water-food-ecosystem responses to climate change … ppt menas kafatos.pdf · water-food-ecosystem...
TRANSCRIPT
Water-Food-Ecosystem Responses to Climate Change and Resilience
1Menas C. Kafatos1Fletcher Jones Endowed Professor of Computational Physics,
Chapman University
Outstanding Visiting Professor, Korea University, Seoul, KoreaAffiliated Researcher, National Observatory of Athens, Greece
2Chapman University, 3Korea University, 4UCLA
Stability, robustness, vulnerability and resilience of agricultural systems
to variousengineering processes, such as information networks,
electronic circuits or flight control systems, in airplanes in order
to make them capable of operating under a wide range of con-
straints (Fowlkes et al. 1995).
More recently, the concept of robustness has also been used
by biologists to describetheability of living systemsto maintain
specific functionalities despite unpredictable environmental or
genetic perturbations (Kitano 2004). For example, biological
robustness can be illustrated by the ability of genomes to com-
pensate for the loss of function in one gene by means of other
copiesof thisgene(Gu et al. 2003). Based on theseobservations
from engineering and biological sciences, robustness has been
described as an intrinsic property of complex adaptive systems
(Carlson and Doyle 2002) and as an important trait for thespe-
cies’ capacity toevolvethrough natural selection (Wagner 2008).
Comprisingboth technical andbiological domains, agricul-
tural systems can also be defined as complex and adaptive
systems. Hence, the robustness concept was recently intro-
duced into agricultural sciences and has been used in an in-
creasing number of scientific papers to represent thecomplex
interactions between the biotechnical factors of agricultural
systems and external drivers of change (de Goede et al.
2013; ten Napel et al. 2006; Verhagen et al. 2010). In these
papers, robustness has been mainly defined as the ability to
minimize the variability of specific agricultural outputs
despite the occurrence of explicitly defined perturbations
(Fig. 2b).
A large part of the literature recently devoted to this subject
deals with robustness as a key breeding goal for animal farms
(Knap2005; Sauvant andMartin2010; Star et al.2008).Theaim
isto select animalsthat achieveahigh production level in awide
diversity of environmental conditions, including stressful condi-
tions. Thesestressorscan bediseasechallenges, extremetemper-
atures, low-quality feed or challenges dueto changes in housing
or management (Merks et al. 2012). However, robustness has
also been discussed in thecontext of cropping systems exposed
to climatic or biotic perturbations. For example, Sabatier et al.
(2013) compared the robustness of two contrasting types of
management strategies for a cacao agroecosystem in Indonesia
facing pest outbreaks and pesticide changes.
Applied to agricultural systemsfacinganenvironment sub-
ject to perturbations, two forms of robustness are frequently
distinguished and sometimescalled, respectively, passiveand
active robustness: (i) resistance, i.e. thewithstanding or toler-
ance of perturbations, and (ii) flexibility, i.e. the ability to
adapt theconfiguration of thesystem in order to limit damage
(ten Napel et al. 2006). For example, robustnesson apig farm
level can include genetic components of heat stress tolerance
in pigs (passive robustness) and temperature control systems
to adjust indoor conditions in real time (active robustness).
Fig. 2 Illustration of stability, robustness, vulnerability and resilience concepts (adapted from Mumby et al. (2014) and deGoede et al. (2013))
N. Urruty et al.
Urruty et al., 2016
Constancy of agricultural outputs over long periods of time or across various spatial environments
Ability to maintain desired levels of agricultural outputs despite the occurrence of perturbations
Degree to which agricultural systems are likely to be harmed due to perturbations
Ability to absorb change and to anticipate future perturbations through adaptive capacity
Agricultural systems are facing multiple and unpredictable perturbations
The impact on a sunflower field of salted sea water flooding induced by Xynthia storm in 2010, in Rochefort area (France)
2.1 Agr icultural systems
Agricultural systems are socio-ecological systems, compris-
ing biotechnical and social factors, and dedicated to the pro-
duction of productive, economic, environmental and social
outputs (Renting et al. 2009). On the one hand, biotechnical
factors consist of biological and technical components linked
through feedback mechanisms (ten Napel et al. 2011).
Biological componentscomprisenot only domesticated plant
and animal species but also non-domesticated species like
pests and pollinators of crops. Technical components consist
of engineering elementsdesigned to optimizeagricultural out-
puts(e.g. irrigation systemand decision support tools). On the
other hand, social factors refer to farmers’ actions and atti-
tudesand in which may beconsidered separately thepsycho-
logical make-up of the farmer and the characteristics of the
farm household (Edwards-Jones2006).
According to thisbasic conceptual scheme, theagricultural
outputs of a farm are highly influenced by the interaction
between thedifferent components that constitutebiotechnical
and social factors. However, agricultural systemsarealso em-
bedded in larger systems such as food, institutional or social
systems. Hence, they are also influenced by external drivers
which can be a source of unpredictable changes for farmers.
2.2 A more changeable environment
External driversof agricultural systemsencompassbio-geophys-
ical, social, economic and political environments that determine
how agricultural activitiesareperformed. Thesedriverscan vary
significantly in time and space and therefore can affect
agricultural systems positively or negatively. Depending on the
frequency, duration and predictability of thesechanges, Maxwell
(1986) distinguished four different types of perturbations that
affect agricultural systems: noise when perturbations occur on a
regular basis and are usually expected by farmers, shocks when
perturbationsareunusual and difficult to anticipate, cycleswhen
the variation is due to cyclical changes, and trends when the
change isgradual over time.
In terms of trends, global warming is expected to impact
agricultural activitiesgradually in thefuture: by theend of the
twenty-first century, temperature is projected to riseby 1.4 to
5.8 °CwhileatmosphericCO2 concentrationcould reach three
to four timesthepre-industrial levels(IPCC 2014). In Europe,
simulations of future climate have suggested an increase of
average temperature and a slight decrease in rainfall (Trnka
et al. 2011). Livestock systemsmay also beimpacted by glob-
al warming, directly by the effects of heat on animal health,
growth and reproduction and, indirectly, for herbivores,
through impacts on the productivity of pastures and forage
crops(Maracchi et al. 2005). Climatechange isalso expected
to increasetherisk of potential pest pressure in agricultureby
providing more suitable environmental conditions for exotic
pests to adapt acrossareaswhich werepreviously detrimental
for their survival (Lamichhane et al. 2014). In this context of
gradual changes, farmersand researcherscan partly anticipate
the impacts on agricultural activities through mitigation and
adaptationprograms(Olesen et al. 2011; Reidsmaet al. 2010).
For example, many research and implementation projects are
currently dealing with adaptation strategiesusing local knowl-
edgeand low inputsfor soil protection and water management
in the context of climate change (Meynard et al. 2012).
Beyond average trends, agricultural systems are also ex-
posed to less predictable perturbations, such as climatic or
economic shocks. These perturbations, exhibiting various in-
tensities and durations, can also heavily impact agricultural
activities. For example, climate variability is considered to
explain part of wheat yield stagnation in Europe since the
middle of the 1990s (Brisson et al. 2010; Moore and Lobell
2014), while food price volatil ity has negatively impacted
farmers’ income stability in recent years (Huchet-Bourdon
2011). In addition to these individual perturbations, local is-
suesmay also interact with global economic issuesand further
increase overall perturbations. For example, due to the speci-
ficities of the world agricultural market (inelastic demand for
agricultural products, high seasonality and relatively long pro-
duction period coupled with a short shelf-life for many agri-
cultural products), asevereclimatic shock, such asdrought on
grainproduction inanexportingcountry, may havesignificant
repercussionson international, national and local marketsand,
therefore, on food security and political stability on local and
global scales (Sternberg 2012).
Furthermore, the relationship between agricultural systems
and their external driversrequiresthat theintrinsicsensitivity of
agricultural systems to exogenous perturbations be taken into
account. For example, theimpact of market volatility during the
Fig. 1 Agricultural systems are facing multiple and unpredictable
perturbations. The impact on a sunflower field of salted sea water
flooding induced by Xynthia storm in 2010, in Rochefort area (France).
Photo credit: INRA
Stability, robustness, vulnerability and resilience. A review
Photo credit: INRA
Key drivers for improving the ability of agricultural systems to cope with perturbations
• Increasing diversity at different levels Increased structural diversity Genetic diversity in monoculture Diversify field with noncrop vegetation Crop rotations Polycultures Agroforestry Mixed landscapes
• Increasing the adaptive capacity of agricultural systems Improvements in the design of agricultural systems Implementation of technical components New fertilizers Decision support tools to prevent abiotic/biotic risks Collective actions between stakeholders that voluntarily share their
goals and production tools
Urruty et al., 2016
Concept: Professor W.K. Lee
▪ Korean Peninsula▪ Same Temperate Zone, BUT Differences in
Vertical Environment- North Korea : Deforested, Degraded- South Korea : Well Vegetated, Green Cover
▪ Why Mid-Latitude▪ Same Temperate Zone, BUT Differences in
Horizontal Environment- Central Asia : Semi-Arid, Dry, Desertification- Coastal Area (Korea, West China, Black Sea,
Mediterranean, SW USA): Vegetated
Example: Southwestern United StatesProject Director: Menas Kafatos, Chapman University
(NIFA Award: 2011- 67004-30224)Co-PDs: S. H. Kim, B. Myoung, D. Stack, N. Hatzopoulos (Chapman Univ.), J. Kim (UCLA),
D. Medvigy (Princeton Univ.), R. Walko (Univ. of Miami)
ClimateArid to semi-arid regions such as East Mediterranean, that are among the most vulnerable sectors to future climate change.
AgricultureCalifornia is the nation’s most productive agricultural state ($35 billion agricultural industry). Of the ten most productive agricultural counties in the United States, nine are in California, and the San Joaquin Valley is the single richest agricultural region in the world
Assessment model hierarchy
Streamflow projection made with bias-corrected climate model data
Map RCM data onto geographic areas of interests
Quality Control of met forcing dataEvaluation/bias
correction
Bias-corrected RCM data (PR, T, etc.)
Agricultural model(APSIM)
Crop productivityassessment
Management decisions,
Policy makers
GCMs + emissions scenarios
Global climate scenarios
RCMs models
Downscaled climate scenarios
GIS
Obs. PR, T, etc..
Observations
A schematic illustration of the data flow from climate projection to crop productivity assessment in a typical nested modeling using a agricultural model.
Differences of averaged meteorological variables between historical run (1981 to 2000) and future projection (2031-2050) during growing season.
Future Projection of Climate Variables in the SW US
Future Projection of Maize Yield Potential Changes
Maze Yp changes between historical run (1981 to 2000) and future projection (2031 to 2050).
Boksoon Myoung1, Seung Hee Kim1, David Stack1, Jinwon Kim2, and Menas Kafatos1
1Center of Excellence in Earth Systems Modeling and Observations, Chapman Univ., USA2Joint Institute for Regional Earth System Sciences and Engineering, UCLA, USA
Temperature, Sowing and Harvest Dates, and Yield Potential of Maize in the Southwestern US
WARM: Warm low-elevation regionsCOOL: Cool high-elevation regionsINT: Intermediate regions
(kg/ha)(month)
(day) OPT-FIXED YLD diff (%)
COOL
INT
GS: Growing season (from SD to HD)
RegionSD
(Sowing date)
HD(Harvest
date)
LGS(Length of GS)
YLD(Yield
Poten.)
WARMVery early
(Mar)Early
(Jun & Jul)Short Low
COOL Early (Apr) Late
(Sep & Oct)
Long High
INTLate
(May & Jun)
Late (Sep) Short High
Suitable management decisions can substantially enhance yield potential over many places.
Summary of the variables
Results of the crop model simulation (21-year averaged)
Local climate and optimal growing season
• RED (WARM): Early planting/harvesting favors higher yields due to the extremely hot summer.
• BLUE (COOL): Early planting and late harvesting (lengthening GS) favor higher yields.
• YELLOW (INT): Late planting/harvesting favors higher yields, which can take advantage of “mild” (25~35°C) summer climate.
SD
HD
GS
Yield Efficiency (YLD/LGS)
Yield vs. LGS
INT region (r=0.83*)
WARM and COOL regions (r=0.71*)
kg/ha per day
Spatially, the longer growing season, the higher yield.
Yield efficiency is higher in INT owing to the mild temperature ranges for maize growth.
Interannual correlations
YLD and LGS are positively correlated on interannual time scales as well, except for the extremely hot southern regions.
RED indicates Warmer sowing season → Early SD → Longer LGS → Higher YLD: Higher Tmin increases yields especially over the COOL region.
BLUE indicates:Hotter harvest season → Freq. heat damages → Lower YLD:Lower Tmax increases yields especially over the WARM region.
Assessments of Future Maize Yield Potential Changes in the Korean
Peninsula Using Multiple Crop Models
Seung Hee Kim, Chul-Hee Lim, Jinwon Kim,
Woo-Kyun Lee, Menas Kafatos
• The Korean Peninsula has unique agricultural environment due to the differences of political and
socio-economical system. NK has been suffering lack of food supplies caused by natural
disasters, land degradation and political failure. The neighboring developed country SK has
better agricultural system but very low food self-sufficiency rate (around 1% of maize). Maize is
an important crop in both countries since it is staple food for NK and SK is No. 2 maize
importing country in the world after Japan. Therefore evaluating maize yield potential (Yp) in the
two distinct regions is essential to assess food security under climate change and variability.
Motivation
Multi-RCM and Multi-Crop Model Super Ensemble Approaches
Map RCM data onto geographic areas of interests
Quality Control of met forcing dataEvaluation/bias
correction
Bias-corrected RCM data (PR, T, etc.)
Agricultural model(APSIM)
Crop productivityassessmentManagement
decisions,Korean Policy
makers
GCMs + emissions scenarios
Global climate scenarios
RCMs over East AsiaDownscaled
climate scenarios
Obs. PR, T, etc..
ObservationsGIS information
over Korea
Agroecosystem model(EPIC and GEPIC) Agricultural
water demandassessment
Future projection
Differences of averaged meteorological variables between historical run (1981 to 2000) and future projection (2031-2050) during growing season (Apr to July).
[%]
Fixed Planting Date Optimal Planting Date
Yp in Mid-Century
3/21
3/31
4/10
4/20
4/30
5/10
5/20
5/30
6/9
2031 2033 2035 2037 2039 2041 2043 2045 2047 2049
Adaptation (shifting planting date)
The optimal planting date is shifted about 20 days earlier
Assessing Relationship Between Climate Indices and Wildfire Potential
in the Southwestern United States
Seung Hee Kim1, Boksoon Myoung1, Jinwon Kim2,
Francis Fujioka1, Menas C. Kafatos1
1Center of Excellence in Earth Systems Modeling and Observations, Chapman University, Orange, CA USA
2Joint Institute for Regional Earth System Sciences and Engineering, UCLA, Los Angeles, CA USA
Wildfires• Wildfires have significant inter-annual variability, mainly resulted from the
inter-annual variability of atmospheric condition
• Early warm season (March-June) temperature variability over the Western US is critical for wildfire potentials.
• Nevertheless, the connections between climate variability and the weather in WUS during the early warm season has received less attention than those during winters.
• There are several studies on relationship between long-term atmospheric anomalies and fire activities but little studies have been done on relationship between climate variability and wildfire potential using drought indices.
• This study aims to identify multiple climate indices closely related with droughts in the WUS region and determine their effect on local wildfire potential using Keetch-Byram Drought Index (KBDI) which has been used to assessing wildfire potential.
• This is done by addressing the long-term variability of KBDI using multi-decadal reanalysis data sets, and then investigating the joint impacts of multiple climate indices on the regional wildfire potential.
• Moisture content within the vegetation/fuel.
• LFM= (total weight-dry weight)/dry weight X 100
• One of the most important factors for assessing fire behavior since it is closely associated with fire ignition, propagation, and intensity.
• Fire danger level– LFM > 120%: Low
– 80%<LFM<120%: Moderate
– 60%<LFM<80%: High
– LFM<60%: Extreme
What is LFM?
BackgroundClimate and wildfire in the Western U.S.
Climatological monthly changes of LFM (black line) and precipitation (blue bars). The climatological LFM-based fire season are indicated with purple and red arrows. The period with potential impacts of AO/ENSO and NAO on precipitation and LFM is displayed with the thick green and yellow arrows.
Methodology
Climate variabilityNAO: North Atlantic OscillationENSO (NINO3.4): El Niño-Southern Oscillation
Atmospheric data35-year (1979-2013) North American Regional Reanalysis (NARR) (32km)
Drought IndexKeetch-Byram Drought Index (KBDI)
where T is the daily maximum temperature, R the mean annual rainfall, Q the current KBDI. This equation describes the drying rate of the soil.
Correlation analysisMonthly correlations between climate indices and KBDI
Evaluations of the empirical model
• LFM transition from a wet season to a dry season (e.g., 90% LFM) are well estimated by EVI on interannual time scales, while magnitudes and timings of max/min LFM are not.
• Temperature information improves the model performance especially in dry seasons.
NDVI and EVI
Conclusions
Maize yields are highly sensitive to planting dates both spatially and interannually, depending on local climates in SWUS.
– Cool climate regionsEarly planting is favorable for higher yields primarily by increasing the length of growing season. In these regions, warmer conditions in the sowing period tend to result in high yields by advancing the sowing date and increasing length of a growing season. – Warm/hot climate regionsYields are less correlated with the length of growing season. Instead, maize yields are associated with temperature variations during the harvesting period due to adverse effects of extreme high temperature events on maize development. – Intermediate climate regionsSimilar to the characteristics of the cool climate regions but with the relatively high yields. The high yield efficiency over this region is due to the optimal temperature ranges for maize growth. In this region, yields were less sensitive to planting dates.