Asian Water Cycle Symposium 2016

Towards monitoring and prediction of severe droughts by integrating numerical simulation and satellite observation

Transcript

1. Towards monitoring and prediction of severe droughts by integrating numerical simulation and satellite observation Yohei Sawada Ph.D Candidate River and Environmental Engineering Laboratory, Department of Civil Engineering, the University of Tokyo 2016/3/2 Asian Water Cycle Symposium 2016
2. 1. Introduction – Two major challenges [Photos from The Telegraph http://www.telegraph.co.uk]
3. 1.1. Challenge 1: How can we get the 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.
4. 1.2. Challenge 2: Monitoring droughts in data-scarce regions. obs obs Initial Condition Parameter Optimization t e.g., Soil moisture  Even if the model were perfect, we cannot forecast very well without good initial conditions and model parameters.  How can we get the observations to improve our forecast in the ungauged areas??
5. 2. Challenge (1): How can we get the 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 Meteorological Ecological Hydrological
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] LAI 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. Challenge (2): Monitoring droughts in data-scarce regions obs obs Initial Condition Parameter Optimization t e.g., Soil moisture  How can we get the observations to train the numerical simulation in the ungauged areas ? Satellite!!
11. 3.1. 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  By assimilating this data, we can improve the skill of eco-hydrological model to simultaneously calculate soil moisture and vegetation dynamics. AMSR-E AMSR2
12. 3.2. Coupled Land and Vegetation Data Assimilation System (CLVDAS) 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: 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.
14. 3.4. 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.
15. 3.5. Results (1) 2010-2011 drought in reanalysis LAI anomaly of 2010-2011 droughts in “reanalysis”.
16. 3.6. Results (3) 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, JGR-A, submitted]  Ecosystem damage of the Horn of Africa drought is predictable 10 months before.
17. 3.6. Results (3) 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, JGR-A, submitted]
18. 3.6. Results (3) 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, JGR-A, submitted]
19. 3.6. Results (3) 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) [Sawada and Koike, JGR-A, submitted]  Ensemble stream prediction (with no meteorological prediction skill) can predict ecosystem damages to some extent in the short lead time predictions.
20. 4. 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
21. 5. Conclusions • By explicitly simulating ecosystem damages in addition to hydrological deficits, we can get the holistic view of severe drought progress. • We make it possible to monitor and predict droughts in the data scarce regions by using the globally applicable satellite data and data assimilation technology.  Towards early-warning system of mega-droughts in the data scarce regions by integrating numerical simulation and satellite observations.