This document summarizes three computational modeling studies that address water resources and agricultural development challenges. The first study uses the SWAT model to examine the impacts of water harvesting and ecological sanitation technologies on smallholder maize yields, evapotranspiration, and river flows in South Africa. The second looks at projected impacts of climate change on irrigation water demands in Europe and India. The third discusses using hydrological models to help Niger, Botswana, and MENA region countries assess climate change impacts on floods, droughts, agriculture, and energy and build local capacity. The document emphasizes that computational tools are most useful when partnered with local data, stakeholders, and sustainable management efforts.

1. Computational approaches to address
water resources and agricultural
development challenges –
Examples from Africa, India and Europe
Jafet Andersson et al.

2. Major challenges
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: Barret C. Science 12 Feb 2010
Source: www.fao.org/economic/ess/food-security-statistics

3. 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

4. 1. Impacts of management alternatives
in South Africa
Strategy: Intensified
management of available
resources in smallholder
farming  increased
productivity  more food
Technologies
• In situ water harvesting
• External water harvesting
• Human urine fertilization
(Ecosan)

5. Water Harvesting (WH)
o Ensemble of systems
o Balance variability in precipitation and
evaporation to overcome dry-spells by
enhancing soil moisture in critical dry
periods → enhanced crop yields
External WH

6. Ecological sanitation (Ecosan)
www.susana.org
o Human excreta:
 Conventionally: polluting waste
 Ecosan: nutrient resource to
improve soil fertility in agriculture
o Human urine fertilization:
o Low pathogen
concentrations
o Most nutrients

7. Knowledge gap → Aims
o Field-scale effects studied:
big variability between
different soil types, climatic
regimes, seasons, and
type of technology
o Potential effects on large
scales is unclear
o 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?

8. 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: Voronoi
o Prediction uncertainty: SUFI-2

9. Yield (t ha–1)
2
Results: Current smallholder maize yields and
biophysical constraints
0
Barbier (2000)
Basic food security
Current smallholder
systems in South Africa
W = water, N = nitrogen, P = phosphorous,
T = temperature

10. Smallholder maize yield
South Africa
Yield (t ha–1)
Barbier (2000)
2
 Reasons:
o Soil fertility
0%
0.3%
0.3%
12%
30%
12% Significant increase  N ↑
o 12.3%
o N still primary
30% Consistent in space and time, and
constraint
o
0
for all parameterizations
Thukela

11. Smallholder maize yield
Ecosan nutrients limited by:
o N volatilization
o Urine quantity
Reasons:
o Soil fertility (N)
o Little runoff (0–17 mm
o
o
Commercial
250%
520%
129%
450%
20%
12%
30%
129%
36%
131%
o
season-1 for Thukela)
Low soil WHC
Uncontrolled storage
→ lateral flows
Pond storage: 95%
inflow → outflow

12. Evaporation & Transpiration
Ecosan → ↓ N stress → ↑ H2O uptake
T
ET = 370
mm/season on
average
H2O
ESC
H2O

13. River flow
Impacts
o Limited impact
o Low-flows: ↑ & 
o Peaks: ∆
Reason
o Discharge
sustained by subsurface flows Sub-surface flows
o Pond capacity
Surface runoff

14. 2. Climate change & impacts on water resources
HYPE
Impacts
Hydrological
model

15. Example: Irrigation water demands
in large-scale agricultural systems
Europe
Current irrigation water demands (mm/yr)
India

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

17. 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
Contacts:
Jafet.Andersson@smhi.se
http://hypeweb.smhi.se
Selected publications
Andersson, 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 Largescale 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