Standardized Precipitation Index Research Articles

Characteristics of extreme meteorological droughts over the Brazilian Pantanal throughout the 21st century

The Brazilian Pantanal is a remarkable ecosystem, encompassing vital aspects such as fish, fruit, and grain production, greenhouse gas emission regulation, temperature and precipitation modulation, and groundwater recharge. However, 2019 and 2020 experienced prolonged and severe drought that profoundly impacted the Pantanal region. Although these impacts are still under review, their effects on wildlife, vegetation, and the local population are undeniable. To enhance our understanding of the magnitude and consequences of the 2019/2020 drought, the present study conducted a comprehensive evaluation of drought occurrences in the Pantanal biome. Utilizing two well-established drought indices, the standard precipitation index (SPI) and the standard precipitation evapotranspiration index (SPEI), we analyzed the available observational data provided by the Climate Research Unit (CRU). Additionally, we endeavored to project droughts throughout the 21st century, thereby facilitating the visualization of potential reoccurrences of hazardous events in the Pantanal area. Two regional climate model (RCM) ensembles, the Eta ensemble (E1) and CORDEX ensemble (E2), along with representative concentration pathways (RCP4.5 and RCP8.5 scenarios) were used to project the severity of droughts in the Brazilian Pantanal until 2098/2099. The findings indicate that the hydrological year of 2019/2020 was the driest on record in the Pantanal over the past 80 years. The E1 ensemble did not project such an extreme event, whereas the E2 ensemble forecasted extreme droughts before the 2040s under the RCP4.5 and RCP8.5 scenarios. Using the SPEI, both ensembles showed similar results, with extreme droughts projected toward the end of the 21st century. Notably, the SPI demonstrated effectiveness in detecting short-term droughts, while the SPEI was more efficient for long-term assessments, projecting extreme drought events for the last two decades. The SPEI, employed with the Eta and CORDEX ensembles, can provide valuable insights into drought projections. Moreover, the potential recurrence of extreme droughts, characterized by significant below-average precipitation spanning vast areas throughout the hydrological year, poses a severe threat to the Pantanal biome in this century.

Assessment of meteorological, hydrological and groundwater drought in the Konya closed basin, Türkiye

Water scarcity, and drought in particular, is a major challenge worldwide, causing direct and indirect negative effects on ecological systems and water resources, as well as social and economic aspects of life. Climate change and increasing human pressure are contributing to increasing the likelihood of droughts, impacting regions which were not used to be dry. To address this challenge properly, studies should be performed at a multi-scale level, addressing hydrological and hydrogeological drought. Focusing on the Konya Closed Basin in Türkiye, data derived from nineteen stations were used to analyze drought conditions, looking at multiple meteorological—Standardized Precipitation Index (SPI), Z Score Index (ZSI), China Z Index (CZI), Modified China Z Index (MCZI)—hydrological—Streamflow Drought Index (SDI), Surface Water Supply Index (SWSI)—and hydrogeological—Standardized Groundwater Level Index (SGI)—assessment indices for different time scales (1, 3, 6,12, 24, 36 and 48 months). The results show that extreme drought (ED) conditions computed by SPI at 1 month (1.9–2.5%) were higher than that of all indices for all stations. Moderate drought occurred at least according to the ZSI-1. It was determined that the percentage of SPI and CZI had greater Extremely, Severely and Moderately Dry events (10.7–13.4% for CZI; 10.7–14.2% for SPI) than those of ZSI and MCZI. On the other part, MCZI has shown fewer total drought events (6.2–10%). Since 2008, extremely dry conditions in the Konya Closed Basin are generally caused by groundwater drought, which is higher than meteorological and hydrological droughts. The results reported in this work might help in better planning drought-resilient strategies in the basin, which will be paramount in light of climate change.

Irregularity and time series trend analysis of rainfall in Johor, Malaysia

Due to its effect on weather and its propensity to cause catastrophic incidents, climate change has garnered considerable global attention. Depending on the area, the effects of climate change may vary. Rainfall is among the most significant meteorological factors associated with climate change. In Malaysia, changes in rainfall distribution pattern have led to many floods and droughts events which lead to La Nina and El Nino where Johor is one of the states in southern part that usually affected. Thus, rainfall trend analysis is important to identify changes in rainfall pattern as it gives an initial overview for future analysis. This research aims to evaluate historical rainfall data of Johor between 1991 to 2020. Normality and homogeneity tests were used to ensure the quality of data followed by Mann-Kendall and Sen’s slope analysis to determine rainfall trend as the rainfall data is not normally distributed (p > 0.05). Standardized precipitation anomaly, coefficient of variation, precipitation concentration index and rainfall anomaly index were used to identify rainfall variability and intensity while standard precipitation index was used to evaluate drought severity. The lowest annual rainfall recorded was 1725.07 mm in 2016 and the highest was 2993.19 mm in 2007. Annual rainfall and seasonal rainfall showed a declining trend although it is not statistically significant (p> 0.05). Results reveal that Johor experienced extreme wet and dry years, leading to drought and flood incidents. Major floods arose in 2006, 2007, 2008, 2010 and 2011 while driest years occurred in 1997, 1998 and 2016 which led to El Nino phenomenon. March and April were identified as the driest months among all. Thus, the findings from this study would assist researchers and decision-makers in the development of applicable adaptation and mitigation strategies to reduce climate change impact. It is recommended that more data analysis from more stations should be done in the future research study to obtain a clearer view and more comprehensive results.

Quantifying Meteorological Drought Severity for the Watersheds in Euphrates River Basin, Central Euphrates District, Iraq

Drought is a major natural extreme event that can cause significant damage to water resources. This study investigated the implications of drought for Watersheds in Euphrates River basin in Iraq which includes Hilla, Karbala, Najaf, Diwaniyah, and Najaf.. It was found that had an adverse impact on the study region. Among the most often used drought assessment indices globally are the Standard Precipitation Index (SPI) and its version, the Standard Precipitation and Evapotranspiration Index (SPEI).The study evaluated several probability distribution functions to model rainfall and water balance in Iraq. The Kolmogorov–Smirnov (K–S) test and the Anderson–Darling (A–D) test were used to test the goodness-of-fit for rainfall and water balance. The log-normal distribution was found to be the best fit for rainfall at approximately 40% of the stations considered, while the generalized logistic distribution (genlog) was the best fit for water balance at approximately 80% of the stations considered. The study also found that the extreme droughts in Samwah in 2012 and Diwiniyah in 1999 had the highest severity values of -2.8957 in SPI-12 and SPEI-12, respectively.

Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania

Study regionThis study refers to the Wami river sub-catchments in Eastern Tanzania. Study FocusThe five-machine learning (ML) algorithms, including long short-term memory (LSTM), multivariate adaptive regression spline (MARS), support vector machine (SVM), extreme learning machine (ELM), and M5 Tree, were used to predict the most widely used drought index, the standard precipitation index (SPI), at six and nine months. Algorithms were established using monthly rainfall data for the period from 1990 to 2022 at five meteorological stations distributed across the Wami River sub-catchment: Barega, Dakawa, Dodoma, Kongwa, and Mandera stations. New hydrological insights for the region. The predicted results of all five ML algorithms were evaluated using several statistical metrics, including Pearson’s correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and Nash Sutcliffe efficiency (NSE). The prediction results revealed that LSTM perform better in predicting drought conditions using SPI6 (6-month SPI) and SPI9 (9-month SPI) with the highest NSE of 0.99 in all five stations, and R of 0.99 in four stations except at Kongwa station, where R range from 0.75 to 0.99. These prediction results will aid decision-makers and planners to develop a drought monitoring and drought early warning system in order to strengthen the governance and resilience to the catchment and people on the impacts of water scarcity and climate change.

A support vector machine based drought index for regional drought analysis

The increased global warming has increased the likelihood of recurrent drought hazards. Potential links between the frequency of extreme weather events and global warming have been suggested by earlier research. The spatial variability of meteorological factors over short distances can cause distortions in conclusions or limit the scope of drought analysis in a particular region when extreme values predominate. Therefore, it is challenging to make trustworthy judgments regarding the spatiotemporal characteristics of regional drought. This study aims to improve the quality and accuracy of regional drought characterization and the process of continuous monitoring. The new drought indicator presented in this study is called the Support Vector Machine based drought index (SVM-DI). It is created by adding different weights to an SVM-based X-bar chart that is displayed with regional precipitation aggregate data. The SVM-DI application site is located in Pakistan's northern area. Using the Pearson correlation coefficient for pairwise comparison, the study compares the SVM-DI and the Regional Standard Precipitation Index (RSPI). Interestingly, compared to RSPI, SVM-DI shows more pronounced regional characteristics in its correlations with other meteorological stations, with a significantly lower Coefficient of Variation. These results confirm that SVM-DI is a useful tool for regional drought analysis. The SVM-DI methodology offers a unique way to reduce the impact of extreme values and outliers when aggregating regional precipitation data.

Evaluating the impacts of flooding on crop yields by different meteorological indices: A regional case study in the middle-lower reach of the Yangtze River, China

Flooding is a worldwide destructive disaster that severely restricts agricultural production. Meteorological indices are common tools for regionally assessing the impact of flooding on crop yields, but their performances are rarely compared. In this work, six convenient meteorological indices, namely, precipitation (P), standardized precipitation anomaly (PA), China Z index (CZI), standardized precipitation index (SPI), standardized precipitation and evapotranspiration index (SPEI), and standardized antecedent precipitation and evapotranspiration index (SAPEI), were employed to establish correlations between flooding intensity and crop meteorological yield (FL-Ym correlation). Four major crops in the middle-lower reach of the Yangtze River (cotton, oilseed rape, wheat, and maize) were selected as the study crops. The results indicated that the flooding intensities quantified by the SPI, SPEI, and CZI had strong interconnections, whereas those quantified by the P and SAPEI were less related to the others. In the cases with stronger negative FL-Ym correlations, different indices were more likely to yield consistent identifications. In terms of the districts witnessing significant FL-Ym correlations, in only 26%, 41%, 44%, and 75% of them (respectively corresponding to cotton, wheat, maize, and oilseed rape), the correlations were consistently identified as significant by the majority of the indices, demonstrating the nonnegligible influence of index selection. The relative performances of the examined indices varied with the employed calculation periods (whole growth period and single critical stage), whereas the SPEI was generally the best performer. The SAPEI performed best in assessing the flooding impact during crop critical growth stages, in sharp contrast with its mediocre performance over the whole crop growth period. According to the results of multiple indices, flooding during the middle stages of the study crops exerted the greatest negative impact. Additionally, oilseed rape and Anhui Province were identified as the crop and the region most affected by flooding. This work can provide support for flooding disaster assessment and agricultural water management.

Does snow storage affect the Palmer drought severity index? Revisiting PDSI drought indicator via conceptual model and large-scale information

Climate change and its impacts have significantly altered the hydro-climatological conditions worldwide. Accurate monitoring of drought is a crucial work for assessing water availability and environmental suitability. The Palmer Drought Severity Index (PDSI) is a key indicator used to measure hydrological drought. However, its application in mountainous and snow-covered regions is limited due to the indicator's inadequate representation of snow hydrology and its impact during spring and summer. In this research, the authors aimed to enhance the PDSI drought indicator by incorporating snow storage and snowmelt processes via conceptual approach and large-scale snow information. Four different scenarios were developed and tested in the Gheshlagh watershed, a mountainous basin in western Iran. To ensure a comprehensive analysis, the Standardized Precipitation Index (SPI) was also employed to evaluate the results. Additionally, an alternative water balance model, better suited for the watershed, was used in place of the Palmer two-soil-layer water balance model. While the simulated indicators showed good agreement with the benchmark PDSI model, the results revealed significant changes in drought indicators, particularly during summer and spring, when considering snow storage. Moreover, the impact of using a different water balance model was found to be more influential than incorporating snow storage in the PDSI framework. Based on the findings, the authors recommend revisiting the PDSI approach by incorporating region-specific water balance models and accounting for the effects of snow storage.

Meteorological drought monitoring in Kızılırmak Basin, Türkiye

Drought, a major phenomenon impacting water resources, viability, sustainability, and the economy, has been one of the most important hydrological concerns. In the literature, it has been classified into four groups that are meteorological, agricultural, hydrological, and socio-economic. Meteorological drought expresses the precipitation deficits when they are significantly below those recorded normal times. In this study, using the Standard Precipitation Index (SPI) and mean monthly precipitation records of 17 stations which have been obtained from the General Directorate of Meteorology of Türkiye, a drought monitoring analysis has been conducted for Kızılırmak Basin, which is the second largest water basin of the country and water source of many provinces allowing for the time scales of 1, 3, 6, 9, 12, and 24 months considering the cases of “dry” (SPI ≤ − 1.5) and “wet” (SPI ≥ 1.5). To detect the possible trends in two categories of Severe and Extreme Drought, SED (SPI ≤ − 1.5), and Severe and Extreme Wet, SEW (SPI ≥ 1.5) of all time scales, a developed form of innovative trend analysis (ITA) is performed by adding two vertical lines. In addition, a traditional the Mann–Kendall test is applied to the SPI series. The findings indicate that dry occurrences tend to outnumber wet occurrences across various time scales. Analysis reveals that a significant majority of results across all time scales exhibit a consistent trend (89%), with a notable increase in the SEW category (62.74%) and a decrease in the SED category (60.78%). Mann–Kendall analysis demonstrates that 67% of the observed trends show a decrease, while 33% show an increase across all time scales.

Hydrological drought assessment using the standardized groundwater index and the standardized precipitation index in the Berg River Catchment, South Africa

Study regionBerg River Catchment (BRC), South Africa (SA). Study focusHydrological droughts threaten water security and climate Change and groundwater abstractions may exacerbate their impact. Thus, early detection and prediction of droughts is essential. The integration of the Standardised Groundwater Index (SGI) with the Standardised Precipitation Index (SPI), has been applied worldwide, it but not explored in South Africa (SA). The aim of this study was to evaluate the integration of the SGI and SPI for hydrological drought assessment within the BRC, SA. New hydrological insights for the regionThe sensitivity of the SGI to selected Probability Distribution Functions (PDFs) was examined, resulting in the Gamma and Log-Normal PDFs being preferred in the BRC. SGI-Gamma and SPI-Gamma comparisons validated the SGI method and shed light on the key factors influencing groundwater-related droughts in the BRC. The following was revealed: Hydrological droughts in the BRC are influenced by climate factors, surface and groundwater interactions, and groundwater abstractions. When conducting hydrological drought assessments using the SGI and the SPI, the initial focus should be on catchment-scale assessments before expanding to national-scale evaluations. Groundwater in the BRC exhibit significant resilience to droughts induced by climate factors, and thus useful to mitigate the risk of future droughts. Finally, The SGI performed satisfactorily in the BRC with limited groundwater level data.

Examining the spatial and temporal vulnerability to drought in Odisha, India

ABSTRACT The research focuses on investigating the spatial and temporal vulnerability to drought in Odisha, India, utilizing the Standard Precipitation Index (SPI), Drought Index (DI), and Rainfall Anomaly Index (RAI). The historical precipitation data are utilized to identify areas with prolonged rainfall deficits, quantifying the severity and duration of drought conditions. Preliminary analysis includes Normality and Homogeneity tests to ensure the validity of the precipitation datasets. The results depict the use of a non-parametric test for which Mann–Kendall trend test and Sen's Slope test are conducted. The districts exhibiting significant decreasing trends are identified as most critical for drought assessment. The findings from drought indices indicate that most districts have been affected by drought for approximately 50 years, with some districts experiencing severe or extreme conditions for over 10 years. Among the nine districts exhibiting a decreasing trend, four districts demonstrate a significant trend at a 5% significance level. These four districts are experiencing extremely and severely dry conditions, and while they display a significant decreasing trend, they are potentially vulnerable to the adverse impacts of drought conditions in the future. The study will serve in understanding and addressing the vulnerability to drought in Odisha, providing crucial insights for effective mitigation and management strategies.

Investigating the Relationship between Precipitation and Vegetation Dynamics with Emphasis on Agricultural Land Cover in the Atrak Basin Area

The present study aimed to analyze the dynamics of vegetation within the Atrak catchment area, as well as its interplay with precipitation patterns. Moreover, additional emphasis was placed on exploring the impact of these dynamics on agricultural land cover type. To achieve this objective, the Enhanced Vegetation Index (EVI) derived from MODIS data and the Comprehensive Historical and Real-Time Satellite-based Precipitation (CHRIPS) data were utilized for the period from 2003 to 2021. Additionally, the Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI) were employed to discern various degrees of drought and pluvial years within the Atrak basin. The study revealed that the years 2008, 2014, 2017, and 2021 exhibited the lowest vegetation coverage, while the years 2010, 2016, and 2019 showcased the most extensive vegetation extent. Notably, it was revealed from the VCI index that the year 2008 was the driest, and the year 2016 was the wettest. Furthermore, based on the SPI index findings, the years 2007, 2019, and 2020 were identified as pluvial years, while in the years 2008, 2014, and 2021 drought conditions occurred. All other years were classified as exhibiting normal conditions. Regarding seasonality, the observations ascertain that the spring season substantiates the most extensive vegetation cover, and a high correlation between spring precipitation and vegetation coverage was observed. Additionally, the anomaly detection outcomes indicate that the eastern regions of the basin have experienced an upward trend compared to the average of the first decade of the studied period.

Orographic effects on droughts in a monsoon climate with the world's highest rainfall

AbstractDrought, a recurring natural phenomenon in South Asia's monsoon climate, presents challenges in delineating its spatiotemporal patterns within complex topographies. This study investigated the impact of the orographic barrier in the rice‐dominated agricultural region of northeastern India and Bangladesh on drought characteristics during 1951–2020, employing the relative Standardized Precipitation Index (rSPI) and relative Standardized Precipitation‐Evapotranspiration Index (rSPEI) across 3‐, 6‐ and 12‐month scales. The results indicate that even in the rainiest region of the world, droughts extend beyond the limits of the dry season inherent in the monsoon regime. These mostly regional droughts exhibit weak correlations with the core of the Indian subcontinent and other parts of Bangladesh. The region's orographic barrier has a greater influence on drought intensity than on frequency. The rSPI index, which depends solely on rainfall, may overestimate drought intensity and frequency in regions with high seasonal/annual rainfall and substantial intermonthly variability. In contrast, the rSPEI index, which depends on both rainfall and potential evapotranspiration (PET), better reflects the spatial variation of drought in complex terrain, identifying the leeward hinterland of the orographic barrier as the most drought‐prone area. The two indices give similar results for drought characteristics away from the barrier. Furthermore, the orographic barrier exerts a negligible influence on the trends in rSPI and rSPEI. Principal component analysis (PCA) highlights the influences of the rainfall coefficient of variation and elevation on rSPI, while the PET coefficient of variation strongly influences rSPEI. Strategies to minimize the adverse effects of drought in complex topography and year‐round cropping should be local and season‐specific. These include using shorter‐growing, drought‐resistant rice varieties and adjusting planting schedules in rain shadow areas during the summer monsoon. These efforts should be complemented by integrating indigenous irrigation methods with modern practices such as roof water harvesting and tube wells in winter.

Projections of meteorological drought events in the upper Kızılırmak basin under climate change scenarios

AbstractClimate change, whose negative impacts are becoming increasingly apparent as a result of human actions, intensifies the drought problems to dangerous levels. The development of local-scale drought projections is crucial to take necessary precautions for potential risks and possible effects of drought. In this study, drought analysis was conducted in the Upper Kızılırmak Basin using the standard precipitation index (SPI) method for the near future (2020–2049), mid-century (2050–2074), and late century (2075–2099). The precipitation data required for the SPI were gathered from the data sets developed for the SSP climate change scenarios of the four chosen global climate models. Precipitation data has been made more convenient for local analysis studies with the statistical downscaling method. Forecasts have been created for the temporal variation and spatial distribution of drought events. The study findings indicate that, under the SSP 2-4.5 scenario, drought-related effects of climate change will decrease until 2100. On the other hand, the number and severity of drought events, as well as the duration of dry periods, will increase until 2100 under the SSP 5-8.5 scenario. According to the SSP 5-8.5 scenario, consisting of the most pessimistic forecasts, moderate drought will last 0–60 months, severe drought will last 0–30 months, and extreme drought will last 0–20 months in different regions of the area in the late century. The spatial distribution of droughts will differ based on the SPI index and climate change scenarios. Comparison of SPI and CZI data showed that both indices are effective in meteorological drought analyses.

Spatial and temporal characteristics of drought in the Mu Us Sandy Land based on the Standardized Precipitation Index

The environment of the Mu Us Sandy Land, northern Shaanxi, is fragile, so the temporal and spatial evolution of drought can provide a reference for ecological construction and agricultural production. Based on the daily precipitation data of five meteorological stations from 1967 to 2020, the spatial and temporal evolution characteristics of drought in the Mu Us Sandy Land of northern Shaanxi were analyzed using the methods Standardized Precipitation Index (SPI) and run-length theory. The results show that 1) the smaller the time scale, the higher the sensitivity of the SPI to primary precipitation. 2) The annual, summer, autumn, and winter SPI showed an upward and a wetting trend, and the fastest wetting speed is observed in summer, while spring showed an increasing trend of drought. 3) In the past 54 years, the duration, degree, and intensity of drought events at SPI-3 and SPI-12 scales in the Mu Us Sandy Land of northern Shaanxi showed an insignificant decreasing trend, but the decreasing rate at the SPI-12 scale was faster than that at the SPI-3 scale. The serious periods of drought are November 2018 to May 2019 and April 1999 to July 2021. 4) The duration of drought events at two timescales in each region showed a decreasing trend. The longest durations of drought were in Yulin and Jingbian; Shenmu and Dingbian are the regions with the fastest reduction rate of drought degree at the two scales. Hengshan shows rapid reduction of drought intensity at the two scales and also the region with large average drought intensity. These results are helpful in understanding and describing drought events for drought risk management under the condition of global warming.

The Impact of Climate Change on Hydro-Meteorological Droughts in the Chao Phraya River Basin, Thailand

This study evaluated the impacts of climate change on hydro-meteorological droughts in the Chao Phraya River Basin (CPRB), Thailand under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). We used three Reginal Climate Models (RCMs) of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment—Southeast Asia (SEACLID/CORDEX-SEA), which are bias corrected. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow for future periods. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were estimated and used for drought characterization at three time scales (3, 6, and 12 months). The lag time between meteorological and hydrological droughts is approximately 1–3 months. The results suggest that the CPRB is likely to experience less frequent hydro-meteorological drought events in the future. The meteorological drought is projected to be longer, more severe, and intense. The severity of hydrological drought tends to decrease, but the intensity could increase. Climate change has been discovered to alter drought behaviors in the CPRB, posing a threat to drought monitoring and warning because droughts will be less predictable in future climate scenarios. The characterization of historical and future droughts over the CPRB is therefore valuable in developing an improved understanding of the risks of drought.

Characterize and analysis of meteorological and hydrological drought trends under future climate change conditions in South Wollo, North Wollo, and Oromia Zones, in Ethiopia

This research was conducted on North Wollo, South Wollo, and Oromia special zones, in Ethiopia. The study aimed to analyze the temporal and spatial variability of meteorological and hydrological drought trends using the selected drought indices and to predict its future trend in the selected areas. To achieve these objectives, meteorological and hydrological data were collected from the Ethiopian Meteorology Institute and the Ministry of Water and Energy respectively. The historical and future drought condition was analyzed by using the standardized precipitation index (SPI), reconnaissance drought index (RDI), and streamflow drought index (SDI) from the drought indicator calculator (DrinC) software. Based on the availability of the data, for historical drought analysis, ten meteorological stations with thirty-two years of daily data were selected. For the future scenario, RCP 4.5 was used to downscale the future climate data and to forecast SPI and RDI values. Also, an artificial neural network (ANN) was applied to forecast the future streamflow data using Python software, then the future hydrological drought was determined using the forecasted streamflow data. The result indicates that all zones were historically affected by severe to extreme droughts, especially 1984, 1986, 1987, 1989, 1991, 1992, 2003, 2007, 2010, 2013, and 2014 years. From 1984 to 1992 the probability of severe to extreme drought occurrence was on average of two years intervals and from 1992 to 2003 there is a huge gap. From the future drought analysis results, the probability of severe to extreme drought occurrence will be at five-year intervals on average. Based on the analyzed results, the frequency of severe to extreme drought occurrence of historical drought which was two and three years was increased to five years for the future conditions on average. But, these are short intervals and the magnitude of the event is very high. So, the regional water and energy office and other concerned bodies in the area have to plan a good drought mitigation mechanism and should develop a drought early warning system for the communities in and around the study area.

Detecting the interactions between vegetation greenness and drought globally

Drought changes can profoundly influence vegetation growth while vegetation, in turn, may impact drought by modulating water and energy exchanges between the land and atmosphere. Previous studies have primarily focused on the unidirectional effects of drought on vegetation dynamics. However, the impacts of vegetation on drought and their bidirectional dependencies have remained underexplored. In this paper, we employ the Leaf Area Index (LAI) and two drought indices (the Standardized Precipitation Index, SPI, and, the Standardized Precipitation Evapotranspiration Index, SPEI) to investigate the interaction between vegetation greenness and drought globally over the period 2001 to 2020. The results demonstrate a statistically significant and consistent trend between LAI and SPI (SPEI) in 66.9% (54.01%) of global vegetated lands. SPI commonly displays a 2-month delayed response to LAI, whereas SPEI shows no evident lag, implying a more rapid response of SPEI to LAI compared to SPI. A substantial proportion of global vegetated areas, around 66% for LAI-SPI and 52% for LAI-SPEI, exhibit a significant positive relationship. The Granger causality test reveals a bidirectional causal relationship between vegetation greenness and drought across approximately 20% of global vegetated areas. Vegetation greenness is identified as the Granger cause of SPI (SPEI) in >60% (30%) of global vegetated lands, providing statistical observational evidence indicating that vegetation can influence drought in many regions within terrestrial ecosystems. Interestingly, the ratio of areas where LAI serves as the Granger cause of SPI is more extensive than the corresponding ratio where SPI is identified as the Granger cause of LAI, a pattern opposite to that observed with SPEI. Overall, our investigation into the interactions between vegetation greenness and drought holds significance for enhancing our understanding of land-atmosphere interplays in the context of climate change and contributes to advancing sustainable development for ecosystem services.

On the timescale of drought indices for monitoring streamflow drought considering catchment hydrological regimes

Abstract. There is a wide variety of drought indices, yet a consensus on suitable indices and temporal scales for monitoring streamflow drought remains elusive across diverse hydrological settings. Considering the growing interest in spatially distributed indices for ungauged areas, this study addresses the following questions: (i) What temporal scales of precipitation-based indices are most suitable to assess streamflow drought in catchments with different hydrological regimes? (ii) Do soil moisture indices outperform meteorological indices as proxies for streamflow drought? (iii) Are snow indices more effective than meteorological indices for assessing streamflow drought in snow-influenced catchments? To answer these questions, we examined 100 near-natural catchments in Chile with four hydrological regimes, using the standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), empirical standardised soil moisture index (ESSMI), and standardised snow water equivalent index (SWEI), aggregated across various temporal scales. Cross-correlation and event coincidence analysis were applied between these indices and the standardised streamflow index at a temporal scale of 1 month (SSI-1), as representative of streamflow drought events. Our results underscore that there is not a single drought index and temporal scale best suited to characterise all streamflow droughts in Chile, and their suitability largely depends on catchment memory. Specifically, in snowmelt-driven catchments characterised by a slow streamflow response to precipitation, the SPI at accumulation periods of 12–24 months serves as the best proxy for characterising streamflow droughts, with median correlation and coincidence rates of approximately 0.70–0.75 and 0.58–0.75, respectively. In contrast, the SPI at a 3-month accumulation period is the best proxy over faster-response rainfall-driven catchments, with median coincidence rates of around 0.55. Despite soil moisture and snowpack being key variables that modulate the propagation of meteorological deficits into hydrological ones, meteorological indices are better proxies for streamflow drought. Finally, to exclude the influence of non-drought periods, we recommend using the event coincidence analysis, a method that helps assessing the suitability of meteorological, soil moisture, and/or snow drought indices as proxies for streamflow drought events.

Decadal changes in surface CO2 concentrations and CO2 fluxes in a mountain lake

AbstractClimate warming impacts biogeochemical cycles in lakes. However, the factors controlling CO2 dynamics in mountain lakes over multidecadal scales are poorly understood. Here, we capitalized on long‐term monthly data (1995–2022) of oligotrophic mountain Lake Tovel and calculated surface CO2 concentrations and flux by applying geochemical relationships and the thin boundary layer approach. Advanced time‐series and regression modeling was used to determine temporal patterns and environmental parameters explaining surface CO2 concentrations and flux. Surface CO2 concentrations were highest from 2009 to 2017 (annual mean: 109.1 μmol CO2 L−1) but lower before and after this period. Concomitantly, the air–water CO2 flux (μmol CO2 m−2 d−1) showed a period of lowest (mean1995–2010: 6.4 ± 0.7), highest (mean2011–2017: 35.7 ± 2.1), and intermediate emissions (mean2018–2022: 19.3 ± 4.7). Temporal modeling showed that hypolimnetic and deep hypolimnetic dissolved oxygen (DO) had the same change points and trends as surface CO2 concentrations. In multiple linear regression, hypolimnetic DO, silica, and the standardized precipitation index (pseudo‐R2adj. = 0.62; p < 0.01) best predicted annual mean surface CO2 concentrations. Regression results and the overlap between temporal trend patterns indicated that surface CO2 concentrations of Lake Tovel were positively influenced by external (loading of allochthonous carbon) and internal (lake autumn mixing) factors. The recent decline in surface CO2 concentrations from the year 2018 was attributed to increased stratification that offset lake autumn mixing and thus lead to the observed decline. These results help us to better understand the carbon cycle in mountain lakes in a changing climate.