Articles | Volume 21, issue 3
https://doi.org/10.5194/nhess-21-995-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-995-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Drought impact in the Bolivian Altiplano agriculture associated with the El Niño–Southern Oscillation using satellite imagery data
Claudia Canedo-Rosso
CORRESPONDING AUTHOR
Division of Water Resources Engineering, Lund University, P.O. Box
118, 22100 Lund, Sweden
Instituto de Hidráulica e Hidrología, Universidad Mayor de
San Andrés, Cotacota 30, La Paz, Bolivia
Stefan Hochrainer-Stigler
International Institute for Applied Systems Analysis (IIASA),
Schlossplatz 1, 2361 Laxenburg, Austria
Georg Pflug
International Institute for Applied Systems Analysis (IIASA),
Schlossplatz 1, 2361 Laxenburg, Austria
Institute of Statistics and Operations Research, Faculty of
Economics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna,
Austria
Bruno Condori
Inter-American Institute for Cooperation on Agriculture (IICA),
Defensores del Chaco 1997, La Paz, Bolivia
Ronny Berndtsson
Division of Water Resources Engineering, Lund University, P.O. Box
118, 22100 Lund, Sweden
Center for Middle Eastern Studies, Lund University, P.O. Box 201,
22100 Lund, Sweden
Related authors
Claudia Canedo Rosso, Lars Nyberg, and Ilias Pechlivanidis
EGUsphere, https://doi.org/10.5194/egusphere-2025-1843, https://doi.org/10.5194/egusphere-2025-1843, 2025
Short summary
Short summary
Severe droughts have increasingly affected water supply, farming, and forestry in Sweden. This study explored how drought risks have changed over time and across regions using meteorological and hydrological data. Results showed that droughts are becoming more frequent in central and south-eastern Sweden, while northern areas are getting wetter. These insights can support early warnings and help guide decisions on drought preparedness and climate adaptation.
Kai Schröter, Pia-Johanna Schweizer, Benedikt Gräler, Lydia Cumiskey, Sukaina Bharwani, Janne Parviainen, Chahan M. Kropf, Viktor Wattin Håkansson, Martin Drews, Tracy Irvine, Clarissa Dondi, Heiko Apel, Jana Löhrlein, Stefan Hochrainer-Stigler, Stefano Bagli, Levente Huszti, Christopher Genillard, Silvia Unguendoli, Fred Hattermann, and Max Steinhausen
Nat. Hazards Earth Syst. Sci., 25, 3055–3073, https://doi.org/10.5194/nhess-25-3055-2025, https://doi.org/10.5194/nhess-25-3055-2025, 2025
Short summary
Short summary
With the increasing negative impacts of extreme weather events globally, it is crucial to align efforts to manage disasters with measures to adapt to climate change. We identify challenges in systems and organizations working together. We suggest that collaboration across various fields is essential and propose an approach to improve collaboration, including a framework for better stakeholder engagement and an open-source data system that helps gather and connect important information.
Nicole van Maanen, Marleen de Ruiter, Wiebke Jäger, Veronica Casartelli, Roxana Ciurean, Noemi Padron, Anne Sophie Daloz, David Geurts, Stefania Gottardo, Stefan Hochrainer-Stigler, Abel López Diez, Jaime Díaz Pacheco, Pedro Dorta Antequera, Tamara Febles Arévalo, Sara García González, Raúl Hernández-Martín, Carmen Alvarez-Albelo, Juan José Diaz-Hernandez, Lin Ma, Letizia Monteleone, Karina Reiter, Tristian Stolte, Robert Šakić Trogrlić, Silvia Torresan, Sharon Tatman, David Romero Manrique de Lara, Yeray Hernández González, and Philip J. Ward
EGUsphere, https://doi.org/10.5194/egusphere-2025-3075, https://doi.org/10.5194/egusphere-2025-3075, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
Disaster risk management faces growing challenges from multiple, changing hazards. Interviews with stakeholders in five European regions reveal that climate change, urban growth, and socio-economic shifts increase vulnerability and exposure. Measures to reduce one risk can worsen others, highlighting the need for better coordination. The study calls for flexible, context-specific strategies that connect scientific risk assessments with real-world decision-making.
Claudia Canedo Rosso, Lars Nyberg, and Ilias Pechlivanidis
EGUsphere, https://doi.org/10.5194/egusphere-2025-1843, https://doi.org/10.5194/egusphere-2025-1843, 2025
Short summary
Short summary
Severe droughts have increasingly affected water supply, farming, and forestry in Sweden. This study explored how drought risks have changed over time and across regions using meteorological and hydrological data. Results showed that droughts are becoming more frequent in central and south-eastern Sweden, while northern areas are getting wetter. These insights can support early warnings and help guide decisions on drought preparedness and climate adaptation.
Julius Schlumberger, Robert Šakić Trogrlić, Jeroen C. J. H. Aerts, Jung-Hee Hyun, Stefan Hochrainer-Stigler, Marleen de Ruiter, and Marjolijn Haasnoot
EGUsphere, https://doi.org/10.5194/egusphere-2024-3655, https://doi.org/10.5194/egusphere-2024-3655, 2024
Short summary
Short summary
This study presents a dashboard to help decision-makers manage risks in a changing climate. Using interactive visualizations, it simplifies complex choices, even with uncertain information. Tested with 54 users of varying expertise, it enabled accurate responses to 71–80 % of questions. Users valued its scenario exploration and detailed data features. While effective, the guidance and set of visualizations could be extended and the prototype could be adapted for broader applications.
Georg C. Pflug, Viktoria Kittler, and Stefan Hochrainer-Stigler
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-194, https://doi.org/10.5194/nhess-2023-194, 2024
Preprint withdrawn
Short summary
Short summary
Multi-hazard events can be devastating and there are indications that in such situations the exposed risk-bearers are affected more severely compared to single-hazard events. We present some statistical modeling approaches to determine possible interrelationships of hazards and tested them for the specific case of the countries within the Danube Region. We especially focused on the question whether certain hazards are more likely to occur due to preceding hazardous events.
Philip J. Ward, James Daniell, Melanie Duncan, Anna Dunne, Cédric Hananel, Stefan Hochrainer-Stigler, Annegien Tijssen, Silvia Torresan, Roxana Ciurean, Joel C. Gill, Jana Sillmann, Anaïs Couasnon, Elco Koks, Noemi Padrón-Fumero, Sharon Tatman, Marianne Tronstad Lund, Adewole Adesiyun, Jeroen C. J. H. Aerts, Alexander Alabaster, Bernard Bulder, Carlos Campillo Torres, Andrea Critto, Raúl Hernández-Martín, Marta Machado, Jaroslav Mysiak, Rene Orth, Irene Palomino Antolín, Eva-Cristina Petrescu, Markus Reichstein, Timothy Tiggeloven, Anne F. Van Loon, Hung Vuong Pham, and Marleen C. de Ruiter
Nat. Hazards Earth Syst. Sci., 22, 1487–1497, https://doi.org/10.5194/nhess-22-1487-2022, https://doi.org/10.5194/nhess-22-1487-2022, 2022
Short summary
Short summary
The majority of natural-hazard risk research focuses on single hazards (a flood, a drought, a volcanic eruption, an earthquake, etc.). In the international research and policy community it is recognised that risk management could benefit from a more systemic approach. In this perspective paper, we argue for an approach that addresses multi-hazard, multi-risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards.
Cited articles
Aceituno, P.: On the Functioning of the Southern Oscillation in the South
American Sector. Part I: Surface Climate, Mon. Weather Rev., 116, 505–524,
https://doi.org/10.1175/1520-0493(1988)116<0505:OTFOTS>2.0.CO;2, 1988.
Alva, A. K., Moore, A. D., and Collins, H. P.: Impact of Deficit Irrigation
on Tuber Yield and Quality of Potato Cultivars, J. Crop Improv.,
26, 211–227, https://doi.org/10.1080/15427528.2011.626891,
2012.
Anderson, W., Seager, R., Baethgen, W., and Cane, M.: Life cycles of
agriculturally relevant ENSO teleconnections in North and South America,
Int. J. Climatol., 37, 3297–3318,
https://doi.org/10.1002/joc.4916, 2017.
Bartholmes, J. C., Thielen, J., Ramos, M. H., and Gentilini, S.: The european flood alert system EFAS – Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts, Hydrol. Earth Syst. Sci., 13, 141–153, https://doi.org/10.5194/hess-13-141-2009, 2009.
Beaudoing, H. and Rodell, M.: NASA/GSFC/HSL: GLDAS Noah Land Surface Model L4 monthly 0.25 × 0.25 degree V2.0, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/9SQ1B3ZXP2C5, 2019.
Beck, P. S. A., Atzberger, C., Høgda, K. A., Johansen, B., and Skidmore,
A. K.: Improved monitoring of vegetation dynamics at very high latitudes: A
new method using MODIS NDVI, Remote Sens. Environ., 100, 321–334,
https://doi.org/10.1016/j.rse.2005.10.021, 2006.
Bhuiyan, C. and Kogan, F. N.: Monsoon variation and vegetative drought
patterns in the Luni Basin in the rain-shadow zone, Int. J. Remote Sens., 31,
3223–3242, https://doi.org/10.1080/01431160903159332, 2010.
BID: Analisis ambiental y social, in: Programa de Saneamiento del Lago
Titicaca, Banco Interamericano de Desarrollo (BID), La Paz, Bolivia, 2016.
Blacutt, L. A., Herdies, D. L., de Gonçalves, L. G. G., Vila, D. A., and
Andrade, M.: Precipitation comparison for the CFSR, MERRA, TRMM3B42 and
Combined Scheme datasets in Bolivia, Atmos. Res., 163, 117–131,
https://doi.org/10.1016/j.atmosres.2015.02.002, 2015.
Buxton, N., Escobar, M., Purkey, D., and Lima, N.: Water scarcity, climate
change and Bolivia: Planning for climate uncertainties, SEI discussion
brief, Stockholm Environment Institute, Davis, USA, 4 pp., 2013.
CAF: Las lecciones de El Niño, Bolivia. Memorias del fenómeno El
Niño 1997–1998, retos y propuestas para la región andina.,
Corporación Andina de Fomento (CAF), Caracas, Venezuela, 2000.
Chuai, X. W., Huang, X. J., Wang, W. J., and Bao, G.: NDVI, temperature and
precipitation changes and their relationships with different vegetation
types during 1998–2007 in Inner Mongolia, China, Int. J. Climatol., 33,
1696–1706, https://doi.org/10.1002/joc.3543, 2013.
Condom, T., Rau, P., and Espinoza, J. C.: Correction of TRMM 3B43 monthly
precipitation data over the mountainous areas of Peru during the period
1998–2007, Hydrol. Process., 25, 1924–1933,
https://doi.org/10.1002/hyp.7949, 2011.
Corbari, C., Sobrino, J. A., Mancini, M., and Hidalgo, V.: Land surface temperature representativeness in a heterogeneous area through a distributed energy-water balance model and remote sensing data, Hydrol. Earth Syst. Sci., 14, 2141–2151, https://doi.org/10.5194/hess-14-2141-2010, 2010.
Cui, L. and Shi, J.: Temporal and spatial response of vegetation NDVI to
temperature and precipitation in eastern China, J. Geogr. Sci., 20, 163–176, https://doi.org/10.1007/s11442-010-0163-4, 2010.
Desinventar Sendai: Disaster loss data for sustainable development goals and Sendai framework monitoring system. United nations office for disaster risk reduction (UNDRR), available at: https://www.desinventar.net/, last access: 1 June 2020.
Duan, Y., Wilson, A. M., and Barros, A. P.: Scoping a field experiment: error diagnostics of TRMM precipitation radar estimates in complex terrain as a basis for IPHEx2014, Hydrol. Earth Syst. Sci., 19, 1501–1520, https://doi.org/10.5194/hess-19-1501-2015, 2015.
EM-DAT: The emergency events database. Center for research on the epidemiology of disasters (CRED), available at: https://www.emdat.be/, last access: 15 July 2020.
FAO: 2015–2016 El Nino early action and response for agriculture, Food security and nutrition, and Food and Agricultural Organization of the United Nations (FAO), Rome, Italy, 43 pp., ISBN 978-92-5-109383-2, 2016.
FAO: Global early warning – Early action report on food security and
agriculture July–September 2017, Early Warning – Early Action (EWEA),
Agricultural Development Economics Division (ESA), and Food and Agricultural
Organization of the United Nations (FAO), Rome, Italy, ISBN 978-92-5-109806-6, 27,
2017.
Funk, C.: Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS), United States Geological Survey (USGS) and Climate Hazard Center of the University of California, Santa Barbara, available at: https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_monthly/netcdf/ (last access: 22 February 2021), 2015.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The
climate hazards infrared precipitation with stations – a new environmental
record for monitoring extremes, Sci. Data, 2, 150066,
https://doi.org/10.1038/sdata.2015.66, 2015.
Garcia, M., Raes, D., and Jacobsen, S.-E.: Evapotranspiration analysis and
irrigation requirements of quinoa (Chenopodium quinoa) in the Bolivian
highlands, Agr. Water Manage., 60, 119–134,
https://doi.org/10.1016/S0378-3774(02)00162-2, 2003.
Garcia, M., Raes, D., Jacobsen, S. E., and Michel, T.: Agroclimatic
constraints for rainfed agriculture in the Bolivian Altiplano, J. Arid
Environ., 71, 109–121,
https://doi.org/10.1016/j.jaridenv.2007.02.005, 2007.
Garcia, M., Condori, B., and Del Castillo, C.: Agroecological and agronomic cultural practices of quinoa in South America, in: Quinoa: Improvement and Sustainable Production, edited by: Murphy, K. and Matanguihan, J., John Wiley & Sons. Inc., Hoboken, USA, 25–46, ISBN 978-1-118-62805-8, 2015.
Garcia, M. and Alavi, G.: Bolivia, in: Atlas de Sequía de América
Latina y el Caribe, edited by: Núñez Cobo, J. and Verbist, K.,
UNESCO y CAZALAC, La Serena, Chile, 29–42, 2018 (in Spanish).
Garreaud, R., Vuille, M., and Clement, A. C.: The climate of the Altiplano:
observed current conditions and mechanisms of past changes, Palaeogeogr.
Palaeocl., 194, 5–22,
https://doi.org/10.1016/S0031-0182(03)00269-4, 2003.
Garreaud, R. D. and Aceituno, P.: Interannual rainfall variability over the
South American Altiplano, J. Climate, 14, 2779–2789,
https://doi.org/10.1175/1520-0442(2001)014<2779:Irvots>2.0.Co;2, 2001.
Geerts, S., Raes, D., Garcia, M., Mendoza, J., and Huanca, R.: Crop water
use indicators to quantify the flexible phenology of quinoa (Chenopodium
quinoa Willd.) in response to drought stress, Field Crops Res., 108,
150–156, https://doi.org/10.1016/j.fcr.2008.04.008, 2008.
Geerts, S., Raes, D., Garcia, M., Miranda, R., Cusicanqui, J. A., Taboada,
C., Mendoza, J., Huanca, R., Mamani, A., Condori, O., Mamani, J., Morales,
B., Osco, V., and Steduto, P.: Simulating Yield Response of Quinoa to Water
Availability with AquaCrop, Agron. J., 101, 499–508,
https://doi.org/10.2134/agronj2008.0137s, 2009.
Helman, D., Givati, A., and Lensky, I. M.: Annual evapotranspiration retrieved from satellite vegetation indices for the eastern Mediterranean at 250 m spatial resolution, Atmos. Chem. Phys., 15, 12567–12579, https://doi.org/10.5194/acp-15-12567-2015, 2015.
Holben, B. N.: Characteristics of maximum-value composite images from
temporal AVHRR data, Int. J. Remote Sens., 7, 1417–1434,
https://doi.org/10.1080/01431168608948945, 1986.
Huang, M., Piao, S., Ciais, P., Peñuelas, J., Wang, X., Keenan, T. F.,
Peng, S., Berry, J. A., Wang, K., Mao, J., Alkama, R., Cescatti, A., Cuntz,
M., De Deurwaerder, H., Gao, M., He, Y., Liu, Y., Luo, Y., Myneni, R. B.,
Niu, S., Shi, X., Yuan, W., Verbeeck, H., Wang, T., Wu, J., and Janssens, I.
A.: Air temperature optima of vegetation productivity across global biomes,
Nat. Ecol. Evol., 3, 772–779,
https://doi.org/10.1038/s41559-019-0838-x, 2019.
Iizumi, T., Luo, J.-J., Challinor, A. J., Sakurai, G., Yokozawa, M., Sakuma,
H., Brown, M. E., and Yamagata, T.: Impacts of El Niño Southern
Oscillation on the global yields of major crops, Nat. Commun., 5,
3712, https://doi.org/10.1038/ncomms4712, 2014.
INE: Censo Agropecuario de Bolivia 2013, first ed., The National Institute
of Statistics (INE) of Bolivia, La Paz, 143 pp., 2015 (in Spanish).
IPCC: Managing the Risks of Extreme Events and Disasters to Advance Climate
Change Adaptation. A Special Report of Working Groups I and II of the
Intergovernmental Panel on Climate Change, Cambridge University Press,
Cambridge, UK and New York, USA, 582, 2012.
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner,
G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V.,
and Midgley, G. F., Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, 1535 pp., 2013.
Ji, L. and Peters, A. J.: Assessing vegetation response to drought in the
northern Great Plains using vegetation and drought indices, Remote Sens.
Environ., 87, 85–98,
https://doi.org/10.1016/S0034-4257(03)00174-3, 2003.
Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman,
G. G., Panov, N., and Goldberg, A.: Use of NDVI and Land Surface Temperature
for Drought Assessment: Merits and Limitations, J. Climate, 23, 618–633,
https://doi.org/10.1175/2009jcli2900.1, 2010.
Kogan, F. and Guo, W.: Strong 2015–2016 El Niño and implication to
global ecosystems from space data, Int. J. Remote Sens., 38, 161–178,
https://doi.org/10.1080/01431161.2016.1259679, 2017.
Kogan, F. N.: Application of vegetation index and brightness temperature for
drought detection, Adv. Space Res., 15, 91–100,
https://doi.org/10.1016/0273-1177(95)00079-T, 1995.
Kogan, F. N.: Satellite-Observed Sensitivity of World Land Ecosystems to El
Niño/La Niña, Remote Sens. Environ., 74, 445–462,
https://doi.org/10.1016/S0034-4257(00)00137-1, 2000.
Kutner, M. H., Nachtsheim, C. J., and Neter, J.: Applied linear regression models, forth ed., McGraw-Hill/Irwin Series: Operations and decision sciences, Ohio, US, 701 pp., ISBN 978-0073014661, 2004.
Legates, D. R. and Willmott, C. J.: Mean seasonal and spatial variability
in gauge-corrected, global precipitation, Int. J. Climatol., 10, 111–127,
https://doi.org/10.1002/joc.3370100202, 1990a.
Legates, D. R. and Willmott, C. J.: Mean seasonal and spatial variability
in global surface air temperature, Theor. Appl. Climatol., 41, 11–21,
https://doi.org/10.1007/BF00866198, 1990b.
Moran, M. S., Clarke, T. R., Inoue, Y., and Vidal, A.: Estimating crop water
deficit using the relation between surface-air temperature and spectral
vegetation index, Remote Sens. Environ., 49, 246–263,
https://doi.org/10.1016/0034-4257(94)90020-5, 1994.
Null, J.: El Niño and La Niña Years and Intensities, available at: https://ggweather.com/enso/oni.htm (last access: 11 February 2020), 2018.
Ochoa, A., Pineda, L., Crespo, P., and Willems, P.: Evaluation of TRMM 3B42 precipitation estimates and WRF retrospective precipitation simulation over the Pacific–Andean region of Ecuador and Peru, Hydrol. Earth Syst. Sci., 18, 3179–3193, https://doi.org/10.5194/hess-18-3179-2014, 2014.
Ottlé, C. and Vidal-Madjar, D.: Estimation of land surface temperature
with NOAA9 data, Remote Sens. Environ., 40, 27–41,
https://doi.org/10.1016/0034-4257(92)90124-3, 1992.
Paredes-Trejo, F. J., Álvarez Barbosa, H., Peñaloza-Murillo, M. A.,
Moreno, M. A., and Farias, A.: Intercomparison of improved satellite
rainfall estimation with CHIRPS gridded product and rain gauge data over
Venezuela, Atmosphere, 29, 323–342,
https://doi.org/10.20937/atm.2016.29.04.04, 2016.
Paredes-Trejo, F. J., Barbosa, H. A., and Lakshmi Kumar, T. V.: Validating
CHIRPS-based satellite precipitation estimates in Northeast Brazil, J. Arid
Environ., 139, 26–40,
https://doi.org/10.1016/j.jaridenv.2016.12.009, 2017.
Pinzon, J. E. and Tucker, C. J.: NDVI: Normalized Difference Vegetation Index-3rd generation using GIMMS from AVHRR sensors, Retrieved from Climate Data Guide, edited by: National Center for Atmospheric Research Staff, available at: https://climatedataguide.ucar.edu/climate-data/ndvi-normalized-difference-vegetation-index-3rd-generation-nasagfsc-gimms (last access: 19 July 2020), 2018.
Quiroz, R., Yarlequé, C., Posadas, A., Mares, V., and Immerzeel, W. W.:
Improving daily rainfall estimation from NDVI using a wavelet transform,
Environ. Model. Softw., 26, 201–209,
https://doi.org/10.1016/j.envsoft.2010.07.006, 2011.
Ramirez-Rodrigues, M. A., Asseng, S., Fraisse, C., Stefanova, L., and
Eisenkolbi, A.: Tailoring wheat management to ENSO phases for increased
wheat production in Paraguay, Climate Risk Management, 3, 24–38,
https://doi.org/10.1016/j.crm.2014.06.001, 2014.
Rencher, A. C.: Methods of Multivariate Analysis, John Wiley & Sons, New
York, 1995.
Rivera, J. A., Marianetti, G., and Hinrichs, S.: Validation of CHIRPS
precipitation dataset along the Central Andes of Argentina, Atmos.
Res., 213, 437–449,
https://doi.org/10.1016/j.atmosres.2018.06.023, 2018.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteor. Soc., 85, 381–394,
https://doi.org/10.1175/bams-85-3-381, 2004.
Sánchez, N., Martínez-Fernández, J., González-Piqueras, J.,
González-Dugo, M. P., Baroncini-Turrichia, G., Torres, E., Calera, A.,
and Pérez-Gutiérrez, C.: Water balance at plot scale for soil
moisture estimation using vegetation parameters, Agr. Forest
Meteor., 166–167, 1–9,
https://doi.org/10.1016/j.agrformet.2012.07.005, 2012.
Santos, J. L.: The Impact of El Niño – Southern Oscillation Events on South America, Adv. Geosci., 6, 221–225, https://doi.org/10.5194/adgeo-6-221-2006, 2006.
Santoso, A., Hendon, H., Watkins, A., Power, S., Dommenget, D., England, M.
H., Frankcombe, L., Holbrook, N. J., Holmes, R., Hope, P., Lim, E.-P., Luo,
J.-J., McGregor, S., Neske, S., Nguyen, H., Pepler, A., Rashid, H., Gupta,
A. S., Taschetto, A. S., Wang, G., Abellán, E., Sullivan, A., Huguenin,
M. F., Gamble, F., and Delage, F.: Dynamics and Predictability of El
Niño–Southern Oscillation: An Australian Perspective on Progress and
Challenges, B. Am. Meteor. Soc., 100, 403–420,
https://doi.org/10.1175/bams-d-18-0057.1, 2019.
Satgé, F., Bonnet, M.-P., Gosset, M., Molina, J., Hernan Yuque Lima, W.,
Pillco Zolá, R., Timouk, F., and Garnier, J.: Assessment of satellite
rainfall products over the Andean plateau, Atmos. Res., 167, 1–14,
https://doi.org/10.1016/j.atmosres.2015.07.012, 2016.
SENAMHI: Sismet, gauged precipitation and temperature monthly datasets, Servicio Nacional de Meteorología e Hidrología (SENAMHI) de Bolivia, available at: http://senamhi.gob.bo/index.php/sismet, last access: 15 September 2019 (in Spanish).
Shinoda, M.: Seasonal phase lag between rainfall and vegetation activity in
tropical Africa as revealed by NOAA satellite data, Int. J. Climatol., 15,
639–656, https://doi.org/10.1002/joc.3370150605, 1995.
Thibeault, J., Seth, A., and Wang, G. L.: Mechanisms of summertime
precipitation variability in the Bolivian Altiplano: present and future,
Int. J. Climatol., 32, 2033–2041, https://doi.org/10.1002/joc.2424, 2012.
Thompson, L. G., Mosley-Thompson, E., and Arnao, B. M.: El Niño-Southern
Oscillation events recorded in the stratigraphy of the tropical Quelccaya
ice cap, Peru, Science, 226, 50–53,
https://doi.org/10.1126/science.226.4670.50, 1984.
Tippett, M. K., Barnston, A. G., and Li, S.: Performance of Recent
Multimodel ENSO Forecasts, J. Appl. Meteorol. Clim., 51, 637–654,
https://doi.org/10.1175/jamc-d-11-093.1, 2012.
UNDP: Tras las huellas del cambio climático en Bolivia. Estado del arte
del conocimiento sobre adaptación al cambio climático: agua y
seguridad alimentaria, UNDP-Bolivia, La Paz, Bolivia, 144 pp., 2011 (in Spanish).
UNEP: Diagnostico Ambiental del Sistema Titicaca-Desaguadero-Poopo-Salar de Coipasa (Sistema TDPS) Bolivia – Perú, United Nations Environment Programme (UNEP), Washington, D.C., 1996 (in Spanish).
UNISDR: Drought Risk Reduction Framework and Practices: Contributing to the
Implementation of the Hyogo Framework for Action, United Nations secretariat
of the International Strategy for Disaster Reduction (UNISDR), Geneva,
Switzerland, 2009.
UNISDR: Making Development Sustainable: The Future of Disaster Risk
Management, Global Assessment Report on Disaster Risk Reduction, United
Nations Office for Disaster Risk Reduction (UNISDR), Geneva, Switzerland, 2015.
van Loon, C. D.: The effect of water stress on potato growth, development,
and yield, Am. Potato J., 58, 51–69,
https://doi.org/10.1007/BF02855380, 1981.
Verbist, K., Amani, A., Mishra, A., and Cisneros, B. J.: Strengthening
drought risk management and policy: UNESCO International Hydrological
Programme's case studies from Africa and Latin America and the Caribbean,
Water Policy, 18, 245–261, https://doi.org/10.2166/wp.2016.223,
2016.
Vicente-Serrano, S. M., Chura, O., López-Moreno, J. I., Azorin-Molina,
C., Sanchez-Lorenzo, A., Aguilar, E., Moran-Tejeda, E., Trujillo, F.,
Martínez, R., and Nieto, J. J.: Spatio-temporal variability of droughts
in Bolivia: 1955–2012, Int. J. Climatol., 35, 3024–3040,
https://doi.org/10.1002/joc.4190, 2015.
Vuille, M.: Atmospheric circulation over the Bolivian Altiplan10.1175/1520-0493(1988)1o during dry
and wet periods and extreme phases of the Southern Oscillation, Int. J.
Climatol., 19, 1579–1600, https://doi.org/10.1002/(SICI)1097-0088(19991130)19:14<1579::AID-JOC441>3.0.CO;2-N, 1999.
Wilhite, D. A. and Glantz, M. H.: Understanding: The Drought Phenomenon:
The Role of Definitions, in: Planning for Drought: Toward a Reduction of
Social Vulnerability, edited by: Wilhite, D. A., Easterling, W. E., and
Wood, D. A., Westview Press, Boulder, USA, 10–30, 1985.
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, second ed.,
Academic Press, Oxford, UK, 648 pp., 2006.
Willmott, C. J. and Matsuura, K.: Smart Interpolation of Annually Averaged
Air Temperature in the United States, J. Appl. Meteorol., 34,
2577–2586, https://doi.org/10.1175/1520-0450(1995)034<2577:sioaaa>2.0.co;2, 1995.
Willmott, C. J. and Matsuura, K.: Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series 1950–1999 provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, available at: https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html (last access: 1 May 2020), 2001.
Yarleque, C., Vuille, M., Hardy, D. R., Posadas, A., and Quiroz, R.:
Multiscale assessment of spatial precipitation variability over complex
mountain terrain using a high-resolution spatiotemporal wavelet
reconstruction method, J. Geophys. Res.-Atmos., 121, 12198–12216,
https://doi.org/10.1002/2016jd025647, 2016.
Zhou, J. and Lau, K.-M.: Does a monsoon climate exist over South America?,
J. Climate, 11, 1020–1040,
https://doi.org/10.1175/1520-0442(1998)011<1020:damceo>2.0.co;2, 1998.
Short summary
Drought is a major natural hazard that causes large losses for farmers. This study evaluated drought severity based on a drought classification scheme using NDVI and LST, which was related to the ENSO anomalies. In addition, the spatial distribution of NDVI was associated with precipitation and air temperature at the local level. Our findings show that drought severity increases during El Niño years, and as a consequence the socio-economic drought risk of farmers will likely increase.
Drought is a major natural hazard that causes large losses for farmers. This study evaluated...
Altmetrics
Final-revised paper
Preprint