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Indices are used for representing complex phenomena; however, concerns usually arise regarding their objectivity and reliability, particularly dealing with their uncertainties during the development process. The current overarching objective is to reveal the significance of employing different weighting techniques in the application of the Standard...
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environmental and social impacts. The vulnerability to droughts, however, is complex to assess and strongly depends on the sectoral focus as well as on the geographical context of the assessment. This report presents the results of an expert survey that was conducted to weigh drought vulnerability indicators according to their relevance for agricul...
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... Initially, primitive composite models such as Multivariate Standardized Drought Index (MSDI 1 ) were developed, utilizing simplified methods that can be seen as advanced versions of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) indices. Furthermore, MSDI 1 represents an improvement over P-ET ref input models, which tend to overestimate drought events in desert and semi-desert climates (Fassouli et al. 2021;Tsesmelis et al. 2019). ...
In order to improve the reliable monitoring and prediction of drought behavior, it is crucial to comprehensively consider composite indices. Initially, four univariate drought indices were evaluated: the Standardized Precipitation Index, the Standardized Soil Moisture Index of the top two layers (SSI1 and SSI2), and the Standardized Precipitation Evapotranspiration Index. Subsequently, this paper introduces five composite drought indices: the Multivariate Standardized Drought Index, the modified Aggregate Drought Index, the Joint Drought Deficit Index, the Machine Learning (ML-based) Drought Index (SVMs), and the Artificial Intelligence (AI-based) Drought Index (ANF-PSOs) models based on precipitation (P), soil moisture at two layers (SM1 and SM2), and reference evapotranspiration as input variables. These drought indices are formulated using P-ETref inputs, P-SM1 inputs, and P-SM2 inputs. The study covers 30 main basins classified into six climate zones, including coastal wet, mountain, semi mountain, semi desert, desert, and coastal desert across Iran from 1979 to 2021. The performance of the studied models was evaluated using the correlation coefficient and root mean square error in comparison with SPI at the same time scales as the target model. Drought characteristics, including the number of drought events, duration, frequency, and intensity, were determined for each model at monthly, seasonal, and yearly time scales. The results revealed that the recommended P-SM1 inputs for SVMs and ANF-PSOs models significantly outperformed the MSDIs, modified ADIs, and JDIs models. The results indicated that the introduced composite models effectively captured the comprehensive SM situation without being heavily influenced by individual parameters. The behavioral patterns of these indices remained consistent, except for the specific performance of ETref, which caused some inconsistencies. Moreover, a comparison among different climates revealed that ETref played a prominent role in the discrepancies observed in the output of the composite models, resulting in a strong relationship between P and ETref. Consequently, when constructing composite indices, the information conveyed by ETref was more readily disregarded by JDI and ADI but retained in ANF-PSO. This research sheds light on the mechanisms of these ML-based and AI-based composite approaches in integrating different drought features. It offers valuable insights into the performance of composite drought models and provides benchmarks, particularly in dry climates, to enhance drought monitoring methods.
... Multivariate drought index (MDI) can simultaneously reflect the multiple characteristics of meteorological, hydrological, and agricultural droughts and accurately captures more drought events . Considering atmospheric water deficit, river basin water shortage and crop irrigation deficiency (Um et al., 2022), SPEI, standardized runoff index (SRI) and standardized soil moisture index (SSMI) were selected (representing meteorology, hydrology, and agricultural drought, respectively) to construct MDI. Weighting method (Tsesmelis et al., 2019), fuzzy comprehensive method and other methods are effective methods to construct MDI, but the subjectivity is too strong. Projection pursuit model (PPM) is a statistical method for processing and analyzing high-level data , which can comprehensively, objectively, and quantitatively evaluate drought risk when it is used to construct MDI. ...
... The expert knowledge weighting methods (Ex-sp and Ex-ov) require a highly complex and time-consuming procedure to obtain feedback [86] and might have led to a greater degree of subjectivity to the DV evaluation [27]. The random weighting method is more time-consuming than the equal weighting technique, which has been used in many DV studies [48,84,87,88] due to its simplicity and ease of reproducibility. However, the factors unlikely have the same influence. ...
Current approaches to identify vulnerability to drought often lack ground-truthing and transparent methodology; they are also often biased by expert subjectivity. Furthermore, most drought vulnerability assessments are seldom system-specific but of general social relevance. Consequently, resultant vulnerability maps are typically a general snapshot of case-specific decisions, lacking teeth in their statistical soundness and system-specific relevance. Thus, this study presents an impact-based method for a transparent selection of effective vulnerability factors for six vulnerable systems (social, economic, agricultural, ecological, water resources, and energy-industry) The study is based on a backward multivariate linear regression (MLR) model, which links vulnerability factors to historical drought impacts for the case of Iran. The drought vulnerability index (DVI) and the comprehensive drought vulnerability index (CDVI) are calculated by aggregating the vulnerability of the six systems. Four different weighting methods are then applied to combine driving vulnerability factors to assess the vulnerability of each system for three timesteps (2005, 2010, and 2015). The vulnerability values are presented as ranges by combining the results of four weighting methods. Furthermore, identified driving vulnerability factors are classified by their manageability. A range of 154 vulnerability factors were tested as possible drivers, from which 44 were identified as effective factors. Overall, the results especially highlight that the social and economic systems are vulnerable to drought. From a systemic perspective, socioeconomic factors, such as infrastructure development, health conditions, and industrialization, might play a crucial role in reducing vulnerability. Thus, they should be prioritized in vulnerability reduction programs.
... Nevertheless, a full assessment of drought in the region requires the characterisation of drought, which permits activities such as early drought alarm and drought risk mitigation; this assessment would improve the preparation and catastrophe planning (Tsesmelis et al. 2019;Garca In principle, the paradigm for assessing vulnerability to disasters described in this study may be employed to update drought vulnerability information with real-time data for adjustments to drought mitigation techniques. ...
Drought is one of the major barriers to the socio-economic development of a region. To manage and reduce the impact of drought, drought vulnerability modelling is important. The use of an ensemble machine learning technique i.e. M5P, M5P -Dagging, M5P-Random SubSpace (RSS) and M5P-rotation forest (RTF) to assess the drought vulnerability maps (DVMs) for the state of Odisha in India was proposed for the first time. A total of 248 drought-prone villages (samples) and 53 drought vulnerability indicators (DVIs) under exposure (28), sensitivity (15) and adaptive capacity (10) were used to produce the DVMs. Out of the total samples, 70% were used for training the models and 30% were used for validating the models. Finally, the DVMs were authenticated by the area under curve (AUC) of receiver operating characteristics, precision, mean-absolute-error, root-mean-square-error, K-index and Friedman and Wilcoxon rank test. Nearly 37.9% of the research region exhibited a very high to high vulnerability to drought. All the models had the capability to model the drought vulnerability. As per the Friedman and Wilcoxon rank test, significant differences occurred among the output of the ensemble models. The accuracy of the M5P base classifier improved after ensemble with RSS and RTF meta classifiers but reduced with Dagging. According to the validation statistics, M5P-RFT model achieved the highest accuracy in modelling the drought vulnerability with an AUC of 0.901. The prepared model would help planners and decision-makers to formulate strategies for reducing the damage of drought.
... The standardized precipitation evapotranspiration index (SPEI) takes PET into account, but in fact in arid and semi-arid areas where potential evapotranspiration is greater than precipitation, the monthly total PET is actually the amount of water that is not available and therefore cannot be evaporated and transpired. Its use may lead to inaccurate estimates of drought events, such as overestimating droughts [30][31][32][33]. The standardized precipitation index (SPI) is a standardized value that expresses the actual precipitation as a deviation from the probability distribution function of precipitation. ...
In northern China, precipitation fluctuates greatly and drought occurs frequently, which mark some of the important threats to agricultural and animal husbandry production. Understanding the meteorological dry-wet change and the evolution law of drought events in northern China has guiding significance for regional disaster prevention and mitigation. Based on the standardized precipitation index (SPI), this paper explored the spatio-temporal evolution of meteorological dry-wet in northern China. Our results showed that arid area (AA) and semi-arid area (SAA) in the west showed a trend of wetting at inter-annual and seasonal scales, while humid area (HA) and semi-humid area (SHA) in the east showed a different dry-wet changing trend at different seasons under the background of inter-annual drying. AA and HA showed obvious “reverse fluctuation” characteristics in summer. The drought frequency (DF) and drought intensity (DI) were high in the east and low in the west, and there was no significant difference in drought duration (DD) and drought severity (DS) between east and west. The DD, DS and DI of AA and SAA showed a decreasing trend, while the DD and DS of HA and SHA showed a slight increasing trend, and the DS decreased. In summer and autumn, the main influencing factors of drying in the east and wetting in the west were PNA, WP, PDO and TP1, and the fluctuations of NAO-SOI, NAO-AMO and PNA-NINO3.4 jointly determined the characteristics of SPI3 reverse fluctuations of HA and AA in summer.
... The comparison of SPI and SPEI is made to assess the impact of potential evapotranspiration which is a metric of the atmospheric evaporative demand (AED) to determine the drought in the study areas as well as the uncertainty in the results obtained using the SPI. SPI or SPEI values of 6 and 12 months are proposed as more appropriate for denoting droughts in arid and semi-arid regions, applied in several studies (e.g., [76,[80][81][82][83]). Accordingly, the SPI-6, SPEI-6, SPI-12, and SPEI-12 are selected for the drought characterization in Greece. ...
Future changes in drought characteristics in Greece were investigated using dynamically downscaled high-resolution simulations of 5 km. The Weather Research and Forecasting model simulations were driven by EC-EARTH output for historical and future periods, under Representative Concentration Pathways 4.5 and 8.5. For the drought analysis, the standardized precipitation index (SPI) and the standardized precipitation-evapotranspiration index (SPEI) were calculated. This work contributed to achieve an improved characterization of the expected high-resolution changes of drought in Greece. Overall, the results indicate that Greece will face severe drought conditions in the upcoming years, particularly under RCP8.5, up to 8/5 y of severity change signal. The results of 6-month timescale indices suggest that more severe and prolonged drought events are expected with an increase of 4 months/5 y, particularly in areas of central and eastern part of the country in near future, and areas of the western parts in far future. The indices obtained in a 12-month timescale for the period 2075–2099 and under RCP8.5 have shown an increase in the mean duration of drought events along the entire country. Drought conditions will be more severe in lowland areas of agricultural interest (e.g., Thessaly and Crete).
... In each survey, experts are asked to conduct a weighting process according to the susceptibility of each element to drought impacts [42]. There is also another type of weighting technique based on vulnerability index [43] which uses the Standardized Drought Vulnerability Index (SDVI). ...
... Exposure is one of the most important concepts in disaster risk analysis. Based on the exact definition of UNDRR terminology, exposure to some natural hazards may be described as "being in the wrong place at the wrong time" [43]. In the case of drought, exposure usually focuses on life damages and losses [43], which is determined by several factors, such as population and livestock density, utilization of land for agriculture (percentage of irrigated farms), as well as water extraction, especially for the industrial sector [44]. ...
... Based on the exact definition of UNDRR terminology, exposure to some natural hazards may be described as "being in the wrong place at the wrong time" [43]. In the case of drought, exposure usually focuses on life damages and losses [43], which is determined by several factors, such as population and livestock density, utilization of land for agriculture (percentage of irrigated farms), as well as water extraction, especially for the industrial sector [44]. Exposure mapping sometimes implies the estimation of population and the number of infrastructures which are under the impact of disasters consequences. ...
A drought risk map has been developed at the national scale by using remote-sensing satellite data over Iran by combining output layers resulting from three main components of a risk-evaluation procedure including Hazard Quantification (HQ), Vulnerability Assessment (VA) and Identification of Elements at Risk (IER) in a GIS environment. In this respect, Drought Severity (DS) was calculated by using the monthly Normalized Difference Vegetation Index (NDVI) (over 31 years from 1986–2016). Iran landcover classification and a slope map, population density maps, and irrigated farm percentages at the provincial scale were utilized within the drought risk evaluation (DRE) process. The final risk map reveals that the northwest of the country, with a climate similar to the central European weather conditions, is exposed to the maximum drought risk. In contrast, the areas with an arid climate, mainly located in the middle of Iran, exhibits minimum risk against drought. Based on the risk map, the southern part of the Caspian Sea shows very low drought risk due to the moderate and subtropical climate in this region. The outputs of this research will provide advice and warnings to help decision makers reduce drought risk consequences after prioritizing risk areas at the administrative scale.
... Due to the differences in environmental and terrain factors in each region, different weighting methods may obtain different results. However, there is currently no fixed standard for the weight of each factor in the model (Tsesmelis et al. 2019). In recent years, AHP (Mokarram et al. 2021) and PCA (Alonso et al. 2019;Aryal and Zhu 2021) have been widely and successfully applied in drought research. ...
Droughts in winter and spring are one of the most prominent natural disasters in the Yunnan Province in China. They occur frequently, with long durations and have a wide range of damage, which has a serious impact on social and economic development, as well as agricultural production and, therefore, strongly impacts the lives of the people living in the region. The traditional drought monitoring model does not take terrain into consideration, thereby affecting the comparative nature of results, as baseline conditions are not the same. Therefore, this study proposed a comprehensive drought monitoring model considering the influence of terrain factors to improve the evaluation effect. Firstly, based on NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measurement Mission (TRMM 3B43) data, vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (TRCI), and three terrain factors ground elevation (DEM), slope (SLOPE), aspect (ASPECT) were selected as model parameters. Then, a comprehensive drought monitoring model without considering terrain factors (Model A) and a comprehensive drought monitoring model of considering terrain factors (Model B) were constructed by using multiple linear regression models. Finally, the effects of the two models were evaluated by using standardized precipitation evapotranspiration index (SPEI) in southwest Yunnan Province, China, and model B was used to analyze the drought in winter and spring in the study area from 2008 to 2019. The results showed that (1) the correlation coefficient of model B was higher than that of model A in winter and spring and the standard error of model B was lower than that of model A. (2) The grade consistency rate of Model A and SPEI was 0.92 in winter and 0.33 in spring; the grade consistency between model B and SPEI was 0.83 in winter and 0.75 in spring, and therefore the monitoring effect of model B was more stable. (3) There were periodic droughts during the study period, and the degree of drought in spring was less than in winter. Medium and severe droughts were observed in winter. Thus, this study concluded that the effect of terrain has an important influence on the evaluation of droughts. The comprehensive drought monitoring model which considers topographic factors can effectively identify the occurrence of drought, and therefore provide significant input with regards to disaster prevention and mitigation policies in southwest Yunnan.
... Thus, a drought may be inaccurately estimated (Karavitis et al., 2012a;Tsesmelis et al., 2019). ...
Tree ring chronologies are considered critical when investigating important relations between tree growth and climate. In the present study, we studied the impact of precipitation and temperature on Abies cephalonica, an endemic Greek species, by evaluating radial growth data from tree ring analysis. The two sampling stands had elevations of 988m and 1.274m above sea level respectively and were found on Mountain Ghiona in central Greece. The hyperbola two-parameter function was used for the determination of the tree growth model against time for both stands. After constructing and solving the equations, the Average Tree Ring Width Index (ARWI) values were calculated and the averaged values per site were standardized and plotted. Due to the lack of temperature data, during the last 30years, we used the Standardized Precipitation Index (SPI) which requires only precipitation data, in order to investigate the impact of extreme drought on Abies cephalonica growth at both elevations. Firstly, a 6-month time step (SPI6) was calculated and the period from March to August was selected for the relative growth correlations. The SPI12 was also calculated (October-September) and correlated, representing the hydrological year. Furthermore, separate correlations between selected average temperatures, precipitations (period 2009-2019) and growth have been tested. The potential possible use of SPI to reveal negative or positive growth due to extreme climate events was suggested. The extreme drought event of 1985 (SPI <-2) was followed by an important growth decrease for both stands. Moreover, the extremely wet year of 1971 (SPI>2) was followed by increased growth in the following year (1972) regarding both stands. Other milder events, as indicated by the SPI, were less easily imprinted on the measurable tree ring growth. A decline in growth was observed after 1998 for both elevations, not connected with SPI but probably associated with observed defoliations by the Choristoneura murinana in the area.
... In 13.1% and 4.7% respectively 1.00, 1.80 and 2.00). The annual rainfall map based on daily data (1991-2014) and the co-kriging (rainfall and Digital Elevation Model) interpolation method transformed the point data to spatial values. ...
Natural resources degradation poses multiple challenges, particularly to environmental
and economic processes. It is usually difficult to identify the degree of degradation and the critical vulnerability values in the affected systems. Thus, among other tools, indices (composite indicators) may also describe these complex systems or phenomena. In this approach, the Water and Land Resources Degradation Index was applied to the fifth largest Mediterranean island, Crete, for the 1999–2014 period. The Water and Land Resources Degradation Index uses 11 water and soil resources related indicators: Aridity Index, Water Demand, Drought Impacts, Drought Resistance Water Resources Infrastructure, Land Use Intensity, Soil Parent Material, Plant Cover, Rainfall, Slope,
and Soil Texture. The aim is to identify the sensitive areas to degradation due to anthropogenic interventions and natural processes, as well as their vulnerability status. The results for Crete Island indicate that prolonged water resources shortages due to low average precipitation values or high water demand (especially in the agricultural sector), may significantly affect Water and Land degradation processes. Hence, Water and Land Resources Degradation Index could serve as an extra tool to assist policymakers to improve their decisions to combat Natural Resources degradation.
