Modeling rain-fed maize vulnerability to droughts using the standardized precipitation index from satellite estimated rainfall—Southern Malawi case study

Jayanthi, Harikishan; Husak, G. J.; Funk, Chris; Magadzire, Tamuka; Chavula, Adams; Verdin, J. P.

doi:10.1016/j.ijdrr.2013.02.001

Cited by 40 publications

(22 citation statements)

References 16 publications

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“…For eastern Africa, the precipitation deficit in southeastern Ethiopia during 2009 [60], the extensive vegetative drought over Kenya and Tanzania in 2005/2006 [61] as well as the most striking, severe 2010/2011 drought period that affected large parts of the Horn of Africa, particularly Ethiopia, Kenya and Somalia e.g., [13,[24][25][26], could be identified. Southern African drought events mentioned in literature such as the prolonged and extensive drought in 2002/2003 [29,62], the 2007/2008 drought period in Zimbabwe [27,28] and the 2004/2005 drought in southern Malawi [63] could also be affirmed through the results of our study. Table 4.…”

Section: Monitoring Agricultural Droughts Over Africasupporting

confidence: 81%

Identifying Droughts Affecting Agriculture in Africa Based on Remote Sensing Time Series between 2000–2016: Rainfall Anomalies and Vegetation Condition in the Context of ENSO

2017

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Droughts are amongst the most destructive natural disasters in the world. In large regions of Africa, where water is a limiting factor and people strongly rely on rain-fed agriculture, droughts have frequently led to crop failure, food shortages and even humanitarian crises. In eastern and southern Africa, major drought episodes have been linked to El Niño-Southern Oscillation (ENSO) events. In this context and with limited in-situ data available, remote sensing provides valuable opportunities for continent-wide assessment of droughts with high spatial and temporal resolutions. This study aimed to monitor agriculturally relevant droughts over Africa between 2000–2016 with a specific focus on growing seasons using remote sensing-based drought indices. Special attention was paid to the observation of drought dynamics during major ENSO episodes to illuminate the connection between ENSO and droughts in eastern and southern Africa. We utilized Tropical Rainfall Measuring Mission (TRMM)-based Standardized Precipitation Index (SPI) with 0 . 25 ∘ resolution and Moderate-resolution Imaging Spectroradiometer (MODIS)-derived Vegetation Condition Index (VCI) with 500 m resolution as indices for analysing the spatio-temporal patterns of droughts. We combined the drought indices with information on the timing of site-specific growing seasons derived from MODIS-based multi-annual average of Normalized Difference Vegetation Index (NDVI). We proved the applicability of SPI-3 and VCI as indices for a comprehensive continental-scale monitoring of agriculturally relevant droughts. The years 2009 and 2011 could be revealed as major drought years in eastern Africa, whereas southern Africa was affected by severe droughts in 2003 and 2015/2016. Drought episodes occurred over large parts of southern Africa during strong El Niño events. We observed a mixed drought pattern in eastern Africa, where areas with two growing seasons were frequently affected by droughts during La Niña and zones of unimodal rainfall regimes showed droughts during the onset of El Niño. During La Niña 2010/2011, large parts of cropland areas in Somalia (88%), Sudan (64%) and South Sudan (51%) were affected by severe to extreme droughts during the growing seasons. However, no universal El Niño- or La Niña-related response pattern of droughts could be deduced for the observation period of 16 years. In this regard, we discussed multi-year atmospheric fluctuations and characteristics of ENSO variants as further influences on the interconnection between ENSO and droughts. By utilizing remote sensing-based drought indices focussed on agricultural zones and periods, this study attempts to contribute to a better understanding of spatio-temporal patterns of droughts affecting agriculture in Africa, which can be essential for implementing strategies of drought hazard mitigation.

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Section: Monitoring Agricultural Droughts Over Africasupporting

confidence: 81%

Identifying Droughts Affecting Agriculture in Africa Based on Remote Sensing Time Series between 2000–2016: Rainfall Anomalies and Vegetation Condition in the Context of ENSO

2017

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“…Several authors have proposed or performed quantitative assessments of vulnerability under this approach [36][37][38][39][40][41][42]. The vulnerability metric developed by Luers et al [37] can be considered a representative example.…”

Section: The Model-based Approach For Vulnerability Quantificationmentioning

confidence: 99%

Reconciling Drought Vulnerability Assessment Using a Convergent Approach: Application to Water Security in the Elqui River Basin, North-Central Chile

Núñez

Vergara

Leyton

et al. 2017

Water

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Drought has been, is and most likely will remain one of the most significant socio-natural disasters affecting society and the environment worldwide. One priority objective in the adoption of national drought policies is to promote standardized approaches to vulnerability assessment. To reach this objective, however, there is a need to address the noticeable lack of reconciliation between the two major epistemic frameworks that have been used to determine who is vulnerable and why: the so-called outcome and contextual frameworks. This study presents a novel procedure called the convergent approach to assess drought vulnerability under an integral framework. The procedure has been applied to the study of the vulnerability of water security to drought in water-use sectors in a basin located in north-central Chile. The study is justified by the role that drought plays as the major threat to water security in a context of global water crisis. The results show that the convergent approach outperforms traditional procedures such as those based on composite indicators, showing sound robustness and reaching sufficient levels of reliability and validity. The potential expansion of this approach to other applications, such as those related to global estimations of vulnerability to drought, is also discussed.

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“…There are several studies reporting regional vulnerability maps for rainfed crops through vulnerability and risk models using crop yield data and socioeconomic data (Antwi-Agyei et al, 2012;Jayanthi et al, 2013;Malcomb et al, 2014;Krishnamurthy et al, 2014).…”

Section: Materiales Y Métodosmentioning

confidence: 99%

“…Existen diferentes estudios que reportan mapas de vulnerabilidad regional para cultivos de temporal mediante modelos de vulnerabilidad y riesgo que utilizan datos de rendimiento del cultivo y datos socioeconómicos (AntwiAgyei et al, 2012;Jayanthi et al, 2013;Malcomb et al, 2014;Krishnamurthy et al, 2014).…”

Section: Introductionunclassified

Modelo de vulnerabilidad y riesgo de la producción de maíz de temporal en Nayarit, México

Bojórquez-Serrano

Álvarez-Bravo

Vidal-Martínez

et al. 2017

Remexca

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En los últimos años se ha incrementado la recurrencia de los impactos de fenómenos meteorológicos en México. Esto ha detonado la exigencia de un mejor entendimiento de la vulnerabilidad y el riesgo ante eventos de este tipo, particularmente en el sector agropecuario. En Nayarit se tiene documentada en serie de tiempo, los impactos a la producción de maíz de temporal (rendimientos promedios municipales), sin embargo no se ha cuantificado el grado de vulnerabilidad ni el nivel de riesgo de esta actividad económica. El objetivo del presente trabajo fue evaluar el grado de vulnerabilidad y el riesgo de la zona productora de maíz de temporal en el estado de Nayarit, México. Se utilizó la “Guía Metodológica para la Evaluación de la Vulnerabilidad ante Cambio Climático”, en tres etapas: 1) conocer el quien, a qué y por qué la zona productora de maíz es vulnerable; 2) se modeló mediante indicadores la dinámica del objeto vulnerable; y 3) se validó el modelo de vulnerabilidad y riesgo. Con lo anterior, se identificó a la precipitación de verano como el factor de peligro, en tres niveles de intensidad con su respectivo impacto en el rendimiento en términos de probabilidad Asimismo, mediante una selección de indicadores de vulnerabilidad para tres tipos de factores: físicos, económicos y sociales, fue posible generar a través de un modelo de regresión lineal múltiple la correspondiente ecuación que permitió explicar la dinámica del rendimiento de grano del maíz de temporal. Al alimentar este modelo con datos de la serie de tiempo 1980-2012, se generó y validó un modelo de riesgo, el cual al selecciona el año 2010, permitió representar e identificar la distribución de la vulnerabilidad municipal para la precipitación pluvial como el factor de riesgo en estudio.

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Modeling rain-fed maize vulnerability to droughts using the standardized precipitation index from satellite estimated rainfall—Southern Malawi case study

Cited by 40 publications

References 16 publications

Identifying Droughts Affecting Agriculture in Africa Based on Remote Sensing Time Series between 2000–2016: Rainfall Anomalies and Vegetation Condition in the Context of ENSO

Identifying Droughts Affecting Agriculture in Africa Based on Remote Sensing Time Series between 2000–2016: Rainfall Anomalies and Vegetation Condition in the Context of ENSO

Reconciling Drought Vulnerability Assessment Using a Convergent Approach: Application to Water Security in the Elqui River Basin, North-Central Chile

Modelo de vulnerabilidad y riesgo de la producción de maíz de temporal en Nayarit, México

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