Drought impact in the Bolivian Altiplano agriculture associated with the El Nino-Southern Oscillation using satellite imagery data

被引:17
|
作者
Canedo-Rosso, Claudia [1 ,2 ]
Hochrainer-Stigler, Stefan [3 ]
Pflug, Georg [3 ,4 ]
Condori, Bruno [5 ]
Berndtsson, Ronny [1 ,6 ]
机构
[1] Lund Univ, Div Water Resources Engn, POB 118, S-22100 Lund, Sweden
[2] Univ Mayor San Andres, Inst Hidraul & Hidrol, Cotacota 30, La Paz, Bolivia
[3] Int Inst Appl Syst Anal IIASA, Schlosspl 1, A-2361 Laxenburg, Austria
[4] Univ Vienna, Fac Econ, Inst Stat & Operat Res, Oskar Morgenstern Pl 1, A-1090 Vienna, Austria
[5] Interamer Inst Cooperat Agr IICA, Defensores Chaco 1997, La Paz, Bolivia
[6] Lund Univ, Ctr Middle Eastern Studies, POB 201, S-22100 Lund, Sweden
基金
瑞典研究理事会;
关键词
QUINOA CHENOPODIUM-QUINOA; LAND-SURFACE-TEMPERATURE; AIR-TEMPERATURE; SPATIAL VARIABILITY; RAINFALL ESTIMATION; VEGETATION; PRECIPITATION; NDVI; MECHANISMS; IRRIGATION;
D O I
10.5194/nhess-21-995-2021
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Drought is a major natural hazard in the Bolivian Altiplano that causes large agricultural losses. However, the drought effect on agriculture varies largely on a local scale due to diverse factors such as climatological and hydrological conditions, sensitivity of crop yield to water stress, and crop phenological stage among others. To improve the knowledge of drought impact on agriculture, this study aims to classify drought severity using vegetation and land surface temperature data, analyse the relationship between drought and climate anomalies, and examine the spatio-temporal variability of drought using vegetation and climate data. Empirical data for drought assessment purposes in this area are scarce and spatially unevenly distributed. Due to these limitations we used vegetation, land surface temperature (LST), precipitation derived from satellite imagery, and gridded air temperature data products. Initially, we tested the performance of satellite precipitation and gridded air temperature data on a local level. Then, the normalized difference vegetation index (NDVI) and LST were used to classify drought events associated with past El Nino-Southern Oscillation (ENSO) phases. It was found that the most severe drought events generally occur during a positive ENSO phase (El Nino years). In addition, we found that a decrease in vegetation is mainly driven by low precipitation and high temperature, and we identified areas where agricultural losses will be most pronounced under such conditions. The results show that droughts can be monitored using satellite imagery data when ground data are scarce or of poor data quality. The results can be especially beneficial for emergency response operations and for enabling a proactive approach to disaster risk management against droughts.
引用
收藏
页码:995 / 1010
页数:16
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