Probabilistic estimates of drought impacts on agricultural ...
Drought Assessment + Impacts: A Preview
-
Upload
jenkins-macedo -
Category
Technology
-
view
122 -
download
1
description
Transcript of Drought Assessment + Impacts: A Preview
![Page 1: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/1.jpg)
Drought Assessment +
Impacts Preview
Remote Sensing for Global Environmental Change Richard MacLean Jenkins Macedo
November 4, 2013
![Page 2: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/2.jpg)
What is Drought? An Oklahoma Experience
URL: http://www.youtube.com/watch?v=oRSFMLByat0
![Page 3: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/3.jpg)
U.S. DROUGHT MONITOR
Source: URL: http://www.youtube.com/watch?v=XAY4fmPH8sU
![Page 4: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/4.jpg)
“Drought-induced reduction in global terrestrial net primary production from
2000 through 2009.” Zhao & Running, 2010
![Page 5: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/5.jpg)
PURPOSE • to test the hypothesis whether warming climate of the
past decade continued to increase Net Primary Production (NPP), or if different climate constraints were more important?
![Page 6: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/6.jpg)
APPROACH • MODIS Gross Primary Production/NNP Algorithm
o Data frame § Remote sensing datasets
• calculate global 1-km MODIS NPP from 2000 through 2009. • used collection 5 (C5) 8-day composite 1-km fraction of photosynthetically active
radiation (FPAR) and Leaf Area Index (LAI) from the MODIS sensor as remotely sensed vegetation property dynamics to the algorithm.
• collection 4 (C4) MODIS 1-km land cover (MOD12Q1) • collection 5 (C5) MODIS Climate Model Grid (CMG) 0.5 degree 8-day snow cover
(MOD10C2) • Collection 5 (C5) MODIS 16-day 1-km NDVI/EVI (MOD12A2.
§ Meteorological Datasets • reanalysis dataset from the National Center for Environmental Prediction (NCEP) • a Palmer Drought Severity Index (PDSI) ta 0.5 degree resolution was used.
o evaluate environmental water stress combining information from evaporation and precipitation.
![Page 7: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/7.jpg)
“A remotely sensed global terrestrial drought severity index.” Mu et al, 2013
![Page 8: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/8.jpg)
PURPOSE • the authors first discussed the various strengths models and concepts of
drought indices and noted that most of those models rely heavily on both reanalysis meteorological and remotely sensed data, which contains substantial uncertainties.
• Mu et al., 2007, 2009, 2011b developed a MODIS ET model to estimate
ET and PET using MODIS data. o using the MODIS ET/PET model and NDVI (Huete et al. 2002) data
products they calculated remotely sensed drought severity index (DSI) globally.
![Page 9: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/9.jpg)
APPROACH • MODIS ET/PET
o Data frame § Remotely sensed inputs data
• MOD16 ET & PET primary inputs to calculate DSI globally. o for all terrestrial ecosystems at continuous 8-day, monthly, and annual steps
at 1-km spatial resolution. • Daily meteorological reanalysis data and 8-day remotely sensed vegetation
property dynamics from MODIS as inputs. • used the Penman-Monteith equation (P-M) to calculate global remotely sensed ET,
and integrates both P-M and Priestley-Taylor (1972) methods to estimate PET. • ET algorithm account for several parameters such as surface energy partitioning,
environmental constraints on ET, wet and moist soil surfaces, and transpiration from canopy stomata.
• Atmospheric relative humidity to quantify proportion of wet soil and wet canopy components.
![Page 10: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/10.jpg)
“Regional aboveground live carbon losses
due to drought-induced tree dieback in piñon-juniper ecosystems”
Huang, C., G.P. Asner, N.N. Barger, J.C. Neff, M.L. Floyd, 2010
![Page 11: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/11.jpg)
PURPOSE • Monitor landscape level
changes in C storage associated with large scale mortality events.
• Quantify the change in piñon-juniper aboveground biomass (AGB) with remote sensing techniques. source: wikimedia commons
![Page 12: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/12.jpg)
APPROACH • Multi year Landsat (ETM
+) time series of dry season Photosynthetic Veg (PV) cover.
• Paired with field measurements of standing live and dead biomass.
source: Huang et al., 2010
![Page 13: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/13.jpg)
“Drought stress and carbon uptake in an Amazon forest measured with spaceborn
imaging spectroscopy” Asner, G.P., D. Nepstad, G. Cardinot, D. Ray,
2004
![Page 14: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/14.jpg)
Purpose • Potential for significant
decrease in Amazonian carbon accumulation driven by El Niño/Southern Oscillation
• Standard remotely sensed greenness may miss small changes in leaf area during droughts.
source: NASA Earth Observatory
![Page 15: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/15.jpg)
Approach • Image spectroscopy with EO-1
Hyperion data • “[Q]uantify relative differences in
canopy water content and carbon uptake resulting from drought stress”
• Precipitation exclusion ground study used to correlate spectroscopy with water stress
• Related spectroscopy estimates of PAR and soil water to model of NPP
![Page 16: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/16.jpg)
Drought in the United States
The data cutoff for Drought Monitor maps is Tuesday at 7 a.m. Eastern Time. The maps, which are based on analysis of the data, are released each Thursday at 8:30 a.m. Eastern Time.
![Page 17: Drought Assessment + Impacts: A Preview](https://reader034.fdocuments.net/reader034/viewer/2022052303/5562a69fd8b42a2e6e8b480b/html5/thumbnails/17.jpg)
Bibliography
Asner, G.P., Nepstad, D., Cardinot, G., and Ray, D. (2004). Drought Stress and Carbon Uptake in an Amazon Forest Measured with Spaceborne Imaging Spectroscopy. PNAS, Vol. 101, No. 16, pg. 6039-6044.
Huang, C., Anser, G.P., Barger, N.N., Neff, J.C., and Floyd, M.L. (2010). Regional Aboveground
Live Carbon Losses due to Drought-Induced Tree Dieback in Pinon-Juniper Ecosystems. Remote Sensing of Environment, Vol. 114, pg. 1471-1479.
Mu, Q., Zhao, M., Kimball, J.S., McDowell, N.G., and Running, S.W. (2013). A Remotely Sensed
Global Terrestrial Drought Severity Index. American Meteorological Society, pg. 83-98. Zhao, M. & Running, S.W. (2010). Drought-Induced Reduction in Global Terrestrial Net Primary
Production from 2000 through 2009. Science, Vol. 329, pg. 940-943.