HR: 09:45h
AN: B11F-08    [Abstracts]
TI: Developing a Nationwide Early Warning System of Meteorological Disasters for the Mongolian Pastoralism
AU: * Shinoda, M
EM: shinoda@alrc.tottori-u.ac.jp
AF: Arid Land Research Center, 1390 Hamasaka, Tottori, 680-0001, Japan
AU: Tachiiri, K
EM: tachiiri@jamstec.go.jp
AF: Frontier Research Center for Global Change, 3173-25 Showamachi, Kanazawa-ku, Yokohama, 236-0001, Japan
AU: Tachiiri, K
EM: tachiiri@jamstec.go.jp
AF: Institute of Arid Asian Studies, Meiji University, 1-9-1 Eifukucho, Suginami-ku, Tokyo, 168- 8555, Japan
AU: Morinaga, Y
EM: ykmorinaga@yahoo.co.jp
AF: Institute of Arid Asian Studies, Meiji University, 1-9-1 Eifukucho, Suginami-ku, Tokyo, 168- 8555, Japan
AU: Klinkenberg, B
EM: brian@geog.ubc.ca
AF: Department of Geography, The University of British Columbia, 1984 West Mall, Vancouver, BC V6T 1Z2, Canada
AB: Among natural disasters, drought affected the most people worldwide during the past few decades. Since the late 1970s, there has been a shift in El Niño-Southern Oscillation toward more warm events, closely related to a worldwide trend for intensified drought. Pastoral livestock husbandry, a major industry in Mongolia, has repeatedly suffered from drought and dzud (anomalous climatic and/or land-surface conditions leading to significant livestock mortality in winter-spring) due to its dry, cold climate. Droughts and dzuds between 1999 and 2002 killed 8.2 million livestock, which accounts for about one forth of the total number of livestock in Mongolia, and 3.0 million female livestock miscarried. The present paper proposes an early warning system (EWS) of the Mongolian meteorological disasters that is suitable for the environment and socio-economy. Although the state-of-the-art long-range weather forecasting has not yet produced reliable quantitative information, timely and accurate monitoring of the climate memory of land-surface anomaly conditions (such as soil moisture, pasture, and livestock) that resulted, with a time lag, from summer deficit rainfall will enable us to deliver early warnings of possible drought and dzud and finally to mitigate their effects on livestock husbandry. With this background in mind, the first attempt has been made to integrate operationally observed ground data and newly introduced remote sensing data in the context of climate memory to be overlaid on a nationwide map. A regression tree model is being developed in order to predict livestock mortality; the predictor variables included two indices developed from remote sensing data—the Normalized Difference Vegetation Index (NDVI) and the Snow Water Equivalent (SWE) —as well as the previous year's livestock numbers and mortality. According to the regression tree model, the most serious livestock mortality in winter-spring was associated with low NDVI values in August of the previous year (i.e., poor vegetation conditions in late summer), high SWE values in December of the previous year (i.e., significant snow accumulation by mid-winter), and a high previous year's mortality.
DE: 0402 Agricultural systems
DE: 0468 Natural hazards
DE: 0480 Remote sensing
DE: 0736 Snow (1827, 1863)
DE: 1812 Drought
SC: Biogeosciences [B]
MN: 2007 Fall Meeting