Spatiotemporal Drought Assessment Based on Gridded Standardized Precipitation Index (SPI) in Vulnerable Agroecosystems

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Spatiotemporal Drought Assessment Based on Gridded Standardized Precipitation Index (SPI) in Vulnerable Agroecosystems


1. Introduction

Interannual temperature and precipitation anomalies have been observed on a global scale, a fact that is primarily attributed to human activities, such as the burning of fossil fuels, deforestation, and/or land-use changes. All these activities have been releasing a great amount of greenhouse gases into the atmosphere, affecting the natural environment and human health. Consequently, the projected temperature rise may potentially lead to more frequent and intense heat waves, deteriorating the drought episodes. Drought can be defined as a prolonged period of below-average precipitation, which can lead to reduced amounts of river flow, lower groundwater levels, and as a result, various anomalies in the ecosystems, which can affect agricultural and industry activities [1,2]. Drought events are affecting several regions across the Earth and are increasingly frequent, especially in Europe. Recently, Europe has experienced several severe drought events, especially in southern and southeastern regions. Droughts may have a strong impact on the socioeconomic structure of every society, especially in semi-arid regions that are characterized by vulnerable agriculture, such as the Mediterranean region, which receives a shortage of rainfall and has a scarce water supply [2]. Drought events can be attributed to climate change and/or increased demand for water resources. Due to their significance, it is obvious that more actions are needed to define the main causes of this problem and to ensure that society is prepared for the unexpectedly increasing frequency and severity of those events in the future [3]. However, to mitigate the effects of droughts, governments and local authorities should develop specific drought management plans and more sustainable water use practices [4]. Specifically, in Greece, droughts are a frequent situation [5], and a change in the frequency and severity has also been recorded in recent years [6,7], frequently leading to dangerous wildfires, having serious consequences in several regions of the country [8,9,10,11,12].
Drought severity is usually defined through indices [13]. Drought indices are numerical values based on ground (in situ) and/or remotely sensed data [14,15,16,17,18,19,20,21] and are computed for each specific region. For the computation of each specific index, precipitation values are first needed, enriched with temperature or evapotranspiration values according to data availability [22,23]. Some indices also include soil moisture and crop growth indicators. Drought indices is an important tool for the scientific community and/or the related stakeholders, helping to identify risky areas and inform decision-makers about water resource management practices [24,25,26,27,28,29]. Drought indices should make a standardized way to evaluate the extent of drought conditions and the related changes over time. Generally, drought indices can have some strong capabilities but also some limitations. For instance, the Standardized Precipitation Index (SPI) uses precipitation data to quantify drought/wet conditions, whereas the Palmer Drought Severity Index (PDSI) combines climatic variables such as temperature and precipitation to make a more comprehensive drought assessment [22].
There are several review articles in the international literature presenting and comparing various drought indices [30,31,32,33,34,35,36,37,38]. There are also several extensive studies on drought forecasting based on such indices [39,40,41,42,43,44,45,46,47]. Remote sensing methodologies can also play an important role in the computation of drought indices. This connection has been achieved using vegetation indices (VIs) which have been introduced in the literature using a combination of surface reflectance values between two or more spectral bands related to satellite systems. Among over a hundred Vis, only a small part has been systematically studied [47]. One of the most prevalent remote sensing indices for the assessment of drought—among others—is the Normalized Difference Vegetation Index [48,49]. NDVI or similar indices like Vegetation Condition Index (VCI) derived from satellite systems can provide satisfactory temporal and spatial coverage over the Earth, making the related computations potentially easier and relatively less expensive than conventional ones [46,48,49]. The SPI has been selected to be adopted for the study area because it has already been tested globally with very reliable results [47,50,51,52]. The SPI is based on a probability distribution function and is a standardized index, and as a result, it can be applied to various climate types all over the world. Numerous research studies investigated drought events using the SPI [52,53,54]. Previous studies in the region of Thessaly investigated the impact of drought to assess stress on crop yields and to evaluate the effectiveness of drought on crop yield assessment [53,54]. Other studies have used the SPI and other drought indices to investigate the frequency and severity of drought events, as well as to identify the most vulnerable regions to drought, especially in Greece [5,55,56,57,58]. More recently, Anagnostopoulou [58], Paparizos et al. [59], Georgoulias et al. [60], and Politi et al. [61] have attempted a future projection using the SPI under different climate conditions, whereas Kourgialas [62] used the SPI for the study of hydrological extremes in the region. The necessity of the current research is dictated by the frequent advent of drought events in the study area which is the largest agricultural region of the country affecting the primary cultivation crops (e.g., cotton; wheat; corn) of Thessaly. The innovative element of this paper is the integrated spatiotemporal drought assessment in multiple time scales through the estimation of the SPI making use of remotely sensed data, such as CHIRPS. Therefore, satellite-derived data from CHIRPS are utilized instead of conventional meteorological data for the computation of the Standardized Precipitation Index (SPI). Consequently, the objectives of this study are: (i) to conduct a spatiotemporal analysis of drought severity using the SPI for the period 1981–2020 in Thessaly, Greece; (ii) to identify both dry and wet periods; (iii) to classify the degree of drought/wetness conditions using a classification scheme for multiple timescales; and (iv) to calculate and classify SPI12 for each month from 1981 to 2020.

4. Discussion

The study mapped the spatial variability of SPI12 across the study area for the driest and wettest years, as well as the intra-annual spatial variability for the driest and wettest years, enriched with the percentage contribution of extreme conditions. The mean annual SPI12 values showed that there were very few hydrological years with extreme meteorological conditions. Hence, the Thessaly region has experienced extreme environmental conditions over the entire timeframe, including two consecutive years of hydrological drought in 1988–1989 and 1989–1990, and two extremely wet years in 2002–2003 and 2009–2010. The driest year (1989–1990) had a severe impact on the entire region, with almost all parts of the region experiencing high degrees of drought. In this context, Loukas and Vasiliades [75] stated that the hydrological year 1989–1990 was one of the driest years recorded in Thessaly when working with SPI values. Karavitis et al. [76] showed that 1989–1990 was indeed one of the driest years throughout Greece. The same pattern is validated from other similar studies [5,77]. Vasiliades [78] revealed that the hydrological year 1976–1977 was the driest recorded year, whereas the second one was 1989–1990, which was characterized as a severe drought event. Unfortunately, the hydrological year 1976–1977 is not included in the present study, and as a result, the relevant time series is different from the previous ones, which has an impact on the computation of the index, especially when dealing with the specific parameters driven for the estimation of SPI values.
In contrast, the wettest year (2002–2003) showed very wet conditions prevailing in several parts of the study area. This is also validated by Kourtis et al. [77]. A previous study using the Reconnaissance Drought Index (RDI) is also in accordance with the previous assumption [78]. In the wettest hydrological year, the study finds a very similar pattern for most of the months, with almost the entire area characterized by very wet conditions from January to August, with some exceptions with differentiated spatial patterns presented in a few months. In December, the study observes a gradient pattern of wetness from west to east. The study also finds that from January to August, extremely wet conditions prevail in almost the entire study area, and moderate to very wet conditions in December and September, whereas only in October and November, from 64 to 77% of the region is characterized by normal conditions.
Regarding the intra-annual variation, January and February were the driest months recorded during the hydrological year 1989–1990 according to [75]. The present study comes in accordance with this finding (Table 3). Further analysis highlights that the spatial distribution of drought severity in the driest hydrological year is similar for most months, with almost the entire region characterized by extreme drought severity, except for some parts in the northern and northwestern territory which face a moderate degree of drought. However, there is a differentiated pattern in October and November, where most of the region is characterized by moderate drought in October and a differentiated pattern of drought severity in November. The study also revealed that 71–99% of the study area (in terms of pixels) suffers from extreme drought conditions for all months except October and November. Figure 6 illustrates an intra-annual comparison for the hydrological year 1989–1990 between SPI12 values derived from the present methodology and conventional values retrieved from [78]. Although the shape of the lines presents a similar behavior, it is obvious that the presented methodology illustrates greater absolute values than the conventional one. This may be explained by the different time series used for the normalization of SPI12 values between the two methodologies.
The analysis revealed differentiated spatial patterns among the regions of the Thessaly area. The central, western, and northern parts of the territory experienced moderate drought conditions, whereas some of the central and eastern regions faced slight drought conditions. The very eastern region, next to the sea, was characterized by normal conditions. In contrast, the northwestern region was characterized by very wet conditions, followed by the neighboring territory in the center of the study area, which faced moderate wet conditions. The last region, located in the southeast, was characterized by normal conditions. Indeed, as stated in [79], Pindos Mountain divides continental Greece into western and eastern regions, and the typical climate of mountainous regions with high annual precipitation and strong gradients of precipitation and temperature is gradually converted to the Mediterranean type. The use of the SPI12 index can help to identify extreme conditions and understand the spatial and temporal patterns of these conditions, which can aid in developing strategies to manage the impacts of drought and wetness on the region’s agricultural systems [80].
In the same context, a geospatial analysis of future projections of extreme drought incidents can provide a valuable preventative tool for the most affected regions. Politi et al. [61] presented a similar approach to making future projections in Greece using SPI and ERA5 data. The authors have already begun to conduct a preliminary analysis of other Mediterranean regions to enhance climate resilience.
Overall, the study provides a comprehensive analysis of the spatial and temporal patterns of environmental extremes (drought/wetness) across the study domain, based on the SPI12 index. The results can help to understand the severity and frequency of drought and wetness events, a fact that is important for rational water resources management and planning, agriculture, and other sectors that may be affected by climate variability. The results of the current analysis can contribute to the enhancement of the spatial resilience of the Thessaly region, an area with high agricultural importance in Greece. Finally, based on the above results, farmers should take advantage of precision agriculture technologies, exploiting, for instance, Internet of things (IoT) devices for the estimation of actual water consumption [81] as well as smart irrigation systems to improve water use efficiency, adopting state-of-the-art wireless communication technologies and sophisticated irrigation control and monitoring systems [82,83]. In the same context, [84] developing sensor cloud-based precision agriculture for water resource optimization reduces the environmental footprint of the agricultural sector and enhances crop productivity. Such technologies should be applied, especially in those areas that are characterized by a high degree of drought as mapped in the above analysis.

5. Conclusions

In this paper, an integrated spatiotemporal drought assessment of the Thessaly region was conducted, taking advantage of the Standardized Precipitation Index (SPI12) for almost 30 years (1981–2020). The index was computed using monthly CHIRPS data as the main input. The results show that the region experienced two consecutive dry hydrological years (1988–1989 and 1989–1990), which can be considered moderately and extremely dry conditions, respectively. Additionally, wet conditions were observed in two cases. The first one was the hydrological year 2002–2003, considered extremely wet, whereas 2009–2010 experienced moderately wet conditions. The spatial variability of the index was mapped for both dry and wet cases, and as a result, a considerable spatial variation was found throughout the region. An intra-annual geospatial analysis was also made, indicating that drought severity was high for most of the months in the driest hydrological year. The same but reverse pattern was almost found for the wettest hydrological year.

These research results were validated by previous studies in the region and can constitute a useful tool for water management practices. Future studies will concentrate on other regions with significant agricultural importance in Greece or in the Mediterranean basin, especially in similar semi-arid climates. Additionally, the authors have already started to use future projections of extreme incidents in order to estimate drought forecasts and contribute to a more feasible water management policy.


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