Land, water and Land, water and ecosystem nexus for ecosystem nexus for climate risk management climate risk management Yoshiki Yamagata, Tokuta Yokohata National Institute for Environmental Studies Akihiko Ito, Naota Hanasaki, Etsushi Kato (NIES), , , ( ), Kazuya Nishina, Yoshimitsu Masaki (NIES), Tsuguki Kinoshita, Motoko Inatomi (Ibaraki Univ), Gen Sakurai, Toshichika Iizumi (NIAES), k h h k ( ) Masashi Okada, Motoki Nishimori (NIAES) 6 th Dec 2013, ICA ‐ RUS workshop
Contents Contents • Background, scope and objective k d d bj i – Land ‐ Water ‐ Ecosystem “nexus” approach Land Water Ecosystem nexus approach • Status and key findings – Land : Land use change, down scaling – Water: Future water scenario, water scarcity Water: Future water scenario water scarcity – Ecosystem: Model uncertainty, global crop yield • Challenges for the future
Land, water, ecosystem “nexus” • Land – A basis for human life (agricultural/urban area) and A basis for human life (agricultural/urban area) and ecosystem (forest etc) – Land use change affects/controls climate change Land use change affects/controls climate change • Water – Used for food (agriculture etc), energy , and human life U d f f d ( i lt t ) d h lif – Water resources is affected by climate change • Ecosystem E t – Provides food (agriculture etc) as well as energy (bio crop) – Ecosystem (vegetation etc) affects/controls climate change “Nexus approach” (trans ‐ sectoral multi ‐ scales) is Nexus approach (trans ‐ sectoral, multi ‐ scales) is essential for climate risk management
Our “nexus” approach Integration (sub ‐ 1) Y. Yamagata, T. Yokohata, E. Kato (NIES) Synergy/trade ‐ off analysis, Urban growth + Downscaling technique y gy/ y , g g q Crop calendar ・ Runoff Eco ‐ system (sub ‐ 2) Water resouces (sub ‐ 3) Floodplain area A. Ito (NIES) ( ) N. Hanasaki (NIES) N. Hanasaki (NIES) Forest management Operation of reservoirs Vegetation ･ LAI Sustainability of eco ‐ system services Sustainability of water use Crop calendar Forest/Grassland Land use Irrigation Agricultural Irrigation productivity Fertilizer ecosystem demand Land use Land use Land use (sub ‐ 4) Agriculture (sub ‐ 5) Land use ･ Fertilizer T. Kinoshita (U. Ibaraki) ( ) M. Nishimori (NIAES) ( ) Crop management Sustainability of crop Crop productivity Sustainability of land use productivity Model input : Socio ‐ economic scenario (RCP, SSP etc.), climate scenario (CMIP4/5) Population, GDP, future “story ‐ line”, changes in climate (temperature, precipitation etc)
Objectives j • Low ‐ carbon scenario? – Sustainability of intensive mitigation/adaptation options, such as negative emission? – Potential of future Bio ‐ Energy Carbon Capture and Storage (BECCS) and 2 degree target: by E. Kato Storage (BECCS) and 2 degree target: by E. Kato • Business as usual (high ‐ carbon) scenario? – Interaction between land, water, ecosystem? – “Climate Boundary”: how resilient are we? • Development of models and data ‐ bases – Coupling of land ‐ water ‐ ecosystem models Coupling of land water ecosystem models
Development of “Integrated terrestrial model” Socio ‐ economic + Climate scenario GDP, population, Temperature, precipitation, .. Water resources Afforestation/ Water use by human deforestation Erosion ac activity (agriculture, y (ag cu u e, industry) is estimated. Eco ‐ system Irrigation from river is Water use The exchange of C and N considered. CO2 emissions （ Agriculture etc ） （ Agriculture, etc. ） between atmosphere ‐ from land use vegetation ‐ soil is Fertilizer Greenhouse gas calculated. Changes in Crop productivity budget budget input i t GHG are estimated. CO2 emissions from forest fire L Land use d A Agriculture i l Land （ MATSIRO ） & （ ） Land ‐ use change (cropland ‐ forest) Crop productivity is Climate （ MIROC ） is calculated based on future socio ‐ estimated . The production Soil water, temperature are Soil water, temperature are economic scenarios. Economic i i E i of bio ‐ energy crop for calculated based on the (e.g., trade) +natural (e.g. mitigation option is water ‐ energy budget. inclination) factors are considered. considered.
L Land d Modelling of land use change Modelling of land use change, Development of down scaling method
Spatially explicit urban growth model Latest urban modeling Socio ‐ economic Spatial interactions scenarios Spatial Econometrics Neo Economic Geography (NEG) SSP RCP Strong Develop new Urban Growth models Input data Validation Urban GIS statistics Algorithm development Test Conduct Simulations Conduct Simulations Satellite R/S data Satellite R/S data U b Urban growth model th d l Feedback Checking with data (MODIS, DMSP etc.) using R/S and GIS data Spatial autocorrelation Spatial autocorrelation Economic agglomeration Feedback Population, GDP ： 50km grid Population, GDP: Country Downscale urban growth with bottom up modeling
Creation of future gridded population and GDP of the world Rank-size rule based Gravity model based
Database for gridded population Database for gridded population Tatem, A. J., Campiz, N., Gething, P. W., Snow, R. W., & Linard, C. (2011). The effects of spatial population dataset choice on estimates of population at risk of disease. Population health metrics , 9 (1), 4. Population Count Grid v3 （ PCGv3 ） by SEDAC is freely available, and most widely used. is freely available, and most widely used.
Problem of SEDAC population database Mesh block size is about 4 km x 4km Mesh block size is about 4 km x 4km Saudi Arabia 2000 Saudi Arabia, 2000 Creating using areal weighting, and overly smoothed. First of all, we have tried to build a spatial statistical Fi t f ll h t i d t b ild ti l t ti ti l model to refine this data set. 11
New method application to the SEDAC population database the SEDAC population database New spatial statistical downscaling method g Yamagata, Seya, and Murakami (2013) y y μ μ N N N N N N y y N μ N μ ˆ ( ( )( )( ) ) μ x [ ] [ ] i i i ˆ β β X X N N N N 1 X X 1 X X N N N N 1 y y ( ( ( ( ) ) ) ) ( ( ) ) i i i i i i i i i i Ny y y 0 . t . ˆ s Using PCGv3 Without refinement Spatial Explanatory variable may leads to biased autocorrelation results, including future estimates. lt i l di f t ti t × Areal weighting l i h i Area Allocation by land × Land use use ArcGIS10.2 ○ ○ NA NA Our new method drastically improve (A ‐ to ‐ P kriging) × Regression based Arbitrary (possibly plural) the downscaling accuracy. 12 ○ New method Arbitrary (possibly plural)
Land use model: Constraint by yield, inclination Cropland and inclination （ Italy ） Cropland with inclination > 0.3 deg (USA) 1.00 0.90 ] urban/cropland [-] 0.80 0 80 op ratio op ratio 0.70 0.60 0.50 0.40 Cro Cro ratio of u 0.30 0.20 0.10 0.00 1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 2 2 3 4 5 5 6 7 7 8 9 9 Yield [tons/ha] Inclination [0.1 degree] Cropland in Australia p Cropland in Australia C op a d Cropland in Canada Cropland in Canada Ca ada 90 25000000 70 25000000 80 60 20000000 20000000 70 50 60 15000000 15000000 40 50 40 30 10000000 10000000 30 Model Model 20 20 5000000 5000000 Observed Observed 10 10 0 0 0 0 1970 1975 1980 1985 1990 1995 2000 2005 人口分布 1970 1975 1980 1985 1990 1995 2000 2005 1970 1980 1990 2000 2005 1970 1980 1990 2000 2005
Land, water and Land, water and ecosystem nexus for ecosystem nexus for climate risk management climate risk management Yoshiki Yamagata, Tokuta Yokohata National Institute for Environmental Studies Akihiko Ito, Naota Hanasaki, Etsushi Kato (NIES), , , ( ), Kazuya Nishina, Yoshimitsu Masaki (NIES), Tsuguki Kinoshita, Motoko Inatomi (Ibaraki Univ), Gen Sakurai, Toshichika Iizumi (NIAES), k h h k ( ) Masashi Okada, Motoki Nishimori (NIAES) 6 th Dec 2013, ICA ‐ RUS workshop
Water Water Future scenario of water use Future scenario of water use, water scarcity
Global water scarcity assessment Global hydrological model with human activities SSP: Shared Socioeconomic Pathways We also developed a scenario matrix of SSP SSP is a global socio ‐ economic scenario, the • and RCP. We analyzed the results successor of SRES Five different views of the successor of SRES. Five different views of the with/without climate policy. h/ h l l world are depicted. • SSP doesn’t include scenarios on water. We developed a compatible water use scenario . p p Hanasaki et al. 2013a,b, Hydrology and Earth System Sciences
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