Evaluación de la probabilidad de destrucción de áreas de cultivo por inundaciones en el Perú
DOI:
https://doi.org/10.17268/manglar.2021.041Resumen
Esta investigación tiene como objetivo estimar la probabilidad de pérdidas (destrucción) de hectáreas agrícolas por inundaciones en función de las hectáreas inundadas. Para ello se utilizó la base de datos de desastres del Instituto Nacional de Defensa Civil (INDECI) del Perú para el periodo 2003 – 2017; en donde se realizó un análisis descriptivo de las perdidas agrícolas por inundaciones, luego se construyó una curva que relaciona la probabilidad de pérdida de áreas agrícolas en función de las áreas inundadas, separando la probabilidad en percentiles P0, P33 y P66 para las regiones de costa, selva alta, selva baja y sierra. Los resultados muestran que las inundaciones ocupan el tercer lugar para el promedio anual acumulado de hectáreas perdidas y afectadas en comparación con otros desastres naturales, por otra parte, para las regiones de la costa, selva alta, selva baja y sierra aproximadamente el 20%, 20%, 50% y 15% posee una tasa de destrucción de 0,8 a 1,0 en proporción de ha perdida / ha inundada respectivamente. La bondad de ajuste R2 para los modelos van desde 0,94 hasta 0,995, lo que indica la confiabilidad para la predicción de la probabilidad de hectáreas perdidas por inundaciones en función de las áreas inundadas en las regiones de costa, selva alta, selva baja y sierra del Perú.
Citas
Alfieri, L., Bisselink, B., Dottori, F., et al. (2017). Global projections of river flood risk in a warmer world. Earth’s Future, 5(2), 171–182.
Barco, D., & Vargas, P. (2009). El cambio climático y sus efectos en el Perú. Moneda, 143, 25-29.
Barrientos-Felipa, P. (2018). La agricultura peruana y su capacidad de competir en el mercado internacional. Equidad y Desarrollo, 32, 143-179.
Bhakta, B., Sawano, H., Ohara, M., et al. (2019). Methodology for Agricultural Flood Damage Assessment. Recent Advances in Flood Risk Management.
Brémond, P., & Grelot, F. (2013). Review Article: Economic evaluation of flood damage to agriculture - Review and analysis of existing methods. Natural Hazards and Earth System Sciences, 13(10), 2493–2512.
Bui, D. T., Panahi, M., Shahabi, H., et al. (2018). Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods. Scientific Reports, 8(1), 1–14.
CENEPRED. (2015). Manual Para la Evaluación de Riesgos originados por Fenómenos Naturales (2a ed.). Disponible en: http://www.sigpad.gov.co/sigpad/paginas_detalle.aspx?idp=112
Chiarelli, D. D., Davis, K. F., Rulli, M. C., et al. (2016). Climate change and large-scale land acquisitions in Africa: Quantifying the future impact on acquired water resources. Advances in Water Resources, 94, 231–237.
Davis, K. F., Rulli, M. C., Garrassino, F., et al. (2017). Water limits to closing yield gaps. Advances in Water Resources, 99, 67–75.
Dottori, F., Szewczyk, W., Ciscar, J. C., et al. (2018). Increased human and economic losses from river flooding with anthropogenic warming. Nature Climate Change, 8(9), 781–786.
Douglas, I. (2005). Ecosystems and human well-being. Encyclopedia of the Anthropocene, 4, 185-197.
Erdlenbruch, K., Thoyer, S., Grelot, F., et al. (2009). Risk-sharing policies in the context of the French Flood Prevention Action Programmes. Journal of Environmental Management, 91(2), 363–369.
FAO. (2016). Damages and Losses from climate-related disasters in agricultural sectors. Disponible en: http://www.fao.org/3/a-i6486e.pdf
Förster, S., Kuhlmann, B., Lindenschmidt, E., et al. (2008). Assessing flood risk for a rural detention area. Hazards Earth Syst. Sci, 8(2), 311–322.
Gerl, T., Kreibich, H., Franco, G., et al. (2016). A Review of Flood Loss Models as Basis for Harmonization and Benchmarking. En 4th International Symposium on Flood Defense: Managing Flood Risk, Reliability and Vulnerability (ISFD4), Los Angeles, 1 set, 2015.
Giang, P. Q., & Phuong, T. T. (2018). Evaluation of loss of rice production due to climate change reinforced flood in vietnam using hydrological model and GIS. Environment Asia, 11(3), 62–78.
Glas, H. Jonckheere, M.; Mandal, A., et al. (2017). A GIS-based tool for flood damage assessment and delineation of a methodology for future risk assessment: case study for Annotto Bay, Jamaica. Natural Hazards, 88(3), 1867–1891.
INDECI. (2017). Emergencias por desastres para el periodo 2003 - 2017, Perú. Matriz de base de datos. Disponible en: https://www.indeci.gob.pe/compend_estad/2018/index.html
IPCC. (2014). Climate Change. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. 3-22.
Kwak, Y., Shrestha, B. B., Yorozuya, A., et al. (2015). Rapid Damage Assessment of Rice Crop after Large-Scale Flood in the Cambodian Floodplain Using Temporal Spatial Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7), 3700–3709.
Lesk, C., Rowhani, P., & Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature, 529, 84–87.
Merz, B., Kreibich, H., Schwarze, R., et al. (2010). Natural Hazards and Earth System Sciences “Assessment of economic flood damage”. Hazards Earth Syst. Sci, 10, 1697–1724.
MINAGRI. (2012). Plan de Gestión de Riesgos y Adaptación al cambio climático en el sector agrario para el periodo 2012 - 2021 - PLANGRACC - A.
MINAGRI. (2016). Politica Nacional Agraria. Disponible en: http://minagri.gob.pe/portal/download/pdf/p-agraria/politica-nacional-agraria.pdf
MINAGRI. (2019). PLAN NACIONAL DE CULTIVOS: Campaña Agrícola 2019-2020. Disponible en: https://cdn.www.gob.pe/uploads/document/file/471867/Plan_ Nacional_de_Cultivos_2019_2020b.pdf
Misra, A. K. (2014). Climate change and challenges of water and food security. International Journal of Sustainable Built Environment, 3(1), 153–165.
Nga, P. H., Takara, K., & Cam Van, N. (2018). Integrated approach to analyze the total flood risk for agriculture: The significance of intangible damages – A case study in Central Vietnam. International Journal of Disaster Risk Reduction, 31, 862–872.
Pacetti, T., Caporali, E., y Rulli, M. C. (2017). Floods and food security: A method to estimate the effect of inundation on crops availability. Advances in Water Resources, 110, 494–504.
Pinos, J., Orellana, D., & Timbe, L. (2020). Assessment of microscale economic flood losses in urban and agricultural areas: case study of the Santa Bárbara River, Ecuador. Natural Hazards, 103(2), 2323–2337.
Psomiadis, E., Soulis, K. X., Zoka, M., et al. (2019). Synergistic approach of remote sensing and gis techniques for flash-flood monitoring and damage assessment in Thessaly plain area, Greece. Water (Switzerland), 11(3), 1–21.
Rosso, R., & Rulli, M. C. (2002). An integrated simulation method for flash-flood risk assessment: 2. Effects of changes in land-use under a historical perspective. Hydrology and Earth System Sciences, 6(2), 285–294.
Rulli, M. C., Bellomi, D., Cazzoli, A., et al. (2016). The water-land-food nexus of first-generation biofuels. Scientific Reports, 6(1), 1–10.
Shokoohi, A., Ganji, Z., Samani, J. M. V., et al. (2018). Analysis of spatial and temporal risk of agricultural loss due to flooding in paddy farms. Paddy and Water Environment, 16, 737–748.
Shrestha, B. B., Sawano, H., Ohara, M., et al. (2016). Improvement in Flood Disaster Damage Assessment Using Highly Accurate IfSAR DEM. Journal of Disaster Research, 11(6), 1137–1149.
Tanoue, M., Hirabayashi, Y., & Ikeuchi, H. (2016). Global-scale river flood vulnerability in the last 50 years OPEN. Nature Publishing Group, 6, 1–9.
Tupac-Yupanqui, R., Lavado-Casimiro, W., & Felipe-Obando, O. (2017). Regionalización de las precipitaciones máximas. Disponible en: https://www.m-culture.go.th/mculture_th/download/king9/ Glossary_about_HM_King_Bhumibol_Adulyadej’s_Funeral.pdf
Vanham, D. 2016. Does the water footprint concept provide relevant information to address the water-food-energy-ecosystem nexus? Ecosystem Services, 17, 298–307.
Vozinaki, A. E. K., Karatzas, G. P., Sibetheros, I. A., et al. (2015). An agricultural flash flood loss estimation methodology: the case study of the Koiliaris basin (Greece), February 2003 flood. Natural Hazards, 79(2), 899–920.
Wang, Y., Liu, G., Guo, E., et al. (2018). Quantitative agricultural flood risk assessment using vulnerability surface and copula functions. Water (Switzerland), 10(9), 1–16.
Win, S., Zin, W. W., & Kawasaki, A. (2020). Development of Flood Damage Estimation Model for Agriculture – Case Study in the Bago Floodplain, Myanmar. Journal of Disaster Research, 15(3), 242–255.
Win, S., Zin, W. W., Kawasaki, A., & San, Z. M. L. T. (2018). Establishment of flood damage function models: A case study in the Bago River Basin, Myanmar. International Journal of Disaster Risk Reduction, 28, 688–700.
Young, K. R., & León, B. (2009). Natural Hazards in Peru. Causation and Vulnerability. Developments in Earth Surface Processes, 13, 165–180.
Zhai, G., Fukuzono, T., & Ikeda, S. (2006). An empirical model of fatalities and injuries due to floods in Japan. Journal of the American Water Resources Association, 42(4), 863–875.
Zulkafli, Z., Buytaert, W., Manz, B., et al. (2016). Projected increases in the annual flood pulse of the Western Amazon. Environmental Research Letters, 11(1),1–9.
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2022 Casiano Aguirre Escalante, David Quispe Janampa, Ricardo Martín Chávez Asencio, Alberto Franco Cerna Cueva, Manuel Emilio Reategui Inga, Beatriz Milagros Vásquez Bendezú
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Manglar is an open access journal distributed under the terms and conditions of Creative Commons Attribution 4.0 International license
