Risk identification of agricultural drought for sustainable agroecosystems

Abstract

Abstract. Drought is considered as one of the major natural hazards with significant impact to agriculture, environment, society and economy. Droughts affect sustainability of agriculture and may result in environmental degradation of a region, which is one of the factors contributing to the vulnerability of agriculture. This paper addresses agrometeorological or agricultural drought within the risk management framework. Risk management consists of risk assessment, as well as a feedback on the adopted risk reduction measures. And risk assessment comprises three distinct steps, namely risk identification, risk estimation and risk evaluation. This paper deals with risk identification of agricultural drought, which involves drought quantification and monitoring, as well as statistical inference. For the quantitative assessment of agricultural drought, as well as the computation of spatiotemporal features, one of the most reliable and widely used indices is applied, namely the Vegetation Health Index (VHI). The computation of VHI is based on satellite data of temperature and the Normalized Difference Vegetation Index (NDVI). The spatiotemporal features of drought, which are extracted from VHI are: areal extent, onset and end time, duration and severity. In this paper, a 20 year (1981–2001) time series of NOAA/AVHRR satellite data is used, where monthly images of VHI are extracted. Application is implemented in Thessaly, which is the major agricultural drought-prone region of Greece, characterized by vulnerable agriculture. The results show that agricultural drought appears every year during the warm season in the region. The severity of drought is increasing from mild to extreme throughout the warm season with peaks appearing in the summer. Similarly, the areal extent of drought is also increasing during the warm season, whereas the number of extreme drought pixels is much less than those of mild to moderate drought throughout the warm season. Finally, the areas with diachronic drought persistence can be located. Drought early warning is developed using empirical functional relationships of severity and areal extent. In particular, two second-order polynomials are fitted, one for low and the other for high severity drought classes, respectively. The two fitted curves offer a forecasting tool on a monthly basis from May to October. The results of this drought risk identification effort are considered quite satisfactory offering a prognostic potential. The adopted remote sensing data and methods have proven very effective in delineating spatial variability and features in drought quantification and monitoring.

Risk Assessment of Drought in the Yangtze River Delta Based on Natural Disaster Risk Theory

“…Based on the framework, the relative drought level or the absolute risk is calculated for the purpose of disaster risk zoning and risk management [5,6]. Regarding techniques, some studies use information diffusion theory, GIS, and remote sensing techniques to identify the degrees and levels of drought risk, drought evaluation, or drought monitoring [7][8][9]. These studies of drought risk assessment have built a solid foundation.…”

Monitoring 2015 drought in West Java using Normalized Difference Water Index (NDWI)

“…Drought is an effect of deficit rainfall in some period of time [3]. El Niño in 2015 given specific impact on water availability in West Java Province.…”

“…Therefore, drought assessment can be used for drought monitoring in a quick and cost-effective way to increase adaptability of water retention. One of common methods for drought monitoring is by using satellite based indices in which has proven to be an effective way and an easy tool from previous study [3][4].…”

Designing an Index based insurance using NDVI as an impact indicator of drought and flood risks in rice cultivation : an application to the region of Babahoyo (Ecuador)

“…Drought risk models have been assessed through different approaches mainly based on drought indices. For example, many authors have obtained drought risk categories from values of different vegetation indices (Dalezios et al, 2014). Another group of researchers have preferred to use climatic indices based on precipitation (Shahid & Behrawan, 2008;Strzepek, Yohe, Neumann, & Boehlert, 2010) or combined with temperature .…”