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The Relationship of Drought Frequency and Duration to Time Scales
... The annual average rainfall was 252.5 mm (Table 1). Using Standardized Precipitation index (SPI), the beginning, end, and severity of the drought were assessed (McKee et al. 1993(McKee et al. , 1995Soro et al. 2014). The duration of the drought was calculated using these data sets. ...
... The defining environmental characteristic of the research area is a severe water deficit. Monthly estimates of meteorological drought were acquired from September 2007 to August 2015 using the Standardized Precipitation Index (SPI) (McKee et al. 1993(McKee et al. , 1995Trabelsi et al. 2023). SPI is utilized to characterize the major events of the observed droughts in the study area, as outlined in Table 2. Precipitation primarily occurred during autumn and winter seasons when the evaporative demand was low (McKee et al. 1993(McKee et al. , 1995 and is based on precipitation data only. ...
... Monthly estimates of meteorological drought were acquired from September 2007 to August 2015 using the Standardized Precipitation Index (SPI) (McKee et al. 1993(McKee et al. , 1995Trabelsi et al. 2023). SPI is utilized to characterize the major events of the observed droughts in the study area, as outlined in Table 2. Precipitation primarily occurred during autumn and winter seasons when the evaporative demand was low (McKee et al. 1993(McKee et al. , 1995 and is based on precipitation data only. SPI offers advantages such as easy calculation, data requirements, and independence from mean precipitation, enabling comparisons across different climatic zones. ...
In olive-growing regions, climate change exacerbates challenges like high temperatures and water scarcity, necessitating a deeper understanding of its impact on olive trees behaviour to cope with drought. This study assesses the ecophysiological performance of 12-year-old olive trees ‘Chemlali Sfax’ (local) and ‘Koroneiki’ (Greek) and drought response mechanisms for developing agronomic strategies that ensure sustainable yields despite adverse climatic conditions in the Mediterranean region (Tunisia). Leaf water content declined from February, with ‘Chemlali Sfax’ and ‘Koroneiki’ maintaining higher content (59%) in June 2012. ‘Koroneiki’ exhibited significantly reduced relative water content (48.09%), leaf area (396.82 mm2), stomatal (346.59 ST/mm2), and trichomic densities (135.66 TR/mm2) compared to ‘Chemlali Sfax’. Soil moisture in the 40–80 cm horizon was higher in April, with ‘Koroneiki’ reaching 5.40% compared to ‘Chemlali Sfax’ at 4.42%. ‘Chemlali Sfax’ significantly outperformed ‘Koroneiki’ in yield, yielding 1129.2 kg/ha compared to 956.9 kg/ha during the 2011–2012 period. Despite these differences, both cultivars showed promising productivity potential under arid, rain-fed conditions, with an average water utilization of 0.8 kg/m3. ‘Chemlali Sfax’ and ‘Koroneiki’ demonstrated favourable adaptability and yield stability in arid conditions, suggesting its potential suitability for cultivation in such environments. Future research directions include exploring the effects of planting both cultivars at identical densities and combinations on olive oil and fruit qualities, as well as exploring additional ecophysiological parameters to enhance understanding of olive tree responses to water stress and drought tolerance mechanisms.
... The standardized precipitation index (SPI) method, developed by McKee et al. (1993), is a widely preferred approach for evaluating meteorological drought (Bazrafshan et al., 2023;Kartal, 2023). This method is advantageous over other drought indices because it only requires precipitation data to calculate the index value. ...
... The calculation of the SPI is presented on the basis of the following equation (McKee et al., 1993): ...
... The SPI value is calculated by dividing the difference between precipitation and its mean over a specified period by the standard deviation. According to the criteria of McKee et al. (1993), SPI index values are classified based on the drought classification provided in Table 1 (Balram & Fanai, 2021). Situations with a negative SPI index are termed drought, while positive values are called wet conditions. ...
The division and evaluation of data series used in monitoring drought into different time intervals is a practical approach to detecting the spatial and temporal extent of drought spread. This study aimed to determine meteorological drought’s spatial and temporal distribution using overlapping and consecutive periods and cycles of the standardized precipitation index (SPI) time series in the Mediterranean region, Turkey. In the scope of the research, SPI values for the SPI12, SPI6 (1), and SPI6 (2) seasons were calculated for consecutive and overlapping hydrological years (1978–1998/21 years, 1978–2008/31 years, and 1978–2018/41 years) at 28 meteorological stations. Autocorrelation, Mann–Kendall, and Sen slope trend tests were applied at a 5% significance level for each season (SPI12, SPI6 (1), and SPI6 (2)) and different time scales (21, 31, and 41 years). For each season and period, maps of the SPI drought class, average formation of drought class, Mann–Kendall (MK) trend, and Sen’s slope (SS) trend test statistics for the Mediterranean region were obtained, and the spatial distribution rate of trends was determined by drawing hypsometric curves. Changes in drought occurrence at different time scales were thoroughly evaluated with the changing length of data recording. Consequently, it was determined that the mild wet (MIW) and mild drought (MID) classes dominate the study area in the Mediterranean region. Significant and nonstationary changes detected in extreme wet and drought occurrences (extreme wet, EW; severe wet, SW; extreme drought, ED; severe drought, SD) were found to pose a risk in the study area. It was observed that there were spatially and temporally insignificant decreasing drought trends in the Mediterranean basin, considering that the time scales of these trends slowed down. Despite a nonsignificant trend from the MID drought class to the MIW drought class, it is predicted that the MIW and MID classes will maintain their dominance in the Mediterranean region. The central part of the study area (central Mediterranean basin) is the region with the highest drought risk.
... In this regard, it is very important to identify hydrological and meteorological droughts in the region of river basins and lakes, since it is not surprising that drought is one of the factors causing changes in the level and area of lakes, which in turn plays an important role in maintaining the ecological system of this region. The following drought indices are used to determine hydrological drought in the lake basin: Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) (McKee et al. 1993;Shukla & Wood 2008;Li et al. 2016). ...
... The SPI, recommended by WMO for drought monitoring (WMO 2009) was proposed by McKee et al. (1993) and is based on the use of time series of monthly precipitation amounts. The calculation procedure involves transforming the precipitation time series using a gamma distribution and then normalizing the resulting probabilities into a SPI: ...
... SDI values were calculated using DrinC (http://drinc.ewra.net/) (Tigkas et al. 2015(Tigkas et al. , 2022 and SPI values were calculated using SPI Generator Application software (McKee et al. 1993;Edwards & McKee 1997;Ali et al. 2020). ...
Hydrological droughts occur due to a variety of hydrometeorological phenomena, such as a lack of precipitation, reduced snow cover, and high evaporation. The values of these factors vary depending on the climate and the severity of drought events. Droughts caused by a lack of precipitation and continuing in the warm season have a longer periodicity. This important statement raises the question of whether climate change may exacerbate the phenomenon of drought. Therefore, understanding the changes in the formation of hydrological droughts is key to foreseeing possible changes in the future. This scientific study analyses the spread of hydrometeorological droughts in the Ile-Balkash basin using standardized precipitation indices and the drought index of river runoff. Lake Balkash plays an important role in the hydrological cycle and is a valuable freshwater resource, especially in dry years. Prolonged droughts in the area have serious consequences, such as deterioration of water quality and loss of wetlands, which are important to the ecological system and migratory birds. The analysis shows that during the period of instrumental observations, several extreme hydrological droughts were observed in this area (1943–1946, 1973–1975, and 1983–1987), which emphasizes the relevance and importance of scientific research on the problem of drought.
... However, temperature is a crucial parameter in discerning droughts since certain temperature conditions can offset the effects of rainfall. Moreover, the conventional SPI metric is based on the assumption that rainfall adheres to a gamma distribution (McKee et al. 1993). However, our research has unveiled that the gamma distribution may be insufficient for effectively modeling rainfall data. ...
... The new metric MSDI is depicted in Fig. 7 whereas the SPI is depicted in Fig. 8. Fitting this long-term precipitation record to a gamma probability distribution is the first step, the cumulative probability of observed precipitation is computed and then inversely transformed by a standard normal distribution with mean 0 and variance 1. The resulting index is the SPI (McKee et al. 1993). In Fig. 7, if an MSDI value extends beyond the red (bold) line, it indicates an extreme dry event whereas, if the index value falls between the red (thin) line and the red (bold) line, it is categorized as a dry condition. ...
... Furthermore, the developed two-dimensional index system has been compared with the already existing, most widely used drought monitoring index SPI, built upon the analysis of precipitation data only. The common approach of developing the SPI involves fitting a gamma distribution to the precipitation data (McKee et al. 1993). In our study we integrate both precipitation and ground temperature to calculate MSDI. ...
Drought is a global threat caused by the persistent challenges of climate change. It is important to identify drought conditions based on weather variables and their patterns. In this study, we enhanced the Standardized Precipitation Index (SPI) by integrating ground temperature data to develop a more comprehensive metric for evaluating drought severity: the Multivariate Standardized Drought Index. Our metric offers a dual assessment of drought severity, taking into account both the intensity of the drought and its duration. We employ this evaluation in a primary paddy cultivation region of Sri Lanka, with the aim of shedding light on the prevailing drought conditions affecting paddy crops due to insufficient water supply and prolonged periods of elevated temperatures. Additionally, we calibrate our metric by aligning it with historical drought records and subsequently compare the outcomes with those derived from the conventional SPI.
... tion to define the relationship of probability to precipitation. Next, an estimate of the inverse normal can be used "to calculate the precipitation deviation for a normally distributed probability density with a mean of zero and standard deviation of unity" (McKee et al., 1993, Section 2.0). ...
... When McKee et al. (1993) first proposed the SPI, they recommended using a monthly precipitation dataset with "ideally a continuous period of at least 30 years" (McKee et al., 1993, Section 2.0). They further discussed how decision makers use hydrologic data "as a percent of average using recent climatic history (the last 30 to 100 years)", hence the goal of developing a drought index within that comparative reference period (McKee et al., 1993, Section 1.0). ...
... When McKee et al. (1993) first proposed the SPI, they recommended using a monthly precipitation dataset with "ideally a continuous period of at least 30 years" (McKee et al., 1993, Section 2.0). They further discussed how decision makers use hydrologic data "as a percent of average using recent climatic history (the last 30 to 100 years)", hence the goal of developing a drought index within that comparative reference period (McKee et al., 1993, Section 1.0). ...
Should drought be considered an extreme dry period based on the entire record of available data? Or, should drought be considered a low in precipitation variability within the context of a present, contemporary climate? The two most common reference periods are the full period of record (all observed data or as much as possible) and a 30-year reference climatology. However, climate non-stationarity may render the "all-data" ap-proach an inaccurate or obsolete comparison unless a trend is factored in. The aim of this review is to explore the literature for approaches to addressing these issues. The World Meteorological Organization (WMO) has recommended a 30-year reference period for most climatological applications since 1935, but for drought assessments and drought indices the modus operandi has been to use as much data as possible. However, in the literature, the “all data” approach has been challenged by evident impacts from climate change-induced non-stationarity. Over the past several years, as potential errors in drought assessments became more apparent due to a stationarity assumption when applying drought indices, several studies have adopted shorter reference periods, with 30-years being the most common. Furthermore, several recent papers have recommended using short reference periods with more frequent data updates for drought assessments to be representative of a contemporary climate. Additionally, at least 18 non-stationary drought indices have been proposed in efforts to retain long datasets and account for non-stationarity in the climate system.
... The calculated LST ( Figure 5) and NDVI scatter plot produces a space with a trapezoidlike shape [45,46]. As depicted in Figure 6, every region within this space carries valuable information regarding the water content of both the canopy and soil, enabling the extraction of details about the orchard's water status. ...
... Regarding the availability of data and the rainfed agricultural system in Tamale, Ghana, alongside the popularity of the standardized precipitation index (SPI), the SPI index was chosen over other meteorological drought indices such as Palmer drought severity Index (PDSI), percent of normal precipitation, standardized precipitation-evapotranspiration Index (SPEI), effective drought index (EDI), rainfall anomaly index (RAI) and deciles index. SPI is the most common drought index, developed by [46]. It is based on precipitation data for different time scales. ...
... In order to calculate SPI, long-term monthly precipitation data are needed. For SPI calculation, it has been assumed that the frequency distribution of precipitation data (g(x)) follows the two-parameter gamma probability distribution [46,47]: ...
The study focused on the prediction of the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana. It investigated the temporal relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and TVDI. The SPI was calculated based on utilizing precipitation data from the World Meteorological Organization (WMO) database (2010–2022) and CMIP6 projected precipitation data (2023–2050) from 35 climate models representing various Shared Socioeconomic Pathway (SSP) climate change scenarios. Concurrently, TVDI was derived from Landsat 8/9 satellite imagery, validated using thermal data obtained from unmanned aerial vehicle (UAV) surveys. A comprehensive cross-correlation analysis between TVDI and SPI was conducted to identify lag times between these indices. Building on this temporal relationship, the TVDI was modeled as a function of SPI, with varying lag times as inputs to the Wavelet-Adaptive Neuro-Fuzzy Inference System (Wavelet-ANFIS). This innovative approach facilitated robust predictions of TVDI as an agricultural drought index, specifically relying on SPI as a predictor of meteorological drought occurrences for the years 2023–2050. The research outcome provides practical insights into the dynamic nature of drought conditions in the Tamale mango orchard region. The results indicate significant water stress projected for different time frames: 186 months for SSP126, 183 months for SSP245, and 179 months for both SSP370 and SSP585. This corresponds to a range of 55–57% of the projected months. These insights are crucial for formulating proactive and sustainable strategies for agricultural practices. For instance, implementing supplemental irrigation systems or crop adaptations can be effective measures. The anticipated outcomes contribute to a nuanced understanding of drought impacts, facilitating informed decision-making for agricultural planning and resource allocation.
... Later, the computed probabilities of the variable are transformed to a Normal distribution with a mean of zero and a standard deviation of one (Kumar et al., 2009). The most well-known standardized index is the standardized precipitation index (SPI) which was first introduced by Mckee et al. (1993). It is calculated with monthly precipitation which is fitted to twoparameter gamma probability distribution and then transformed into a normal distribution (Kumar et al., 2009;Keyantash and Dracup, 2002;Hayes et al., 1999). ...
... The calculation of SSI is based on the same principles as SPI by McKee et al. (1993) which essentially is the difference in streamflow value from the mean for a given period divided by the standard deviation, where the mean and standard deviation is established using past records. However, the disadvantage of directly using this simple method is that streamflow values are typically not normally distributed and likely to provide an uncertain non-exceedance probability. ...
... However, the disadvantage of directly using this simple method is that streamflow values are typically not normally distributed and likely to provide an uncertain non-exceedance probability. As a result, McKee et al. (1993) suggests resolving it by determining a good fitting cdf for the streamflow values of each calendar month using maximum likelihood estimation and then transforming it to a normal distribution with mean zero and standard deviation of one. Thus, the resulting computation of standardized streamflow is linearly proportional to streamflow deficit and allows specification of probability, percent of average, and accumulated streamflow deficits. ...
... However, it is important to note that this step does not distort the probabilistic CDF values (i.e., this transformation can be performed in both directions without information loss). This approach is functionally similar to methods used to compute other standardized drought indices, such as the Standardized Precipitation Index (SPI, [(McKee et al., 1993)]), described in greater depth below. For this analysis, SMI obs values were truncated at 2 and 2. This is consistent with common drought assessment practices, where values that exceed 2 and 2 are considered extreme and treated equal to 2 or 2 (representing roughly the 2nd and 98th percentiles) (M. ...
... The SPI (McKee et al., 1993) was designed to standardize precipitation time series across an observational record in order to compute standardized precipitation anomalies in both time and space. To calculate the SPI we first aggregated (summed) the time series of precipitation based on the timescale of interest (10, 20 … 730 days) for each day of the year. ...
Accurate drought assessments are critical for mitigating the deleterious impacts of water scarcity on communities across the world. In many regions, deficits in soil moisture represent a key driver of drought conditions. However, relationships between soil moisture and widely used drought indicators have not been thoroughly evaluated. In addition, there has not been an in‐depth assessment of the accuracy of operational soil moisture models used for drought monitoring. Here, we used 2,405 observed time series of soil moisture from 637 long‐term monitoring stations across the conterminous United States to test the ability of meteorological drought indices and soil moisture models to accurately characterize soil moisture drought. The optimal timescales for meteorological drought indices varied substantially by depth, but were ∼30 days for depth averaged conditions; progressively longer timescales (∼10–80 days) represent progressively deeper soil moisture (2–36 in.). However, soil moisture models (including Short‐term Prediction Research and Transition Center, Soil Moisture Active Passive L4, and Topofire) significantly outperformed the meteorological drought indices for predicting standardized soil moisture anomalies and drought conditions. Additionally, soil moisture models represent near instantaneous conditions, implicitly aggregating antecedent data thereby eliminating the need for timescales, providing a more effective and convenient method for soil moisture drought monitoring. We conclude that soil moisture models provide a straightforward and favorable alternative to meteorological drought indices that better characterize soil moisture drought.
... The standardized precipitation index (SPI), originally developed at Colorado State University in the United States, is a widely utilized tool for quantifying rainfall deficits and monitoring drought situations 13,14 . A drought event is defined as a period during which the SPI value consistently remains negative, and a drought event concludes when the SPI turns positive. ...
... A drought event is considered to commence when the SPI value consistently remains negative and concludes when the SPI becomes positive. The detailed SPI computational methodology can be found in Guttman 64,65 , McKee et al. 13,14 , and Hayes et al. 6 . The SPI has been widely employed in numerous studies to analyse meteorological droughts 24,25,33,60,[66][67][68][69] . ...
Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates.
... Standardized Precipitation Index (SPI): The data used in the SPI method are relatively easy to find (McKee et al. 1993). In the SPI, which can generally be calculated on time scales of 1, 3, 6, 9, 12, 24 and 36 months, short-term time periods create important outputs for agricultural water requirement and water potential. ...
... The precipitation series is best represented by the gamma probability distribution (Thom 1966). As a result of the calculations performed in the method, the following equations (Eqs. 1, 2, 3) are reached (Thom 1966;McKee et al. 1993McKee et al. , 1995Mishra & Singh 2010). A decrease in SPI values below 0 is defined as the onset of drought, while negative index values indicate a dry period. ...
In the Egirdir Lake Basin (Turkey), the six drought indices (i.e., SPI, PNI, DI, CZI, RAI and ZSI methods) were calculated for the three stations at 57 years between 1965 and 2022 on a 1-month and 1-year time scale. A positive correlation was determined between the drought index values calculated by the SPI, RAI, ZSI and CZI methods for three meteorological stations, even if the calculation method remains conceptually very different. Extremely dry periods and compatible results were determined in similar years according to all drought analysis methods for 1-year time scale at all stations. The RAI index gave the highest overall drought value (− 4.5) over the 1-year time scale compared to other indexes. In the analyzes of all drought methods, a very high correlation was determined for each station on a 1-year time scale. The correlation between the drought indices determined by different methods increased depending on the time scale. According to the drought analysis, it has been determined that there are dry periods for long periods, especially in recent years. It has been determined that this situation is compatible with the level drops in Egirdir Lake.
... SPI is a drought index that focuses solely on precipitation data. It calculates the deviation of precipitation from the long-term average for a given timescale, such as monthly or annually [11]. SPI is a dimensionless index that allows for comparisons across different locations and seasons, making it useful for monitoring drought conditions globally. ...
... In the past few decades, many drought indices have been used in studies to measure drought conditions, such as rainfall deciles (RDs) [9], the Palmer drought severity index (PDSI) [10], the standardized precipitation index (SPI) [11], the standardized precipitation evapotranspiration index (SPEI) [12], and the modified version of PDSI [13]. RDs are useful IOD refers to the difference in sea surface temperatures between the western and eastern parts of the Indian Ocean. ...
East Asia is a region that is highly vulnerable to drought disasters during the spring season, as this period is critical for planting, germinating, and growing staple crops such as wheat, maize, and rice. The climate in East Asia is significantly influenced by three large-scale climate variations: the Pacific Decadal Oscillation (PDO), the El Niño–Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) in the Pacific and Indian Oceans. In this study, the spring meteorological drought was quantified using the standardized precipitation evapotranspiration index (SPEI) for March, April, and May. Initially, coupled climate networks were established for two climate variables: sea surface temperature (SST) and SPEI. The directed links from SST to SPEI were determined based on the Granger causality test. These coupled climate networks revealed the associations between climate variations and meteorological droughts, indicating that semi-arid areas are more sensitive to these climate variations. In the spring, PDO and ENSO do not cause extreme wetness or dryness in East Asia, whereas IOD does. The remote impacts of these climate variations on SPEI can be partially explained by atmospheric circulations, where the combined effects of air temperatures, winds, and air pressure fields determine the wet/dry conditions in East Asia.
... For example, Mooley and Parthasarathy (1982) defined the severity of a drought through dividing actual rainfall by average rainfall and dividing the result by the standard variation. The standardized precipitation index (SPI) (Mckee et al., 1993) is based on precipitation only, which makes it easily usable from widely available weather data. It is a continuous statistical method which compares rainfall variability to historic rainfall. ...
A review of research related to resilience of forages to drought in Nordic countries
... Drought exposure index, used in this study to separate drought from non-drought years, was obtained from the Standardized Precipitation Index (SPI) [29]. To obtain SPI, first, a suitable cumulative probability distribution function (here Gamma distribution) [30,31] is fitted to the precipitation. ...
Drought events have significant impacts on agricultural production in Sub-Saharan Africa (SSA), as agricultural production in most of the countries relies on precipitation. Socio-economic factors have a tremendous influence on whether a farmer or a nation can adapt to these climate stressors. This study aims to examine the extent to which these factors affect maize vulnerability to drought in SSA. To differentiate sensitive regions from resilient ones, we defined a crop drought vulnerability index (CDVI) calculated by comparing recorded yield with expected yield simulated by the Environmental Policy Integrated Climate (EPIC) model during 1990–2012. We then assessed the relationship between CDVI and potential socio-economic variables using regression techniques and identified the influencing variables. The results show that the level of fertilizer use is a highly influential factor on vulnerability. Additionally, countries with higher food production index and better infrastructure are more resilient to drought. The role of the government effectiveness variable was less apparent across the SSA countries due to being generally stationary. Improving adaptations to drought through investing in infrastructure, improving fertilizer distribution, and fostering economic development would contribute to drought resilience.
... In this study, we used the Standardized Precipitation Index (SPI) to assess the occurrence of meteorological drought in Bolivia. The SPI, proposed by [42], has gained widespread use due to its strong theoretical foundation, robustness, and versatility in drought analysis. SPI's primary advantage is its ability to analyze drought impacts across various temporal scales, allowing for the identification of different drought types [43]. ...
This study evaluated the accuracy of two new generation satellite rainfall estimates (SREs): Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Integrated Multi-satellite Retrieval for GPM (IMERG) over Bolivia’s complex terrain. These SREs were compared against rainfall data from rain gauge measurements on a point-to-pixel basis for the period 2002–2020. The evaluation was performed across three regions with distinct topographical settings: Altiplano (Highland), Valles (Midland), and Llanos (Lowland). IMERG exhibited better accuracy in rainfall detection than CHIRPS, with the highest rainfall detection skills observed in the Highland region. However, IMERG’s higher rainfall detection skill was countered by its higher false alarm ratio. CHIRPS provided a more accurate estimation of rainfall amounts across the three regions, exhibiting low random errors and relative biases below 10%. IMERG tended to overestimate rainfall amounts, with marked overestimation by up to 75% in the Highland region. Bias decomposition revealed that IMERG’s high false rainfall bias contributed to its marked overestimation of rainfall. We showcase the utility of long-term CHIRPS data to investigate spatio-temporal rainfall patterns and meteorological drought occurrence in Bolivia. The findings of this study offer valuable insights for choosing appropriate SREs for informed decision-making, particularly in regions of complex topography lacking reliable gauge data.
... This study considers three SRPP products from 1984 to 2019, including CHIRPSv2, PERSIANN-CDR, and ERA5-Land (ERA-5L), for drought monitoring due to their spatial and temporal resolution, which is essential for capturing precipitation patterns. It should be noted that drought estimation requires long-term datasets (> 30 years) on a monthly scale, especially for SPI/SPEI computation 18,56 .These three long-term SRPPs provide valuable opportunities to evaluate drought from a climatological perspective, especially for sparse gauge networks, such as East Africa 57 . In addition, previous studies (Table S1) extensively consider these SRPPs for drought monitoring in similar climate characteristics. ...
This study introduces a novel Hybrid Ensemble Machine-Learning (HEML) algorithm to merge long-term satellite-based reanalysis precipitation products (SRPPs), enabling the estimation of super drought events in the Lake Victoria Basin (LVB) during the period of 1984 to 2019. This study considers three widely used Machine learning (ML) models, including RF (Random Forest), GBM (Gradient Boosting Machine), and KNN (k-nearest Neighbors), for the emerging HEML approach. The three SRPPs, including CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station), ERA5-Land, and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record), were used to merge for developing new precipitation estimates from HEML model. Additionally, classification and regression models were employed as base learners in developing this algorithm. The newly developed HEML datasets were compared with other ML and SRPP products for super-drought monitoring. The Standardized precipitation evapotranspiration index (SPEI) was used to estimate super drought characteristics, including Drought frequency (DF), Drought Duration (DD), and Drought Intensity (DI) from machine learning and SRPPs products in LVB and compared with RG observation. The results revealed that the HEML algorithm shows excellent performance (CC = 0.93) compared to the single ML merging method and SRPPs against observation. Furthermore, the HEML merging product adeptly captures the spatiotemporal patterns of super drought characteristics during both training (1984–2009) and testing (2010–2019) periods. This research offers crucial insights for near-real-time drought monitoring, water resource management, and informed policy decisions.
... The standardized precipitation index (SPI) is an indicator representing the probability of precipitation occurrence in a certain period, which was first proposed to assess climate and drought change [43]. It has the advantages of simple calculation, strong stability, and can eliminate the temporal and spatial difference of precipitation [44]. ...
Extreme drought and flood events, as well as their combined events, pose significant challenges to global sustainable socio-economic development and ecological health. However, the impact of dry–wet combination events (DWCEs) on vegetation vulnerability remains to be investigated. The Loess Plateau (LP) was selected as the study area to explore the response time of vegetation to precipitation index changes by optimal correlation coefficient; then, the impact of different DWCEs on vegetation vulnerability under moderate and severe scenarios was analyzed; finally, a vegetation loss probability model was constructed based on the copula function and Bayesian framework, to quantify the vegetation loss probability under DWCEs stress. The results indicate that: (1) normalized difference vegetation index (NDVI) shows an upward trend in spring, summer, and autumn, with the proportion of areas are 90.5%, 86.2%, and 95.4%, respectively, and show an insignificant trend in winter; (2) the response time of vegetation to precipitation index changes tends to be one or two seasons; (3) moderate scenarios have more influence than severe scenarios, dry-to-wet events (DWEs), wet-to-dry events (WDE) and continuous dry events (CDE) in spring-summer have a significant impact on summer vegetation of Ningxia and Shanxi, and WDE and CDE have a higher impact on autumn vegetation. (4) in terms of the probability of vegetation loss, DWE, and CDE cause higher losses to summer vegetation, while WDE and CDE cause higher losses to autumn vegetation. This study quantifies the impact of adjacent seasonal DWCE stress on future vegetation vulnerability.
... As a measure of drought risk, we use the Standardized Precipitation Index (SPI), produced by the Copernicus European Drought Observatory (EDO). The indicator has been developed by [32], and is fully described in [33]. See also European Drought Observatory, December 2023, SPI Factsheet for Europe; https://edo.jrc.ec.europa.eu/documents/facts ...
Nature-related financial risks have emerged as critical concerns for policymakers and financial actors. Central to this issue are ecosystem services, which play an integral role in various production processes but may be interrupted due to the degradation of nature. This article delves into the vulnerability of European SMEs by combining firm-level exposures to ecosystem service dependencies with regional information on the relative abundance of ecosystem services provisioning and the risk of natural hazards. Focusing on long-term debt positions to gauge financial stability implications, the results reveal moderate nature risks for European SMEs at the current stance but also highlight a possible concentration of risks and a need to further refine the use of available indicators.
... Indeed, preliminary studies (e.g., McKee et al. 1993;Wallace and Hobbs 2006) showed that the higher the standardized anomaly, the more extreme events are observed. This dynamic is evident in the last three rows of Fig. 1, which displays the standardized anomalies of precipitation from June 19 to 21, 2015, in southern Cameroon. ...
Understanding the atmospheric conditions that trigger extreme precipitation events is paramount for advancing weather forecasting. For this purpose, the present study aims diagnosing the physical and synoptic processes that underpinned the June 20, 2015, extreme rainfall event in the city of Douala, using ERA5 reanalysis datasets. The results reveal the occurrence of stronger mass convergence (5.0 × 10⁻⁴Kg.m⁻¹s⁻¹) on June 20 compared to the days before (2.24 × 10⁻⁴Kg.m⁻¹s⁻¹, June 19) and after (2.85 × 10⁻⁴Kg.m⁻¹s⁻¹, June 21). This convergence originated from the eastern equatorial Atlantic Ocean. This process initiated during the night at 0000 UTC and progressively intensified, reaching its peak (21.59 Kg.m⁻¹s⁻¹) at 0900 UTC corresponding to the peak of precipitation (4.4 mm/hour). Afterwards, it gradually decreased from 1200 UTC to 2100 UTC. In response to the increased moisture availability, atmospheric instability was enhanced due to a strengthening of latent static energy, which led to an increase in moist static energy. This resulted in anomalously strong and deep convection on June 20 compared to June 19 and 21. The present work demonstrates that analyzing moisture and energy budgets can assist forecasters in nowcasting such extreme precipitation events over southern Cameroon with a high level of accuracy.
... Aside from short-term changes in water availability, it is also imperative to understand the long-term dynamics to identify drought legacy effects on the current vegetation states (Schwalm et al., 2017). To this end, the standardized precipitation index (spi) (McKee et al., 1993) and standardized precipitation evapotranspiration index (spei) (Vicente-Serrano et al., 2010) were used to characterize these legacy effects. The spi and spei were calculated at 1-, 3-, 6-, 9-, 12-, and 24month aggregation lengths. ...
Drought is a devastating natural disaster, during which water shortage often manifests itself in the health of vegetation. Unfortunately, it is difficult to obtain high-resolution vegetation drought impact information that is spatially and temporally consistent. While remotely sensed products can provide part of this information, they often suffer from data gaps and limitations with respect to their spatial or temporal resolution. A persistent feature among remote-sensing products is the trade-off between the spatial resolution and revisit time: high temporal resolution is met with coarse spatial resolution and vice versa. Machine learning methods have been successfully applied in a wide range of remote-sensing and hydrological studies. However, global applications to resolve drought impacts on vegetation dynamics still need to be made available, as there is significant potential for such a product to aid with improved drought impact monitoring. To this end, this study predicted global vegetation dynamics based on the enhanced vegetation index (evi) and the popular Random forest (RF) regressor algorithm at 0.1°. We assessed the applicability of RF as a gap-filling and downscaling tool to generate global evi estimates that are spatially and temporally consistent. To do this, we trained an RF regressor with 0.1° evi data, using a host of features indicative of the water and energy balances experienced by vegetation, and evaluated the performance of this new product. Next, to test whether the RF is robust in terms of spatial resolution, we downscale the global evi: the model trained on 0.1° data is used to predict evi at a 0.01° resolution. The results show that the RF can capture global evi dynamics at both a 0.1° resolution (RMSE: 0.02–0.4) and at a finer 0.01° resolution (RMSE: 0.04–0.6). Overall errors were higher in the downscaled 0.01° product compared with the 0.1° product. Nevertheless, relative increases remained small, demonstrating that RF can be used to create downscaled and temporally consistent evi products. Additional error analysis revealed that errors vary spatiotemporally, with underrepresented land cover types and periods of extreme vegetation conditions having the highest errors. Finally, this model is used to produce global, spatially continuous evi products at both a 0.1 and 0.01° spatial resolution for 2003–2013 at an 8 d frequency.
... the Standarized Precipiation Index (SPI) index was suggested by McKee et al. (1993) to understand the severity and duration of droughts by analyzing unusual rainfall patterns. Selecting the appropriate probability distribution for precipitation datasets and employing suitable parameter estimation methods are challenging aspects in computing the SDI index. ...
Selecting appropriate Global Climate Models (GCMs) presents a significant challenge for accurate climate projections. To address this, a novel framework based on information theory based minimum redundancy and maximum relevancy (MRMR) method identifies top-performing GCMs across the entire study region using multicriteria decision analysis methodology. A subset of the ten best-performing models out of twenty-two GCMs is chosen for multi-model ensemble analysis. Five MME methods are selected to assess the ensemble performance of the ten selected GCMs, categorized into simple, regression-based, geometric-based, and machine learning ensembles. This study evaluates the effectiveness of the MME method based on a comprehensive index called the extended distance between indices of simulation and observation. An Adaptive Multimodel Standardized Drought Index (AMSDI) has been developed based on the optimal MME method. For the application of the framework and the proposed index, historical precipitation data from 1950 to 2014 were utilized from 28 grid points in the Punjab province of Pakistan as the reference dataset. Additionally, simulations from 22 models of the Coupled Model Intercomparison Project phase 6, both past and future, were employed for the estimation procedure. In AMSDI indicator, we used improved multimodel ensemble of precipitation for future drought characterization under various future scenarios. Outcome associated with this research show that AMSDI effectively have ability to effectively identifiy extreme drought events for all three future scenarios. In conclusion, the AMSDI method is shown to be effective and flexible, improving accuracy in monitoring droughts.
... Subsequently, hierarchical clustering was performed to the rotated PC scores employing Ward's linkage algorithm and Euclidean distance measures. This combination has been shown to detect observations that exhibit similarities and group them together (Table 2) Mckee et al. (1993) introduced the Standardized Precipitation Index (SPI) based on long-term rainfall data for monitoring and analysis of wet and dry periods ( Table 3). ...
For the present study, a quantitative statistical approach has been used to understand the spatiotemporal hydrological variability in the Luni River Basin from 1980 to 2020. To delineate homogeneous precipitation regions, we utilized the Principal Component Analysis (PCA) and Ward’s method of Hierarchical Clustering Analysis (HCA) on the precipitation-derived variables. Five homogenous precipitation regions were identified for the Luni River Basin, Pali-Ajmer (Region 1), Jodhpur-Nagaur (Region 2), Jodhpur-Jaisalmer (Region 3), Barmer-Balotra (Region 4) and northern part of the coastal reaches of Gujarat (Region 5). The calculation of wet and dry periods for a 3-month (or seasonal) scale has been undertaken using metrics like the Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and self-calibrated Palmer Drought Severity Index (scPDSI) spanning for a period from 1980 to 2020. These indices indicate significant occurrences of major floods in the years 1990, 1996, 2001, 2006, 2010, 2016, and 2019, along with major droughts during 1984, 1987, and 2002. Cross-Wavelet Analysis (CWA) was utilized to discern the impact of large-scale climatic anomalies, including the Southern Oscillation Index (SOI), Pacific Sea Surface Temperature (SST), Multivariate ENSO Index (MEI), and Indian Ocean Dipole (IOD), on 3-month precipitation, revealing strong teleconnections with the basin’s topographic variations and dynamic hydrology. Finally, the present study portrays the occurrences of various hydrological events, including floods and droughts, spanning the last four decades in an inland river basin such as the Luni, which is susceptible to major climatic phenomena such as El Niño, ENSO, SOI, SST and IOD.
... For the purpose of defining meteorological drought, McKee developed the Standardized Precipitation Index (SPI), a powerful tool for estimating the severity and duration of drought occurrences [15]. This index has proven to be valuable not only in short-term agricultural research but also in long-term analyses of subsurface waters, river flows, and lake water levels [16]. ...
Drought prediction is a crucial task in climate-related forecasting, with significant implications for agriculture, water resource management, and disaster preparedness. In this study, we present a Long Short-Term Memory (LSTM) neural network model to predict drought using the Standardized Precipitation Index (SPI) values from multiple regions, namely Sreemangal, Jessore, and Syl-het. The dataset was preprocessed, and the data was split into training, testing, and validation sets. The LSTM model was trained with a custom R-squared metric to evaluate its performance. Additionally, we compared the LSTM model's performance against traditional statistical models, including Multiple Linear Regression (MLR), Random Forest, and a calculated value from another model. The results demonstrated that the LSTM model exhibited superior predictive accuracy, achieving higher R-squared values and lower mean squared error (MSE) and mean absolute error (MAE) values compared to the other models. This study highlights the effectiveness of the LSTM model in drought prediction and its potential to enhance decision-making in drought-prone regions. 1
... PCI ranges from 0 to 1, wherein low precipitation leads to a value nearer zero and higher periods of rainfall lead to PCI values near 1. The Standardized Precipitation Index (SPI) is widely used in large-scale drought monitoring [37]. The index regards the continuous time series of precipitation at a certain time scale (such as 1, 3, 6, and 12 months) as obeying a certain probability density function distribution (such as gamma distribution), then derives the corresponding cumulative probability function and then converts it into a standard normal distribution. ...
Driven by continuously evolving precipitation shifts and temperature increases, the frequency and intensity of droughts have increased. There is an obvious need to accurately monitor drought. With the popularity of machine learning, many studies have attempted to use machine learning combined with multiple indicators to construct comprehensive drought monitoring models. This study tests four machine learning model frameworks, including random forest (RF), convolutional neural network (CNN), support vector regression (SVR), and BP neural network (BP), which were used to construct four comprehensive drought monitoring models. The accuracy and drought monitoring ability of the four models when simulating a well-documented Inner Mongolian grassland site were compared. The results show that the random forest model is the best among the four models. The R2 range of the test set is 0.44–0.79, the RMSE range is 0.44–0.72, and the fitting accuracy relationship could be described as RF > CNN > SVR ≈ BP. Correlation analysis between the fitting results of the four models and SPEI found that the correlation coefficient of RF from June to September was higher than that of the other three models, though we noted the correlation coefficient of CNN in May was slightly higher than that of RF (CNN = 0.79; RF = 0.78). Our results demonstrate that comprehensive drought monitoring indices developed from RF models are accurate, have high drought monitoring ability, and can achieve the same monitoring effect as SPEI. This study can provide new technical support for comprehensive regional drought monitoring.
... This study also highlights the impact of variable climates. Extended drought periods have already occurred and future projections in the Eastern Thermaikos Gulf forecast that such periods will last for up to 90 months [47,48]. Alternating long wet and drought periods also influence groundwater level variation, as depicted in Figure 10. ...
Groundwater is a primary source of drinking water; however, groundwater depletion constitutes a common phenomenon worldwide. The present research aims to quantify groundwater depletion in three aquifers in Greece, including the porous aquifers in the Eastern Thermaikos Gulf, Mouriki, and the Marathonas basin. The hypothesis is to reverse the phenomenon by adopting an environmentally acceptable methodology. The core of the suggested methodology was the simulation of groundwater using MODFLOW-NWT and the application of managed aquifer recharge (MAR) by using water from small dams after the generation of hydropower. Surface runoff and groundwater recharge values were obtained from the ArcSWAT simulation. The predicted future climatic data were obtained from the Coordinated Regional Climate Downscaling Experiment (CORDEX), considering the Representative Concentration Pathway (RCP) 4.5 and the climate model REMO2009. Groundwater flow simulations from 2010 to 2020 determined the existing status of the aquifers. The simulation was extended to the year 2030 to forecast the groundwater regime. In all three sites, groundwater depletion occurred in 2020, while the phenomenon will be exacerbated in 2030, as depicted in the GIS maps. During 2020, the depletion zones extended 11%, 28%, and 23% of the aquifers in Mouriki, the Eastern Thermaikos Gulf, and the Marathonas basin, respectively.
... Posteriormente, as séries do SPEI foram empregadas para identificar atributos relacionados à seca, tais como sua duração, intensidade e severidade. A classificação do SPEI é feita de acordo com a categorização do SPI desenvolvida por McKee et al. (1993), cujos limites estão indicados na Tabela 2. (Allen et al., 1998), portanto é recomendada para a maioria dos usos, incluindo análises climatológicas de longo prazo. O conjunto de dados pode ser acessado por meio do endereço eletrônico da Global SPEI database. ...
Resumo A seca é um fenômeno natural, que causa impactos severos em diferentes setores da sociedade. Para avaliar a seca, são utilizados índices que permitem comparar eventos em diferentes regiões e escalas temporais. O SPEIbase é uma base de dados em grade que fornece estimativas do SPEI em escala global, com ampla abrangência temporal (1901 a 2022), resolução espacial de 0,5° x 0,5° e segue em processo de atualização. O objetivo deste estudo foi avaliar o SPEIbase v2.9 na representação dos padrões espaço-temporal da seca na Região Sudeste Brasileira. Para isso, foram comparados os valores da SPEIbase via dados de oito estações meteorológicas convencionais (EMC), para o período de 1961 a 2010. A SPEIbase apresentou resultados satisfatórios para a Região Sudeste Brasileira. Os dados em grade apresentaram tendência linear positiva para todas as oito EMC, com correlação forte (0,70 < r < 0,90) para mais de 50% das estações. Além disso, a SPEIbase apresentou desempenho satisfatório na identificação da frequência e intensidade das secas. O SPEIbase é um importante produto que pode ser usado para a análise de secas na Região Sudeste Brasileira.
Vegetation is closely related to the variability of various meteorological factors. Due to the spatiotemporally complex relationship between vegetation and meteorological drought, the assessment of vegetation drought vulnerability remains challenging. This study focuses on the Korean Peninsula and uses four drought indices that can account for different causes of meteorological drought and three representative vegetation indices. A copula-based bivariate joint probability model is constructed between meteorological drought indices and vegetation indices, taking into account the propagation time and delay time from meteorological drought to vegetation drought. From this, the vegetation drought vulnerability is calculated for each combination of meteorological drought index and vegetation index. The spatiotemporal vegetation drought vulnerability of the Korean Peninsula is quantitatively assessed by weighting the vulnerability for each combination based on the correlation strength. The results showed that while vulnerability to each combination was largely consistent, there were meteorological drought indices and vegetation indices that were dominant in certain seasons. Our research can be used as a tool to provide a basis for understanding and responding to the risk of meteorological drought on vegetation.
Drought is a disaster that has a bad impact, especially in the city of Banjarbaru. There are various ways to reduce the impact of drought in the future, one of which is by looking for information regarding the predicted drought index for the following year. The data used in this research to find the drought index value is Banjarbaru City rainfall data for 2007-2022. Seasonal Autoregressive Integrated Moving Average (SARIMA) method is a calculated method for predicting rainfall data and the data obtained is a forecast of rainfall in the city of Banjarbaru for the next 12 periods, namely SARIMA (0,2,3) (0,1,1) 12. This model is a model that is suitable for use because it has fulfilled several tests, namely stationarity of variance and mean, significance test, white noise test and normality test with an AIC value of 1022,60 and an equation model obtained from SARIMA (0,2,3) (0,1,1) 12 is (1-B) 2 (1-B 12) Zt=(1+1,77B-0,54B 2 + 0,23B 3)(1-0,96B 12)εt. After obtaining forecast rainfall data for the next 12 periods. Rainfall data for 2007-2022 and forecast results for 2023 were used to find the drought index value using the Standardized Precipitation Index (SPI) method. It was found that the highest negative drought index value occurred in January, namely-1,774, including the dry category and the drought index had a positive value The highest occurred in June, namely 0,582, including the normal category. The calculation results of this drought index forecast are used to provide additional information to anticipate drought disasters in the future.
Study region: Six regions in Algeria have been selected as follows: Ain Elhadjel, Msaad, Bous-saada, Elkantara, M'sila and M'doukel. Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variational Mode Decomposition (VMD) technique and the Extreme Learning Machine (ELM) algorithm as a preprocessing technique for predicting future droughts. The first 6 and 12-month SPI values 1, 2, and 3-month lead time values were estimated with the ELM algorithm. After that, meteorological variables and Standard Precipitation Index (SPI) values, divided into subcomponents with VMD, are presented to the ELM model, and a drought forecasting model is developed. Model performances were evaluated according to various visual and statistical criteria. New hydrological insights for the region: Soft computing techniques have become the preferred method for producing predictions due to their ability to minimize development time, require minimal input, and offer a relatively less complex approach when compared to dynamic or physical models. As a result of the analysis, it has been determined that the highest prediction accuracies are generally obtained in VMD-ELM models and SPI predictions with a 1-month lead time. The study outputs give important ideas to mite donors regarding water resource planning
In this article, we focus on studying the nexus between moisture transport deficit and drought occurrence in nine key regions across the world where the magnitude of meteorological drought is projected to increase from 1850 to 2100 under a high anthropogenic emission scenario. These regions are central America, southwestern South America, northern Brazil, the Amazon, northeastern Brazil, the western Mediterranean, southern Africa, the eastern Mediterranean, and southwestern Australia. Using the Lagrangian particle dispersion model FLEXPART, we identify the specific moisture sources of the regions (the own region, the nearby continental source and the oceanic sources) and obtain their contributions to the precipitation in the regions for the period 1980–2018. For each region and specific moisture source, the conditional probability of meteorological drought occurrence given an equivalent contribution deficit from the source is estimated using copula models, a statistical methodology that allows us to capture complex relationships between variables. We identify the dominant moisture source in each region, which is the source for which the contribution deficit maximises drought probability. A variety of cases are found: in three regions, the dominant source is the region itself, in one region, it is the nearby terrestrial source, and in five regions, it is an oceanic source. In general, contribution deficits from specific moisture sources are associated with only slightly greater drought probabilities than those from major global moisture sources. We also reveal that the source that contributes the most to precipitation in a given region is not necessarily the dominant source of drought in the region. These results highlight the importance of understanding the role of dominant moisture sources and moisture transport deficits on meteorological drought occurrence at a regional scale.
Estimation of accurate soil physical and hydraulic properties are of prime importance for the management of water resources in agriculture-dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using six years (2015–2021) of satellite soil moisture observations from NASA’s Soil Moisture Active Passive (SMAP) mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that control model soil moisture simulations, such as Ksat (Saturated hydraulic conductivity), expt (exponent parameter in Campbell’s equation for hydraulic conductivity), and bd (Bulk density) were optimized for the Lower Mekong River (LMR) basin. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment, were estimated using the RHEAS framework’s VIC Routing module and Soil Moisture Deficit Index (SMDI) module, respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4 % and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9 %. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional drought reports of LMR basin. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions, informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR basin and beyond.
Climate change accelerates the changes in the hydrological cycle, which increases or decreases the intensity and duration of the drought events. This study assesses the drought risk under future climatic projections by computing drought hazard and drought vulnerability at a sub-basin scale. A multivariate drought hazard index (MDHI) considers deficits in precipitation and streamflow to quantify droughts. MDHI was analyzed using the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Streamflow Drought Index (SDI). Soil and Water Assessment Tool (SWAT+) simulated hydrological variables under baseline and future SSP scenarios were used to estimate MDHI. Drought vulnerability assessment is estimated by combining exposure, sensitivity and adaptive capacity indicators. The results show that the Drought Hazard Index (DHI) are likely to decrease in future climate change scenarios in comparison with the baseline. The Drought Vulnerability Index (DVI) reveals more than 50% of the basin falls under the high and very high vulnerable category. Based on the drought risk assessment, during the baseline period 48.82%, and 15.25% of the total basin area fall under high and very high drought risk. In the case of the future SSP245 and SSP585 scenarios, the high drought risk area gets reduced extensively with 15.09% and 1.02% respectively. The drought risk will be lower for future scenarios compared to baseline. This study aids the policymakers and water managers in prioritizing the projects on a sub-basin level to solve drought risk in future climatic conditions.
A sharp drop in groundwater level as a result of indiscriminate extraction over a long period of time leads to the drying up of groundwater flows, which is called the phenomenon of groundwater drought. In this regard, this research aims to investigate the process of change and reduction of groundwater level, which is characterised by the phenomenon of groundwater drought. Based on this, the Groundwater Resource Index (GRI) was used to evaluate the drought condition of groundwater and analyse its spatial and temporal patterns based on groundwater level data of 21 observation wells between 1993 and 2019. ArcGIS software was used to create zone maps. The results of the research show that certain areas of the study area have experienced moderate to severe drought since 2001. In addition, the GRI zonation maps show that the southern and south-eastern regions of the aquifer have been more sensitive to drought than other parts of the aquifer during the defined period. The spatio-temporal pattern of groundwater drought in the aquifer shows that after a period of moderate drought from 2001 to 2003, the condition of the aquifer improved slightly, and generally stable conditions were established from 2001 to 2010, but since 2011, the occurrence of drought has intensified and the aquifer has been in severe to very severe drought conditions. These conditions highlight the need for careful attention and implementation of management measures. One of the study's recommendations is to use satellite data on groundwater levels to assess the progress of the drought, and compare it with the findings of this study.
Groundwater is an important natural freshwater reserve on which billions of habitants depend for their diverse utilisation. Global water demand has far exceeded the total available water resources which in turn have put a serious concern on food security. India, is one of the largest agricultural user of groundwater in the world where there has been a large scale revolutionary shift from surface water management to a widespread groundwater abstraction. Increased industrialisation, rapid population growth, climate change, changes in the land use and land cover, has influenced the extensive use of groundwater which simultaneously affects the groundwater level. Groundwater drought occurs when this groundwater level falls below the critical level. In the present study, analysis of groundwater drought of the state of Bihar, India, has been conducted using a drought index called Standardized Groundwater Index (SGI) and the spatial and temporal distribution of SGI has been reflected using GIS approach. The rainfall and groundwater data of 38 districts of Bihar from 2002-2019 has been used and the data was divided seasonally into pre- monsoon, monsoon, post- monsoon and winter seasons. Indian Meteorological Department (IMD) norms have been considered for division of seasons. Further, the trend analysis of groundwater level data of all districts of Bihar for all seasons were studied using Mann-Kendall test statistics and Thein Sen’s slope estimator. The results of SGI spatial and temporal distribution showed that districts namely Aurangabad, Gaya, Buxar, Bhojpur, Kishanganj, Katihar, Kaimur, Rohtas, Nawada, Saran Chappra, Siwan, Samastipur, Supaul are prone to the critical groundwater drought condition. The trend analysis results showed that 45% of the districts were showing decline in the groundwater level particularly in pre-monsoon season.
Permitting and regulatory obligations require mine operators to periodically report the spatial extent of mine-induced water-level changes, and to provide water-level monitoring data for review, along with explanations of all hydrologic stresses causing water-level trends. This study couples a baseline-estimation method with a curve-fitting trend-analysis approach to determine all natural, mining, and non-mining aquifer stresses affecting water levels in wells. The approach was applied to the Twin Creeks Mine monitoring network in north-central Nevada, USA. Stresses identified in wells were used to delineate the approximate extent of pumping effects and indicated that drawdowns had not coalesced between mining and non-mining pumping areas. Trend-analysis results indicated that 10 study-area well hydrographs have natural trends, and statistical methods identified three of these wells as having statistically significant downward trends. Thus, caution should be used when interpreting the meaning of statistically significant downward trends, because the downward trend may be climate driven rather than pumping related. The curve-matching approach requires the development of a baseline water-level trend to understand expected natural fluctuations. The baseline trend assumes a dynamic-equilibrium natural condition, where long-term net changes in groundwater levels are zero. The baseline trend and trend analysis require a recharge proxy for the study area. This study developed recharge proxies using winter precipitation data, peak reservoir-storage volumes, and metered mine-water discharge to a surface-water channel. The curve-matching approach also can be used to identify and remove erroneous data, reconcile water-level and pumping datasets, or build hydrologic conceptualizations.
This work focuses on quantifying favorable rainfall patterns during the rainy season to help estimate the probability of receiving maximum seasonal vegetation growth and determining favorable timing for providing supplementary irrigation. Our study was conducted within the Demilitarized Zone located on the northern side of the State of Kuwait. Landsat satellite imagery was utilized to estimate the vegetation coverage for 17 rainy years from 1991 to 2019. Later on, corresponding meteorological data was analyzed to determine the relationship between rainfall patterns and vegetation growth. This way, we estimate the probability of receiving favorable rainfall events that will result in maximum vegetation growth. The results showed a significant variation in vegetation coverage within the examined seasons, which was significantly influenced by accumulated rainfall and the number of rainfall events (P < 0.0001). Total rainfall also had a significant influence on vegetation. Thus, the frequency, distribution, and occurrence of rainfall events were found to play a vital role in accelerating vegetation growth. It was also illustrated that native vegetation was highly influenced by rainfall events received during the early stages of the season from October to November. However, the probability of obtaining favorable rainfall events was found to be low (< 5%). It was also found that the total annual rainfall significantly decreased in the past ten years, which was below the average rainfall (< 108 mm). A conceptual model of Native Desert Vegetation Rainfall Response (NDVR, Model) was presented according to the multiple lines of evidence on the vegetation-rainfall interaction. Based on the model, supplementary irrigation is recommended in the revegetation planning due to the impacts of climate change. Further studies need to be conducted to evaluate the sustainability and long-term consequences of irrigating native desert plants.
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. ABSTRACT: Using a comprehensive drought measure and a panel autoregressive distributed lag model, the paper finds that worsening drought conditions can result in long-term scarring of real GDP per capita growth and affect long-term price stability in Fragile and Conflict-Affected States (FCS), more so than in other countries, leaving them further behind. Lower crop productivity and slower investment are key channels through which drought impacts economic growth in FCS. In a high emissions scenario, drought conditions will cut 0.4 percentage points of FCS' growth of real GDP per capita every year over the next 40 years and increase average inflation by 2 percentage points. Drought will also increase hunger in FCS, from alreay high levels. The confluence of lower food production and higher prices in a high emissions scenario would push 50 million more people in FCS into hunger. The macroeconomic effects of drought in FCS countries are amplified by their low copying capacity due to high public debt, low social spending, insufficient trade openness, high water insecurity, and weak governance. RECOMMENDED CITATION: Tintchev, Kalin and Laura Jaramillo, 2024."Hanging Out to Dry? Long-term Macroeconomic Effects of Drought in Fragile and Conflict-Affected States," IMF Working Paper 24/XX JEL Classification Numbers: O1, O4, Q1, Q2, Q5
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