Diogo S. Martins

SPI Modes of Drought Spatial and Temporal Variability in Portugal: Comparing Observations, PT02 and GPCC Gridded Datasets

Regional drought modes in Portugal are identified applying the Principal Component Analysis (PCA) and Varimax rotation to the Standardized Precipitation Index (SPI) computed on various time scales using the three precipitation datasets covering the period 1950–2003: (i) The observation dataset composed of 193 rain-gauges distributed almost uniformly over the country, (ii) the PT02 high-resolution gridded dataset provided by the Portuguese Meteorological Institute, and (iii) the GPCC dataset with 0.5° spatial resolution. Results suggest that the three datasets well agree in identifying the principal drought modes, i.e. two sub-regions in northern and southern Portugal with independent climate variability. The two sub-regions appear stable when the SPI time scale is varied from 3- to 24-month, and the associated rotated principal component scores (RPCs) do not show any statistically significant linear trend. The degree of similarity between the rotated loadings or REOFs of different SPI time scales for the three used datasets was examined through the congruence coefficients, whose results show a good agreement between the three datasets in capturing the main Portuguese sub-regions. A third spatial mode in central-eastern Portugal was identified for SPI-24 in PT02, with the associated RPC characterized by a statistically significant downward trend. The stability of the identified sub-regions as a function of studied time period was also evaluated applying the same methodologies to a set of three different time windows and it was found that the southern sub-region is very stable but the northern and central-eastern sub-regions are very sensitive to the selected time window.

Vulnerability of Bulgarian agriculture to drought and climate variability with focus on rainfed maize systems

Bulgarian agriculture is affected by droughts and, likely, by climate change. Thus, aiming at assessing its vulnerability, this study includes a general characterization of climate variability in eight selected locations, both in northern and southern Bulgaria. Trend tests were applied to monthly precipitation, maximum and minimum temperature and to the Standardized Precipitation Index with two-month time step (SPI-2) relative to the period of 1951–2004. Negative trends were identified for precipitation and SPI-2 at various locations, mainly in the Thrace Plain, indicating that dryness is likely to be increasing in Bulgaria. The vulnerability of rainfed maize systems to drought was studied using the previously calibrated WinISAREG model and the Stewart’s yield model to compute both the relative yield decrease (RYD) due to water stress and the corresponding net irrigation required to overcome those losses. Results identified a strong relation between SPI-2 for July–August (SPI-2July–Aug) and RYD. Results also show that yield losses are higher when the soils have a smaller soil water holding capacity. For the various regions under study, thresholds for RYD were defined considering the related economic impacts and the influence of soil characteristics on the vulnerability of the rainfed maize systems. Finally, to support drought risk management, SPI-2July–Aug thresholds were developed to be used as indicators of the economic risk of rainfed maize for various climate regions and soil groups in Bulgaria.

Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales

The spatial and temporal variability of droughts were studied for the Northeast Algeria using SPI and RDI computed with monthly precipitation data from 123 rainfall stations and CFSR reanalysis monthly temperature data covering the period 1979–80 to 2013–14. The gridded temperature data was interpolated to all the locations having precipitation data, thus providing to compute SPI and RDI with the time scales of 3-, 6- and 12-month with the same observed rainfall data. Spatial and temporal patterns of droughts were obtained using Principal Component Analysis in S-Mode with Varimax rotation applied to both SPI and RDI. For all time scales of both indices, two principal components were retained identifying two sub-regions that are similar and coherent for all SPI and RDI time scales. Both components explained more than 70% and 74% of drought spatial variability of SPI and RDI, respectively. The identified sub-regions are similar and coherent for all SPI and RDI time scales. The Modified Mann-Kendall test was used to assess trends of the RPC scores, which have shown non-significant trends for decreasing drought occurrence and severity in both identified drought sub-regions and all time scales. Both indices have shown a coherent and similar behavior, however with RDI likely showing to identify more severe and moderate droughts in the southern and more arid sub-region which may be due to its ability to consider influences of global warming. Results for RDI are quite uniform relative to time scales and show smaller differences among the various climates when compared with SPI. Further assessments covering the NW and NE of Algeria using longer time series should be performed to better understand the behavior of both indices.

Comparing SPI and RDI Applied at Local Scale as Influenced by Climate

Drought and wetness events were studied in the Northeast Algeria with SPI and RDI. The study area includes a variety of climatic conditions, ranging from humid in the North, close to the Mediterranean Sea, to arid in the South, near the Sahara Desert. SPI only uses precipitation data while RDI uses a ratio between precipitation and potential evapotranspiration (PET). The latter was computed with the Thornthwaite equation, thus using temperature data only. Monthly precipitation data were obtained from 123 rainfall stations and monthly temperature data were obtained from CFSR reanalysis gridded temperature data. Both data sets cover the period 1979–80 to 2013–14. Using ordinary kriging, the gridded temperature data was interpolated to all the locations having precipitation data, thus providing to compute SPI and RDI with the same observed rainfall data for the 3-, 6- and 12-month time scales. SPI and RDI were therefore compared at station level and results and have shown that both indices revealed more sensitive to drought when applied in the semi-arid and arid zones. Differently, more wetness events were detected by RDI in the more humid locations. Comparing both indices, they show a coherent and similar behavior, however RDI shows smaller differences among climate zones and time-scales, which is an advantage relative to the SPI and is likely due to including PET in RDI.

Correction to: Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales

Due to an oversight, Figure 1 image and Figure 4 caption were incorrectly captured in the original publication.