Future Earth workshop

Future Earth International Co-Design Workshop @ Tokyo

Transcript

1. Towards monitoring and predicting mega-droughts A model-data integration approach Yohei Sawada RIKEN Advanced Institute for Computational Science 2017/1/16 Future Earth International Co-Design Workshop
2. 1. Introduction [Photos from The Telegraph http://www.telegraph.co.uk]
3. 1.1. Motivation [Shukla et al., 2015] California drought 2.2 million dollar total economic loss [Howitt et al., 2014] Australian Millennium drought [van Dijk et al., 2013] The worst record since European settlement of Australia [Gergis et al., 2012] Amazon drought [Lewis., 2011] Response of largest carbon sink to drought is uncertain [e.g., Morton et al., 2014; Samanta et al., 2010] Horn of Africa & Sahel drought [Anderson et al., 2013] The food shortages affected 31 million people [e.g., Boyd et al., 2013] Future change in aridity is very uncertain. [e.g., Dai, 2011; Roderick et al., 2015]
4. 1.2. Needs to have a holistic view of droughts Natural climate variability Precipitation deficiency, high temperature etc… Soil water deficiency Plant water stress, reduced biomass and yield Reduced stream flow, inflow to reservoirs, Groundwater deficiency, …… Economic, Social, and Environmental impacts [from National Drought Mitigation Center, University of Nebraska-Lincoln, USA] See also [Mishra and Singh, 2010] Meteorological Ecological Hydrological  Drought is multi-sector and multi-scale phenomena. Couplings between hydrology and ecology are important to quantify droughts.
5. 2. Modeling approach to get the holistic view of droughts Model (numerical simulation) Data (observation)
6. 2.1. Ecohydrological model: WEB-DHM-Veg GBHM(river model) Coupling ＋ Hydro-SiB(Land surface model) Dynamic Vegetation Model (DVM)  WEB-DHM-Veg can simulate soil moisture, groundwater, river discharge, and vegetation growth (and their interactions).
7. 2.2. Strategy of ecohydrological drought quantification Ecohydrological Model In-situ observed rainfall JRA25 reanalysis [Onogi et al., 2007] Biomass Soil Moisture Groundwater River discharge Satellite LAI (AVHRR) Calibration & Validation In-situ river discharge Calibration & Validation Agricultural Drought Index Nationwide crop production & Reports about past droughts Validation Hydrological Drought Index Drought Analysis Drought Indices - Standardized Anomaly Index (SA index) – [Jaranilla-Sanchez et al., 2011]
8. 2.3. Model validation Drought indices (SA index) Green:simulated annual peak LAI and Orange:nationwide crop production  The drought index calculated from the model-estimated annual peak of leaf area index correlates well with the drought index from nationwide annual crop production. R =0.89 Drought Nash = 0.66 R = 0.80 River Discharge at Jendouba site Blue: Simulated river discharge Red: Observed river discharge Bar: Rainfall [Sawada et al., 2014, Water Resour. Res.]
9. 2.4. Ecohydrological drought analysis on 1988-1989 drought Drought indices Blue: River discharge Gray: Groundwater level Green: Leaf Area Index Drought Agricultural Drought Hydrological Drought  Historic agricultural droughts predominantly occurred prior to hydrological droughts and the hydrological drought lasted much longer, even after crop production has recovered. [Sawada et al., 2014, Water Resour. Res.]
10. 3. A model-data integration approach Model (numerical simulation) Data (observation)
11. 3.1. Why do we need a model-data integration? Model (numerical simulation) Data (observation) Good: - We can calculate everything - We can predict the future Bad: - (generally) less accurate Good: - (generally) more accurate Bad: - We can observe a small subset of the world - We cannot predict the future  Data Assimilation is strongly recommended to improve our skill of monitoring and predicting natural disasters.
12. 3.2. Coupled Land and Vegetation Data Assimilation System (CLVDAS) [Sawada and Koike, 2014 JGR-A; Sawada et al, 2015 JGR-A] Ecohydrological model Soil moisture Vegetation(LAI) Temperature Radiative Transfer Model Estimated TB Core-Model Pass1: Parameter Optimization Parameter Core-Model Estimated TB Satellite observed TB Schuffled Complex Evolution COST Pass2: Data Assimilation ~1year Soil Moisture, LAI ensemble Core-Model Estimated TB ~5days Satellite observed TB COST Genetic Particle Filter
13. 3.3. Application of Passive Microwave Remote Sensing Radiative Transfer in microwave region Radiation from soil  depends on Surface Soil Moisture Attenuation by canopy Radiation from canopy  depend on Vegetation water content • Microwave brightness temperature is influenced by surface soil moisture, vegetation water content, and temperature [e.g., Paloscia and Pampaloni, 1988] • It is not strongly influenced by atmospheric condition AMSR-E AMSR2
14. 3.4. Example of validation@ West Africa Calibration Validation LAI Surface Soil Moisture Green: Optimized, Black：Default, Red：Observed, Yellow：Observed (Microwave VOD (NASA LPRM))  Optimization improves the skill of estimating surface soil moisture and vegetation dynamics at the same time. [Sawada and Koike, 2014 JGR-A]
15. 4. Towards monitoring and predicting mega-droughts
16. 4.1. Case study 1: Horn of Africa drought [FAO, 2011] [Anderson et al., 2012]  We cannot have the access to many ground observations to develop the drought prediction system.
17. 4.2. Strategy of ecohydrological drought forecast 2003 2004 2005 2006 2007 2008 2009 2010 2011 Observed meteorological forcings (satellite- based) Microwave land observation CLVDAS (Reanalysis) Drought CLVDAS (Ensemble Stream Prediction) CLVDAS (Real Predicion) NOAA GFDL Meteorological forecast No in-situ data is used.
18. 4.3.1. Results (1) 2010-2011 drought in reanalysis LAI anomaly of 2010-2011 droughts in “reanalysis”. [Sawada and Koike, 2016, JGR-A]
19. 4.3.2. Results (2) Predictions: starting from 1 Sep 2010 Gray: Climatorogy Green: Horn of Africa drought (reanalysis) Leaf Area Index timeseries CLVDAS (Ensemble Stream Prediction) CLVDAS (Real Predicion) [Sawada and Koike, 2016 JGR-A]  Ecosystem damage of the Horn of Africa drought is predictable 10 months before.
20. 4.3.2. Results (2) Predictions: starting from 1 Oct 2010 Leaf Area Index timeseries CLVDAS (Ensemble Stream Prediction) CLVDAS (Real Predicion) Gray: Climatorogy Green: Horn of Africa drought (reanalysis) [Sawada and Koike, 2016 JGR-A]
21. 4.3.2. Results (2) Predictions: starting from 1 Jan 2011 Leaf Area Index timeseries CLVDAS (Ensemble Stream Prediction) CLVDAS (Real Predicion) Gray: Climatorogy Green: Horn of Africa drought (reanalysis) [Sawada and Koike, 2016 JGR-A]
22. 4.3.2. Results (2) Predictions: starting from 1 Mar 2011 Leaf Area Index timeseries CLVDAS (Ensemble Stream Prediction) CLVDAS (Real Predicion) Gray: Climatorogy Green: Horn of Africa drought (reanalysis)  Ensemble stream prediction (with no meteorological prediction skill) can predict ecosystem damages to some extent in the short lead time predictions. [Sawada and Koike, 2016 JGR-A]
23. 4.3.3. Results(3) Prototype of drought monitoring and prediction system with many thanks to Profs. Eiji Ikoma and Masaru Kitsuregawa @ Univ. of Tokyo
24. 4.4. Case study 2: North Africa Wheat production 2005 Morocco Drought @Morocco @Algeria @Tunisia LAI anomaly from CLVDAS CLVDAS can reproduce the decrease of nationwide crop production [Sawada and Koike, in prep]
25. 4.4. Case study 2: North Africa Wheat production 2007 Morocco Drought @Morocco @Algeria @Tunisia LAI anomaly from CLVDAS CLVDAS can reproduce the decrease of nationwide crop production [Sawada and Koike, in prep]
26. 4.4. Case study 2: North Africa Wheat production 2008 Morocco, Algeria & Tunisia Drought @Morocco @Algeria @Tunisia LAI anomaly from CLVDAS CLVDAS can reproduce the decrease of nationwide crop production [Sawada and Koike, in prep]
27. 4.4. Case study 2: North Africa Wheat production 2010 Tunisia Drought @Morocco @Algeria @Tunisia LAI anomaly from CLVDAS CLVDAS can reproduce the decrease of nationwide crop production [Sawada and Koike, in prep]
28. 4.5. Case study 2: North Africa - Forecast 2008 Morocco, Algeria & Tunisia Drought  A meteorological seasonal forecast is not perfect. There many things to do...
29. 4.6. Case study 3: Economic impact of drought @ Pakistan 2007 winter 2007 summer w/o irrigation Satellite w/ irrigation Economic model  Linkage between agricultural degradation and economic growth was investigated. [Suzuki, 2015; Ishiwata et al., 2015]
30. 5. Towards real-time drought early warning system Met. Reanalysis Satellite (AMSR-E, AMSR2, MODIS, GRACE,.....) CLVDAS past present future L-V reanalysis Met. Hindcast Met. Seasonal Forecast L-V hindcast L-V real-time forecast Data Analysis Tool river discharge (GRDC) Soil moisture network (SCAN, CEOP,…) Database (DIAS) Crop production (FAOSTAT) Other Data Ecohydrological Drought analysis [Jaranilla-Sanchez et al., 2011, WRR] [Sawada et al. 2014, WRR] Hydrological Drought Index Agricultural Drought Index Socio-Economical Drought analysis [Suzuki, 2015] [Yokomatsu et al., 2015] SocioEconomical Drought Index Decision Makers
31. 6. Conclusions Q1. Did the use of DIAS expand the inter-disciplinary aspects of your research? If so, what were the benefits?  DIAS is designed to go across the boundary of disciplines. It is necessary to solve the natural disaster related issues. Q2. Did your research benefit from the data and model integration DIAS offers?  DIAS is the only-one platform ever which makes it possible to the inter- disciplinary drought monitoring & prediction in the real-time basis. Q3. With regard to the Sustainable Development Goals and the Future Earth project, how should DIAS be developed? What changes and improvements would you like to see?  DIAS should be the leader of open science. We need to improve the transparency of data management and computer codes, which is necessary to contribute to decision making.