Figure 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Drought is a natural phenomenon caused by extreme and persistent precipitation shortage. This shortfall causes impacts on hydrology, agriculture, and the economy of a country. Secondly, drought/dryness has certain unique characteristics (severity, duration) among natural hazards which makes it difficult to classify the persistent and subjective net...
Contexts in source publication
Context 1
... literature, several drought realizations were identified by (Wilhite & Glantz 1985). From the research, four major types of drought such as meteorological, agricultural, hydrological, and socioeconomic droughts are classified ( Figure 1). Meteorological and hydrological droughts are linked with a reduction in precipitation. ...
Context 2
... the literature, several drought realizations have identified by Wilhite & Glantz (1985). From the research, four major types of drought are classified as meteorological, agricultural, hydrological, and socioeconomic droughts ( Figure 1). Meteorological and hydrological droughts are linked with a reduction in precipitation. ...
Similar publications
Water is the most important substance needed by living things apart from air. Long droughts have serious impacts on society and the environment resulting in a lack of clean water supply, both in terms of quality and quantity. The purpose of the article is to contribute to a better understanding of overcoming the clean water crisis by utilizing arti...
Unprecedented heatwaves accompanying severe droughts hit South Europe in May‒July 2022. From the interdisciplinary perspective, this study revealed that the extreme climate events can intensify European energy crisis through pushing up electricity demand and limiting renewable energy supply that makes up more than one‐third of gross electricity con...
Central Chile has experienced a protracted megadrought since 2010 (up to date), with annual precipitation deficits ranging from 25 % to 70 %. Drought propagation has been intensified during this time, with streamflow reductions up to 30 % larger than those expected from historical records. This intensification has been attributed to the cumulative...
The effects of extreme weather events and the resilience of the energy sector have become the subject of regulatory initiatives and ongoing research. We demonstrate the vulnerability of the German power sector to climate change and provide a qualitative and quantitative analysis of emerging risks from two types of extreme weather events: droughts a...
We assess the spatiotemporal characteristics of historical and projected future drought over southern Ethiopia using the Standardized Precipitation Evapotranspiration Index and the K‐means clustering method. Historical assessment is done for the period 1981–2018 while projected drought is analysed over two consecutive future periods, that is, 2021–...
Citations
... Drought is a significant challenge for agriculture, affecting farmers worldwide due to insufficient irrigation and reduced rainfall (Orimoloye et al., 2022). This natural phenomenon is complex and not limited to specific regions or timeframes, making it difficult to monitor and manage (Faiz et al., 2021). Drought stress is primarily caused by a decrease in precipitation, leading to prolonged dry periods. ...
Ensuring global food security and achieving sustainable agricultural productivity remains one of the foremost challenges of the contemporary era. The increasing impacts of climate change and environmental stressors like drought, salinity, and heavy metal (HM) toxicity threaten crop productivity worldwide. Addressing these challenges demands the development of innovative technologies that can increase food production, reduce environmental impacts, and bolster the resilience of agroecosystems against climate variation. Nanotechnology, particularly the application of nanoparticles (NPs), represents an innovative approach to strengthen crop resilience and enhance the sustainability of agriculture. NPs have special physicochemical properties, including a high surface-area-to-volume ratio and the ability to penetrate plant tissues, which enhances nutrient uptake, stress resistance, and photosynthetic efficiency. This review paper explores how abiotic stressors impact crops and the role of NPs in bolstering crop resistance to these challenges. The main emphasis is on the potential of NPs potential to boost plant stress tolerance by triggering the plant defense mechanisms, improving growth under stress, and increasing agricultural yield. NPs have demonstrated potential in addressing key agricultural challenges, such as nutrient leaching, declining soil fertility, and reduced crop yield due to poor water management. However, applying NPs must consider regulatory and environmental concerns, including soil accumulation, toxicity to non-target organisms, and consumer perceptions of NP-enhanced products. To mitigate land and water impacts, NPs should be integrated with precision agriculture technologies, allowing targeted application of nano-fertilizers and nano-pesticides. Although further research is necessary to assess their advantages and address concerns, NPs present a promising and cost-effective approach for enhancing food security in the future.
... Due to the non-availability of site-specific spatiotemporal observations on various drought indicators such as soil moisture, ET, groundwater level, reservoir level, streamflow, etc., in many parts of the world, conventional global drought assessment methods have usually been observed to be based on single indicator(s) to represent a specific type of drought (Sivakumar et al. 2011); for example SPI for monitoring meteorological droughts. However, objective characterization of drought conditions generally requires the integration of several drought-related variables or indices (Faiz et al. 2021). With advancements in geospatial technology, it is now possible to monitor agricultural, hydrologic and socio-economic droughts at varied spatio-temporal scales and even over areas having inadequate data or lacking in-situ monitoring stations through the use of a number of promising hybrid or composite drought indicators thereby leading to improved drought monitoring (Hobbins et al. 2016;Faiz et al. 2018;Flint et al. 2018;Xu et al. 2018;Shen et al. 2019;Chattopadhyay et al. 2020;Kulkarni et al. 2020;Danodia et al. 2021;Kumar et al. 2021;Abdourahamane et al. 2022;Pandya et al. 2022;Prajapati et al. 2022). ...
The present investigation was primarily aimed at cross-comparing the agricultural drought declaration potential of a 4-month averaged Composite Drought Index (CDIavg) vs. its end of the season monthly (i.e. CDIsep) values over one of the most drought-prone states (i.e. Karnataka) of India. The afore-stated composite drought indices were based on a combination of three indicators, namely Standardized Precipitation Index (SPI), Evaporative Stress Index (ESI) and Normalized Difference Vegetation Index anomaly (NDVIanamoly). Because kharif season (June to September) is considered to be one of the most important seasons for farming in India, the drought declaration potential of the developed composite drought indices (i.e., CDIavg and CDIsep) was evaluated in terms of their cross-comparison with the government declared droughts and crop yield anamolies during 18 - kharif cropping seasons of the validation years from 2001 to 2018. The investigation revealed superior performance of the (objective and spatially distributed) composite drought index-based approaches over the conventional (subjective and lumped) drought declaration protocols being followed by the state and the central governments. Amongst the proposed two composite drought index-based approaches, the end of the season composite drought index (i.e., CDIsep) based protocol was observed to be comparatively more precise in targeting varied category drought hit areas. The investigation thus demonstrated immense potential of the validated approach for rational allocation of government relief funds, in proportion to the intensity and extent of droughts, across the study area.
... Due to the importance of streamflow drought, there have been ongoing efforts to simulate and predict drought occurrence and severity [8,[14][15][16][17][18][19]. Many studies and models have shown skill in simulating certain drought events; however, there are many different types of drought indicators [11,20,21], and the methodologies used to evaluate models are Water 2024, 16, 2996 2 of 22 inconsistent [22]. Differences in the methodologies used to evaluate drought simulations can make model intercomparison difficult, as is the case more broadly in hydrology [23,24]. ...
Hydrologic models are the primary tools that are used to simulate streamflow drought and assess impacts. However, there is little consensus about how to evaluate the performance of these models, especially as hydrologic modeling moves toward larger spatial domains. This paper presents a comprehensive multi-objective approach to systematically evaluating the critical features in streamflow drought simulations performed by two widely used hydrological models. The evaluation approach captures how well a model classifies observed periods of drought and non-drought, quantifies error components during periods of drought, and assesses the models’ simulations of drought severity, duration, and intensity. We apply this approach at 4662 U.S. Geological Survey streamflow gages covering a wide range of hydrologic conditions across the conterminous U.S. from 1985 to 2016 to evaluate streamflow drought using two national-scale hydrologic models: the National Water Model (NWM) and the National Hydrologic Model (NHM); therefore, a benchmark against which to evaluate additional models is provided. Using this approach, we find that generally the NWM better simulates the timing of flows during drought, while the NHM better simulates the magnitude of flows during drought. Both models performed better in wetter eastern regions than in drier western regions. Finally, each model showed increased error when simulating the most severe drought events.
... Droughts are commonly identified using drought indices which are derived from drought indicators (e.g. precipitation deficits, low soil moisture, low streamflow) (Mukherjee et al., 2018;Faiz et al., 2021;Yihdego et al., 2019;Bachmair et al., 2016). Most drought indices are representative of one specific drought type; for example the Standardized Precipitation Index (SPI; McKee et al., 1993) captures meteorological droughts, the Soil Moisture Anomaly (SMA; Orlowsky and Seneviratne, 2013) captures soil moisture drought, and the Standardized Runoff Anomaly (SRA; Gudmundsson and Seneviratne, 2015b) describes hydrological droughts. ...
The co-occurrence of meteorological, agricultural, and hydrological droughts (multivariate compound droughts) in Switzerland during growing season is problematic due to limitations in water abstractions from rivers during low-flow periods, while at the same time the need for irrigation is high. We analyse compound droughts for 52 catchments in Switzerland during the extended summer season (May–October) using the transient climate and hydrological scenarios for Switzerland (CH2018 and Hydro-CH2018) for both a scenario with mitigation (representative concentration pathway 2.6 (RCP2.6), 8 model chains) and a scenario without mitigation (RCP8.5, 20 model chains). In the RCP8.5 scenario the number of compound drought days is projected to significantly increase by mid-century across all greater regions of Switzerland. The increased frequency is mainly a result of more frequent events (significant) rather than longer event durations (non-significant). Models generally agree on the sign of change. By 2085, compound drought events are projected to occur in median once per catchment per extended summer season north of the Alps and every 1–2 years south of the Alps. Further, the increases in compound drought days mainly occur between May–October, leading to a shift in the main agricultural production season and a more pronounced seasonality with the highest occurrence probabilities between mid-July and the beginning of October. Coupled to the increase in days and events, significantly more catchments are projected to be affected by compound droughts at the same time. In the RCP2.6 (mitigation) scenario, the increase in the number of compound drought days and events is not significant by the end of the 21st century. In comparison with RCP8.5, the number of compound drought days is reduced by 50 %–55 % north of the Alps and by up to 75 % south of the Alps by the end of the century. This emphasizes the need for coordinated adaptation in combination with mitigation measures taken at an early stage.
... Several researchers [15][16][17]51,66,81,[90][91][92][93][94][95][96][97][98][99][100][101][102][103] have evaluated the effectiveness of the SPI and SPEI. Dai [51], Ellis et al. [94], Quiring [97], and White and Walcott [95] noted that the SPI is based only on precipitation and does not consider evapotranspiration, which makes it more suited to monitoring meteorological and hydrological droughts rather than agricultural drought, although Quiring [97] and Vicente-Serrano et al. [81] concluded that it is more effective than PDSI, Palmer Z Index, Effective Drought Index (EDI), and percent of normal precipitation. ...
... Quiring [97] also noted that, since percent of normal precipitation only relates precipitation to a base period and not to the historical variation in precipitation, it cannot be used to compare drought conditions over space or time. Naumann et al. [90], Vicente-Serrano et al. [92], and Homdee et al. [98] determined that the SPEI is more effective when compared with the SPI, but Vicente-Serrano et al. [91] concluded that the SPEI shows different sensitivity to precipitation and reference evapotranspiration as a function of the climatology and Faiz et al. [99] noted that the SPEI is not suitable for colder regions where winter temperatures are mostly below zero and potential evapotranspiration is essentially zero. Seasonality is also important in the application of the SPI [22,66,101], with the SPI more effective during the wet season than during the dry season. ...
Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought-monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America and to enhance the utility of drought-monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impact information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and its indicators are changing over time as the climate warms and by recommending ways to strengthen the use of indices and indicators in drought decision making.
... The results of the homogeneity test suggest that, for trend analysis (and for climate-related studies), one should consider the detected breakpoint to ensure that the only variations to be detected are caused by climate and not by non-climate factors [24,48]. Also, the use of long-term and reliable rainfall data is a prerequisite for the accurate identification of the drought tendency of any region [66]. ...
Understanding the spatiotemporal distribution of extreme rainfall and meteorological drought on a watershed scale could be beneficial for local management of any water resources system that supports dam operation and river conservation. This study considered the watershed of Angat as a case, given its economic importance in the Philippines. A series of homogeneity tests were initially conducted on each rainfall dataset from monitoring stations in and near the watershed, followed by trend analysis to determine the rate and direction of change in the annual and seasonal rainfall extreme indices in terms of intensity, duration, and frequency. Three indices, using the rainfall deviation method (%DEV), percent of normal rainfall index (PNRI), and Standardized Precipitation Index (SPI), were also used to identify meteorological drought events. Generally, rainfall in the watershed has an increasing annual PCPTOT (4–32 mm/year), with increasing frequency and intensity in heavy rainfall and wet days. A significant increasing trend (α = 5%) in the seasonal PCPTOT (7–65 mm/year) and R10mm (1.7–10.0 days/decade) was particularly observed in all stations during the Amihan Monsoon Season (Dec–Feb). The observed increasing rainfall intensity and frequency, if it continues in the future, could have an implication both for the water resources operation to satisfy the multiple objectives of Angat Reservoir and for the flood operation that prevents damage in the downstream areas. The effect of each ENSO (El Niño- Southern Oscillation) phase on the rainfall is unique in magnitude, intensity, and duration. The seasonal reversal of the ENSO in the extreme rainfall and meteorological drought signals in Angat Watershed was also evident. The identified meteorological drought events in the watershed based on SPI-12 persisted up to 12–33 months, could reduce more than 60% (PNRI < 40%) of the normal rainfall. Insights from the study have implications for the hydrology of the watershed that should be considered for the water resources management of the Angat Reservoir.
... Several researchers [15][16][17]51,66,81,[90][91][92][93][94][95][96][97][98][99][100][101][102][103] have evaluated the effectiveness of the SPI and SPEI. Dai [51], Ellis et al. [94], Quiring [97], and White and Walcott [95] noted that the SPI is based only on precipitation and does not consider evapotranspiration, which makes it more suited to monitoring meteorological and hydrological droughts rather than agricultural drought, although Quiring [97] and Vicente-Serrano et al. [81] concluded that it is more effective than PDSI, Palmer Z Index, Effective Drought Index (EDI), and percent of normal precipitation. ...
... Quiring [97] also noted that, since percent of normal precipitation only relates precipitation to a base period and not to the historical variation of precipitation, it cannot be used to compare drought conditions over space or time. Naumann et al. [90], Vicente-Serrano et al. [92], and Homdee et al. [98] determined that the SPEI is more effective when compared with the SPI, but Vicente-Serrano et al. [91] concluded that the SPEI shows different sensitivity to precipitation and reference evapotranspiration as a function of the climatology and Faiz et al. [99] noted that the SPEI is not suitable for colder regions where winter temperatures are mostly below zero and potential evapotranspiration is essentially zero. Seasonality is also important in the application of the SPI [22,66,101], with the SPI more effective during the wet season than during the dry season. ...
: Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America, and to enhance the utility of drought monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impacts information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and the colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and the warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and indicators are changing over time as the climate warms, and by recommending ways to strengthen the use of indices and indicators in drought decision-making.
... Its fixed time scale is the main shortcoming of PDSI, which makes it unable to calculate the characters of the scaling formula necessary to evaluate dry/wet conditions. Since dry/wet conditions are multiscale phenomena, sometimes PDSI is not a proper choice to indicate drought/flood conditions because it could lag emerging droughts by several months (Faiz et al. 2021). Therefore, Vicente-Serrano et al. proposed SPEI to solve this problem, which has been used in different regions of the world (McClaran and Wei 2014). ...
Drought is a gradual phenomenon that occurs slowly and directly impacts human life and agricultural products. Due to its significant damage, comprehensive studies must be conducted on drought events. This research employs precipitation and temperature from a satellite-based gridded dataset (i.e., NASA-POWER) and runoff from an observation-based gridded dataset (i.e., GRUN) to calculate hydrological and meteorological gical droughts in Iran during 1981–2014 based on the Standardised Precipitation-Evapotranspiration Index (SPEI) and Hydrological Drought Index (SSI) indices, respectively. In addition, the relationship between the meteorological and hydrological droughts is assessed over various regions of Iran. Afterward, this study employed the Long Short-Term Memory (LSTM) method to predict the hydrological drought based on the meteorological drought over the northwest region of Iran. Results show that hydrological droughts are less dependent on precipitation in the northern regions and the coastal strip of the Caspian Sea. These regions also have a poor correlation between meteorological and hydrological droughts. The correlation between hydrological and meteorological drought in this region is 0.44, the lowest value among the studied regions. Also, on the margins of the Persian Gulf and southwestern Iran, meteorological droughts affect hydrological droughts for 4 months. Besides, except the central plateau, most regions experienced meteorological and hydrological droughts in the spring. The correlation between droughts in the center of the Iranian plateau, which has a hot climate, is less than 0.2. The correlation between these two droughts in the spring is stronger than in other seasons (CC = 0.6). Also, this season is more prone to drought than other seasons. In general, hydrological droughts occurred one to two months after the meteorological drought in most regions of Iran. LSTM model for northwest Iran showed that the predicted values had a high correlation with the observed values, and their RMSE was less than 1 in this region. CC, RMSE, NSE, and R-square of the LSTM model are 0.7, 0.55, 0.44, and 0.6, respectively. Overall, these results can be used to manage water resources and allocate water downstream to deal with hydrological droughts.
... While PDSI already incorporates many key factors such as precipitation, temperature, duration, and soil moisture, there may be limitations in directly using PDSI to assess the impact of drought. [9] Therefore, this paper proposes a method to convert PDSI into a drought coefficient to more accurately reflect the impact of drought on ecosystems. The drought coefficient can comprehensively consider PDSI and other environmental variables that may affect the severity of drought, allowing us to more accurately quantify and predict the impact of drought on ecosystems. ...
Biodiversity and ecosystem interactions are a hot topic in environmental science. Since the Industrial Revolution, human activities have intensified the globalization of climate change, leading to changes in precipitation patterns on different continents, among which the exacerbation of drought cycles is one. To investigate the relationship between biodiversity and ecosystems under climate change, a plant species interaction model based on the Drought coefficient and biodiversity effect model is proposed in this study. This model utilized the data collected between 2008 and 2017 from Xinyuan County, China, to quantify the impact of drought on plant communities, determine the growth rate of each species, and investigate how the plant community as a whole change with the interaction among species and drought cycles. The results show that the existence of drought-resistant plants in the community can enhance the adaptability of the region to drought. Furthermore, the transformation of plant communities from competitive species to facilitative species can also enhance the overall drought resistance of the region.
... The availability of water that is below the needs and demands of humans or the environment is generally called the drought period (Wilhite and Pulwarty, 2018). Commonly, droughts are categorized into; meteorological (M) (related to a shortage of precipitation), hydrological (H) (associated with a deficit of river discharge), agricultural (linked with the decline of soil moisture [SM]) and socio-economic (when all the above drought events come with a negative impact on society) (Faiz et al., 2021). M drought may lead to soil water deficiency before runoff generation. ...
... Therefore, agricultural or SM drought may affect the H drought occurrence (Bhardwaj et al., 2020;. To identify and assess the impact of these droughts, several drought indices have been used in different countries (Bhardwaj et al., 2020;Faiz et al., 2021). For example, Standardized Precipitation Index (SPI) (McKee et al., 1993) is usually used for M droughts events; Standardized Streamflow Index (SSI) is developed to analyse H droughts (Shukla and Wood, 2008); Standardized SM Index (SSMI) drought can be used for SM or agricultural droughts (Farahmand and AghaKouchak, 2015). ...
... We employed three widely used drought indices (SPI, SSI, SSMI) to represent the drought typology and propagation from M to SM/agricultural and H drought (McKee et al., 1993;Barker et al., 2016;Van Loon and Laaha, 2015;Bhardwaj et al., 2020;Askarimarnani et al., 2021;Faiz et al., 2021). SPI, SSI, and SSMI are calculated based on nonparametric distribution (Gringorten, 1963) due to higher value of goodness-of-fit statistics compared to observed values over selected basins (Farahmand and AghaKouchak, 2015). ...
Understanding and exploring hydrological (H) or soil moisture (SM) drought due to meteorological (M) drought under changing climate is crucial for drought early warning. Previously, different methods were used to calculate drought propagation from one state (M to another [H or SM]). However, each method has its pros and cons and thus cannot describe appropriate propagation attributes. In this study, a time series analysis is carried out to explore the propagation process in the Yellow River Basin (YRB) and Hai River Basin (HRB) of China. Standardized Streamflow Index (SSI), Standardized SM Index (SSMI), and Standardized Precipitation Index (SPI) 1‐month time series were calculated using streamflow, satellite‐based SM, and precipitation from 1979 to 2016. The time series framework identifies the average drought propagation as 3.4–8.3 months (M–H) and 2.3–5 months (M–SM) in YRB, while 2.8–7.7 months (M–H) and 2.3–8.5 months (M–SM) in HRB. Cross‐wavelet analysis indicated that periodic characteristics of M drought are responsible for these droughts. Overall, the findings of this study may help to minimize the drought hazards posed by M droughts.