Virtual �eld Regional crop water production functions and available water

Table of Contents

1 Project overview

2 Virtual �eld

3 Regional crop water production functions and available water

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SAPHIR: Saxonian Platform for High Performance IRrigation

• EU �nanced project (2012-14), 7 young scientists employed

• focus area: Saxony

• 20% of area was irrigated before 1990, now 11% of vegetables

• more dry periods in spring/summer, higher variability of rainfall

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Project overview• Main intention: get information about potential yields andirrigation water reqirements under climate change

sortenspezifischePflanzenparameter

standortspezifischeBodenparameter

virtuelles Feld

ExperimenteOptimierung des Bewässerungsmanagements

ÖkonomieFeldskala

ÖkonomieRegionalskala

€

Q

Q

YUnsicherheitKlima

UnsicherheitBoden

KostenKosten

BewässerungswürdigkeitZusatzwasserbedarf

Gewinnfunktion

Erträge Erträge

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Virtual �eld

• Simulation of processes in the �eld (model↔�eld observations)

• use of mechanistic models→ spatial and temporal transfer

2

INTRODUCTION The loss of agrochemicals into aquifers and surface waters in humid regions is an inevitable consequence of intensive agriculture. In large parts of Europe, for instance, the input of nitrogen to agricultural systems and subsequent losses are so large that they constitute a threat to both the quality of surface and ground waters (EEA, 1995). In most agricultural systems the main loss of nitrogen is due to leaching of nitrate from the fields. The fact that laboratory and field measurements necessary for assessment of nitrogen leaching from agricultural fields are expensive have prompted the development of agro-ecosystem models capable of simulating the nitrogen dynamics in agricultural soils and in particular simulating the leaching. In Denmark this led to the development of the Daisy model (Hansen et al., 1990, 1991a). This model has since then been used extensively (e.g. Blicher-Mathiesen et al., 1990; Blicher-Mathiesen et al., 1991; Hansen et al., 1991b; Hansen et al., 1992; Hansen and Svendsen, 1994, 1995a,b,c; Hansen et al., 1999, Jensen and Østergaard, 1993; Jensen et al., 1992; Jensen et al., 1993; Jensen et al., 1994a,b; Jensen et al., 1996; Magid and Kølster, 1995; Mueller et al., 1997; Petersen et al., 1995; Refsgaard et al., 1999, Styczen and Storm, 1993a,b). The model applications comprise both scientific studies and management related studies aimed at decision support. In addition, the model has been validated in a number of major comparative tests (Vereecken et al., 1991; Hansen et al., 1991a,c; Willigen, 1991; Diekkrüger et al., 1995; Svendsen et al., 1995; Smith et al., 1997; Jensen et al., 1997). Hence, Daisy can be considered a well-tested model. Daisy is a one-dimensional agro-ecosystem model that, in brief, simulates crop growth, water and heat balances, organic matter balance, the dynamics of ammonium and nitrate in agricultural soil based on information on management practices and weather data, Fig. 1. Recently, the simulation of the fate of pesticides has been included in the model. The simulation of the organic matter balance and the nitrogen dynamics is strongly interconnected, hence the organic matter model is considered an integral part of the overall nitrogen balance model. Weather data are used as driving variables. The minimum data requirement is daily values of global radiation, air temperature and precipitation. However, much more detailed information can be utilized by the model, e.g. hourly values of global radiation, air temperature, relative humidity, wind speed, and precipitation. The present chapter offers a relatively detailed description of the Daisy model. Figure 1. Schematic representation of the agro-ecosystem model Daisy. The model comprises three main modules, viz. a bioclimate, a vegetation, and a soil component.

Mac

ropo

res

Pes

ticid

e

Mac

ropo

res

Nitr

ate

Mac

ropo

res

Am

mon

ium

Mac

ropo

res

Org

anic

Mat

ter

Mac

ropo

res

Hea

t

Soi

l m

atrix

Mac

ropo

res

Wat

er

SoilUptake

Turnover

Sorption

Transport

Phase change

BioclimateSVAT

Light distributionInterception

Snow accumulation

VegetationGrowth

PhotosynthesisRespiration

Uptake

Numeric layer

Parameters:Soil Data

VegetationData

Driving variables:

Weather DataManagement

Data

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Irrigation experiments

• in 2013/14 conduction of �eld experiments with white cabbage

• three di�erent scheduling strategies were tested:

• irrigation schedules using di�erent kc values (SWB)• automatically drip irrigated using a tensiometer∗(T)• using the mechanistic model Daisy calibrated against �eld dataof 2012/13∗(D)

• T performed best, SWB overestimated crop waterrequirements

∗an irrigation of 15mm was triggered when a soil tension of -250 hPa (or400 hPa) at 30 cm soil depth was reached

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Experimental data collection

• leaf area index (LAI)

• plant heights

• stomatal conductivity

• biomass partitioning and yield

• soil tension (every 15min, at 30, 60 and 90 cm soil depth)

Figure: Tensiometers installed in white cabbage

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Model calibration - plant variables

0

10

20

30

40

50

60

13 49 55 62 70 74 83 90 97 105

118

126

day after transplanting

plan

t hei

ght [

cm]

Abbildung: Observed (boxplots) and simulated plant heights (2013)

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Model calibration - soil hydraulic dynamics

77 91 108 122 138

0

10

20

30

40

days after transplanting

rain

fall

and

irrig

atio

n [m

m] -600

-500

-400

-300

-200

-100

0

soi

l ten

sion

Ψ [h

Pa]

predictedobserved

Abbildung: Observed and simulated soil tension at 30 cm soil depth

• �eld data+mechanistic crop model→ adequate simulation ofprocesses, transfer (climate change scenarios)

• overcome gap between scienti�c research and farmers

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Site analysis - economic decision support tool• generation of synthetic climate data based on observed data• using the stochastic weather generator LARS-WG• drip irrigation water requirements ranged from 30 to 195mm

0

0.2

0.4

0.6

0.8

1

7 7.5 8 8.5 9 9.5 10

ψ(y

ield

)

DM yield in t/ha

histogramkde

Figure: Simulated yield (DM, in t ha−1) of white cabbage (300 years)10 / 16


Simulation based estimation of Kc values

• 300 Kc curves for 300 synthetic years (fully irrigated cabbage)

• red curve: applied in 2014; green: recommended (SWB)

0 20 40 60 80 100 1200

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Tage nach Pflanzung

k c

90 %80 %50 %medianGeisenheim

14.5. 2.6. 23.6. 13.7. 2.8. 22.8. 11.9.

95% Quantil

25% Quantil Bereiche:

90% Quantil

75% Quantil

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Regional crop water production functionsBasics Generating SCWPF Regional SCWPF SCWPF variation Water availabilityInput data

regions with climatic water balanceP − ETa < 200mm

agricultural regions

masked soils and masked raster cells fromdisaggregated climate data

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• Mask: CWB<200mm, agriculture, raster: 5x5 km, 5 mostimportant soils per masked cell, several crops

• climate: WEREX V, soil: Bodenkonzeptkarte, land use: InVeKoS

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Potato crop water production function

• simulated using the physically-based crop growth model Daisy

050

100150

200250

300350

400

8

10

12

14

16

180

0.2

0.4

0.6

0.8

1

I in mm/a

DM Y in t/ha

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Potato yield (no irrigation, WEREX A1b EH5)

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Potato yield with irrigation (210 mm, WEREX A1b EH5)

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Regionalised water availability

• Planned: combination with progonosis of water balance

• water balance from KliWES www.wasserhaushaltsportal.sachsen.de

• RD (in�ow to direct runo� storage) and RG2 (in�ow to slow aquifer- interpretation as groundwater recharge)

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german irrigation project saphir

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