Computational approaches to address water resources and agricultural development challenges

– Examples from Africa, India and Europe

Jafet Andersson et al.

o Undernourishment (1 billion)

o Population growth (+1-2 b., 2025)o Climate change (warmer, more

extremes, etc.)o Water and nutrient requirements

for food production → environmental degradation & resource scarcity

o Sustainable food production & natural resources management?

Source: Barret C. Science 12 Feb 2010

Source: www.fao.org/economic/ess/food-security-statistics

Major challenges

Source: Barret C. Science 12 Feb 2010

Computer models can assist in providing quantitative information

Spatiotemporally dynamic integration of multiple processes, feedbacks and scales

Systematic assessments Estimates in unmonitored

areas Not a solution to all problems

but one of many tools

WFD information requested for 38 000 catchments vs. observations available at 330 sites → PUB

Technologies• In situ water harvesting • External water harvesting• Human urine fertilization

(Ecosan)

1. Impacts of management alternativesin South Africa

Strategy: Intensified management of available resources in smallholder farming increased productivity more food

Water Harvesting (WH)

External WH

o Ensemble of systemso Balance variability in precipitation and

evaporation to overcome dry-spells by enhancing soil moisture in critical dry periods → enhanced crop yields

Ecological sanitation (Ecosan)

o Human excreta: Conventionally: polluting waste Ecosan: nutrient resource to

improve soil fertility in agriculture

o Human urine fertilization:o Low pathogen

concentrationso Most nutrients

www.susana.org

Knowledge gap → Aimso Field-scale effects studied:

big variability between different soil types, climatic regimes, seasons, and type of technology

o Potential effects on large scales is unclearo Isolated or widespread

impact on yields?

o Return flows to rivers: ecosystems, reservoir management?

o How does it vary in space and time? Any consistent patterns? What to prioritize in different areas?

Approach

o Scales: Thukela River (29,000 km2) South Africa (1,800,000 km2)

o Scenarios based on data from field research & crop requirements

o Essential variables: maize yields, evaporation and river flow

o SWAT computer model: process-based (hydrology,

vegetation, nutrient cycles etc.) explicit in space and time human management

o Climate processing: Voronoio Prediction uncertainty: SUFI-2

Results: Current smallholder maize yields and biophysical constraints

Yie

ld (

t ha–

1)

0

2

Current smallholder systems in South Africa

W = water, N = nitrogen, P = phosphorous, T = temperature

Barbier (2000)Basic food security

0.3%

Smallholder maize yieldY

ield

(t h

a–1

)0

2

Baseli

ne

In si

tu W

H

Extern

al W

H

(10%

)Exte

rnal

WH

(50%

)Eco

san

Ecosa

n +

in sit

u W

H

Ecosa

n +

exte

rnal

WH

12%

30%0% 0.3%

12%

30%

12.3%

Reasons:o Soil fertility

o N still primary constraint

Thukela

South Africa

o Significant increase N ↑o Consistent in space and time, and

for all parameterizations

Bar

bier

(20

00)

Reasons:o Soil fertility (N)o Little runoff (0–17 mm

season-1 for Thukela)o Low soil WHCo Uncontrolled storage

→ lateral flowso Pond storage: 95%

inflow → outflow

20%12%30%

Smallholder maize yield

Baseli

ne

Ecosa

n

Full fe

rt.

129%450%

Full ir

rig.

36%

Ecosa

n & F

ull ir

rig.

250%520%

Full fe

rt. &

Full

irrig

.

129% 131%

Full fe

rt. &

in sit

u W

H

Full fe

rt. &

exte

rnal

WH

Ecosan nutrients limited by:o N volatilizationo Urine quantity

Commercial

Evaporation & TranspirationEcosan → ↓ N stress → ↑ H2O uptake

ESC

T

H2O

H2O

ET = 370 mm/season on average

River flow

Impactso Limited impact

o Low-flows: ↑ & o Peaks: ∆

Reasono Discharge sustained

by sub-surface flows

o Pond capacity

Surface runoff

Sub-surface flows

2. Climate change & impacts on water resources

HYPE

Impacts

Hydrological model

Example: Irrigation water demands in large-scale agricultural systems

Europe India

Current irrigation water demands (mm/yr)

• Climate change floods, droughts, agriculture & energy

• Participatory approach • Open model & data• Shared development and

analysis• Future refinements &

application possible without external actors

• Local ownership facilitates sustainable management & adaptation

3. Computation & Capacity Development: Niger, Botswana, MENA

Final remarks Computational approaches can be useful to

assess challenges and opportunities for water resources and agricultural development (South Africa, India, Europe, West Africa)

Models are not enough (people, data, policy, institutions etc.)

Parnter with us: SMHI research: hydrometeorological

modelling & capacity development Swedish IHP: linking water scientists in

Sweden and Africa

Selected publicationsAndersson, J.C.M., Zehnder, A.J.B., Wehrli, B., Jewitt, G.P.W., Abbaspour, K.C., and Yang, H. (2013). Improving crop yield and

water productivity by ecological sanitation and water harvesting in South Africa, Environ. Sci. Technol., 47:9, 4341-4348

Andersson, J.C.M.; Pechlivanidis, I.G.; Gustafsson, D.; Donnelly, C. ; and B. Arheimer (2013) Key Factors for Improving Large-scale Hydrological Model Performance. Proceedings of the 13th International Conference on Environmental Science and Technology, Athens, Greece. Paper no. CEST13_0753

Andersson, J.C.M., Zehnder, A.J.B., Rockström, J., and Yang, H. (2011) Potential impacts of water harvesting and ecological sanitation on crop yield, evaporation and river flow regimes in the Thukela River basin, South Africa, Agr. Water Manage., 98:7,1113-1124

Contacts:Jafet.Andersson@smhi.se http://hypeweb.smhi.se