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Drought is an important natural hazard that is expected to increase in frequency and intensity as a consequence of climate change. This study aimed to evaluate the impact of future changes in the temperature and precipitation regime of Spain on agricultural droughts, using novel static and dynamic drought indices. Statistically downscaled climate change scenarios from the model HadGEM2-CC, under the scenario representative concentration pathway 8.5 (RCP8.5), were used at a total of 374 sites for the period 2006 to 2100. The evolution of static and dynamic drought stress indices over time show clearly how drought frequency, duration and intensity increase over time. Values of static and dynamic drought indices increase over time, with more frequent occurrences of maximum index values equal to 1, especially towards the end of the century (2071–2100). Spatially, the increase occurs over almost the entire area, except in the more humid northern Spain, and in areas that are already dry at present, which are located in southeast Spain and in the Ebro valley. This study confirms the potential of static and dynamic indices for monitoring and prediction of drought stress.
María Del Pilar Jiménez-Donaire; Juan Vicente Giráldez; Tom Vanwalleghem. Impact of Climate Change on Agricultural Droughts in Spain. Water 2020, 12, 3214 .
AMA StyleMaría Del Pilar Jiménez-Donaire, Juan Vicente Giráldez, Tom Vanwalleghem. Impact of Climate Change on Agricultural Droughts in Spain. Water. 2020; 12 (11):3214.
Chicago/Turabian StyleMaría Del Pilar Jiménez-Donaire; Juan Vicente Giráldez; Tom Vanwalleghem. 2020. "Impact of Climate Change on Agricultural Droughts in Spain." Water 12, no. 11: 3214.
The early and accurate detection of drought episodes is crucial for managing agricultural yield losses and planning adequate policy responses. This study aimed to evaluate the potential of two novel indices, static and dynamic plant water stress, for drought detection and yield prediction. The study was conducted in SW Spain (Córdoba province), covering a 13-year period (2001–2014). The calculation of static and dynamic drought indices was derived from previous ecohydrological work but using a probabilistic simulation of soil moisture content, based on a bucket-type soil water balance, and measured climate data. The results show that both indices satisfactorily detected drought periods occurring in 2005, 2006 and 2012. Both their frequency and length correlated well with annual precipitation, declining exponentially and increasing linearly, respectively. Static and dynamic drought stresses were shown to be highly sensitive to soil depth and annual precipitation, with a complex response, as stress can either increase or decrease as a function of soil depth, depending on the annual precipitation. Finally, the results show that both static and dynamic drought stresses outperform traditional indicators such as the Standardized Precipitation Index (SPI)-3 as predictors of crop yield, and the R2 values are around 0.70, compared to 0.40 for the latter. The results from this study highlight the potential of these new indicators for agricultural drought monitoring and management (e.g., as early warning systems, insurance schemes or water management tools).
María Del Pilar Jiménez-Donaire; Juan Vicente Giráldez; Tom Vanwalleghem. Evaluation of Drought Stress in Cereal through Probabilistic Modelling of Soil Moisture Dynamics. Water 2020, 12, 2592 .
AMA StyleMaría Del Pilar Jiménez-Donaire, Juan Vicente Giráldez, Tom Vanwalleghem. Evaluation of Drought Stress in Cereal through Probabilistic Modelling of Soil Moisture Dynamics. Water. 2020; 12 (9):2592.
Chicago/Turabian StyleMaría Del Pilar Jiménez-Donaire; Juan Vicente Giráldez; Tom Vanwalleghem. 2020. "Evaluation of Drought Stress in Cereal through Probabilistic Modelling of Soil Moisture Dynamics." Water 12, no. 9: 2592.