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.