Post on 05-Dec-2014
description
transcript
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